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Assessing coastal species distribution models through the integration of terrestrial, oceanic and atmospheric data
Author(s) -
Rickbeil Gregory J. M.,
Coops Nicholas C.,
Drever Mark C.,
Nelson Trisalyn A.
Publication year - 2014
Publication title -
journal of biogeography
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.7
H-Index - 158
eISSN - 1365-2699
pISSN - 0305-0270
DOI - 10.1111/jbi.12340
Subject(s) - environmental science , overfitting , environmental data , generalized additive model , shore , species distribution , habitat , physical geography , scale (ratio) , terrestrial ecosystem , ecology , geography , oceanography , ecosystem , cartography , computer science , statistics , geology , biology , mathematics , machine learning , artificial neural network
Aim When considering multiple species in distribution models, environmental variables should be selected that describe the group of species' environmental requirements. This can, however, be challenging in locations such as coastal areas, where different species may respond to terrestrial, oceanic and/or atmospheric conditions. Here, we evaluate the use of remotely sensed ( RS ) terrestrial, oceanic and interpolated climate variables, as well as a more detailed shore‐zone data set, as a means of modelling the distributions of coastal bird species with diverse habitat requirements. Location Coastal British Columbia, Canada. Methods Boosted regression trees were used to model the distributions of 60 species of coastal birds using each environmental variable group, run individually and in combination. Models were assessed for their predictive ability and model fit, as well as for model overfitting. Results Models that incorporated terrestrial data were found to produce the highest model fit and predictive ability of the broad‐scale environmental groups. Incorporating the fine‐scale shore‐zone data offered little improvement, as did selecting the best model in terms of predictive ability by species. Model fit and predictive ability were also found to vary by functional feeding group, with insectivores and benthivores being the best‐modelled. Main conclusions Having access to both broad‐scale RS environmental data and more detailed coastal shore‐zone data produced modest improvements over employing RS environmental data alone. Models using only the terrestrial data set, however, performed similarly to the best single model type (terrestrial + shore‐zone), indicating that broad‐scale environmental data also offer an effective means of estimating coastal bird distributions. Testing multiple environmental variable groups in different combinations and selecting the best model allowed models to be optimized by species; conversely, the results of a ‘one model fits all’ approach were comparable to those of the best models, indicating that a single‐model approach is also valid.