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Economic valuation of critical habitat closures Berman, Matthew; Gregr, Edward; Ishimura, Gakushi; Coatta, Ryan; Flinn, Rowenna; Sumaila, U. Rashid; Trites, Andrew 2008

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   ISSN 1198-6727  Fisheries Centre Research Reports 2008 Volume 16 Number 8     Economic Valuation of Critical Habitat Closures       Fisheries Centre, University of British Columbia, Canada   Economic Valuation of Critical Habitat Closures    By  Matthew Berman1 Edward Gregr2 Gakushi Ishimura2 Ryan Coatta2 Rowenna Flinn2 U. Rashid Sumaila2 and Andrew Trites2  1. Institute of Social and Economic Research, University of Alaska Anchorage, Anchorage AK, USA  2. Fisheries Centre, The University of British Columbia, Vancouver, BC, Canada         Fisheries Centre Research Reports 16(8) 102 pages © published 2008 by  The Fisheries Centre, University of British Columbia  2202 Main Mall Vancouver, B.C., Canada, V6T 1Z4       ISSN 1198-6727   Fisheries Centre Research Reports 16(8) 2008   ECONOMIC VALUATION OF CRITICAL HABITAT CLOSURES    By Matthew Berman, Edward J. Gregr, Gakushi Ishimura, Ryan Coatta, Rowenna Flinn, U. Rashid Sumaila, and Andrew Trites   CONTENTS    Page DIRECTOR’S FOREWORD ...................................................................................................................................... 1 ABSTRACT ........................................................................................................................................................... 2 INTRODUCTION ................................................................................................................................................... 3 METHODS ........................................................................................................................................................... 5 RESULTS ........................................................................................................................................................... 10 DISCUSSION ......................................................................................................................................................33 CONCLUSIONS ...................................................................................................................................................34 ACKNOWLEDGEMENTS.......................................................................................................................................34 REFERENCES ..................................................................................................................................................... 35 APPENDICES...................................................................................................................................................... 37 Appendix A. Equations for Spatial Distribution of Catch Per Unit of Effort (CPUE), Estimated from the Summer 2001 NMFS Gulf of Alaska Bottom Trawl Survey .............................. 37 1. Equations with Wind -- only shown if absolute value of t on wind > 1.........................................38 2. Equations without wind ................................................................................................................ 45 Appendix B. Equations for Spatial Distribution of Catch Per Unit of Effort (CPUE) for Summer and Winter, Bering Sea/Aleutian Islands and Gulf of Alaska, Estimated from 2001 NMFS Bottom Trawl Fisheries Observer Data .......................................................................... 58 1. Winter bottom trawl.......................................................................................................................58 2. Summer bottom trawl selection equation (IMR7)........................................................................ 81       A Research Report from the Fisheries Centre at UBC 102 pages © Fisheries Centre, University of British Columbia, 2008   FISHERIES CENTRE RESEARCH REPORTS ARE ABSTRACTED IN THE FAO AQUATIC SCIENCES AND FISHERIES ABSTRACTS (ASFA) ISSN 1198-6727  Economic Valuation Of Critical Habitat Closures, Berman et al.   1 DIRECTOR’S FOREWORD Humans have developed fisheries spanning entire oceans, and have the capability to overexploit the resources in any region within a very short time, as attested by numerous now-defunct fisheries.  Therefore, a consensus is slowly emerging that management of fisheries, rather than focusing only on the amount of fishing effort deployed, also needs to be structured in space, with different ocean areas being targeted differently, and/or at different times, depending on the resources and habitat that they provide. In fact, ocean zoning is emerging as a major element of Ecosystem-Based (Fisheries) Management, because ecosystems are spatial entities. Ecosystem-Based (Fisheries) Management implies, among other things, redirecting fishing effort away from previously fished areas to protect animals or habitats whose continued existence is considered crucial. For the eastern North Pacific ocean, the rookeries and haulouts of Steller sea lions and the feeding areas surrounding them provide a clear example of areas that need protection. The cost to the fisheries of closing such areas can be evaluated and balanced against the risk of damage to natural resources in the area. This cost will be some fraction of the value of the catch that could be made in the areas to be closed, its value depending on the extent to which substitute areas are available to the fishery. This report presents a spatial model of fleet operations through which such costs can be evaluated; as such, it is of interest to anyone interested in spatial management and marine protected areas. Some colleagues claim not to know what Ecosystem-Based (Fisheries) Management means, or even that it does not mean anything concrete. This report shows what Ecosystem-Based (Fisheries) Management can mean, and it poses questions and proposes an approach for answering them that would not have seen the light of day when single-species approaches reigned. So, gradually, we are getting there.    Daniel Pauly Director, UBC Fisheries Centre   Economic Valuation Of Critical Habitat Closures, Berman et al.  2 ABSTRACT We developed methods to estimate the spatial variation in economic values of ocean fisheries, and applied the methods to estimate the cost of closing groundfish fisheries in Steller sea lion Critical Habitat in the Bering Sea and Gulf of Alaska. The research addressed two related goals: (1) explicitly linking spatial variability of fisheries biomass and profitability over time to environmental variables; and (2) developing estimates of opportunity costs of time and area closures to the fishing industry at scales relevant to management. The approach involved two stages of statistical analyses. First, environmental conditions measured at 3 km and 9 km spatial scales and two-week and one-month intervals were used to predict fish biomass and fisheries catch per unit of effort (CPUE). Environmental variables included bathymetry, remotely sensed physical and biological observations, and output from a physical oceanographic circulation model. Second, we used predicted CPUE and spatial regulatory and cost factors to explain the spatial distribution of fishing effort over time. Our results suggested that 2001 Critical Habitat closures cost the North Pacific groundfish trawl fisheries 5-40 percent of their total potential net earnings. The improved methods for estimating opportunity costs of fisheries closures we present have direct applications to evaluating boundary changes to marine protected areas and other spatial management decisions. Limitations include the extensive data requirements and the need to bootstrap confidence intervals. If further research demonstrates the robustness and stability of the estimated relationships over time, the methods could project spatial fishery effects of climate variability and change, leading to dynamic spatial models linking fisheries with ecosystems.   Economic Valuation Of Critical Habitat Closures, Berman et al.   3 INTRODUCTION Resource managers are increasingly requested to make decisions to restrict commercial fishing for the benefit of protected species, with uncertainty about the value of the reserved habitat to the fishing industry, and to the species at risk. Critical Habitat designations for Steller sea lions (SSLs; Eumetopias jubatus) since 2000 in the Gulf of Alaska and Bering Sea have been especially disruptive to fisheries for pollock (Theragra chalcogramma), Atka mackerel (Pleurogrammus monopterygius), and other groundfish (National Marine Fisheries Service 2001a). However, claims of high annual losses by fisheries organizations cannot be independently evaluated due to the absence of a scientifically defensible method to estimate the cost of the closures around critical habitat areas. The main official study documenting the economic impact of SSL critical habitat designation (National Marine Fisheries Service 2001a) contains only qualitative analyses of the closures on industry profits. Pending proposals to close additional areas to fishing in the North Pacific to protect ‘essential fish habitat’ or ‘habitat areas of particular concern’, and possible future closures to protect other marine species could further reduce the area available for fishing. The controversy surrounding these actions suggests that there is an urgent need to develop objective methods to quantify their cost.  Quantitative economic analyses of North Pacific habitat closures have largely been limited to describing what has become known as ‘revenue at risk’ (National Marine Fisheries Service 2001a; Tetra Tech 2004; North Pacific Fishery Management Council 2004). Revenue at risk represents an estimate of the ex-vessel gross revenue that could reasonably be expected to derive from fishing in the area proposed to be closed, based on historic catches when the area was open to fishing. This is a completely inadequate measure of the losses that the industry – and society – would endure from such closures. Under fisheries regulated by Total Allowable Catch (TAC), fishing effort generally moves from closed areas to areas that remain open. Total catch and gross revenue will remain the same as before unless the restrictions are so severe that some TAC remains uncaught, an unlikely outcome for overcapitalized fisheries like those of the North Pacific. True ex-vessel gross revenue losses are probably close to zero in most cases.  Although most habitat closures are unlikely to substantially affect total catches, market value, and gross revenues, the expansion of time and area closures on the fishery nevertheless imposes real costs on the industry. Such costs may include higher travel costs to reach open areas, higher operating costs from lower catch rates and interrupted trawls, search costs and costs of learning how to fish profitably in new areas. These costs are described qualitatively in regulatory review documents (National Marine Fisheries Service 2001a; Tetra Tech 2004; North Pacific Fishery Management Council (2004). While these industry costs represent real losses to society, they are not closely related to the so-called revenue at risk.  Methods do exist for estimating the costs of fishery time and area closures based on extensions of the Random Utility Model (RUM; McFadden 1981). RUM was initially developed to model transportation mode choice (Ben-Akiva and Lerman 1985; Domencich and McFadden 1975). Early applications to natural resources focused on estimating demand for recreational fisheries and associated non-market values (Bockstael 1989). RUM was first extended to commercial fisheries by Bockstael (1983), and has increasingly been used to model spatial economic decisions in fisheries (Dupont 1993; Holland and Sutinen 2000). Haynie and Layton (2004) estimated a spatial choice model for groundfish trawl fishing in the Bering Sea, establishing an initial milestone toward quantifying the cost of critical habitat closures. In this project we addressed two specific limitations of the standard approach used by Haynie and Layton (2004) – generality and usefulness to management.  First, Haynie and Layton (2004) did not address costs for a large offshore fishing fleet. The second, and more fundamental, limitation arises from the imposition of an unrealistic choice set on fishers. Haynie and Layton (2004) divided the fishing ground into 29 geographic areas, based on statistical reporting, of which 18 contained most of the fishing. This artificial division is unlikely to correspond to actual fleet choices. The Bering Sea/Aleutian Islands area is a huge expanse with a complex coastal and subsurface geography; a realistic choice set for the trawl fleet would contain a much larger set of more precise locations. The theoretical justification for RUM requires that the choices represent a complete set of discrete, independent, and available alternatives (McFadden 1981). Empirical applications of RUM are appropriate to the extent that they model a realistic decision-making problem for individual agents. Like the Economic Valuation Of Critical Habitat Closures, Berman et al.  4 applications to recreational fishing, commercial fishing applications work best when they involve specific alternative fisheries and discrete alternative fishing sites (Berman, Haley, and Kim 1997). We therefore aimed to develop a realistic method of valuing habitat-driven fishery closures. To be useful to managers, the method must satisfy four criteria. It must: 1. Be consistent with RUM, benefiting from RUM's theoretical and practical advantages; 2. Differentiate among a large number of small areas distributed over a large geographic space, so that it is relevant to decisions regarding marine mammal critical habitat; 3. Recognize costs of reduced fishing flexibility to an at-sea processing fleet as well as the shore- based fleet; 4. Provide estimates of impacts on fisheries linked directly to ecological variables that are consistent with habitat models for SSLs and other protected species potentially interacting with fisheries. In this way, estimated fisheries values can be compared directly to habitat requirements, both of which may vary over time. This work could therefore be considered a test of two primary hypotheses about Bering Sea and Gulf of Alaska fisheries: 1. Data on measured and modeled environmental variables can predict spatial variation in the density of catchable fish biomass at the small temporal and spatial scales relevant to realistic modeling of fishing fleet choice sets and management needs; and 2. Resulting predictions of fish biomass, along with data on prices and indicators of fishing costs, can predict spatial choices of the shore-based and offshore fishing fleet in a way that can be used to derive profit functions under the assumptions of RUM. Of course, closing marine habitat to fishing may benefit fisheries in the long run, enabling higher future catches outside the boundaries of the closed areas as stocks rebuild. These benefits could be significant, but estimating such benefits is outside the modest scope of this project. We also did not attempt to estimate the value of protected species saved through closures to fisheries. We addressed only the short- term cost to the fishery of foregone fishing opportunities – the cost that often poses the main obstacle to the creation of such reserves. Our goal was to design and demonstrate a method to quantify the net cost to the fishing industry of closing areas to fishing that satisfies the above four criteria. Our intent was to improve existing economic models of spatial choice in fisheries by relaxing unrealistic restrictions on spatial decision-making while incorporating detailed and flexible geographic scales. The research plan included four specific objectives: • Develop and test a scientifically defensible method to value commercially fished areas at flexible temporal and spatial scales relevant to management decisions; • Demonstrate a specific application of the method by estimating the cost to Bering Sea and Gulf of Alaska groundfish trawl fisheries of changes in Steller sea lion critical habitat closures; • Create maps of relative fisheries values for comparison to maps of relative importance to SSL recovery, in order to assist management decision-making under uncertainty; • Generalize the method to evaluate fishery time and area closures for any protected species, or for marine conservation generally.  Economic Valuation Of Critical Habitat Closures, Berman et al.   5 METHODS Theoretical approach For this study, cost of habitat protection means the opportunity costs, or profits foregone, from time and area closures and gear restrictions. Valuing this cost started with a model of fishing fleet decision-making consistent with the assumptions of RUM. The RUM has been widely used to model spatial economic decisions in fisheries (Dupont 1993; Holland and Sutinen 2000). Its advantages include the ability to model choices among multiple spatial alternatives, straightforward computation using maximum likelihood techniques, and direct derivation of welfare estimates under a reasonable set of assumptions. We extended the RUM approach to address the goals of the project by making the following five assumptions: 1. The probability of use of each alternative (when it is open to fishing) is based on the RUM; 2. Modeled alternatives are small geographic units with similar fish habitat; 3. Expected catch in each alternative unit depends on predicted fish density times geographic area; 4. Since alternatives are very small in relation to the total fishery area, the probability that any vessel uses a given area during each fishing day is small (generally < 1%); 5. A large number of vessel-days per month are observed in each modeled fishery (generally > 100). Under these assumptions, the number of landings in an area during a specified time can be approximated by the Poisson probability distribution. Using probabilistic models of count data to approximate the RUM is new for commercial fisheries, but analogous to an approach proposed by Guimares et al. (2003) to model siting decisions regarding industrial facilities. Since the underlying choice probabilities conform to the assumptions of RUM, we may invoke it to estimate the value of each small choice area from the estimated parameters of the profit function, following Small and Rosen (1981). Berman (2006) describes the specific technical approach developed under the project for implementing the extension of RUM to commercial fisheries, consistent with the above five assumptions. Suppose the utility that an agent in group i derives from selecting choice j at occasion k, is [ ]ijk ijk ijk ijkU V η ε= + +  (1) where Vijk  represents the profit for alternative k, ηijk is a random term with a zero mean whose distribution may be correlated with observed data, and ηijk is a random term with a zero mean with an independently and identically distributed type one extreme value distribution. The random variable η may, for example, model systematic but unobserved differences in operating costs or other information that varies among vessels in a fishery. For a given value of ηijk, the conditional probability πijk that a fisher from group i  chooses area j ∈ Jk is given by:  log  - 'ijk ijk ijk ikVπ α η γ= +  (2) Where      log  ijk k V ik j J ijke αγ η∈= +∑  (3) Under the five assumptions listed above, the conditional probability for the number of vessels yijk from group i observed catch in area j during occasion k  may be approximated by a Poisson distribution:  Economic Valuation Of Critical Habitat Closures, Berman et al.  6 ( ) exp( ) / ! ijk ijk ijkprob y y y yλ λ= = −   (4) where  exp( ' )jk k jk ijk kn xλ β η γ= + −   (5) The unconditional probability distribution for yjk depends on the distribution of η. However, equations (4) and (5) represent a form of "overdispersion" in the Poisson model (Cameron and Trivedi 1986). For example, if eη is assumed to have a gamma distribution, the Poisson approximation to the mixed logit becomes a negative binomial model, whose parameters can be estimated easily with conventional maximum likelihood.  Since the underlying choice probabilities conform to the assumptions of RUM, we may invoke RUM to estimate the conditional value of an area from the estimated parameters δ, ε, and gk following Small and Rosen (1981). Given that the vessel has chosen to take a fishing trip, the ex-vessel price, and the relevant geographical information, the difference in value between two subsets J1k and J2k of the choice set Jk is related to the parameter γ'ik:  1 2 1 2  ( / ) log ( / )ijk ijk k k k k V V J J k j J ijk k j J ijkS S n e n e α αα η α η∈ ∈− = − + − +∑ ∑   (6) The opportunity cost of closing area j to fishing during choice occasion k reduces to: log[1/(1 )] /  k jkn π α− −   (7) Since the expected value of ηijk = 0, a point estimate of the opportunity cost may be derived by evaluating the coefficients of a negative binomial regression for Equation (2). However, the complexities that Hensher and Greene (2003) outline for derivation of welfare estimates with mixed logit models apply here. In general, bootstrapping is necessary to generate confidence intervals around the point estimate. Data sources This was a data-intensive project. Although we did not generate new data from field observations, much of the work on the project involved processing primary data, including environmental indicators, fish catch and effort, fisheries openings and habitat regulations, and indicators of prices and costs. Spatial resolution: A key issue that had to be determined at the start of the project was the identification of the appropriate spatial and temporal scales for the analysis. Three criteria drove the decision: realistic behavioural choices of fishing fleets, management needs, and the resolution of the available environmental data. We decided to analyse the data at multiple scales, with different types of analyses at each scale. The three scales selected were: 1. The Alaska Department of Fish and Game (ADF&G) statistical area (statarea). Each statarea measures one-half degree of latitude by one degree of longitude (roughly 30 x 40 km), with the areas further subdivided in coastal areas around landforms. In any given year, about 700 of approximately 1,700 statareas report some groundfish fishing activity. The statarea was used for testing the feasibility of the approach (Berman 2006), but the available data support more detailed spatial scales, which also better represent fishing decisions and satisfy management needs; 2. A 9 x 9 km2 grid, covering the entire Gulf of Alaska and Bering Sea regions. This geography was selected as the finest scale that was supported when considering the resolution of the available satellite data. There were approximately 45,000 9 x 9 km2 grid cells within the U.S. Exclusive Economic Zone (EEZ) in the Bering Sea and Gulf of Alaska; and Economic Valuation Of Critical Habitat Closures, Berman et al.   7 3. Within the Gulf of Alaska, we analysed the data on a 3 x 3 km2 grid. This downscaling was possible for the Gulf of Alaska because of the availability of oceanographic model output (described below). The area contained within the model area inside the U.S. EEZ contained about 36,000 3 x 3 km2 cells. Environmental indicators: Environmental indicators used in the project included bathymetry, remote sensing data, and oceanographic model output. Data sources on fish biomass density and fishing effort provided some limited additional environmental measurements, as described below.  Remote sensing data were obtained for four indicators: sea surface temperature, sea surface height, wind, and chlorophyll-a. Each indicator has its own strengths and weaknesses. Temperature is a basic habitat indicator for most species. However, cloudiness disrupts satellite measurements, and the limitation of remotely sensed temperature to surface waters compromises the utility of this indicator for bottom- dwelling fish. Sea surface height is measured as an anomaly from mean sea level. Sea surface height is affected by salinity, temperature, and persistent atmospheric pressure anomalies, all of which could indicate habitat variation. However satellite measurements of sea surface height tend to produce inaccurate readings near shallow coastlines affected by tidal action. Wind, inferred from satellite readings of wave height, indicates surface mixing and could be an important indicator of surface enrichment. However, interference with land causes wind data to be unavailable in coastal areas where significant fishing takes place. Chlorophyll-a provides a measure of primary production. However because it is derived from colour images, clouds and low light conditions in winter combine to yield few if any valid readings in December and January. Sea ice also affects both colour and radar satellite measurements. We derived monthly climatologies from available remotely sensed data, beginning in 1993, except for chlorophyll data, which began in 1997.  For the Gulf of Alaska, Al Hermann and Dave Musgrave provided output from a Regional Ocean Modeling System (ROMS) model developed for the Gulf of Alaska (Hermann et al. 2002; Hermann and Stabeno 1996). Model outputs were provided at a 3 x 3 km2 resolution, summarized as 2-week averages, for all of calendar year 2001. ROMS model indicators included temperature, salinity, and velocity vectors in three directions at 30 different vertical levels. In addition, the model also calculated a mixed layer depth and sea surface height anomaly. Each of the three types of environmental indicators has advantages and disadvantages. The ROMS output has the ability to ‘see’ the ocean in three dimensions, as well as infer ocean dynamics through currents and eddies. However, it is difficult to validate the model to assess its accuracy at the fine scale we used. (For example, modeled and remote-sensed sea surface height are positively correlated, but the correlation coefficient is only 0.3, so we included both indicators for the 3 x 3 km2 Gulf of Alaska analyses.) ROMS model output was available only for a portion of the Gulf of Alaska (GOA). Remotely sensed data provide direct measurements of dynamic environmental conditions. However, as discussed above, sea ice, clouds, and low light conditions reduce the spatial and temporal extent of these data. Bathymetry provided the best quality and coverage, and served as an important but limited habitat indicator. For the statistical analyses, we extracted four levels from the ROMS output: surface, bottom, and the levels immediately above and below the mixed layer depth. We calculated horizontal velocity from the two horizontal vectors. In addition, to reduce collinearity of variables at different depths, we represented the surface and bottom values as differences from the level below the mixed layer. Remotely sensed data generally supported monthly climatologies at 7 – 10 km resolutions. After selecting a minimum usable quality level based on available quality flags, we interpolated the remote sensing data to common 9 x 9 km2 (entire study area) and 3 x 3 km2 (GOA) grids. Metadata (Gregr and Coatta 2008) contain the technical specifications for the sources and processing of the environmental data. After developing the five data sets of environmental climatologies, we generated slopes (rates of change across space) for bathymetry, sea surface temperature (SST), and sea surface height (SSH). The formula (using SSH as an example), was : sshslope = (180/3.14159)*arctan{ [((SSHn-SSHs)/2) 2 + ((SSHw-SSHe)/2)2 ]1/2 } (8) The subscripts n, s, e, w denote the adjacent cells to the north , south, east and west, respectively. Economic Valuation Of Critical Habitat Closures, Berman et al.  8 Since bathymetry is measured in metres, and horizontal distances in kilometres, we divided the formula result by 1000 to obtain bottom slope in degrees. Slopes for SST and SSH represent fronts that could indicate areas of unique habitat. We used linear interpolation as needed to map the 12 monthly climatolgies onto 26 2-week intervals for the 3 x 3 km2 GOA study area. Data on fish density and fishing effort: We derived data measuring spatial fish density and fishing effort from three sources. ADF&G landings data provide information on effort and catch (including bycatch and discards) delivered to shore-based processors at the spatial resolution of the ADF&G statarea. Data provided by ADF&G include round weight and number of boats, summarized by species, gear, port landed, and month for two calendar years (1998 and 2002). Complete data are available when data represent more than three vessels in an observation. ADF&G also indicated which statareas in a given month had at least one but less than three vessels delivering a given species, gear, port, month combination. We combined all trawl and all fixed gears to increase the number of data points. Since completing a fish ticket is voluntary for the offshore sector, we used only onshore landings in the statistical analysis. ADF&G data were used for testing the methods for mapping spatial fisheries values (Berman 2006), but had insufficient spatial resolution to value fisheries closures related to SSL critical habitat. Alaska Fisheries Science Center personnel kindly provided NMFS trawl biomass survey data for the Gulf of Alaska in 2001 (Alaska Fisheries Science Center 2001). NMFS uses the survey for area-wide stock assessments. The survey records represent individual trawl hauls at over 500 points, providing considerable spatial variation. However, the survey had limited spatial and temporal coverage. All data points lie west of Middleton Island in the Central Gulf. The survey was taken in late spring and early summer, starting in mid-May and ending in mid-July. The survey moved systematically from west to east. Trawl depths for the survey were limited to less than 600 metres. Despite these limitations, the NMFS survey data do provide a spatially random sample taken with standard gear over a large geographic area. The survey data included 421 points that fell within the ROMS model spatial coverage. In addition to haul weight by species and haul duration, each haul also recorded time and location, surface temperature and gear temperature. The fishery catch and effort data used for the 9 x 9 km2 study came from the NMFS fisheries observer program. NMFS made available to the project individual haul records for the North Pacific groundfish fishery from 1993 through 2003. Geographic coverage for the observer data extends to the entire Alaskan fishing grounds. The data include all gears and all species caught, including bycatch and discards. For 2001, approximately 44,000 hauls were observed, including 100 percent of hauls in the Bering Sea and about 30 percent of the hauls in the Gulf of Alaska. Data on species round weights in each haul were estimated from sampling portions of each observed haul. Most observed hauls included bottom depth and fishing depth as well as time and location. In order to join the different data sets spatially, we related the survey locations and GOA observer trawl ending locations and dates to the ROMS model 3 x 3 km2 grid and two-week time step. We also aggregated all observer data to the 9 x 9 km2 grid and at a monthly time step. Observed catch locations may represent a spatially biased sample of fish density, since the fishing fleet is preferentially targeting (or may be avoiding in the case of unwanted bycatch) areas of the ocean where concentrations of fish are more likely to be found. However, one can test and correct for this bias arising from nonrandom spatial selection at the scale of grid, although not at finer scales. Data on fisheries openings and habitat regulations: Fishing seasons, and in-season time and area closures by gear and sector (inshore vs. offshore where different) were derived from public sources (National Marine Fisheries Service 2001b; 2001c). The primary spatial reference for most in-season fisheries regulations is the NMFS three-digit management area. We also obtained information on seasonal time and area closures and gear restrictions related to bycatch, Steller sea lion critical habitat, and other environmental regulations from applicable sections of the Code of Federal Regulations archived on the NMFS Alaska region website (National Marine Fisheries Service 2001d). NMFS management support personnel provided digital spatial data delimiting the geographic boundaries applicable to each separate environmental regulation. In all, we mapped 78 separate closures, each applying to different gears, fisheries, and time periods. We interpolated where necessary to address spatial and temporal overlap between closed areas and periods and the modeled spatial grid cells and time periods. Economic Valuation Of Critical Habitat Closures, Berman et al.   9 Indicators of prices and costs: Primary economic factors relating to the value of catch opportunities consisted of targeted fish species prices and the distance traveled to access fishing areas. Ex-vessel prices for trawl and fixed gear landings in the GOA and Bering Sea were obtained from Alaska Fisheries Science Center (2003). For all fisheries, we calculated distance to port as the one-way distance from the catch grid cell to the grid cell of the nearest port used by any vessel of that gear type (trawl or fixed gear), based on ADF&G landings data. Although fixed-gear boats land catch at numerous ports, trawl vessels landed nearly all their catch at eight Alaska ports in 2001: Dutch Harbor, Akutan, King Cove, Sand Point, Kodiak, Kenai, and Cordova. For offshore fisheries, we also considered the distance between consecutive offshore hauls, but rejected that measure in favour of a different approach, which we describe below. Statistical methods We conducted our analyses at three different spatial scales, using increasingly detailed information about the oceanic environment and fishing-related costs and revenues as we moved from coarser to finer spatial scales. We first estimated monthly results for the Bering Sea and Gulf of Alaska at the resolution of ADF&G statistical areas with data for calendar year 1998. Fishing was relatively unregulated in terms of spatial closures in 1998, with the exception of regulations set by NMFS management areas. After this year, habitat closures, which could not be modeled adequately at this coarse a spatial resolution, increasingly began to affect fishing location choices. We then estimated results for the Bering Sea and Gulf of Alaska at 9 km resolution, using a monthly time step for calendar year 2001. This was the year NMFS adjusted the Steller sea lion regulatory regime to comply with a court order. We had originally hoped to examine these data for additional years, in order to determine the stability of the relationships over the years. However, the complexity of fisheries and environmental regulations, which changed spatially and temporally each year, made this impractical. Finally, we estimated results for the Gulf of Alaska, 2001, at a 3 km resolution using the ROMS output to predict environmental conditions at fishing locations. We estimated equations to explain variation in CPUE as a function of the environmental variables available at each particular scale. For both the 9 and 3 km analyses, data from the NMFS observer program included all species caught in each haul, regardless of whether that species was targeted or not. Consequently, a catch of zero could be inferred for hauls that did not report any catch of a given species. In order to use the zero-landings information to improve predictions of the spatial distribution of different species, we estimated CPUE equations from the observer data using censored regressions (tobit). On the other hand, data available from ADF&G were not available by individual vessel or haul, and included information only from vessels that reported landings of that species. Since it was not possible to infer zero catches from such data, we estimated CPUE for the statareas using ordinary least squares. For all CPUE equations, we obtained a large improvement in the statistical fit by using a loglinear specification, or more precisely (to accommodate the zero observations), the natural logarithm of CPUE + 1. For the 9 and 3 km analyses, we tested and corrected for sample selection bias as necessary to take into account potential correlation between the spatial distribution of variables determining fish distribution and the distribution of fishing effort. To quantify how predicted CPUE, economic variables, and regulatory factors influenced the distribution of fishing effort, we estimated Poisson and negative binomial regressions (Berman 2006). Berman (2006) also illustrated how closely the Poisson regression approximates the multinomial logit model, using example fisheries estimated from the ADF&G statareas. For the 9 and 3 km studies, all equations were estimated using standard maximum likelihood techniques.  