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Abstract: Analysis of growth and biomass turnover in natural forests of Eucalyptus regnans, the world's tallest angiosperm, reveals it is also the world's most productive forest type, with fire disturbance an important mediator of net primary productivity (NPP). A comprehensive empirical database was used to calculate the averaged temporal pattern of NPP from regeneration to 250 years age. NPP peaks at 23.1 ± 3.8 (95% interquantile range) Mg C ha−1 year−1 at age 14 years, and declines gradually to about 9.2 ± 0.8 Mg C ha−1 year−1 at 130 years, with an average NPP over 250 years of 11.4 ± 1.1 Mg C ha−1 year−1, a value similar to the most productive temperate and tropical forests around the world. We then applied the age‐class distribution of E. regnans resulting from relatively recent historical fires to estimate current NPP for the forest estate. Values of NPP were 40% higher (13 Mg C ha−1 year−1) than if forests were assumed to be at maturity (9.2 Mg C ha−1 year−1). The empirically derived NPP time series for the E. regnans estate was then compared against predictions from 21 global circulation models, showing that none of them had the capacity to simulate a post‐disturbance peak in NPP, as found in E. regnans. The potential importance of disturbance impacts on NPP was further tested by applying a similar approach to the temperate forests of conterminous United States and of China. Allowing for the effects of disturbance, NPP summed across both regions was on average 11% (or 194 Tg C/year) greater than if all forests were assumed to be in a mature state. The results illustrate the importance of accounting for past disturbance history and growth stage when estimating forest primary productivity, with implications for carbon balance modelling at local to global scales. [ABSTRACT FROM AUTHOR]

Carbon farming presents an opportunity for the land sector to generate income and transition to more sustainable land management practices. In Australia, establishing a carbon project and earning carbon credits is complex, with project proponents needing to satisfy eligibility requirements and adhere to rigorous measurement, verification, and reporting protocols of approved methods. To address these challenges, a human centered design (HCD) approach was used to deliver a digital solution, serving landowners' needs related to method discovery and reconfiguring how the methodological and scientific complexity of abatement potentials was delivered. The solution, called LOOC-C (pronounced "Look-see"), supports the discovery of abatement methods that are available for a given land area and provides an initial estimate of the potential quantum of carbon sequestered/emitted and the nature of co-benefits associated with each eligible method. Reporting on LOOC-C development and its observed impact demonstrates the role that human centered digital tools have in promoting land management actions that are both sustainable and reasonable to undertake. It equally demonstrates the power of integrating environmental market and user requirements with a robust design methodology. With similar opportunities in environmental markets globally, additional applications of an HCD approach are proposed. In 2012, the Australian government established the Emissions Reduction Fund (ERF) to reward landowners (via awarding Australian Carbon Credit Units, or ACCUs) for the implementation of management practices that either sequester carbon and/or reduce emissions of greenhouse gases. Rigorous eligibility and method requirements are intended to provide confidence in abatement outcomes, but they introduce significant complexity that participants must overcome. 11 years later, uncertainties in the implementation and ACCU generation potential of ERF projects and implications on productivity/co-benefits have limited uptake and the quantum of ACCU generation of land sector enterprises. Digital tools that support the discovery of options and provide estimated potential outcomes, such as the LOOC-C tool described in this paper (), can generate interest and empowerment, helping to initiate decisions toward market participation. [ABSTRACT FROM AUTHOR]

Accurate assessment of environmental variables is vital to understanding the global issues of land-use change and climate change, but is hindered by their high spatial and temporal heterogeneity. Extensive surveys are needed to model such large-scale problems, with their success dependent on adequate sampling protocols. We present a robust method for designing efficient sampling protocols for environmental variables. The SIMAP method involves the following steps: 1) Selecting sites that cover a representative range of spatial variability, 2) Intensive and spatially-accurate surveys within sites, 3) construction of continuous Maps that replicate the spatial and statistical variation of the surveys, 4) Accuracy simulations based on sampling of these maps and 5) determining a sampling Protocol for subsequent broader surveys. To illustrate the method, we used estimation of soil C stocks in mixed-species tree plantings and pastures to estimate carbon sequestration following reforestation. Soil C was surveyed intensively from these two land uses at several farms that covered a large rainfall gradient to provide contrasting datasets. In this example, sampling simulations showed that a systematic design generally required one less sample than a restricted-random design to achieve the same accuracy, while a simple-random design required substantially more samples. We found taking a minimum of 30 soil samples was needed for both bulk density and C concentration to accurately estimate soil C content within a 1-ha plot in a pasture or tree planting, which suggests many previous surveys of soil C were sampled inadequately. The SIMAP method could be readily applied to a range of abiotic and biotic variables, with the construction of maps allowing most sampling intensities and designs to be tested. Adequate sampling intensities differ widely among environmental variables, so the SIMAP method enables researchers to determine which variables require more investment. For many variables, costs may be minimised while maintaining a high accuracy of the sampling design via bulking of well-mixed samples prior to analysis. [ABSTRACT FROM AUTHOR]

