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Evidence exists that tree mortality is accelerating in some regions of the tropics1,2, with profound consequences for the future of the tropical carbon sink and the global anthropogenic carbon budget left to limit peak global warming below 2 °C. However, the mechanisms that may be driving such mortality changes and whether particular species are especially vulnerable remain unclear3–8. Here we analyse a 49-year record of tree dynamics from 24 old-growth forest plots encompassing a broad climatic gradient across the Australian moist tropics and find that annual tree mortality risk has, on average, doubled across all plots and species over the last 35 years, indicating a potential halving in life expectancy and carbon residence time. Associated losses in biomass were not offset by gains from growth and recruitment. Plots in less moist local climates presented higher average mortality risk, but local mean climate did not predict the pace of temporal increase in mortality risk. Species varied in the trajectories of their mortality risk, with the highest average risk found nearer to the upper end of the atmospheric vapour pressure deficit niches of species. A long-term increase in vapour pressure deficit was evident across the region, suggesting that thresholds involving atmospheric water stress, driven by global warming, may be a primary cause of increasing tree mortality in moist tropical forests.

Reintroductions of coarse woody debris (CWD) to Australia’s temperate woodlands have been proposed to offset the impacts of long-term tree removal. However, the magnitude of the reduction in the rate of surface-soil moisture loss due to CWD is not known. Gravimetric soil moisture content was measured at different distances from CWD for 12 samples in a cleared temperate woodland. Sampling was conducted at regular intervals following a major rain event in late summer. Lower soil bulk densities near CWD indicated higher levels of soil carbon. A multivariate nonlinear statistical model was constructed to explain the observed soil moisture content as a function of time after rainfall, distance from CWD and CWD diameter. The model demonstrated that rates of soil moisture loss increased with increasing distance from CWD. Drying times near CWD were 40% longer than drying times at reference distances from CWD. The model also showed that CWD diameter influenced the rate of soil moisture loss, with larger diameters yielding reductions in soil moisture loss over greater distances from CWD. Locations of greater soil moisture availability associated with CWD may be particularly advantageous for organisms sensitive to low soil moisture levels and may increase productivity, particularly in water-limited ecosystems.

Problems arising from application of the representative criterion for conservation and natural heritage evaluation are discussed. An ecological basis to this criterion is suggested that focuses on those key environmental factors dominating biotic response. A methodology is proposed that utilizes computer-based methods of establishing and interrogating spatial data bases (geographic information systems), environmental modeling, and numeric analysis. An example is presented illustrating some of the advantages and limitations of classification and dimension reduction techniques in both defining bioenvironments and displaying their spatial distribution. The advantages of this method for representativeness evaluation are that it maximizes the utility of available data, is explicit and repeatable, and enables large areas to be analyzed at relatively fine scales.

This paper examines the characteristics of four different methods of estimating the fractal dimension of profiles. The semi-variogram, roughness-length, and two spectral methods are compared using synthetic 1024-point profiles generated by three methods, and using two profiles derived from a gridded DEM and two profiles from a laser-scanned soil surface. The analysis concentrates on the Hurst exponent H,which is linearly related to fractal dimension D,and considers both the accuracy and the variability of the estimates of H.The estimation methods are found to be quite consistent for Hnear 0.5, but the semivariogram method appears to be biased for Happroaching 0 and 1, and the roughness-length method for Happroaching 0. The roughness-length or the maximum entropy spectral methods are recommended as the most suitable methods for estimating the fractal dimension of topographic profiles. The fractal model fitted the soil surface data at fine scales but not at broad scales, and did not appear to fit the DEM profiles well at any scale.

The low spatial density of streamflow gauging stations limits the accuracy of spatial streamflow estimates in many parts of the world. Strategies to improve runoff estimates in the absence of dense measurements have tended to focus on estimating parameters of runoff models in ungauged regions, through so‐called parameter regionalization methods. However, parameter regionalization can be affected by overdependence on calibration at gauged sites, model parameter equifinality, and ensuing estimation errors. As a result, spatial model runoff estimates typically exhibit spatially correlated biases. This analysis attempts to enhance the use of observations in spatial runoff estimation. Specifically, we assessed the potential to reduce systematic errors by spatially interpolating residuals (i.e., errors) between prior grid‐based streamflow estimates for Australia at 0.05° × 0.05° grid from the Australian Bureau of Meteorology's calibrated, operational Australian Water Resources Assessment Landscape model (AWRA‐L) and streamflow gauging records from 780 unimpeded, relatively small catchments. We analyzed spatial autocorrelation in residuals and tested an efficient two‐step correction approach involving a uniform correction and subsequent kriging of residuals. The approach removed an average of 41% of systematic bias in the model estimates and also improved other model performance measures. Further reduction in errors at shorter timescales may be achievable through a temporally hierarchical correction scheme. We developed a method to reduce spatially variable bias in modeled runoff at the continental scaleThe two‐step regression and kriging scheme removed an average 41% of bias in cross validationCorrections were applied to continuous daily fields of runoff at a 0.05 × 0.05° grid across Australia