Economic Valuation Of Critical Habitat Closures, Berman et al.  10 RESULTS Statistical analysis proceeded sequentially in three stages. First we estimated censored normal (tobit) regression equations explaining the spatial distribution of CPUE in the data source at each time step, using the coefficients to project variation in expected CPUE over the entire study area. Second, we estimated Poisson and negative binomial regressions explaining the spatial distribution of fishing effort at each time step as a function of projected CPUE and economic factors, to obtain a set of spatial profit functions. Third, we used the coefficients from the spatial profit functions to estimate the opportunity cost to the fishery of closing specific areas related to designation of Critical Habitat for Steller sea lions. Berman (2006) discussed the analysis at the level of the ADF&G statarea. This resolution was ideal for developing the theoretical method, and demonstrating the ability of the Poisson regression to approximate closely the multinomial logit equation for the large number of alternatives needed to model site choice realistically. Although Berman (2006) estimated a distribution of value for onshore groundfish fisheries across more than 1,000 statareas in the North Pacific, the spatial resolution of the statarea was too coarse to apply to evaluating the complex spatial geography of Steller sea lion habitat closures, so we do not discuss it further here. Instead, we focus on results obtained for the Bering Sea/Aleutian Islands and Gulf of Alaska (North Pacific) at a 9 km resolution, based on remote sensed environmental data, and for the Gulf of Alaska (GOA) at a 3 km resolution, using output of the GOA ROMS model in addition to the remote sensing data. Spatial fish density Results from the GOA 2001 bottom trawl survey. Because the trawl survey is a spatially random sample taken during a relatively brief (approximately 2-month) time period, estimating CPUE is straightforward. The spatial sampling is stratified to improve accuracy (National Marine Fisheries Service 2001c). Since our interest is in understanding spatial variation fish density rather than total area- wide biomass, we ignored the strata weights in our analysis. The 2001 GOA survey contains 521 sample points, of which 415 observations lie within the boundary of the ROMS model. Nearly all the 106 excluded sample points were in the west end of the GOA (western portion of NMFS regulatory area 610). Figure 1 displays the geographic location of 2001 trawl survey points in comparison to the observed hauls in that year. The 2001 Gulf of Alaska trawl survey was conducted from May 20 through July 23. Oceanographic model outputs were obtained for 14-day periods. The Julian days representing the start of each model period, determined by the correspondence between the available survey data and the ROMS model output, are 137, 151, 165, 179, and 193. Table 1 contains the precise definition of variables included in the 3 km Gulf of Alaska CPUE analysis using the survey data. The dependent variable is the natural logarithm of CPUE, defined as total round weight for each species divided by trawl duration (kg/hour). Individual species considered included pollock, Pacific cod, black cod, and halibut. In addition, we aggregated all flatfish and rockfish species into two species group categories. These represent the main target and bycatch fisheries for groundfish vessels in the GOA. We estimated separate equations for all hauls, and for hauls with average fish weight greater than a given threshold (representing hauls with likely commercial value), derived from the distribution of average fish weights in hauls for each species or species group. Wind data were available for only about two-thirds of the observations. Consequently, we estimated separate equations with and without wind. This yielded four equations for each species: all and large fish, with and without wind.  Figure 1. Location of 2001 GOA trawl survey points (orange) and observed haul locations (green). Economic Valuation Of Critical Habitat Closures, Berman et al.   11 Appendix A contains the complete set of CPUE equation results estimated from the 2001 trawl survey for the Gulf of Alaska study. Equations were estimated as censored normal regressions (tobit), by maximum likelihood. Due to high collinearity of the set of modeled and observed environmental variables, we dropped variables with a probability > 0.3 (absolute value of t-statistic approximately equal to 1) from the equations to increase robustness of the predictions. With separate intercepts for each time period, coefficients should be interpreted as effects of spatial anomalies. In other words, coefficients, except for those for the time periods and constant term, would be identical if all variables were transformed to represent deviations from the respective time-period mean. Since only one observation had wind data in the first time period, the wind equations combine intercepts for the first two periods. The tables in Appendix A display the respective CPUE equations in pairs. The bottom equation on the page is the censored regression; the top equation reports the corresponding ordinary least squares regression for reference. Censoring the equation at a minimum of zero is necessary to avoid generating predictions of negative CPUE, especially in the equations for large fish. Unfortunately, no generally recognized measure of goodness of fit such as R2 exists for the censored equations, which combine a probit and a linear regression. Figure 2 summarizes the results in Appendix A by displaying the level of statistical significance and direction of effect of the set of environmental variables in the multivariate analysis for all fish. Quadratic associations imply an optimal habitat that lies within the range of observations for that variable. The equations fit best for black cod and rockfish, and least well for Pacific cod. The linear regression equations excluding small fish have lower R2 than the corresponding equations for all fish, but not necessarily a worse fit if censoring is taken into account. In general, the equations show a different pattern of significant variables across species, suggesting habitat selection. Several modeled environmental variables such as salinity, temperature at depth and velocity variables have significant estimated effects for each species, suggesting that the ROMS model successfully enables downscaling the CPUE analysis to the 3km scale. Economic Valuation Of Critical Habitat Closures, Berman et al.  12  Table 1. Environmental variables used for 3 km Gulf of Alaska CPUE equations. Variable Definition Source constant constant term (intercept for period 12) calculated herm13 dummy variable for two-week period starting with julian day 151 trawl survey date herm14 dummy variable for two-week period starting with julian day 165 trawl survey date herm15 dummy variable for two-week period starting with julian day 179 trawl survey date herm16 dummy variable for two-week period starting with julian day 193 trawl survey date ldepth natural logarithm of bottom depth, in metres trawl survey l2dep square of nat. log of bottom depth calculated timeldep julian day times ldepth trawl survey lslope natural logarithm of slope at 3km resolution NOAA bathymetry l2slope square of nat. log of slope calculated lmld natural logarithm of mixed layer depth, in metres Hermann model lmld_dep nat. log. of mixed layer depth times nat. log. of bottom depth calculated surftemp surface temperature trawl survey geartemp gear temperature at fishing depth trawl survey gtemp2 square of gear temperature calculated tempdep temperature at first model level below mixed layer depth Hermann model mstemp temperature at depth minus surface temp Hermann model bmtemp bottom temperature minus temperature at depth Hermann model lstem natural logarithm of surface temperature trawl survey lgtem natural logarithm of gear temperature at fishing depth trawl survey l2gtem square of natural logarithm of gear temperature calculated lmtem natural logarithm of temperature below mixed layer depth Hermann model lmstem nat. log of temperature at depth minus nat. log of surface temp Hermann model lbmtem nat. log of bottom temperature minus nat. log of temp at depth Hermann model lmsal natural logarithm of salinity below mixed layer depth Hermann model lmssal nat. log of salinity at depth minus nat. log of surface salinity Hermann model lbmsal nat. log of bottom salinity minus nat. log of salinity at depth Hermann model vervelbm vertical velocity at level below mixed layer depth (10-3m/s) Hermann model msvervel vertical velocity at depth minus vertical vel. at surface (cm/s) Hermann model bmvervel vertical velocity on bottom minus vertical vel. at depth (cm/s) Hermann model horvelbm horizontal velocity at level below mixed layer depth (cm/s) Hermann model mshorvel horizontal velocity at depth minus hor. vel. at surface (cm/s) Hermann model bmhorvel horizontal velocity on bottom minus hor. vel. at depth (cm/s) Hermann model ssh modeled sea surface height, in metres Hermann model sshrs average monthly sea surface height, in metres x 10-2 Remote-sensed lchla natural logarithm of chlorophyll, current period Remote-sensed lchla1 natural logarithm of chlorophyll, one-period (14-day) lag Remote-sensed lchla2 natural logarithm of chlorophyll, two-period (28-day) lag Remote-sensed lwind natural logarithm of average wind speed Remote-sensed  Economic Valuation Of Critical Habitat Closures, Berman et al.   13 Equations including wind  Variable P. cod pollock black cod halibut flatfish rockfish Modeled environment Bottom depth 22 ++ 22 -- 22 22 Bottom slope + + ++ Mixed layer depth ++ -- Bottom temp. 22 22 - + Temp. gradient + ++ ++ + - Salinity at mld 22 2 ++ - + Salinity gradient - -- + ++ Vertical velocity - Vert. vel. gradient + Horiz. velocity ++ -- Hor. vel. gradient - + -- Sea surface height + Remote sensed environment Sea surface temp. -- - Sea surface height ++ + - Chlorophyll-a ++ ++ ++ Lagged chl-a + + -- Wind + - - R sq. OLS 0.28 0.33 0.64 0.35 0.30 0.45   Equations excluding wind  Variable P. cod pollock black cod halibut flatfish rockfish Modeled environment Bottom depth 22 ++ + -- 22 2 Bottom slope 2 + + ++ Mixed layer depth ++ -- Bottom temp. 22 22 22 - + Temp. gradient + ++ ++ + -- Salinity at mld 22 2 2 - 2 Salinity gradient - -- ++ ++ Vertical velocity Vert. vel. gradient ++ - Horiz. velocity -- ++ -- Hor. vel. gradient -- + -- Sea surface height + Remote sensed environment Sea surface temp. -- - Sea surface height + - Chlorophyll-a ++ ++ ++ ++ Lagged chl-a + -- -- R sq. OLS 0.27 0.33 0.58 0.35 0.30 0.48 Significant positive association + Positive association < .01 ++ Significant negative association - Positive association < .01 -- Significant quadratic association 2 Quadratic association < .01 22  Figure 2. Variables explaining the spatial distribution of fishing effort, Gulf of Alaska, Summer 2001: Equations including wind (top panel) and excluding wind (bottom panel). Red denotes a significant positive association, blue a significant negative association, and purple a significant quadratic effect. Economic Valuation Of Critical Habitat Closures, Berman et al.  14 Results from the North Pacific 2001 NMFS observer bottom trawl hauls. NMFS observer data provided a large sample of CPUE taken over the entire area and time period used by the fishery. However, unlike the trawl survey, the observer data – indeed any data derived from commercial fishing activity – do not constitute a spatially random sample. One would assume that the fishing fleet preferentially samples areas of the ocean that have target and desired bycatch species present. The fleet may also avoid areas with concentrations of unwanted bycatch species. Not taking this correlation into account could lead to the classic problem of sample selection bias, where the explanatory variables in the relationship predicting CPUE are correlated with the error term. We included all hauls in each CPUE equations, not just those hauls targeting the given modeled species, and ignored the differential rate of observer sampling in the GOA vs. BSAI regions. Including all hauls, rather than just those hauls targeting the given modeled species, increases the sample size and geographic dispersion, as well as reducing the effect of selection bias. However, one must assume that selection may still exist. The simplest method to correct for selection bias is to apply the Heckman (1979) procedure. The Heckman correction involves a two-step process. First one estimates a probit equation for the probability that a grid cell has observed fishing activity. This is essentially a reduced-form equation that combines the variables predicting CPUE with those predicting distribution of fishing effort. From the probit equation, one calculates the inverse Mills ratio (IMR), defined as the ratio of the standard normal density to the cumulative probability for each observation, and includes the IMR as an additional explanatory variable in the CPUE equation. Heckman (1979) discusses how to adjust the standard errors for the second step equation. A more statistically-efficient method would be to estimate the combined probit selection equation and CPUE equation using full information maximum likelihood (FIML). This approach would be preferred in theory where the CPUE equation is a censored regression (observed hauls with zero catch of a given species). In the present case, however, the situation is more complex. The FIML procedure considers the joint probability of the fishing location and presence of a species at that location jointly as a bivariate normal distribution. Applying FIML to estimate CPUE for each species would yield a different set of coefficients for the probit selection equation for each species, based on correlation with the particular CPUE equation. However, the probit equation models the same location choice for all species, so its coefficients should be the same across all species. This more accurately models the process when the species addressed in the CPUE equation represents the dominant target species for the entire fishery (such as pollock in pelagic trawl hauls). On the other hand, applying the Heckman (1979) procedure to a censored regression would estimate the probability that a species is present, along with the CPUE, given the probability of a haul occurring at that location. This more closely models the process when there are many target species represented in the data set of hauls (as is the case for bottom trawl hauls). In theory one could estimate FIML results for the system of equations for all the species combined, along with a single probit selection equation. However, since each species would have its own correlation coefficient with the probit, the FIML multivariate normal equation would be infeasible to estimate in practice. Economic Valuation Of Critical Habitat Closures, Berman et al.   15  Table 2. Environmental variables for the 9 km Bering Sea/Aleutian Islands and Gulf of Alaska CPUE equations. Variable Definition Source constant constant term (intercept for January) calculated feb dummy variable for observation in February observer haul date mar dummy variable for observation in March observer haul date apr dummy variable for observation in April observer haul date may dummy variable for observation in May observer haul date jun dummy variable for observation in June observer haul date jul dummy variable for observation in July observer haul date aug dummy variable for observation in August observer haul date sep dummy variable for observation in September observer haul date oct dummy variable for observation in October observer haul date nov dummy variable for observation in November observer haul date dec dummy variable for observation in December observer haul date goa dummy variable for observation in Gulf of Alaska observer data ldepth natural logarithm of bottom depth, in metres observer data l2dep square of nat. log of bottom depth calculated timeldep month times ldepth calculated slope slope at 3km resolution, in degrees NOAA bathymetry 2slope square slope calculated sst surface temperature, degrees C remote sensed sst2 square of absolute value of surface temperature calculated sstslope square of sea surface temperature, in degrees calculated ssh average monthly sea surface height, in metres x 10-2 remote sensed sshslope slope of sea surface height, in degrees calculated lwind natural logarithm of average wind speed remote sensed mwind natural log. of average wind speed, monthly means for missing values  calculated mchla natural logarithm of chlorophyll, monthly means for missing values remote sensed mchla1 previous month’s value for mchla remote sensed gtimelde goa times timeldep calculated gsst goa times sst calculated gssh goa times ssh calculated glwind goa times lwind calculated gmwind goa times mwind  calculated gmchla goa times mchla calculated gmchla1 goa times mchla1 calculated bottomt observer haul location  (=1 if haul observed; 0 if no haul observed) observer data imrx inverse Mills ratio from applicable probit equation number x calculated portdist distance to port calculated xxxtrawl openings for trawl fishery xxx: pol=pollock, cod=P. cod, atk=Atka mackerel NMFS regulations xxxtssl habitat closures for trawl fishery xxx: same as above, plus mix=other trawl NMFS regulations Economic Valuation Of Critical Habitat Closures, Berman et al.  16 Using the 2001 observer haul data, we estimated censored CPUE regressions both with the Heckman method and using FIML for the main target and desired bycatch species for trawl fisheries. As defined above, these are pollock, Pacific cod, Atka mackerel, black cod, and rockfish and flatfish species groups. Where multiple hauls were observed in the same grid cell during the same month, we averaged the CPUE for the respective hauls. We estimated separate equations for winter and summer seasons, where summer is defined as the months of May through October, in order to allow for different behaviour of groundfish during spawning and non-spawning seasons. Table 2 summarizes the exact definition of the explanatory variables available for this analysis, including those needed to estimate the selection probit equations.  As in the previous analysis using survey data, the dependent variable is the natural logarithm of CPUE plus one, where CPUE is defined as total weight (extrapolated to the haul from observed samples) divided by haul duration. CPUE units in the observer equations are tonnes per hour. Although the survey CPUE is measured with standard gear, heterogeneity of the trawl fishing fleet could affect measured CPUE. We created a measure of ‘standard CPUE’, defined as the CPUE for a standard, or average-sized boat. We computed standard CPUE in two steps. First, we created an inferred capacity from the maximum haul weight for all species combined, observed for each vessel over the entire year. Then we created standard CPUE by multiplying observed CPUE by the ratio of the average inferred capacity for the fleet (113.8 tonnes) to the inferred capacity of the vessel. For all species, equations for standard CPUE showed a significantly better fit. As discussed in the next section, however, while the environmental data produced a much better fit for the standard CPUE equations for Pacific cod than for the equations for raw CPUE, predictions from the raw CPUE equations provided a much better prediction of the distribution of the Pacific cod fishing fleet. We therefore report both standard and raw CPUE equations for Pacific cod. We included only data from bottom trawl hauls, in order to maintain as much consistency as possible. Since the pollock fishery is primarily a pelagic trawl fishery (exclusively so by regulation in the Bering Sea), we also estimated equations for pollock from pelagic trawl hauls. Pollock CPUE equations estimated for the two gear groups produced very similar results. We therefore report only the bottom trawl results. Appendix B shows the complete estimation results for three sets of equations for each species each season: (1) ordinary least squares using the Heckman (1979) procedure (with corrected standard errors); (2) censored regressions using the Heckman approach, and (3) FIML results. The tables leave out the FIML probit coefficients, which differ slightly for each species. Instead, we show the very similar probit equations for the first stage Heckman selection process. Including all hauls, rather than just those hauls targeting the given modeled species, reduced the effect of selection bias. Indeed, the selection effect, as measured by the coefficient on the IMR, is not significantly different from zero in many of the CPUE equations, and the three specifications often yielded conflicting results regarding the significance and direction of  selection bias . Although the selection effect is not robust among the specifications, the three equations generally yielded similar effects for the set of environmental variables. Figure 3 summarizes the results for the censored regressions (second specification) in Appendix B.  Economic Valuation Of Critical Habitat Closures, Berman et al.   17 Winter months  Variable pollock P. cod A. mackerel black cod rockfish flatfish Measured environment Bottom depth 22 22 22 22 22 Depth over time + + -- ++ Bottom slope 22 22 22 22 22 Remote sensed environment Sea surface temp. 22 22 22 22 SST slope - - + - Sea surface height ++ ++ -- ++ -- ++ SSH slope ++ ++ Wind speed + -- -- Chlorophyll-a -- ++ Lagged chl-a - - R sq. OLS 0.20 0.38 0.48 0.32 0.31 0.30 Selection bias + + -- -  Summer months  Variable pollock P. cod A. mackerel black cod rockfish flatfish Measured environment Bottom depth 22 22 22 + 22 22 Depth over time + + Bottom slope 22 -- 22 -- 22 22 Remote sensed environment Sea surface temp. + 22 22 22 SST slope + Sea surface height + - ++ ++ SSH slope + Wind speed - Chlorophyll-a -- - ++ Lagged chl-a - -- - -- R sq. OLS 0.24 0.23 0.39 0.36 0.50 0.29 Selection bias - -- -- ++ - Significant positive association + Positive association < .01 ++ Significant negative association - Positive association < .01 -- Significant quadratic association 2 Quadratic association < .01 22  Figure 3. Variables explaining spatial distribution of fishing effort, Bering Sea/Aleutian Islands and Gulf of Alaska, 2001: Winter months (top panel) and Summer months (bottom panel). Red denotes a significant positive association, blue a significant negative association, and purple a significant quadratic effect. Source: Appendix B  Economic Valuation Of Critical Habitat Closures, Berman et al.  18 The observer CPUE equations fit best for Atka mackerel in winter and rockfish in summer, and least well for pollock in both seasons, and Pacific cod in summer. One might expect that the habitat indicators would have less ability to explain location and density of more mobile species. The equations differ significantly between summer and winter for most species, especially with respect to the direction and significance of the remote-sensed variables. Since the environmental variables change strongly between summer and winter, as noted in means and standard deviations displayed with the equation results, the different equation coefficients do not necessarily imply that the fish have moved far between summer and winter habitats. Spatial distribution of effort The aim of the statistical analysis is to derive a profit function – that is, a relationship for Vijk (Equation 2) based on associating the spatial distribution of observing fishing effort with a set of factors representing spatial variation in fishing costs and revenues. We hypothesized that spatial variation in predicted CPUE – whether derived from survey data or from the fishing fleet as a whole – would be highly correlated with spatial variation in revenues from fishing. Important spatial cost factors (discussed above) include distance to port and fishery regulations creating time and area closures and gear restrictions. Each statistical relationship that predicts the spatial distribution of CPUE for a given species can potentially generate an estimated equation for the spatial distribution of effort. We first discuss results derived from the Gulf of Alaska 2001 bottom trawl survey, and then examine results estimated using CPUE predictions derived from fisheries observer data. Results from the GOA 2001 bottom trawl survey. In practice, limits on spatial and temporal scales and geographic extents in available predictions of CPUE determine the scale and boundaries of the analyses that can be performed for spatial distribution of effort. The Gulf of Alaska 2001 bottom trawl survey generates profit functions at a very fine spatial scale (3 x 3 km2 grid). However, our analyses using the trawl survey are limited in spatial extent by the geographic coverage of the GOA oceanographic model since the CPUE predictions rely on the model output. For the GOA, we also limited the choice set to the bottom depths included in the survey (less than 600 m), which also approximates the deepest trawl haul observed in the GOA fisheries. The time frame for the survey data collection also limited the GOA effort analysis. The 2001 survey was conducted during the summer months – late May through late July. The only fisheries open for a substantial portion of the survey period were the sablefish and halibut longline fisheries, and some flatfish trawl fisheries. Rockfish trawling was open during a three-week period in July. To estimate equations relevant to the pollock and Pacific cod trawl fisheries – the largest GOA fisheries and the ones mainly affected by Steller sea lion critical habitat designations – we had to project the CPUE equations past the end of the range of the data used to estimate those relationships. We hypothesized that the relationship of the environmental variables to CPUE estimated during the survey period held for the duration of trawl fishing in 2001 (late August to late October). Examining whether an equation estimated from early summer data predicts the distribution of fishing effort in fall, controlling for other relevant factors, tests the hypothesis of stability of the relationship, providing a mechanism to validate the overall method. One could in theory also project the CPUE equations forward into the early spring and winter months. However, we doubt that such an out-of-season prediction would be valid, due to possible behavioural changes in groundfish during the spawning season. Whether or not the equations are used to predict CPUE beyond the time horizon of the data used to estimate the relationships, generating predictions for the entire Gulf of Alaska involves projecting outside the range of the independent variables observed in the roughly 400 survey sample locations. This is basically a problem of boundary conditions. The range of modeled temperature and salinity, as well as remote-sensed variables such as chlorophyll-a and sea surface temperature, extends beyond the range of these variables in sampled areas. This is particularly a problem in estuaries and other coastal environments. The problem is magnified by the fact that the CPUE equations are estimated as loglinear equations (which yields a much better fit), so the errors, after converting to CPUE units, are exponential. One way of handling the data range problem is to limit the CPUE predictions to the range of the observations on the independent variables within the sampled points. This approach would be appropriate for handling out-of-sample prediction within the time horizon of the survey data set. Unfortunately, such Economic Valuation Of Critical Habitat Closures, Berman et al.   19 an approach becomes problematic when extending predictions over time, as the ranges of the variables change seasonally. We opted instead to use the predictions generated by the equations for large fish, and censor a small percentage of high CPUE predictions. In essence, this approach assumes that the CPUE equations generate good predictions of suitable habitat for mature fish, but cannot predict high abundance locations within that habitat. The rationale is based on the scale of spatial aggregations of fish compared to the spatial scale of the data. Within any 9x9 km2 area representing a grid cell, we expected that the trawl survey would find the species present in significant numbers if environmental conditions favour it, but would only randomly find large aggregations within suitable habitat. The fishery, on the other hand, will search for such aggregations within the local area.  Given the censoring approach, there remains the question of how to choose the limit for each fishery. We determined the censor with a stepwise process based on the log likelihood of the equation for the distribution of fishing effort. We reduced the upper limit of predicted CPUE by one integer level of predicted natural logarithm of CPUE at a time, until the log likelihood stopped increasing. This occurred for pollock at a value of 7, and at a value of 3 for Pacific cod. One test of the validity of such an approach is whether the profit functions it generates appear reasonable. Table 3 shows the best-fitting negative binomial regressions for the distribution of GOA shore-based pollock and Pacific cod trawl fishing effort during fall 2001, as a function of censored CPUE predictions generated from the 2001 bottom trawl survey. The shore-based pollock and Pacific cod fisheries are the main GOA fisheries affected by the final Steller sea lion regulations, which went into effect on July 17 of that year. Because average CPUE changes each time period, often along with fisheries and habitat regulations, a separate intercept term is required for each time period to represent the overall value of fishing opportunities during the period: the parameter γ'k in Equation (5). The constant term represented the intercept for model period 19 (August 23-September 5), the first summer period during which any landings were recorded for either fishery. Coefficients for the other periods represented effects relative to period 19. Pacific cod trawling occurred in the GOA in period 19, but was closed for the duration of period 21. Fishing ended for both fisheries on October 31 (period 23).  