Coupling Biocrusts and Vegetation Dynamics to Improve Predictions of Dryland Change

University Of California-Davis, Davis CA

Investigators

Abstract

Environmental changes worldwide have pushed ecosystems towards their limit, and there is a growing need to better predict ecosystem responses to these external pressures. Prior work suggests that spatial patterns found in many ecosystems change in a predictable way when approaching a tipping point. These spatial patterns can be harbingers of imminent whole ecosystem change. Drylands have been used as pivotal systems in developing such a theory. The theory predicts that as the climate becomes drier, patterns of dryland vegetation shift from bare gaps in homogenous vegetation to labyrinthine or striped vegetation cover, then to a spotty pattern, before catastrophically shifting to a bare state. Operationalizing an early warning system of ecosystem change using such spatial patterns requires accurate characterization of significant feedbacks; otherwise, “false alarms” could lead to costly resource misallocations. Unfortunately, predictions from current theory are limited to only a small area of drylands globally. This is likely because models currently ignore biological soil crusts, or biocrusts. Biocrusts are associations between soil particles and living microorganisms (e.g., cyanobacteria, lichens, mosses, bacteria) that live within or immediately on top of, the upper few millimeters of soils. Biocrusts can cover more surface area than vascular plants and play a critical role in the dynamics of water, energy, and nutrients. They also alter core processes that underlie vegetation spatial patterns. Neglecting biocrusts poses significant uncertainties in predicting dryland change driven by climate. Current theory of ecosystem spatial self-organization considers only one homogenous assemblage of organisms, in the case of drylands, vascular plants. Conceptualizing drylands as an integral biocrust-vascular plant complex requires new theories and models that integrate inter-specific interactions (e.g., competition, facilitation) and spatial self-organization. This research develops such theories and models, using drylands as a focal study area. The project investigates biocrust patch dynamics by (1) manipulating rainfall regimes in the lab and tracking biocrust patch formation and (2) monitoring drylands of the U.S. Southwest. Dryland pattern formation models link vascular plants with biocrusts. Comparing these models, with prior models that do not consider biocrusts, elucidates the role of biocrusts in dryland resilience and predicts how ecosystem state (e.g., productivity) and spatial patterns change with altered rainfall regimes under climate change. Lastly, multi-source remote sensing imagery and deep learning approaches guide large-scale biocrust mapping. The resultant models predict changes to drylands in the U.S. Southwest over the next few decades of anticipated climate change. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.