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Ecosystem Services of the British Columbia Coast: Modeling the Impacts of Agriculture on the Provision.. Solomon, Cody; Thompson, Allison 2010-05-05

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i  ECOSYTEM SERVICES OF THE BRITISH COLUMBIA COAST: MODELING THE IMPACTS OF AGRICULTURE ON TE PROVISION OF SHELFISH by CODY SOLMON1 & ALISON THOMPSON1 1B.Sc. Hons. University of British Columbia A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DGREE OF BACHELOR OF SCIENCE (HONURS) in THE FACULTY OF SCIENCE (Environmental Sciences)  This thesi conforms to the required standard  ......................................    ......................................     Kai M. A. Chan             Rebeca G. Martone Supervisors  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) APRIL 2010 ii ABSTRACT   The coastal ecosystems of British Columbia, Canada (BC) are highly impacted by human activities; this i due, in part, to a lck of understanding of the efcts that ultiple human activities may have on ecosystems and the services they provide for humans. Understanding the functioning of ecosyste services i important for both conservation eforts and natural resources management. We create a conceptual model of BC’s coastal ecosystem services and adopt a echanistic approach to identifying and quantifying the linkages by which human activities may cumulatively impact ecosystem services. From this model e investigate the relationship betwen one human activity, agriculture, and one ecosystem service, the provision of shelfish, by identifying and quantifying the specifc linkages betwen the two. We use a Geographic Information System (GIS) to model the impacts that agricultural runoff, in the form of nitrate, may have on the suitability of shelfish harvest ites (both wild and commercial) in BC. We use three diferent models to investigate the ways that nitrogenous runoff ay cause changes to ecosystem functions, through eutrophication and the production of harmful algal blooms (HABs), HAB toxicity, and the subsequent closure of shelfish harvesting sites. From our results, we identify a trade-off betwen fertilzer use on agricultural land and potential production of shelfish in BC. A lack of spatialy and temporaly explict data, long with uncertainty of the relationships in the literature, is the ost iportant limitaion to the applicability of our models to BC. We recommend further work investigating these relationships and an expansion of coastl monitoring systes in BC. iii ACKNOWLEDGEMNTS  Special thanks to our supervisors Rebeca G. Martone and Kai M. A. Chan for their invaluable support and contribution to our resarch. Also thanks to Brian Klinkenberg of the University of Britsh Columbia (Vancouver) Geography department for his assitance with spatial modeling in GIS; as wel as Douw Steyn and the entire Earth and Ocean Science department for all that they provided to help us along the way.         iv Table of Contents  TITLE PAGE….........................................................................................................................i ABSTRACT..............................................................................................................................ii ACKNOWLEDGEMENTS......................................................................................................iii TABLE OF CONTETS..........................................................................................................iv LIST OF FIGURES..................................................................................................................vi INTRODUCTION.....................................................................................................................1  Introduction to ecosystem services................................................................................1  Conceptual model..........................................................................................................5  Coastal ecosystem services of British Columbia...........................................................6 METHODS..............................................................................................................................12  Spatial mapping of agriculture and nitrate runoff........................................................12 The models...................................................................................................................14 RESULTS................................................................................................................................17  Agriculture and nitrate runoff in BC............................................................................17  Model 1........................................................................................................................18 Model 2........................................................................................................................18 Model 3........................................................................................................................24  DISCUSION..........................................................................................................................26 v Spatial mapping...........................................................................................................26 HAB – Toxin – Shelfish Relationship........................................................................30 Limitaions...................................................................................................................31 CONCLUSION........................................................................................................................35 BIBLIOGRAPHY....................................................................................................................37 APENDICES .........................................................................................................................40vi List of Figures  Figure 1.1 Four categories of ecosystem services.....................................................................2 Figure 1.2 How anthropogenic ativities afect ecosystem services.........................................3 Figure 1.3 Conceptual model ilustrating the process by which human activities may cumulatively impact ecosystem services in British Columbia, Canada....................................9 Figure 2.1 Decison tree for algal bloom forecasting..............................................................15 Figure 3.1 Study area and location of agriculture, British Columbia, Canada....................... 17 Figure 3.2 Linear regresion results of model 1......................................................................18 Figure 3.3 Model 2 results using 99.5% decay........................................................................20 Figure 3.4 Model 2 results using 66.7% decay........................................................................21 Figure 3.5 Model 2 results using 50% decay...........................................................................22 Figure 3.6 Model 2 results using 33.3% decay........................................................................23 Figure 3.7 Model 3 hydrodynamic stability results for least stratifed conditions...................24 Figure 4.1 Shelfish culture capability in British Columbia ....................................................29 1 1. INTRODUCTION   Introduction to Ecosystem services Ecosystem services are the process and conditions by which humans benefit from the ecosystes around us (Costanza et al. 1997; Milennium Ecosystem Asesment (MA) 2003, 2005). These benefits can be provided by ‘natural’ ecosystes, such as water filtration from pristine wetlands, or from engineered and managed systems such as the provisioning of food from agriculture. There are four generaly aceptd categories of ecosystem services: supporting, e.g. nutrient cycling and waste procesing; provisioning e.g. the production of food; cultural, e.g. recreation and spiritual value; and regulating, e.g. carbon sequestration that mitgates climate change (Figure 1.1) (MA 2005; Chan et al. 2009). Obtaining the benefits provided by ecosystem services constiutes an integral part of the world’s socio-ecological and economic systes and in the absence of humans there are no services (Bennet et al. 2009). Clean drinking water, food production, climate regulation and aesthetic and spiritual values are al examples of ecosystem services and al of these can be negatively affectd by misuse