Now accepting applications for working groups!
In line with our mission to foster collaboration across a broad range of disciplines related to environmental data science, the Environmental Data Science Innovation & Inclusion Lab is now accepting applications for its second cohort of working groups. These groups will play a pivotal role in promoting the integrative approach ESIIL champions, pooling knowledge and expertise from various disciplines to tackle environmental issues with a data-driven perspective. Please click here to view the 2024 Request for Proposals (RFP) and supporting documents.
What are working groups?
Working groups are self-organized research teams focused on well-defined scientific questions that advance environmental data science and require insights from a diverse group of researchers and other stakeholders. A single working group may have up to 15 participants and a quorum (50% or more) shall meet in person up to 2 times over a 2 year period, with each meeting lasting between 3 and 5 days.
Want to learn more?
Thank you to everyone who attended an information session or the networking event! ESIIL is continuing to host office hours to answer your questions and connect you with others interested in tackling scientific questions that advance environmental data science. 
Office Hours
ESIIL is holding office hours to answer any last minute questions you have before submitting your proposal. Click HERE to sign up for a time slot or email Rachel.Lieber@colorado.edu.
Find Collaborators
If you are interested in joining a working group but don’t yet have a team or are looking for collaborators to add to your proposal, please add your information to our to our Working Group Connections sheet.
Important Dates
Proposals must be submitted in PDF format to esiil@colorado.edu by November 22, 2024.
Funding decisions will be announced by early February 2025, with anticipated start dates of Working Groups in Summer or Fall 2025.
We look forward to receiving your proposals and working with you to advance environmental data science.
Questions? Email Ty.Tuff@colorado.edu.
ESIIL's First Cohort of Working Groups
Advancing Fungal Dispersal Ecology through Traits and Data Harmonization
Short Title: Fungal dispersal traits
PI: Bala Chauhary (Dartmouth College), Co-PIs: Cameron Egan (University of Southern California), Kabir Peay (Stanford University)
Fungi have captured the imagination of the public for their pivotal roles in ecosystems, the parts they play in world foods and culture, and their curious impacts to human health and well-being. But why do we see certain fungi in certain locations? How did they get there and what are the ways that fungi move from place to place? This working group studies the relatively uncharted discipline of fungal dispersal ecology. Combining global datasets, we aim to better understand how beneficial and pathogenic fungi disperse at a variety of spatial scales.
Harmonizing Natural Methane Datasets using Knowledge Guided Machine Learning
Short title: AI for Natural Methane
PI: Youmi Oh (University of Colorado Boulder), Co-PIs: Sparkle Malone (Yale University), Gavin McNicol (University of Illinois Chicago), Licheng Liu (University of Minnesota)
Atmospheric methane (CH4) is the second most powerful greenhouse gas after carbon dioxide and grew at the fastest rate ever recorded in 2020-2022. Slowing or reversing the accelerating growth in atmospheric CH4 will require an improved understanding of the global CH4 budget, which is currently underconstrained. Natural CH4 budgets are responsible for ~40% of the total global CH4 budgets but remain the most uncertain factor. This AI for natural CH4 working group aims to build a novel framework that integrates scientific knowledge and machine learning to harmonize simulated and observed datasets from global wetlands and soil sinks to quantify the spatial and temporal changes of global natural CH4 fluxes. Specifically, we will harmonize every possible form of the global natural CH4 datasets, including field-based CH4 fluxes from chamber and eddy-covariance measurements and simulated CH4 fluxes from bottom-up process-based models and top-down atmospheric assimilation models. As an output of this working group, we will generate and publicly share harmonized measurement datasets and global natural CH4 flux products from 1980 to present.
Interdisciplinary Approaches to Modeling Extreme Wildfire Events
Short Title: Modeling Extreme Wildfires
PI: Melissa Lucash (University of Oregon), Co-PIs: Robert Scheller (North Carolina State University), Branda Nowell (North Carolina State University), Sam Flake (North Carolina State University), James Lamping (University of Oregon)
Extreme wildfire events are increasing, driven by the expansion of urban communities closer to forests, land management practices which suppressed wildfire, and climate change. These events have a low probability of occurring, but they exhibit exceptional fire behavior characteristics and produce severe consequences for forests and humans. While the media and field studies focus on extreme wildfire events like the Lahaina wildfire in Hawaii and the Paradise fire in California, most estimates of wildfire risk report averages, but not extreme events, and they often underestimate the effects of climate change. Our workshops will form a cohesive, interdisciplinary research team to: 1) synthesize our current understanding of extreme fire behavior, 2) isolate the current gaps in our understanding of the social and biophysical drivers that cause extreme wildfire events and risk to communities, 3) develop a roadmap for improving the representation of extreme events into models that represent social and biophysical processes, and 4) integrate a widely-used model of forest change into High Performance Computers to generate improved predictions of wildfire risk and the probability of extreme wildfire events. We will initially investigate extreme fires in the Sierra Nevada Mountains of California, interior Alaska, and the Southern Appalachians.
