http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Phenology, the timing of life-cycle events and phases of plants and animals, and their relationship with the environment, especially climate, is an integrative record of the effects of global warming and other environmental changes on biological processes. Current phenology studies focus primarily on structural changes such as bud break, flowering, leaf coloring, and leaf fall. However, the functional aspects of plant activities, although invisible, also provide quantitative measures of plant responses to changes in environmental conditions and underlie the structural changes. Ecosystem processes such as biogeochemical fluxes also show seasonal changes and can be decomposed to key transition dates and phase durations, that characterize the exchanges of mass and energy between plants and the environment and may exert mutual feedback. To address these gaps, this work aims to understand the seasonality of key ecosystem processes. This work consists of four primary contributions in the fields of ecology and phenology. First, chapter 2 generated a standardized flux seasonality data base from global FLUXNET2015 sites, which will help improve assessments of cyclical changes and evaluate the influence that climate and natural and human-caused disturbances have on these rhythms. Second, chapter 3 explored the asynchrony of GPP and ecosystem respiration as well as their sensitivity to temperature, which emphasizes the importance of considering both photosynthesis and respiration in predicting net carbon fluxes induced by phenological shifts. Third, chapter 4 assessed the temporal effects on phenology metrics with eddy covariance data, remote sensing data, and PhenoCam data, in which I quantified the phenology errors caused by data noise and aggregation and found the phenology errors due to temporal resolution mainly come from the data noise. Fourth, chapter 5 quantified the misrepresentation of greenness and photosynthetic phenology in 17 terrestrial biosphere models as well as errors in annual GPP caused by incorrect representation of seasonality. The results showed large differences in seasonality of vegetation greenness and photosynthesis between satellite data and Terrestrial Biosphere Models, which led to an error of 40.57 ± 82.45 g C m^-2 yr^-1 in annual GPP. This work can help to improve the seasonality of ecosystem processes and their response to changing climate, which can improve the understanding of the mechanisms of the seasonality of ecosystem processes and the simulations in the terrestrial biosphere models. The electronic version of this dissertation is accessible from https://hdl.handle.net/1969.1/198689
"Major Subject: Ecology and Conservation Biology"
Doctor of Philosophy Texas A&M University 2022 https://hdl.handle.net/1969.1/198689