Hall, Kimberly (Kim): The Nature Conservancy (Project Lead)
Project Funding:
2017 - 2021
NRA: 2016 NASA: Ecological Forecasting
Funded by NASA
Abstract:
Maintaining the ability of biota to move to suitable climates in response to climate change is now essential for biodiversity conservation. Identifying natural areas with diverse climatic options and enhancing connectivity among them has thus become a key climate adaptation strategy for The Nature Conservancy, implemented through our Conserving Nature’s Stage (CNS) initiative. There is urgent demand to expand this strategy, but a lack of consistent national datasets and inefficient computational algorithms are hindering progress. We propose to use NASA Earth observations and parallel computing algorithms to enable fast and consistent analyses to inform climate-smart conservation across large landscapes.
CNS analyses start with spatial data and models of topoclimate and human impacts to the landscape. These are used to identify locally-connected areas that will provide access to diverse climatic conditions needed for species to persist and adapt to climate change. These data are then used with Circuitscape connectivity analysis software to identify networks of climatically-diverse sites that can facilitate broad-scale movements and range shifts.
CNS products have enormous conservation impact. They have resulted in tens of millions of dollars being directed to land protection and are broadly used by NGOs and agencies in decision making.
But expanding our projects to larger analysis areas has revealed data gaps and computational limitations that are hindering progress. We have an opportunity to overcome these challenges by using NASA Earth observations and advanced computational tools enabled by the Julia scientific computing language to:
1) develop empirically-based and validated topoclimate and human modification datasets for the conterminous US;
2) develop and release a much more powerful version of Circuitscape needed to analyze high-resolution satellite datasets and to conduct sensitivity and scenario analyses; and
3) combine these data and models with novel connectivity analyses to produce consistent and seamless forecasts of important areas for climate adaptation across large study regions.
Our models will use NASA MODIS Aqua and Terra-derived LST, NDVI, and snow data; SRTM; Landsat-derived land use data; and VIIRS night-lights data. We will validate topoclimate and human modification models using in-situ meteorological data and an extensive database of high-resolution aerial photography.
Circuitscape is the world’s leading connectivity modeling platform; significant improvements will benefit users around the world who apply Circuitscape in diverse fields like landscape genetics, conservation planning, wildfire management, and epidemiology. New algorithms will allow much larger datasets to be analyzed. Faster
analyses will enable users to quantify uncertainty, conduct ensemble modeling, and identify “no regrets” conservation actions where model agreement is high.
Our work will immediately advance the rigor and utility of CNS decision support while benefiting agency and NGO partners. We will release data, analysis products, and open-source tools to help practitioners make smart conservation decisions for present and future biodiversity.
RELEVANCE: We directly a
nswer NASA’s Ecological Forecasting Program call by addressing connectivity decision making at spatial scales sufficient for satellite remote sensing to have a positive impact. As outlined in NASA’s Earth Science Applications Plan, we will use Earth observations to address biodiversity conservation; software enhancements will allow users to incorporate uncertainty, estimates of error, and the comparison of alternative policy outcomes into connectivity analyses, a goal of the Program.
Our proposal builds on previous NASA-funded work and is heavily leveraged by matching funds. Our team includes international leaders in connectivity, topoclimate, climate change, and scientific computing from NGOs, private industry, and academia.
Publications:
Dickson, B. G., Albano, C. M., Anantharaman, R., Beier, P., Fargione, J., Graves, T. A., Gray, M. E., Hall, K. R., Lawler, J. J., Leonard, P. B., Littlefield, C. E., McClure, M. L., Novembre, J., Schloss, C. A., Schumaker, N. H., Shah, V. B., Theobald, D. M. 2018. Circuit-theory applications to connectivity science and conservation. Conservation Biology. 33(2), 239-249. DOI: 10.1111/cobi.13230
Landau, V., Shah, V., Anantharaman, R., Hall, K. 2021. Omniscape.jl: Software to compute omnidirectional landscape connectivity. Journal of Open Source Software. 6(57), 2829. DOI: 10.21105/joss.02829
Littlefield, C. E., McRae, B. H., Michalak, J. L., Lawler, J. J., Carroll, C. 2017. Connecting today's climates to future climate analogs to facilitate movement of species under climate change. Conservation Biology. 31(6), 1397-1408. DOI: 10.1111/cobi.12938
More details may be found in the following project profile(s):
