Quantifying nocturnal bird migration at high resolution is essential for (1) understanding the phenology of migration and its drivers, (2) identifying critical spatio-temporal protection zones for migratory birds, and (3) assessing the risk of collision with artificial structures. We propose a tailored geostatistical model to interpolate migration intensity monitored by a network of weather radars. The model is applied to data collected in autumn 2016 from 69 European weather radars. To validate the model, we performed a cross-validation and also compared our interpolation results with independent measurements of two bird radars. Our model estimated bird densities at high resolution (0.2 latitude–longitude, 15 min) and assessed the associated uncertainty. Within the area covered by the radar network, we estimated that around 120 million birds were simultaneously in flight (10–90 quantiles: 107–134). Local estimations can be easily visualized and retrieved from a dedicated interactive website. This proof-of-concept study demonstrates that a network of weather radar is able to quantify bird migration at high resolution and accuracy. The model presented has the ability to monitor population of migratory birds at scales ranging from regional to continental in space and daily to yearly in time. Near-real-time estimation should soon be possible with an update of the infrastructure and processing software.
1. Over the past decades, tracking technologies have become more ubiquitous and helped uncover crucial spatio-temporal relationships in nature. To extend the tracking of small animals and reduce any potential adverse impact of devices, methodologies compatible with light-weight devices are sought after. Measured by light-weight geolocators, atmospheric pressure provides an untapped opportunity for global geopositioning, as its natural temporal variation is unique to each location.2. In this study, we estimate the position of birds by comparing pressure data recorded by the geolocator with reference data from a global weather reanalysis database. The method produces a likelihood map of the position based on (1) a mask of the locations for which the ground level elevation matches the pressure measured by the geolocator and (2) a likelihood of the mismatch between the temporal timeseries measured by the geolocator and the reanalysis dataset. This new method is introduced step by step and applied to 16 tracks from nine long-and short-distance migrant species.Global positioning with pressure sensors 2 3. Using known positions of double-tagged individuals (light and pressure data), we demonstrate that our method is almost three times more accurate than light-based positioning with an average error of 44 km in our trials. In contrast to the traditional light-based approach, pressure geolocation can provide useful information for short stationary periods (less than a day) and is not affected by the equinox-problem, nor any shading effects due to weather or animal behaviour. To facilitate the application of the method, we developed an R package (GeoPressureR), together with a user guide (GeoPressureManual) and starting code (GeoPressureTemplate).4. The use of pressure sensors to position animals has the potential to become widespread, thanks to affordable light-weight devices (<0.4 g) with a method to precisely and accurately estimate position, identify stopover locations, ultimately map a full migratory trajectory. This method broadens the reach of application of affordable light-weight tracking devices to short-distant migrants (>200km), forest-dwelling species, nocturnal animals and altitudinal migrants.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.