Selecting discriminant function models for predicting the expected richness of aquatic macroinvertebrates
By J. Sickle, D. Huff, Charles P. Hawkins. 2006
76 citations recorded by the source corpus.
Subfields: Ecology, Environmental Chemistry, Nature and Landscape Conservation
View Paper: https://www.semanticscholar.org/paper/8345528bfb093271fbdd4f97318daf2fb09cf7aa
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Ecology
- Yong Cao
- D. Carlisle
- M. Potapova
- M. Meador
- James Falcone
- M. A. Kosterman
- J. Olson
- Charles P. Hawkins
In Environmental Chemistry
In Nature and Landscape Conservation
- Yong Cao
- D. Carlisle
- M. Meador
- M. Potapova
- James Falcone
- M. A. Kosterman
- J. Olson
- Charles P. Hawkins
Citations in the corpus
- Modeling natural environmental gradients improves the accuracy and precision of diatom-based indicators (2007, methodology): “We used the allpossible-subsets procedure of Van Sickle et al. (2006) to identify the variables in a discriminant function model that best predicted group membership and, thus, the taxonomic composition of RC sites.”
- Biological assessments of Appalachian streams based on predictive models for fish, macroinvertebrate, and diatom assemblages (2008, methodology): “The all-subsets multiple discriminant analysis routines of Van Sickle et al. (2006) were used to select a set of predictor variables that were unrelated to human disturbance and that produced the most precise and accurate estimates of the probabilities of capture (Pc) for each taxon at each referenc”