Windle2010145

Référence

Windle, M.J.S., Rose, G.A., Devillers, R. and Fortin, M.-J. (2010) Exploring spatial non-stationarity of fisheries survey data using geographically weighted regression (GWR): An example from the Northwest Atlantic. ICES Journal of Marine Science, 67(1):145-154. (Scopus )

Résumé

Analyses of fisheries data have traditionally been performed under the implicit assumption that ecological relationships do not vary within management areas (i.e. assuming spatially stationary processes). We question this assumption using a local modelling technique, geographically weighted regression (GWR), not previously used in fisheries analyses. Outputs of GWR are compared with those of global logistic regression and generalized additive models (GAMs) in predicting the distribution of northern cod off Newfoundland, Canada, based on environmental (temperature and distance from shore) and biological factors (snow crab and northern shrimp) from 2001. Results from the GWR models explained significantly more variability than the global logistic and GAM regressions, as shown by goodness-of-fit tests and a reduction in the spatial autocorrelation of model residuals. GWR results revealed spatial regions in the relationships between cod and explanatory variables and that the significance and direction of these relationships varied locally. A k-means cluster analysis based on GWR t-values was used to delineate distinct zones of species-environment relationships. The advantages and limitations of GWR are discussed in terms of potential application to fisheries ecology. © 2009 International Council for the Exploration of the Sea.

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@ARTICLE { Windle2010145,
    AUTHOR = { Windle, M.J.S. and Rose, G.A. and Devillers, R. and Fortin, M.-J. },
    TITLE = { Exploring spatial non-stationarity of fisheries survey data using geographically weighted regression (GWR): An example from the Northwest Atlantic },
    JOURNAL = { ICES Journal of Marine Science },
    YEAR = { 2010 },
    VOLUME = { 67 },
    NUMBER = { 1 },
    PAGES = { 145-154 },
    NOTE = { cited By 24 },
    ABSTRACT = { Analyses of fisheries data have traditionally been performed under the implicit assumption that ecological relationships do not vary within management areas (i.e. assuming spatially stationary processes). We question this assumption using a local modelling technique, geographically weighted regression (GWR), not previously used in fisheries analyses. Outputs of GWR are compared with those of global logistic regression and generalized additive models (GAMs) in predicting the distribution of northern cod off Newfoundland, Canada, based on environmental (temperature and distance from shore) and biological factors (snow crab and northern shrimp) from 2001. Results from the GWR models explained significantly more variability than the global logistic and GAM regressions, as shown by goodness-of-fit tests and a reduction in the spatial autocorrelation of model residuals. GWR results revealed spatial regions in the relationships between cod and explanatory variables and that the significance and direction of these relationships varied locally. A k-means cluster analysis based on GWR t-values was used to delineate distinct zones of species-environment relationships. The advantages and limitations of GWR are discussed in terms of potential application to fisheries ecology. © 2009 International Council for the Exploration of the Sea. },
    AFFILIATION = { Fisheries Conservation Group, Marine Institute, Memorial University of Newfoundland, St John's, NL A1C 5R3, Canada; Department of Geography, Memorial University of Newfoundland, St John's, NL A1C 5R3, Canada; Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, ON M5S 3G5, Canada },
    AUTHOR_KEYWORDS = { Atlantic cod; Fisheries ecology; Generalized additive models; Geographically weighted regression; Logistic regression; Non-stationarity; Northwest Atlantic; Spatial modelling },
    DOCUMENT_TYPE = { Article },
    DOI = { 10.1093/icesjms/fsp224 },
    SOURCE = { Scopus },
    URL = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-73549095760&doi=10.1093%2ficesjms%2ffsp224&partnerID=40&md5=b69a5531cb0b1fbfd58073ae4abcbd03 },
}

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