When the densities of a population in different locations change at different rates over time, spatial synchrony and asynchrony are likely to occur. This results in relative abundance trends from surveys that cover different areas. Reasons for spatial synchrony and asynchrony include environmental variability, nonlinear density-dependence, dispersal, and species interactions. This synchrony phenomena has long been realized but has not considered in commonly-used stock assessment models, which often assume that all indices are independent and represent a common population trend.
In their paper, Jiao et al. point out that the statistical catch-at-age (SCA) models do not account for spatial synchrony and asynchrony associated with spatial autocorrelation (the degree to which marine creatures and their associated data values tend to be spatially clustered together), dispersal, and environmental noise, and this limitation often reduces the value of statistical inference on key parameters associated with population dynamics and management reference points. This study presents a new method for addressing this problem by modeling the indices from different surveys using spatial hierarchical Bayesian models.
The authors use Atlantic weakfish as an example, a species for which there are state-wide and expansive coastal surveys. Three spatial SCA models were developed to mimic different potential spatial patterns. The performance of these newly-developed models was compared with a commonly-used Bayesian SCA model that assumes relative abundance indices are spatially independent. A simulation study was also conducted to evaluate the uncertainty resulting from model selection and the robustness of the recommended model.
The new approach allows for better integration of different surveys where spatial synchrony and asynchrony is a factor. The methodology provides important input to current fisheries stock assessments and has substantial implications for future stock assessment model development and evaluation in a changing environment.
Ocean trawl. Photo: New Jersey Divison of Fish and Wildlife
Integrating spatial synchrony/asynchrony of population distribution into stock assessment models: a spatial hierarchical Bayesian statistical catch-at-age approach
Authors: Yan Jiao, Rob O'Reilly, Eric Smith and Don Orth
ICES J. Mar. Sci. (2016) doi: 10.1093/icesjms/fsw036 First published online: 30 March 2016