Identifying hotspots and priority areas for xenarthran research and conservation
Feijó, Anderson et al. (2022), Identifying hotspots and priority areas for xenarthran research and conservation, Dryad, Dataset, https://doi.org/10.5061/dryad.2rbnzs7ph
Aim: Limited funds for conservation and research require the development of prioritization schemes. Traditionally, biodiversity metrics were used to delineate priority areas; however, a growing realization emphasizes that logistic factors should be also considered. Here, we combine species richness, past collection efforts, degree of habitat loss and accessibility to define priority areas and spatially orient fieldwork in a cost-effective manner for xenarthrans, one of the four main mammalian radiation.
Methods: We assessed spatial patterns of species richness in Xenarthra and identify diversity hotspots based on species distribution models. Spatial patterns and biases in the Xenarthra past collection efforts were analyzed using a comprehensive database including 33,464 individual records. Finally, we produced priority area indices relating species richness and collection efforts with levels of habitat loss and accessibility (roads and rivers network) to highlight important but neglected areas.
Results: Collection efforts were concentrated to a small portion of the Neotropics (central-southern Brazil, eastern Bolivia and north-western Argentina) and were biased toward access routes. Major xenarthran diversity hotspots include the Amazonian lowlands of Bolivia and the dry Chaco of Paraguay and Argentina. Priority areas for research varied depending on the metric analyzed. Amazon holds a high diversity that remains poorly explored. Central Argentina and northeastern Brazil are priority areas for research and conservation given the low sampling efforts, high diversity and endemic species, high levels of habitat loss and a dense road network.
Main conclusions: Most areas of the Neotropics lack a proper assessment of the xenarthran assemblage, reflecting extensive knowledge shortfalls. Overall, sites close to roads tend to be better sampled, but many areas with a dense road network are under-sampled, being good candidates for low-cost studies. There is an alarming spatial overlap between Xenarthra diversity hotspots and areas facing intense human modification. Some of the priority areas for research should also be viewed as priority areas for conservation.
Individual xenarthra species distribution models:
For each of the 34 taxa, we compared a set of Maxent models using the ‘maxnet’ algorithm implemented in the ENMeval 2.0 R package. To reduce spatial correlation and sampling bias, we excluded duplicated localities within 2.5° grid resolution, the same spatial resolution of the environmental layers used. This more coarse resolution was preferred given the variety of methods used to record species occurrence. Models vary in complexity and include regularization multipliers ranging from 1 to 5 with five combinations of feature classes (L, LQ, LQH, and H; where L=linear, Q=quadratic, H=hinge). We select 13 less correlated (r <0.8) climate predictors from the full set of 19 layers obtained from WorldClim. Isothermality, temperature annual range, mean temperature of warmest quarter and coldest quarter, precipitation of wettest and driest quarter were discarded. We also included as environmental predictors elevation and mean annual leaf-area index (LAI) given that most of the xenarthrans are either restricted to forest or open-biome and their distributions are constrained by mountain ranges. LAI monthly estimation was retrieved for the year 2020 and used here as a proxy of vegetation type. The performance of our models was tested by partitioning the localities into testing and training bins using the “checkerboard2” method. Five thousand background points were randomly selected for model training from a buffer area that extended five decimal degrees from the most marginal records. The best model was selected based on spatial cross-validation metrics using the lowest average 10 percentile omission rate followed by the highest area under the curve (AUC) average. Models had overall low omission rates and high AUC (Table S1), indicating low overfit and high performance in discriminating occurrences from background points
Xenarthra richness map
We used the logistic output of each species best model in which the probability of presence varies from 0 to 1 and allows direct comparison across models. Each logistic output was masked with a five-degree buffered convex hull based on species-specific occurrence points and constrained considering known geographic barriers to dispersal, such as the Andes and major rivers. The rationale for limiting the logistic output is to provide a better approximate estimation of the species’ occupied areas while avoiding potential but inaccessible regions. Finally, to reduce overprediction in species-poor areas, grids with suitability values lower than the minimum training present threshold were removed. The 34 final SDM suitability outputs were then stacked to produce the Xenarthra richness map. Areas outside of the known range of the group (e.g. Pacific coast of Chile) were removed.
Priority areas for research per country
To identify priority areas for future research on Xenarthra, we relate four metrics (species richness, availability of access routes (combining roads and river), degree of habitat alteration, and presence of protected areas) with sampling effort. For habitat alteration, we used the recent dataset of global human modification (Theobald et al., 2020). This dataset combined numerous human activities (e.g., urbanization, crop and pasture lands, livestock grazing, logging, mining, roads) to estimate the current (~2017) degree of habitat change across the globe (see Theobald et al., 2020). We consider priority areas those with high diversity, high levels of habitat alteration, closer to access routes or protected areas that have been poorly sampled. To avoid bias associated with distinct units, density grid values were normalized from 0 to 1. For each metric, we calculated an individual index dividing the normalized values by the log-transformed sampling effort, producing a 0–1 scale where values close to one indicate a higher density of each metric but lower sampling effort. Finally, we produced a unified index that incorporates information of all four metrics. The unified index was calculated as the ratio of the arithmetic mean of species richness, routes, habitat alteration and protected area normalized values with the log-transformed sampling effort.
To assist project planning at a national level, we provide prioritization maps per country.
Density of species richness (a), access routes (b), protected areas (c) and levels of human alteration (d) relative to collection effort. (e) Map showing all four indexes combined into a unified index. Warmer colors represent areas with high density of each factor but low density of sampling effort. Maps in equal-area projection at 100 km grid resolution.
Second Tibetan Plateau Scientific Expedition and Research Program, Award: 2019QZKK0402/2019QZKK0501
Chinese Academy of Sciences, Award: 2021PB0021