Canadian Journal of Fisheries and Aquatic Sciences

Transcription

1 Management strategy analysis for multispecies fisheries including technical interactions and human behavior in modeling management decisions and fishing Journal: Canadian Journal of Fisheries and Aquatic Sciences Manuscript ID cjfas r1 Manuscript Type: Article Date Submitted by the Author: 23-Aug-2017 Complete List of Authors: Ono, Kotaro; University of Oslo, Centre for Ecological and Evolutionary Synthesis (CEES), Department of Biosciences Haynie, Alan; National Marine Fisheries Service - NOAA, Alaska Fisheries Science Center Hollowed, Anne; Alaska Fisheries Science Center, Ianelli, James; Alaska Fisheries Science Center, McGilliard, Carey; National Marine Fisheries Service, Alaska Fisheries Science Center, Punt, André; University of Washington, School of Aquatic and Fishery Sciences Is the invited manuscript for consideration in a Special Issue? : Keyword: N/A multispecies, FISHERY MANAGEMENT < General, BYCATCH < General, management strategy evaluation

2 Page 1 of 58 Canadian Journal of Fisheries and Aquatic Sciences Management strategy analysis for multispecies fisheries including technical interactions and human behavior in modeling management decisions and fishing Authors: Kotaro Ono 123*, Alan C. Haynie 4, Anne B. Hollowed 4, James N. Ianelli 4, Carey R. McGilliard 4, André E. Punt 3 (in alphabetic order after me) Affiliations and addresses: 11 1 Centre for Ecological and Evolutionary Synthesis (CEES), Department of Biosciences, 12 University of Oslo, P.O. Box 1066 Blindern, NO-0316 Oslo, Norway Centre for Coastal Research (CCR), University of Agder, P.O. Box 422, 4604 Kristiansand, Norway 3 School of Aquatic and Fishery Sciences, University of Washington, Box , Seattle, Washington USA 4 Alaska Fisheries Science Center, National Marine Fisheries Service, 7600 Sand Point Way NE, Seattle, WA 98115, U.S.A. * corresponding author (kotaro.ono@ibv.uio.no) Abstract 1

3 Page 2 of A multispecies fishery management strategy evaluation (MSE) framework based on the example of the groundfish fishery in the Bering Sea and Aleutian Islands region of Alaska was used to examine the interplay between a bycatch species and three groundfish species. The study introduces a framework for a realistic multispecies fishery MSE by accounting for fleet dynamics, multispecies fishery quota allocation, and the temporal dynamics of technical interactions. The quota allocation and the fleet dynamics models were implemented using linear programming, and regression approaches were used to realistically project future users behavioral response to changes in the fishery. Models were calibrated and then validated using historical and out-of-sample data, respectively. The results highlight the importance of accounting for technical interactions and their inter-annual dynamics for both quota allocation and fleet dynamics to design a realistic multispecies fishery MSE (without them, the amount of lost yield increased). Particular attention should therefore be paid to understanding human behavior as well as its uncertainty and to refining approaches to incorporate this information into a multispecies fishery management strategy analysis. 37 2

4 Page 3 of 58 Canadian Journal of Fisheries and Aquatic Sciences Introduction Multispecies fisheries are characterized by a complex combination of harvested animals, which vary in productivity and commercial importance. Some species may be productive and commercially valuable, while others are less productive or prohibited and not targeted by fishers. The latter are commonly referred to as bycatch. Most fisheries have to deal with bycatch issues as it is seldom possible to target and catch single species (Davies et al. 2009). Bycatch becomes especially problematic when there are species with very restrictive catch limits, either because they are protected by law, relatively unproductive, or are depleted (Hall et al. 2000). Institutions 47 without bycatch controls and monitoring can potentially overfish weak stocks (Hilborn et al ). In contrast, institutions that monitor and manage all catches (both landed and discarded) might have to trade-off the amount of bycatch with the catch of target species, depending on the overlap between the species (Worm et al. 2009; Ono et al. 2013; Kuriyama et al. 2016). In addition to bycatch, multispecies fisheries can have other constraints. For example, the groundfish fishery in the Bering Sea and Aleutian Islands (BSAI) region of the US Exclusive Economic Zone has a 2 million-ton cap in total groundfish catch (49 FR 397), which is well below the sum of individual species target catch limits in most years. Additionally, some areas are closed to fishing to protect marine mammal habitat and achieve other conservation goals. These types of constraints and catch restrictions make the effective management of a multispecies fishery particularly complex. One approach that has become popular in the field of fishery science to evaluate the outcomes of management measures given uncertainty is management strategy evaluation (MSE) (Smith et al. 1999; Bunnefeld et al. 2011; Punt et al. 2016). MSE is a simulation framework that 3

5 Page 4 of attempts to model the entire management system, including the true state of the resource, the data collection process, the estimation of stock status, the management advice/decision processes, and the fleet dynamics. MSEs have been conducted to examine consequences of new approaches to stock assessment, changes in fishing gear, and alternative management actions for a fishery (Punt et al. 2016). However, most applications of MSE have ignored technical interactions in multispecies fisheries. Here, the term technical interactions refers to situations where target and bycatch species are caught together during a fishing event due to the imperfect selectivity of fishing gear. When formulating a multispecies fishery MSE, technical interactions matter in at least two steps: how to set the annual quota (or total allowable catch limit) for each species in the fishery and how to model the fleet dynamics (i.e. how the fishing fleets operate to catch their quota and achieve economic goals). Additionally, annual changes in technical interactions need to be realistically modeled in the MSE. Some MSE frameworks include some elements of fleet dynamics (e.g. Atlantis (Fulton et al. 2011), BIOMAS (Ives et al. 2013), FLR (Kell et al. 2007), and ISIS-FISH (Pelletier et al. 2009)), but a multispecies fishery quota allocation model with trade-offs and constraints (i.e. based on constrained optimization) as well as the temporal dynamics of technical interactions have never been included in an MSE. Fleet dynamics and quota allocation models require extensive data on catch composition by species (both target and bycatch) to determine the fishing strategies by vessel, fleet (group of vessels), or métier. A métier is defined as a group of fishing operations targeting a specific assemblage of species using a specific gear, at a specific time and area (EC 2008). The concept of métier arose in Europe during the late 1980s (Laurec et al. 1991), and involves aggregating fishing activities into several homogeneous categories to make them easier to analyze (Deporte et al. 2012). However, accounting for technical interactions is not as simple 4

