• Energy Research

AbstractClimate change will impact gross primary productivity (GPP), net primary productivity (NPP), and carbon storage in wooded ecosystems. The extent of change will be influenced by thermal acclimation of photosynthesis—the ability of plants to adjust net photosynthetic rates in response to growth temperatures—yet regional differences in acclimation effects among wooded ecosystems is currently unknown. We examined the effects of changing climate on 17 Australian wooded ecosystems with and without the effects of thermal acclimation of C3 photosynthesis. Ecosystems were drawn from five ecoregions (tropical savanna, tropical forest, Mediterranean woodlands, temperate woodlands, and temperate forests) that span Australia's climatic range. We used the CABLE‐POP land surface model adapted with thermal acclimation functions and forced with HadGEM2‐ES climate projections from RCP8.5. For each site and ecoregion we examined (a) effects of climate change on GPP, NPP, and live tree carbon storage; and (b) impacts of thermal acclimation of photosynthesis on simulated changes. Between the end of the historical (1976–2005) and projected (2070–2099) periods simulated annual carbon uptake increased in the majority of ecosystems by 26.1%–63.3% for GPP and 15%–61.5% for NPP. Thermal acclimation of photosynthesis further increased GPP and NPP in tropical savannas by 27.2% and 22.4% and by 11% and 10.1% in tropical forests with positive effects concentrated in the wet season (tropical savannas) and the warmer months (tropical forests). We predicted minimal effects of thermal acclimation of photosynthesis on GPP, NPP, and carbon storage in Mediterranean woodlands, temperate woodlands, and temperate forests. Overall, positive effects were strongly enhanced by increasing CO2 concentrations under RCP8.5. We conclude that the direct effects of climate change will enhance carbon uptake and storage in Australian wooded ecosystems (likely due to CO2 enrichment) and that benefits of thermal acclimation of photosynthesis will be restricted to tropical ecoregions.

Forests are an important global carbon sink but their responses to climate change are uncertain. Tree stems, as the predominant carbon pool, represent net productivity in temperate eucalypt forests but the drivers of growth in these evergreen forests remain poorly understood partly because the dominant tree species lack distinct growth rings. Disentangling eucalypt species' growth responses to climate from other factors, such as competition and disturbances like fire, remains challenging due to a lack of long-term growth data. We measured monthly stem-diameter changes (as basal area increment, BAI) of two co-occurring dominant eucalypts from different sub-genera (Eucalyptus obliqua and E. rubida) over nearly four years. Our study included seven sites in a natural temperate forest of south-eastern Australia, and we used linear mixed-effects models to examine the relative importance to monthly BAI of species, monthly climate variables (temperature and rainfall), inter-tree competition, and recent fire history (long-unburnt, prescribed fire, wildfire). Monthly BAI peaked in spring and autumn and was significantly different between species during spring and summer. BAI variation was most clearly associated with temperature, increasing in hyperbolic response curves up to maximum mean temperatures of ~ 15-17 °C and thereafter decreasing. Temperature optima for maximum monthly BAI were 1 to 2 °C warmer for E. rubida than E. obliqua. While less important than temperature, rainfall, particularly autumn rainfall, also helped explain patterns in monthly BAI, with inter-tree competition and recent fire history of comparatively minor importance. Our study provides the first comprehensive field-based evidence of different growth niches for eucalypts from different subgenera in natural temperate mixed forests. It highlights the importance of intra-annual climate to understanding productivity variation in temperate evergreen forests and provides insights into the mechanisms underpinning the successful co-existence of different tree species as well as their relative vulnerabilities to changing climates.

AbstractFire regimes are changing across the globe in response to complex interactions between climate, fuel, and fire across space and time. Despite these complex interactions, research into predicting fire regime change is often unidimensional, typically focusing on direct relationships between fire activity and climate, increasing the chances of erroneous fire predictions that have ignored feedbacks with, for example, fuel loads and availability. Here, we quantify the direct and indirect role of climate on fire regime change in eucalypt dominated landscapes using a novel simulation approach that uses a landscape fire modelling framework to simulate fire regimes over decades to centuries. We estimated the relative roles of climate‐mediated changes as both direct effects on fire weather and indirect effects on fuel load and structure in a full factorial simulation experiment (present and future weather, present and future fuel) that included six climate ensemble members. We applied this simulation framework to predict changes in fire regimes across six temperate forested landscapes in south‐eastern Australia that encompass a broad continuum from climate‐limited to fuel‐limited. Climate‐mediated change in weather and fuel was predicted to intensify fire regimes in all six landscapes by increasing wildfire extent and intensity and decreasing fire interval, potentially led by an earlier start to the fire season. Future weather was the dominant factor influencing changes in all the tested fire regime attributes: area burnt, area burnt at high intensity, fire interval, high‐intensity fire interval, and season midpoint. However, effects of future fuel acted synergistically or antagonistically with future weather depending on the landscape and the fire regime attribute. Our results suggest that fire regimes are likely to shift across temperate ecosystems in south‐eastern Australia in coming decades, particularly in climate‐limited systems where there is the potential for a greater availability of fuels to burn through increased aridity.

