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In the age of big data, scientific progress is fundamentally limited by our capacity to extract critical information. We show that recasting multispecies distribution modeling as a ranking problem allows analyzing ubiquitous citizen-science observations with unprecedented efficiency. Based on 6.7M observations, we jointly modeled the distributions of 2477 plant species and species aggregates across Switzerland, using deep neural networks (DNNs). Compared to commonly-used approaches, multispecies DNNs predicted species distributions and especially community composition more accurately. Moreover, their setup allowed investigating understudied aspects of ecology: including seasonal variations of observation probability explicitly allowed approximating flowering phenology, especially for small, herbaceous species; reweighting predictions to mirror cover-abundance allowed mapping potentially canopy-dominant tree species nationwide; and projecting DNNs into the future allowed assessing how distributions, phenology, and dominance may change. Given their skill and their versatility, multispecies DNNs can refine our understanding of the distribution of plants and well-sampled taxa in general.

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’

Land use is the main direct driver of biodiversity loss and Southern-Asia is, globally, one of the regions under the highest land-use change. Here we estimate how mammals that play a key role in the ecosystem functioning will cope with landscape transformations. We used the a state-of-the-art spatially-explicit agent-based model (RangeShifter) combining local density-dependence on fecundity, stage-structured demographics and dispersal to predict the occupancy and abundance for large-body size carnivorous species (Panthera tigris, Panthera pardus) mid-sized and small carnivorous (Cuon alpinus, Felis chaus, Vulpes vulpes and Prionailurus bengalensis) and two Cetartiodactyla species (Sus scrofa and Gazella benetti) in Southern Asia. In addition, we estimated how species-richness changed through time. The model was projected to the period 1850 to 2100 under two socio-economic pathways, representing an intermediate scenario (SSP2-4.5) and a fossil-fueled development scenario (SSP5-8.5). We found mixed-response to land-use across species. We estimate the mean total proportion of remaining individuals to be 0.60 (SD = 0.24) under SSP2 and 0.64 (SD = 0.37) under SSP5 compared to baseline land use in 1850. The drop in the total number of occupied cells is of lower magnitude (SSP2: mean = 0.82, SD = 0.27; SSP5: mean = 0.84, SD = 0.32). Mean species richness per cell followed a decline throughout the 20th century (mean = 0.90, SD = 0.15) followed by increase from current time up to 2100 under both scenarios (SSP2: mean = 0.95, SD = 0.18; SSP5: mean = 0.97, SD = 0.22). Our results support biotic homogenization with spread of widespread species and restriction of forest-specialists. We confirm a disproportionate and negative influence of loss of non-disturbed patches, and lower landscape permeability in large mammals, potentially leading to considerable change in mammalian biomass in the ecosystem. These findings suggest that a middle-road socio-economic pathway (SSP2) is not enough to maintain or recover populations compared to pre-disturbance levels.

Time is running out to limit further devastating losses of biodiversity and nature's contributions to humans. Addressing this crisis requires accurate predictions about which species and ecosystems are most at risk to ensure efficient use of limited conservation and management resources. We review existing biodiversity projection models and discover problematic gaps. Current models usually cannot easily be reconfigured for other species or systems, omit key biological processes, and cannot accommodate feedbacks with Earth system dynamics. To fill these gaps, we envision an adaptable, accessible, and universal biodiversity modeling platform that can project essential biodiversity variables, explore the implications of divergent socioeconomic scenarios, and compare conservation and management strategies. We design a roadmap for implementing this vision and demonstrate that building this biodiversity forecasting platform is possible and practical.

Monitoring trends in animal populations in arid regions is challenging due to remoteness and low population densities. However, detecting species' tracks or sign 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) when surveys were restricted to existing 2-ha plots; 2) when surveys were optimised to target all species equally; 3) when surveys were optimised 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 due to relatively low levels of initial occupancy. Our results suggest that surveying 200 of the existing plots annually (with a small subset re-surveyed twice within a year) will have at least an 80% chance at detecting 30% declines in occupancy for 4 of the 5 invasive species modelled and 1 of the 6 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 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 for monitoring broad-scale occupancy trends in desert species with both widespread and restricted distributions.

