School of Agriculture, Food and Ecosystem Sciences - Research Publications
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ItemExploring how functional traits modulate species distributions along topographic gradients in Baxian Mountain, North China(NATURE PORTFOLIO, 2022-01-19)The associations between functional traits and species distributions across environments have attracted increasing interest from ecologists and can enhance knowledge about how plants respond to the environments. Here, we applied a hierarchical generalized linear model to quantifying the role of functional traits in plant occurrence across topographic gradients. Functional trait data, including specific leaf area, maximum height, seed mass and stem wood density, together with elevation, aspect and slope, were used in the model. In our results, species responses to elevation and aspect were modulated by maximum height and seed mass. Generally, shorter tree species showed positive responses to incremental elevation, while this trend became negative as the maximum height exceeded 22 m. Most trees with heavy seeds (> 1 mg) preferred more southerly aspects where the soil was drier, and those light-seed trees were opposite. In this study, the roles of maximum height and seed mass in determining species distribution along elevation and aspect gradients were highlighted where plants are confronted with low-temperature and soil moisture deficit conditions. This work contributes to the understanding of how traits may be associated with species occurrence along mesoscale environmental gradients.
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ItemUsing data calibration to reconcile outputs from different survey methods in long-term or large-scale studies(SPRINGER, 2022-03)Understanding the impact of management interventions on the environment over decadal and longer timeframes is urgently required. Longitudinal or large-scale studies with consistent methods are best practice, but more commonly, small datasets with differing methods are used to achieve larger coverage. Changes in methods and interpretation affect our ability to understand data trends through time or across space, so an ability to understand and adjust for such discrepancies between datasets is important for applied ecologists. Calibration or double sampling is the key to unlocking the value from disparate datasets, allowing us to account for the differences between datasets while acknowledging the uncertainties. We use a case study of livestock grazing impacts on riparian vegetation in southeastern Australia to develop a flexible and powerful approach to this problem. Using double sampling, we estimated changes in vegetation attributes over a 12-year period using a pseudo-quantitative visual method as the starting point, and the same technique plus point-intercept survey for the second round. The disparate nature of the datasets produced uncertain estimates of change over time, but accounting for this uncertainty explicitly is precisely the objective and highlights the need to look more closely at this very common problem in environmental management, as well as the potential benefits of the double sampling approach.
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ItemAusTraits, a curated plant trait database for the Australian flora(NATURE PORTFOLIO, 2021-09-30)We introduce the AusTraits database - a compilation of values of plant traits for taxa in the Australian flora (hereafter AusTraits). AusTraits synthesises data on 448 traits across 28,640 taxa from field campaigns, published literature, taxonomic monographs, and individual taxon descriptions. Traits vary in scope from physiological measures of performance (e.g. photosynthetic gas exchange, water-use efficiency) to morphological attributes (e.g. leaf area, seed mass, plant height) which link to aspects of ecological variation. AusTraits contains curated and harmonised individual- and species-level measurements coupled to, where available, contextual information on site properties and experimental conditions. This article provides information on version 3.0.2 of AusTraits which contains data for 997,808 trait-by-taxon combinations. We envision AusTraits as an ongoing collaborative initiative for easily archiving and sharing trait data, which also provides a template for other national or regional initiatives globally to fill persistent gaps in trait knowledge.
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ItemCombining functional traits, the environment and multiple surveys to understand semi-arid tree distributions(WILEY, 2018-11)QUESTIONS: Relationships between species, their functional traits and environmental gradients can now be more fully understood with trait‐based multi‐species distribution models (trait‐SDMs). However, general patterns are yet to emerge from founding studies using these models, which are mostly case studies at a single scale. Here, we address the generality of trait–environment relations by asking whether these relationships hold for different sampling schemes, environmental variables and species sets. METHODS: We focus on the drought and fire‐resistant “mallee” eucalypts of a semi‐arid region of southeast Australia, which are likely to face new climates and disturbance regimes under global change. We use hierarchical regression modelling to test how trait–environment relationships change for two data sets representing an extensively collected, multipurpose data set and an intensively collected data set stratified along environmental gradients. RESULTS: Three functional traits (specific leaf area, maximum height and seed mass) explained a substantial portion of the occurrence of species along soil, water and climatic gradients, with the relationship between seed mass and soil type robust across all tests. Other trait–environment relationships changed depending on study design and species set, with soil and substrate variables more important relative to climate (precipitation) for the intensively sampled survey. Remotely sensed variables were good surrogates for some field‐based measures (soil type), but not others (land form: dune or swale). In particular, airborne soil radiometric data show promise as a spatially continuous substitute for soil texture. CONCLUSIONS: Trait‐SDMs are a powerful tool for quantifying ecological interactions, but generalizations will only be possible when sample design, scale and environmental variables are carefully considered. We show that important ecological relationships can be diluted or missed entirely in broad scale trait–environment studies that rely on remotely sensed climate variables alone. Relationships that are robust to differences in study design, growth form and ecosystem (e.g., heavier seeds on sandy soil) are the most likely to reveal general ecological processes.
