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Environmental factors, such as temperature, precipitation, and elevation, explain most of the variation in species richness at the global scale. Nevertheless, richness patterns may have different drivers across taxa and regions. To date, a comprehensive global examination of how various factors such as climate or topography drive patterns of species richness across all terrestrial vertebrates, using the same methods and predictors, has been lacking. Recent advances in species-distribution data allowed us to model and examine the richness pattern of all terrestrial tetrapods comprehensively. We tested the relationship between environmental and biogeographical variables and richness of amphibians (5983 species), birds (9630), mammals (5004), reptiles (8939), and tetrapods as a whole, globally, and across biogeographical realms. We studied the effects of climatic, ecological, and biogeographic drivers using generalized additive models. Richness patterns and their environmental associations varied among taxa and realms. Overall precipitation was the predominant richness predictor. However, temperature was more important in realms where both cold and warm conditions exist. In the Indomalayan realm, elevational range was very important. Richness patterns of mammals, birds, and amphibians were strongly related to precipitation whereas reptile richness was mostly associated with temperature. Our results support the universal importance of precipitation but also suggest that future global-scaled research should incorporate other relevant variables other than climate, such as elevational range, to gain a better understanding of the richness–environment relationship. By doing so, we can further advance our knowledge of the complex relationships between biodiversity and the environment.

The expanding use of community science platforms has led to an exponential increase in biodiversity data in global repositories. Yet, understanding of species distributions remains patchy. Biodiversity data from social media can potentially reduce the global biodiversity knowledge gap. However, practical guidelines and standardized methods for harvesting such data are nonexistent. Following data privacy and protection safeguards, we devised a standardized method for extracting species distribution records from Facebook groups that allow access to their data. It involves 3 steps: group selection, data extraction, and georeferencing the record location. We present how to structure keywords, search for species photographs, and georeference localities for such records. We further highlight some challenges users might face when extracting species distribution data from Facebook and suggest solutions. Following our proposed framework, we present a case study on Bangladesh's biodiversity—a tropical megadiverse South Asian country. We scraped nearly 45,000 unique georeferenced records across 967 species and found a median of 27 records per species. About 12% of the distribution data were for threatened species, representing 27% of all species. We also obtained data for 56 DataDeficient species for Bangladesh. If carefully harvested, social media data can significantly reduce global biodiversity knowledge gaps. Consequently, developing an automated tool to extract and interpret social media biodiversity data is a research priority.

Citizen science plays a crucial role in helping monitor biodiversity and inform conservation. With the widespread use of smartphones, many people share posts that contain biodiversity information on social media, but this information is still not widely used in conservation. Focusing on Bangladesh, a tropical mega-diverse and mega-populated country, we examine the importance of social media records in conservation decision-making. We show that adding Facebook data to the Global Biodiversity Information Facility (GBIF) data improved the accuracy of conservation planning assessments by identifying additional important conservation areas in the northwest, southeast and central parts of Bangladesh, extending priority conservation areas by 4,000-10,000 km2. Community efforts are needed to drive the implementation of the ambitious Kunming-Montreal Global Biodiversity Framework targets, especially in mega-diverse tropical countries with a lack of reliable and up-to-date species distribution data. We highlight that conservation planning can be enhanced by including available data gathered from social media platforms.

The Red List of Threatened Species, published by the International Union for Conservation of Nature (IUCN), is a crucial tool for conservation decision-making. However, despite substantial effort, numerous species remain unassessed or have insufficient data available to be assigned a Red List extinction risk category. Moreover, the Red Listing process is subject to various sources of uncertainty and bias. The development of robust automated assessment methods could serve as an efficient and highly useful tool to accelerate the assessment process and offer provisional assessments. Here, we aimed to (1) present a machine learning-based automated extinction risk assessment method that can be used on less known species; (2) offer provisional assessments for all reptiles-the only major tetrapod group without a comprehensive Red List assessment; and (3) evaluate potential effects of human decision biases on the outcome of assessments. We use the method presented here to assess 4,369 reptile species that are currently unassessed or classified as Data Deficient by the IUCN. The models used in our predictions were 90% accurate in classifying species as threatened/nonthreatened, and 84% accurate in predicting specific extinction risk categories. Unassessed and Data Deficient reptiles were considerably more likely to be threatened than assessed species, adding to mounting evidence that these species warrant more conservation attention. The overall proportion of threatened species greatly increased when we included our provisional assessments. Assessor identities strongly affected prediction outcomes, suggesting that assessor effects need to be carefully considered in extinction risk assessments. Regions and taxa we identified as likely to be more threatened should be given increased attention in new assessments and conservation planning. Lastly, the method we present here can be easily implemented to help bridge the assessment gap for other less known taxa.

