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Aim Concern about climate change, with the subsequent emergence of carbon markets and policy initiatives such as REDD (reducing carbon emissions by decreasing deforestation and forest degradation), have focused attention on assessing and monitoring terrestrial carbon reserves. Most effort has focused on above-ground forest biomass. Soil has received less attention despite containing more carbon than above-ground terrestrial biomass and the atmosphere combined. Our aim was to explore how well soil carbon concentration could be estimated on three continents from existing climate, topography and vegetation-cover data. Location Peru, Brazil, Argentina, Australia, China. Methods Soil carbon concentration and leaf area index ( LAI) as well as GIS-derived climate and topography variables for 65 temperate and 43 tropical, forest and woodland ecosystems, were either directly measured or estimated from freely available global datasets. We then used multiple regressions to determine how well soil carbon concentration could be predicted from LAI, climate and topography at a given site. We compared our measurements with top soil carbon estimates from the Food and Agriculture Organization of the United Nations ( FAO) harmonized world soil map. Results Our empirical model based on estimates of temperature, water availability and plant productivity provided a good estimate of soil carbon concentrations ( R2 = 0.79). In contrast, the values of topsoil carbon concentrations from the FAO harmonized world soil map correlated poorly with the measured values of soil carbon concentration ( R2 = 0.0011). Main conclusions The lack of correlation between the measured values of soil carbon and the values from the FAO harmonized world soil map indicate that substantial improvements in the production of soil carbon maps are needed and possible. Our results demonstrate that the inclusion of freely available GIS data offers improved estimates of soil carbon and will allow the creation of more accurate soil carbon maps. [ABSTRACT FROM AUTHOR]

Aim To determine the relationship between the distribution of climate, climatic heterogeneity and pteridophyte species richness gradients in Australia, using an approach that does not assume potential relationships are spatially invariant and allows for scale effects (extent of analysis) to be explicitly examined. Location Australia, extending from 10° S to 43° S and 112° E to 153° E. Method Species richness within 50 × 50 km grid cells was determined using point distribution data. Climatic surfaces representing the distribution and availability of water and energy at 1 km and 5 km cell resolutions were obtained. Climate at the 50 km resolution of analysis was represented by their mean and standard deviation in that area. Relationships were assessed using geographically weighted linear regression at a range of spatial bandwidths to investigate scale effects. Results The parameters and the predictive strength of all models varied across space at all extents of analysis. Overall, climatic variables representing water availability were more highly correlated to pteridophyte richness gradients in Australia than those representing energy. Their variance in cells further increased the strength of the relationships in topographically heterogeneous regions. Relationships with water were strong across all extents of analysis, particularly in the tropical and subtropical parts of the continent. Water availability explained less of the variation in richness at higher latitudes. Main conclusions This study brings into question the ability of aspatial and single-extent models, searching for a unified explanation of macro-scaled patterns in gradients of diversity, to adequately represent reality. It showed that, across Australia, there is a positive relationship between pteridophyte species richness and water availability but the strength and nature of the relationship varies spatially with scale in a highly complex manner. The spatial variance, or actual complexity, in these relationships could not have been demonstrated had a traditional aspatial global regression approach been used. Regional scale variation in relationships may be at least as important as more general relationships for a true understanding of the distribution of broad-scale diversity. [ABSTRACT FROM AUTHOR]

