Biology subjects

Van Doren, B. M.

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

9 recordsLinked to original sources

bioRxiv2026

The Dark Ecology Dataset: Measurements of Aerial Biomass in US Weather Radar from 1995 to 2025

The US NEXRAD radar network has monitored the aerosphere over the US and its territories continuously since the 1990s and archived nearly 300 million radar volume scans. These data contain a wealth of information about the movements of birds, bats, and insects. Historically, this biological information was difficult to access due to the amount of data and challenges in analyzing it. In the last 15 years, fueled by computational and methodological advances, large-scale aeroecology research has blossomed. However, comprehensive analyses of the NEXRAD archive remain very costly. We collected measurements from every volume scan in the NEXRAD archive--nearly 300 million data files total--to assemble a dataset of aerial biological activity over the US from 1995 to 2025. The core data are vertical profiles, which summarize biological activity at different heights above the radar station for each volume scan. We also provide time series data products that aggregate vertical profiles to point measurements at radar stations across time. These data products can support a range of aeroecology analyses at significantly reduced effort.

ecology↗

bioRxiv2025

Population-level migration modeling of North American birds through data integration with BirdFlow

BackgroundAccurate information on population-level movements of migratory animals is essential for understanding migration and for designing effective conservation strategies in a changing world. Yet such information remains scarce for most migratory species due to the effort and expense needed to collect data across their full distribution ranges. BirdFlow is a probabilistic modeling framework that infers population-level movements from weekly species distribution maps produced by the participatory science project eBird. However, BirdFlow models have only been tuned for a handful of species using high-resolution individual tracking data, which is not available for most migratory species. MethodsHere, we introduce a general tuning and evaluation framework for BirdFlow that enables the first large-scale integration of distributional and individual-level data to infer animal movement across continents and hundreds of migratory species, eliminating reliance on any single individual-tracking data source. By generalizing the BirdFlow model parametrization, we enable tuning and validation using multiple complementary data sources, including GPS tracks, banding recoveries, and radio telemetry data from the Motus Wildlife Tracking System. We investigate the efficacy of this approach by (1) investigating predictive performance compared to null models; (2) validating the biological plausibility of BirdFlow models by comparing movement properties such as route straightness, number of stopovers, and migration speed between model-generated routes and real movement tracks; and (3) comparing the performance of models tuned on species-specific movement data to models tuned using hyperparameters transferred from other species. ResultsOur results show that BirdFlow models produced by the new tuning framework achieve biologically realistic performance, even for prediction horizons of thousands of kilometers and several months. When species-specific data are unavailable, models can still be tuned using data from other phylogenetically adjacent species to achieve improved performance. ConclusionsBy integrating eBird Status & Trends abundance surfaces with data from banding recaptures, radio telemetry, and GPS tracking, we scale BirdFlow model to 153 North American migratory species, representing the first collection of continental-scale population-level movement and forecasting models. Species-specific tuning improves population-level movement forecasts, while taxonomically informed hyperparameter transfer supports the modeling of data-limited species. Overall, our work offers a foundation for more accurate predictions across hundreds of species for research in ecology and conservation, disease surveillance, aviation, and public outreach.

ecology↗

bioRxiv2024

Migration strategies of a high-latitude breeding songbird (Setophaga coronata coronata) revealed using multi-sensor geolocators and stable isotopes

Seasonal migration allows animals to use habitat where conditions are unfavorable for part of the year but may constrain breeding ranges due to the costs of longer migrations as ranges expand poleward. In species with large ranges, high latitude breeding populations may employ different migratory strategies allowing them to persist far from other core non-breeding areas. The myrtle warbler (Setophaga coronata coronata) has two disjunct non-breeding ranges in North and Central America-one along the Gulf Coast and the other on the Pacific. Previous work indirectly linked birds breeding in Alaska with the Pacific non-breeding area, suggesting that high latitude populations evolved a shorter migration route. We directly tested this hypothesis using geolocators measuring both light and atmospheric pressure to track Alaskan myrtle warbler migration in fine detail and inferred non-breeding areas using hydrogen isotopes for a larger sample of birds breeding in Alaska, British Columbia, and Alberta. We found, contrary to expectations, that all geolocator-tracked birds and most birds with stable isotope data migrated to the southeastern United States, while only a small subset of birds ([~]5%) likely wintered on the Pacific Coast. We additionally demonstrate the advantages of pressure geolocation for characterizing migratory behavior at a fine scale.

