Sydne Record
University of Maine, ME·Ecology & Evolution
Why do certain species live where they do, and how will they respond as the climate, land use and disturbances like fire change? These questions are central to protecting biodiversity, which underpins healthy ecosystems and human wellbeing.
- Fields
Ecology & Evolution
Computer Science
Plant Science & Agriculture
- Topics
biodiversity
National Ecological Observatory Network
intraspecific trait variation
- Grants
- 3 known grants, through Dec 2027
What they work on
- Develop machine learning algorithms that automatically measure body traits of NEON beetle specimens from images, and test how well they work across different image types.
- Identify which beetle traits, including variation among individuals of the same species, are most closely tied to beetle diversity across NEON sites continent-wide.
- Test how spatial scale shapes body size variation within species of small mammals, fish and ground beetles at NEON sites, and how that variation relates to biodiversity.
- Build public geospatial layers of disturbance, past land use, climate, geology and topography, and model how these drivers together explain biodiversity from plot to continental scales.
- Assemble flowering times for more than 4,400 US plant species by combining NEON and National Phenology Network observations with digitized herbarium specimens, including crowdsourced records from CrowdCurio.
- Model how fire history, soil quality, land use and climate drive differences in flowering and fruiting timing, and forecast near- and long-term phenology changes for populations, species and communities.
More about the research
Answering them requires data collected the same way across a whole continent. The National Ecological Observatory Network (NEON), a set of standardized field sites across the United States, provides that kind of data. Across three projects, the lab uses NEON data with herbarium records, citizen science and machine learning to link traits of individual organisms and the timing of life events to environmental drivers at local to continental scales.
Why it matters
These projects could give conservation planners better baselines and forecasts for biodiversity under climate change, land-use change and disturbance. Automated beetle imaging could support insect monitoring amid concerns about wide-scale insect declines. Flowering forecasts could show how shifting seasons may affect plants and the animals that rely on their flowers and fruits. The public datasets will be available for other researchers to use.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
EPSCOR Research Fellows: NSF: Advancing National Ecological Observatory Network-Enabled Science and Workforce Development at the University of Maine with Artificial Intelligence
NSF · Jan 2025 – Dec 2027
Collaborative Research: MRA: Modeling and forecasting phenology across spatiotemporal and taxonomic scales using ecological observatory and mobilized digital herbarium data
NSF · Aug 2022 – Jun 2027
Collaborative Proposal: MRA: Local- to continental-scale drivers of biodiversity across the National Ecological Observatory Network (NEON)
NSF · Oct 2022 – Nov 2026
Closest labs
Eric Rickart
University of Utah, UT·Ecology & Evolution
Digitize morphological and reproductive traits, such as body measurements and breeding condition, from about 1.2 million non-marine mammal specimens from western North America across 19 U.S. museums.
Steven Hall
University of Wisconsin-Madison, WI·Earth & Environmental Science
Test whether variation in soil organic matter properties and cycling across U.S. ecosystems can be explained by three fundamental axes running from fast to slow biogeochemical rates, using samples and data from NEON sites.
Theodore Stankowich
California State University-Long Beach, CA·Ecology & Evolution
Digitize and publish morphological (body shape and size) and reproductive trait data from about 1.2 million non-marine mammal specimens from western North America, held in 19 U.S. natural history museums.
The school
University of Maine: 10 labs on PI Pages, $199M a year on research, 81 research doctorates a year.
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