Process-based models are becoming increasingly used tools for understanding how species are likely to respond to environmental changes and to potential management options. RangeShifter is one such modelling platform, which has been used to address a range of questions including identifying effective reintroduction strategies, understanding patterns of range expansion and assessing population viability of species across complex landscapes.Here we introduce a new version, RangeShifter 2.0, which incorporates important new functionality. It is now possible to simulate dynamics over user-specified, temporally changing landscapes. Additionally, the genetic and evolutionary capabilities have been strengthened, notably by introducing an explicit genetic modelling architecture, which allows for simulation of neutral and adaptive genetic processes. Furthermore, emigration, transfer and settlement rules can now all evolve, allowing for sophisticated simulation of the evolution of dispersal. We illustrate the potential application of RangeShifter 2.0’s new functionality by two examples. The first illustrates the range expansion of a virtual species across a dynamically changing UK landscape. The second demonstrates how the software can be used to explore the concept of evolving connectivity in response to land-use modification, by examining how movement rules come under selection over landscapes of different structure and composition.RangeShifter 2.0 is built using object-oriented C++ providing computationally efficient simulation of complex individual-based, eco-evolutionary models. The code has been redeveloped to enable use across operating systems, including on high performance computing clusters, and the Windows GUI has been enhanced. Furthermore, the recoding of the package has supported the development of a new version running under the R platform, RangeShiftR.RangeShifter 2.0 will facilitate the development of in-silico assessments of how species will respond to environmental changes and to potential management options for conserving or controlling them. By making the code available open source, we hope to inspire further collaborations and extensions by the ecological community.

Generating spatial predictions of species distribution is a central task for research and policy. Among the currently most widely used tools for this purpose are correlative species distribution models (cSDMs). Their basic assumption of a species distribution in equilibrium with its environment, however, is rarely met in real data and prevents dynamic projections. Process-based, dynamic SDMs (dSDMs) promise to overcome these limitations as they explicitly represent transient dynamics and enhance spatio-temporal transferability. Software tools for implementing dSDMs become increasingly available, yet their parameterisation 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 non-equilibrium situation. We construct an individual-based model with the RangeShiftR modelling platform and calibrate it using Bayesian inference. This allows the integration of heterogeneous data sources, such as parameter estimates from published literature and observational data from monitoring schemes, and consistent quantification of parameter uncertainties. Our monitoring data encompass counts of breeding pairs at 267 sites across Switzerland over an annual time-series of 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 and excellent predictive accuracy of spatial and temporal projections, respectively, well representing the observed population dynamics over the last two decades. Results suggest that reproductive success was the most decisive factor driving the observed range expansion. According to our predictions, the Swiss red kite population fills most of its range but density will further increase before stabilising mid century.With our case study we demonstrate the practicality of data integration and validation for dSDMs using available tools. This approach can improve predictive performance compared to cSDMs. The workflow exemplified here can be adopted for any population for which prior knowledge on demographic and dispersal parameters as well as spatio-temporal observations of abundance or occupancy are available. The resulting calibrated model provides refined insights into the ecology of a species and its predictions can inform conservation and management.

Complex distribution data can be summarised by grouping species with similar or overlapping distributions to unravel patterns in species distributions and separate trends (e.g., of habitat loss) among spatially unique groups. However, such classifications are often heuristic, lacking the transparency, objectivity, and data-driven rigour of quantitative methods, which limits their interpretability and utility. Here, we develop and illustrate the clustering of spatially associated species, a methodological framework aimed at statistically classifying species using explicit measures of interspecific spatial association. We investigate several association indices and clustering algorithms and show how these methodological choices engender substantial variations in clustering outcome and performance. To facilitate robust decision making, we provide guidance on choosing methods appropriate to the study objective(s). As a case study, we apply the framework to model tree distributions in Borneo to evaluate the impact of land-cover change on separate species groupings. We identified 11 distinct clusters that unravelled ecologically meaningful patterns in Bornean tree distributions. These clusters then enabled us to quantify trends of habitat loss tied to each of those specific clusters, allowing us to discern particularly vulnerable species clusters and their distributions. This study demonstrates the advantages of adopting quantitatively derived clusters of spatially associated species and elucidates the potential of resultant clusters as a spatially explicit framework for investigating distribution-related questions in ecology, biogeography, and conservation. By adopting our methodological framework and publicly available codes, practitioners can leverage the ever-growing abundance of distribution data to better understand complex spatial patterns among species distributions and the disparate effects of global changes on biodiversity.Statement of authorshipSEHP and ELW conceived the idea and designed methodology. SEHP conducted all analyses and developed the methodological framework with key inputs from ELW, JWFS, and DZ. All authors contributed to the interpretation of the results. SEHP and ELW wrote the first draft of the manuscript. All authors provided feedback on the writing.