The negative binomial equations in Table 3 exhibit a high degree of dispersion: the variance scale factor is around 100: that is, the variance is 100 times the mean. The high variance creates convergence problems for the algorithm, as small changes in the scale factor have little effect on the log likelihood or the other equation coefficients. The variance multiplier for pollock had to be approximated by estimating the equation with a fixed value for the scale factor, changing the value until the log likelihood stopped increasing. Nevertheless, the equation results for both pollock and Pacific cod appeared reasonable. The coefficients on expected censored CPUE are positive and significant, and the coefficients on distance to port are significant and negative. We excluded grid cells from the choice set that were subject to regulatory closures during the entire model period. For pollock, some hauls occurred in areas that were open for a portion of the period. The coefficients on the regulatory variables – the fraction of the time included in a fishery opening and the fraction of the time subject to a habitat closure, respectively – have the expected signs and are highly significant. For Pacific cod, hardly any hauls were observed in such partially closed areas, so no coefficients could be estimated. GOA directed trawl fisheries besides pollock and Pacific cod include rockfish and flatfish fisheries. These fisheries are covered under a diverse set of regulations and are exempt from most, but not all the Steller sea lion habitat regulations. A distinguishing characteristic of these fisheries is the dependence on retained bycatch for valuable species – primarily black cod, but also rockfish for some vessels when the directed fishery is closed – for additional revenue. For the shore-based fishery in particular, it is often difficult to determine what the target species is for a given vessel based on an individual observed haul. Consequently, we estimate a single equation for ‘other’ mixed trawl fisheries. Economic Valuation Of Critical Habitat Closures, Berman et al.  20 Table 4 shows the best-fitting negative binomial regressions for the distribution of fishing effort estimated for GOA other trawl fisheries. For the shore-based fisheries, large rockfish, black cod, and flatfish all significantly predict location choice. Using the same censoring procedure as before, the best fit is achieved at a censor of 3 for rockfish, 4 for flatfish and black cod, and 6 for pollock. The constant term represents period 12; the intercepts for the remaining periods represent effects relative to period 12. Port distance is again negative as expected, and highly significant. Pollock is abundant in the GOA, but of lower value, especially for smaller fish. The coefficient on all pollock is negative and significant. With the variety of desired and unwanted species, a tradeoff between expected revenue and CPUE for other onshore trawl fisheries similar to those displayed in Figure 4 was not straightforward to construct. However, the coefficient on port distance was similar to those for the other two shore-based fisheries. Several offshore trawl fisheries operate in the Gulf of Alaska. All GOA pollock is allocated to the shore-based sector, and too little Pacific cod is allocated to the offshore sector to estimate a choice equation. No mother ships operated in the GOA in 2001, but a number of trawl catcher-processors prosecuted the rockfish and flatfish fisheries. During the period in summer that rockfish was permitted as a target species (model periods 15 and 16), enough hauls (279) were observed to estimate a choice equation for offshore fishing effort. The primary target for most of these hauls appeared to be rockfish. However, black cod, which has a high market value, remains an important bycatch species.  Distance to port is not important for GOA offshore fisheries, since no portion of the Gulf lies particularly far from at least one port with ability to accommodate a mid-sized trawl catcher-processor. As mentioned above, we considered the distance between consecutive offshore hauls as a measure of travel cost. While highly significant when included in the equation, such an approach to measuring travel cost is of little practical value, for two reasons. First, modeling distance between hauls requires constructing a separate choice set for each grid cell in which a haul is observed, since that cell becomes the starting point from which to measure distance to the next haul. One would have to construct thousands of choice sets, which, even with sampling, would involve millions of observations in order to model the fishery. In other words, it negates the advantage of the count models for partial aggregation of the large spatial choice set. Second, knowing that a cell was selected because it was close to a previous haul location provides insufficient information for predicting spatial values. One would need to model the set of consecutive hauls in a nested choice framework. In particular, one might assume a nested choice model, where the probability of selecting a grid cell is a function of that cell’s CPUE and the “inclusive value” (McFadden 1981), representing the value of the set of future opportunities from the series of consecutive future hauls. The inclusive value at each level depends on the value of the unknown parameter for the inclusive value at a lower choice level (corresponding to the next haul). With dozens of consecutive hauls during a fishery, and with thousands of choices for each haul, the likelihood function would be infeasible to compute, even if one could construct the set of inclusive values data set.  Given these challenges, we decided to experiment with a simple approach that addressed travel distance conceptually in a nested model, but in a way that was easy to understand conceptually and straightforward to estimate. The inclusive value at each stage (haul) is a function of CPUE, the distance to a subsequent haul, and the subsequent haul’s inclusive value. If travel distance between hauls is typically low, much of the variance in inclusive value will be captured with a measure of average CPUE in the neighbourhood around the grid cell in question. Figure 4. Profitability tradeoffs implied by the distribution of fishing effort: Gulf of Alaska 2001. 0 1 2 3 4 5 0 6 12 18 24 30 36 42 48 54 60 66 72 78 84 90 Distance to port (km) Summer/fall Pollock catch Summer/fall Pacific codEconomic Valuation Of Critical Habitat Closures, Berman et al.   21 Table 3. Negative binomial regressions for distribution of Fall 2001 Gulf of Alaska onshore pollock and Pacific cod trawl fishing effort: Maximum likelihood estimates (t-statistics in parentheses). The dependent variable was the number of target fishery hauls observed. pollock Pacific cod Constant -8.791 *** -4.517 *** (-6.35) (-11.0) Period 20 1.929 *** -- (2.64)  Period 21 0.9192  -2.281 *** (0.79) (-5.17) Period 22 -1.110 *** 0.6378 *** (-4.84) (2.61) Period 23 -0.5618 ** -0.6534 ** (-2.05) (-2.02) Fishery regulatory opening 5.5433 ***  (3.91)  Fishery SSL habitat closure -1.4517 ***  (-5.39)  Expected survey CPUE 0.0007414 *** 0.07873 *** (3.02) (3.49) Distance to port (km) -0.01665 *** -0.01223 *** (-14.36) (-7.46) Variance Scale 100 136.28 *** -- (6.42) Log-likelihood -898.5455 -875.1 Initial Log-Likelihood -1350.877 -1312.6 Likelihood ratio squared 904.7 875.0 Observations 105,205 93,219 ** p < 0.05 *** p < 0.01  Economic Valuation Of Critical Habitat Closures, Berman et al.  22  Table 4. Negative binomial regressions for distribution of Summer and Fall 2001 Gulf of Alaska onshore and offshore ‘Other’ trawl fishing effort: Maximum likelihood estimates (t-statistics in parentheses). The dependent variable was the number of target fishery hauls observed.  onshore  offshore  Constant -5.6947 *** -6.6377 ***  (-4.92) (-25.67)  Period 13 -1.7916 *    (-1.86)   Period 14 -1.9221 *    (-1.86)   Period 15 0.25172    (0.28)   Period 16 0.37629 0.3170   (0.56) (1.59)  Period 17 0.05481    (0.08)   Period 19 -0.04711    (-0.07)   Period 21 -3.1653    (-1.35)   Period 22 -0.08453    (-0.12)   Period 23 -1.6386    (-1.53)   Expected CPUE, rockfish 0.09464 *   0.02694 ***  (1.73) (3.06)  Expected CPUE, flatfish 0.03951 **    (2.15)   Expected CPUE, black cod 0.02056 * 2.082E-04 *  (1.69) (1.83)  Expected CPUE, pollock -0.01313 **    (-2.07)   Expected neighbourhood    0.06038 **     CPUE, rockfish (2.94)  Distance to port (km) -0.01282 ***    (-5.40)   Variance Scale 50 150   -- --  Log-likelihood -308.0 -946.5  Initial Log-Likelihood -2840 -1246  Likelihood ratio squared 5064 *** 599.7 *** Observations    22,403 42,435  *p < 0.1 ** p < 0.05 ***p < 0.01  Economic Valuation Of Critical Habitat Closures, Berman et al.   23 In the hauls observed from the North Pacific trawl fisheries in 2001, travel distance between hauls was less than 30 km about 80% of the time. We constructed a measure of rockfish neighbourhood CPUE that was a simple average of the predicted CPUE of all grid cells within 30 km of each cell. Cells within this range that were on land or otherwise unavailable for fishing – for example, with water depths deeper than 600 m – were included in the average with a zero value. The second column of coefficients in Table 4 shows the results of estimating the negative binomial equation for spatial choice for the GOA offshore ‘Other’ trawl fishery. The constant term for the offshore fishery represents period 15; the intercept for period 16 represents an effect relative to period 15. The censoring procedure produced the highest log likelihood at a value of 4 for the natural log of rockfish CPUE, 8 for black cod, and 3 for the 30 km rockfish neighbourhood CPUE. The coefficient on rockfish CPUE, black cod bycatch, and the rockfish neighbourhood CPUE are all positive and significant.  Results for the 2001 North Pacific trawl fisheries at 9 km resolution. Scaling up to the North Pacific and moving from the oceanographic model output to remote-sensed observations as the basis for predicting CPUE required several additional factors to be addressed. In particular, the offshore fisheries in the Bering Sea are large and complex. The Atka mackerel fishery is an important offshore fishery that is specifically included in the Steller sea lion habitat regulations. A number of large mother ship fleets operate in the Bering Sea. However, observers on mother ships cannot determine exactly where the haul was caught, and many hauls could have occurred in a grid cell other than the one recorded for the mother ship when the haul was brought aboard. Consequently, we consider only catcher-processors to represent the offshore fleet.  The larger catcher processors that operate in the Bering Sea can trawl to deeper levels. We therefore used 700 m as the maximum depth. We did not censor the CPUE predictions, since they derive from equations estimated from the fisheries with the same data set. The larger grid cell size, however, limits the flexibility of the neighbourhood CPUE calculation. Neighbourhood CPUE for the offshore fisheries in the 9 km analysis averaged CPUE in all grid cells within 20 km of the target cell, and excluded the central cell from the calculation. In addition, we used data for all 12 months of the year, so the choice equations included hauls for the entire period in 2001 during which directed fishing for that target species was permitted. Tables 5 and 6 show the negative binomial regression results for the distribution of fishing effort for 2001 Bering Sea and Gulf of Alaska shore-based and offshore trawl fisheries, respectively. We considered pollock, Pacific cod, Atka mackerel (offshore only), and rockfish target fisheries. The fishing regulations for the diverse Bering Sea flatfish fisheries are so complex that we did not attempt to estimate equations for this fishery, but rather used rockfish as a basis from which to generalize to the other trawl fisheries. The constant term represents the month of January; the other months represent effects relative to January for each month where hauls were observed. In addition to separate intercepts for each time period, we used a separate intercept for GOA grid cells to represent the effects of a difference in the observer coverage, as well as differences in fishery regulations in the Gulf of Alaska. Where a t-statistic is missing for the variance scale factor, the scale was estimated iteratively, as discussed above. Partial months’ fishery openings and habitat closures (as discussed above for the GOA) again significantly explain the distribution of effort for pollock, as well as the offshore Atka mackerel fishery. Offshore pollock fishing is prohibited within the Catcher Vessel Operating Area (CVOA). The positive, significant coefficient for the CVOA suggests a congestion effect, or some pathway by which competition with the offshore fishery affects profitability of the onshore fleet.  Economic Valuation Of Critical Habitat Closures, Berman et al.  24  Table 5. Negative binomial regressions for the distribution of 2001 Bering Sea and Gulf of Alaska shore- based trawl fisheries maximum likelihood estimates (t-statistics in parentheses). Dependent variable: number of target fishery hauls observed.  pollock  P. cod  rockfish Constant -1.2498 * -4.6277 *** -6.8094 ***  (-1.90) (-6.14) (-4.25) February 2.4079 ** 2.0584 ** 1.231   (2.39) (2.19) (0.97) March 3.6583 *** 5.4063 *** 4.3474 **  (3.33) (4.73) (2.34) April -0.74609  3.4613 *** 4.5357 **  (-0.69) (3.56) (2.35) May   2.112 *  (1.75) June -2.2732 ***  0.59191   (-2.69) (0.46) July -0.4371  0.26946  2.9205   (-0.42) (0.31) (1.30) August -2.6464 * -0.6889  2.8411   (-1.80) (-0.42) (1.33) September -2.5728 * 0.62073  3.7546 **  (-1.66) (0.62) (2.03) October -3.0826 * 0.29461  2.3453 *  (-1.95) (0.18 (1.90) GOA -2.5295 *** -0.9924  2.587 **  (-4.48) (-0.83) (2.24) GOA*February -0.95673  -0.6407     (-1.09) (-0.40)  GOA*March -1.2965  -4.5917 ** -3.2224 **  (-1.12) (-2.10) (-2.07) GOA*April   -1.6775   (-1.04) GOA*July   -0.0331   (-0.02) Economic Valuation Of Critical Habitat Closures, Berman et al.   25  Table 5 (continued). Negative binomial regressions for the distribution of 2001 Bering Sea and Gulf of Alaska shore-based trawl fisheries.  pollock  P. cod  rockfish GOA*August 4.1665 **  -2.4645   (2.51) (-1.25) GOA*September 3.5723 ** 0.11671  -1.9815   (2.12) (0.08) (-1.29) GOA*October 3.3818 ** 1.9185     (2.13) (0.98)  Fishery opening 0.46144      (0.32)  Habitat closure -7.4125 **   (-2.32)  Catcher Vessel Op. Area 3.2991 **      (2.59)  Expected target CPUE 0.94094 *** 1.0686 *** 0.54686 ***  (3.62) (2.75) (2.72) Distance to port (km) -0.01415 *** -0.00984 *** -0.01252 ***  (-6.40) (-6.36) (-7.42) Variance Scale 22.052 *** 108.43 *** 38.552 ***  (10.17) (5.23) (4.51)   Log-likelihood -1029.8 -363.89 -375.82) Initial Log-Likelihood -18566 -6594.6 -3831.8 Likelihood ratio squared 35073 *** 12461 *** 6912.0 *** Observations 14,504 15,800 18,732 *p < 0.1 ** p < 0.05 ***p < 0.01  Economic Valuation Of Critical Habitat Closures, Berman et al.  26  Table 6. Negative binomial regressions for the distribution of 2001 Bering Sea and Gulf of Alaska offshore trawl fisheries maximum likelihood estimates (t-statistics in parentheses). Dependent variable: number of target fishery hauls observed.  pollock  P. cod CPUE  P. cod standard  Atka mackerel  rockfish  Constant -5.3002 *** -5.8285 *** -5.4243 *** -4.8533 *** -2.2217 ***  (-3.42)  (-6.14) (-6.08) (-3.06) (-8.87)  February -3.3406  1.4846  1.6753 * -3.8038  0.86159 ***  (-1.40)  (1.57) (1.78) (-1.48) (2.95)  March -2.2833  3.3841 *** 3.5669 *** 2.0219  1.9069 ***  (-0.96)  (3.16) (3.46) (1.00) (4.68)  April -2.6535  1.4848  1.5528   2.9953 **  (-1.11)  (1.20) (1.34) (8.45)  May -9.195 *** -0.5648  -0.5947   0.54855   (-3.30)  (-0.41) (-0.44) (1.29)  June -4.564 * 0.70685  0.8148   1.0766 ***  (-1.90)  (0.77) (0.95) (3.23)  July -3.5481  0.58327  0.61193   1.9931 ***  (-1.48)  (0.59) (0.68) (5.25)  August -3.4948  0.87803  0.75062   2.7179 ***  (-1.45)  (0.85) (0.78) (7.19)  September -2.463  0.02562  -0.2418  -2.1931  2.2756 ***  (-1.03)  (0.02) (-0.21 (-1.01) (6.76)  October -3.4555  0.93725  0.70636   2.5314 ***  (-1.44)  (0.62) (0.50 (6.29)  GOA   -3.7882 ** -4.0564 ***  -4.2784 ***    (-2.55) (-2.92 (-10.28)  GOA*February       1.4902 ***     (2.69)  GOA*March       0.41813      (0.57)  GOA*April   2.8132 * 3.3909 **  0.88828 **    1.95 2.59 (1.98)  GOA*May   3.9998 ** 4.3063 ***  4.0211 ***    2.40 2.65 (7.63)  Economic Valuation Of Critical Habitat Closures, Berman et al.   27  Table 6 (continued). Negative binomial regressions for the distribution of 2001 Bering Sea and Gulf of Alaska offshore trawl fisheries.   pollock  P. cod CPUE  P. cod standard  Atka mackerel  rockfish  GOA*July      1.126 **     (2.02) GOA*September      -0.9723      (-0.87) Fishery opening 6.9813 *   9.2642 *   (1.79)  (1.885)  Habitat closure -6.2714       (-1.29)   Exp. target CPUE 0.91187 *** 2.0902 ** 0.68025 * 0.32935 ** 1.3225 ***  (4.91)  (2.45) (1.75) (2.006)  (6.30) Neighbour CPUE 0.88797 ***      (3.39)   Dist. to port (km) -0.00521 *** -0.00370 *** -0.00337 *** -0.00306 * -0.00560 ***  (-12.60)  (-3.95) (-3.80) (-1.853)  (-16.40) Variance Scale 18.678 *** 100 100 35  24.25 ***  (15.02)  -- -- --  (12.62)      Log-likelihood -2412.55  -299.26 -306.46 -78.062  -1978.9 Initial Log- Likelihood  -18440.5  -3231.2 -3231.2 -1688.2  -15879 Likelihood ratio squared  32056  *** 5863.9  *** 5849.5  *** 3220.33  *** 27799  *** Observations  11,832  16,556 16,556 370  17,824 *p < 0.1 ** p < 0.05 ***p < 0.01  The coefficients on expected target CPUE are all positive and significant. For onshore Pacific cod, we show the equation estimated with raw CPUE as well as the equations for standard CPUE used for the other fisheries. For reasons that we cannot explain, the equation for raw CPUE fits much better for shore-based Pacific cod. The coefficients on distance to port are all negative and highly significant in the onshore fisheries. Interestingly, the coefficients on port distance are also significant and negative for all the offshore fisheries as well. However, the magnitude of the coefficients for the shore-based fishery is about three times as large as for the offshore fishery. Figures 5 and 6 illustrate the tradeoff between distance to port and expected revenues per standard haul implied by the spatial distribution of fishing effort for the onshore and offshore fisheries, respectively. For the onshore fisheries, rockfish and Pacific cod appear to be more sensitive to distance from port than pollock. However, the expected revenue measure assumed average annual ex-vessel prices for pollock. During the winter season, roe adds significant value to the pollock fishery, but we lack reliable data on the implied ex-vessel value of roe. If roe values were included, the slope for pollock would be steeper, implying less, if any, difference between pollock and the other fisheries. The slopes of the profitability tradeoffs implied by the choice equations are much lower across the board than those for the onshore fisheries. The scales of the two figures are identical to facilitate the comparison. The offshore fisheries include an additional target species, Atka mackerel. The Atka mackerel target fishery is a geographically and temporally compressed fishery that is heavily constrained by Steller sea lion Economic Valuation Of Critical Habitat Closures, Berman et al.  28 regulations. The combination of fisheries and habitat regulations left relatively few cell-months available for fishing, so results for this fishery should be considered less reliable than those for the other fisheries. Nevertheless, the tradeoffs shown in Figure 6 suggest that the estimated profit functions for Atka mackerel imply similar economic tradeoffs between remoteness and expected CPUE as the other offshore fisheries. The neighbourhood CPUE variable is positive and highly significant for the offshore pollock fishery. Indeed, the coefficient on the neighbourhood expected CPUE does not differ significantly from the coefficient on the cell’s expected CPUE. The neighbourhood CPUE did not explain the distribution of effort for the other three offshore fisheries modelled. Since nearly half of all consecutive hauls took place within 9 km of the previous haul, the results suggest that the spatial scale might be too coarse to pick up the neighbourhood effect for these less mobile fisheries. Instead, the coefficient for expected CPUE for the target cell includes whatever neighbourhood effect may exist.  Spatial values and the cost of habitat protection The negative binomial regression equations (Tables 3 through 6) generated estimates of the spatial distribution of fishery values for the various target fisheries and fleet sectors. The equations estimated for the GOA at a 3 km resolution may be considered more accurate representations of the spatial relationships, but are more limited to the GOA summer fisheries. The 3 km GOA results best illustrate the fine- scales effect of regulations and effects of changes to boundaries of individual closure units. The 9 km results best illustrate the overall impact of the Endangered Species Act Critical Habitat program as a whole on the North Pacific groundfish fleet. To assess the impact of the Steller sea lion habitat closures, one must first establish a baseline for comparison. Since the North Pacific groundfish fisheries are regulated under a total allowable catch (TAC) regime, one may make the assumption that any expansion or contraction of fishing opportunities would result in only a slight change in the number of hauls made by the fleet during the year. Instead, there would be a redistribution of a fixed level of fishing effort and catch among available fishing locations. Consequently, one may assume that habitat regulations do not change the number of hauls, and focus on estimated changes in the value per haul. Figure 5. Profitability tradeoffs implied by the distribution of fishing effort: 2001 Onshore trawl fisheries. Figure 6. Profitability tradeoffs implied by the distribution of fishing effort: 2001 Offshore trawl fisheries.  Economic Valuation Of Critical Habitat Closures, Berman et al.   29 The equations for fishing location choice were estimated based on the fisheries openings and habitat closures in effect during 2001, so the 2001 regime would ordinarily become the baseline for the analysis. However, the Steller sea lion regulatory regime actually changed twice during 2001, first in June 10, due to the expiration of the congressionally mandated delay in implementing the judicial order requiring a revision to the Reasonable and Prudent Alternatives (RPA), and again on July 17 when NMFS completed the regulations to comply with the order. This created an unstable baseline that complicated the analysis. To address this moving baseline, we estimated the spatial distribution of relative values by evaluating Equations (6) and (7) assuming that fishery TAC and bycatch regulations remained as they were in 2001, but that all Steller sea lion habitat regulations were removed. Hypothetically removing the Critical Habitat fishing restrictions inflates the estimated total value of the fishery above that actually realized by the fleet under the habitat regulations in effect during 2001. However, it provides a constant baseline with which to compare changes in relative value caused by the imposition of the habitat regulations. GOA 3 km resolution. As mentioned above, trawl fishing was effectively closed in the GOA in the summer of 2001 until July 17. Figure 7 shows the estimated baseline net value of summer fishing in the Gulf of Alaska in 2001, assuming that all Steller sea lion Critical Habitat Closures were removed. We computed the estimates in the table by evaluating Equation (7) with the coefficients shown in Tables 3 and 4, and applying a scale factor unique to each fishery. To scale the values, we first converted the scale from expected trawl survey CPUE to expected catch per fishery haul by multiplying by ratio of average catch of the target species in trawl fishery hauls to predicted survey CPUE for the grid cells where hauls were observed. This adjusted for the difference between survey CPUE and expected fishery catch. We then multiplied by average ex- vessel prices to convert the units from tons of fish per haul to dollars per haul. Finally, we scaled up to total fishery value by multiplying the result for each month by the number of hauls observed during that month in summer 2001, divided by the estimated percentage of hauls in the GOA sampled by observers.1  The estimates in Figure 7 suggest that the mixed trawl offshore fishery is more profitable than any of the onshore fisheries. However, the mixed trawl fishery includes values for sablefish bycatch, for which reliable ex-vessel price data do not exist. Using the estimates in Figure 7 as a base, it is possible to estimate the opportunity cost to the summer and fall 2001 fisheries of the Steller sea lion habitat closures that came into effect on July 17. Figure 8 shows the estimated costs for the three shore-based trawl fisheries, as well as for the offshore rockfish trawl fishery. The numbers are computed by evaluating Equation (6) with J1k representing the                                                  1 According to the National Marine Fisheries Service, “The portion of the catch sampled by observers varies by region, vessel-type, gear-type, and target fishery. Since 2001, vessels with observers in the BSAI have accounted for approximately 90% of the groundfish tonnage caught and observers have sampled the catch from about three-fourths of the hauls/sets. Vessels with observers in the GOA have accounted for approximately 40% of the groundfish tonnage caught and observers have sampled the catch from about two- thirds of the hauls/sets.” (http://www.afsc.noaa.gov/FMA/spatial_data.htm)  Figure 7. Net value of summer 2001 Gulf of Alaska trawl fisheries. Figure 8. Estimated cost per haul of Steller sea lion closures for onshore Gulf of Alaska trawl fisheries, summer and fall, 2001. Economic Valuation Of Critical Habitat Closures, Berman et al.  30 choice set that would have been available if fishing had been permitted within the Steller sea lion closure areas, and J2k equals the choice set actually available for each fishery and time period. Since we only have an approximate figure for the estimated number of hauls for each target fishery, Figure 8 shows the cost in terms of dollars per haul. The estimated cost for Pacific cod shore-based trawl fishery is quite large: around $1,800 per haul. The estimated cost for the pollock fishery is about half, but still substantial. These are the two main fisheries affected by the Steller sea lion closures. The estimated costs for the other onshore and offshore fisheries are small. The Steller sea lion regulations closed relatively few areas of the Gulf of Alaska to all trawl fishing, and those areas – such as three nautical mile no fishing zones around rookeries and haulouts – were of relatively little importance to these fisheries.  Figure 9 shows the relative importance of the Steller sea lion closure areas compared to the areas remaining open by expressing the costs in terms of the percentage of baseline values. The figures suggest that the closures cost the Pacific cod trawl fishery about 38% of the profit per haul, and the pollock fishery about 28%. The cost to the other trawl fisheries was slight: 0.2% of the operating profits. The numbers in Figure 9 are measured as a percentage of the potential profit, defined as the baseline profits in Figure 7. The baseline, as discussed above, represents a somewhat arbitrary standard. Nevertheless, the results suggest that the cost to the two fisheries specifically targeted for regulation to protect Steller sea lion foraging around rookeries and haulouts in the GOA is substantial, while the cost to other trawl fisheries is slight. The spatially detailed analysis for the GOA provided an opportunity to measure the change in the cost to the fisheries resulting from relatively small changes in the boundaries to the closed areas. We chose the Chiniak Gully research area to illustrate the capabilities of the method. The Chiniak Gully closure was a relatively small area of the GOA off Kodiak Island that was closed to all trawling during the month of August 2001 so that NMFS could conduct research on the effect of fishing on localized depletion of fish stocks. Figure 10 shows the estimated cost of the August 2001 research trawl closure, using the methods described. The cost is measured in terms of the average cost per haul during the portion of the closure period that the respective target fishery was open to trawl fishing in the GOA. Again, the estimated cost is highest for the Pacific cod fishery: nearly $200 per haul. It turns out the Chiniak Gully was a highly profitable area for the Pacific cod trawl fishery during this time, as it had both high expected CPUE values and was close to the major fishing port of Kodiak. The cost to the pollock fishery was much less, but still significant. The cost for the other shore-based trawl fisheries was relatively small. In this case, the other most valuable trawl target fisheries in the Chiniak Gully area (rockfish) was closed in the GOA. The trawl Figure 9. Estimated cost of Steller sea lion closures as percentage of profit for onshore Gulf of Alaska trawl fisheries, summer and fall, 2001. Figure 10. Estimated cost per haul of August 2001 Chiniak Gulley trawl closure for onshore Gulf of Alaska trawl fisheries. Figure 11. Estimated cost of August 2001 Chiniak Gulley trawl closure as percentage of profit for onshore Gulf of Alaska trawl fisheries. Economic Valuation Of Critical Habitat Closures, Berman et al.   31 sablefish bycatch allowance had also already been reached. While flatfish trawling was still permitted, estimated profitability for this target fishery was much lower in the GOA in summer and fall.  To illustrate this point, Figure 11 shows the costs of the Chiniak Gully closure expressed as the percentage change in the value per haul. The percentage costs are much more closely aligned. According to these estimates, the research trawl closure cost the trawl fisheries about 2-4 percent of profits, with the lowest percentage cost falling on the pollock fishery, and the highest on the Pacific cod fishery. North Pacific, 9 km resolution. We projected CPUE for the 9 km study of the North Pacific as a whole from equations estimated from the trawl fishery itself, unlike the 3 km GOA results which were derived independently from the NMFS trawl survey. However, CPUE for each species was estimated from the groundfish fishing fleet as a whole, using data on all bottom trawl hauls from all fisheries rather than just from hauls targeting that particular species. This means that the projections of CPUE from the statistical equations do not represent the magnitude of target fishery hauls likely to be realized by any particular component of the fishery prosecuting a target species, even though we believe the relative CPUE estimates are valid. For example, the expected CPUE from a standard bottom trawl haul is likely to underestimate expected catches of very large pollock catcher-processors, and over-predict CPUE of smaller Pacific cod catcher vessels. Because of the selection bias discussed above, there is no simple procedure for adjusting the expected standard bottom trawl haul CPUE for all fisheries to the expected target fishery haul. This created a scale problem that made it difficult to estimate a baseline value, as presented for the GOA in Figure 7, or even a cost per haul, as described for Figure 8.  With Atka mackerel, we have the additional problem of a small number of hauls and limited geographic dispersion, making the estimated equation imprecise. However, since the method of estimating changes in net fisheries values relies on evaluating the difference in a logarithmic function (Equation 6), changing the scale factor for the base value has only a slight effect on the resulting change in value. Consequently, we showed the estimated costs of Steller sea lion habitat closures in terms of the percentage change in value per haul. Scaling issues with assessing the percentage change in values are basically limited to uncertainty in weighting of GOA values relative to the generally much larger BSAI values. As mentioned, the Steller sea lion regulatory regime changed twice in 2001: first when the Congressional moratorium expired on June 10, and again on July 17, when NMFS completed work on the new regime. We assessed the effect of the changing regulatory environment on the fisheries by estimating the relative fisheries values associated with a set of successive regulatory regimes. The hypothetical baseline regime N et  v al ue  (%  c ha ng e)  Figure 12. Estimated percentage change in net value of North Pacific trawl fisheries with three Steller sea lion habitat protection regimes. Economic Valuation Of Critical Habitat Closures, Berman et al.  32 involved no Steller sea lion habitat closures, but retained trawl fishery closures associated with other management objectives (for example, crab bycatch), as well as the general fishery TAC and bycatch-related closures. We defined the first regime as the set of restrictions that related to all trawl fisheries. Most were in place before NMFS designated Critical Habitat for Steller sea lions. We defined Regime 2 as the set of regulations in place on January 1, 2001, and Regime 3 as the set of regulations that went into effect on July 17, 2001. Only minor adjustments to the Steller sea lion regulations have occurred since 2001. Trawl fishing was effectively closed between June 10 and July 17, so we made no attempt to value that intervening period. Table 7 shows the results of evaluating Equation (6) for the three regimes. The results appear quite different from those shown for the GOA. Figure 12 illustrates the relative magnitude of the changes for different fisheries and regulatory regimes.   Table 7. Estimated cost of three regimes of Steller sea lion protective measures as a percentage of net profit per haul: Bering Sea/Aleutian Islands and Gulf of Alaska trawl fisheries, 2001.   