Increasing impacts of climatic change and anthropogenic disturbances on natural ecosystems are leading to population declines or extinctions of many species worldwide. In Australia, recent climatic change has caused population declines in some native fauna. The projected increase in mean annual temperature by up to 4°C by the end of the 21st century is expected to exacerbate these trends. The greater glider (Petauroides volans), Australia’s largest gliding marsupial, is widely distributed along the eastern coast, but has recently experienced drastic declines in population numbers. Its association with hollow‐bearing trees, used for nesting, has made it an important species for the conservation of old‐growth forest ecosystems. Fires and timber harvesting have been identified as threats to the species. Greater gliders have disappeared however from areas that have experienced neither raising questions about the role of other factors in their decline. A unique physiology and strictEucalyptusdiet make them vulnerable to high temperatures and low water availability. As such, climatic conditions may drive habitat selection and recent climatic trends may be contributing to observed population declines. Using presence:absence data from across its distribution in Victoria, coupled with high spatial and temporal resolution climatic data and machine‐learning modeling, we tested the influence of climatic, topographic, edaphic, biotic, and disturbance variables on greater glider occupancy and habitat suitability. We found that climatic variables, particularly those related to aridity and extreme weather conditions, such as number of nights warmer than 20°C, were highly significant predictors of greater glider occurrence. Climatic conditions associated with habitat suitability have changed over time, with increasing aridity across much of its southeastern distribution. These changes in climate are closely aligned with observed population declines across this region. At higher elevation, some areas where the greater glider is observed at high densities, conditions have become wetter, which is improving habitat quality. These areas are of growing significance to greater glider conservation as they will become increasingly important as climatic refugia in the coming decades. Protecting these areas of habitat will be critical for facilitating the conservation of greater gliders as the broader landscape becomes less hospitable under future climatic change.

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.

Extreme weather can have significant impacts on plant species demography; however, most studies have focused on responses to a single or small number of extreme events. Long‐term patterns in climate extremes, and how they have shaped contemporary distributions, have rarely been considered or tested. BIOCLIM variables that are commonly used in correlative species distribution modelling studies cannot be used to quantify climate extremes, as they are generated using long‐term averages and therefore do not describe year‐to‐year, temporal variability. We evaluated the response of 37 plant species to base climate (long‐term means, equivalent to BIOCLIM variables), variability (standard deviations) and extremes of varying return intervals (defined using quantiles) based on historical observations. These variables were generated using fine‐grain (approx. 250 m), time‐series temperature and precipitation data for the hottest, coldest and driest months over 39 years. Extremes provided significant additive improvements in model performance compared to base climate alone and were more consistent than variability across all species. Models that included extremes frequently showed notably different mapped predictions relative to those using base climate alone, despite often small differences in statistical performance as measured as a summary across sites. These differences in spatial patterns were most pronounced at the predicted range margins, and reflect the influence of coastal proximity, continentality, topography and orographic barriers on climate extremes. Species occupying hotter and drier locations that are exposed to severe maximum temperature extremes were associated with better predictive performance when modelled using extremes. Understanding how plant species have historically responded to climate extremes may provide valuable insights into our understanding of contemporary distributions and help to make more accurate predictions under a changing climate.

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.

Agroforestry is one nature-based solution that holds significant potential for improving the sustainability and resilience of agricultural systems. Quantifying these benefits is challenging in agroforestry systems, largely due to landscape complexity and the diversity of management approaches. Digital tools designed for agroforestry typically focus on timber and crop production, and not the broader range of benefits usually considered in assessments of ecosystem services and natural capital. The objectives of this review were to identify and evaluate digital tools that quantify natural capital benefits across eight themes applicable to agroforestry systems: timber production and carbon sequestration, agricultural production, microclimate, air quality, water management, biodiversity, pollination, and amenity. We identified and evaluated 63 tools, 9 of which were assessed in further detail using Australia as a case study. No single tool was best suited to quantify benefits across each theme, suggesting that multiple tools or models could be combined to address capability gaps. We find that model complexity, incorporation of spatial processes, accessibility, regional applicability, development speed and interoperability present significant challenges for the tools that were evaluated. We recommend that these challenges be considered as opportunities to develop new, and build upon existing, tools to enhance decision support in agroforestry systems.