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 explicitily describe temporal changes in species' occupancy via colonisation 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 dataset on 69 Swiss birds, we tested whether dynamic occupancy models improve 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 parameterised models using calibration datasets 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 dynamic occupancy models. However, none of the models performed well at identifying sites where range changes are likely to occur. In terms of estimating population trends, dynamic occupancy models 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 dynamic occupancy models 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.

Predictions from species distribution models (SDMs) that rely on presence-only data are strongly influenced by how pseudo-absences are derived. However, which strategies to generate pseudo-absences give rise to faithful SDMs in complex mountainous terrain, and whether species-specific or generic strategies perform better remain open questions.Here, across 500 plant species, we investigated comprehensively how predictions of SDMs at a 93 m spatial resolution are influenced by pseudo-absence strategies, using the complex topography of the Swiss mountains as a model system. We used five generic (random, equal-stratified, proportional-stratified, target, density) and three species-specific (target specific, density specific and geographic specific) approaches to derive pseudo-absence data. We conducted performance tests for each of our eight strategies in combination with (a) spatial bias, generated within our occurrence dataset on sites with highest sampling density, to investigate how this common bias problem influences the performance of pseudo-absence sampling strategies, and (b) a new approach to reduce model extrapolation in environmental space by including background data from all environmental conditions of the study area. SDMs were evaluated against an independent and well-sampled dataset of true presences and absences.The random, the density (generic), and the geographic specific (species-specific) strategies consistently performed best, even in cases of strong spatial sampling bias in the occurrence data. Including a background of environmentally stratified pseudo-absences improved predictions of species distributions towards environmental extremes, and significantly reduced spatial extrapolations of model predictions in environmental space.Our results indicate that both generic and species-specific pseudo-absence strategies allow estimating robust SDMs and we provide clear recommendations which strategies to choose in complex terrain and when presence data are prone to high sampling bias. In datasets with strong sampling bias, most pseudo-absence strategies produce extrapolation problems and we additionally recommend environmentally stratified pseudo-absences in these cases. Overall, in species rich datasets the use of complex and computationally demanding, species-specific pseudo-absence strategies may not always be justified compared to simpler generic approaches.

The field of distributional ecology has seen considerable recent attention, particularly surrounding the theory, protocols, and tools for Ecological Niche Modeling (ENM) or Species Distribution Modeling (SDM). Such analyses have grown steadily over the past two decades—including a maturation of relevant theory and key concepts—but methodological consensus has yet to be reached. In response, and following an online course taught in Spanish in 2018, we designed a comprehensive English-language course covering much of the underlying theory and methods currently applied in this broad field. Here, we summarize that course, ENM2020, and provide links by which resources produced for it can be accessed into the future. ENM2020 lasted 43 weeks, with presentations from 52 instructors, who engaged with >2500 participants globally through >14,000 hours of viewing and >90,000 views of instructional video and question-and-answer sessions. Each major topic was introduced by an “Overview” talk, followed by more detailed lectures on subtopics. The hierarchical and modular format of the course permits updates, corrections, or alternative viewpoints, and generally facilitates revision and reuse, including the use of only the Overview lectures for introductory courses. All course materials are free and openly accessible (CC-BY license) to ensure these resources remain available to all interested in distributional ecology.

Dispersal is a key life-history trait for most species and 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 aim to estimate and compare empirical dispersal kernels for European breeding birds considering average dispersal, natal (before first breeding) and breeding dispersal (between subsequent breeding attempts), 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 average, the natal and the breeding 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 feature in almost all bird species. The overall phylogenetic signal in both median and long dispersal distances was low (Pagel’s λ < 0.40), implying a high degree of taxonomic generality in our findings. As expected in birds, natal dispersal was 5 Km greater as an average than breeding dispersal for most species (88% species analysed).Our comprehensive analysis of empirical kernels indicates 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.

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 λ0.7) 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 Pagel0,7) 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.