Accurate 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 genus Eucalyptus and 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 of stand-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). [ABSTRACT FROM AUTHOR]

Abstract: Employing rangelands for climate change mitigation is hindered by conflicting reports on the direction and magnitude of change in soil organic carbon (ΔSOC) following changes in woody cover. Publications on woody thickening and deforestation, which had led to uncertainty in ΔSOC, were re-evaluated, and the dimensional-dependence of their data was determined. To model the fundamentals of SOC flux, linked SOC pools were simulated with first-order kinetics. Influences from forest development timelines and location of mature trees, with a potential for deep-set roots, were considered. We show that controversy or uncertainty has arisen when ΔSOC data were not measured along sufficient lengths of the three Cartesian axes and the time axis, i.e. in 4D. Thickening and deforestation experiments have particularly neglected factors affecting the time and depth axes, and sometimes neglected all four axes. Measurements of thickening must use time-spans beyond the calculable breakeven date – when thickening just recovers the SOC lost through land degradation: then all ecosystems are likely to incur net sequestration. The similarity between half-life of carbon pools, and the half-time required for sequestration, mandates that millennial time-spans must be considered in design of SOC experiments. Spatial and temporal averaging of ΔSOC data that accounted for environmentally dependent decomposition rates, revealed that deforestation to pasture incurred a higher and longer-term net emission than earlier reported. Published reports on thickening or deforestation appear no longer contradictory when one considers that they only presented views from lengths of the 4D axes that were too limited. Adoption of this understanding into carbon accounting will allow more precise estimates of carbon fluxes for emission trading schemes and national reports. [Copyright &y& Elsevier]

Understanding the relationship between disturbance regimes and species diversity has been of central interest to ecologists for decades. For example, the intermediate disturbance hypothesis proposes that diversity will be highest at intermediate levels of disturbance. Although peaked (hump-shaped) diversity-disturbance relationships (DDRs) have been documented in nature, many other DDRs have been reported as well. Here, we begin to theoretically unify these diverse empirical findings by showing how a single simple model can generate several different DDRs, depending on the aspect of disturbance that is considered. Additionally, we elucidate the competition-mediated mechanism underlying our results. Our findings have the potential to reconcile apparently conflicting empirical results on the effects of disturbance on diversity. [ABSTRACT FROM AUTHOR]

The concept of community assembly through trait-based environmental filtering has played a key role in our understanding of how communities change over space and time, however, the importance of spatial scale in the filtering process remains unclear. We propose that different environmental filters may operate at different spatial scales, and that filters at finer scales would be nested within those acting at coarser scales. We tested for the existence of spatially nested sets of trait-based filters in a temperate native grassland by applying the recently proposed maximum entropy (MaxEnt) approach to trait-based community assembly, which we extend through a trait selection procedure. We found that different traits were important in influencing the abundances of species at the three different spatial scales examined (micro-habitat, habitat, landscape), supporting the idea that trait based filtering processes operating at coarse spatial scales can be quite distinct from those operating at fine scales. Despite this result, we identified several traits which were frequently related to abundance at all spatial scales. Taken together, our results support the proposition that trait-based environmental filters at finer spatial scales are nested within those operating at coarser scales. We compared our results to those obtained using a simpler trait-by-trait analytical approach (correlation analysis and MaxEnt on individual traits). The capacity for MaxEnt to incorporate multiple traits simultaneously provided unique insights into the important traits at each spatial scale and presents significant advantages over existing univariate and multivariate approaches. [ABSTRACT FROM AUTHOR]

A randomisation test is described for assessing relative abundance predictions from the maximum entropy approach to biodiversity. The null model underlying the test randomly allocates observed abundances to species, but retains key aspects of the structure of the observed communities; site richness, species composition, and trait covariance. Three test statistics are used to explore different characteristics of the predictions. Two are based on pairwise comparisons between observed and predicted species abundances (RMSE, RMSESqrt). The third statistic is novel and is based on community-level abundance patterns, using an index calculated from the observed and predicted community entropies (EDiff). Validation of the test to quantify type I and type II error rates showed no evidence of bias or circularity, confirming the dependencies quantified by Roxburgh and Mokany (2007) and Shipley (2007) have been fully accounted for within the null model. Application of the test to the vineyard data of Shipley et al. (2006) and to an Australian grassland dataset indicated significant departures from the null model, suggesting the integration of species trait information within the maximum entropy framework can successfully predict species abundance patterns. The paper concludes with some general comments on the use of maximum entropy in ecology, including a discussion of the mathematics underlying the Maxent optimisation algorithm and its implementation, the role of absent species in generating biased predictions, and some comments on determining the most appropriate level of data aggregation for Maxent analysis. [ABSTRACT FROM AUTHOR]

