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The fishermen as predator : numerical responses of British Columbia gillnet fishermen to salmon abundance Millington, Peter 1984

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THE FISHERMEN AS PREDATOR: NUMERICAL RESPONSES OF BRITISH COLUMBIA GILLNET FISHERMEN TO SALMON ABUNDANCE. By PETER MILLINGTON M. Env. St., University Of Adelaide, 1982 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE STUDIES (Department, of Zoology) We accept t h i s thesis as conforming to the required standard COLUMBIA 1 984 THE UNIVERSITY OF BRITISH A p r i l 1984 (c^ Peter Millington, In presenting t h i s thesis i n p a r t i a l f u l f i l m e n t of the requirements fo r an advanced degree at the University of B r i t i s h Columbia, I agree that the Library s h a l l make i t f r e e l y a v a i l a b l e f o r reference and study. I further agree that permission f o r extensive copying of t h i s t h e s i s f o r s c h o l a r l y purposes may be granted by the head of my department or by h i s or her representatives. I t i s understood that copying or p u b l i c a t i o n of t h i s t h e s i s for f i n a n c i a l gain s h a l l not be allowed without my written permission. Department of The University of B r i t i s h Columbia 1956 Main Mall Vancouver, Canada V6T 1Y3 DE-6 f3/81"> i i ABSTRACT Fishermen are predators whose functional and numerical responses to prey can be studied. I review the fishery l i t e r a t u r e and discuss how much of i t can be viewed in the context of a predator-prey system. I examined the numerical response, aggregation and movement of boats in response to f i s h abundance, in the B r i t i s h Columbia salmon g i l l n e t f l e e t using catch data by area for the period 1979 to 1981. In any one year, for the whole coast, there was a strong relationship between the return (catch value per week per boat) and the number of g i l l n e t boats f i s h i n g in the following week. I investigated three hypotheses to explain the within season movement of the g i l l n e t f l e e t between areas (or sites) along the coast: fixed or t r a d i t i o n a l movement, which can be equated to an innate pattern of behaviour not substantially modified by learning; movement by fishermen to maximize individual return, which tends to equalize the return per unit time in a l l s i t e s ; and movement in which the drive to maximize individual return is modified by d i f f e r e n t i a l foraging costs and benefits between s i t e s . At the individual area l e v e l , none of the hypotheses was s u f f i c i e n t to consistently explain the variation in boat numbers within a season. Fixed movement patterns do not adequately explain movement although they may be useful in the short term. Movement by fishermen to maximize individual returns did not consistently explain movement into and out of pa r t i c u l a r areas, and resultant area returns did not approach the p r o v i n c i a l average return. Special features of each area appear to modify fishermen's attempts to maximize their i n d i d i v i d u a l returns such that each area tends to a c h a r a c t e r i s t i c return with respect to adjacent areas in any one year. However there i s much v a r i a b i l i t y in these returns between years. I compared the a b i l i t y of the l a t t e r two hypotheses (equalization of return between s i t e s and d i f f e r e n t i a l foraging costs) to predict boat numbers in p a r t i c u l a r areas in the following weeks. In some areas with i d e n t i f i a b l e features of location or boat type this approach worked well, but in most cases i t was confounded by t r a d i t i o n a l and economic factors. Application of these hypotheses to components of the f l e e t , such as combination g i l l n e t t e r s and t r o l l e r s , and pure g i l l n e t t e r s , may provide more insight into the mechanisms driving the numerical response. i v TABLE OF CONTENTS ABSTRACT i i L I S T OF TABLES v L I S T OF FIGURES v i i ACKNOWLEDGEMENTS v i i i INTRODUCTION 1 LITERATURE REVIEW 3 F u n c t i o n a l r e s p o n s e s 4 N u m e r i c a l r e s p o n s e s 18 METHODS 21 D e s c r i p t i o n of t h e F i s h e r y 21 D a t a s e t 25 D a t a a n a l y s i s 29 NUMERICAL RESPONSES OF FISHERMEN 30 N u m e r i c a l r e s p o n s e s of t h e salmon g i l l n e t f l e e t 30 RESULTS AND DISCUSSION 35 T y p e s of movement 35 F i x e d movement p a t t e r n s 35 M a x i m i z i n g i n d i v i d u a l b o a t r e t u r n 42 D i f f e r e n t i a l c o s t s and b e n e f i t s between a r e a s 61 P r e d i c t i n g movement 69 CONCLUSIONS 85 LITERATURE CITED 87 V LIST OF TABLES Table I. Classes of variables which affect the functional responses of predators to prey 8 Table II. Percentage catch value by area for g i l l n e t boats 1979-1981 28 Table I I I . Breakdown of g i l l n e t boat type by area 36 Table IV. P r o b a b i l i t i e s that boats f i s h certain area pairs - 1979 38 Table V. P r o b a b i l i t i e s that boats f i s h certain area pairs -1980 39 Table VI. P r o b a b i l i t i e s that boats f i s h certain area pairs - 1981 '. 4 0 Table VII. P r o b a b i l i t i e s of fi s h i n g area pairs - Bella Coola 4 1 Table VIII. Number of boats in week N + 1 vs landed value per boat in week N. Si g n i f i c a n t regressions 46 Table IX. Number of boats in week N + 1 vs landed value per boat in week N. Regression s t a t i s t i c s & cor r e l a t i o n c o e f f i c i e n t s 47 Table X. Average CPUE in each area each year 66 Table XI. Comparison of approaches to predict boat numbers 77 Table XII. Weekly observed and predicted boat numbers -1981 78 Table XIII. Contributions to 50% of sum of squared deviations 81 Table XIV. Mean and median returns in f i r s t and la s t week fished 1 979 to 1 981 vi i LIST OF FIGURES Figure 1. Types of functional response 5 Figure 2. S t a t i s t i c a l Map - B r i t i s h Columbia waters 22 Figure 3. Catch value by area 1979 - 1981 26 Figure 4. Number of boats in week N + 1 vs landed value per boat in week N - 1979 to 1981 - B r i t i s h Columbia 31 Figure 5. Number of mobile boats in week N + 1 vs landed value per boat week N - 1979 - areas 4 £ 29 44 Figure 6. Number of stationary boats in week N + 1 vs landed value per boat in week N - area comparisons 50 Figure 7. Number of stationary boats in week N vs landed value per boat in week N + 1 - Fraser River 52 Figure 8. Number of mobile boats in week N + 1 vs landed value per boat in week N - Bella Coola 55 Figure 9. Number of stationary boats in week N + 1 vs landed value per boat in week N - Bella Coola 57 Figure 10. Number of boats operating in each area each week - 1979 59 Figure 11. Total number of boats operating 1979 to 1981 ... 62 Figure 12. Value of catch per boat per week in each area -1979 64 Figure 13. RPA by area from 1979 to 1981 67 Figure 14. Number of boats observed and number predicted by FORCAST for Barkley Sound in 1981 74 v i i i ACKNOWLEDGEMENTS I would l i k e to thank rny supervisor Professor Ray Hilborn for his help and ideas during the course of t h i s work. I would also l i k e to thank the members of my Committee, Professors N. J. Wilimovsky and C. C. Lindsey for their constructive comments about my thesis. Two professional fishermen, Mr M. W. C. Forrest of the B r i t i s h Columbia Gi'llnet Association and Mr T. Sakata gave me the benefit of their experience. Thanks to my fellow students Linda Berg, Mike Lapointe, E l i e Moussalli and Rick Taylor who a l l helped in many ways. I am also grateful to Sue E r t i s and B i l l Webb of the Biosciences Data Centre for their help when I was stuck. F i n a l l y special thanks to my wife Donna for her patience, fortitude and encouragement. 1 INTRODUCTION There exists a large body of theory concerning predator-prey interactions developed for natural systems which can be applied to the study of f i s h i n g and fishermen. Fishing can be regarded as i n d u s t r i a l hunting, with the fisherman as a super-predator, and much of the t h e o r e t i c a l bases of predator-prey systems can equally well be applied to man. Fishermen can be studied from at least three d i f f e r e n t frames of reference; the f i s h , the fisherman and the manager. For e f f e c t i v e management there must be a sound theoret i c a l underpinning to explain and predict changes in the other two frames of reference. There has been much work on f i s h resources, but there has been r e l a t i v e l y l i t t l e systematic study of fishermen. Most papers which model fi s h i n g operations ignore or i m p l i c i t l y assume that the entire phenomenon is a predator-prey system. Only a limited number of authors have e x p l i c i t l y discussed their work in t h i s context (e.g. Peterman et a l . 1979, Peterman 1980) or have considered the management implications of such an approach (Larkin 1 9 7 9 , Dickie 1979). The basic components of such predator-prey systems are the functional and numerical responses of the predator to prey density. The essential and subsidiary components of the predator-prey system described by Holling (1959, 1965, 1966) provide a convenient framework to study the c h a r a c t e r i s t i c s of a predator; the fisherman. I w i l l review the l i t e r a t u r e and examine how much of i t can be considered in t h i s context. 2 Using the B r i t i s h Columbia (B. C.) salmon g i l l n e t fishermen as an example, I w i l l examine how a numerical predator response (movement and aggregation of boats) i s related to prey density (measured in terms of catch value). I w i l l investigate three alternative hypotheses to explain t h i s aggregation: fixed or t r a d i t i o n a l movement, which can be equated-to a fixed pattern of behaviour not substantially modified by learning; movement by fishermen to maximize individual return, which tends to equalize the return per unit time in a l l s i t e s (or areas); and movement in which the tendency to maximize individual return per unit time is modified by d i f f e r e n t i a l foraging costs and benefits between s i t e s . I w i l l show how useful these hypotheses can be as management tools to predict the within season movement of the salmon g i l l n e t f i s h i n g boats along the B. C. Coast. F i n a l l y I w i l l outline b r i e f l y the usefulness of considering f i s h i n g within the context of a predator-prey system and what future work can be done in t h i s area. 3 LITERATURE REVIEW Solomon (1949) used two terms to categorize the two kinds of responses in a predator-prey system. Functional responses are changes in the number of attacks or the consumption rate of the predator as a function of prey or predator density. Numerical responses are changes in predator density or abundance with changing prey density (Holling 1959). Total predation can then be expressed in terms of these two kinds of response. Numerical responses arise from predator movement in and out of an area or changes in b i r t h or death rates. Murdoch and Oaten (1975) pointed out that i f the response i s from movement i t could be either a numerical or an aggregative functional response to prey patchiness. Which of these i s considered more appropriate i s a question of scale, i . e. i f movements are over large distances, i t i s better to consider the response as numerical. Many B. C. salmon g i l l n e t t e r s do move large distances during the course of as season, from the Nass River to the Fraser River, so th i s i s the approach I have adopted. From the f i s h e r i e s management viewpoint, the numerical response, e. g. an increase in boat numbers, i s of primary importance. For example, to achieve his desired salmon escapement goal from the anticipated run of f i s h , the fishery manager must.adjust the opening duration in an area as only time can l i m i t the k i l l from the flood of predators (fishermen) which can be expected. Calculation of the t o t a l opening time would be helped i f the number of boats (and hence t o t a l e f f o r t ) which w i l l f i s h an area could be predicted. 