Walter Jetz

Aim: To test whether individual movement mechanisms inferred from animal tracking data can be scaled up to explain landscape-scale patterns of population density and improve predictions of population redistribution under future environmental change. Location: Texas Gulf Coast, USA Time Period: 2009–2018 Major Taxa Studied: Whooping Crane ( Grus americana ) Methods: We parameterized individual-level step-selection functions using 14,898 GPS locations from 22 Whooping Cranes and propagated the resulting movement and habitat-selection mechanisms across the landscape to derive steady-state distributions of space use. We compared these movement-informed predictions with correlative species distribution models fitted to the same data. Predictions were evaluated using withheld tracking observations and fully independent population-level aerial-survey data. We then projected both approaches under sea-level-rise scenarios to assess how future habitat loss and fragmentation alter crane space use. Results: Movement mechanisms derived from individual-level tracking data captured the mechanistic dependence of population density on habitat quality and spatial configuration. When projected across the landscape, these mechanisms reproduced fully independently observed density patterns and outperformed conventional species distribution models. Because predictions arose from movement mechanisms rather than occurrence-environment correlations, movement-informed models also captured the effects of habitat reconfiguration under sea-level rise. They projected pronounced population redistribution and an expansion of core space-use areas of up to 80% which conventional models missed. Main Conclusions: Individual movement mechanisms derived from biologging data can scale up to explain current population structure and predict future redistribution under environmental change. By mechanistically accounting for how habitat configuration constrains accessibility, movement-informed models reveal severe impacts of fragmentation that are missed by conventional distribution models. Rapidly expanding biologging datasets therefore offer a scalable basis for connecting individual behavior to macroecological patterns and improving biodiversity forecasts in changing landscapes.

The number of species introduced outside of their historical ranges by human activity continues to rise ¹ . A subset of these species establishes, form self-sustaining populations, and some – invasive alien species – go on to cause substantial harm to biodiversity and ecosystems ² . Preventing new invasive alien species from establishing is the key focus of interventions, because post-establishment management is costly and often fails ^3,4 . However, it remains unclear how effective multilateral efforts have been in curbing the rise. Here we show that the emergence of new invasive alien species across countries is slowing, and trends are similarly negative across geographically diverse countries. Using data and modelling advances, we find a 35% reduction in the establishment of new invasive alien species over a policy-relevant 50-year time frame. The findings directly inform the assessment of progress for the invasive alien species target of the Kunming-Montreal Global Biodiversity Framework ⁵ , and provide a global baseline for monitoring rates of invasive alien species establishment. Furthermore, the slowdown suggests that policy and investment over recent decades to prevent invasive alien species from entering and establishing in countries have had a positive effect.

Increasingly variable and extreme weather can push organisms beyond their physiological thermal or hydric tolerances and limit where they can persist, affecting their geographic distributions and biodiversity patterns. For example, organisms often live near their physiological limits at range edges and may be unable to persist in these regions under variable and extreme conditions. However, it remains unclear how climatic variability and extreme weather define continental‐scale species distributions and biodiversity patterns. Here we link hundreds of millions of citizen science bird observations from 2004 to 2024 to high‐resolution maps of within‐year climatic variability and extreme weather risk, exploring how these factors define both summer and winter distributions and biodiversity patterns for 535 North American species across 2901 individual models. We find that species distribution models accounting for climatic variability or extreme weather risk performed better than others at predicting richness and individual species presences across 220 well‐surveyed sites. Such models predicted narrower geographic distributions than models relying on only climatic means, especially at the range edges, resulting in species' ranges that were truncated on average by 6% in summer and 10% in winter when climatic variability and extreme weather variables were included. These effects were observed in both seasons but were particularly strong in winter, a time with greater short‐term weather variability than summer. Richness estimates were substantially lower when climatic variability or extreme weather were accounted for, especially in the US southwest and central plains (up to 35 fewer species), regions highly prone to extreme heat, cold, and drought. Our results suggest that more mechanistically informed biodiversity predictions that account for climatic variability and extreme weather are critical for reliably predicting distributional and biodiversity patterns.

