School of Agriculture, Food and Ecosystem Sciences - Theses
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ItemThe mechanisms through which fire shapes plant life cycles in heathlands( 2023-11)Fire is a key driver of plant diversity, and many plants have adaptations that help them thrive in fire–prone ecosystems. However, changes to fire activity threaten thousands of plants worldwide. To understand the future of plant populations under fire regime changes, empirical research on fire’s influence on demographic processes is required. This thesis explored how patterns of fire influence plant species across their life cycle, from seeds to mature life stages, and how this relates to plant functional traits. I examined a Mediterranean-type heathland ecosystem as a case study, to examine the mechanisms through which fires impact plants at different life-stages, including those that take place above and below ground. I established 57 study sites in Gariwerd, southeastern Australia, which has experienced substantial variation in fire history. First, I investigated whether knowledge of plant traits can be used to make robust predictions for how fire influences plant relative abundance. I deductively assigned species to plant functional types, based on their persistence traits, establishment capacity, and the timing of key life stages, and made a priori predictions on how relative abundance changes as a product of time since fire. Using empirical data I collected on species relative abundance, I then built nonlinear models to test species’ model conformity to a priori predictions for plant functional types. Predictions of the direction of changes in relative abundance (increase or decrease from 0-81 years since fire) were correct for 18 of 24 species modelled. Predictions of the shape of changes in relative abundance were not as accurate, but still useful: 13 out of 24 species showed ‘excellent’ conformity with shape predictions, 7 ‘good’ conformity, and 4 ‘poor’. This suggests plant functional types can be used to generalise fire responses across species that share similar traits, and thus inform fire management and biodiversity conservation. Second, I examined how fire severity and time since fire interact to influence plant maturity. I collected data on the proportion of plants that had reached reproductive maturity at a site. I used this field data, alongside satellite-based fire severity mapping, to build non-linear models of plant-fire relationships. The results indicated that the proportion of mature plants was influenced by time since fire, regardless of fire severity. For example, for Banksia marginata, the proportion of mature plants increased from 13% (1-year post-fire) to 58% (15 years post-fire), and maturity of this species showed minimal variation between low and high severity fire. Interestingly, no relationships were observed between time since fire and the relative abundance of plants. That is, only when plant life stages were considered, did I detect an effect of fire on plants. Ecological studies that distinguish between plant life stages will help to predict the impacts of fire on populations and enhance decision-making. Third, I investigated how time since fire and mean fire interval influence canopy seedbank production, based on a suite of plant traits. I surveyed all individual plants with canopy cones present at each of the 57 study sites. On each mature individual, I measured plant height and width, and counted the number of cones. I sampled a subset of these cones across individual plants, and then germinated them in a laboratory trial. I used regression models to explore the relationship between fire frequency and variables relating to different aspects of canopy seedbank production. The interval between fires influenced canopy seedbank production and viability. For example, no canopy cones were observed on plants at short mean fire intervals: such as fire intervals more frequently than every 18 years for the obligate seeder tree Callitris rhomboideia. Quantifying the fire intervals which supports canopy seedbanks provides a new understanding of an important above ground process and helps to determine how frequently to burn ecosystems containing serotinous species. Last, I examined how time since fire and fire frequency influence the occurrence of different species in the soil seedbank and, again, examined ecological relationships through the lens of plant traits. I sampled the soil seedbank at 57 sites, treated soil samples with heat and smoke product to promote germination, and grew seedlings in a germination trial lasting 14 months. I used non-linear modelling to explore relationships between fire and species occurrence. Fire frequency influences the occurrence of species in the soil seedbank, and the nature of these relationships depends on plant traits such as plant and seed longevity. For example, frequent fires (every <15 years) will reduce the occurrence of herbaceous species with long-lived seed. However, for other types of plants, such as perennials with short-lived seed, I observed no relationship between fire and soil seedbank occurrence, demonstrating many species have soil seedbanks resilient to frequent fires. Overall, my research advances understanding of how fire impacts different species and groups of plants across their life cycle. Notably, a mix of field research, laboratory studies and empirical models provide evidence that the traits of plants can be used to identify how fire affects species in the soil and canopy seedbanks, and as juvenile and mature plants. By examining plant life stages above and below ground, this work also helps to define the fire regimes that support plants in the heathy woodlands of Gariwerd. Because it is based on mechanisms, I anticipate that the trait-based approaches I have developed and tested could be used to understand and predict fire-related changes in plant populations in a wide range of ecosystems.