4 Even given t h i s viewpoint the greater body of work to date has concentrated on the functional rather than numerical responses of fishermen. Functional responses Holling (1959) i d e n t i f i e d three basic forms of the functional response: type 1, which produces density independent mortality up to a sa t i a t i o n point; type 2 which produces an inversely density-dependent mortality over a l l ranges of prey abundance; and type 3, which produces direct density-dependent mortality up to point where the predator is satiated or runs out of time. A modified type 2 (or 'type 4') response may occur when there i s a refuge for the prey which protects a portion of the population (Figure 1). A l l three types of functional response could be, or have been, observed in fishermen. A type 1 functional response implies a gear or fisherman whose searching and/or catching e f f i c i e n c y climbs steadily to a saturation point. The type 2 functional response has been noted by Peterman (1980) in the B. C. native indian food fishery for salmon, where the gear used included dip nets, gaffs, g i l l n e t s and traps. The asymptote in the type 2 functional response arises from handling time or search e f f i c i e n c y l i m i t a t i o n s (Holling 1959). The gear used by the indians can only sweep a limited volume of water and handling times at high densities become important because s i g n i f i c a n t time is needed to bring in, empty and reset the g i l l n e t s and traps (Peterman 1980). gure 1. Types of functional response. For a further explanation of each type see text. 6 7 The type 3 or S-shaped curve i s t y p i c a l of many vertebrates where there is more than one prey species, as the predator may not 'switch' to a prey species when i t i s at low density. That i s , switching can give a larger than expected number of attacks when a species i s abundant r e l a t i v e to other prey (Murdoch 1969) . This type 3 response should be the one most commonly observed in f i s h i n g , where the fisherman has a number of species or stock available to him. Holling (1959) i d e n t i f i e d five classes of variables which affe c t prey mortality from predation: 1. Density of the predator population 2. Density of the prey population 3. Predator c h a r a c t e r i s t i c s 4. Prey c h a r a c t e r i s t i c s 5. Density and quality of alternate foods for the predator. Only the f i r s t two variables are essential in the predator-prey process (Holling 1959). These groups include, in turn, essential and subsidiary components (Table I) and subcomponents. These functional responses have been applied to f i s h themselves e. g. P a c i f i c salmon (Larkin 1971, Peterman and Gatto 1978), but e x p l i c i t consideration with respect to man has been limited to work by Peterman (1980). In f i s h i n g terms, the exploitation and interference functional response components together comprise competition between f i s h i n g boats. The effects of exploitation competition may include instances where each unit of gear competes with each TABLE I CLASSES OF VARIABLES WHICH AFFECT THE FUNCTIONAL RESPONSES OF PREDATORS TO PREY 1. Density of the predator population Essential Exploitation - as predators compete for the same resource, the chance of discovering an unattacked prey decreases with increasing predator density Interference - between competitors Subsidiary Social f a c i l i t a t i o n - s o c i a l contact can stimulate an increase in predation with increasing predator density Avoidance learning by prey - the greater the predator density the greater the chance each prey w i l l aquire an e f f e c t i v e way of avoiding attack 2. Density of the prey population Essential Rate of successful search -reactive distance of predator to prey -speed of predator movement -speed of prey movement -capture success Time exposed to predators -non feeding vs feeding times Time spent handling prey -pursuit time -eating time -time for digestive pause Subsidiary Hunger Learning by predator Inhibit i o n by prey -development of defence mechanisms 3. Predator c h a r a c t e r i s t i c s Swimming speed Visual acuity 4. Prey c h a r a c t e r i s t i c s Caloric value of prey Prey exposure time to predator Prey attractiveness to predator - p a l a t a b i l i t y -defence mechanisms Strength of stimulus used by predator to locate prey -size of prey -habits of prey -colours of prey 5. Density and quality of alternate foods for the predators Switching From Holling (1959, 1965, 1966); G r i f f i t h s and H o l l i (1969). 9 other unit of gear (e. g. trawlers), or where f i s h compete for a unit of space on the gear e. g. hooks on longlines (Rothschild 1977). Descriptions of interference competition are mostly anecdotal. Trawler skippers are known to misdirect other nearby skippers over unproductive ground or lure them into time wasting t r i p s , (Andersen and Wadel 1972, Tunstall 1962) and a purse seine skipper may sometimes d i r e c t l y interfere with the fishing operation of another vessel (Orbach 1977). Various s o c i a l mechanisms have developed to mitigate inference competition (McCay 1978), mostly through spacing mechanisms such as community recognition of rights to favoured fis h i n g access points (Andersen and S t i l e s 1973, Breton 1973), or ac t i v e l y defended t e r r i t o r i e s (Acheson 1975). However, there may not be generally accepted, or automatic, long term property rights to these access points, and fishermen may have to compete for them by being f i r s t to f i s h there in a given season (and thus establishing temporary 'ownership') or occupying a l l good fi s h i n g locations with the gear (Andersen and S t i l e s 1973). Social f a c i l i t a t i o n should increase the average fisherman's catch with increasing concentration of fi s h i n g boats, and t h i s most often occurs via the flow of radio information between a l l , or sub-sets of, fishermen. In many cases cooperative information flow is limited to kin groups, friends or company boats through the use of low power radios (Lofgren 1972) or code groups (Orbach 1977, Tunstall 1962). Information is an indeterminate, dynamic and scarce 1 0 r e s o u r c e ( A n d e r s e n and Wadel 1972), and v a r i o u s s o c i a l s t r u c t u r e s a p p e a r t o m i t i g a t e s o c i a l f a c i l i t a t i o n t h r o u g h a n o t h e r f o rm o f i n t e r f e r e n c e - d i s i n f o r m a t i o n . F o r example many f i s h e r m e n a r e not o n l y i n c o m p e t i t i o n w i t h o t h e r company's b o a t s , but a l s o w i t h s k i p p e r s i n t h e same company; t h e i r s u r v i v a l i n t h e h i e r a c h y i s m e asured r e l a t i v e t o t h e s e o t h e r s k i p p e r s ( T u n s t a l l 1962, A n d e r s e n 1973). The s k i p p e r t h e r e f o r e p l a y s a z e r o sum game; h i s l o s s i s some-one e l s e ' s g a i n ( A n d e r s e n 1972). D i s i n f o r m a t i o n t a k e s t h e f o r m o f n o n - c o m m i t t a l e x c h a n g e s on t h e r a d i o or w i t h - h o l d i n g i n f o r m a t i o n , d e c e p t i v e s t r a t e g i e s , u n d e r - s t a t e d c a t c h e s , o v e r - s t a t e d c a t c h e s o r even r a d i o s i l e n c e ( A n d e r s e n 1972, A n d e r s e n 1973, O r b a c h 1977, T u n s t a l l 1962). The o v e r a l l f l e e t p e r f o r m a n c e i s t h e r e f o r e a f f e c t e d by i n e f f i c i e n c i e s i n b o t h c a t c h and s e a r c h . The d i s i n f o r m a t i o n i s n o t t o t a l however; t h e b a s i c need o f a l l s k i p p e r s f o r a m i n i m a l i n f o r m a t i o n f l o w , and i n a d v e r t e n t s l i p s , c o n t r i b u t e t o t h i s f l o w of i n f o r m a t i o n . When a l a r g e p e r c e n t a g e of t h e f l e e t w i t h d r a w s from f i s h i n g t h e r e may be an even l a r g e r p e r c e n t a g e c a t c h r e d u c t i o n ( A n d e r s e n and S t i l e s 1973). The d i r e c t outcome o f s o c i a l f a c i l i t a t i o n i s u s u a l l y a r e a l i n c r e a s e i n e f f e c t i v e f i s h i n g e f f o r t as t h e number of g e a r u n i t s i n c r e a s e . R o t h s c h i l d (1977) c a l l e d t h i s i n c r e a s e t h e c o o p e r a t i o n e f f e c t . As f i s h l e a r n a v o i d a n c e t e c h n i q u e s f r o m e n c o u n t e r s w i t h n a t u r a l p r e d a t o r s so t h e y l e a r n f r o m e n c o u n t e r s w i t h f i s h i n g g e a r . T h e s e e n c o u n t e r s may i n c l u d e f l i g h t f r o m t r a w l g e a r , 11 p u l l i n g the mouth from hooks, or, once caught, release as undersized or otherwise i l l e g a l . Fish learning to avoid fi s h i n g gear has been most often observed in freshwater angling, and some species (e. g. bass, Micropterus spp.) appear to learn at a faster pace than others (e. g. carp, Cyprinus carpio ) (Beukema 1970a, Hackney and Linkous 1978, Schneider 1973). The rate of learning is also modified by the type of stimulus presented e. g. the avoidance learning rate is greater in pike ( Esox lucius ) with spinners than with l i v e baited hooks (Beukema 1970b). In commercial f i s h e r i e s , the common gear type e. g. nets, gives the f i s h less 'choice' in i t s response and the f i s h play a more passive part (Allen 1963). Avoidance i s more of a f l i g h t response to predators in general and capture depends more on the f i s h size i . e. whether too large or too small for the mesh. Secondly, for marine stocks the size of the area they commonly occupy means that the encounter rate for the individual w i l l be be f a i r l y low in most f i s h e r i e s , with less opportunity for learning through repeated encounters. Thirdly, given the e f f i c i e n c y of modern fi s h i n g gear, i t i s not inconceivable that the proportion of f i s h escaping, once encountered at any time, is also f a i r l y low. However, some gears, such as lobster pots or hook and l i n e , allow for learning responses in the f i s h similar to those of freshwater species subject to angling. Searching i s a major component of f i s h i n g e s p e c i a l l y in encirclement methods such as purse seining, while sub-components such as speed of the predator (f i s h i n g boat or gear), capture 12 success and perception distance are constantly changing with improved technology (Mangel and Clark 1982). Success rates in locating prey depend on past experience, time of year, weather etc. (Orbach 1 977); searching is not -a random a c t i v i t y and stocks are not randomly d i s t r i b u t e d . Saila and Flowers (1969) applied operations research methods to model the search process of f i s h i n g boats taking t h i s non-random d i s t r i b u t i o n into account. Others have developed stochastic theories of search, considering contagiously or randomly d i s t r i b u t e d prey and allowing for a variable'perception radius, escapement and capture/handling delays (Paloheimo and Dickie 1964, Paloheimo 1971 a, 1971b). Success rates were shown to vary with prey a c t i v i t y and abundance (Mangel and Clark 1982). The time spent handling prey considered in terms of pursuit, eating, and digestive pause are, together with the time exposed to the prey (feeding vs non-feeding time), intimately bound together in the f i s h e r i e s context within the concept of e f f o r t . The optimal a l l o c a t i o n of time spent on these various components has been addressed in part by simple models of foraging behaviour in animals (Pyke et a l . 1977) and the actual time spent in these a c t i v i t i e s can be accounted for t h e o r e t i c a l l y in various search models (Paloheimo 1971a, 1971b). However, there do not appear to be any authors who have considered the p r a c t i c a l aspects of these problems in f i s h i n g boats within the context of a predator-prey system. Shardlow (1983) investigated the rel a t i o n s h i p between catch per unit e f f o r t of salmon anglers and salmon abundance, where 1 3 both were estimated simultaneously. Unlike previous studies using h i s t o r i c a l data to estimate abundance, the proportion of the immediately surrounding population caught by a numerical unit of f i s h i n g e f f o r t , the c a t c h a b i l i t y c o e f f i c i e n t , was not constant but increased with increasing salmon abundance. Studies of the interaction between fis h i n g gear and f i s h showed that feeding f a c i l i t a t i o n between salmon may be the underlying mechanism, producing increased c a t c h a b i l i t y with abundance. That i s , one salmon chasing a lure w i l l a t t r a c t others and the affe c t of t h i s i s greater at higher abundance. • Rothschild (1972, 1977) has addressed the problem of properly measuring f i s h i n g e f f o r t in terms of real inputs such as hours fished, rather than in terms of the numerical f r a c t i o n of the population caught. In most f i s h e r i e s , technological developments, together with increases in s k i l l are constantly changing e f f o r t levels i . e. decreasing the non-feeding, eating and digestive pause phases and increasing the feeding time. For modelling and management purposes therefore, a large amount of time i s spent trying to relate changes in e f f o r t and differences in boats to some standard (see Pope 1975). Hunger, or the need to go f i s h i n g has been addressed in the l i t e r a t u r e from both the s o c i o l o g i c a l (Orbach 1977, Tunstall 1962), and economic standpoints (Smith 1981). External economic pressures arid internal motivations constitute t h i s drive. Unlike most carnivores, fishermen do not generally appear to have a personal sa t i a t i o n point, other than the l i m i t a t i o n s of equipment. More i s always better. 