6 Page 5 of 58 Canadian Journal of Fisheries and Aquatic Sciences as defining métiers because the behavior of fishers (and managers) may change in response to a variety of constraints including regulations, markets demands and price, stock conditions, and environmental conditions. Therefore, it is necessary to appropriately account for these factors when modeling both the quota allocation process and the fleet dynamics in a constrained multispecies fishery to conduct a realistic MSE. We present a multispecies fishery MSE framework based on the example of the BSAI groundfish fishery that includes fleet dynamics, multispecies fishery quota allocation, and some of the inter-annual dynamics of technical interactions. Linear programming was used to optimize the quota allocation and the fleet dynamics models, and regression approaches were used to realistically project future users (both managers and fishers) behavioral response to changes in the fishery. This framework was then tested to examine how the inclusion of technical interactions and its temporal dynamics in the quota allocation and fleet dynamics models of the multispecies fishery MSE may increase realism and is likely to improve management actions. Finally, the sensitivity of the model outcomes to a range of biological and management scenarios was tested Methods The Bering Sea (BS) and Aleutian Islands (AI) The BS is the northernmost part of the Pacific Ocean and comprises a large continental shelf that abruptly drops through a narrow slope to a deep-water basin on the west (Fig 1). The BS covers about 2 million km 2 and is bordered on the east and northeast by Alaska, on the north by the Bering Strait (that leads to Arctic Ocean), on the west by Russia's Siberia and Kamchatka Peninsula, and on the south by the Alaska Peninsula and the AI. The BS is a productive 5

7 Page 6 of ecosystem that provides resources for fish, marine mammals, sea birds and humans. This productivity is due to both upwelling along the continental slope (Springer et al. 1996) and the seasonal sea ice coverage that triggers the spring phytoplankton bloom (Alexander and Niebauer 1981). The resources in the BS ecosystem are under the jurisdiction of the United States, the US State of Alaska, and the Russia Federation. The AI are a chain of more than 150 mostly volcanic islands that extend about 1,600 km westward from the tip of Alaska towards Russia s Kamchatka Peninsula (Fig 1). Similarly to the BS, the AI support abundant wildlife, including seabirds (Delinger 2006) and marine mammals such as Steller sea lions (Eumetopia jubatus) and northern fur seals (Callorhinus ursinus) (Muto et al. 2016). Here the focus was on the U.S. Bering Sea and Aleutian Islands (BSAI), which includes the AI and the portion of the BS in the Exclusive Economic Zone (EEZ) of the United States. The BSAI is managed under a fishery management plan (FMP) established under the Magnuson- Stevens Fishery Conservation and Management Act The BSAI groundfish fishery The groundfish fishery in the US portion of BS and AI (the BSAI GF) includes several types of fishing gear, primarily bottom trawl, pelagic trawl, longline, and pots. The fishery catches a wide variety of species including walleye pollock (Gadus chalcogrammus), Pacific cod (Gadus macrocephalus), and yellowfin sole (Limanda apsera) (NPFMC 2015). Pollock is the most abundant target species (mainly caught using pelagic trawls), and supports one of the largest commercial fisheries in the world, with an average annual catch of more than 1,200,000 mt (Ianelli et al. 2015), or about 66% of the total groundfish catch in the BSAI GF during Pacific cod and yellowfin sole constituted the next most important species in terms of 6

8 Page 7 of 58 Canadian Journal of Fisheries and Aquatic Sciences catch (12 and 8% respectively of the total BSAI GF catch during ). As opposed to pollock and yellowfin sole, cod is caught using several gear types, including bottom trawl, longline, and pot gear. Every year, the total allowable catch (TAC) for each of the 26 species (or species complexes) in the BSAI GF are set by the North Pacific Fishery Management Council (NPFMC) based on information on stock status relative to a suite of reference points and other socio-ecological and economic considerations, such as the 2-million-ton BSAI ecosystem cap (49 FR 397). The TAC is consequently often less than the sum of the species-specific acceptable biological catches (ABCs), which are the target catch level allowable for each species individually that accounts for scientific uncertainty. Catch limits are also set annually for a few major bycatch species in the BSAI GF (the prohibited species catch, PSC, most of which are constrained by a bycatch cap). Prohibited species are valuable target species for fishers outside of the BSAI GF, but cannot be retained by the groundfish fishery. One example is Pacific halibut (Hippoglossus stenolepis, hereafter referred to as halibut) which is a transboundary species that ranges along the west coast of North America from the US State of Oregon to the BS. Halibut supports a longline commercial fishery in Alaska (annual value exceeding USD 100,000,000 (Fissel et al. 2015)) managed by the International Pacific Halibut Commission and a substantial recreational fishery. The species is also a bycatch species in the BSAI GF that is managed under a bycatch cap. Four sectors share the fixed halibut bycatch limits. These are the Amendment 80 (A80), trawl limited access (non-a80 trawlers), community development quota, and freezer longline sectors ( 7