and degradation of the environment. The United Nations’ Milennium Ecosystem Asesment (2005) found that “60 per cent of the ecosystem services that support life on Earth... are being degraded or used unsustainably.” The causes of these negative impacts, which are most commonly the result of human activities, wil be referred to as drivers, i.e. the factors that drive cosystem change (Figure 1.1). It is important to note that while the majority of this paper is concerned with negative impacts to ecosystem services, both activities and drivers can force positve changes, as in the case of ecological restoration. Human activities can impact the functioning of ecosystem services in a multiude of ways. Ecosystem services, the benefits humans obtain from ecosystes, can change due to changes in ecosyste service providers – the ecosystems and ecosystem components that generate the services themselves. Often, societis modify their environments to maximize the  2 productivity of provisioning services, as in the case of agriculture (Kareiva et al. 2007; Foley t al. 2005). These modifications, however, can cause adverse reactions in the same, or other, ecosystem service providers, which in turn afects the benefits humans may gain from ecosystem services. Human activities may impact one or several ecosystem services, and a variety of human activities may impact a single ecosystem service provider, or service (Figure 1.2). The relationship betwen these impacts i largely unknown; they may be additive, multiplicative, offseting, etc. Trade-offs occur when a human activity increase production of an ecosystem service while negatively afecting another. A common example of a trade-off is the intensive use of fertilzer on agriculture lands that can cause nutrients such as nitrogen and phosphorus to leach out of soil into nearby water bodies and  acelrate the proces of eutrophication, resulting in harmful algal blooms that may reduce disolved oxygen, negatively impacting water quality, fish and other aquatic wildlife populations. This was the case in the Gulf of Mexico, where the production of corn-based ethanol increased nutrient runoff into the Misisippi River. The resulting eutrophication caused the water Figure 1.1 There are four categories of ecosystem services as defined by the Milennium Ecosystm Asesment (2003, 2005).  Anthropogenic ativitis fect drivers, which an iturn afect ecosyst srvices (figure modified from MA 2005 and Chan et al. 2009).  3 along the shelf of the gulf to become hypoxic (Donner & Kucharik 2008). A synergy occurs when the efect of one or a cbination of activities i greater than the sum of the activity or activities alone. A synergy may occur within one ecosystem service/provider, or across multiple. An example of a synergy is the improvement in water quality and filtration and flood control, both supporting services, that results from wetland restoration, a human activity (Hey t al. 2004; Zedlr 2003). Cross-system linkages are those in which a human ctivity occurring in one ecosystem drives change in another, e.g. how land use afects coastal waters (Halpern et al. 2009). Al of the previously described linkages are possible ways in which the cumulative impacts to ecosystem services may be manifested. Understanding the diferent types of linkages and how they operate is an esential step in evaluating the diferent types of relationships betwen human activities and ecosystem service providers and services and how they operate on a case-to-case basi; both of these are esential for succesful ecosystem-based management. The concept of ecosystem services alows managers to explictly evaluate the linkages betwen activities, their ipacts and the changes to benefits that humans ultimately receive. Figure 1.2 Anthropogenic ativities, whetr intentional or not, drive change in ecosystems that hen can directly afet he provisioning of srvices or cause interations such as trade-offs whih then hange the benefits that society reives. Figure redrawn from Bennet et al. 2009). ! 4  The ecosystem services concept is an increasingly important lens through which to perform both conservation and management of both resources and land-use (Nelson et al. 2009; Ban et al. in prep). Asesing decisons in terms of changes to ecosystem services alows for an integrated approach where trade-offs can be asesd for a variety of management objectives, al revolving around impacts to human wel-being (Balvanera et al. 2001; Singh 2002; Kremen & Ostfeld 2005). This i in contrast to more traditional management approaches that focus on a single objective or single problem, often leading to mis-calulation or mis-identifcation of the benefits of various actions across sectors of society. Similarly, management practies that aim to maximize the production of one service often end up decreasing the productivity of a variety of other services (Bennet et al. 2009).  An ecosystem services framework for management must include al ecosystem services for the relevant are in order to be efective, and the scale of asesment must be local (Bennet et al. 2009). Despite the increase in popularity of the ecosyste services framework and its integrated approach, there is litle information available on the interactions beten these srvices at a regional scale, which is the pertinent scale of much management (Chan et al. 2006). Mapping ecosystem services i integral for the ecological understanding of their function and spatial extent, and provides a benefical lens through which to perform ecosystem-based management (Kremen and Ostfeld, 2005). Initiatives to map or catlogue ecosyste services at a global scale bring atention to the need for integrated management approaches, but do not give managers the detailed understanding that is necesary in order to ases trade-offs and make informed decisons (Naidoo et al. 2008, Bennet et al. 2009).  Globaly, cumulative impacts of human activities to marine ecosystems have been mapped and each region given an ipact score; the framework provided by this global asesment has proven succesful for more local scales as wel (Halpern et al. 2008, Halpern et al. 2009). The 2005 Milennium Ecosystem Asesment evaluated threats to ecosystem services. More local approaches have set up a fraework for evaluating the impact of land-use changes and conservation scenarios on ecosystem services, a result directly benefical to managers (Chan et al 2006). Barbier t al. (2008), Egoh et al. (2008), and Naidoo & Rickets (2006) produced maps of ecosystem services to ases specifc ecological questions with important management consequences. Others have atempted to  5 catlogue and map services in various regions, which can help land-use managers to prioritize various areas (Ban et al. in prep.; Chan et al. 2009). In this paper, we aim to build on the concept of evaluating cumulative human impacts to ecosystems, and extend this to the cumulative impacts of human activities on the ecosystem services theselves. Cumulative impacts are "spatialy or temporaly acumulated changes" caused by human activities that afect the functioning of ecosystem service providers and thus the production of ecosystem services (Forrex 2010). A quantitaive evaluation of the impacts of human activities on a variety of ecosystem services would alow for efective management based on the ability to spatialy prioritize conservation initiatives, and succesfully ases the trade-offs of various land-use changes. To date, the most recent proposed method for asesing human impact to ecosystem services identifes two relationships: the relationships betwen drivers and ecosystem services and the interactions among multiple ecosystem services (Bennet et al. 2009). It is well-recognized that the relationship betwen ecosyste services and the way that human actions afect them is complex. Although drivers can directly impact the provisioning of ecosystem services in many case, they also have indirect efects which may be more important and which require a more complex set of steps to identify and understand. For exaple, the relationship betwen nutrient runoff (a driver) and shelfish provision (a service) cannot be understood without the understanding of the relationships betwen nutrient runoff and harmful algal blooms, and the efect of harmful algal blooms on toxins and so on shelfish. We therefore expand on the driver-service methodology and arguing that the process governing change to ecosystem services follow a pathway with multiple steps.  