Leveraging Biodiversity Data to Explore Ecosystem Dynamics
Short title: BioViewPoint
PI: Ruben Remelgado (TU Dresden), Co-PI: Kimberly Thompson (German Centre for Integrative Biodiversity Research)
Satellites are a powerful tool to understand how the world is changing. Satellites equipped with cameras, in particular, record light reflected by the Earth's surface across different wavelengths. We can use these data to understand the environmental conditions that influence the distribution of life on Earth, or Earth’s 'biodiversity'. However, just as environmental changes influence where species are and how many species exist in a particular place, species also influence their environment. In other words, biodiversity itself helps shape the world around us. This means that knowledge on how species are distributed in space and time can offer insights into our environment and on how this might change in the future. To explore this concept, our project will use a combination of species observations, pictures taken from satellites, and artificial intelligence to describe how biodiversity affects the environment. We think that just as we can use these satellite photos to understand the environment's impact on biodiversity, we can also use observations of different species to map the Earth's surface the way satellites perceive it. Through this research we can also better understand how different species contribute to keeping their environment healthy, which will support policymakers in creating legislation to protect Earth’s biodiversity and the environments we share with them.
Linking Phenological Change to Range Change in North American Plant Species
Short Title: Macrophenology
PI: Sydne Record (University of Maine), Co-PIs: Linda Black Elk (North American Indigenous Food Systems), Kai Zhu (University of Michigan), Technical Lead: Eric Sokol (NEON)
Plant phenology — the seasonal timing of leaf-out, budding, flowering, fruiting, and leaf-off — is the most easily observable and well-documented biological response to climate change. Changes in phenology have many implications for ecosystem services relevant to society (e.g., food security, carbon sequestration, seasonal allergies). Often we need to know where and when these seasonal changes in plants occur, which is challenging given their sensitivity to changes in climate. However, little is known about how plant phenology influences a species’ ability to persist in different locations in the face of climate change, how rare versus common species respond to climate, or how native vs. invasive plants may respond to climate change differently. The proposed working group (WG) will tackle these questions by combining different types of phenological data with species distribution data, which will require access to advanced computing resources. We will use a co-production framework with Indigenous scholars to develop a research approach and products that are needed and of value to their communities. The Environmental Science Innovation and Inclusion Lab (ESIIL) is the premier location for this work given their in-house knowledge of bringing together people with diverse perspectives and top computer infrastructure.
Maka Sitomniya: Preserving Mother Earth by Asserting Lakota Sovereignty in Earth Data Science
Short title: EDS for Maka Sitomniya
PI: Phil Two Eagle (Sicangu Lakota Treaty Council), Co-PIs: Joni Tobacco (Great Plains Tribal Water Alliance), Elisha Wakinyan Zi Yellow Thunder (South Dakota State University)
The world is faced with growing threats from multiple, interacting environmental challenges ranging from chemical pollution to increasing demands and diminishing supplies of freshwater to loss of biodiversity to the climate crisis. Indigenous communities are particularly vulnerable to these threats as a result of a long history of injustice. At the same time, their holistic worldview, long tenure on the lands and waters and time-tested stewardship practices provide the local knowledge necessary to understand and respond to environmental challenges. What is lacking are the resources and technical expertise to combine Indigenous Knowledges with the latest advances in data collection and analysis.
Climate change vulnerability assessment, mitigation and adaptation all depend on timely and reliable data. Recent advances in remote sensing technology and environmental data science (EDS) provide powerful tools for planners and decision-makers, but only if the data and analyses are accessible to them. Our Working Group is not focused on specific technological advances, but on enabling Tribes to access and use EDS for their own purposes. Our Indigenous-led group, consisting predominantly of Indigenous scientists and Tribal College faculty, proposes to facilitate the adoption of EDS by creating a DataCube and workflow that are customizable for the needs of specific Tribes and useful for training to build Tribal capacity. Advancing EDS is not just about pushing the edges of the science, it must also be about expanding accessibility and use of the science to benefit society, and extending its reach into communities that would otherwise be excluded.
Synthesizing Patterns and Drivers of Zooplankton Community Dynamics Worldwide
Short Title: Worldwide FreshH20 Zoops
PI: Michael Meyer (U.S. Geological Survey), Co-PIs: Stephanie Figary (University of Vermont), Rachel Pilla (Oak Ridge National Laboratory), Jason Stockwell (University of Vermont), Celia Symons (University of California, Irvine)
Lakes provide critical ecosystem services that are under threat from global environmental change. However, these freshwater ecosystems are underrepresented in biodiversity research relative to marine and terrestrial environments, despite the recognized worldwide biodiversity crisis in freshwater. Zooplankton are microscopic aquatic animals that serve as important metrics of biodiversity in lakes because they are critical food for young fish and consumers of algae. When changing zooplankton diversity alters these ecosystem functions, algae can grow to excess and lead to harmful algae blooms that negatively influence human health, tourism, and property values. We have developed the largest dataset of freshwater zooplankton biodiversity in the world, and will use it to examine how zooplankton assemblages have responded to a changing climate and how such changes are likely to impact lake water quality and ecosystem function. Our dataset contains > 60,000 samples from 289 lakes (34 countries, 6 continents) , with data up to 60 years ago. Drawing from our group’s diverse backgrounds and expertise, we will synthesize zooplankton biodiversity change worldwide and share our results and data.