Tapping Soil Survey Information for Rapid Assessment of Sagebrush Ecosystem Resilience and Resistance

Maestas, Jeremy D.; Campbell, Steven; Chambers, Jeanne C.; Pellant, Mike; Miller, Richard F.

doi:10.1016/j.rala.2016.02.002

Cited by 69 publications

(81 citation statements)

References 5 publications

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“…The degree of model agreement regarding sagebrush presence or absence varied in relation to landscape features, but model agreement of no occupancy was generally greater in areas with low mean annual precipitation, high annual grass cover, and lower fertile island cover. Specific to the findings of this study, these characteristics generally correspond to areas widely regarded as low resistance and resilience landscapes for sagebrush reestablishment after fire (Maestas et al ). Thus, models widely agreeing on the absence of sagebrush in this area are not surprising.…”

Section: Discussionsupporting

confidence: 74%

Cannot see the random forest for the decision trees: selecting predictive models for restoration ecology

Barnard

Germino

Pilliod

et al. 2019

Restoration Ecology

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Improving predictions of restoration outcomes is increasingly important to resource managers for accountability and adaptive management, yet there is limited guidance for selecting a predictive model from the multitude available. The goal of this article was to identify an optimal predictive framework for restoration ecology using 11 modeling frameworks (including machine learning, inferential, and ensemble approaches) and three data groups (field data, geographic data [GIS], and a combination thereof). We test this approach with a dataset from a large postfire sagebrush reestablishment project in the Great Basin, U.S.A. Predictive power varied among models and data groups, ranging from 58% to 79% accuracy. Finer‐scale field data generally had the greatest predictive power, although GIS data were present in the best models overall. An ensemble prediction computed from the 10 models parameterized to field data was well above average for accuracy but was outperformed by others that prioritized model parsimony by selecting predictor variables based on rankings of their importance among all candidate models. The variation in predictive power among a suite of modeling frameworks underscores the importance of a model comparison and refinement approach that evaluates multiple models and data groups, and selects variables based on their contribution to predictive power. The enhanced understanding of factors influencing restoration outcomes accomplished by this framework has the potential to aid the adaptive management process for improving future restoration outcomes.

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Section: Discussionsupporting

confidence: 74%

Cannot see the random forest for the decision trees: selecting predictive models for restoration ecology

Barnard

Germino

Pilliod

et al. 2019

Restoration Ecology

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“…Projected declines may be slowed or halted by targeting fire suppression in remaining areas of intact sagebrush with high densities of breeding sage-grouse. ecologists increasingly emphasize management practices that understand factors driving resilience to wildfire and resistance to cheatgrass (hereinafter, R&R), which are influenced strongly by soil moisture and temperature regimes in semiarid ecosystems such as the Great Basin (14,18). However, responses of vertebrate populations inhabiting sagebrush ecosystems have not been linked empirically to altered disturbance regimes (e.g., the cheatgrass-fire cycle) or underlying factors influencing sagebrush ecosystem R&R across large spatiotemporal scales despite their obvious importance from a conservation perspective (10,19).…”

Section: Significancementioning

confidence: 99%

Wildfire, climate, and invasive grass interactions negatively impact an indicator species by reshaping sagebrush ecosystems

