The interactions between vegetation and climate are complex and critical to our ability to predict and mitigate climate change. Savanna ecosystems, unique in their structure and composition, are particularly dynamic and their carbon cycling has been identified as highly significant to the global carbon budget. Understanding the responses of these dynamic ecosystems to environmental conditions is therefore central to both ecosystem management and scientific knowledge. Longleaf pine ecosystems are highly biodiverse and unique savanna ecosystems located in the south-eastern USA – an important current carbon sink and key area identified for future carbon sequestration. These ecosystems depend on fire to maintain their structure and function, and the longleaf pine tree itself (Pinus palustris Mill.) has been noted for its resilience to drought, fire, pests and storms and is thus becoming increasingly attractive as both a commercial forestry species and a provider of other ecosystem services. Previous process-based models tested in the south-eastern USA have been shown to fail in conditions of drought or rapid disturbance. Consequently, in order to inform management and understand better the physiology of these ecosystems, there is a need for a process-based model capable of upscaling leaf-level processes to the stand scale to predict GPP of longleaf pine savannas. P.palustris exists across a wide range of soil moisture conditions, from dry sandy well-drained soils (xeric) to claypan areas with higher moisture content (mesic). Previous work has demonstrated that this species adjusts many aspects of its physiology in response to these differing soil conditions, even under identical climate. The research in this thesis supports these previous findings, but additionally explores, with the assistance of the Soil Plant Atmosphere model (SPA), the productivity response of P. palustris across the soil moisture gradient. Contrary to expectations, measurements, field observations and modelling suggest that P. palustris trees growing in already water-limited conditions cope better with exceptional drought than their mesic counterparts. At the leaf-level, xeric P. palustris trees were found to have higher measured net photosynthesis, but the lower stand density and leaf area at this site meant that in non-drought conditions mesic P. palustris annual gross primary productivity (GPP) was 23% greater than xeric annual GPP. Initial upscaling of leaf-level processes to the canopy scale using the SPA model found that, during the growing season when other components of longleaf pine ecosystems are active, the longleaf pine may only be responsible for around 65% of the total productivity. Other important components of longleaf pine savannas are oaks and grasses which, with pine, constitute 95% of longleaf pine ecosystem biomass. Each of these groups, however, responds differently to fire and water availability. Despite this, the other components of longleaf pine savannas have received limited research attention and have never been modelled using a process-based model such as SPA. As integral components of longleaf pine carbon budgets, it is essential that the physiology and productivity of oaks and grasses in this system are better understood. The research in this thesis studied the productivity response of these groups during drought across a soil moisture gradient, and found that oak and pines at each site appear to fill separate ecohydrological niches depending on whether or not they are growing in a xeric or mesic habitat. As expected, the highest drought tolerance was found in the C4 grass, wiregrass (Aristida stricta), at both xeric and mesic sites. In order to further explore the contributions of the different functional groups in longleaf pine savannas, the SPA model was adapted to run with concurrent functional groups and to represent the different photosynthetic pathways of the understorey grasses (C4) and the canopy trees (C3). The aim of this part of the thesis was to represent better a savanna ecosystem in a process-based model and explore and quantify the contributions of each functional group diurnally, seasonally, annually and interannually. Modelling results suggest that accurately representing the phenology not only of trees but of grasses, is critical to capturing ecosystem GPP and its variability. This phenology may not only be seasonally controlled, but also dictated by fire. Overall, this research highlights the importance of continued research into savanna and savanna-like ecosystems. Additionally, it provides an insight into the responses of multiple ecosystem components to an extreme drought, and how these responses differ at leaf, stand and landscape scales. The thesis also employs a little-used method of combining eddy-covariance data with a process-based model to separate out different ecosystem components, a method becoming more common but not yet widely tested.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:669324 |
| Date | January 2013 |
| Creators | Wright, Jennifer Kathryn |
| Contributors | Williams, Mathew; Mencuccini, Maurizio; Mitchell, Robert; Starr, Gregory |
| Publisher | University of Edinburgh |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://hdl.handle.net/1842/11675 |
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