School of Agriculture, Food and Ecosystem Sciences - Research Publications
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ItemNo Preview AvailableCanopy Composition and Spatial Configuration Influences Beta Diversity in Temperate Regrowth Forests of Southeastern Australia(MDPI, 2023-03)Structural features of the overstorey in managed and unmanaged forests can significantly influence plant community composition. Native Acacia species are common in temperate eucalypt forests in southeastern Australia. In these forests, intense disturbances, such as logging and wildfire, lead to high densities of regenerating trees, shrubs, and herbs. The tree layer is dominated by Acacia and Eucalyptus, that compete intensely for resources in the first decades after stand establishment. The relative abundance and size of Acacia and Eucalyptus varies widely due to stochastic factors such as dispersal, microsite variability, and weather and climatic conditions. This variability may influence the structure and composition of the herbaceous and shrub species. In the temperate forests of southeastern Australia, understorey plant diversity is assumed to be influenced by Acacia species density, rather than Eucalyptus density. To quantify the influence of Acacia and Eucalyptus density on plant community composition, we used remote sensing and machine learning methods to map canopy composition and then compare it to understorey composition. We combined unoccupied aerial vehicle (UAV or drone) imagery, supervised image classifications, and ground survey data of plant composition from post-logging regrowth forests in the Central Highlands of southeastern Australia. We found that aggregation and patch metrics of Eucalyptus and Acacia were strongly associated with understorey plant beta diversity. Increasing aggregation of Acacia and the number of Acacia patches had a significant negative effect on plant beta diversity, while the number of Eucalyptus patches had a positive influence. Our research demonstrates how accessible UAV remote sensing can be used to quantify variability in plant biodiversity in regrowth forests. This can help forest managers map patterns of plant diversity at the stand-scale and beyond to guide management activities across forested landscapes.
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ItemNo Preview AvailableA new method employing species-specific thresholding identifies acoustically overlapping bats(WILEY, 2022-11)Abstract Passive acoustic detectors are increasingly used for monitoring biodiversity, particularly for echolocating bat species (Microchiroptera). However, identification of calls collected at large scales is hindered by substantial variation within and between species, and the considerable time investment needed to manually identify acoustic data. We use acoustic data from 14 species of echolocating bats, occurring in temperate forests and woodlands of southeastern Australia to build a supervised classification model that identifies species from large acoustic datasets. Acoustic data from hand‐release (39,567) and free‐flying (8851) bat calls were used to build a predictive model, which was then validated using field‐collected calls (149,097) from the same region. We maximized the model fit per species by validating the associated confidence scores against manually identified presence and absence values. This allowed us to model the identification success of each species as a function of the confidence score. From this relationship, we set specific thresholds for accepting species identification, enabling more accurate classification of calls and identification of multiple bat species within a single acoustic recording. Including calls from manually identified free‐flying bats improved overall identification accuracy, including a 60% improvement for bats that navigate in open spaces. Assigning species‐specific thresholds achieved substantial improvements in overall model confidence, with functionally meaningful changes in the identification of species exhibiting considerable acoustic overlap in time and frequency measures. Research into the ecological requirements of species is hampered by problems with identification. Our research illustrates that internal train–test validation overestimates model accuracy particularly for species that were in low abundance or for uncommon species, which are acoustically similar to more common ones. Recognizing this, we set specific thresholds per species below which identifications were not accepted. Our method is particularly relevant in locations with high overlap in species' call parameters, which can result in false negatives in preference for species that are easier to identify because of the common practice of assigning one species per acoustic recording. This research proposes a cautious method to substantially reduce the burden of manual identification of large acoustic datasets.
