Toni Lyn Morelli

Toni Lyn Morelli is an Adjunct Assistant Professor in the Department of Environmental Conservation at the University of Massachusetts Amherst and a Research Ecologist at the U.S. Geological Survey (USGS). Her work focuses on wildlife ecology, climate change impacts, and translational ecology to support conservation efforts. She holds a Ph.D. in Ecology and Evolution from Stony Brook University (2008) and a B.S. in Zoology from Michigan State University (2001). Her research integrates field studies, ecological modeling, genetics, and collaborative approaches to address biodiversity threats under climate change.

Dr. Morelli’s expertise includes species distribution modeling, occupancy modeling, geospatial analysis, and decision analysis. She leads projects such as the Northeast Regional Invasive Species and Climate Change (RISCC) Management Network and studies climate change refugia to enhance ecosystem resilience. Her contributions to DEIA (Diversity, Equity, Inclusion, and Accessibility) initiatives include leadership roles in the USGS EMA DEIA Leadership Council and Northeast CASC Diversity Committee.

• Affiliations: USGS Northeast Climate Science Center, Northeast Climate Adaptation Science Center, Refugia Research Coalition
• Grants: USFWS Species Support Partnership (2021–2023), USGS Natural Resources Preservation Program (2019–2021)

Her awards include the University of Massachusetts’ Inaugural DEI Faculty Award (2021), USGS STAR Award (2016), and recognition as a top Associate Editor for Conservation Science & Practice (2018–2022). She collaborates with organizations like the National Park Service and USA National Phenology Network to operationalize climate adaptation strategies and improve invasive species management.

Dr. Morelli advises on climate-vulnerable species and ecosystems, particularly in boreal forests and vernal pools, while advancing interdisciplinary approaches to conservation challenges. She also contributes to policy-relevant initiatives, such as updating taxonomy systems and analyzing citizen science data for natural resource management.