The objective of this lecture by Andrey Krasovskiy (IIASA) is to model the suitability of tree species under various climatic conditions. This will provide important information for policymakers to manage future tree species distribution in the Alpine region. While future climates pose a risk to some native tree species, e.g. Norway Spruce, non-native tree (NNT) species can become more suitable in some areas. While NFI data on tree species can be limited, crowdsourcing provides a global outlook on the distribution of tree species. Global data is valuable, as it provides information on historical species suitability under various climatic conditions. Using these data sources we deal with presence-only data. We only have information about locations where species were observed, and no information on whether they were present in other pixels. Several methods are developed for such modeling, including Random Forest Classifier and MaxEnt.
In this lecture, we will provide an example of modeling suitability maps for Black Locust (Robinia pseudoacacia) using relatively high-resolution climatic data (19 bioclimatic variables) for historical and future climates (RCP4.5 and RCP8.5). Our results show that climate change can have a significant impact on NNT species distribution. This methodology can be applied to a variety of tree species, and optimal composition analyzed. Comparison with other methods and suitability maps still needs to be done. Future analysis will include soil information as an additional suitability factor.
In the first lecture of Chapter 6, Andrey Krasovskiy (IIASA) shares the insight of his research on how to use tree species suitability under various climatic conditions.