Vegetation classification maps are typically static and lack a predictive nature; however, models based on environmental variables allow us to make predictions that are transferable across space and time. Ecological niche models predict the geographic distribution of a species’ suitable habitat by relating its occurrence records with variables likely associated with its environmental requirements, a method widely used for individual species but not often for entire ecosystems. However, climatically suitable areas may be further restricted by anthropogenic processes such as deforestation. Therefore, it is important to consider land cover in addition to environmental suitability to accurately predict distribution of vegetation types. We tested the utility of niche models to predict the geographic distribution of two montane forest types in the mountains of eastern Mexico: cloud forests and pine-oak forests. We used bioclimatic variables from the Chelsa dataset and occurrence points drawn randomly from the INEGI VI land-use and vegetation classification dataset to build Maxent models using Wallace v1.0.6. Using the Global Forest Change dataset, we removed areas with less than 70% current forest cover from the niche model predictions to approximate the remaining suitable area after accounting for deforestation.
Results/Conclusions
The modeling results revealed that the most suitable areas for cloud forests occur on the eastern slopes of the Sierra Madre Oriental, while suitable areas for pine-oak forests occur on the western ones. When accounting for current forest cover, we observed a substantial decrease in areas highly suitable for cloud forests. In comparison, pine-oak forests lost a lower proportion of their highly suitable areas. The cloud forests lost greater areas in the southern region while the pine oak forests lost more in the north. These results highlight how habitat loss due to anthropogenic processes pose threats for forest-dwelling species. Therefore, these models could help conservation efforts aiming to preserve the biodiversity of montane forest systems. In our ongoing analyses, we are refining the implementation of niche models to define boundaries where predicted suitable conditions overlap for both forests. Additionally, future directions of this work will involve projecting these forests to future climate scenarios. Understanding the future climate for these areas will identify the extent of elevational change in the forests.