Performing Department
VM: Extension/Public Programs
Non Technical Summary
This proposal aims to develop and validate a near-real-time predictive waterfowl tracking system in two areas (Central Valley of California and Mid-Atlantic coast across Delaware, Maryland, Virginia, and North Carolina) with high waterfowl and commercial poultry populations (Figure 1). After validation, this approach could be further expanded to other poultry dense areas of the U.S. that spatially and temporally interface with waterfowl. Our goal is to leverage a series of well- demonstrated and cutting edge remote sensing technologies and research products including:NEXt generation RADar (NEXRAD) for waterfowl roosting location and densityHigh spatial resolution and temporal frequency waterfowl telemetry (i.e. location tracking) dataSatellite imagery and land-based sensing for tracking dynamic changes in surface water and land cover (i.e. waterfowl habitat) which are used to build predictive statistical models with respect to waterfowl location and density in near real-time in the Central Valley of California and the Mid-Atlantic coast andHigh-sensitivity ultra-filtration of wetland water and whole-segment M-RTPCR of AIV of wetland water, sediment and cloacal waterfowl samples . We will leverage NEXRAD, telemetry, and the geospatial habitat data to identify and target selected habitat (water and sediment) at high and low waterfowl densities in order to understand the viral ecology of AIV in wetlands in close proximity to commercial poultry.Specifically, we will collect cloacal swabs from waterfowl, wetland sediment and water for ultra-filtration (e.g. 10 gallons of wetland water will be filtered to less than 50 ml in order to concentrate virus). Subsequent M-segment RT-qPCR, whole-segment M-RTPCR and sequencing will be used to establish the relationship between waterfowl density and AIV viral prevalence in water, sediment and associated waterfowl. Our initial results which are in review by the Journal Transboundary & Emerging Diseases4 demonstrate that ultra-filtration in combination with the whole segment RT-PCR is superior to commonly used detection methods for AIV in water (i.e. no filtration with M-segment qPCR) which are well known to lack sensitivity and are not considered representative of AIV prevalence.5The above described data will be integrated into near-real-time predictive models which will be further validated and subsequently deployed as a web-app that poultry farmers, veterinarians, and regulators (USDA-APHIS and state departments of agriculture) can use as a risk-management tool to assess and manage risk related to AIV transmission to poultry from waterfowl.The combined analysis of waterfowl location, density, AIV presence and the molecular ecology of AIVs in waterfowl and the associated environment can better inform producers and other stakeholders enabling enhanced biosecurity practices (i.e. changes in husbandry practices, repairs to farm facilities and equipment, increased utilization of car-wash stations, foot baths, limiting sharing of equipment and workers between risky and non-risky farms). Similarly, the analysis of these data could significantly improve decision making (i.e. processing a flock early, placing of a flock later, and triaging veterinary visits) at an individual farm level during times of increased risk. To promote enhanced biosecurity decisions, from an extension perspective in year one we will use our university extension capabilities to develop a multi-disciplinary advisory team which will help guide web-app development with the goal of maximizing adoption of the web-app for various stakeholders. During years 2-4, the web-app will be beta tested and disseminated to stakeholders in a series of extension- based workshops across the Central Valley of California and the Mid-Atlantic Coast area by UC Davis and University of Delaware faculty and extension faculty. Additionally, surveys evaluating the web-app will be delivered to producers and other relevant stakeholders in years 2-4 electronically and via in-person extension based meetings in order to optimize the functionality/usability of the web-app.
Animal Health Component
100%
Research Effort Categories
Basic
(N/A)
Applied
100%
Developmental
(N/A)
Goals / Objectives
Process recent NEXRAD observations of waterfowl distributions in CA and Mid-Atlantic.Analyze historical telemetry data of waterfowl distributions in the Pacific Flyway.Collect new telemetry data of waterfowl distributions in the Mid-Atlantic.Develop statistical models to predict waterfowl distributionsbased upon NEXRAD and telemetry observations and remotely-sensed environmental predictor variables including satellite imagery.Sample water, sediment & waterfowl for AIV abundance in 6 watersheds (3 in CA & 3 in the Mid-Atlantic).Link waterfowl species within sampled watersheds to likely AIV host species and test for correlation between AIV abundance and various waterfowl density model outputs.Develop web-app of near-real-time predicted waterfowl distributions in the Central Valley of California and the Mid-Atlantic.Extension Tasks:Develop multi-disciplinary extension advisory team (separate bi-annual meetings in CA and DE.In order to evaluate progress: survey producers & other stakeholders to optimize web-app development.Hold extension based meeting for beta testing, feedback, and analytics of the web-app.?From a broader perspective:A. Produce near real time predictions of wild waterfowl distributions in regions of high commercial poultry density in the Central Valley of California and the Mid-Atlantic coast based on NEXRAD (Figure 1 and 2b), models that utilize satellite and land-based remote sensing (Figure 2a) and waterfowl telemetry data (Figure 3).Use historical waterfowl distribution data collected in Objective I to identify 6 wetlands across the Pacific and Atlantic Flyways of high waterfowl density (i.e., potentially high AIV risk) to further validate the efficacy of sampling wetlands (water and sediment) for AIVs using ultrafiltration of wetland water linked to whole-segment RT-PCR and metagenomics of those whole-segment AIVs in wetlands. These results will be compared to direct sampling of waterfowl via standard molecular and virus isolation methods. Samples (sediment and 10L water samples) will be taken from each wetland and associated waterfowl quarterly over a 2-year period in order to better understand the qualitative and quantitative ebb and flow of the virus in the wetland environment. We will use e-Bird 15, a citizen science dataset of bird observations, to identify waterfowl species present in the study areas and link the results to our wetland sequencing data. These data will be used to help validate the species information gained by comparing the AIV sequences (from the whole-segment M-RTPCR) with the hosts those sequences were associated with using the NIAID Influenza Research Database (IRD).16Develop a user-friendly web-app that stakeholders and poultry producers in California Central Valley and the Mid-Atlantic (Figure 1) can use as a near real-time predictive tool to understand waterfowl location, density, and AIV presence and type.Develop a robust extension-based advisory team and associated extension-based workshops and on-line tools to increase awareness and adoption of the predictive web-app.
Project Methods
Process recent NEXRAD observations of waterfowl distributions in CA and Mid-Atlantic.Analyze historical telemetry data of waterfowl distributions in the Pacific Flyway.Collect new telemetry data of waterfowl distributions in the Mid-Atlantic.Develop statistical models to predict waterfowl distributionsbased upon NEXRAD and telemetry observations and remotely-sensed environmental predictor variables including satellite imagery.Sample water, sediment & waterfowl for AIV abundance in 6 watersheds (3 in CA & 3 in the Mid-Atlantic) (Table 2).Link waterfowl species within sampled watersheds to likely AIV host species and test for correlation between AIV abundance and various waterfowl density model outputs.Develop web-app of near-real-time predicted waterfowl distributions in the Central Valley of California and the Mid-Atlantic.Extension Tasks:Develop multi-disciplinary extension advisory team (separate bi-annual meetings in CA and DE.In order to evaluate progress: survey producers & other stakeholders to optimize web-app development.Hold extension based meeting for beta testing, feedback, and analytics of the web-app.