DOI: 10.5061/dryad.2rbnzs7ph
title: Identifying hotspots and priority areas for xenarthran research and conservation

description: 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. Location: Neotropics. 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.
description: <strong>Individual xenarthra species distribution
 models:</strong> 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 (<i>r </i>&lt;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
 <strong>Xenarthra richness map</strong>
 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.
 <strong>Priority areas for research per
 country</strong> 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. Legend: 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.