Abstract: In this study, we sought to address the issue of how to derive an extended synthetic record of fire incidence and timing at regional scale that would be representative of a short remotely-sensed calibration record. We used annual rainfall and simulated annual ground stratum growth to develop multiple regression relationships for prediction of annual fire probability and proportion of late (August–November) fires from AVHRR NDVI fire footprint data across Australian tropical savannas. Relationships were examined using spatial averaging in moving windows varying from 3×3 to 61×61 pixels in size. Model fits as measured by R 2 improved as window size increased, but output layers became smoother and less representative of natural heterogeneity. A 25×25 pixel window was selected as the best compromise between model fit and smoothing. A 113-year synthetic record of annual fire probability and proportion of late fires was generated using the spatially explicit layers of model coefficients. The statistical properties of the synthetic fire probabilities were compared with those derived from the available fire footprint record, using a simple vegetation classification based on ground stratum type for spatial stratification. The two data sets showed a strong correspondence for both burned area and fire probability; spatial variation in mean and coefficient of variation of fire probability was representative of that observed in the historical record. There was significant temporal variation in the synthetic annual fire probability for different vegetation zones across the tropical savanna region for the full 113-year length of record. This simple approach could readily be applied to other areas of the world provided rainfall data are available and annual ground stratum growth can be simulated with a suitable model or estimated with remote sensing. [Copyright &y& Elsevier]

Abstract: This paper uses a scenario analysis system – Range-ASSESS – to examine the potential for gains and losses of soil carbon in the Australian rangelands as affected by grazing and climate. The analysis involves a factorial examination of the effect of stocking rates and all possible 5-year historical climates between 1889 and 1999. The analysis also looks at the sensitivity of results to the method of calculation of safe carrying capacity, and to the thresholds used to calculate grazing and dryness indices that drive transitions in state and transition models. The analysis showed that different vegetation zones produced different responses to changes in stocking depending upon the spatial distribution of dryness index, nature of carbon state and transition model, rules governing transitions, and relative significance of soil carbon. At a stocking density equivalent to 100% of 1997 levels, the soil carbon loss from rangelands was about 400 Mt C in 40% of the 5-year periods using a sensitive growth deviation threshold to determine dryness index. If a less sensitive threshold was used, potential loss was reduced to about 200 Mt C. If the grazing pressure threshold for a grazing index of four is adjusted to a more generous level, then potential losses in the dry periods are substantially reduced. The analysis is intended to be indicative of a likely approximate outcome rather than a quantitative measure of system response. The results indicate that the interpretation of the effect of the drought-grazing pressure interaction on perennial plant survival, and consequent organic carbon input to soils, is a major source of uncertainty and a critical area for more experimental measurement. [Copyright &y& Elsevier]

Coexistence mechanisms that require environmental variation to operate contribute importantly to the maintenance of biodiversity. One famous hypothesis of diversity maintenance under disturbance is the intermediate disturbance hypothesis (IDH). The IDH proposes patterns of peaked diversity under intermediate disturbance regimes, based on a tension between competitively superior species and species which can rapidly colonize following disturbance. We review the literature, and describe recent research that suggests that more than one underlying mechanism can generate this unimodal diversity pattern in disturbed environments. Several exciting emerging research areas are identified, including interactions between disturbance types, operation of the IDH in multi-trophic systems, and changes in disturbance regimes. However, empirical work is still focussed on describing the IDH pattern, with little emphasis on identifying its mechanistic basis. We discuss how to extend methods for identifying different coexistence mechanisms, developed in the theoretical literature, to experimental research. In an attempt to operationalize these various ideas we outline a hypothetical IDH research programme. A solid understanding of the life history attributes of the component species and their responses to disturbance will facilitate identification of the coexistence mechanism(s) underlying the IDH pattern, and provide a framework by which empirical and theoretical results can be more fully integrated. [ABSTRACT FROM AUTHOR]