animal behavior and cognition↗

bioRxiv2023

Nighthawk: acoustic monitoring of nocturnal bird migration in the Americas

O_LIAnimal migration is one of natures most spectacular phenomena, but migratory animals and their journeys are imperiled across the globe. Migratory birds are among the most well-studied animals on Earth, yet relatively little is known about in-flight behavior during nocturnal migration. Because many migrating bird species vocalize during flight, passive acoustic monitoring shows great promise for facilitating widespread monitoring of bird migration. C_LIO_LIHere, we present Nighthawk, a deep learning model designed to detect and identify the vocalizations of nocturnally migrating birds. We trained Nighthawk on the in-flight vocalizations of migratory birds using a diverse dataset of recordings from across the Americas. C_LIO_LIOur results demonstrate that Nighthawk performs well as a nocturnal flight call detector and classifier for dozens of avian taxa, both at the species level and for broader taxonomic groups (e.g., orders and families). The model accurately quantified nightly nocturnal migration intensity and species phenology and performed well on data from across North America. Incorporating modest amounts of additional annotated audio (50-120 h) into model training yielded high performance on target datasets from both North and South America. C_LIO_LIBy monitoring the vocalizations of actively migrating birds, Nighthawk provides a detailed window onto nocturnal bird migration that is not presently attainable by other means (e.g., radar or citizen science). Scientists, managers, and practitioners could use acoustic monitoring with Nighthawk for a number of applications, including: monitoring migration passage at wind farms; studying airspace usage during migratory flights; monitoring the changing migrations of species susceptible to climate change; and revealing previously unknown migration routes and behaviors. Overall, this work will empower diverse stakeholders to efficiently monitor migrating birds across the Western Hemisphere and collect data in aid of science and conservation. Nighthawk is freely available at https://github.com/bmvandoren/Nighthawk. C_LI

ecology↗

bioRxiv2023

Structural genomic variation and migratory behavior in wild songbirds

Structural variants (SVs) are a major source of genetic variation, but accurate descriptions in natural populations and connections with phenotypic traits remain scarce. We integrated advances in genomic sequencing and animal tracking to begin filling this knowledge gap in the Eurasian blackcap. Specifically, we (i) characterized the genome-wide distribution, frequency and overall fitness effects of SVs using haplotype-resolved assemblies for 79 birds, and (ii) used these SVs to study the genetics of seasonal migration. We detected >15K SVs. Many SVs overlapped repetitive regions and exhibited evidence of purifying selection suggesting they have overall deleterious effects on fitness. We used estimates of genomic differentiation to identify SVs exhibiting evidence of selection in blackcaps with different migratory strategies. Insertions and deletions dominated these SVs and were associated with genes that are either directly (e.g., regulatory motifs that maintain circadian rhythms) or indirectly (e.g., through immune response) related to migration. We also broke migration down into individual traits (direction, distance and timing) using existing tracking data and tested if genetic variation at the SVs we identified could account for phenotypic variation at these traits. This was only the case for one trait - direction - and one specific SV (a deletion on chromosome 27) accounted for much of this variation. Our results highlight the evolutionary importance of SVs in natural populations and provide insight into the genetic basis of seasonal migration.

evolutionary biology↗

bioRxiv2020

The genomic landscape of divergence across the speciation continuum in island-colonising silvereyes (Zosterops lateralis)