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. Federal State of Berlin, Germany German Research Foundation http://dx.doi.org/10.13039/501100001659

Land-use change remains the main driver of biodiversity loss, and fragmentation and habitat loss are expected to lead to further population declines and species losses . We apply a recently developed R package for a spatially-explicit mechanistic simulation model (RangeShiftR), which incorporates habitat suitability, demographic as well as dispersal processes to understand temporal effects of land-use change (Land-use harmonization scenarios for the 1900-2100 period) on abundance and richness of mammalian species in South-Asia. We then compare land-use scenarios with and without protected areas to understand if current spatial conservation strategies are able to sustain viable populations independently of the land-use scenarios followed. Our approach is innovative in assessing how land-use scenarios can influence animal populations through underlying ecological processes.

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.

Reliable estimates of nest‐switching are required to study avian mating systems and manage wild populations, yet different estimation methods have rarely been integrated or assessed. Through a literature review and case study, we reveal that three common methods for assessing nest‐switching blend different components, producing a wide range of estimates. Careful component definition and reporting are essential to properly estimate this behaviour. published

Reliably modelling the demographic and distributional responses of a species to environmental changes can be crucial for successful conservation and management planning. Process-based models have the potential to achieve this goal, but so far they remain underused for predictions of species’ distributions. Individual-based models offer the additional capability to model inter-individual variation and evolutionary dynamics and thus capture adaptive responses.We present RangeShiftR, an R package that provides flexible and fast simulations of spatial eco-evolutionary dynamics and species’ responses to environmental changes. It implements the individual-based simulation software RangeShifter for the widely used statistical programming platform R. The package features additional auxiliary functions to support model specification and analysis of results. We provide an outline of the package’s functionality, describe the underlying model structure with its main components and present a short example.RangeShiftR offers substantial model complexity, especially for the demographic and dispersal processes. It comes with comprehensive documentation and elaborate tutorials to provide a low entry level. Thanks to the implementation of the core code in C++, the computations are fast. The complete source code is published under a public licence, making adaptations and contributions feasible.The RangeShiftR package facilitates the application of individual-based and mechanistic modelling to eco-evolutionary questions by operating a flexible and powerful simulation model from R. It allows effortless interoperation with existing packages to create streamlined workflows that can include data preparation, integrated model specification, and results analysis. Moreover, the implementation in R strengthens the potential for coupling RangeShiftR with other models.

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.

A recommendation of: F. Laroche, M. Balbi, T. Grebert, F. Jabot & F. Archaux Three points of consideration before testing the effect of patch connectivity on local species richness: patch delineation, scaling and variability of metrics 10.1101/640995

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., 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. 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. 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.

Recent years have seen an exponential increase in the amount of data available in all sciences and application domains. Macroecology is part of this "Big Data" trend, with a strong rise in the volume of data that we are using for our research. Here, we summarize the most recent developments in macroecology in the age of Big Data that were presented at the 2018 annual meeting of the Specialist Group Macroecology of the Ecological Society of Germany, Austria and Switzerland (Gfo). Supported by computational advances, macroecology has been a rapidly developing field over recent years. Our meeting highlighted important avenues for further progress in terms of standardized data collection, data integration, method development and process integration. In particular, we focus on (a) important data gaps and new initiatives to close them, for example through space- and airborne sensors, (b) how various data sources and types can be integrated, (c) how uncertainty can be assessed in data-driven analyses and (d) how Big Data and machine learning approaches have opened new ways of investigating processes rather than simply describing patterns. We discuss how Big Data opens up new opportunities, but also poses new challenges to macroecological research. In the future, it will be essential to carefully assess data quality, the reproducibility of data compilation and analytical methods, and the communication of uncertainties. Major progress in the field will depend on the definition of data standards and workflows for macroecology, such that scientific quality and integrity are guaranteed, and collaboration in research projects is made easier.

Many species migrate long distances annually between their breeding and wintering areas1. Although global change affects both ranges, impact assessments have generally focused on breeding ranges and ignored how environmental changes influence migrants across geographical 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 that face multiple global change risks are not listed by International Union for Conservation of Nature as threatened or near threatened. To neglect seasonal migration in impact assessments could thus seriously misguide species’ conservation. Climate and land-cover change can affect the summer and winter ranges and migration distances of migratory birds. Accounting for all of these factors, rather than just summer range as is typical, significantly increases the number of species under threat.