1 4 While i t is possible to measure the external pressures to f i s h , e. g. to earn a l i v i n g or pay off a debt load etc., any models are complicated by non-tangible motivations. For example Anderson (1980) i d e n t i f i e d the worker s a t i s f a c t i o n bonus as a factor in economic modelling. This pleasure factor in commercial fishermen means they are w i l l i n g to subsidize their f i s h i n g from other sources, including off-season employment or even unemployment insurance (Ferris and Plourde 1982). If the worker s a t i s f a c t i o n bonus i s positive with respect to other industries, i t means there may be increased perception of benefits beyond those calculated from d o l l a r revenues. This i s the case for the salmon fishery along the western North American coast (Smith 1981). The l e v e l of a skipper's motivation and his learning c a p a b i l i t y a f f e c t both his f i s h i n g strategy and the risks he i s w i l l i n g to take within that strategy. Rothschild (1972) considered that s k i l l may be more important than the c a p a b i l i t i e s of the f i s h i n g boat in i t s contribution to fi s h i n g e f f o r t , but that in many analyses s k i l l was ignored as i t cannot be properly formulated. The degree of s k i l l required for success w i l l vary with circumstances. For example, at low prey densities when the decision environment becomes more complicated the number of decision variables to be considered means a ' s k i l l e d ' skipper w i l l show a r e l a t i v e l y better catch than when the prey densities are higher (Rothschild 1977). Cove (1973) measured differences in f i s h i n g strategies based on the skipper's assessment of risk in three types of 1 5 f i s h e r y . The d e g r e e o f r i s k v a r i e d w i t h t h e u n c e r t a i n t y of t h e r e s o u r c e , t h e s k i p p e r ' s m o t i v a t i o n and t h e c a p a b i l i t y of t h e g e a r . R i s k s measured i n c l u d e d f i s h i n g n e a r d a n g e r o u s f e a t u r e s , s e l e c t i o n of f i s h i n g a r e a s on t h e b a s i s of p a s t c a t c h e s ( a v e r a g e o r good) and i n d e p e n d e n t v e r s u s g r e g a r i o u s f i s h i n g h a b i t s . F o r example, t h e s k i p p e r ' s s u r v i v a l i n a company f i s h i n g b o a t d e p e n d s on h i s p e r f o r m a n c e r e l a t i v e t o h i s p o s i t i o n i n t h e h i e r a c h y ( A n d e r s e n 1973, T u n s t a l l 1962). Cove (1973) f o u n d t h e h i g h e s t r i s k t a k i n g i n t r a w l e r s was i n c i r c u m s t a n c e s of low u n c e r t a i n t y , low c a p a b i l i t y ( o f t h e b o a t ) and h i g h m o t i v a t i o n ( i . e. a s k i p p e r low on t h e company h i e r a c h y ) . However even t h i s d e g r e e o f r i s k t a k i n g o n l y e x p l a i n e d 42% of t h e c a t c h v a r i a t i o n . T h i s ' s k i p p e r ' e f f e c t was a l s o s t u d i e d by P a l s s o n and D u r r e n b e r g e r (1982) amongst I c e l a n d i c g i l l n e t and l o n g l i n e f i s h e r m e n u s i n g p a t h a n a l y s i s t e c h n i q u e s . They f o u n d t h e s k i p p e r e f f e c t t o be q u i t e weak, w i t h t h e d i f f e r e n t i a l s u c c e s s i n c e r t a i n s k i p p e r s b e i n g a c c o u n t e d f o r i n terms of l a r g e r boat s i z e and g r e a t e r number o f f i s h i n g t r i p s , and t h a t a major component o f a s k i p p e r ' s ' s k i l l ' i s h i s a b i l i t y t o m a x i m i z e t h e s i z e o f t h e b o a t he i s g i v e n t o command and t h e number of t r i p s he can u n d e r t a k e i n a y e a r . In t h e B. C. p u r s e s e i n e f l e e t 40% of t h e d i f f e r e n c e s i n c a t c h i n g power between b o a t s was a t t r i b u t e d t o s k i p p e r and crew s k i l l and 25% t o a r e a s p e c i a l i z a t i o n ( H i l b o r n and L e d b e t t e r 1984). The s i g n i f i c a n c e of s k i p p e r s k i l l i n t h e c a t c h p r o c e s s t h e r e f o r e v a r i e s c o n s i d e r a b l y between f i s h e r i e s . 1 6 Inhibition by prey takes the form of defence mechanisms, both physical and chemical. Besides general defences adapted to a l l types of natural predators e. g. spines, poisonous secretions etc., f i s h do not appear to have developed any sp e c i f i c defence mechanisms d i r e c t l y as a result of predation by man. However, there are indications that f i s h i n g pressure has reduced the mean size of several salmon species (Ricker 1981), thus a l t e r i n g their v u l n e r a b i l i t y to f i s h i n g gear. In f i s h i n g the physical predator c h a r a c t e r i s t i c s are those of boat and gear type. In fis h i n g boats the 'evolutionary' pressures are strong and force rapid change from experience in the fishery and external inputs,, both managerial and technological. The common property nature of the resource is an additional spur to change as participants attempt to maximize their individual catches in open competition. Results from attempts to attribute catch to boat c h a r a c t e r i s t i c s have been mixed. As mentioned previously Palsson and Durrenberger (1981) found boat size one of two factors s i g n i f i c a n t l y correlated with catch. Carlson (1975) found f i s h i n g boat c h a r a c t e r i s t i c s accounted for 50% of the landed weight and 83% of the landed value of US catches from Georges Bank. However, Hilborn and Ledbetter (1984) could only account for 10% of the differences in catch from B. C. purse seiners by examining cert a i n boat a t t r i b u t e s such as length, although they did not take into account differences in gear to explain some of these differences. A major d i f f i c u l t y encountered i s how to standardize a 1 7 dynamic c h a r a c t e r i s t i c such as e f f o r t (e. g. see Pope 1975, Rothschild 1977), esp e c i a l l y where gross c h a r a c t e r i s t i c s such as engine power may account for some variation (Hovart and Michielsen 1975), but where a d i f f i c u l t to measure c h a r a c t e r i s t i c , such as the hanging r a t i o of a g i l l n e t , may also have a s i g n i f i c a n t e f f e c t . The prey c h a r a c t e r i s t i c s considered important by Holling (1959) (Table I) translate in f i s h e r i e s terms into such i l l -defined ones as palatabi1ity, market demand and market value. The actual c a l o r i c value of the product matters l i t t l e ; i t s attractiveness in the market place matters most of a l l . Extreme selective pressure by predators may change c h a r a c t e r i s t i c s such as the exposure time of the prey to the predator i . e. the period during which i t is vulnerable, or the size, habits colours etc. of the animals such that the e f f i c i e n c y of the predator's method of prey location i s reduced. The p o s s i b i l i t y that f i s h i n g has caused such changes has merited serious theoretical discussion (Calaprice 1969, Thorpe and Koonce 1981), and the a l t e r a t i o n of run timings in some salmon stocks is one example (Vaughan 1947). Parrish (1963), although primarily considering the selection process from the f isherman's viewpoint did outline the main selection processes a r i s i n g from f i s h i n g operations: 1. Those caused by differences in the d i s t r i b u t i o n of the fis h i n g f l e e t and components of the fi s h i n g stock 2. Those caused by the variation in habits and behaviour of components of the f i s h stock in the exploited area 18 3. Those caused by the inherent properties of the fishing gear. Each of these processes operate to select between species in a multi-species fishery, between abundance leve l s of a single species, and between d i f f e r e n t components of a stock - sizes, ages, sexes etc. In a multi-species fishery, there may be switching, i . e. a type 3 functional response (Beddington 1979). Larkin (1979) c a l l e d fishermen accomplished switchers, as man consumes a wide range of species, uses various f i s h i n g styles and has an arsenal of technology available to f a c i l i t a t e switching. The theory of optimal foraging strategy (Pyke et a l . 1977) indicates that selection of prey items by fishermen is an important mechanism a f f e c t i n g prey size in a f i s h i n g system (Dickie 1979). A major obstacle to studying the functional responses of fishermen has been to obtain an accurate count of prey abundance. Modern sonar and underwater viewing equipment does now allow a simultaneous estimate of prey abundance and predator ( f i s h boat) numbers and attack rates (e. g. see Shardlow 1983). Numerical responses Larkin (1979) categorized approaches to modelling f i s h populations into 4 broad types: Lotka-Volterra models, single species models, the ecosystem approach, and the functional approach. Each has i t s l i m i t a t i o n s . Although there has been some application of the Lotka-Volterra models in f i s h e r i e s (May et a l . 1979) they are not considered good predictive tools for management (Larkin 1979). The single species models 19 (e. g. Beverton and Holt 1957, Schaefer 1957, Ricker 1975) lump the effect of man the predator under the category of harvest, but have been the most widely used management tools to date, although their management u t i l i t y has not been properly evaluated. The ecosystem approach i s quite complex and demanding of data, as i s the functional approach, and the management u t i l i t y of both has to date been unclear. I have outlined already how a few authors have made a start on modelling functional responses and applying them to the management context (e. g. Peterman 1980). The single species approach could be regarded as modelling the numerical response of the prey, usually represented by harvest, to the predator density but very l i t t l e has been done to investigate the numerical response of the predator to prey density. Peterman et a l . (1979) when examining f l e e t dynamics of B. C. t r o l l e r s on chinook salmon showed a numerical response -within season aggregation - similar to natural predators, with clear bounds on responses due to management regulations. They considered that changes in f i s h i n g e f f i c i e n c y , f l e e t size, f l e e t composition etc. to be equivalent to the predator-reproductive • numerical response. Loucks and S u t c l i f f e (1978) outlined a simple model of f i s h i n g boat mobility (responsive e f f o r t ) to perceived stock abundance and ocean climate, and found that the Canadian east coast cod fishery to be prosecuted by a 'perceptive f i s h i n g industry akin to a natural predator-prey system'. Ledbetter (1981) considered t h i s numerical response to be 20 better understood in the f i s h e r i e s context as a response to information (e. g. past catch per unit e f f o r t - CPUE) and this was assumed by Hilborn and Ledbetter (1979), in their analysis of the movement dynamics of the B. C. purse seine f l e e t . These authors examined a number of hypotheses to explain and predict movement of the f l e e t . If each vessel is attempting to maximize i t s return and there is optimal boat d i s t r i b u t i o n with respect to f i s h abundance, the CPUE along the coast should roughly be equal. However, the CPUEs from the individual areas and the pr o v i n c i a l CPUE were found to be d i f f e r e n t . This was attributed the d i f f e r e n t i a l costs and benefits of fi s h i n g certain areas. Such an, approach does show promise as a management tool and I w i l l examine the a p p l i c a b i l i t y of these concepts to the B.'C. salmon g i l l n e t f l e e t . 21 METHODS Description of the Fishery The B. C. salmon fishery is a highly regulated one commencing in A p r i l in some areas and continuing u n t i l November in others. For management and s t a t i s t i c a l purposes, the Department of Fisheries and Oceans has divided the B. C. coast into 29 main areas (Figure 2). The fishery i s characterized by brief openings of a half, one or two days in these areas or in subareas, usually s t a r t i n g at 1800 hours on a Sunday. There is ample scope for movement between these areas between openings, but unless the area is nearby or eas i l y accessible there i s a considerable penalty for doing so during an opening. This factor, together with the nature of the data set, led me to consider one week as the basic unit of e f f o r t . The fisherman must decide on the basis of his recent catch, his boat's c a p a b i l i t y , past year's experience, conversations with other fishermen and information from the f i s h e r i e s managers, whether to change areas between openings. Many of the boats are 'combination' boats i . e. they are licenced both for t r o l l i n g and g i l l n e t t i n g . Thus a combination licence holder must decide on the appropriate time to change gear and enter or leave the g i l l n e t fishery. Many of the fishermen operate on a part-time basis and are considerably less mobile, often only fi s h i n g one or two s t a t i s t i c a l areas adjacent to their place of residence or work. Some of the t r o l l e r s may also only g i l l n e t one area. These 22 Figure 2. S t a t i s t i c a l Map - B r i t i s h Columbia waters. Source: Canada, Department of Fisheries and Oceans (1982) . 