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ItemTransferability of trait-based species distribution models(WILEY, 2021-01)The need for reliable prediction of species distributions dependent upon traits has been hindered by a lack of model transferability testing. We tested the predictive capacity of trait‐SDMs by fitting hierarchical generalised linear models with three trait and four environmental predictors for 20 eucalypt taxa in a reference region. We used these models to predict occurrence for a much larger set of taxa and target areas (82 taxa across 18 target regions) in south‐eastern Australia. Median predictive performance for new species in target regions was 0.65 (area under receiver operating curve) and 1.24 times random (area under precision recall curve). Prediction in target regions did not worsen with increasing geographic, environmental or community compositional distance from the reference region, and was improved with reliable trait–environment relationships. Transfer testing also identified trait–environment relationships that did not transfer. These results give confidence that traits and transfer testing can assist in the hard problem of predicting environmental responses for new species, environmental conditions and regions.
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ItemNo Preview AvailableThe neglected tool in the Bayesian ecologist's shed: a case study testing informative priors' effect on model accuracy(WILEY-BLACKWELL, 2015-01)Despite benefits for precision, ecologists rarely use informative priors. One reason that ecologists may prefer vague priors is the perception that informative priors reduce accuracy. To date, no ecological study has empirically evaluated data-derived informative priors' effects on precision and accuracy. To determine the impacts of priors, we evaluated mortality models for tree species using data from a forest dynamics plot in Thailand. Half the models used vague priors, and the remaining half had informative priors. We found precision was greater when using informative priors, but effects on accuracy were more variable. In some cases, prior information improved accuracy, while in others, it was reduced. On average, models with informative priors were no more or less accurate than models without. Our analyses provide a detailed case study on the simultaneous effect of prior information on precision and accuracy and demonstrate that when priors are specified appropriately, they lead to greater precision without systematically reducing model accuracy.
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ItemUnderstanding co-occurrence bymodelling species simultaneously with a Joint Species DistributionModel (JSDM)(WILEY, 2014-05)Summary A primary goal of ecology is to understand the fundamental processes underlying the geographic distributions of species. Two major strands of ecology – habitat modelling and community ecology – approach this problem differently. Habitat modellers often use species distribution models (SDMs) to quantify the relationship between species’ and their environments without considering potential biotic interactions. Community ecologists, on the other hand, tend to focus on biotic interactions and, in observational studies, use co‐occurrence patterns to identify ecological processes. Here, we describe a joint species distribution model (JSDM) that integrates these distinct observational approaches by incorporating species co‐occurrence data into a SDM. JSDMs estimate distributions of multiple species simultaneously and allow decomposition of species co‐occurrence patterns into components describing shared environmental responses and residual patterns of co‐occurrence. We provide a general description of the model, a tutorial and code for fitting the model in R. We demonstrate this modelling approach using two case studies: frogs and eucalypt trees in Victoria, Australia. Overall, shared environmental correlations were stronger than residual correlations for both frogs and eucalypts, but there were cases of strong residual correlation. Frog species generally had positive residual correlations, possibly due to the fact these species occurred in similar habitats that were not fully described by the environmental variables included in the JSDM. Eucalypt species that interbreed had similar environmental responses but had negative residual co‐occurrence. One explanation is that interbreeding species may not form stable assemblages despite having similar environmental affinities. Environmental and residual correlations estimated from JSDMs can help indicate whether co‐occurrence is driven by shared environmental responses or other ecological or evolutionary process (e.g. biotic interactions), or if important predictor variables are missing. JSDMs take into account the fact that distributions of species might be related to each other and thus overcome a major limitation of modelling species distributions independently.