Performance of a state‐space multispecies model: What are the consequences of ignoring predation and process errors in stock assessments?

Abstract

Having a realistic representation of ecosystems in fisheries models is important in the context of ecosystem‐based fisheries management (EBFM). While different modelling approaches support EBFM, accounting for trophic interactions and uncertainty in stock dynamics is important for management advice. Multispecies models exist, but are rarely used for assessments. Most stock assessments are single species models and predation is subsumed into natural mortality, which is often an assumed known value. The use of state‐space assessment models, which account for stochasticity in unobserved processes (process errors), is increasing. However, many stocks are managed assuming deterministic processes. Little is known of how ignoring predation and process errors in stock assessment can impact the perception of the stocks and therefore fisheries management. We developed an age‐structured multispecies operating model that simulated data with errors in observations, recruitment and fish abundance. Four estimation models (EMs) that differed according to whether or not they accounted for predation or process errors were fitted to the simulated data. Relative differences between true and predicted outputs were estimated as a measure of bias. Equilibrium unfished biomass was estimated for each model as a proxy reference point. Ignoring predation had the largest impact on stock perception and resulted in large bias in parameters, derived outputs and absolute or relative reference points. Estimating unobserved processes was not sufficient in limiting the bias when natural mortality was misspecified. Ignoring process errors had limited bias but the bias increased when no contrasts existed in fishing mortality over time. Looking solely at likelihood values to choose among models is misleading and predictive ability could be used to prevent selecting models that overfit the data. Synthesis and applications. Ignoring trophic interactions that occur in marine ecosystems induces bias in stock assessment outputs and results in low model predictive ability with subsequently biased reference points. While it may be difficult to estimate natural mortality when no data exist to inform it, stock managers should remember that, if predation is large, assuming a constant mortality over time and/or age could have large consequences on stock perception and reference point estimates and affect resulting management advice.

Sea urchin harvest inside marine protected areas: an opportunity to investigate the effects of exploitation where trophic upgrading is achieved

“…This phenomenon has been observed worldwide ( Daskalov, 2002 ; Loh et al, 2015 ; Stuhldreier et al, 2015 ) and the increase in resource depletion has led to the development of holistic approaches to fisheries management (ecosystem-based fisheries management, ( Link, 2010 ) and multispecies models of predation, where trophic pressures vary and the assumption of constant natural mortality is neglected ( Jurado-Molina, Livingston & Ianelli, 2005 ; Kinzey & Punt, 2009 ). Moreover, recent investigations have explicitly highlighted how ignoring natural trophic interactions affects stock assessment model performance and fisheries management ( Farina et al, 2020 ; Trijoulet, Fay & Miller, 2020 ).…”

Can we fish on stocks that need rebuilding? Illustrating the trade-offs between stock conservation and fisheries considerations

“…Reference points are uncertain due to modeling choice and errors but also due to lack of understanding of biological and environmental drivers of the variations in productivity. Also, reference points usually ignore external factors that can affect fish mortality such as environmental factors or multispecies interactions (Trijoulet et al, 2018(Trijoulet et al, , 2019(Trijoulet et al, , 2020.…”

Model-based estimates of reference points in an age-based state-space stock assessment model

“…Instead, uncertainty related to these can be evaluated directly within the assessment model (Methot and Wetzel, 2013) or by, for example, local sensitivity (e.g., Cadigan and Wang, 2016;Zheng et al, 2019) or sensitivity analyses (e.g., Zhu et al, 2012). Moreover, the reference point estimates ignore external factors that are not explicitly considered in the models such as environmental effects (e.g., Miller et al, 2016b), spatial variation (Punt et al, 2020), mixed fisheries considerations (e.g., Albertsen et al, 2018;Rindorf et al, 2016) or multispecies interactions (e.g., Trijoulet et al, 2018Trijoulet et al, , 2019Trijoulet et al, , 2020.…”