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. Using data from two European bird atlases and from the North American Breeding Bird Survey between the 1980s and 2010s, we analysed range overlap and climate niche overlap based on kernel density estimation. Phylogenetic multiple regression was used to assess the effect of species morphological, ecological and biogeographic traits on range and niche metrics. European birds shifted their ranges north and north-eastwards, US birds westwards. Range unfilling was lower than expected by null models, and niche expansion was more common than niche unfilling. Also, climate niche tracking was generally lower in US birds and poorly explained by species traits. Overall, our results suggest that dispersal limitations were minor in range shifting birds in Europe and the USA while delayed extinctions from unfavourable areas seem more important. Regional differences could be related to differences in land use history and monitoring schemes. Comparative analyses of range and niche shifts provide a useful screening approach for identifying the importance of transient dynamics and time-lagged responses to climate change. This article is part of the theme issue 'Ecological novelty and planetary stewardship: biodiversity dynamics in a transforming biosphere'.

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. Process-based, dynamic SDMs (dSDMs) promise to overcome these limitations as they explicitly represent transient dynamics and enhance spatiotemporal transferability. Software tools for implementing dSDMs are becoming increasingly available, but their parameter estimation can be complex. Here, we test the feasibility of calibrating and validating a dSDM using long-term monitoring data of Swiss red kites (Milvus milvus). This population has shown strong increases in abundance and a progressive range expansion over the last decades, indicating a nonequilibrium situation. We construct an individual-based model using the RangeShiftR modeling platform and use Bayesian inference for model calibration. This allows the integration of heterogeneous data sources, such as parameter estimates from published literature and observational data from monitoring schemes, with a coherent assessment of parameter uncertainty. Our monitoring data encompass counts of breeding pairs at 267 sites across Switzerland over 22 years. We validate our model using a spatial-block cross-validation scheme and assess predictive performance with a rank-correlation coefficient. Our model showed very good predictive accuracy of spatial projections and represented well the observed population dynamics over the last two decades. Results suggest that reproductive success was a key factor driving the observed range expansion. According to our model, the Swiss red kite population fills large parts of its current range but has potential for further increases in density. We demonstrate the practicality of data integration and validation for dSDMs using RangeShiftR. This approach can improve predictive performance compared to cSDMs. The workflow presented here can be adopted for any population for which some prior knowledge on demographic and dispersal parameters as well as spatiotemporal observations of abundance or presence/absence are available. The fitted model provides improved quantitative insights into the ecology of a species, which can greatly aid conservation and management efforts.

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. It is also not well understood how widely used approaches to assess or project the responses of species to changing environmental conditions can deal with time lags. It, therefore, remains unclear to what extent time lags in species distributions are accounted for in biodiversity assessments, scenarios and models; this has ramifications for policymaking and conservation science alike. This perspective piece reflects on lagged species responses to environmental change and discusses the potential consequences for species distribution models (SDMs), the tools of choice in biodiversity modelling. We suggest ways to better account for time lags in calibrating these models and to reduce their leverage effects in projections for improved biodiversity science and policy.

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. We conclude that novel community data offer many opportunities to advance our understanding of fundamental ecological processes, including community assembly, biotic interactions, micro- and macroevolution, and overall ecosystem functioning.

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. Ectothermic species, which are directly dependent on the thermal environment are expected to show a different response to temporal or spatial variability in temperature than endothermic groups that can decouple their internal temperature from that of their surroundings. Here, we explore the degree to which spatial variability and long-term temporal variability in temperature and precipitation change niche estimates for ectothermic (730 amphibian, 1276 reptile), and endothermic (1961 mammal) species globally. We use three different species distribution modelling (SDM) algorithms to quantify the effect of spatial and temporal climate variability, based on global range maps of all species and climate data from 1979 to 2013. All SDMs were cross-validated and accessed for their performance using the Area under the Curve (AUC) and the True Skill Statistic (TSS). The mean performance of SDMs using only climatic means as predictors was TSS = 0.71 and AUC = 0.90. The inclusion of spatial variability offers a significant gain in SDM performance (mean TSS = 0.74, mean AUC = 0.92), as does the inclusion of temporal variability (mean TSS = 0.80, mean AUC = 0.94). Including both spatial and temporal variability in SDMs shows the highest scores in AUC and TSS. Accounting for temporal rather than spatial variability in climate improved the SDM prediction especially in ectotherm groups such as amphibians and reptiles, while for endothermic mammals no such improvement was observed. These results indicate that including long term climate interannual climate variability into niche estimations matters most for ectothermic species that cannot decouple their physiology from the surrounding environment as endothermic species can.

