Sampling!effort! No*data*from*QC*North* Shore*or*An&cos&*Island* No*data*from*upper*Bay* of*fundy*

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1 Spa2al%paIerns%in%larval%produc2on%of%American%lobster%Homarus(americanus%in%Atlan2c%Canada% Marthe%Larsen%Haarr1,%Rémy%RocheIe1,%Michel%Comeau2,%Bernard%SainteRMarie3,%John%Tremblay4% 1Biology!Department,!University!of!New!Brunswick!Saint!John,!Saint!John!NB.!2Gulf!Region,!Fisheries!and!Oceans!Canada,!Moncton!NB.!3Maurice!Lamontagne!Ins'tute,!Fisheries!and!Oceans!Canada,!MontJJoli!QC.!4Bedford!Ins'tute!of!Oceanography,!Fisheries!and!Oceans!Canada,!Dartmouth!NS* Industry%partners:*Fish*Food*and*Allied*Workers*Union,*Guysborough*County*Inshore*Fishermen s*associa&on,*associa&on*des*pêcheurs*propriétaires*des*iles*de*la*madeleine,*regroupement*des*pêcheurs*professionnels*du*sud*de*la*gaspésie,*mari&me*fishermen s*union,* LFA*30*Fishermen s*associa&on,*richmond*county*fishermen s*associa&on,*homarus*inc.*eastern*shore*fishermen s*protec&ve*associa&on,*fishermen*and*scien&sts*research*society,*lfa34*management*board,*fundy*north*fishermen s*associa&on,*gulf*nova*sco&a* Fishermen s*coali&on,*northumberland*fishermen s*associa&on,*prince*county*fishermen s*associa&on,*lfa*27*management*board.* Measure*female*size,*egg* development*stage*and* clutch*quality* No*data*from*QC*North* Shore*or*An&cos&*Island* 17,239*berried*females* sampled*on*342*trips*by* 61*vessels*in*2011*(year* 1*of*5)* Sampling!effort! No*data*from*upper*Bay* of*fundy* Will*escape*vents*in*traps*allow* small*berried*females*to*escape?* eggs% AZard*1985.*Campbell*and* Robinson*1983.*Ennis*1981.** Single*day*sampling*in* collabora&on*with*dfo* No*late*spring/summer*fishery,*may*need*out* of*season*sampling* eggs% female%size% female%size% Fecundity* *size*rela&onships* *are*not*the*same*in*all*areas** Evaluate*poten&al*limita&ons*in* data*that*may*affect*es&mates*of* egg*produc&on* female*size7at7maturity* eggs% eggs% female%size% female%size% Tremblay*et!al.*2011.*CSAS*Res.*Doc.* * LFA Objective: Estimate spatial patterns in larval production based on berried (eggbearing) female demography to provide biological input into biophysical model of larval drift and connectivity Abundance!of!berried!females! 0* *2,000* number*of*eggs* Mean!#!of!eggs!per!trap! hauled! female*size* 0.8* *0.9* Mean!#!of!berried!females!per! trap!hauled! Mean!size!of!berried!females! 36,000* *38,000* 0* *0.01* (index*based*on*catch7per7unit7effort)* One fisherman every km of coastline collecting data on all berried females in catch one day a week throughout season Trap*and*opening* size*may*limit*the* size*of*lobsters*that* can*be*caught* Es'mated!egg!produc'on! Data*will*become*available*from* government*sampling* 70775*mm* *mm* Known*size7fecundity*rela&onships* used*with*berried*female*size*and* abundance*to*es&mate*egg* produc&on* Acknowledgments:%This*research*is*made*possible*by*an*unprecedented*collabora&on*between*fishermen,*academics*and*government*scien&sts,*and*the*contribu&on*of*people*too*numerous*to*list*here.*The*following*have*been*instrumental*in*the*development*and* realiza&on*of*the*project:*marc*allain*as*project*facilitator,*pazy*king*and*the*fsrs*team;*david*decker,*jackie*baker*and*the*ffaw;*mario*desraspe*and*the*appim;*o'neil*clou&er,*jean*côté*and*the*rppsg;*mar&n*mallet*and*dounia*daoud*(homarus*inc.);*ginny*boudreau* and*the*gcifa;*kevin*squires*and*the*mfu.*angelica*silva*(dfo*bio)*made*significant*contribu&ons*to*the*design*of*the*berried*female*sampling*protocol,*julien*gaudeze*(dfo*sabs)*enabled*sampling*of*berried*females*with*grand*manan*fishermen,*and*robert*macmillan* (PEI*FARD)*is*sharing*data*for*PEI.*This*project*is*funded*by*the*Natural*Sciences*and*Engineering*Research*Council*of*Canada.% % %

