School of Agriculture, Food and Ecosystem Sciences - Theses
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ItemClimate and climate change effects on carbon uptake and storage in Australia’s wooded ecosystems( 2022)Forest ecosystems are central to the land carbon sector due to their capacity to store and sequester carbon. Many studies have demonstrated that forest carbon uptake and storage is strongly dependent upon climatic conditions. However, the effects of climate on forest carbon uptake and storage in different biomes are still uncertain. Climate change may alter carbon dynamics within forest ecosystems through the direct effects of increased temperature, increased CO2 concentration and changing precipitation regimes. Yet forests may also adjust to changing climate through mechanisms such as thermal acclimation. In this thesis I used three modelling approaches (machine learning, boundary-line analysis, and a land-surface model) to examine how climate of the recent past, present, and future affect carbon uptake (as Gross Primary Productivity, GPP) and storage (as above-ground biomass, AGB) in Australian forests. Furthermore, I explored how current GPP adjusted to thermal regimes and how acclimation affected carbon uptake and storage in the future. In my first quantitative chapter (Chapter 2), I explored relationships between carbon storage (as AGB) with climate and soil in Australian forests across the continent. I developed RandomForest models with climate-only, soil-only, or climate plus soil variables to examine whether climate or soils are better predictors of forest biomass at the continental scale and to identify the most important predictor variables. In this chapter I demonstrated that climate (particularly temperature and the timing of precipitation) was more important than soil for explaining variation in AGB across Australia’s forests. In Chapter 3, I used boundary-line analysis to examine the ecosystem temperature response of carbon uptake (as GPP) in 17 wooded ecosystems representing five distinct ecoregions. These responses were represented as a convex parabolic curve that was similar in shape among ecoregions – narrow in tropical forests and broader in woodlands. I then derived the thermal optima of GPP (Topt) from these curves for each ecosystem and examined the relationship between Topt and mean air temperatures across sites. My analysis revealed a strong positive linear relationship between Topt and mean air temperature that indicated GPP was optimised to the present climate. Finally, in Chapter 4, I predicted how carbon uptake and storage will be affected by climate change in these 17 ecosystems and examined the effects of thermal acclimation of photosynthesis on these predictions. I used the CABLE-POP land surface model adapted with thermal acclimation of photosynthetic functions and forced with climate projections from the extreme climate scenario RCP8.5. My simulations indicated that increased temperature, CO2 concentration and changed precipitation patterns will have a positive effect on future carbon uptake and storage in the majority of the 17 ecosystems. Furthermore, thermal acclimation of photosynthesis is likely to enhance this effect in tropical ecosystems. My results confirm that carbon uptake and storage in Australian forests are fundamentally linked to temperature and precipitation regimes, and that these forests may be capable of adjusting to climatic conditions. My research indicates that the direct effects of climate change are likely to enhance the storage and sink capacity of Australia’s forests in the future. While I did not assess the indirect effects of climate change on carbon cycles through changes to disturbance regimes, overall, my thesis suggests that carbon uptake and above-ground biomass carbon stores in Australia’s forests are likely to be resilient to climate change.
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ItemUnderstanding the Importance of Microbial Biogeography to Australian Winemaking( 2020)Microbes are a vital part of ecosystems and play key roles in the essential processes of the functioning. In agriculture, microbial ecology has wide reaching impacts on crop growth and quality commodity production. As a high value agricultural product, wine is a useful model for elucidating the effects of microbial ecology from the vineyard to the winery. Microbial growth and metabolism is an inherent component of wine production, influencing grapevine health and productivity, conversion of sugar to ethanol during fermentation, and the flavour, aroma and quality of finished wines. Recent advances in genetic sequencing and metagenomic approaches has extended our understanding of microbial distribution patterns and established the unique biogeography model in viticulture. While the contributions of microbial biogeography to wine metabolites and regional distinctiveness (known as terroir, a well-recognised and celebrated character in wine industry), and by which mechanisms, remain tenuous. This thesis focuses on the microbial biogeography of wine, the interplay between microbial patterns and affecting factors, and how these patterns drive wine quality and styles. I begin by investigating the distribution patterns of bacteria and fungi at large scale, and their roles in shaping wine characteristics. Samples were collected from vineyard soil, grape must, and wine ferments across six geographically separated wine-producing regions in southern Australia (~ 400 km). Soil and grape must microbiota exhibited distinctive regional patterns, as well as wine aroma profiles. Associations among soil and wine microbiota, abiotic factors (weather and soil properties), and wine regionality were modelled, highlighting that fungal communities was the most important driver of wine aroma profiles. Source tracking wine-related fungi in the vineyard suggests that soil is a source reservoir of grape- and must-associated fungi which