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ItemMulti-scale mapping of Australia's terrestrial and blue carbon stocks and their continental and bioregional drivers(SPRINGERNATURE, 2023-06-01)Abstract The soil in terrestrial and coastal blue carbon ecosystems is an important carbon sink. National carbon inventories require accurate assessments of soil carbon in these ecosystems to aid conservation, preservation, and nature-based climate change mitigation strategies. Here we harmonise measurements from Australia’s terrestrial and blue carbon ecosystems and apply multi-scale machine learning to derive spatially explicit estimates of soil carbon stocks and the environmental drivers of variation. We find that climate and vegetation are the primary drivers of variation at the continental scale, while ecosystem type, terrain, clay content, mineralogy and nutrients drive subregional variations. We estimate that in the top 0–30 cm soil layer, terrestrial ecosystems hold 27.6 Gt (19.6–39.0 Gt), and blue carbon ecosystems 0.35 Gt (0.20–0.62 Gt). Tall open eucalypt and mangrove forests have the largest soil carbon content by area, while eucalypt woodlands and hummock grasslands have the largest total carbon stock due to the vast areas they occupy. Our findings suggest these are essential ecosystems for conservation, preservation, emissions avoidance, and climate change mitigation because of the additional co-benefits they provide.
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ItemAcacia Density, Edaphic, and Climatic Factors Shape Plant Assemblages in Regrowth Montane Forests in Southeastern Australia(MDPI, 2023-06)A fundamental requirement of sustainable forest management is that stands are adequately regenerated after harvesting. To date, most research has focused on the regeneration of the dominant timber species and to a lesser degree on plant communities. Few studies have explored the impact of the regeneration success of dominant tree species on plant community composition and diversity. In this study, we quantified the influence of variability in tree density and climatic and edaphic factors on plant species diversity in montane regrowth forests dominated by Eucalyptus regnans in the Central Highlands of Victoria in southeastern Australia. We found that Acacia density shaped plant biodiversity more than Eucalyptus density. Edaphic factors, particularly soil nutrition and moisture availability, played a significant role in shaping species turnover and occurrence. Our findings suggest that the density of Acacia is a key biotic filter that influences the occurrence of many understorey plant species and shapes plant community turnover. This should be considered when assessing the impacts of both natural and anthropogenic disturbances on plant biodiversity in the montane forests of southeastern Australia.
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ItemRiparian fungal communities respond to land-use mediated changes in soil properties and vegetation structure(SPRINGER, 2022-06)Abstract Purpose Owing to their topographic location and nutrient rich soils, riparian forests are often converted to pastures for grazing. In recent decades, remnant riparian forests cleared for grazing pastures have been restored with native species. The impacts of such land-use changes on soil fungal communities are unclear, despite the central roles that soil fungi play in key ecosystem processes. We investigated how soil fungal taxonomic and functional composition are affected by land-use change at different depths, and if variation in soil fungal communities is related to edaphic properties and extant vegetation. Methods The study was conducted in six waterways in south-eastern Australia, each comprising three land-use types: remnant riparian forest, cleared forest converted to pasture, and pastures restored with native plants. We surveyed three strata of vegetation and sampled top-soil and sub-soil to characterise physicochemical properties and soil fungal communities. ITS1 region sequences were used to assign soil fungal taxonomic and functional composition. Results Fungal taxonomic and functional composition infrequently varied with land-use change or soil depth. Overall, environmental properties (soil and vegetation) explained 35–36% of variation in both fungal taxonomic and functional composition. Soil fungal taxonomic composition was related to soil fertility (N, P, K, pH and Ca) and ground cover characteristics, whereas functional composition was related to clay content, sub-canopy cover and tree basal area. Conclusion Across the six studied waterways, fungal taxonomic and functional composition were more strongly associated with land-use mediated changes in site-scale soil physicochemical properties and vegetation structure than broad-scale classes of land-use type.
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ItemAusTraits, a curated plant trait database for the Australian flora(NATURE PORTFOLIO, 2021-09-30)We introduce the AusTraits database - a compilation of values of plant traits for taxa in the Australian flora (hereafter AusTraits). AusTraits synthesises data on 448 traits across 28,640 taxa from field campaigns, published literature, taxonomic monographs, and individual taxon descriptions. Traits vary in scope from physiological measures of performance (e.g. photosynthetic gas exchange, water-use efficiency) to morphological attributes (e.g. leaf area, seed mass, plant height) which link to aspects of ecological variation. AusTraits contains curated and harmonised individual- and species-level measurements coupled to, where available, contextual information on site properties and experimental conditions. This article provides information on version 3.0.2 of AusTraits which contains data for 997,808 trait-by-taxon combinations. We envision AusTraits as an ongoing collaborative initiative for easily archiving and sharing trait data, which also provides a template for other national or regional initiatives globally to fill persistent gaps in trait knowledge.
