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Student Research Talks

Soundscape Mapping: Understanding Geospatial Patterns in Soundscapes 

Colin Quinn

I will briefly discuss the field of ecoacoustics and focus on research from my dissertation. This includes applying convolutional neural networks to identify ecologically meaningful sounds, applying soundscape metrics in a species distribution modeling framework to understand their spatiotemporal relationships with environmental characteristics, and exploring biotic soundscape responses following wildfires.

Opportunities Using Thermal Imaging

Jen Diehl

Leaf temperature is central to every canopy-atmosphere exchange. Yet the availability of high-resolution thermal imagery has been limited, inhibiting a clear understanding of these processes. My research aims to analyze thermal data across various scales, from regional (remote sensing using ECOSTRESS) to local (flux tower site). To aid in this effort, we have been collecting and processing two long-term thermal imaging datasets: a mixed temperate forest in Massachusetts (Harvard Forest) and a subalpine conifer forest in Colorado (NIWOT Ridge), which has the world’s longest in-situ thermal record. Additionally, we have begun to explore the potential use of more readily available flux data, such as longwave radiation, as a proxy for leaf temperature. We have developed equations that account for tower-based thermal interferences and can apply them to estimate surface temperature using flux longwave radiation. We aim to use these datasets and alternative methods to improve model constraints and better understand the complex interplay between leaf temperature and canopy-atmosphere exchange.

Predicting tree high temperature thresholds in southwest US and the tropics

Ben Wiebe

Identifying Potential Sources of Model Uncertainty Using Functional Benchmarks

Jeralyn Poe

Seeing the trees for the forest: Predicting ecosystem processes from structural and functional diversity

Kyla Dahlin

Associate Professor

Michigan State University

Department of Geography, Environment, & Spatial Sciences

Program in Ecology, Evolution, & Behavior

Abstract

The terrestrial biosphere is the largest source of uncertainty in the global carbon budget. We know that Earth system models (ESMs) over-simplify patterns and processes, yet we lack global scale information to constrain these models. This situation is, however, rapidly changing with the development of new observational tools, new ESMs, and new approaches to data analysis. To help constrain and inform ESMs, my group is working to understand ecological diversity at multiple spatial and temporal scales. We map biodiversity patterns based on in-situ data, geodiversity, and forest structure; we quantify plant functional traits in four-dimensions (x-y-z and through time); and we work to address “known unknowns” in the Earth system, like semi-arid ecosystem ecology, phenology, and historic influences. These projects all target the same fundamental question: How can we better quantify the globe’s myriad dimensions of ecological diversity to improve forecasts of the Earth system?

Bio

Dr. Kyla Dahlin is an associate professor at Michigan State University in the Department of Geography, Environment, and Spatial Sciences, where she has worked since 2015. Her research group aims to understand and quantify ecological patterns and processes from local to global scales through the application of emerging technologies, including air- and space-borne remote sensing, spatial statistics, and process-based modeling. Kyla is currently on sabbatical at NASA’s Jet Propulsion Lab (JPL) in the Carbon Cycle and Ecosystems group, which has been supported by JPL, MSU, and an NSF CAREER award. Outside of work, Dr. Dahlin enjoys spending time with her family, growing and making things, and riding bicycles.

web: www.ersamlab.com

twitter: @bristleweed

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