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Weather radar plays an important role in monitoring aerial animal migration, providing a stable data source for biological studies with large-scale coverage and consecutive-time samples. The accurate estimation of bird density from weather radar echoes is fundamental for quantitative biological studies. We analyzed the bird observation model in weather radar, and proposed a method to build the bird quantification model by jointly utilizing dual-polarization Doppler weather radar and scanning bird radar. We designed a detailed process to remove tracks or echoes from non-bird targets, ensuring the effectiveness of bird observations. The field experiments validated the quantification method, showing that the average radar cross section of birds in Jinan was 19.09 dBscm (i.e., 81.19 cm2; 95% confidence interval, CI: 18.92–19.27 dBscm) for the S-band weather radar, with an R2 of 0.79. In addition, through the correlation analysis, we found that the ground terrain may affect the distribution pattern of aerial bird density.

Radar is an effective tool for continuous monitoring and quantification of aerial bird movement and used to study migration and local flight behaviour. However, systems with automated tracking algorithms do not provide the level of processing sufficient to guarantee reliable data. Therefore, post‐processing such radar data is required but often non‐trivial, especially in challenging environments such as open sea. We present a post‐processing framework that implements knowledge of the radar system and bird biology to filter the data and retrieve reliable, high‐quality tracking data. The framework is split into three modules, each with a specific aim: (I) sub‐setting based on prior knowledge of the radar system and bird flight, (II) improving bird track quality and (III) detecting and removing spatio‐temporal sections of data that have a clear bias for false observations. The effectiveness of the framework is demonstrated with a case study comparing track densities inside and outside an offshore wind farm, and by applying the workflow to a dataset of visually validated radar tracks. Application of Module I resulted in a dataset of 520.894 bird tracks (19.5% of total data) within a 10.4 km 2 area. Additionally, 18.734 tracks were corrected for geometric errors in Module II, and Module III identified 236 of 719 observation hours and an area of 1.55 km 2 as unreliable for spatio‐temporal analysis. No difference in track densities was found between the area inside and outside the wind farm when using the post‐processed data, whereas using the unprocessed bird tracks, lower track densities were observed outside the wind farm. Of the visually validated radar tracks, the framework removed 85% of false positive bird tracks, while retaining 80% of true positive bird tracks. The framework provides a logical workflow to increase the reliability and quality of a bird radar dataset while being adaptable to the radar system and its surroundings. This is a first step towards standardising the post‐processing methodology for automated bird radar systems, which can facilitate comparative analyses of bird movement in space and time and improve the quality of ecological impact assessments.

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.

Worldwide, wildlife–aircraft strikes cost more than US$1.2 billion in aircraft damage and downtime and jeopardize the safety of aircrews, passengers, and animals. Radar has long been used to monitor flying animal movements and can be a useful tool for strike mitigation. In the USA, the Avian Hazard Advisory System (AHAS) is an early‐warning system that integrates data from next‐generation weather radar (NEXRAD) weather surveillance radars (WSRs) with historic bird occurrence data to quantify avian activity and forecast the relative bird risk within a ~9.3‐km radius of military and civilian airfields. Bird detection radars (BDRs) with both horizontal‐surveillance and vertical‐scanning components are also available for monitoring local avian activity at airports, but we have little information regarding the congruence of broad‐scale warnings and local avian activity where WSRs and BDRs overlap. We quantified trends in biological activity recorded at hourly intervals by a BDR at an airfield in Texas, USA, and in the most frequently assigned AHAS risk forecasts for that site during the same intervals. We then examined the strength of association between these datasets by season and time of day to determine when information from BDRs might best complement forecasts from the broad‐scale AHAS system. We found a strong overall association between the datasets but weak or moderate agreement during daylight periods, when most strikes occur. NEXRAD WSRs see only limited bird activity near the Earth's surface, where the majority of damaging strikes take place and, not surprisingly, AHAS warnings during our study were best predicted by the BDR at higher altitudes. Our results suggest BDRs might best complement early‐warning systems, like AHAS, as part of integrated strike mitigation plans at airfields with large numbers of hazardous birds flying at low altitudes during daylight hours, especially in late afternoon.

1. In the context of growing demand for offshore wind energy production in recent years, much effort has been made to determine the collision risk that offshore wind turbines pose to birds. Currently, only limited species-specific data on migrating birds' avoidance rates and associated mortality at offshore wind farms exist. 2. During a 4-year study, bird detection radar was used to monitor behavioural responses and flight changes of migrating pink-footed geese in relation to two offshore wind farms during and after construction. 3. Radar recorded a total of 979 goose flocks migrating through the whole study area, of which 571 were visually confirmed as 39 957 pink-footed geese Anser brachyrhynchus. Overall, we calculated that 97·25% of all flocks recorded by radar, in 2009 and 2010 combined, migrated without any risk of additional mortality associated with the constructed wind farms. 4. We identified a growing tendency of geese to avoid the wind farms and calculated that, for 2009 and 2010 combined, avoidance was exhibited by 94·46% of the original 292 flocks predicted to enter the wind farms. 5. Synthesis and applications. Migratory geese responded to offshore wind farms by adopting strong horizontal and vertical avoidance behaviour. For the first time, wind farm avoidance rates have been recorded for pink-footed geese, and these rates will allow more robust impact assessments to be undertaken for both this species and waterfowl in general. Remote sensing techniques should be used to undertake long-term impact assessments at offshore wind farms to provide an evidence-base for assessing the mortality risk for migratory birds.

