Species Distribution Modeling in Latin America: A 25-Year Retrospective Review

Abstract

Species distribution modeling (SDM) is a booming area of research that has had an exponential increase in use and development in recent years. We performed a search of scientific literature and found 5,533 documents published from 1993 to 2018 using SDM, representing a global network of 4,329 collaborating institutions from 155 countries, with Brazil and of Brasilia were the most productive. From this body of literature, the most frequently modeled taxonomic groups were Chordata and Insecta, and the most common realms of application were conservation planning and management, climate change, species conservation, epidemiology, evolutionary biology, and biological invasions. From the 36 modeling methods identified to generate SDMs, MaxEnt is used in 73.5% of the papers, followed by Genetic Algorithm for Rule-Set Prediction (GARP) with 18.7%, and just 7.4% of the papers compared between 3 and 10 modeling methods. In Latin American countries, productivity in SDM research could be improved as the network of collaborations diversifies and connects with other productive countries (such as United ). The scientific collaboration between Latin American countries should be increased, as the most prolific countries (Brazil, Mexico, Argentina, and Colombia) share less than 10% of its productivity. Some of the main challenges for SDM development in Latin America include bridging the gaps from (a) software use to research productivity and (b) translation to decision-making. To address these challenges, we propose to strengthen communities of practice where modelers, species experts, and decisionmakers come together to discuss and develop SDM to shift and enhance current paradigms on how science and decisionmaking are linked.

Ecological niche modelling predicts significant impacts of future climate change on two endemic rodents in eastern Africa

“…Ecological niche models have often been used to model and project rodent distributions and niches, but a large proportion of these studies are restricted to species found in the Americas (Martínez-Salazar et al 2012;Bean et al 2014;Kubiak et al 2017;Flores-Zamarripa & Fernández 2018;Urbina-Cardona et al 2019;Pardi et al 2020). African rodents have also been studied using ENM techniques; Taylor et al (2015) showed that trends in the distribution of Afromontane rodents reflect changes in biomes predicted by past, present, and future climate scenarios.…”

Assessment of climate change impacts on one of the rarest apes on Earth, the Cao Vit Gibbon Nomascus nasutus

“…Species distribution modeling (SDM) is widely used to study biogeography, biodiversity patterns, evolutionary ecology, and to inform conservation efforts including reintroduction planning and predicting species' distribution shifts in response to climate change (Urbina-Cardona et al 2019). Overall, SDM explores and utilizes possible interactions between species occurrence records and environmental variables to calculate and produce a probability map of environmental suitability for a species in a given area.…”

“…SDM can help identify the potential distribution range of an endangered species that may be included in protected area planning (Thapa et al 2018), resolve taxonomy issues for species groups whose other traits may not be clearly distinguishable and conclusive (van Schingen et al 2016), predict vulnerable regions for invasive species risk (Ren et al 2016), determine areas of refugia that may be home to many rare and endemic species (Tang et al 2018), and examine processes influencing biodiversity and evolution over a long timescale (Musher et al 2020). The wide range of applications has pushed the development of many different SDMs approaches, among which Maximum Entropy (Maxent) is among the most commonly used (Urbina-Cardona et al 2019). Compared to other modeling approaches, Maxent only requires presence records, and still produces robust results when only a small number of occurrences is available (Elith et al 2006, Phillips et al 2006, Pearson et al 2007.…”

Current and future predicting habitat suitability map of Cunninghamia konishii Hayata using MaxEnt model under climate change in Northern Vietnam

“…SDM has become a valuable tool for assessing habitat suitability and resource conservation to protect important plant species and to predict suitable cultivation regions . SDM is widely applied to analyze potential distribution of endangered species and to predict changes of species distributions under different climate change scenarios (Urbina-Cardona et al 2019). Species distribution modeling includes biophysical correlation model, climate element index method, generalized linear model (GLM), multivariate adaptive regression splines (MARS), genetic algorithm for rule-set production (GARP), maximum entropy (MaxEnt) and boosted regression trees (Sharma et al 2018).…”