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Zurell, D.

Publications and source records attributed to Zurell, D..

18 recordsLinked to original sources

North American bird occupancy dynamics attributed to climate and land use change

Evidence is accumulating that global change is altering species distributions. Yet, detailed knowledge is missing about the relative and joint contribution of different drivers to observed species responses. Here, we implemented an impact attribution framework based on counterfactual simulations to assess the impact of climate and land use change on occupancy dynamics of North American breeding birds. We used a Bayesian framework to fit process-explicit dynamic occupancy models to long-term survey data for 159 species from 1995 to 2019, and quantified predictive performance using spatial and temporal cross-validation. We then assessed the relative importance and effect direction of climate and land use change while accounting for model predictive accuracy. Results indicate that climate change negatively affected 90 % of the species and land use change negatively impacted 96 %. Climate change emerged as more important than land use change for driving changes in occupancy across species. Remarkably, the effects of both drivers were mostly antagonistic rather than acting additively or synergistically. Climate was the most important driver for bird communities in the western USA, while land use change dominated in the southeast, and combined climate and land use change in the northeast. Our analysis demonstrates that recent changes in North American bird distributions are shaped by multiple global change drivers acting in concert. The effect of recent climate and land use change were mostly antagonistic, and thus trends in bird occupancy dynamics could not be understood by studying the impact of those drivers in isolation. By disentangling the effects of climate and land use change on biodiversity trends, impact attribution approaches can improve our understanding of global change impacts and can support conservation planning and more accurate and realistic projections of biodiversity response to global change.

ecology↗

Past and future phenology changes of zoonotic vector-borne diseases under climate and land-use change

Environmental changes are reshaping the distribution and seasonal dynamics of vector-borne diseases, with important implications for public health. Tick-borne encephalitis virus (TBEV) and West Nile virus (WNV) cause growing concern in Europe, with rising case numbers and ever-expanding circulation areas. The transmission risk of TBEV and WNV follows characteristic seasonal patterns, driven largely by weather-dependent activity of their arthropod vectors. The relative roles of climate and land-use change on the seasonal dynamics and spread of these diseases and their vectors remain, however, poorly quantified. Here, we assess the spread and phenology of TBEV and WNV in response to historical and future climate and land-use changes across Europe. We developed spatiotemporal species distribution models (SDMs) for the viruses and their primary vector species, generating monthly environmental suitability predictions from the 1970s to 2050s. Virus models incorporated vector suitability as a nested predictor to capture the dependence of virus occurrence on vector presence. To disentangle drivers of observed changes, we applied counterfactual historical simulations, attributing shifts in seasonal transmission risk to climate or land-use changes. Historical attribution results show that land-use changes mainly affected absolute vector suitability, whereas climatic changes drove shifts in seasonal transmission risk. Transmission risk is projected to rise continent-wide for both TBEV and WNV over the coming decades. Further, TBEV is projected to undergo pronounced phenological shifts, with a dominant spring peak and a delayed autumn peak extending into October. Prolonged seasonal transmission windows are projected to create hotspots that both intensify and expand across large regions. Taken together, our findings underscore the need for coordinated transnational efforts to manage the projected health burden of TBEV and WNV across Europe, and support upstream prevention by providing climate-informed guidance on intervention timing and spatial prioritisation.

ecology↗

Landscape context drives within-species variation in dispersal kernels and limits transferability

Dispersal is a key ecological trait that ensures connectivity, gene flow, and range dynamics, yet empirical information about the dispersal process remains scarce. While interspecific differences have been synthesized, intraspecific variation driven by individual movement and landscape context remains poorly understood. Here, we use the individual-based modelling platform RangeShifter to simulate movement trajectories of four animal species, from insects to mammals, across neutral landscapes varying in habitat amount and fragmentation. From these simulations we derived dispersal kernels using five probability density functions and analysed their response to landscape context. Across species, log-normal (fat-tailed) kernels consistently best described dispersal, but both median and long-distance dispersal varied strongly with habitat configuration. Habitat loss consistently reduced abundances, whereas fragmentation per se more strongly shaped dispersal kernels, providing mechanistic insight into a central debate in landscape ecology. Our results highlight that dispersal kernels do not transfer reliably across environments and should be treated as emergent, context-dependent properties. Progress in predicting species responses to global change will require empirical studies explicitly integrated with mechanistic modelling to uncover generalizable links between dispersal, traits, and landscape context.