9 Page 8 of The A80 sector was established in 2008 when the NPFMC allowed cooperative formation among non-pollock groundfish catcher processors. This is a group of bottom trawlers, who belong to one or two harvesting cooperatives, and are given target allocations of Pacific cod, yellowfin sole, flathead sole (Hippoglossoides elsassodon), northern rock sole (Lepidopsetta polyxystra), Atka mackerel (Pleurogrammus monopterygius), and Pacific ocean perch (Sebastes alutus) in the BSAI. They also target other limited-entry species (e.g. arrowtooth flounder, Atheresthes stomias) in the BSAI and numerous species in the Gulf of Alaska. The remaining BSAI trawlers include the American Fisheries Act (AFA) pelagic trawl vessels that normally target pollock with pelagic nets but switch to bottom trawl gear for part of the year, bottom trawl catcher vessels that target a variety of groundfish, deliver to inshore processors, and do not belong to the A80 sector, and trawl catcher vessels that deliver to catcher processors who function as motherships for some Pacific cod and Atka mackerel. Community development quota (CDQ) is allocated to certain Western Alaska groups (that represent villages/communities) to support their economic development. This quota is typically leased to fishing companies and caught using trawls, longlines, and pots. Finally, the freezer longline sector mostly targets Pacific cod, but also catches other quota species including skates and Greenland turbot (Hippoglossoides reinhardtius) Métiers in the BSAI groundfish fishery A cluster analysis was performed on the observer catch weight by species collected between (2009 and 2015 were omitted for model validation; see below) to define métiers in the BSAI GF. Observers are trained biologists aboard fishing vessels who collect information about fishing activities including fishing date, fishing location (NMFS area, Fig 1), vessel size, type, gear, catch composition, fishing sector, and biological samples such as scales, tissues, 8

10 Page 9 of 58 Canadian Journal of Fisheries and Aquatic Sciences otoliths and stomachs. Previous research (e.g. Deporte et al and many studies cited within) have performed a principal component analysis (PCA) before the actual clustering but this step was skipped in this study to avoid possible subjectivity in selecting the number of axes to retain from the PCA analysis (different methods might suggest different number of axes). The cluster analysis involved subsetting the data by the 64 combinations of NMFS area and sectors (data for all years were aggregated). The data for each combination were then partitioned into 2-15 clusters based on catch composition using the partitioning around medoids algorithm through the function pam in the R package cluster (Maechler et al. 2016). The optimal number of clusters (which maximizes within-cluster similarity and maximizes between-cluster dissimilarity) was then chosen using the silhouette index (Rousseeuw 1987; Cope et al. 2009), a measure of how similar an object is to its own cluster compared to other clusters. The clusters derived from the 185 analysis of all 64 data subsets are the métiers representing the BSAI groundfish fishery General description of the model The MSE involved five steps each year where the species dynamics were represented using a multispecies, age-structured population dynamics model (the Operating model OM; Appendix A) (Fig 2): (i) generate data from the OM for use in the assessment method, (ii) apply the assessment method independently for each species and calculate next year s ABC, (iii) mimic the management decision process in choosing the TAC for each species based on the proposed ABCs (the actual target) and the constraints of the fishery, (iv) apply a fleet dynamics model to determine the realized catch for each species, and (v) update the population dynamics. Steps (i) through (v) were repeated for 15 years (to examine the consequences beyond the longest generation time of the modeled animals), and the 15-year cycle was replicated 50 times (results did not qualitatively change after 50 iterations) to capture the range of possible outcomes. 9

11 Page 10 of The OM The OM consisted of age- and sex-structured population dynamics models for three species that did not interact biologically (i.e. predator-prey and competition dynamics were ignored. Readers are referred to the discussion section for the validity of this assumption). The three target species were parameterized to roughly mimic the dynamics of the Pacific cod (Thompson 2015), walleye pollock (Ianelli et al. 2015) and yellowfin sole (Wilderbuer et al. 2015) in the BSAI as reflected by recent stock assessments (Table 1; Appendix A). In addition to the three target species, the OM included halibut as a bycatch species. The population dynamics of halibut were not modeled explicitly and halibut was only included in the OM as an annual fixed total halibut bycatch limit (as the halibut bycatch limit is currently set independent of its stock status). Halibut bycatch cap was allocated by sector in this study The expected recruitment for each species was assumed to follow a Beverton and Holt stock- recruitment relationship with lognormally distributed annual deviations, where the degree of variance, σ R, was species specific (Equation A.6; Table 1). The OM had a 51-year burn-in period during which the population was gradually fished down starting in year 5 to reach the relative biomass level estimated in the latest stock assessment reports by year 51 (Table 1; Fig 3). As some combinations of recruitment deviations (i.e. the 51-year time series of randomly sampled recruitment deviation) precluded the OM from achieving the specified depletion level, only the combinations that satisfied this condition were kept. The projection continued from year 52 until year 66, and the population dynamics were updated each year The assessment method (AM) and the harvest control rule (HCR) The AM was based on a single-species, age- and sex-structured population dynamics model that matched the dynamics of each species in the OM (the CAB model: Cope et al. 2003, 10

12 Page 11 of 58 Canadian Journal of Fisheries and Aquatic Sciences Appendix B). The AM estimated unfished spawning stock biomass, fishery and survey selectivity-at-age (assumed to be logistic), recruitment deviations, and fishing mortality. The remaining parameters, including the growth parameters, natural mortality, recruitment variability, steepness (Hilborn and Walters 1992), and survey catchability, were fixed at their true values (Table 1, Appendix B). The quality and quantity of data that are available for assessment purposes determines AM performance (Magnusson and Hilborn 2007; Ono et al. 2015). Two data scenarios were therefore considered: a data-rich (Base case) and a data-limited case (see the scenarios listed below). For the data-rich case, two types of data were assumed to be available: some age-composition data (from the fishery and survey) and an index of abundance (Appendix C). For the data-limited case, the same data types were assumed to be available, but with a lower survey frequency, lower sample size and subject to more observation error (Appendix C). The ABC for each species was calculated based on the estimate of current spawning stock biomass, B t, using a harvest control rule (HCR) that defined the target harvest rate, F ABC. The HCR for this paper was the tier-3 HCR in BSAI fisheries (NPFMC 2015)) (see appendix A for how to obtain ABC from F ABC ): 236 F ( B αb ) t 40% ABC, t = max 0, F40% min 1, (1 α ) B 40% (1) where F 40% was the estimated harvest level that leads to an equilibrium spawning potential ratio (i.e. number of eggs that could be produced by an average recruit over its lifetime when the stock is fished divided by the number of eggs that could be produced by an average recruit over its lifetime when the stock is unfished) of 0.40, α=0.05 was a buffer to account for uncertainty, and B 40% was the estimated long-term average spawning stock biomass that would be obtained under 11

