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Patterning the distribution of threatened crayfish and their exotic analogues using self-organizing maps

Published online by Cambridge University Press:  03 June 2010

DOROTHÉE KOPP ,
FRÉDÉRIC SANTOUL ,
NICOLAS POULET ,
ARTHUR COMPIN  and
RÉGIS CÉRÉGHINO
DOROTHÉE KOPP
Affiliation:
EcoLab, UMR 5245, Université de Toulouse, bât 4R3, 118 route de Narbonne, 31062 Toulouse Cedex 9, France
FRÉDÉRIC SANTOUL
Affiliation:
EcoLab, UMR 5245, Université de Toulouse, bât 4R3, 118 route de Narbonne, 31062 Toulouse Cedex 9, France
NICOLAS POULET
Affiliation:
Office National de l'Eau et des Milieux Aquatiques, 16 avenue Louison Bobet, 94132 Fontenay-sous-Bois, France
ARTHUR COMPIN
Affiliation:
EcoLab, UMR 5245, Université de Toulouse, bât 4R3, 118 route de Narbonne, 31062 Toulouse Cedex 9, France
RÉGIS CÉRÉGHINO*
Affiliation:
EcoLab, UMR 5245, Université de Toulouse, bât 4R3, 118 route de Narbonne, 31062 Toulouse Cedex 9, France
*
*Correspondence: Professor Régis Céréghino Tel.: +33 561 558 436 e-mail: cereghin@cict.fr
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Summary

Ability to demonstrate statistical patterns of distribution by threatened species and by their potential competitors will determine success in forecasting locations at greatest risk, and ability to target management efforts. A self-organizing map algorithm (SOM) was used to derive probabilities of presence of native (Austropotamobius pallipes) and exotic (Orconectes limosus, Pacifastacus leniusculus and Procambarus clarkii) crayfish species with respect to physical and land-cover variables in a large stream system, using a simple presence-absence dataset of species. Crayfish were sampled at 128 sites representing 86 rivers. The probability of occurrence of the native species increased at higher elevations above sea level and lower temperatures; populations appeared to be mostly confined to headwater streams where exotic competitors were unable to withstand the colder conditions. The distribution of exotic species was correlated with anthropogenic factors, such as the degree of urbanization and agricultural land area. Complementary modelling tools, such as GIS and SOMs, can help to maximize the information extracted from available data in the context of biological conservation.

Keywords

freshwaterland useneural networksoccurrencespecies distribution
Type
Papers
Information
Environmental Conservation , Volume 37 , Issue 2 , June 2010 , pp. 147 - 154
Copyright
Copyright © Foundation for Environmental Conservation 2010

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