ecology↗

Red List criteria underestimate climate-related extinction risk of range-shifting species

Climate change causes global species redistribution and elevates extinction risk, making early identification of vulnerable species critical for timely conservation. The IUCN Red List provides guidelines for assessing climate-related extinction risk using species distribution models (SDMs) and spatially explicit population models (SEPMs). However, a systematic evaluation of these guidelines is currently missing. Using simulations of virtual species with diverse life-history traits and range dynamics, we found that SDMs consistently underestimated extinction risk for range-shifting species. This was due to a concave relationship between population size and habitat loss, which contradicts the linear assumption in the Red List guidelines. For range-contracting species, SDMs provided adequate warning times. Probabilistic extinction estimates from SEPMs provided delayed warning for all species, particularly for highly threatened ones. Our results reveal key limitations of current Red List guidelines under climate change. Based on our findings, we provide tentative recommendations for updating the IUCN Red List guidelines.

ecology↗

Climatic niche conservatism in non-native plants depends on introduction history and biogeographic context

Many tools informing preventive invasion management build on the assumption that introduced species will conserve their climatic niches outside their native ranges. Previous research testing the validity of this assumption found contradictory results regarding niche conservatism vs. niche switching for non-native species. An open question is in how far these contradictions reflect context dependency, yet only few studies compared the niche dynamics of species introduced to multiple regions. Here, we used an ordination-based approach to quantify the climatic niche changes (stability, unfilling, expansion) of 316 plant species introduced to eight different regions across the world, including the Pacific region with extreme isolation between island groups. We then performed multiple phylogenetic regressions to assess how the regional context and species characteristics affect niche dynamics. Niche conservatism varied across regions, even within species. While niche expansion into previously unoccupied climates was generally low, niche unfilling varied strongly between regions. Generally, region-specific introduction history and species biogeographic attributes were more important for explaining niche changes than ecological traits. Niche expansion was consistently higher for species with small native range sizes, and niche stability increased. In contrast, niche unfilling decreased with time since introduction which could suggest that the lack of niche conservatism observed in many regions might be transient and potentially related to dispersal limitations. Overall, our results shed light on the context dependency of climatic niche changes when species are introduced to new regions, highlighting that the species and region-specific context should be accounted for when assessing the potential for niche changes. Significance StatementAs non-native species are introduced to new regions by humans, they may occupy the same climatic conditions as in their native ranges, leave parts of their native niche unfilled or expanding into previously unoccupied conditions. Knowing to which extent these niche dynamics generally occur is essential for understanding and managing biological invasions. Here, we evaluate regional differences in the climatic niche dynamics of non-native plants that were introduced to multiple regions across the world. We found marked variation across regions, influenced by factors such as the time that has passed since species were introduced, or biogeographic attributes of both the native and non-native ranges. These findings indicate that a lack of apparent niche changes is likely a temporary phenomenon.

ecology↗

Uncertainty in blacklisting potential Pacific plant invaders using species distribution models

O_LIInvasive alien species pose a growing threat to global biodiversity, underscoring the need for evidence-based prevention strategies. Species distribution models (SDMs) are a widely used tool to estimate the potential distribution of alien species and to inform blacklists based on establishment risk. Yet, data limitations and modelling decisions can introduce uncertainty in these predictions. Here, we aim to quantify the contribution of four key sources of uncertainty in SDM-based blacklists: species occurrence data, environmental predictors, SDM algorithms, and thresholding methods for binarising predictions. C_LIO_LIFocusing on 82 of the most invasive plant species on the Hawaiian Islands, we built SDMs to quantify their establishment potential in the Pacific region. To assess uncertainty, we systematically varied four modelling components: species occurrence data (native vs. global), environmental predictors (climatic vs. edapho-climatic), four SDM algorithms, and three thresholding methods. From these models, we derived blacklists using three alternative blacklisting definitions and quantified the variance in establishment risk scores and resulting species rankings attributable to each source of uncertainty. C_LIO_LISDMs showed fair predictive performance overall. Among the sources of uncertainty, thresholding method had the strongest and most consistent influence on risk scores across all three blacklist definitions but resulted in only minor changes in blacklist rankings. In contrast, algorithm choice had the most pronounced effect on blacklist rankings, followed by smaller but important effects of species occurrence data and environmental predictors. Notably, models based only on native occurrences often underestimated establishment potential. C_LIO_LISDMs can provide valuable support for planning the preventive management of alien species. However, our findings show that blacklist outcomes are highly sensitive to modelling decisions. While ensemble modelling across multiple algorithms is a recommended best practice, our results reinforce the importance of incorporating global occurrence data when available and carefully evaluating the trade-offs of including additional environmental predictors. Given the strong influence of thresholding on risk scores, we emphasise the need for transparent, context-specific threshold selection. More broadly, explicitly assessing uncertainty in SDM outputs can improve the robustness of blacklists and support scientifically informed, precautionary decision-making, particularly in data-limited situations where pragmatic modelling choices must be taken. C_LI

