School of Botany - Research Publications
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ItemPredicting distribution changes of a mire ecosystem under future climates(Wiley, 2014-04-01)Aim Mire ecosystems are threatened by global climate change but have important roles in biodiversity conservation, carbon storage, landscape‐scale hydrological function and in providing ecosystem services. We aimed to: (1) estimate change in areas environmentally suitable for mires under future climates; (2) evaluate the sensitivities of projected change to uncertainties in future climate and model structure; (3) evaluate the effect of global mitigation actions on distribution change; (4) identify potential climate refuges for future adaptation actions. Methods We developed and evaluated correlative bioclimatic models for an Australian mire ecosystem by: (1) selecting environmental predictors representing ecological processes that mediate ecosystem occurrence and dynamics; (2) using a high‐performance modelling algorithm; (3) quantifying predictive performance by cross‐validation; (4) cross‐checking responses to predictor variables between different algorithms; (5) comparing the modelled responses with expected mechanistic responses; (6) evaluating extrapolation risks by quantifying the deviation between future and current environmental domains of the study area and by assessing the temporal constancy of correlations between variables; (7) using a geographically stratified cross‐validation to verify spatial consistency of the model; and (8) quantifying the robustness of predictions of climate change impacts to uncertainty in both climate and ecological models. Results All combinations of global circulation models and distribution model projected declines of at least 30% in both area and suitability of environments for the mire ecosystem and in projecting a contraction of range to the southwest. We identified a likely refuge in the south of the distribution and two less certain, emerging areas of suitable environment west and south of the current distribution. Main Conclusions We conclude that southern mire ecosystems are highly susceptible to climate change. Our approach will be useful for the prediction of climate impacts on other ecosystems for which there is enough knowledge to map distributions and develop plausible hypotheses about environmental factors that influence them.
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ItemDetecting Extinction Risk from Climate Change by IUCN Red List Criteria(WILEY, 2014-06)Anthropogenic climate change is a key threat to global biodiversity. To inform strategic actions aimed at conserving biodiversity as climate changes, conservation planners need early warning of the risks faced by different species. The IUCN Red List criteria for threatened species are widely acknowledged as useful risk assessment tools for informing conservation under constraints imposed by limited data. However, doubts have been expressed about the ability of the criteria to detect risks imposed by potentially slow-acting threats such as climate change, particularly because criteria addressing rates of population decline are assessed over time scales as short as 10 years. We used spatially explicit stochastic population models and dynamic species distribution models projected to future climates to determine how long before extinction a species would become eligible for listing as threatened based on the IUCN Red List criteria. We focused on a short-lived frog species (Assa darlingtoni) chosen specifically to represent potential weaknesses in the criteria to allow detailed consideration of the analytical issues and to develop an approach for wider application. The criteria were more sensitive to climate change than previously anticipated; lead times between initial listing in a threatened category and predicted extinction varied from 40 to 80 years, depending on data availability. We attributed this sensitivity primarily to the ensemble properties of the criteria that assess contrasting symptoms of extinction risk. Nevertheless, we recommend the robustness of the criteria warrants further investigation across species with contrasting life histories and patterns of decline. The adequacy of these lead times for early warning depends on practicalities of environmental policy and management, bureaucratic or political inertia, and the anticipated species response times to management actions.
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ItemWhat do we gain from simplicity versus complexity in species distribution models?(WILEY, 2014-12)Species distribution models (SDMs) are widely used to explain and predict species ranges and environmental niches. They are most commonly constructed by inferring species' occurrence–environment relationships using statistical and machine‐learning methods. The variety of methods that can be used to construct SDMs (e.g. generalized linear/additive models, tree‐based models, maximum entropy, etc.), and the variety of ways that such models can be implemented, permits substantial flexibility in SDM complexity. Building models with an appropriate amount of complexity for the study objectives is critical for robust inference. We characterize complexity as the shape of the inferred occurrence–environment relationships and the number of parameters used to describe them, and search for insights into whether additional complexity is informative or superfluous. By building ‘under fit’ models, having insufficient flexibility to describe observed occurrence–environment relationships, we risk misunderstanding the factors shaping species distributions. By building ‘over fit’ models, with excessive flexibility, we risk inadvertently ascribing pattern to noise or building opaque models. However, model selection can be challenging, especially when comparing models constructed under different modeling approaches. Here we argue for a more pragmatic approach: researchers should constrain the complexity of their models based on study objective, attributes of the data, and an understanding of how these interact with the underlying biological processes. We discuss guidelines for balancing under fitting with over fitting and consequently how complexity affects decisions made during model building. Although some generalities are possible, our discussion reflects differences in opinions that favor simpler versus more complex models. We conclude that combining insights from both simple and complex SDM building approaches best advances our knowledge of current and future species ranges.
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ItemResponse to Kriticos et al.(Pensoft, 2014-09-02)
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ItemBiocrust morphogroups provide an effective and rapid assessment tool for drylands(WILEY, 2014-12)Biological soil crusts (biocrusts) occur across most of the world's drylands and are sensitive indicators of dryland degradation. Accounting for shifts in biocrust composition is important for quantifying integrity of arid and semi-arid ecosystems, but the best methods for assessing biocrusts are uncertain. We investigate the utility of surveying biocrust morphogroups, a reduced set of biotic classes, compared to species data, for detecting shifts in biocrust composition and making inference about dryland degradation.We used multivariate regression tree (MRT) analyses to model morphogroup abundance, species abundance and species occurrence data from two independent studies in semi-arid open woodlands of south-eastern Australia. We advanced the MRT method with a 'best subsets' model selection procedure, which improved model stability and prediction.Biocrust morphogroup composition responded strongly to surrogate variables of ecological degradation. Further, MRT models of morphogroup data had stronger explanatory power and predictive power than MRT models of species abundance or occurrence data. We also identified morphogroup indicators of degraded and less degraded sites in our study region.Synthesis and applications. Sustainable management of drylands requires methods to assess shifts in ecological integrity. We suggest that biocrust morphogroups are highly suitable for assessment of dryland integrity because they allow for non-expert, rapid survey and are informative about ecological function. Furthermore, morphogroups were more robust than biocrust species data, showed a strong response to ecological degradation and were less influenced by environmental variation, and models of morphogroup abundance were more predictive.