13 Page 12 of a fishing mortality rate of F 40% and average recruitment. Average recruitment was calculated as the average estimated recruitment between the first year of the simulation period with catch data to the most recent year Management decisions/quota allocation model Two quota allocation strategies were examined: (i) a naïve manager strategy that ignored technical interactions and set the total allowable catch (TAC) for each species independently of the TAC for the other species and equal to the species ABC. The TAC by species was determined by proportionally reducing each species ABC if their sum was larger than a cap. The cap was chosen as 1.7 million tons (instead of 2 million tons mentioned earlier because the 251 average sum of cod, pollock and yellowfin sole TACs between equaled that amount ( The naïve manager strategy was contrasted with (ii) a conscientious manager strategy that mimicked the current quota allocation process in the BSAI, which considered technical interactions while setting the TAC. The conscientious manager strategy involved applying linear programming (Press et al. 2007), a constrained optimization approach, to mimic the process of selecting the TAC by species in the BSAI. This involved finding next year s total catches by métier k, C k,t+1, that maximized the expected revenue, Θ t+ 1, while meeting several constraints (described below). The expected revenue at time t+1 was calculated as: nspecies nmetier obs t+ 1 pj, t+ 1 Ck, t+ 1 Pj, k, t j= 1 k= 1 Θ = (2) where p j,t+1 was the net price of species j in year t+1 as perceived by the decision makers and P obs j, k, t was the observed proportion of species j in the catch for métier k and year t. The net price 12

14 Page 13 of 58 Canadian Journal of Fisheries and Aquatic Sciences reflects decision makers relative preferences in allocating quota across species. Decision makers were assumed to base their future quota allocations on past catch composition data. p j,t+1, and 266 P were input parameters in this model and C k,t+1 was the variable to be optimized. Θ t+ 1 was obs j, k, t then maximized with the constraints that: (i) Each target species TAC was below its acceptable biological catch, ABC j,t n metier obs Ck, t+ 1 Pj, k, t ABC j, t+ 1 (4) k= 1 (ii) The fixed halibut bycatch limit of 4,426t (the 2014 halibut bycatch cap) was not exceeded. 271 n metier obs Ck, t + 1 Pbycatch, k, t 4426 (5) k= (iii) The total catch across all target species was less than the 1.7-million-ton cap. 273 n metier n species k = 1 j= 1 C p obs k, t+ 1 j, k, t (6) (iv) Total catch by métier k at time t+1, C k,t+1, was bounded by the métier-specific fishery expansion factors {λ 1,k, λ 2,k }. These bounds controlled the year-to-year variability in catch within a métier and represented the amount of flexibility a fishery had to expand or limit métier use (Murawski and Finn 1986), i.e.: 278 λ C C λ C 1, k k,2014 k, t+ 1 2, k k,2014 (7) This model was designed to match the current quota allocation process in the BSAI. It obs involved setting the initial catch proportion by species P j, k,2014, the initial catch by métier C k,2014, the fishery expansion factors {λ 1,k, λ 2,k }, and the perceived net price, p j,t+1 as follows: 13

15 Page 14 of obs (i) The initial catch proportions by species P j, k,2014 were obtained from the métier analysis where information for the four species of interest were only kept for each métier cluster: cod, pollock, yellowfin sole and halibut. The catch proportions by species were updated for each obs projection year, P j, k, t + 1 (see below). (ii) The initial catch by métier C k,2014 was set to the average total catch by métier between (iii) The fishery expansion factors for métier k {λ 1,k, λ 2,k }, were determined from the change in total catch within métier k between with respect to the average catch for métier k. λ 1,k was the ratio between the minimum and the average total catch for métier k, λ 1,k [0,1] 291 and λ 2,k was the ratio between the maximum and the average total catch for métier k. Some métiers were only observed for a single year. For those cases, {λ 1,k, λ 2,k } were imputed from the corresponding sector-specific averages (each sector had many métiers). (iv) The perceived net price for each species controlled the decision makers preference to allocate more quota to some species than others. Decision maker s future perceived net prices, p j,t+1, were calculated based on the catch allocation by species (see Appendix D for more details) Fleet dynamic model: calculating the realized catch for next year Fleet dynamics were not static over time. Fishers have to adapt their behavior in response to, for example, changes in catch limits (TAC or bycatch limit), environmental conditions, species abundance, and market conditions. Fleet dynamics were modeled using the linear programming approach described in the previous section with a few modifications: 14

16 Page 15 of 58 Canadian Journal of Fisheries and Aquatic Sciences obs (i) For each projection year t+1, the métier catch composition, P j, k, t + 1, was modified for species j based on its spawning stock biomass relative to that in 2014, SSB j,2014. If a species became more abundant than in 2014, its proportion in the métier catch composition increased proportionally (see Appendix E). P SSB obs = obs j, t+ 1 j, k, t 1 Pj, k, t SSB (8) + j, (ii) Each species catch was limited by its TAC: 309 n metier obs Ck, t+ 1 Pj, k, t+ 1 TAC j, t+ 1 (9) k= (iii) Fishers targeting/avoidance behavior in catching cod, pollock, yellowfin sole and halibut changed over time in response to regulatory constraints. The realized net price reflected fishers targeting choice and future realized net prices, p j,t+1, were calculated based on the catch by species (Appendix D) Scenarios Several scenarios examined the impact of management measures and environmental uncertainty for the BSAI groundfish fishery (Table 2). (i) Halibut bycatch limits. Halibut bycatch limits are currently set independently of halibut stock status. Sensitivity to various halibut bycatch limits was therefore examined: 4,426t (base case; 2014 bycatch cap 1 ), 2,318t (the lowest halibut bycatch mortality value in history i.e. the 2015 value) and 1,500t (a hypothetical lower value). 1 The bycatch cap of 4,426t was in place since 2012 through The limit was reduced in 2016 to 3,830t. 15

