Cargando…
The dimensionality reductions of environmental variables have a significant effect on the performance of species distribution models
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 o...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10659948/ https://www.ncbi.nlm.nih.gov/pubmed/38020673 http://dx.doi.org/10.1002/ece3.10747 |
_version_ | 1785137657624395776 |
---|---|
author | Zhang, Hao‐Tian Guo, Wen‐Yong Wang, Wen‐Ting |
author_facet | Zhang, Hao‐Tian Guo, Wen‐Yong Wang, Wen‐Ting |
author_sort | Zhang, Hao‐Tian |
collection | PubMed |
description | 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 dimensionality of high‐dimensional environmental data. Then, we established five SDMs based on the environmental variables of dimensionality reduction for 23 real plant species and nine virtual species, and compared the predictive performance of those with the SDMs based on the selected environmental variables through Pearson's correlation coefficient (PCC). In addition, we studied the effects of DRTs, model complexity, and sample size on the predictive performance of SDMs. The predictive performance of SDMs under DRTs other than KPCA is better than using PCC. And the predictive performance of SDMs using linear DRTs is better than using nonlinear DRTs. In addition, using DRTs to deal with environmental variables has no less impact on the predictive performance of SDMs than model complexity and sample size. When the model complexity is at the complex level, PCA can improve the predictive performance of SDMs the most by 2.55% compared with PCC. At the middle level of sample size, the PCA improved the predictive performance of SDMs by 2.68% compared with the PCC. Our study demonstrates that DRTs have a significant effect on the predictive performance of SDMs. Specifically, linear DRTs, especially PCA, are more effective at improving model predictive performance under relatively complex model complexity or large sample sizes. |
format | Online Article Text |
id | pubmed-10659948 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-106599482023-11-01 The dimensionality reductions of environmental variables have a significant effect on the performance of species distribution models Zhang, Hao‐Tian Guo, Wen‐Yong Wang, Wen‐Ting Ecol Evol Research Articles 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 dimensionality of high‐dimensional environmental data. Then, we established five SDMs based on the environmental variables of dimensionality reduction for 23 real plant species and nine virtual species, and compared the predictive performance of those with the SDMs based on the selected environmental variables through Pearson's correlation coefficient (PCC). In addition, we studied the effects of DRTs, model complexity, and sample size on the predictive performance of SDMs. The predictive performance of SDMs under DRTs other than KPCA is better than using PCC. And the predictive performance of SDMs using linear DRTs is better than using nonlinear DRTs. In addition, using DRTs to deal with environmental variables has no less impact on the predictive performance of SDMs than model complexity and sample size. When the model complexity is at the complex level, PCA can improve the predictive performance of SDMs the most by 2.55% compared with PCC. At the middle level of sample size, the PCA improved the predictive performance of SDMs by 2.68% compared with the PCC. Our study demonstrates that DRTs have a significant effect on the predictive performance of SDMs. Specifically, linear DRTs, especially PCA, are more effective at improving model predictive performance under relatively complex model complexity or large sample sizes. John Wiley and Sons Inc. 2023-11-20 /pmc/articles/PMC10659948/ /pubmed/38020673 http://dx.doi.org/10.1002/ece3.10747 Text en © 2023 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Articles Zhang, Hao‐Tian Guo, Wen‐Yong Wang, Wen‐Ting The dimensionality reductions of environmental variables have a significant effect on the performance of species distribution models |
title | The dimensionality reductions of environmental variables have a significant effect on the performance of species distribution models |
title_full | The dimensionality reductions of environmental variables have a significant effect on the performance of species distribution models |
title_fullStr | The dimensionality reductions of environmental variables have a significant effect on the performance of species distribution models |
title_full_unstemmed | The dimensionality reductions of environmental variables have a significant effect on the performance of species distribution models |
title_short | The dimensionality reductions of environmental variables have a significant effect on the performance of species distribution models |
title_sort | dimensionality reductions of environmental variables have a significant effect on the performance of species distribution models |
topic | Research Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10659948/ https://www.ncbi.nlm.nih.gov/pubmed/38020673 http://dx.doi.org/10.1002/ece3.10747 |
work_keys_str_mv | AT zhanghaotian thedimensionalityreductionsofenvironmentalvariableshaveasignificanteffectontheperformanceofspeciesdistributionmodels AT guowenyong thedimensionalityreductionsofenvironmentalvariableshaveasignificanteffectontheperformanceofspeciesdistributionmodels AT wangwenting thedimensionalityreductionsofenvironmentalvariableshaveasignificanteffectontheperformanceofspeciesdistributionmodels AT zhanghaotian dimensionalityreductionsofenvironmentalvariableshaveasignificanteffectontheperformanceofspeciesdistributionmodels AT guowenyong dimensionalityreductionsofenvironmentalvariableshaveasignificanteffectontheperformanceofspeciesdistributionmodels AT wangwenting dimensionalityreductionsofenvironmentalvariableshaveasignificanteffectontheperformanceofspeciesdistributionmodels |