Conditions conducive to fires are becoming increasingly common and widespread under climate change. Recent fire events across the globe have occurred over unprecedented scales, affecting a diverse array of species and habitats. Understanding biodiversity responses to such fires is critical for conservation. Quantifying post‐fire recovery is problematic across taxa, from insects to plants to vertebrates, especially at large geographic scales. Novel datasets can address this challenge. We use presence‐only citizen science data from iNaturalist, collected before and after the 2019–2020 megafires in burnt and unburnt regions of eastern Australia, to quantify the effect of post‐fire diversity responses, up to 18 months post‐fire. The geographic, temporal, and taxonomic sampling of this dataset was large, but sampling effort and species discoverability were unevenly spread. We used rarefaction and prediction (iNEXT) with which we controlled sampling completeness among treatments, to estimate diversity indices (Hill numbers: q = 0–2) among nine broad taxon groupings and seven habitats, including 3885 species. We estimated an increase in species diversity up to 18 months after the 2019–2020 Australian megafires in regions which were burnt, compared to before the fires in burnt and unburnt regions. Diversity estimates in dry sclerophyll forest matched and likely drove this overall increase post‐fire, while no taxon groupings showed clear increases inconsistent with both control treatments post‐fire. Compared to unburnt regions, overall diversity across all taxon groupings and habitats greatly decreased in areas exposed to extreme fire severity. Post‐fire life histories are complex and species detectability is an important consideration in all post‐fire sampling. We demonstrate how fire characteristics, distinct taxa, and habitat influence biodiversity, as seen in local‐scale datasets. Further integration of large‐scale datasets with small‐scale studies will lead to a more robust understanding of fire recovery. [ABSTRACT FROM AUTHOR]

Aim: A key ecological debate is whether ecosystem functions are distinctly influenced by biological diversity across broad scales. Although recent work has emphasized the importance of links between ecosystem functions and measures of ecological specialization as proxies of biodiversity, few studies have analysed macroecological relationships empirically in diverse environments. We tested whether gross primary productivity (GPP) in the Australian Wet Tropics (WT) was distinctly related to community‐level measures of the ecological specialization of component tree species across climate space, after accounting for climate drivers. Location: Wet Tropics, Australia. Time period: 1976–2012. Major taxa studied: Nine hundred and forty‐eight WT tree species. Methods: Using all geographically valid herbarium records for WT trees, we quantified the realized climatic niche widths using continental surfaces for maximum temperature of the warmest period and total annual rainfall. The median realized niche width for all tree species occurring at 510 sites was used to approximate ecological specialization within communities. To separate climatic effects on GPP, we applied a novel analysis using the difference in GPP and the difference in median community realized niche width between site pairs with similar climatic conditions. Linear models were then run on the difference in GPP between site pairs (response) and the difference in climatic niche widths (predictor). Results: For climatically similar sites in drier areas, GPP was higher in sites composed of species with narrower temperature niches (average R2 = 0.087, average t‐statistic = −3.45). Conversely, for climatically similar sites in drier areas, GPP was lower in sites composed of species with narrower rainfall niches (average R2 = 0.171, average t‐statistic = 5.06). Main conclusions: Wet Tropics sites with more thermal specialists had higher productivity, whereas sites with more moisture specialists had lower productivity. These findings suggest that physiological specialization across climate space can influence primary productivity at broad scales, but in inconsistent ways. [ABSTRACT FROM AUTHOR]

Invasive weeds represent one of the greatest threats to ecosystem integrity worldwide, with climate change predicted to allow expansion of weed ranges in coming decades. One of Australia's worst weeds is lantana (Lantana camara) which, given the potential for climatic change, is of increasing concern to those managing the mountainous regions in the country's southeast. In order to identify potential additional threats lantana may pose for Australia's valued biodiversity, this research develops a habitat suitability model for lantana in a portion of the Greater Blue Mountains World Heritage Area under current and simulated warmer conditions. Minimum temperature was found to be the most important predictor correlated with potential lantana establishment, explaining over 88% of the variation in lantana presence predicted by the model. Currently, 8% of the study area was found to be suitable for lantana, with this figure reaching 94% after a simulated 2°C rise in temperature anticipated by 2050. The sharp increase in suitable habitat highlights the importance of keeping the weed's range restricted in the study area. The strong link between temperature and predicted lantana establishment confirms prior research and further stresses the threat this weed poses to the area's biodiversity values as the climate warms. In addition, the model identified low-lying riparian areas as potential incursion pathways for the weed to travel further inland. Given the weed's invasiveness, potential for adverse impacts, and high capacity for dispersal, these pathways should not be overlooked when monitoring potential invasion of mountainous regions by lantana and other tropical weeds. [ABSTRACT FROM AUTHOR]