Conceptual Model We identify five aspects of the human activity – ecosystem service relationship: human activities, drivers, ecosystem change-agents, ecosyste service providers and ecosystem services (Figure 1.3). Activities include both the purposeful manipulation of ecosystes and the indirect efects that human societis can have on ecosystems. The component of the activity that enters an ecosystem is a driver, e.g. coastal development is an  6 activity and the pollution, sewage and runoff entering the marine environment are the drivers. Drivers cause changes in ecosystems through ecosystem change-agents: ecosystem changes, process, phenomena, or elents that are frequent focal points of changes in ecosystem service providers. Ecosystem change-agents can either take the form of biological process, e.g. oxygen consumption and harmful algal blooms, or abiotic factors, e.g. heavy metals. Ecosystem service providers are afectd by threats; the providers are also what make ecosystem services possible, providing humans with tangible and intangible benefits that we cal ecosyste services. The factor connecting al these together in a cyclical nature is society; humans require ecosystem services for wel-being, but also change these srvices directly or indirectly through their actions. By adopting a mechanistic approach that evaluates impacts for each linkage, we aim to provide a more complet understanding of the dynamics betwen human activities and ecosystem services. Our conceptual model provides a comprehensive way of understanding both the relationships betwen human activities and ecosystem services, and the relationships betwen multiple ecosystem services. Coastal Ecosystem Services in Britsh Columbia In this thesi, we investigated the cumulative impacts of multiple human activities and multiple ecosystem services of the coastal marine ecosystems in British Columbia (BC). We first identify a set of human activities and ecosystem services of the BC coast and the relationships betwen these. We then selct a set of linkages that we quantify and model in GIS; the spatialy explict results wil alow the asesment of various scenarios of human activity changes on the BC coast. There is very litle understanding to date of ecosystem services in the marine environment, although marine environments are highly degraded globaly (Ban et al. in prep). Coastal ecosystems are also much more vulnerable to human stresors than are open ocean ecosystems (Teck et al. 2009). The ecosystem stres caused by human activities integral to BC's economy and culture has taken its toll on coastal BC ecosystems. Approximately 98% of the BC continental shelf and slope are afectd by anthropogenic ativities, while only 6% of the coast is currently protectd (Ban & Alder 2008). A global map of human impacts to ecosystems clasifes the BC coast as ranging from medium to high impact (Halpern et al. 2008). The results of this global map and knowledge of local levels of degradation along with the reliance of BC's economy on ocean-related  7 activities highlight the importance of a local understanding of the impacts that human activities have on the functioning of BC's coastal ecosystem services. We have created a conceptual model that adapts work done by Ban et al. (in prep) catloguing BC's coastal ecosystem services into our multiple-step format (Figure 1.3). Although the ecosystem services and human activities afecting them were compiled and described in terms of benefitng from or improving/degrading the production of ecosystem services, we aim to take a closer look at these relationships, quantify them and examine how multiple human activities can cause cumulative impacts to ecosyste services. It is beyond the scope of our analysis to research the entire set of linkages described in our conceptual diagram, so we selcted a set of linkages to explore in full, by modeling and mapped in a spatialy explict seting, when possible.  The human activity we have chosen to investigate is agriculture, and the ecosystem service provider is shelfish production, a subset of the ecosystem service 'food provision'. Agriculture contributed 1% to the BC GDP in 1996, and employed 1.8% of the workforce (BC Ministry of Finance and Corporate Relations 1999). Although the agriculture sector is relatively smal in BC, its impact on coastal ecosystems through runoff may be significant. Fertilzer travels from farms into aquatic eosystes in the form of nutrient runoff, the driver of ecosystem change for this linkage. Nutrient runoff enriches ecosystems, acelrating the proces of eutrophication and alowing phytoplankton to bloom if physical and chemical conditions are compliant (Heisler t al. 2008; Anderson et al. 2002; Selner et al. 2003). Eutrophication leading to harmful algal blooms i the ecosystem change-agent we wil be investigating. Finaly, these harmful algal blooms release toxins into the water which may be taken up and concentrated by shelfish, afecting the viability of both commercial shelfish aquaculture and wild shelfish harvest. The linkages described here are deonstrated in figure 1.3 with arrows and bold letring. For each linkage we are separately modeling and characterizing the impacts; this mechanistic approach wil alow us to evaluate the impact of agriculture on shelfish production in the most comprehensive way possible. Shelfish production can also be considered a cultural service, as shelfish harvesting has traditionaly been practied both for subsistence and recreation historicaly in BC (Quayle 1969, etc.). We explore the impacts of agriculture on both shelfish aquaculture sites and potential wild  8 harvest sites, therefore addresing impacts to two diferent ecosystem services. Although it is dificult to quantify impacts to cultural ecosystem services, we are able to show potential are lost to recreational shelfish harvesting, as wel as aquaculture site closures. Changes to the value of the service are indicated by mapping bloom probability; the option value of the service decrease as habita suitability decrease, e.g. bloom event probability increase, and with it the potential toxicity of the water to shelfish. We wil also evaluate the trade-offs beten two types of agriculture and the provision of shelfish in coastal BC. We use three diferent models to generate three diferent impact scenarios, and discuss the outcome of each.  9              Figure 1.3 A conceptual model ilustrating the process by which human activities may cumulatively impact the provision of ecosystem services in coastal BC. The bold typeface with te arrows identifies the components that we wil be vluting in detail. Arrows represnt dirct rlationships and may flow linearl from one "step" t the next, e.g. drivers directly impacting chang-agents, or may irectly impact a component hat tey are not right next to, e.g. an ecosystem driver directly impacting an ecoste service provider. Figure adpted fro research in Ba et al. (in pre.). ! 10 As of 1990, the British Columbian shelfish aquaculture industry was losing $2 milion per year due to harvesting site closures (Shumway 1990). As of 2008 there were 503 licenses isued for shelfish harvesting sites in BC (BC Shelfish Growers Asociation 2008). Shelfish site closures are due to two factors: toxin contamination from harmful algal blooms (HABs) or unhealthy fecal coliform levels from sewage inputs. The first recorded death due to harmful algal bloom toxicity in BC was in 1793, when one of Captain George Vancouver's crew died after eating Mussels collectd along the BC coast (Quayle 1969). By 1927 it was recognized that the toxins produced by harmful algal blooms were a serious threat on the west coast of North America (Horner et al. 1997). At this time there was only one toxin recognized as harmful, Paralytic Shelfish Poison (PSP) caused by the Dinoflagelate genus Alexandrium, of which there are multiple toxin-causing species (Horner et al. 1997). Domoic aid poison (DAP) is the other major toxin on the west coast of North America that is detrimental to human health. DAP causes amnesiac shelfish poisoning (ASP), and the organis responsible is the diatom genus Pseudo-nitzschia, again with multiple toxin-producing species (CFIA 2008). DAP causing ASP is a relatively new problem; it was first recognized in 1991 on the west coast of California (Horner et al. 1997). Both of these toxins are harmful to human health and can sometimes be deadly (CFIA 2008). Currently, al shelfish harvesting sites on the BC coast are monitored on a daily basi for PS and DAP. A third toxin-caused condition is diarrhetic shelfish poison (DSP), but this toxin is very rare and not monitored on a regular basi, so wil not be considered in our analysis. Shelfish harvesting sites are closed when PSP values are 80 micrograms per 100 grams of shelfish, or ASP is over 14 micrograms per 100g of shelfish.  