Using the California Waterfowl Tracker to Assess Proximity of Waterfowl to Commercial Poultry in the Central Valley of California

Acosta, Sarai; Kelman, Todd; Feirer, Shane T.; Matchett, Elliott L.; Smolinsky, Jaclyn A.; Pitesky, Maurice; Buler, Jeffrey J.

doi:10.1637/aviandiseases-d-20-00137

Cited by 7 publications

(5 citation statements)

References 15 publications

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“…The ability to detect and distinguish between different biological patterns that are created by various phenomena in weather radars has an enormous effect on our understanding of aeroecology and its consequences for our lives (Acosta et al, 2021; Bacciu et al, 2019; Cohen et al, 2022; Nilsson et al, 2021; Van Doren & Horton, 2018). In this study, we developed the first automatic algorithm for detecting flocks of migrating soaring birds, an important biological phenomenon involving many millions of birds that travel across large spatial extents.…”

Section: Discussionmentioning

confidence: 99%

Section: F I G U R Ementioning

confidence: 99%

“…Consequently, weather radars around the world are exploited to better understand different broad scale behaviours and movement of aerial organisms in detail, including quantification of biomass fluxes (Dokter et al, 2018; Farnsworth et al, 2016; Hu et al, 2016; Nilsson et al, 2019; Van Doren & Horton, 2018) and mapping of stopover sites along migration flyways (Buler & Dawson, 2014; Cohen et al, 2021; Schekler et al, 2022). In addition, extracting biological data from weather radars allows us to manage human‐wildlife conflicts such as flight safety (Ginati et al, 2010; Kranstauber et al, 2022; Van Gasteren et al, 2018), crop damage (Markkula et al, 2008), risks due to collision with wind energy facilities (Cohen et al, 2022), and the dispersal of pathogens (Acosta et al, 2021) and pollinators (Wotton et al, 2019). These efforts minimize financial consequences, provide economic incentives, decrease risks to human lives and conserve aerial animals (Bauer et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

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Automatic detection of migrating soaring bird flocks using weather radars by deep learning

et al. 2023

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The use of weather radars to detect and distinguish between different biological patterns greatly improves our understanding of aeroecology and its consequences for our lives. Importantly, it allows us to quantify passerine bird migration at different scales. Yet, no algorithm to detect soaring bird flocks in weather radar is available, precluding our ability to study this type of migration over large spatial scales. We developed the first automatic algorithm for detecting the migration of flocks of soaring birds, an important bio‐flow phenomenon involving many millions of birds that travel across large spatial extents, with implications for risk of bird‐aircraft collisions. The algorithm was developed with a deep learning network for semantic segmentation using U‐Net architecture. We tested several models with different weather radar products and with image sequences for flock movement identification. The best model includes the radial velocity product and a sequence of two previous images. It identifies 93% of soaring bird flocks that were tagged by a human on the radar image, with a false discovery of less than 20%. Large birds such as those detected by the algorithm pose a serious risk for flight safety of civilian and military transportation and therefore the application of this algorithm can substantially reduce bird‐strikes, leading to reduced financial losses and threats to human lives. In addition, it can help overcome one of the main challenges in the study of bird migration by automatically and continuously detecting flocks of large birds over wide spatial scales without the need to equip the birds with tracking devices, unravelling the abundance, timing, spatial flyways, seasonal trends and influences of environmental conditions on the migration of bird flocks.

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Section: Discussionmentioning

confidence: 99%

Section: F I G U R Ementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automatic detection of migrating soaring bird flocks using weather radars by deep learning

et al. 2023

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“…Our case study illustrates how this leads to comprehensive bird migration forecasts, which can be used to anticipate the spread and accumulation of migrants over time. Such insights are crucial to, for example, develop targeted containment strategies for the spread of pathogens like avian influenza (Acosta et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

“…Knowing when and where individuals or populations will move in the near future enables stakeholders to take targeted conservation measures and to systematically mitigate human‐wildlife conflicts. Examples range from the temporary removal of man‐made barriers, obstacles, or sources of pollution (Liechti et al, 2013; Marschall et al, 2011; van Doren et al, 2021), to preventing local transmission of infectious diseases (Acosta et al, 2021; Lisovski et al, 2018), and protecting human infrastructure and agriculture from damage through mass movements (Leskinen et al, 2009; van Gasteren et al, 2019). Focusing such efforts on specific locations and times where interventions are most needed can greatly improve the ecological and societal impact while reducing the associated costs (Horton et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Learning to predict spatiotemporal movement dynamics from weather radar networks

et al. 2022

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The potential of remote sensing for improved infectious disease ecology research and practice

Teitelbaum,

Ferraz,

De La Cruz

et al. 2024

Proc. R. Soc. B.

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Outbreaks of COVID-19 in humans, Dutch elm disease in forests, and highly pathogenic avian influenza in wild birds and poultry highlight the disruptive impacts of infectious diseases on public health, ecosystems and economies. Infectious disease dynamics often depend on environmental conditions that drive occurrence, transmission and outbreaks. Remote sensing can contribute to infectious disease research and management by providing standardized environmental data across broad spatial and temporal extents, often at no cost to the user. Here, we (i) conduct a review of primary literature to quantify current uses of remote sensing in disease ecology; and (ii) synthesize qualitative information to identify opportunities for further integration of remote sensing into disease ecology. We identify that modern advances in airborne remote sensing are enabling early detection of forest pathogens and that satellite data are most commonly used to study geographically widespread human diseases. Opportunities remain for increased use of data products that characterize vegetation, surface water and soil; provide data at high spatio-temporal and spectral resolutions; and quantify uncertainty in measurements. Additionally, combining remote sensing with animal telemetry can support decision-making for disease management by providing insights into wildlife disease dynamics. Integrating these opportunities will advance both research and management of infectious diseases.

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Using the California Waterfowl Tracker to Assess Proximity of Waterfowl to Commercial Poultry in the Central Valley of California

Cited by 7 publications

References 15 publications

Automatic detection of migrating soaring bird flocks using weather radars by deep learning

Automatic detection of migrating soaring bird flocks using weather radars by deep learning

Learning to predict spatiotemporal movement dynamics from weather radar networks

The potential of remote sensing for improved infectious disease ecology research and practice

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