Monitoring for spatial regimes in rangelands
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.
Section snippets
Study site
We set this study in The Nature Conservancy's Niobrara Valley Preserve, Nebraska, USA. Bisected east-west by the Niobrara River, the Niobrara Valley hosts a suite of plant communities ranging from glaciated and Rocky Mountain-associated communities on its northern shore to relict Pleistocene communities and Great Plains-associated species on its southern shore (Bessey, 1887). These adjacent, compositionally-distinct communities are ideal for testing the spatial regimes monitoring approach in a
Results
We detected spatial regimes that aligned with observed and historical plant community locations (Fig. 1, Fig. 2). Spatial variance detected the approximate spatial boundaries of all four historical plant communities (tallgrass prairie, burnt ponderosa pine woodland, deciduous forest, Sandhills Prairie) within the study area (Fig. 2a; Fig. 1). STARS also detected 4 spatial regimes, the first corresponding very closely in location with the northern tallgrass prairie, the second apparently
Discussion
Using traditional rangeland monitoring methods alongside established regime shift detection methods, we successfully detected boundaries between ecological states (i.e., spatial regimes) in a complex rangeland system. Most of these spatial regime locations match historic locations of plant communities in the Niobrara Valley going back 130 years (Bessey, 1887; Kantak, 1995). But spatial regimes monitoring also detected the wildfire-driven state transition of the relict ponderosa pine woodland
Declaration of Competing Interest
The authors have no competing interests to state.
Acknowledgements
The US Department of Defense's Strategic Environmental Research Development Program W912HQ-15-C-0018, USDA NIFA McIntire Stennis project 1008861, the University of Nebraska-Lincoln's Institute of Agriculture and Natural Resources, and The Nature Conservancy Nebraska Chapter's J.E. Weaver Competitive Grants Program supported this work. We thank Courtney Everhart and Phoebe Hartvigsen for help collecting data.
References (17)
- et al.
Quantifying spatial resilience
Journal of Applied Ecology
(2016) Determination of the number of zones in a biostratigraphical sequence
New Phytologist
(1996)A meeting-place for two floras
Bulletin of the Torrey Botanical Club
(1887)- et al.
Do early warning indicators consistently predict nonlinear change in long‐term ecological data?
Journal of Applied Ecology
(2016) - Dakos, V., & Lahti, L. (2013). R Early Warning Signals Toolbox. The R Project for Statistical...
- et al.
Alternative stable states and spatial indicators of critical slowing down along a spatial gradient in a savanna ecosystem
Global Ecology and Biogeography
(2017) rioja: Analysis of Quarternary Science Data
R package version
(2017)Terrestrial plant communities of the Middle Niobrara Valley, Nebraska
(1995)
Cited by (5)
Geographical boundaries and historical explanations of within-scale resilience
2024, Ecological ComplexityTracking spatial regimes in animal communities: Implications for resilience-based management
2022, Ecological IndicatorsCitation Excerpt :H1) Spatial regime boundaries identified via independent vegetation and animal datasets will synchronize in space and time. Specifically, we expect animal community boundary strength to peak at weak-moderate vegetation boundary strengths, and we also expect animal community boundary strength will decline at strong vegetation boundaries because bird communities will shift from grassland to woodland communities due to grassland birds’ sensitivity to even minimal woody plant cover (Thompson et al., 2014; Roberts et al., 2021). ( H2) Because fire is a critical negative feedback for maintaining the grassland regime that historically dominated our study area (Twidwell et al., 2020), animal community boundary strength should decline as fire frequency increases.
Spatiotemporal variability in Swedish lake ecosystems
2022, PLoS ONEInformed cover measurement: Guidelines and error for point-intercept approaches
2021, Applications in Plant Sciences