Periodic environments determine the life cycle of many animals across the globe and the timing of important life history events, such as reproduction and migration. These adaptive behavioural strategies are complex and can only be fully understood (and predicted) within the framework of natural selection in which species adopt evolutionary stable strategies. We present sOAR, a powerful and user-friendly implementation of the well-established framework of optimal annual routine modelling. It allows determining optimal animal life history strategies under cyclic environmental conditions using stochastic dynamic programming. It further includes the simulation of population dynamics under the optimal strategy. sOAR provides an important tool for theoretical studies on the behavioural and evolutionary ecology of animals. It is especially suited for studying bird migration. In particular, we integrated options to differentiate between costs of active and passive flight into the optimal annual routine modelling framework, as well as options to consider periodic wind conditions affecting flight energetics. We provide an illustrative example of sOAR where food supply in the wintering habitat of migratory birds significantly alters the optimal timing of migration. sOAR helps improving our understanding of how complex behaviours evolve and how behavioural decisions are constrained by internal and external factors experienced by the animal. Such knowledge is crucial for anticipating potential species’ response to global environmental change. This article is protected by copyright. All rights reserved.

Species’ ranges are primarily limited by the physiological (abiotic) tolerance of the species, described by their fundamental niche. Additionally, demographic processes, dispersal, and interspecific interactions with other species are shaping species distributions, resulting in the realised niche. Understanding the complex interplay between these drivers is vital for making robust biodiversity predictions to novel environments. Correlative species distribution models have been widely used to predict biodiversity response but also remain criticised, as they are not able to properly disentangle the abiotic and biotic drivers shaping species’ niches. Recent developments have thus focussed on (i) integrating demography and dispersal into species distribution models, and on (ii) integrating interspecific interactions. Here, I review recent demographic and multi-species modelling approaches and discuss critical aspects of these models that remain underexplored in general and in respect to birds, for example, the complex life histories of birds and other animals as well as the scale dependence of interspecific interactions. I conclude by formulating modelling guidelines for integrating the abiotic and biotic processes that limit species’ ranges, which will help to disentangle the complex roles of demography, dispersal and interspecific interactions in shaping species niches. Throughout, I pinpoint complexities of avian life cycles that are critical for consideration in the models and identify data requirements for operationalizing the different modelling steps. This article is protected by copyright. All rights reserved.

Increased human-induced environmental changes and global warming alter bird migration timing and routes. Recently, many Holarctic species, including white storks, Ciconia ciconia, were reported to overwinter at higher latitudes, closer to breeding grounds. We aimed to understand the causes and implications of this phenomenon by examining bird survival and behaviour during overwintering in Europe versus Africa. We compared GPS and body acceleration data of 54 juvenile (first-year) white storks that originated from the same European natal population and overwintered in Europe or Africa. All six juveniles that overwintered in Europe survived through their first year, which was significantly higher than only 38% of the 48 overwintering in the species' traditional grounds in Africa. During overwintering, storks in Europe differed from those in Africa by (1) reducing movement and foraging range, (2) spending less time flying and more time resting, thus using less energy (estimated from overall dynamic body acceleration) and (3) reducing foraging effort, while relying more on anthropogenic resources (landfills and agricultural areas). Timing affected overwintering site as juveniles that overwintered in Europe hatched and started migrating later. We emphasize, however, that late hatching by itself did not yield a survival benefit as not all late juveniles curtailed their migration. We suggest that wintering in Europe was less demanding compared to Africa which may explain the increased survival of juveniles that wintered in Europe. Our findings correspond to the general increase in the European wintering population of white storks, and shed light on the contemporary trend of shortened bird migration; a phenomenon with potentially broad ecological implications. published

Studying the causes and consequences of route selection in animal migration is important for understanding the evolution of migratory systems and how they may be affected by environmental factors at various spatial and temporal scales. One key decision during migration is whether to cross “high transport cost” areas, or to circumvent them. Soaring birds may face this choice when encountering waterbodies where convective updrafts are weak or scarce. Crossing these waterbodies requires flying using energetically costly flapping flight, while circumventing them over land permits energetically cheap soaring.We tested how several atmospheric factors (e.g., wind, thermal uplift) and geographic, seasonal and state-related factors (sex and age) affected route selection in migrating white storks (Ciconia ciconia). We used 196 GPS tracks of 70 individuals either crossing or circumventing the north-easternmost section of the Mediterranean Sea, over Iskenderun Bay in southern Turkey.We found that westward and southward winds promoted a cross-bay journey in spring and autumn, respectively, acting as tailwinds. Also, overall weaker winds promoted a sea crossing in spring. Sea crossing was associated with flapping flight and higher values of Overall Dynamic Body Acceleration (ODBA) and resulted in higher ground speed than travel over land.The combined environmental conditions and the effects of route selection on movement-related energy costs and speed were likely responsible for an increase in the time spent flying and distance travelled of migrating storks that decided to cross the bay during spring. Notably, daily travel distances of spring migrants crossing the bay were 60 kilometres longer than those of land-detouring birds, allowing them to reach their destination faster but likely incurring a higher energetic flight cost. No such benefit was found during autumn.Our findings confirm that atmospheric conditions can strongly affect bird route selection. Consequently, migration timing, speed and movement-related energy expenditure differed considerably between the two migratory seasons and the two route choices, highlighting a time-energy trade-off in the migration of white storks.

