Distribution modeling applied to deficient data species assessment: A case study with Pithecopus nordestinus (Anura, Phyllomedusidae)

Abstract

The arboreal frog Pithecopus nordestinus is geographically present in almost all Brazilian Northeast territory and Minas Gerais State. It is currently classified as deficient data (DD) by IUCN Red List of Endangered Species, requiring further knowledge about its geographic distribution and population status. In this context, the species distribution modeling can be applied, since its basis uses species occurrence records and environmental variables related to bioclimatic and landscape features. This kind of method predicts the species suitability of certain organism in the geographic space. We obtained 159 P. nordestinus occurrence records, covering all the previously known distribution of the species. These records were collected from direct field sampling, scientific literature, museum collections, and available online databases. We used four species distribution modeling algorithms to obtain the potential range (extent of occurrence) and available habitat for this frog through habitat area analysis proposed by IUCN. The generated models can be considered as excellent, with mean AUC value of 0.981. The environmental variables related to temperature and radiation were the most important to the construction of this distribution model. Our results indicate that the forested areas of the Atlantic Forest domain and forest patches inside the Caatinga biome present the highest suitability values for the species occurrence and the major part of available habitats, a fact possibly related to the known arboreal habit of this amphibian. We thus provide a new distribution area for P. nordestinus more broadly than previously known and a new polygon for conservation purposes based on extent of occurrence, and an increase of occupancy based on habitat area analysis. The identification of additional areas where the P. nordestinus occurrence was not yet well known, new habitats for possible dispersal or recolonization; and the selection of conservation hotspots applied to this species are direct applications from our study. In addition, the methodological procedures used here may serve as a baseline tool for new investigations with focus on still deficient data species and its ecological and conservation planning requirements.

Reconstructing the historical distribution and local extinction of the giant otter Pteronura brasiliensis in the Atlantic Forest of South America

“…Distribution models with TSS and AUC values >0.90 are considered as excellent for predicting geographical patterns of animals (Silva et al, 2020; Garbino, Silva & Silva, 2021). Therefore, the model presented here may be considered an accurate estimation of the historical range of the giant otter in the Atlantic Forest.…”

“…AUC and TSS values vary from 0 to 1 based on the sensitivity of the species' response to environmental variables and models. In this study, models with AUC > 0.7 and TSS > 0.4 were selected (Buisson et al, 2010) using the mean suitability value of each grid and the minimum omission method (Silva et al, 2017; Silva et al, 2020). A jackknife test was used to identify the environmental variables that best explained the species occurrence (Phillips, Anderson & Schapire, 2006).…”

“…historical presence only) were used for habitat suitability modelling in the Biomod2 package (Thuiller et al, 2009): artificial neural networks (Ripley, 1996), generalized boosted models (Friedman, 2001), random forest (Breiman, 2001), and maximum entropy (Phillips, Anderson & Schapire, 2006). These four algorithms were chosen because they are better suited for historical data, where true absences cannot be ascertained (Andrade, Velazco & Marco, 2020;Silva et al, 2020). For each algorithm, 10,000 background records were randomly distributed throughout the study area, using 70% of the records for training and 30% for evaluating model fitting.…”

Distribution and habitat use of the Madagascar Peregrine Falcon: first estimates for area of habitat and population size

“…We view these range maps as work in progress, which can be continuously updated as new occurrence data are collected. Indeed, using SDMs to map and update species range limits is gaining in use (e.g., Breiner et al 2017;Ramesh et al 2017;Da Silva et al 2020).…”

Range-wide habitat use and Key Biodiversity Area coverage for a lowland tropical forest raptor across an increasingly deforested landscape

“…Indeed, HSMs can predict more complex and ecologically realistic geographic ranges compared to IUCN range maps (Breiner et al 2017;Herkt et al 2017). Using model-based interpolation based on the AOH guidelines but adapted to a correlative modelling approach (Da Silva et al 2020), may also be more effective for highlighting species-specific biodiversity area gaps by identifying higher coverage of suitable pixels (Di Marco et al 2017).…”