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An Analysis of the Influence of Annual Thermal Variables on the Occurrence of Fifteen Warmwater Fishes
Author(s) -
Scheller Robert M.,
Snarski Virginia M.,
Eaton John G.,
Oehlert Gary W.
Publication year - 1999
Publication title -
transactions of the american fisheries society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.696
H-Index - 86
eISSN - 1548-8659
pISSN - 0002-8487
DOI - 10.1577/1548-8659(1999)128<0257:aaotio>2.0.co;2
Subject(s) - multivariate statistics , nonparametric statistics , statistics , parametric statistics , environmental science , linear discriminant analysis , streams , sampling (signal processing) , fish <actinopterygii> , regression , mathematics , biology , fishery , computer science , computer network , filter (signal processing) , computer vision
Multisource fish‐sampling data and U.S. Geological Survey temperature data from streams throughout the United States were used to investigate the influence of derived thermal regime variables on the presence or absence of 15 common warmwater fish species. The 3‐year average annual thermal regime was calculated for streams where presence or absence was known for these 15 species. Six variables estimated to be of biological importance to the winter and summer survival and recruitment of a species, including measures of feeding and nonfeeding periods, were calculated from these thermal regimes. Stepwise discriminant analysis and multiple regression were used to select optimal variables for creating multivariate models. Parametric and nonparametric multivariate discriminant analyses were then performed to test our ability to correctly classify presence or absence using the thermal variables. These statistical empirical models were able to correctly predict presence or absence with greater than 90% accuracy for 13 of 15 species. Nonparametric ( K th nearest neighbor) analyses had marginally more accurate predictions than parametric (linear) analyses. This technique may allow for an improved estimation of potential changes in distribution under various global warming scenarios.