Effects on the suitability maps of orchid SDMs
Ecological suitability maps of target species generated by SDMs are
commonly an invaluable reference for biodiversity conservation planning.
The effects of human influences and physiological differences on the
accuracy of orchid SDMs are one of the objects of our exploration.
However, more vital we wonder about their effects on orchid suitability
maps. In our study, this variation was evident (see Appendix S2.2 for
the detailed predicted suitability area by different modeling
strategies).
At first, considering the orchids lifeforms classification, we
established the spatial comparison between the overall layer (modeled by
all-data) and the total layer (generated by superposition of the
epiphytic, mycoheterotrophic, and terrestrial orchids suitability map),
which showed that there was an enormous variance between them. In the
GLM models, the changed area was 39499 km2 (6.59% of
the total study area). In the MaxEnt models, the changed area was 28018
km2 (4.68%). In the RF models, the changed area was
19974 km2 (3.33%). Moreover, it was usually shown
that the overall modeling predicted suitability area was more than the
total classification modeling results. To explore these discrepancy
areas, we plotted the changed area map. Geopolitically, we counted the
changed area for all counties separately in our study area. On this
basis, we did the double-ranking to find the critical regions in the
change. The results showed that the eastern, western, and southeastern
regions of the study area were the vital changed regions, especially
Zayü County, Longyang District, and Tengchong City, which always was at
the forefront of considering the sequential and area weights no matter
the modeling strategies (see Fig. 3).
Secondly, in exploring the effect of human interference on suitability
maps, we obtained 12 groups of comparison variables based on different
models with varying data sets (see Appendix S2.2). The results showed
that models containing the HI factor predicted fewer suitability areas
than without this factor (more than 66%), with only four groups showing
the opposite, namely, G-m, M-e, R-e, and R-m. To further explore the
effect of the HI factor on different lifeforms orchids, we calculated
the changed area in each county for all models separately and did the
same double-ranking exercise (see Appendix S3.2 for details) and plotted
Fig. 4. This figure indicated that when the HI factor was included in
the model variables, the change in the predicted area caused by it had
the greatest impact on terrestrial orchids, followed by
mycoheterotrophic orchids and epiphytic orchids. Region analysis
displayed that Zayü, Tengchong, and Yangyuan counties were the most
changed in all comparisons (see Appendix S2.3, we have mapped the
differences of all model strategies).