Modelling species distributions using regression quantiles

Type Article
Date 2008-02
Language English
Author(s) Vaz SandrineORCID1, Martin C2, Eastwood P3, Ernande BrunoORCID4, 5, Carpentier Andre1, Meaden G2, Coppin Franck1
Affiliation(s) 1 : IFREMER, Lab Ressources Halieut, F-62321 Boulogne, France.
2 : Canterbury Christ Church Univ, Dept Geog & Life Sci, Canterbury CT1 1QU, Kent, England.
3 : Ctr Environm Fisheries & Aquaculture Sci, Lowestoft Lab, Lowestoft NR33 0HT, Suffolk, England.
4 : IFREMER, Lab Ressources Halieut, F-14520 Port En Bessin, France.
5 : Int Inst Appl Syst Anal, Evolut & Ecol Program, A-2361 Laxenburg, Austria.
Source Journal of Applied Ecology (0021-8901) (Blackwell science), 2008-02 , Vol. 45 , N. 1 , P. 204-217
DOI 10.1111/j.1365-2664.2007.01392.x
WOS© Times Cited 55
Keyword(s) Marine fish, Limiting factors, Habitat, Geographical information systems, Distribution models
Abstract 1. Species distribution modelling is an important and well-established tool for conservation planning and resource management. Modelling techniques based on central estimates of species responses to environmental factors do not always provide ecologically meaningful estimates of species-environment relationships and are being increasingly questioned.

2. Regression quantiles (RQ) can be used to model the upper bounds of species-environment relationships and thus estimate how the environment is limiting the distribution of a species. The resulting models tend to describe potential rather than actual patterns of species distributions.

3. Model selection based on null hypothesis testing and backward elimination, followed by validation procedures, are proposed here as a general approach for constructing RQ limiting effect models for multiple species.

4. This approach was applied successfully to 16 of the most abundant marine fish and cephalopods in the eastern English Channel. Most models were validated successfully and null hypothesis testing for model selection proved effective for RQ modelling.

5. Synthesis and applications. Modelling the upper bounds of species-habitat relationships enables the detection of the effects of limiting factors on species' responses. Maps showing potential species distributions are also less likely to underestimate species responses' to the environment, and therefore have subsequent benefits for precautionary management.
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