A novel spatiotemporal stock assessment framework to better address fine‐scale species distributions: Development and simulation testing

• Published in: Fish and Fisheries
• Main Authors: Cao, Jie, Thorson, James T., Punt, André E., Szuwalski, Cody
• Other Authors: Joint Institute for the Study of the Atmosphere and Ocean (, JISAO, ) under NOAA Cooperative Agreement
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 2019
• Subjects: Bering Sea; Chionoecetes opilio; northern shrimp; Pandalus borealis; Snow crab
• Online Access: http://dx.doi.org/10.1111/faf.12433; https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1111%2Ffaf.12433; https://onlinelibrary.wiley.com/doi/pdf/10.1111/faf.12433; https://onlinelibrary.wiley.com/doi/full-xml/10.1111/faf.12433

Abstract Characterizing population distribution and abundance over space and time is central to population ecology and conservation of natural populations. However, species distribution models and population dynamic models have rarely been integrated into a single modelling framework. Consequently, fine‐scale spatial heterogeneity is often ignored in resource assessments. We develop and test a novel spatiotemporal assessment framework to better address fine‐scale spatial heterogeneities based on theories of fish population dynamic and spatiotemporal statistics. The spatiotemporal model links species distribution and population dynamic models within a single statistical framework that is flexible enough to permit inference for each state variable through space and time. We illustrate the model with a simulation–estimation experiment tailored to two exploited marine species: snow crab ( Chionoecetes opilio , Oregoniidae) in the Eastern Bering Sea and northern shrimp ( Pandalus borealis , Pandalidae) in the Gulf of Maine. These two species have different types of life history. We compare the spatiotemporal model with a spatially aggregated model and systematically evaluate the spatiotemporal model based on simulation experiments. We show that the spatiotemporal model can recover spatial patterns in population and exploitation pressure as well as provide unbiased estimates of spatially aggregated population quantities. The spatiotemporal model also implicitly accounts for individual movement rates and can outperform spatially aggregated models by accounting for time‐and‐size varying selectivity caused by spatial heterogeneity. We conclude that spatiotemporal modelling framework is a feasible and promising approach to address the spatial structure of natural resource populations, which is a major challenge in understanding population dynamics and conducting resource assessments and management.