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ItemPredicting plant species distributions using climate-based model ensembles with corresponding measures of congruence and uncertainty(WILEY, 2022-03-17)Aim The increasing availability of regional and global climate data presents an opportunity to build better ecological models; however, it is not always clear which climate dataset is most appropriate. The aim of this study was to better understand the impacts that alternative climate datasets have on the modelled distribution of plant species, and to develop systematic approaches to enhancing their use in species distribution models (SDMs). Location Victoria, southeast Australia and the Himalayan Kingdom of Bhutan. Methods We compared the statistical performance of SDMs for 38 plant species in Victoria and 12 plant species in Bhutan with multiple algorithms using globally and regionally calibrated climate datasets. Individual models were compared against one another and as SDM ensembles to explore the potential for alternative predictions to improve statistical performance. We develop two new spatially continuous metrics that support the interpretation of ensemble predictions by characterizing the per-pixel congruence and variability of contributing models. Results There was no clear consensus on which climate dataset performed best across all species in either study region. On average, multi-model ensembles (across the same species with different climate data) increased AUC/TSS/Kappa/OA by up to 0.02/0.03/0.03/0.02 in Victoria and 0.06/0.11/0.11/0.05 in Bhutan. Ensembles performed better than most single models in both Victoria (AUC = 85%; TSS = 68%) and Bhutan (AUC = 86%; TSS = 69%). SDM ensembles using models fitted with alternative algorithms and/or climate datasets each provided a significant improvement over single model runs. Main conclusions Our results demonstrate that SDM ensembles, built using alternative models of the same climate variables, can quantify model congruence and identify regions of the highest uncertainty while mitigating the risk of erroneous predictions. Algorithm selection is known to be a large source of error for SDMs, and our results demonstrate that climate dataset selection can be a comparably significant source of uncertainty.
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ItemSoil Bacterial Community Responds to Land-Use Change in Riparian Ecosystems(MDPI, 2021-02)Riparian forests were frequently cleared and converted to agricultural pastures, but in recent times these pastures are often revegetated in an effort to return riparian forest structure and function. We tested if there is a change in the soil bacterial taxonomy and function in areas of riparian forest cleared for agricultural pasture then revegetated, and if soil bacterial taxonomy and function is related to vegetation and soil physicochemical properties. The study was conducted in six riparian areas in south-eastern Australia, each comprising of three land-use types: remnant riparian forest, cleared forest converted to pasture, and revegetated pastures. We surveyed three strata of vegetation and sampled surface soil and subsoil to characterize physicochemical properties. Taxonomic and functional composition of soil bacterial communities were assessed using 16S rRNA gene sequences and community level physiological profiles, respectively. Few soil physiochemical properties differed with land use despite distinct vegetation in pasture relative to remnant and revegetated areas. Overall bacterial taxonomic and functional composition of remnant forest and revegetated soils were distinct from pasture soil. Land-use differences were not consistent for all bacterial phyla, as Acidobacteria were more abundant in remnant soils; conversely, Actinobacteria were more abundant in pasture soils. Overall, bacterial metabolic activity and soil carbon and nitrogen content decreased with soil depth, while bacterial metabolic diversity and evenness increased with soil depth. Soil bacterial taxonomic composition was related to soil texture and soil fertility, but functional composition was only related to soil texture. Our results suggest that the conversion of riparian forests to pasture is associated with significant changes in the soil bacterial community, and that revegetation contributes to reversing such changes. Nevertheless, the observed changes in bacterial community composition (taxonomic and functional) were not directly related to changes in vegetation but were more closely related to soil attributes.