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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.

Many plant species are well-adapted to historical fire regimes. An increase in the severity, frequency, and extent of wildfires could compromise the regenerative capacity of species, resulting in permanent shifts in plant diversity. We surveyed extant vegetation and soil seed banks across two forest types with contrasting historical fire regimes—Shrubby Dry Forest (fire return interval: 10–20 years) and Sub-Alpine Woodland (50–100 years). Over the past 20 years, both forests have been subject to repeated, high-severity wildfires at intervals significantly shorter than their historical return intervals. We examined the soil seed bank response to fire-cued germination, and whether the plant diversity in soil seed banks and extant vegetation demonstrated similar responses to short-interval, high-severity wildfires. The soil seed bank demonstrated a positive response to heat in combination with smoke, and for the Sub-Alpine Woodland, this was limited to sites more frequently burnt by fire. With an increase in fire frequency, there was a decline in species richness and Shannon’s Diversity and a shift in species composition in both extant vegetation and the soil seed bank. The fire frequency effects on the relative richness of trait associations were restricted to the Shrubby Dry Forest, and included an increase in short-lived obligate seeders, wind-dispersed species, and ant-dispersed shrubs in burnt relative to long unburnt sites in both extant vegetation and the soil seed bank. Graminoids were the most abundant component of the soil seed banks of Sub-Alpine Woodlands, and this increased with more frequent fire, with a similar trend (p = 0.06) in extant vegetation. Clear shifts in plant diversity in both soil seed banks and extant vegetation in forest types with contrasting historical fire regimes suggest that emerging fire regimes are pushing ecosystems beyond their historical range of variability, including potentially more flammable states and a decline in the buffering capacity of soil seed banks.

Old-growth forests provide many ecosystem services and benefits. However, they are becoming increasingly rare and thus are an urgent priority for conservation. Accurately mapping old-growth forests is a critical step in this process. Here, we used LiDAR, an improved individual tree crown delineation algorithm for broadleaved forests, Gaussian mixture modelling, and a rule-based classification key to map the extent and location of old-growth forests across a topographically and ecologically complex landscape of 337,548 ha in southeastern Australia. We found that variation in old growth extent was largely driven by the old growth definition, which is a human construct, rather than by uncertainty in the technical aspect of the work. Current regulations define a stand as old growth if it was recruited prior to 1900 (i.e., >120 years old) and is undisturbed (i.e., 250 cm in diameter at breast height) were located within 50 m of cool temperate rainforests and cool temperate mixed forests (i.e., streamlines). We discuss the implication of our findings for the conservation and management of high-conservation-value forests in the region.

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.

Measurement(s) plant trait Technology Type(s) digital curation Sample Characteristic - Organism Viridiplantae Sample Characteristic - Location Australia Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.14545755

Soil seed banks play an important role in plant species persistence in fire-prone systems. Response to fire related germination cues often reflect historical fire regimes and can be important in maintaining ecotones between different forest types. We assessed the effects of heat and/or smoke on the soil stored seed banks across an ecotone of eucalypt to rainforest overstorey comprising wet forest, cool temperate mixed forest, and cool temperate rainforest in south-eastern Australia. Soils from five replicates of each forest type were subjected to very low (45 °C), low (65 °C) and high (90 °C) heat with or without two different smoke treatments: –smoke-infused vermiculite, and karrikinolide—a phytoreactive compound derived from smoke. Soils were placed in a glasshouse and germinants were identified and counted at weekly intervals. Response to fire cues was consistent among forest types despite underlying differences in the diversity of soil seed banks. There was no overall response of species richness, abundance, or composition to fire cues. Phanerophytes and ant-dispersed species with hard seed coats demonstrated positive response, and endozoochores negative response, to high heat independent of smoke. Endozoochores were concentrated (albeit at low densities) in cool temperate rainforest with no overall effect of seral affiliation on response to fire cues. Given the lack of response to karrikinolide, response to soil disturbance would most likely be associated with mechanical seed abrasion and/or exposure to increased light availability than to non-fire related production of smoke products. Forest type was a stronger determinant of floristics in the germinated soil seed bank than simulated fire related germination cues. Both smoke treatments had little influence on floristics in the germinated seed bank suggesting other, non-fire disturbances such as treefalls and soil turnover by fauna may be more important for germination for many of the species in these forest types.

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.