23 STATISTICAL MAP 25 'stationary' boats can be contrasted with the 'mobile' ones which move between a larger number of areas. D i s t r i b u t i o n of catch value by area for the years 1979 to 1981 are i l l u s t r a t e d in Figure 3. The areas which together contributed an average of over 76% of the catch value in a l l years are considered in more d e t a i l (Table I I ) . Two in pa r t i c u l a r were subject to special examination because of their o v e r a l l contribution to the p r o v i n c i a l salmon catch: Area 4, Lower Skeena River, and Area 29, Gulf/Fraser River. Together, catches from these two areas constituted between 21 and 61% of the p r o v i n c i a l salmon g i l l n e t catch by value beteween 1979 and 1981 inc l u s i v e . Data set The Department of Fisheries and Oceans compile data from s l i p s made out when a boat s e l l s i t s f i s h , which is usually within one or two days of capture. The sales s l i p information includes the date, boat number, s t a t i s t i c a l area, and catch d e t a i l s such as species, pounds, pieces and value of f i s h landed. This data base has been described in some d e t a i l by Wong (1983). Sales s l i p and boat at t r i b u t e information i s available for the years for the years 1967 to 1981, but mobility has been increasing in recent years, with fishermen road-hauling their boats between areas (M. W. C. Forrest, B. C. G i l l n e t Association, personal communication), so l a t e r years are examined in d e t a i l . 26 F i g u r e 3. C a t c h v a l u e by a r e a 1979 - 1981. For a d e s c r i p t i o n of a r e a numbers see F i g u r e 1 and T a b l e I I . 1979 c a t c h ||| ; 1980 c a t c h \ \ \ ; 1981 c a t c h / / / . TABLE II PERCENTAGE CATCH VALUE BY AREA FOR GILLNET BOATS 1979 - 1981 Area Area Name Year Number 1979 1980 1981 Average 1 Queen C h a r l o t t e I s l a n d s (North) 0 . 14 0 .92 1 . 13 0 . 73 2 Queen C h a r l o t t e I s l a n d s (W & E) 0 .05 0 .45 0 .66 0 . 39 3* Nass R i v e r 4 . 23 1 1 . 42 4 . 24 6 .63 4* Lower Skeena 31 .07 1 1 .47 28 .40 23 .65 5 G r e n v i 1 l e / P r i n c i p e 1 .4 1 5 . 56 1 . 74 2 .90 6 B u t e d a l e 0 . 97 4 . 40 1 .99 2 . 45 7* B e l l a Bel l a 5 . 19 1 1 . 10 3 .60 6 63 8* Bel l a Coo l a 9 . 49 1 1 . 76 7 . 92 9 .72 9 R i v e r s I n l e t 1 . 17 0 . 45 2 .69 1 . 44 10 Smith I n l e t 0 . 73 0 .97 3 . 57 1 . 76 1 1 Seymour/Be 1i ze 0 .66 0 .80 0 . 74 0. . 73 12* A l e r t Bay - J o h n s t o n e S t r a i t s 2 .84 7 . 36 8 . 56 6 . 25 13 Quath i ask i 1 . 28 2 19 0 . 89 1 . .45 14 Comox/Oua1icum Beach 0 .09 0 . 66 0 . 84 0 . 53 15 Powe11 R i ve r 0 .01 0. .00 0 .00 0. .00 16 Pender Harbour 0 .02 0. . 13 1 . 99 0. .71 17 Nana i mo/Ladysm i th 0 .03 0. 02 0. . 13 0. 06 18 Cow i chan 0. .01 0 00 0 01 0. 01 19 'V i c t o r i a ' 0 .00 0. 00 0. 00 0. 00 20* Juan de Fuca 2 , 27 4 . 21 3 . 59 3 . 36 21 ' O u t e r N i t i n a t ' 0, .00 0. 00 0. .00 0. 00 22 N i t i n a t Lake 0. .00 0. 79 O. 00 0. 26 23* Bark ley Sound 6 . 79 8 . 99 9 . 33 8 . 37 24 C l ayoquot Sound 0. 01 0. 21 0. 08 0. 10 25 Nootka Sound 0. 00 1. 45 0. 35 0. 60 26 Kyoquot Sound 0. 00 0. 52 0. 26 0. 26 27 Q u a t s i n o Sound 0. 05 0. 00 0. 02 0. 02 28 Howe Sound 0. 61 0. 01 0. 00 0. 21 29* G u l f / F r a s e r R i v e r 29. 69 10. 10 16 . 29 18 . 69 31 + Other a reas 1.21 1.81 0. 95 1 . 32 * = a rea s c o n s i d e r e d in d e t a i l + = mos t l y USA water s . 29 The data allow at least three ways of examining catch per unit e f f o r t (CPUE); pieces, weight and landed value. The price of d i f f e r e n t salmon species varies and fishermen trade off the expected d o l l a r return from one species against that of another; i . e. an expected high catch of a low value species against an expected low catch of a high value one. Therefore I considered the best index of performance was to be landed value in dollars per boat per week of f i s h i n g . This conforms with the methods used by Hilborn and Ledbetter (1979, 1984). Data analysis After necessary manipulation to break down the data set into area s p e c i f i c information and boat type s p e c i f i c information, I used a number of s t a t i s t i c a l techniques to test relationships between data, including two way analysis of variance, Spearman's rank cor r e l a t i o n c o e f f i c i e n t and linear regression. The actual tests used and the assumptions made in their application are outlined with the results of each of the t e s t s . 30 NUMERICAL RESPONSES OF FISHERMEN Numerical responses of the salmon g i l l n e t f l e e t The f l e e t of g i l l n e t boats f i s h i n g for salmon off the B r i t i s h Columbia coast is one of three components of a fishing f l e e t which has a great excess capacity. This was highlighted by the'recent Commission on P a c i f i c Fisheries Policy (Pearse 1982). A major topic for discussion within the f i s h i n g community and amongst managers i s the introduction of new management regimes to reduce the overcapacity through such mechanisms as buy-back and area l i c e n c i n g . For e f f e c t i v e management, attempts must be made to determine how the f i s h i n g f l e e t -moves within season along the B. C. coast, what determines these movements, and to predict how they w i l l change, esp e c i a l l y given known changes in f i s h abundance. In a s p e c i f i c fishery Hilborn and Ledbetter (1979) modelled the numerical response of purse seiners to prey density (salmon), but does th i s also apply to another group of predators - the g i l l n e t t e r s ? These boats p o t e n t i a l l y pose greater a n a l y t i c a l problems as there are two levels of switching involved; not only between species of salmon but between gears, as many are also licenced to t r o l l . A predictable numerical response to increases in CPUE is evident for 1979 to 1981 inclusive i . e. the number of g i l l n e t boats in a p a r t i c u l a r week i s related to the d o l l a r return per boat in the previous week (Figure 4). Does t h i s relationship hold for p a r t i c u l a r s i t e s or areas 31 Figure 4. Number of boats in week N + 1 vs landed value per boat in week N - 1979 to 1981 - B r i t i s h Columbia. 1979 - + ; 1980 - X ; 1981 - l> . N U M B E R O F B Q A T 5 W E E K N + 1 H> K ru ru a? o U1 o t/i o o o o o o o o o o o o ui.. o o 81 o o zz 33 along the B r i t i s h Columbia coast, and i f so how can i t be explained? Hilborn and Ledbetter (1979) put forward three hypotheses about mobility behaviour of the B. C. salmon purse seine f l e e t : T r a d i t i o n a l : fishermen f i s h in t r a d i t i o n a l patterns and the best prediction of e f f o r t for an area w i l l be the h i s t o r i c a l e f f o r t in that area. Coast wide equalization: fishermen w i l l move from area to area to maximize their catch, such that the CPUE in a l l areas approaches approximately the same value. Area s p e c i f i c d e s i r a b i l i t y : each area has i t s own unique costs and d e s i r a b i l i t y and fishermen w i l l move between areas so as to maintain the r e l a t i v e average CPUE's in the di f f e r e n t areas. Tr a d i t i o n a l or fixed movement patterns can be equated to a 'no learning' situation in other forgers or predators; there is an innate pattern of behaviour not modified by foraging success in any pa r t i c u l a r s i t e or area. Coast wide equalization of CPUE i s equivalent to a situa t i o n where foragers or predators i n d i v i d u a l l y move to maximize their energy return, with the result that the energy return for each area or s i t e per unit time i s approximately equal. In thi s case the individual forager or predator takes into account developments in adjacent areas i f such information i s a v a i l a b l e . Area s p e c i f i c d e s i r a b i l i t y occurs where s i t e assessment is influenced by d i f f e r e n t i a l costs of foraging e. g. there are 34 other factors such as the s i t e ' s exposure to other predators (or weather), or the degree of aggressive interaction with other foragers that must be taken into account (see Pyke et a l . 1977). Similar information from adjacent s i t e s i s also assimilated, i f available, to weigh the advantages of further movement. I analysed data from the B. C. salmon g i l l n e t f l e e t for the years 1979 to 1981 inclusive to see whether a predictable numerical response is evident and which of these three hypotheses best explained the aggregation of boats in particular areas at p a r t i c u l a r times. I then tested the r e l a t i v e value of these hypotheses as management tools. I discuss the possible mechanisms which affect the numerical response of the g i l l n e t t e r s and how thi s in turn modifies the outcome of any predict ion. 35 RESULTS AND DISCUSSION Types of movement As I was investigating the aggregation of fis h i n g boats in response to anticipated or observed f i s h abundance, I considered i t important to d i f f e r e n t i a t e between boats which moved between areas and those that did not. Like Hilborn and Ledbetter (1979) I lab e l l e d the former mobile, and the l a t t e r stationary. Stationary boats by implication react in d i f f e r i n g degrees to changes in f i s h density and hence CPUE by entering or leaving the fishery in their area or s i t e only . Mobile boats are influenced by the q u a l i t i e s of other areas and move accordingly. The'number of boats fi s h i n g each major area from 1979 to 1981 are outlined in Table I I I . The t o t a l number of boats fishing in any one year averaged about 2500, of which approximately 30% fished only one area. The greatest proportion of stationary boats was in the Fraser River, where the r a t i o of stationary to mobile was 1:2.5. This contrasts with the Nass (Area 3) where the r a t i o was 1:200. Fixed movement patterns Although fixed movement patterns by f i s h i n g boats implies a situa t i o n where there is no learning, and much information is ignored, a number of constraints may reinforce such a pattern and the degree of 'no learning' i s r e l a t i v e . The constraints include regulatory a r t i f a c t s such as h i s t o r i c a l sequences of 1979 1981 TABLE III BREAKDOWN OF GILLNET BOAT TYPE BY AREA NUMBER OF EACH BOAT TYPE FISHING EACH MAJOR AREA Area 8 12 20 23 29 a l 1 + s t a t s * mob i1e 1980 a l 1 + s t a t s * mob i1e 629 3 626 712 13 699 793 42 751 900 28 872 500 1 1 489 84 1 12 829 603 17 586 640 27 613 455 23 432 878 59 819 279 16 263 329 7 322 353 24 329 618 59 618 1253 462 790 1053 321 732 a l 1 + s t a t s * mob i1e 636 16 620 1001 84 917 581 6 575 655 25 .630 698 78 620 217 15 202 635 88 547 1 141 316 825 TOTAL NUMBER OF GILLNET BOATS BY TYPE BY YEAR Year 1979 1980 1981 a l 1 + s t a t s * mob i1e 2345 677 1668 2570 680 1890 264 1 793 1848 + a l l = mob i l e p l u s s t a t i o n a r y v e s s e l s ( i . e. tha t s e a s o n ) . * s t a t s = s t a t i o n a r y boa t s For an e x p l a n a t i o n of a r e a numbers see T a b l e II a l l v e s s e l s f i s h i n g a r e a 37 openings. Other positive reinforcements are natural cycle patterns of salmon abundance which leads to anti c i p a t i o n of the run size, strengthened in turn by pa r t i c u l a r good or bad years in that area. The hypothesis that fishermen move according to t r a d i t i o n a l patterns i s supported by anthropological data in other f i s h e r i e s (Andersen and Wadel 1972), and in the B. C. salmon purse seine f l e e t (H. Hsu, quoted in Hilborn and Ledbetter 1979). However, such an idea i s d i f f i c u l t to test s a t i s f a c t o r i l y except by exclusion of other hypotheses. Also a data set covering many years i s required to predict trends and these trends are l i k e l y to be confounded by changes in regulations. The sytematic base for t h i s hypothesis i s poor, so i f unexpected changes do occur i t i s often d i f f i c u l t to i s o l a t e the reason, e. g. such as the regulation changes. From the data set i t i s possible to examine the relationship between fis h i n g areas and determine the pro b a b i l i t y that a boat f i s h i n g one area w i l l also f i s h another area. This was calculated by dividing the t o t a l number of boats which fished a p a r t i c u l a r area at some time during the year into the number which also fished in another p a r t i c u l a r area that year. The p r o b a b i l i t y that a boat f i s h i n g one area in 1979 w i l l f i s h another area that year i s given in Table IV (for 1980 & 1981 p r o b a b i l i t i e s see Tables V & VI res p e c t i v e l y ) . Unfortunately I did not examine data covering a four year time span or longer so that a comparison could be made between years of dominant sockeye salmon runs, where, for example I would have AREA TABLE IV PROBABILITIES THAT BOATS FISH CERTAIN AREA PAIRS 1979 31 11.0 1 1 1 1 1 1 I ! 1 1 1 1 ! 1 I 1 1 1 1 1 1 1 1 30 1 1. 01 1 1 1 1 t 1 1 1 1 1 1 1 1 1 1 1 1 1 1 I 1 29 1 11. 01 * I • 1 I 1 • . 21 1 i 1. 171 1 * . 01 . 01 • 1. 03 . 091. 191 041. 031. 131. 191 141. 03!. 081. 181. 121 1. 01 28 1 1 1 0 1 1 . 01 1 1 1. 0! i 11. 01 1 ! 1. 011.01 1 11. O i l . 01 I 1 11.011.01 1 27 1 1. 601 1. 0! 1 1 . 101 ! ! : 1 1. lO 1. 30 i . 201. 101. 201. 30! 101 1. 101. 101. 101 I 26 1 : i 1 1 1 1 1 1 1 ! I 1 ! i . : ! 1 ! I ! I 1 23 1 t i 1 1 1 t ! I 1 1 1 1 1 1 i 1 1 1 1 1 1 1 24 ! 1 1. Oi I 11.0 1. 01 1 !. 40! 1 1. 20 ! 1 ! 1 i . 20! i . 2 0! 1. 201 1. 20! 23 i !. 74 1 • 1 * : 1 !. 01 1.01 i 1. 43! 1 * . 01 1. 12 . OBI. 19!. 111. 07!. OS!. 231. 181. 041. 02!. 131. 03 1. 01 1. 01 22 1 1 1 1 1 1 1 1 1 ! I 1 1 1 1 1 1 ! I I 1 I 21 1 1 1 1 1 t i 1 1 1 I 1 i 1 1 1 I 1 1 I 1 1 ! 20 1 1. 761 • j 1 1. 01 . 331 1 11. 01 1 . 