Harmful algal blooms may be toxic (producing toxins), or noxious (causing anoxia and clogging the gils of filter feding animals); we are considering only the toxic efects of HABs (Anderson et al. 2002). In addition to afecting the production of shelfish, HABs can also be responsible for beach closures, impacting the provisioning of cultural/recreational services in BC. Harmful algal blooms have become a worldwide isue, causing problems on every continent (Anderson et al. 2002). Globaly, the incidence of harmful algal blooms has been increasing, although at the same tie HAB-monitoring systems have also become much more advanced (Horner et al. 1997; Anderson et al. 2002; Heisler t al. 2008). A worldwide  1 increase in fertilzer use producing nutrient-rich runoff has occurred coincident with the increase in HABs. Although the rise in runoff may not be the root cause of increased HAB frequency, it is generaly agreed that runoff is a factor in the development and intensity of HABs. Despite the increase in HABs over time, and their efect on human health through the vector of shelfish, current understanding of the HAB - toxin relationship is uncertain at best, although recent advances in the forecasting of blooms have been made (Anderson et al. 2002; Shumway et al. 1990; Hodgkis & Ho 1997; Wong et al. 2009; Pickel et al. 2009). This complexity is due to the intricate nature of ocean dynamics; factors ranging from temperature, salinity, ocean current speed and direction, acro- and micro-nutrients, and nutrient ratios have al been implicated as having an influence on the formation of HABs (Heisler t al. 2008). Due to data limitaions, we have asumed that the formation of HABs is a simple proces that can be predicted by nitrate concentration. We have found two datasets alowing us to predict occurrence of HAB based on nitrate or fertilzer use, and one model that employs stability and nitrate concentration criteria. Using several models wil alow us to evaluate or make predictions within a range of values developed from among diferent asumptions regarding the relationship betwen HABs and nutrient runoff. Given that the data necesary to validate our models either have not been collectd or are unavailable, we are unable to test how acurate our modeling approaches are in the context of BC. 12 2. METHODS     We performed a literature search, using search engines Web of Science and Google Scholar, to investigate the current knowledge of relationships betwen agriculture and the production of shelfish. We broke the relationship down into the various components based on our framework: the relationship betwen agriculture and nutrient runoff, nutrient runoff and harmful algal blooms, harmful algal blooms and toxin production, and toxin production and shelfish production. We sarched a variety of governmental websites (The Pacifc Division of the Department of Fisheries and Oceans, Canadian Food Inspection Agency, Environment Canada, British Columbia Ministry of Land and Agriculture) for data on agriculture and shelfish harvesting in BC. Although we were able to find data on shelfish aquaculture in BC, we were unable find data on wild shelfish fisheries, which proves limitng for our results. Not only did we sarch the scientifc literature for qualitaive information on each linkage, but we sarched for quantitaive relationships, especialy numerical data, and also for data in a format compatible with ArcGIS 9.3. We found that data for al aspects of the agriculture-shelfish relationship are scarce, especialy Canadian data, and that there is no general scientifc consensus on any of the linkages either. In light of the uncertainties in the science, we opted to use a variety of models to compare the cumulative impacts, and analyze the resulting scenarios.  Spatial mapping of agriculture and nitrate runoff  Environmental Systems Research Instiute's geographical information systems (GIS) software suite ArcGIS 9.3 was used for the spatial modeling and data analysis component of our study. Al data sources used for the GIS model can be found in Appendix 1. Geospatial data for land use, rivers and watersheds in BC were al used as GIS layers. Land use data was kept at its native hectare resolution but al modeling was performed at square kilometre resolution. A layer was created to identify each km2 cel containing a river’s entry to the ocean; we manualy removed those rivers with no agriculture in their watershed from the  13 layer (as they would not contribute a significant source of fertilzer runoff). A cost-distance map was created to calculate the radius of runoff difusion into the ocean from each river outh in such a way that it would follow along coastlines and travel around islands. To quantify the relationship betwen agriculture and fertilzer use, we used agriculture, fertilzer use, nitrate concentration, and total volume of water flux data (all average yearly values) for the Fraser River watershed (Appendix 1). We used the average value for fertilzer use per hectare in Canada. Yearly fertilzer use for the Fraser River watershed was calculated; this number was compared with yearly average nitrogen concentrations for the Fraser River. These values were then extrapolated to create a relationship betwen fertilzer use and nitrogen loading in waters, which was applied to diferent watersheds throughout BC.  In order to determine yearly average concentrations of nitrate for al of BC’s rivers, a relationship was built betwen catchment area and flow rate (Appendix 1). Average yearly figures for nitrate concentration were generated using total mas of nitrate and volume of water per river. In the case of watersheds with multiple major rivers, the nitrate loading and flow rates were equaly distributed. Once nitrate concentration per river was determined, the concentration was difused equaly in each direction from a point source cl at the mouth of the river into the ocean. This was preformed individualy for each river major river mouth, for a total of 65 times (there were 65 rivers used in this study).  Each operation produced an individual layer representing the difusion of nitrate from that particular river. These layers were then summed such that the value in cel is representd by:  ,where [NO3-]i is nitrate concentration of the water in a cel, i represents the river and layer number (1-65), mi s the yearly mas flux of NO3- by river in micrograms, Vi is the yearly volume of water flux by river in litres (so that mi/Vi = initial concentration at mouth of river i), ! is the decay coeficent (one minus the rate of decay, betwen 0 and 1), and xis the distance from the mouth of river i.  The mas flux of NO3-, m, was determined by calculating the ratio of tonnes of nitrate per year that flows through the Fraser River (extracted from measured average concentration, 151 "g/L, and measured yearly flow rate, 112 km2/yr) to the total amount of fertilzer applied on agricultural land in the Fraser watershed (calculated  14 by multiplying the average value for fertilzer use in Canada, 56 kg/ha, with the total area of agriculture within the watershed, 500,000 ha).  This ratio of total mas of NO3- runoff to fertilzer application and agriculture area was then applied to al other watersheds in the study to determine the mas flux for each river given a known area of agriculture within the cathment.  The volume flux, V, was calculated in order to derive a staistical relationship betwen watershed area and flow rate, (flow rate = 0.0118*watershed area (km2) + 895) specifc for British Columbia, using hydrometric data from Environment Canada's Water Survey program (Appendix 1). Halpern et al. (2008) use a similar method to model difusion, employing a decay rate of 99.5% (i.e. !=0.005, so that the value in a given cel is one half of one percent the value in the previous cel). We used a range of decay coeficents for our model: 99.5, 66.7, 50 and 33.3 percent decay, and present results for each. Coastal currents and trapping by confined waterways and inlets were not included in our model due to a lack of spatialy explict data.   