Fleet  Regime 1 (%) Regime 1 to regime 2 (%) Regime 2 to regime 3 (%) Cumulative, three regimes (%) Pollock catcher boats 1.0 18.1 -3.9 15.2 Pollock catcher-processors 0.2 2.0 0.3 2.5 P. cod catcher boats 0.6 4.8 5.9 11.3 P. cod catcher-processors 0.4 4.6 2.8 7.8 Atka mackerel catcher-processors 0.5 3.0 3.8 7.3 Other trawl catcher boats 1.1 -- -- 1.1 Other trawl catcher processors 0.04 -- -- 0.04   Our estimates suggested that the largest cost of the Steller sea lion regulations for the North Pacific fisheries has fallen on pollock catcher boats, as a result of the shift from Regime 1 to Regime 2. The revised regulations in summer 2001 actually mitigated some of that cost for the onshore pollock fleet, while increasing the cost substantially for the Pacific cod and Atka mackerel fleets. The costs to the offshore fleets are smaller across the board than the costs estimated for the shore-based fleets. In particular, the costs for the offshore pollock fleet appear relatively small: almost an order of magnitude smaller than the costs to the shore-based fleet. One important regulatory closure areas affecting the offshore pollock fleet is the large Steller Sea Lion Conservation Area (SSLCA) in the southeastern Bering Sea. In 2001, SSLCA catch limits did not affect pollock catcher-processors. Pollock fishing within the SSLCA closed early for the mother ship sector in the pollock A season, which might have led to a substantial estimated cost. However, as discussed above, we cannot estimate a reliable equation for mother ships, so we cannot estimate the cost to that sector of the fleet. Economic Valuation Of Critical Habitat Closures, Berman et al.   33 DISCUSSION This report presented an exploratory study designed to develop a new set of methods for estimating spatial values of ocean fisheries at fine spatial scales, and to apply the methods to estimate the cost to the North Pacific groundfish fisheries of spatial closures. As an exploratory study, the criteria for success should be somewhat different from what they might be for a more applied study that was designed to provide findings more readily applicable to management decisions. The two general hypotheses – that environmental conditions explain and can predict the spatial distribution of fish density, and that predicted spatial fish densities predict the distribution of fishing effort at fine spatial scales – received strong empirical support. The estimates of the cost of habitat closures developed from the statistical analyses appear plausible. The cost estimates varied widely for different sectors of the trawl fleet, ranging, for all North Pacific fisheries, from a 10-15% loss of profits for pollock and Pacific cod catcher boats, to negligible or modest for pollock catcher processors and the rockfish and flatfish fisheries. Estimated costs for Atka mackerel and Pacific cod catcher-processors lay midway between the two extremes. Using a different and spatially more precise data set for estimating CPUE in the GOA, we found that the estimated regulatory costs to the Pacific cod and pollock shore-based trawl fisheries were even greater: about twice as high as for the North Pacific. The question that arises is how confident we can be that these estimates reflect true conditions for the fishing fleet. As discussed above, confidence intervals can only be derived from bootstrapping. In this case, bootstrapping is very cumbersome, since we have in essence a three-stage statistical procedure. The CPUE equations for the North Pacific involved a two-stage sample selection bias correction. The predictions then feed into a third-stage probabilistic count model, which itself was only an approximation, albeit one likely to be accurate, for a multinomial logit model that underlies the derived welfare estimates. Since our goal was to demonstrate the method rather than derive numerically precise values, we did not attempt such a computationally complex procedure to generate confidence intervals, and leave this for further research. Nevertheless, the empirical results for the different statistical analyses, combined with general characteristics of the RUM approach, provided some insights into the likely nature and magnitude of the uncertainty in the estimates. First, the coefficient on CPUE in the negative binomial regressions becomes a multiplicative scale factor in Equations (6) and (7) for the estimated values. The total values and values per haul are inversely proportional to the magnitude of this coefficient. Confidence intervals around this coefficient are quite precise (t-statistics around 3) for all fisheries except offshore Pacific cod and Atka mackerel. Estimated costs for these fisheries were in the intermediate range. However, the coefficient on CPUE scales total values estimated both with and without the habitat regulations. The estimated values are relatively insensitive to the magnitude of the coefficient (Figure 7). A much bigger source of uncertainty in the estimates derived from uncertainty in the predictions of CPUE itself. The equations explaining the distribution of pollock and Pacific cod spatial densities were generally less precise than those of the other fisheries. Further work to improve the ability to explain and predict pollock and Pacific cod spatial distribution would have priority for improving the accuracy of the estimates of the cost of fisheries closures. Another question that arises is whether the statistical results and the cost estimates that follow from them represent an artifact of special conditions present in 2001, or whether the relationships are stable over time. It was our original intent to test the stability of CPUE predictions across a number of years. The requirement to compile, analyze, and code the extremely complex and continuously-changing spatial regulatory environment for the North Pacific fisheries in order to correct for selection bias in the CPUE equations made it impossible for us to conduct such stability tests. Clearly, this remains a high priority for future research. As an exploratory study, the refinements discussed here should be undertaken before applying the results in a management context. Once questions about the stability and robustness of the results are resolved, the analysis can be applied directly in a management context to estimate the costs and benefits of all proposed regulatory changes that involve time and area closures for the groundfish fleet. The analysis of the Chiniak Gully research closure provided a simple example of the kind of information that the analysis can provide to management and the public.  Economic Valuation Of Critical Habitat Closures, Berman et al.  34 CONCLUSIONS The project aimed to design and demonstrate a method to quantify the net cost to industry of closing protected areas to fishing that (1) could take advantage of RUM’s theoretical and practical advantages; (2) could be applied at a spatial scale relevant to decisions regarding marine protected areas; (3) included estimates of costs of reduced fishing flexibility to an at-sea processing fleet as well as the shore-based fleet; and (4) provided estimates of fisheries impacts linked directly to environmental variables relevant to habitat models for Steller sea lions and other protected species potentially interacting with fisheries. Our goal was to improve existing economic models of spatial choice in fisheries by relaxing unrealistic restrictions on spatial decision-making while incorporating detailed and flexible geographic scales. We successfully addressed the four stated objectives. We developed and tested a scientifically defensible method to value fishery use areas at flexible temporal and spatial scales relevant to management decisions. The method produced predictions of relative value across the entire U.S. EEZ in the North Pacific at a detailed spatial scale in different seasons. The method could easily be generalized to evaluate fishery time and area closures for any protected species or for marine conservation generally. Finally, we demonstrated an application of the method to estimate cost to Bering Sea and GOA groundfish trawl fisheries of changes to Steller sea lion critical habitat closures.  The method generated plausible statistical results that distinguished relatively costly regulations from those involving relatively modest or negligible costs, and the relative burdens on different sectors of the fishing fleet. This approach provides management with credible independent estimates of the effect of regulations on net profits for the first time. While the actual magnitude of the estimated costs may remain uncertain, the percentage changes in costs are likely to be reliable. Further research will be required to demonstrate the robustness and stability of the estimated relationships over time, as well as to compute bootstrapped confidence intervals around estimates of values and costs. The main weakness of the method is its requirement for managing and analyzing large volumes of spatially explicit data. The constraint is both a problem of computational resources and one of human resources. Taking the next step towards implementing the method as a standard practice for NMFS as part of the regulatory review process would require that the agency make an institutional commitment to developing the analytical capability to manage and process large environmental and regulatory datasets. While the agency certainly has this capability, it has in the past been deployed to other research and management tasks. ACKNOWLEDGEMENTS The authors acknowledge the large number of individuals and organizations who provided data and assistance with its interpretation. Remote-sensed data for the study were obtained from a variety NOAA and NASA public internet data distribution sites. The Alaska Fisheries Science Center provided trawl survey data for the Gulf of Alaska. The NOAA Alaska Region provided data from the fisheries observer program. Dave Musgrave and Al Hermann provided unpublished oceanographic model output developed at NOAA PMEL. Steve Lewis provided geo-spatial data on fishery regulations from the NOAA Alaska Region. The North Pacific Universities Marine Mammal Research Consortium and the North Pacific Marine Science Foundation provided financial support for this and related research. Finally, the authors acknowledge many individual staff members of the North Pacific Fishery Management Council, Alaska Fisheries Science Center, PMEL, and representatives of the fishing industry for useful comments and feedback on the study design and preliminary results. Economic Valuation Of Critical Habitat Closures, Berman et al.   35 REFERENCES Alaska Fisheries Science Center. 2001. 2001 Gulf of Alaska biennial groundfish assessment survey, resource assessment and conservation engineering division, AFSC Quarterly Research Reports, July-Sept 2001, http://www.afsc.noaa.gov/Quarterly/jas2001/divrpts_race.htm#2001_GOA_biennial. Alaska Fisheries Science Center. 2003 North Pacific groundfish stock assessment and fishery evaluation reports. http://www.afsc.noaa.gov/refm/stocks/assessments.htm. Ben-Akiva, M., and Lerman, S. 1985. Discrete choice analysis: Theory and application to travel demand. Cambridge, MA: MIT Press. Berman, M. 2006. Modeling spatial choice in ocean fisheries.  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A tractable approach to the firm location decision problem. Review of Economics and Statistics 85(1): 201-204. Haynie, A., and Layton, D. 2004. Estimating the economic impact of the Steller sea lion conservation area: Developing and applying new methods for evaluating spatially complex area closures. IIFET 2004 Japan Proceedings (International Institute of Fisheries Economics and Trade). Corvallis, Oregon. Heckman, J. 1979. Sample selection bias as a specification error. Econometrica, 47: 153-161. Hensher, D.A., and Greene, W.H. 2003. The mixed logit model: The state of practice. Transportation 30(2): 133-76. Hermann, A.J., Haidvogel, D. B., Dobbins, E. L., and Stabeno, P. J. 2002. Coupling global and regional circulation models in the coastal Gulf of Alaska. Progress in Oceanography. 53: 335-367. Hermann, A. J. and Stabeno, P. J. 1996. An eddy resolving model of circulation on the western Gulf of Alaska shelf: Model development and sensitivity analyses. 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Economic Valuation Of Critical Habitat Closures, Berman et al.  36 National Marine Fisheries Service. 2001d. 2001 Steller sea lion protection measures and groundfish harvest specifications. http://www.fakr.noaa.gov/sustainablefisheries/2001hrvstspecssl.htm. North Pacific Fishery Management Council. 2004. Regulatory impact review/regulatory flexibility analysis for amendments 84/76/19/11/8 to the BSAI groundfish FMP (#84), GOA groundfish FMP (#76), BSAI crab FMP (#19), scallop FMP (#11), and the salmon FMP (#8) and regulatory amendments to provide habitat areas of particular concern. Initial Draft, September, 2004. Roberts, R.J., 1993. Ulcerative dermal necrosis (UDN) in wild salmonids. In: Bruno, D.W. (Ed.), Pathological conditions of wild salmonids. Fisheries Research: 17: 3-14. Small, K., and Rosen, H. 1981. Applied welfare economics with discrete choice models. Econometrica. 49: 105-130. Stewart, D.A., Agnew, D., Boyd, R., Briggs, R., Toland, P., 1993. The derivation of changes in Nephrops per unit effort values for the Northern Ireland fishing fleet. Fisheries Research 17: 273-292. Tetra Tech FW, Inc. 2004. Draft environmental impact statement for essential fish habitat identification and conservation in Alaska, Appendix C, regulatory impact review/initial regulatory flexibility analysis. National Marine Fisheries Service, January, 2004. Economic Valuation Of Critical Habitat Closures, Berman et al.   37 APPENDICES APPENDIX A. EQUATIONS FOR SPATIAL DISTRIBUTION OF CATCH PER UNIT OF EFFORT (CPUE), ESTIMATED FROM THE SUMMER 2001 NMFS GULF OF ALASKA BOTTOM TRAWL SURVEY Economic Valuation Of Critical Habitat Closures, Berman et al.  38 1. Equations with Wind (only shown if absolute value of t for wind > 1) Pacific cod, average weight > 0.5 kg    Limited Dependent Variable Model - CENSORED regression    Ordinary least squares regression. Dep. Variable  LBPCOD   Observations  264 Weights  ONE    Mean of LHS  0.1617331E+01 Std.Dev of LHS  0.2134344E+01  StdDev of resid.  0.1898167E+01 Sum of squares  0.8791417E+03  R-squared  0.2662060E+00 Adj. R-squared  0.2090663E+00  F[ 19, 244]  0.4658862E+01 Prob value  0.3991631E-08  Log-likelihood  -0.5333954E+03 Restr.(b=0) Log-l  -0.5742529E+03  Amemiya Pr. Criter.  0.3875997E+01 Akaike Info.Crit.  0.4192389E+01  ANOVA Source  Variation  Deg. freedom Mean Square   Regression 0.3189352E+03  19. 0.1678607E+02   Residual 0.8791417E+03  244. 0.3603040E+01   Total  0.1198077E+04  263. 0.4555426E+01  N(0,1) used for significance levels.    Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant  3651.8 1333. 2.739 0.0062  J162  2.2328  0.8727 2.559 0.0105  0.2765 0.4481  J177  2.5571 1.467 1.743 0.0814  0.2500 0.4338  J192  3.6429 2.045 1.781 0.0749  0.2462 0.4316  LDEPTH  21.023 5.532 3.800 0.0001  4.8944 0.5090  L2DEP  -1.9598  0.5609  -3.494 0.0005 24.2132 5.0025  LSTEM  -3.1445 1.299  -2.420 0.0155  2.3164 0.1872  LGTEM 33.528 21.26 1.577 0.1148  1.7553 0.1431  LSLOPE 0.35871  0.3821 0.939 0.3478  3.2087 0.9759  L2SLOPE -0.69028E-01  0.7476E-01  -0.923 0.3559 11.2445 5.3610  LMTEM -0.64826 4.017  -0.161 0.8718  2.0037 0.1970  LMSTEM  19.805 6.670 2.969 0.0030 -0.0902 0.0553  LMSAL  -2138.6 776.6  -2.754 0.0059  3.4659 0.0278  LBMSAL -46.985 13.30  -3.532 0.0004  0.0383 0.0242  BMHORVEL 0.51526E-01  0.5817E-01 0.886 0.3757  2.8017 2.9325  SSHRE  0.36187E-01  0.1089 0.332 0.7397 -3.4680 1.6767  LWIND 1.4795 1.103 1.342 0.1796  1.7965 0.1697  L2GTEM -8.2877 5.829  -1.422 0.1550  3.1014 0.5184  L2MSAL  306.44 113.3 2.705 0.0068 12.0132 0.1912  LMLD_DEP -0.12687  0.1491  -0.851 0.3949  7.2535 3.0037  *****************************************************************************  Limited Dependent Variable Model - CENSORED regression    Maximum Likelihood Estimates    Log-Likelihood.............. -363.32    Threshold values for the model: Lower  0.0000 Upper **********   N(0,1) used for significance levels.    Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant  11000. 3022. 3.640 0.0003  J162  5.4903 2.000 2.745 0.0061  0.2765 0.4481  J177  7.8656 3.386 2.323 0.0202  0.2500 0.4338  J192  11.843 4.843 2.445 0.0145  0.2462 0.4316  LDEPTH  82.904 18.82 4.404 0.0000  4.8944 0.5090  L2DEP  -7.9522 1.898  -4.189 0.0000 24.2132 5.0025  LSTEM  -8.4387 2.728  -3.093 0.0020  2.3164 0.1872  LGTEM 159.31 61.83 2.577 0.0100  1.7553 0.1431  LSLOPE  2.7343 1.409 1.941 0.0523  3.2087 0.9759  L2SLOPE -0.48996  0.2570  -1.906 0.0566 11.2445 5.3610  LMTEM  -14.693 9.276  -1.584 0.1132  2.0037 0.1970  LMSTEM  44.992 15.32 2.936 0.0033 -0.0902 0.0553  LMSAL  -6532.9 1765.  -3.702 0.0002  3.4659 0.0278  LBMSAL -109.08 30.66  -3.558 0.0004  0.0383 0.0242  BMHORVEL 0.14673  0.1244 1.179 0.2384  2.8017 2.9325  SSHRE  0.29768  0.2418 1.231 0.2182 -3.4680 1.6767  LWIND 2.9177 2.369 1.232 0.2181  1.7965 0.1697  L2GTEM -39.537 16.70  -2.367 0.0179  3.1014 0.5184  L2MSAL  942.04 257.4 3.660 0.0003 12.0132 0.1912  LMLD_DEP -0.38099  0.3333  -1.143 0.2530  7.2535 3.0037  Sigma 3.3130  0.2491  13.300 0.0000 Economic Valuation Of Critical Habitat Closures, Berman et al.   39 Pollock, average weight > 0.25 kg  Limited Dependent Variable Model - CENSORED  regression   Ordinary least squares regression.  Dep. Variable LBPOLL    Observations 263  Weights ONE   Mean of LHS 0.8345154E+00  Std.Dev of LHS 0.1541075E+01  StdDev of resid. 0.1293798E+01  Sum of squares 0.4034130E+03  R-squared 0.3516626E+00  Adj. R-squared 0.2951685E+00  F[ 21,  241] 0.6224763E+01  Prob value 0.0000000E+00  Log-likelihood  -0.4294374E+03  Restr.(b=0) Log-l  -0.4864222E+03  Amemiya Pr. Criter. 0.1813936E+01  Akaike Info.Crit. 0.3432984E+01  ANOVA Source  Variation  Deg. freedom  Mean Square  Regression  0.2188139E+03  21. 0.1041971E+02  Residual  0.4034130E+03 241. 0.1673913E+01  Total 0.6222269E+03 262. 0.2374912E+01  N(0,1) used for significance levels.   Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant -4281.5 1321.  -3.240 0.0012  J162 0.79908  0.6140 1.301 0.1931  0.2776 0.4487  J177 0.44705 1.052 0.425 0.6710  0.2510 0.4344  J192 0.27878E-01 1.474 0.019 0.9849  0.2433 0.4299  LDEPTH  3.3759 4.608 0.733 0.4638  4.8978 0.5069  L2DEP -0.49393E-01  0.4374  -0.113 0.9101 24.2447 4.9856  LSTEM -0.93955  0.8902  -1.055 0.2912  2.3153 0.1868  LGTEM 33.014 14.54 2.271 0.0232  1.7545 0.1428  LMLD  4.3831 1.636 2.680 0.0074  1.5058 0.6399  LMTEM 1.9085 2.834 0.673 0.5007  2.0028 0.1968  LMSTEM -10.846 5.027  -2.157 0.0310 -0.0900 0.0553  LMSAL 2462.0 767.5 3.208 0.0013  3.4665 0.0261  LBMSAL  16.422 9.721 1.689 0.0912  0.0380 0.0236  HORVELM -0.72220E-01  0.3501E-01  -2.063 0.0391 15.3185  11.0040  MSHORVEL 0.57074E-01  0.3631E-01 1.572 0.1159 12.8499  10.7104  BMHORVEL 0.63602E-01  0.4546E-01 1.399 0.1618  2.8098 2.9352  SSHRE  0.85775E-01  0.7878E-01 1.089 0.2762 -3.4664 1.6797  LCHLA  0.27958  0.2416 1.157 0.2471  0.7104 0.4982  LWIND  -1.4766  0.7600  -1.943 0.0520  1.7980 0.1683  L2GTEM -8.5751 4.008  -2.139 0.0324  3.0986 0.5174  L2MSAL -357.96 111.5  -3.211 0.0013 12.0172 0.1800  LMLD_DEP -0.60579  0.3453  -1.755 0.0793  7.2726 2.9935 *****************************************************************************  Limited Dependent Variable Model - CENSORED  regression   Maximum Likelihood Estimates   Log-Likelihood..............  -229.59   Threshold values for the model: Lower 0.0000  Upper **********    N(0,1) used for significance levels.   Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant -11936. 4863.  -2.454 0.0141  J162  1.0769 1.963 0.549 0.5832  0.2776 0.4487  J177 -3.0453 3.449  -0.883 0.3773  0.2510 0.4344  J192 -5.0420 4.815  -1.047 0.2951  0.2433 0.4299  LDEPTH  70.138 23.28 3.013 0.0026  4.8978 0.5069  L2DEP  -5.6319 2.157  -2.611 0.0090 24.2447 4.9856  LSTEM  -3.4402 2.771  -1.242 0.2144  2.3153 0.1868  LGTEM 145.78 78.76 1.851 0.0642  1.7545 0.1428  LMLD  16.865 7.083 2.381 0.0173  1.5058 0.6399  LMTEM 18.050 8.915 2.025 0.0429  2.0028 0.1968  LMSTEM -25.364 16.00  -1.585 0.1129 -0.0900 0.0553  LMSAL 6797.1 2819. 2.411 0.0159  3.4665 0.0261  LBMSAL -37.846 34.58  -1.094 0.2738  0.0380 0.0236  HORVELM -0.16061  0.1148  -1.400 0.1616 15.3185  11.0040  MSHORVEL 0.12218  0.1142 1.070 0.2848 12.8499  10.7104  BMHORVEL 0.14484  0.1442 1.004 0.3152  2.8098 2.9352  SSHRE  0.23584  0.2197 1.073 0.2831 -3.4664 1.6797  LCHLA 1.5638  0.7075 2.210 0.0271  0.7104 0.4982  LWIND  -3.9803 2.408  -1.653 0.0984  1.7980 0.1683  L2GTEM -40.774 23.23  -1.755 0.0792  3.0986 0.5174  L2MSAL -997.94 409.5  -2.437 0.0148 12.0172 0.1800  LMLD_DEP -2.5134 1.402  -1.793 0.0730  7.2726 2.9935  Sigma 2.5898  0.2373  10.916 0.0000Economic Valuation Of Critical Habitat Closures, Berman et al.  40 Black cod, average weight > 0.75 kg  Limited Dependent Variable Model - CENSORED  regression   Ordinary least squares regression.  Dep. Variable LBBCOD    Observations 263  Weights ONE   Mean of LHS 0.1955044E+01  Std.Dev of LHS 0.2467206E+01  StdDev of resid. 0.1637353E+01  Sum of squares 0.6487836E+03  R-squared 0.5931938E+00  Adj. R-squared 0.5595734E+00  F[ 20,  242] 0.1764389E+02  Prob value 0.1147677E-35  Log-likelihood  -0.4919181E+03  Restr.(b=0) Log-l  -0.6101916E+03  Amemiya Pr. Criter. 0.2894990E+01  Akaike Info.Crit. 0.3900518E+01  ANOVA Source  Variation  Deg. freedom  Mean Square  Regression  0.9460386E+03  20. 0.4730193E+02  Residual  0.6487836E+03 242. 0.2680924E+01  Total 0.1594822E+04 262. 0.6087107E+01  N(0,1) used for significance levels.   Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant  982.14 1653. 0.594 0.5524  J162  1.4588  0.7794 1.872 0.0612  0.2776 0.4487  J177  2.9388 1.288 2.282 0.0225  0.2510 0.4344  J192  3.2753 1.809 1.811 0.0701  0.2433 0.4299  LDEPTH  1.1602 4.030 0.288 0.7735  4.8978 0.5069  L2DEP  0.36078  0.3653 0.988 0.3234 24.2447 4.9856  LGTEM 1.4113 1.407 1.003 0.3159  1.7545 0.1428  LSLOPE 0.85200E-01  0.1255 0.679 0.4973  3.2086 0.9778  LMLD  2.9710 2.025 1.467 0.1423  1.5058 0.6399  LMTEM  -9.6338 3.446  -2.795 0.0052  2.0028 0.1968  LMSTEM  6.5858 5.942 1.108 0.2677 -0.0900 0.0553  LMSAL  -594.38 961.2  -0.618 0.5363  3.4665 0.0261  LMSSAL  30.364 11.99 2.532 0.0113  0.0127 0.0195  VERTVEL  -6.7201 4.589  -1.464 0.1431 -0.0032 0.0232  HORVELM -0.96752E-01  0.4494E-01  -2.153 0.0313 15.3185  11.0040  MSHORVEL 0.71843E-01  0.4711E-01 1.525 0.1273 12.8499  10.7104  SSHRE  0.29249  0.1014 2.884 0.0039 -3.4664 1.6797  LCHLA  0.69575  0.3049 2.282 0.0225  0.7104 0.4982  LWIND  -1.3734  0.9021  -1.523 0.1279  1.7980 0.1683  L2MSAL  90.307 139.8 0.646 0.5184 12.0172 0.1800  LMLD_DEP -0.76351  0.4338  -1.760 0.0784  7.2726 2.9935  *****************************************************************************  Limited Dependent Variable Model - CENSORED  regression   Maximum Likelihood Estimates   Log-Likelihood..............  -289.92   Threshold values for the model: Lower 0.0000  Upper **********    N(0,1) used for significance levels.   Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant  4045.1 3639. 1.112 0.2663  J162  3.7377 1.571 2.379 0.0174  0.2776 0.4487  J177  8.5246 2.728 3.125 0.0018  0.2510 0.4344  J192  8.7241 3.771 2.313 0.0207  0.2433 0.4299  LDEPTH  67.698 11.49 5.892 0.0000  4.8978 0.5069  L2DEP  -5.3179 1.044  -5.091 0.0000 24.2447 4.9856  LGTEM 12.842 3.608 3.560 0.0004  1.7545 0.1428  LSLOPE 0.31280  0.2515 1.244 0.2137  3.2086 0.9778  LMLD  7.5796 5.144 1.473 0.1406  1.5058 0.6399  LMTEM  -25.431 6.902  -3.685 0.0002  2.0028 0.1968  LMSTEM  34.202 12.47 2.744 0.0061 -0.0900 0.0553  LMSAL  -2525.1 2113.  -1.195 0.2322  3.4665 0.0261  LMSSAL  66.766 20.31 3.288 0.0010  0.0127 0.0195  VERTVEL  -12.226 7.999  -1.529 0.1264 -0.0032 0.0232  HORVELM -0.17359  0.8271E-01  -2.099 0.0358 15.3185  11.0040  MSHORVEL 0.12531  0.8490E-01 1.476 0.1399 12.8499  10.7104  SSHRE  0.79853  0.1900 4.204 0.0000 -3.4664 1.6797  LCHLA 1.4385  0.5722 2.514 0.0119  0.7104 0.4982  LWIND  -7.3941 2.844  -2.600 0.0093  1.7980 0.1683  L2MSAL  378.44 307.1 1.232 0.2179 12.0172 0.1800  LMLD_DEP -2.0157 1.010  -1.995 0.0461  7.2726 2.9935  Sigma 2.2199  0.1569  14.152 0.0000  Economic Valuation Of Critical Habitat Closures, Berman et al.   41 Halibut, all  Limited Dependent Variable Model - CENSORED  regression   Ordinary least squares regression.  Dep. Variable LHAL    Observations 263  Weights ONE   Mean of LHS 0.3605741E+01  Std.Dev of LHS 0.2128749E+01  StdDev of resid. 0.1730145E+01  Sum of squares 0.7303901E+03  R-squared 0.3848164E+00  Adj. R-squared 0.3394340E+00  F[ 18,  244] 0.8479421E+01  Prob value 0.1578245E-16  Log-likelihood  -0.5074982E+03  Restr.(b=0) Log-l  -0.5713854E+03  Amemiya Pr. Criter. 0.3209655E+01  Akaike Info.Crit. 0.4003788E+01  ANOVA Source  Variation  Deg. freedom  Mean Square  Regression  0.4568817E+03  18. 0.2538232E+02  Residual  0.7303901E+03 244. 0.2993402E+01  Total 0.1187272E+04 262. 0.4531572E+01  N(0,1) used for significance levels.   Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant  44.989 14.16 3.177 0.0015  J162 0.75663  0.8639 0.876 0.3811  0.2776 0.4487  J177 0.33069 1.355 0.244 0.8072  0.2510 0.4344  J192  1.5001 1.870 0.802 0.4224  0.2433 0.4299  L2DEP -0.37442  0.1345  -2.784 0.0054 24.2447 4.9856  TIMELDEP -0.75653E-02  0.5589E-02  -1.354 0.1759  864.3330 121.2768  LSTEM  -1.6490 1.288  -1.280 0.2006  2.3153 0.1868  LGTEM  -35.150 14.24  -2.468 0.0136  1.7545 0.1428  LSLOPE 0.14877  0.1247 1.193 0.2328  3.2086 0.9778  LMLD -6.5972 1.900  -3.473 0.0005  1.5058 0.6399  LMTEM 4.6740 2.798 1.671 0.0948  2.0028 0.1968  LMSTEM -8.2449 6.496  -1.269 0.2043 -0.0900 0.0553  LMSSAL -23.534 11.02  -2.135 0.0328  0.0127 0.0195  VERTVEL  -8.3319 4.776  -1.745 0.0811 -0.0032 0.0232  LCHLA1 0.92643  0.5006 1.851 0.0642  0.8483 0.4133  LCHLA2  -0.59286  0.4783  -1.239 0.2152  0.7532 0.4622  LWIND  -1.0785  0.9022  -1.195 0.2319  1.7980 0.1683  L2GTEM  9.2649 3.838 2.414 0.0158  3.0986 0.5174  LMLD_DEP  1.5925  0.4067 3.916 0.0001  7.2726 2.9935 *****************************************************************************  Limited Dependent Variable Model - CENSORED  regression   Maximum Likelihood Estimates   Log-Likelihood..............  -494.55   Threshold values for the model: Lower 0.0000  Upper **********    N(0,1) used for significance levels.   Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant  54.944 17.38 3.162 0.0016  J162  1.0732 1.055 1.017 0.3090  0.2776 0.4487  J177 0.45641 1.651 0.276 0.7822  0.2510 0.4344  J192  1.9509 2.286 0.853 0.3935  0.2433 0.4299  L2DEP -0.53155  0.1676  -3.172 0.0015 24.2447 4.9856  TIMELDEP -0.93874E-02  0.6829E-02  -1.375 0.1693  864.3330 121.2768  LSTEM  -2.2799 1.565  -1.456 0.1453  2.3153 0.1868  LGTEM  -42.553 17.52  -2.429 0.0151  1.7545 0.1428  LSLOPE 0.18518  0.1520 1.218 0.2232  3.2086 0.9778  LMLD -9.7177 2.423  -4.010 0.0001  1.5058 0.6399  LMTEM 6.4038 3.458 1.852 0.0640  2.0028 0.1968  LMSTEM -10.717 7.975  -1.344 0.1790 -0.0900 0.0553  LMSSAL -31.531 14.04  -2.247 0.0247  0.0127 0.0195  VERTVEL  -9.1408 5.735  -1.594 0.1110 -0.0032 0.0232  LCHLA1  1.2576  0.6193 2.031 0.0423  0.8483 0.4133  LCHLA2  -0.85596  0.5911  -1.448 0.1476  0.7532 0.4622  LWIND  -1.4691 1.079  -1.361 0.1735  1.7980 0.1683  L2GTEM  11.049 4.707 2.347 0.0189  3.0986 0.5174  LMLD_DEP  2.3412  0.5258 4.452 0.0000  7.2726 2.9935  Sigma 2.0556  0.1081  19.013 0.0000 Economic Valuation Of Critical Habitat Closures, Berman et al.  42 Halibut, average weight > 1 kg   Limited Dependent Variable Model - CENSORED  regression   Ordinary least squares regression.  Dep. Variable LBHAL   Observations 263  Weights ONE   Mean of LHS 0.3518902E+01  Std.Dev of LHS 0.2186825E+01  StdDev of resid. 0.1869869E+01  Sum of squares 0.8531244E+03  R-squared 0.3191008E+00  Adj. R-squared 0.2688706E+00  F[ 18,  244] 0.6352760E+01  Prob value 0.8742407E-12  Log-likelihood  -0.5279236E+03  Restr.(b=0) Log-l  -0.5784645E+03  Amemiya Pr. Criter. 0.3749004E+01  Akaike Info.Crit. 0.4159115E+01  ANOVA Source  Variation  Deg. freedom  Mean Square  Regression  0.3998135E+03  18. 0.2221186E+02  Residual  0.8531244E+03 244. 0.3496411E+01  Total 0.1252938E+04 262. 0.4782206E+01  N(0,1) used for significance levels.   Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant  11.477 26.56 0.432 0.6657  J162  2.0763  0.7037 2.951 0.0032  0.2776 0.4487  J177  2.2745  0.7962 2.857 0.0043  0.2510 0.4344  J192  3.8644 1.145 3.374 0.0007  0.2433 0.4299  LDEPTH  7.9750 6.291 1.268 0.2049  4.8978 0.5069  L2DEP  -1.3080  0.6029  -2.169 0.0301 24.2447 4.9856  LSTEM  -2.4061 1.214  -1.982 0.0475  2.3153 0.1868  LGTEM  -45.552 20.23  -2.252 0.0244  1.7545 0.1428  LSLOPE 0.14587  0.1339 1.089 0.2760  3.2086 0.9778  LMLD -6.8059 2.245  -3.031 0.0024  1.5058 0.6399  LMSAL 8.8554 7.039 1.258 0.2084  3.4665 0.0261  LMSSAL -15.872 12.68  -1.252 0.2106  0.0127 0.0195  VERTVEL  -8.6660 5.158  -1.680 0.0929 -0.0032 0.0232  LCHLA -0.46292  0.4457  -1.039 0.2990  0.7104 0.4982  LCHLA1  1.5084  0.7001 2.155 0.0312  0.8483 0.4133  LCHLA2  -0.99361  0.5437  -1.827 0.0676  0.7532 0.4622  LWIND -0.90053  0.9947  -0.905 0.3653  1.7980 0.1683  L2GTEM  11.884 5.566 2.135 0.0327  3.0986 0.5174  LMLD_DEP  1.6428  0.4881 3.366 0.0008  7.2726 2.9935  *****************************************************************************  Limited Dependent Variable Model - CENSORED  regression   Maximum Likelihood Estimates   Log-Likelihood..............  -507.60   Threshold values for the model: Lower 0.0000  Upper **********    N(0,1) used for significance levels.   Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant  10.845 33.49 0.324 0.7460  J162  2.9686  0.8959 3.314 0.0009  0.2776 0.4487  J177  3.2217 1.005 3.205 0.0014  0.2510 0.4344  J192  5.4084 1.453 3.722 0.0002  0.2433 0.4299  LDEPTH  10.045 7.874 1.276 0.2021  4.8978 0.5069  L2DEP  -1.7466  0.7566  -2.309 0.0210 24.2447 4.9856  LSTEM  -3.2524 1.516  -2.146 0.0319  2.3153 0.1868  LGTEM  -57.220 25.20  -2.271 0.0232  1.7545 0.1428  LSLOPE 0.18943  0.1675 1.131 0.2581  3.2086 0.9778  LMLD -10.747 2.949  -3.644 0.0003  1.5058 0.6399  LMSAL 12.680 9.100 1.393 0.1635  3.4665 0.0261  LMSSAL -22.188 16.42  -1.351 0.1766  0.0127 0.0195  VERTVEL  -9.7879 6.355  -1.540 0.1235 -0.0032 0.0232  LCHLA -0.68626  0.5705  -1.203 0.2290  0.7104 0.4982  LCHLA1  2.1564  0.8975 2.403 0.0163  0.8483 0.4133  LCHLA2 -1.4602  0.6995  -2.087 0.0369  0.7532 0.4622  LWIND  -1.3376 1.243  -1.076 0.2819  1.7980 0.1683  L2GTEM  14.752 6.915 2.133 0.0329  3.0986 0.5174  LMLD_DEP  2.5799  0.6495 3.972 0.0001  7.2726 2.9935  Sigma 2.2760  0.1224  18.602 0.0000 Economic Valuation Of Critical Habitat Closures, Berman et al.   43 Flatfish, all  Limited Dependent Variable Model - CENSORED  regression   Ordinary least squares regression.  Dep. Variable LFLAT   Observations 263  Weights ONE   Mean of LHS 0.5386837E+01  Std.Dev of LHS 0.1870004E+01  StdDev of resid. 0.1406774E+01  Sum of squares 0.4789211E+03  R-squared 0.4772697E+00  Adj. R-squared 0.4340689E+00  F[ 20,  242] 0.1104769E+02  Prob value 0.2508541E-23  Log-likelihood  -0.4519995E+03  Restr.(b=0) Log-l  -0.5373022E+03  Amemiya Pr. Criter. 0.2137033E+01  Akaike Info.Crit. 0.3596955E+01  ANOVA Source  Variation  Deg. freedom  Mean Square  Regression  0.4372705E+03  20. 0.2186353E+02  Residual  0.4789211E+03 242. 0.1979013E+01  Total 0.9161916E+03 262. 0.3496915E+01  N(0,1) used for significance levels.   Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant -2358.9 1289.  -1.830 0.0672  J162 0.66428  0.5854 1.135 0.2565  0.2776 0.4487  J177  -0.42864 1.012  -0.424 0.6719  0.2510 0.4344  J192 -1.4083 1.426  -0.988 0.3232  0.2433 0.4299  LDEPTH  32.631 4.354 7.495 0.0000  4.8978 0.5069  L2DEP  -2.8540  0.4220  -6.763 0.0000 24.2447 4.9856  TIMELDEP -0.62780E-02  0.4152E-02  -1.512 0.1306  864.3330 121.2768  LGTEM  -16.487 15.51  -1.063 0.2878  1.7545 0.1428  LSLOPE 0.33820  0.1084 3.119 0.0018  3.2086 0.9778  LMTEM 4.7554 2.765 1.720 0.0854  2.0028 0.1968  LMSAL 1384.7 747.8 1.852 0.0641  3.4665 0.0261  LBMSAL -58.652 10.65  -5.509 0.0000  0.0380 0.0236  VERTVEL 4.4290 3.923 1.129 0.2590 -0.0032 0.0232  HORVELM -0.10453  0.3921E-01  -2.666 0.0077 15.3185  11.0040  MSHORVEL 0.91852E-01  0.4088E-01 2.247 0.0246 12.8499  10.7104  BMHORVEL -0.17495  0.4726E-01  -3.702 0.0002  2.8098 2.9352  LCHLA1 0.48358  0.4142 1.167 0.2430  0.8483 0.4133  LCHLA2  -0.76722  0.4142  -1.852 0.0640  0.7532 0.4622  LWIND 1.6687  0.7756 2.151 0.0314  1.7980 0.1683  L2GTEM  5.6575 4.273 1.324 0.1855  3.0986 0.5174  L2MSAL -209.67 108.5  -1.932 0.0534 12.0172 0.1800  *****************************************************************************  Limited Dependent Variable Model - CENSORED  regression   Maximum Likelihood Estimates   Log-Likelihood..............  -455.13   Threshold values for the model: Lower 0.0000  Upper **********    N(0,1) used for significance levels.   Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant -2443.5 1278.  -1.913 0.0558  J162 0.52961  0.5841 0.907 0.3645  0.2776 0.4487  J177  -0.84760 1.020  -0.831 0.4060  0.2510 0.4344  J192 -1.9431 1.432  -1.357 0.1749  0.2433 0.4299  LDEPTH  35.563 4.490 7.920 0.0000  4.8978 0.5069  L2DEP  -3.1205  0.4350  -7.173 0.0000 24.2447 4.9856  TIMELDEP -0.67135E-02  0.4128E-02  -1.626 0.1039  864.3330 121.2768  LGTEM  -19.705 15.49  -1.272 0.2032  1.7545 0.1428  LSLOPE 0.33531  0.1075 3.119 0.0018  3.2086 0.9778  LMTEM 5.8421 2.774 2.106 0.0352  2.0028 0.1968  LMSAL 1434.7 741.2 1.935 0.0529  3.4665 0.0261  LBMSAL -62.703 10.64  -5.892 0.0000  0.0380 0.0236  VERTVEL 4.6344 3.903 1.187 0.2351 -0.0032 0.0232  HORVELM -0.96556E-01  0.3916E-01  -2.465 0.0137 15.3185  11.0040  MSHORVEL 0.85591E-01  0.4072E-01 2.102 0.0355 12.8499  10.7104  BMHORVEL -0.18795  0.4721E-01  -3.981 0.0001  2.8098 2.9352  LCHLA1 0.46835  0.4126 1.135 0.2563  0.8483 0.4133  LCHLA2  -0.76605  0.4146  -1.848 0.0646  0.7532 0.4622  LWIND 1.6930  0.7700 2.199 0.0279  1.7980 0.1683  L2GTEM  6.6162 4.270 1.549 0.1213  3.0986 0.5174  L2MSAL -217.59 107.6  -2.022 0.0431 12.0172 0.1800  Sigma 1.3934  0.6260E-01  22.258 0.0000 Economic Valuation Of Critical Habitat Closures, Berman et al.  44 Rockfish, all  Ordinary least squares regression.  Dep. Variable LROCK   Observations 263  Weights ONE   Mean of LHS 0.3033271E+01  Std.Dev of LHS 0.2706313E+01  StdDev of resid. 0.2062773E+01  Sum of squares 0.1021207E+04  R-squared 0.4678222E+00  Adj. R-squared 0.4190393E+00  F[ 22,  240] 0.9589869E+01  Prob value 0.1974883E-21  Log-likelihood  -0.5515720E+03  Restr.(b=0) Log-l  -0.6345193E+03  Amemiya Pr. Criter. 0.4627143E+01  Akaike Info.Crit. 0.4369369E+01  ANOVA Source  Variation  Deg. freedom  Mean Square  Regression  0.8977141E+03  22. 0.4080519E+02  Residual  0.1021207E+04 240. 0.4255031E+01  Total 0.1918921E+04 262. 0.7324128E+01 Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant  1504.2 1944. 0.774 0.4390  J162  -0.75671  0.8060  -0.939 0.3478  0.2776 0.4487  J177  -0.27890 1.490  -0.187 0.8515  0.2510 0.4344  J192  -0.76191 2.095  -0.364 0.7162  0.2433 0.4299  LDEPTH -2.5107 6.417  -0.391 0.6956  4.8978 0.5069  L2DEP  0.28820  0.6328 0.455 0.6488 24.2447 4.9856  TIMELDEP 0.78801E-02  0.6279E-02 1.255 0.2095  864.3330 121.2768  LGTEM 22.477 22.86 0.983 0.3254  1.7545 0.1428  LSLOPE  -0.70144E-01  0.1629  -0.430 0.6669  3.2086 0.9778  LMTEM  -6.4377 5.463  -1.178 0.2387  2.0028 0.1968  LBMTEM -6.5865 2.821  -2.335 0.0196 -0.1522 0.1850  LMSAL  -916.79 1128.  -0.813 0.4163  3.4665 0.0261  LMSSAL  24.445 15.53 1.574 0.1154  0.0127 0.0195  LBMSAL  29.658 15.83 1.874 0.0610  0.0380 0.0236  VERTVEL  -7.7301 5.687  -1.359 0.1740 -0.0032 0.0232  HORVELM -0.32319E-01  0.1793E-01  -1.802 0.0715 15.3185  11.0040  BMHORVEL 0.94278E-01  0.7277E-01 1.296 0.1951  2.8098 2.9352  SSHRE -0.61026E-01  0.1284  -0.475 0.6345 -3.4664 1.6797  LCHLA 1.4035  0.5139 2.731 0.0063  0.7104 0.4982  LCHLA1 -1.0980  0.4983  -2.203 0.0276  0.8483 0.4133  LWIND  0.34816 1.214 0.287 0.7742  1.7980 0.1683  L2GTEM -6.3082 6.300  -1.001 0.3167  3.0986 0.5174  L2MSAL  138.63 163.6 0.847 0.3969 12.0172 0.1800  *****************************************************************************  Limited Dependent Variable Model - CENSORED  regression   Maximum Likelihood Estimates   Log-Likelihood..............  -479.26   Threshold values for the model: Lower 0.0000  Upper **********    N(0,1) used for significance levels.   Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant  2518.5 2439. 1.032 0.3019  J162  -0.94423 1.057  -0.894 0.3715  0.2776 0.4487  J177  -0.23051 2.011  -0.115 0.9087  0.2510 0.4344  J192  -0.70044 2.845  -0.246 0.8056  0.2433 0.4299  LDEPTH  16.379 12.49 1.311 0.1899  4.8978 0.5069  L2DEP  -1.5830 1.226  -1.291 0.1967 24.2447 4.9856  TIMELDEP 0.82648E-02  0.8136E-02 1.016 0.3097  864.3330 121.2768  LGTEM 52.069 41.42 1.257 0.2088  1.7545 0.1428  LSLOPE  -0.22848  0.2106  -1.085 0.2781  3.2086 0.9778  LMTEM  -8.2066 6.995  -1.173 0.2407  2.0028 0.1968  LBMTEM -11.663 3.758  -3.104 0.0019 -0.1522 0.1850  LMSAL  -1560.6 1416.  -1.102 0.2705  3.4665 0.0261  LMSSAL  24.014 19.63 1.224 0.2211  0.0127 0.0195  LBMSAL  49.506 22.24 2.226 0.0260  0.0380 0.0236  VERTVEL  -8.9489 7.202  -1.243 0.2140 -0.0032 0.0232  HORVELM -0.41086E-01  0.2331E-01  -1.763 0.0779 15.3185  11.0040  BMHORVEL 0.19113  0.9863E-01 1.938 0.0527  2.8098 2.9352  SSHRE -0.16915  0.1654  -1.023 0.3064 -3.4664 1.6797  LCHLA 1.6080  0.6656 2.416 0.0157  0.7104 0.4982  LCHLA1 -1.3805  0.6614  -2.087 0.0369  0.8483 0.4133  LWIND 2.5312 2.195 1.153 0.2487  1.7980 0.1683  L2GTEM -15.351 11.72  -1.310 0.1902  3.0986 0.5174  L2MSAL  233.79 205.7 1.136 0.2558 12.0172 0.1800  Sigma 2.4862  0.1358  18.308 0.0000Economic Valuation Of Critical Habitat Closures, Berman et al.   45 2. Equations without wind  Pacific cod, all  Limited Dependent Variable Model - CENSORED  regression   Ordinary least squares regression.  Dep. Variable LPCOD   Observations 377  Weights ONE   Mean of LHS 0.1951593E+01  Std.Dev of LHS 0.2121660E+01  StdDev of resid. 0.1870424E+01  Sum of squares 0.1259456E+04  R-squared 0.2558793E+00  Adj. R-squared 0.2228073E+00  F[ 16,  360] 0.7737031E+01  Prob value 0.8994911E-15  Log-likelihood  -0.7623066E+03  Restr.(b=0) Log-l  -0.8180181E+03  Amemiya Pr. Criter. 0.3656244E+01  Akaike Info.Crit. 0.4134252E+01  ANOVA Source  Variation  Deg. freedom  Mean Square  Regression  0.4330865E+03  16. 0.2706791E+02  Residual  0.1259456E+04 360. 0.3498488E+01  Total 0.1692542E+04 376. 0.4501442E+01  N(0,1) used for significance levels.   Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant  1423.6 690.7 2.061 0.0393  J162 0.52740  0.3813 1.383 0.1666  0.3263 0.4695  J177 0.55088  0.4990 1.104 0.2696  0.2334 0.4236  J192 0.90287  0.5848 1.544 0.1226  0.2334 0.4236  LDEPTH  12.498 3.273 3.818 0.0001  4.8044 0.5626  L2DEP  -1.1314  0.3373  -3.354 0.0008 23.3976 5.3509  LSTEM  -2.6242  0.8848  -2.966 0.0030  2.2955 0.1867  LGTEM 46.412 11.78 3.941 0.0001  1.7873 0.1745  LSLOPE 0.43460  0.3255 1.335 0.1818  3.2026 0.9801  L2SLOPE -0.66421E-01  0.6250E-01  -1.063 0.2879 11.2148 5.3014  LMSAL  -846.40 402.8  -2.101 0.0356  3.4545 0.0358  LMSSAL -19.928 8.351  -2.386 0.0170  0.0135 0.0274  LBMSAL -28.354 7.911  -3.584 0.0003  0.0410 0.0305  MSHORVEL -0.95601E-02  0.1150E-01  -0.831 0.4060 10.9195  10.0521  LCHLA2 0.47311  0.2280 2.075 0.0380  0.8487 0.5049  L2GTEM -11.400 3.124  -3.649 0.0003  3.2248 0.6559  L2MSAL  119.69 58.60 2.042 0.0411 11.9349 0.2450  *****************************************************************************  Limited Dependent Variable Model - CENSORED  regression   Maximum Likelihood Estimates   Log-Likelihood..............  -614.09   Threshold values for the model: Lower 0.0000  Upper **********    N(0,1) used for significance levels.   Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant  2375.4 1188. 2.000 0.0455  J162 0.37777  0.6423 0.588 0.5564  0.3263 0.4695  J177  -0.32678E-01  0.8509  -0.038 0.9694  0.2334 0.4236  J192 0.46898 1.010 0.464 0.6424  0.2334 0.4236  LDEPTH  33.600 6.485 5.181 0.0000  4.8044 0.5626  L2DEP  -3.2230  0.6741  -4.781 0.0000 23.3976 5.3509  LSTEM  -4.5960 1.464  -3.140 0.0017  2.2955 0.1867  LGTEM 107.99 22.11 4.885 0.0000  1.7873 0.1745  LSLOPE  1.5487  0.7957 1.946 0.0516  3.2026 0.9801  L2SLOPE -0.25802  0.1450  -1.780 0.0751 11.2148 5.3014  LMSAL  -1448.1 692.3  -2.092 0.0365  3.4545 0.0358  LMSSAL -35.562 14.72  -2.416 0.0157  0.0135 0.0274  LBMSAL -45.527 12.99  -3.504 0.0005  0.0410 0.0305  MSHORVEL -0.29942E-01  0.2054E-01  -1.458 0.1449 10.9195  10.0521  LCHLA2 0.88429  0.3869 2.286 0.0223  0.8487 0.5049  L2GTEM -26.392 5.780  -4.566 0.0000  3.2248 0.6559  L2MSAL  204.76 100.7 2.034 0.0420 11.9349 0.2450  Sigma 2.7979  0.1501  18.634 0.0000 Economic Valuation Of Critical Habitat Closures, Berman et al.  46 Pacific cod, average weight > 0.5 kg  Limited Dependent Variable Model - CENSORED  regression   Ordinary least squares regression.  Dep. Variable LBPCOD    Observations 381  Weights ONE   Mean of LHS 0.1749955E+01  Std.Dev of LHS 0.2159548E+01  StdDev of resid. 0.1960828E+01  Sum of squares 0.1391835E+04  R-squared 0.2146230E+00  Adj. R-squared 0.1755711E+00  F[ 18,  362] 0.5495840E+01  Prob value 0.1895555E-10  Log-likelihood  -0.7874233E+03  Restr.(b=0) Log-l  -0.8334465E+03  Amemiya Pr. Criter. 0.4036584E+01  Akaike Info.Crit. 0.4233193E+01  ANOVA Source  Variation  Deg. freedom  Mean Square  Regression  0.3803519E+03  18. 0.2113066E+02  Residual  0.1391835E+04 362. 0.3844847E+01  Total 0.1772186E+04 380. 0.4663649E+01  N(0,1) used for significance levels.   Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant  224.30 555.1 0.404 0.6862  J162  1.5314  0.6262 2.445 0.0145  0.3228 0.4682  J177  1.6346  0.9857 1.658 0.0972  0.2336 0.4237  J192  1.8798 1.395 1.347 0.1778  0.2257 0.4186  LDEPTH  11.891 3.224 3.688 0.0002  4.7937 0.5688  L2DEP  -1.0322  0.3354  -3.077 0.0021 23.3024 5.3937  LSTEM  -1.7338  0.9764  -1.776 0.0758  2.2923 0.1872  LGTEM 46.860 12.18 3.848 0.0001  1.7847 0.1747  LSLOPE 0.42390  0.3414 1.242 0.2144  3.2009 0.9755  L2SLOPE -0.56517E-01  0.6615E-01  -0.854 0.3929 11.1949 5.2778  LMTEM  -1.4291 2.297  -0.622 0.5338  1.9673 0.2118  LMSTEM  6.0243 3.853 1.564 0.1179 -0.0912 0.0537  LMSAL  -145.42 324.5  -0.448 0.6540  3.4544 0.0352  LBMSAL -33.179 8.041  -4.126 0.0000  0.0403 0.0301  HORVELM  0.53499E-01  0.4454E-01 1.201 0.2297 12.9840  10.5078  MSHORVEL -0.64555E-01  0.4692E-01  -1.376 0.1689 10.7954  10.0679  LCHLA  0.39234  0.2179 1.801 0.0718  0.7226 0.5474  L2GTEM -11.733 3.220  -3.644 0.0003  3.2155 0.6557  L2MSAL  17.370 47.43 0.366 0.7142 11.9342 0.2413  *****************************************************************************  Limited Dependent Variable Model - CENSORED  regression   Maximum Likelihood Estimates   Log-Likelihood..............  -570.80   Threshold values for the model: Lower 0.0000  Upper **********    N(0,1) used for significance levels.   Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant  1189.3 1114. 1.067 0.2858  J162  3.5561 1.311 2.713 0.0067  0.3228 0.4682  J177  4.8798 2.087 2.338 0.0194  0.2336 0.4237  J192  5.9164 2.954 2.003 0.0452  0.2257 0.4186  LDEPTH  32.248 7.843 4.112 0.0000  4.7937 0.5688  L2DEP  -2.8980  0.8139  -3.561 0.0004 23.3024 5.3937  LSTEM  -4.0984 1.988  -2.061 0.0393  2.2923 0.1872  LGTEM 218.32 42.14 5.181 0.0000  1.7847 0.1747  LSLOPE  1.4928 1.027 1.453 0.1463  3.2009 0.9755  L2SLOPE -0.20927  0.1871  -1.119 0.2633 11.1949 5.2778  LMTEM  -9.4971 5.004  -1.898 0.0577  1.9673 0.2118  LMSTEM  15.221 8.363 1.820 0.0687 -0.0912 0.0537  LMSAL  -805.20 651.8  -1.235 0.2167  3.4544 0.0352  LBMSAL -62.086 16.83  -3.689 0.0002  0.0403 0.0301  HORVELM  0.13319  0.9186E-01 1.450 0.1471 12.9840  10.5078  MSHORVEL -0.16556  0.9713E-01  -1.705 0.0883 10.7954  10.0679  LCHLA  0.47038  0.4492 1.047 0.2951  0.7226 0.5474  L2GTEM -56.370 11.21  -5.028 0.0000  3.2155 0.6557  L2MSAL  110.77 95.23 1.163 0.2448 11.9342 0.2413  Sigma 3.4294  0.2084  16.455 0.0000 Economic Valuation Of Critical Habitat Closures, Berman et al.   47 Pollock, all  Limited Dependent Variable Model - CENSORED  regression   Ordinary least squares regression.  Dep. Variable LPOLL   Observations 380  Weights ONE   Mean of LHS 0.1580394E+01  Std.Dev of LHS 0.2058696E+01  StdDev of resid. 0.1726682E+01  Sum of squares 0.1079278E+04  R-squared 0.3280923E+00  Adj. R-squared 0.2965386E+00  F[ 17,  362] 0.1039791E+02  Prob value 0.0000000E+00  Log-likelihood  -0.7375332E+03  Restr.(b=0) Log-l  -0.8130837E+03  Amemiya Pr. Criter. 0.3122657E+01  Akaike Info.Crit. 0.3976490E+01  ANOVA Source  Variation  Deg. freedom  Mean Square  Regression  0.5270111E+03  17. 0.3100065E+02  Residual  0.1079278E+04 362. 0.2981431E+01  Total 0.1606289E+04 379. 0.4238230E+01  N(0,1) used for significance levels.   Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant -1937.3 643.6  -3.010 0.0026  J162 0.92638  0.4346 2.131 0.0330  0.3237 0.4685  J177  1.2384  0.5070 2.443 0.0146  0.2342 0.4241  J192 0.76996  0.7230 1.065 0.2869  0.2237 0.4173  LDEPTH  3.8528  0.6249 6.166 0.0000  4.7950 0.5690  LGTEM 38.828 8.212 4.728 0.0000  1.7844 0.1748  LSLOPE  -0.82837E-01  0.1030  -0.804 0.4211  3.1984 0.9756  LMLD  2.5593 1.333 1.920 0.0548  1.5198 0.6260  LMSAL 1139.0 375.3 3.035 0.0024  3.4550 0.0336  LBMSAL -27.278 7.020  -3.886 0.0001  0.0399 0.0287  VERTVEL 5.4075 4.504 1.201 0.2299 -0.0025 0.0202  HORVELM -0.19863E-01  0.1096E-01  -1.812 0.0700 13.0181  10.5005  BMHORVEL 0.45647E-01  0.4401E-01 1.037 0.2996  2.7418 2.9262  LCHLA  0.72940  0.2705 2.696 0.0070  0.7227 0.5481  LCHLA1  -0.23812  0.2881  -0.826 0.4086  0.9196 0.4705  L2GTEM -9.7652 2.148  -4.547 0.0000  3.2144 0.6562  L2MSAL -171.94 54.64  -3.147 0.0016 11.9379 0.2306  LMLD_DEP -0.50035  0.3005  -1.665 0.0960  7.1724 2.8464   *****************************************************************************  Limited Dependent Variable Model - CENSORED  regression   Maximum Likelihood Estimates   Log-Likelihood..............  -628.14   Threshold values for the model: Lower 0.0000  Upper **********    N(0,1) used for significance levels.   Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant -3281.3 1050.  -3.126 0.0018  J162  1.0767  0.6594 1.633 0.1025  0.3237 0.4685  J177  1.6396  0.7861 2.086 0.0370  0.2342 0.4241  J192 0.80336 1.106 0.727 0.4675  0.2237 0.4173  LDEPTH  5.8535  0.9975 5.868 0.0000  4.7950 0.5690  LGTEM 60.649 13.48 4.498 0.0000  1.7844 0.1748  LSLOPE  -0.16146  0.1544  -1.045 0.2958  3.1984 0.9756  LMLD  4.2850 2.116 2.025 0.0428  1.5198 0.6260  LMSAL 1919.7 611.2 3.141 0.0017  3.4550 0.0336  LBMSAL -38.469 10.70  -3.596 0.0003  0.0399 0.0287  VERTVEL 9.0343 6.867 1.316 0.1883 -0.0025 0.0202  HORVELM -0.36579E-01  0.1690E-01  -2.164 0.0304 13.0181  10.5005  BMHORVEL 0.12661  0.6929E-01 1.827 0.0677  2.7418 2.9262  LCHLA  0.88612  0.4046 2.190 0.0285  0.7227 0.5481  LCHLA1  -0.44773  0.4311  -1.038 0.2991  0.9196 0.4705  L2GTEM -15.501 3.570  -4.342 0.0000  3.2144 0.6562  L2MSAL -287.83 89.00  -3.234 0.0012 11.9379 0.2306  LMLD_DEP -0.86254  0.4681  -1.843 0.0654  7.1724 2.8464  Sigma 2.4187  0.1218  19.859 0.0000   Economic Valuation Of Critical Habitat Closures, Berman et al.  48 Pollock, average weight > 0.25 kg  Limited Dependent Variable Model - CENSORED  regression   Ordinary least squares regression.  Dep. Variable LBPOLL    Observations 380  Weights ONE   Mean of LHS 0.1134761E+01  Std.Dev of LHS 0.1956043E+01  StdDev of resid. 0.1648476E+01  Sum of squares 0.9918778E+03  R-squared 0.3159901E+00  Adj. R-squared 0.2897541E+00  F[ 14,  365] 0.1204414E+02  Prob value 0.4875549E-22  Log-likelihood  -0.7214881E+03  Restr.(b=0) Log-l  -0.7936468E+03  Amemiya Pr. Criter. 0.2824742E+01  Akaike Info.Crit. 0.3876253E+01  ANOVA Source  Variation  Deg. freedom  Mean Square  Regression  0.4582150E+03  14. 0.3272964E+02  Residual  0.9918778E+03 365. 0.2717473E+01  Total 0.1450093E+04 379. 0.3826102E+01  N(0,1) used for significance levels.   Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant -1348.8 592.5  -2.276 0.0228  J162 0.89908  0.4101 2.192 0.0284  0.3237 0.4685  J177  1.1277  0.4548 2.480 0.0132  0.2342 0.4241  J192  1.2104  0.6751 1.793 0.0730  0.2237 0.4173  LDEPTH  3.9858  0.5955 6.693 0.0000  4.7950 0.5690  LGTEM 34.701 7.729 4.490 0.0000  1.7844 0.1748  LMLD  3.2231 1.268 2.542 0.0110  1.5198 0.6260  LMSAL 797.98 345.8 2.308 0.0210  3.4550 0.0336  LBMSAL -31.406 6.667  -4.711 0.0000  0.0399 0.0287  HORVELM -0.11829E-01  0.9293E-02  -1.273 0.2031 13.0181  10.5005  LCHLA  0.95612  0.2569 3.721 0.0002  0.7227 0.5481  LCHLA1  -0.45142  0.2729  -1.654 0.0981  0.9196 0.4705  L2GTEM -8.6805 2.021  -4.296 0.0000  3.2144 0.6562  L2MSAL -122.34 50.37  -2.429 0.0151 11.9379 0.2306  LMLD_DEP -0.59225  0.2860  -2.071 0.0384  7.1724 2.8464  *****************************************************************************  Limited Dependent Variable Model - CENSORED  regression   Maximum Likelihood Estimates   Log-Likelihood..............  -421.77   Threshold values for the model: Lower 0.0000  Upper **********    N(0,1) used for significance levels.   Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant -4805.1 1799.  -2.672 0.0075  J162  3.1049 1.175 2.643 0.0082  0.3237 0.4685  J177  3.4288 1.305 2.628 0.0086  0.2342 0.4241  J192  4.6284 1.930 2.399 0.0165  0.2237 0.4173  LDEPTH  12.433 2.086 5.961 0.0000  4.7950 0.5690  LGTEM 127.18 30.49 4.171 0.0000  1.7844 0.1748  LMLD  12.360 4.333 2.853 0.0043  1.5198 0.6260  LMSAL 2799.5 1046. 2.676 0.0075  3.4550 0.0336  LBMSAL -82.426 18.95  -4.350 0.0000  0.0399 0.0287  HORVELM -0.35186E-01  0.2606E-01  -1.350 0.1769 13.0181  10.5005  LCHLA 2.7487  0.7121 3.860 0.0001  0.7227 0.5481  LCHLA1 -1.6501  0.7357  -2.243 0.0249  0.9196 0.4705  L2GTEM -33.342 8.316  -4.009 0.0001  3.2144 0.6562  L2MSAL -423.15 152.5  -2.775 0.0055 11.9379 0.2306  LMLD_DEP -2.1187  0.9060  -2.339 0.0194  7.1724 2.8464  Sigma 3.5479  0.2618  13.551 0.0000   Economic Valuation Of Critical Habitat Closures, Berman et al.   49 Black cod, all  Limited Dependent Variable Model - CENSORED  regression   Ordinary least squares regression.  Dep. Variable LBCOD   Observations 380  Weights ONE   Mean of LHS 0.1691693E+01  Std.Dev of LHS 0.2331162E+01  StdDev of resid. 0.1611987E+01  Sum of squares 0.9458542E+03  R-squared 0.5407595E+00  Adj. R-squared 0.5218347E+00  F[ 15,  364] 0.2857420E+02  Prob value 0.0000000E+00  Log-likelihood  -0.7124609E+03  Restr.(b=0) Log-l  -0.8603153E+03  Amemiya Pr. Criter. 0.2707911E+01  Akaike Info.Crit. 0.3834005E+01  ANOVA Source  Variation  Deg. freedom  Mean Square  Regression  0.1113751E+04  15. 0.7425007E+02  Residual  0.9458542E+03 364. 0.2598500E+01  Total 0.2059605E+04 379. 0.5434315E+01  N(0,1) used for significance levels.   Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant -11.569 14.36  -0.806 0.4204  J162  1.5302  0.5078 3.013 0.0026  0.3237 0.4685  J177  2.0762  0.8046 2.580 0.0099  0.2342 0.4241  J192  3.0962 1.111 2.786 0.0053  0.2237 0.4173  LDEPTH  2.6106  0.2619 9.967 0.0000  4.7950 0.5690  LGTEM  -3.8113 7.670  -0.497 0.6193  1.7844 0.1748  LSLOPE 0.53129E-01  0.9596E-01 0.554 0.5798  3.1984 0.9756  LMTEM  -4.5406 1.910  -2.377 0.0175  1.9668 0.2118  LMSTEM  10.651 3.081 3.457 0.0005 -0.0907 0.0529  LBMTEM -4.4453 1.154  -3.851 0.0001 -0.1087 0.1885  LMSAL 3.2199 4.153 0.775 0.4381  3.4550 0.0336  VERTVEL  -6.2213 4.241  -1.467 0.1424 -0.0025 0.0202  HORVELM -0.62759E-01  0.3714E-01  -1.690 0.0911 13.0181  10.5005  MSHORVEL 0.47594E-01  0.3944E-01 1.207 0.2275 10.8239  10.0659  LCHLA1 0.49223  0.1937 2.541 0.0111  0.9196 0.4705  L2GTEM  1.1889 2.007 0.592 0.5536  3.2144 0.6562  *****************************************************************************  Limited Dependent Variable Model - CENSORED  regression   Maximum Likelihood Estimates   Log-Likelihood..............  -427.20   Threshold values for the model: Lower 0.0000  Upper **********    N(0,1) used for significance levels.   Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant -186.07 37.80  -4.923 0.0000  J162  3.5569 1.245 2.856 0.0043  0.3237 0.4685  J177  6.3356 2.073 3.056 0.0022  0.2342 0.4241  J192  9.0142 2.881 3.129 0.0018  0.2237 0.4173  LDEPTH  6.9424  0.7007 9.908 0.0000  4.7950 0.5690  LGTEM 132.99 27.97 4.754 0.0000  1.7844 0.1748  LSLOPE 0.27057  0.2252 1.202 0.2295  3.1984 0.9756  LMTEM  -15.878 4.536  -3.500 0.0005  1.9668 0.2118  LMSTEM  16.773 8.181 2.050 0.0403 -0.0907 0.0529  LBMTEM -9.3727 2.637  -3.555 0.0004 -0.1087 0.1885  LMSAL 16.582 9.264 1.790 0.0735  3.4550 0.0336  VERTVEL  -9.2226 8.656  -1.066 0.2866 -0.0025 0.0202  HORVELM -0.19006  0.7697E-01  -2.469 0.0135 13.0181  10.5005  MSHORVEL 0.15606  0.7938E-01 1.966 0.0493 10.8239  10.0659  LCHLA1  1.2566  0.4294 2.927 0.0034  0.9196 0.4705  L2GTEM -36.502 7.835  -4.659 0.0000  3.2144 0.6562  Sigma 2.5864  0.1625  15.913 0.0000      Economic Valuation Of Critical Habitat Closures, Berman et al.  50 Black cod, average weight > 0.75 kg  Limited Dependent Variable Model - CENSORED  regression   Ordinary least squares regression.  Dep. Variable LBBCOD    Observations 374  Weights ONE   Mean of LHS 0.1696323E+01  Std.Dev of LHS 0.2349267E+01  StdDev of resid. 0.1628311E+01  Sum of squares 0.9438974E+03  R-squared 0.5414875E+00  Adj. R-squared 0.5195922E+00  F[ 17,  356] 0.2473081E+02  Prob value 0.0000000E+00  Log-likelihood  -0.7038005E+03  Restr.(b=0) Log-l  -0.8496170E+03  Amemiya Pr. Criter. 0.2779005E+01  Akaike Info.Crit. 0.3859895E+01  ANOVA Source  Variation  Deg. freedom  Mean Square  Regression  0.1114710E+04  17. 0.6557120E+02  Residual  0.9438974E+03 356. 0.2651397E+01  Total 0.2058608E+04 373. 0.5519056E+01  N(0,1) used for significance levels.   Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant -15.959 20.30  -0.786 0.4317  J162  1.7021  0.5732 2.969 0.0030  0.3289 0.4704  J177  2.2423  0.8924 2.513 0.0120  0.2353 0.4248  J192  3.2224 1.185 2.719 0.0065  0.2273 0.4196  LDEPTH  2.9182  0.3463 8.427 0.0000  4.8091 0.5586  LGTEM  -10.021 8.246  -1.215 0.2242  1.7859 0.1739  LSLOPE 0.11155  0.9676E-01 1.153 0.2490  3.1972 0.9813  LMTEM  -2.0865 1.928  -1.082 0.2792  1.9714 0.2087  LMSTEM  8.5575 3.145 2.721 0.0065 -0.0915 0.0528  LMSAL 4.6443 6.711 0.692 0.4889  3.4556 0.0334  LMSSAL  9.1180 6.368 1.432 0.1522  0.0127 0.0241  LBMSAL -9.0701 6.851  -1.324 0.1855  0.0404 0.0286  HORVELM -0.56622E-01  0.3755E-01  -1.508 0.1316 13.1751  10.4944  MSHORVEL 0.51108E-01  0.3954E-01 1.293 0.1962 10.9839  10.0643  SSHRE  0.12401  0.5373E-01 2.308 0.0210 -3.5265 1.8770  LCHLA1 0.83496  0.3605 2.316 0.0206  0.9176 0.4615  LCHLA2  -0.43952  0.3528  -1.246 0.2128  0.8485 0.5044  L2GTEM  2.6929 2.173 1.239 0.2153  3.2197 0.6533  *****************************************************************************  Limited Dependent Variable Model - CENSORED  regression   Maximum Likelihood Estimates   Log-Likelihood..............  -391.25   Threshold values for the model: Lower 0.0000  Upper **********    N(0,1) used for significance levels.   Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant -466.94 89.95  -5.191 0.0000  J162  4.1629 1.509 2.759 0.0058  0.3289 0.4704  J177  7.3562 2.529 2.909 0.0036  0.2353 0.4248  J192  8.7336 3.343 2.613 0.0090  0.2273 0.4196  LDEPTH  7.1558  0.9911 7.220 0.0000  4.8091 0.5586  LGTEM 115.82 32.66 3.546 0.0004  1.7859 0.1739  LSLOPE 0.31719  0.2308 1.374 0.1694  3.1972 0.9813  LMTEM  -10.210 5.835  -1.750 0.0801  1.9714 0.2087  LMSTEM  13.796 8.799 1.568 0.1169 -0.0915 0.0528  LMSAL 97.421 29.22 3.334 0.0009  3.4556 0.0334  LMSSAL  34.048 15.62 2.180 0.0292  0.0127 0.0241  LBMSAL  70.359 31.05 2.266 0.0234  0.0404 0.0286  HORVELM -0.17467  0.8019E-01  -2.178 0.0294 13.1751  10.4944  MSHORVEL 0.13521  0.7991E-01 1.692 0.0906 10.9839  10.0643  SSHRE  0.17729  0.1218 1.455 0.1456 -3.5265 1.8770  LCHLA1  2.1955  0.8847 2.482 0.0131  0.9176 0.4615  LCHLA2 -1.1974  0.8400  -1.425 0.1540  0.8485 0.5044  L2GTEM -31.303 9.360  -3.344 0.0008  3.2197 0.6533  Sigma 2.4939  0.1595  15.633 0.0000      Economic Valuation Of Critical Habitat Closures, Berman et al.   51 Halibut, all  Limited Dependent Variable Model - CENSORED  regression   Ordinary least squares regression.  Dep. Variable LHAL    Observations 374  Weights ONE   Mean of LHS 0.3650597E+01  Std.Dev of LHS 0.2093095E+01  StdDev of resid. 0.1690549E+01  Sum of squares 0.1020290E+04  R-squared 0.3756375E+00  Adj. R-squared 0.3476549E+00  F[ 16,  357] 0.1342395E+02  Prob value 0.4611915E-27  Log-likelihood  -0.7183536E+03  Restr.(b=0) Log-l  -0.8064352E+03  Amemiya Pr. Criter. 0.2987862E+01  Akaike Info.Crit. 0.3932372E+01  ANOVA Source  Variation  Deg. freedom  Mean Square  Regression  0.6138407E+03  16. 0.3836505E+02  Residual  0.1020290E+04 357. 0.2857955E+01  Total 0.1634131E+04 373. 0.4381047E+01  N(0,1) used for significance levels.   Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant  9.9394 13.38 0.743 0.4577  J162  1.4329  0.4986 2.874 0.0041  0.3289 0.4704  J177  1.8114  0.7062 2.565 0.0103  0.2353 0.4248  J192  3.0988 1.052 2.946 0.0032  0.2273 0.4196  LDEPTH -3.5200 1.056  -3.332 0.0009  4.8091 0.5586  TIMELDEP -0.65529E-02  0.4476E-02  -1.464 0.1432  846.4649 124.6727  LGTEM  -1.8108  0.9826  -1.843 0.0653  1.7859 0.1739  LSLOPE 0.26355  0.9636E-01 2.735 0.0062  3.1972 0.9813  LMLD -6.3529 1.375  -4.621 0.0000  1.5095 0.6250  LMSAL 4.6533 3.664 1.270 0.2041  3.4556 0.0334  LMSSAL -5.7320 6.048  -0.948 0.3433  0.0127 0.0241  VERTVEL  -6.9483 4.442  -1.564 0.1177 -0.0025 0.0202  SSHRE  0.91710E-01  0.5381E-01 1.704 0.0883 -3.5265 1.8770  LCHLA -0.44326  0.2856  -1.552 0.1206  0.7189 0.5424  LCHLA1 0.50492  0.4884 1.034 0.3013  0.9176 0.4615  LCHLA2  -0.43661  0.3964  -1.102 0.2707  0.8485 0.5044  LMLD_DEP  1.5506  0.3099 5.004 0.0000  7.1526 2.8620   *****************************************************************************  Limited Dependent Variable Model - CENSORED  regression   Maximum Likelihood Estimates   Log-Likelihood..............  -704.05   Threshold values for the model: Lower 0.0000  Upper **********    N(0,1) used for significance levels.   Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant  11.614 16.40 0.708 0.4788  J162  1.8384  0.6079 3.024 0.0025  0.3289 0.4704  J177  2.3043  0.8580 2.686 0.0072  0.2353 0.4248  J192  4.0739 1.286 3.167 0.0015  0.2273 0.4196  LDEPTH -5.0082 1.320  -3.795 0.0001  4.8091 0.5586  TIMELDEP -0.80978E-02  0.5475E-02  -1.479 0.1391  846.4649 124.6727  LGTEM  -2.6236 1.198  -2.189 0.0286  1.7859 0.1739  LSLOPE 0.31328  0.1187 2.640 0.0083  3.1972 0.9813  LMLD -9.5375 1.780  -5.358 0.0000  1.5095 0.6250  LMSAL 6.6920 4.539 1.474 0.1404  3.4556 0.0334  LMSSAL -12.453 8.046  -1.548 0.1217  0.0127 0.0241  VERTVEL  -7.7724 5.352  -1.452 0.1465 -0.0025 0.0202  SSHRE  0.11360  0.6591E-01 1.723 0.0848 -3.5265 1.8770  LCHLA -0.59920  0.3528  -1.698 0.0894  0.7189 0.5424  LCHLA1 0.71807  0.5988 1.199 0.2304  0.9176 0.4615  LCHLA2  -0.57077  0.4851  -1.177 0.2394  0.8485 0.5044  LMLD_DEP  2.2913  0.4032 5.683 0.0000  7.1526 2.8620  Sigma 2.0165  0.8828E-01  22.841 0.0000   Economic Valuation Of Critical Habitat Closures, Berman et al.  52 Halibut, average weight > 1 kg   Limited Dependent Variable Model - CENSORED  regression   Ordinary least squares regression.  Dep. Variable LBHAL   Observations 374  Weights ONE   Mean of LHS 0.3444155E+01  Std.Dev of LHS 0.2216326E+01  StdDev of resid. 0.1933219E+01  Sum of squares 0.1323016E+04  R-squared 0.2779140E+00  Adj. R-squared 0.2391580E+00  F[ 19,  354] 0.7170858E+01  Prob value 0.0000000E+00  Log-likelihood  -0.7669414E+03  Restr.(b=0) Log-l  -0.8278307E+03  Amemiya Pr. Criter. 0.3937192E+01  Akaike Info.Crit. 0.4208243E+01  ANOVA Source  Variation  Deg. freedom  Mean Square  Regression  0.5091980E+03  19. 0.2679990E+02  Residual  0.1323016E+04 354. 0.3737335E+01  Total 0.1832214E+04 373. 0.4912103E+01  N(0,1) used for significance levels.   Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant -17.851 16.40  -1.089 0.2763  J162  2.1568  0.5878 3.669 0.0002  0.3289 0.4704  J177  2.9741  0.8362 3.557 0.0004  0.2353 0.4248  J192  4.6804 1.250 3.745 0.0002  0.2273 0.4196  LDEPTH  6.9992 3.233 2.165 0.0304  4.8091 0.5586  L2DEP  -1.0170  0.2741  -3.710 0.0002 23.4383 5.3224  TIMELDEP -0.85529E-02  0.5611E-02  -1.524 0.1274  846.4649 124.6727  LSTEM  -1.2177  0.9641  -1.263 0.2066  2.2942 0.1865  LGTEM  -2.2955 1.137  -2.019 0.0435  1.7859 0.1739  LSLOPE 0.20461  0.1130 1.811 0.0702  3.1972 0.9813  LMLD -6.5637 1.674  -3.921 0.0001  1.5095 0.6250  LMSAL 6.1386 4.272 1.437 0.1508  3.4556 0.0334  LMSSAL -5.4676 7.412  -0.738 0.4607  0.0127 0.0241  VERTVEL  -6.1099 5.097  -1.199 0.2306 -0.0025 0.0202  BMHORVEL 0.73806E-01  0.4734E-01 1.559 0.1190  2.7209 2.9310  SSHRE  0.12446  0.6225E-01 1.999 0.0456 -3.5265 1.8770  LCHLA -0.29316  0.3297  -0.889 0.3739  0.7189 0.5424  LCHLA1 0.49847  0.5590 0.892 0.3725  0.9176 0.4615  LCHLA2  -0.53682  0.4543  -1.182 0.2374  0.8485 0.5044  LMLD_DEP  1.6323  0.3694 4.419 0.0000  7.1526 2.8620   *****************************************************************************  Limited Dependent Variable Model - CENSORED  regression   Maximum Likelihood Estimates   Log-Likelihood..............  -730.62   Threshold values for the model: Lower 0.0000  Upper **********    N(0,1) used for significance levels.   Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant -26.168 21.25  -1.231 0.2182  J162  2.9904  0.7632 3.918 0.0001  0.3289 0.4704  J177  4.0626 1.084 3.747 0.0002  0.2353 0.4248  J192  6.5508 1.633 4.013 0.0001  0.2273 0.4196  LDEPTH  9.6981 4.345 2.232 0.0256  4.8091 0.5586  L2DEP  -1.4655  0.3729  -3.930 0.0001 23.4383 5.3224  TIMELDEP -0.11694E-01  0.7292E-02  -1.604 0.1088  846.4649 124.6727  LSTEM  -1.5266 1.247  -1.224 0.2209  2.2942 0.1865  LGTEM  -3.3208 1.476  -2.250 0.0244  1.7859 0.1739  LSLOPE 0.24185  0.1470 1.646 0.0998  3.1972 0.9813  LMLD -10.655 2.305  -4.624 0.0000  1.5095 0.6250  LMSAL 8.7282 5.634 1.549 0.1213  3.4556 0.0334  LMSSAL -13.145 10.58  -1.242 0.2141  0.0127 0.0241  VERTVEL  -7.1632 6.462  -1.108 0.2677 -0.0025 0.0202  BMHORVEL 0.10196  0.6190E-01 1.647 0.0995  2.7209 2.9310  SSHRE  0.16483  0.8050E-01 2.048 0.0406 -3.5265 1.8770  LCHLA -0.44976  0.4303  -1.045 0.2959  0.7189 0.5424  LCHLA1 0.81429  0.7266 1.121 0.2624  0.9176 0.4615  LCHLA2  -0.77288  0.5905  -1.309 0.1906  0.8485 0.5044  LMLD_DEP  2.6076  0.5114 5.099 0.0000  7.1526 2.8620  Sigma 2.4188  0.1108  21.824 0.0000  Economic Valuation Of Critical Habitat Closures, Berman et al.   53 Flatfish, all (obervations where wind data available)  Limited Dependent Variable Model - CENSORED  regression   Ordinary least squares regression.  Dep. Variable LFLAT   Observations 263  Weights ONE   Mean of LHS 0.5386837E+01  Std.Dev of LHS 0.1870004E+01  StdDev of resid. 0.1417238E+01  Sum of squares 0.4880812E+03  R-squared 0.4672717E+00  Adj. R-squared 0.4256181E+00  F[ 19,  243] 0.1121803E+02  Prob value 0.0000000E+00  Log-likelihood  -0.4544909E+03  Restr.(b=0) Log-l  -0.5373022E+03  Amemiya Pr. Criter. 0.2161307E+01  Akaike Info.Crit. 0.3608296E+01  ANOVA Source  Variation  Deg. freedom  Mean Square  Regression  0.4281105E+03  19. 0.2253213E+02  Residual  0.4880812E+03 243. 0.2008564E+01  Total 0.9161916E+03 262. 0.3496915E+01  N(0,1) used for significance levels.   Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant -2553.6 1295.  -1.972 0.0487  J162 0.77812  0.5873 1.325 0.1852  0.2776 0.4487  J177  -0.23570 1.016  -0.232 0.8165  0.2510 0.4344  J192 -1.4423 1.436  -1.004 0.3152  0.2433 0.4299  LDEPTH  33.119 4.380 7.561 0.0000  4.8978 0.5069  L2DEP  -2.9114  0.4243  -6.862 0.0000 24.2447 4.9856  TIMELDEP -0.66245E-02  0.4180E-02  -1.585 0.1130  864.3330 121.2768  LGTEM  -19.776 15.55  -1.272 0.2034  1.7545 0.1428  LSLOPE 0.33226  0.1092 3.042 0.0023  3.2086 0.9778  LMTEM 4.0266 2.764 1.457 0.1452  2.0028 0.1968  LMSAL 1494.1 751.6 1.988 0.0468  3.4665 0.0261  LBMSAL -52.429 10.32  -5.079 0.0000  0.0380 0.0236  VERTVEL 3.9298 3.946 0.996 0.3193 -0.0032 0.0232  HORVELM -0.10193  0.3948E-01  -2.582 0.0098 15.3185  11.0040  MSHORVEL 0.88084E-01  0.4114E-01 2.141 0.0323 12.8499  10.7104  BMHORVEL -0.16235  0.4724E-01  -3.437 0.0006  2.8098 2.9352  LCHLA1 0.47578  0.4173 1.140 0.2542  0.8483 0.4133  LCHLA2  -0.74886  0.4172  -1.795 0.0726  0.7532 0.4622  L2GTEM  6.5509 4.285 1.529 0.1263  3.0986 0.5174  L2MSAL -224.49 109.1  -2.057 0.0397 12.0172 0.1800  *****************************************************************************  Limited Dependent Variable Model - CENSORED  regression   Maximum Likelihood Estimates   Log-Likelihood..............  -457.53   Threshold values for the model: Lower 0.0000  Upper **********    N(0,1) used for significance levels.   Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant -2642.8 1286.  -2.055 0.0399  J162 0.63877  0.5875 1.087 0.2770  0.2776 0.4487  J177  -0.66665 1.027  -0.649 0.5162  0.2510 0.4344  J192 -1.9952 1.446  -1.380 0.1677  0.2433 0.4299  LDEPTH  36.156 4.533 7.976 0.0000  4.8978 0.5069  L2DEP  -3.1888  0.4389  -7.266 0.0000 24.2447 4.9856  TIMELDEP -0.70319E-02  0.4163E-02  -1.689 0.0912  864.3330 121.2768  LGTEM  -23.145 15.55  -1.488 0.1367  1.7545 0.1428  LSLOPE 0.32942  0.1085 3.036 0.0024  3.2086 0.9778  LMTEM 5.1148 2.779 1.840 0.0657  2.0028 0.1968  LMSAL 1546.6 746.4 2.072 0.0383  3.4665 0.0261  LBMSAL -56.455 10.35  -5.457 0.0000  0.0380 0.0236  VERTVEL 4.0933 3.931 1.041 0.2977 -0.0032 0.0232  HORVELM -0.93561E-01  0.3952E-01  -2.367 0.0179 15.3185  11.0040  MSHORVEL 0.81467E-01  0.4106E-01 1.984 0.0473 12.8499  10.7104  BMHORVEL -0.17543  0.4729E-01  -3.709 0.0002  2.8098 2.9352  LCHLA1 0.45520  0.4164 1.093 0.2743  0.8483 0.4133  LCHLA2  -0.74056  0.4183  -1.771 0.0766  0.7532 0.4622  L2GTEM  7.5530 4.289 1.761 0.0782  3.0986 0.5174  L2MSAL -232.77 108.4  -2.148 0.0317 12.0172 0.1800  Sigma 1.4064  0.6318E-01  22.259 0.0000  Economic Valuation Of Critical Habitat Closures, Berman et al.  54 Flatfish, all, full sample  Limited Dependent Variable Model - CENSORED  regression   Ordinary least squares regression.  Dep. Variable LFLAT   Observations 381  Weights ONE   Mean of LHS 0.5595923E+01  Std.Dev of LHS 0.1801840E+01  StdDev of resid. 0.1528464E+01  Sum of squares 0.8503779E+03  R-squared 0.3107195E+00  Adj. R-squared 0.2804214E+00  F[ 16,  364] 0.1025543E+02  Prob value 0.1805557E-20  Log-likelihood  -0.6935645E+03  Restr.(b=0) Log-l  -0.7644509E+03  Amemiya Pr. Criter. 0.2440443E+01  Akaike Info.Crit. 0.3729997E+01  ANOVA Source  Variation  Deg. freedom  Mean Square  Regression  0.3833403E+03  16. 0.2395877E+02  Residual  0.8503779E+03 364. 0.2336203E+01  Total 0.1233718E+04 380. 0.3246627E+01  N(0,1) used for significance levels.   Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant  66.658 16.34 4.078 0.0000  J162 0.57483  0.3955 1.453 0.1462  0.3228 0.4682  J177 0.47079  0.4818 0.977 0.3285  0.2336 0.4237  J192  -0.32654  0.6573  -0.497 0.6193  0.2257 0.4186  LDEPTH  11.020 2.312 4.767 0.0000  4.7937 0.5688  L2DEP -0.98788  0.2190  -4.510 0.0000 23.3024 5.3937  LGTEM 1.7617  0.9067 1.943 0.0520  1.7847 0.1747  LSLOPE 0.30548  0.9090E-01 3.361 0.0008  3.2009 0.9755  LMLD -1.7468 1.243  -1.405 0.1601  1.5147 0.6330  LMSTEM  6.0196 3.273 1.839 0.0659 -0.0912 0.0537  LMSAL  -26.729 4.994  -5.353 0.0000  3.4544 0.0352  LBMSAL -22.951 5.972  -3.843 0.0001  0.0403 0.0301  HORVELM -0.11637  0.3563E-01  -3.266 0.0011 12.9840  10.5078  MSHORVEL 0.90229E-01  0.3723E-01 2.423 0.0154 10.7954  10.0679  BMHORVEL -0.97203E-01  0.3809E-01  -2.552 0.0107  2.7609 2.9459  LCHLA -0.26639  0.1685  -1.581 0.1139  0.7226 0.5474  LMLD_DEP 0.31329  0.2699 1.161 0.2457  7.1489 2.8796   *****************************************************************************  Limited Dependent Variable Model - CENSORED  regression   Maximum Likelihood Estimates   Log-Likelihood..............  -700.72   Threshold values for the model: Lower 0.0000  Upper **********    N(0,1) used for significance levels.   Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant  68.759 16.44 4.183 0.0000  J162 0.54184  0.3975 1.363 0.1729  0.3228 0.4682  J177 0.41089  0.4848 0.848 0.3967  0.2336 0.4237  J192  -0.40207  0.6611  -0.608 0.5431  0.2257 0.4186  LDEPTH  11.118 2.323 4.786 0.0000  4.7937 0.5688  L2DEP -0.99673  0.2202  -4.527 0.0000 23.3024 5.3937  LGTEM 1.8515  0.9121 2.030 0.0424  1.7847 0.1747  LSLOPE 0.30430  0.9130E-01 3.333 0.0009  3.2009 0.9755  LMLD -1.9632 1.255  -1.564 0.1178  1.5147 0.6330  LMSTEM  6.2444 3.290 1.898 0.0577 -0.0912 0.0537  LMSAL  -27.404 5.023  -5.456 0.0000  3.4544 0.0352  LBMSAL -23.924 6.012  -3.979 0.0001  0.0403 0.0301  HORVELM -0.11518  0.3585E-01  -3.213 0.0013 12.9840  10.5078  MSHORVEL 0.88527E-01  0.3744E-01 2.364 0.0181 10.7954  10.0679  BMHORVEL -0.10137  0.3833E-01  -2.645 0.0082  2.7609 2.9459  LCHLA -0.28096  0.1695  -1.658 0.0973  0.7226 0.5474  LMLD_DEP 0.34779  0.2720 1.278 0.2011  7.1489 2.8796  Sigma 1.5348  0.5703E-01  26.914 0.0000   Economic Valuation Of Critical Habitat Closures, Berman et al.   55 Flatfish, average weight > 0.5 kg  Limited Dependent Variable Model - CENSORED  regression   Ordinary least squares regression.  Dep. Variable LBFLAT    Observations 380  Weights ONE   Mean of LHS 0.3833270E+01  Std.Dev of LHS 0.3136294E+01  StdDev of resid. 0.2825974E+01  Sum of squares 0.2882993E+04  R-squared 0.2266596E+00  Adj. R-squared 0.1880997E+00  F[ 18,  361] 0.5878116E+01  Prob value 0.1970590E-11  Log-likelihood  -0.9242151E+03  Restr.(b=0) Log-l  -0.9730520E+03  Amemiya Pr. Criter. 0.8385436E+01  Akaike Info.Crit. 0.4964290E+01  ANOVA Source  Variation  Deg. freedom  Mean Square  Regression  0.8449811E+03  18. 0.4694340E+02  Residual  0.2882993E+04 361. 0.7986130E+01  Total 0.3727974E+04 379. 0.9836343E+01  N(0,1) used for significance levels.   Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant  2245.4 1293. 1.737 0.0824  J162  2.2061  0.9116 2.420 0.0155  0.3237 0.4685  J177  2.9253 1.484 1.971 0.0487  0.2342 0.4241  J192  4.1548 2.011 2.066 0.0389  0.2237 0.4173  LDEPTH -10.339 4.653  -2.222 0.0263  4.7950 0.5690  L2DEP 1.3349  0.4847 2.754 0.0059 23.3147 5.3955  LGTEM 71.379 17.96 3.974 0.0001  1.7844 0.1748  LSLOPE 0.60310  0.1700 3.548 0.0004  3.1984 0.9756  LMLD -1.5218  0.7217  -2.109 0.0350  1.5198 0.6260  LMTEM  -4.4718 3.565  -1.254 0.2097  1.9668 0.2118  LMSTEM  19.905 6.696 2.973 0.0030 -0.0907 0.0529  LMSAL  -1312.3 753.6  -1.741 0.0816  3.4550 0.0336  LMSSAL -25.502 14.33  -1.779 0.0752  0.0125 0.0240  LBMSAL -23.659 11.77  -2.010 0.0444  0.0399 0.0287  HORVELM -0.13246  0.6602E-01  -2.006 0.0448 13.0181  10.5005  MSHORVEL 0.11947  0.6950E-01 1.719 0.0856 10.8239  10.0659  LCHLA1 0.70629  0.3459 2.042 0.0412  0.9196 0.4705  L2GTEM -18.465 4.740  -3.896 0.0001  3.2144 0.6562  L2MSAL  188.68 109.7 1.720 0.0855 11.9379 0.2306   *****************************************************************************  Limited Dependent Variable Model - CENSORED  regression   Maximum Likelihood Estimates   Log-Likelihood..............  -794.34   Threshold values for the model: Lower 0.0000  Upper **********    N(0,1) used for significance levels.   Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant  3835.4 1951. 1.966 0.0493  J162  3.0538 1.387 2.202 0.0277  0.3237 0.4685  J177  4.3090 2.286 1.885 0.0594  0.2342 0.4241  J192  6.6923 3.094 2.163 0.0306  0.2237 0.4173  LDEPTH -21.097 7.160  -2.947 0.0032  4.7950 0.5690  L2DEP 2.5970  0.7463 3.480 0.0005 23.3147 5.3955  LGTEM 125.55 28.99 4.330 0.0000  1.7844 0.1748  LSLOPE 0.98501  0.2755 3.575 0.0003  3.1984 0.9756  LMLD -2.5635 1.111  -2.308 0.0210  1.5198 0.6260  LMTEM  -7.6277 5.537  -1.378 0.1683  1.9668 0.2118  LMSTEM  30.724 10.13 3.034 0.0024 -0.0907 0.0529  LMSAL  -2244.3 1138.  -1.973 0.0485  3.4550 0.0336  LMSSAL -42.533 21.92  -1.941 0.0523  0.0125 0.0240  LBMSAL -41.613 18.34  -2.269 0.0233  0.0399 0.0287  HORVELM -0.19004  0.1014  -1.874 0.0610 13.0181  10.5005  MSHORVEL 0.17784  0.1062 1.674 0.0941 10.8239  10.0659  LCHLA1  1.0526  0.5328 1.976 0.0482  0.9196 0.4705  L2GTEM -32.519 7.659  -4.246 0.0000  3.2144 0.6562  L2MSAL  323.27 165.6 1.952 0.0510 11.9379 0.2306  Sigma 4.0471  0.2044  19.798 0.0000   Economic Valuation Of Critical Habitat Closures, Berman et al.  56 Rockfish, all  Limited Dependent Variable Model - CENSORED  regression   Ordinary least squares regression.  Dep. Variable LROCK   Observations 415  Weights ONE   Mean of LHS 0.2230611E+01  Std.Dev of LHS 0.2518493E+01  StdDev of resid. 0.1838761E+01  Sum of squares 0.1359179E+04  R-squared 0.4823993E+00  Adj. R-squared 0.4669485E+00  F[ 12,  402] 0.3122170E+02  Prob value 0.2573607E-49  Log-likelihood  -0.8350287E+03  Restr.(b=0) Log-l  -0.9716781E+03  Amemiya Pr. Criter. 0.3486955E+01  Akaike Info.Crit. 0.4086885E+01  ANOVA Source  Variation  Deg. freedom  Mean Square  Regression  0.1266743E+04  12. 0.1055619E+03  Residual  0.1359179E+04 402. 0.3381043E+01  Total 0.2625922E+04 414. 0.6342808E+01  N(0,1) used for significance levels.   Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant  1422.6 369.8 3.847 0.0001  J162 -1.2637  0.3160  -3.999 0.0001  0.3108 0.4634  J177 -1.3701  0.4226  -3.242 0.0012  0.2578 0.4380  J192 -1.3041  0.5398  -2.416 0.0157  0.2217 0.4159  LDEPTH -3.6561 2.092  -1.748 0.0805  4.7674 0.5731  L2DEP  0.57280  0.2187 2.619 0.0088 23.0556 5.4133  L2SLOPE -0.13411E-01  0.1962E-01  -0.684 0.4942 11.1722 5.3092  LBMTEM -3.8807 1.039  -3.734 0.0002 -0.1073 0.1828  LMSAL  -846.88 216.5  -3.911 0.0001  3.4529 0.0380  LBMSAL  10.041 5.904 1.701 0.0890  0.0399 0.0320  VERTVEL  -8.6183 4.686  -1.839 0.0659 -0.0024 0.0196  BMHORVEL 0.52898E-01  0.4025E-01 1.314 0.1888  2.6973 2.8813  L2MSAL  126.49 31.69 3.991 0.0001 11.9241 0.2597  *****************************************************************************  Limited Dependent Variable Model - CENSORED  regression   Maximum Likelihood Estimates   Log-Likelihood..............  -668.81   Threshold values for the model: Lower 0.0000  Upper **********    N(0,1) used for significance levels.   Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant  1841.9 550.6 3.345 0.0008  J162 -1.8584  0.5091  -3.650 0.0003  0.3108 0.4634  J177 -1.8095  0.7284  -2.484 0.0130  0.2578 0.4380  J192 -2.2790  0.9834  -2.318 0.0205  0.2217 0.4159  LDEPTH  11.207 4.418 2.537 0.0112  4.7674 0.5731  L2DEP -0.84570  0.4490  -1.883 0.0596 23.0556 5.4133  L2SLOPE -0.51155E-01  0.3195E-01  -1.601 0.1093 11.1722 5.3092  LBMTEM -5.8966 1.814  -3.250 0.0012 -0.1073 0.1828  LMSAL  -1131.7 322.8  -3.506 0.0005  3.4529 0.0380  LBMSAL  30.484 10.56 2.887 0.0039  0.0399 0.0320  VERTVEL  -12.762 6.933  -1.841 0.0656 -0.0024 0.0196  BMHORVEL 0.14213  0.6724E-01 2.114 0.0345  2.6973 2.8813  L2MSAL  170.48 47.33 3.602 0.0003 11.9241 0.2597  Sigma 2.5557  0.1232  20.744 0.0000      Economic Valuation Of Critical Habitat Closures, Berman et al.   57 Rockfish, average weight > 0.5 kg  Limited Dependent Variable Model - CENSORED  regression   Ordinary least squares regression.  Dep. Variable LBROCK    Observations 391  Weights ONE   Mean of LHS 0.1339421E+01  Std.Dev of LHS 0.2389698E+01  StdDev of resid. 0.2223165E+01  Sum of squares 0.1853424E+04  R-squared 0.1678070E+00  Adj. R-squared 0.1345193E+00  F[ 15,  375] 0.5041109E+01  Prob value 0.3734648E-08  Log-likelihood  -0.8590191E+03  Restr.(b=0) Log-l  -0.8949307E+03  Amemiya Pr. Criter. 0.5144713E+01  Akaike Info.Crit. 0.4475801E+01  ANOVA Source  Variation  Deg. freedom  Mean Square  Regression  0.3737325E+03  15. 0.2491550E+02  Residual  0.1853424E+04 375. 0.4942464E+01  Total 0.2227156E+04 390. 0.5710657E+01  N(0,1) used for significance levels.   Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant -64.247 20.25  -3.173 0.0015  J162  -0.74014  0.5727  -1.292 0.1962  0.3171 0.4660  J177  -0.74802  0.7823  -0.956 0.3390  0.2302 0.4215  J192 -1.8074 1.160  -1.558 0.1193  0.2327 0.4231  TIMELDEP 0.93623E-02  0.3178E-02 2.946 0.0032  842.1405 126.7421  LSTEM  0.11830 1.023 0.116 0.9079  2.2926 0.1903  LGTEM 11.193 10.23 1.094 0.2741  1.7858 0.1758  L2SLOPE -0.22014E-01  0.2438E-01  -0.903 0.3664 11.2754 5.3109  LMLD  1.7334 1.323 1.311 0.1900  1.5073 0.6411  LMSAL 14.083 6.198 2.272 0.0231  3.4524 0.0389  LBMSAL  6.4959 7.952 0.817 0.4140  0.0410 0.0323  VERTVEL  -13.389 5.760  -2.324 0.0201 -0.0024 0.0200  MSHORVEL -0.13167E-01  0.1447E-01  -0.910 0.3629 10.5688  10.0444  BMHORVEL 0.79452E-01  0.5379E-01 1.477 0.1397  2.7515 2.9133  L2GTEM -3.0617 2.711  -1.130 0.2587  3.2198 0.6594  LMLD_DEP -0.45503  0.2926  -1.555 0.1199  7.1040 2.9228   *****************************************************************************  Limited Dependent Variable Model - CENSORED  regression   Maximum Likelihood Estimates   Log-Likelihood..............  -471.12   Threshold values for the model: Lower 0.0000  Upper **********    N(0,1) used for significance levels.   Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant -410.09 86.33  -4.750 0.0000  J162 -1.9281 1.745  -1.105 0.2692  0.3171 0.4660  J177 -1.3962 2.429  -0.575 0.5655  0.2302 0.4215  J192 -7.0661 3.660  -1.931 0.0535  0.2327 0.4231  TIMELDEP 0.37971E-01  0.1029E-01 3.689 0.0002  842.1405 126.7421  LSTEM  -6.2988 3.264  -1.930 0.0536  2.2926 0.1903  LGTEM 171.57 49.20 3.487 0.0005  1.7858 0.1758  L2SLOPE -0.87869E-01  0.7214E-01  -1.218 0.2232 11.2754 5.3109  LMLD  8.5802 4.772 1.798 0.0722  1.5073 0.6411  LMSAL 69.276 23.65 2.929 0.0034  3.4524 0.0389  LBMSAL  58.733 29.96 1.961 0.0499  0.0410 0.0323  VERTVEL  -33.644 16.27  -2.068 0.0386 -0.0024 0.0200  MSHORVEL -0.63866E-01  0.4067E-01  -1.570 0.1163 10.5688  10.0444  BMHORVEL 0.30568  0.1795 1.703 0.0886  2.7515 2.9133  L2GTEM -47.574 13.63  -3.491 0.0005  3.2198 0.6594  LMLD_DEP -2.1410 1.009  -2.121 0.0339  7.1040 2.9228  Sigma 5.0184  0.3764  13.334 0.0000    Economic Valuation Of Critical Habitat Closures, Berman et al.  58 APPENDIX B. EQUATIONS FOR SPATIAL DISTRIBUTION OF CATCH PER UNIT OF EFFORT (CPUE) FOR SUMMER AND WINTER, BERING SEA/ALEUTIAN ISLANDS AND GULF OF ALASKA, ESTIMATED FROM 2001 NMFS BOTTOM TRAWL FISHERIES OBSERVER DATA  1. Winter bottom trawl  Selection equation, equations with chlorophyll (IMR2)   Binomial Probit Model    Maximum Likelihood Estimates   Log-Likelihood..............  -803.90   Restricted (Slopes 0) Log-L.  -4647.4   Chi-Squared (29)............ 7687.1   Significance Level..........  0.32173E-13   N(0,1) used for significance levels.   Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant -8.0095 1.824  -4.392 0.0000  FEB -0.58440  0.2262  -2.583 0.0098  0.1770 0.3817  MAR -0.27818  0.2437  -1.141 0.2537  0.1625 0.3689  APR -0.33022  0.2787  -1.185 0.2361  0.1592 0.3659  NOV -0.51956  0.2642  -1.967 0.0492  0.1691 0.3748  DEC  -1.3110  0.2641  -4.963 0.0000  0.1633 0.3697  GOA -0.28827  0.1897  -1.520 0.1285  0.2505 0.4333  LDEPTH  3.9029  0.6864 5.686 0.0000  4.4569 0.9573  L2DEP -0.45071  0.7256E-01  -6.212 0.0000 20.7804 8.8173  SLOPE  0.25926E-01  0.1530E-01 1.694 0.0902  3.9482 8.2979  SLOPE2 0.17093E-03  0.3905E-03 0.438 0.6616 84.4372 287.8697  SST  0.21348  0.8084E-01 2.641 0.0083  2.4452 2.6567  SST2  -0.50597E-01  0.1390E-01  -3.641 0.0003 11.6614  14.7372  SSTSLOPE 0.22275E-02  0.3028E-02 0.736 0.4620 13.8289  13.0156  SSH -0.12662E-01  0.1032E-01  -1.227 0.2198 -3.4514 7.7605  SSHSLOPE -0.20823E-02  0.2055E-02  -1.013 0.3110 32.0651  19.2719  MWIND -0.43418  0.3434  -1.264 0.2061  2.4399 0.1314  MCHLA -0.30384  0.1549  -1.962 0.0498  0.3811 0.3658  MCHLA1 0.16545  0.1648 1.004 0.3153  0.4223 0.3927  DWIND -0.32717  0.1485  -2.204 0.0276  0.2554 0.4361  DCHLA -0.38978  0.1410  -2.765 0.0057  0.4946 0.5000  DCHLA1  -0.27557  0.1247  -2.211 0.0271  0.6494 0.4772  POLTRAWL  -0.18045  0.1398  -1.291 0.1967  0.4259 0.4510  CODTRAWL 0.64341  0.1811 3.552 0.0004  0.7303 0.3938  ATKTRAWL  1.4558  0.3287 4.429 0.0000  0.0193 0.1173  POLTSSL  0.23449E-01  0.1772 0.132 0.8947  0.1648 0.3710  CODTSSL  0.48325  0.2669 1.810 0.0702  0.1515 0.3577  ATKTSSL -0.84069  0.3077  -2.732 0.0063  0.1387 0.3448  MIXTSSL  -1.1763  0.3151  -3.733 0.0002  0.0913 0.2871  PORTDIST  -0.17414E-01  0.2034E-02  -8.562 0.0000 63.9429  39.7322  Frequencies of actual & predicted outcomes  Predicted outcome has maximum probability.     Predicted    Actual  0  1  TOTAL    0  9161  3 9164  1  1652 13 1665    Total 10813 16  10829 Economic Valuation Of Critical Habitat Closures, Berman et al.   59 Selection equation, equations without chlorophyll (IMR3)   Binomial Probit Model    Maximum Likelihood Estimates   Log-Likelihood..............  -827.24   Restricted (Slopes 0) Log-L.  -4125.9   Chi-Squared (24)............ 6597.3   Significance Level..........  0.32173E-13   N(0,1) used for significance levels.   Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant -8.2873 1.709  -4.848 0.0000  FEB -0.36374  0.1918  -1.897 0.0579  0.1912 0.3933  MAR  0.20799  0.1758 1.183 0.2369  0.1597 0.3663  APR  0.19676  0.1960 1.004 0.3154  0.1459 0.3530  NOV -0.46857E-01  0.1865  -0.251 0.8016  0.1877 0.3905  DEC -0.73421  0.2215  -3.314 0.0009  0.1502 0.3573  GOA -0.16994  0.1915  -0.887 0.3749  0.2269 0.4189  LDEPTH  3.6863  0.6554 5.624 0.0000  4.6349 0.8986  L2DEP -0.42507  0.6937E-01  -6.127 0.0000 22.2899 8.6848  SLOPE  0.23553E-01  0.1582E-01 1.489 0.1366  4.6016 9.0754  SLOPE2 0.22750E-03  0.4127E-03 0.551 0.5815  103.5289 316.6736  SST  0.29195  0.8387E-01 3.481 0.0005  2.9045 2.1915  SST2  -0.62001E-01  0.1418E-01  -4.371 0.0000 12.8857  13.7415  SSTSLOPE 0.29445E-02  0.3113E-02 0.946 0.3442 13.5910  12.1874  SSH -0.13987E-01  0.1072E-01  -1.304 0.1922 -3.2951 6.6821  SSHSLOPE  -0.29155E-02  0.2061E-02  -1.415 0.1572 30.6658  18.2006  LWIND -0.45863  0.3288  -1.395 0.1631  2.4428 0.1455  POLTRAWL  -0.13340  0.1389  -0.960 0.3368  0.4196 0.4519  CODTRAWL 0.58830  0.1838 3.201 0.0014  0.7504 0.3823  ATKTRAWL  1.7320  0.3335 5.194 0.0000  0.0221 0.1248  POLTSSL  0.58119E-01  0.1852 0.314 0.7536  0.1396 0.3466  CODTSSL  0.35259  0.2944 1.198 0.2311  0.1300 0.3351  ATKTSSL -0.69082  0.3294  -2.097 0.0360  0.1257 0.3305  MIXTSSL  -1.1321  0.3149  -3.595 0.0003  0.0785 0.2675  PORTDIST -0.18465E-01  0.2015E-02  -9.165 0.0000 59.9196  38.0269  Frequencies of actual & predicted outcomes  Predicted outcome has maximum probability.     Predicted    Actual  0  1  TOTAL    0  7101  3 7104  1  1577  7 1584    Total  8678 10 8688     Economic Valuation Of Critical Habitat Closures, Berman et al.  60 Pollock, standard CPUE  Sample Selection Model   Two stage least squares regression. Dep. Variable LOGPOLLS    Observations  1653  Weights ONE   Mean of LHS 0.4938884E+00  Std.Dev of LHS 0.5930371E+00  StdDev of resid. 0.5295141E+00  Sum of squares 0.4547848E+03  R-squared 0.2022732E+00 Adj. R-squared 0.1875187E+00  F[ 30, 1622] 0.1370925E+02  Prob value 0.3217295E-13  Log-likelihood  -0.1278888E+04  Restr.(b=0) Log-l  -0.1481315E+04  Amemiya Pr. Criter. 0.2856435E+00 Akaike Info.Crit. 0.1584862E+01  Standard error corrected for selection.....  0.52995    Correlation of disturbance in regression   and Selection Criterion (Rho)..............  0.44612E-01    N(0,1) used for significance levels.   Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant  1.1154 1.140 0.979 0.3278  FEB -0.15042  0.1829  -0.822 0.4108  0.1506 0.3578  MAR -0.37005  0.3468  -1.067 0.2859  0.3315 0.4709  APR -0.65578  0.5082  -1.290 0.1969  0.2989 0.4579  NOV  -2.2821 1.489  -1.532 0.1255  0.0901 0.2865  DEC  -2.4468 1.622  -1.509 0.1314  0.0321 0.1762  GOA  -1.3082 1.051  -1.245 0.2130  0.1688 0.3747  TIMELDEP 0.50111E-01  0.3452E-01 1.452 0.1466 17.6167  11.9465  LDEPTH  -0.36414  0.4314  -0.844 0.3986  4.5252 0.4984  L2DEP -0.46261E-03  0.4333E-01  -0.011 0.9915 20.7261 4.8625  SLOPE -0.25176E-01  0.5478E-02  -4.595 0.0000  3.9975 8.9265  SLOPE2 0.37079E-03  0.1313E-03 2.825 0.0047 95.6138 342.6782  SST  0.16426  0.4515E-01 3.638 0.0003  3.1065 1.3323  SST2  -0.30844E-01  0.1080E-01  -2.856 0.0043 11.2998 7.7299  SSTSLOPE  -0.28840E-02  0.1265E-02  -2.279 0.0227 13.1123  11.4851  SSH  0.12895E-01  0.5204E-02 2.478 0.0132 -5.3569 5.1763  SSHSLOPE  -0.95708E-03  0.8205E-03  -1.166 0.2434 26.4713  17.4774  MWIND  0.34503  0.1548 2.229 0.0258  2.3861 0.1216  MCHLA  0.87458E-01  0.9232E-01 0.947 0.3435  0.4752 0.3091  MCHLA1  -0.80405E-01  0.9173E-01  -0.877 0.3807  0.4445 0.3390  DWIND  0.12052  0.6758E-01 1.783 0.0745  0.0490 0.2159  DCHLA -0.12035  0.6483E-01  -1.856 0.0634  0.2390 0.4266  DCHLA1  -0.30161E-02  0.5271E-01  -0.057 0.9544  0.3696 0.4829  GLDEPTH  0.93711  0.1131 8.286 0.0000  0.8006 1.7913  GTIMELDE  -0.86798E-01  0.2006E-01  -4.326 0.0000  2.3168 5.6658  GSST  -0.85188E-01  0.9814E-01  -0.868 0.3854  0.8209 1.8344  GSSH  -0.27424E-01  0.1321E-01  -2.075 0.0380 -0.6570 2.5355  GMWIND  -0.79279  0.3654  -2.170 0.0300  0.3997 0.8888  GMCHLA  -0.12001  0.1420  -0.845 0.3981  0.0888 0.2504  GMCHLA1  0.34060  0.3861 0.882 0.3777  0.0497 0.1298  IMR2 0.23642E-01  0.5027E-01 0.470 0.6381  1.8333 0.4828 Economic Valuation Of Critical Habitat Closures, Berman et al.   61 Pollock, standard CPUE (cont.)   Limited Dependent Variable Model - CENSORED  regression   Maximum Likelihood Estimates   Log-Likelihood..............  -1398.2   Threshold values for the model: Lower 0.0000  Upper **********    N(0,1) used for significance levels.   Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant  1.0848 1.336 0.812 0.4167  FEB -0.25756  0.2090  -1.232 0.2179  0.1508 0.3579  MAR -0.65751  0.3979  -1.652 0.0985  0.3317 0.4710  APR  -1.0791  0.5832  -1.850 0.0643  0.2979 0.4575  NOV  -3.7238 1.715  -2.171 0.0299  0.0899 0.2861  DEC  -4.0426 1.869  -2.162 0.0306  0.0320 0.1760  GOA  -2.3676 1.303  -1.817 0.0692  0.1683 0.3742  TIMELDEP 0.85701E-01  0.3983E-01 2.152 0.0314 17.5926  11.9387  LDEPTH  -0.38976  0.5074  -0.768 0.4424  4.5262 0.4981  L2DEP -0.69750E-02  0.5060E-01  -0.138 0.8904 20.7345 4.8581  SLOPE -0.41843E-01  0.6649E-02  -6.293 0.0000  4.0358 8.9858  SLOPE2 0.56804E-03  0.1606E-03 3.537 0.0004 96.9843 345.8335  SST  0.16687  0.5200E-01 3.209 0.0013  3.1077 1.3310  SST2  -0.34356E-01  0.1227E-01  -2.800 0.0051 11.3046 7.7231  SSTSLOPE  -0.33352E-02  0.1474E-02  -2.262 0.0237 13.0894  11.4765  SSH  0.16015E-01  0.6061E-02 2.642 0.0082 -5.3576 5.1789  SSHSLOPE  -0.10924E-02  0.9511E-03  -1.149 0.2507 26.5137  17.5059  MWIND  0.43538  0.1826 2.385 0.0171  2.3863 0.1215  MCHLA  0.98047E-01  0.1037 0.945 0.3445  0.4746 0.3089  MCHLA1  -0.74557E-01  0.1026  -0.727 0.4672  0.4437 0.3389  DWIND  0.10749  0.8102E-01 1.327 0.1846  0.0489 0.2156  DCHLA -0.14061  0.7484E-01  -1.879 0.0603  0.2394 0.4269  DCHLA1 0.14144E-01  0.5972E-01 0.237 0.8128  0.3703 0.4830  GLDEPTH 1.5109  0.1392  10.853 0.0000  0.7982 1.7891  GTIMELDE  -0.14220  0.2449E-01  -5.807 0.0000  2.3099 5.6586  GSST  -0.27316  0.1174  -2.326 0.0200  0.8184 1.8322  GSSH  -0.47740E-01  0.1682E-01  -2.838 0.0045 -0.6551 2.5319  GMWIND  -0.90118  0.4478  -2.013 0.0442  0.3985 0.8878  GMCHLA  -0.27502  0.2025  -1.358 0.1744  0.0885 0.2501  GMCHLA1  0.58058  0.4593 1.264 0.2062  0.0496 0.1296  IMR2  -0.31325E-01  0.5813E-01  -0.539 0.5900  1.8289 0.4856  Sigma  0.58721  0.1127E-01  52.082 0.0000    Economic Valuation Of Critical Habitat Closures, Berman et al.  62 Pollock, standard CPUE (cont.)   ML Estimates of Selection Model    Maximum Likelihood Estimates   Log-Likelihood..............  -4626.4   LHS is CENSORED. Tobit Model fit by MLE.   FIRST 30 estimates are probit equation.    N(0,1) used for significance levels.   Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant -4.1264 1.232  -3.349 0.0008  FEB  0.98613E-01  0.1709 0.577 0.5639  MAR  0.37777  0.3067 1.232 0.2181  APR  0.58488  0.4458 1.312 0.1895  NOV 1.1308 1.270 0.891 0.3731  DEC 1.3228 1.384 0.956 0.3393  GOA -0.14458 1.335  -0.108 0.9138  TIMELDEP -0.34752E-01  0.2935E-01  -1.184 0.2364  LDEPTH  1.7974  0.4519 3.977 0.0001  L2DEP -0.22567  0.4269E-01  -5.287 0.0000  SLOPE -0.19990E-01  0.8378E-02  -2.386 0.0170  SLOPE2 0.56498E-03  0.2294E-03 2.463 0.0138  SST  0.36798  0.4871E-01 7.554 0.0000  SST2  -0.53505E-01  0.1187E-01  -4.509 0.0000  SSTSLOPE 0.14164E-03  0.1674E-02 0.085 0.9325  SSH  0.21621E-01  0.6149E-02 3.516 0.0004  SSHSLOPE  -0.33315E-02  0.1036E-02  -3.214 0.0013  MWIND  0.53113E-01  0.2104 0.252 0.8007  MCHLA  0.12475E-01  0.1135 0.110 0.9125  MCHLA1 0.19246  0.1211 1.590 0.1119  DWIND -0.18806  0.7618E-01  -2.468 0.0136  DCHLA -0.37030  0.7953E-01  -4.656 0.0000  DCHLA1  -0.27602  0.6146E-01  -4.491 0.0000  GLDEPTH 1.1358  0.1040  10.921 0.0000  GTIMELDE  -0.11683  0.2030E-01  -5.755 0.0000  GSST  -0.35817  0.8837E-01  -4.053 0.0001  GSSH  -0.43281E-01  0.1345E-01  -3.217 0.0013  GMWIND -1.0219  0.4806  -2.126 0.0335  GMCHLA  -0.11160  0.2064  -0.541 0.5888  GMCHLA1 -0.41817  0.3863  -1.082 0.2791  SIGMA(1) 0.88965  0.1663E-01  53.499 0.0000  RHO(1,2) 0.97103  0.5738E-02 169.218 0.0000Economic Valuation Of Critical Habitat Closures, Berman et al.   63 Pacific Cod, CPUE  Sample Selection Model   Two stage least squares regression. Dep. Variable LOGPCOD   Observations  1653  Weights ONE   Mean of LHS 0.4016436E+00 Std.Dev of LHS 0.4578468E+00  StdDev of resid. 0.3778806E+00  Sum of squares 0.2316115E+03  R-squared 0.3183967E+00  Adj. R-squared 0.3057900E+00  F[ 30, 1622] 0.2525611E+02  Prob value 0.3217295E-13  Log-likelihood  -0.7211968E+03  Restr.(b=0) Log-l  -0.1053648E+04  Amemiya Pr. Criter. 0.1454717E+00  Akaike Info.Crit. 0.9100990E+00  Standard error corrected for selection.....  0.37804    Correlation of disturbance in regression   and Selection Criterion (Rho).............. -0.34165E-01    N(0,1) used for significance levels.   Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant  -0.34035  0.8704  -0.391 0.6958  FEB  0.26840  0.1298 2.067 0.0387  0.1506 0.3578  MAR  0.27857  0.2469 1.128 0.2592  0.3315 0.4709  APR  0.21542  0.3635 0.593 0.5535  0.2989 0.4579  NOV -0.38747 1.065  -0.364 0.7159  0.0901 0.2865  DEC -0.59297 1.157  -0.512 0.6084  0.0321 0.1762  GOA  0.60395  0.7511 0.804 0.4214  0.1688 0.3747  TIMELDEP 0.87670E-02  0.2468E-01 0.355 0.7225 17.6167  11.9465  LDEPTH 0.60747  0.3311 1.835 0.0665  4.5252 0.4984  L2DEP -0.91019E-01  0.3371E-01  -2.700 0.0069 20.7261 4.8625  SLOPE  0.31871E-01  0.4234E-02 7.528 0.0000  3.9975 8.9265  SLOPE2  -0.56813E-03  0.9254E-04  -6.140 0.0000 95.6138 342.6782  SST -0.26935  0.3413E-01  -7.892 0.0000  3.1065 1.3323  SST2 0.43395E-01  0.7856E-02 5.524 0.0000 11.2998 7.7299  SSTSLOPE  -0.15933E-02  0.9065E-03  -1.758 0.0788 13.1123  11.4851  SSH  0.16291E-01  0.3711E-02 4.390 0.0000 -5.3569 5.1763  SSHSLOPE 0.14028E-02  0.5978E-03 2.347 0.0189 26.4713  17.4774  MWIND  0.11299  0.1097 1.030 0.3032  2.3861 0.1216  MCHLA -0.43405  0.6560E-01  -6.616 0.0000  0.4752 0.3091  MCHLA1  -0.19937  0.6630E-01  -3.007 0.0026  0.4445 0.3390  DWIND  0.16400  0.4901E-01 3.347 0.0008  0.0490 0.2159  DCHLA  0.11784E-01  0.4757E-01 0.248 0.8043  0.2390 0.4266  DCHLA1  -0.79232E-01  0.3865E-01  -2.050 0.0404  0.3696 0.4829  GLDEPTH -0.45225  0.8058E-01  -5.612 0.0000  0.8006 1.7913  GTIMELDE 0.16988E-01  0.1431E-01 1.187 0.2351  2.3168 5.6658  GSST 0.61049E-01  0.6931E-01 0.881 0.3784  0.8209 1.8344  GSSH 0.28799E-02  0.9423E-02 0.306 0.7599 -0.6570 2.5355  GMWIND 0.39574  0.2607 1.518 0.1291  0.3997 0.8888  GMCHLA 0.24716  0.1015 2.436 0.0149  0.0888 0.2504  GMCHLA1 -0.45681  0.2783  -1.642 0.1007  0.0497 0.1298  IMR2  -0.12916E-01  0.6029E-01  -0.214 0.8304  1.1629 0.3469  Economic Valuation Of Critical Habitat Closures, Berman et al.  64 Pacific Cod, CPUE (cont.)   Limited Dependent Variable Model - CENSORED  regression   Maximum Likelihood Estimates   Log-Likelihood..............  -836.10   Threshold values for the model: Lower 0.0000  Upper **********    N(0,1) used for significance levels.   Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant -4.5811  0.9639  -4.753 0.0000  FEB  0.45632  0.1401 3.257 0.0011  0.1510 0.3581  MAR  0.63246  0.2668 2.370 0.0178  0.3309 0.4707  APR  0.75070  0.3913 1.919 0.0550  0.2983 0.4577  NOV 1.1011 1.152 0.956 0.3393  0.0900 0.2862  DEC 1.0698 1.255 0.853 0.3938  0.0320 0.1761  GOA 1.5165  0.8081 1.877 0.0606  0.1685 0.3744  TIMELDEP -0.26526E-01  0.2670E-01  -0.994 0.3204 17.5967  11.9453  LDEPTH  2.3686  0.3710 6.385 0.0000  4.5260 0.4983  L2DEP -0.27027  0.3711E-01  -7.283 0.0000 20.7325 4.8607  SLOPE  0.30314E-01  0.4177E-02 7.257 0.0000  4.0364 8.9912  SLOPE2  -0.50497E-03  0.9921E-04  -5.090 0.0000 97.0856 346.0301  SST -0.24102  0.3388E-01  -7.114 0.0000  3.1073 1.3317  SST2 0.39899E-01  0.8133E-02 4.906 0.0000 11.3035 7.7274  SSTSLOPE  -0.94942E-03  0.9545E-03  -0.995 0.3199 13.0978  11.4800  SSH  0.20636E-01  0.3927E-02 5.255 0.0000 -5.3587 5.1819  SSHSLOPE 0.10670E-02  0.6211E-03 1.718 0.0858 26.4999  17.4975  MWIND  0.80353E-01  0.1154 0.696 0.4863  2.3863 0.1216  MCHLA -0.45601  0.6975E-01  -6.537 0.0000  0.4747 0.3091  MCHLA1  -0.18075  0.6914E-01  -2.614 0.0089  0.4438 0.3390  DWIND  0.15257  0.5143E-01 2.966 0.0030  0.0489 0.2158  DCHLA -0.28402E-01  0.4859E-01  -0.585 0.5588  0.2397 0.4271  DCHLA1  -0.12141  0.3971E-01  -3.057 0.0022  0.3708 0.4832  GLDEPTH -0.71826  0.9939E-01  -7.226 0.0000  0.7991 1.7900  GTIMELDE 0.44300E-02  0.1623E-01 0.273 0.7849  2.3127 5.6615  GSST 0.73247E-01  0.7624E-01 0.961 0.3367  0.8194 1.8331  GSSH  -0.55089E-02  0.1040E-01  -0.530 0.5963 -0.6558 2.5334  GMWIND 0.49946  0.2768 1.805 0.0711  0.3990 0.8882  GMCHLA 0.34294  0.1083 3.166 0.0015  0.0886 0.2502  GMCHLA1 -0.46969  0.3011  -1.560 0.1188  0.0496 0.1297  IMR2 0.93349E-01  0.3800E-01 2.457 0.0140  1.8293 0.4856  Sigma  0.39417  0.7186E-02  54.851 0.0000   Economic Valuation Of Critical Habitat Closures, Berman et al.   65 Pacific Cod, CPUE (cont.)   ML Estimates of Selection Model    Maximum Likelihood Estimates   Log-Likelihood..............  -4088.5   LHS is CENSORED. Tobit Model fit by MLE.   FIRST 30 estimates are probit equation.    N(0,1) used for significance levels.   Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant -7.2310  0.8258  -8.756 0.0000  FEB  0.45676  0.1256 3.637 0.0003  MAR  0.82684  0.2313 3.575 0.0004  APR 1.1856  0.3407 3.480 0.0005  NOV 2.5329  0.9725 2.605 0.0092  DEC 2.6828 1.051 2.553 0.0107  GOA  0.73664  0.6481 1.137 0.2557  TIMELDEP -0.64675E-01  0.2215E-01  -2.920 0.0035  LDEPTH  3.4550  0.3167  10.908 0.0000  L2DEP -0.38971  0.2937E-01 -13.270 0.0000  SLOPE  0.37766E-01  0.4092E-02 9.228 0.0000  SLOPE2  -0.44846E-03  0.9988E-04  -4.490 0.0000  SST  0.77759E-02  0.2956E-01 0.263 0.7925  SST2  -0.10414E-04  0.8001E-02  -0.001 0.9990  SSTSLOPE 0.55426E-03  0.9146E-03 0.606 0.5445  SSH  0.21176E-01  0.4207E-02 5.033 0.0000  SSHSLOPE  -0.41704E-03  0.6674E-03  -0.625 0.5321  MWIND -0.69156E-01  0.1025  -0.675 0.5000  MCHLA -0.38958  0.7398E-01  -5.266 0.0000  MCHLA1  -0.38286E-02  0.9325E-01  -0.041 0.9673  DWIND -0.71155E-01  0.4193E-01  -1.697 0.0897  DCHLA -0.16925  0.4913E-01  -3.445 0.0006  DCHLA1  -0.25188  0.4418E-01  -5.702 0.0000  GLDEPTH -0.37785  0.7760E-01  -4.869 0.0000  GTIMELDE 0.16939E-01  0.1290E-01 1.313 0.1892  GSST  -0.15007E-01  0.5949E-01  -0.252 0.8008  GSSH 0.13846E-01  0.7467E-02 1.854 0.0637  GMWIND 0.40362  0.2273 1.776 0.0757  GMCHLA 0.14655  0.8716E-01 1.681 0.0927  GMCHLA1 -0.79825  0.2400  -3.326 0.0009  SIGMA(1) 0.60701  0.1138E-01  53.345 0.0000  RHO(1,2) 0.96156  0.5675E-02 169.443 0.0000Economic Valuation Of Critical Habitat Closures, Berman et al.  66 Pacific cod, standard CPUE  Sample Selection Model   Two stage least squares regression. Dep. Variable LOGPCODS    Observations  1653  Weights ONE   Mean of LHS 0.5852257E+00  Std.Dev of LHS 0.6311426E+00  StdDev of resid. 0.4941459E+00  Sum of squares 0.3960602E+03  R-squared 0.3866361E+00 Adj. R-squared 0.3752915E+00  F[ 30, 1622] 0.3408112E+02  Prob value 0.3217295E-13  Log-likelihood  -0.1164618E+04  Restr.(b=0) Log-l  -0.1584256E+04  Amemiya Pr. Criter. 0.2487594E+00 Akaike Info.Crit. 0.1446604E+01  Standard error corrected for selection.....  0.51396    Correlation of disturbance in regression   and Selection Criterion (Rho)..............  0.32420    N(0,1) used for significance levels.   Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant -1.8615 1.131  -1.646 0.0997  FEB  0.23511  0.1699 1.384 0.1665  0.1506 0.3578  MAR -0.50209E-01  0.3220  -0.156 0.8761  0.3315 0.4709  APR -0.34159  0.4737  -0.721 0.4709  0.2989 0.4579  NOV  -2.3463 1.384  -1.695 0.0901  0.0901 0.2865  DEC  -2.6294 1.505  -1.747 0.0806  0.0321 0.1762  GOA 2.2027  0.9816 2.244 0.0248  0.1688 0.3747  TIMELDEP 0.47112E-01  0.3206E-01 1.470 0.1417 17.6167  11.9465  LDEPTH  1.3597  0.4333 3.138 0.0017  4.5252 0.4984  L2DEP -0.19238  0.4435E-01  -4.338 0.0000 20.7261 4.8625  SLOPE  0.32883E-01  0.5784E-02 5.686 0.0000  3.9975 8.9265  SLOPE2  -0.54462E-03  0.1239E-03  -4.396 0.0000 95.6138 342.6782  SST -0.25440  0.4350E-01  -5.848 0.0000  3.1065 1.3323  SST2 0.41775E-01  0.1022E-01 4.087 0.0000 11.2998 7.7299  SSTSLOPE  -0.24015E-02  0.1207E-02  -1.989 0.0467 13.1123  11.4851  SSH  0.32392E-01  0.4910E-02 6.597 0.0000 -5.3569 5.1763  SSHSLOPE 0.21011E-02  0.7808E-03 2.691 0.0071 26.4713  17.4774  MWIND  0.16945  0.1458 1.163 0.2450  2.3861 0.1216  MCHLA -0.48255  0.8668E-01  -5.567 0.0000  0.4752 0.3091  MCHLA1  -0.17339  0.8625E-01  -2.010 0.0444  0.4445 0.3390  DWIND  0.91711E-01  0.6415E-01 1.430 0.1528  0.0490 0.2159  DCHLA -0.98280E-01  0.6258E-01  -1.570 0.1163  0.2390 0.4266  DCHLA1  -0.18044  0.5224E-01  -3.454 0.0006  0.3696 0.4829  GLDEPTH -0.59690  0.1053  -5.668 0.0000  0.8006 1.7913  GTIMELDE 0.15717E-01  0.1872E-01 0.840 0.4011  2.3168 5.6658  GSST  -0.79708E-01  0.9060E-01  -0.880 0.3790  0.8209 1.8344  GSSH 0.11185E-01  0.1230E-01 0.909 0.3633 -0.6570 2.5355  GMWIND 0.41358  0.3414 1.211 0.2258  0.3997 0.8888  GMCHLA 0.62346E-01  0.1325 0.471 0.6380  0.0888 0.2504  GMCHLA1 -0.32412  0.3622  -0.895 0.3709  0.0497 0.1298  IMR2 0.16663  0.7790E-01 2.139 0.0324  1.1639 0.3593 Economic Valuation Of Critical Habitat Closures, Berman et al.   67 Pacific cod, standard CPUE (cont.)   Limited Dependent Variable Model - CENSORED  regression   Maximum Likelihood Estimates   Log-Likelihood..............  -1251.5   Threshold values for the model: Lower 0.0000  Upper **********    N(0,1) used for significance levels.   Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant -6.3956 1.267  -5.048 0.0000  FEB  0.45232  0.1837 2.462 0.0138  0.1510 0.3581  MAR  0.34229  0.3499 0.978 0.3280  0.3309 0.4707  APR  0.24404  0.5131 0.476 0.6344  0.2983 0.4577  NOV -0.61666 1.511  -0.408 0.6833  0.0900 0.2862  DEC -0.69585 1.646  -0.423 0.6724  0.0320 0.1761  GOA 3.2275 1.058 3.050 0.0023  0.1685 0.3744  TIMELDEP 0.79667E-02  0.3502E-01 0.228 0.8200 17.5967  11.9453  LDEPTH  3.2265  0.4874 6.620 0.0000  4.5260 0.4983  L2DEP -0.37358  0.4863E-01  -7.681 0.0000 20.7325 4.8607  SLOPE  0.24030E-01  0.5474E-02 4.390 0.0000  4.0364 8.9912  SLOPE2  -0.43733E-03  0.1301E-03  -3.363 0.0008 97.0856 346.0301  SST -0.25286  0.4443E-01  -5.691 0.0000  3.1073 1.3317  SST2 0.40570E-01  0.1066E-01 3.804 0.0001 11.3035 7.7274  SSTSLOPE  -0.20002E-02  0.1252E-02  -1.597 0.1102 13.0978  11.4800  SSH  0.36651E-01  0.5154E-02 7.111 0.0000 -5.3587 5.1819  SSHSLOPE 0.20999E-02  0.8134E-03 2.582 0.0098 26.4999  17.4975  MWIND  0.18850  0.1513 1.246 0.2127  2.3863 0.1216  MCHLA -0.47418  0.9140E-01  -5.188 0.0000  0.4747 0.3091  MCHLA1  -0.19012  0.9065E-01  -2.097 0.0360  0.4438 0.3390  DWIND  0.11420  0.6719E-01 1.700 0.0892  0.0489 0.2158  DCHLA -0.97964E-01  0.6375E-01  -1.537 0.1244  0.2397 0.4271  DCHLA1  -0.16553  0.5207E-01  -3.179 0.0015  0.3708 0.4832  GLDEPTH -0.92506  0.1280  -7.229 0.0000  0.7991 1.7900  GTIMELDE  -0.69064E-02  0.2118E-01  -0.326 0.7443  2.3127 5.6615  GSST  -0.63529E-01  0.9966E-01  -0.637 0.5238  0.8194 1.8331  GSSH  -0.16994E-02  0.1360E-01  -0.125 0.9005 -0.6558 2.5334  GMWIND 0.60086  0.3624 1.658 0.0973  0.3990 0.8882  GMCHLA 0.19110  0.1419 1.347 0.1779  0.0886 0.2502  GMCHLA1 -0.67475E-01  0.3927  -0.172 0.8636  0.0496 0.1297  IMR2 0.81921E-01  0.4980E-01 1.645 0.1000  1.8293 0.4856  Sigma  0.51663  0.9425E-02  54.815 0.0000   Economic Valuation Of Critical Habitat Closures, Berman et al.  68 Pacific cod, standard CPUE (cont.)   ML Estimates of Selection Model    Maximum Likelihood Estimates   Log-Likelihood..............  -4566.3   LHS is CENSORED. Tobit Model fit by MLE.   FIRST 30 estimates are probit equation.    N(0,1) used for significance levels.   Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant -6.3867 1.286  -4.966 0.0000  FEB  0.45635  0.2193 2.081 0.0375  MAR  0.37182  0.4336 0.858 0.3911  APR  0.31363  0.6440 0.487 0.6263  NOV -0.32962 1.885  -0.175 0.8612  DEC -0.38884 2.036  -0.191 0.8486  GOA 3.3144  0.9396 3.527 0.0004  TIMELDEP 0.14914E-02  0.4311E-01 0.035 0.9724  LDEPTH  3.2197  0.4993 6.448 0.0000  L2DEP -0.36956  0.4759E-01  -7.765 0.0000  SLOPE  0.25667E-01  0.4666E-02 5.501 0.0000  SLOPE2  -0.47429E-03  0.1005E-03  -4.718 0.0000  SST -0.26058  0.4351E-01  -5.989 0.0000  SST2 0.41794E-01  0.1195E-01 3.497 0.0005  SSTSLOPE  -0.20963E-02  0.1247E-02  -1.681 0.0927  SSH  0.35638E-01  0.5527E-02 6.448 0.0000  SSHSLOPE 0.21735E-02  0.8050E-03 2.700 0.0069  MWIND  0.20655  0.1310 1.576 0.1149  MCHLA -0.45320  0.1115  -4.064 0.0000  MCHLA1  -0.19234  0.1506  -1.277 0.2014  DWIND  0.11284  0.4940E-01 2.284 0.0224  DCHLA -0.90226E-01  0.6071E-01  -1.486 0.1372  DCHLA1  -0.15534  0.5928E-01  -2.621 0.0088  GLDEPTH -0.94200  0.1275  -7.389 0.0000  GTIMELDE  -0.40522E-02  0.2032E-01  -0.199 0.8420  GSST  -0.66750E-01  0.9346E-01  -0.714 0.4751  GSSH  -0.28497E-03  0.1100E-01  -0.026 0.9793  GMWIND 0.60259  0.3084 1.954 0.0507  GMCHLA 0.19075  0.1428 1.336 0.1817  GMCHLA1 -0.87730E-01  0.3621  -0.242 0.8085  SIGMA(1) 0.51882  0.8805E-02  58.922 0.0000  RHO(1,2) 0.13195  0.1042 1.266 0.2054  Economic Valuation Of Critical Habitat Closures, Berman et al.   69 Atka mackerel, standard CPUE  Sample Selection Model   Two stage least squares regression. Dep. Variable LOGATKAS  Observations  1581  Weights ONE   Mean of LHS 0.9712528E-01  Std.Dev of LHS 0.5179737E+00  StdDev of resid. 0.3773482E+00 Sum of squares 0.2217039E+03  R-squared 0.4770010E+00 Adj. R-squared 0.4692753E+00  F[ 23, 1557] 0.6174180E+02  Prob value 0.3217295E-13  Log-likelihood  -0.6904281E+03  Restr.(b=0) Log-l  -0.1202811E+04  Amemiya Pr. Criter. 0.1445532E+00  Akaike Info.Crit. 0.9037674E+00  ANOVA Source  Variation  Deg. freedom  Mean Square  Regression  0.2022049E+03  23. 0.8791520E+01  Residual  0.2217039E+03  1557. 0.1423917E+00  Total 0.4239088E+03  1580. 0.2682967E+00  N(0,1) used for significance levels.   Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant -2.5075  0.7676  -3.267 0.0011  FEB  0.36633  0.1212 3.023 0.0025  0.1467 0.3540  MAR  0.71386  0.2365 3.018 0.0025  0.3302 0.4704  APR 1.4063  0.3496 4.023 0.0001  0.3017 0.4591  NOV 7.2145 1.047 6.891 0.0000  0.0942 0.2923  DEC 7.5766 1.158 6.542 0.0000  0.0335 0.1801  GOA  0.24173  0.7217 0.335 0.7377  0.1569 0.3638  TIMELDEP -0.18106  0.2456E-01  -7.371 0.0000 17.8641  12.1146  LDEPTH  1.2558  0.2925 4.293 0.0000  4.5226 0.4979  L2DEP -0.73829E-01  0.2914E-01  -2.534 0.0113 20.7017 4.8728  SLOPE  0.41723E-01  0.4121E-02  10.125 0.0000  3.6965 8.5220  SLOPE2  -0.46303E-03  0.1018E-03  -4.548 0.0000 86.2419 319.1512  SST  0.47862E-01  0.3521E-01 1.359 0.1741  3.1015 1.2961  SST2 0.15012E-02  0.7932E-02 0.189 0.8499 11.2162 7.6731  SSTSLOPE 0.19233E-02  0.9740E-03 1.975 0.0483 12.9840  11.0375  SSH -0.55611E-01  0.3852E-02 -14.438 0.0000 -5.4090 5.1606  SSHSLOPE 0.13469E-02  0.6036E-03 2.231 0.0257 25.7042  17.0200  LWIND -0.21809  0.1102  -1.979 0.0478  2.3851 0.1233  GLDEPTH -0.46192  0.7445E-01  -6.205 0.0000  0.7443 1.7397  GTIMELDE 0.80349E-01  0.1155E-01 6.956 0.0000  2.1758 5.5440  GSST  -0.48696E-01  0.7236E-01  -0.673 0.5010  0.7697 1.7962  GSSH 0.59079E-01  0.1065E-01 5.548 0.0000 -0.5913 2.3521  GLWIND 0.47128  0.2537 1.858 0.0632  0.3706 0.8612  IMR3  -0.27612E-01  0.3475E-01  -0.795 0.4268  1.8369 0.4658   Economic Valuation Of Critical Habitat Closures, Berman et al.  70 Atka mackerel, standard CPUE (cont.)   Limited Dependent Variable Model - CENSORED  regression   Maximum Likelihood Estimates   Log-Likelihood..............  -301.66   Threshold values for the model: Lower 0.0000  Upper **********    N(0,1) used for significance levels.   Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant -34.660 11.60  -2.988 0.0028  FEB 3.8767 1.453 2.669 0.0076  0.1467 0.3540  MAR 7.4859 2.822 2.653 0.0080  0.3302 0.4704  APR 11.374 4.143 2.745 0.0060  0.3017 0.4591  NOV 38.969 26.25 1.485 0.1376  0.0942 0.2923  DEC 47.706 13.72 3.477 0.0005  0.0335 0.1801  GOA  -8.4889 9.698  -0.875 0.3814  0.1569 0.3638  TIMELDEP -1.0691  0.2922  -3.659 0.0003 17.8641  12.1146  LDEPTH  14.818 4.787 3.095 0.0020  4.5226 0.4979  L2DEP  -1.2947  0.4866  -2.661 0.0078 20.7017 4.8728  SLOPE  0.17656  0.2213E-01 7.978 0.0000  3.6965 8.5220  SLOPE2  -0.25598E-02  0.4588E-03  -5.580 0.0000 86.2419 319.1512  SST  0.21850  0.2181 1.002 0.3165  3.1015 1.2961  SST2 0.31178E-01  0.5390E-01 0.578 0.5630 11.2162 7.6731  SSTSLOPE 0.20967E-01  0.6283E-02 3.337 0.0008 12.9840  11.0375  SSH -0.14980  0.2583E-01  -5.799 0.0000 -5.4090 5.1606  SSHSLOPE 0.84141E-02  0.4288E-02 1.962 0.0497 25.7042  17.0200  LWIND  -1.7169  0.5096  -3.369 0.0008  2.3851 0.1233  GLDEPTH -0.73699  0.9166  -0.804 0.4214  0.7443 1.7397  GTIMELDE 0.72536E-01  0.1191 0.609 0.5426  2.1758 5.5440  GSST -2.0576  0.6663  -3.088 0.0020  0.7697 1.7962  GSSH 0.39086E-01  0.1059 0.369 0.7120 -0.5913 2.3521  GLWIND  8.2018 3.358 2.442 0.0146  0.3706 0.8612  IMR3 0.14239  0.2502 0.569 0.5692  1.8369 0.4658  Sigma 1.2081  0.8352E-01  14.466 0.0000  Economic Valuation Of Critical Habitat Closures, Berman et al.   71 Atka mackerel, standard CPUE (cont.)   