01 * 1. 01 ! . 081. 181 . 061. 031. 09 ! . 241 191 . 031. 041. 16!. 08! 1 19 1 1 I 1 i i : 1 1 1 ! I 1 I I I 1 1 I I I 1 1 18 ! 1. 601 1 ! . 201 1 !. 40! 1 1. 0 1 i 1 1 1 1. 201 1 ! i I 1 I 17 1 1. 831 1 1 . 231 1 1. 08! 1 1. 01 !. 08! .231. 13! 1 ! ! 1 1 1 1. 23! 1 1. 08 16 1 11. o: 1 1 : i I ! 1 1. 0 1 !. 371. 291. 43! 1. 431 14! 1 1 1 I ! IS 1 1. 30! 1 1 i i 1 1 1 1. 0! .731. 23!. 23! !. 30!. 301. 23! I 1. 231 ! 1 14 1 1. 701 . 01 1 t 1. 01 .441 : !. 291 1 . 01 11. 01 .221. 37!. 161. 07!. 14 !. 281. 151. 071. 081. 22!. 121 1. 02 13 1 I. 361 • 1 1 1 . 131 1 1. 10! 1 1 . 01 1 . 01 1. 101 1. 01. 48!. 061. 06!. 191. 271. 22!. 10!. 081. 26!. 21 1 1 • 12 1 !. 321 * 1 . 01 1 1 1 .131 1 1. 11 1 1 » . 011 « 1. 081 . 22! 1 .01. 171. 18!. 27 !. 321. 36!. 131. 131. 32!. 2 3 ! 1. 01 11 1 1. 481 . 021 1 t . 331 1 1. 131 1 . 021 . 01 1. 141 .111. 6611. 01. 471. 421. 84!. 47!. 03!. 031. 21 1. 111. 011 10 1 1. 331 . 01 1 t 1 .201 1 !. 071 1 .021 1. 03! . 101. 661. 4311. 01. 31 I. 731. 231. 01 1. 021. 181. 091. 011 9 1 i . 341 • 1 . 01 1 1 i . lOI t 1. OBI 1 ! . 01 1. 031 . 141. 421. 17!. 2211 .01. 8BI. 291. 041. 031. 121. 0 7 ! « 1 8 1 1. 40! • 1 • 1 1 • . 141 1 1.11! 1 • ! • « 1. 041 . 10!. 391. 161. 13!. 4211 . 01. 481. 091. 071. 291. 181 • 1 1 . 0 7 1 1. 361 • 1 1 . 131 1 1. I l l 1 • I » 1. 031 . 10!. 321. 111. 06!. 17!. 3811. 01. 181. 22!. 321. 39 I • 1. 20 6 1 1. 341 1 1. 01 . 071 1 1. 031 1 1. 04! . 121. 311. 031. 01 1. 071. 311. 491 1. 01. 281. 901. 721. 011. 02 3 1 i . 27 t • S t I . 021 1 I. 031 1 1. 021 .031. 16!. 021. 01 I. 02!. 12!. 301. 1411. 0!. 98!. 84! » I. 04 4 1 1. 271 • 1 • I 1 1 • . 06! 1 1. 031 1 1 • 1 » 1. 02! .071. 17!. 031. 03 i . 04 1. 211. 311. 191. 43! 1. 01. 771. 011. 03 3 1 1. 241 • 1 • 1 1 t . 03( 1 I. 031 1 1. 02 .071. 171. 021. 021. 031. 171. 31 I. 21 1. 49!. 9711. 01. 01 1. 03 2 1 1. 221 1 1.11 . 221 1 I 1 1 1 1. 111. 111. 111. 111. 221 221. 111. 111. 361. 3611. 01 1 1 1. 381 1 1 . 091 1 1 ! 1 . 03 1. 06 .06!. 161 1 I 1. 131. 231. 131. 411. 731. 691 11. 0 31 30 29 28 27 26 25 24 23 22 21 20 19 18 17 16 13 14 13 12 11 10 9 8 7 6 5 4 3 2 1 AREA I f a boat f i s h e s an a r e a l i s t e d a l o n g the s i d e , the f i g u r e s g i v e n a r e the p r o b a b i l i t y t h a t i t m i l l a l s o f i s h an a r e a l i s t e d a l o n g t h e base. • •= p r o b a b i l i t y l e s s t h a n .01 F o r an e x p l a n a t i o n of a r e a numbers see T a b l e I I . 00 CO AREA 31 11.0 TABLE V PROBABILITIES THAT BOATS FISH CERTAIN AREA PAIRS - 1980 !. 04 1. 02! 1. 02!. 02!. 02!. 02! , 02! I. 02! i . 0 2 : . 02 : I. 021. 021 1. 02 i . 021. 02 i . 02 1. 021. 02 30 I 11. o : I I ! I I I I I I I 29 i 28 11.0 27 I 11. 01 » I • I i i . o n . o i : I. 06! . 021. 331. 09! I.021 I I . O i l . O i l . 011.01 I I . O! 1. 03!. 03 I .911. 06 t. 30 I . 06 1. 07 1. 03 1. 24 I . 191. 08 I . 021. 181. 121. 021.10 11.01 11.011.01 11.011.01 I 1. Oi 1. O i l . O i l . 011.011.0 I. 301 II. 01. 301. 381 30!. 231 I » 1. 301. 38 11. 01. 381. 381 I. 38! . 63! . 13! I. 23! . 381 I. 13 26 I 23 1.01 I. 071 I. 0711. 01. 401. 031. 40S. 15! I. 121 I. 021 I. 271 I. 631. 301. 271. 021. 3BI. 431. 131. 131. 23!. 201. 071. 13 I. 391. 01 I. 021. 1311.01. 131.711. 281 I. 431 I. 021. 02! I. 221. 14!. 03!. 121. 121. 02 1. 181. 24 1. 121. 021. 181. 14 1. 031. 08 24 1.01 I. 311. 01 I I. 041. 341 1. 01. 80!. 171 1. 241 I. O i l . O i l . 03!. 11 I. 21 1. 071. 071. 03 1. 111. 141. 061. 01 1. 201. 13 1. 031. 03 23 I * 22 1.01 21 I 20 I • 19 I IB I 17 03 16 I 13 I 14 I 13 I • 12 I « 11 ! 10 I 9 I. 01 8 I * 7 I 6 I • 3 I » 4 I « 3 I • 2 I • I I * I. 601 I. 60!. « I, 01 I. 01 I . 01 I. 041. 06!. 191 281 09 I I . 01. 22 I 07 1. 83!1. O! I . 381 I. 67 1 » I . 03 1. 02 ! .03! * ! • I. 12!. 16!. 291. 08!. 09 1. 03 1. 21 1. 221. 08! . 02 1. 02!. 131.03!. 07 21 I. 20!. 331. 131. 14!. 02 i. 20 i . 30 i . 13!. 03!. 261. 20!. 041. 07 I I 1. 6BI I 33! I « I. I I 04!. 01 I I I _ I !. 231. 161. t. 021. 221. 031. 721 ! 1. O! . 21 S. 20!. 32 121 I 12 S. 04 I. 231. 23!. 11 !. 04 04I. 10 I 171 I. 831 I. 301 11171 1. 34! I. 17! . 17! . 37!. . 171 I . 34!. 14! I.97! . I. 871 03! I 03 1. 49 I. 031. 261. 14! 031 1. 01. 091 . 0811. 01 . 01 i . 031 17! 26! 471 111. 171 161.111 . 06 I. . 081 . 06! 03! 171. 111. 161. 131. 09! . 20 03 1. 26 031. I OBI 1^11 I I I 61 I. 30! 161 1 321. 181 301 I 11.01 !. SO I I 021 i l l 0 1 01 I. 41 I. I. SOU. 01 .031.041 I . 021. 031 . 01 I. 031 . 031. 041 . 02!. 03! . 071. 04! . 02!. 021 . 01 I. 011 • I. 01 I .011 » I . 01 !. 01 ! . 01 I. 01! .011.01! . 031. 041 II . 01 1.311 ! I 131. 071 I 071. ! I 331. 291 09! 20! 061 20! 08! I. 391 I. 181 1. O . 15 . 51 I . 57! 22! 13! 331. 401 411. 451 16! .081. 17 !. 451 I. 35! » I. « I. 01 I 01 !. 0 2 i _ ? 7 ! 02!. 21 I . 01 I• i 1. 0 . 25 141. 081 !. 06! . 23! . 24! .OBI. . 101. 401. 321 421. 32! .031. 13 .071. 13 041. 121. 061 13! 061 14! I. 1 2| 1. 25! 10! 25! 1. O! . BO I . 141 1. 01 471. 53! 701. 541 1. 401 !. 441 .021. .021 031. 321 031. 321 321 311 . 571. 181 1. 0!. 24! . 141. . 101. 061 06! 39!. 34 I 261. 19 1 L 0 2 l . 10 . 01 1. 09 . 021 04 . 03 1. 09 09!. 01 I. I l l 031 131 03! ! L 2 2 I I. 161 16! 131 811. 61 I. 31 I 31 I .761. 37 I .911. 33! I. S8I. I. 391 01 I » I 021. 21 I 01 !. 21 ! 331 24! 4711. 01 21 !. 131 171 22! .041. .031. 271. 1 1 ! 321. 21 ! 04!. 031. OS! 051 05! 061 I. 12! I. 10! 091 091 641 331 171 101 1.0!. 621 . 4711. OI I. 241 I. 1BI . 01 I • I O i l . 17! O i l . I l l 21 I 20! 081. 04 1 04!. 03 I . 38! 1. 01 . 131. .281. 611. SOI. 82!. 631. 161. 13 26!. 17 021. O i l . 041 02! 06! 031 !. 08! 1. 05! 07! 051 48!. 341. 051 04! 321. 73 I 13!. 301 1. 081 I. 21 ! * I * I 1. 051 02!. 15! 121 171 04 1.02 I 051. 031 471 40! 1. O! . 27! .931. 70 I 1.01. 71 I .J32\. IB . 21 I. 16 « I. » I. 02!. 021. 03! 031 05! 04! !. 09! I. 08! 071 07! 41 i . 40!. 07! 071 221. 571 191. 59! I. 18! I. OBI 1. 47! • I # I 021.11! O i l . 07! 01!. 181 171 08! 221 05!. 01 I 01 I. 01 I 07 1. 02 I 391. 331. 34 i . 251 331 20! 9011. O I 74 1. 69 I 62 I . 34! . 241. 17 1J5I. 13 . 1611. 0 021 04! 021 06! 031 03! I. 031 1.14! 061 13! 23!. 49! . 01 I 07! 081. 34! 23!. SOI I • I 31 30 29 28 27 26 25 24 23 22 21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 AREA I f a boat f i s h e s an area l i s t e d a l o n g t h e s i d e , the f i g u r e s g i v e n a r e t h e p r o b a b i l i t y t h a t i t w i l l a l s o f i s h an a r e a l i s t e d a l o n g t he base. • - p r o b a b i l i t y l e s s than .01 Fo r an e i p l a n a t l o n of a r e a numbers see T a b l e I I . PROBABILITIES THAT BOATS FISH CERTAIN AREA PAIRS - 1981 AREA 31 I I I I I I _ J _ I I I I I I I J l I I I I I I I _ I I I I I I I I I 30 I : I I I I I I I I I I I I I I I I I I I I I I I I I I I I I 29 I I 11.01 I I. 011. 041. 021. 331 I 1.13! » I « 1.011.231 1. 29!. 071. 281. 041. 131. 131. 231. 161. 031. 021. 22!. 14 1. 02! T l 4 28 I I I I I I l_ I I I ! I ! I I I I I I I I I I I I l ~ I I 7 ~ T 7 27 I I I I I I I ! I I I I ! I I I I 1 * 1 I » I I I I I I • I * I • I • I 26 I I 1.261 I 11.02.041 1.311 I 1.411 I t 1.221 1. 37!. 111. 691. 261. 261. 111. 261. 281. 201. 041. 431. 28 1.117 28 23 I I 1.361 I I. 0211. 01. 061. 631 I l._44l I . O i l . O i l 271 1. 361. 101. 27 1. 071. 111. 131. 131. 10~ 1021. 041. 221. 12 1. 211. 16 24 I I 1.691 I I 1711. 01. 72! I 1.241 I I.071.071 I. 661. 171. 661. 141. 141.211. 171. 171. 141. 031. 34 1. 24 I 1.28 23 I I 1.621 I 1. 031. 081. 0311 01.011 I 231 » I • I.021.231 1.411 071. 291. 051. 141. 15 1. 19 1. 141. OS 1. 021. IB 1. 10 1.021. 10 22 I I I I I I I I I 11.01 I I I I I I I I I I I I I II.OI I 11.011.01 11.0 21 I I 1 1 I I l_ I I I I I I I I I I I 1 I I I I ! I I I I 1 I I 20 I I 1.671 I I. 031. 171. 031. 741 I 11.01 • I • I . O i l . 3 0 1 1. 301. 071. 281. 061. 111. 12 !. 171. 101. 04 1. 03!. 221. 13 1. 011. 12 19 I I 11.01 I I I I 11.01 I 11.011.01 11.011.01 I I . O i l . 011.0! I I I I 11.01 11.0! I I . 011.0 18 I I 1.671 I I 1.331 1.671 I 1.331 11.01 1.331 ! 331 1.671.331.331.331.33! I I I 1 I I 17 I I 1.711 I I I. 061. 121. 391 I I. 181.061 11.01.331 1. 331. 291. 63 I 1. 41 1. 3S 1. 351. 061. 06 1. 06!. 24 1. 06 1. 121. 24 16 I I 1.841 I I. 041. 071. 011. 47! I 1.211 » I • I.0211.01 1. 46!. 211. 311. 081. 231. 221. 281. 191. 091. 041. 331. 221. 041. 23 13 I I I I I I I I I I I I | I I I I I I I I I I I I ! I I I I I 14 I I 1.361 I. 01 I. 031. 081. 031. 441 I 1.181 • I • I . O i l . 2 4 1 11. 01. 14 1. 421. 081. 191. 21 1. 34 1. 271. 091. 061. 331. 23 1. 031. 14 13 I I 1.491 I I. 031. 031. 031. 271 I 1.091.011 I.031.371 1. 4611.01. 621. 031. 241. 291. 411. 241. 131. 031. 391. 28'". 031.17 12 I I 1.461 I • I. 031. 031. 031. 271 I 1.091 • I • I. 021. 221 1. 331. 1311. 01. 11 1. 311. 33 1. 481. 31 1. 101. 041. 371. 23 1. 031. 14 11 I I 1.471 I I. 191. 061. 041. 331 I 1.131 I . O i l 1.231 1. 31!. 091. 8 2 I I . 01. 331. 34 1. 381. 321. 061. 061.411. 26 1. 07 1. 19 10 I I 1.331 I I. 031. 031. 01 I. 32! I 1.091 I • 1.031.231 1. 411. 131. 771. 1811.01. 661.721. 321. 031. 031.131. 061. 021. 06 9 I I 1.331 I I. 021. 041. 021. 291 I 1.081 t • I. 021. 201 1. 371. 131. 731. 161. 3311. O 1. 721. 361. 061. 031. 261. 13 1. 021. 09 8 I I 1.441 I I. 021. 021. 011. 191 I 1.061 I • I . O i l . 131 1. 301. 111. 321. 091. 301. 3711. 01. 351. 101. 03!. 421. 27 1. 031. 09 7 I I 1.31! I I. 031. O i l . O i l . 151 • I 1 0 4 1 I I • i.101 1. 271. 07 1. 371. 03!. 131. 21 1. 6211. OI . 21 I. l O I . 661. 47 1. 131. 14 6 I I 1.231 I • I. 04!. 01 I. 01 I. 11 I I 1.031 • I I • I. I l l 1. 191. 081. 231. 021. 06!. 08 1. 24 1. 431 1. 01. 331. 931. 60 !. 191. 16 3 I I I. I l l I I. O i l . O i l 1.061 I 1.041 I I • 1.031 I. 151. 041. 131. 031. 031. 041. 141. 241 . 3811. 01. 971. 641. 231. 13 4 I I 1.251 I • !. 021. 021. 011. 121 • 1 1.03! » I I • I. 101 1. 201. 071. 261. 041. 041. 09 1. 271. 381 . 231. 231 1. 01. 61 1. 191. 13 3 7 I 1.231 I • I. 021. 021. 01 I. 101 • I 1.051 I I • I. H I 1.211. 08!. 261. 041. 031. 07 1. 281. 43!. 261. 241. 9611. 01. 221. 16 2 7 I 1.131 I I. 0 3 1 . O i l 1.061 I 1.01! • I I . O i l . 0 6 1 1. 141. 04 I. IS 1. 031. 031. 03 1. 151. 331. 24 !. 261. 871. 6411. 01. 21 1 7 7 7757! I I. 061. 051. 031. 23! » I 1.101 » I 1.021.271 1.311. 111. 36!. 071. 071. 121. 221. 301 . 171. H I . 361. 39 1. 171 1. O I f a b o a t f i s h e s an a r e a l i s t e d a l o n g t h e s i d e , the f i g u r e s g i v e n a r e the p r o b a b i l i t y t h a t i t w i l l a l s o f i s h an a r e a l i s t e d a l o n g t h e base. • - p r o b a b i l i t y l e s s t han .01 F o r an e x p l a n a t i o n o f a r e a numbers see T a b l e II. 31 30 29 28 27 26 25 24 23 22 21 20 19 18 17 16 13 14 13 12 11 10 9 8 7 6 3 4 3 2 1 41 expected very similar p r o b a b i l i t i e s of fi s h i n g certain area pairs, in response to anticipated f i s h numbers. Consider the relationship between boats fi s h i n g area 8 (Bella Bella) and the immediately surrounding productive areas (Table VII). The averages for the immediately adjacent Bella TABLE VII PROBABILITIES OF FISHING AREA PAIRS - BELLA P r o b a b i l i t i e s of Year fis h i n g area 8 1979 1 980 1981 If f i s h area 4 0.29 0.22 0.48 If f i s h area 7 0.58 0.47 0.62 If f i s h area 12 0.52 0.47 0.27 Coola area (Area 7) are r e l a t i v e l y constant, and owe more to immediate geographical proximity than run strengths in any p a r t i c u l a r year. The probabilites for areas 4 (Skeena River) and 12 (Johnstone S t r a i t s ) show no consistent pattern and may i l l u s t r a t e the need for a longer time series of data, especially for 1978 where sockeye and pink salmon catches were high in a l l four areas, and were higher than any of the following years (Canada 1981). This type of approach contains much information about gross area relationships e. g. i f an area licencing scheme was being considered, but is not p a r t i c u l a r l y useful in predicting week to week boat numbers. Such an approach may be possible by following sequential movement of a substantial subsample of boats over the weeks for a number of seasons, but would not 42 explain these movements. This type of approach does have larger scale long term benefits, as i t supplies useful information about the dependence of boats on p a r t i c u l a r areas and area combinations i f , for example, area licencing schemes needed to be implemented. Maximizing individual boat return If each fisherman i s foraging ( i . e. moving) to maximize his return in a p a r t i c u l a r week then the net ef f e c t should be movement between areas or s i t e s u n t i l the returns in every area over the medium term are approximately equal. That i s , fishermens' movements along the coast and their aggregation in areas at-certain opening times should lead to CPUE in a l l areas tending towards a province wide average. If fishermen are purely economic maximizers rather than responding in t r a d i t i o n a l fishing patterns they would respond quickly to varying lev e l s of CPUE along the B. C. coast. One observation which should r e f l e c t t h i s i s that movement to, or entry into, an area increases with increasing CPUE. If I assume that knowledge of the previous weeks' CPUE from an opening in an area i s f a i r l y widespread, I would infer that the number of boats f i s h i n g an area in the week immediately after a certain catch would be related to the CPUE