17 Page 16 of (ii) Data availability. Sensitivity was explored to a reduced frequency, sample size, and precision of the index of abundance and the age-composition data (Appendix C). Results were also compared to the case were true values were used instead (perfect assessment). (iii) What happened if fishing fleets became more or less flexible in their fishing dynamics in response to regulations? Halibut bycatch could be reduced in the future with further implementation of deck sorting (which leads to lower mortality through less time out of the water), changing fishing incentives, halibut excluders, or other new technologies. This would increase fishers fishing opportunities by allowing them to fish on more diverse locations and times of the year. Conversely, spatial fishing opportunities could be further limited if closed areas were expanded or halibut densities increased. Sensitivity was examined to situations where catches within métiers become more or less restrictive. This involved reducing or increasing the fishery expansion factors for each métier. The new sets of fishery expansion factors to be tested were {max(0.99,3/2*λ 1,k ), min(1.01, 2/3*λ 2,k )} and {0.5*λ 1,k, 2*λ 2,k } Performance indicators Performance of quota allocation approaches was evaluated using metrics that considered the status of the stock, catch stability and total catch, and the catch to quota ratio (i.e. were fishers capable of fully exploiting the allocated quota?). The status of the stock was evaluated as the average probability over the 15-year projection period and across replicates to render the stock overfished (probability that stock biomass fell below a minimum stock size threshold defined as 0.5 B 35% ) at any point in time and across replicates. Catch stability was examined by calculating the standard deviation of the species catch during the 15-year projection period and across replicates. The average catch by species during the 15-year projection period and across 16

18 Page 17 of 58 Canadian Journal of Fisheries and Aquatic Sciences replicates was also calculated. Finally, the catch to quota ratio i.e. the ratio between the realized catch and the TAC was examined by computing the 15-year average catch to quota ratio by species across replicates Model calibration and validation metrics The quota allocation algorithm and the fleet dynamics model required a way to project the perceived and realized net price in the future. This projection involved regressing the calculated net prices by species between against the estimated ABCs (for the quota allocation model) or TAC (for the fleet dynamics model) (Appendix D). Models were then cross-validated using the 2009 and 2015 observed TAC and catch by species to check whether the projection 353 method was able to reproduce out of sample observations Model calibration (i.e. the process of calculating the perceived and realized net price by species using the data) and model validation (i.e. projecting the 2009 and 2015 perceived and realized net prices and performing a cross-validation with the 2009 and 2015 data) was evaluated by examining the mean absolute relative error (MARE) between the observed TAC (or catch) and model expectations (outputs from the linear programming model). The lower the MARE, the more accurate was the model Results Calibration of the quota allocation and fleet dynamics models The average MARE across all years for the quota allocation model was (minimum and maximum 0.050). This meant that there was an average of 3.5% absolute relative error between model expected and observed TAC allocations between 2010 and 2014 (Table 3). The 17

19 Page 18 of average MARE across all years for the fleet dynamics models was (minimum 0.028, maximum 0.113) which is equivalent to an average of 5.5% absolute relative error (Table 3) Validation of the quota allocation and fleet dynamics models The MARE for 2009 was 1.1% for the quota allocation model and 12.9% for the fleet dynamics model. The MARE for 2015 was 16.3% for the quota allocation model and 6.4% for the fleet dynamics model (Table 3) Base case description (conscientious manager) The average catch during the 15-year projection period by species was around 240,000t for cod, 1.25million t for pollock and 100,000t for yellowfin sole, leading to 1.6 million t of average total catch (sum of all three species) for the base case (Fig 4i). The total catch by year during the projected period varied (Fig 4i). Total catch equaled 1.7 million t during the first three years of the projection period with low among-simulation variability (mostly due to stability of pollock catch). However, the total catch started to fluctuate between 400,000t and 1.7 million t, with a larger among-replicate variability thereafter (mostly due to fluctuation in pollock recruitment leading to lower catch). The among-replicate difference in total catch peaked at 1.3 million t, with pollock being the largest contributor, followed by cod then yellowfin sole (Fig 4i). The average annual catches of cod decreased then stabilized over the course of the 15-year projection period, whereas the average annual yellowfin sole catches kept decreasing, and catches of pollock fluctuated during the projection period (Fig 4i). These trajectories were similar to those of the spawning stock biomass (Appendix F, Figure F1). The average probability of overfished species was zero for all scenarios. The average catch to quota ratio (across all three species) during the 15-year projection fluctuated between 91.5% and 99.5% across replicates (Fig 5iv, Consc_Base ). Yellowfin sole 18

20 Page 19 of 58 Canadian Journal of Fisheries and Aquatic Sciences showed the largest amount of catch to quota mismatch across replicates, with a 15-year averaged catch to quota ratio between 62.7% and 95.4% (Fig 5iii), cod was next with a value between 91.2% and 100% (Fig 5i), and pollock was the most reliably caught species with a ratio between 92.9% and 100% (Fig 5ii) Naïve vs. conscientious manager Overall, the average total catch during the 15-year projection period was larger for a conscientious manager than a naïve manager (that set quotas without accounting for technical interaction) by 300,000t (Fig 4i). The difference was largest for pollock (200,000t difference in 15-year average catch), followed by cod (80,000t difference), then yellowfin sole (30,000t 397 difference). The naïve manager approach led to a much lower among-replicate-15-year average catch to quota ratio (between 69.9% and 90.9%) than a conscientious manager. Pollock was poorest, with a 15-year averaged ratio between 66.9% and 95.5% (Fig 5ii) Influence of the halibut bycatch limit (conscientious manager) The average catch by species during the 15-year projection period did not change much even at the historical low halibut bycatch mortality limit (2,318t) for the conscientious manager approach. The average catch was 240,000t for cod, 1.25million t for pollock, and 110,000t for yellowfin sole, leading to 1.6 million t of average total catch (Fig 4ii). However, annual catch variability (i.e. among-replicate variation) increased slightly compared to the base case, with a bycatch limit of 2,318t (Fig 4ii vs. i). When the bycatch limit decreased to 1,500t, the average catch by species decreased by more than 20% on average. Cod catch declined to 150,000t, pollock catch to 1 million t, yellowfin sole catch remained constant (110,000t), and the average total catch decreased to about 1.25 million t (Fig 4iii). Furthermore, the naïve manager approach 19