ML Estimates of Selection Model    Maximum Likelihood Estimates   Log-Likelihood..............  -3700.3   LHS is CENSORED. Tobit Model fit by MLE.   FIRST 25 estimates are probit equation.    N(0,1) used for significance levels.   Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant -25.650 16.27  -1.577 0.1149  FEB 2.5945 1.996 1.300 0.1936  MAR 5.3704 3.816 1.407 0.1594  APR 7.8817 5.663 1.392 0.1640  NOV 30.922 4108. 0.008 0.9940  DEC 38.664 18.61 2.078 0.0377  GOA 4.7623 6.405 0.743 0.4572  TIMELDEP -0.86924  0.3946  -2.203 0.0276  LDEPTH  11.894 6.859 1.734 0.0829  L2DEP  -1.0056  0.7298  -1.378 0.1682  SLOPE  0.17329  0.2808E-01 6.172 0.0000  SLOPE2  -0.29793E-02  0.5322E-03  -5.598 0.0000  SST  0.23424  0.1940 1.207 0.2273  SST2  -0.19454E-01  0.5202E-01  -0.374 0.7084  SSTSLOPE 0.89657E-02  0.5497E-02 1.631 0.1029  SSH -0.11840  0.2624E-01  -4.513 0.0000  SSHSLOPE 0.11803E-01  0.5106E-02 2.312 0.0208  LWIND  -1.8455  0.5698  -3.239 0.0012  GLDEPTH -0.77086 1.749  -0.441 0.6594  GTIMELDE 0.55854E-01  0.1737 0.321 0.7478  GSST -1.3869  0.8370  -1.657 0.0975  GSSH 0.35503E-01  0.1585 0.224 0.8227  GLWIND  1.8487  0.5711 3.237 0.0012  SIGMA(1)  1.5309  0.1964 7.796 0.0000  RHO(1,2)  -0.64896  0.1448  -4.482 0.0000Economic Valuation Of Critical Habitat Closures, Berman et al.  72 Black cod, standard CPUE  Sample Selection Model   Two stage least squares regression. Dep. Variable LOGBCODS    Observations  1653  Weights ONE   Mean of LHS 0.1877368E-01  Std.Dev of LHS 0.1331380E+00  StdDev of resid. 0.1099445E+00 Sum of squares 0.1960639E+02  R-squared 0.3176522E+00  Adj. R-squared 0.3050317E+00  F[ 30, 1622] 0.2516956E+02  Prob value 0.3217295E-13  Log-likelihood 0.1319602E+04  Restr.(b=0) Log-l 0.9880533E+03  Amemiya Pr. Criter. 0.1231448E-01  Akaike Info.Crit.  -0.1559107E+01  Standard error corrected for selection.....  0.11313    Correlation of disturbance in regression   and Selection Criterion (Rho).............. -0.28055    N(0,1) used for significance levels.    Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant  2.4274  0.2362  10.276 0.0000  FEB  0.79184E-02  0.3793E-01 0.209 0.8346  0.1506 0.3578  MAR  0.53221E-01  0.7198E-01 0.739 0.4597  0.3315 0.4709  APR  0.61085E-01  0.1059 0.577 0.5639  0.2989 0.4579  NOV  0.35221  0.3101 1.136 0.2560  0.0901 0.2865  DEC  0.39322  0.3375 1.165 0.2440  0.0321 0.1762  GOA  0.78183E-01  0.2188 0.357 0.7208  0.1688 0.3747  TIMELDEP -0.80420E-02  0.7177E-02  -1.121 0.2625 17.6167  11.9465  LDEPTH -1.0480  0.8925E-01 -11.742 0.0000  4.5252 0.4984  L2DEP  0.12000  0.8940E-02  13.423 0.0000 20.7261 4.8625  SLOPE  0.19162E-03  0.1173E-02 0.163 0.8703  3.9975 8.9265  SLOPE2  -0.15904E-04  0.2722E-04  -0.584 0.5591 95.6138 342.6782  SST -0.62122E-02  0.9342E-02  -0.665 0.5061  3.1065 1.3323  SST2 0.24332E-02  0.2249E-02 1.082 0.2792 11.2998 7.7299  SSTSLOPE 0.78424E-04  0.2648E-03 0.296 0.7671 13.1123  11.4851  SSH -0.12560E-03  0.1084E-02  -0.116 0.9078 -5.3569 5.1763  SSHSLOPE 0.22311E-03  0.1731E-03 1.289 0.1974 26.4713  17.4774  MWIND -0.29226E-01  0.3219E-01  -0.908 0.3639  2.3861 0.1216  MCHLA  0.27555E-01  0.1913E-01 1.441 0.1497  0.4752 0.3091  MCHLA1  -0.29923E-01  0.1915E-01  -1.562 0.1182  0.4445 0.3390  DWIND -0.21355E-01  0.1412E-01  -1.513 0.1303  0.0490 0.2159  DCHLA -0.62132E-02  0.1338E-01  -0.464 0.6424  0.2390 0.4266  DCHLA1  -0.65625E-02  0.1078E-01  -0.609 0.5427  0.3696 0.4829  GLDEPTH  0.32166E-01  0.2342E-01 1.374 0.1696  0.8006 1.7913  GTIMELDE 0.14320E-01  0.4161E-02 3.442 0.0006  2.3168 5.6658  GSST  -0.81180E-01  0.2034E-01  -3.992 0.0001  0.8209 1.8344  GSSH 0.65528E-02  0.2737E-02 2.394 0.0167 -0.6570 2.5355  GMWIND 0.40875E-01  0.7596E-01 0.538 0.5905  0.3997 0.8888  GMCHLA  -0.70419E-01  0.2949E-01  -2.388 0.0170  0.0888 0.2504  GMCHLA1 -0.18011  0.8041E-01  -2.240 0.0251  0.0497 0.1298  IMR2  -0.31739E-01  0.1040E-01  -3.053 0.0023  1.1205 0.4131 Economic Valuation Of Critical Habitat Closures, Berman et al.   73 Black cod, standard CPUE (cont.)   Limited Dependent Variable Model - CENSORED  regression   Maximum Likelihood Estimates   Log-Likelihood..............  -220.25   Threshold values for the model: Lower 0.0000  Upper **********    N(0,1) used for significance levels.   Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant  3.8818 1.656 2.345 0.0190  FEB  0.10332  0.2868 0.360 0.7187  0.1514 0.3586  MAR -0.22769E-01  0.5783  -0.039 0.9686  0.3329 0.4714  APR -0.18441  0.8442  -0.218 0.8271  0.2969 0.4570  NOV  -2.5711 7.886  -0.326 0.7444  0.0895 0.2856  DEC  -2.2801 16.09  -0.142 0.8873  0.0319 0.1757  GOA -0.62586 1.308  -0.478 0.6323  0.1683 0.3742  TIMELDEP 0.43199E-01  0.5101E-01 0.847 0.3970 17.5742  11.9220  LDEPTH -1.9790  0.6414  -3.085 0.0020  4.5267 0.4973  L2DEP  0.24043  0.6431E-01 3.739 0.0002 20.7384 4.8504  SLOPE -0.26325E-02  0.1051E-01  -0.251 0.8022  4.0268 8.9715  SLOPE2  -0.24089E-03  0.3034E-03  -0.794 0.4272 96.6545 345.2532  SST -0.99882E-01  0.7856E-01  -1.271 0.2036  3.1098 1.3297  SST2 0.25173E-01  0.1688E-01 1.491 0.1359 11.3145 7.7193  SSTSLOPE  -0.45292E-03  0.2235E-02  -0.203 0.8394 13.0559  11.4707  SSH  0.33618E-01  0.1035E-01 3.248 0.0012 -5.3600 5.1714  SSHSLOPE 0.29973E-02  0.1254E-02 2.391 0.0168 26.4905  17.4916  MWIND -0.14052  0.2238  -0.628 0.5301  2.3863 0.1215  MCHLA  0.26636  0.2143 1.243 0.2140  0.4744 0.3084  MCHLA1  -0.70390  0.3591  -1.960 0.0500  0.4432 0.3385  DWIND -0.54923  0.2197  -2.500 0.0124  0.0487 0.2153  DCHLA  0.11690  0.9831E-01 1.189 0.2344  0.2386 0.4263  DCHLA1  -0.29439E-01  0.1487  -0.198 0.8431  0.3720 0.4835  GLDEPTH  0.23380  0.1471 1.589 0.1120  0.7980 1.7888  GTIMELDE  -0.64460E-02  0.2493E-01  -0.259 0.7959  2.3070 5.6523  GSST  -0.21247  0.1357  -1.566 0.1174  0.8186 1.8326  GSSH  -0.52664E-01  0.1856E-01  -2.838 0.0045 -0.6535 2.5277  GMWIND 0.23113  0.4277 0.540 0.5889  0.3985 0.8879  GMCHLA  -0.84100E-01  0.2490  -0.338 0.7356  0.0885 0.2498  GMCHLA1 -0.17378  0.6210  -0.280 0.7796  0.0494 0.1294  IMR2  -0.28799  0.8733E-01  -3.298 0.0010  1.8280 0.4850  Sigma  0.37722  0.2295E-01  16.440 0.0000  Economic Valuation Of Critical Habitat Closures, Berman et al.  74 Black cod, standard CPUE (cont.)   ML Estimates of Selection Model    Maximum Likelihood Estimates   Log-Likelihood..............  -3543.2   LHS is CENSORED. Tobit Model fit by MLE.   FIRST 30 estimates are probit equation.    N(0,1) used for significance levels.   Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant  2.9620 2.057 1.440 0.1499  FEB  0.11760  0.3187 0.369 0.7121  MAR  0.42049E-03  0.6920 0.001 0.9995  APR -0.19232  0.9516  -0.202 0.8398  NOV  -2.4524 1232.  -0.002 0.9984  DEC  -1.6980 327.3  -0.005 0.9959  GOA -0.37774 1.709  -0.221 0.8251  TIMELDEP 0.38390E-01  0.4995E-01 0.769 0.4422  LDEPTH -1.5918  0.7620  -2.089 0.0367  L2DEP  0.19778  0.7363E-01 2.686 0.0072  SLOPE -0.51571E-02  0.1535E-01  -0.336 0.7368  SLOPE2  -0.10672E-03  0.4393E-03  -0.243 0.8080  SST -0.77259E-01  0.1102  -0.701 0.4833  SST2 0.25042E-01  0.2403E-01 1.042 0.2973  SSTSLOPE  -0.23213E-03  0.2909E-02  -0.080 0.9364  SSH  0.34729E-01  0.1487E-01 2.336 0.0195  SSHSLOPE 0.29496E-02  0.1493E-02 1.975 0.0482  MWIND -0.23381  0.3027  -0.773 0.4398  MCHLA  0.22720  0.3550 0.640 0.5222  MCHLA1  -0.64249  0.6993  -0.919 0.3582  DWIND -0.55175  0.3753  -1.470 0.1415  DCHLA  0.60389E-01  0.1296 0.466 0.6413  DCHLA1  -0.10837  0.2019  -0.537 0.5914  GLDEPTH  0.22527  0.1480 1.522 0.1281  GTIMELDE  -0.80087E-02  0.2776E-01  -0.289 0.7729  GSST  -0.26293  0.1854  -1.418 0.1562  GSSH  -0.54842E-01  0.2298E-01  -2.386 0.0170  GMWIND 0.21476  0.5189 0.414 0.6789  GMCHLA  -0.12324  0.4085  -0.302 0.7629  GMCHLA1 -0.31465  0.9677  -0.325 0.7451  SIGMA(1) 0.41090  0.4339E-01 9.471 0.0000  RHO(1,2)  -0.41769  0.2496  -1.674 0.0942 Economic Valuation Of Critical Habitat Closures, Berman et al.   75 Rockfish, standard CPUE  Sample Selection Model   Two stage least squares regression. Dep. Variable LOGROCKS    Observations  1653  Weights ONE   Mean of LHS 0.9454465E-01  Std.Dev of LHS 0.3560142E+00  StdDev of resid. 0.2955764E+00  Sum of squares 0.1417067E+03  R-squared 0.3102881E+00  Adj. R-squared 0.2975315E+00  F[ 30, 1622] 0.2432356E+02  Prob value 0.3217295E-13  Log-likelihood  -0.3151357E+03  Restr.(b=0) Log-l  -0.6378122E+03  Amemiya Pr. Criter. 0.8900384E-01  Akaike Info.Crit. 0.4187970E+00  Standard error corrected for selection.....  0.29652    Correlation of disturbance in regression   and Selection Criterion (Rho).............. -0.94950E-01   N(0,1) used for significance levels.    Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant 0.85655  0.6326 1.354 0.1757  FEB  0.83449E-02  0.1019 0.082 0.9348  0.1506 0.3578  MAR -0.53336E-01  0.1937  -0.275 0.7830  0.3315 0.4709  APR -0.29167E-01  0.2848  -0.102 0.9184  0.2989 0.4579  NOV  0.81535  0.8349 0.977 0.3288  0.0901 0.2865  DEC  0.82764  0.9089 0.911 0.3625  0.0321 0.1762  GOA -0.88707  0.5878  -1.509 0.1313  0.1688 0.3747  TIMELDEP -0.21835E-01  0.1932E-01  -1.130 0.2585 17.6167  11.9465  LDEPTH  -0.17497  0.2391  -0.732 0.4643  4.5252 0.4984  L2DEP  0.31109E-01  0.2394E-01 1.299 0.1938 20.7261 4.8625  SLOPE  0.36580E-01  0.3118E-02  11.732 0.0000  3.9975 8.9265  SLOPE2  -0.61869E-03  0.7224E-04  -8.565 0.0000 95.6138 342.6782  SST -0.11381E-01  0.2497E-01  -0.456 0.6486  3.1065 1.3323  SST2 0.51224E-02  0.6026E-02 0.850 0.3953 11.2998 7.7299  SSTSLOPE  -0.60695E-03  0.7056E-03  -0.860 0.3897 13.1123  11.4851  SSH -0.19526E-01  0.2901E-02  -6.731 0.0000 -5.3569 5.1763  SSHSLOPE 0.90913E-03  0.4612E-03 1.971 0.0487 26.4713  17.4774  MWIND -0.21572  0.8579E-01  -2.514 0.0119  2.3861 0.1216  MCHLA  0.32825E-01  0.5114E-01 0.642 0.5210  0.4752 0.3091  MCHLA1  -0.68217E-02  0.5122E-01  -0.133 0.8941  0.4445 0.3390  DWIND -0.66462E-01  0.3771E-01  -1.763 0.0780  0.0490 0.2159  DCHLA  0.29790E-01  0.3571E-01 0.834 0.4042  0.2390 0.4266  DCHLA1  -0.17345E-01  0.2874E-01  -0.603 0.5462  0.3696 0.4829  GLDEPTH -0.29733E-02  0.6303E-01  -0.047 0.9624  0.8006 1.7913  GTIMELDE 0.20665E-01  0.1119E-01 1.847 0.0648  2.3168 5.6658  GSST 0.66855E-01  0.5470E-01 1.222 0.2217  0.8209 1.8344  GSSH 0.16746E-01  0.7368E-02 2.273 0.0230 -0.6570 2.5355  GMWIND 0.17361  0.2040 0.851 0.3947  0.3997 0.8888  GMCHLA  -0.27873E-01  0.7928E-01  -0.352 0.7252  0.0888 0.2504  GMCHLA1 -0.11691  0.2161  -0.541 0.5885  0.0497 0.1298  IMR2  -0.28155E-01  0.2768E-01  -1.017 0.3091  1.1206 0.4129 Economic Valuation Of Critical Habitat Closures, Berman et al.  76  Rockfish, standard CPUE (cont.)   Limited Dependent Variable Model - CENSORED  regression   Maximum Likelihood Estimates   Log-Likelihood..............  -513.72   Threshold values for the model: Lower 0.0000  Upper **********    N(0,1) used for significance levels.   Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant -11.694 3.018  -3.875 0.0001  FEB -0.41568  0.5026  -0.827 0.4082  0.1510 0.3582  MAR  -1.2020  0.9679  -1.242 0.2143  0.3339 0.4718  APR  -2.1255 1.460  -1.456 0.1455  0.2972 0.4572  NOV  -5.9906 15.44  -0.388 0.6979  0.0897 0.2858  DEC  -7.1441 28.30  -0.252 0.8007  0.0319 0.1758  GOA 2.3880 1.931 1.237 0.2163  0.1685 0.3744  TIMELDEP 0.11025  0.9308E-01 1.184 0.2362 17.5917  11.9199  LDEPTH  4.4729 1.162 3.850 0.0001  4.5260 0.4973  L2DEP -0.38035  0.1172  -3.246 0.0012 20.7319 4.8508  SLOPE  0.77617E-01  0.8660E-02 8.963 0.0000  3.9858 8.9047  SLOPE2  -0.12622E-02  0.1915E-03  -6.592 0.0000 95.1317 341.8117  SST -0.22672  0.8609E-01  -2.634 0.0084  3.1086 1.3300  SST2 0.60631E-01  0.2358E-01 2.572 0.0101 11.3079 7.7204  SSTSLOPE  -0.35217E-02  0.2518E-02  -1.399 0.1619 13.0662  11.4736  SSH -0.67012E-01  0.1124E-01  -5.961 0.0000 -5.3578 5.1643  SSHSLOPE 0.18710E-02  0.1694E-02 1.104 0.2694 26.4753  17.4747  MWIND -0.96248  0.3144  -3.061 0.0022  2.3861 0.1214  MCHLA -0.39986  0.4040  -0.990 0.3223  0.4748 0.3084  MCHLA1  -0.73409  0.6102  -1.203 0.2289  0.4438 0.3384  DWIND  0.11798E-01  0.1161 0.102 0.9191  0.0487 0.2154  DCHLA  0.24601  0.1559 1.578 0.1145  0.2377 0.4258  DCHLA1 0.34102E-01  0.1879 0.182 0.8560  0.3706 0.4831  GLDEPTH  -1.0949  0.2173  -5.038 0.0000  0.7990 1.7896  GTIMELDE 0.97137E-01  0.3384E-01 2.871 0.0041  2.3097 5.6551  GSST  -0.11523  0.1893  -0.609 0.5426  0.8195 1.8335  GSSH 0.77479E-01  0.2297E-01 3.373 0.0007 -0.6543 2.5291  GMWIND  1.0618  0.6392 1.661 0.0967  0.3990 0.8884  GMCHLA 0.55717  0.4411 1.263 0.2066  0.0886 0.2500  GMCHLA1 -0.25825  0.7854  -0.329 0.7423  0.0495 0.1295  IMR2  -0.39212E-01  0.9918E-01  -0.395 0.6926  1.8279 0.4850  Sigma  0.67367  0.2677E-01  25.163 0.0000  Economic Valuation Of Critical Habitat Closures, Berman et al.   77 Rockfish, standard CPUE (cont.)   ML Estimates of Selection Model    Maximum Likelihood Estimates   Log-Likelihood..............  -3838.3   LHS is CENSORED. Tobit Model fit by MLE.   FIRST 30 estimates are probit equation.    N(0,1) used for significance levels.   Variable Coefficient Std. Error  t-ratio Prob.  ----------------------------------------------------------------------------  Constant -11.433 3.360  -3.403 0.0007  FEB -0.44676  0.5944  -0.752 0.4523   MAR  -1.1717 1.155  -1.015 0.3103  APR  -2.1339 1.771  -1.205 0.2283    NOV  -5.5952 487.8  -0.011 0.9908    DEC  -6.9311 3230.  -0.002 0.9983    GOA 2.1761 2.591 0.840 0.4011   TIMELDEP 0.11665  0.1176 0.992 0.3214    LDEPTH  4.3433 1.333 3.259 0.0011  L2DEP -0.36760  0.1366  -2.692 0.0071  SLOPE  0.74790E-01  0.9806E-02 7.627 0.0000  SLOPE2  -0.12404E-02  0.2055E-03  -6.037 0.0000  SST -0.22718  0.1004  -2.264 0.0236    SST2 0.60625E-01  0.2731E-01 2.220 0.0264    SSTSLOPE  -0.37243E-02  0.3075E-02  -1.211 0.2258    SSH -0.67868E-01  0.1097E-01  -6.185 0.0000    SSHSLOPE 0.18257E-02  0.1723E-02 1.059 0.2894  MWIND -0.93293  0.2593  -3.598 0.0003    MCHLA -0.42820  0.3984  -1.075 0.2824   MCHLA1  -0.85148  0.8477  -1.004 0.3151   DWIND  0.24351E-01  0.1556 0.157 0.8756    DCHLA  0.25822  0.1700 1.519 0.1288    DCHLA1 0.97265E-01  0.2350 0.414 0.6790    GLDEPTH  -1.0709  0.2429  -4.409 0.0000  GTIMELDE 0.95116E-01  0.4150E-01 2.292 0.0219   GSST  -0.94056E-01  0.2143  -0.439 0.6607   GSSH 0.77905E-01  0.2516E-01 3.096 0.0020    GMWIND  1.0637  0.8297 1.282 0.1998    GMCHLA 0.59288  0.4589 1.292 0.1964    GMCHLA1 -0.22814 1.057  -0.216 0.8292    SIGMA(1) 0.68102  0.2746E-01  24.803 0.0000  RHO(1,2)  -0.17018  0.1592  -1.069 0.2851  Economic Valuation Of Critical Habitat Closures, Berman et al.  78 Flatfish, standard CPUE  Sample Selection Model   Two stage least squares regression. Dep. Variable LOGFLATS    Observations  1653  Weights ONE   Mean of LHS 0.1157577E+01  Std.Dev of LHS 0.8117898E+00  StdDev of resid. 0.6808442E+00  Sum of squares 0.7518762E+03  R-squared 0.2961645E+00  Adj. R-squared 0.2831466E+00  F[ 30, 1622] 0.2275053E+02  Prob value 0.3217295E-13  Log-likelihood  -0.1694409E+04  Restr.(b=0) Log-l  -0.2000332E+04  Amemiya Pr. Criter. 0.4722421E+00  Akaike Info.Crit. 0.2087609E+01  Standard error corrected for selection.....  0.68107    Correlation of disturbance in regression   and Selection Criterion (Rho).............. -0.30324E-01   N(0,1) used for significance levels.    Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant  6.1487 1.457 4.221 0.0000  FEB -0.97080  0.2348  -4.135 0.0000  0.1506 0.3578  MAR  -2.0991  0.4461  -4.706 0.0000  0.3315 0.4709  APR  -3.2206  0.6562  -4.908 0.0000  0.2989 0.4579  NOV  -8.5532 1.924  -4.447 0.0000  0.0901 0.2865  DEC  -8.8377 2.094  -4.221 0.0000  0.0321 0.1762  GOA  -3.5439 1.354  -2.618 0.0089  0.1688 0.3747  TIMELDEP 0.19577  0.4452E-01 4.397 0.0000 17.6167  11.9465  LDEPTH -2.0645  0.5506  -3.750 0.0002  4.5252 0.4984  L2DEP  0.21101  0.5513E-01 3.827 0.0001 20.7261 4.8625  SLOPE -0.73404E-01  0.7173E-02 -10.233 0.0000  3.9975 8.9265  SLOPE2 0.11978E-02  0.1661E-03 7.209 0.0000 95.6138 342.6782  SST  0.24649  0.5749E-01 4.288 0.0000  3.1065 1.3323  SST2  -0.74073E-01  0.1388E-01  -5.338 0.0000 11.2998 7.7299  SSTSLOPE  -0.25282E-02  0.1624E-02  -1.557 0.1195 13.1123  11.4851  SSH  0.40100E-01  0.6678E-02 6.005 0.0000 -5.3569 5.1763  SSHSLOPE  -0.12754E-02  0.1061E-02  -1.202 0.2295 26.4713  17.4774  MWIND -0.25255  0.1974  -1.279 0.2008  2.3861 0.1216  MCHLA 1.1488  0.1177 9.757 0.0000  0.4752 0.3091  MCHLA1 0.86893E-01  0.1179 0.737 0.4612  0.4445 0.3390  DWIND -0.22258  0.8680E-01  -2.564 0.0103  0.0490 0.2159  DCHLA -0.13774  0.8220E-01  -1.676 0.0938  0.2390 0.4266  DCHLA1  -0.88872E-01  0.6615E-01  -1.344 0.1791  0.3696 0.4829  GLDEPTH  0.14648  0.1452 1.009 0.3130  0.8006 1.7913  GTIMELDE 0.88725E-02  0.2577E-01 0.344 0.7307  2.3168 5.6658  GSST 0.38910  0.1260 3.088 0.0020  0.8209 1.8344  GSSH  -0.65376E-01  0.1697E-01  -3.852 0.0001 -0.6570 2.5355  GMWIND 0.50701  0.4698 1.079 0.2805  0.3997 0.8888  GMCHLA -1.0678  0.1826  -5.847 0.0000  0.0888 0.2504  GMCHLA1  0.56283  0.4977 1.131 0.2581  0.0497 0.1298  IMR2  -0.20653E-01  0.6372E-01  -0.324 0.7458  1.1206 0.4129 Economic Valuation Of Critical Habitat Closures, Berman et al.   79 Flatfish, standard CPUE (cont.)   Limited Dependent Variable Model - CENSORED  regression   Maximum Likelihood Estimates   Log-Likelihood..............  -1721.2   Threshold values for the model: Lower 0.0000  Upper **********    N(0,1) used for significance levels.   Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant  6.8473 1.489 4.600 0.0000  FEB  -1.0425  0.2388  -4.366 0.0000  0.1511 0.3582  MAR  -2.2391  0.4534  -4.939 0.0000  0.3311 0.4708  APR  -3.4395  0.6642  -5.178 0.0000  0.2985 0.4577  NOV  -9.4973 1.947  -4.878 0.0000  0.0900 0.2863  DEC  -9.8708 2.121  -4.655 0.0000  0.0320 0.1761  GOA  -3.6358 1.371  -2.653 0.0080  0.1686 0.3745  TIMELDEP 0.22064  0.4513E-01 4.889 0.0000 17.6044  11.9448  LDEPTH -2.3806  0.5638  -4.223 0.0000  4.5258 0.4984  L2DEP  0.24184  0.5660E-01 4.273 0.0000 20.7312 4.8619  SLOPE -0.81900E-01  0.7336E-02 -11.164 0.0000  4.0238 8.9794  SLOPE2 0.12876E-02  0.1759E-03 7.321 0.0000 96.7721 345.8994  SST  0.22309  0.5891E-01 3.787 0.0002  3.1071 1.3321  SST2  -0.73641E-01  0.1409E-01  -5.226 0.0000 11.3031 7.7297  SSTSLOPE  -0.28536E-02  0.1661E-02  -1.718 0.0858 13.1029  11.4816  SSH  0.46324E-01  0.6920E-02 6.694 0.0000 -5.3603 5.1831  SSHSLOPE  -0.12551E-02  0.1076E-02  -1.166 0.2436 26.4725  17.4673  MWIND -0.19251  0.2035  -0.946 0.3442  2.3863 0.1216  MCHLA 1.1717  0.1205 9.726 0.0000  0.4749 0.3090  MCHLA1 0.66035E-01  0.1195 0.552 0.5806  0.4441 0.3390  DWIND -0.18149  0.8857E-01  -2.049 0.0404  0.0489 0.2158  DCHLA -0.11872  0.8521E-01  -1.393 0.1635  0.2393 0.4268  DCHLA1  -0.48766E-01  0.6881E-01  -0.709 0.4785  0.3704 0.4831  GLDEPTH  0.16995  0.1475 1.152 0.2492  0.7996 1.7904  GTIMELDE 0.51625E-02  0.2618E-01 0.197 0.8437  2.3140 5.6629  GSST 0.42715  0.1283 3.331 0.0009  0.8199 1.8335  GSSH  -0.69744E-01  0.1727E-01  -4.039 0.0001 -0.6562 2.5341  GMWIND 0.47162  0.4768 0.989 0.3226  0.3992 0.8884  GMCHLA -1.0817  0.1852  -5.839 0.0000  0.0887 0.2503  GMCHLA1  0.59237  0.5041 1.175 0.2400  0.0496 0.1297  IMR2  -0.10502  0.6598E-01  -1.592 0.1114  1.8292 0.4858  Sigma  0.68992  0.1216E-01  56.747 0.0000    Economic Valuation Of Critical Habitat Closures, Berman et al.  80 Flatfish, standard CPUE (cont.)   ML Estimates of Selection Model    Maximum Likelihood Estimates   Log-Likelihood..............  -5040.6   LHS is CENSORED. Tobit Model fit by MLE.   FIRST 30 estimates are probit equation.    N(0,1) used for significance levels.   Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant  6.1914 1.555 3.981 0.0001  FEB -0.95373  0.2786  -3.423 0.0006  MAR  -2.0431  0.5603  -3.646 0.0003  APR  -3.1565  0.8300  -3.803 0.0001  NOV  -8.6509 2.469  -3.504 0.0005  DEC  -8.9204 2.686  -3.321 0.0009  GOA  -3.4433 1.535  -2.244 0.0249  TIMELDEP 0.19935  0.5732E-01 3.478 0.0005  LDEPTH -2.1238  0.5971  -3.557 0.0004  L2DEP  0.21786  0.6183E-01 3.524 0.0004  SLOPE -0.82142E-01  0.7867E-02 -10.441 0.0000  SLOPE2 0.13501E-02  0.1894E-03 7.129 0.0000  SST  0.23645  0.6877E-01 3.438 0.0006  SST2  -0.72843E-01  0.1542E-01  -4.725 0.0000  SSTSLOPE  -0.26437E-02  0.1914E-02  -1.381 0.1672  SSH  0.46902E-01  0.7795E-02 6.017 0.0000  SSHSLOPE  -0.14632E-02  0.1132E-02  -1.293 0.1960  MWIND -0.23509  0.1815  -1.295 0.1952  MCHLA 1.1495  0.1240 9.271 0.0000  MCHLA1 0.85693E-01  0.1634 0.525 0.5999  DWIND -0.20470  0.1068  -1.917 0.0552  DCHLA -0.14862  0.9172E-01  -1.620 0.1052  DCHLA1  -0.83526E-01  0.6834E-01  -1.222 0.2216  GLDEPTH  0.17628  0.1497 1.177 0.2391  GTIMELDE 0.48022E-02  0.2465E-01 0.195 0.8456  GSST 0.37656  0.1239 3.039 0.0024  GSSH  -0.70388E-01  0.1600E-01  -4.401 0.0000  GMWIND 0.46083  0.5310 0.868 0.3855  GMCHLA -1.0867  0.2070  -5.250 0.0000  GMCHLA1  0.50834  0.5185 0.980 0.3269  SIGMA(1) 0.69040  0.1242E-01  55.593 0.0000  RHO(1,2)  -0.93186E-02  0.1119  -0.083 0.9336 Economic Valuation Of Critical Habitat Closures, Berman et al.   81 2. Summer bottom trawl selection equation (IMR7)   Binomial Probit Model    Maximum Likelihood Estimates   Log-Likelihood..............  -1125.6   Restricted (Slopes 0) Log-L.  -5571.7   Chi-Squared (29)............ 8892.3   Significance Level..........  0.32173E-13   N(0,1) used for significance levels.   Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant -8.5656 1.161  -7.379 0.0000  JUN -0.56585  0.1549  -3.652 0.0003  0.1624 0.3689  JUL  0.14640  0.1579 0.927 0.3538  0.1657 0.3718  AUG  0.25194  0.1892 1.331 0.1831  0.1720 0.3774  SEP  0.64802E-01  0.1891 0.343 0.7318  0.1658 0.3719  OCT  0.32963  0.1780 1.852 0.0641  0.1670 0.3730  GOA -0.58712  0.2235  -2.627 0.0086  0.2506 0.4334  LDEPTH  3.5841  0.4522 7.927 0.0000  4.4273 0.9897  L2DEP -0.40491  0.4853E-01  -8.344 0.0000 20.5808 9.0589  SLOPE  0.27611E-01  0.1268E-01 2.178 0.0294  3.8647 8.1433  SLOPE2 0.13874E-03  0.3151E-03 0.440 0.6597 81.2443 275.9448  SST  0.14330  0.5170E-01 2.772 0.0056  6.9487 3.5439  SST2  -0.80494E-02  0.3063E-02  -2.628 0.0086 60.6119  47.4101  SSTSLOPE  -0.12476E-02  0.1904E-02  -0.655 0.5123 21.5992  16.9362  SSH -0.52594E-02  0.9747E-02  -0.540 0.5895 -3.5008 4.7903  SSHSLOPE  -0.25610E-02  0.1810E-02  -1.415 0.1571 27.8622  18.4854  MWIND -0.55131  0.2237  -2.464 0.0137  2.0435 0.2532  MCHLA  0.18774  0.6358E-01 2.953 0.0031  1.0271 0.5401  MCHLA1 0.18947  0.5607E-01 3.379 0.0007  1.0011 0.5225  DWIND -0.46088  0.1402  -3.288 0.0010  0.1624 0.3689  DCHLA -0.19016  0.1275  -1.491 0.1360  0.0892 0.2850  DCHLA1  -0.25206  0.1414  -1.783 0.0746  0.1103 0.3133  POLTRAWL  -0.41931  0.1364  -3.074 0.0021  0.6967 0.4456  CODTRAWL 0.34204  0.2689 1.272 0.2034  0.7855 0.3899  ATKTRAWL  1.0861  0.3081 3.525 0.0004  0.0105 0.0962  POLTSSL -0.68190  0.2108  -3.235 0.0012  0.1797 0.3654  CODTSSL  0.21356  0.2041 1.046 0.2955  0.1739 0.3605  ATKTSSL  0.30112  0.1609 1.872 0.0613  0.1545 0.3416  MIXTSSL  -1.7791  0.3092  -5.755 0.0000  0.0891 0.2847  PORTDIST  -0.13200E-01  0.1235E-02 -10.686 0.0000 66.4344  40.5635  Frequencies of actual & predicted outcomes  Predicted outcome has maximum probability.     Predicted    Actual  0  1  TOTAL   0  9883  8 9891  1  2096  9 2105    Total 11979 17  11996    Economic Valuation Of Critical Habitat Closures, Berman et al.  82 Pollock , standard CPUE  Sample Selection Model   Two stage least squares regression. Dep. Variable LOGPOLLS    Observations  2084  Weights ONE   Mean of LHS 0.5367812E+00  Std.Dev of LHS 0.6599354E+00  StdDev of resid. 0.5753294E+00  Sum of squares 0.6795509E+03  R-squared 0.2396060E+00  Adj. R-squared 0.2284945E+00  F[ 30, 2053] 0.2156386E+02  Prob value 0.3217295E-13  Log-likelihood  -0.1789390E+04  Restr.(b=0) Log-l  -0.2090430E+04  Amemiya Pr. Criter. 0.3359276E+00  Akaike Info.Crit. 0.1747015E+01  Standard error corrected for selection.....  0.57811    Correlation of disturbance in regression   and Selection Criterion (Rho).............. -0.10700    N(0,1) used for significance levels.   Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant -4.9508  0.8283  -5.977 0.0000  JUN -0.14520  0.1470  -0.988 0.3232  0.0614 0.2402  JUL -0.33695  0.2141  -1.574 0.1155  0.2692 0.4436  AUG -0.64359  0.3005  -2.141 0.0322  0.2116 0.4085  SEP  -1.0007  0.3844  -2.603 0.0092  0.1569 0.3638  OCT  -1.4103  0.4666  -3.022 0.0025  0.1934 0.3950  GOA  -3.6998  0.7372  -5.019 0.0000  0.2404 0.4274  TIMELDEP 0.53041E-01  0.2063E-01 2.571 0.0101 35.5014 7.3817  LDEPTH  2.0966  0.3288 6.376 0.0000  4.5578 0.5740  L2DEP -0.25616  0.3397E-01  -7.541 0.0000 21.1029 5.4919  SLOPE -0.27787E-01  0.5262E-02  -5.280 0.0000  4.3812 9.3259  SLOPE2 0.31872E-03  0.1174E-03 2.715 0.0066  106.1247 364.6537  SST  0.10120  0.3953E-01 2.560 0.0105  8.1178 2.4189  SST2  -0.57602E-02  0.2925E-02  -1.969 0.0489 71.7304  37.6320  SSTSLOPE  -0.13557E-02  0.9203E-03  -1.473 0.1407 20.1822  15.0117  SSH -0.29468E-02  0.6169E-02  -0.478 0.6329 -3.7809 3.8750  SSHSLOPE 0.20828E-02  0.8658E-03 2.406 0.0161 22.9680  16.3217  MWIND  0.34110E-01  0.1392 0.245 0.8065  2.0168 0.2648  MCHLA -0.70842E-01  0.4116E-01  -1.721 0.0852  1.0076 0.4184  MCHLA1 0.42190E-02  0.2952E-01 0.143 0.8864  1.0345 0.5422  DWIND  0.88128E-01  0.8618E-01 1.023 0.3065  0.0288 0.1673  DCHLA  0.18086  0.6690E-01 2.703 0.0069  0.0509 0.2198  DCHLA1  -0.18743  0.7207E-01  -2.601 0.0093  0.0398 0.1956  GLDEPTH  0.34532  0.8189E-01 4.217 0.0000  1.1783 2.1088  GTIMELDE 0.11532E-01  0.8576E-02 1.345 0.1787  8.5557  15.7173  GSST  -0.16840E-01  0.2986E-01  -0.564 0.5728  2.3308 4.3117  GSSH  -0.19891E-02  0.1075E-01  -0.185 0.8532 -0.8258 2.2383  GMWIND 0.42804  0.2508 1.707 0.0879  0.4961 0.8907  GMCHLA 0.27099  0.8707E-01 3.112 0.0019  0.2574 0.4944  GMCHLA1  0.80668E-01  0.8036E-01 1.004 0.3155  0.2479 0.4876  IMR7  -0.61856E-01  0.4819E-01  -1.284 0.1993  1.9159 0.4657 Economic Valuation Of Critical Habitat Closures, Berman et al.   83 Pollock , standard CPUE (cont.)   Limited Dependent Variable Model - CENSORED  regression   Maximum Likelihood Estimates   Log-Likelihood..............  -1922.1   Threshold values for the model: Lower 0.0000  Upper **********    N(0,1) used for significance levels.   Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant -7.8763 1.039  -7.581 0.0000  JUN -0.12605  0.1785  -0.706 0.4800  0.0612 0.2398  JUL -0.29978  0.2642  -1.135 0.2566  0.2697 0.4439  AUG -0.57325  0.3724  -1.539 0.1238  0.2109 0.4080  SEP -0.89335  0.4775  -1.871 0.0614  0.1573 0.3642  OCT  -1.2361  0.5789  -2.135 0.0327  0.1937 0.3953  GOA  -6.5638  0.9770  -6.719 0.0000  0.2425 0.4287  TIMELDEP 0.48001E-01  0.2536E-01 1.893 0.0583 35.5104 7.3774  LDEPTH  3.4986  0.4334 8.072 0.0000  4.5577 0.5732  L2DEP -0.39135  0.4309E-01  -9.083 0.0000 21.1014 5.4836  SLOPE -0.38735E-01  0.6325E-02  -6.125 0.0000  4.4027 9.3546  SLOPE2 0.45389E-03  0.1403E-03 3.234 0.0012  106.8498 365.4387  SST  0.38036E-01  0.4857E-01 0.783 0.4335  8.1238 2.4194  SST2  -0.11547E-02  0.3548E-02  -0.325 0.7448 71.8300  37.6711  SSTSLOPE  -0.13083E-02  0.1065E-02  -1.229 0.2191 20.1950  15.0092  SSH -0.45853E-02  0.7180E-02  -0.639 0.5231 -3.7775 3.8726  SSHSLOPE 0.16441E-02  0.1022E-02 1.609 0.1077 23.0271  16.3453  MWIND -0.79378E-01  0.1617  -0.491 0.6235  2.0169 0.2647  MCHLA -0.43299E-01  0.4703E-01  -0.921 0.3572  1.0078 0.4181  MCHLA1  -0.31080E-02  0.3347E-01  -0.093 0.9260  1.0345 0.5419  DWIND -0.40418E-01  0.1086  -0.372 0.7098  0.0287 0.1670  DCHLA  0.21986  0.7699E-01 2.856 0.0043  0.0507 0.2194  DCHLA1  -0.19798  0.8351E-01  -2.371 0.0177  0.0397 0.1953  GLDEPTH  0.63679  0.1047 6.085 0.0000  1.1873 2.1130  GTIMELDE 0.11580E-01  0.1060E-01 1.092 0.2748  8.6344  15.7704  GSST  -0.33178E-01  0.3736E-01  -0.888 0.3745  2.3526 4.3267  GSSH  -0.18198E-01  0.1383E-01  -1.316 0.1881 -0.8332 2.2430  GMWIND 0.84511  0.3283 2.574 0.0100  0.5004 0.8934  GMCHLA 0.48930  0.1085 4.508 0.0000  0.2599 0.4961  GMCHLA1  0.19610  0.9800E-01 2.001 0.0454  0.2504 0.4896  IMR7  -0.11926  0.5738E-01  -2.078 0.0377  1.9109 0.4671  Sigma  0.64576  0.1118E-01  57.744 0.0000   Economic Valuation Of Critical Habitat Closures, Berman et al.  84 Pollock , standard CPUE (cont.)   ML Estimates of Selection Model    Maximum Likelihood Estimates   Log-Likelihood..............  -6240.2   LHS is CENSORED. Tobit Model fit by MLE.   FIRST 30 estimates are probit equation.    N(0,1) used for significance levels.   Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant -8.8557 1.161  -7.624 0.0000  JUN -0.21361  0.2191  -0.975 0.3297  JUL -0.34851  0.3123  -1.116 0.2645  AUG -0.64471  0.4357  -1.480 0.1390  SEP -0.97808  0.5669  -1.725 0.0845  OCT  -1.3192  0.6900  -1.912 0.0559  GOA  -6.4392 1.170  -5.503 0.0000  TIMELDEP 0.55940E-01  0.2903E-01 1.927 0.0540  LDEPTH  3.8321  0.4988 7.682 0.0000  L2DEP -0.43531  0.4700E-01  -9.261 0.0000  SLOPE -0.34657E-01  0.7108E-02  -4.876 0.0000  SLOPE2 0.42922E-03  0.1748E-03 2.456 0.0141  SST  0.53129E-01  0.6353E-01 0.836 0.4030  SST2  -0.17815E-02  0.4292E-02  -0.415 0.6781  SSTSLOPE  -0.10514E-02  0.1149E-02  -0.915 0.3600  SSH -0.48489E-02  0.6528E-02  -0.743 0.4576  SSHSLOPE 0.15819E-02  0.1004E-02 1.576 0.1151  MWIND -0.19282  0.1593  -1.210 0.2261  MCHLA -0.27358E-02  0.5160E-01  -0.053 0.9577  MCHLA1 0.13265E-01  0.3232E-01 0.410 0.6815  DWIND -0.10874  0.9448E-01  -1.151 0.2498  DCHLA  0.17846  0.7966E-01 2.240 0.0251  DCHLA1  -0.22222  0.7962E-01  -2.791 0.0053  GLDEPTH  0.62990  0.1295 4.864 0.0000  GTIMELDE 0.84452E-02  0.1222E-01 0.691 0.4893  GSST  -0.36456E-01  0.4259E-01  -0.856 0.3920  GSSH  -0.19688E-01  0.1557E-01  -1.264 0.2061  GMWIND 0.86262  0.3922 2.199 0.0279  GMCHLA 0.47273  0.1117 4.233 0.0000  GMCHLA1  0.20261  0.1042 1.944 0.0519  SIGMA(1) 0.64627  0.8627E-02  74.912 0.0000  RHO(1,2) 0.35777E-01  0.9503E-01 0.376 0.7066  Economic Valuation Of Critical Habitat Closures, Berman et al.   85 Pacific cod, CPUE   Sample Selection Model   Two stage least squares regression. Dep. Variable LOGPCOD   Observations  2084  Weights ONE   Mean of LHS 0.2972981E+00  Std.Dev of LHS 0.3440143E+00  StdDev of resid. 0.3100060E+00  Sum of squares 0.1973010E+03  R-squared 0.1875518E+00  Adj. R-squared 0.1756797E+00  F[ 30, 2053] 0.1579768E+02  Prob value 0.3217295E-13  Log-likelihood  -0.5007468E+03  Restr.(b=0) Log-l  -0.7327900E+03  Amemiya Pr. Criter. 0.9753332E-01  Akaike Info.Crit. 0.5103137E+00  Standard error corrected for selection.....  0.32875    Correlation of disturbance in regression   and Selection Criterion (Rho).............. -0.39356    N(0,1) used for significance levels.   Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant -1.2723  0.4103  -3.101 0.0019  JUN  0.11579  0.7965E-01 1.454 0.1460  0.0614 0.2402  JUL -0.41659E-01  0.1154  -0.361 0.7181  0.2692 0.4436  AUG -0.58546E-01  0.1618  -0.362 0.7175  0.2116 0.4085  SEP  0.93405E-01  0.2067 0.452 0.6514  0.1569 0.3638  OCT  0.67453E-01  0.2510 0.269 0.7881  0.1934 0.3950  GOA 1.4409  0.3975 3.625 0.0003  0.2404 0.4274  TIMELDEP -0.17606E-01  0.1107E-01  -1.590 0.1118 35.5014 7.3817  LDEPTH 0.81896  0.1703 4.809 0.0000  4.5578 0.5740  L2DEP -0.78711E-01  0.1749E-01  -4.501 0.0000 21.1029 5.4919  SLOPE  0.47438E-02  0.2890E-02 1.642 0.1007  4.3812 9.3259  SLOPE2  -0.15830E-03  0.6482E-04  -2.442 0.0146  106.1247 364.6537  SST  0.43391E-01  0.2125E-01 2.042 0.0411  8.1178 2.4189  SST2  -0.10287E-02  0.1575E-02  -0.653 0.5137 71.7304  37.6320  SSTSLOPE 0.83700E-05  0.5032E-03 0.017 0.9867 20.1822  15.0117  SSH -0.68258E-02  0.3352E-02  -2.036 0.0417 -3.7809 3.8750  SSHSLOPE 0.12321E-02  0.4771E-03 2.582 0.0098 22.9680  16.3217  MWIND  0.19648E-01  0.7717E-01 0.255 0.7990  2.0168 0.2648  MCHLA -0.75122E-01  0.2270E-01  -3.309 0.0009  1.0076 0.4184  MCHLA1  -0.28885E-01  0.1600E-01  -1.805 0.0711  1.0345 0.5422  DWIND  0.27416  0.4677E-01 5.862 0.0000  0.0288 0.1673  DCHLA  0.45396E-01  0.3648E-01 1.244 0.2134  0.0509 0.2198  DCHLA1  -0.75853E-02  0.3905E-01  -0.194 0.8460  0.0398 0.1956  GLDEPTH -0.36074  0.4416E-01  -8.169 0.0000  1.1783 2.1088  GTIMELDE 0.21356E-01  0.4631E-02 4.612 0.0000  8.5557  15.7173  GSST  -0.33675E-01  0.1607E-01  -2.096 0.0361  2.3308 4.3117  GSSH 0.25757E-01  0.5804E-02 4.438 0.0000 -0.8258 2.2383  GMWIND  -0.12599  0.1353  -0.931 0.3518  0.4961 0.8907  GMCHLA 0.10916  0.4694E-01 2.325 0.0200  0.2574 0.4944  GMCHLA1  0.42643E-01  0.4329E-01 0.985 0.3246  0.2479 0.4876  IMR7  -0.12938  0.2677E-01  -4.833 0.0000  1.1542 0.4255  Economic Valuation Of Critical Habitat Closures, Berman et al.  86 Pacific cod, CPUE (cont.)   Limited Dependent Variable Model - CENSORED  regression   Maximum Likelihood Estimates   Log-Likelihood..............  -752.97   Threshold values for the model: Lower 0.0000  Upper **********    N(0,1) used for significance levels.   Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant -3.7195  0.5497  -6.767 0.0000  JUN  0.73074E-01  0.9327E-01 0.783 0.4334  0.0614 0.2400  JUL -0.94603E-01  0.1408  -0.672 0.5017  0.2694 0.4438  AUG -0.16166  0.2004  -0.807 0.4198  0.2114 0.4084  SEP -0.51867E-01  0.2574  -0.201 0.8403  0.1572 0.3641  OCT -0.86989E-01  0.3124  -0.278 0.7807  0.1932 0.3949  GOA 2.2360  0.4688 4.770 0.0000  0.2407 0.4276  TIMELDEP -0.12539E-01  0.1385E-01  -0.905 0.3654 35.5039 7.3800  LDEPTH  1.9559  0.2269 8.621 0.0000  4.5580 0.5738  L2DEP -0.21204  0.2374E-01  -8.933 0.0000 21.1043 5.4895  SLOPE  0.52226E-02  0.3241E-02 1.611 0.1071  4.3922 9.3408  SLOPE2  -0.16501E-03  0.7142E-04  -2.310 0.0209  106.5008 365.0076  SST  0.40015E-01  0.2453E-01 1.632 0.1028  8.1200 2.4204  SST2  -0.66932E-03  0.1811E-02  -0.370 0.7117 71.7742  37.6846  SSTSLOPE 0.41485E-03  0.5517E-03 0.752 0.4521 20.1927  15.0243  SSH -0.51768E-02  0.3694E-02  -1.401 0.1611 -3.7785 3.8752  SSHSLOPE 0.88669E-03  0.5294E-03 1.675 0.0940 22.9994  16.3473  MWIND  0.38426E-01  0.8328E-01 0.461 0.6445  2.0166 0.2648  MCHLA -0.67076E-01  0.2473E-01  -2.712 0.0067  1.0074 0.4183  MCHLA1  -0.35456E-01  0.1760E-01  -2.015 0.0439  1.0342 0.5422  DWIND  0.28878  0.5112E-01 5.649 0.0000  0.0288 0.1672  DCHLA  0.36174E-01  0.4144E-01 0.873 0.3827  0.0508 0.2197  DCHLA1  -0.19423E-01  0.4524E-01  -0.429 0.6677  0.0398 0.1955  GLDEPTH -0.49552  0.5405E-01  -9.168 0.0000  1.1795 2.1094  GTIMELDE 0.21449E-01  0.5321E-02 4.031 0.0001  8.5635  15.7202  GSST  -0.48657E-01  0.1829E-01  -2.660 0.0078  2.3350 4.3166  GSSH 0.31160E-01  0.6686E-02 4.660 0.0000 -0.8271 2.2387  GMWIND  -0.16473  0.1553  -1.061 0.2888  0.4965 0.8907  GMCHLA 0.14357  0.5368E-01 2.675 0.0075  0.2577 0.4945  GMCHLA1  0.63966E-01  0.4930E-01 1.297 0.1945  0.2481 0.4877  IMR7  -0.11801  0.2947E-01  -4.005 0.0001  1.9112 0.4671  Sigma  0.33502  0.5614E-02  59.678 0.0000   Economic Valuation Of Critical Habitat Closures, Berman et al.   87 Pacific cod, CPUE (cont.)   