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ARS Home » Plains Area » Las Cruces, New Mexico » Range Management Research » People & Locations » Heather Savoy

Heather M Savoy
Range Management Research
Biologist

Phone: (575) 646-6453
Fax: (575) 646-5889

2995 KNOX ST.
LAS CRUCES, NM 880038003

Publications (Clicking on the reprint icon Reprint Icon will take you to the publication reprint.)
Broad-scale climate drivers of a vector-borne livestock disease movement from Mexico into the United States - (Abstract Only)
Harnessing the power of AI technologies for ecology: The knowledge learning analysis system (KLAS) for spatially-distributed, continuous ecological data - (Abstract Only)
Peters, D.C., Ramirez, G., Anderson, J.P., Savoy, H.M., Huang, H. 2020. Harnessing the power of AI technologies for ecology: The knowledge learning analysis system (KLAS) for spatially-distributed, continuous ecological data [abstract]. Ecological Society of America Meeting. August 3-6, 2020, Virtual. Poster #85379.
Full Genomic Sequencing of Vesicular Stomatitis Virus Isolates from the 2004–2006 US Outbreaks Reveals Associations of Viral Genetics to Environmental Variables - (Abstract Only)
AI recommender system with ML for agricultural research - (Peer Reviewed Journal)
Peters, D.C., Savoy, H.M., Ramirez, G., Huang, H. 2020. AI recommender system with ML for agricultural research. IEEE IT Professional. 22:29-32.
Modifying connectivity to promote state change reversal in drylands - (Peer Reviewed Journal)
Full Genomic Sequencing Of Vesicular Stomatitis Virus Isolates From The 2004-2006 US Outbreaks Reveals Associations of Viral Genetics To Environmental Variables - (Abstract Only)
Complex disease problems across scales: perspectives on advancing disease ecology with trans-disciplinary research - (Abstract Only)
The DASH portal: Supporting geoHealth research by automating geospatial data tasks - (Abstract Only)
AI and machine learning to improve understanding and prediction of complex ecosystem dynamics - (Abstract Only)
A transdisciplinary framework for predictive disease ecology based on cross-scale interactions: Insights from long-term data - (Abstract Only)