The Models We used three diferent models to evaluate the impact of nitrate runoff on the formation of harmful algal blooms. This was done for multiple reasons. In general, there is good reason to believe that nutrient runoff is a factor in the formation of HABs. Strong correlations have been demonstrated globaly betwen total nitrogen input and phytoplankton production in marine and estuarine waters (Horner et al. 1997; Anderson et al. 2002; Heisler et al. 2008). In the coastal waters of China and Japan, increased nutrient loadings have coincided with the development and expansion of large biomas HABs, impacting fisheries and human health. These same regions have recorded reduced incidences of HABs when controls on nutrient input were put in place (Anderson et al. 2002). However, there is uncertainty around the relationship betwen nutrient runoff in HABs specifc to BC, in part because there is very litle available data for harmful algal blooms or nitrate loading and ambient concentration, as wel as the other environmental conditions that may either prevent or faciltae blooms (Anderson et al. 2002; Barbier t al. 2008; Selner et al. 2003; Robinson & Brown 1983). The four diferent models present a range of response variables, i.e. HABs per year, likelihood of a HAB. They also involve the input of a variety of parametrs; from merely nitrogen concentration or fertilzer use, to those combined with wind speed, depth and  15 mixing conditions. For each model, we used the program Dathief to extract time sries or spatial data, and fit staistical relationships betwen nutrient runoff and HAB formation. The first model is a staistical model relating incidence of HABs to fertilzer use for Chinese coastal waters. We extracted time sries data of both fertilzer use (milions of tonnes per year) and HAB (events per year) located in Chinese coastal waters (Anderson et al. 2002, Zhang 1994). The natural log of HAB events were plotted against fertilzer use and a linear regresion model was used to describe the relationship and to predict HAB events per year based on fertilzer use in BC, which was calculated by totaling the area of agriculture (Figure 3.1), multiplying that by the average fertilzer use per hectare per year. For the second model we extracted time sries data of nitrate concentrations (mg/L) per month and monthly HAB events, from 1986 to 1989, also obtained from the Chinese coastal region (Hodgkis & Ho 1997). We found that when nitrate concentration was over 50 "g/L there was a 0.76 probability of at least one HAB incident per month, and used this figure as our bloom “threshold”. Because this model involves nitrate concentrations from al sources, and we lacked data on background nitrate lvels, our use of the model required an asumption of negligible background concentrations of nitrate lvels (se discussion). The third model used involved methodology adapted from a forecasting model developed and applied to the coastal waters of China (Wong et al. 2009). This model uses a decison-making tree to forecast environmental conditions favourable for HABs to occur Figure 2.1 Decision tre for algal bloom forecasting involving hydroynamic stabilty and nutrient hreshld. Redrawn frm Won et al. (209).  16 (Figure 2.1). The model's first step determines whetr hydrodynamic conditions are suitable for bloom formation; if so, nutrient concentrations are considered. If both mixing and nutrient conditions are favourable, a bloom is predicted to form; otherwise it wil not. Hydrodynamic stability (E in Figure 2.1) is a complex measure of how stable a water column is; in order for a bloom to occur this figure must be les than the critical turbulence threshold, representd as         (! refers to algal growth rate and l is depth of euphotic zone). The stability parametr, E, is a function of wind speed at 10 metres, W10 , water column depth, H, depth averaged tidal current, U, and the bulk Richardson number, Ri . It is given by:  , where  when W10> 4.2 ms-1, and  when W10< 4.2 ms-1. The bulk Richardson number, Ri , is given by:                     , where # is water density and Us is the surface current (an empirical function of water column depth and wind speed obtained from three-dimensional hydrodynamic modeling), and is bounded by zero and 15 "above which there is no further reduction in difusivity by stratifcation" (Wong et al.!2009).  Due to constraints in the availability of spatial data, we were only able to obtain the average wind speed and bathymetric (water column depth) data for BC (Appendix 1).  The remaining parametrs were estimated from values used in the Wong et al. study (2009). We have adapted and simplifed this model by making asumptions about al parametrs excepting those requiring or related to wind speed and depth to acommodate for the liited amount of spatialy explict data we were able to obtain for the BC coast. For al three models, we asumed the same relationship betwen the occurrence of HABs and their efect on the viability of shelfish production sites. Due to data limitaions and current lack of scientifc understanding, we asumed that the occurrence of a HAB would always produce enough toxicity to prevent any suitable shelfish habita from being harvested, be it aquaculture or recreational/wild harvest sites. There are relationships in the scientifc literature linking HABs (in terms of celular abundance) to toxin production but the connection betwen toxin production and its subsequent uptake and concentration by shelfish is unknown (Flynn 2002; Gedaria et al. 2007; Parkhil et al. 1999; Marchetti et  17 al. 2004). For this reason, we were unable to use toxin production to model potential shelfish closures.  18 3. RESULTS   Agriculture and Nitrate Runoff in BC                    Figure 3.1 A map of the coastl draing watersheds and locations of agricultre in Britsh Columbia, Canda. ! 19 Model 1 The first model is a staistical relationship betwen fertilzer use (milions of tonnes per year) and HABs (events per year), which we used to predict incidents of HABs based on fertilzer use. The equation obtained by linear regresion is ln(HABs/yr) = 0.17*(fertilzer use in tons/yr) + 0.44, with an r2 value of 0.885. The total amount of agricultural area in BC (Figure 3.1) is 1367275 hectares of land, which was converted to 76.5 thousand tonnes of fertilzer for al of BC, using the average fertilzer use from Staistics Canada (2006). This amount of fertilzer produced a value of 1.45 HABs/year for al of BC. It was impossible to display this model in a spatialy explict format because frtilzer use for al of BC is significantly lower than the lowest data point extracted, and required extrapolating nearly to the y-axis.     Model 2 The probability of at least one HAB event per month when nitrate concentration is over 50 "g/L is 0.76. We asumed that at least one HAB per month was enough to cause a shelfish harvest closure and therefore did not consider the occurrence of multiple blooms for a given !"!#$"%"%#$"&"&#$"'"'#$"("(#$"!" $" %!" %$" &!" &$"!"#$%&'()*+,-./*,01!12*,.3'*.#41!!15"'.56.05""*'()*+,-.Figure 3.2 Linear regrsion (black line) of the rlationship betwen fertilzer use and natural log of HABs per year (blue diamonds). Original data frm Zhang (194). Apenix 3 contains additonal information  suitabilty of linear regression.  20 are.  Due to the high variability in ambient nitrate concentrations in the waters of British Columbia's coast, an arbitrary value of 5 "g/L was chosen to represent background levels for visualization purposes. This value is slightly above the lowest detectable concentration of nitrate, 3.1 "g/L, but not high enough to influence HAB formation (Pickel et al. 2009). Figures 3.3, 3.4, 3.5, and 3.6 show the spatial distribution in concentrations of nitrate due to riverine plumes using decay coeficents of 0.005, 0.33, 0.5, and 0.67 respectively. Insets show two areas of interest; the Gulf Islands and Victoria region around south Vancouver Island where relatively high NO3- concentrations were produced from our model, and the Courtenay and Comox region on east Vancouver Island where there is a large density of shelfish harvesting sites (representd as purple dots).  Red pixels represent areas that have average concentrations over our nitrate threshold of 50 "g/L, and thus have a 76% probability of experiencing a bloom and thus a closure.  From Figure 3.3 it can be sen that using such a low decay coeficent (0.995; that used by Halpern et al.) produces limited results as the nitrate concentration in river plumes often becomes insignificant by the second cel (i.e. no efect is sen beyond a 2km radius of the river mouth), which is at odds with satelite images of river plumes.  Environment Canada staes that in 2004 there were 161 square kilometrs of shelfish harvesting closures as a direct result of agriculture runoff; we have listed the total area of cels with concentrations above our threshold in the description of each map.  