To meet the ambitious objectives of biodiversity and climate conventions, the international community requires clarity on how these objectives can be operationalized spatially and how multiple targets can be pursued concurrently. To support goal setting and the implementation of international strategies and action plans, spatial guidance is needed to identify which land areas have the potential to generate the greatest synergies between conserving biodiversity and nature’s contributions to people. Here we present results from a joint optimization that minimizes the number of threatened species, maximizes carbon retention and water quality regulation, and ranks terrestrial conservation priorities globally. We found that selecting the top-ranked 30% and 50% of terrestrial land area would conserve respectively 60.7% and 85.3% of the estimated total carbon stock and 66% and 89.8% of all clean water, in addition to meeting conservation targets for 57.9% and 79% of all species considered. Our data and prioritization further suggest that adequately conserving all species considered (vertebrates and plants) would require giving conservation attention to ~70% of the terrestrial land surface. If priority was given to biodiversity only, managing 30% of optimally located land area for conservation may be sufficient to meet conservation targets for 81.3% of the terrestrial plant and vertebrate species considered. Our results provide a global assessment of where land could be optimally managed for conservation. We discuss how such a spatial prioritization framework can support the implementation of the biodiversity and climate conventions.

Our knowledge of the conservation status of reptiles, the most diverse class of terrestrial vertebrates, has improved dramatically over the past decade, but still lags behind that of the other tetrapod groups. Here, we conduct the first comprehensive evaluation (~92% of the world's ~1714 described species) of the conservation status of skinks (Scincidae), a speciose reptile family with a worldwide distribution. Using International Union for Conservation of Nature (IUCN) criteria, we report that ~20% of species are threatened with extinction, and nine species are Extinct or Extinct in the Wild. The highest levels of threat are evident in Madagascar and the Neotropics, and in the subfamilies Mabuyinae, Eugongylinae and Scincinae. The vast majority of threatened skink species were listed based primarily on their small geographic ranges (Criterion B, 83%; Criterion D2, 13%). Although the population trend of 42% of species was stable, 14% have declining populations. The key threats to skinks are habitat loss due to agriculture, invasive species, and biological resource use (e.g., hunting, timber harvesting). The distributions of 61% of species do not overlap with protected areas. Despite our improved knowledge of the conservation status of the world's skinks, 8% of species remain to be assessed, and 14% are listed as Data Deficient. The conservation status of almost a quarter of the world's skink species thus remains unknown. We use our updated knowledge of the conservation status of the group to develop and outline the priorities for the conservation assessment and management of the world's skink species.

Technology is transforming societies worldwide. A major innovation is the emergence of robotics and autonomous systems (RAS), which have the potential to revolutionize cities for both people and nature. Nonetheless, the opportunities and challenges associated with RAS for urban ecosystems have yet to be considered systematically. Here, we report the findings of an online horizon scan involving 170 expert participants from 35 countries. We conclude that RAS are likely to transform land use, transport systems and human-nature interactions. The prioritized opportunities were primarily centred on the deployment of RAS for the monitoring and management of biodiversity and ecosystems. Fewer challenges were prioritized. Those that were emphasized concerns surrounding waste from unrecovered RAS, and the quality and interpretation of RAS-collected data. Although the future impacts of RAS for urban ecosystems are difficult to predict, examining potentially important developments early is essential if we are to avoid detrimental consequences but fully realize the benefits.