The intermediate disturbance hypothesis (IDH) has been used for several decades as an explanation for the coexistence of species in ecological communities. It is intuitively simple, but deceptively so. We show, via discussion and examples, that the IDH is not one mechanism of coexistence, but rather summarizes a set of similar phenomena that can arise from the action of several different coexistence mechanisms. These underlying mechanisms are defined by the various ways in which species differ in their response to disturbance-induced spatial and temporal variability in resources and environmental conditions. As an example, the original specification of the IDH required patchy disturbances for coexistence. However, because the underlying mechanisms of coexistence can also operate at the within-patch scale, patchy disturbances are not a necessary requirement for coexistence under intermediate-disturbance regimes. These conclusions are illustrated through the analysis of three models: a spatial within-patch model, a spatial between-patch model, and a purely temporal model. All three generate similar patterns of coexistence under intermediate disturbance, yet underlying that coexistence lie at least two quite-distinct mechanisms of species coexistence: the storage effect and relative nonlinearity. The results from our analyses suggest that, as a promoter of species coexistence, the IDH is both broader in scope and richer in detail than has previously been recognized. [ABSTRACT FROM AUTHOR]

Abstract. Piovesan & Adams (2000) criticized the EUROFLUX network of carbon flux measurements based on the assumption that the results were 'surprising', as they appeared to contradict the generally accepted relationship that rates of soil and plant respiration increase with increasing temperature. The EUROFLUX studies showed an overall opposite trend, with higher rates of ecosystem respiration at the cooler, northern latitudes. In this paper the concept of 'surprising' results in ecology is reviewed and the original claims of Piovesan & Adams reassessed. It is concluded that results from the EUROFLUX network do not contradict the established respiration/temperature relationship for at least two reasons. First, ecosystem respiration is the net outcome of a wide range of underlying processes and a simple respiration/ temperature relationship may not be observed if other nontemperature dependent processes dominate. Second, relatively higher temperatures at the northern hemisphere sites during the period over which the EUROFLUX observations were made are consistent with the hypothesis that the measured fluxes reflect a non-equilibrium, transient loss of carbon. It is further concluded that the potential importance of inter-year variability for determining short-term patterns of carbon flux may provide the key to reconciling the temperature/respiration relationship highlighted by Piovesan & Adams, and the latitudinal respiration trends evident in the EUROFLUX results. [ABSTRACT FROM AUTHOR]

A new approach is introduced for determining the intrinsic guild classification of a group of species. Previous delimitations of intrinsic guilds have used evidence of spatial distributions (i.e. species co-occurrences), but this is rather indirect evidence. The new method is based on the results of species pairwise competition experiments, and thus uses direct data on species interactions. As with the spatial-distribution intrinsic guild approach, no prior assumptions are made about the classification, nor about which characters are related to guild membership. The method is applied to the results of two published experiments. For one, little independent evidence is available to judge the classification. There is no correlation between the guild classification obtained and gross morphology, but there is no reason to expect any such correlation. For the second experiment, intrinsic guild classifications had previously been obtained from distributional data, and the experimentally-based intrinsic classifications was identical to a distributionally-based one. We suggest that combining evidence from field distributions with experimental evidence offers a rigorous way to determine the true guild structure of communities, offering convincing conclusions when the two lines of evidence converge. [ABSTRACT FROM AUTHOR]

Prescribed fire to reduce forest fuels has been routinely applied to reduce wildfire risk in many parts of the world. It has also been proposed that prescribed fire can be used to mitigate greenhouse gas (GHG) emissions. Although prescribed fire creates emissions, if the treatment also decreases the incidence of subsequent wildfires, it is possible for the net outcome to be an emissions decline. Previous studies have suggested prescribed fire, at the frequencies required to materially impact wildfire occurrence, generally leads to net emissions increases. A focus on emissions means any change in carbon storage within the ecosystem remains unaccounted for; because living, dead, and soil carbon pools are characterized by different residence times, a re-distribution of carbon amongst these pools may either reduce or increase long-term ecosystem carbon stores. A full ecosystem carbon model has been developed to investigate the implications of prescribed fire management on total Net Ecosystem Carbon Balance (NECB), inclusive of both emissions and carbon storage. Consistent with previous work, the results suggested limited potential for reducing net GHG emissions through applying prescribed fire, with higher emissions from prescribed fire approximately offset by lower emissions and avoided carbon losses from the subsequent reduction in wildfire frequency. For example, shortening the prescribed fire interval from 25 to 10 years resulted in a NECB sequestration that was typically less than ±0.4 Mg C ha−1 yr−1, or less than approximately 0.1% of the total ecosystem carbon storage. Hence, whilst there was limited opportunity for achieving emission abatement outcomes from changing prescribed fire management, there were no significant emission penalties for doing so. These results suggest land managers should be free to adopt prescribed fire regimes to target specific management outcomes, without significantly impacting net emissions or total ecosystem carbon storage over the long term. • Novel data-driven model of forest fuels, fire, and ecosystem carbon. • Low potential for reducing wildfire emissions through applying prescribed fire (PF). • Increased emissions from PF offset by reduced carbon losses from wildfire. • Potential to modify PF management without adversely impacting total carbon balance. • Managers can adopt different prescribed fire regimes to target specific outcomes. [ABSTRACT FROM AUTHOR]

Studies the distribution patterns of organisms. Problems associated with spatial autocorrelation; Description of algorithms for simulating spatial structure.

