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Advisor(s)
Abstract(s)
Climate change is already a reality, and habitat loss is affecting relentlessly tree species distributions. The
strawberry tree (Arbutus unedo L., Ericaceae) is a Mediterranean evergreen tree used in this article as a case study
to evince its expected threatening situation in the upcoming age. This research work seeks to identify the
bioclimatic and physiographic variables that have the most impact on the strawberry tree’s spatial distribution in
Portugal, acquiring vital knowledge for the design of accurate conservation and afforestation plans for the use
and conservation of strawberry tree, working as a guide under a climate change scenario. For that, twenty-one
bioclimatic variables, two physiographic attributes (altitude and slope), and the Emberger Index (EI) were used
together with 318 observations of strawberry trees, to build a scalable Bayesian procedure, based on machine
learning techniques, aimed to assess the species’ future habitat evolution through three temporal scenarios: (i)
Control Run (1960–1990); (ii) 2050 and (iii) 2070. The results indicate for 2050 a 30% loss of the humid subregion
and a 35% increase in the semi-arid sub-region towards the north. In 2070, it is expected a 2% recuperation
for the sub-humid area, but an 8% loss of the humid sub-area. Under these extreme climate change
conditions, it is anticipated an almost complete loss of habitat for the strawberry tree in the south of Portugal.
The expected ecological evolvability may trigger future migration paths and new refuges’ settlement in the
northern sub-region for the succeeding decades and suggesting after 2070 the possibility of habitat switch and
species drifting.
Description
Keywords
Arbutus unedo Climate change Bayesian network Habitat suitability Migration Natural refuge
Pedagogical Context
Citation
GERASSIS, Saki [et al.] (2021) - Future habitat suitability for species under climate change: lessons learned from the strawberry tree case study. Forest Ecology and Management. ISSN 0378-1127. Vol. 491, p. 119150. Doi: https://doi.org/10.1016/j.foreco.2021.119150
Publisher
Elsevier