Forecasting potential invaders to prevent future biological invasions worldwide.

The ever-increasing and expanding globalisation of trade and transport underpins the escalating global problem of biological invasions. Developing biosecurity infrastructures is crucial to anticipate and prevent the transport and introduction of invasive alien species. Still, robust and defensible forecasts of potential invaders are rare, especially for species without known invasion history.

Multispecies deep learning using citizen science data produces more informative plant community models.

In the age of big data, scientific progress is fundamentally limited by our capacity to extract critical information. Here, we map fine-grained spatiotemporal distributions for thousands of species, using deep neural networks (DNNs) and ubiquitous citizen science data. Based on 6.

Fitting individual-based models of spatial population dynamics to long-term monitoring data.

Generating spatial predictions of species distribution is a central task for research and policy. Currently, correlative species distribution models (cSDMs) are among the most widely used tools for this purpose. However, a fundamental assumption of cSDMs, that species distributions are in equilibrium with their environment, is rarely fulfilled in real data and limits the applicability of cSDMs for dynamic projections.

Range and climate niche shifts in European and North American breeding birds.

Species respond dynamically to climate change and exhibit time lags. Consequently, species may not occupy their full climatic niche during range shifting. Here, we assessed climate niche tracking during recent range shifts of European and United States (US) birds.

Potential sources of time lags in calibrating species distribution models.

The Anthropocene is characterized by a rapid pace of environmental change and is causing a multitude of biotic responses, including those that affect the spatial distribution of species. Lagged responses are frequent and species distributions and assemblages are consequently pushed into a disequilibrium state. How the characteristics of environmental change-for example, gradual 'press' disturbances such as rising temperatures due to climate change versus infrequent 'pulse' disturbances such as extreme events-affect the magnitude of responses and the relaxation times of biota has been insufficiently explored.

Novel community data in ecology-properties and prospects.

New technologies for monitoring biodiversity such as environmental (e)DNA, passive acoustic monitoring, and optical sensors promise to generate automated spatiotemporal community observations at unprecedented scales and resolutions. Here, we introduce 'novel community data' as an umbrella term for these data. We review the emerging field around novel community data, focusing on new ecological questions that could be addressed; the analytical tools available or needed to make best use of these data; and the potential implications of these developments for policy and conservation.

Interannual climate variability improves niche estimates for ectothermic but not endothermic species.

Climate is an important limiting factor of species' niches and it is therefore regularly included in ecological applications such as species distribution models (SDMs). Climate predictors are often used in the form of long-term mean values, yet many species experience wide climatic variation over their lifespan and within their geographical range which is unlikely captured by long-term means. Further, depending on their physiology, distinct groups of species cope with climate variability differently.

Local colonisations and extinctions of European birds are poorly explained by changes in climate suitability.

Climate change has been associated with both latitudinal and elevational shifts in species' ranges. The extent, however, to which climate change has driven recent range shifts alongside other putative drivers remains uncertain. Here, we use the changing distributions of 378 European breeding bird species over 30 years to explore the putative drivers of recent range dynamics, considering the effects of climate, land cover, other environmental variables, and species' traits on the probability of local colonisation and extinction.

Detecting and attributing the causes of biodiversity change: needs, gaps and solutions.

This issue addresses the multifaceted problems of understanding biodiversity change to meet emerging international development and conservation goals, national economic accounting and diverse community needs. Recent international agreements highlight the need to establish monitoring and assessment programmes at national and regional levels. We identify an opportunity for the research community to develop the methods for robust detection and attribution of biodiversity change that will contribute to national assessments and guide conservation action.

Predicting extinctions with species distribution models.

Predictions of species-level extinction risk from climate change are mostly based on species distribution models (SDMs). Reviewing the literature, we summarise why the translation of SDM results to extinction risk is conceptually and methodologically challenged and why critical SDM assumptions are unlikely to be met under climate change. Published SDM-derived extinction estimates are based on a positive relationship between range size decline and extinction risk, which empirically is not well understood.

Standardised empirical dispersal kernels emphasise the pervasiveness of long-distance dispersal in European birds.

Dispersal is a key life-history trait for most species and is essential to ensure connectivity and gene flow between populations and facilitate population viability in variable environments. Despite the increasing importance of range shifts due to global change, dispersal has proved difficult to quantify, limiting empirical understanding of this phenotypic trait and wider synthesis. Here, we introduce a statistical framework to estimate standardised dispersal kernels from biased data.

Designing a large-scale track-based monitoring program to detect changes in species distributions in arid Australia.

Monitoring trends in animal populations in arid regions is challenging due to remoteness and low population densities. However, detecting species' tracks or signs is an effective survey technique for monitoring population trends across large spatial and temporal scales. In this study, we developed a simulation framework to evaluate the performance of alternative track-based monitoring designs at detecting change in species distributions in arid Australia.

Seasonal niche tracking of climate emerges at the population level in a migratory bird.

Seasonal animal migration is a widespread phenomenon. At the species level, it has been shown that many migratory animal species track similar climatic conditions throughout the year. However, it remains unclear whether such a niche tracking pattern is a direct consequence of individual behaviour or emerges at the population or species level through behavioural variability.

Rapid changes in seed dispersal traits may modify plant responses to global change.

When climatic or environmental conditions change, plant populations must either adapt to these new conditions, or track their niche via seed dispersal. Adaptation of plants to different abiotic environments has mostly been discussed with respect to physiological and demographic parameters that allow local persistence. However, rapid modifications in response to changing environmental conditions can also affect seed dispersal, both via plant traits and via their dispersal agents.

Global predictors of alien plant establishment success: combining niche and trait proxies.

Biological invasions are on the rise globally. To reduce future invasions, it is imperative to determine the naturalization potential of species. Until now, screening approaches have relied largely on species-specific functional feature data.

Long-distance migratory birds threatened by multiple independent risks from global change.

Many species migrate long distances annually between their breeding and wintering areas1. While global change affects both ranges, impact assessments have generally focused on breeding ranges and ignore how environmental changes influence migrants across geographic regions and the annual cycle2,3. Using range maps and species distribution models, we quantified the risk of summer and winter range loss and migration distance increase from future climate and land cover changes on long-distance migratory birds of the Holarctic (n=715).

Outstanding Challenges in the Transferability of Ecological Models.

Predictive models are central to many scientific disciplines and vital for informing management in a rapidly changing world. However, limited understanding of the accuracy and precision of models transferred to novel conditions (their 'transferability') undermines confidence in their predictions. Here, 50 experts identified priority knowledge gaps which, if filled, will most improve model transfers.