RESUMO

Species distribution models (SDMs) have become an essential tool for the management and conservation of imperiled species. However, many at-risk species are rare and characterized by limited data on their spatial distribution and habitat relationships. This has led to the development of SDMs that integrate multiple types and sources of data to leverage more information and provide improved predictions of habitat associations. We developed a novel integrated species distribution model to predict habitat suitability for jaguars (Panthera onca) in the border region between northern Mexico and the southwestern USA. Our model combined presence-only and occupancy data to identify key environmental correlates, and we used model results to develop a probability of use map. We adopted a logistic regression modeling framework, which we found to be more straightforward and less computationally intensive to fit than Poisson point process-based models. Model results suggested that high terrain ruggedness and the presence of riparian vegetation were most strongly related to habitat use by jaguars in our study region. Our best model, on average, predicted that there is currently 25,463 km2 of usable habitat in our study region. The United States portion of the study region, which makes up 38.6% of the total area, contained 40.6% of the total usable habitat. Even though there have been few detections of jaguars in the southwestern USA in recent decades, our results suggest that protection of currently suitable habitats, along with increased conservation efforts, could significantly contribute to the recovery of jaguars in the USA.

Assuntos

RESUMO

Estimating α-diversity and species distributions provides baseline information to understand factors such as biodiversity loss and erosion of ecosystem services. Yet, species surveys typically cover a small portion of any country's landmass. Public, global databases could help, but contain biases. Thus, the magnitude of bias should be identified and ameliorated, the value of integration determined, and application to current policy issues illustrated. The ideal integrative approach should be powerful, flexible, efficient, and conceptually straightforward. We estimated distributions for >6,000 species, integrating species sightings (S) from the Global Biodiversity Information Facility (GBIF), systematic survey data (S2 ), and "bias-adjustment kernels" (BaK) using spatial and species trait databases (S2 BaK). We validated our approach using both locational and species holdout sets, and then applied our predictive model to Panama. Using sightings alone (the most common approach) discriminated relative probabilities of occurrences well (area under the curve [AUC] = 0.88), but underestimated actual probabilities by ~4,000%, while using survey data alone omitted over three-quarters of the >6,000 species. Comparatively, S2 BaK had no systematic underestimation, and substantially stronger discrimination (AUC = 0.96) and predictive power (deviance explained = 47%). Our model suggested high diversity (~200% countrywide mean) where urban development is projected to occur (the Panama Canal watershed) and also suggested this is not due to higher sampling intensity. However, portions of the Caribbean coast and eastern Panama (the Darién Gap) were even higher, both for total plant biodiversity (~250% countrywide mean), and CITES listed species. Finally, indigenous territories appeared half as diverse as other regions, based on survey observations. However, our model suggested this was largely due to site selection, and that richness in and out of indigenous territories was roughly equal. In brief, we provide arguably the best estimate of countrywide plant α-diversity and species distributions in the Neotropics, and make >6,000 species distributions available. We identify regions of overlap between development and high biodiversity, and improve interpretation of biodiversity patterns, including for policy-relevant CITES species, and locations with limited access (i.e., indigenous territories). We derive a powerful, flexible, efficient and simple estimation approach for biodiversity science.

Assuntos

RESUMO

Malaria is an important health issue in French Guiana. Its principal mosquito vector in this region is Anopheles darlingi Root. Knowledge of the spatial distribution of this species is still very incomplete due to the extent of French Guiana and the difficulty to access most of the territory. Species distribution modeling based on the maximal entropy procedure was used to predict the spatial distribution of An. darlingi using 39 presence sites. The resulting model provided significantly high prediction performances (mean 10-fold cross-validated partial area under the curve and continuous Boyce index equal to, respectively, 1.11-with a level of omission error of 20%-and 0.42). The model also provided a habitat suitability map and environmental response curves in accordance with the known entomological situation. Several environmental characteristics that had a positive correlation with the presence of An. darlingi were highlighted: nonpermanent anthropogenic changes of the natural environment, the presence of roads and tracks, and opening of the forest. Some geomorphological landforms and high altitude landscapes appear to be unsuitable for An. darlingi. The species distribution modeling was able to reliably predict the distribution of suitable habitats for An. darlingi in French Guiana. Results allowed completion of the knowledge of the spatial distribution of the principal malaria vector in this Amazonian region, and identification of the main factors that favor its presence. They should contribute to the definition of a necessary targeted vector control strategy in a malaria pre-elimination stage, and allow extrapolation of the acquired knowledge to other Amazonian or malaria-endemic contexts.

Assuntos

RESUMO

Background. Just as for most other tortoise species, the once common Chaco tortoise, Chelonoidis chilensis (Testudinidae), is under constant threat across it distribution in Argentina, Bolivia and Paraguay. Despite initial qualitative description of the species distribution and further individual reports of new locations for the species, there is no description of the species distribution in probabilistic terms. With this work we aim to produce an updated predictive distribution map for C. chilensis to serve as a baseline management tool for directed strategic conservation planning. Methods. We fitted a spatially expanded logistic regression model within the Bayesian framework that accounts for uncertainty on presence-only and generated pseudo-absence data into the parameter estimates. We contrast the results with reported data for the national networks of protected areas to assess the inclusion of the species in area-based conservation strategies. Results. We obtained maps with predictions of the occurrence of the species and reported the model's uncertainty spatially. The model suggests that potential suitable habitats for the species are continuous across Argentina, West Paraguay and South Bolivia, considering the variables, the scale and the resolution used. The main limiting variables were temperature-related variables, and precipitation in the reproductive period. Discussion. Given the alarming low density and coverage of protected areas over the distribution area of C. chilensis, the map produced provides a baseline to identify areas where directed strategic conservation management actions would be more efficient for this and other associated species.