PS 4-42 - Quantifying dryland ecological dynamics at regional scales using monitoring data, remote sensing, and expert knowledge

Background/Question/Methods

Ecological dynamics in dryland ecosystems are often non-linear, with transitions between putative ecological states difficult to reverse, particularly following land degradation. Land degradation is a threat to drylands globally and will be compounded by climate change. Therefore, a primary need for avoiding and reversing dryland land degradation is information about what states are possible, drivers of transitions between states, and potential management actions to restore degraded states. State-and-transition models (STMs) describe persistent plant communities and ecological conditions that are possible (the ‘state’) within a given abiotic setting and the drivers or actions that can cause shifts between states (the ‘transitions’). STMs are widely used to guide and inform resource conservation decisions but are often based on expert opinion and local quantitative and qualitative surveys rather than rigorous and broadscale scientific analysis. Data-driven STMs have been developed for some lands, but these efforts typically involve intensive field sampling campaigns that are difficult to expand to regional or national scales due to time and resource requirements. Further, new timeseries land cover products provide opportunities to better incorporate temporal dynamics into STMs.

Results/Conclusions

We leveraged newly available digital soil maps of the Upper Colorado River Basin in the southwestern US and large field-based vegetation and soil cover monitoring databases (collected by the US Bureau of Land Management, Natural Resources Conservation Service, and National Park Service) in a repeatable workflow for developing data-driven STMs. We then used indicators of ecosystem stability or variability from time series remote sensing, documentation from local STMs created using expert knowledge, and available regional research to provide further context and descriptions of the data-driven states, including likely drivers of transitions. Results suggest that alternative stable states with reduced ecosystem services are common across the region, often likely driven by drought and land use. In particular, states with loss of perennial grass cover leading to invasive annual dominance, shrub encroachment, or elevated bare soil exposure are evident in the monitoring data. The workflow described here can serve as template for development, documenting, and mapping ecological dynamics at regional scales which we foresee as critical information for meeting land conservation and climate change mitigation priorities.