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Various types of radar systems are increasingly being used to monitor aerial biodiversity. Each of these types has different detection capabilities and sensitivities to environmental conditions, which affect the quantity and quality of the measured objects of interest. Radar wind profilers have long been known to detect birds, but their use in ornithology has remained limited, largely because of biologists' unfamiliarity with these systems. Although the potential of radar wind profilers for quantitative bird monitoring has been illustrated with time series of raw data, a comparison with a similar radar system more established in biology is missing. Here, we compare nocturnal bird migration patterns observed by a radar wind profiler during October 2019 and April 2021 with those from a dedicated bird radar BirdScan MR1. The systems were located 50 km apart with an altitudinal difference of about 850 m. The nightly migration intensities measured with both systems were highly correlated in both spring and autumn (Pearson correlation coefficient ≈ 0.8, P < 0.001), but estimated traffic measured by the radar wind profiler was on average five times higher in spring and nine times higher in autumn. Low ratios of the migration traffic rates of the Birdscan MR1 to those of the radar wind profiler occurred primarily in clear conditions. In both radar systems, migration occurred at significantly higher altitudes in spring than in autumn. Discrepancies in absolute numbers between both systems are likely due to both system‐inherent and external environmental and topographical factors, but also different quantification approaches. These findings support the capacity of radar wind profilers for aerial biomonitoring, independent of environmental conditions, and open up further avenues for studying the impact of weather on bird migration at detailed temporal and altitudinal scales. Radar wind profilers used in meteorology have long been known to register birds, but their use in ornithology has remained limited, largely because of biologists’ unfamiliarity with these systems. Here, we compare nocturnal bird migration patterns observed by a radar wind profiler during October 2019 and April 2021 with those from a dedicated bird radar. The nightly migration intensities measured with both systems highly correlated in both spring and autumn. Low ratios of the migration traffic rates of the Birdscan MR1 to those of the radar wind profiler occurred primarily in clear conditions. In both radar systems, migration occurred at significantly higher altitudes in spring than in autumn. Discrepancies between both systems are likely due to both system‐inherent and external environmental factors, but also due to different quantification approaches. These findings support the capacity of radar wind profilers for aerial biomonitoring, independent of environmental conditions.

Weather radar networks have great potential for continuous and long-term monitoring of aerial biodiversity of birds, bats, and insects. Biological data from weather radars can support ecological research, inform conservation policy development and implementation, and increase the public’s interest in natural phenomena such as migration. Weather radars are already used to study animal migration, quantify changes in populations, and reduce aerial conflicts between birds and aircraft. Yet efforts to establish a framework for the broad utilization of operational weather radar for biodiversity monitoring are at risk without suitable data policies and infrastructure in place. In Europe, communities of meteorologists and ecologists have made joint efforts toward sharing and standardizing continent-wide weather radar data. These efforts are now at risk as new meteorological data exchange policies render data useless for biodiversity monitoring. In several other parts of the world, weather radar data are not even available for ecological research. We urge policy makers, funding agencies, and meteorological organizations across the world to recognize the full potential of weather radar data. We propose several actions that would ensure the continued capability of weather radar networks worldwide to act as powerful tools for biodiversity monitoring and research.

