Species Ecological Envelopes under Climate Change Scenarios: A Case Study for the Main Two Wood-Production Forest Species in Portugal

Abstract

Species ecological envelope maps were obtained for the two main Portuguese wood-production species (Eucalyptus globulus Labill. and Pinus pinaster Aiton) and projected future climate change scenarios. A machine learning approach was used to understand the most influential environmental variables that may explain current species distribution and productivity. Background and Objectives: The aims of the study were: (1) to map species potential suitability areas using ecological envelopes in the present and to project them in the future under climate change scenarios; (2) to map species current distributions; (3) to map species current productivity; and (4) to explore the most influential environmental variables on species current distribution and productivity. Materials and Methods: Climate, elevation data, and soil data sets were used to obtain present and future species ecological envelopes under two climate change scenarios. The official land cover maps were used to map species distributions. Forest inventory data were used to map the species productivity by geostatistical techniques. A Bayesian machine learning approach, supported by species distributions and productivity data, was used to explore the most influential environmental variables on species distribution and productivity and to validate species ecological envelopes. Results: The species ecological envelope methodology was found to be robust. Species’ ecological envelopes showed a high potential for both species’ afforestation. In the future, a decrease in the country’s area potentiality was forecasted for both species. The distribution of maritime pine was found to be mainly determined by precipitation-related variables, but the elevation and temperature-related variables were very important to differentiate species productivity. For eucalypts, species distribution was mainly explained by temperature-related variables, as well as the species productivity. Conclusions: These findings are key to support recommendations for future afforestation and will bring value to policy-makers and environmental authorities in policy formulation under climate change scenarios.

Modelling Maritime Pine (Pinus pinaster Aiton) Spatial Distribution and Productivity in Portugal: Tools for Forest Management

“…Nowadays, access to big spatial data on climate and other environmental variables has fostered the use of powerful techniques from artificial intelligence and spatial statistics, such as machine learning (ML) and geostatistical modeling, which, coupled with geographic information systems (GIS) allow for the construction of simulated maps for the species' habitat suitability and productivity under the impacts of climate change [1][2][3][4][5][6][7][8][9][10][11]. Indeed, statistical modelling techniques such as classical regression (CR), generalized linear models (GLM), algorithmic modelling based on machine learning (ML), e.g., Bayesian networks (BNs), maximum entropy (MaxENT), and classification and regression trees (CART) have become increasingly popular [12].…”

“…Regarding forest species' productive potential mapping, several methodological approaches have been essayed in various studies [6][7][8][9][10][11]. In Portugal, forest species' productive potential maps for present and future climate change scenarios are available, for each one of the seven forest management regions the country, as divided in [13].…”

“…Yet, in a recent study [11], current species' potential maps were produced for Portugal using the species ecological envelopes methodology, proposed by DGRF (Direção Geral dos Recursos Florestais) [14], and afterwards, projected into the future under climate change scenarios. Moreover, in that study a Bayesian ML approach was used to explore the most influential environmental variables on current species' distribution and productivity for the species essayed (Maritime pine and Eucalypt).…”

“…However, the impact of climate change in Maritime pine forest area must not be neglected. Indeed, several studies have consistently reported a future decrease of potential areas of the Maritime pine, particularly in south of the Tagus river and the inland of Portugal, associated with a species' migration trend from south to north and from the interior to coastal areas [11].…”

“…The present study focused on producing Maritime pine distribution and productivity maps by using a ML modelling approach. In a previous study [11], the authors used a predefined set of environmental variables to produce an ecological envelope map for the species' potential distribution. Afterwards, a Bayesian ML approach highlighted that the species' current distribution was mainly determined by precipitation-related variables, and that elevation-and temperature-related variables were very important to differentiate the species' current productivity.…”