Dr. Amy J.S. Davis
My research leverages machine learning, eco-geographical relationships and spatial statistics to understand and predict patterns of biodiversity in response to drivers of global change across spatial scales. This kind of research requires combining different datasets on species distributions that have originated from a diverse assortment of collection methods, geographic extents (e.g. watersheds to continents), taxonomic and spatial biases, and varying degrees of sampling effort. As a result, I am also enthusiastic about developing methodology that allows drawing reliable inferences from imperfect data.
I have developed and managed databases and data quality standards as a postdoc in invasive species for the United States Environmental Protection Agency and Ghent University, and more recently as a data scientist for IPSOS European Public Affairs.
Currently, I manage the GloNAF (Global Naturalized Alien Flora) database. GloNAF is a living database that is continually pruned, fertilized and given new data to grow.
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Publications
Davis AJS, Groom Q, Adriaens T, Vanderhoeven S, De Troch R, Oldoni D, Desmet P, Reyserhove L, Lens L and Strubbe D (2024) Reproducible WiSDM: a workflow for reproducible invasive alien species risk maps under climate change scenarios using standardized open data. Frontiers in Ecology and Evolution 12:1148895 (DOI: 10.3389/fevo.2024.1148895)
Strubbe D, Jiménez L, Barbosa AM, Davis AJS, Lens L & Rahbek C (2023) Mechanistic models project bird invasions with accuracy. Nature Communications 14:2520 (DOI:10.1038/s41467-023-38329-4)