Logistic regression as a tool for defining habitat requirements of two common gammarids

Logistic regression as a tool for defining habitat requirements of two common gammarids 1. Logistic regression predicts the probability of occurrence of a species as a function of environmental variables. This technique was applied to a large data set describing the distribution of two common gammarid species, Gammarus fossarum and G. pulex, in streams in the Netherlands, to evaluate its usefulness in defining habitat requirements. 2. A method is presented that derives optimum habitat ranges for environmental variables from logistic regression equations. The calculated optimum habitat ranges, which are related to the maximum likelihood of presence in the field, agreed with habitat requirements and ecological tolerances in the literature. 3. Single logistic regressions provide good descriptions of the optimum habitat requirements and multiple logistic regressions give insight into the relative importance of each environmental variable. It is the combination that makes logistic regression a valuable tool for constructing habitat suitability indices. 4. Current velocity, pH, Kjeldahl nitrogen, total phosphorus, ammonium nitrogen, conductivity, width and depth are, in this sequence, the most important environmental variables in predicting the probability of occurrence of G. fossarum, whereas current velocity, Kjeldahl nitrogen, pH and depth are the most important variables for the prediction of the probability of occurrence of G. pulex. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Freshwater Biology Wiley

Logistic regression as a tool for defining habitat requirements of two common gammarids

Loading next page...
 
/lp/wiley/logistic-regression-as-a-tool-for-defining-habitat-requirements-of-two-TCd9duXTE4
Publisher
Wiley
Copyright
Copyright © 1998 Wiley Subscription Services, Inc., A Wiley Company
ISSN
0046-5070
eISSN
1365-2427
DOI
10.1046/j.1365-2427.1998.00304.x
Publisher site
See Article on Publisher Site

Abstract

1. Logistic regression predicts the probability of occurrence of a species as a function of environmental variables. This technique was applied to a large data set describing the distribution of two common gammarid species, Gammarus fossarum and G. pulex, in streams in the Netherlands, to evaluate its usefulness in defining habitat requirements. 2. A method is presented that derives optimum habitat ranges for environmental variables from logistic regression equations. The calculated optimum habitat ranges, which are related to the maximum likelihood of presence in the field, agreed with habitat requirements and ecological tolerances in the literature. 3. Single logistic regressions provide good descriptions of the optimum habitat requirements and multiple logistic regressions give insight into the relative importance of each environmental variable. It is the combination that makes logistic regression a valuable tool for constructing habitat suitability indices. 4. Current velocity, pH, Kjeldahl nitrogen, total phosphorus, ammonium nitrogen, conductivity, width and depth are, in this sequence, the most important environmental variables in predicting the probability of occurrence of G. fossarum, whereas current velocity, Kjeldahl nitrogen, pH and depth are the most important variables for the prediction of the probability of occurrence of G. pulex.

Journal

Freshwater BiologyWiley

Published: Jun 1, 1998

There are no references for this article.

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create folders to
organize your research

Export folders, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off