Name
Title | ||
|---|---|---|
Study on selecting sensitive environmental variables in modelling species spatial distribution. |
Abstract | ||
|---|---|---|
ABSTRACTThis study explores the effects of different environmental variables on the accuracy of species distribution models. Forest inventory and analysis data sets were used to generate absence and pseudo-absence points of chestnut oak (Quercus prinus) in the central and southern Appalachian mountain region of the US. We simulate chestnut oak distribution using different criteria for selecting environmental variables: (1) the selection of sensitive variables using factor analysis and the calculation of a sensitivity index, (2) principal components analysis. Factor analysis to environmental variables at both occurrence and pseudo-absence points was conducted to calculate the sensitivity index for each environmental variable. The identification of sensitive variables may use the factor loadings of first one or two factors of environmental variables. Modelling with sensitive variables (mean Kappa u003e 0.60; mean true skill statistic (TSS) u003e 0.60) can enhance model accuracy more than using PCA variables or all ... |
Year | Venue | Field |
|---|---|---|
2016 | Annals of GIS | Econometrics,Kappa,Statistic,Species distribution,Forest inventory,Factor analysis,Statistics,Environment variable,Geography,Principal component analysis,Spatial distribution |
DocType | Volume | Issue |
Journal | 22 | 1 |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hongshuo Wang | 1 | 1 | 0.77 |
| Desheng Liu | 2 | 24 | 4.82 |
| Darla Munroe | 3 | 0 | 0.34 |
| Kai Cao | 4 | 99 | 11.94 |
| Christine Biermann | 5 | 0 | 0.34 |