Biology subjects

Van Doren, B.

Publications and source records attributed to Van Doren, B..

3 recordsLinked to original sources

bioRxiv2022

BirdVox: Machine listening for bird migration monitoring

The steady decline of avian populations worldwide urgently calls for a cyber-physical system to monitor bird migration at the continental scale. Compared to other sources of information (radar and crowdsourced observations), bioacoustic sensor networks combine low latency with a high taxonomic specificity. However, the scarcity of flight calls in bioacoustic monitoring scenes (below 0.1% of total recording time) requires the automation of audio content analysis. In this article, we address the problem of scaling up the detection and classification of flight calls to a full-season dataset: 6672 hours across nine sensors, yielding around 480 million neural network predictions. Our proposed pipeline, BirdVox, combines multiple machine learning modules to produce per-species flight call counts. We evaluate BirdVox on an annotated subset of the full season (296 hours) and discuss the main sources of estimation error which are inherent to a real-world deployment: mechanical sensor failures, sensitivity to background noise, misdetection, and taxonomic confusion. After developing dedicated solutions to mitigate these sources of error, we demonstrate the usability of BirdVox by reporting a species-specific temporal estimate of flight call activity for the Swainsons Thrush (Catharus ustulatus).

ecology↗

bioRxiv2022

bi-axial orientation could explain range expansion in a migratory songbird

The likelihood of a new migratory route evolving is a function of the associated fitness payoff, and the probability that the route arises in the first place. Cross-breeding studies suggest that young birds migrate in a direction intermediate between their parents, though this would seemingly not explain how highly divergent migratory trajectories arise in apparently sympatric populations. It has been suggested that diametrically opposed reverse migratory trajectories might be surprisingly common, and if such routes were heritable it follows that they could underlie the rapid evolution of divergent migratory trajectories. Here, we used Eurasian blackcap (Sylvia atricapilla; blackcap) ringing recoveries and geolocator trajectories to investigate whether a recently-evolved northwards autumn migratory route could be explained by the reversal of each individuals expected southwards migratory direction. We found that northwards migrants were recovered closer to the sites specified by a precise axis reversal than would be expected by chance, consistent with the rapid evolution of new migratory routes via bi-axial variation in orientation. We suggest that the surprisingly high probability of axis reversal might allow birds to expand their wintering ranges rapidly, and hence propose that understanding how direction is encoded is crucial when characterising the genetic basis of migratory direction and how this relates to route evolution.

zoology↗

bioRxiv2022

BirdFlow: Learning Seasonal Bird Movements from Citizen Science Data

Large-scale monitoring of seasonal animal movement is integral to science, conservation, and outreach. However, gathering representative movement data across entire species ranges is frequently intractable. Citizen science databases collect millions of animal observations throughout the year, but it is challenging to infer individual movement behavior solely from observational data. We present BO_SCPLOWIRDC_SCPLOWFO_SCPLOWLOWC_SCPLOW, a probabilistic modeling framework that draws on citizen science data from the eBird database to model the population flows of migratory birds. We apply the model to 11 species of North American birds, using GPS and satellite tracking data to tune and evaluate model performance. We show that BO_SCPLOWIRDC_SCPLOWFO_SCPLOWLOWC_SCPLOW models can accurately infer individual seasonal movement behavior directly from eBird relative abundance estimates. Supplementing the model with a sample of tracking data from wild birds improves performance. Researchers can extract a number of behavioral inferences from model results, including migration routes, timing, connectivity, and forecasts. The BO_SCPLOWIRDC_SCPLOWFO_SCPLOWLOWC_SCPLOW framework has the potential to advance migration ecology research, boost insights gained from direct tracking studies, and serve a number of applied functions in conservation, disease surveillance, aviation, and public outreach.

ecology↗