21 Page 20 of could not be run as the allocated quota (that ignored technical interaction) was sometimes outside the range of feasible catch options (see Equation 7) with a bycatch limit of 1,500t. The mismatch between catch and quota during the 15-year projection period generally decreased with a lower halibut bycatch limit due to feedback changes in relative abundance and trends in species net price (Fig 5i-iv) (see discussion for explanation) Influence of data availability (conscientious manager) Data availability and quality did not substantially affect the average catch by species over the projection period (only a small decrease in average cod and pollock catch for the data-poor and data-rich case compared to the base case) (Fig 6ii,iii vs. 6i). Annual catch variability, however, 419 increased slightly under a data poor scenario (especially at the start of the projection period) (Fig ii). The average catch to quota ratio during the 15-year projection period decreased for the data- poor scenario and increased under perfect assessment (Fig 7i-iv). Effectively, the variability in pollock recruitment dwarfs the impact on catch from different levels of information Influence of flexibility in fishing dynamics (conscientious manager) Increased flexibility in fishing dynamics (i.e. a wider range for the fishery expansion factors) slightly decreased the average total catch over the 15-year projection period (1,540,000t vs. 1,595,000t) and increased the individual species among-replicate catch variability for cod, pollock and yellowfin sole (84,000t, 223,000t, and 41,000t vs. 54,000t, 207,000t, and 21,000t respectively) compared to the base case (Fig 8ii vs. 8i) (see discussion for interpretation). Under this scenario, the average catch of cod increased (257,000t vs. 240,000t) while the average catches of pollock and yellowfin sole decreased (1,219,000t, and 65,000t vs. 1,253,000t, and 103,000t). Additionally, the average catch to quota ratio for cod increased compared to the base case and decreased for yellowfin sole (Fig 9i-iv). 20

22 Page 21 of 58 Canadian Journal of Fisheries and Aquatic Sciences Lesser flexibility in fishing dynamics also decreased the average total catch over the 15-year period (1,468,000t vs. 1,595,000t) (Fig 8iii vs. 8i). Both cod (183,000t vs. 240,000t) and pollock average catch (1,181,000t vs. 1,253,000t) decreased compared to the base case, but the yellowfin sole average catch stayed the same. The average catch to quota ratio for all three species did not change much compared to the base case (Fig 9i-iv) Discussion Synopsis and interpretation 440 Base case interpretation 441 In this proof of concept paper, an MSE based on the example of three main species (by catch volume) in the BSAI GF that included for the first time a fleet dynamics model, multispecies quota allocation, and some temporal dynamics of technical interactions was developed. The study highlighted the importance of accounting for technical interactions and its temporal dynamics in quota allocation modelling and the fleet dynamics to design a realistic multispecies fishery MSE. Model projections were quite different between scenarios that ignored ( naïve manager approach) or included ( conscientious manager approach) technical interactions in the quota allocation process. The conscientious manager approach produced on average 20% higher catches than the naïve manager. Nonetheless, some mismatch persisted between the allocated quota and the realized catch during the projected years. The base model predicted a 15- year average catch to quota ratio between % for cod, % for pollock and % for yellowfin sole. These numbers are somewhat similar to the catch to quota ratio observed in the BSAI groundfish fishery between 2009 and 2015: % for cod, % for pollock, and % for yellowfin sole respectively. Moreover, while the base model 21

23 Page 22 of predicted that on average, 94% of the 1.7 million t overall catch limit would be caught annually over the projection years, there was considerable year to year variability due to changes in pollock abundance (the most abundant species of the three that also has a high recruitment variability). When pollock abundance was low, the total catch was well below the 1.7 million t catch limit. In 2009 for example, quota was set to 176,540t for cod, 815,000t for pollock, and 210,000t for yellowfin sole (a total of 1,201,540t for the three species), and the fishery caught 175,756t, 810,857t, and 107,513t respectively (a total of 1,094,126t) Effect of halibut bycatch limit The fishery could achieve total catches similar to the base case even at a lower halibut 464 bycatch mortality limit (i.e. 2,318t: the historical low value reached in 2015). However, a halibut bycatch limit of 1,500t decreased the average total catch by more than 300,000t (a 20% average drop compared to the base case). Such bycatch reduction has never been observed in the past and this result should be interpreted with caution as it is difficult (if not impossible) to predict changes in fleet dynamics in response to a major policy change based on historical empirical data alone (Reimer et al. 2017). The model also predicted a reduced mismatch between catch and quota with a lower halibut bycatch limit (2,318t) compared to the base case. Under a lower halibut bycatch limit, fishers tried to catch more yellowfin sole and cod at the start of the time series (see Table D1; conditional on every other species TAC except halibut being equal, yellowfin sole realized net price decreases as halibut bycatch cap increases, while pollock realized net price increases as halibut bycatch cap increases). This result is however a significant abstraction from the actual manner in which vessels attempt to catch the TAC that they are allocated. This induced biomass time series for cod and yellowfin sole that were slightly less variable than for the base case for some replicates (i.e. fewer ups and downs) (Appendix F). A 22

