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Modelling stream fish species distribution in a river network: the relative effects of temperature versus physical factors
Author(s) -
Buisson L.,
Blanc L.,
Grenouillet G.
Publication year - 2008
Publication title -
ecology of freshwater fish
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.667
H-Index - 55
eISSN - 1600-0633
pISSN - 0906-6691
DOI - 10.1111/j.1600-0633.2007.00276.x
Subject(s) - perch , habitat , pike , ecology , generalized additive model , environmental science , spatial distribution , context (archaeology) , species distribution , relative species abundance , watershed , fish <actinopterygii> , biology , fishery , abundance (ecology) , geography , statistics , paleontology , remote sensing , mathematics , machine learning , computer science
– The relative influence of temperature versus local physical factors on the spatial distribution of riverine fish species was investigated in a large watershed of south‐western France. Using generalised additive models and hierarchical partitioning, we modelled the ecological responses of 28 fish species to a set of five environmental predictors, and we quantified the independent effect of each predictor. The spatial distribution of fish species was primarily determined by both mean temperature and position along the upstream–downstream gradient. However, responses to these environmental factors varied according to the species considered. Fish species with strong thermal requirements (e.g., common carp, black bullhead, Atlantic salmon) were mainly sensitive to temperature whereas longitudinal gradient was of primary importance for downstream species (e.g., common bream, largemouth bass, pike perch). Both the statistical methods used gave concordant results and appeared complementary. This dual‐approach, quantifying the relative contribution of each environmental factor, appears particularly useful to understand the spatial distribution of stream fish species. Separating the effects of temperature versus habitat factors is crucial to accurately predict species distribution modifications in the current context of global change.