A new role for effort dynamics in the theory of harvested populations and data-poor stock assessment

Abstract

Research shows that population status can be predicted using catch data, but there is little justification for why these predictions work or how they account for changes in fisheries management. We demonstrate that biomass can be reconstructed from catch data whenever fishing mortality follows predictable dynamics over time (called “effort dynamics”), and we develop a state-space catch only model (SSCOM) for this purpose. We use theoretical arguments and simulation modeling to demonstrate that SSCOM can, in some cases, estimate population status from catch data. Next, we use meta-analysis to estimate effort dynamics for US West Coast groundfishes before and after fisheries management changes in the mid-1990s. We apply the SSCOM using meta-analytic results to data for eight assessed species and compare results with stock assessment and data-poor methods. Results indicate general agreement among all three methods. We conclude that effort dynamics provides a theoretical basis for using catch data to reconstruct biomass and has potential for conducting data-poor assessments. However, we still recommend that index and compositional data be collected to allow application of data-rich methods.

Estimating fisheries reference points from catch and resilience

“…For example, the DCAC method (MacCall 2009), which estimates a sustainable catch-level below MSY, requires catch, relative depletion, M and F msy /M as inputs. The SSCOM method (Thorson et al 2013), which predicts stock status and productivity, requires catch, priors for unexploited biomass, initial effort and parameters of an effort-dynamics model. The COM-SIR method (Vasconcellos and Cochrane 2005), which estimates stock status, production and exploitation rates, requires catch, priors for r and k, relative bioeconomic equilibrium and increase in harvest rate over time as inputs.…”

“…Meyer and Millar 1999;McAllister et al 2001;Vasconcellos and Cochrane 2005;Thorson et al 2013), but differs in its emphasis on informative priors for k, based on maximum catch modulated by productivity, for q, based on equilibrium catch, for r, based on more complex modelling of the distribution of r, and for relative biomass ranges, based on default rules or expert opinion. However, as CMSY is a simplified Bayesian implementation of a data-limited production model, it seems appropriate to compare CMSY results with the results of a full Bayesian implementation of a surplus production estimation model, rather than with results obtained from various stock assessment methods with different assumptions and often unavailable levels of uncertainty.…”

Improving estimates of population status and trend with superensemble models

“…Biomass is assumed to follow a Schaefer model and harvest dynamics are assumed to follow a logistic model. The model is fit with a sampling‐importance‐resampling algorithm (Rosenberg et al., ). SSCOM (state‐space catch‐only model) is a hierarchical model that, similar to COM‐SIR, is based on a coupled harvest‐dynamics model (Thorson, Minto, Minte‐Vera, Kleisner, & Longo, ). SSCOM estimates unobserved dynamics in both fishing effort and the fished population based on a catch time series and priors on r , the maximum rate of increase of fishing effort and the magnitude of various forms of stochasticity.…”

An asymmetric logistic regression model for ecological data

“…The catch may vary, however, due to external factors such as fishing duration, gear technology, unreported fishing, regulations and subsidies given to fisheries (Arnason, Kelleher & Willmann ; Carruthers, Walters & McAllister ; Thorson et al . ).…”

“…We therefore omitted these variables for simplicity from our analysis although the lag could relate to reconstruction of unobserved fishery dynamics (Thorson et al . ).…”