ML Estimates of Selection Model    Maximum Likelihood Estimates   Log-Likelihood..............  -5068.9   LHS is CENSORED. Tobit Model fit by MLE.   FIRST 30 estimates are probit equation.    N(0,1) used for significance levels.   Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant -4.2442  0.6238  -6.804 0.0000  JUN  0.60673E-01  0.9264E-01 0.655 0.5125  JUL -0.10202  0.1355  -0.753 0.4516  AUG -0.17891  0.1959  -0.913 0.3611  SEP -0.81157E-01  0.2507  -0.324 0.7461  OCT -0.11978  0.3072  -0.390 0.6966  GOA 2.2362  0.4178 5.352 0.0000  TIMELDEP -0.97058E-02  0.1348E-01  -0.720 0.4717  LDEPTH  2.1088  0.2630 8.018 0.0000  L2DEP -0.22970  0.2727E-01  -8.424 0.0000  SLOPE  0.61017E-02  0.3329E-02 1.833 0.0668  SLOPE2  -0.17241E-03  0.7232E-04  -2.384 0.0171  SST  0.44229E-01  0.2528E-01 1.750 0.0802  SST2  -0.79041E-03  0.1924E-02  -0.411 0.6812  SSTSLOPE 0.46403E-03  0.6213E-03 0.747 0.4551  SSH -0.52626E-02  0.3588E-02  -1.467 0.1425  SSHSLOPE 0.10845E-02  0.5087E-03 2.132 0.0330  MWIND  0.22658E-01  0.8935E-01 0.254 0.7998  MCHLA -0.63126E-01  0.2815E-01  -2.243 0.0249  MCHLA1  -0.30799E-01  0.2229E-01  -1.382 0.1670  DWIND  0.27695  0.3481E-01 7.956 0.0000  DCHLA  0.30429E-01  0.5056E-01 0.602 0.5473  DCHLA1  -0.31148E-01  0.4778E-01  -0.652 0.5144  GLDEPTH -0.49579  0.5434E-01  -9.123 0.0000  GTIMELDE 0.20498E-01  0.5050E-02 4.059 0.0000  GSST  -0.49139E-01  0.1878E-01  -2.616 0.0089  GSSH 0.31766E-01  0.5106E-02 6.221 0.0000  GMWIND  -0.15793  0.1317  -1.199 0.2306  GMCHLA 0.14375  0.4085E-01 3.519 0.0004  GMCHLA1  0.61543E-01  0.4136E-01 1.488 0.1367  SIGMA(1) 0.34485  0.7897E-02  43.669 0.0000  RHO(1,2)  -0.27904  0.9210E-01  -3.030 0.0024  Economic Valuation Of Critical Habitat Closures, Berman et al.  88 Pacific cod, standard CPUE   Sample Selection Model   Two stage least squares regression. Dep. Variable LOGPCODS    Observations  2084  Weights ONE   Mean of LHS 0.4752573E+00  Std.Dev of LHS 0.5026082E+00  StdDev of resid. 0.4405847E+00  Sum of squares 0.3985179E+03  R-squared 0.2312093E+00 Adj. R-squared 0.2199752E+00  F[ 30, 2053] 0.2058093E+02  Prob value 0.3217295E-13  Log-likelihood  -0.1233296E+04  Restr.(b=0) Log-l  -0.1522892E+04  Amemiya Pr. Criter. 0.1970024E+00 Akaike Info.Crit. 0.1213336E+01  Standard error corrected for selection.....  0.47276    Correlation of disturbance in regression   and Selection Criterion (Rho).............. -0.42883    N(0,1) used for significance levels.   Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant -3.0312  0.5839  -5.191 0.0000  JUN  0.10253E-01  0.1133 0.090 0.9279  0.0614 0.2402  JUL -0.34272  0.1640  -2.089 0.0367  0.2692 0.4436  AUG -0.49949  0.2300  -2.172 0.0299  0.2116 0.4085  SEP -0.43265  0.2937  -1.473 0.1408  0.1569 0.3638  OCT -0.63212  0.3566  -1.773 0.0763  0.1934 0.3950  GOA 1.5999  0.5649 2.832 0.0046  0.2404 0.4274  TIMELDEP 0.74061E-02  0.1573E-01 0.471 0.6378 35.5014 7.3817  LDEPTH  1.4534  0.2422 6.001 0.0000  4.5578 0.5740  L2DEP -0.15923  0.2490E-01  -6.395 0.0000 21.1029 5.4919  SLOPE -0.47634E-02  0.4127E-02  -1.154 0.2484  4.3812 9.3259  SLOPE2  -0.87827E-04  0.9267E-04  -0.948 0.3433  106.1247 364.6537  SST  0.98077E-01  0.3022E-01 3.246 0.0012  8.1178 2.4189  SST2  -0.36883E-02  0.2240E-02  -1.647 0.0996 71.7304  37.6320  SSTSLOPE 0.35150E-03  0.7179E-03 0.490 0.6244 20.1822  15.0117  SSH  0.63242E-04  0.4775E-02 0.013 0.9894 -3.7809 3.8750  SSHSLOPE 0.15362E-02  0.6806E-03 2.257 0.0240 22.9680  16.3217  MWIND  0.10685  0.1100 0.972 0.3313  2.0168 0.2648  MCHLA -0.12999  0.3234E-01  -4.020 0.0001  1.0076 0.4184  MCHLA1  -0.36242E-01  0.2283E-01  -1.588 0.1123  1.0345 0.5422  DWIND  0.38428  0.6654E-01 5.775 0.0000  0.0288 0.1673  DCHLA  0.12941  0.5197E-01 2.490 0.0128  0.0509 0.2198  DCHLA1  -0.27154E-01  0.5566E-01  -0.488 0.6257  0.0398 0.1956  GLDEPTH -0.44470  0.6278E-01  -7.084 0.0000  1.1783 2.1088  GTIMELDE 0.25827E-01  0.6586E-02 3.921 0.0001  8.5557  15.7173  GSST  -0.35985E-01  0.2283E-01  -1.576 0.1150  2.3308 4.3117  GSSH 0.35849E-01  0.8251E-02 4.345 0.0000 -0.8258 2.2383  GMWIND  -0.37602E-01  0.1924  -0.195 0.8450  0.4961 0.8907  GMCHLA 0.10319  0.6673E-01 1.546 0.1220  0.2574 0.4944  GMCHLA1  0.71789E-01  0.6151E-01 1.167 0.2432  0.2479 0.4876  IMR7  -0.20273  0.3795E-01  -5.342 0.0000  1.1540 0.4264 Economic Valuation Of Critical Habitat Closures, Berman et al.   89 Pacific cod, standard CPUE (cont.)   Limited Dependent Variable Model - CENSORED  regression   Maximum Likelihood Estimates   Log-Likelihood..............  -1400.0   Threshold values for the model: Lower 0.0000  Upper **********    N(0,1) used for significance levels.   Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant -6.3560  0.7936  -8.009 0.0000  JUN -0.62224E-01  0.1325  -0.470 0.6387  0.0614 0.2400  JUL -0.46524  0.1995  -2.332 0.0197  0.2694 0.4438  AUG -0.70861  0.2837  -2.497 0.0125  0.2114 0.4084  SEP -0.71877  0.3645  -1.972 0.0486  0.1572 0.3641  OCT -0.95623  0.4424  -2.161 0.0307  0.1932 0.3949  GOA 2.5754  0.6638 3.880 0.0001  0.2407 0.4276  TIMELDEP 0.19013E-01  0.1958E-01 0.971 0.3314 35.5039 7.3800  LDEPTH  3.0260  0.3295 9.184 0.0000  4.5580 0.5738  L2DEP -0.34505  0.3424E-01 -10.076 0.0000 21.1043 5.4895  SLOPE -0.64795E-02  0.4597E-02  -1.409 0.1587  4.3922 9.3408  SLOPE2  -0.75276E-04  0.1015E-03  -0.742 0.4581  106.5008 365.0076  SST  0.86105E-01  0.3538E-01 2.434 0.0149  8.1200 2.4204  SST2  -0.28320E-02  0.2598E-02  -1.090 0.2757 71.7742  37.6846  SSTSLOPE 0.90923E-03  0.7848E-03 1.159 0.2466 20.1927  15.0243  SSH  0.24922E-02  0.5253E-02 0.474 0.6352 -3.7785 3.8752  SSHSLOPE 0.99416E-03  0.7513E-03 1.323 0.1858 22.9994  16.3473  MWIND  0.14132  0.1184 1.194 0.2326  2.0166 0.2648  MCHLA -0.12416  0.3519E-01  -3.528 0.0004  1.0074 0.4183  MCHLA1  -0.53522E-01  0.2503E-01  -2.138 0.0325  1.0342 0.5422  DWIND  0.40964  0.7310E-01 5.604 0.0000  0.0288 0.1672  DCHLA  0.13303  0.5882E-01 2.262 0.0237  0.0508 0.2197  DCHLA1  -0.29947E-01  0.6410E-01  -0.467 0.6403  0.0398 0.1955  GLDEPTH -0.61108  0.7630E-01  -8.009 0.0000  1.1795 2.1094  GTIMELDE 0.26484E-01  0.7525E-02 3.520 0.0004  8.5635  15.7202  GSST  -0.55655E-01  0.2602E-01  -2.139 0.0325  2.3350 4.3166  GSSH 0.42989E-01  0.9458E-02 4.545 0.0000 -0.8271 2.2387  GMWIND  -0.78227E-01  0.2201  -0.355 0.7223  0.4965 0.8907  GMCHLA 0.13823  0.7603E-01 1.818 0.0690  0.2577 0.4945  GMCHLA1  0.10721  0.6978E-01 1.537 0.1244  0.2481 0.4877  IMR7  -0.22882  0.4230E-01  -5.410 0.0000  1.9112 0.4671  Sigma  0.47643  0.7996E-02  59.582 0.0000  Economic Valuation Of Critical Habitat Closures, Berman et al.  90 Pacific cod, standard CPUE (cont.)   ML Estimates of Selection Model    Maximum Likelihood Estimates   Log-Likelihood..............  -5719.8   LHS is CENSORED. Tobit Model fit by MLE.   FIRST 30 estimates are probit equation.    N(0,1) used for significance levels.   Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant -7.4990  0.8471  -8.852 0.0000  JUN -0.10092  0.1376  -0.733 0.4634  JUL -0.48861  0.1929  -2.532 0.0113  AUG -0.75684  0.2736  -2.766 0.0057  SEP -0.79366  0.3505  -2.264 0.0235  OCT  -1.0382  0.4283  -2.424 0.0154  GOA 2.5939  0.5660 4.583 0.0000  TIMELDEP 0.25656E-01  0.1893E-01 1.355 0.1754  LDEPTH  3.3667  0.3535 9.525 0.0000  L2DEP -0.38555  0.3659E-01 -10.538 0.0000  SLOPE -0.41864E-02  0.4831E-02  -0.867 0.3861  SLOPE2  -0.92706E-04  0.1096E-03  -0.845 0.3978  SST  0.96412E-01  0.3881E-01 2.484 0.0130  SST2  -0.31448E-02  0.2783E-02  -1.130 0.2585  SSTSLOPE 0.10493E-02  0.8380E-03 1.252 0.2105  SSH  0.23009E-02  0.5296E-02 0.434 0.6640  SSHSLOPE 0.12845E-02  0.7238E-03 1.775 0.0760  MWIND  0.10022  0.1332 0.753 0.4518  MCHLA -0.10925  0.4291E-01  -2.546 0.0109  MCHLA1  -0.42077E-01  0.3249E-01  -1.295 0.1952  DWIND  0.37687  0.5157E-01 7.309 0.0000  DCHLA  0.11479  0.6983E-01 1.644 0.1002  DCHLA1  -0.53688E-01  0.6483E-01  -0.828 0.4076  GLDEPTH -0.61036  0.7158E-01  -8.527 0.0000  GTIMELDE 0.24273E-01  0.7297E-02 3.327 0.0009  GSST  -0.57593E-01  0.2552E-01  -2.257 0.0240  GSSH 0.44010E-01  0.7312E-02 6.019 0.0000  GMWIND  -0.64972E-01  0.1895  -0.343 0.7318  GMCHLA 0.13433  0.5970E-01 2.250 0.0244  GMCHLA1  0.10391  0.5838E-01 1.780 0.0751  SIGMA(1) 0.49707  0.1360E-01  36.545 0.0000  RHO(1,2)  -0.32736  0.9276E-01  -3.529 0.0004 Economic Valuation Of Critical Habitat Closures, Berman et al.   91 Atka mackerel, standard CPUE   Sample Selection Model   Two stage least squares regression. Dep. Variable LOGATKAS    Observations  2084  Weights ONE   Mean of LHS 0.1138042E+00  Std.Dev of LHS 0.5121301E+00  StdDev of resid. 0.3874007E+00  Sum of squares 0.3081128E+03  R-squared 0.4275091E+00  Adj. R-squared 0.4191435E+00  F[ 30, 2053] 0.5110278E+02  Prob value 0.3217295E-13  Log-likelihood  -0.9652031E+03  Restr.(b=0) Log-l  -0.1562004E+04  Amemiya Pr. Criter. 0.1523118E+00  Akaike Info.Crit. 0.9560491E+00  Standard error corrected for selection.....  0.38994    Correlation of disturbance in regression   and Selection Criterion (Rho).............. -0.13468    N(0,1) used for significance levels.   Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant -3.4712  0.5074  -6.841 0.0000  JUN -0.67829  0.9920E-01  -6.838 0.0000  0.0614 0.2402  JUL  -1.1060  0.1442  -7.673 0.0000  0.2692 0.4436  AUG  -1.2967  0.2023  -6.411 0.0000  0.2116 0.4085  SEP  -1.5553  0.2586  -6.015 0.0000  0.1569 0.3638  OCT  -1.9524  0.3140  -6.219 0.0000  0.1934 0.3950  GOA -0.97079  0.4965  -1.955 0.0506  0.2404 0.4274  TIMELDEP 0.79617E-01  0.1385E-01 5.747 0.0000 35.5014 7.3817  LDEPTH  1.5793  0.2108 7.492 0.0000  4.5578 0.5740  L2DEP -0.24881  0.2161E-01 -11.515 0.0000 21.1029 5.4919  SLOPE  0.60933E-01  0.3525E-02  17.284 0.0000  4.3812 9.3259  SLOPE2  -0.81759E-03  0.7889E-04 -10.364 0.0000  106.1247 364.6537  SST  0.71823E-01  0.2647E-01 2.713 0.0067  8.1178 2.4189  SST2  -0.70958E-02  0.1965E-02  -3.611 0.0003 71.7304  37.6320  SSTSLOPE  -0.35416E-03  0.6188E-03  -0.572 0.5671 20.1822  15.0117  SSH  0.10969E-01  0.4147E-02 2.645 0.0082 -3.7809 3.8750  SSHSLOPE 0.13210E-02  0.5863E-03 2.253 0.0242 22.9680  16.3217  MWIND -0.13900E-01  0.9491E-01  -0.146 0.8836  2.0168 0.2648  MCHLA -0.95928E-01  0.2804E-01  -3.421 0.0006  1.0076 0.4184  MCHLA1  -0.77336E-02  0.1969E-01  -0.393 0.6945  1.0345 0.5422  DWIND  0.31106  0.5794E-01 5.368 0.0000  0.0288 0.1673  DCHLA  0.87823E-01  0.4508E-01 1.948 0.0514  0.0509 0.2198  DCHLA1 0.26594  0.4818E-01 5.520 0.0000  0.0398 0.1956  GLDEPTH  0.25282E-01  0.5511E-01 0.459 0.6464  1.1783 2.1088  GTIMELDE  -0.99106E-02  0.5767E-02  -1.718 0.0857  8.5557  15.7173  GSST 0.10888  0.2009E-01 5.419 0.0000  2.3308 4.3117  GSSH  -0.16702E-01  0.7246E-02  -2.305 0.0212 -0.8258 2.2383  GMWIND  -0.64004E-01  0.1689  -0.379 0.7047  0.4961 0.8907  GMCHLA 0.47865E-01  0.5866E-01 0.816 0.4145  0.2574 0.4944  GMCHLA1  0.81060E-01  0.5412E-01 1.498 0.1342  0.2479 0.4876  IMR7  -0.52518E-01  0.3285E-01  -1.599 0.1099  1.1538 0.4263  Economic Valuation Of Critical Habitat Closures, Berman et al.  92 Atka mackerel, standard CPUE (cont.)   Limited Dependent Variable Model - CENSORED  regression   Maximum Likelihood Estimates   Log-Likelihood..............  -499.01   Threshold values for the model: Lower 0.0000  Upper **********    N(0,1) used for significance levels.   Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant -71.966 9.669  -7.443 0.0000  JUN -0.53299  0.7549  -0.706 0.4801  0.0633 0.2435  JUL -0.26201 1.205  -0.217 0.8279  0.2678 0.4429  AUG  0.70599 1.787 0.395 0.6928  0.2117 0.4086  SEP 1.4722 2.293 0.642 0.5209  0.1560 0.3630  OCT 1.4626 2.851 0.513 0.6079  0.1946 0.3960  GOA 3.3275 3.532 0.942 0.3461  0.2436 0.4293  TIMELDEP -0.57410E-01  0.1199  -0.479 0.6320 35.4982 7.3771  LDEPTH  28.726 4.040 7.110 0.0000  4.5562 0.5722  L2DEP  -2.8680  0.3611  -7.943 0.0000 21.0860 5.4745  SLOPE  0.16240  0.1591E-01  10.207 0.0000  4.3530 9.2912  SLOPE2  -0.23148E-02  0.3170E-03  -7.303 0.0000  105.2351 363.2146  SST  0.35657  0.1572 2.268 0.0233  8.1293 2.4187  SST2  -0.46840E-01  0.1205E-01  -3.887 0.0001 71.9170  37.6799  SSTSLOPE 0.35578E-02  0.4259E-02 0.835 0.4035 20.1658  14.9954  SSH  0.48134E-01  0.2299E-01 2.094 0.0363 -3.7812 3.8648  SSHSLOPE 0.11133E-02  0.3546E-02 0.314 0.7535 22.9580  16.2960  MWIND  0.45062  0.5215 0.864 0.3875  2.0168 0.2650  MCHLA -0.34119  0.2358  -1.447 0.1478  1.0088 0.4175  MCHLA1  -0.33524  0.1792  -1.870 0.0614  1.0362 0.5414  DWIND  0.40500  0.2792 1.451 0.1469  0.0285 0.1666  DCHLA  0.33889  0.2470 1.372 0.1701  0.0504 0.2189  DCHLA1 0.74358  0.2210 3.365 0.0008  0.0395 0.1948  GLDEPTH  -1.2782  0.4338  -2.947 0.0032  1.1915 2.1141  GTIMELDE  -0.35227E-01  0.3543E-01  -0.994 0.3201  8.6620  15.7768  GSST 0.53089  0.1346 3.943 0.0001  2.3647 4.3352  GSSH  -0.97291E-01  0.5316E-01  -1.830 0.0672 -0.8373 2.2455  GMWIND 0.19559 1.123 0.174 0.8617  0.5022 0.8939  GMCHLA  -0.98987  0.4409  -2.245 0.0248  0.2617 0.4980  GMCHLA1 -0.17773  0.4437  -0.401 0.6887  0.2523 0.4920  IMR7  -0.23128E-01  0.1741  -0.133 0.8943  1.9113 0.4659  Sigma 1.1871  0.6330E-01  18.752 0.0000   Economic Valuation Of Critical Habitat Closures, Berman et al.   93 Atka mackerel, standard CPUE (cont.)   ML Estimates of Selection Model    Maximum Likelihood Estimates   Log-Likelihood..............  -4808.5   LHS is CENSORED. Tobit Model fit by MLE.   FIRST 30 estimates are probit equation.    N(0,1) used for significance levels.   Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant -54.075 8.874  -6.094 0.0000  JUN  0.35337  0.8760 0.403 0.6867  JUL  0.11495 1.508 0.076 0.9392  AUG 1.2717 2.289 0.556 0.5785  SEP 1.7955 2.963 0.606 0.5445  OCT 2.0186 3.662 0.551 0.5814  GOA 1.0775 4.573 0.236 0.8137  TIMELDEP -0.10824  0.1534  -0.706 0.4804  LDEPTH  21.902 3.711 5.901 0.0000  L2DEP  -2.1076  0.3086  -6.830 0.0000  SLOPE  0.13907  0.1884E-01 7.380 0.0000  SLOPE2  -0.23223E-02  0.4010E-03  -5.792 0.0000  SST  0.15489  0.2127 0.728 0.4665  SST2  -0.31409E-01  0.1520E-01  -2.066 0.0389  SSTSLOPE  -0.21312E-02  0.4937E-02  -0.432 0.6660  SSH  0.39028E-01  0.2478E-01 1.575 0.1153  SSHSLOPE 0.28872E-02  0.4043E-02 0.714 0.4752  MWIND 1.1562  0.5865 1.972 0.0487  MCHLA -0.67795  0.2351  -2.883 0.0039  MCHLA1  -0.49768  0.1921  -2.591 0.0096  DWIND  0.91228  0.3394 2.688 0.0072  DCHLA  0.73018  0.2850 2.562 0.0104  DCHLA1 0.87158  0.2442 3.570 0.0004  GLDEPTH  -1.1428  0.5303  -2.155 0.0312  GTIMELDE  -0.13840E-01  0.4993E-01  -0.277 0.7816  GSST 0.48191  0.1817 2.652 0.0080  GSSH  -0.61423E-01  0.7320E-01  -0.839 0.4014  GMWIND 0.64325 1.615 0.398 0.6905  GMCHLA  -0.72321  0.5613  -1.289 0.1976  GMCHLA1 -0.85615E-01  0.5399  -0.159 0.8740  SIGMA(1)  1.6226  0.1807 8.979 0.0000  RHO(1,2)  -0.82007  0.5919E-01 -13.855 0.0000 Economic Valuation Of Critical Habitat Closures, Berman et al.  94 Black cod, standard CPUE   Sample Selection Model   Two stage least squares regression. Dep. Variable LOGBCODS    Observations  2084  Weights ONE   Mean of LHS 0.7045839E-01  Std.Dev of LHS 0.2518961E+00  StdDev of resid. 0.2016822E+00 Sum of squares 0.8350725E+02  R-squared 0.3586414E+00  Adj. R-squared 0.3492694E+00  F[ 30, 2053] 0.3826724E+02 Prob value 0.3217295E-13  Log-likelihood 0.3951616E+03  Restr.(b=0) Log-l  -0.8327635E+02  Amemiya Pr. Criter. 0.4128078E-01  Akaike Info.Crit.  -0.3494833E+00  Standard error corrected for selection.....  0.21504    Correlation of disturbance in regression   and Selection Criterion (Rho).............. -0.41018    N(0,1) used for significance levels.   Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant  1.8098  0.2727 6.635 0.0000  JUN  0.14077  0.5192E-01 2.711 0.0067  0.0614 0.2402  JUL  0.16869  0.7505E-01 2.248 0.0246  0.2692 0.4436  AUG  0.19537  0.1052 1.857 0.0633  0.2116 0.4085  SEP  0.18826  0.1344 1.400 0.1614  0.1569 0.3638  OCT  0.16351  0.1632 1.002 0.3164  0.1934 0.3950  GOA  -1.0550  0.2586  -4.079 0.0000  0.2404 0.4274  TIMELDEP -0.49002E-02  0.7198E-02  -0.681 0.4960 35.5014 7.3817  LDEPTH  -0.90681  0.1134  -7.995 0.0000  4.5578 0.5740  L2DEP  0.11537  0.1170E-01 9.863 0.0000 21.1029 5.4919  SLOPE -0.69613E-02  0.1903E-02  -3.659 0.0003  4.3812 9.3259  SLOPE2 0.45571E-04  0.4235E-04 1.076 0.2819  106.1247 364.6537  SST -0.29274E-01  0.1384E-01  -2.115 0.0344  8.1178 2.4189  SST2 0.86234E-03  0.1025E-02 0.841 0.4003 71.7304  37.6320  SSTSLOPE 0.52476E-04  0.3278E-03 0.160 0.8728 20.1822  15.0117  SSH -0.42687E-04  0.2183E-02  -0.020 0.9844 -3.7809 3.8750  SSHSLOPE 0.58802E-03  0.3113E-03 1.889 0.0589 22.9680  16.3217  MWIND  0.11473  0.5041E-01 2.276 0.0229  2.0168 0.2648  MCHLA -0.15123E-02  0.1481E-01  -0.102 0.9187  1.0076 0.4184  MCHLA1  -0.18364E-01  0.1042E-01  -1.762 0.0781  1.0345 0.5422  DWIND  0.53786E-01  0.3039E-01 1.770 0.0767  0.0288 0.1673  DCHLA  0.53918E-01  0.2379E-01 2.266 0.0234  0.0509 0.2198  DCHLA1 0.46058E-01  0.2544E-01 1.810 0.0703  0.0398 0.1956  GLDEPTH  0.20449  0.2873E-01 7.118 0.0000  1.1783 2.1088  GTIMELDE 0.11063E-02  0.3020E-02 0.366 0.7141  8.5557  15.7173  GSST 0.34821E-01  0.1044E-01 3.334 0.0009  2.3308 4.3117  GSSH  -0.49733E-02  0.3780E-02  -1.316 0.1883 -0.8258 2.2383  GMWIND 0.22938E-02  0.8804E-01 0.026 0.9792  0.4961 0.8907  GMCHLA  -0.74513E-01  0.3051E-01  -2.442 0.0146  0.2574 0.4944  GMCHLA1 -0.63518E-01  0.2817E-01  -2.255 0.0241  0.2479 0.4876  IMR7  -0.88207E-01  0.1802E-01  -4.896 0.0000  1.1568 0.4254 Economic Valuation Of Critical Habitat Closures, Berman et al.   95 Black cod, standard CPUE (cont.)   Limited Dependent Variable Model - CENSORED  regression   Maximum Likelihood Estimates   Log-Likelihood..............  -475.33   Threshold values for the model: Lower 0.0000  Upper **********    N(0,1) used for significance levels.   Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant  1.2131 2.136 0.568 0.5700  JUN  0.54405  0.3012 1.806 0.0709  0.0632 0.2433  JUL  0.45107  0.3643 1.238 0.2157  0.2679 0.4430  AUG  0.42911  0.5001 0.858 0.3908  0.2114 0.4084  SEP  0.58552  0.6314 0.927 0.3538  0.1563 0.3632  OCT  0.47957  0.7677 0.625 0.5322  0.1943 0.3958  GOA  -1.4643  0.8430  -1.737 0.0824  0.2437 0.4294  TIMELDEP 0.20330E-01  0.3037E-01 0.669 0.5033 35.4959 7.3762  LDEPTH -1.0072  0.8306  -1.213 0.2253  4.5567 0.5722  L2DEP  0.18929  0.7475E-01 2.532 0.0113 21.0905 5.4739  SLOPE -0.94604E-02  0.7722E-02  -1.225 0.2205  4.3824 9.3282  SLOPE2  -0.43875E-03  0.2070E-03  -2.120 0.0340  106.1783 364.3158  SST -0.13363  0.7330E-01  -1.823 0.0683  8.1266 2.4220  SST2 0.58258E-02  0.4761E-02 1.224 0.2211 71.8882  37.6890  SSTSLOPE  -0.19264E-02  0.1486E-02  -1.297 0.1948 20.1915  15.0350  SSH -0.21699E-01  0.1219E-01  -1.779 0.0752 -3.7830 3.8678  SSHSLOPE 0.26229E-02  0.1163E-02 2.255 0.0241 23.0019  16.3287  MWIND -0.41253  0.1958  -2.107 0.0351  2.0167 0.2648  MCHLA -0.24595  0.1010  -2.436 0.0149  1.0086 0.4172  MCHLA1  -0.10473  0.6237E-01  -1.679 0.0931  1.0359 0.5412  DWIND  0.37205  0.1130 3.293 0.0010  0.0290 0.1678  DCHLA  0.28535  0.1020 2.797 0.0052  0.0504 0.2187  DCHLA1 0.14997  0.9825E-01 1.527 0.1269  0.0399 0.1958  GLDEPTH  0.32492  0.1238 2.625 0.0087  1.1919 2.1142  GTIMELDE  -0.43333E-01  0.1475E-01  -2.937 0.0033  8.6648  15.7758  GSST 0.52365E-01  0.3993E-01 1.311 0.1898  2.3660 4.3357  GSSH 0.19796E-01  0.1484E-01 1.334 0.1824 -0.8388 2.2465  GMWIND 0.95612  0.3191 2.996 0.0027  0.5024 0.8940  GMCHLA 0.15454  0.1236 1.250 0.2112  0.2619 0.4981  GMCHLA1 -0.13879E-01  0.9517E-01  -0.146 0.8841  0.2526 0.4922  IMR7  -0.59326  0.9035E-01  -6.566 0.0000  1.9115 0.4680  Sigma  0.48232  0.1793E-01  26.899 0.0000  Economic Valuation Of Critical Habitat Closures, Berman et al.  96 Black cod, standard CPUE (cont.)   ML Estimates of Selection Model    Maximum Likelihood Estimates   Log-Likelihood..............  -4800.0   LHS is CENSORED. Tobit Model fit by MLE.   FIRST 30 estimates are probit equation.    N(0,1) used for significance levels.   Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant -1.8232 2.353  -0.775 0.4384  JUN  0.55945  0.4032 1.387 0.1653  JUL  0.48645  0.5183 0.938 0.3480  AUG  0.48453  0.7006 0.692 0.4892  SEP  0.52862  0.8851 0.597 0.5504  OCT  0.35517 1.074 0.331 0.7409  GOA  -1.3881 1.064  -1.305 0.1919  TIMELDEP 0.35108E-01  0.4106E-01 0.855 0.3925  LDEPTH  -0.19972  0.9987  -0.200 0.8415  L2DEP  0.93209E-01  0.9095E-01 1.025 0.3054  SLOPE -0.17968E-02  0.8992E-02  -0.200 0.8416  SLOPE2  -0.48634E-03  0.2269E-03  -2.143 0.0321  SST -0.11236  0.1066  -1.054 0.2918  SST2 0.43708E-02  0.6897E-02 0.634 0.5262  SSTSLOPE  -0.15135E-02  0.1960E-02  -0.772 0.4401  SSH -0.27004E-01  0.1562E-01  -1.729 0.0839  SSHSLOPE 0.32944E-02  0.1333E-02 2.472 0.0134  MWIND -0.41676  0.2745  -1.518 0.1289  MCHLA -0.20416  0.1667  -1.225 0.2207  MCHLA1  -0.60272E-01  0.9581E-01  -0.629 0.5293  DWIND  0.29563  0.1353 2.185 0.0289  DCHLA  0.23332  0.1148 2.032 0.0422  DCHLA1 0.89461E-01  0.1318 0.679 0.4972  GLDEPTH  0.35292  0.1715 2.057 0.0397  GTIMELDE  -0.50061E-01  0.2251E-01  -2.224 0.0261  GSST 0.56143E-01  0.6438E-01 0.872 0.3831  GSSH 0.26269E-01  0.1766E-01 1.488 0.1368  GMWIND 0.94383  0.4029 2.343 0.0191  GMCHLA 0.13359  0.1906 0.701 0.4833  GMCHLA1 -0.42591E-01  0.1296  -0.329 0.7425  SIGMA(1) 0.60758  0.6781E-01 8.961 0.0000  RHO(1,2)  -0.65815  0.1227  -5.366 0.0000 Economic Valuation Of Critical Habitat Closures, Berman et al.   97 Rockfish, standard CPUE  Sample Selection Model   Two stage least squares regression. Dep. Variable LOGROCKS    Observations  2084  Weights ONE   Mean of LHS 0.3189948E+00 Std.Dev of LHS 0.8042483E+00  StdDev of resid. 0.5704857E+00 Sum of squares 0.6681570E+03  R-squared 0.4965948E+00  Adj. R-squared 0.4892387E+00  F[ 30, 2053] 0.6750753E+02  Prob value 0.3217295E-13  Log-likelihood  -0.1771771E+04 Restr.(b=0) Log-l  -0.2502574E+04  Amemiya Pr. Criter. 0.3302952E+00  Akaike Info.Crit. 0.1730106E+01  Standard error corrected for selection.....  0.57731    Correlation of disturbance in regression   and Selection Criterion (Rho)..............  0.18122    N(0,1) used for significance levels.   Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant -3.1113  0.7469  -4.165 0.0000  JUN -0.50678E-01  0.1460  -0.347 0.7285  0.0614 0.2402  JUL  0.18668  0.2123 0.879 0.3792  0.2692 0.4436  AUG -0.19276  0.2978  -0.647 0.5175  0.2116 0.4085  SEP -0.74679E-01  0.3807  -0.196 0.8445  0.1569 0.3638  OCT  0.28892  0.4623 0.625 0.5320  0.1934 0.3950  GOA  -1.9658  0.7314  -2.688 0.0072  0.2404 0.4274  TIMELDEP 0.42586E-02  0.2040E-01 0.209 0.8346 35.5014 7.3817  LDEPTH  1.5964  0.3102 5.147 0.0000  4.5578 0.5740  L2DEP -0.16010  0.3176E-01  -5.041 0.0000 21.1029 5.4919  SLOPE  0.50755E-01  0.5203E-02 9.754 0.0000  4.3812 9.3259  SLOPE2  -0.45105E-03  0.1165E-03  -3.872 0.0001  106.1247 364.6537  SST -0.17723  0.3899E-01  -4.546 0.0000  8.1178 2.4189  SST2 0.14955E-01  0.2894E-02 5.167 0.0000 71.7304  37.6320  SSTSLOPE 0.13599E-02  0.9128E-03 1.490 0.1363 20.1822  15.0117  SSH  0.23040E-01  0.6114E-02 3.769 0.0002 -3.7809 3.8750  SSHSLOPE 0.13567E-02  0.8649E-03 1.569 0.1167 22.9680  16.3217  MWIND -0.26801  0.1398  -1.917 0.0552  2.0168 0.2648  MCHLA  0.23437E-01  0.4124E-01 0.568 0.5698  1.0076 0.4184  MCHLA1  -0.10672  0.2902E-01  -3.678 0.0002  1.0345 0.5422  DWIND  0.64795E-01  0.8539E-01 0.759 0.4480  0.0288 0.1673  DCHLA  0.24483  0.6637E-01 3.689 0.0002  0.0509 0.2198  DCHLA1 0.32186  0.7106E-01 4.529 0.0000  0.0398 0.1956  GLDEPTH  0.35931  0.8116E-01 4.427 0.0000  1.1783 2.1088  GTIMELDE  -0.31584E-01  0.8495E-02  -3.718 0.0002  8.5557  15.7173  GSST 0.89732E-01  0.2959E-01 3.033 0.0024  2.3308 4.3117  GSSH  -0.39775E-01  0.1067E-01  -3.728 0.0002 -0.8258 2.2383  GMWIND 0.18776  0.2487 0.755 0.4503  0.4961 0.8907  GMCHLA  -0.12895  0.8632E-01  -1.494 0.1352  0.2574 0.4944  GMCHLA1  0.19343  0.7970E-01 2.427 0.0152  0.2479 0.4876  IMR7 0.10462  0.4816E-01 2.172 0.0298  1.1547 0.4262  Economic Valuation Of Critical Habitat Closures, Berman et al.  98 Rockfish, standard CPUE (cont.)   Limited Dependent Variable Model - CENSORED  regression   Maximum Likelihood Estimates   Log-Likelihood..............  -1058.7   Threshold values for the model: Lower 0.0000  Upper **********    N(0,1) used for significance levels.   Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant -59.038 4.225 -13.975 0.0000  JUN -0.15847E-01  0.4224  -0.038 0.9701  0.0633 0.2436  JUL 1.2045  0.6004 2.006 0.0448  0.2671 0.4426  AUG 1.1343  0.8765 1.294 0.1956  0.2119 0.4088  SEP 2.3211 1.119 2.074 0.0380  0.1562 0.3631  OCT 3.1534 1.383 2.281 0.0226  0.1948 0.3961  GOA 5.7371 1.695 3.385 0.0007  0.2429 0.4289  TIMELDEP -0.84149E-01  0.5672E-01  -1.484 0.1379 35.5010 7.3800  LDEPTH  21.942 1.623  13.521 0.0000  4.5561 0.5725  L2DEP  -1.9408  0.1388 -13.979 0.0000 21.0856 5.4770  SLOPE  0.89567E-01  0.9907E-02 9.040 0.0000  4.3567 9.2949  SLOPE2  -0.97000E-03  0.2125E-03  -4.565 0.0000  105.3350 363.3731  SST -0.18383  0.9804E-01  -1.875 0.0608  8.1259 2.4168  SST2 0.11912E-01  0.7003E-02 1.701 0.0890 71.8521  37.6225  SSTSLOPE 0.49697E-02  0.2508E-02 1.981 0.0475 20.1507  14.9848  SSH  0.32366E-01  0.1621E-01 1.997 0.0458 -3.7796 3.8661  SSHSLOPE 0.24707E-02  0.2035E-02 1.214 0.2248 22.9461  16.2950  MWIND -0.46247  0.3564  -1.298 0.1944  2.0170 0.2650  MCHLA  0.45035  0.1383 3.257 0.0011  1.0089 0.4176  MCHLA1  -0.29367  0.9248E-01  -3.176 0.0015  1.0362 0.5416  DWIND  0.35857E-01  0.1803 0.199 0.8424  0.0286 0.1666  DCHLA  0.12472  0.1533 0.814 0.4158  0.0505 0.2190  DCHLA1 0.58706  0.1424 4.123 0.0000  0.0395 0.1949  GLDEPTH -0.96351  0.1986  -4.852 0.0000  1.1882 2.1125  GTIMELDE  -0.56791E-01  0.1835E-01  -3.095 0.0020  8.6394  15.7671  GSST 0.18872  0.6882E-01 2.742 0.0061  2.3558 4.3273  GSSH  -0.69470E-01  0.2373E-01  -2.927 0.0034 -0.8329 2.2418  GMWIND  -0.18797E-01  0.5551  -0.034 0.9730  0.5009 0.8934  GMCHLA  -0.55850  0.1975  -2.829 0.0047  0.2612 0.4978  GMCHLA1  0.30757  0.1641 1.874 0.0609  0.2516 0.4917  IMR7 0.33952  0.1143 2.970 0.0030  1.9112 0.4661  Sigma  0.98623  0.2846E-01  34.654 0.0000  Economic Valuation Of Critical Habitat Closures, Berman et al.   99 Rockfish, standard CPUE (cont.)   ML Estimates of Selection Model    Maximum Likelihood Estimates   Log-Likelihood..............  -5380.0   LHS is CENSORED. Tobit Model fit by MLE.   FIRST 30 estimates are probit equation.    N(0,1) used for significance levels.   Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant -56.474 4.850 -11.644 0.0000  JUN  0.22905  0.4983 0.460 0.6458  JUL 1.2618  0.6869 1.837 0.0662  AUG 1.2949 1.002 1.292 0.1963  SEP 2.4800 1.277 1.942 0.0521  OCT 3.3829 1.587 2.132 0.0330  GOA 5.2336 2.035 2.572 0.0101  TIMELDEP -0.10408  0.6720E-01  -1.549 0.1215  LDEPTH  21.260 1.869  11.377 0.0000  L2DEP  -1.8490  0.1647 -11.226 0.0000  SLOPE  0.82802E-01  0.1053E-01 7.863 0.0000  SLOPE2  -0.96891E-03  0.2243E-03  -4.319 0.0000  SST -0.22583  0.9279E-01  -2.434 0.0149  SST2 0.14440E-01  0.6475E-02 2.230 0.0257  SSTSLOPE 0.35699E-02  0.2713E-02 1.316 0.1882  SSH  0.30588E-01  0.1629E-01 1.877 0.0605  SSHSLOPE 0.26823E-02  0.2115E-02 1.268 0.2047  MWIND -0.33975  0.4520  -0.752 0.4523  MCHLA  0.30897  0.1708 1.809 0.0705  MCHLA1  -0.33693  0.1063  -3.171 0.0015  DWIND  0.17681  0.1896 0.932 0.3512  DCHLA  0.24109  0.1746 1.381 0.1674  DCHLA1 0.64309  0.1702 3.778 0.0002  GLDEPTH -0.96905  0.2408  -4.025 0.0001  GTIMELDE  -0.49021E-01  0.2095E-01  -2.340 0.0193  GSST 0.19512  0.6870E-01 2.840 0.0045  GSSH  -0.65231E-01  0.2516E-01  -2.593 0.0095  GMWIND 0.53487E-01  0.6346 0.084 0.9328  GMCHLA  -0.44256  0.2267  -1.952 0.0509  GMCHLA1  0.30180  0.2029 1.487 0.1369  SIGMA(1) 0.99435  0.2637E-01  37.712 0.0000  RHO(1,2)  -0.36229E-01  0.1442  -0.251 0.8016 Economic Valuation Of Critical Habitat Closures, Berman et al.  100 Flatfish, standard CPUE   Sample Selection Model   Two stage least squares regression. Dep. Variable LOGFLATS    Observations  2084  Weights ONE   Mean of LHS 0.1443222E+01  Std.Dev of LHS 0.7989369E+00  StdDev of resid. 0.6750596E+00 Sum of squares 0.9355633E+03  R-squared 0.2857214E+00  Adj. R-squared 0.2752838E+00  F[ 30, 2053] 0.2737428E+02  Prob value 0.3217295E-13  Log-likelihood  -0.2122535E+04  Restr.(b=0) Log-l  -0.2488766E+04  Amemiya Pr. Criter. 0.4624842E+00  Akaike Info.Crit. 0.2066732E+01  Standard error corrected for selection.....  0.67523    Correlation of disturbance in regression   and Selection Criterion (Rho).............. -0.26454E-01   N(0,1) used for significance levels.   Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant  8.5247  0.8862 9.619 0.0000  JUN -0.28334  0.1729  -1.639 0.1012  0.0614 0.2402  JUL -0.42274  0.2512  -1.683 0.0924  0.2692 0.4436  AUG -0.52272  0.3524  -1.483 0.1380  0.2116 0.4085  SEP -0.68092  0.4506  -1.511 0.1308  0.1569 0.3638  OCT -0.85019  0.5471  -1.554 0.1202  0.1934 0.3950  GOA -0.88905  0.8652  -1.028 0.3042  0.2404 0.4274  TIMELDEP 0.66403E-01  0.2414E-01 2.751 0.0059 35.5014 7.3817  LDEPTH -3.3393  0.3685  -9.061 0.0000  4.5578 0.5740  L2DEP  0.32054  0.3777E-01 8.486 0.0000 21.1029 5.4919  SLOPE -0.76981E-01  0.6136E-02 -12.546 0.0000  4.3812 9.3259  SLOPE2 0.10598E-02  0.1371E-03 7.731 0.0000  106.1247 364.6537  SST  0.10518E-01  0.4612E-01 0.228 0.8196  8.1178 2.4189  SST2  -0.17401E-02  0.3424E-02  -0.508 0.6113 71.7304  37.6320  SSTSLOPE 0.19921E-02  0.1076E-02 1.851 0.0641 20.1822  15.0117  SSH  0.30263E-01  0.7218E-02 4.193 0.0000 -3.7809 3.8750  SSHSLOPE 0.46397E-03  0.1020E-02 0.455 0.6492 22.9680  16.3217  MWIND -0.12633  0.1653  -0.764 0.4448  2.0168 0.2648  MCHLA  0.72674E-01  0.4883E-01 1.488 0.1367  1.0076 0.4184  MCHLA1 0.32083E-01  0.3426E-01 0.936 0.3491  1.0345 0.5422  DWIND -0.43080E-01  0.1009  -0.427 0.6695  0.0288 0.1673  DCHLA  0.19638  0.7851E-01 2.501 0.0124  0.0509 0.2198  DCHLA1  -0.24133  0.8384E-01  -2.878 0.0040  0.0398 0.1956  GLDEPTH  0.43883  0.9601E-01 4.571 0.0000  1.1783 2.1088  GTIMELDE  -0.56250E-01  0.1005E-01  -5.598 0.0000  8.5557  15.7173  GSST  -0.38102E-01  0.3501E-01  -1.088 0.2764  2.3308 4.3117  GSSH 0.29712E-01  0.1263E-01 2.353 0.0186 -0.8258 2.2383  GMWIND 0.84513  0.2942 2.872 0.0041  0.4961 0.8907  GMCHLA  -0.21575  0.1022  -2.111 0.0348  0.2574 0.4944  GMCHLA1 -0.18307  0.9431E-01  -1.941 0.0523  0.2479 0.4876  IMR7  -0.17862E-01  0.5775E-01  -0.309 0.7571  1.1541 0.4255 Economic Valuation Of Critical Habitat Closures, Berman et al.   101 Flatfish, standard CPUE (cont.)   Limited Dependent Variable Model - CENSORED  regression   Maximum Likelihood Estimates   Log-Likelihood..............  -2189.2   Threshold values for the model: Lower 0.0000  Upper **********    N(0,1) used for significance levels.   Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant  10.023  0.9941  10.083 0.0000  JUN -0.17792  0.1766  -1.007 0.3137  0.0614 0.2401  JUL -0.34136  0.2578  -1.324 0.1854  0.2695 0.4438  AUG -0.37872  0.3620  -1.046 0.2954  0.2115 0.4085  SEP -0.49852  0.4633  -1.076 0.2819  0.1568 0.3637  OCT -0.64007  0.5624  -1.138 0.2550  0.1933 0.3950  GOA -0.89924  0.8867  -1.014 0.3105  0.2408 0.4277  TIMELDEP 0.53463E-01  0.2489E-01 2.148 0.0317 35.5004 7.3800  LDEPTH -3.8565  0.3947  -9.771 0.0000  4.5579 0.5739  L2DEP  0.38824  0.4089E-01 9.495 0.0000 21.1036 5.4907  SLOPE -0.83725E-01  0.6378E-02 -13.128 0.0000  4.3791 9.3241  SLOPE2 0.11046E-02  0.1426E-03 7.748 0.0000  106.0738 364.5736  SST -0.61101E-02  0.4745E-01  -0.129 0.8975  8.1203 2.4210  SST2  -0.10966E-02  0.3518E-02  -0.312 0.7553 71.7806  37.6925  SSTSLOPE 0.16883E-02  0.1102E-02 1.532 0.1254 20.1982  15.0259  SSH  0.30473E-01  0.7427E-02 4.103 0.0000 -3.7812 3.8741  SSHSLOPE 0.43550E-03  0.1040E-02 0.419 0.6755 22.9797  16.3266  MWIND -0.45419E-01  0.1675  -0.271 0.7862  2.0167 0.2648  MCHLA  0.36737E-01  0.4934E-01 0.745 0.4565  1.0076 0.4183  MCHLA1 0.19910E-01  0.3539E-01 0.563 0.5737  1.0345 0.5421  DWIND  0.44944E-01  0.1032 0.435 0.6633  0.0288 0.1672  DCHLA  0.23501  0.8068E-01 2.913 0.0036  0.0508 0.2197  DCHLA1  -0.26924  0.8763E-01  -3.072 0.0021  0.0398 0.1956  GLDEPTH  0.43692  0.9853E-01 4.434 0.0000  1.1800 2.1098  GTIMELDE  -0.51209E-01  0.1033E-01  -4.959 0.0000  8.5676  15.7228  GSST  -0.40244E-01  0.3596E-01  -1.119 0.2631  2.3361 4.3173  GSSH 0.31127E-01  0.1292E-01 2.410 0.0160 -0.8275 2.2392  GMWIND 0.79485  0.3017 2.635 0.0084  0.4967 0.8909  GMCHLA  -0.18950  0.1047  -1.810 0.0703  0.2578 0.4945  GMCHLA1 -0.20333  0.9659E-01  -2.105 0.0353  0.2482 0.4878  IMR7  -0.14461  0.5784E-01  -2.500 0.0124  1.9115 0.4670  Sigma  0.69039  0.1092E-01  63.215 0.0000   Economic Valuation Of Critical Habitat Closures, Berman et al.  102 Flatfish, standard CPUE (cont.)   ML Estimates of Selection Model    Maximum Likelihood Estimates   Log-Likelihood..............  -6509.3   LHS is CENSORED. Tobit Model fit by MLE.   FIRST 30 estimates are probit equation.    N(0,1) used for significance levels.   Variable Coefficient Std. Error  t-ratio Prob.  Var. Mean Var. st. dev.  Constant  8.8227 1.188 7.425 0.0000  JUN -0.23979  0.1790  -1.339 0.1805  JUL -0.35902  0.2652  -1.354 0.1758  AUG -0.41255  0.3767  -1.095 0.2735  SEP -0.54322  0.4816  -1.128 0.2593  OCT -0.67442  0.5914  -1.140 0.2542  GOA -0.83326  0.8942  -0.932 0.3514  TIMELDEP 0.58686E-01  0.2602E-01 2.256 0.0241  LDEPTH -3.4442  0.5165  -6.668 0.0000  L2DEP  0.33991  0.4794E-01 7.090 0.0000  SLOPE -0.81097E-01  0.6405E-02 -12.661 0.0000  SLOPE2 0.10962E-02  0.1424E-03 7.696 0.0000  SST  0.14665E-02  0.6364E-01 0.023 0.9816  SST2  -0.12020E-02  0.4484E-02  -0.268 0.7887  SSTSLOPE 0.20152E-02  0.1043E-02 1.932 0.0533  SSH  0.30362E-01  0.6908E-02 4.395 0.0000  SSHSLOPE 0.43279E-03  0.9993E-03 0.433 0.6650  MWIND -0.12628  0.1637  -0.771 0.4405  MCHLA  0.71385E-01  0.5448E-01 1.310 0.1901  MCHLA1 0.35210E-01  0.3798E-01 0.927 0.3539  DWIND -0.18057E-01  0.9320E-01  -0.194 0.8464  DCHLA  0.20191  0.8879E-01 2.274 0.0230  DCHLA1  -0.28890  0.9005E-01  -3.208 0.0013  GLDEPTH  0.43885  0.9877E-01 4.443 0.0000  GTIMELDE  -0.54051E-01  0.9655E-02  -5.598 0.0000  GSST  -0.43807E-01  0.3895E-01  -1.125 0.2607  GSSH 0.30922E-01  0.1132E-01 2.733 0.0063  GMWIND 0.81385  0.3022 2.693 0.0071  GMCHLA  -0.20505  0.9217E-01  -2.225 0.0261  GMCHLA1 -0.20576  0.7758E-01  -2.652 0.0080  SIGMA(1) 0.69119  0.1175E-01  58.815 0.0000  RHO(1,2)  -0.44590E-01  0.9834E-01  -0.453 0.6502  

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