School of Agriculture, Food and Ecosystem Sciences - Research Publications
Permanent URI for this collection
Search Results
1 - 3 of 3
-
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.
-
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.
-
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.