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TREE REGRESSION ANALYSIS ON THE NESTING HABITAT OF SMALLMOUTH BASS
Author(s) -
Rejwan Cynthia,
Collins Nicholas C.,
Brunner L. Jerry,
Shuter Brian J.,
Ridgway Mark S.
Publication year - 1999
Publication title -
ecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.144
H-Index - 294
eISSN - 1939-9170
pISSN - 0012-9658
DOI - 10.1890/0012-9658(1999)080[0341:traotn]2.0.co;2
Subject(s) - nesting (process) , habitat , bass (fish) , ecology , geography , tree (set theory) , biology , mathematics , engineering , mechanical engineering , mathematical analysis
Habitat conditions may determine locations of patchily distributed smallmouth bass nests in lakes, since young smallmouth bass are fragile and are therefore vulnerable to suboptimal physical characteristics in their typically variable nesting area, the littoral zone. Knowing which habitat conditions are important to nest locations would ultimately be useful in protecting optimal nesting areas from anthropogenic disturbances in lakes. To evaluate factors related to the nest distributions of a smallmouth bass ( Micropterus dolomieui ) population, physical habitat conditions were measured at 36 1‐km‐long and 31 100‐m‐long sites in Lake Opeongo (Ontario, Canada). Both tree regression analysis (a recently devised and unique statistical tool) and standard multiple regression were used to determine the relationships between nest density and four habitat variables. Tree regression analysis does not require assumptions of linearity or homoscedasticity of variances, and it automatically identifies interactions among variables. Furthermore tree regression results were more accurate and more precise than standard multiple‐regression results. Mechanisms underlying the significant relationships between nest densities and both littoral zone temperatures and shoreline reticulation in 1‐km‐long sites and the nonsignificant results at the 100‐m‐long scale, are discussed. Cross‐validation results quantify the difficulty in extrapolating sample findings to whole populations in ecological research.