ecology↗

Dispersal syndromes allow understanding but not predicting dispersal ability across the tree of life.

Dispersal is fundamental to many ecological and evolutionary processes, yet understanding the determinants and predictability of dispersal remains a crucial challenge. Dispersal syndromes, which describe patterns in the covariation of traits associated with dispersal, can help to gain a deeper understanding of the evolutionary dynamics of dispersal and its implications for range dynamics and ecosystem functioning in the face of global change. However, the extent to which such dispersal syndromes are generalizable across a large taxonomic scale has been hampered by low availability of standardized dispersal data across species. In this study, we used the most comprehensive and up-to-date empirical dispersal dataset for European birds to investigate the formation of dispersal syndromes and their effectiveness in predicting dispersal across species. We found body mass, feeding guild, and life-history traits shape dispersal syndromes in birds. Yet, only body mass and life history accurately predicted dispersal for unassessed species, while even multi-trait dispersal syndromes poorly predicted dispersal for phylogenetically distant species. These results shed light on the complex nature of bird dispersal and emphasize the need for refined approaches in elucidating the mechanisms and constraints underlying dispersal evolution. Our study highlights the importance of considering multiple factors and expanding empirical datasets to enhance our understanding of dispersal in avian populations.

zoology↗

The best of two worlds: using stacked generalization for integrating expert range maps in species distribution models

Species distribution models (SDMs) are powerful tools for assessing suitable habitat across large areas and at fine spatial resolution. Yet, the usefulness of SDMs for mapping species realized distributions is often limited, since data biases or missing information on dispersal barriers or biotic interactions hinder them from accurately delineating species range limits. One way to overcome this limitation is to integrate SDMs with expert range maps, which provide coarse-scale information on the extent of species ranges that is complementary to information offered by SDMs. Here, we propose a new approach for integrating expert range maps in SDMs based on an ensemble method called stacked generalization. Specifically, our approach relies on training a meta-learner regression model using predictions from one or more SDM algorithms alongside the distance of training points to expert-defined ranges as predictor variables. We demonstrate our approach with an occurrence dataset for 49 bat species covering four biodiversity hotspots in the Eastern Mediterranean, Western Asia, and Central Asia. Our approach offers a flexible method to integrate expert range maps with any combination of SDM modeling algorithms, thus facilitating the use of algorithm ensembles. In addition, it provides a novel, data-driven way to account for uncertainty in expert-defined ranges not requiring prior knowledge about their accuracy, which is often lacking. Our approach holds considerable promise for better understanding species distributions, and thus for biogeographical research and conservation planning. In addition, our work highlights the overlooked potential of stacked generalization as an ensemble method in species distribution modeling.

ecology↗

Rank-based deep learning from citizen-science data to model plant communities

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.

ecology↗

Fitting individual-based models of species range 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, cSDMs fundamental assumption of species distributions in equilibrium with their environment is rarely met in real data and limits their applicability for 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 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 non-equilibrium situation. We construct an individual-based model with the RangeShiftR modelling platform and use Bayesian inference for model calibration. This allows the integration of heterogeneous data sources, such as parameter estimates from published literature as well as observational data from monitoring schemes, with 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 RangeShifteR. 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 spatio-temporal observations of abundance or presence/absence are available. The fitted model provides improved quantitative insights into the ecology of a species, which may greatly help conservation and management actions. Open Research statementThis submission uses novel code which is provided in an external repository. All data and code required to replicate the presented analyses are provided as private-for-peer review via a public GitHub repository under the following link: https://github.com/UP-macroecology/Malchow_IBMcalibration_2023 Upon acceptance, this repository will be archived and versioned with Zenodo and its DOI provided. For this study, a tagged development version of the R package RangeShiftR was used that is available at: https://github.com/RangeShifter/RangeShiftR-package/releases/tag/v.1.1-beta.0

ecology↗

Demography-environment relationships improve mechanistic understanding of range dynamics under climate change