In this issue of the Journal of Vegetation Science, Michalet et al. used species removal experiments to detect direct and indirect species interactions in a sub-alpine grassland. They found evidence for competition, facilitation and a range of indirect interactions at the species level, but no measurable effects when aggregated at the community level. Their results raise fundamental questions regarding the role and importance of indirect interactions in structuring ecological communities. [ABSTRACT FROM AUTHOR]

Greenhouse gas (GHG) accounting of emissions from land use, land-use change, and forestry necessarily involves consideration of landscape fire. This is of particular importance for Australia given that natural and human fire is a common occurrence, and many ecosystems are adapted to fire, and require periodic burning for plant regeneration and ecological health. Landscape fire takes many forms, can be started by humans or by lightning, and can be managed or uncontrolled. We briefly review the underlying logic of greenhouse gas accounting involving landscape fire in the 2020 Australian Government GHG inventory report. The treatment of wildfire that Australia chooses to enact under the internationally agreed guidelines is based on two core assumptions (a) that effects of natural and anthropogenic fire in Australian vegetation carbon stocks are transient and they return to the pre-fire level relatively quickly, and (b) that historically and geographically anomalous wildfires in forests should be excluded from national anthropogenic emission estimates because they are beyond human control. It is now widely accepted that anthropogenic climate change is contributing to increased frequency and severity of forest fires in Australia, therefore challenging assumptions about the human agency in fire-related GHG emissions and carbon balance. Currently, the national inventory focuses on forest fires; we suggest national greenhouse gas accounting needs to provide a more detailed reporting of vegetation fires including: (a) more detailed mapping of fire severity patterns; (b) more comprehensive emission factors; (c) better growth and recovery models from different vegetation types; (d) improved understanding how fires of different severities affect carbon stocks; and (e) improved analysis of the human agency behind the causes of emissions, including ignition types and fire-weather conditions. This more comprehensive accounting of carbon emissions would provide greater incentives to improve fire management practices that reduce the frequency, severity, and extent of uncontrolled landscape fires. [ABSTRACT FROM AUTHOR]

The Full Carbon Accounting Model (FullCAM) is used by the Australian Government for international reporting of greenhouse gas emissions and to predict carbon stock changes for carbon abatement projects. It is also used by landowners, companies and researchers, and is therefore regularly applied at local to continental scales. FullCAM is a stand level model with daily, monthly or annual temporal resolutions, depending on the users' preference. FullCAM has been developed over the past 20 years, however, no studies have reviewed and described the current structure, parameters, calibration, and uses of FullCAM, which was the objective of this study. We also discuss the potential for further developments that could improve FullCAM's ability to model responses to disturbances and climate, while also increasing its local accuracy. • FullCAM simulates carbon follows between live biomass, standing dead, debris and soil. • FullCAM is used at local, regional and continental scales, and daily, monthly or annual steps. • FullCAM is used for native forests, woodlands and savannas, as well as plantations. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

