Monitoring for spatial regimes in rangelands

Abstract

In rangelands, monitoring spatial regime boundaries (i.e., boundaries between ecological states) could provide early warnings of state transitions, elucidate the spatial nature of state transitions, and quantify management outcomes. Here, we test the ability of established regime shift detection methods and traditional, local-scale rangeland monitoring data to identify spatial regime boundaries in a complex rangeland system. We collected plant community composition data via point-intercept sampling at 400 evenly-spaced locations along a 4000m transect. We then applied three statistical regime shift detection methods to identify spatial regimes and compared outcomes of each statistical method. Statistical detection of spatial regimes held up to traditional field monitoring practices. Spatial regime locations matched historic plant communities in the study site going back 130 years, but we also detected a localized wildfire-driven state transition: a relict ponderosa pine (Pinus ponderosa) spatial regime transitioned to a bur oak (Quercus macrocarpa) – annual grass regime. The spatial regimes monitoring approach capitalizes on the existence of spatial boundaries between states to track ecological states as they move, expand, contract, or disappear. This is an advancement over traditional time series approaches to regime shift/state transition detection which only detect state transitions if enough sites transition. Existing local-scale rangeland monitoring, used strategically, can complement current coarse, broad-scale applications of spatial regimes monitoring by detecting subtle, fine-scale boundaries that broad-scale monitoring cannot capture.

Introduction

In rangelands, monitoring for spatial regimes could aid in managing for resilience of desirable ecological states (Roberts et al. 2018). As extensions of resilience and alternative state theories, spatial regimes are defined as the spatial extent and boundaries of an ecological state (Allen et al. 2016). Spatial regimes exhibit strong spatial order and can move, contract, expand at the expense of neighboring spatial regimes, or collapse and reorganize in response to disturbance, changes in feedbacks, and management (Uden et al., 2019). Because the spatial regimes concept acknowledges these spatial aspects of ecological states, monitoring for movement in spatial regime boundaries can provide early warnings of state transitions years before traditional temporal regime shift (i.e., state transition) detection methods (Roberts et al., 2019). Spatial regimes monitoring has identified subtle boundaries such as sub-continental and oceanic boundaries between bird and plankton communities via community composition data (Sundstrom et al., 2017). It also has strong potential to quantify management outcomes: for example, management seeking to prevent state transitions from perennial- to annual-grass dominated states in sagebrush steppe could track over time if, where, and how much perennial:annual spatial regime boundaries expanded or contracted in response to fire management (Uden et al., 2019).

To-date, the spatial regime monitoring approach has only been tested at broad scales, and it remains unclear if it could be applied to local rangeland monitoring. If tracking spatial regimes could be incorporated into existing rangeland monitoring, it could allow managers to map and track spatial regimes at fine scales, ask spatially-explicit questions concerning state transitions, and quantify local management outcomes. Here, we test the ability of established regime shift detection methods and traditional, local-scale rangeland monitoring data to identify spatial regime boundaries in a complex rangeland system.