Note that the areas surrounding the Fraser River and Victoria (top left and bottom middle of the insets on the right) have some of the largest afectd areas, the implications of which can be sen from the lack of current shelfish tenures (as wel as the areas left blank in Figure 4.1) these regions are permanently closed to shelfish harvesting due to a variety of reasons including agriculture runoff and sewage outflow (CFIA, DFO).  The much more sparsely populated north coast region, including the Haida Gwai islands, were included in the analysis but due to the vast amount of land area and relatively smal amount of agriculture, no substantial results were produced, so these areas are not shown on the maps. 21              Figure 3. Result of model 2 using a decay rate of 99.5% (! = 0.005). Although dificult o se using this high decay rate, there are atotal of 34 square kilometers that are above our 76% HAB probabilty threshold, all ocated at the mouths of rivers.  2               Figure 3.4 Result of model 2 using a decay rate of 67% (! = 0.33, value in cel is one third the value in previous cel). Ther are atoal of 83 kilometers that are abve ur 76% HAB probabilty threshold.  23              Figure 3.5 Result of model 2 using adecay rate of 50% (! = 0.50, value in cell is one half the value in previous cell). There are a total of 170 kilometrs that are abve or 76% HAB prbabilty threshold.  24              Figure 3.7 Result of model 2 using a decay rate of 33% (! = 0.67, value in cell is two thirds the value in previous cell). Ther are a total of 495 kilometrs that are above our 76% HAB probabilty threshold.  25 Model 3 The Wong et al. (2009) model is by far the most comprehensive and thus required more parametr to run than the other models.  Dat availability for most of these parametrs was a major constraint, so many of the parametrs had to be estimated.  Figure 3.7 shows the hydrodynamic stability using extreme values for the bulk Richardson number and tidal current velocity (0 and 9.1 cm s-1 respectively, i.e. the lower bound for Ri and the maximum measured value for current) deonstrating an unrealisticaly unstratifed environment for the purpose of showing that even when using extrem values for these parametrs al of the shoreline remains stable with respect to the average critical turbulence threshold (         ) from Wong et al. (2009) of 2.82x10-4 m2 s-1 (conditions that favour the formation of blooms occur when E <         ).  Figure 3.7: Map showing hydroynamic stability, E, using measured yearly wind sped and bathymetric data. For this scenario depth averged curent was set to thximm vlue from Wog et l. (209) at 9.1 cm s-1 and th bulk Richardson numbr was set to the theoretical minimu of zer, in order to portray the least tratifed conditions, deonstrting that ven when these variabls ar set o xtremes all of the coastline areas remain stable with respect to the critcal turblence threshold.! 26  Given that the entire BC coast mets the hydrodynamic stability condition, the next step in the decison tree (Figure 2.1) from this model is to determine areas in which the nitrate lvel exceded the triggering level in order to conclude whetr or not a bloom is likely to occur.  The triggering level used in the study was 533 !g/L.  Applying this triggering level to our modeled concentrations due to agriculture yields no locations where a bloom is likely to occur. 27 4. DISCUSION    We implented three models to predict HAB formation from agricultural fertilzer use because of the uncertainty of these relationships in the literature and general dificulty of obtaining the needed data. The first model provides a simple linear relationship betwen increasing fertilzer use over time and with it an increasing quantity of harmful algal bloom events. The second model uses a diferent approach by asigning a probability for a bloom given a concentration of nitrate. The third model, the forecasting model, is diferent from the previous two as it incorporates another variable based on hydrodynamic stability as a condition for bloom formation. We compared the results of this set of three models as an alternative to performing model validation techniques, which were impossible due to data limitaion. The benefit of comparing three models i that we were able to generate a reasonable range of outces, and also examine the dynamic betwen fertilzer runoff and HAB production at a variety of scales. We were also able to identify aspects of the linkage that are least sound; this motivated our recommendations for further monitoring and data collection practies in British Columbia. The results of our three models varied greatly; this i due in part to difering scales and units of input parametrs. The first model required extrapolating the linear regresion line nearly to the y-axis, leading to unreliable outcomes that we were unable to display spatialy. Though this model cannot acurately be applied to British Columbia due to a large diference in the magnitude of fertilzer use it does show with some degree of confidence that the two are in fact related. The second model was constrained by the fact that it could not be used to generate explict predictions, but rather probabilites. Having said that, this model does demonstrate a strong relationship betwen elevated nitrate concentrations and HAB events, supporting the conclusions dran from the first model. Another source of uncertainty to model 2 is the applicability of HAB event probabilites from the location of data collection to BC. The most important limitaion of results generated by the third model is the number of parametrs required that had to be estimated using data from China. This model has the highest potential to acurately model and predict HAB formation in BC, both spatialy and  28 temporaly and thus we base the majority of our recommendations around the factors currently impeding its application in BC. Our models may under predict the quantity of HAB events and thus closures due to the relatively smal agriculture sector in BC and the uncertainty surrounding the magnitude of change that fertilzer runoff can cause in marine systems. Discrepancies betwen the results of the three models demonstrate a need for scientifc research on the formation, frequency, and location of HABs, and their potential efect on shelfish and shelfish harvesting, specifc to the BC coast. Despite the diferences in each of the models, one common theme is that nitrate loading has a measurable efect on formation of harmful algal blooms, toxin production, and shelfish closures. Changes to potential shelfish harvesting ares represent potential changes to the ecosystem service provision, both cultural and provisioning. This information is important for managers in BC involved in policy creation and decison making, as manageent can benefit from efective consideration of tradeoffs and strategis for minimizng the impact of human activities, specifcaly agriculture, on marine systems and their functioning. Al of the factors which acted to limit our application of these models in BC can be considered directions and objectives for further scientifc research.  Applicability of the models to BC  The most limitng factor in the understanding of the ecological relationships betwen activities and services in BC is the lack of data. Not only is data lcking on specifc relationships, but basic oceanic data is lacking for the BC coast, as wel. A lack in both understanding of specifc relationships and general data limited the design of our model, and our ability to verify evaluate the model projections. By constructing a variety of models demonstrating the efects of agriculture on potential shelfish production, we have deonstrated how the efect of a single activity can have a significant impact on an ecosystem service in BC. As more and more of the linkages become understood, and data becomes available, the results of a cumulative impact model wil become much more  29 significant. Currently, shelfish site closures are determined by measuring shelfish for biotoxin contamination (PSP, ASP) on a regular basi. The employment of a HAB forecasting and monitoring program, siilar to the one in place in China, along with shelfish biotoxin monitoring would alow for the development of a framework for discovering the specifc causes of shelfish contamination, and managing those threats. The current method is a reactive approach; we recomend a proactive approach, as it would lead to beter management of the ecological factors contributing to shelfish biotoxin contamination. The Pacifc branch of the Department of Fisheries and Oceans Canada provides spatial data on shelfish culture capability for Manila clams, Pacifc oysters, and Japanese scalops in British Columbia.  