might be translocated via xylem sap. I then move on to elucidate the fungal ecology within vineyards. Fungal communities were characterised over space and time that associated with the grapevine (grapes, flowers, leaves, roots, root zone soil) during the annual growth cycle (flowering, fruit set, veraison, and harvest). Fungi were significantly influenced by the grapevine habitat and plant development stage, with little influences from the geographic location (<5 km). The developmental stage of veraison, where grapes undergo a dramatic change in metabolism and start ripening process, saw a distinct shift in fungal communities. A core fungal microbiota of grapevines (based on abundance-occupancy models) existed over space and time which drove the seasonal community succession. Beyond coinciding with the changing plant metabolism and physiology, strong correlations with solar radiation and water status suggests that the core microbiota changes with respect to the changing environments during plant development. I further investigate fungal contributions to wine aroma profiles by quantifying multiple layers of fungi, combining metagenomics and population genetics. Fungal communities were characterised associated with Pinot Noir and Chardonnay grape must/juice and ferments coming from three wine estates (including 11 vineyards) in the Mornington Peninsula wine region. At this scale (< 12 km), fungal communities, yeast populations, and Saccharomyces cerevisiae populations differentiated between geographic origins (estate/vineyard), with influences from the grape variety. During spontaneous fermentation, growth and dominance of S. cerevisiae reshaped the fungal community and structured the biodiversity at strain level. Associations between fungal microbiota and wine metabolites highlights the primary role of S. cerevisiae in determining wine aroma profiles at sub-regional scale. Overall, this thesis provides a significant body of knowledge to the microbial ecology field. Using vineyards, grapes, and wine as a model system, these findings relate microbial biogeography, environments, and quality agricultural commodity production. It provides fundamental perspectives to conserve the biodiversity and functioning for sustainable agriculture under the changing climate.
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ItemImproving species distribution models using extreme value theory and climate dataset ensembles( 2020)The development of climate datasets at fine spatial and temporal scales has commonly been driven by the need to better understand vegetation distributions and ecological systems. While a wide range of global, national and regional climate datasets have been developed over the last two decades, they are rarely compared directly in the ecological literature. This thesis evaluates a range of climate interpolation techniques and investigates how the spatial and temporal characteristics of climate datasets may be utilised to improve the predictive performance of plant species distribution models (SDM). A series of spline-based and geostatistical methods for interpolating temperature variables are first compared across Victoria, southeast Australia. Secondary predictors (thermal remote sensing data and local topographic indices) which indirectly capture mesoscale microclimate and cold air drainage regimes were found to improve monthly mean minimum temperature interpolations by up to 39%. Thermal remote sensing data only reduced root mean square error (RMSE) by up to 6% for maximum temperature across Victoria and was most effective during the summer months. The interpolation methods used in southeast Australia were subsequently transferred to the Royal Himalayan Kingdom of Bhutan to validate their effectiveness in a novel climate. In Bhutan, the predictive performance of minimum temperature interpolations was also improved considerably (up to 23% reduction in RMSE) when using thermal remote sensing data and local topographic indices as spatial covariates. Thermal remote sensing data also reduced the RMSE for maximum temperature interpolations by up to 16% in Bhutan. Interannual variability of climate extremes were used to evaluate how the temporal characteristics of climate may be used to improve the predictive performance of SDMs. Generalised Extreme Value (GEV) distributions were fitted to monthly climate data to generate variables which account for the skewed distribution of extremes. Models incorporating interannual variability (drawn from a range of expected return intervals) improved predictive performance compared to models using seasonal extremes only for 28 of 37 species assessed. Iteratively fitting models using alternate expected return intervals typically acted on the leading and trailing edges of current distributions, indicating that such methods may be useful for model calibration and characterising climate-driven source-sink population dynamics. The impact of spatial disparities in climate on the predictive performance of plant SDMs was evaluated using three distinct datasets developed for Victoria as part of this research, in addition to two global datasets (WorldClim v1 and v2). Individual models were compared against one another and as ensembles to explore the potential for alternate predictions to complement one another. The Victorian datasets demonstrated a significant improvement over the original WorldClim dataset (up to 17.3% mean increase in D2) and trended towards an improvement relative to WorldClim v2; however, no significant differences were found when comparing the alternate Victorian datasets. Multi-model ensembles achieved a mean increase of up to 13.8% and 29.2% in D2 relative to individual models when using regional and global datasets, respectively. Ensembles provide a pragmatic method to improve the predictive performance of SDMs and allow a trade-off between the uncertainties and potential biases embedded in competing climate datasets.