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Populations of many migratory taxa have been declining over recent decades. Although protected areas are a cornerstone for conservation, their role in protecting migratory species can be incomplete due to the dynamic distributions of these species. Here, we use a pan-European citizen science bird occurrence dataset (EurobirdPortal) with Spatiotemporal Exploratory Modelling to assess how the weekly distributions of 30 passerine and near passerine species overlap with protected areas in Europe and compare this to range adjusted policy protection targets. Thirteen of our 30 species were inadequately covered by protected areas for some, or all, of the European part of their annual cycle under a target based on the 2020 Convention on Biodiversity framework and none were adequately covered under a target based on the 2030 Convention on Biodiversity framework. Species associated with farmland had the lowest percentage of their weekly distribution protected. The percentage of a species’ distribution within protected areas was positively correlated with its long-term population trend, even after accounting for confounding factors, suggesting a positive influence of protected areas on long-term trends. This emphasises the positive contribution that an informed expansion of the European protected area system could play for the future conservation of migratory land birds. Populations of many migratory taxa have been declining over recent decades. This study examines how well protected areas in Europe cover the dynamic distributions of migratory birds throughout their annual cycles and finds that many species are inadequately protected, especially farmland birds, and that higher protected area coverage correlates with more positive long-term population trends.

Numerous organisms depend on the physical structure of their habitats, but incorporating such information into ecological niche analyses has been limited by the lack of adequate data over broad spatial extents. The increasing availability of high‐resolution measurements from country‐wide airborne laser scanning (ALS) surveys – a light detection and ranging (LiDAR) technology – now provides unprecedented opportunities for characterizing habitat structure. Here, we use country‐wide ALS data in combination with presence–absence observations of birds from a national monitoring scheme in the Netherlands to quantify niche filling, niche overlap and niche separation of three closely‐related wetland birds (great reed warbler, Eurasian reed warbler and Savi's warbler). We developed a workflow to derive LiDAR metrics capturing different aspects of vertical and horizontal vegetation structure and used a principal component analysis (PCA), niche equivalency and niche similarity tests to analyse the fine‐scale breeding habitat niches of these warbler species in the Netherlands. The widespread Eurasian reed warbler almost completely filled the available wetland habitat space (93%) whereas the two other species showed considerably less niche filling (64% and 74%, respectively). Substantial niche overlap occurred among all species, but each species occupied a distinct part of the habitat space. The great reed warbler mainly occurred in tall and vertically complex wetland vegetation and was absent in areas with large proportions of reedbeds. The Eurasian reed warbler occupied all parts of the wetland habitat space, whereas the Savi's warbler mainly occurred in large homogenous reedbeds with low vegetation height. Our results demonstrate that broad‐scale ecological niche analyses can incorporate the fine‐scale 3D habitat preference of species with unprecedented detail (e.g. 10 m resolution), and thus go much beyond quantifying the climate niche and 2D habitat information from land cover maps. This is important to identify habitat features and priorities for biodiversity conservation in wetlands and other habitats.

Following the call from the United Nations Convention on Biological Diversity “Cities & Biodiversity Outlook” project to better preserve urban biodiversity, this paper presents stakeholder-specific statements for bird conservation in city environments. Based upon the current urban bird literature we focus upon habitat fragmentation, limited habitat availability, lack of the native vegetation and vegetation structure as the most important challenges facing bird conservation in cities. We follow with an overview of the stakeholders in cities, and identify six main groups having the greatest potential to improve bird survival in cities: i) urban planners, urban designers and (landscape) architects, ii) urban developers and engineers, iii) homeowners and tenants, iv) companies and industries, v) landscaping and gardening firms, vi) education professionals. Given that motivation to act positively for urban birds is linked to stakeholder-specific advice, we present ten statements for bird-friendly cities that are guided by an action perspective and argument for each stakeholder group. We conclude with a discussion on how the use of stakeholder-specific arguments can enhance and rapidly advance urban bird conservation action.

Collisions between aircraft and birds, so-called “bird strikes,” can result in serious damage to aircraft and even in the loss of lives. Information about the distribution of birds in the air and on the ground can be used to reduce the risk of bird strikes and their impact on operations en route and in and around air fields. Although a wealth of bird distribution and density data is collected by numerous organizations, these data are not readily available nor interpretable by aviation. This paper presents two national efforts, one in the Netherlands and one in the United States, to develop bird avoidance nodels for aviation. These models integrate data and expert knowledge on bird distributions and migratory behavior to provide hazard maps in the form of GIS-enabled Web services. Both models are in operational use for flight planning and flight alteration and for airfield and airfield vicinity management. These models and their presentation on the Internet are examples of the type of service that would be very useful in other fields interested in species distribution and movement information, such as conservation, disease transmission and prevention, or assessment and mitigation of anthropogenic risks to nature. We expect that developments in cyber-technology, a transition toward an open source philosophy, and higher demand for accessible biological data will result in an increase in the number of biological information systems available on the Internet.

