Conclusion
Reducing the uncertainty of SDMs and improving the accuracy of model
precision and predicted suitability map is among the unavoidable issues
of SDMs applied to biogeography research. The extensive ecological
adaptability of orchids may lead to more uncertainty in the
correspondence between orchids’ locality and environmental information
when the absence of preprocessing occurrence data in orchid SDMs. This
has been confirmed in our study. Multiple modeling approaches and
validation demonstrate that classification modeling based on
physiological characteristics enhances the accuracy of orchid SDMs. Such
a feature is also reflected in the suitability maps of the model
predictions. Mountainous areas with heterogeneous environments in the
Hengduan Mountains hold the hotspots of orchids. But different living
forms of orchids are influenced by distinct environmental variables,
thus presenting diverse critical regions geographically. In addition, we
propose a method to quantify human activity that allows it could be a
significant non-natural predictor of input models. It does improve model
accuracy in our study, but not a critical variable affecting the
geographic distribution pattern of orchids. Importantly we provide a
method that can be borrowed and referenced by biogeographic studies of
other regions or species, which is flexible and replacement. Our
research underscores that, in addition to conventional model
optimization methods, classification modeling based on physiological
characteristics and incorporation of human activity also make valuable
contributions to orchid SDMs. Ultimately, we expect that our study will
inspire the thinking of related researchers in modeling SDMs to promote
further biogeographic and conservation studies of orchids or other
species.