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Past and Future Modeling of Ecological Indicators for the South Atlantic Landscape Conservation Cooperative
Science Center Objects
The South Atlantic Landscape Conservation Cooperative (LCC) has developed a Conservation Blueprint: a “living spatial plan to conserve natural and cultural resources for future generations.” This blueprint is a data-driven plan based on terrestrial, freshwater, marine, and cross-ecosystem indicators to measure the overall health of South Atlantic ecosystems.
The Science Issue and Relevance: The South Atlantic Landscape Conservation Cooperative (LCC) has developed a Conservation Blueprint: a “living spatial plan to conserve natural and cultural resources for future generations.” This blueprint is a data-driven plan based on terrestrial, freshwater, marine, and cross-ecosystem indicators to measure the overall health of South Atlantic ecosystems.
Currently, there are 30 indicators in the blueprint, with spatial output completed for current conditions of the indicators. The USGS Wetland and Aquatic Research Center (WARC) is assisting the LCC by modeling past and future conditions of the indicators based on existing spatial layers, identifying gaps in data, and proposing solutions for filling those gaps.
Methodology for Addressing the Issue: To address the modeling needs of the Blueprint, WARC scientists are first compiling and validating available data and current indicator models from LCC sources. These existing blueprint models are compared to published LCC output to verify model function and data integrity.
All base layers, input data sets, and relationships among them are laid out in ArcMap ModelBuilder for a straightforward visual framework that allows for clear interpretation of modeled outputs. With this framework, current and predicted future climate and landscape conditions can be translated into accessible graphical outputs of conditions and changes in indicator habitats and species of interest to inform management decisions.
Future Steps: Working with LCC personnel to prioritize indicator models, the next step is to develop past and future model output of the existing indicator models. Where data and base forecasting layers are available, past and future models for indicators will be completed. Where existing modeling frameworks are not amenable to modification, new modeling approaches will be developed using available data layers. Finally, where insufficient data or forecasting of base layer models exist for projection of indicator models, these data and modeling gaps will be identified and a plan to address the gaps will be proposed.