Factors affecting seasonal habitat use of native brook trout ( Salvelinus fontinalis ) in urban headwater streams

2021 ◽  
Author(s):  
Scott G. Blair ◽  
Lee F.G. Gutowsky ◽  
Michael G. Fox
Keyword(s):  
Habitat Use ◽  
Brook Trout ◽  
Salvelinus Fontinalis ◽  
Headwater Streams ◽  
Factors Affecting ◽  
Seasonal Habitat Use

Ontogenetic and diel variation in stream habitat use by brook trout (Salvelinus fontinalis) in a headwater stream

2011 ◽  
Vol 26 (1) ◽  
pp. 143-152 ◽  
Author(s):  
James H. Johnson ◽  
Robert M. Ross ◽  
David S. Dropkin ◽  
Lori A. Redell
Keyword(s):  
Habitat Use ◽  
Brook Trout ◽  
Salvelinus Fontinalis ◽  
Headwater Stream ◽  
Stream Habitat ◽  
Diel Variation

Selection of Factors Affecting the Presence of Brook Trout (Salvelinus fontinalis) in Adirondack Lakes: A Case Study

1992 ◽  
Vol 49 (3) ◽  
pp. 597-608 ◽  
Author(s):  
J. J. Beauchamp ◽  
S. W. Christensen ◽  
E. P. Smith
Keyword(s):  
Logistic Regression ◽  
Brook Trout ◽  
Salvelinus Fontinalis ◽  
Model Development ◽  
Predictive Ability ◽  
Absolute Magnitude ◽  
Regression Diagnostics ◽  
Data Set ◽  
Factors Affecting ◽  
Adirondack Lakes

We used multiple logistic regression techniques to develop models for estimating the probability of brook trout (Salvelinus fontinalis) presence/absence as a function of observable water chemistry variables and watershed characteristics. The data set consists of the Adirondack Lakes Survey Corporation data collected on 1469 lakes during 1984–87. Two models fitted to a randomly selected development subset of lakes, using two sets of candidate explanatory/predictor variables of particular interest, were compared on the basis of coefficient consistency and predictive ability. In addition to the usual maximum likelihood logistic regression results, we also applied collinearity and other associated diagnostics and variable-selection procedures designed specifically for the logistic regression model to arrive at parsimonious models. Both models correctly predicted fish presence in more than 85% of the model development set and more than 80% of the lakes in the verification data. For those variables appearing in both models, the signs of the estimated coefficients were the same and in agreement with expectation. The removal of influential observations, as indicated by the logistic regression diagnostics, caused all of the estimated coefficients to increase in absolute magnitude. This results in a model which is more sensitive to changes in the explanatory variables.

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