|Document Type:||Journal Article|
|Title:||Probability of stochastic depletion: an easily interpreted diagnostic for stock assessment modelling and fisheries management|
|Author:||James T. Thorson, Olaf Jensen, R. Hilborn|
|Journal:||International Council for the Exploration of the Sea Techniques in Marine Environmental Sciences|
|Keywords:||stochastic depletion, recruitment variability, rebuilding plan, management strategy evaluation, fishery collapse,|
Marine fish populations have high variation in cohort strength, and the production of juveniles (recruitment) may have persistent positive or negative residuals (autocorrelation) after accounting for spawning biomass. Autocorrelated recruitment will occur whenever average recruitment levels change between oceanographic regimes, but may also indicate environmental and biological variation on shorter time-scales. Here, we use estimates of recruitment variability and autocorrelation to simulate the stationary distribution of spawning biomass for 100 real-world stocks when unfished, fished at FMSY, or fished following a 40-10 control rule where fishing mortality decreases as a function of spawning biomass. Results show that unfished stocks have spawning biomass (SB) below its determinstic value (SB0) 58% of the time, and below 0.5SB0 5% of the time on average across all stocks. Similarly, stocks fished at the level producing determinstic maximum sustainable yield (FMSY) are below its deterministic prediction of spawning biomass (SBMSY) 60% of the time and below 0.5SBMSY 8% of the time. These probabilities are greater for stocks with high recruitment variability, positive autocorrelation, and high natural mortality, and these traits are associated with clupeids and
|Full Text URL:||http://icesjms.oxfordjournals.org/content/72/2/428.abstract?etoc|
|Theme:||Recovery and rebuilding of marine and coastal species|
Describe the relationships between human activities and species recovery, rebuilding and sustainability.
Characterize the population biology of species, and develop and improve methods for predicting the status of populations.