Microhabitat Selection for Nesting and Brood-Rearing by the Greater Sage-Grouse in Xeric Big Sagebrush

Kirol, Christopher P.; Beck, Jeffrey L.; Dinkins, Jonathan B.; Conover, Michael R.

doi:10.1525/cond.2012.110024

Cited by 46 publications

(8 citation statements)

References 62 publications

(21 reference statements)

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“…The relationship between plot-and landscape-level factors and PrGRSG-scat in our study (i.e. selection for sagebrush and avoidance of steep slopes) was generally consistent with other studies which used scat (Kirol et al 2012;Arkle et al 2014;Lockyer et al 2015) or radio-telemetry methods to investigate habitat use by GRSG in either burned or unburned areas (Schroeder & Vander Haegen 2011;Anthony et al 2021b;Poessel et al 2022).…”

Section: Discussionsupporting

confidence: 90%

Relationship of greater sage‐grouse to natural and assisted recovery of key vegetation types following wildfire: insights from scat

Germino

Anthony

Kluender

et al. 2022

Restoration Ecology

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Megafires are creating severe conservation problems worldwide for wildlife that have obligate dependencies on plant species that are foundational but fire‐intolerant. Wildfire‐induced loss of native perennials and increases in exotic annual grasses threaten greater sage‐grouse (GRSG, Centrocercus urophasianus) in its sagebrush steppe habitat in western North America. Post‐fire restoration using herbicides, seeding, and planting of native perennials such as sagebrush are common, but there are few assessments of GRSG response to the treatments. We measured the presence of GRSG scat and modeled the probability of GRSG presence (PrGRSG‐scat) in relation to variation in plot‐level and landscape‐level predictors, and land treatments, in an intensive, repeat sampling from 2017 to 2020 of 113,000 ha area burned in 2015 in the Soda Megafire (Oregon and Idaho, U.S.A.). GRSG scat was present in less than 200 of more than 8,000 observations, as would be expected for a philopatric species (i.e. high fidelity to home site) returning to degraded habitat. PrGRSG‐scat was positively associated with sagebrush presence at the plot level and was positively related to elevation, lower‐angle slopes, and proximity to sagebrush seedling outplant islands. The statistical significance of relationships of PrGRSG‐scat to restoration treatments was marginal at best, with the largest effect being a positive response of PrGRSG‐scat to pre‐emergent herbicide sprayed to reduce exotic annual grasses. More time may be required for restored sagebrush steppe to meet GRSG needs or for GRSG to “adopt” the restored vegetation. Moreover, whereas scat is a convenient and non‐invasive method to monitor GRSG, its post‐fire scarcity weakens the strength of statistical inference on GRSG recovery patterns and response to restoration.

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Section: Discussionsupporting

confidence: 90%

Relationship of greater sage‐grouse to natural and assisted recovery of key vegetation types following wildfire: insights from scat

Germino

Anthony

Kluender

et al. 2022

Restoration Ecology

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“…for nesting sites (Connelly, Schroeder, Sands and Braun, 2000;Hagen, Connelly and Schroeder, 2007) Selection for areas of greater cover and herbaceous heights for nest sites can be higher in xeric sagebrush habitats types (e.g. Wyoming big sagebrush sites receiving annually <25 cm of precipitation; Kirol, Beck, Dinkins and Conover, 2012;Boyd, Beck and Tanaka, et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

“…Early brood-rearing habitat is generally defined by greater canopy cover of sagebrush, with greater abundances of forb and insects (Connelly, Schroeder, Sands and Braun, 2000;Aldridge and Brigham, 2002;Kirol, Beck, Dinkins and Conover, 2012). Late brood-rearing habitat is generally defined by more mesic sites that contain forbs and insects (Holloran, 1999;Connelly, Schroeder, Sands and Braun, 2000;Connelly, Rinkes and Braun, 2011;Kirol, Beck, Dinkins and Conover, 2012). Also, sage-grouse broods have been shown to avoid areas with higher densities of predators (Dinkins, Conover, Kirol and Beck, 2012;Dinkin, Conover, Kirol, Beck and Frey, 2014;Mabray and Conover, 2015).…”

