DATASETS, MODEL LEARNING AND AI
The Cascadia bioregion is a busy, data-rich place.
Our coalition partners have lots going on, with their own initiatives, including some inter-organizational efforts. But these are too often siloed. And the pace and complexity of change at different scales is increasing beyond their capacity.
Our partners and collaborators say they want an integrated biodiversity early warning system to enable fast, reliable responses based on systems analysis, model learning and other multiscale, up-to-task interoperability tools.
This system will link real-world needs and cutting-edge methods, citizen science volunteerism, remotely sensed data, machine- and model-learning and predictive tools for significantly better policy, planning, management and rapid action.
Cascadia Biodiversity Evidence also offers important system-learning opportunities with paired networks in Polynesia (Berkeley Institute of Data Science/Gump Research Station, Tahiti), the Sierra Nevadas (Eric Berlow), and elsewhere.
Geospatial data, interoperability and model-training are essential, to extrapolate and understand biodiversity vulnerability.
Species data
- population, range, phenology, reproduction, migration
Ecological communities
Natural capital proxy data
Land cover and ecosystem data
Social and cadastral data
Economic and political data
Model learning and output data