• A comprehensive sensitivity analysis was completed for the FullCAM model. • Carbon stock outputs were most sensitive to background mortality rates, age, site conditions, decomposition/turnover parameters and stand structure. • Model applications showed the importance of background mortality in relation to fire and harvesting. • Sensitivity of FullCAM outputs is very context dependent in relation to age and site conditions. • Implications for further developing FullCAM are discussed. In Australia, the Full Carbon Accounting Model (FullCAM) is used by the Australian Government for international reporting of greenhouse gas emissions and to predict carbon stock changes for carbon abatement projects. Consequently, over the last 20 years, it has been routinely applied at continental, regional and local scales, and has been subject to on-going development to improve its accuracy and representativeness. Given its importance, a sensitivity analysis could facilitate our understanding of model behaviour and aid the planning of future work and data collection. In particular, the sensitivity of a given model parameter is often context dependent such that it depends on the level of other parameters or variables. Key FullCAM parameters have generally been calibrated using data from empirical studies, with differing levels of confidence based on the sample size and data quality. The objective of this study was to apply a sensitivity analysis to examine (i) the sensitivity of FullCAM carbon stock outputs to its parameters and inputs, and how the sensitivity indices compare with the sample sizes used to calculate the respective parameters, (ii) the context dependency in terms of how the sensitivity varies with age, potential maximum biomass (a key FullCAM parameter), and disturbance severity or type, and (iii) to identify the implications for further development of FullCAM for woody vegetation systems. Of the 67 parameters tested, FullCAM carbon stock outputs were most sensitive to background mortality rates, age, potential maximum biomass, climate variability, age of maximum growth, decomposition or turnover parameters, and stand structure (regenerating or mature). The context dependency of the sensitivity analysis followed a consistent pattern depending on two main conditions, such that the sensitivity was higher when (i) the carbon stock was large and (ii) the parameter had a strong influence on that carbon stock. Several strong context dependencies occurred because the size of carbon stocks often vary through space and time within an ecosystem, and different processes (i.e. parameters) are more important at different times and locations. There was a strong context dependency in relation to age and potential maximum biomass, and FullCAM simulations indicated an interaction between fire disturbances and vegetation types. Lastly, background mortality was one of the least available inputs, but one to which FullCAM outputs were very sensitive. FullCAM was applied to show that the long-term contributions of low rates of background mortality to standing dead and debris C stocks were often as large as those from frequent (every 30 years) and intense harvesting and fire events because of the regular (annual) contribution of mortality and its accumulating influence of C stocks. [ABSTRACT FROM AUTHOR]

Ecologists have long studied the effects of disturbance on species diversity. More recently, researchers have become interested in understanding how the various aspects of disturbance interact to influence community diversity. While the effects of temporal autocorrelation have also received some attention, the potential for manipulating disturbance autocorrelation to achieve management goals has not been theoretically explored., We consider the interactions between temporal autocorrelation of disturbance occurrence and disturbance intensity at varying disturbance frequencies. Using an annual plant model, we show that when intensity and frequency are kept constant, changing the temporal autocorrelation of disturbance occurrence can also affect competitive outcomes. Additionally, we show that when species coexist, the degree of autocorrelation can affect which species reaches higher densities., We describe several examples (including prescribed burning, grazing and mowing) that outline how manipulation of temporal autocorrelation may be used to achieve conservation and eradication goals at no additional cost., Synthesis and applications. Our results provide important insights into, and have potential application to, land management and conservation. While changing the intensity and frequency of human-induced disturbances can be costly, adjusting the temporal autocorrelation of disturbance occurrence may be considered a 'no-cost manipulation'. In instances where a land manager lacks the funds or resources to manipulate other aspects of disturbance, such as intensity and frequency, changing the temporal autocorrelation may provide an effective, economical alternative. [ABSTRACT FROM AUTHOR]

Disturbance is a key factor shaping ecological communities, but little is understood about how the effects of disturbance processes accumulate over time. When disturbance regimes change, historical processes may influence future community structure, for example, by altering invasibility compared to communities with stable regimes. Here, we use an annual plant model to investigate how the history of disturbance alters invasion success. In particular, we show how two communities can have different outcomes from species introduction, solely due to past differences in disturbance regimes that generated different biotic legacies. We demonstrate that historical differences can enhance or suppress the persistence of introduced species, and that biotic legacies generated by stable disturbance history decay over time, though legacies can persist for unexpectedly long durations. This establishes a formal theoretical foundation for disturbance legacies having profound effects on communities, and highlights the value of further research on the biotic legacies of disturbance. [ABSTRACT FROM AUTHOR]

Above‐ground biomass in forests is critical to the global carbon cycle as it stores and sequesters carbon from the atmosphere. Climate change will disrupt the carbon cycle hence understanding how climate and other abiotic variables determine forest biomass at broad spatial scales is important for validating and constraining Earth System models and predicting the impacts of climate change on forest carbon stores. We examined the importance of climate and soil variables to explaining above‐ground biomass distribution across the Australian continent using publicly available biomass data from 3130 mature forest sites, in 6 broad ecoregions, encompassing tropical, subtropical and temperate biomes. We used the Random Forest algorithm to test the explanatory power of 14 abiotic variables (8 climate, 6 soil) and to identify the best‐performing models based on climate‐only, soil‐only and climate plus soil. The best performing models explained ~50% of the variation (climate‐only: R2 = 0.47 ± 0.04, and climate plus soils: R2 = 0.49 ± 0.04). Mean temperature of the driest quarter was the most important climate variable, and bulk density was the most important soil variable. Climate variables were consistently more important than soil variables in combined models, and model predictive performance was not substantively improved by the inclusion of soil variables. This result was also achieved when the analysis was repeated at the ecoregion scale. Predicted forest above‐ground biomass ranged from 18 to 1066 Mg ha−1, often under‐predicting measured above‐ground biomass, which ranged from 7 to 1500 Mg ha−1. This suggested that other non‐climate, non‐edaphic variables impose a substantial influence on forest above‐ground biomass, particularly in the high biomass range. We conclude that climate is a strong predictor of above‐ground biomass at broad spatial scales and across large environmental gradients, yet to predict forest above‐ground biomass distribution under future climates, other non‐climatic factors must also be identified. [ABSTRACT FROM AUTHOR]

