The Tropical Andes, known for its rich biodiversity, faces threats like habitat loss and climate change, making conservation in these areas crucial. Traditional conservation efforts often focus on taxonomic diversity (TD), overlooking functional diversity (FD), which is vital for ecosystem health. Understanding the role of communitymanaged lands in biodiversity protection is also essential for achieving conservation goals. This study used the Frugivoria trait database to assess FD of frugivorous birds and mammals in the Tropical Andes, species critical for seed dispersal and forest health. We analyzed the spatial alignment and mismatch between TD and FD in high diversity areas and evaluated the spatial distribution of Functionally Unique, Specialized, and Endangered (FUSE) species, which have irreplaceable functional roles. Our findings reveal spatial discrepancies between FD and TD, highlighting the limitations of relying solely on TD for conservation. Additionally, we examined biodiversity levels in strict and less strict protected areas (PAs), finding that strict PAs do not always contain higher biodiversity levels than less strict ones. Some strict PAs better represent FUSE species distributions, but many areas with more FUSE species and higher TD remain unprotected. Notably, community-managed lands exhibited higher FD than other PAs in the same IUCN PA category, underscoring the importance of Indigenous Lands and community-managed areas in protecting FD. As global conservation aims to protect 30 % of Earth's land by 2030, our findings emphasize the need to incorporate multiple biodiversity dimensions and utilize other effective area-based conservation measures (OECMs) for effective and sustainable conservation in the Tropical Andes.

Improving models of species' distributions is essential for conservation, especially in light of global change. Species distribution models (SDMs) often rely on mean environmental conditions, yet species distributions are also a function of environmental heterogeneity and filtering acting at multiple spatial scales. Geodiversity, which we define as the variation of abiotic features and processes of Earth's entire geosphere (inclusive of climate), has potential to improve SDMs and conservation assessments, as they capture multiple abiotic dimensions of species niches, however they have not been sufficiently tested in SDMs. We tested a range of geodiversity variables computed at varying scales using climate and elevation data. We compared predictive performance of MaxEnt SDMs generated using CHELSA bioclimatic variables to those also including geodiversity variables for 31 mammalian species in Colombia. Results show the spatial grain of geodiversity variables affects SDM performance. Some variables consistently exhibited an increasing or decreasing trend in variable importance with spatial grain, showing slight scale-dependence and indicating that some geodiversity variables are more relevant at particular scales for some species. Incorporating geodiversity variables into SDMs, and doing so at the appropriate spatial scales, enhances the ability to model species-environment relationships, thereby contributing to the conservation and management of biodiversity. This article is part of the Theo Murphy meeting issue 'Geodiversity for science and society'.

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.

Motivation: Biodiversity in many areas is rapidly declining because of global change. As such, there is an urgent need for new tools and strategies to help identify, monitor and conserve biodiversity hotspots. This is especially true for frugivores, species consuming fruit, because of their important role in seed dispersal and maintenance of forest structure and health. One way to identify these areas is by quantifying functional diversity, which measures the unique roles of species within a community and is valuable for conservation because of its relationship with ecosystem functioning. Unfortunately, the functional trait information required for these studies can be sparse for certain taxa and specific traits and difficult to harmonize across disparate data sources, especially in biodiversity hotspots. To help fill this need, we compiled Frugivoria, a trait database containing ecological, life-history, morphological and geographical traits for mammals and birds exhibiting frugivory. Frugivoria encompasses species in contiguous moist montane forests and adjacent moist lowland forests of Central and South America-the latter specifically focusing on the Andean states. Compared with existing trait databases, Frugivoria harmonizes existing trait databases, adds new traits, extends traits originally only available for mammals to birds also and fills gaps in trait categories from other databases. Furthermore, we create a cross-taxa subset of shared traits to aid in analysis of mammals and birds. In total, Frugivoria adds 8662 new trait values for mammals and 14,999 for birds and includes a total of 45,216 trait entries with only 11.37% being imputed. Frugivoria also contains an open workflow that harmonizes trait and taxonomic data from disparate sources and enables users to analyse traits in space. As such, this open-access database, which aligns with FAIR data principles, fills a major knowledge gap, enabling more comprehensive trait-based studies of species in this ecologically important region.Main Types of Variable Contained: Ecological, life-history, morphological and geographical traits.Spatial Location and Grain: Neotropical countries (Mexico, Guatemala, Costa Rica, Panama, El Salvador, Belize, Nicaragua, Ecuador, Colombia, Peru, Bolivia, Argentina, Venezuela and Chile) with contiguous montane regions.Time Period and Grain: IUCN spatial data: obtained February 2023, spanning range maps collated from 1998 to 2022. IUCN species data: obtained June 2019-September 2022. Newly included traits: span 1924 to 2023.Major Taxa and Level of Measurement: Classes Mammalia and Aves; 40,074 species-level traits; 5142 imputed traits for 1733 species (mammals: 582; birds: 1147) and 16 sub-species (mammals).

