The Relative Importance of Fire History, Management Treatments, Biotic, and Abiotic Factors on the Abundance of Key Vegetative Components in an Endangered Sagebrush-Steppe Ecosystem

Raymondi, Ann Marie

doi:10.18122/b29h8m

Cited by 2 publications

(3 citation statements)

References 99 publications

(127 reference statements)

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“…Military installations also have restricted access to the public and to grazing, and have therefore preserved pristine community types. Raymondi [2017] also found a positive relationship between sagebrush cover and distance to the nearest grazing allotment with biocrust cover for unburned areas, and with native non-sagebrush shrubs in burned areas. This gradient of biocrust cover with its known cover associations betoken the BOP as a suitable study location for exploring remote sensing approaches.…”

Section: Study Areamentioning

confidence: 70%

“…The added factor of invasive species within the study area mean that biocrust is often replaced by Bromus tectorum (cheatgrass) or other invasive species. B. tectorum cover has a negative relationship with biocrust cover [Bansal and Sheley, 2016;Raymondi, 2017]. The other end of the spectrumcommunities of native vegetation with robust biocrust cover -owe their thanks to the history of scientific study in the BOP and to the military installations in the area.…”

Section: Study Areamentioning

confidence: 99%

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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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Section: Study Areamentioning

confidence: 70%

Section: Study Areamentioning

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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“…Two species were used to ascertain whether the symbiosis varies between different hosts, and to gain a broader understanding of possible effects of the isolated fungus on plants. Furthermore, the restoration of sagebrush habitats often involves seeding with P. secunda (Raymondi, 2017). Thus, microorganisms present in this plant and the surrounding soil could potentially spread to and alter the microbiome and performance of A. tridentata (Serpe et al, 2020).…”

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A Symbiosis Between a Dark Septate Fungus, an Arbuscular Mycorrhiza, and Two Plants Native to the Sagebrush Steppe

Carpenter¹

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Plant roots form symbioses with various fungi, including arbuscular mycorrhizae (AMFs) and dark septate endophytes (DSEs). The symbiosis between plants and AMFs has been extensively studied and is generally considered to be mutualistic. Much less is known about the symbiosis between plants and DSE. In sagebrush habitats, DSEs are common, but their effects on the vegetation are unclear. As a first step to study these effects, I isolated and cultured a DSE from the roots of the shrub Artemisia tridentata. Based on partial sequences of five genes and phylogenetic analyses, the isolated fungus was a non-described species within the Darksidea or a closely related sister group. Subsequently, I performed experiments in vitro and in potted plants to determine the effect of the isolated DSE on root tissue integrity, colonization by the AMF Rhizophagus irregularis, and plant biomass. These experiments were conducted in two plant species, A. tridentata and the native grass Poa secunda. Plants were exposed to one of four treatments: no inoculation (-AMF-DSE), inoculation with the DSE isolate (-AMF+DSE), inoculation with R. irregularis (+AMF-DSE), and inoculation with both fungi (+AMF+DSE). Microscopic observations revealed that the DSE hyphae grew along the root surface and penetrated epidermal and cortical cells without damage to them. In A. tridentata, the hyphae also reached the stele. For both species, total DSE colonization in the –AMF+DSE treatment was similar to that in the +AMF+DSE treatment, indicating the presence of AMF did not alter DSE colonization. Inoculation with DSE did not affect total AMF colonization of A. tridentata; however, it increased total colonization of P. secunda from 16.9 (+5.6%) in the +AMF-DSE treatment to 42.6 (+2.9%) in the +AMF+DSE treatment. Also, in both species, the presence of the DSE more than doubled the frequency of AMF intraradical storage structures, which consisted of vesicles plus intraradical spores. These results suggest that via increases in AMF colonization, DSE could lead to a beneficial effect on the host plants. However, neither on its own nor through co-inoculation with AMF, did the DSE isolate affect plant biomass. Thus, under the two conditions tested, the symbiosis was commensalistic. Further work is needed to evaluate the symbiosis in settings that better mimic the natural environment.

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The Relative Importance of Fire History, Management Treatments, Biotic, and Abiotic Factors on the Abundance of Key Vegetative Components in an Endangered Sagebrush-Steppe Ecosystem

Cited by 2 publications

References 99 publications

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

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

A Symbiosis Between a Dark Septate Fungus, an Arbuscular Mycorrhiza, and Two Plants Native to the Sagebrush Steppe

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