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. The 16 contributions of this issue address six major aspects of biodiversity assessment: connecting policy to science, establishing observation, improving statistical estimation, detecting change, attributing causes and projecting the future. These studies are led by experts in Indigenous studies, economics, ecology, conservation, statistics, and computer science, with representations from Asia, Africa, South America, North America and Europe. The results place biodiversity science in the context of policy needs and provide an updated roadmap for how to observe biodiversity change in a way that supports conservation action via robust detection and attribution science. This article is part of the theme issue 'Detecting and attributing the causes of biodiversity change: needs, gaps and solutions'.

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. Based on this, we compare empirical dispersal kernels for European breeding birds considering age (average dispersal; natal, before first breeding; and breeding dispersal, between subsequent breeding attempts) and sex (females and males) and test whether different dispersal properties are phylogenetically conserved. We standardised and analysed data from an extensive volunteer-based bird ring-recoveries database in Europe (EURING) by accounting for biases related to different censoring thresholds in reporting between countries and to migratory movements. Then, we fitted four widely used probability density functions in a Bayesian framework to compare and provide the best statistical descriptions of the different age and sex-specific dispersal kernels for each bird species. The dispersal movements of the 234 European bird species analysed were statistically best explained by heavy-tailed kernels, meaning that while most individuals disperse over short distances, long-distance dispersal is a prevalent phenomenon in almost all bird species. The phylogenetic signal in both median and long dispersal distances estimated from the best-fitted kernel was low (Pagel's λ < 0.25), while it reached high values (Pagel's λ >0.7) when comparing dispersal distance estimates for fat-tailed dispersal kernels. As expected in birds, natal dispersal was on average 5 km greater than breeding dispersal, but sex-biased dispersal was not detected. Our robust analytical framework allows sound use of widely available mark-recapture data in standardised dispersal estimates. We found strong evidence that long-distance dispersal is common among European breeding bird species and across life stages. The dispersal estimates offer a first guide to selecting appropriate dispersal kernels in range expansion studies and provide new avenues to improve our understanding of the mechanisms and rules underlying dispersal events.

La dispersión es un rasgo clave del ciclo vital de la mayoría de las especies y es esencial para garantizar la conectividad y el flujo genético entre poblaciones y contribuir a la viabilidad de la población en contextos de ambiente variable. A pesar de que la dispersión es clave para estudiar los cambios en el área de distribución de las especies debido al cambio global, la dispersión es difícil de cuantificar, lo que limita la comprensión empírica de este rasgo fenotípico y su síntesis más amplia. Aquí introducimos un marco de trabajo estadístico para estimar de manera estandarizada los kernels de dispersión a partir de datos sesgados. Basándonos en este marco, comparamos los kernels de dispersión empíricos para las aves reproductoras europeas considerando la edad (dispersión media vital; natal, antes de la primera reproducción; y dispersión reproductora, entre los intentos de reproducción posteriores) y el sexo (hembras y machos), además de explorar si las diferentes propiedades de dispersión se conservan filogenéticamente. Estandarizamos y analizamos los datos de una extensa base de datos de anillamiento de aves en Europa (EURING), basada en voluntarios, teniendo en cuenta los sesgos relacionados con los diferentes umbrales de comunicación de las anillas entre países y con los movimientos migratorios. A continuación, ajustamos, en un marco bayesiano, cuatro funciones de probabilidad ampliamente utilizadas para comparar y proporcionar las mejores descripciones estadísticas de los diferentes kernels de dispersión por edad y sexo para cada especie de ave. Los movimientos de dispersión de las 234 especies de aves europeas analizadas se explicaron estadísticamente mejor mediante kernels de cola pesada, lo que significa que, aunque la mayoría de los individuos se dispersan en distancias cortas, la dispersión a larga distancia es un fenómeno prevalente en casi todas las especies de aves. La señal filogenética tanto en las distancias de dispersión medias como en las largas estimadas a partir del kernel mejor ajustado fue baja (λ de Pagel < 0,25), mientras que alcanzó valores altos (λ de Pagel >0,7) al comparar las estimas de distancia de dispersión para los kernels de cola pesada. Como se esperaba en las aves, la dispersión natal fue en promedio 5 km mayor que la dispersión reproductiva, pero no se detectó una dispersión sesgada por sexo. Nuestro robusto marco analítico permite un buen uso de los datos de marcaje y recaptura disponibles para la estimación estandarizada de las distancias de dispersión. Hemos encontrado pruebas sólidas de que la dispersión a larga distancia es común entre las especies de aves reproductoras europeas y en todas las etapas de la vida. Las estimas de dispersión ofrecen un primer paso para seleccionar los kernels de dispersión adecuados para los estudios de expansión del rango de distribución y proporcionar nuevas vías de investigación para mejorar nuestra comprensión de los mecanismos y procesos que subyacen a los eventos de dispersión.

