Revised distributional estimates for the recently discovered olinguito (Bassaricyon neblina), using museum and science records

In the context of global change, a necessary first step for the conservation of species is gaining a good understanding of their distributional limits. This is especially important for biodiversity hotspots with high endemism such as the Northern Andes. The olinguito (Procyonidae: Bassaricyon neblina) is a recently described, medium-sized carnivoran found in Northern Andean cloud forests. A preliminary distributional model was published along with the original description, and I here provide revised distributional estimates using updated locality records and more current ENM methods. I build ecological niche models in Maxent using occurrence data (georeferenced museum records and citizen science-derived photo-vouchers) and bioclimatic variables. Optimal models were selected via two different approaches, AICc and performance on withheld data. The occurrence data used here show climatic signals different from those for data used in the original description of the species. The AICc-optimal model aligned more closely with current knowledge of the species’ elevational limits. This model shows more extensive suitable area in northern Colombia, and highlights areas for future sampling, such as the central portion of the Western Cordillera of Colombia, mid- and northern portions of the Central Cordillera of Colombia, southwestern Colombia, and the eastern slopes of Eastern Andes in Ecuador. Future conservation planning for this species should also take into account key threats, including deforestation and climate change

Temporal matching of occurrence localities and forest cover data helps improve range estimates and predict climate change vulnerabilities

Improved quantification of species\u27 ranges is needed to provide more accurate estimates of extinction risks for conservation planning. Highland tropical biodiversity may be particularly vulnerable to the anthropogenic changes in land cover and climate and is subject to overestimation of geographic range size in IUCN assessments. Here, we demonstrate a novel and practical approach for quantifying inferred range reductions based upon temporal matching of recent species occurrence localities and vegetation data. As an illustration pertinent to montane forest-associated species with limited distribution data, we use Gymnuromys roberti, an endemic Malagasy rodent with a Least Concern conservation status. We estimated climatic suitability and climate change vulnerability using species distribution modeling (SDM). We then determined deforestation tolerance thresholds for the species by temporally matching recent occurrence localities with percent forest cover values from MODIS forest cover layers. Finally, we applied these thresholds in postprocessing SDM-based range estimates. These estimates demonstrate that the lack of sufficient forest cover substantially reduces the species\u27 current estimated range compared with the IUCN range map. Projections to 2050 suggest that there will be a loss of climatic suitability over three quarters of the currently suitable habitat along with increased fragmentation, highlighting the need to include climate change vulnerability assessments as an integral part of conservation planning. Broader application of SDMs could assist practitioners at various stages of conservation planning, stressing the need for improved accessibility of methodologically complex SDM approaches

changeRangeR: An R package for reproducible biodiversity change metrics from species distribution estimates

Conservation planning and decision-making rely on evaluations of biodiversity status and threats that are based upon species' distribution estimates. However, gaps exist regarding automated tools to delineate species' current ranges from distribution estimates and use those estimates to calculate both species- and community-level biodiversity metrics. Here, we introduce changeRangeR, an R package that facilitates workflows to reproducibly transform estimates of species' distributions into metrics relevant for conservation. For example, by combining predictions from species distribution models (SDMs) with other maps of environmental data (e.g., suitable forest cover), researchers can characterize the proportion of a species' range that is under protection, metrics used under the IUCN Criteria A and B guidelines (Area of Occupancy and Extent of Occurrence), and other more general metrics such as taxonomic and phylogenetic diversity and endemism. Further, changeRangeR facilitates temporal comparisons among biodiversity metrics to inform efforts toward complementarity and consideration of future scenarios in conservation decisions. changeRangeR also provides tools to determine the effects of modeling decisions through sensitivity tests. Transparent and repeatable workflows for calculating biodiversity change metrics from SDMs such as those provided by changeRangeR are essential to inform conservation decision-making efforts and represent key extensions for SDM methodology and associated metadata documentation.journal articl

A Test of Species Distribution Model Transferability Across Environmental and Geographic Space for 108 Western North American Tree Species

Predictions from species distribution models (SDMs) are commonly used in support of environmental decision-making to explore potential impacts of climate change on biodiversity. However, because future climates are likely to differ from current climates, there has been ongoing interest in understanding the ability of SDMs to predict species responses under novel conditions (i.e., model transferability). Here, we explore the spatial and environmental limits to extrapolation in SDMs using forest inventory data from 11 model algorithms for 108 tree species across the western United States. Algorithms performed well in predicting occurrence for plots that occurred in the same geographic region in which they were fitted. However, a substantial portion of models performed worse than random when predicting for geographic regions in which algorithms were not fitted. Our results suggest that for transfers in geographic space, no specific algorithm was better than another as there were no significant differences in predictive performance across algorithms. There were significant differences in predictive performance for algorithms transferred in environmental space with GAM performing best. However, the predictive performance of GAM declined steeply with increasing extrapolation in environmental space relative to other algorithms. The results of this study suggest that SDMs may be limited in their ability to predict species ranges beyond the environmental data used for model fitting. When predicting climate-driven range shifts, extrapolation may also not reflect important biotic and abiotic drivers of species ranges, and thus further misrepresent the realized shift in range. Future studies investigating transferability of process based SDMs or relationships between geodiversity and biodiversity may hold promise

Data from: Bayesian hierarchical models suggest oldest known plant-visiting bat was omnivorous

The earliest record of plant visiting in bats dates to the Middle Miocene of La Venta, the world's most diverse tropical palaeocommunity. Palynephyllum antimaster is known from molars that indicate nectarivory. Skull length, an important indicator of key traits such as body size, bite force and trophic specialization, remains unknown. We developed Bayesian models to infer skull length based on dental measurements. These models account for variation within and between species, variation between clades, and phylogenetic error structure. Models relating skull length to trophic level for nectarivorous bats were then used to infer the diet of the fossil. The skull length estimate for Palynephyllum places it among the larger lonchophylline bats. The inferred diet suggests Palynephyllum fed on nectar and insects, similar to its living relatives. Omnivory has persisted since the mid-Miocene. This is the first study to corroborate with fossil data that highly specialized nectarivory in bats requires an omnivorous transition

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changeRangeR: An R package for reproducible biodiversity change metrics from species distribution estimates

Abstract Conservation planning and decision‐making rely on evaluations of biodiversity status and threats that are based upon species' distribution estimates. However, gaps exist regarding automated tools to delineate species' current ranges from distribution estimates and use those estimates to calculate both species‐ and community‐level biodiversity metrics. Here, we introduce changeRangeR, an R package that facilitates workflows to reproducibly transform estimates of species' distributions into metrics relevant for conservation. For example, by combining predictions from species distribution models (SDMs) with other maps of environmental data (e.g., suitable forest cover), researchers can characterize the proportion of a species' range that is under protection, metrics used under the IUCN Criteria A and B guidelines (Area of Occupancy and Extent of Occurrence), and other more general metrics such as taxonomic and phylogenetic diversity and endemism. Further, changeRangeR facilitates temporal comparisons among biodiversity metrics to inform efforts toward complementarity and consideration of future scenarios in conservation decisions. changeRangeR also provides tools to determine the effects of modeling decisions through sensitivity tests. Transparent and repeatable workflows for calculating biodiversity change metrics from SDMs such as those provided by changeRangeR are essential to inform conservation decision‐making efforts and represent key extensions for SDM methodology and associated metadata documentation