The dimensionality reductions of environmental variables have a significant effect on the performance of species distribution models
Haotian Zhang; Wen‐Yong Guo; Wenting Wang · 2023 · Ecology and Evolution
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
How to effectively obtain species-related low-dimensional data from massive environmental variables has become an urgent problem for species distribution models (SDMs). In this study, we will explore whether dimensionality reduction on environmental variables can improve the predictive performance of SDMs. We first used two linear (i.e., principal component analysis (PCA) and independent components analysis) and two nonlinear (i.e., kernel principal component analysis (KPCA) and uniform manifold approximation and projection) dimensionality reduction techniques (DRTs) to reduce the dimensionali
Abstract by Haotian Zhang; Wen‐Yong Guo; Wenting Wang, Ecology and Evolution (2023) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.1002/ece3.10747