The effect of future climate change is poorly studied in the tropics, especially in mountainous areas, yet species living in these environments are predicted to be strongly affected. Newly available high‐resolution environmental data and statistical methods enable the development of forecasting models, but the uncertainty related to climate models can be strong, which can lead to ineffective conservation actions. Predictive studies aimed at providing conservation guidelines often account for a range of future climate predictions (climate scenarios and global circulation models). However, very few studies consider potential differences related to the source of climate data and/or do not account for spatial information (overlap) in uncertainty assessments. We modelled the environmental suitability for Phelsuma borbonica, an endangered reptile native to Reunion Island. Using two metrics of species range change (difference in overall suitability and spatial overlap), we quantified the uncertainty related to the modelling technique (n = 10), sample bias correction, climate change scenario, global circulation models (GCM) and data source (CHELSA vs. Worldclim). Uncertainty was mainly driven by GCMs when considering overall suitability, while for spatial overlap, the uncertainty related to data source became more important than that of GCMs. The uncertainty driven by sample bias correction and variable selection was much higher when assessed based on the spatial overlap. The modelling technique was a strong driver of uncertainty in both cases. We provide a consensus ensemble prediction map of the environmental suitability of P. borbonica to identify the areas predicted to be the most suitable in the future with the highest certainty. Predictive studies aimed at identifying priority areas for conservation in the face of climate change need to account for a wide panel of modelling techniques, GCMs and data sources. We recommend the use of multiple approaches, including spatial overlap when assessing uncertainty in species distribution models.
The effect of future climate change is poorly documented in the tropics, especially in mountainous areas. Yet, species living in these environments are predicted to be strongly affected. Newly available high-resolution environmental data and statistical methods enable the development of forecasting models. Nevertheless, the uncertainty related to climate models can be strong, which can lead to ineffective conservation actions. Predicted studies aimed at providing conservation guidelines often account for a range of future climate predictions (climate scenarios and global circulation models). However, very few studies considered potential differences related to baseline climate data and/or did not account for spatial information (overlap) in uncertainty assessments. We modelled the environmental suitability for Phelsuma borbonica, an endangered reptile native to Reunion Island. Using two metrics of species range change (difference in overall suitability and spatial overlap), we quantified the uncertainty related to the modelling technique (n = 10), sample bias correction, climate change scenario, global circulation models (GCM) and baseline climate (CHELSA versus Worldclim). Uncertainty was mainly driven by GCMs when considering overall suitability, while for spatial overlap the uncertainty related to baseline climate became more important than that of GCMs. The uncertainty driven by sample bias correction and variable selection was much higher when assessed based on spatial overlap. The modelling technique was a strong driver of uncertainty in both cases. We eventually provide a consensus ensemble prediction map of the environmental suitability of P. borbonica to identify the areas predicted to be the most suitable in the future with the highest certainty. Predictive studies aimed at identifying priority areas for conservation in the face of climate change need to account for a wide panel of modelling techniques, GCMs and baseline climate data. We recommend the use of multiple approaches, including spatial overlap, when assessing uncertainty in species distribution models.
First reports of envenoming by South American water snakes Helicops angulatus and Hydrops triangularis from Bolivian Amazon: a one-year prospective study of non-front-fanged colubroid snakebites. Toxicon. 2021;202:53-9. 8. da Silva AM, Mendes VKDG, Monteiro WM, Bernarde PS. Non-venomous snakebites in the Western Brazilian Amazon. Rev Soc Bras Med Trop. 2019;52:e20190120. 9. Ministry of Health of Brazil. How is Brazil taking care of tourists' health? Available at: https://bvsms.saude.gov.br/ bvs/publicacoes/como_cuidar_saude_brasil_ingles.pdf. Accessed July 20, 2021. 10. de Medeiros CR, Duarte MR, de Souza SN. Differential diagnosis between venomous (Bothrops jararaca, Serpentes, Viperidae) and "Nonvenomous" (Philodryas olfersii, Serpentes, Dipsadidae) snakebites: is it always possible?
