Scientific article

Spatiotemporal modeling of mature‐at‐length data using a sliding window approach

Published inEnvironMetrics, p. 148-158p.
Publication date2022-09-14
First online date2022-09-14

Excess bycatch of marine species during commercial fishing trips is a challenging problem in fishery management

worldwide. The aims of this paper are twofold: to introduce methods and provide a practical guide for spatiotemporal modelling of

bycatch data, as well as to apply these methods and present a thorough examination of Greenland shark (Somniosus microcephalus)

bycatch weight in a Canadian Arctic fishery. We introduce the spatially explicit two-part model and offer a step by step guide for

applying the model to any form of bycatch data, from data cleaning, exploratory data analysis, variable and model selection,

model checking, to results interpretation. We address various problems encountered in decision making and suggest that

researchers proceed cautiously and always keep in mind the aims of the analysis when fitting a spatiotemporal model. Results

identified spatiotemporal hotspots and indicated month and gear type were key drivers of high bycatch. The importance of

onboard observers in providing robust bycatch data was also evident. These findings will help to inform conser

Citation (ISO format)
YAN, Yuan et al. Spatiotemporal modeling of mature‐at‐length data using a sliding window approach. In: EnvironMetrics, 2022, p. 148–158p. doi: 10.1002/env.2759
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Article (Published version)
ISSN of the journal1099-095X

Technical informations

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