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assisted forest regeneration
Lab Leader: Leland Werden
Anna Abraham ❍ Abigith Baby ❍ Nicholas Berry ❍ Himesh B ❍ Harvey Bordett ❍
Ellie Dunklee ❍ Kaija Emmanuel ❍ Vladislav Gerasimov ❍ Anthony Hevia ❍ Angela Hsu ❍ Declan Johnson ❍ Eli King ❍ Gerbrand Koren ❍ Scott LaRocca ❍ Yifei Liu ❍ Aditya Narendra ❍ Lorrie Newman ❍ Elie Nsimba Ngembo ❍ Navit Reid ❍ Thomas Sherin ❍ Jennifer Simpson ❍ Arjun Srihari ❍ Luisa Teixeira ❍

There is a tremendous interest in mitigating the impacts of climate change with forest restoration. Many strategies are used to regain tree cover on deforested lands, ranging from low-cost approaches like natural regeneration (letting forest grow back on its own) to resource-intensive approaches such as assisted restoration (e.g., tree planting). Given the limited resources to achieve ambitious international goals, determining the most efficient and effective restoration approach at specific sites is critical to scaling the global restoration movement.

While there have been significant advances in the understanding of where forests are likely to naturally regenerate, the rates of recovery under natural vs. assisted restoration are incredibly variable and often sparsely documented. Current remote sensing techniques and models, while useful in a broad sense, fall short of delivering regionally specific recommendations that can directly guide choices made by restoration practitioners on the ground. Moreover, in situations where assisted restoration approaches are necessary, projects often have a high failure rate, often because inappropriate approaches are selected for site-level conditions.

Building on work synthesizing the global potential for carbon sequestration by natural regeneration (Cook-Patton et al., 2020), our project aims to quantify potential carbon capture and plant biodiversity recovery of forest, savannah, and mangrove assisted restoration projects. To do so we are conducting a systematic literature review, synthesizing unpublished data from field partners, and integrating as much information from non-English sources as possible. There is an incredible wealth of information on restoration outcomes that is not published in the academic literature (Ladouceur et al., 2022) and want to ensure that we integrate this information into this synthesis as much as possible.

Leland Werden is a restoration ecologist in the Crowther Lab at ETH Zürich. He uses large-scale restoration experiments and conducts global data syntheses to bridge the gap between ecological theory and restoration practice.

References: Cook-Patton, S. C., Leavitt, S. M., Gibbs, D., Harris, N. L., Lister, K., Anderson-Teixeira, K. J., Briggs, R. D., Chazdon, R. L., Crowther, T. W., Ellis, P. W., Griscom, H. P., Herrmann, V., Holl, K. D., Houghton, R. A., Larrosa, C., Lomax, G., Lucas, R., Madsen, P., Malhi, Y., … Griscom, B. W. (2020). Mapping carbon accumulation potential from global natural forest regrowth. Nature, 585(7826), 545–550. https://doi.org/10.1038/s41586-020-2686-x

Ladouceur, E., Shackelford, N., Bouazza, K., Brudvig, L., Bucharova, A., Conradi, T., Erickson, T. E., Garbowski, M., Garvy, K., Harpole, W. S., Jones, H. P., Knight, T., Nsikani, M. M., Paterno, G., Suding, K., Temperton, V. M., Török, P., Winkler, D. E., & Chase, J. M. (2022). Knowledge sharing for shared success in the decade on ecosystem restoration.
Ecological Solutions and Evidence, 3(1). https://doi.org/10.1002/2688-8319.12117

Coastal Wetland Forests
Elliott White Jr

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.

Jason Baldes
Gisel Booman

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.

Riparian Ecosystems
Forrest Pound

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.

Bundled Ecological NFT
Philip Taylor

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.

Global Forests
Aron Boettcher

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.

Impact & Risk
Aaron Hirsh
Valérie Lechêne

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.

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