Geology topics

Matthew J. Hardy

Publications and source records attributed to Matthew J. Hardy.

5 recordsLinked to original sources

USGS2026

Using high frequency GPS data to assess wintering goose proximity to commercial poultry facilities on the Delmarva peninsula for avian influenza risk management and surveillance

The ongoing global outbreak of Highly Pathogenic Avian Influenza Virus (HPAIv) Clade 2.3.4.4 H5N1 in poultry, which was first detected in the United States (USA) in February 2022, underscores the importance of improving food biosecurity and wild bird surveillance. The Delmarva Peninsula is vital to wintering waterfowl and to poultry production, thereby increasing the risk of HPAIv outbreaks. By using fine-scale GPS tracking of Greater Snow Geese (GSGO, N = 59) and Canada Geese (CANG, N = 9) over four winters (2019–2023), we 1) demonstrate a risk ranking system for poultry facilities based on exposure to wintering waterfowl, and 2) model this exposure in relation to temporal, geographic, and land cover factors. We showed that an ordinal-percentile ranking system, based on goose points per hour (gp/h) at poultry facilities, effectively predicted HPAIv H5N1 outbreak risks in the Delmarva Peninsula. Seven facilities with outbreaks were in the 78–99th risk percentile, including one ranked among the top 25 of 6,021 facilities. Such ranking criteria could be used to guide biosecurity efforts when response teams’ resources are limited. Notably, one-third of Delmarva poultry facilities had GPS-marked waterfowl present during winters, indicating a significant presence despite our limited sample sizes. We analyzed the variability in goose exposure rates near poultry facilities over four winters. Time (week) during winter was the key predictor of exposure, with greater GSGO presence in mid-late winter (Dec 15 to Feb 28) and CANG exposure peaking in early winter (Nov 28 to Dec 11), then stabilizing until late winter (Feb to Mar 13), whereupon exposure increased again. Colder temperatures (below 0°C) significantly increased GSGO exposure as they sought waste grain for energy. Goose exposure for both species was high throughout the winter, necessitating 24-hour surveillance. Proximity to sewage treatment plants (≤2 km), National Wildlife Refuges (≤5 km), water bodies (≤5 km), and the coast (≤20 km) increased exposure levels. Additionally, inland areas with features such as reservoirs and lakes supported large numbers of geese near facilities.

Delaware, Maryland, Virginia↗

USGS2026

Waterfowl move less in heterogeneous and human-populated landscapes, with implications for spread of avian influenza viruses

Animal movements contribute to the spread of infectious diseases and are driven in part by environmental conditions. We investigated the links among the environment, animal movement, and infectious disease dynamics in waterfowl, which are among the primary wildlife hosts of avian influenza viruses. By combining telemetry data on 4606 individuals from 26 waterfowl species with data on land cover, weather, and vegetation, we found that waterfowl moved less in areas of higher land cover heterogeneity and higher human population density. Moreover, predicted waterfowl movement distances were weakly but positively correlated with distances between detections of H5N1 highly pathogenic avian influenza in wild waterfowl, suggesting that environmental conditions might contribute to the spread of this disease via their effects on bird movements. By considering wildlife movements alongside other drivers of infectious disease dynamics, such as livestock production and human mobility, we move closer to predicting outbreaks and informing interventions.

Ecology Letters↗

USGS2024

Examining inter-regional and intra-seasonal differences in wintering waterfowl landscape associations among Pacific and Atlantic flyways

