Insights into rangeland restoration outcomes from data-driven population models

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Robert K. Shriver, Caitlin M. Andrews, Robert S. Arkle, David M. Barnard, Michael C. Duniway, Matthew J. Germino, David S. Pilliod, David A. Pyke, Justin L. Welty, John B. Bradford

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Evidence that once healthy plant populations are slow or unable to recover after disturbance and seeding is widespread throughout restoration ecology, particularly in water-limited regions like the Great Basin, US. While considerable attention has been given to the role of abiotic conditions (e.g. moisture availability) and plant community composition (e.g. competition), less is known about how endogenous changes to population structure following wildfire influence recovery dynamics. Because recovering populations are dominated by young, small plants which may have lower survival and reproductive rates, their population dynamics may be altered from pre- to post-disturbance conditions. Using data from 531 post-fire big sagebrush populations where seeding occurred, we parameterized structured-population models to explore how changes in sagebrush population size-structure influence recovery dynamics. We find that shifts in population size-structure from large to small plants, and resulting transient dynamics, could significantly impede recovery after disturbance. We estimated that 500 of the 531 populations have the potential to grow and recover once a mature population size structure is established (i.e. asymptotic population growth rate 95% CI overlap with 1). But, we estimate 88% of these 500 populations declined, rather than grew, as they were re-establishing after post-fire seeding. Our modeling indicates that slow recovery was likely driven, in part, by the demographic effects of a population that was composed predominantly of small plants with low survival and reproduction. Our results suggest that alternative restoration approaches, such as planting some larger plants in conjunction with seeding, may accelerate recovery.

Resource Type:
Conference Presentation, SER2021

Pre-approved for CECs under SER's CERP program