24 Page 23 of 58 Canadian Journal of Fisheries and Aquatic Sciences reduced variability in species biomass can potentially reduce mismatch between catch and quota because it reduces variability in métier species composition. While the quota allocation model apportioned TAC based on past métier catch composition (Equation 2), the fleet dynamics model operated on the current observed catch composition (Equation 8). If there was a sudden large increase in the abundance of a species for example, this would change the métier catch composition, but the quota allocation model would not be informed. This information lag could consequently lead to increased mismatch between catch and quota. Therefore, any changes in the fishery (regulations or environmental) that could induce large changes in species composition could potentially induce a larger mismatch between catch and quota. This effect could, however, be counter-balanced by changes in net prices (for both quota allocation and fleet dynamics), but the latter was harder to examine as it was affected by the stock status (ABC) and catch limits 489 (TAC or bycatch limit) of all four species Effect of fishing flexibility The effect of the bycatch limit on fishery performance also differed depending on the fishers behavioral flexibility. For example, additional opportunities to discovering new grounds and reducing halibut mortality might become available to fishers if the use and effectiveness of halibut excluders (Gauvin and Rose 2008) or deck sorting (Gauvin 2013) expanded in the fishery. This situation was mimicked by increasing the fishery expansion factors {λ 1,k, λ 2,k }. The fishery was then able to catch as much as the base case even with a 1,500t halibut bycatch limit. On the other hand, if further restrictions (e.g. closed area or gear restrictions) were to be implemented, the average total catch (sum across the three species) decreased compared to the base case. There are, however, a few caveats to this sensitivity analysis. Specifically, changes in fishing flexibility were modelled by changing the fishery expansion factors only i.e. the flexibility a fishery had in 23

25 Page 24 of selecting to catch more in certain métiers than others. However, changes in flexibility are likely to vary depending on the métier and it is also possible that new métiers would become available (disappear), and species realized net price (for fleet dynamics) could evolve with increased (decreased) flexibility. This explained the result that a greater flexibility in fishing behavior led to a lower average total catch under the base case halibut bycatch limit. The model allocated more catch and quota to cod and less to yellowfin sole (by concentrating effort to métiers catching cod) because the underlying net price models were the same as for the base case. Consequently, this inconsistency further increased the catch to quota mismatch for yellowfin sole and impacted the total average catch over time. In reality, increased flexibility would almost certainly lead to at least some increased catch as fleets could make fine scale adjustments to the location and timing of fishing e.g. in-season decisions Effect of data availability The amount of data used in the assessment did not affect the average catch by species over the projection years much. Catch variability was the main variable that increased with fewer data (especially at the start of the projection period). At the start of the projection period, all stocks were still healthy and this masked the effect of recruitment failures. Therefore, the effect of assessment uncertainty reflected directly on the quota and catch of target species. However, as time progressed, the impact of pollock recruitment failures overshadowed the effect of assessment uncertainty (in combination with the 1.7t cap) and the effect of data availability became less apparent. A non-intuitive finding was that perfect knowledge of the stock status did not help reduce the mismatch between catch and quota nor did it increase the average catch over time. Mismatch was mainly caused by differences in expectation between quota allocation and fleet dynamics, but perfect knowledge of the stock did not help reduce these differences. In 24

26 Page 25 of 58 Canadian Journal of Fisheries and Aquatic Sciences contrast, some level of uncertainty (base case) was beneficial to the overall catch (as the strategy to maximize catch for a year is likely to be different than the one maximizing the long-term catch) as it increased future catches without affecting the catch to quota mismatch. However, too much uncertainty was not good as it increased the catch to quota mismatch Limitations/future research Linear programming was used to model the quota allocation and fleet dynamics of a multispecies fishery based on historical species catch composition data. The model behaved relatively well, with an average of 5% bias in predicting quota and catch by species. However, the model had more difficulties predicting the fleet dynamics (11 or 13% relative error on 533 average) when pollock quota was low (e.g. 2009, 2010), and the quota allocation (16% relative error on average) when pollock ABC was high (2015). The present model was conditional on the current management system, i.e., its performance decreased as the fishery condition moved away from that during Therefore, the model will not perform well if there are any large changes in management in the future similar to the A80 fleet restructuring ( Marked changes in the technical relationship have been observed with the A80 change and it is not realistic to think that the relationships will be static across major management changes such as a large decrease in the halibut quota (Abbott et al 2015; Reimer et al 2017). Similarly, the current MSE should not be used for long-term predictions. As the projection period increases, the likelihood of major changes in fishery management, regulations, environment, and fishing techniques also increases. No predator-prey interactions were assumed and all species were modelled independently in this study. However, this is not the case (Jurado-Molina et al. 2005; Holsman et al. 2016), and future studies should investigate sensitivity to the inclusion of predator-prey dynamics. 25

27 Page 26 of Additionally, while the focus was on the three most abundant species caught in the BSAI, other species are also economically important to the fishery, jointly caught with Pacific cod and yellowfin sole, and are also impacted by the halibut bycatch regulations. Finally, linear programming was used as a tool to create a quick, but realistic, quota allocation and fleet dynamics model. Many detailed fleet dynamics models have been developed over the past decades (Haynie et al 2009; Abbott and Wilen 2011) and they could have been used (especially, if one wanted to include spatial factors as another dimension in the MSE). While possible, model run time is an important factor to consider in an MSE (linear programming only takes a few seconds, as opposed to hours or days for more complicated models) and these sophisticated models might perform as well (or bad) as the simpler linear programming as it remains challenging to predict changes in human behavior in response to a major policy change based on historical empirical data alone (Reimer et al. 2017). Modeling/predicting realistic human behavior (with its associated uncertainty) in an ever-changing socio-ecological system will remain a major challenge to address in the future Conclusion Managing fisheries bycatch while achieving optimal yield from target fisheries is a problem of fisheries management (e.g., Wesley et al. 2013). A multispecies fishery MSE framework based on the example of the BSAI groundfish fishery was developed in this study to examine the interplay between the three most abundant groundfish species in the BSAI and a major bycatch species (i.e. halibut) under different fishing, environmental and management scenarios. The results highlighted the importance of accounting for technical interactions and their temporal dynamics in both quota allocation and fleet dynamics to design a realistic multispecies fishery management strategy analysis. Future studies aimed at building a realistic multispecies fishery 26