A goal of the genomic era is to infer the evolutionary dynamics at play during the process of speciation by analysing the genomic landscape of divergence. However, empirical assessments of genomic landscapes under varying evolutionary scenarios are few, limiting the ability to achieve this goal. Here we combine RAD-sequencing and individual-based simulations to evaluate the genomic landscape in the silvereye (Zosterops lateralis). Using comparisons matched for divergence timeframe and gene flow context, we document how genomic patterns accumulate along the speciation continuum. In contrast to previous predictions, our results provide limited support for the idea that divergence accumulates around loci under divergent selection or that genomic islands widen with time. While a small number of genomic islands were found in populations diverging with and without gene flow, in few cases were SNPs putatively under selection tightly associated with genomic islands. Furthermore, we modelled the transition from localised to genome-wide levels of divergence using individual-based simulations that considered only neutral processes. Our results challenge the ubiquity of existing verbal models that explain the accumulation of genomic differences across the speciation continuum and instead support the idea that divergence both within and outside of genomic islands is important during the speciation process. DATA ACCESSION NUMBERSResequencing data from this study have been submitted to the National Center for Biotechnology Information (NCBI; https://www.ncbi.nlm.nih.gov) under accession number PRJNA489169.

evolutionary biology↗

bioRxiv2018

A continental system for forecasting bird migration

Billions of animals cross the globe each year during seasonal migrations, but efforts to monitor them are hampered by the irregularity and relative unpredictability of their movements. We developed a bird migration forecast system with continental scope by leveraging 23 years of spring observations to learn associations between atmospheric conditions and bird migration intensity. Our models explained up to 81% of variation in migration intensity across the United States at altitudes of 0-3000 m, and performance remained high when forecasting events 24-72 h into the future (68-72% variation explained). We infer that avian migratory movements across the United States frequently exceed 200 million individuals per night and exceed 500 million individuals per night during peak passage. Accurately forecasting bird migration will allow stakeholders to reduce collisions with illuminated buildings, airplanes, and wind turbines, predict movements under climate change scenarios, and engage the public.

ecology↗

bioRxiv2017

Comparative analysis examining patterns of genomic differentiation across multiple episodes of population divergence in birds

Heterogeneous patterns of genomic differentiation are commonly documented between closely related populations and there is considerable interest in identifying factors that contribute to their formation. These factors could include genomic features (e.g., areas of low recombination) that promote processes like linked selection (positive or purifying selection that affects linked neutral sites) at specific genomic regions. Examinations of repeatable patterns of differentiation across population pairs can provide insight into the role of these factors. Birds are well suited for this work, as genome structure is conserved across this group. Accordingly, we re-estimated relative (FST) and absolute (dXY) differentiation between eight sister pairs of birds that span a broad taxonomic range using a common pipeline. Across pairs, there were modest but significant correlations in window-based estimates of differentiation (up to 3% of variation explained for FST and 26% for dXY), supporting a role for processes at conserved genomic features in generating heterogeneous patterns of differentiation. This suggestion was reinforced by linear models identifying several genomic features (e.g., gene densities) as significant predictors of FST and dXY repeatability. FST repeatability was higher among pairs that were further along the speciation continuum (i.e., more reproductively isolated), suggesting that early stages of speciation may be dominated by positive selection that is different between pairs and replaced by processes acting according to shared genomic features as speciation proceeds.

genomics↗

bioRxiv2017

Correlated patterns of genetic diversity and differentiation across an avian family

Comparative studies of genomic differentiation among independent lineages can provide insights into aspects of the speciation process, such as the relative importance of selection and drift in shaping genomic landscapes, the role of genomic regions of high differentiation, and the prevalence of convergent molecular evolution. We investigated patterns of genetic diversity and divergence in stonechats (genus Saxicola), a widely distributed avian species complex with phenotypic variation in plumage, morphology, and migratory behavior, to ask whether similar genomic regions are important in the evolution of independent, but closely related, taxa. We used whole-genome pooled sequencing of 262 individuals from 5 taxa and found that patterns of genetic diversity and divergence are highly similar among different stonechat taxa. We then asked if these patterns remain correlated at deeper evolutionary scales and found that homologous genomic regions have become differentiated in stonechats and the closely related Ficedula flycatchers. Such correlation across a range of evolutionary divergence and among phylogenetically independent comparisons suggests that similar processes may be driving the differentiation of these independently evolving lineages, which in turn may be the result of intrinsic properties of particular genomic regions (e.g., areas of low recombination). Consequently, studies employing genome scans to search for areas important in reproductive isolation should account for corresponding regions of differentiation, as these regions may not necessarily represent speciation islands or facilitate local adaptation.

genomics↗