2023
DOI: 10.21203/rs.3.rs-2899371/v1
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Climate change and low species drought tolerance will negatively impact plant richness in the Neotropical seasonally dry forests

Abstract: Neotropical seasonal dry forest (NSDF) is one of the most threatened ecosystems under global climate change (GCC) predictions, with likely species losses and uneven modification of diversity patterns all through its distribution. Nonetheless, few studies have evaluated the GCC impacts on the diversity patterns of NSDF plants. The lack of continental-scale approaches restricts our understanding of GCC consequences in the high beta-diverse NSDF. We showed some potential impacts of GCC on the distributions of ove… Show more

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Cited by 1 publication
(2 citation statements)
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“…1200 species) and database of available records per species were obtained from the Global Biodiversity Information Facility (GBIF), the Latin American and Caribbean Seasonally Dry Tropical Forest Floristic Network (DRYFLOR; available at: http://www.dryflor.info/data), and supplemented with our fieldwork in specific locations across Mexico and Colombia. First, we carried out a data depuration procedure, that is, we removed records with imprecise occurrence, suspected incorrect taxonomic identity, and those whose associated climate values affected the model's performance (details in Manrique‐Ascencio et al., 2024). Second, we avoided modeling poorly surveyed species since the low sample size in occurrence records can affect the model performance (Elith & Leathwick, 2007; Owens et al., 2013).…”
Section: Methodsmentioning
confidence: 99%
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“…1200 species) and database of available records per species were obtained from the Global Biodiversity Information Facility (GBIF), the Latin American and Caribbean Seasonally Dry Tropical Forest Floristic Network (DRYFLOR; available at: http://www.dryflor.info/data), and supplemented with our fieldwork in specific locations across Mexico and Colombia. First, we carried out a data depuration procedure, that is, we removed records with imprecise occurrence, suspected incorrect taxonomic identity, and those whose associated climate values affected the model's performance (details in Manrique‐Ascencio et al., 2024). Second, we avoided modeling poorly surveyed species since the low sample size in occurrence records can affect the model performance (Elith & Leathwick, 2007; Owens et al., 2013).…”
Section: Methodsmentioning
confidence: 99%
“…info/ data), and supplemented with our fieldwork in specific locations across Mexico and Colombia. First, we carried out a data depuration procedure, that is, we removed records with imprecise occurrence, suspected incorrect taxonomic identity, and those whose associated climate values affected the model's performance (details in Manrique-Ascencio et al, 2024). Second, we avoided modeling poorly surveyed species since the low sample size in occurrence records can affect the model performance (Elith & Leathwick, 2007;Owens et al, 2013).…”
Section: Plant Species: Occurrence Data and Phylogenetic Informationmentioning
confidence: 99%