Non-additive and non-stationary properties in the spatial distribution of a large marine fish population

Density-independent and density-dependent variables both affect the spatial distributions of species. However, their effects are often separately addressed using different analytical techniques. We apply a spatially explicit regression framework that incorporates localized, interactive and threshold...

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Bibliographic Details
Published in:Proceedings of the Royal Society B: Biological Sciences
Main Authors: Ciannelli, Lorenzo, Bartolino, Valerio, Chan, Kung-Sik
Format: Article in Journal/Newspaper
Language:English
Published: The Royal Society 2012
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Online Access:http://dx.doi.org/10.1098/rspb.2012.0849
https://royalsocietypublishing.org/doi/pdf/10.1098/rspb.2012.0849
https://royalsocietypublishing.org/doi/full-xml/10.1098/rspb.2012.0849
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Summary:Density-independent and density-dependent variables both affect the spatial distributions of species. However, their effects are often separately addressed using different analytical techniques. We apply a spatially explicit regression framework that incorporates localized, interactive and threshold effects of both density-independent (water temperature) and density-dependent (population abundance) variables, to study the spatial distribution of a well-monitored flatfish population in the eastern Bering Sea. Results indicate that when population biomass was beyond a threshold a further increase in biomass-promoted habitat expansion in a non-additive fashion with water temperature. In contrast, during years of low population size, habitat occupancy was affected positively only by water temperature. These results reveal the spatial signature of intraspecific abundance distribution relationships as well as the non-additive and non-stationary responses of species spatial dynamics. Furthermore, these results underscore the importance of implementing analytical techniques that can simultaneously account for density-dependent and density-independent sources of variability when studying geographical distribution patterns.