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Using multivariate adaptive regression splines to predict the distributions of New Zealand's freshwater diadromous fish
Author(s) -
LEATHWICK J. R.,
ROWE D.,
RICHARDSON J.,
ELITH J.,
HASTIE T.
Publication year - 2005
Publication title -
freshwater biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.297
H-Index - 156
eISSN - 1365-2427
pISSN - 0046-5070
DOI - 10.1111/j.1365-2427.2005.01448.x
Subject(s) - riparian zone , multivariate adaptive regression splines , fish migration , environmental science , multivariate statistics , streams , habitat , sampling (signal processing) , ecology , streamflow , statistics , regression analysis , drainage basin , geography , mathematics , biology , bayesian multivariate linear regression , cartography , computer science , computer network , filter (signal processing) , computer vision
Summary 1. Relationships between probabilities of occurrence for fifteen diadromous fish species and environmental variables characterising their habitat in fluvial waters were explored using an extensive collection of distributional data from New Zealand rivers and streams. Environmental predictors were chosen for their likely functional relevance, and included variables describing conditions in the stream segment where sampling occurred, downstream factors affecting the ability of fish to move upriver from the sea, and upstream, catchment‐scale factors mostly affecting variation in river flows. 2. Analyses were performed using multivariate adaptive regression splines (MARS), a technique that uses piece‐wise linear segments to describe non‐linear relationships between species and environmental variables. All species were analysed using an option that allows simultaneous analysis of community data to identify the combination of environmental variables best able to predict the occurrence of the component species. Model discrimination was assessed for each species using the area under the receiver operating characteristic curve (ROC) statistic, calculated using a bootstrap procedure that estimates performance when predictions are made to independent data. 3. Environmental predictors having the strongest overall relationships with probabilities of occurrence included distance from the sea, stream size, summer temperature, and catchment‐scale drivers of variation in stream flow. Many species were also sensitive to variation in either the average and/or maximum downstream slope, and riparian shade was an important predictor for some species. 4. Analysis results were imported into a Geographic Information System where they were combined with extensive environmental data, allowing spatially explicit predictions of probabilities of occurrence by species to be made for New Zealand's entire river network. This information will provide a valuable context for future conservation management in New Zealand's rivers and streams.