Exploring spatial non-stationarity of fisheries survey data using geographically weighted regression (GWR): an example from the Northwest Atlantic
Abstract 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: 145–154. Analyses of fisheries data...
Published in: | ICES Journal of Marine Science |
---|---|
Main Authors: | , , , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
Oxford University Press (OUP)
2009
|
Subjects: | |
Online Access: | http://dx.doi.org/10.1093/icesjms/fsp224 http://academic.oup.com/icesjms/article-pdf/67/1/145/29135002/fsp224.pdf |
id |
croxfordunivpr:10.1093/icesjms/fsp224 |
---|---|
record_format |
openpolar |
spelling |
croxfordunivpr:10.1093/icesjms/fsp224 2024-10-29T17:46:26+00:00 Exploring spatial non-stationarity of fisheries survey data using geographically weighted regression (GWR): an example from the Northwest Atlantic Windle, Matthew J. S. Rose, George A. Devillers, Rodolphe Fortin, Marie-Josée 2009 http://dx.doi.org/10.1093/icesjms/fsp224 http://academic.oup.com/icesjms/article-pdf/67/1/145/29135002/fsp224.pdf en eng Oxford University Press (OUP) ICES Journal of Marine Science volume 67, issue 1, page 145-154 ISSN 1095-9289 1054-3139 journal-article 2009 croxfordunivpr https://doi.org/10.1093/icesjms/fsp224 2024-10-08T04:05:23Z Abstract 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: 145–154. 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. Article in Journal/Newspaper northern shrimp Northwest Atlantic Snow crab Oxford University Press Canada Gam ENVELOPE(-57.955,-57.955,-61.923,-61.923) Windle ENVELOPE(162.300,162.300,-77.900,-77.900) ICES Journal of Marine Science 67 1 145 154 |
institution |
Open Polar |
collection |
Oxford University Press |
op_collection_id |
croxfordunivpr |
language |
English |
description |
Abstract 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: 145–154. 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. |
format |
Article in Journal/Newspaper |
author |
Windle, Matthew J. S. Rose, George A. Devillers, Rodolphe Fortin, Marie-Josée |
spellingShingle |
Windle, Matthew J. S. Rose, George A. Devillers, Rodolphe Fortin, Marie-Josée Exploring spatial non-stationarity of fisheries survey data using geographically weighted regression (GWR): an example from the Northwest Atlantic |
author_facet |
Windle, Matthew J. S. Rose, George A. Devillers, Rodolphe Fortin, Marie-Josée |
author_sort |
Windle, Matthew J. S. |
title |
Exploring spatial non-stationarity of fisheries survey data using geographically weighted regression (GWR): an example from the Northwest Atlantic |
title_short |
Exploring spatial non-stationarity of fisheries survey data using geographically weighted regression (GWR): an example from the Northwest Atlantic |
title_full |
Exploring spatial non-stationarity of fisheries survey data using geographically weighted regression (GWR): an example from the Northwest Atlantic |
title_fullStr |
Exploring spatial non-stationarity of fisheries survey data using geographically weighted regression (GWR): an example from the Northwest Atlantic |
title_full_unstemmed |
Exploring spatial non-stationarity of fisheries survey data using geographically weighted regression (GWR): an example from the Northwest Atlantic |
title_sort |
exploring spatial non-stationarity of fisheries survey data using geographically weighted regression (gwr): an example from the northwest atlantic |
publisher |
Oxford University Press (OUP) |
publishDate |
2009 |
url |
http://dx.doi.org/10.1093/icesjms/fsp224 http://academic.oup.com/icesjms/article-pdf/67/1/145/29135002/fsp224.pdf |
long_lat |
ENVELOPE(-57.955,-57.955,-61.923,-61.923) ENVELOPE(162.300,162.300,-77.900,-77.900) |
geographic |
Canada Gam Windle |
geographic_facet |
Canada Gam Windle |
genre |
northern shrimp Northwest Atlantic Snow crab |
genre_facet |
northern shrimp Northwest Atlantic Snow crab |
op_source |
ICES Journal of Marine Science volume 67, issue 1, page 145-154 ISSN 1095-9289 1054-3139 |
op_doi |
https://doi.org/10.1093/icesjms/fsp224 |
container_title |
ICES Journal of Marine Science |
container_volume |
67 |
container_issue |
1 |
container_start_page |
145 |
op_container_end_page |
154 |
_version_ |
1814275775480725504 |