5. CONCLUSIONS 
The study developed the four models that related the 
known CF sites to habitat characteristics and predicted the 
plant’s potential sites in the study area. For the case of Huisun 
area, the four models accurately predicted the potential habitats 
of CFs in Huisun, and substantially reduced the area of field 
survey to less than 10% of the Huisun study area, and were 
implemented efficiently. As a result, they were well suited for 
spatial distribution modeling of CFs. MAXENT was the best 
because it had highest accuracy and reliability among them. 
More importantly, they can prioritize either the field-survey 
areas where it is viable to collect fine spatial-resolution 
microclimatic, edaphic, or biotic data for refining predictions of 
potential habitat in the later rounds of SDM or search areas for 
new population discovery under the conditions of limited 
funding and manpower. However, the outcome showed that it 
is unlikely to accurately extrapolate the spatial patterns of CFs 
from one area to another area with a big separation or to a 
larger area encompassing the original one by predictive models 
merely based on topographic variables, as in the case of our 
entire study area. Follow-up studies will attempt to 
incorporate proxy indicators that can be extracted from 
hyperspectral images or LIDAR DEM and substitute for direct 
parameters, and so that predictive models are applicable on a 
broader scale. 
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