@Article{CavalcanteSamp:2022:MoPoDi,
author = "Cavalcante, Arnobio de Mendon{\c{c}}a Barreto and Sampaio,
Augusto Cesar Praciano",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)}",
title = "Modeling the potential distribution of cacti under climate change
scenarios in the largest tropical dry forest region in South
America",
journal = "Journal of Arid Environments",
year = "2022",
volume = "200",
pages = "e104725",
month = "May",
keywords = "Brazilian semiarid, Cactaceae, Global warming, Caatinga, Species
distribution models, Extinction risk.",
abstract = "Climate change projections for the Brazilian semiarid region for
the rest of this century include increased temperature, reduced
precipitation and aridification. Consequently, alterations in the
distribution of species are expected in the largest seasonally dry
tropical forest in South America (Caatinga), which covers 75% of
Brazil's semiarid region. This study modeled the potential
distribution of eight cactus species native (target species) to
the Caatinga under future climate scenarios and analyzed the range
shifts of these species during the remainder of this century. Two
online biodiversity databases, nine environmental variables and
the Maxent algorithm were used, considering the time intervals
1961-1990, 2041-2060 and 2061-2080, along with two Representative
Concentration Pathway (RCP) scenarios, 4.5 and 8.5. The potential
species distribution models predict that: (1) the future climate
conditions are likely to cause contraction or expansion of the
areas with high habitat suitability (>0.75) of the target species;
(2) species with widespread distribution are likely to be
vulnerable to climate change; (3) for some cactus species, climate
change will provide an opportunity for expansion, but for the
majority it will be a threat to survival; and (4) it is premature
to claim that the future vegetation of the Caatinga will be
dominated by cacti.",
doi = "10.1016/j.jaridenv.2022.104725",
url = "http://dx.doi.org/10.1016/j.jaridenv.2022.104725",
issn = "0140-1963",
language = "en",
targetfile = "Cavalcance_2022_modeling.pdf",
urlaccessdate = "03 maio 2024"
}