Impact assessments increasingly rely on models to project the potential impacts of climate change on species distributions. Ecological niche models have become established as an efficient and widely used method for interpolating (and sometimes extrapolating) species’ distributions. They use statistical and machine-learning approaches to relate species’ observations to environmental predictor variables and identify the main environmental determinants of species’ ranges. Based on this estimated species–environment relationship, the species’ potential distribution can be mapped in space (and time). In this chapter, we explain the concept and underlying assumptions of ecological niche models, describe the basic modelling steps using the silvereye (Zosterops lateralis) as a simple real-world example, identify potential sources of uncertainty in underlying data and in the model, and discuss potential limitations as well as latest developments and future perspectives of ecological niche models in a global change context.

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.

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., Seed dispersal is critical to plant fitness and plant community dynamics. However, measuring and tracking all factors that influence seed dispersal effectiveness for any potential seed-disperser relationship would require an unrealistically large amount of time, materials and financial resources. Building on current frameworks, we suggest that seed dispersal ecology quantify seed dispersal at the scale of plant functional groups, in order to reduce complexity to manageable levels. Based on functional group classifications, plant species can be distinguished by their level of dependence on seed dispersal and its mechanisms.

For much of the twentieth century, many or most scholarly journals in biodiversity, ecology, biogeography and conservation were owned and published by scientific societies and made available to the broader scientific community at relatively low cost. However, the past several decades have seen a dominant process of commercialization of the publication process in this field, either by commercial publishing firms taking over publication of journals owned by a society (e.g., Evolution), or by commercial publishers starting new journals to fill “niches” that were until then empty in the scholarly publishing ecosystem. Diversity and Distributions is an example of this latter category of journals, having been created by Wiley Publishers in 1993 (as Biodiversity Letters). Being a journal owned by a commercial publisher, it has always been accessed by readers via subscription, but the publication process has been free to potential authors whose work has passed peer review.

Aim We assess main ecological determinants affecting the comparative performance of macroecological models (MEMs) that model species richness directly, and stacked species distribution models based on stacking probabilities (pS-SDMs) and binary predictions (bS-SDMs). Specifically, we aimed to understand how statistical effects such as prevalence and environmental heterogeneity are entangled with species' ecology in Swiss avian assemblages. Location Switzerland. Methods We tested for statistical and ecological effects on overprediction and underprediction by regressing species richness residuals against community-averaged values of prevalence, functional traits and functional dissimilarity. Further, we defined bird functional groups through hierarchical clustering and compared accuracy of species richness predictions between groups to understand the differences between model types and ecological determinants thereof. Last, we tested how accuracy of species assemblages constructed from bS-SDMs relates to species' functional characteristics. Results Underprediction of high diversity sites by pS-SDMs and MEMs was mainly explained by prevalence, whereas overprediction of low diversity sites was strongly affected by diet and habitat traits, and increased with functional dissimilarity. Model performances varied strongly between functional groups with more accurate and less biased predictions for generalist species groups. Critically, overprediction bias in richness predictions by bS-SDMs was uncorrelated with assemblage prediction success. Main conclusions The reliability of all community models tested here strongly depended on functional species' characteristics related mainly to diet, foraging and breeding habitat. This underlines the need to incorporate all relevant and species-specific or functional group-specific ecological filters in the models. Improved prediction accuracy of species richness will require finer-resolved environmental predictors that better describe available niche space and account for specific spatial and resource requirements of different species. More research is needed to understand the relationship between accuracy of species richness and species assemblage predictions in bS-SDMs as well as the role of biotic interactions.