Monthly Archives: April 2018

Detecting mortality variation to enhance forage fish population assessments

Nis S Jacobsen¹, Jim Thorson¹ and Tim E Essington^2
1NWFSC
²SAFS
April 17th, 2018 9:00 (PST): FSH 105

Detecting mortality variation to enhance forage fish population assessments

Contemporary stock assessment models used by fisheries management often assume that natural mortality rates are constant over time for exploited fish stocks. This assumption results in biased estimates of fishing mortality and reference points when mortality changes over time. However, it is difficult to distinguish changes in natural mortality from changes in fishing mortality, selectivity and recruitment. Because changes in size structure can be indicate changes in mortality, one potential solution is to use population size-structure and fisheries catch data to simultaneously estimate time-varying natural and fishing mortality. Here we test that hypothesis by simulating the ability to accurate estimate natural and fishing mortality from size structure and catch data and compare performance among four alternative estimation models. We show that it is possible to estimate time-varying natural mortality in a size-based model, even when fishing mortality, recruitment, and selectivity are changing over time. Finally, we apply the model to North Sea sprat, and show that estimates of recruitment and natural mortality are similar to estimates from an alternative multispecies population model fitted to additional data sources. We recommend considering diagnostic tools such as ours within forage fish stock assessment to explore potential trends in natural mortality.

Ensemble models for data-poor assessment: the value of life-history information

Dr. Merrill Rudd^1
1NOAA
April 03, 2018 9:00 (PST): FSH 105

Ensemble models for data-poor assessment: the value of life-history information

Scientists and resource managers need to understand a population’s biological parameters, such as mortality and individual growth rates, to successfully manage exploitation and other risks. In the absence of age data, growth rate estimates come from direct observation, proxies, or patterns in the length data while natural mortality rate estimates are often assumed based on empirical relationships with the growth rate estimates. Length composition of the catch can inform recruitment and fishing mortality, but parameter estimates depend on accurate growth and natural mortality rates. Uncertain or unreliable mortality and growth rates propagate high uncertainty in stock status estimates when relying on length data to inform vital stock assessment parameters. This study uses predictive stacking as a method of model averaging across a distribution of values for growth and mortality. We used the R package FishLife to develop distributions of life history parameters based on a multivariate model with taxonomic structure, drawing combinations of points from these distributions to integrate uncertainty in life history parameters into a data-limited, length-based stock assessment. Through simulation we demonstrate that predictive stacking leads to better assessment performance than assuming the parameter means from FishLife when the true values of life history parameters are unknown. We then applied the predictive stacking method for a U.S. Caribbean stock previously lacking accepted management advice due to debilitating uncertainty in life history parameters. This method will be applicable for stock assessments concerned with properly accounting for uncertainty in biological parameters, ranging from life-history-based to length-or age-based stock assessments.