Coates

Ricca

Prochazka

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

144

183

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Iconic sagebrush ecosystems of the American West are threatened by larger and more frequent wildfires that can kill sagebrush and facilitate invasion by annual grasses, creating a cycle that alters sagebrush ecosystem recovery post disturbance. Thwarting this accelerated grass–fire cycle is at the forefront of current national conservation efforts, yet its impacts on wildlife populations inhabiting these ecosystems have not been quantified rigorously. Within a Bayesian framework, we modeled 30 y of wildfire and climatic effects on population rates of change of a sagebrush-obligate species, the greater sage-grouse, across the Great Basin of western North America. Importantly, our modeling also accounted for variation in sagebrush recovery time post fire as determined by underlying soil properties that influence ecosystem resilience to disturbance and resistance to invasion. Our results demonstrate that the cumulative loss of sagebrush to direct and indirect effects of wildfire has contributed strongly to declining sage-grouse populations over the past 30 y at large spatial scales. Moreover, long-lasting effects from wildfire nullified pulses of sage-grouse population growth that typically follow years of higher precipitation. If wildfire trends continue unabated, model projections indicate sage-grouse populations will be reduced to 43% of their current numbers over the next three decades. Our results provide a timely example of how altered fire regimes are disrupting recovery of sagebrush ecosystems and leading to substantial declines of a widespread indicator species. Accordingly, we present scenario-based stochastic projections to inform conservation actions that may help offset the adverse effects of wildfire on sage-grouse and other wildlife populations.

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“…Last, we estimated the probability of sage‐grouse locations belonging to a set of dominant soil moisture–temperature regimes using a multinomial loglinear model in the r package “nnet”. Soil data were compiled by Maestas, Campbell, Chambers, Pellant, and Miller () (resolution: 0.01 km × 0.01 km). Variation in soil regimes provide indirect support to variation in dominant vegetation characteristics and their resistant/resilient properties (Chambers et al, ), and thus variation in breeding patch quality.…”

Section: Methodsmentioning

confidence: 99%

Extreme site fidelity as an optimal strategy in an unpredictable and homogeneous environment

et al. 2019

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Animal site fidelity structures space use, population demography and ultimately gene flow. Understanding the adaptive selection for site fidelity patterns provides a mechanistic understanding to both spatial and population processes. This can be achieved by linking space use with environmental variability (spatial and temporal) and demographic parameters. However, rarely is the environmental context that drives the selection for site fidelity behaviour fully considered. We use ecological theory to understand whether the spatial and temporal variability in breeding site quality can explain the site fidelity behaviour and demographic patterns of Gunnison sage‐grouse (Centrocercus minimus). We examined female site fidelity patterns across multiple spatial scales: proximity of consecutive year nest locations, space‐use overlap within and across the breeding and brooding seasons, and fidelity to a breeding patch. We also examined the spatial and temporal variability in nest, chick, juvenile and adult survival. We found Gunnison sage‐grouse to be site faithful to their breeding patch, area of use within the patch and generally where they nest, suggesting an “Always Stay” site fidelity strategy. This is an optimal evolutionary strategy when site quality is unpredictable. Further, we found limited spatial variability in survival within age groups, suggesting little demographic benefit to moving among patches. We suggest Gunnison sage‐grouse site fidelity is driven by the unpredictability of predation in a relatively homogeneous environment, the lack of benefits and likely costs to moving across landscape patches and leaving known lek and breeding/brooding areas. Space use and demography are commonly studied separately. More so, site fidelity patterns are rarely framed in the context of ecological theory, beyond questions related to the win‐stay:lose‐switch rule. To move beyond describing patterns and understand the adaptive selection driving species movements and their demographic consequences require integrating movement, demography and environmental variability in a synthetic framework. Site fidelity theory provides a coherent framework to simultaneously investigate the spatial and population ecology of animal populations. Using it to frame ecological questions will lead to a more mechanistic understanding of animal movement, spatial population structuring and meta‐population dynamics. A free Plain Language Summary can be found within the Supporting Information of this article.

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Tapping Soil Survey Information for Rapid Assessment of Sagebrush Ecosystem Resilience and Resistance

Cited by 69 publications

References 5 publications

Cannot see the random forest for the decision trees: selecting predictive models for restoration ecology

Cannot see the random forest for the decision trees: selecting predictive models for restoration ecology

Wildfire, climate, and invasive grass interactions negatively impact an indicator species by reshaping sagebrush ecosystems

Extreme site fidelity as an optimal strategy in an unpredictable and homogeneous environment

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