Sustainable human–wildlife coexistence requires a mechanistic understanding of the many ways that humans affect animals. However, progress is hampered by the lack of accessible data measuring the dynamic presence of people. Here, we leverage mobile-device data to disentangle how human presence and landscape modification differentially influence the use of geographic and environmental space for 37 mammal and bird species across the United States. Human presence affected more than 65% of species, with substantial variation across species. For ~60% of species that responded to human activities, the effects were interdependent—animals tended to react more strongly to human presence in less modified habitats. Our results demonstrate that human presence and landscape modification have complex combined effects on wildlife, which need to be considered for effective management.

Aim: For species with narrow ranges or low population sizes, a deficiency of species occurrence records can limit the capacity to build accurate species distribution models (SDMs). Model‐based integration of data from multiple sources has been offered as a solution to improve predictions of species' distributions at large scales, especially for data‐deficient species, but clear empirical demonstrations for this are lacking. Location: South and Central America. Methods: We applied a state‐of‐the‐art data integration technique to model the distributions of 98 hummingbird species. We fitted SDMs using either presence‐absence (PA) data from eBird or presence‐only (PO) data from eBird and the Global Biodiversity Information Facility (GBIF) and compared them to integrated SDMs, which utilise both PA and PO data. We fitted generalised linear mixed‐effects models and validated them with spatial block cross‐validation and expert range map adjusted validation. We also conducted an experiment using artificially thinned datasets of 47 abundant enough species to assess model performance under different levels of data deficiency. Results: Data integration improved model performance compared with PA models for small‐ranging and data‐deficient species for which PA data covered poorly the environmental conditions in the study area. Still, the difference to PA models was very small. The thinning experiment showed that even a small amount of PO data in data integration improved the predictive accuracy in comparison to PA models. In comparison to PO models, data integration improved models over all species, but especially for data‐rich species with large geographical ranges. Main Conclusions: Overall, data integration enables a more comprehensive capture of available species information and can improve range predictions in comparison to conventional modelling methods.

Global conservation organizations invest heavily in protected areas and management actions to support biodiversity outcomes, often with a particular focus on target species. However, they require more flexible, efficient means to monitor the impact of these interventions at scale. Lack of responsive tools that can regularly track progress and impact of conservation actions can limit effective resourcing and planning for future work. While sufficient in situ data may exist for specific regions, and species, conservation organizations require standardized measurements and aggregate metrics to support decision-making across their global portfolio and countries of activity. This presented work addresses these challenges by leveraging the advanced and proven species-level models and metrics of Map of Life (MOL). This effort utilizes NASA-supported remote sensing workflows and the digital infrastructure that enables calculations of Species Habitat Scores and Species Habitat Indices – formally adopted indicators of Goal A of the UN Global Biodiversity Framework. These indicators rely on species-habitat association calculations, historically developed from expert-informed range maps validated against a single independent source of species presence data. To enhance the accuracy and reliability of these measures, we will implement emerging modelling techniques that integrate diverse data streams, including citizen science records as well as visual, acoustic, and GPS tracking data. Combining species occurrence information from multiple data sources substantially improves model predictions and resulting conservation insights, but these approaches have yet to be widely adopted by conservation practitioners for large-scale decision-making.We show results for implementing these models for mammal species based on camera trap data and citizen science data, with all routines designed to be easily extended to additional taxa. By integrating multi-source datasets with NASA and other Earth Observation products that capture fine-scale climatic and environmental conditions, we can provide annual, range-wide estimates of suitable habitat, habitat connectivity, protection adequacy, and estimate total population size trends for target species. Our advanced species distribution and occupancy modeling approaches enables us to offer both high-resolution maps and species-level trends alongside method-associated uncertainty that will directly support tracking progress towards 30x30 goals and guide adaptive management interventions.