Aim The increasing availability of remote sensing (RS) products from airborne laser scanning (ALS) surveys, synthetic aperture radar acquisitions and multispectral satellite imagery provides unprecedented opportunities for describing the physical structure and seasonal changes of vegetation. However, the added value of these RS products for predicting species distributions and animal habitats beyond land cover maps remains little explored. Here, we aim to assess how metrics derived from different types of high‐resolution (10 m) RS products predict the habitat suitability of wetland birds. Location North‐eastern part of the Netherlands. Methods We built species distribution models (SDMs) with occurrence observations from territory mapping of two selected wetland bird species (great reed warbler and Savi's warbler) and metrics from a Dutch land cover map, country‐wide ALS and Sentinel‐1 and Sentinel‐2 RS products. We then compared model performance, relative variable importance and response curves of the SDMs to assess the contribution and ecological relevance of each RS product and metric. Results Our results showed that ALS and Sentinel metrics improve SDMs with only land cover metrics by 11% and 10% of the Area Under Curve (AUC) for the great reed warbler and the Savi's warbler respectively. Assessments of feature importance revealed that all types of RS products contributed substantially to predicting the habitat suitability of these wetland birds, but that the most important variables vary among species. Main conclusions Our study demonstrates that metrics from different high‐resolution RS products capture complementary ecological information on animal habitats, including aspects such as the proportional cover of habitat types, vegetation density and the horizontal variability of vegetation height. Land cover maps with detailed spatial and thematic information can already achieve high model accuracies, but adding metrics derived from ALS point clouds and Sentinel imagery further improve model accuracy and enhance the understanding of animal–habitat relationships.

Abstract Highly pathogenic avian influenza (HPAI) is a threat to poultry production. It is desirable to be able to forecast HPAI outbreaks to allow for the implementation of elevated biosecurity measures. The Bird Flu Radar tool is an early warning system for HPAI based on wild bird movement and abundance. Here we develop the wild bird movement component of the Bird Flu Radar model by exploiting abundance data, which have greater spatio‐temporal coverage than movement (ring‐recovery or tracking) data. We explore two approaches for estimating bird movement from abundance data, building on recent migratory connectivity studies. In the first, week‐to‐week movement between areas of high abundance was estimated using a graph‐theoretic approach, with abundance in the intervening area also informing connectivity between locations. In the second, movement from breeding areas to wintering areas and back was simulated using an individual‐based model, the parameter values of which were calibrated for each species using weekly abundance maps. The output pseudo‐movements from the individual‐based model were easily integrated into the long‐distance movement model in the early warning system for HPAI, to update the long‐distance movement estimates for all 25 wild bird study species. Overall, we find that there are fundamental shortcomings of abundance data for inferring bird movement. However, when the accuracy of abundance‐derived pseudo‐movements can be confirmed, then they can complement ring‐recovery or tracking data. Spatio‐temporal coverage is still sparser for movement data than for abundance data, and so efforts to develop methods to exploit abundance data are likely to be useful in future endeavours estimating bird movement, and in downstream applications such as forecasting HPAI transmission.

Highly pathogenic avian influenza (HPAI) viruses pose a significant threat to both poultry and wild bird populations. Migratory wild birds play a key role in the intercontinental spread of avian influenza (AI), introducing the virus into poultry populations. In response to frequent AI outbreaks in Europe, the European Food Safety Authority (EFSA) is responsible for HPAI surveillance. A key component of this surveillance includes the integration of 1) outbreak data from Member States and 2) contributions from non‐governmental ornithological organisations like the European Bird Census Council (EBCC) and the European Union for Bird Ringing (EURING), in a predictive spatio‐temporal risk assessment model. Previous data integration and modelling efforts led to the development of an early warning system for predicting HPAI outbreaks accessible through a publicly available online user interface: the Bird Flu Radar. This system has since been improved by expanding the species coverage and refining the existing base models behind the epidemiological model. This report details a further refinement of these base models, by exploring the feasibility of using additional data sources in both the wild bird abundance and movement models. For the wild bird abundance model, weather data provided by the ERA5‐Land dataset was included, specifically the variables daily surface temperature (daily average, minimum and maximum) and snow cover. For the wild bird movement model, bird tracking data from Movebank was included for 19 of the 25 study species. For the remaining 6 study species no public data were available. The insights found for the abundance model, will support future study, understanding and prediction of the response of birds to fluctuating weather conditions. The insights found for the movement model provide a refinement and improvement of the estimates already derived from ring‐recovery data.

