The goal of this thesis was to examine the breeding system and natural history of the Maguire primrose (Primula cusickiana var. maguirei). Maguire primrose is an endemic, threatened subspecies found only along a narrow corridor within Logan Canyon in northern Utah, USA. This plant displays distinct flower distyly, with clear distinction of pin and thrum morphologies (morphs). The timing of Maguire primrose flower blooms was disparate between upper and lower canyon populations, and the flowers experienced cool temperatures occasionally. I captured eight different species of flying insects visiting Maguire primrose flowers, and made 67 observations of insect visitation. Inter-morph outcrossing hand pollinations were the most successful hand pollinations performed, but were usually not as successful as those that were naturally pollinated. Selfing rates were quite low compared to outcrossing reproductive scenarios. Morph-specific fecundity differences were detected in my hand pollinations, but not in the naturally pollinated maternal plants. I did not observe clear fecundity differences based on the canyon location of Maguire primrose.

Agriculture plays a crucial role in ensuring global food security, but its intensification has resulted in groundwater depletion, particularly in arid regions like Saudi Arabia. Although the significance of agriculture in Saudi Arabia is well-recognized, there is limited understanding of the agroinformatics aspects required to manage them at a regional or national level. High-resolution satellite data has the potential to provide valuable insights, including the number, location, size, and crop type of agriculture fields, as well as patterns of behavior. Machine learning techniques have emerged as the state-of-the-art methods to extract agricultural informatics from satellite data due to their efficiency and accuracy. However, in regions like Saudi Arabia where even basic agroinformatics data is not routinely available, the lack of ground truth data required to drive machine learning approaches is a critical consideration in model selection. One potential solution is to create a dataset by collecting field data or interpreting satellite imagery using human interpreters, but this can be time-consuming and labor-intensive. Another option is to explore unsupervised techniques that require limited or no ground truth data, but this can result in accuracy sacrifices. Ultimately, we aim to strike a balance between usability, data availability, accuracy, and computational efficiency when developing solutions to address these issues. In this study, a hybrid machine learning framework was developed to accurately delineate agricultural fields in a regional scale of Saudi Arabia, with high accuracy and stable transferability when applied to different temporal and spatial regions across the country. The framework was used to conduct the first retrospective analysis of agriculture activity over three decades on a national scale, including changes in the number, acreage, field size distribution, and the dynamics of expansion and contraction of center-pivot fields. Additionally, a novel unsupervised framework was developed to identify within-field dynamics and map critical crop phenology stages and crop types, providing valuable information for in-field agricultural practices. The agroinformatics retrieved in this study can provide valuable insights for policymakers, farmers, and other stakeholders involved in agriculture and environmental management and exhibited significant implications for the management and sustainability of agricultural systems in Saudi Arabia and other regions facing similar challenges.

Foraging decisions of large herbivores can be affected by many factors and have great effect on both above- and below-ground ecosystems. Here, I determine to which degree temporal (seasonality), or spatial (habitat) factors influence the foraging behaviour of Svalbard reindeer (Rangifer tarandus platyrhynchus) and asses if small-scale foraging behaviour correlates with an animals’ patch residence time. I analysed behavioural observations on 15 Svalbard reindeer between summer and autumn 2022, recording both behaviour and habitat use. Using mixed-effects multinomial logistic regression, the interaction between seasonal change from summer to autumn and habitat best explained the variation in Svalbard reindeer foraging behaviour. The foraging behaviour “standing-grazing” was found to have the highest observed amount in all habitat types and increased over the growing season. Contrastingly, the foraging behaviour of “walking-grazing” had its highest abundance in the beginning of the study period, and then decreased over the course of the season for all analysed habitats. In addition, “walking-grazing” was found to be significantly correlated with patch residency time and decreased the longer an individual stayed in a patch. With presumable high plant quality early in the season, my results imply how Svalbard reindeer are greatly stationary foragers, but prioritizing to become relatively mobile when it is beneficial. Thus, highlighting how Svalbard reindeer respond dynamically to shifts in foraging conditions. These findings provide knowledge about what factors may impact the foraging behaviours of Arctic herbivores and helps contribute to a broader understanding of how changes in foraging behaviours may alter arctic ecosystems.

