UMD Theses and Dissertations
Permanent URI for this collectionhttp://hdl.handle.net/1903/3
New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a given thesis/dissertation in DRUM.
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Item MULTISCALE, MULTITEMPORAL ASSESSMENT OF CHIMPANZEE (Pan troglodytes) HABITAT USING REMOTELY SENSED DATASETS(2023) Jantz, Samuel M; Hansen, Matthew C; Geography/Library & Information Systems; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)All four sub-species of our closest living relative, the chimpanzee, are listed as endangered by the International Union for the Conservation of Nature (IUCN), and their populations continue to decline due to human activities. Effective conservation efforts require information on their population status and distribution. Traditional field surveys are expensive and impractical for covering large areas at regular time intervals, making it difficult to track population trends. Given that chimpanzees occupy a large range (2.3 x 106 km2), new cost-effective methods and data are needed to provide relevant information on population status and trends across large geographic and time scales. The objective of this dissertation is to help fill this gap by leveraging freely available and regularly updated remotely sensed datasets to map and monitor chimpanzee habitat across their range. This research begins by first producing annual forest cover and change maps for the Greater Gombe (GGE) and Greater Mahale ecosystems (GME) in western Tanzania using Landsat phenological metrics and machine learning methods. Canopy cover was predicted at 30-meter resolution and the Cumulative Sums (CuSum) algorithm was applied to the canopy cover time series to detect forest loss and gain events between 2000-2020. An accuracy assessment showed the CuSum algorithm was able to detect forest loss well but had more difficulty detecting gradual forest gain events. A total of 276,000 ha (+/- 27,000 ha) of gross forest loss was detected between 2000 and 2020 in the GGE and GME; however, loss was not spread equally among the two ecosystems. The results show widespread forest loss in the GME, contrasted with net forest cover gain in the GGE. Next, the annual forest cover maps, and additional derived variables, were used to train an ensemble model to predict the relative encounter rate of chimpanzee nest sightings in the GGE and GME. Model output exhibited a strong linear relationship to chimpanzee abundances and population density estimated from a recent ground survey, enabling model output to be linearly transformed into population estimates. The model predicted the two ecosystems harbor just over 3,000 individuals, which agrees with the upper limit of population estimates from ground surveys. Most importantly, the model can be applied to annually updated variables enabling the detection of potential population shifts caused by changes in landscape condition. Model output indicates a possible population reduction in portions of the GME, while the GGE is predicted to have increased its ability to sustain a larger population. Finally, Random Forests regression was used to relate predictor variables, primarily derived from Landsat data to a coarse resolution, range-wide habitat suitability map enabling the prediction of habitat suitability at 30 meter resolution. The model showed good agreement with the calibration data; however, there was considerable variation in predictive capability among the four chimpanzee sub-species. Elevation, Landsat ETM+ band 5 and Landsat derived canopy cover were the strongest predictors; highly suitable areas were associated with dense tree canopy cover for all but the Nigeria-Cameroon and Central Chimpanzee sub-species. The model can detect changes in suitability to support monitoring and conservation planning across the chimpanzee range. Results from this dissertation highlight the promise of integrating continuously updated satellite data into habitat suitability models to detect changes through time and inform conservation efforts for chimpanzees at multiple scales.Item Understanding organic and conventional management programs and rhizosphere microbiome for sports turf in Maryland(2023) Peddigari, Shravya; Carroll, Mark; Plant Science and Landscape Architecture (PSLA); Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In response to public concerns about exposure to pesticides, some state and local municipalities have placed restrictions on the use of pesticides on athletic fields. When such restrictions are implemented athletic field management often transitions to the use of natural or organic turf care with little understanding of how the transition away from conventional management practices may affect surface conditions and soil microbial properties.This thesis examined the use of organic and conventional management programs on the turf quality, surface hardness, and shear strength of engineered soil cap, hybrid bermudagrass (Cynodon dactylon x Cynodon transvaalensis) athletic fields, as well as the impact of the two programs on the rhizosphere microbiome. Turf quality was assessed by visual means and by obtaining normalized difference vegetative index (NDVI) readings of the turf canopy. Surface hardness was determined using a Clegg impact surface tester. The rotational shear strength of the surface was measured using a shear vane. The study was conducted for 3 years at two different locations; research plots at the University of Maryland Research Facility and on athletic fields at Laytonia Recreational Park, in Gaithersburg, MD. Surface property data was collected monthly. Turf visual quality and NDVI data revealed use of the organic management program led to higher visual quality during spring, which was primarily the result of the spring retention of fall overseeded intermediate ryegrass (Lolium x hybridum Hausskn) and early season use of natural based fertilizers. In the summer months, crabgrass (Digitaria ischaemum Schreb.) encroachment was limited to the organically managed turfgrass. At both locations, clover (Trifolium repens) encroachment developed by the third year of the study, but the presence of this weed had limited impact on turfgrass quality. There were few significant differences in surface hardness and shear strength between the two management practices over the entire study period. The rhizosphere microbiome data, which was collected 12, 20, and 24 months after the initiation of two programs, did not show any significant difference between the organic and conventional management athletic fields in microbial abundance and/or diversity. The results of this study emphasize that the adoption of organic management programs on bermudagrass athletic fields should, in most instances, center on the establishment of acceptable weed tolerance levels for these fields. The use of organic management programs in the transition zone offers a viable alternative to the conventional chemical management of athletic fields, however over time, growing weed seed banks may necessitate the need for the occasional use of conventional herbicide materials.Item Integrating genetic information with macroscale models of species' distributions and phenology: a case study with balsam poplar (Populus balsamifera L.)(2019) Gougherty, Andrew Vincent; Fitzpatrick, Matthew; Marine-Estuarine-Environmental Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)To keep pace with future climate change, forest tree species are often predicted to need to shift their geographic ranges and phenology to minimize exposure to climates they have not experienced in the recent past. While many approaches have been developed to predict range shifts and shifting phenology, most large-scale, spatial techniques do not explicitly account for intraspecific genetic variation. This can be problematic when populations are locally adapted to climate, a common characteristic of plant species, as species-level responses to climate may not be representative of populations. In this dissertation, I use balsam poplar (Populus balsamifera L.), a northern North American deciduous tree species, to test a variety of techniques of integrating genetic information with spatial models of balsam poplar’s distribution and phenology. First, I tested multiple hypotheses, identified in the literature, for their ability to predict genetic diversity in balsam poplar. Results show that diversity in balsam poplar was highest in the center of the range and lowest near the range edge – consistent with the ‘central-periphery hypothesis.’ Second, I tested whether genetically-informed distribution models are more transferable through time, than standard distribution models. Using pollen and fossil records to validate models, I show that standard and genetically-informed distribution models perform similarly through time, but genetically-informed models offer additional insights into where populations may have originated on the landscape during the last glacial maximum. Third, I developed a new approach to predict population’s exposure to future climate change. Using spatial models of adaptive genetic differentiation, I show that populations in the eastern portion of balsam poplar’s range have the greatest predicted exposure to climate change as they would need to migrate the furthest and will see the greatest disruption in their gene-climate association. Fourth, I assessed whether a genomic prediction of common garden observations of phenology can inform phenology measured on the landscape with remote sensing. I show that the genomic prediction was the most important variable explaining the date of spring onset on the landscape, but was relatively unimportant in predicting the heat sum accumulated at the date of spring onset. I also show that model error was correlated with multiple meteorological variables, including winter temperatures – illustrating the challenges of predicting phenology in changing climates.