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. We collated presence-absence records from 550 2-ha track-based plots for 11 vertebrates over 13 years and fitted ensemble species distribution models to predict occupancy in 2018. We simulated plausible changes in species' distributions over the next 15 years and, with estimates of detectability, simulated monitoring to evaluate the statistical power of three alternative monitoring scenarios: (1) where surveys were restricted to existing 2-ha plots, (2) where surveys were optimized to target all species equally, and (3) where surveys were optimized to target two species of conservation concern. Across all monitoring designs and scenarios, we found that power was higher when detecting increasing occupancy trends compared to decreasing trends owing to the relatively low levels of initial occupancy. Our results suggest that surveying 200 of the existing plots annually (with a small subset resurveyed twice within a year) will have at least an 80% chance of detecting 30% declines in occupancy for four of the five invasive species modeled and one of the six native species. This increased to 10 of the 11 species assuming larger (50%) declines. When plots were positioned to target all species equally, power improved slightly for most compared to the existing survey network. When plots were positioned to target two species of conservation concern (crest-tailed mulgara and dusky hopping mouse), power to detect 30% declines increased by 29% and 31% for these species, respectively, at the cost of reduced power for the remaining species. The effect of varying survey frequency depended on its trade-off with the number of sites sampled and requires further consideration. Nonetheless, our research suggests that track-based surveying is an effective and logistically feasible approach to monitoring broad-scale occupancy trends in desert species with both widespread and restricted distributions.

Can dynamic occupancy models improve predictions of species' range dynamics? A test using Swiss birds.

Predictions of species' current and future ranges are needed to effectively manage species under environmental change. Species ranges are typically estimated using correlative species distribution models (SDMs), which have been criticized for their static nature. In contrast, dynamic occupancy models (DOMs) explicitily describe temporal changes in species' occupancy via colonization and local extinction probabilities, estimated from time series of occurrence data. Yet, tests of whether these models improve predictive accuracy under current or future conditions are rare. Using a long-term data set on 69 Swiss birds, we tested whether DOMs improve the predictions of distribution changes over time compared to SDMs. We evaluated the accuracy of spatial predictions and their ability to detect population trends. We also explored how predictions differed when we accounted for imperfect detection and parameterized models using calibration data sets of different time series lengths. All model types had high spatial predictive performance when assessed across all sites (mean AUC > 0.8), with flexible machine learning SDM algorithms outperforming parametric static and DOMs. However, none of the models performed well at identifying sites where range changes are likely to occur. In terms of estimating population trends, DOMs performed best, particularly for species with strong population changes and when fit with sufficient data, while static SDMs performed very poorly. Overall, our study highlights the importance of considering what aspects of performance matter most when selecting a modelling method for a particular application and the need for further research to improve model utility. While DOMs show promise for capturing range dynamics and inferring population trends when fitted with sufficient data, computational constraints on variable selection and model fitting can lead to reduced spatial accuracy of predictions, an area warranting more attention.

Reducing persecution is more effective for restoring large carnivores than restoring their prey.

Large carnivores are currently disappearing from many world regions because of habitat loss, prey depletion, and persecution. Ensuring large carnivore persistence requires safeguarding and sometimes facilitating the expansion of their populations. Understanding which conservation strategies, such as reducing persecution or restoring prey, are most effective to help carnivores to reclaim their former ranges is therefore important. Here, we systematically explored such alternative strategies for the endangered Persian leopard (Panthera pardus saxicolor) in the Caucasus. We combined a rule-based habitat suitability map and a spatially explicit leopard population model to identify potential leopard subpopulations (i.e., breeding patches), and to test the effect of different levels of persecution reduction and prey restoration on leopard population viability across the entire Caucasus ecoregion and northern Iran (about 737,000 km2 ). We identified substantial areas of potentially suitable leopard habitat (~120,000 km2 ), most of which is currently unoccupied. Our model revealed that leopards could potentially recolonize these patches and increase to a population of >1,000 individuals in 100 yr, but only in scenarios of medium to high persecution reduction and prey restoration. Overall, reducing persecution had a more pronounced effect on leopard metapopulation viability than prey restoration: Without conservation strategies to reduce persecution, leopards went extinct from the Caucasus in all scenarios tested. Our study highlights the importance of persecution reduction in small populations, which should hence be prioritized when resources for conservation are limited. We show how individual-based, spatially explicit metapopulation models can help in quantifying the recolonization potential of large carnivores in unoccupied habitat, designing adequate conservation strategies to foster such recolonizations, and anticipating the long-term prospects of carnivore populations under alternative scenarios. Our study also outlines how data scarcity, which is typical for threatened range-expanding species, can be overcome with a rule-based habitat map. For Persian leopards, our projections clearly suggest that there is a large potential for a viable metapopulation in the Caucasus, but only if major conservation actions are taken towards reducing persecution and restoring prey.