Globally, local governments are increasing investment in urban greening projects. However, there is little consideration of whether the species being planted will be resilient to climate change. We assessed the distribution of climatically suitable habitat, now and in the future, for 176 tree species native to Australia, commonly planted across Australia's Significant Urban Areas (SUAs) and currently grown by commercial nurseries. Species' occurrence records were obtained from inventories and herbaria, globally and across Australia, and combined with baseline climate data (WorldClim, 1960–1990) and six climate scenarios for 2030 and 2070 using climatic suitability models (CSMs). CSMs for each species were calibrated and projected onto baseline and future scenarios. We calculated changes in the size of climatically suitable habitat for each species across each SUA, and identified urban areas that are likely to have suitable climate for either fewer or more of our study species under future climate. By 2070, climatically suitable habitat in SUAs is predicted to decline for 73% of species assessed. For 18% of these species, climatically suitable area is predicted to be more than halved, relative to their baseline extent. Generally, for urban areas in cooler regions, climatically suitable habitat is predicted to increase. By contrast, for urban areas in warmer regions, a greater proportion of tree species may lose climatically suitable habitat. Our results highlight changing patterns of urban climatic space for commonly planted species, suggesting that local governments and the horticultural industry should take a proactive approach to identify new climate-ready species for urban plantings. Unlabelled Image • Climate change likely to cause declines in suitable habitat for urban trees. • New urban habitat likely gained by some tree species due to climate change. • Tree species in warmer cities likely to decline more than trees in cooler cities. • Industry and government need climate-ready tree species selection. [ABSTRACT FROM AUTHOR]

Species distribution models (SDMs) frequently project substantial declines in the spatial extent of climatically suitable habitat in response to scenarios of future climate change. Such projections are highly disconcerting. Yet, considerable variation can occur in the direction and magnitude of range changes projected by different SDM methods, even when predictive performance is similar. In this study, we assessed whether particular methods have a tendency to predict substantial loss or gain of suitable habitat. In particular, we asked, “are 14 SDM methods equally likely to predict extreme changes to the future extent of suitable habitat for 220 Australian mammal species?”. We defined five non-mutually exclusive categories of ‘extreme’ change, based on stability or loss of current habitat, or the dislocation of current and future habitat: a) no future habitat (range extinction); b) low stability of current habitat (≤10% remains); c) no gain of habitat in new locations; d) all future habitat is in new locations (i.e. completely displaced from current habitat); and e) substantial increase in size of habitat (future habitat is ≥100% larger than current). We found that some SDM methods were significantly more likely than others to predict extreme changes. In particular, distance-based models were significantly less likely than other methods to predict substantial increases in habitat size; Random Forest models and Surface Range Envelopes were significantly more likely to predict a complete loss of current habitat, and future range extinction. Generalised Additive Models and Generalised Linear Models rarely predicted range extinction; future habitat completely disjunct from current habitat was predicted more frequently than expected by Classification Tree Analysis and less frequently by Maxent. Random Forest generally predicted extreme range changes more frequently than other SDM methods. Our results identify trends among different methods with respect to tendency to predict extreme range changes. These are of significance for climate-impact assessments, with implications for transferability of models to novel environments. Our findings emphasise the need to explore and justify the use of different models and their parameterisations, and to develop approaches to assist with optimisation of models. [ABSTRACT FROM AUTHOR]

Advances in computing power and infrastructure, increases in the number and size of ecological and environmental datasets, and the number and type of data collection methods, are revolutionizing the field of Ecology. To integrate these advances, virtual laboratories offer a unique tool to facilitate, expedite, and accelerate research into the impacts of climate change on biodiversity. We introduce the uniquely cloud-based Biodiversity and Climate Change Virtual Laboratory (BCCVL), which provides access to numerous species distribution modelling tools; a large and growing collection of biological, climate, and other environmental datasets; and a variety of experiment types to conduct research into the impact of climate change on biodiversity. Users can upload and share datasets, potentially increasing collaboration, cross-fertilisation of ideas, and innovation among the user community. Feedback confirms that the BCCVL's goals of lowering the technical requirements for species distribution modelling, and reducing time spent on such research, are being met. [ABSTRACT FROM AUTHOR]