The Central Valley of California (CVC) and Mid-Atlantic (MA) in the U.S. are both critical sites for nationwide food security, and many waterfowl species annually, especially during the winter, providing feeding and roosting locations for a variety of species. Mapping waterfowl distributions, using NEXRAD, may aid in the adaptive management of important waterfowl habitat and allow various government agencies to better understand the interface between wild and domestic birds and commercial agricultural practices. We used 9 years (2014–2023) of data from the US NEXRAD network to model winter waterfowl relative abundance in the CVC and MA as a function of weather, temporal period, environmental conditions, and landcover characteristics using boosted regression tree modelling. We were able to quantify the variability in effect size of 28 different covariates across space and time within two geographic regions which are critical to nationwide waterfowl management and host a high density of nationally important commercial agriculture. In general, weather, geographic (distance to features), and landcover condition (wetness index) predictors had the strongest relative effect on predicting wintering waterfowl relative abundance in both regions, while effects of land cover composition were more regionally and temporally specific. Increased daily mean temperature was a major predictor of increasing relative waterfowl abundance in both regions throughout the winter. Increasing precipitation had differing effects within regions, increasing relative waterfowl abundance in the MA, while decreasing in general within the CVC. Increasing relative waterfowl abundance in the CVC are strongly tied to the flooding of the landscape and rice availability, whereas waterfowl in the MA, where water is less limiting, are generally governed by waste grain availability and emergent wetland on the landscape. Waterfowl relative abundance in the MA was generally higher nearer to the Atlantic coast and lakes, while in the CVC they were higher nearer to lakes. Our findings promote a better understanding of spatial associations of waterfowl to landscape features and may aid in conservation and biosecurity management protocols.

California, Delaware, Maryland, New Jersey, North ↗

USGS2024

Mitigating risk: Predicting H5N1 avian influenza spread with an empirical model of bird movement

Understanding timing and distribution of virus spread is critical to global commercial and wildlife biosecurity management. A highly pathogenic avian influenza virus (HPAIv) global panzootic, affecting ~600 bird and mammal species globally and over 83 million birds across North America (Dec 2023), poses a serious global threat to animals and public health. We combined a large, long-term waterfowl GPS tracking dataset (16 species) with on-ground disease surveillance data (county-level HPAIv detections) to create a novel empirical model that evaluated spatiotemporal exposure and predicted future spread and potential arrival of HPAIv via GPS tracked migratory waterfowl through 2022. Our model was effective for wild waterfowl, but predictions lagged HPAIv detections in poultry facilities and among some highly impacted non-migratory species. Our results offer critical advance warning for applied biosecurity management and planning and demonstrate the importance and utility of extensive multi-species tracking to highlight potential high-risk disease spread locations and more effectively manage outbreaks.

Transboundary and Emerging Diseases↗

USGS2024

Examining inter-regional and intra-seasonal differences in wintering waterfowl landscape associations among Pacific and Atlantic flyways

The Central Valley of California (CVC) and Mid-Atlantic (MA) in the U.S. are both critical sites for nationwide food security (California Poultry Federation 2016, Prosser et al. 2017), and many waterfowl species annually, especially during the winter, providing feeding and roosting locations for a variety of species. Mapping waterfowl distributions, using NEXRAD, may aid in the adaptive management of important waterfowl habitat and allow various government agencies to better understand the interface between wild and domestic birds and commercial agricultural practices. We used 9 years (2014–2023) of data from the US NEXRAD network to model winter waterfowl relative abundance in the CVC and MA as a function of weather, temporal period, environmental conditions, and landcover characteristics using Boosted Regression Tree modelling. We were able to quantify the variability in effect size of 28 different covariates across space and time within two geographic regions which are critical to nationwide waterfowl management and host a high density of nationally important commercial agriculture. In general, weather, geographic (distance to features), and landcover condition (wetness index) predictors had the strongest relative effect on predicting wintering waterfowl relative abundance in both regions, while effects of land cover composition were more regionally and temporally specific. Increased daily mean temperature was a major predictor of increasing relative waterfowl abundance in both regions throughout the winter. Increasing precipitation had differing effects within regions, increasing relative waterfowl abundance in the MA, while decreasing in general within the CVC. Increasing relative waterfowl abundance in the CVC are strongly tied to the flooding of the landscape and rice availability, whereas waterfowl in the MA, where water is less limiting, are generally governed by waste grain availability and emergent wetland on the landscape. Waterfowl relative abundance in the MA was generally higher nearer to the Atlantic coast and lakes, while in the CVC they were higher nearer to lakes. Our findings promote a better understanding of spatial associations of waterfowl to landscape features and may aid in conservation and biosecurity management protocols.

Authorea↗