of that previous week. This can be explored by regressing the two variables against each other. As I showed in Figure 4, t h i s trend of increasing numbers with increasing CPUE i s c e r t a i n l y evident for a l l g i l l n e t t e r s on the B. C. coast in any one year. 43 On a smaller scale t h i s approach was also used for the Georgia S t r a i t commercial t r o l l fishery with data from a 10 year period (Ague et a l . : 1983), where the relationship between CPUE and subsequent boat numbers was very strong, as well as for purse seiners in eight amalgamated areas on the B. C. coast, where the f i t was highly variable between years (Hilborn and Ledbetter 1979). In mobile g i l l n e t boats t h i s relationship should be achieved by more boats moving in from surrounding areas. For stationary g i l l n e t boats this means more of them enter the fishery in their area, either by st a r t i n g to f i s h for the season or by switching from t r o l l i n g . When the CPUE from one week was regressed against the number of mobile boats f i s h i n g in that area the subsequent week, the results were mixed. For example, the slope of the regression l i n e for area 29 (Fraser River) was s i g n i f i c a n t l y d i f f e r e n t from zero (p <0.05) in 1979 (Figure 5) and 1980 but not in 1981 (Tables VIII and IX). In comparison, the rel a t i o n s h i p for area 4 (Skeena River) was not s i g n i f i c a n t in 1979 (Figure 5) or either of the following years. A similar regression analysis for the mobile boats for a l l years in a l l areas indicated that only in area 8 (Bella Coola) did the expected pattern hold for a l l three years (Table VIII). Extending regression analysis to stationary boats and then a l l boats combined was s i m i l a r l y unsuccessful in predicting movement. Only in area 8 were a l l the slopes of the regression 44 F i g u r e 5. Number o f m o b i l e b o a t s i n week N + 1 v s l a n d e d v a l u e p e r b o a t week N - 1979 - a r e a s 4 & 29. A r e a 4 -Skeena R i v e r - + ; A r e a 29 - F r a s e r R i v e r - X . BOO* ^  h 1 1 1 1 1 1 1 1 1 1 0. 500. 1000. 1500. EOOO. E500. 3000- 3500. 4000. 4500* 5000. V A L U E - B O A T I N WEEK N CS) TABLE VI I I NUMBER OF BOATS IN WEEK N + 1 VS LANDED VALUE PER BOAT IN WEEK N SUMMARY TABLE OF SIGNIFICANT REGRESSIONS Area A l l M o b i l e S t a t i o n a r y boa t s boa t s boa t s 3 No ( 1 ) No (0) No (0) 4 No (0) No (0) No ( 1 ) 7 No ( 1 ) No ( 1 ) No (0) 8 Yes Yes Yes 12 No ( 1 ) No (0) No ( 1 ) 20 • No (0) No (0) No (0) 23 No ( 1 ) No (0) No ( 1 ) 29 No (2) No (2) No (2) The f i g u r e s in b r a c k e t s a re the number of y e a r s in which the s l o p e of the r e g r e s s i o n l i n e f o r number of boa t s i n week N + 1 vs l anded v a l u e in week N i s s i g n i f i c a n t l y d i f f e r e n t from z e r o at p < 0 .05 . Thus : Yes = s l o p e s i g n i f i c a n t l y d i f f e r e n t from z e r o i n a l l t h r e e y e a r s No = s l o p e not s i g n i f i c a n t l y d i f f e r e n t from z e r o i n a l l t h r e e yea r s For a more d e t a i l e d e x p l a n a t i o n see the t e x t . For an e x p l a n a t i o n of the a r e a numbers see T a b l e II. For d e t a i l e d s t a t i s t i c s see T a b l e IX. 47 TABLE IX NUMBER OF BOATS IN WEEK N + 1 VS LANDED VALUE PER BOAT IN WEEK N REGRESSION STATISTICS & CORRELATION COEFFICIENTS Area Boat Type Mobile Stationary Combined 1 979 3 1 .00 1 .00 1 .00 (0.06) -(0.01) (0.06) 4 0.29 0.03 0.28 (0.27) (0.73) (0.27) 7 0.02 1 .00 0.01 (0.48) (0.02) (0.51) 8 <0.01 0.04 <0.01 (0.52) (0.30) (0.52) 1 2 1 .00 1 .00 1 .00 (<0.01) (<0.01 ) (<0.01) 20 1 .00 1 .00 .1.00 (0.10) • (0.12) (0.08) 23 1 .00 1 .00 1 .00 (<0.01) (<0.01 ) (0.04) 29 <0.01 <0.01 <0.01 (0.64) (0.58) (0.69) 1 980 3 1 .00 1 .00 1 .00 (0.09) (0.05) (0.09) 4 0.35 0.35 1 .00 (0.43) (0.42) (0.43) 7 1 .00 1 .00 1 .00 (0.01) (0.09) (0.01) 8 <0. 01 <0.01 1 .00 (0.59) (0.44) (0.60) 12 1 .00 1 .00 1 .00 (0.07) (0.04) (0.08) 20 0.28 1 .00 1 .00 (0.13) (0.08) (0.16) 23 0.29 <0.01 1 .00 (0.10) (0.47) (0.08) 29 <0. 01 <0.01 <0.01 (0.43) (0.47) (0.49) P.T.O. 48 TABLE IX (continued) Area Boat Type Mobile Stationary Combined 1 981 3 .07 (0.52) 4 .07 (0.60) 7 1 .00 (<0.01) 8 <0. 01 (0.82) 1 2 . 1 3 (0.19) 20 1 .00 (0.04) 23 .06 (0.33) 29 1 .00 (0.04) 1 .00 .06 (0.09) (0.53) .09 .07 (0.56) (0.60) 1 .00 1 .00 (0.10) (<0.01) <0.01 <0.01 (0.64) (0.83) <0.01 .09 (0.55) (0.24) 1 .00 1 .00 (0.04) (0.04) 1 .00 .09 (<0.01) (0.28) 1 . 00 1 .00 (0.09) (0.06) ( ) = cor r e l a t i o n c o e f f i c i e n t r 2 These are the p r o b a b i l i t i e s that a linear regression does not account for number of boats appearing in the week after a certain catch For an explanation of the area numbers see Table I I . 49 l i n e s s i g n i f i c a n t l y d i f f e r e n t from zero in a l l years for mobile and stationary boats and for both types combined. These poor f i t s were caused by large deviations from the the expected number of boats the next week. These w i l l be discussed in more d e t a i l l a t e r where I attempt to analyse how good the method i s as a predictive t o o l . However, examination of the data from stationary boats did indicate some sort of saturation curve, as found by Hilborn and Ledbetter (1979). There i s a l e v e l of perceived return to the fishermen which must be exceeded before he i s w i l l i n g to forgo alternative employment opportunities or shoulder the cost burden of going f i s h i n g . This saturation curve for some of the areas is i l l u s t r a t e d in Figure 6 for 1979. It i s apparent however, that the CPUE at which saturation occurs varies s i g n i f i c a n t l y between areas. In addition t h i s CPUE le v e l varies for the same area in d i f f e r e n t years. For example the Fraser River has a large number of stationary boats p a r t i c i p a t i n g in the fishery (Table I I I ) . There i s a large 'reservoir' of fishermen within the Vancouver area who g i l l n e t only during the Fraser River openings. The CPUE saturation l e v e l of stationary boats varies considerably, with $200/week att r a c t i n g 450 boats in 1979, but only 220 in 1981 (Figure 7). This type of relationship may be modified by how good the season has been for t r o l l i n g in the combination boats, or alt e r n a t i v e employment opportunities, e s p e c i a l l y in the Vancouver area. Bella Coola (area 8) i s characterized by a central location 50 F i g u r e 6. Number of s t a t i o n a r y b o a t s i n week N + 1 vs l a n d e d v a l u e p e r b o a t i n week N - a r e a c o m p a r i s o n s . A r e a 4 -Skeena R i v e r - + ; A r e a 7 - B e l l a B e l l a - X ; A r e a 12 -J o h n s t o n e S t r a i t s - * ; A r e a 20 - Juan de F u c a - < A r e a 23 - B a r k l e y Sound - . SO' ^  40- I V A L U E - B O A T I N WEEK N ( $ ) 52 Figure 7. Number of stationary boats in week N vs landed value per boat in week N + 1 - Fraser River. 1979 - + ; 1980 - X ; 1981 - p . 500- 1000. 1500 E000. E500. 3000- 3500- 4000. V A L U E - B O A T I N WEEK N CS) C O 54 (Figure 1) and early openings. Movement into this area nearest approaches the 'ideal' conditions necessary for the hypothesis, i . e. there i s high probability that boats based in adjacent areas w i l l f i s h area 8 (Tables IV, V and VI). This may explain why, of a l l areas, the prediction holds here for a l l boat types for a l l years (Figures 8 and 9) Overall therefore, neither an approach regressing catch and numbers or one determining the saturation l e v e l for an area appears sat i s f a c t o r y in predicting e f f o r t in a pa r t i c u l a r area. This i s in marked contrast to the very good relationship obtained for the whole province (Figure 4). I examined the trends in CPUE over the whole fi s h i n g season to see i f the CPUE in each area was about the same in any one week. This 'is dependent on the cost to movement at least to adjoining areas being small, the boat in question coping with the conditions in a l l areas, and the information about catches in adjacent areas being good. Figure 10 indicates the l e v e l of e f f o r t in boat numbers each week of the year in several of the main areas in 1979. Although small numbers of boats do par t i c i p a t e in f i s h e r i e s in several areas within a week, the figures are a good guide to the to t a l number of active boats along the coast in a pa r t i c u l a r week. The timing of the f i s h e r i e s are well d i s t r i b u t e d over the year, allowing great opportunities for boats to move. This is demonstrated for example in areas 4, 23 and 29. Similar patterns were observed for 1980 and 1981, and the t o t a l number of boats g i l l n e t t i n g in a pa r t i c u l a r week i s very similar from 55 Figure 8. Number of mobile boats in week N + 1 vs landed value per boat in week N - Bella Coola. 1979 - + • 1980 - X ; 1981 - £> . NUMBER OF BOATS WEEK N+l 57 Figure 9. Number of stationary boats in week N + 1 vs landed value per boat in week N - Bella Coola. 1979 - + ; 1980 - X ; 1981 - t> . NUMBER OF BOATS WEEK N+l 89 59 F i g u r e 10. Number of b o a t s - 1979. A r e a s 3 t o 8 20 - $ ; A r e a 23 - + ; o p e r a t i n g i n e a c h a r e a e a c h week as numbered. A r e a 12 - o ; A r e a A r e a 29 - X . 60 o "co UJ uJ 5 " C M _o M o CM o o o o o CO o o CO —I— o o o o CM G i v o e J O uaswnN 61 year to year (Figure 11). There i s considerable contrast in e f f o r t between areas caused by boats moving in response to actual or anticipated changes in f i s h numbers, but these do not aff e c t the area differences in CPUE over the year and between years (Figure 12, Table X) , and some areas appear consistently to have higher CPUE's than others e. g. area 4 vs area 29. Assuming that the underlying d i s t r i b u t i o n of CPUE for the major areas i s e s s e n t i a l l y normally d i s t r i b u t e d I undertook a two way analysis of variance of the average CPUE for each area in each year (Table X). I found that although there i s no si g n i f i c a n t difference between years (p < 0.05) the differences between areas in any one year are s i g n i f i c a n t l y d i f f e r e n t . This implies that there are other factors operating in addition to attempts by the fishermen to maximize their return per unit time by moving between areas along the coast. D i f f e r e n t i a l costs and benefits between areas Every area may have unique features of location, weather etc. which makes i t p a r t i c u l a r l y desirable and compensates for the costs and conditions experienced. This is the area s p e c i f i c d e s i r a b i l i t y hypothesis put forward by Hilborn and Ledbetter (1979). The CPUE from each area tends to an average over the years which i s r e l a t i v e l y constant with respect to both the pr o v i n c i a l average and every other area. I have la b e l l e d t h i s constant proportion the RPA. The RPA for the areas' CPUE r e l a t i v e to the pr o v i n c i a l average i s i l l u s t r a t e d in Figure 13. Although the RPAs do vary, p a r t i c u l a r l y for some of the 62 Figure 11. Total number of boats operating 1979 to 1981. 1979 - +; 1980 - X ; 1981 - o . NUMBER OF B O A T S (x1000) 64 F i g u r e 12. V a l u e o f c a t c h p e r b o a t p e r week i n e a c h a r e a -1979. A r e a s 3 t o 8 as numbered. A r e a 12 - o ; A r e a 20 - $ ; A r e a 23 - + ; A r e a 29 - X . C A T C H V A L U E PER B O A T ($) TABLE X AVERAGE CPUE IN EACH ($/WEEK) AREA EACH YEAR Area Year 1979 1 980 1 981 3 1 067 1 537 1 201 ' 4 2792 1 447 2551 7 1604 1 366 1 164 8 1230 1 260 1 402 1 2 875 887 1 482 20 1 21 5 1 229 2097 23 1226 1 008 1 364 29 1 129 510 960 Pro v i n c i a l average 1 582 1 225 1700 For an explanation of area numbers see Table I I . 67 F i g u r e 13. RPA by a r e a from 1979 t o 1981. A r e a 3 - + ; A r e a 4-4 ; A r e a 7 - t> ; A r e a 8 - , ^ ; A r e a 12 - <J ; A r e a 20 - X ; A r e a 23 - > ; A r e a 29 - ^ . 