Estimating spatio-temporal dynamics of size-structured populations

Spatial distributions of structured populations are usually estimated by fitting abundance surfaces for each stage and at each point of time separately, ignoring correlations that emerge from growth of individuals. Here, we present a statistical model that combines spatio-temporal correlations with...

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Bibliographic Details
Published in:Canadian Journal of Fisheries and Aquatic Sciences
Main Authors: Kristensen, Kasper, Thygesen, Uffe Høgsbro, Andersen, Ken Haste, Beyer, Jan E.
Other Authors: Jech, Josef Michael
Format: Article in Journal/Newspaper
Language:English
Published: Canadian Science Publishing 2014
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Online Access:http://dx.doi.org/10.1139/cjfas-2013-0151
http://www.nrcresearchpress.com/doi/full-xml/10.1139/cjfas-2013-0151
http://www.nrcresearchpress.com/doi/pdf/10.1139/cjfas-2013-0151
Description
Summary:Spatial distributions of structured populations are usually estimated by fitting abundance surfaces for each stage and at each point of time separately, ignoring correlations that emerge from growth of individuals. Here, we present a statistical model that combines spatio-temporal correlations with simple stock dynamics to estimate simultaneously how size distributions and spatial distributions develop in time. We demonstrate the method for a cod (Gadus morhua) population sampled by trawl surveys. Particular attention is paid to correlation between size classes within each trawl haul due to clustering of individuals with similar size. The model estimates growth, mortality, and reproduction, after which any aspect of size structure, spatio-temporal population dynamics, as well as the sampling process can be probed. This is illustrated by two applications: (i) tracking the spatial movements of a single cohort through time and (ii) predicting the risk of bycatch of undersized individuals. The method demonstrates that it is possible to combine stock assessment and spatio-temporal dynamics; however, this comes at a high computational cost. The model can be extended by increasing its ecological fidelity, although computational feasibility eventually becomes limiting.