Narrow-ranging species are usually omitted from Species distribution models (SDMs) due to statistical constraints, which may be problematic in conservation planning. The recently available high-resolution climate and land use data enable to increase the eligibility of narrow-ranging species for SDMs, provided their distribution is well known. We modelled the distribution of two narrow-ranging species for which the distribution of their occurrence records is assumed to be nearly comprehensive and unbiased (i.e., the Critically Endangered Manapany day gecko Phelsuma inexpectata and the Endangered golden Mantella frog Mantella aurantiaca). We predict a dramatic decline in climate suitability in the whole current distribution area of both species by 2070, potentially leading to a complete extinction even in the most optimistic scenario. We identified the areas with the best climate suitability in the future, but these remain largely suboptimal regarding species climatic niche. The high level of habitat fragmentation suggests that both species likely need to be at least partly translocated. We propose to consider the use of spatially explicit guidelines for translocation and habitat restoration in order to leave the species a chance to adapt and persist. The effect of climate change remains understudied for the extreme majority of rare and highly threatened species. This study suggests that the level of threats of data-poor and narrow-ranging species already identified as threatened may be underestimated, especially in heterogeneous tropical environments. We stress the need to consider the option of implementing proactive actions for threatened narrow-ranging species.
The effective conservation of a species requires a thorough knowledge of its ecology. Long considered to live exclusively in rocky habitats, the European leaf-toed gecko (Euleptes europaea) has in fact been observed in vegetated and wooded habitats at several locations throughout its range. The tendency of this species to use these habitats seems to be clearly supported by its prehensile tail characteristic of geckos with arboreal behaviour. To better assess tree occupation by E. europaea and other co-occurring geckos, a site-occupancy survey was conducted in 2022 on the testing site of DGA (French MoD the Procurement Agency) of Levant Island (Hyeres, France). Two stands of Eucalyptus sp. containing 68 trees were selected to monitor. One stand lies in an anthropised context, consisting of scattered woodland and clear ground (stand 1), and the other represents a "natural" forest context with dense ground vegetation (stand 2). The results revealed high occupancy by E. europaea in both stands, with an average occupancy probability of 0.57 (CI 0.40;0.72). The Mediterranean house gecko (Hemidactylus turcicus) and Moorish gecko (Tarentola mauritanica) had an average occupancy probability of 0.28 (CI 0.16;0.44) and 0.07 (CI 0.03;0.16) respectively. In stand 2, E. europaea was the only gecko species found, suggesting that it is better adapted to this type of forest habitat, which may represent a refuge for this species. In view of these results, the ecology of this species should be reconsidered and the research broadened by systematically including vegetated and forest habitats.
Species monitoring can be strongly limited by terrain accessibility, impeding our understanding of species ecology and thus challenging their conservation. This is particularly true for species living in the canopy, on cliffs or in dense vegetation. Remote sensing imagery may fill this gap by offering a cost‐effective monitoring approach allowing to improve species detection in inaccessible areas. We investigated the applicability of drone‐based monitoring for a Critically Endangered insular gecko (Phelsuma inexpectata) and two invasive alien gecko species representing a risk for the former (P. grandis and P. laticauda). We determined the approach distance before inducing behavioural response caused by the drone's presence. All three study species showed no escape behaviour to the drone's presence until very close distances (mean distance for P. inexpectata: 33.8 cm; P. grandis: 21.9 cm; P. laticauda: 26.4 cm). We then performed horizontal and vertical approaches with the drone, taking photos every meter starting at 10 m away from the canopy edge to determine an optimal distance for detection while ensuring species‐level identification. We examined a total of 328 photos. We found a bimodality in the number of detected geckos for two species, with different individuals recorded between short and intermediate distances. Therefore, we recommend taking photos at two distances of 2–2.5 and 5 m away from the canopy, ideally facing away from the sun and in low wind conditions. We encourage the application of our methodology for Phelsuma spp., but also for other species of similar size and ecology to improve detection in inaccessible areas.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.