68 69 mid-coast locations, the averages appear to stay in the same order or rank. When the co r r e l a t i o n between the rankings between areas was compared between years using Spearman's Rank Correlation c o e f f i c i e n t , t h i s supposition did not hold up (p < 0.05). Notwithstanding, the contrasts between the Skeena River (Area 4) and Fraser River (Area 29) are p a r t i c u l a r l y s t r i k i n g . The former i s r e l a t i v e l y remote compared to the l a t t e r ; i t probably requires higher CPUE to compensate for t h i s . The areas which intermediate in both remoteness and exposure have CPUE RPAs which are correspondingly intermediate. Predictinq movement Applying each of the three hypotheses put forward by Hilborn and Ledbetter (1979) for purse seiners to explain movement of g i l l n e t t e r s produced mixed r e s u l t s . Fixed (or tr a d i t i o n a l ) movements patterns do not s a t i s f a c t o r i l y explain movement, although they may be useful in the short term i f there are no dramatic changes in the underlying driving forces in the f i shery. Trying to explain movement in terms of individual fishermen maximizing their return worked well for the entire province (Figure 4) but when applied on an area by area basis only in certain locations with special c h a r a c t e r i s t i c s were consistent and s i g n i f i c a n t relationships i d e n t i f i e d (e. g. Bella Coola). Special features of each area appear to modify the drive by fishermen to maximize their individual return to the extent that each area tends to a c h a r a c t e r i s t i c return with respect to 70 adjacent areas in any one year, but except for p a r t i c u l a r locations (e. g. Skeena and Fraser) there i s much v a r i a b i l i t y in these returns from year to year. The question remains as to how good these l a t t e r two hypotheses perform as management tools to predict boat numbers, espe c i a l l y compared with, for example, using the mean number of boats predicted to be operating in each area each season. It i s , I believe, a legitimate test of the hypotheses to start from the position where the only data set 'known' i s that for 1979, applying these data as a base and going forward to see how well the hypotheses explain the 1980 and 1981 data. Given the limited set of data examined, I decided to take the approach that I was a f i s h e r i e s manager, faced with the need to predict how many boats were going to f i s h in a p a r t i c u l a r area in a given week, i . e the anticipated e f f o r t , in response to the anticipated salmon runs. When information i s available on the CPUE in one week and I wanted to predict the number of boats in that area in the following week, i t was possible contrast at least four d i f f e r e n t approaches, each r e f l e c t i n g a d i f f e r i n g l e v e l of knowledge. In the f i r s t two I used as the basis of approach that each area has s p e c i f i c costs and benefits i . e. the area s p e c i f i c d e s i r a b i l i t y hypothesis, and in the l a t t e r two I assumed each fisherman was moving in an attempt to maximize his individual return, with the result that the area CPUE tended toward a p r o v i n c i a l average i . e. the coast wide equalization hypothesi s. 71 In order to calculate the number of boats predicted to be fi s h i n g in a pa r t i c u l a r area in a pa r t i c u l a r week under the area s p e c i f i c d e s i r a b i l i t y hypothesis, I used the following method. I mul t i p l i e d the RPA by the pr o v i n c i a l CPUE to get the CPUE I would predict for that area that week. I then divided this into the area catch of that week to predict the number of boats for the next week. For example, i f in the f i r s t week of 1980 the pr o v i n c i a l CPUE was $1200 per week and the RPA for Barkley Sound was 0.75 then: Predicted area CPUE = 0.75 x 1200 = 900 If the catch value that week was $27 000 then: Predicted number of boats in Barkley Sound in second week = 27000/900 =30. For the coast wide equalization approach I substituted the appropriate CPUE into the regression equation for that area and read off the predicted number of boats in the following week. The slope of regression l i n e used may or may not have been s i g n i f i c a n t l y d i f f e r e n t from zero, but i t was assumed for thi s exercise that the relationship, although not necessarily s i g n i f i c a n t , was v a l i d . Note however, that several of the o r i g i n a l regression slopes were negative. As t h i s did not make sense in p r a c t i c a l terms ( i . e. the number of boats decreasing with increasing catch) I recalculated the regression constraining the slope to pass through the o r i g i n . Although thi s ignores the fact that every area undoubtedly has a pool of boats which constitute the minimum f i r s t week's f i s h i n g boat 72 numbers, t h i s approach at least standardized the predictions from these 'marginal' regression r e l a t i o n s h i p s . The RPAs and CPUE figures varied with the approach used. The four d i f f e r e n t approaches were as follows: This year's RPA and this week's catch (PERFECT). In t h i s I used the area s p e c i f i c RPA and the CPUE of the year in question (1980 or 1981), and the area catch that week, to predict the CPUE and thus the number of boats in that week. This is an u n r e a l i s t i c situation but does represent the approach which u t i l i z e s a l l the latest information available, hence the 'perfect' information. Previous year's RPA and last weeks catch (FORCAST). This approach represents true forecasting in the sense that I used both the RPA from the previous year (1979 for 1980) or years (1979 and 1980 combined for 1981) and the previous week's area catch and p r o v i n c i a l CPUE. Linear regression of catch versus numbers where the  regression equation used was for that area that year (PERFLIN). The regression equation used was derived from the whole year's data. That i s , i t i s the 'perfect' information case for the coast wide equalization hypothesis. Linear regression of catch versus numbers where the  regression equation was that for the previous year (1980) or computed from the lumped data from the previous two years (1981) (FORELIN). This represents the true forecasting approach using the coast wide CPUE equalization hypothesis. The method of comparison was to examine the sum of squared 73 j deviations between the observed number of boats fi s h i n g a pa r t i c u l a r area and the number predicted by each of these d i f f e r e n t approaches. For example, the differences in the observed number of boats in Barkley Sound in 1981 and the number of boats predicted by FORCAST are i l l u s t r a t e d in Figure 14. As a table of sums of squares would not be p a r t i c u l a r l y illuminating, I related these to a common factor, the mean number of boats f i s h i n g each week in each area. That i s , were the sum of squared deviations of observed from predicted for each of the four approaches any bigger than would have been obtained from the sum of squared deviations of boat numbers from a yearly mean? In thi s case the mean was calculated for each area from the mean number of boats fi s h i n g in a l l three years 1979 to 1981 i n c l u s i v e . I used an index S 2 : S 2 = 1 - ( E (observed - pr e d i c t e d ) 2 / £ (observed - mean) 2) Where : predicted = number of boats predicted that week by that approach observed = number of boats actually f i s h i n g that week mean = mean number of boats f i s h i n g per week in that area If the S 2 approaches 1, then the numerator i s closer to zero than the denominator, and the approach i s much better than just using the mean number of boats. If the S 2 i s near 0, then the approach i s about as good as just using the mean number of boats. If the S 2 i s much less than 0 then the approach is worse 74 Figure 14. Number of boats observed and number predicted by FORCAST for Barkley Sound in 1981. Observed number of boats - +. Number of boats predicted by FORCAST - X. S 1 V 0 8 dO U3BWDN 76 t h a n j u s t c o n s i d e r i n g t h e mean number of b o a t s as a p r e d i c t o r of bo a t numbers. The r e s u l t s of t h e s e a n a l y s e s a r e o u t l i n e d i n T a b l e X I . None o f t h e f o u r a p p r o a c h e s p e r f o r m w e l l on t h e b a s i s of t h e i n d e x . In most c a s e s t h e outcomes were not as good a t e x p l a i n i n g movement t h a n by j u s t by t a k i n g t h e a v e r a g e number of b o a t s f i s h i n g t h a t a r e a a c r o s s t h e s e a s o n s . T h a t i s , most of the i n d e x v a l u e s were n e g a t i v e . PERFECT and PERFLIN, b o t h b a s e d on more ' p e r f e c t ' c u r r e n t i n f o r m a t i o n , g e n e r a l l y o u t p e r f o r m e d t h e f o r e c a s t i n g a p p r o a c h e s FORELIN and FORCAST. T h a t i s , t h e y g e n e r a l l y p r o d u c e d v a l u e s w h i c h were g r e a t e r t h a n FORELIN and FORCAST. The b e s t r e l a t i o n s h i p s were f o u n d i n B e l l a C o o l a , p o s s i b l y due t o i t s c e n t r a l l o c a t i o n , and t h e F r a s e r R i v e r , where t h e r e i s a l a r g e number of s t a t i o n a r y b o a t s . The w o r s t r e l a t i o n s h i p s were f o u n d i n J u a n de F u c a S t r a i t ( a r e a 20) and B a r k l e y Sound ( a r e a 23, F i g u r e 14) w h i c h were not o n l y c o n s i s t e n t l y n e g a t i v e , but o f t e n v e r y much worse t h a n t h e a v e r a g e . Can t h e s e r e s u l t s be e x p l a i n e d f u r t h e r ? A r e t h e u n d e r l y i n g d e v i a t i o n s ( e . g. as i n F i g u r e 14) c o n s i s t e n t , o r a r e one or two l a r g e e r r o r s i n an o t h e r w i s e c l o s e t r e n d between o b s e r v e d , and p r e d i c t e d b o a t numbers h a v i n g a l a r g e e f f e c t on t h e i n d e x ? In T a b l e X I I a r e t h e o b s e r v e d and p r e d i c t e d b o a t numbers, and t h e p e r c e n t a g e c o n t r i b u t i o n t o t h e t o t a l sum o f s q u a r e d d e v i a t i o n s between them, f o r t h e Skeena R i v e r , B e l l a C o o l a , B a r k l e y Sound and t h e F r a s e r R i v e r a r e a s i n 1981. In B a r k l e y Sound, f o r FORELIN and PERFLIN, t h e d e v i a t i o n s TABLE XI COMPARISON OF APPROACHES TO PREDICT BOAT NUMBERS A r e a Year FORELIN PERFLIN FORCAST PERFECT 3 80 - 3. .05 .86 - 1 . ,45 .48 81 - 0. ,41 — ( .89 * ,73 < .78 4 80 - o. .92 .92 - 0. ,37 .58 81 .40 i .73 .10 - o! .60 7 80 - 3, .13 .76 .72 .80 81 - 1 , .08 - 2 .09 - o! .63 - o! .53 8 80 .72 .93 - 0. .45 .68 81 .47 .50 4 .54 .67 1 2 80 - 0, .96 .42 4 . 1 0 .67 81 - 4, .94 - o! .22 - 2. .66 - o! .12 20 80 - 0, .74 - 2 .90 - 1 0 , .30 - 5, . 36 81 - 0, .05 - 0, .02 - 1, .00 - 2, .18 23 80 - 1 , .44 - 2 .67 - 0, .82 - 0, . 1 4 81 - 0, .91 - .91 - 5, .48 - 3, .08 29 80 .37 .88 .03 .16 81 .02 .28 - 4, .19 - 2, .53 F o r an e x p l a n a t i o n of e a c h h e a d i n g see t h e t e x t . F o r an e x p l a n a t i o n of a r e a numbers of T a b l e I I . TABLE XII WEEKLY OBSERVED AND PREDICTED BOAT NUMBERS - 1981 Area Week Observed FORELIN PERFLIN FORCAST PERFECT boat Boats %* Boats %* Boat s %* Boats numbers + + . + + 4 29 845 689 0 . 1636 912 4 30 872 628 0 .4003 750 4 31 716 544 0 . 1989 528 4 32 559 53 1 0 .0053 494 4 33 372 524 0 . 1553 475 4 34 375 48 1 0 .0755 362 Average 623 566 587 8 21 33 82 0 .0258 72 8 22 42 84 0 .0189 74 8 23 52 80 0. .0084 70 8 24 6 1 104 0. .0198 93 8 25 56 98 0 .0189 88 8 26 66 1 19 0. .0301 108 8 27 68 120 0. .0290 1 10 8 28 206 146 0. 0386 136 8 29 206 255 0. .0258 243 8 30 250 188 0. 04 12 177 8 31 2 16 332 0. . 1443 320 8 32 263 4 15 0. . 2478 402 8 33 375 • 261 0. 1394 250 8 34 318 177 0. 2 133 166 Average 158 176 165 0 . 0645 498 0 . 5269 825 0 .0010 0 .2139 836 0 .0057 698 0 . 0744 0 . 5080 707 0 .0004 34 1 0 . 3458 0 .0607 346 0 . 1985 225 0 . 2743 0 . 1525 228 0 .0907 172 0 .0984 0 .0024 174 0 . 1768 85 0 . 2068 465 391 0 .017 1 26 0 .0006 40 0 .0008 0 .0115 36 0 .0005 51 0 .0014 0 0036 46 0. .0005 49 0 .0002 0. .0115 45 0. 0032 86 0. .0107 0. .0115 78 0. 0061 58 0. .0001 0. ,0198 52 0. 0025 45 0. .0076 0. 0198 41 0. 0091 39 0. .0144 0. 0550 35 0. 3664 95 0. 2111 0. .0154 85 0. 1834 131 0. 0964 0. 0598 1 18 0. 2183 1 16 0. 3076 0. 12 14 105 0. 1544 239 0. 0091 0. 2 169 216 0. 0277 372 0. 2036 0. 1754 335 0. 0200 381 0. 0006 0. 2593 343 0. 0078 228 0. 1388 112 138 P.T.O T a b l e XII c o n t i n u e d Area Week Observed FORELIN PERFLIN FORCAST PERFECT boat Boats %* Boats %* Boa t s %* Boat s %* numbers + + + + 23 25 31 1 65 0 . 2754 65 0 . 2753 267 0 .0024 527 0 .0909 23 26 372 168 0 . 1894 168 O . 1893 527 0 .0295 729 0 . 2484 23 27 390 165 0 . 2304 165 0 . 2303 729 0 . 1409 614 0 .0978 23 28 267 310 0 .0084 310 0 .0084 614 0 . 1477 447 0 .0631 23 29 203 203 0 .0000 203 0 .0000 447 0 .0730 139 0 .0080 23 30 149 172 0 .0024 172 0 .0024 139 0 .0001 , 95 0 .0057 23 31 43 234 0 . 1660 233 0 . 1642 95 0 .0033 42 0 .0000 23 34 94 173 0 .0284 172 0 .0277 52 0 .0022 102 0 .0001 23 35 227 192 0 .0056 191 0, .0059 102 0 .0192 533 0. . 1825 23 36 223 210 0 .0008 209 0. .0009 533 0 . 1 178 607 0 2874 23 40 206 62 0 .0944 62 0. .0943 82 1 0 . 4638 1 19 0 .0147 Average 226 178 177 393 359 29 28 502 501 0. 0000 566 0. 0235 480 0. 0002 390 0. 0094 29 29 351 353 0, 0000 535 0. 1943 383 0. 0005 63 0. 0619 29 30 421 406 0. 0010 546 0. 0897 62 0. 0655 134 0. 0615 29 31 420 423 0. 0000 549 0. 0955 132 0. 0421 17 1 0. 0463 29 32 705 950 0. 2552 659 0. 012 1 168 0. 1465 1221 0. 1987 29 33 61 1 587 0. 0024 584 0. 0042 1200 0. 1762 625 0. 0001 29 34 916 715 0. 17 18 6 10 0. 5374 6 14 0. 0463 1586 0. 3349 29 35 561 369 0. 1567 538 0. 0030 1558 0. 5048 434 0. 0120 29 36 610 374 0. 2368 539 0. 0289 426 0. 0172 523 0. 0056 29 37 531 394 0. 0798 543 0. 0008 514 0. 0001 638 0. 0085 29 38 601 451 0. 0957 555 0. 0121 627 0. 0003 1 192 0. 