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Species distribution modelling under climate change scenarios for maritime pine (Pinus pinaster Aiton) in Portugal
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Advisor(s)
Abstract(s)
To date, a variety of species potential distribution mapping approaches have been used,
and the agreement in maps produced with different methodological approaches should be assessed.
The aims of this study were: (1) to model Maritime pine potential distributions for the present and
for the future under two climate change scenarios using the machine learning Maximum Entropy
algorithm (MaxEnt); (2) to update the species ecological envelope maps using the same environmental
data set and climate change scenarios; and (3) to perform an agreement analysis for the species
distribution maps produced with both methodological approaches. The species distribution
maps produced by each of the methodological approaches under study were reclassified into presence–
absence binary maps of species to perform the agreement analysis. The results showed that
the MaxEnt-predicted map for the present matched well the species’ current distribution, but the
species ecological envelope map, also for the present, was closer to the species’ empiric potential
distribution. Climate change impacts on the species’ future distributions maps using the MaxEnt
were moderate, but areas were relocated. The 47.3% suitability area (regular-medium-high), in the
present, increased in future climate change scenarios to 48.7%–48.3%. Conversely, the impacts in
species ecological envelopes maps were higher and with greater future losses than the latter. The
76.5% suitability area (regular-favourable-optimum), in the present, decreased in future climate
change scenarios to 58.2%–51.6%. The two approaches combination resulted in a 44% concordance
for the species occupancy in the present, decreasing around 30%–35% in the future under the climate
change scenarios. Both methodologies proved to be complementary to set species’ best suitability
areas, which are key as support decision tools for planning afforestation and forest management
to attain fire-resilient landscapes, enhanced forest ecosystems biodiversity, functionality and
productivity.
Description
Keywords
Species distribution modelling Species ecological envelope MaxEnt software RCP 4.5 to RCP 8.5 climate change scenarios Concordance
Citation
ALEGRIA, C.M.M. [et al.] (2023) - Species distribution modelling under climate change scenarios for maritime pine (Pinus pinaster Aiton) in Portugal. Forests. 14, 591. DOI 14, 591. https://doi.org/10.3390/f14030591
Publisher
MDPI