Spatiotemporal patterns of cheatgrass invasion in Colorado Plateau National Parks

Bishop, Tara B. B.; Munson, Seth M.; Gill, Richard; Belnap, Jayne; Petersen, Steven L.; Clair, Samuel B. St.

doi:10.1007/s10980-019-00817-8

Cited by 26 publications

(25 citation statements)

References 72 publications

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“…, Bishop et al. ) from fall until spring depending on weather patterns (Mack , Meyer et al. ) and has successfully invaded over 210,000 km 2 of the Great Basin in the western United States (Bradley et al.…”

Section: Introductionmentioning

confidence: 99%

Earlier fall precipitation and low severity fire impacts on cheatgrass and sagebrush establishment

et al. 2020

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In arid and semiarid ecosystems, invasion by exotic grasses may be driving state changes in vegetation defined by losses of native shrub communities. Changes in wildfire regimes and fall precipitation timing related to climate change may promote fluctuations in resource availability that reinforces invasion and state changes in vegetation. The objective of this study was to investigate how earlier fall precipitation timing and fire affected the germination, establishment, and growth of the dominant native shrub Wyoming big sagebrush (Artemisia tridentata subsp. wyomingensis), and one of the most problematic invaders, cheatgrass (Bromus tectorum L.) in the Great Basin. We extracted soil cores from Rush Valley, Utah (UT), USA, on the eastern side of the Great Basin ecoregion and placed them in a common garden in Provo, UT, and planted seeds of sagebrush and cheatgrass in individual cores. We measured the response of sagebrush and cheatgrass to experimental fire and two fall precipitation timing pulses in a full factorial design. Water was added for two weeks in early September (early fall treatment) and mid-October (late fall treatment). We measured seedling emergence, plant height, biomass, density, seed production, and survival. Early fall precipitation did not significantly affect the amount of cheatgrass or sagebrush seedling emergence. Early fall precipitation significantly increased cheatgrass density, height, biomass, and seed production, and sagebrush height and biomass, but not density. Surprisingly, cheatgrass did not respond positively to fire. In contrast, fire increased sagebrush density (twofold) and survival. These findings indicate that fire can have positive impacts on sagebrush establishment. The data suggest that projected increases in fall moisture in the Great Basin due to climate change are likely to have positive impacts on both cheatgrass and sagebrush. However, additional studies are needed to identify how fall precipitation timing and fire might impact competitive interactions between sagebrush and cheatgrass and the bearing on invasion success at influencing state changes in the Great Basin.

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Section: Introductionmentioning

confidence: 99%

Earlier fall precipitation and low severity fire impacts on cheatgrass and sagebrush establishment

et al. 2020

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“…topographic gradients, resistance to cheatgrass invasion was driven by temperature and ppt regimes (Chambers et al 2014), and the driver of variable interannual cheatgrass growth was highly variable ppt (Bradley and Mustard 2005). Periods of limited soil moisture reduced plant germination and establishment (Bishop et al 2019), and management activities that sought to exclude nonnative species introduced gaps in vegetation cover that caused increases in resource availability (Rau et al 2014). Both phenomena created space for cheatgrass, an early-season and fast-growing plant, to have invaded by capitalizing on available moisture and nutrients when temperatures warmed, earlier than most native plants (Boyte et al 2016).…”

Section: Discussionmentioning

confidence: 99%

Estimating Abiotic Thresholds for Sagebrush Condition Class in the Western United States

Boyte

Wylie

et al. 2020

Rangeland Ecology & Management

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Sagebrush ecosystems of the western United States can transition from extended periods of relatively stable conditions to rapid ecological change if acute disturbances occur. Areas dominated by native sagebrush can transition from species-rich native systems to altered states where non-native annual grasses dominate, if resistance to annual grasses is low. The non-native annual grasses provide relatively little value to wildlife, livestock, and humans and function as fuel that increases fire frequency. The more land area covered by annual grasses, the higher the potential for fire, thus reducing the potential for native vegetation to reestablish, even when applying restoration treatments. Mapping areas of stability and areas of change using machine-learning algorithms allows both the identification of dominant abiotic variables that drive ecosystem dynamics and the variables' important thresholds. We develop a decision-tree model with rulesets that estimate three classes of sagebrush condition (i.e., sagebrush recovery, tipping point [ecosystem degradation], and stable). We find rulesets that primarily drive development of the sagebrush recovery class indicate areas of midelevations (1 602 m), warm 30-yr July temperature maximums (tmax) (30.62 C), and 30-yr March precipitation (ppt) averages equal to 26.26 mm, about 10% of the 30-yr annual ppt values. Tipping point and stable classes occur at elevations that are lower (1 505 m) and higher (1 939 m), respectively, more mesic during March and annually, and experience lower 30-yr July tmax averages. These defined variable averages can be used to understand current dynamics of sagebrush condition and to predict where future transitions may occur under novel conditions.

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“…Remote sensing approaches to mapping cheatgrass distribution, percent cover, and dynamics (e.g., die-off, potential habitat, phenological metrics) generally fall into three categories: those focusing on spectral signatures or phenological indicators in overhead imagery [10,12,13,[16][17][18][19][20][21]; those based on modeling the ecological niche of cheatgrass using known ranges of biophysical conditions of where cheatgrass is known to occur [22,23]; and those combining elements of those two approaches [7,8,11,15,24,25]. Much attention has been given to deriving phenological indicators of cheatgrass presence from spectral indices, such as the Normalized Difference Vegetation Index (NDVI), because its life cycle differs from many of the native plant species in its North American range.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Classification of Cheatgrass Invasion in the Western United States Using Biophysical and Remote Sensing Data

Larson

Tuor

2021

Remote Sensing

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Cheatgrass (Bromus tectorum) invasion is driving an emerging cycle of increased fire frequency and irreversible loss of wildlife habitat in the western US. Yet, detailed spatial information about its occurrence is still lacking for much of its presumably invaded range. Deep learning (DL) has demonstrated success for remote sensing applications but is less tested on more challenging tasks like identifying biological invasions using sub-pixel phenomena. We compare two DL architectures and the more conventional Random Forest and Logistic Regression methods to improve upon a previous effort to map cheatgrass occurrence at >2% canopy cover. High-dimensional sets of biophysical, MODIS, and Landsat-7 ETM+ predictor variables are also compared to evaluate different multi-modal data strategies. All model configurations improved results relative to the case study and accuracy generally improved by combining data from both sensors with biophysical data. Cheatgrass occurrence is mapped at 30 m ground sample distance (GSD) with an estimated 78.1% accuracy, compared to 250-m GSD and 71% map accuracy in the case study. Furthermore, DL is shown to be competitive with well-established machine learning methods in a limited data regime, suggesting it can be an effective tool for mapping biological invasions and more broadly for multi-modal remote sensing applications.

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Spatiotemporal patterns of cheatgrass invasion in Colorado Plateau National Parks

Cited by 26 publications

References 72 publications

Earlier fall precipitation and low severity fire impacts on cheatgrass and sagebrush establishment

Earlier fall precipitation and low severity fire impacts on cheatgrass and sagebrush establishment

Estimating Abiotic Thresholds for Sagebrush Condition Class in the Western United States

Deep Learning Classification of Cheatgrass Invasion in the Western United States Using Biophysical and Remote Sensing Data

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