MEES Theses and Dissertations

Permanent URI for this collectionhttp://hdl.handle.net/1903/19655

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    USING AN INDIVIDUAL BASED MODEL TO EVALUATE THE EFFECTS OF CLIMATE CHANGE ON THE REPRODUCTIVE PHENOLOGY OF EELGRASS (ZOSTERA MARINA L.) ALONG A LATITUDINAL GRADIENT
    (2017) Foley, Jessica Lynn; Harris, Lora A; Marine-Estuarine-Environmental Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    I explored the effects of climate change on the reproductive biology of the clonal marine angiosperm Zostera marina L. (eelgrass) using an individual-based model. The model captures whole plant ontogeny, morphology, and ecophysiology from seed to reproductive adult to simulate the plasticity of eelgrass in response to environmental variables. Using a latitudinal gradient as a proxy for climate change, virtual seeding experiments were performed in three locations along the East coast of the United States. I simulated the impacts of increased temperatures on Z. marina’s biomass, reproductive phenology, and life history. Warmer temperatures resulted in a modeled decrease of Z. marina’s total biomass, as well as altered reproductive timing and strategy. These results have implications for long term predictions of Z. marina persistence in its traditional biogeographic range, and indicate adaptation via shifts in phenology and reproductive strategy may interact to dampen some negative consequences of increased temperatures.
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    Assessing the influence of abiotic factors and leaf-level properties on the stability of growing-season canopy greenness in a deciduous forest
    (2016) Cunningham, Vanessa M.; Nelson, David M; Elmore, Andrew J; Marine-Estuarine-Environmental Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Maps depicting spatial pattern in the stability of summer greenness could advance understanding of how forest ecosystems will respond to global changes such as a longer growing season. Declining summer greenness, or “greendown”, is spectrally related to declining near-infrared reflectance and is observed in most remote sensing time series to begin shortly after peak greenness at the end of spring and extend until the beginning of leaf coloration in autumn,. Understanding spatial patterns in the strength of greendown has recently become possible with the advancement of Landsat phenology products, which show that greendown patterns vary at scales appropriate for linking these patterns to proposed environmental forcing factors. This study tested two non-mutually exclusive hypotheses for how leaf measurements and environmental factors correlate with greendown and decreasing NIR reflectance across sites. At the landscape scale, we used linear regression to test the effects of maximum greenness, elevation, slope, aspect, solar irradiance and canopy rugosity on greendown. Secondly, we used leaf chemical traits and reflectance observations to test the effect of nitrogen availability and intrinsic water use efficiency on leaf-level greendown, and landscape-level greendown measured from Landsat. The study was conducted using Quercus alba canopies across 21 sites of an eastern deciduous forest in North America between June and August 2014. Our linear model explained greendown variance with an R2=0.47 with maximum greenness as the greatest model effect. Subsequent models excluding one model effect revealed elevation and aspect were the two topographic factors that explained the greatest amount of greendown variance. Regression results also demonstrated important interactions between all three variables, with the greatest interaction showing that aspect had greater influence on greendown at sites with steeper slopes. Leaf-level reflectance was correlated with foliar δ13C (proxy for intrinsic water use efficiency), but foliar δ13C did not translate into correlations with landscape-level variation in greendown from Landsat. Therefore, we conclude that Landsat greendown is primarily indicative of landscape position, with a small effect of canopy structure, and no measureable effect of leaf reflectance. With this understanding of Landsat greendown we can better explain the effects of landscape factors on vegetation reflectance and perhaps on phenology, which would be very useful for studying phenology in the context of global climate change