Achieving societal well-being goals is inextricably linked to the preservation of many ecosystem functions. This dissertation adopts a plant lens, to contribute to our understanding of sustainable ecosystem functioning. Specifically, it sheds light on some plant physiology, phenology, and ecology processes, which matter for sustainable development: tree growth response to high temperatures, annual fluctuations in the timing of plant flowering, and ecological benefits of crop diversity that translate into economic returns. In addition, it illustrates how large-scale data proxies can be used to document large scale patterns that arise from individual plant processes. Chapter 1 documents a new methodology for estimating tree-level temperature response curves, using tree ring data and a degree-day framework. It uses those curves to document harmful impacts of high temperatures for tree growth across the US, and shows that there is limited acclimatization, but some adaptation to those high temperatures in a sample of climate sensitive and long-lived trees. Chapter 2 shows that satellite imagery and deep learning tools can be leveraged, to monitor interannual variations in the timing of plant flowering at large scales. It documents the predictive performance of two models: one adapted to monitoring crop flowering, the other adapted to monitoring shifts in the onset of spring flowering. Finally, chapter 3 highlights remaining gaps between empirical evidence of crop diversity benefits, and portrayal of those benefits in economic models of optimal crop diversity choice. Together, these chapters illustrate that bridging scales and disciplines is a difficult task, although it is necessary for understanding the sustainability of the human environmental footprint.

Ectotherms (including marine turtles) being especially sensitive to climate, are at risk to the accelerated rate of human-driven climate change. This study addresses two concerns associated with marine turtles and climate change – the relationship between the timing of marine turtle nesting and sea surface temperature; and the concern over the feminization of marine turtle populations due to rising sand temperatures. Previous studies of loggerhead sea turtles (Caretta caretta) and green sea turtles (Chelonia mydas) have documented the relationship between sea surface temperatures and nesting phenology. Earlier nesting behaviors in both species have been associated with warmer sea surface temperatures. Also, sex determination for marine turtles is temperature-dependent. Due to current sand temperatures, it is estimated that loggerhead (Caretta caretta) nests along the Atlantic coast of Florida already produce over 89% female hatchlings. Using shade to reduce nest temperature and increase the proportion of male hatchlings is one option for mitigating the impacts of climate change on marine turtle sex ratios. In this study, a 21- year (1988-2008) dataset of hawksbill sea turtle (Eretmochelys imbricata) nesting at Buck Island Reef National Monument, St. Croix, U. S. Virgin Islands was analyzed in a similar manner to previous studies. It was found that warmer sea surface temperatures were associated with longer nesting seasons and later median nesting dates. Additionally, a preliminary sand shading study was conducted in the first field season (2011) with a subsequent loggerhead nest shading study in the following field season (2012). Although hatching success was not significantly impacted, temperatures were significantly reduced in the majority of shaded nests. This practice may not be immediately applicable as a means of managing sex ratios, but it could be used to reverse the temperature effects of nest relocation

With a warming climate and the usage of monocultures in food production it is possible we may face more issues concerning food insecurity in the future as pollinators struggle to find food in agricultural landscapes. Therefore, it is of value to know whether common agricultural weeds are important sources of food for pollinators. Pollinators are vital in the growing of food-crops and may depend on different agricultural weeds during their flying season. This study is based on observational citizen science data on the flowering of 24 agricultural weeds classified as useful to pollinators in the years 2008-2022. Data on first flowering and length of flowering was compared between life history classifications based on life span and time of germination. The results showed that while there is variation both between groups and within groups among the selected species, summer annuals are among the last to start flowering. Species classed as “summer and winter annuals”, i.e that can germinate either in spring or autumn, had a longer flowering period than the other classes. Winter annuals were first to flower while the perennials and “summer and winter annuals” differed more among each other. Weather dependency was also addressed by comparison over years. Overall, the selected species were not significantly affected by mean winter temperature, mean summer temperature or mean summer precipitation.

