Modelling Species Distributions Using Regression Quantiles

Vaz, S., Martin, C.S., Eastwood, P.D., Ernande, B., Charpentier, A., Maeden, G.J., & Coppin, F. (2007). Modelling Species Distributions Using Regression Quantiles. IIASA Interim Report. IIASA, Laxenburg, Austria: IR-07-056

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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 increasingly being 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 successfully applied to 16 of the most abundant marine fish and cephalopods in the Eastern English Channel. Most models were successfully validated 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.

Item Type: Monograph (IIASA Interim Report)
Uncontrolled Keywords: Habitat; Marine fish; Distribution models; Limiting factors; Geographical Information Systems
Research Programs: Evolution and Ecology (EEP)
Depositing User: IIASA Import
Date Deposited: 15 Jan 2016 08:40
Last Modified: 27 Aug 2021 17:20

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