Skip Navigation



ICES Journal of Marine Science: Journal du Conseil Advance Access published online on September 4, 2009
ICES Journal of Marine Science: Journal du Conseil, doi:10.1093/icesjms/fsp224
This Article
Right arrow Full Text
Right arrow Full Text (PDF)
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Windle, M. J. S.
Right arrow Articles by Fortin, M.-J.
PubMed
Right arrow Articles by Windle, M. J. S.
Right arrow Articles by Fortin, M.-J.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© 2009 International Council for the Exploration of the Sea. Published by Oxford Journals. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Exploring spatial non-stationarity of fisheries survey data using geographically weighted regression (GWR): an example from the Northwest Atlantic

Matthew J. S. Windle1, George A. Rose1, Rodolphe Devillers2 and Marie-Josée Fortin3

1 Fisheries Conservation Group, Marine Institute, Memorial University of Newfoundland, St John's, NL, Canada A1C 5R3
2 Department of Geography, Memorial University of Newfoundland, St John's, NL, Canada A1C 5R3
3 Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, ON, Canada M5S 3G5

Correspondence to M. J. S. Windle: tel: +1 709 778 0504; fax: +1 709 778 0669; e-mail: matt.windle{at}mi.mun.ca.

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: 000–000.

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.

Keywords: Atlantic cod, fisheries ecology, generalized additive models, geographically weighted regression, logistic regression, non-stationarity, Northwest Atlantic, spatial modelling

Received 6 April 2009; accepted 6 August 2009.


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?




Disclaimer: Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.