Bioclimatic modeling in the Last Glacial Maximum, Mid-Holocene and facing future climatic changes in the strawberry tree (Arbutus unedo L.)

Abstract

Increasing forest wildfires in Portugal remain a growing concern since forests in the Mediterranean region are vulnerable to recent global warming and reduction of precipitation. Therefore, a long-term negative effect is expected on the vegetation, with increasing drought and areas burnt by fires. The strawberry tree (Arbutus unedo L.) is particularly used in Portugal to produce a spirit by processing its fruits and is the main income for forestry owners. Other applications are possible due to the fruit and leaves’ anti-oxidant properties and bioactive compounds production, with a potential for clinical and food uses. It is a sclerophyllous plant, dry-adapted and fire resistant, enduring the Mediterranean climate, and recently considered as a possibility for afforestation, to intensify forest discontinuity where pines and eucalypts monoculture dominate the region. To improve our knowledge about the species’ spatial distribution we used 318 plots (the centroid of a 1 km2 square grid) measuring the species presence and nine environmental attributes. The seven bioclimatic variables most impacting on the species distribution and two topographic features, slope and altitude, were used. The past, current and future climate data were obtained through WorldClim. Finally, the vulnerability of the strawberry tree to the effects of global climate change was examined in the face of two emission scenarios (RCP 4.5 and 8.5), to predict distribution changes in the years 2050 and 2070, using a species distribution models (MaxEnt). The reduction of suitable habitat for this species is significant in the southern regions, considering the future scenarios of global warming. Central and northern mountainous regions are putative predicted refuges for this species. Forest policy and management should reflect the impact of climate change on the usable areas for forestry, particularly considering species adapted to the Mediterranean regions and wildfires, such as the strawberry tree. The distribution of the species in the Last Glacial Maximum (LGM) and Mid-Holocene (MH) agrees with previous genetic and paleontological studies in the region, which support putative refuges for the species. Two in the southern and coastal-central regions, since the LGM, and one in the east-central mountainous region, considered as cryptic refugia.

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

“…Costa et al [19] used a bioclimatic approach (aridity and thermicity indices) to generate a new composite index to overlap with the species current ranges and for two climate change scenarios (Representative Concentration Pathways scenarios, RCP 4.5 and RCP 8.5, where the numbers refer to the radiative forcing (Wm −2 ) [20]). Ribeiro et al [21] have used a bioclimatic modeling approach to study the influence of environmental variables explaining the presence of a typically adapted species to the Mediterranean region in Portugal for two climate change scenarios (RCP 4.5 and RCP 8.5).…”

Reconstructing the heterogeneity of past woodlands in anthracology using the spatial distribution of charcoals in archaeological layers: Applied to the postglacial occupation of the Abeurador cave (Hérault) in the South of France

“…Arbutus unedo' was found much earlier in this cave, during the Middle Magdalenian (Martínez Varea and Badal Garcia, 2018). Like Ribeiro et al (2019) in the case of Portugal, it can be assumed that in some regions, Arbutus unedo populations suffered from competition with oaks and only increased when the mature oak forest was replaced by an open forest.…”

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].…”