Species responses to climate change are widely detected as range and abundance changes. To better explain and predict them, we need a mechanistic understanding of how the underlying demographic processes are shaped by climatic conditions. We built spatially-explicit, process-based models for eight Swiss breeding bird populations. They jointly consider dispersal, population dynamics and the climate-dependence of three demographic processes - juvenile survival, adult survival and fecundity. The models were calibrated to two-decade abundance time-series in a Bayesian framework. We assessed goodness-of-fit and discriminatory power of the models with different metrics, indicating fair to excellent model fit. The most influential climatic predictors for population performance were the mean breeding-season temperature and the total winter precipitation. Maps of overall growth rate highlighted demographically suitable areas. Further, benefits from contemporary climate change were detected for typical mountain birds, whereas lowland birds were adversely affected. Embedding generic process-based models in a solid statistical framework improves our mechanistic understanding of range dynamics and allows disentangling the underlying abiotic and biotic processes. For future research, we advocate a stronger integration of experimental and empirical measurements and more detailed predictors in order to generate precise insights into the processes by which climate affects populations.

ecology↗

The clustering of spatially associated species unravels patterns in Bornean tree species distributions

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.

ecology↗

Strategies for sampling pseudo-absences for species distribution models in complex mountainous terrain

O_LIPredictions 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. C_LIO_LIHere, 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. C_LIO_LIThe 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. C_LIO_LIOur 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. C_LI

ecology↗

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

O_LIDispersal 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. C_LIO_LIHere 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. C_LIO_LIWe 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. C_LIO_LIThe 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 (Pagels {lambda} < 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). C_LIO_LIOur 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. C_LI

ecology↗

Interannual climate variability data improves niche estimates in species distribution models

AimClimate is an essential element of species niche estimates in many current ecological applications such as species distribution models (SDMs). Climate predictors are often used in the form of long-term mean values. Yet, climate can also be described as spatial or temporal variability for variables like temperature or precipitation. Such variability, spatial or temporal, offers additional insights into niche properties. Here, we test to what degree spatial variability and long-term temporal variability in temperature and precipitation improve SDM predictions globally. LocationGlobal. Time period1979-2013 Major taxa studiesMammal, Amphibians, Reptiles MethodsWe use three different SDM algorithms, and a set of 833 amphibian, 779 reptile, and 2211 mammal species to quantify the effect of spatial and temporal climate variability in SDMs. All SDMs were cross-validated and accessed for their performance using the Area under the Curve (AUC) and the True Skill Statistic (TSS). ResultsMean performance of SDMs with 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 similarly high TSS and AUC scores. Main conclusionsAccounting for temporal rather than spatial variability in climate improved the SDM prediction especially in exotherm groups such as amphibians and reptiles, while for endotermic mammals no such improvement was observed. These results indicate that more detailed information about temporal climate variability offers a highly promising avenue for improving niche estimates and calls for a new set of standard bioclimatic predictors in SDM research.

ecology↗

RangeShifter 2.0: An extended and enhanced platform for modelling spatial eco-evolutionary dynamics and species' responses to environmental changes.

O_LIProcess-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. C_LIO_LIHere 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 2.0s 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. C_LIO_LIRangeShifter 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. C_LIO_LIRangeShifter 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. C_LI

ecology↗

RangeShiftR: an R package for individual-based simulation of spatial eco-evolutionary dynamics and species' responses to environmental change

O_LIReliably 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. C_LIO_LIWe 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 packages functionality, describe the underlying model structure with its main components and present a short example. C_LIO_LIRangeShiftR 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. C_LIO_LIThe 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. C_LI

ecology↗

Causes and consequences of facultative sea crossing in a soaring migrant

O_LIStudying 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. C_LIO_LIWe 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. C_LIO_LIWe 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. C_LIO_LIThe 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. C_LIO_LIOur 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. C_LI

ecology↗