2 Effect of temperature on the behavior of stage IV larvae of the American lobster (Homarus americanus) Mélanie Chiasson 1, Gilles Miron 1, Dounia Daoud 2 and Martin Mallet 2 1 Université de Moncton 2 Homarus inc. Introduction Results Conclusion The lobster industry initiated in 2002 a lobster seeding program in Eastern Canada to increase the stock abundance of the American lobster (Homarus americanus). Seeding programs are carried out in the field after reared larvae reach stage IV. Several environmental parameters can influence the behavior and survival of larvae during the enhancement procedures in the field, including water temperature. The aim of this study was to document, in laboratory, the larval behavior of stage IV lobster larvae within a field simulation seeding context. More precisely, the objectives were to: Larvae tend to leave the substrate more often when they are acclimated at 15 C (Fig.1); Larvae acclimated at 15 C generally reached the substrate more rapidly than those acclimated at 20 C (Figs.2 and 5); Larvae were stressed (observation of tail flicks) when seeding was carried out at low temperature (10 C) particularly if acclimation at high temperature (20 C) was carried out (Figs. 3 and Fig.6); Larvae tend to hide more rapidly in high water temperature (20 C) compared to low water temperature (10 C) (Fig.4) = Time to reach the substrate = Tail flick = Substrate or shelter = Number of time the larva left the substrate describe the behavior of lobster larvae in relation to various water temperatures; verify if a thermal shock can occur during seeding and modify the behavior of lobster larvae; verify if an acclimation at a given temperature could minimize a potential thermal shock; verify if the behavioral response of larvae under a given temperature are similar when observations are carried out from 1 individual vs 1 individual within a group of 5. The information gathered in this study will serve as a fundamental data base for recruitment projects of the lobster node and help refine a general framework for future studies within a more applied context. Material and methods Figure 1 : Number of time (mean ± SE) the American lobster stage IV larva left the substrate in relation to seeding temperatures. Each bar of each seeding temperature represents a larval batch. Figure 4 : Time budget of the American lobster stage IV larva in relation to seeding temperatures. Each graphic represent the mean number of larvae displaying each behavioral categories, all replicates and larval batches confounded. 2009: 1 stage IV larva/experimental enclosure 2010: 5 stage IV larvae/experimental enclosure Same acclimation and seeding temperature treatments each year Larvae are reared in hatchery at 20 C Acclimation prior to treatment (4 days) Figure 2 : Percentage of time (mean ± SE) the American lobster stage IV larva took to reach the substrate in relation to seeding temperatures. Each bar of each seeding temperature represents a larval batch. Figure 5 : Time taken by 3 individuals out of 5 (mean ± SE) to reach the substrate by the American lobster stage IV larva in relation to seeding temperatures. Each bar of each seeding temperature represents a larval batch. Acclimation and seeding temperatures affect the larval behavior of the American lobster stage IV larvae Field validations are still needed to warrant laboratory results and suggest recommendations to the industry Seeding temperature Acknowledgments Figure 3 : Time (mean ± SE) the American lobster stage IV larva took to display the tail flick in relation to seeding temperatures. Each bar of each seeding temperature represents a larval batch. Figure 6: Time (mean ± SE) the the American lobster stage IV larva took to display the tail flick in relation to seeding temperatures. Each bar of each seeding temperature represents a larval batch. We would like to thank Rémy Haché and his team from the Institut de recherche sur les zones côtières for providing valuable support with larval rearing. Michel Comeau and Stéphan Reebs provided equipment and advices during the project. This research has been funded by Homarus Inc., the Department of Fisheries & Oceans, the Université de Moncton and the Canadian Capture Fisheries Research Network (NSERC strategic network).