Early-life behaviour predicts first-year survival in a long-distance avian migrant.

Early-life conditions have critical, long-lasting effects on the fate of individuals, yet early-life activity has rarely been linked to subsequent survival of animals in the wild. Using high-resolution GPS and body-acceleration data of 93 juvenile white storks (Ciconia ciconia), we examined the links between behaviour during both pre-fledging and post-fledging (fledging-to-migration) periods and subsequent first-year survival. Juvenile daily activity (based on overall dynamic body acceleration) showed repeatable between-individual variation, the juveniles' pre- and post-fledging activity levels were correlated and both were positively associated with subsequent survival. Daily activity increased gradually throughout the post-fledging period, and the relationship between post-fledging activity and survival was stronger in individuals who increased their daily activity level faster (an interaction effect). We suggest that high activity profiles signified individuals with increased pre-migratory experience, higher individual quality and perhaps more proactive personality, which could underlie their superior survival rates. The duration of individuals' fledging-to-migration periods had a hump-shaped relationship with survival: higher survival was associated with intermediate rather than short or long durations. Short durations reflect lower pre-migratory experience, whereas very long ones were associated with slower increases in daily activity level which possibly reflects slow behavioural development. In accordance with previous studies, heavier nestlings and those that hatched and migrated earlier had increased survival. Using extensive tracking data, our study exposed new links between early-life attributes and survival, suggesting that early activity profiles in migrating birds can explain variation in first-year survival.

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. Here, we estimated seasonal niche overlap and seasonal niche tracking at the individual and population level of central European white storks (Ciconia ciconia). We quantified niche tracking for both weather and climate conditions to control for the different spatio-temporal scales over which ecological processes may operate. Our results indicate that niche tracking is a bottom-up process. Individuals mainly track weather conditions while climatic niche tracking mainly emerges at the population level. This result may be partially explained by a high degree of intra- and inter-individual variation in niche overlap between seasons. Understanding how migratory individuals, populations and species respond to seasonal environments is key for anticipating the impacts of global environmental changes.

Advancing an interdisciplinary framework to study seed dispersal ecology.

Although dispersal is generally viewed as a crucial determinant for the fitness of any organism, our understanding of its role in the persistence and spread of plant populations remains incomplete. Generalizing and predicting dispersal processes are challenging due to context dependence of seed dispersal, environmental heterogeneity and interdependent processes occurring over multiple spatial and temporal scales. Current population models often use simple phenomenological descriptions of dispersal processes, limiting their ability to examine the role of population persistence and spread, especially under global change. To move seed dispersal ecology forward, we need to evaluate the impact of any single seed dispersal event within the full spatial and temporal context of a plant's life history and environmental variability that ultimately influences a population's ability to persist and spread. In this perspective, we provide guidance on integrating empirical and theoretical approaches that account for the context dependency of seed dispersal to improve our ability to generalize and predict the consequences of dispersal, and its anthropogenic alteration, across systems. We synthesize suitable theoretical frameworks for this work and discuss concepts, approaches and available data from diverse subdisciplines to help operationalize concepts, highlight recent breakthroughs across research areas and discuss ongoing challenges and open questions. We address knowledge gaps in the movement ecology of seeds and the integration of dispersal and demography that could benefit from such a synthesis. With an interdisciplinary perspective, we will be able to better understand how global change will impact seed dispersal processes, and potential cascading effects on plant population persistence, spread and biodiversity.

The total dispersal kernel: a review and future directions.