Understanding animal space use is central to ecology and conservation, and movement‐based habitat selection models provide powerful tools for identifying preferred environments. However, in heterogeneous landscapes, movement constraints limit habitat accessibility, decoupling realised space use from underlying preferences and complicating efforts to scale individual‐level movement processes to landscape distributions. We present a steady‐state distribution (SSD) framework that integrates locally estimated habitat selection and movement into sparse Markovian transition matrices to predict emergent space use. Across simulations and empirical case studies, SSD consistently outperformed occurrence‐based models in predicting space use from individual‐ to population‐level scales. Moreover, SSD proved to be more efficient—and sometimes even more accurate—than more assumptive agent‐based model simulations (ABMs). Our approach bridges a key gap between mechanistic movement models and spatial prediction, providing a scalable and computationally efficient approach for translating individual movement behaviour into landscape‐scale distribution patterns relevant for ecological inference and conservation planning.

In the face of the global biodiversity crisis, accessibility to biodiversity data that are maximally effective for downstream use in science, conservation, and policy is paramount. The Darwin Core standard has played a central role in providing a standardised structure and vocabulary for biodiversity data. However, early iterations of the standard were not optimised to capture the sampling context of biodiversity surveys – survey methods, scope, and sampling effort – which is essential for the correct interpretation and potential reuse of such data. To address this limitation, we present the Humboldt Extension to Darwin Core, a ratified standard designed to accommodate datasets that contain such contextual information. Building upon an initial, previously developed framework, we significantly improved, fully tested, and ratified a final standard, following a community process defined by biodiversity information standards (TDWG), an international standards organisation. The resulting Humboldt Extension adds 55 terms that enrich the Darwin Core, providing the terms needed to capture and share multiple types of biodiversity survey data. We illustrate the benefits of implementing the Humboldt Extension with three case studies and demonstrate how richer data can be used in research, modelling, and to inform decision‐making. We urge the uptake and use of this Extension to facilitate the reuse and synthesis of monitoring data, particularly structured surveys and inventories, for science, conservation, and policy.

Many species exhibit consistent morphological differences between males and females. This sexual dimorphism sparked debate between Charles Darwin, who attributed it to sexual selection favoring male traits that appeal to females, and Alfred Russell Wallace, who suggested that natural selection favored cryptic traits in females to evade predators. Here, using multispectral imaging of wing reflectance and patterning across 274 butterfly and moth species sampled from recent phylogenetic frameworks, we demonstrate that Darwinian and Wallacean models both describe aspects of the evolution of lepidopteran coloration, with Darwinian sexual selection largely acting on visible male traits in diurnal species, while Wallacean natural selection tends to act on female traits of nocturnal species: In butterflies, wing reflectance and color pattern traits exhibit the strongest dimorphism in dorsal and forewing areas, areas predicted to experience strong sexual selection, with male traits evolving more quickly and exhibiting greater between-species disparity than those of females (indicators of sexual selection), but only in wavelengths perceptible to butterflies. In contrast, in nocturnal moths, evolutionary rate and disparity are strongly female biased. Ancestrally nocturnal geometrid moths, where diurnal behavior has evolved repeatedly, confirm this trend of male-biased rates and disparity in diurnal species, especially on dorsal and forewing surfaces.

In the face of rapid ecological decline, biodiversity information is essential for safeguarding life on Earth. Although this information is increasingly valued by governments, businesses, and other stakeholders, it remains insufficiently accessible and usable. Because the rarity and functions of biodiversity vary greatly across land- and seascapes, the global and local ecological and social significance of biodiversity change within and among regions can differ greatly. Measuring biodiversity at both scales is therefore critical for effective monitoring and conservation—an expectation embedded in global frameworks. We review the challenges and opportunities in biodiversity measurement, examining key users, producers, and use cases, and the role of emerging technologies. As catalysts for a more robust, efficient, and collaborative global measurement system we highlight Essential Biodiversity Variables (EBVs) as a flexible foundation to integrate across scales by linking local data into global significance assessments. EBVs also underpin indicators, geospatial data products, and an evolving ‘bag of metrics’ required by different users. We call for improved incentives and organization around thematic and regional networks to produce EBVs and structured end-to-end initiatives and workflows as a blueprint for the next phase of coordinated biodiversity measurement globally