Modelling and mapping the suitability of European forest formations at 1-km resolution

Abstract

Proactive forest conservation planning requires spatially accurate information about the potential distribution of tree species. The most cost-efficient way to obtain this information is habitat suitability modelling i.e. predicting the potential distribution of biota as a function of environmental factors. Here, we used the bootstrap-aggregating machine-learning ensemble classifier Random Forest (RF) to derive a 1-km resolution European forest formation suitability map. The statistical model use as inputs more than 6,000 field data forest inventory plots and a large set of environmental variables. The field data plots were classified into different forest formations using the forest category classification scheme of the European Environmental Agency. The ten most dominant forest categories excluding plantations were chosen for the analysis. Model results have an overall accuracy of 76%. Between categories scores were unbalanced and Mesophitic deciduous forests were found to be the least correctly classified forest category. The model's variable ranking scores are used to discuss relationship between forest category/environmental factors and to gain insight into the model's limits and strengths for map applicability. The European forest suitability map is now available for further applications in forest conservation and climate change issues.

Functional Response Trait Analysis Improves Climate Sensitivity Estimation in Beech Forests at a Trailing Edge

“…In our study, Ellenberg's Quotient, the most widely used composite index in relation with climate adaptation and sensitivity (e.g., [15,16,20,22,23]), did not proved to have a powerful sensitivity indication power due to the low ratio of decay function (Table 2). This conclusion is consistent with the findings that EQ ranked 35th of 44 climatic predictors of European forest distribution [11]. In our study, composite indices did not produce a separate cluster or sub-unit, but they were classified as part of the precipitation-related group, which proved to be a more sensitive indicator of climate change, except for the Ombrothermic Index ( Figure 5).…”

“…In case of climate-induced forests the reliability of climate modes is considered "fair" if AUC > 0.7 and "good" if AUC > 0.8 [11,21]. In our study, median AUC reached "fair" for the main groups of both clusters (AUC = 0.785 for the temperature group, and AUC = 0.768 for the precipitation group).…”

“…In spite of the small difference in significant environmental factors, the primary importance of temperature measures was detected at lower/southern and upper/northern climatic distribution limits of Fagus species at a global scale [16]. Note that the Thermicity index (Ti), which refers to the sum of yearly mean, maximum and minimum temperature of the coldest month, was ranked as the tenth most important predictor for sub-continental beech forest occurrence [11]. Comparing previous findings to our results, this bioclimatic measure proved to be similarly subordinate within the PAT subunit to describe functional distribution performance of beech forests.…”

“…Temperature-related indices are frequently reported to be the best predictors of beech forest distribution in studies throughout Europe. Six predictors from the first 10 for hemi-boreal beech forests; eight important indices for sub-atlantic and sub-continental beech forests; and nine variables for Pannonian-Pontic beech forests turned out to be temperature-related climate measures [11]. Additionally, the temperature gradient was reported to involve the first division level in the TWINSPAN classification of European beech forests at a continental scale [12].…”

An improved species distribution model for Scots pine and downy oak under future climate change in the NW Italian Alps

“…Published models of Scots pine distribution under scenarios of climate change have produced contrasting results (e.g., Casalegno et al 2011;Meier et al 2011), probably as a result of different datasets and processes being included or not in the models (e.g., dispersal constraints, biotic competition, choice of climate, and drought-related variables).…”