I am a research scientist working at the intersection of predictive ecology, biodiversity conservation, and invasion ecology. My research examines how human-mediated processes and the data we rely shape what we think we know about global biodiversity patterns.

By combining large-scale biodiversity data, ecological theory, and reproducible workflows, my work aims to improve inference, prediction, and decision-making in conservation and invasion science under global change.

Human-mediated biodiversity change
Understanding how trade, urbanization, recreation, and other human activities structure biodiversity patterns across regions and taxa.

Predictive modeling and machine learning for ecology
My work integrates species distribution models with machine learning approaches to improve prediction and quantify uncertainty under non-stationary conditions.

Data, bias, and reproducibility
I develop and apply reproducible, open analytical practices to assess how data availability, sampling bias, and modeling choices influence ecological inference, risk assessment, and conservation decision-making.

Selected Publications

• The updated Global Naturalized Alien Flora (GloNAF 2.0) database. Ecology (2025).
• Reproducible WiSDM: a workflow for reproducible invasive alien species risk maps under climate change scenarios. Frontiers in Ecology and Evolution (2024).
• Mechanistic models project bird invasions with accuracy. Nature Communications (2023).
• Recreational freshwater fishing drives non-native aquatic species richness patterns at a continental scale. Diversity and Distributions (2017).
• Multi-temporal trajectories of landscape change explain forest biodiversity in urbanizing ecosystems. Landscape Ecology (2017).
• Accounting for residential propagule pressure improves prediction of urban plant invasion. Ecosphere (2016).

I collaborate widely with researchers working in invasion biology, macroecology, biodiversity informatics, and conservation science. I am particularly interested in projects involving large-scale biodiversity datasets, synthesis across regions or taxa, and reproducible analytical workflows that support evidence-based decision-making.

Please get in touch via email: amy.davis (at) uni-konstanz (dot) de