Highly pathogenic avian influenza (HPAI) viruses pose a significant threat to both poultry and wild bird populations. Migratory wild birds play a key role in the intercontinental spread of avian influenza (AI), introducing the virus into poultry populations. In response to frequent AI outbreaks in Europe, the European Food Safety Authority (EFSA), at the request of the European Commission (EC), produces quarterly and annual epidemiological reports to monitor and analyse AI trends. A key component of this surveillance includes the integration of outbreak data from Member States and contributions from non‐governmental ornithological organisations like the European Bird Census Council (EBCC) and the European Union for Bird Ringing (EURING) together in a predictive spatio‐temporal risk assessment model. Previous data integration and modelling efforts led to the development of an early warning system for predicting HPAI outbreaks accessible through a publicly available online user interface: the Bird Flu Radar. This report presents an improvement of the system by expanding the species coverage and refining the existing base models behind the epidemiological model. Specifically, this report details the exploration to incorporate 12 additional wild bird species into the models, and the changes made to the base models predicting the distribution and movements of wild birds. We demonstrate the improvements respecting the existing base models while at the same time enhancing the effectiveness in predicting HPAI outbreaks and possibly mitigating negative effects in Europe by providing more accurate predictions to different stakeholders.

The Russian breeding population of barnacle geese Branta leucopsis has shown a rapid increase in numbers since 1980, which has coincided with a southwest-wards breeding range expansion within the Russian Arctic. Here barnacle geese also started to occupy coastal and marsh land habitats, in which they were not know to nest on their traditional breeding grounds. While these changes have been well documented by studies and observations throughout the new breeding range of barnacle geese, observations are lacking from the traditional breeding grounds on Novaya Zemlya, as this area is remote and difficult to access. This is especially relevant given rapid climate warming in this area, which may impact local distribution and population size. We used GPS-tracking and behavioural biologging data from 46 individual barnacle geese captured on their wintering grounds to locate nest sites in the Russian Arctic and study nesting distribution in 2008–2010 and 2018–2020. Extrapolating from nest counts on Kolguev Island, we estimate the breeding population on Novaya Zemlya in 2018–2020 to range around 75,250 pairs although the confidence interval around this estimate was large. A comparison with the historical size of the barnacle goose population suggests an increase in the breeding population on Novaya Zemlya, corresponding with changes in other areas of the breeding range. Our results show that many barnacle geese on Novaya Zemlya currently nest on lowland tundra on Gusinaya Zemlya Peninsula. This region has been occupied by barnacle geese only since 1990 and appears to be mainly available for nesting in years with early spring. Tracking data are a valuable tool to increase our knowledge of remote locations, but counts of breeding individuals or nests are needed to further corroborate estimates of breeding populations based on tracking data.

Field monitoring can vary from simple volunteer opportunistic observations to professional standardised monitoring surveys, leading to a trade-off between data quality and data collection costs. Such variability in data quality may result in biased predictions obtained from species distribution models (SDMs). We aimed to identify the limitations of different monitoring data sources for developing species distribution maps and to evaluate their potential for spatial data integration in a conservation context. Using Maxent, SDMs were generated from three different bird data sources in Catalonia, which differ in the degree of standardisation and available sample size. In addition, an alternative approach for modelling species distributions was applied, which combined the three data sources at a large spatial scale, but then downscaling to the required resolution. Finally, SDM predictions were used to identify species richness and high quality areas (hotspots) from different treatments. Models were evaluated by using high quality Atlas information. We show that both sample size and survey methodology used to collect the data are important in delivering robust information on species distributions. Models based on standardized monitoring provided higher accuracy with a lower sample size, especially when modelling common species. Accuracy of models from opportunistic observations substantially increased when modelling uncommon species, giving similar accuracy to a more standardized survey. Although downscaling data through a SDM approach appears to be a useful tool in cases of data shortage or low data quality and heterogeneity, it will tend to overestimate species distributions. In order to identify distributions of species, data with different quality may be appropriate. However, to identify biodiversity hotspots high quality information is needed.