This dataset contains radar recordings of avian spring migration from March to May in two consecutive years. The data was gathered by a BirdScan MR1 at four sites simultaneously. The radars were deployed in two pairs, with a distance of either 6.7 km or 30.4 km between the paired devices. The data itself is available as csv, the variables are described in \"variable_description.pdf\". To access the scripts, download the zipped folder \"hr_birdscan\". Unzip and open the .Rproj file. To run the script, always make sure to first run 00a_setup.R and 00b_functions.R before moving to the other scripts. These then run independently. Note that the full run of the \"01_bsimport.Rmd\" script takes a long time and is not necessary for the following scripts, as all processed and generated data tables are included in the zipped folder. Any figures that are generated by the script can be exported as pdf by running the \"sh.export.figure\" function at the end of each .Rmd file. The pdfs are exported to the output folder.

Once, bird respiration was thought to be responsible for the 10 dB-level fluctuations in the radar signals of birds. Although, recently, many researchers provide evidence against this, there are almost no quantification measurements of the contribution of respiration to bird signals in microwave anechoic chambers. Here, we first measured the radar signals modulated by the respiration of birds in a microwave anechoic chamber. Theoretically, the simulated signal fluctuation caused by the respiration of a 1 kg standard avian target (SAT) duck is approximately 1.2 dB based on the water sphere model. Then, experimentally, in a microwave anechoic chamber, we measured the signal fluctuations produced by the respiration movement of ducks using a dynamic system composed of a network analyzer and a high-speed camera. We tracked continuous radar data of a living duck and a dead duck within the S-band, X-band, and Ku-band, and then presented them using low-resolution range profiles (LRRP) and high-resolution range profiles (HRRP). The results indicate that respiration movement causes periodic signal fluctuation with a respiration rate of approximately 0.7 Hz, but the amplitudes within S-band, X-band, and Ku-band are approximately 1 dB level, much less than the 10 dB level. Respiration is not responsible for the 10 dB-level periodic signal fluctuation in radar echoes from birds.

The S-bandWSR-88D weather radar is sensitive enough to observe biological scatterers like birds and insects. However, their non-spherical shapes and frequent collocation in the radar resolution volume create challenges in identifying their echoes. We propose a method of extracting bird (or insect) features by coherently averaging dual polarization measurements from multiple radar scans, containing bird (insect) migration. Additional features are also computed to capture aspect and range dependence, and the variation of these echoes over local regions. Next, ridge classifier and decision tree machine learning algorithms are trained, first only with the averaged dual pol inputs and then different combinations of the remaining features are added. The performance of all models for both methods, are analyzed using metrics computed from the test data. Further studies on different patterns of birds/insects, including roosting birds, bird migration and insect migration cases, are used to further investigate the generality of our models. Overall, the ridge classifier using only dual polarization variables was found to perform consistently well across all these tests. Our recommendation is that this classifier can be used operationally on the US Next-Generation Radars (NEXRAD), as a first step in classifying biological echoes. It would be used in conjunction with the existing Hydrometeor Classification Algorithm (HCA), where the HCA would first separate biological from non-biological echoes, then our algorithm would be applied to further separate biological echoes into birds and insects. To the best of our knowledge, this study is the first to train a machine learning classifier that is capable of detecting diverse patterns of bird and insect echoes, based on dual polarization variables at each range gate.

1. Advances in information technology are increasing the use of radar as a tool to investigate and monitor bird migration movements. We set up a field campaign to compare and validate outputs from different radar systems. 2. Here we compare the pattern of nocturnal bird migration movements recorded by four different radar systems at a site in southern Sweden. Within the range of the weather radar (WR) Ängelholm, we operated a \"BirdScan\" (BS) dedicated bird radar, a standard marine radar (MR), and a tracking radar (TR). 3. The measures of nightly migration intensities, provided by three of the radars (WR, BS, MR), corresponded well with respect to the relative seasonal course of migration, while absolute migration intensity agreed reasonably only between WR and BS. Flight directions derived from WR, BS and TR corresponded very well, despite very different sample sizes. Estimated mean ground speeds differed among all four systems. The correspondence among systems was highest under clear sky conditions and at high altitudes. 4. Synthesis and applications. While different radar systems can provide useful information on nocturnal bird migration, they have distinct strengths and weaknesses, and all require supporting data to allow for species level inference. Weather radars continuously detect avian biomass flows across a wide altitude band, making them a useful tool for monitoring and predictive applications at regional to continental scales that do not rely on resolving individuals. BirdScan and marine radar's strengths are in local and low altitude applications, such as collision risks with man-made structures and airport safety, although marine radars should not be trusted for absolute intensities of movement. In quantifying flight behaviour of individuals, tracking radars are the most informative.