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Trees are pivotal to global biodiversity and nature’s contributions to people, yet accelerating global changes threaten global tree diversity, making accurate species extinction risk assessments necessary. To identify species that require expert-based re-evaluation, we assess exposure to change in six anthropogenic threats over the last two decades for 32,090 tree species. We estimated that over half (54.2%) of the assessed species have been exposed to increasing threats. Only 8.7% of these species are considered threatened by the IUCN Red List, whereas they include more than half of the Data Deficient species (57.8%). These findings suggest a substantial underestimation of threats and associated extinction risk for tree species in current assessments. We also map hotspots of tree species exposed to rapidly changing threats around the world. Our data-driven approach can strengthen the efforts going into expert-based IUCN Red List assessments by facilitating prioritization among species for re-evaluation, allowing for more efficient conservation efforts.

 

Species distribution models (SDMs) that integrate presence‐only and presence–absence data offer a promising avenue to improve information on species' geographic distributions. The use of such ‘integrated SDMs’ on a species range‐wide extent has been constrained by the often limited presence–absence data and by the heterogeneous sampling of the presence‐only data. Here, we evaluate integrated SDMs for studying species ranges with a novel expert range map‐based evaluation. We build new understanding about how integrated SDMs address issues of estimation accuracy and data deficiency and thereby offer advantages over traditional SDMs.

South and Central America.

1979–2017.

Hummingbirds.

We build integrated SDMs by linking two observation models – one for each data type – to the same underlying spatial process. We validate SDMs with two schemes: (i) cross‐validation with presence–absence data and (ii) comparison with respect to the species' whole range as defined with IUCN range maps. We also compare models relative to the estimated response curves and compute the association between the benefit of the data integration and the number of presence records in each data set.

The integrated SDM accounting for the spatially varying sampling intensity of the presence‐only data was one of the top performing models in both model validation schemes. Presence‐only data alleviated overly large niche estimates, and data integration was beneficial compared to modelling solely presence‐only data for species which had few presence points when predicting the species' whole range. On the community level, integrated models improved the species richness prediction.

Integrated SDMs combining presence‐only and presence–absence data are successfully able to borrow strengths from both data types and offer improved predictions of species' ranges. Integrated SDMs can potentially alleviate the impacts of taxonomically and geographically uneven sampling and to leverage the detailed sampling information in presence–absence data.

 

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. 

Accumulating evidence on the impact of climate change on droughts, highlights the necessity for developing effective adaptation and mitigation strategies. Changes in future drought risk and severity in Australia are quantified by analyzing nine Coupled Model Intercomparison Project Phase 6 climate models. Historic conditions (1981–2014) and projections for mid-century (2015–2050) and end-century (2051–2100) from four shared socioeconomic pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5) are examined. Drought events are identified using both the standardized precipitation index and the standardized precipitation evapotranspiration index. The spatial-temporal evolution of droughts is addressed by quantifying the areal extent of regions under moderate, severe and extreme drought from historic to end-century periods. Drought characteristics derived from the models are used to develop severity–duration–frequency curves using an extreme value analysis method based on ordinary events. Under SSP5-8.5, a tenfold increase in the area subject to extreme droughts is projected by the end of the century, while a twofold increase is projected under SSP1-2.6. Increase in extreme droughts frequency is found to be more pronounced in the southern and western regions of Australia. For example, frequency analysis of 12 month duration droughts for the state of South Australia indicates that, under SSP5-8.5, drought severities currently expected to happen on average only once in 100 years could happen as often as once in 3 years by the end of the century, with a 33 times higher risk (from 1% to 33%), while under SSP1-2.6, the increase is fivefold (1%–5%). The significant difference in the increase of drought risk between the two extreme scenarios highlights the urge to reduce greenhouse gases emission in order to avoid extreme drought conditions to become the norm by the end of the century.

 

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. 

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

Biodiversity contributes to the ecological and climatic stability of the Amazon Basin1,2, but is increasingly threatened by deforestation and fire3,4. Here we quantify these impacts over the past two decades using remote-sensing estimates of fire and deforestation and comprehensive range estimates of 11,514 plant species and 3,079 vertebrate species in the Amazon. Deforestation has led to large amounts of habitat loss, and fires further exacerbate this already substantial impact on Amazonian biodiversity. Since 2001, 103,079–189,755 km2 of Amazon rainforest has been impacted by fires, potentially impacting the ranges of 77.3–85.2% of species that are listed as threatened in this region5. The impacts of fire on the ranges of species in Amazonia could be as high as 64%, and greater impacts are typically associated with species that have restricted ranges. We find close associations between forest policy, fire-impacted forest area and their potential impacts on biodiversity. In Brazil, forest policies that were initiated in the mid-2000s corresponded to reduced rates of burning. However, relaxed enforcement of these policies in 2019 has seemingly begun to reverse this trend: approximately 4,253–10,343 km2 of forest has been impacted by fire, leading to some of the most severe potential impacts on biodiversity since 2009. These results highlight the critical role of policy enforcement in the preservation of biodiversity in the Amazon.