Uncontrolled, large fires are a major threat to the biodiversity of protected heath landscapes. The severity of the fire is an important factor influencing vegetation recovery. We used airborne imaging spectroscopy data from the Airborne Prism Experiment (APEX) sensor to: (1) investigate which spectral regions and spectral indices perform best in discriminating burned from unburned areas; and (2) assess the burn severity of a recent fire in the Kalmthoutse Heide, a heathland area in Belgium. A separability index was used to estimate the effectiveness of individual bands and spectral indices to discriminate between burned and unburned land. For the burn severity analysis, a modified version of the Geometrically structured Composite Burn Index (GeoCBI) was developed for the field data collection. The field data were collected in four different vegetation types: Calluna vulgaris-dominated heath (dry heath), Erica tetralix-dominated heath (wet heath), Molinia caerulea (grass-encroached heath), and coniferous woodland. Discrimination between burned and unburned areas differed among vegetation types. For the pooled dataset, bands in the near infrared (NIR) spectral region demonstrated the highest discriminatory power, followed by short wave infrared (SWIR) bands. Visible wavelengths performed considerably poorer. The Normalized Burn Ratio (NBR) outperformed the other spectral indices and the individual spectral bands in discriminating between burned and unburned areas. For the burn severity assessment, all spectral bands and indices showed low correlations with the field data GeoCBI, when data of all pre-fire vegetation types were pooled (R2 maximum 0.41). Analysis per vegetation type, however, revealed considerably higher correlations (R2 up to 0.78). The Mid Infrared Burn Index (MIRBI) had the highest correlations for Molinia and Erica (R2 = 0.78 and 0.42, respectively). In Calluna stands, the Char Soil Index (CSI) achieved the highest correlations, with R2 = 0.65. In Pinus stands, the Normalized Difference Vegetation Index (NDVI) and the red wavelength both had correlations of R2 = 0.64. The results of this study highlight the superior performance of the NBR to discriminate between burned and unburned areas, and the disparate performance of spectral indices to assess burn severity among vegetation types. Consequently, in heathlands, one must consider a stratification per vegetation type to produce more reliable burn severity maps.

Acoustic recordings have emerged as a promising tool to monitor nocturnal bird migration, as it can uniquely provide species‐level detection of migratory movements under the darkness of the night sky. This study explores the use of acoustics to quantify nocturnal bird migration across Europe, a region where research on the topic remains relatively sparse. We examine three migration intensity measures derived from acoustic recordings, that is, nocturnal flight call rates, nocturnal flight passage rates and species diversity, in the French Pyrenees in 2021 and 2022. To assess the effectiveness of these acoustic measurements, we compare them with migratory traffic rates estimated by a dedicated bird radar at three taxonomic levels: all birds, passerines and thrushes. We also test if weather conditions influence these relationships and whether combining acoustic data from multiple simultaneous sites improve the predictive performance. Nocturnal flight passage rates, that is, the number of estimated passing birds independent of call abundance, outperformed predictions using species diversity or nocturnal flight call rates. The predictive accuracy of the acoustics data increased with taxonomic detail: predicting thrush migration using acoustics was far more accurate (R2 = 63%) than for passerines (R2 = 29%) or birds in general (R2 = 27%). Prediction using simultaneous acoustics measurements from several sites strongly reduced the uncertainty of the quantification. We did not find any evidence that weather conditions affected the predictive performance of the acoustics data. Accurate, automated monitoring of migratory flows is crucial as many bird species face steep population declines. Acoustic monitoring offers valuable species‐specific insights, making it a powerful tool for nocturnal bird migration studies. This study advances the integration of acoustic methods into bird monitoring by testing their benefits and limitations and provides recommendations and guidelines to enhance the effectiveness of future studies using acoustic data. In this study, we explore three migration intensity measures derived from acoustic recordings, that is, bird call and passage rates, and species diversity, for their usefulness in quantifying nocturnal bird migration rates at three taxonomic levels: birds, passerines and thrushes, as estimated by a dedicated bird radar. Bird passage rates, that is, the number of estimated passing birds independently of call abundance, outperformed predictions using species diversity or the commonly used bird call rates. The predictive accuracy of the acoustic data increased with taxonomic detail. Prediction using simultaneous acoustic measurements from several sites strongly reduced the uncertainty of the quantification. We found no evidence that weather conditions affected the predictive performance of the acoustic data. Many migratory bird species show strong population declines. Accurate and automated quantification of migratory flows is increasingly urgent. Acoustic monitoring offers valuable species‐specific insights, making it a powerful tool for nocturnal bird migration studies.

