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Vast amounts of Primary Biodiversity Data exist online (~109 records, each documenting an individual species at a point in space and time). These data hold immense but unrealized promise for science and society, including use in biogeographic research addressing issues such as zoonotic diseases, invasive species, threatened species and habitats, and climate change. Ongoing and envisioned changes in biodiversity informatics involving data providers, aggregators, and users should catalyze improvements to allow efficient use of such data for diverse analyses. We discuss relevant issues from the perspective of modeling species distributions, currently the most common use of Primary Biodiversity Data. Key cross-cutting principles for progress include harnessing feedback from users and increasing incentives for improving data quality. Critical challenges include: (1) establishing individual and collective stable unique identifiers across all of biodiversity science, (2) highlighting issues regarding data quality and representativeness, and (3) improving feedback mechanisms. Such changes should lead to ever-better data and increased utility and impact, including greater data integration with various research areas within and beyond biogeography (e.g., population demography, biotic interactions, physiology, and genetics). Building on existing pilot functionalities, biodiversity informatics could see transformative changes over the coming decade via a combination of community consensus building, coordinated efforts to justify and secure funding, and technical innovations.

Aim: Insects are one of the least studied taxa, with most species lacking basic ecological and biogeographical information. This problem is particularly acute in the tropics, where low sampling effort hampers accurate estimates of species richness at scale and potentially confounds efforts to identify the drivers of biogeographical gradients. Here, we evaluate the quality of the data on the distribution and diversity of odonate species in the Neotropics, while also examining the influence of sampling completeness on climate–richness relationships using a comprehensive database of odonates. Location: The Neotropics. Taxon: Odonata. Methods: Using 56,535 records collected from 1970 to 2021, we assess whether climate–species richness models vary under different scenarios of survey completeness. Results: Our survey compilation revealed that most Neotropical diversity of Odonata likely remains unknown. Only 1% of the one‐degree cells covering the Neotropics held reliable information on odonate species richness, with particularly severe gaps in the Caribbean, Central America, northeastern Brazil and northern Chile. Temperature, precipitation and potential evapotranspiration exert consistent effects on Odonata richness across the entire Neotropics, regardless the level of survey completeness. Whereas seasonality‐related variables are less important predictors of species richness at the biogeographical scale. Main Conclusions: By highlighting areas where inventories are more reliable and identifying regions that require increased data collection efforts and mobilization, our assessment offers a roadmap for improving the reliability of odonate inventories in the Neotropics. Furthermore, our findings underscore the importance of accounting for varying levels of survey completeness in macroecological models to reveal robust climate–species richness relationships. Simultaneously, they highlight strong climatic predictors of species richness, irrespective of survey effort intensity. These predictors provide a solid foundation for modelling and predicting odonate species richness in the Neotropics. [ABSTRACT FROM AUTHOR]

We compiled 46 broadscale data sets of species richness for a wide range of terrestrial plant, invertebrate, and ectothermic vertebrate groups in all parts of the world to test the ability of metabolic theory to account for observed diversity gradients. The theory makes two related predictions: (1) In-transformed richness is linearly associated with a linear, inverse transformation of annual temperature, and (2) the slope of the relationship is near -0.65. Of the 46 data sets, 14 had no significant relationship; of the remaining 32, nine were linear, meeting prediction 1. Model I (ordinary least squares, OLS) and model II (reduced major axis, RMA) regressions then tested the linear slopes against prediction 2. In the 23 data sets having nonlinear relationships between richness and temperature, split-line regression divided the data into linear components, and regressions were done on each component to test prediction 2 for subsets of the data. Of the 46 data sets analyzed in their entirety using OLS regression one was consistent with metabolic theory (meeting both predictions), and one was possibly consistent. Using RMA regression, no data sets were consistent. Of 67 analyses of prediction 2 using OLS regression on all linear data sets and subsets, two were consistent with the prediction, and four were possibly consistent. Using RMA regression, one was consistent (albeit weakly), and four were possibly consistent. We also found that the relationship between richness and temperature is both taxonomically and geographically conditional, and there is no evidence for a universal response of diversity to temperature. Meta-analyses confirmed significant heterogeneity in slopes among data sets, and the combined slopes across studies were significantly lower than the range of slopes predicted by metabolic theory based on both OLS and RMA regressions. We conclude that metabolic theory, as currently formulated, is a poor predictor of observed diversity gradients in most terrestrial systems.