First of all: We are delighted that you are interested in leading a Collaborative Earth Lab! In less than one page, please answer the following questions, then email us your responses at email@example.com.
What is the scientific question to be addressed or the technological tool to be built?
Keep in mind that, ideally, the work can be completed, or a significant benchmark attained, within six to nine months of work by a Lab of four members, each working approximately ten hours per week. This is not a strict requirement, but an approximate guide to assist in scoping.
How will answering this scientific question or building this tool catalyze greater ecological conservation or regeneration?
In short, what is your theory of change?
What are the skills you think you will need on your team to complete this work?
This will help members of the CE community know where they can put their expertise to work. If you are unsure how to answer the question at this stage, CE staff can provide some guidance. You will have opportunities to adjust the composition of your team at certain benchmarks along the way.
Please include also brief bios of the prospective Lab Leader or co-Leaders.
This can come from another source, such as an organizational or personal website. CE staff will edit the bio for presentation to the Collaborative Earth community.
Finally, if your lab is selected, you will be asked to talk about your lab—what it is and why it matters—for about ten to fifteen minutes at the Collaborative Earth Pitch Event. If you can't make the event, you can simply make a short video of yourself. Your written description of the lab as well as your video describing the lab will be made available on the Collaborative Earth website.
Thank you for joining us in pioneering a new way of doing science!
The goal of our lab is to create a high-spatial resolution map of coastal forested wetlands at global scale. If we know precisely where these ecologically critical but fragile forests are located, we can manage freshwater flows to counteract saltwater introgression due to rising sea levels, and we can assist in their migration inland, preserving their critical function in protecting coastlines and sequestering carbon.
Across the continent, a number of first nations are in the process of reintroducing bison to the grasslands in which they were once the primary grazer and an ecologically vital species. Initial experiences and evolutionary considerations suggest that this may be ecologically beneficial in terms of grassland biodiversity, carbon cycle, and resilience to climate change. However, these questions have not yet been studied at scale. In this lab, we will leverage remote sensing to scale up from ground measurements, establishing the large-scale patterns of bison impact.
Beaver dams are known to result in greener, more drought-resilient waterways in semi-arid environments. We are using computer vision to spot dams in satellite imagery, generating a large dataset that we can use to train models that will tell us what the ecological effects of a dam will be at any point on a waterway. The goal is to create a tool to guide efficient restoration through the introduction of small dams.
Markets in voluntary carbon credits are increasingly providing a flow of capital for regenerating ecosystems. The problem is, thriving and resilient ecosystems are not just carbon. We need to find ways to structure credits to incentivize the diverse and functional ecosystems we want, not merely high-concentrations of carbon. We will design the technological tools to support a market in bundled ecological credits.
We are building an accurate and global model for predicting potential rates of reforestation and resulting carbon sequestration. Such a model could have a transformational impact on global reforestation efforts by opening new streams of financing in the form of carbon credit futures.
Leveraging The Earthshot Institute’s broad scientific and technical expertise, the Impact and Risk Lab helps investors and governments who earnestly want to forecast, measure, and address the socio-ecological risks to and/or impacts from their work. For a given system, we build simple process-based models to identify key socio-ecological risks and outcomes. We then draw on big data to improve and train our models, generating quantitative predictions and developing measurement systems for verification.