Gone with the wind: Inferring bird migration with light‐level geolocation, wind and activity measurements

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.

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“…no MCMC) while integrating information of pressure, light, flight duration as well as wind support (e.g. Werfeli et al, 2022).…”

Section: Discussionmentioning

confidence: 99%

Global positioning with animal‐borne pressure sensors

et al. 2023

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“…Compared to Werfeli et al (2022), the computation of wind is further improved by integrating the variation of windspeed over time, space, and altitude encountered throughout the flight.…”

Section: Model Strengthsmentioning

confidence: 99%

“…Briedis, Beran, et al, 2020). In this context, wind data have recently been shown to considerably improve position estimates because of the strong influence of wind on a bird's ground speed (Werfeli et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Reconstructing bird trajectories from pressure and wind data using a highly optimized hidden Markov model

et al. 2023

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Tracking technologies have widely expanded our understanding of bird migration routes, destinations and underlying strategies. However, determining the entire trajectory of small birds equipped with lightweight geolocators remains a challenge. We develop a highly optimized hidden Markov model (HMM) for reconstructing bird trajectories. The observation model is defined by pressure and, optionally, light measurements, while the movement model incorporates wind data to constrain consecutive positions based on realistic airspeeds. To reduce the computational costs associated with a large state space, we prune the HMM states and transitions based on flight and observation constraints to efficiently model the entire trajectory. The approach presented is based on a mathematically exact procedure and is fast to compute. We demonstrate how to compute (1) the most likely trajectory, (2) the marginal probability map of each stationary period, (3) simulated trajectories and (4) the wind conditions (wind support/drift) encountered by the bird during each migratory flight. We construct a version of an HMM optimized for reconstructing a bird's migration trajectory based on lightweight geolocator data. To render this approach easily accessible to researchers, we designed a dedicated R package GeoPressureR ( https://raphaelnussbaumer.com/GeoPressureR/).

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“…By using wind data to refine the possible distance covered by a bird, we improve the accuracy and precision of the trajectory. Compared to Werfeli et al (2022), the computation of wind is further improved by integrating the variation of windspeed over time, space and altitude encountered throughout the flight.…”

Section: Model Strengthsmentioning

confidence: 99%

“…In addition, identifying the exact duration of flights (using activity data or pressure) can inform the movement model of the SSM by constraining the distance between consecutive positions, assuming a distribution of ground speeds (e.g., Briedis, Beran, et al, 2020). In this context, wind data has recently been shown to considerably improve position estimates because of the strong influence of wind on a bird's ground speed (Werfeli et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Reconstructing bird trajectories from pressure and wind data using a highly optimised hidden Markov model

Nussbaumer

Gravey

Briedis

et al. 2022

Preprint

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1. Tracking technologies have widely expanded our understanding of bird migration routes, destinations, and underlying strategies. However, determining the entire trajectory of small birds equipped with lightweight geolocators remains a challenge. Statistical trajectory models that incorporate both flight behaviour and sensor information provide the most accurate estimates of a bird’s full trajectory. 2. We develop a highly optimized hidden Markov model (HMM) for reconstructing bird trajectories. The observation model is defined by pressure and, optionally, light measurements, while the movement model incorporates wind data to constrain consecutive positions based on realistic airspeeds. Due to the large state space, the computational requirements for HMM calculations are high and may exceed available memory. We prune the HMM states and transitions based on flight and observation constraints to efficiently model the entire trajectory. 3. The approach presented is based on a mathematically exact procedure and is fast to compute. We demonstrate how to compute: (1) the most likely trajectory, (2) the marginal probability map of each stopover, (3) simulated trajectories, and (4) the wind conditions (wind support/drift) encountered by the bird during each migratory flight. 4. We construct a version of an HMM optimized for reconstructing a bird’s migration trajectory based on lightweight geolocator data. The focus of this work is to render this approach easily accessible to researchers through the dedicated R package GeoPressureR (https://raphaelnussbaumer.com/GeoPressureR/).

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Gone with the wind: Inferring bird migration with light‐level geolocation, wind and activity measurements

Cited by 4 publications

References 41 publications

Global positioning with animal‐borne pressure sensors

Global positioning with animal‐borne pressure sensors

Reconstructing bird trajectories from pressure and wind data using a highly optimized hidden Markov model

Reconstructing bird trajectories from pressure and wind data using a highly optimised hidden Markov model

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