A map of suitable locations is shown (Figure 4.1) as a reference for, and as a complemnt to our results.  The parametrs from which they determined this information are not explict. Dat are not included for this map around the cites of Vancouver and Victoria, because these locations are permanently closed to al shelfish harvesting due to high volumes of sewage outflow and other forms of runoff. Although agricultural runoff was not the only impact considered in the creation of this map, the regions designated as "poor" areas for shelfish harvesting generaly coincide with the threshold exceding areas generated by model 2 and suggest that al three of our models were conservative.  30 Figure 4.1  Map showing shelfish cultre capbility for Manila clams, Pacific oysters, and Japnes scallops in Britsh Clumbia. Redrawn from DFO Canada 201. ! 31 Spatial Mapping The GIS model we constructed is spatialy explict but lacks a temporal dimension; this i its most important limitaion. Al linkages defining the agriculture-shelfish relationship are sensitve to teporal variations. For example, fertilzer is applied to agricultural land seasonaly, and rainfal, which influences the amount of fertilzer runoff entring waterways, varies both seasonaly and annualy. Nutrient inputs from fertilzer runoff can be both episodic and chronic in nature, both types of which influence the formation and persistence of HABs although the way in which they do so may difer (Heisler et al. 2008; Anderson et al. 2002). Another limitaion of the GIS model is that it is unable to acount for 3-dimensional dynamics, such as water column structure. HAB formation depends on mixing dynamics in the water column, which are determined by temperature, salinity, wind speed, bathymetry, upweling conditions, and direction and speed of currents (Wong et al. 2009). Additionaly, it is very dificult to model difusion of nitrate concentration from river-based runoff inputs into the ocean without taking into acount vertical structure of the estuary into which runoff is flowing. Direction and extent of difusion depends currents, flushing time and estuary depth; al of thes factors influence how long nutrients are available to phytoplankton for uptake (Anderson et al. 2002). Conversely, mapping our models spatialy has alowed us to compare and define areas in BC where agriculture afects the production of shelfish most intensely.  HAB - Toxin - Shelfish Relationship There are many uncertainties in the HAB-toxin relationship; these uncertainties are expresed in the asumptions we made to model this linkage. Dinoflagelate species alone are able to produce a suite of twelve diferent toxins, each of which varies in its biological activity (Boyer et al. 1987; John & Flynn 2000). We asumed that al toxins produced by al species had the same efect on shelfish, and al toxins produced by a bloom of harmful algae were capable closing a site's availability to shelfish harvesting. Both Pseudo-nitzschia and Alexandrium, the most important toxin-producers in BC, exude more toxins under nutrient- and temperature-stresed conditions (Boyer et al. 1987; Flynn 2002). Toxicity of these  32 species i inversely related to growth rate, and increase as temperatures become ls optimal and phosphorus limitaion increase; Pseudo-nitzschia produce more toxins when their silca requirement is not met (Boyer et al. 1987; Flynn 2002). We were not able to include any of these additional variables in our model, which adds a source of uncertainty to our results.  The largest unknown in the relationship betwen agriculture and shelfish production is the relationship betwen toxin production by HAB species and shelfish toxin uptake. This relationship is very complex, both spatialy and temporaly, and can be influenced by a multiude of factors, the full range of which is unknown. Lab experiments have been able to easure toxin production by a selction of HAB species; the toxins are measured as concentration in water (Flynn 2002; Marcheti et al. 2004; Parkil & Cembela 1999; John & Flynn 2000). To date, the movement of these toxins in water and how the toxins are concentrated and taken up by shelfish has not been succesfully measured. Closures of shelfish aquaculture sites are determined by measuring the toxicity in the shelfish; toxicity in water is not measured along with shelfish toxicity (CFIA 2008).   Limitations A limitaion common to the application of models 1-3 to BC is the source of the data that inform the; al three odels were based on datasets from the coastal waters of China. Chinese coastal waters are subtropical westrn Pacifc, whereas BC coastal waters are temperate eastern Pacifc. These two regions difer in both annual average temperature and precipitaion paterns, upweling dynamics, currents, nutrient conditions, wind speed, rate of nutrient turnover, and salinity (Meuwig 1999). Despite these diferences, both fertilzer use (leading to nutrient runoff) and HABs have increased globaly over the same tie scale as the data used for models 1 and 2 (Heisler t al. 2008; Parsons & Dortch 2002). Since mixing and nutrient ratios have both been recently described as important factors in defining favourable bloom conditions, these variables wil create the largest uncertainty in using Chinese data on BC coastal waters (Heisler t al. 2008; Wong et al. 2009; ). Although the specifc relationships may difer from China to BC (and wil be discussed for each model separately), the general trend can be applied to diferent regions globaly, as long as factors  3 potentialy causing uncertainty are recognized. This conclusion applies for model 3 as wel, since it is a model created for Chinese waters that we have applied to the BC coast. Another limitaion common to al models i the variable ambient concentration of nitrate throughout the coastal ocean, and the variability in celular concentrations that make up HABs and cause toxicity. Natural nitrate concentrations vary widely throughout BC's coast; because of this the efect of additional nitrate from agricultural runoff also varies widely. We were not able to acount for the efect of natural nitrate concentrations, as spatialy explict nitrate data is not available for al of BC. The lack of this information implies that al of our models are conservative as we only considered nitrate from one source and were not adding this nitrate to an already-present baseline. Similarly, the concentration of cels that actualy makes up a HAB varies by species and location. Generaly, this concentration is considered to be 10^6 cels/L, and can increase to as high as 10^9 cels/L (Anderson et al. 2002). Despite this, concentrations as low as 100s of cels/L have been observed to cause enough toxicity in shelfish to harm humans due concentration of toxins by the shelfish feding mechanism (Selner et al. 2003). Although concentrations this low may not be explictly considered HABs, they are stil of concern for our purposes, as we are ultimately concerned about the change in the ecosystem service provider that is shelfish production. The r2 value for model 1 is very high, suggesting a strong relationship betwen fertilzer use and the formation of HABs. The causal relationship is likely not as strong as i suggested by the correlation coeficent, as both variables are increasing over time. It does imply, though, that eutrophication caused by fertilzer runoff is a contributing factor to the development and persistence of HABs (Trainer et al. 2003; Turner & Rabalais 1991; Parsons & Dortch 2002; Heisler t al. 2008). Another source of uncertainty is the scientist’ definition of a HAB. The amounts of fertilzer in the dataset are an order of magnitude larger than BC's annual fertilzer use and produce a relatively insignificant number of HABs per year. This uggest that the authors' methodology was centred on a conservative definition of what constiutes a HAB. The scale of the Chinese frtilzer dat was the entire coastline; this may have led to the authors considering a number of smaler HABs occurring along the entire coastline at the same tie to be "one" HAB event. Using data for al of the Chinese  34 coast and atempting to apply it to a Canadian province comes with a large source of uncertainty as the total amount of fertilzer used in China is many times larger than that for British Columbia, and generates a very conservative estimate of HABs. The data from model 2 were measured from China so the above discussion about uncertainty of its application to a diferent location applies. Another source of uncertainty is that we are considering anything equal to or over one HAB per month to be one HAB. Following from this, we are then asuming that one bloom per month can generate enough toxins to close a shelfish site from possible harvesting.  