28 Page 27 of 58 Canadian Journal of Fisheries and Aquatic Sciences MSE for the BSAI groundfish fishery or in other systems with significant bycatch constraints should consider expanding the current model to include the whole suite of species exploited and managed in the fishery, the predator-prey relationships, as well as refined quota and fleet dynamics models that better account for the complexity and uncertainty in human behavior (via the use of technical interactions or other ways) Acknowledgements Kotaro Ono was partially funded by the Joint Institute for the Study of the Atmosphere and Ocean (JISAO) under NOAA Cooperative Agreement NA15OAR , Contribution No and the Research Council of Norway through the SkagCore project. We thank Matthew Reimer (University of Alaska Anchorage), the Scientific and Statistical Committee, Ingrid Spies (Alaska Fishery Science Center), Tom Wilderbuer (Alaska Fishery Science Center), the associate editor and two anonymous reviewers who helped improve this manuscript References Abbott, J.K., Haynie, A.C., Reimer, M.N., Hidden flexibility: institutions, incentives, and the margins of selectivity in fishing. Land Econ. 91, doi: /le Abbott, J.K., Wilen, J.E., Dissecting the tragedy: a spatial model of behavior in the commons. J. Environ. Econ. Manage. 62, doi: /j.jeem Alexander, V., Niebauer, H.J., Oceanography of the eastern Bering Sea ice-edge zone in spring. Limnol. Oceanogr. 26, Bunnefeld, N., Hoshino, E., Milner-Gulland, E.J., Management strategy evaluation: A powerful tool for conservation? Trends Ecol. Evol. 26, doi: /j.tree

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33 Page 32 of Punt, A.E., Butterworth, D.S., de Moor, C.L., De Oliveira, J.A.A., Haddon, M., Management strategy evaluation: best practices. Fish Fish. 17, doi: /faf Reimer, M.N., Abbott, J.K., Haynie, A.C., Empirical Models of Fisheries Production : Conflating Technology with Incentives? Mar. Resour. Econ. 32. doi: / Rousseeuw, P.J., Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, Smith, A.D.M., Sainsbury, K.J., Stevens, R.A., Implementing effective fisheriesmanagement systems - management strategy evaluation and the Australian partnership approach. ICES J. Mar. Sci. 56, doi: /jmsc Springer, A.M., McRoy, C.P., Flint, M. V, The Bering Sea Green Belt: shelf-edge processes and ecosystem production. Fish. Oceanogr. 5, doi: /j tb00118.x Thompson, G.G., Assessment of the Pacific cod stock in the Eastern Bering Sea. In stock assessment and fishery evaluation report for the groundfish resources of the Bering Sea/Aleutian Islands regions. Wesley, P.S., Benaka, L.R., Estimating the economic impacts of bycatch in U.S. commercial fisheries. Mar. Policy 38, doi: /j.marpol Wilderbuer, T.K., Nichol, D.G., Ianelli, J.N., Assessment of the yellowfin sole stock in the Bering Sea and Aleutian Islands. In Stock assessment and fishery evaluation report for the groundfish resources of the Bering Sea/Aleutian Islands regions. Worm, B., Hilborn, R., Baum, J.K., Branch, T.A., Collie, J.S., Costello, C., Fogarty, M.J., Fulton, E.A., Hutchings, J.A., Jennings, S., Jensen, O.P., Lotze, H.K., Mace, P.M., McClanahan, 32

34 Page 33 of 58 Canadian Journal of Fisheries and Aquatic Sciences T.R., Minto, C., Palumbi, S.R., Parma, A.M., Ricard, D., Rosenberg, A.A., Watson, R., Zeller, D., Rebuilding global fisheries. Science 325, doi: /science

35 Page 34 of 58 Table 1: Summary of the parameters for cod, pollock, and yellowfin sole, and how those parameters are treated in the assessment model (AM). All values were taken from the respective stock assessment documents (i.e. Thompson (2015) for cod, Ianelli et al. (2015) for pollock and Wilderbuer et al. (2015) for yellowfin sole). Parameters (units) Biology Symbol Estimated in the AM (Y/N) Species Cod Pollock Yellowfin sole Virgin spawning stock biomass (t) SSB 0 derived from R 0 800,000 5,200,000 1,000,000 Initial (year 65) SSB level with respect to SSB 0 (%) NA Natural mortality (year -1 ) M N Maximum age (year) a plus N Log mean virgin recruitment (unitless) Log(R 0 ) Y Recruitment variability (unitless) σ R N Steepness (unitless) h N Growth parameters Length at age Female asymptotic length (cm) L,f N Male asymptotic length (cm) L,m N Female growth rate (yr -1 ) k f N Male growth rate (yr -1 ) k m N Female length-weight scaling (kg.cm -βf ) α f N 6e-06 7e-6 5e-6 Male length-weight scaling (kg.cm -βm ) α m N 6e-06 7e-6 9e-6 Female allometric factor (unitless) β f N Male allometric factor (unitless) β m N Maturity Age-at-50% maturity (yr) Age-at-95% maturity (yr) Selectivity commercial Fishery age-at-50% selectivity (yr) Fishery age-at-95% selectivity (yr) Selectivity survey Survey age-at-50% selectivity (yr) Survey age-at-95% selectivity (y) mat a 50 N mat a 95 N fish a 50 Y a Y fish 95 surv a 95 Y surv a 95 Y

36 Page 35 of 58 Canadian Journal of Fisheries and Aquatic Sciences Table 2: List of scenario name, abbreviation, and description Scenario name Abbreviation Description Base (4426t) _Base Base case Min PSC (2318t) _PSC PSC limit of 2,318t 1500t 1500 PSC limit of 1,500t Data poor Perfect assessment _poor _perfect Fewer data (both in terms of quantity and quality) are available for performing stock assessments The true state of the stock is known without error More flexible Less flexible _more _less Vessels are more flexible in their fishing dynamics Vessels are less flexible in their fishing dynamics

37 Page 36 of 58 Table 3: Mean absolute relative error (MARE) for model calibration ( ) and validation (2009 and 2015). Results are shown for the quota allocation model and for the vessel dynamics. MARE represents model accuracy. The lower the value, the more accurate is the model. MARE YEAR Quota allocation Vessel dynamics

38 Page 37 of 58 Canadian Journal of Fisheries and Aquatic Sciences Figure 1: Map of Alaskan waters including the Bering Sea and Aleutian Islands. The boxes with numbers corresponds to the NMFS statistical areas.

39 Page 38 of 58 Figure 2: Management strategy evaluation cycle used in this study