Estimates of species’ ranges can inform many aspects of biodiversity research and conservation-management decisions. Many practical applications need high-precision range estimates that are sufficiently reliable to use as input data in downstream applications. One solution has involved expert-generated maps that reflect on-the-ground field information and implicitly capture various processes that may limit a species’ geographic distribution. However, expert maps are often subjective and rarely reproducible. In contrast, species distribution models (SDMs) typically have finer resolution and are reproducible because of explicit links to data. Yet, SDMs can have higher uncertainty when data are sparse, which is an issue for most species. Also, SDMs often capture only a subset of the factors that determine species distributions (e.g., climate) and hence can require significant post-processing to better estimate species’ current realized distributions. Here, we demonstrate how expert knowledge, diverse data types, and SDMs can be used together in a transparent and reproducible modeling workflow. Specifically, we show how expert knowledge regarding species’ habitat use, elevation, biotic interactions, and environmental tolerances can be used to make and refine range estimates using SDMs and various data sources, including high-resolution remotely sensed products. This range-refinement approach is primed to use various data sources, including many with continuously improving spatial or temporal resolution. To facilitate such analyses, we compile a comprehensive suite of tools in a new R package, maskRangeR, and provide worked examples. These tools can facilitate a wide variety of basic and applied research that requires high-resolution maps of species’ current ranges, including quantifications of biodiversity and its change over time.

Improved quantification of species' 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' 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.

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.

Understanding a species' distributional limits is a necessary step for developing conservation priorities. The olinguito (Procyonidae: Bassaricyon neblina) is a recently described, medium-sized carnivoran found in Northern Andean cloud forests. Here, we provide revised distributional estimates for this species using current ecological niche modeling methods (using Maxent and bioclimatic variables), taking into account sampling bias and using updated locality information including 9 photo-vouchered citizen science records that increased the original data set by 60%. Optimal models were selected via 2 approaches: Akaike's Information Criterion corrected for small sample size (AICc) and performance on withheld data. The AICc-optimal model aligned more closely with current knowledge of the species' elevational limits. This model improves on the previously modeled distribution map for the species, especially in terms of elevational discrimination in the Central Cordillera of Colombia, where high mountain peaks are considered unsuitable. Compared with the original map, the revised map suggests more extensive suitable area in northern Colombia, possibly due to the inclusion of new citizen science records, and highlights 6 priority areas for future sampling that have no olinguito records: the northern portion of the Western Cordillera of Colombia, southern portion of the Western Cordillera of Colombia, western slopes of the Andes in southwestern Colombia, both versants of the middle portion of the Central Cordillera of Colombia, western slopes of the Andes in central and southern Ecuador, and eastern slopes of the Andes in Ecuador. This study improves knowledge of the olinguito's range, increases our understanding of this species' natural and taxonomic history, and provides an example of modeling the distributions of poorly known species with small sample sizes.

Background There is ample evidence that biotic factors, such as biotic interactions and dispersal capacity, can affect species distributions and influence species' responses to climate change. However, little is known about how these factors affect predictions from species distribution models (SDMs) with respect to spatial grain and extent of the models. Objectives Understanding how spatial scale influences the effects of biological processes in SDMs is important because SDMs are one of the primary tools used by conservation biologists to assess biodiversity impacts of climate change. Data sources and study eligibility criteria We systematically reviewed SDM studies published from 2003-2015 using ISI Web of Science searches to: (1) determine the current state and key knowledge gaps of SDMs that incorporate biotic interactions and dispersal; and (2) understand how choice of spatial scale may alter the influence of biological processes on SDM predictions. Synthesis methods and limitations We used linear mixed effects models to examine how predictions from SDMs changed in response to the effects of spatial scale, dispersal, and biotic interactions. Results There were important biases in studies including an emphasis on terrestrial ecosystems in northern latitudes and little representation of aquatic ecosystems. Our results suggest that neither spatial extent nor grain influence projected climate-induced changes in species ranges when SDMs include dispersal or biotic interactions. Conclusions We identified several knowledge gaps and suggest that SDM studies forecasting the effects of climate change should: 1) address broader ranges of taxa and locations; and 1) report the grain size, extent, and results with and without biological complexity. The spatial scale of analysis in SDMs did not affect estimates of projected range shifts with dispersal and biotic interactions. However, the lack of reporting on results with and without biological complexity precluded many studies from our analysis.

There is an urgent need for more ecologically realistic models for better predicting the effects of climate change on species' potential geographic distributions. Here we build ecological niche models using MAXENT and test whether selecting predictor variables based on biological knowledge and selecting ecologically realistic response curves can improve cross-time distributional predictions. We also evaluate how the method chosen for extrapolation into nonanalog conditions affects the prediction. We do so by estimating the potential distribution of a montane shrew (Mammalia, Soricidae, Cryptotis mexicanus) at present and the Last Glacial Maximum (LGM). Because it is tightly associated with cloud forests (with climatically determined upper and lower limits) whose distributional shifts are well characterized, this species provides clear expectations of plausible vs. implausible results. Response curves for the MAXENT model made using variables selected via biological justification were ecologically more realistic compared with those of the model made using many potential predictors. This strategy also led to much more plausible geographic predictions for upper and lower elevational limits of the species both for the present and during the LGM. By inspecting the modeled response curves, we also determined the most appropriate way to extrapolate into nonanalog environments, a previously overlooked factor in studies involving model transfer. This study provides intuitive context for recommendations that should promote more realistic ecological niche models for transfer across space and time.

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.