School of Agriculture, Food and Ecosystem Sciences - Theses
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ItemStormwater runoff retention and tree growth in passively irrigated street tree systems( 2023-11)Urbanisation creates extensive impervious surface cover which alters natural hydrological cycles. Impervious surfaces generate large volumes of stormwater runoff, which degrades receiving waterway ecosystems when conventionally drained. To protect urban streams, runoff volumes need to be significantly reduced to restore pre-development flow regimes. Street trees have considerable potential to increase the demand for stormwater, by transpiring large volumes of water if irrigated with runoff via ‘passive irrigation’. Passive irrigation systems can also benefit trees by reducing drought stress and increasing growth and therefore providing shading and cooling benefits. However, lack of knowledge on system design and tree species selection is preventing widespread uptake of passive irrigation systems for street trees. This thesis aimed to identify the drivers of runoff retention, tree growth, water use and drought response, to inform the design of and tree species selection for passive irrigation systems. Runoff retention and tree growth were quantified for alternative system designs with different inlet types and storage volumes in the field. Using this data, runoff retention was also modelled for a range of soil exfiltration rates, climates and storage sizes. Tree water use, drought response and growth were assessed in a glasshouse study to inform species selection in relation to system storage. In the field, systems with inlets designed to exclude sediment captured less runoff. However, the exfiltration rate of the surrounding heavy clay soil ultimately limited runoff retention. Therefore, inlet selection will likely be driven by maintenance requirements and cost, rather than runoff retention. However, where storage volume was large relative to the contributing catchment area, or where exfiltration rates were faster, runoff retention was substantially greater. Passively irrigated trees in the field grew faster than control trees; however, this was likely due to trees having both adequate drainage, to avoid waterlogging, as well as an internal water storage. In the glasshouse study, trees with an internal water storage and low stomatal sensitivity showed lower drought induced leaf loss compared to those without, whereas trees with high stomatal sensitivity and an internal water storage showed lower drought stress and recovered rapidly after drought. This thesis demonstrated that passive irrigation systems have significant potential to reduce runoff volume entering waterways. However, large storage volumes will be required to facilitate exfiltration in heavy-textured soils. Passive irrigation systems can improve tree growth when waterlogging is avoided and internal water storage provided, which will likely increase evapotranspiration, runoff retention and canopy growth in the future. As per other stormwater control measures, maintenance requirements must be considered during design and greater oversight during construction is required to ensure systems perform as intended. Designs need to flexible and adapt to local soil and climate conditions. Matching tree species, or more accurately, tree water use strategies, to system design is required to ensure systems maintain function in future climates.
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ItemLinking fire, climate, connectivity and animal conservation( 2023-05)Australia’s forests and woodlands have been severely fragmented by agriculture, urbanisation and forestry, resulting in loss of habitat for native species. Habitat loss and fragmentation affect landscape structure, defined as the composition and configuration of land cover types. Species persistence in modified landscapes depends on the landscape structure and the availability of resources, which may be altered through management practices, such as prescribed fire. Fire can be used in the ecological management of many terrestrial ecosystems, where its application or suppression alter habitat structure and the availability and configuration of key resources. Current approaches to ecological fire management do not consider the influence of the surrounding landscape or the effect of fire on animal movement (connectivity) within or among habitat patches. The implications of current fire regimes for long-term population persistence are also overlooked. Connectivity is linked to persistence because it maintains dispersal, gene flow and genetic diversity, helping species combat environmental change and avoid extinction. Incorporating connectivity into fire management will help conserve biodiversity in fragmented landscapes. In my research program I investigated how 1) species richness and mammal community composition and 2) individual species respond to habitat, fire and landscape structure using data from remote-sensing cameras collected in south-eastern Australia. Additionally, I combine fire simulation modelling and connectivity analyses using genetic data to 3) compare future connectivity for two small mammals, yellow-footed antechinus (Antechinus flavipes) and heath mouse (Pseudomys shortridgei) under alternative fire regimes and predicted climate scenarios. These findings will inform an understanding of how fire, habitat and climate influence mammal communities, and species’ distributions, connectivity, and persistence. In Chapter 2, my first data chapter, I evaluate the relative influence of fire (time since fire and fire frequency), vegetation type, land use diversity and annual rainfall on ground-dwelling mammal community composition and species richness. Findings suggest that vegetation type and rainfall have the greatest influence on the mammal community and the vegetation type treeless heath is of great importance to critical weight range mammals. In Chapter 3, I focus on 1) the influence of vegetation structure, fire, and annual rainfall on the occurrence of 18 mammal species in two vegetation types (heathy woodland and treeless heath) at the local and landscape scale and 2) if species’ life history traits influence their responses to local and landscape scale predictors. At the local scale understory complexity influenced the occurrence of nine species in both vegetation types. In the woodland species occurrences were also influenced by annual rainfall and basal area while in treeless heath time since fire and fire frequency were important drivers. At the landscape scale, the extent of mature vegetation was most influential in the treeless heath while fire age-class diversity and the extent of native vegetation was most influential in the woodland. All tested species life history traits (size, diet, nest requirement, mean annual offspring and native status) were found to influence species responses at the site scale while only one trait (mean annual offspring) was found to predict species responses at the landscape scale. In Chapter 4, I combine fire simulations and connectivity analyses to compare future connectivity under four alternative fire regimes (0%, 1%, 2% and 4% of total available area burnt per year) and predicted future climate, for two small mammals, heath mouse (Pseudomys shortridgei) and yellow-footed antechinus (Antechinus flavipes). Genetic modelling found that heath mouse and yellow-footed antechinus considered the early post-fire age class low resistance (high connectivity) and pastural farmland to be high resistance (low connectivity). The results of the simulations suggest that the composition of the future landscape will shift towards more recently burnt vegetation, increasing connectivity for the heath mouse and yellow-footed antechinus. Overall, my research has provided new insights into how vegetation type and structure, fire and landscape structure influence mammal species distributions and contributed new information towards an important knowledge gap; how fire regimes influence animal movement and connectivity. Collectively, the findings will inform how fire regimes may influence species distributions and connectivity for long-term persistence in fire-prone landscapes.
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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.