This data repository contains the results of the NatureMap ( naturemap.earth/) conservation prioritization effort. The maps were created by jointly optimizing biodiversity and NCPs such as carbon and/or water. Maps are supplied at both 10km and 50km resolution and all maps that aim to find priority areas for all species considered in the analysis, utilize a series of representative sets. The ranks for each layer are area-specific and can be used to extract summary statistics by simple subsetting. For example, to obtain the top 30% of land area for biodiversity and carbon, one needs to create a mask of all areas lower than a value of 30 from the respective ranked layers. For convenience two files are supplied that contain the fraction of land area per grid cell times 1000. Multiplying those with the cell area (100km2, respectively 2500km2) gives the exact amount of land area in a given grid cell. These are labelled "globalgrid_mollweide_**km.tif " can be used to create masks for the priority maps. The geographic projection is World Mollweide Equal Area projection.

Sera from 114 first-degree relatives of insulin-dependent diabetes mellitus (type I diabetes) patients and 81 healthy individuals living in Germany were analyzed for antibodies to rat brain glutamic acid decarboxylase (GAD-ab) using an immunoprecipitation assay. The determination of GAD-ab in the 81 islet cell antibody (ICA) and insulin autoantibody (IAA) negative healthy individuals established a normal range (mean ± 2 SD); 2 healthy individuals (2.5) possessed GAD-ab levels above this range, but became negative on follow-up. None of 86 ICA-/IAA- first-degree relatives had GAD-ab; whereas, 42.9 of 28 ICA+ and/or IAA+ relatives were positive for GAD-ab. Presence of GAD-ab was negatively correlated with IAA (P = 0.02) and positively with ICA (P = 0.0006). Follow-up samples were obtained from 25 of 28 ICA+ and/or IAA+ relatives with a mean (± SD) follow-up period of 20.6 ± 12.1 months. In these 25 relatives, GAD-ab were positive in 70 ICA+/IAA-, 0 ICA-/IAA+, and 57.1 ICA+/IAA+ relatives in the first sample and in 57.1 ICA+/IAA-, 0 ICA-/IAA+, and 70 ICA+/IAA+ relatives in the last sample. GAD-ab, once detected, persisted in 9 of 11 GAD-ab+ relatives. Of the relatives, 2 converted to GAD-positivity, concomitant with the appearance of ICA, and 2 others lost GAD-ab during follow-up. Of the 28 ICA+ and/or IAA+ relatives, 6 progressed to overt type I diabetes on follow-up, and GAD-ab were detectable in 4 of these relatives. In conclusion, this study indicates that GAD-ab in first-degree relatives are significantly correlated with ICA, are independent of the appearance of IAA, and are present in 66.7 of relatives who subsequently developed type I diabetes.

IDDM results from immune-mediated destruction of insulin-producing pancreatic β-cells in individuals genetically susceptible for the disease. There is evidence that the 65-kDa isoform of GAD plays a critical role in the induction of autoimmune diabetes in NOD mice. In humans, it is still unclear when and to what β-cell antigens autoreactive lymphocytes become activated during early disease. We conducted a prospective study from birth, BABY- DIAB, among children of mothers with IDDM or gestational diabetes or fathers with IDDM, and we investigated the temporal sequence of antibody responses to islet cells (ICA), insulin (IAA), GAD (GADA), and the protein tyrosine phosphatase IA-2/ICA512 (IA-2A). Of 1,019 children included at birth, we have currently followed 513 to the age of 9 months, 214 to the age of 2 years, and 37 to the age of 5 years. At birth, all antibody specificities were frequent in newborns of diabetic mothers but not fathers and are suggested to be transplacentally acquired because they are strongly correlated with antibody levels in their diabetic mothers. In early childhood, antibody levels were <99th percentile of control subjects in the majority of children. However, 37 children exhibited elevated antibody levels; these were most frequently detected at the age of 2 years. The antibody prevalence at age 2 years was 2.3 for ICA, 7 for IAA, 4.2 for GADA, and 2.8 for IA-2A (8.9 positive for at least one antibody). Children of diabetic fathers were positive for at least one antibody more frequently than were children of diabetic mothers (9 months of age: 8.5 vs. 3.6; 2 years of age: 16.7 vs. 7.9). There was no specific sequence in the appearance of positive autoantibodies, but 13 (35) antibody-positive cases already had more than one ICA before the age of 2 years and 7 (19) showed reactivity to three islet cell antigens before age 5 years. The presence of multiple antibodies confers high risk for the future development of diabetes; three of six childre