Studying nocturnal bird migration is challenging because direct visual observations are difficult during darkness. Radar has been the means of choice to study nocturnal bird migration for several decades, but provides limited taxonomic information. Here, to ascertain the feasibility of enhancing the taxonomic resolution of radar data, we combined acoustic data with vertical‐looking radar measurements to quantify thrush (Family: Turdidae) migration. Acoustic recordings, collected in Helsinki between August and October of 2021–2022, were used to identify likely nights of high and low thrush migration. Then, we built a random forest classifier that used recorded radar signals from those nights to separate all migrating passerines across the autumn migration season into thrushes and non‐thrushes. The classifier had a high overall accuracy (≈0.82), with wingbeat frequency and bird size being key for separation. The overall estimated thrush autumn migration phenology was in line with known migratory patterns and strongly correlated (Pearson correlation coefficient ≈0.65) with the phenology of the acoustic data. These results confirm how the joint application of acoustic and vertical‐looking radar data can, under certain migratory conditions and locations, be used to quantify ‘family‐level’ bird migration. This study addresses the challenge of studying nocturnal bird migration, typically hindered by limited taxonomic information from radar data. To enhance resolution, we combined acoustic recordings with vertical‐looking radar measurements, focusing on thrush migration. Using a random forest classifier, we achieved a high accuracy in distinguishing thrushes from non‐thrushes during autumn migration, relying on key factors like wingbeat frequency and bird size. The estimated thrush migration phenology aligned with known patterns and correlated strongly with acoustic data. Our study provides the first example of combining acoustic and radar data to extract taxonomic information, enabling the quantification of family‐level migration from radar data.

Background Understanding the evolution of migration requires knowledge of the patterns, sources, and consequences of variation in migratory behaviour, a need exacerbated by the fact that many migratory species show rapid population declines and require knowledge-based conservation measures. We therefore need detailed knowledge on the spatial and temporal distribution of individuals across their annual cycle, and quantify how the spatial and temporal components of migratory behaviour vary within and among individuals. Methods We tracked 138 migratory journeys undertaken by 64 adult common terns ( Sterna hirundo ) from a breeding colony in northwest Germany to identify the annual spatiotemporal distribution of these birds and to evaluate the individual repeatability of eleven traits describing their migratory behaviour. Results Birds left the breeding colony early September, then moved south along the East Atlantic Flyway. Wintering areas were reached mid-September and located at the west and south coasts of West Africa as well as the coasts of Namibia and South Africa. Birds left their wintering areas late March and reached the breeding colony mid-April. The timing, total duration and total distance of migration, as well as the location of individual wintering areas, were moderately to highly repeatable within individuals (repeatability indexes: 0.36–0.75, 0.65–0.66, 0.93–0.94, and 0.98–1.00, respectively), and repeatability estimates were not strongly affected by population-level inter-annual variation in migratory behaviour. Conclusions We found large between-individual variation in common tern annual spatiotemporal distribution and strong individual repeatability of several aspects of their migratory behaviour.

In the context of rapid climate change, phenological advance is a key adaptation for which evidence is accumulating across taxa. Among vertebrates, phenotypic plasticity is known to underlie most of this phenological change, while evidence for micro-evolution is very limited and challenging to obtain. In this study, we quantified phenotypic and genetic trends in timing of spring migration using 8,032 dates of arrival at the breeding grounds obtained from observations on 1,715 individual common terns (Sterna hirundo) monitored across 27 years, and tested whether these trends were consistent with predictions of a micro-evolutionary response to selection. We observed a strong phenotypic advance of 9.3 days in arrival date, of which c. 5% was accounted for by an advance in breeding values. The Breeder’s equation and Robertson’s Secondary Theorem of Selection predicted qualitatively similar evolutionary responses to selection, and these theoretical predictions were largely consistent with our estimated genetic pattern. Overall, our study provides rare evidence for micro-evolution underlying (part of) an adaptive response to climate change in the wild, and illustrates how a combination of adaptive micro-evolution and phenotypic plasticity facilitated a shift towards earlier spring migration in this free-living population of common terns. Lay Summary Empirical evidence for evolutionary change underlying vertebrate adaptation to current global change is very rare. This may be due to phenotypic plasticity being the main mechanism underlying adaptation, or to challenges associated with the empirical testing of genetic changes in the wild, in particular data limitations. In this study, we tested whether an observed phenotypic advance in the timing of arrival from spring migration in a wild seabird population was due to an evolutionary response (i.e., genetic change) and/or to phenotypic plasticity or other non-genetic effects. To do so, we applied a bivariate “animal model” to a 27-year data set from a pedigreed population of common terns located at the North Sea coast of Germany. We found an evolutionary response to selection favoring earlier arriving individuals. Additionally, we could show that two different theoretical models predict a qualitatively similar evolutionary response as the one we estimated, both in terms of direction and magnitude. As such, our study provides a rare empirical case where estimated and predicted evolutionary responses agree, and suggests an evolutionary response in the timing of avian spring migration, although it played a smaller role than phenotypic plasticity in the common tern response to rapid climate change. Overall, these findings show the use of disentangling the relative, and often complementary, contributions of plastic and evolutionary changes to better understand adaptive processes and predict responses to future changes.