While our model did acount for flow rate in terms of catchment area, it did not acount for a decay coeficent that varies with river output.  In other words, a large river wil most likely difuse more slowly over distance than a smaler river due to total volume of water entering the ocean.  Another caveat to our method of difusion is that we were unable to acount for direction-specifc advection and transport of nitrate by ocean currents and mixing; we asumed that the diference in densites betwen river/estuarine water and ocean water was great enough to have 100% stratifcation (i.e. no loss of nitrate due to vertical mixing).  It is important to note that our modeled values for [NO3-] only represent concentrations as a direct result of agriculture runoff; other sources of nitrate (.g. coastal upweling, waste from aquaculture) were not included in this analysis.  In determining concentrations within rivers, using data from the Fraser, we asumed that 100% of the nitrate loading was from agricultural runoff.  There are, of course, other sources of nitrogen within the natural and manmade environments, such as plant liter and sewage outflow, but we could not acount for their representative fractions.  Although census data is available for quantity of livestock, whose waste is a source of nitrate and thus an inherent contribution to [NO3-] in rivers, we could not break down the percentages by land use that livestock agriculture occupies because the census data was not aerial.  Thus every catchment was asumed to have the same compositon of types of agriculture as within the Fraser watershed, though one can be fairly certain there is a high degree of spatial variability in agriculture types (e.g. fruit orchards, grain fields, fodder crops) and fertilzation methods (e.g. organic, animal manure, chemical) that can contribute to sources of variation in our result  35 Model 3 was the most comprehensive model applied to our study. Due to data constraints, we were unable to include al of the variables that the creators of this forecasting model suggested. Our simplifcations decrease the acuracy of the predictions, although this odel is the most comprehensive of those that we employed.  Although the coastal waters of British Columbia were shown to be hydrodynaicaly stable even under unrealistic conditions, the nitrate triggering parametr used by the authors was higher than our maxium modeled NO3- concentration, which resulted in zero predicted HABs.  A HAB-triggering level tailored to British Columbia's coast using empirical data would increase the applicability of this model to BC.  In terms of forecasting, this model would be beter for real world application if actual measured oceanic nitrate concentrations were used rather than their theoretical component from agriculture alone, as there are many sources of nitrogen to the coastal region of BC mising from our model (Mackas & Harrison 1997).  Another constraint to the application of this forecasting model in BC is that to keep consistent with the temporal scale of nitrate data, we had to use yearly averaged wind sped values rather than using real-time data. This decison tree forecasting proces implented into a geographic information system does however provide a solid fraework from which to actively monitor and predict where blooms are likely to occur in real time given adequate data availability. Despite the variety of results produced by our models and the lack of their current applicability to BC, our models show that there definitely exist a trade-off betwen fertilzer use on agricultural land and shelfish production in BC. Beter management of agricultural runoff can be implented without necesarily reducing agricultural production. With lower amounts of nutrient runoff our models al predict an increase in the number of available shelfish harvesting sites, and therefore the theoretical production of shelfish increase. With efctive management of agriculture in BC, both these food provision services can increase at the same tie. 36 5. CONCLUSION   Our approach provides a framework for identifying the ecological impacts of human activities on ecosystem services. The methods provided can be expanded on and used to identify locations in BC where management should target in order to ases and minimize cumulative impacts to ecosystem services, and also maximize the productivity of a variety of ecosyste services. Highly ipacted services can be targets for sustainable management and/or conservation, and areas where beter management practies for high-impact activities may be identifed. Using a mechanistic approach alows managers to identify which aspect(s) of the activity-service relationship is causing the highest impact and target that specifc linkage.  The results of our spatial mapping identify regions of BC where beter management regulations of a human activity can benefit not only the specifc service (shelfish production) that we investigated, but a variety of ecosystem services, including food provision, regulating services, and cultural/recreational services. Shelfish harvesting is an important part of both the BC culture and economy and on top of this shelfish can act to purify water by filtering out toxins and bacteria. If fertilzer use in BC were beter managed in order to avoid runoff, the provision of shelfish in BC could theoreticaly increase due to les site closures, without a decrease in agricultural production. Using the GIS as an analysis tool, diferent scenarios for runoff management can be asesd in terms of the gain or loss of potential shelfish harvesting areas, and the change in the provision of the service can be quantifed. If fertilzer runoff was properly managed in BC, more shelfish habita would become available for harvesting, increasing revenue and generating employment. Additionaly, the cultural service that the harvest of wild shelfish provides would be increased, and the water filtration capability of shelfish would expand. In creating a conceptual model of the linkages betwen human activities and ecosystem services in BC, and modeling one of these pathways, we have demonstrated a framework for the evaluation of cumulative impacts to ecosystem services in coastal BC. We  37 have identifed an important trade-off betwen an ecosystem driver caused by agriculture on shelfish production, as wel as the gaps in knowledge of this relationship. 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APENDICES Appendix 1 Spatial (GIS) data sources:  • Britsh Columbia Watershed Atlas (Minstry of Environment) <http://w.env.gov.bc.ca/fish/watershed_atlas_aps/watershed_code/index.html> • Canadin Wind Energy Atlas (Environment Canda) <http:/w.windatlas.ca/en/index.php> • Water Survey (Environment Canda) <http:/scitech.pyr.ec.gc.ca/waterweb/> • Water Quality – Fraser River Monitoring Program (Environment Canda) <http:/w.waterquality.ec.gc.ca/waterqualityweb/realtimeindex.aspx> • Integrated Land Management Bureau (Province of Britsh Columbia) <http://w.hectaresbc.org>   Other dat sources: • Statistics Canada 2006 Census of Agriculture o <http:/w.stacn.gc.ca/-ra206/index-eng.htm>   43 Appendix 2 Map of British Columbia for reference to coastal study area.  Redrawn from information available from Google Maps.                       4 Appendix 3 Additional information for model 1 linear regresion.  Original data extracted from Anderson et al. (2002) (redrawn from Smil 2001 and Zhang 1994) using the program Dathief. The data did not appear to be linear, so y-values were ln-transformed (Figure 3.2).   A histogram displaying the approximately normal distribution of residuals of the linear regresion performed on ln(HABs per year) vs. fertilzer use (milions of tonnes per year) (Figure 3.2). The far left value (-0.8 to -0.6) looks like an outlier at a glance, but is not, as it is only one unit greater than its neighbour to the right. !"#!"$!"%!"&!"'!"(!"!" '" #!" #'" $!" $'"!"#$%&'()%*(+),%-()./0/1()%2$(%&3/00/45$%46%.45$%'()%*(+),%!"#"$"%"&"'"(")""*!+,"-."*!+(""*!+("-."*!+&""*!+&"-."*!+$""*!+$"-."!""!"-."!+$" "!+$"-."!+&""!+&"-."!+(""!+("-."!+,""!+,"/01"/2.34" 45  X-axis values (fertilzer use) plotted versus residuals of the linear regresion (Figure 3.2) showing approximate homoscedasticty of the residuals about the x-axis.  *!+,"*!+("*!+&"*!+$"!"!+$"!+&"!+("!+,"!" '" #!" #'" $!" $'"7($/89+0$%-()./0/1()%2$(%&3/00/45$%46%.455($:*(+),%

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