Effects on the accuracy of orchid SDMs
The AUC values under all modeling strategies exceeded 0.8, with both Kappa and TSS values exceeding 0.4, indicating that our constructed models outperformed the random model and performed well in the prediction orchids habitat (see Appendix S2.1 for details of the model accuracy results).
We compared the accuracy of the orchid lifeforms classification models constructed based on physiological traits with the models generated by all-data, obtaining 27 groups of comparative data under different approaches for modeling and verifying, of which 15 groups (55.6%) showed that the accuracy of the classification modeling was higher than that of the all-data models. However, we also observed that in the RF models, only the R-t model had higher Kappa values than the all-data model, while in the other comparison groups, the accuracy showed equal or slightly lower values (ranging from 0.001-0.09). When we temporarily discounted the kind of models, the proportional improvement of model accuracy by orchids lifeforms classification was able to reach more than 70%. We performed a T-test on these differences and found general significant differences between classification modeling and all-data modeling (see Fig. 1). The situation was quite distinct when we examined the effect of human influences on the accuracy of the models. Based on the constructed models, we created 32 groups of comparative data, 23 of which showed (71.9%) that the accuracy of the orchid model with the inclusion of the HI factor was higher than that of the model without this variable, and in those contrasts groups, the maximum difference did not exceed 0.04. However, we performed T-test in these comparisons, and significance was present in only two groups (M- t and G-all), which did not appear to be a generalization seemingly (see Fig. 2).