Section: Introductionmentioning

confidence: 99%

Sage-Grouse Nesting, Brood and Winter Habitat Characteristics in Montana

Woodward¹,

Sika

Wambolt³

et al. 2016

EMSD

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Greater sage-grouse (Centrocercus urophasianus) habitat characteristics were studied in central Montana primarily on Wyoming big sagebrush (Artemisia tridentata Nutt. ssp. wyomingensis Beetle & Young) dominated rangeland. The primary objective was to compare shrub and herbaceous parameters within (use, random or non-use) and between seasonal habitats (nest, brood, winter). Two study sites (Musselshell and Golden Valley counties), and 2 years (2004 and 2005) were compared. Nest, brood, and random sites were compared for herbaceous cover, and grass height (n = 648). Nest, brood, random, winter use and winter non-use sites were evaluated for shrub cover, density, and height. All differences were considered significant at P ≤ 0.05. Sage-grouse nested in areas with greater total shrub cover and height, and taller live and residual grass than was randomly available. No differences were found between brood and paired random sites for any of the herbaceous or shrub parameters measured. Shrub cover and density were greater at winter use sites than non-use sites. Winter use sites had less shrub cover than nest sites. The nest and brood habitat had similar shrub cover, density, and height on the study area. Sage-grouse habitats should be managed to include sagebrush, forbs, and grass. Herbaceous vegetation was more important during nesting and brood rearing than during the winter. Therefore, some portions of sage-grouse habitat may benefit from management for greater herbaceous cover, but not at the expense of removing sagebrush. Sagebrush cover from 10 to 15 percent was the most consistent component of sage-grouse habitat.

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“…It should be noted here that the methods outlined in this paper do not distinguish directly between biocrust cover underneath a shrub versus interspace. Percent biocrust cover (inbetween and under shrub canopies) has been found to be an important factor in microhabitat selection for nesting sage-grouse [Kirol et al, 2012].…”

Section: Discussionmentioning

confidence: 99%

“…Biocrusts are also sensitive to long term changes in precipitation and temperature [Ferrenberg et al, 2015]. Observing changes in biocrust cover and composition spatially and through time can be used to assess ecosystem health, disturbance, and changes in climate [Belnap et al, 2001;Kirol et al, 2012;Blay et al, 2017].…”

Section: What Are Biological Soil Crusts?mentioning

confidence: 99%

Remote Sensing Time-Series Analysis, Machine Learning, and K-Means Clustering Improves Dryland Vegetation and Biological Soil Crust Classification

Enterkine¹

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Dryland and semi-arid vegetation communities, although appearing to the casual observer as relatively simplistic and homogeneous, are in fact the opposite. Upon further inspection, semi-arid vegetation is highly complex and heterogeneous at almost any scale. The same holds true for biological soil crust. Growing concern about global changes in climate, nutrient cycles, and land use have required increasing scrutiny of our understanding of these communities and all of their constituents, as we seek to improve forecasting models and inform land management decisions. This thesis aims to provide insight to the paradigm of how we create and interpret vegetation classifications in a semi-arid ecosystem. In the first chapter, I examine the potential of new remote sensing imaging platforms in combination with machine learning algorithms and cloud computing as they apply to time-series analyses for vegetation classification. The results of this indicate that sinusoidal approximations ("Harmonic Models") of vegetation indices are able to predict vegetation cover with nearly the same accuracy as monthly composites, and that a combination of both perform no better than either. Additionally, I examine how assigning classes to training data (e.g. species-level, plant functional type) influence the classification accuracy, interpretability, and potential uses. Stricter class membership requirements at increasingly aggregated scales (e.g. PFT) lead to greater accuracies. Finding the implication of this conclusion unsatisfactoryat the extreme, everything was either cheatgrass or bare, the shrub class succumbing almost entirely to vii errors of omission-I investigated approaching other methods to assign classes to field data that captured more of the realities of semi-arid vegetation within our study area. To this extent, k-means clustering was used to determine what community classes were present in the field data. The outcome of this approach is a class where each potential constituent cover has a known distribution. Overall accuracies were found to be lower for this approach. However, the classification outcomes quantify overlapping distributions of cover types (e.g. 'sagebrush' or 'shrub') between classes. These accuracies are assessed using 'fuzzy' confusion matrices. This enables more information to be preserved through the remote sensing classification process, and reserves more interpretation for the map user than a typical 'hard' classification. Importantly the distributions of cover types are likely most representative of field conditions and thus more useful to land managers making holistic decisions about restoration or fuel management. For the second chapter, I delved deeper into the potentials of new remote sensing and computing platforms to predict biological soil crust cover. The growing field of research on biological soil crust points to potentially significant implications for nutrient and water cycling, in addition to positive effects on native vascular vegetation. However, spatial data are lacking due to remot...

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Microhabitat Selection for Nesting and Brood-Rearing by the Greater Sage-Grouse in Xeric Big Sagebrush

Cited by 46 publications

References 62 publications

Relationship of greater sage‐grouse to natural and assisted recovery of key vegetation types following wildfire: insights from scat

Relationship of greater sage‐grouse to natural and assisted recovery of key vegetation types following wildfire: insights from scat

Sage-Grouse Nesting, Brood and Winter Habitat Characteristics in Montana

Remote Sensing Time-Series Analysis, Machine Learning, and K-Means Clustering Improves Dryland Vegetation and Biological Soil Crust Classification

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