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Massive biological databases of species occurrences, or georeferenced locations where a species has been observed, are essential inputs for modeling present and future species distributions. Location accuracy is often assessed by determining whether the observation geocoordinates fall within the boundaries of the declared political divisions. This otherwise simple validation is complicated by the difficulty of matching political division names to the correct geospatial object. Spelling errors, abbreviations, alternative codes, and synonyms in multiple languages present daunting name disambiguation challenges. The inability to resolve political division names reduces usable data, and analysis of erroneous observations can lead to flawed results. Here, we present the Geographic Name Resolution Service (GNRS), an application for correcting, standardizing, and indexing world political division names. The GNRS resolves political division names against a reference database that combines names and codes from GeoNames with geospatial object identifiers from the Global Administrative Areas Database (GADM). In a trial resolution of political division names extracted from >270 million species occurrences, only 1.9%, representing just 6% of occurrences, matched exactly to GADM political divisions in their original form. The GNRS was able to resolve, completely or in part, 92% of the remaining 378,568 political division names, or 86% of the full biodiversity occurrence dataset. In assessing geocoordinate accuracy for >239 million species occurrences, resolution of political divisions by the GNRS enabled the detection of an order of magnitude more errors and an order of magnitude more error-free occurrences. By providing a novel solution to a significant data quality impediment, the GNRS liberates a tremendous amount of biodiversity data for quantitative biodiversity research. The GNRS runs as a web service and is accessible via an API, an R package, and a web-based graphical user interface. Its modular architecture is easily integrated into existing data validation workflows. 

Abstract Alpine grassland vegetation supports globally important biodiversity and ecosystems that are increasingly threatened by climate warming and other environmental changes. Trait-based approaches can support understanding of vegetation responses to global change drivers and consequences for ecosystem functioning. In six sites along a 1314 m elevational gradient in Puna grasslands in the Peruvian Andes, we collected datasets on vascular plant composition, plant functional traits, biomass, ecosystem fluxes, and climate data over three years. The data were collected in the wet and dry season and from plots with different fire histories. We selected traits associated with plant resource use, growth, and life history strategies (leaf area, leaf dry/wet mass, leaf thickness, specific leaf area, leaf dry matter content, leaf C, N, P content, C and N isotopes). The trait dataset contains 3,665 plant records from 145 taxa, 54,036 trait measurements (increasing the trait data coverage of the regional flora by 420%) covering 14 traits and 121 plant taxa (ca. 40% of which have no previous publicly available trait data) across 33 families. 

Spatially continuous data on functional diversity will improve our ability to predict global change impacts on ecosystem properties. We applied methods that combine imaging spectroscopy and foliar traits to estimate remotely sensed functional diversity in tropical forests across an Amazon-to-Andes elevation gradient (215 to 3537 m). We evaluated the scale dependency of community assembly processes and examined whether tropical forest productivity could be predicted by remotely sensed functional diversity. Functional richness of the community decreased with increasing elevation. Scale-dependent signals of trait convergence, consistent with environmental filtering, play an important role in explaining the range of trait variation within each site and along elevation. Single- and multitrait remotely sensed measures of functional diversity were important predictors of variation in rates of net and gross primary productivity. Our findings highlight the potential of remotely sensed functional diversity to inform trait-based ecology and trait diversity-ecosystem function linkages in hyperdiverse tropical forests. 

Quaternary climate change reduced and homogenized angiosperm tree diversity across large landscapes worldwide.