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11 Decision Analysis and Influence Diagram Modeling in Fisheries Management Danielle Edwards, Sarah Hawkshaw, Ben Nelson and Murdoch McAllister University of British Columbia, Vancouver, BC Introduction Fisheries management frequently involves a number of biological, economic and socio-cultural objectives that are in conflict. A key role of management is to make decisions with respect to these multiple objectives, often requiring difficult choices and trade-offs. Further complicating this challenge is the high level of uncertainty surrounding fisheries systems, which can result in unanticipated consequences of these complex decisions. Techniques and approaches, such as decision analysis, have been developed to more explicitly account for the uncertainties that confound fisheries management, to better understand risks and expected outcomes, and to provide guidance for transparent decision making in the face of conflicting objectives and uncertainty. Decision Analysis Decision analysis is a structured way to evaluate the consequences of alternative management actions. It provides a framework for decision making that accounts for multiple management objectives, uncertainty and the potential consequences (short and/or long-term) of management options. The benefits of a decision analysis approach to fisheries management is that it provides a formalized structure for evaluating complex decision problems, requiring clear articulation of management objectives, alternative actions, hypotheses, and expected outcomes from each action. Decision analysis is not intended to prescribe actions. This systematic approach to decision making is intended to help decision makers and stakeholders think through complex management decisions [1], improve transparency of the decision making process, and facilitate the communication of management options and decisions. Framework for Decision Analysis [1,2] : 1.Identify management objectives (e.g., viable fishing industry, healthy fish stocks) 2.Identify policy performance indices (e.g., average annual catch, expected stock size) 3.Formulate the utility function (i.e., combining policy performance indices into a single value that you want to maximize (benefits) or minimize (losses)) 4.Identify alternative management actions (e.g., TACs, size limits, gear restrictions) 5.Identify alternative hypotheses (e.g., current stock status, stock productivity) 6.Evaluate the weight of evidence in support of the alternative hypotheses 7.Evaluate the potential consequences of each management action under each alternative hypothesis 8.Rank the actions according to their expected utility 9.Evaluate sensitivity of ranking to assumptions, objectives 10.Communicate results to decision makers / stakeholders (e.g., through tools such as decision tables and Influence Diagram Models) Influence Diagram Modeling Influence Diagram Models (IDM) are graphical and mathematical representations of the key elements of a system. These elements are comprised of chance nodes to account for different states of nature, decision nodes representing discrete management options and utility nodes summarizing the expected outcome for each decision or combination of decisions. The IDM also explicitly identifies the probabilistic relationships between each of these elements. The IDM approach offers many benefits [3], helping to focus thinking about a system through the development of the model, providing a means to explicitly include uncertainty and having the capacity to handle large and complex model sets. IDMs are also valuable in that they provide a relatively userfriendly interface for investigating decision problems. They can function as a bridge between simulation modeling and decision making, offering a more concise and easier to understand output than the outputs more traditionally associated with simulation modeling. As well, due to their fast response time, they can be used directly within management meetings to investigate potential outcomes from different management actions. Trawl Mesh Size Recruitment assumption Example influence diagram model used in an analysis of Baltic cod management [4] Key Elements of IDM Yearly catch Effort by Gillnets Utility = chance node (e.g., stock size) Risk for recruitment failure Effort by Trawls Growth rate Critical spawning biomass = decision node (e.g., TAC, gear restrictions, effort controls) = utility node (e.g., one or a combination of: net profit, risk of stock recruitment failure, annual catch) CCFRN Project Examples: The Use of Decision Analysis and Influence Diagram Modeling to Communicate In-season Management Options for the WCVI Salmon Troll Fishery Evaluating Management Strategies for the BC Small Boat Groundfish Fishery Using Influence Diagram Modeling A Bayesian Influence Diagram Model for the Evaluation of Management Actions Related to Seal-Chinook and Coho Fishery Interactions in BC [1] Keeney, R.L Making Better Decision Makers. Decision Analysis, 1(4): [2] McAllister, M.K. and Kirkwood G.P Using Bayesian decision analysis to help achieve a precautionary approach for managing a precautionary approach for managing developing fisheries. Canadian Journal of Fisheries and Aquatic Sciences, 55(12) [3] Uusitalo, L Advantages and challenges of Bayesian networks in environmental modelling. Ecological Modelling, 203: [4] Kuikka S., Hildén M., Gislason H., Hansson S., Sparholt H., Varis O Modeling environmentally driven uncertainties in Baltic cod (Gadus morhua) management by Bayesian influence diagrams. Canadian Journal of Fisheries and Aquatic Sciences, 56(4): Canadian Capture Fisheries Research Network AGM 2011