In the forested landscapes of southeastern Australia, bushfires and timber harvesting are the primary catalysts for regeneration in Eucalyptus regnans, E. delegatensis, and high elevation mixed species (HEMS) forests. Quantifying the role of climate, topography and edaphic conditions on plant regeneration is important for understanding current and future risks of climate change. In this study, we investigated the post-disturbance regeneration dynamics in these forests. We sought to determine the direct impact of climate variability on regeneration and the interactive effects of climate, topography, and edaphic factors on the regeneration success of Eucalyptus. Data was collated from regeneration surveys conducted in 881 coupes, totaling ~55,000 ha of forest harvested and regenerated between 2004 and 2012. The time-period analyzed included the Millennium Drought, an intense and prolonged drought that lasted from 1996 to 2010. To test the influence of climatic, topographic, and edaphic variables on the occurrence and abundance of Eucalyptus regeneration, we used machine learning models. We found that regeneration success was closely tied to the timing of the Millennium Drought. Between 2004 and 2005 Eucalyptus seedlings occurred in less than 50% of survey plots across all three forest types. Declines in regeneration during the period of drought were greater in E. regnans and E. delegatensis than HEMS forests, suggesting that regeneration in the HEMS forests is more resistant to drought. We found that seasonal precipitation and temperature had the greatest influence on regeneration success of Eucalyptus. Responses varied by species, however autumn precipitation had the strongest influence on abundance of regeneration of E. regnans and E. delegatensis, while summer temperature was more important for HEMS species. Our findings highlight the importance of seasonal and annual climate variability on Eucalyptus regeneration and portend potential declines in regeneration success in a warmer and drier future for E. regnans and E. delegatensis.

Predictive vegetation mapping is an essential tool for managing and conserving high conservation-value forests. Cool temperate rainforests (Rainforest) and cool temperate mixed forests (Mixed Forest, i.e., rainforest spp. overtopped by large remnant Eucalyptus trees) are threatened forest types in the Central Highlands of Victoria. Logging of these forest types is prohibited; however, the surrounding native Eucalyptus forests can be logged in some areas of the landscape. This requires accurate mapping and delineation of these vegetation types. In this study, we combine niche modelling, multispectral imagery, and LiDAR data to improve predictive vegetation mapping of these two threatened ecosystems in southeast Australia. We used a dataset of 1586 plots partitioned into four distinct forest types that occur in close proximity in the Central Highlands: Eucalyptus, Tree fern, Mixed Forest, and Rainforest. We calibrated our model on a training dataset and validated it on a spatially distinct testing dataset. To avoid overfitting, we used Bayesian regularized multinomial regression to relate predictors to our four forest types. We found that multispectral predictors were able to distinguish Rainforest from Eucalyptus forests due to differences in their spectral signatures. LiDAR-derived predictors were effective at discriminating Mixed Forest from Rainforest based on forest structure, particularly LiDAR predictors based on existing domain knowledge of the system. For example, the best predictor of Mixed Forest was the presence of Rainforest-type understorey overtopped by large Eucalyptus crowns, which is effectively aligned with the regulatory definition of Mixed Forest. Environmental predictors improved model performance marginally, but helped discriminate riparian forests from Rainforest. However, the best model for classifying forest types was the model that included all three classes of predictors (i.e., spectral, structural, and environmental). Using multiple data sources with differing strengths improved classification accuracy and successfully predicted the identity of 88% of the plots. Our study demonstrated that multi-source methods are important for capturing different properties of the data that discriminate ecosystems. In addition, the multi-source approach facilitated adding custom metrics based on domain knowledge which in turn improved the mapping of high conservation-value forest.

Modern approaches to predictive ecosystem mapping (PEM) have not thoroughly explored the use of ‘characteristic’ gradients, which describe vegetation structure (e.g., light detection and ranging (lidar)-derived structural profiles). In this study, we apply a PEM approach by classifying the dominant stand types within the Central Highlands region of south-eastern Australia using both lidar and species distribution models (SDMs). Similarity percentages analysis (SIMPER) was applied to comprehensive floristic surveys to identify five species which best separated stand types. The predicted distributions of these species, modelled using random forests with environmental (i.e., climate, topography) and optical characteristic gradients (Landsat-derived seasonal fractional cover), provided an ecological basis for refining stand type classifications based only on lidar-derived structural profiles. The resulting PEM model represents the first continuous distribution map of stand types across the study region that delineates ecotone stands, which are seral communities comprised of species typical of both rainforest and eucalypt forests. The spatial variability of vegetation structure incorporated into the PEM model suggests that many stand types are not as continuous in cover as represented by current ecological vegetation class distributions that describe the region. Improved PEM models can facilitate sustainable forest management, enhanced forest monitoring, and informed decision making at landscape scales.