Abstract
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.
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Acknowledgements
We thank the many Texas A&M University undergraduate and graduate students who provided assistance with field work. We acknowledge the City of Bryan/College Station-Brazos Valley Solid Waste Management Agency for logistical assistance with field studies. We also thank the City of Bryan/College Station-Brazos Valley Solid Waste Management Agency, the Texas Department of Transportation, and the Ladybird Johnson Wildflower Center (Austin, TX)—Endangered Species Conservation Grant Program Award #12419 for providing funding. Finally, we thank the anonymous reviewer and Associate Editor for their time and effort, and the manuscript is greatly improved as a result of their comments.
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Communicated by Daniel Sanchez Mata.
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Wang, HH., Wonkka, C.L., Treglia, M.L. et al. Incorporating local-scale variables into distribution models enhances predictability for rare plant species with biological dependencies. Biodivers Conserv 28, 171–182 (2019). https://doi.org/10.1007/s10531-018-1645-4
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DOI: https://doi.org/10.1007/s10531-018-1645-4