2606 Average 566 502 566 560 634 * Pe r cen tage c o n t r i b u t i o n of that week to the t o t a l d e v i a t i o n s q u a r e d . + Boat numbers For an e x p l a n a t i o n of a rea numbers see T a b l e II. U 3 80 from t h e mean were e v e n l y d i s t r i b u t e d , w i t h t h e l a r g e s t s i n g l e o c c u r r e n c e i n b o t h b e i n g l e s s t h a n 28%. N e i t h e r method f o l l o w e d t h e o b s e r v e d b o a t numbers w e l l . In t h e FORCAST, one s i n g l e e v e n t i n t h e l a s t week c o n t r i b u t e d 46% of t o t a l d e v i a t i o n , w h i l e i n PERFECT t h e l a r g e s t s i n g l e o c c u r r e n c e was l e s s t h a n 29%. B o t h t h e s e l a t t e r a p p r o a c h e s f o l l o w e d t h e d y n a m i c s r a t h e r b e t t e r t h a n t h e l i n e a r a p p r o a c h e s . However, i n none of t h e a p p r o a c h e s d i d t h e p r e d i c t i o n s f o r a p a r t i c u l a r week have c o n s i s t e n t l y l a r g e o r s m a l l d e v i a t i o n s . In B e l l a C o o l a ( a r e a 8), t h e p r e d i c t i o n s f o r a l l f o u r methods worked w e l l . In a l l f o u r c a s e s t h e l a r g e s t s i n g l e c o n t r i b u t i o n t o t h e t o t a l sum of s q u a r e d d e v i a t i o n s was l e s s t h a n 30%. In FORELIN and PERFLIN t h e c o n t r i b u t i o n s were low i n t h e f i r s t t e n weeks, w i t h t h e l a s t f o u r weeks c o n t r i b u t i n g a l m o s t a l l t h e d e v i a t i o n s . The p a t t e r n was more v a r i a b l e i n PERFECT and FORCAST, but a g a i n a l m o s t a l l of t h e d e v i a t i o n s were i n f o u r of t h e f o u r t e e n weeks. However, i n n e i t h e r B a r k l e y Sound nor B e l l a C o o l a was t h e r e a c o n s i s t e n t p a t t e r n t o t h e d e v i a t i o n s . T a b l e X I I I s e t s o u t t h e number of weeks f i s h e d i n e a c h a r e a e a c h y e a r f o r w h i c h p r e d i c t i o n s were made, and t h e number of weeks w h i c h t o g e t h e r c o n t r i b u t e d o v e r 50% of t h e d e v i a t i o n s . I t i s e v i d e n t i n a l l f o u r methods i n a l l 8 main a r e a s t h a t i n t h e m a j o r i t y of c a s e s o n l y one o r two weeks c o n t r i b u t e most t o t h e d e v i a t i o n s , and have a marked e f f e c t on t h e u s e f u l n e s s of t h e i n d e x . However, a l t h o u g h t h e s e one o r two i n s t a n c e s may form h a l f t h e t o t a l s e a s o n i n some a r e a s (Skeena R i v e r ) , i n most TABLE XIII CONTRIBUTION TO 50% OF SUM OF SQUARED DEVIATIONS Area Number Minimum number of weeks where t o t a l of c o n t r i b u t i o n to sum of squared d e v i a t i o n s >= 50%. FORELIN PERFLIN FORCAST PERFECT 3 4 7 8 12 20 23 29 8 4 19 17 14 1 1 13 16 3 4 7 8 12 20 23 29 7 6 6 14 12 6 1 1 1 1 For an e x p l a n a t i o n of a r e a numbers see T a b l e II 82 t h e y c o n s t i t u t e o n l y 10-20% o f t h e s e a s o n . T h a t i s , t h e ' d i s t r i b u t i o n ' of t h e sum of s q u a r e d d e v i a t i o n s from t h e o b s e r v e d d e p e n d more on t h e a r e a t h a n t h e method of p r o d u c i n g t h e i n d e x , as t h e number o f o c c a s i o n s c o n t r i b u t i n g o v e r 50% of t h e d e v i a t i o n s a r e v e r y s i m i l a r between methods i n any p a r t i c u l a r a r e a ( T a b l e X I I I ) . However, t h e r e does not a p p e a r t o be any c o n s i s t e n c y a b o u t when t h e s e l a r g e d e v i a t i o n s w i l l o c c u r , i . e. i t i s not p o s s i b l e t o p r e d i c t w h i c h o f t h e p r e d i c t i o n s w i l l be v e r y l a r g e . The l i n e a r a p p r o a c h e s (PERFLIN and FORELIN) a r e i n g e n e r a l more a c c u r a t e i n t h e i r p r e d i c t i o n s , i n c o m p a r i s o n w i t h mean a n n u a l b o a t numbers ( T a b l e XI) but n e i t h e r r e f l e c t f l e e t d y n a m i c s w e l l . The a r e a s p e c i f i c d e s i r a b i l i t y methods (PERFECT and FORCAST) a r e l e s s a c c u r a t e i n p r e d i c t i o n but do f o l l o w f l e e t d y n a m i c s c l o s e l y e i t h e r w i t h a l a g (FORCAST e. g. see F i g u r e 14) or i m m e d i a t e l y ( P ERFECT). In f a c t , PERFECT may r e f l e c t t h e r e a l w o r l d b e t t e r t h a n FORCAST as t h e b o a t s i n t h e f l e e t may have but w i t h i n week i n f o r m a t i o n b a s e d on knowledge of when t h e r u n s peak and good c o m m u n i c a t i o n . What c o u l d be t h e f a c t o r s c o n t r i b u t i n g t o t h e s e l a r g e d e v i a t i o n s ? I f t h e r e a r e a l a r g e number of s t a t i o n a r y b o a t s , t h e s e do n o t r e a c t t o d e v e l o p m e n t s i n a d j o i n i n g a r e a s , and t h u s t h e number o f b o a t s w i t h i n t h a t a r e a w i l l n o t v a r y as much as e x p e c t e d i f t h e b o a t s were t r u e l y m o b i l e . S t a t i o n a r y b o a t s u s u a l l y c o n t i n u e f i s h i n g once s t a r t e d f o r t h e s e a s o n . T h i s t r e n d i s e s p e c i a l l y e v i d e n t f o r t h e F r a s e r R i v e r , w h i c h i s a l s o one of t h e two a r e a s t h a t t h e l i n e a r a p p r o a c h worked 83 consistently well. The c r i t i c a l entry and exit CPUE may also vary during the season, further contributing to the v a r i a t i o n . Each fisherman should have a certain d o l l a r 'baseline' based on economic c r i t e r i a such as operating costs, which should be exceeded before the fisherman commences f i s h i n g or continues to f i s h . If the entry and exit CPUE does not vary I would expect that the catch per week in the f i r s t week should be approximately the same as that in the last week. At f i r s t glance t h i s does not appear to be the case as the mean dol l a r return in the f i r s t week and l a s t week fished by each boat in a l l three years are di f f e r e n t (Table XIV). Furthermore, I performed a chi-squared test comparing the frequency d i s t r i b u t i o n of earnings in the f i r s t week fished to the la s t week fished by each boat in a l l three years. That i s , the numbers of boats earning between $0-$100, $ 101-$200, $20l-$300 etc., in their f i r s t week of f i s h i n g and la s t week of fi s h i n g in any one year. I found not only was the frequency d i s t r i b u t i o n from the f i r s t week in any one year s i g n i f i c a n t l y d i f f e r e n t from the l a s t week in that year (p < 0.05), but that the f i r s t weeks and l a s t weeks respectively across each year were s i g n i f i c a n t l y d i f f e r e n t from each other. These results imply in part that economic c r i t e r i a are modified for example by ' t r a d i t i o n a l ' behaviour where the fishermen always start or stops in a certain week or before or after a ce r t a i n opening in an area. However, even th i s idea must be q u a l i f i e d , in l i g h t of the 84 TABLE XIV MEAN AND MEDIAN RETURNS IN FIRST AND LAST WEEK FISHED 1979 TO 1981 Ye a r Week *Mean r e t u r n +Median r e t u r n ($) ($) 1979 F i r s t 582 260 1979 L a s t 660 377 1980 F i r s t 827 498 1980 L a s t 768 512 1981 F i r s t 850 403 1981 L a s t 732 390 *Mean d o l l a r e a r n i n g s of b o a t s t h a t week +Median d o l l a r e a r n i n g s above o r below week w h i c h 50% of t h e f l e e t e n t e r o r l e a v e t h e f i s h e r y F i r s t - d o l l a r e a r n i n g i n f i r s t week t h a t b o a t f i s h e d L a s t - d o l l a r e a r n i n g i n l a s t week t h a t boat f i s h e d median r e t u r n t o t h e f l e e t i n t h e f i r s t and l a s t week f i s h e d by e a c h b o a t ( T a b l e X I V ) . T h i s i s t h e median d o l l a r e a r n i n g s above (or below) w h i c h 50% of t h e f l e e t e n t e r ( o r l e a v e ) t h e f i s h e r y . A l t h o u g h d i f f e r e n t i n 1979, t h e median v a l u e s a r e a l m o s t e q u a l i n 1980 and 1981. D e v i a t i o n s can a l s o be a s c r i b e d t o v a r i a b i l i t y i n t o t a l e a r n i n g s . The t o t a l r e p o r t e d d o l l a r l a n d i n g s from t h e g i l l n e t f l e e t f o r 1979, 1980, and 1981 were a p p r o x i m a t e l y $34, $35 and $41 m i l l i o n r e s p e c t i v e l y i . e. not m a r k e d l y d i f f e r e n t , e s p e c i a l l y a f t e r t a k i n g i n f l a t i o n i n t o a c c o u n t . A l t h o u g h I d i d not have i n f o r m a t i o n on t h e c o s t s f a c e d by f i s h e r m e n o r a l t e r n a t i v e employment o p p o r t u n i t i e s t h e s e may have had a s i g n i f i c a n t e f f e c t , e s p e c i a l l y as t h e c u r r e n t r e c e s s i o n may have f o r c e d f i s h e r m e n t o go f i s h i n g . 85 CONCLUSIONS C o n s i d e r i n g f i s h i n g as a p r e d a t o r - p r e y s y s t e m p r o v i d e s a c o n v e n i e n t framework f o r c o h e s i v e c o n s i d e r a t i o n of a d i s p a r a t e l i t e r a t u r e a b o u t e x p l o i t a t i o n o f a q u a t i c r e s o u r c e s . W i t h o p e n i n g s of l i m i t e d d u r a t i o n i n g e o g r a p h i c a l l y s e p a r a t e s i t e s , f i s h e r m e n may behave l i k e e n e r g y ( i . e. e c o n o m i c ) m a x i m i z e r s and e x h i b i t t h e a p p r o p r i a t e n u m e r i c a l r e s p o n s e . T h a t i s , t h e y may a t t e m p t t o m a x i m i z e t h e d o l l a r r e t u r n i n an o p e n i n g , and move between s i t e s t o a t t a i n t h i s g o a l . The f u n c t i o n a l and n u m e r i c a l r e s p o n s e s of f i s h e r m e n t o p r e y d e n s i t y have i m p o r t a n t management i m p l i c a t i o n s . Not l e a s t a l o n g t h e B. C. c o a s t i s t h e p o t e n t i a l a b i l i t y t o p r e d i c t boat movements and t h u s c o n t r o l e f f o r t . The a p p r o a c h has shown p r o m i s e f o r t r o l l e r s i n G e o r g i a S t r a i t and p u r s e s e i n e r s a l o n g t h e whole B. C. c o a s t , as w e l l as g i l l n e t t e r s a l o n g t h e whole c o a s t , where i t i s p o s s i b l e t o p r e d i c t b o a t numbers i n one week w i t h knowledge of t h e r e t u r n (CPUE) i n t h e p r e v i o u s week. When t h i s a p p r o a c h i s a p p l i e d on a f i n e r s c a l e i . e. on an a r e a by a r e a b a s i s t o p r e d i c t b o a t numbers i t o n l y worked i n some a r e a s a t c e r t a i n t i m e s . Even a l l o w i n g f o r c o n s i s t e n t d i f f e r e n c e s between a r e a s , s u c h as t h e i r s i t e s p e c i f i c a d v a n t a g e s , was not s u f f i c i e n t t o o b t a i n r e l i a b l e p r e d i c t i o n s . The a p p r o a c h was c o n f o u n d e d by a number o f f a c t o r s . G i l l n e t f i s h e r m e n a p p e a r t o be more bound by t r a d i t i o n (do not l e a r n ) and l e s s by e c o n o m i c c r i t e r i a t h a n B.C. p u r s e s e i n e 86 f i s h e r m e n who have a f a r h i g h e r c a p i t a l i n v e s t m e n t , and more e c o n o m i c i n c e n t i v e s t o movement. F u r t h e r c o m p l i c a t i n g f a c t o r s a r e t h e t e n d e n c y o f many f i s h e r m e n t o go f i s h i n g e a r l i e r t h a n e x p e c t e d t o t e s t g e a r or t o f i n d o u t where t h e f i s h a r e ( f i s h i n g f o r i n f o r m a t i o n ) . In c o n t r a s t , t h e y may f i s h l a t e r t h a n e x p e c t e d t o q u a l i f y f o r unemployment i n s u r a n c e . L o c a t i o n may a l s o c o n f o u n d f l e x i b i l i t y . T h e r e may i n f a c t be s i g n i f i c a n t i n v e s t m e n t i n moving t o some o f t h e more remote a r e a s ( e . g. Nass or • Skeena) w h i c h e n c o u r a g e s f i s h e r m e n t o s t a y t h e r e i n f a c e o f poor r e t u r n s . In c o m p a r i s o n , c e n t r a l l o c a t i o n s s u c h as B e l l a B e l l a , B e l l a C o o l a and J o h n s t o n e S t r a i t s o f f e r much more s c o p e f o r f l e x i b i l i t y . F u r t h e r s t u d y of' s e v e r a l a s p e c t s of t h i s p r o b l e m may i s o l a t e t h e s e c o m p l i c a t i n g f a c t o r s . The g i l l n e t f l e e t has two components - t h e p u r e g i l l n e t t e r s and t h e c o m b i n a t i o n t r o l l e r -g i l l n e t t e r s . A n a l y s i s of t h e s e s e p a r a t e l y may be more i l l u m i n a t i n g . I t s h o u l d a l s o be p o s s i b l e t o f o l l o w i n d e t a i l t h e movement o f a p r o p o r t i o n of t h e f l e e t w i t h i n a s e a s o n t o see whether c o n s i s t e n t t r e n d s emerge, and how t h e s e a r e a s s o c i a t e d w i t h s u c h f a c t o r s as home p o r t , b o a t s i z e and l e v e l o f i n d e b t e d n e s s . F i n a l l y , a measure o f t h e t o t a l p r e d a t i o n i n an a r e a s h o u l d be p o s s i b l e by s t u d y i n g t h e n u m e r i c a l and f u n c t i o n a l r e s p o n s e s of f i s h e r m e n s i m u l t a n e o u s l y , i f methods t o e s t i m a t e p r e y d e n s i t y i n r e a l t i m e c a n be f u r t h e r r e f i n e d . 87 LITERATURE CITED A c h e s o n , J . M. ( 1 9 7 5 ) . The l o b s t e r f i e f s : e c onomic and e c o l o g i c a l e f f e c t s of t e r r i t o r i a l i t y i n t h e Maine l o b s t e r i n d u s t r y . Human E c o l o g y 3_: 183-207. A l l e n , K. R. ( 1 9 6 3 ) . The i n f l u e n c e of b e h a v i o u r on t h e c a p t u r e of f i s h w i t h b a i t s . I n t e r . Comm. N o r t h w e s t A t l a n t i c F i s h i n g S p e c . Pub. 5:5-7. A n d e r s e n , R. R. ( 1 9 7 2 ) . Hunt and d e c e i v e : i n f o r m a t i o n management i n N e w f o u n d l a n d d e e p - s e a t r a w l e r f i s h i n g . 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