Weather radar systems have become a central tool in the study of nocturnal bird migration. Yet, while studies have sought to validate weather radar data through comparison to other sampling techniques, few have explicitly examined the impact of range and topographical blockage on sampling detection—critical dimensions that can bias broader inferences. Here, we assess these biases with relation to the Cheyenne, WY Next Generation Weather Radar (NEXRAD) site, one of the large‐scale radars in a network of 160 weather surveillance stations based in the United States. We compared local density measures collected using a mobile, vertically looking radar with reflectivity from the NEXRAD station in the corresponding area. Both mean nightly migration activity and within night migration activity between NEXRAD and the mobile radar were strongly correlated (r = 0.85 and 0.70, respectively), but this relationship degraded with both increasing distance and beam blockage. Range‐corrected NEXRAD reflectivity was a stronger predictor of observed mobile radar densities than uncorrected reflectivity at the mean nightly scale, suggesting that current range correction methods are somewhat effective at correcting for this bias. At the within night temporal scale, corrected and uncorrected reflectivity models performed similarly up to 65 km, but beyond this distance, uncorrected reflectivity became a stronger predictor than range‐corrected reflectivity, suggesting range limitations to these corrections. Together, our findings further validate weather radar as an ornithological tool, but also highlight and quantify potential sampling biases. We compared local density measures of migratory birds collected using a mobile, vertically looking radar with reflectivity from a Next Generation Weather Radar (NEXRAD) station across varying distances and topographies. Broadly, mean nightly migration activity and within night migration activity between NEXRAD and the mobile radar were strongly correlated (r = 0.85 and 0.70, respectively), but this relationship degraded with both increasing distance and beam blockage.

Appropriate management of (semi-)natural areas requires detailed knowledge of the ecosystems present and their status. Remote sensing can provide a systematic, synoptic view at regular time intervals, and is therefore often suggested as a powerful tool to assist with the mapping and monitoring of protected habitats and vegetation. In this study, we present a multi-step mapping framework that enables detailed NATURA 2000 (N2000) heathland habitat patch mapping and the assessment of their conservation status at patch level. The method comprises three consecutive steps: (1) a hierarchical land/vegetation type (LVT) classification using airborne AHS imaging spectroscopy and field reference data; (2) a spatial re-classification to convert the LVT map to a patch map based on life forms; and (3) identification of the N2000 habitat type and conservation status parameters for each of the patches. Based on a multivariate analysis of 1325 vegetation reference plots acquired in 2006–2007, 24 LVT classes were identified that were considered relevant for the assessment of heathland conservation status. These labelled data were then used as ground reference for the supervised classification of the AHS image data to an LVT classification map, using Linear Discriminant Analysis in combination with Sequential-Floating-Forward-Search feature selection. Overall classification accuracies for the LVT mapping varied from 83% to 92% (Kappa ≈ 0.82–0.91), depending on the level of detail in the hierarchical classification. After converting the LVT map to a N2000 habitat type patch map, an overall accuracy of 89% was obtained. By combining the N2000 habitat type patch map with the LVT map, two important conservation status parameters were directly deduced per patch: tree and shrub cover, and grass cover, showing a strong similarity to an independent dataset with estimates made in the field in 2009. The results of this study indicate the potential of imaging spectroscopy for detailed heathland habitat characterization of N2000 sites in a way that matches the current field-based workflows of the user.