A one-year study was conducted in the Central Coast of California at a commercial vineyard to evaluate the effect of supplemental irrigation on dry-farmed Vitis vinifera L. cv. Zinfandel with varying vine ages during the 2021 growing season. The experimental block was historically dry-farmed on own-rooted Zinfandel vines, older vines were replaced as production quality decreased with a genetically identical scion grafted onto St. George (Vitis vinifera Scheel) rootstock. Six total treatments were included in this study, with Young vines (5 to 12 years old), Old vines (40 to 60 years old), and Control (2:1 ratio of old to young vines, and representation of the block). Each vine age treatment included both non-irrigated and irrigated vines, the total vine sample contained half irrigated and half non-irrigated. Irrigation was manually applied at véraison and véraison + 4 weeks, based on age-specific ETc, and to replenish 95% ETc. Results indicated no significant changes in phenological progression, leaf senescence, and physical berry analysis caused by supplemental irrigation during key developmental stages. Vine age was the primary driver of significant variation observed for most parameters. However, irrigated vines had slightly higher phenological progression leading up to harvest, although not statistically significant. Due to this minor trend, irrigated vines in each vine age group were harvested before the non-irrigated vines starting with young, control, and old vines. Additionally, lower leaf water potential was found at pre-dawn during the second irrigation application at véraison + 4 weeks. Applying supplemental irrigation during the growing season resulted in no significant impacts on vine performance. Results suggest the potential for implementing a dry-farmed management in vineyards to help adapt to climate changes and water scarcity issues.

Gopher tortoises are ecosystem engineers whose burrows provide habitat to >350 species. Prescribed fire is used to manage tortoise habitat, but fire timing is mostly restricted to the vegetative dormant season. Restricted fire timing in combination with white-tailed deer competition may negatively affect tortoises. To address these concerns, we quantified these species’ dietary overlap and conducted a field experiment to examine impacts of fire phenology on plants and animals. Although tortoises and deer consumed ~75% of the same plants, their diets were statistically dissimilar. Fire altered plant community composition and increased foliar crude protein and phosphorus while decreasing calcium. Deer detections were unaffected, but tortoises were detected more in fire treatment plots. We simultaneously monitored burrow and surface temperatures and found burrows provide thermal refuge. Our data suggests that fire timing affects plants in ways that can affect gopher tortoises, and burrows may mitigate some negative impacts of climate change.

No description available.

This dissertation investigated the practicality and expediency of applying remote sensing data fusion products to the analysis of dryland vegetation phenology. The objective of the first study was to verify the quality of the output products of the spatial and temporal adaptive reflectance fusion method (STARFM) over the dryland Arizona study site. Synthetic 30 m resolution images were generated from Landsat-5 Thematic Mapper (TM) data and a range of 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance datasets and assessed via correlation analysis with temporally coincident Landsat-5 imagery. The accuracy of the results (0.61 < R2 < 0.94) justified subsequent use of STARFM data in this environment, particularly when the imagery were generated from Nadir Bi-directional Reflectance Factor (BRDF)-Adjusted Reflectance (NBAR) MODIS datasets. The primary objective of the second study was to assess whether synthetic Landsat data could contribute meaningful information to the phenological analyses of a range of dryland vegetation classes. Start-of-season (SOS) and date of peak greenness phenology metrics were calculated for each STARFM and MODIS pixel on the basis of enhanced vegetation index (EVI) and normalized difference vegetation index (NDVI) time series over a single growing season. The variability of each metric was calculated for all STARFM pixels within 500 m MODIS extents. Colorado Plateau Pinyon Juniper displayed high amounts of temporal and spatial variability that justified the use of STARFM data, while the benefit to the remaining classes depended on the specific vegetation index and phenology metric. The third study expanded the STARFM time series to five years (2005-2009) to examine the influence of site characteristics and climatic conditions on dryland ponderosa pine (Pinus ponderosa) forest phenological patterns. The results showed that elevation and slope controlled the variability of peak timing across years, with lower elevations and shallower slopes linked to higher levels of variability. During drought conditions, the number of site variables that controlled the timing and variability of vegetation peak increased. / Ph. D.