2016 Multi-Species Stock Assessment for Walleye Pollock, Pacific cod, and Arrowtooth Flounder in the Eastern Bering Sea

February 13, 2016

Multi-species statistical catch-at-age models (MSCAA) are an example of a class of multi-species ‘Models with Intermediate Complexity for Ecosystem assessments’ (i.e., MICE; Plagányi et al., 2014), which have particular utility in addressing both strategic and tactical EBFM questions (Hollowed et al. 2013; Fogarty 2014; Link and Browman 2014; Plagányi et al., 2014). MSCAA models may increase forecast accuracy, may be used to evaluate propagating effects of observation and process error on biomass estimates (e.g., Curti 2013; Ianelli et al., in press), and can quantifyclimate and trophic interactions on species productivity. As such MSCAA models can address long recognized limitations of prevailing single species management, notably non-stationarity in mortality and maximum sustainable yield (MSY), and may help reduce risk of overharvest (Link 2010;Plagányi et al., 2014; Fogarty 2014). Because multispecies biological references points (MBRPs) from MSCAA model are conditioned on the abundance of other species in the model (Collie and Gislason 2001; Plagányi et al., 2014; Fogarty 2014), they may also have utility in setting harvest limits for multi-species fleets, evaluating population dynamics in marine reserves or non-fishing areas, and quantifying trade-offs that emerge among fisheries that impact multiple species in afoodweb (see reviews in Pikitch et al., 2004; Link 2010;Levin et al., 2013;Link and Browman 2014; Fogarty 2014).

Depending on their structure, MSCAAmodelscan be used to evaluate climate-and fisheries-driven changes to trophodynamic processes, recruitment, and species abundance (Plagányi et al., 2014).MSCAA modelsdiffer somewhat among systems and species, but most use abundance and diet datato estimate fishing mortality, recruitment, stock size, and predation mortality simultaneously for multiple speciesin a statistical framework. Similar toage structured single species stock assessment models widelyused to set harvest limits, MSCAAmodels are based on a population dynamics model, the parameters of which are estimated using survey and fishery data and maximum likelihood methods (e.g., Jurado-Molina et al., 2005; Kinzey and Punt, 2009; Van Kirk et al., 2010; Kempf 2010; Curti et al., 2013; Tsehaye et al., 2014). Unlike most single-species models(but see Hollowed et al. 2000b; Spencer et al. 2016), MSCAA models additionally separate natural mortality into residual and annually varying predation mortality, and modelthe latter as a series of predator-prey functional responses. Thus, natural mortality rates for each species in MSCAA modelsdepend on the abundance of predators in a given year and vary annually with changes in recruitment and harvest of each species in the model.

MSCAA modelshave specific utility in quantifying direct and indirect effects of fisheries harvest on species abundance and size distributions (see reviews in Hollowed et al., 2000a, 2013; Link 2010; Fogarty 2014; Link and Browman 2014; Plagányi et al., 2014), which is important for EBFM and trade-off analyses of various management strategies. Rapidly shifting climate conditions are also of growing concern in fisheries management as changes in physical processes are known to influence individual growth, survival, and reproductive success of fish andshellfish (Hanson et al., 1997; Kitchell et al., 1977; Morita et al., 2010; Hollowed et al., 2013, Cheung et al., 2015). Climate-driven changes in water temperature can directly impact metabolic costs, prey consumption, and somatic or gonadal tissue growth, with attendant indirect effects on survival, production, and sustainable harvest rates (e.g., Hanson et al., 1997; Morita et al., 2010, Cheung et al., 2015). Temperature-dependent predation, foraging,metabolic,and growth rates are common in more complex spatially-explicit food webor whole of ecosystem models such asGADGET (e.g., Howell and Bogstad 2010; Taylor et al., 2007), Atlantis (e.g., Fulton et al., 2011; Kaplan et al., 2012; 2013), and FEAST (Ortiz et al., in press). Temperature functions for growth and predation can also be incorporated into MSCAA models, allowing this class of models to be used to evaluateinteractingclimate, trophodynamic, and fishery influences on recommended fishingmortality rates.

Numerousstudies point to the importance of using multi-species models for EBFM (see review in Link 2010). Multi-species production models produced different estimates of abundances and harvest rates than single species models forNortheast US marine ecosystems (Gamble and Link, 2009; Tyrrell et al., 2011), and MSY of commercial groundfish stocks estimated from aggregated production models are different than the sum of MSY estimates from single-species assessments (Mueter and Megrey, 2006; Gaichas et al., 2012;Smith et al., 2015). Multi-speciesmodels have been used to demonstrate long-term increases in yield of Icelandic stocks of Atlantic cod (Gadus morhua) and reductions in capelin (Mallotus villosus) and Northern shrimp (Pandalus borealis) catch associated with short-term decreases in cod harvest (Danielsson et al., 1997). Kaplan et al. (2013) demonstrated the disproportionately large ecosystem impacts of applying the same Fx(e.g., Fx, or the harvest rate that reduces spawning stock biomass to x% of unfished spawning stock biomass, SSB0; Caddy and Mahon, 1995; Collie and Gislason, 2001)harvest control rule approach to forage fish as is used for groundfish in the northeast Pacific, and trophodynamics in a southern Benguela ecosystem resulted in higher carrying capacity for small pelagic species under fishing (versus no-fishing) scenarios (Smith et al., 2015).

Last updated by Alaska Fisheries Science Center on 04/22/2019

Research in Alaska North Pacific Groundfish Stock Assessments Walleye Pollock