Large areas of Australia’s natural woodlands have been cleared over the last two centuries, and remaining woodlands have experienced degradation from human interventions and anthropogenic climate change. Restoration of woodlands is thus of high priority both for government and society. Revegetation of deforested woodlands is increasingly funded by carbon markets, with accurate predictions of site-level carbon capture an essential step in the decision making to restore. We compared predictions of carbon in above-ground biomass using both the IPCC Tier 2 modelling approach and Australia’s carbon accounting model, FullCAM, to independent validation data from ground-based measurements. The IPCC Tier 2 approach, here referred to as the FastTrack model, was adjusted to simulate carbon capture by mixed-species forests for three planting configurations: direct seeding, tubestock planting, and a mix thereof. For model validation, we collected data on above-ground biomass, crown radius, and canopy cover covering an age range of 9–35 years from 20 plantings (n = 6044 trees). Across the three planting configurations, the FastTrack model showed a bias of 2.4 tC/ha (+4.2% of the observed mean AGB), whilst FullCAM had a bias of −24.6 tC/ha (−42.9% of the observed mean AGB). About two-thirds of the error was partitioned to unsystematic error in FastTrack and about one-quarter in FullCAM, depending on the goodness-of-fit metric assessed. Model bias differed strongly between planting configurations. For the FastTrack model, we found that additional canopy cover data estimated from satellite images obtained at different years can improve the carbon capture projections. To attain the highest accuracy of carbon projection at the site level, we recommend using a model with parameters calibrated for the specific planting configuration using local representative data.

Wildfires have increased in size and frequency in recent decades in many biomes, but have they also become more severe? This question remains under-examined despite fire severity being a critical aspect of fire regimes that indicates fire impacts on ecosystem attributes and associated post-fire recovery. We conducted a retrospective analysis of wildfires larger than 1000 ha in south-eastern Australia to examine the extent and spatial pattern of high-severity burned areas between 1987 and 2017. High-severity maps were generated from Landsat remote sensing imagery. Total and proportional high-severity burned area increased through time. The number of high-severity patches per year remained unchanged but variability in patch size increased, and patches became more aggregated and more irregular in shape. Our results confirm that wildfires in southern Australia have become more severe. This shift in fire regime 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.

AbstractGross primary productivity (GPP) of wooded ecosystems (forests and savannas) is central to the global carbon cycle, comprising 67%–75% of total global terrestrial GPP. Climate change may alter this flux by increasing the frequency of temperatures beyond the thermal optimum of GPP (Topt). We examined the relationship between GPP and air temperature (Ta) in 17 wooded ecosystems dominated by a single plant functional type (broadleaf evergreen trees) occurring over a broad climatic gradient encompassing five ecoregions across Australia ranging from tropical in the north to Mediterranean and temperate in the south. We applied a novel boundary‐line analysis to eddy covariance flux observations to (a) derive ecosystem GPP–Ta relationships and Topt (including seasonal analyses for five tropical savannas); (b) quantitatively and qualitatively assess GPP–Ta relationships within and among ecoregions; (c) examine the relationship between Topt and mean daytime air temperature (MDTa) across all ecosystems; and (d) examine how down‐welling short‐wave radiation (Fsd) and vapour pressure deficit (VPD) influence the GPP–Ta relationship. GPP–Ta relationships were convex parabolas with narrow curves in tropical forests, tropical savannas (wet season), and temperate forests, and wider curves in temperate woodlands, Mediterranean woodlands, and tropical savannas (dry season). Ecosystem Topt ranged from 15℃ (temperate forest) to 32℃ (tropical savanna—wet and dry seasons). The shape of GPP–Ta curves was largely determined by daytime Ta range, MDTa, and maximum GPP with the upslope influenced by Fsd and the downslope influenced by VPD. Across all ecosystems, there was a strong positive linear relationship between Topt and MDTa (Adjusted R2: 0.81; Slope: 1.08) with Topt exceeding MDTa by >1℃ at all but two sites. We conclude that ecosystem GPP has adjusted to local MDTa within Australian broadleaf evergreen forests and that GPP is buffered against small Ta increases in the majority of these ecosystems.

AbstractAccurate ground‐based estimation of the carbon stored in terrestrial ecosystems is critical to quantifying the global carbon budget. Allometric models provide cost‐effective methods for biomass prediction. But do such models vary with ecoregion or plant functional type? We compiled 15 054 measurements of individual tree or shrub biomass from across Australia to examine the generality of allometric models for above‐ground biomass prediction. This provided a robust case study because Australia includes ecoregions ranging from arid shrublands to tropical rainforests, and has a rich history of biomass research, particularly in planted forests. Regardless of ecoregion, for five broad categories of plant functional type (shrubs; multistemmed trees; trees of the genusEucalyptusand closely related genera; other trees of high wood density; and other trees of low wood density), relationships between biomass and stem diameter were generic. Simple power‐law models explained 84–95% of the variation in biomass, with little improvement in model performance when other plant variables (height, bole wood density), or site characteristics (climate, age, management) were included. Predictions of stand‐based biomass from allometric models of varying levels of generalization (species‐specific, plant functional type) were validated using whole‐plot harvest data from 17 contrasting stands (range: 9–356 Mg ha−1). Losses in efficiency of prediction were <1% if generalized models were used in place of species‐specific models. Furthermore, application of generalized multispecies models did not introduce significant bias in biomass prediction in 92% of the 53 species tested. Further, overall efficiency ofstand‐level biomass prediction was 99%, with a mean absolute prediction error of only 13%. Hence, for cost‐effective prediction of biomass across a wide range of stands, we recommend use of generic allometric models based on plant functional types. Development of new species‐specific models is only warranted when gains in accuracy of stand‐based predictions are relatively high (e.g. high‐value monocultures).