Spatial climate datasets currently available for Bhutan are limited by weather station data availability, spatial resolution or interpolation methodology. This article presents new datasets for monthly maximum temperature, minimum temperature, precipitation and vapour pressure climate normals interpolated for the 1986-2015 reference period using trivariate smoothing splines. The inclusion of standardized day time Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) values as partial spline dependencies reduced cross validated root mean square error (RMSE) for maximum temperature by up to 16.0% and was most effective between March and September. Using both a topographic index of relative elevation and standardized night time MODIS LST values as partial spline dependencies reduced monthly mean minimum temperature RMSE by up to 23.4%. Neither variable was effective for minimum temperature interpolation between June and September. High humidity, extensive cloud cover and heavy precipitation occur during these months, which are likely to suppress the formation of temperature inversions that typically form under clear, calm conditions. These new temperature and precipitation surfaces show distinct differences from the WorldClim and CRU CL 2.0 datasets, which do not use weather stations within Bhutan for calibration. New precipitation surfaces better describe the heavy rainfall experienced in the southern foothills while retaining the effect of orography throughout the central valleys and ranges. The development of vapour pressure surfaces also allow for the calculation of ecologically important variables such as vapour pressure deficit, and may also be useful for solar radiation modelling in the region. The different datasets presented in this article will facilitate ecological and agricultural research in Bhutan and provide high quality surfaces needed for future climate change scenarios. [ABSTRACT FROM AUTHOR]

ABSTRACT Spatial climate datasets currently available for Bhutan are limited by weather station data availability, spatial resolution or interpolation methodology. This article presents new datasets for monthly maximum temperature, minimum temperature, precipitation and vapour pressure climate normals interpolated for the 1986-2015 reference period using trivariate smoothing splines. The inclusion of standardized day time Moderate Resolution Imaging Spectroradiometer ( MODIS) land surface temperature ( LST) values as partial spline dependencies reduced cross validated root mean square error ( RMSE) for maximum temperature by up to 16.0% and was most effective between March and September. Using both a topographic index of relative elevation and standardized night time MODIS LST values as partial spline dependencies reduced monthly mean minimum temperature RMSE by up to 23.4%. Neither variable was effective for minimum temperature interpolation between June and September. High humidity, extensive cloud cover and heavy precipitation occur during these months, which are likely to suppress the formation of temperature inversions that typically form under clear, calm conditions. These new temperature and precipitation surfaces show distinct differences from the WorldClim and CRU CL 2.0 datasets, which do not use weather stations within Bhutan for calibration. New precipitation surfaces better describe the heavy rainfall experienced in the southern foothills while retaining the effect of orography throughout the central valleys and ranges. The development of vapour pressure surfaces also allow for the calculation of ecologically important variables such as vapour pressure deficit, and may also be useful for solar radiation modelling in the region. The different datasets presented in this article will facilitate ecological and agricultural research in Bhutan and provide high quality surfaces needed for future climate change scenarios. [ABSTRACT FROM AUTHOR]

The surge in global efforts to understand the causes and consequences of drought on forest ecosystems has tended to focus on specific impacts such as mortality. We propose an ecoclimatic framework that takes a broader view of the ecological relevance of water deficits, linking elements of exposure and resilience to cumulative impacts on a range of ecosystem processes. This ecoclimatic framework is underpinned by two hypotheses: (i) exposure to water deficit can be represented probabilistically and used to estimate exposure thresholds across different vegetation types or ecosystems; and (ii) the cumulative impact of a series of water deficit events is defined by attributes governing the resistance and recovery of the affected processes. We present case studies comprising Pinus edulis and Eucalyptus globulus, tree species with contrasting ecological strategies, which demonstrate how links between exposure and resilience can be examined within our proposed framework. These examples reveal how climatic thresholds can be defined along a continuum of vegetation functional responses to water deficit regimes. The strength of this framework lies in identifying climatic thresholds on vegetation function in the absence of more complete mechanistic understanding, thereby guiding the formulation, application and benchmarking of more detailed modelling. [ABSTRACT FROM AUTHOR]

