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DOI10.1007/s10531-018-1645-4
Incorporating local-scale variables into distribution models enhances predictability for rare plant species with biological dependencies
Wang, Hsiao-Hsuan1; Wonkka, Carissa L.2; Treglia, Michael L.3; Grant, William E.1; Smeins, Fred E.4; Rogers, William E.4
发表日期2019
ISSN0960-3115
EISSN1572-9710
卷号28期号:1页码:171-182
英文摘要

The conservation of rare species is typically challenging because of incomplete knowledge about their biology and distributions. Species distribution models (SDMs) have emerged as an important tool for improving the efficiency of rare species conservation. However, these models must include biologically relevant predictor variables at scales appropriate for discriminating suitable and unsuitable habitat. We used a species distribution modelling tool, maximum entropy (Maxent), to assess the relative influence of biologically relevant topographic characteristics, land cover features, geological formations, and edaphic factors on the occurrence of the endangered endemic orchid Spiranthes parksii (Navasota ladies' tresses). Our final model produced an excellent AUC value (0.984), with the permutation importance to model fit of predictor variables representing topographic characteristics, land cover features, geological formations, and edaphic factors summing to 8.17, 35.12, 10.43, and 46.28%, respectively. Local-scale edaphic variables were the most informative, with soil taxonomic units explaining the highest amount of variance (36.40%) of all variables included in the model. These results document the importance of local edaphic characteristics in discriminating between suitable and unsuitable habitat for S. parksii, and emphasize the importance of including local-scale edaphic factors in SDMs for species such as S. parksii with specialized habitat requirements and close relationships with other organisms.


WOS研究方向Biodiversity & Conservation ; Environmental Sciences & Ecology
来源期刊BIODIVERSITY AND CONSERVATION
文献类型期刊论文
条目标识符http://gcip.llas.ac.cn/handle/2XKMVOVA/91555
作者单位1.Texas A&M Univ, Dept Wildlife & Fisheries Sci, College Stn, TX 77843 USA;
2.Univ Nebraska, Dept Agron & Hort, Lincoln, NE 68583 USA;
3.Nature Conservancy, New York City Program, New York, NY 10001 USA;
4.Texas A&M Univ, Dept Ecosyst Sci & Management, College Stn, TX 77843 USA
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Wang, Hsiao-Hsuan,Wonkka, Carissa L.,Treglia, Michael L.,et al. Incorporating local-scale variables into distribution models enhances predictability for rare plant species with biological dependencies[J],2019,28(1):171-182.
APA Wang, Hsiao-Hsuan,Wonkka, Carissa L.,Treglia, Michael L.,Grant, William E.,Smeins, Fred E.,&Rogers, William E..(2019).Incorporating local-scale variables into distribution models enhances predictability for rare plant species with biological dependencies.BIODIVERSITY AND CONSERVATION,28(1),171-182.
MLA Wang, Hsiao-Hsuan,et al."Incorporating local-scale variables into distribution models enhances predictability for rare plant species with biological dependencies".BIODIVERSITY AND CONSERVATION 28.1(2019):171-182.
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