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ItemQuantifying fire-severity patterns using optical remote sensing data in temperate eucalypt forests of south-eastern Australia( 2020)Wildfires have significant biophysical and ecological impacts on ecosystems worldwide from local to regional and national scales. The magnitude of such impacts is related to wildfire severity. Recent increases in wildfire occurrence have been associated with climate change, however whether there has also been a change in fire severity remains underexamined in many biomes. Better understanding of fire-severity patterns is required for effective wildfire management, particularly in the fire-prone landscapes of temperate south-eastern Australia, which support a diversity of forests varying in species composition, structure, and post-fire regeneration strategies. Thus, the overarching aims of my Thesis were to accurately quantify wildfire severity at landscape scales and to examine spatial and temporal variation in wildfire severity across a range of forest types in Victoria, south-eastern Australia. To meet the overarching aims, my Thesis involves: (1) identification of optimal optical spectral indices for mapping fire severity across the dominant and most fire-prone forest types in Victoria; (2) a comparison of the accuracy of two different fire-severity mapping approaches, namely single spectral indexing thresholding and machine learning; (3) using the acquired knowledge, the development of fire-severity maps for large (>1000 ha) wildfires occurring in Victoria between 1987 and 2017, and a retrospective analysis of changes in spatial patterns of high-severity fires over that period; and (4) an analysis of the relative importance of four groups of environmental variables (namely fire weather, fuel, topography and climate) as predictors of high-severity fire extent and landscape configuration. My evaluation of remote sensing based spectral indices indicated that the best-performing indices of fire severity varied with forest type and forest functional group, but that there is scope to group forests by structure and fire-regeneration strategy to simplify fire-severity classification in heterogeneous forest landscapes. Results from my comparative analysis confirmed that machine learning outperformed the spectral index thresholding approach for mapping fire severity in most cases, increasing overall accuracy by 11% on a forest-group basis, and 16% on an individual wildfire basis. My results also confirmed that the accuracy achieved with a reduced set of predictor variables that included the previously identified optimal indices of fire severity was not improved by adding more variables. Greater overall accuracies (by 12% on average) were achieved when in-situ data (rather than data from other fires) were used to train the machine-learning algorithm. As such, my study demonstrates the utility of machine-learning algorithms for streamlining a robust fire-severity mapping approach across heterogeneous forested landscapes. Analysis of spatial patterns highlighted that high-severity wildfires in temperate Australian forests have increased in extent and aggregation in recent decades. The total and proportional high-severity burned area increased through time from 1987 to 2017. While the number of high-severity patches per year remained unchanged in that period, the variability in high-severity patch size increased, and high-severity patches became more aggregated and more irregular in shape. Finally, key findings from my models on the relative importance of environmental drivers (climate, fire weather, fuel, and topography) were that fuel type and fire weather were the most important predictors of the extent and configuration of high-severity fires in Australian temperate forests. My Thesis presents one of the most comprehensive analyses of fire-severity patterns from remote sensing data in Australia. My research results support the reliable estimation of wildfire severity from optical images using machine-learning algorithms once optimal spectral indices are identified and when in-situ training data are available for individual fires. Importantly, the quantified shifts in fire regimes across Victoria’s forested landscapes may have critical consequences for ecosystem dynamics, as fire-adapted temperate forests are more likely to be burned at high severities relative to historical ranges, a trend that seems set to continue under projections of a hotter, drier climate in south-eastern Australia. It is therefore critical that forest scientists and land managers continue to acknowledge and quantify changing wildfire-severity patterns so that they are better informed to address the ecological consequences.
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ItemImpacts of short-interval wildfires on tree demography and forest structure in temperate Australia( 2019)Fire is a powerful agent of disturbance in terrestrial ecosystems, and it shapes vegetation composition and patterns globally. This is particularly true in south-eastern Australia where forests are dominated by species of the genus Eucalyptus, many of which have the capacity to recover from high-severity fire by resprouting from epicormic or basal buds. Climate change is predicted to yield more severe fire weather and lengthen fire seasons in temperate Australia, leading to increased wildfire frequency in these forests. While increased fire frequency – resulting in wildfire intervals of under a decade – are known to negatively affect fire-sensitive eucalypts (obligate seeders which have a juvenile period of 10 – 15 years) less is known about how such changes impact fire-tolerant, resprouting eucalypts. This Thesis examines the impacts of recent wildfires in south-eastern Australia, where a series of large wildfires burned over four million hectares of land, leading to the burning of different types of fire-tolerant eucalypt forests (basal resprouters, epicormic resprouters) by high-severity wildfires once, twice, and sometimes three times between 2003 and 2013. In the context of this massive natural experiment in the landscape, my overarching aim was to quantify the impacts of short-interval wildfire on eucalypt tree demography and regeneration, to improve understanding of potential fire-related changes to the structure and resilience of fire-tolerant forests. Short-interval high-severity wildfires significantly increased whole-tree mortality and decreased the abundance of both resprouts and seedlings in basally resprouting eucalypt forests. In these sub-alpine forests, dominated by snow gum (E. pauciflora), more frequent wildfire (two and particularly three short-interval fires) also increased the cover of grasses at the expense of shrubs. In mixed-species eucalypt forests, which occupy extensive tracts of low elevation landscapes in south-eastern Australia, resprouting occurs from both basal and epicormic buds. In these forests, the dynamics of both topkill (i.e. stem, but not whole-tree, mortality) and whole-tree mortality have important ramifications for forest structure. After a single high-severity wildfire, small-diameter stems were typically topkilled; after two short-interval wildfires, the diameter of stems topkilled increased. Additionally, the overall likelihood of either basal or epicormic resprouting decreased after two short-interval wildfires. This decline in resprouting capacity indicated that the size class most vulnerable to ‘resprout failure’ after multiple wildfires was intermediate sized stems (in the vicinity of 20 - 30 cm DBHUB), rather than smaller or larger stems. Seedling regeneration also decreased in these forest types after short-interval wildfires, suggesting that, as for fire-sensitive forests, immaturity risk may be a relevant factor for fire-tolerant forests. Short-interval wildfires reduced the total and aboveground carbon stocks of mixed-species forests, while also increasing the proportion of carbon mass in the dead pool, indicating that resprouter forests might not be perpetually secure carbon stocks under emerging fire regimes. My Thesis highlights that fire-tolerant forests may not be as invulnerable to changes in fire frequency as widely assumed, and that management interventions will likely be required to counteract increasing tree mortality and decreasing tree regeneration if predictions of more frequent and severe wildfires in temperate Australia are realised.