There is increasing reliance on ecological models to improve our understanding of how ecological systems work, to project likely outcomes under alternative global change scenarios and to help develop robust management strategies. Two common types of spatiotemporally explicit ecological models are those focussed on biodiversity composition and those focussed on ecosystem function. These modelling disciplines are largely practiced separately, with separate literature, despite growing evidence that natural systems are shaped by the interaction of composition and function. Here we call for the development of new modelling approaches that integrate composition and function, accounting for the important interactions between these two dimensions, particularly under rapid global change. We examine existing modelling approaches that have begun to combine elements of composition and function, identifying their potential contribution to fully integrated modelling approaches. The development and application of integrated models of composition and function face a number of important challenges, including biological data limitations, system knowledge and computational constraints. We suggest a range of promising avenues that could help researchers overcome these challenges, including the use of virtual species, macro-ecological relationships and hybrid correlative-mechanistic modelling. Explicitly accounting for the interactions between composition and function within integrated modelling approaches has the potential to improve our understanding of ecological systems, provide more accurate predictions of their future states and transform their management. [ABSTRACT FROM AUTHOR]

Carbon finance offers the potential to change land management and conservation planning priorities. We develop a novel approach to planning for improved land management to conserve biodiversity while utilizing potential revenue from carbon biosequestration. We apply our approach in northern Australia's tropical savanna, a region of global significance for biodiversity and carbon storage, both of which are threatened by current fire and grazing regimes. Our approach aims to identify priority locations for protecting species and vegetation communities by retaining existing vegetation and managing fire and grazing regimes at a minimum cost. We explore the impact of accounting for potential carbon revenue (using a carbon price of US$14 per tonne of carbon dioxide equivalent) on priority areas for conservation and the impact of explicitly protecting carbon stocks in addition to biodiversity. Our results show that improved management can potentially raise approximately US$5 per hectare per year in carbon revenue and prevent the release of 1-2 billion tonnes of carbon dioxide equivalent over approximately 90 years. This revenue could be used to reduce the costs of improved land management by three quarters or double the number of biodiversity targets achieved and meet carbon storage targets for the same cost. These results are based on generalised cost and carbon data; more comprehensive applications will rely on fine scale, site-specific data and a supportive policy environment. Our research illustrates that the duel objective of conserving biodiversity and reducing the release of greenhouse gases offers important opportunities for cost-effective land management investments. [ABSTRACT FROM AUTHOR]

• We measured soil organic carbon that is typically ignored close to large trees. • 90% of the total soil organic carbon was within ~2.6 m of the surface. • Under large tree trunks soil organic carbon was four times more concentrated. • The stand-level had ~7% [95% CI: 3–12%] more soil carbon than previously estimated. • There may be higher emissions from past forest disturbances than have been inferred. Typical soil organic carbon (SOC) measurements do not account for the higher SOC concentration adjacent to, inside and under the trunks of large trees, or for the root volume which displaces soil and thereby reduces spatial density of SOC. Any net difference between these two omissions could have a significant impact on carbon accounts for the conversion of a primary forest with large trees to a type of land cover with much smaller trees, or no trees, such as to a secondary forest on short harvest cycles, or to deforested land, respectively. To improve knowledge of carbon stocks in primary forests, for better carbon management and climate change modelling, we sampled SOC and soil bulk density directly under large tree trunks, inside tree trunks, in the humus mounds in the buttress region, and under the humus mounds. The measurements were in primary Eucalyptus regnans mixed-forest. SOC was formulated as a function of depth. Adjacent to the trees, 90% of the total cumulative SOC was estimated to be within ~2.6 m of the mineral soil surface. That SOC was compared with an earlier measurement in the same locality of SOC in-between trees, away from the trunk and buttress. The SOC under large tree trunks was about four times more concentrated than in-between trees. Formulae that link SOC, root volume, and buttress shape, to tree diameter and ground slope were applied to forest stands within 54.4 ha of primary forest. When the under-trunk SOC was tallied with the organic soils associated with the buttress region and in nearly decomposed logs, SOC at the unit-area-level increased by ~7% [95% CI: 3–12%] relative to the in-between-tree SOC alone, and the absolute increase was 21 Mg ha−1 [95% CI: 10–37 Mg ha−1] of SOC. Our results suggest that, at least for land use change that fells mature trees >1 m in diameter, there may have been higher greenhouse gas emissions from past forest attrition than have been inferred. Globally, we identified 50 example tree species, other than E. regnans , that may also have extra SOC at the stand-level in the absence of fire. Additional SOC per hectare was positively correlated with basal area of trees, which increases with the number of large trees in a stand. The maintenance of large trees will help ensure higher levels of forest carbon. The protection of medium-sized trees will be necessary to ensure existence of large trees in the future. [ABSTRACT FROM AUTHOR]