The distribution and abundance of plants across the world depends in part on their ability to move, which is commonly characterized by a dispersal kernel. For seeds, the total dispersal kernel (TDK) describes the combined influence of all primary, secondary and higher-order dispersal vectors on the overall dispersal kernel for a plant individual, population, species or community. Understanding the role of each vector within the TDK, and their combined influence on the TDK, is critically important for being able to predict plant responses to a changing biotic or abiotic environment. In addition, fully characterizing the TDK by including all vectors may affect predictions of population spread. Here, we review existing research on the TDK and discuss advances in empirical, conceptual modelling and statistical approaches that will facilitate broader application. The concept is simple, but few examples of well-characterized TDKs exist. We find that significant empirical challenges exist, as many studies do not account for all dispersal vectors (e.g. gravity, higher-order dispersal vectors), inadequately measure or estimate long-distance dispersal resulting from multiple vectors and/or neglect spatial heterogeneity and context dependence. Existing mathematical and conceptual modelling approaches and statistical methods allow fitting individual dispersal kernels and combining them to form a TDK; these will perform best if robust prior information is available. We recommend a modelling cycle to parameterize TDKs, where empirical data inform models, which in turn inform additional data collection. Finally, we recommend that the TDK concept be extended to account for not only where seeds land, but also how that location affects the likelihood of establishing and producing a reproductive adult, i.e. the total effective dispersal kernel.

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. Studying such changes empirically is challenging, due to the high variability in dispersal success, resulting from environmental heterogeneity, and substantial phenotypic variability of dispersal-related traits of seeds and their dispersers. The exact mechanisms that drive rapid changes are often not well understood, but the ecological implications of these processes are essential determinants of dispersal success, and deserve more attention from ecologists, especially in the context of adaptation to global change. We outline the evidence for rapid changes in seed dispersal traits by discussing variability due to plasticity or genetics broadly, and describe the specific traits and biological systems in which variability in dispersal is being studied, before discussing some of the potential underlying mechanisms. We then address future research needs and propose a simulation model that incorporates phenotypic plasticity in seed dispersal. We close with a call to action and encourage ecologists and biologist to embrace the challenge of better understanding rapid changes in seed dispersal and their consequences for the reaction of plant populations to global change.

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. Such information is, however, time-consuming and expensive to collect, thwarting the screening of large numbers of potential invaders. We propose to resolve such data limitations by developing indicators of establishment success of alien species that can be readily derived from open-access databases. These indicators describe key features of successfully established aliens, including estimates of potential range size, niche overlap with human-disturbed environments, and proxies of species traits related to their palaeoinvasions and local dominance capacities. We demonstrate the utility of this new approach by applying it to two large and highly invasive plant groups: Australian acacias and eucalypts. Our results show that these indicators robustly predict establishment successes and failures in each clade independently, and that they can cross-predict establishment in these two clades. Interestingly, the indicator identified as most important was species potential range size on Earth, a variable too rarely considered as a predictor. By successfully identifying key features that predispose Australian plants to naturalize, we provide an objective and cost-effective protocol for flagging high-risk introductions.

Employing plant functional groups to advance seed dispersal ecology and conservation.

Seed dispersal enables plants to reach hospitable germination sites and escape natural enemies. Understanding when and how much seed dispersal matters to plant fitness is critical for understanding plant population and community dynamics. At the same time, the complexity of factors that determine if a seed will be successfully dispersed and subsequently develop into a reproductive plant is daunting. Quantifying all factors that may influence seed dispersal effectiveness for any potential seed-vector relationship would require an unrealistically large amount of time, materials and financial resources. On the other hand, being able to make dispersal predictions is critical for predicting whether single species and entire ecosystems will be resilient to global change. Building on current frameworks, we here posit that seed dispersal ecology should adopt plant functional groups as analytical units to reduce this complexity to manageable levels. Functional groups can be used to distinguish, for their constituent species, whether it matters (i) if seeds are dispersed, (ii) into what context they are dispersed and (iii) what vectors disperse them. To avoid overgeneralization, we propose that the utility of these functional groups may be assessed by generating predictions based on the groups and then testing those predictions against species-specific data. We suggest that data collection and analysis can then be guided by robust functional group definitions. Generalizing across similar species in this way could help us to better understand the population and community dynamics of plants and tackle the complexity of seed dispersal as well as its disruption.

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). Risk estimates are largely independent of each other and magnitudes vary geographically. If seasonal range losses and increased migration distances are not considered, we strongly underestimate the number of threatened species by 18-49% and the overall magnitude of risk for 17-50% species. Many of the analysed species facing multiple global change risks are not listed by IUCN as threatened or near threatened. Neglecting seasonal migration in impact assessments could thus seriously misguide species' conservation.

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. These are summarized into six technical and six fundamental challenges, which underlie the combined need to intensify research on the determinants of ecological predictability, including species traits and data quality, and develop best practices for transferring models. Of high importance is the identification of a widely applicable set of transferability metrics, with appropriate tools to quantify the sources and impacts of prediction uncertainty under novel conditions.