School of Agriculture, Food and Ecosystem Sciences - Research Publications
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ItemDisentangling the four demographic dimensions of species invasiveness(WILEY, 2016-11)A definitive list of invasive species traits remains elusive, perhaps due to inconsistent ways of identifying invasive species. Invasive species are typically identified using one or more of four demographic criteria (local abundance, geographic range, environmental range, spread rate), referred to here as the demographic dimensions of invasiveness. In 112 studies comparing invasive and non‐invasive plant traits, all 15 combinations of the four demographic dimensions were used to identify invasive species; 22% of studies identified invasive species solely by high abundance, while 25% ignored abundance. We used demographic data of 340 alien herbs classified as invasive or non‐invasive in Victoria, Australia, to test whether the demographic dimensions are independent and which dimensions influence invasive species listing in practice. Species' abundances, spread rates and range sizes were independent. Relative abundance best explained the invasiveness classification. However, invasive and non‐invasive species each spanned the full range of each demographic dimension, indicating that no dimension clearly separates invasive from non‐invasive species. Graminoids with longer minimum residence times were more frequently classified as invasive, as were forbs occurring near edges of native vegetation fragments. Synthesis. Conflating multiple forms of invasiveness, by not distinguishing invasive species that are identified using different demographic criteria, may obscure traits possessed by particular subsets of invasive species. Traits promoting high abundance likely differ from those enabling fast spread and broad ranges. Examining traits linked with the four demographic dimensions of invasiveness will highlight species at risk of becoming dominant, spreading quickly or occupying large ranges.
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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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ItemMovement re-established but not restored: Inferring the effectiveness of road-crossing mitigation for a gliding mammal by monitoring use(ELSEVIER SCI LTD, 2013-03)Wildlife crossing structures are commonly used to mitigate the barrier and mortality impacts of roads on wildlife. For arboreal mammals, canopy bridges, glider poles and vegetated medians are used to provide safe passage across roads. However, the effectiveness of these measures is unknown. We investigate the effect of canopy bridges, glider poles and vegetated medians on squirrel glider movement across a freeway in south-east Australia. We monitored structures directly using motion-triggered cameras and passive integrated transponder (PIT) scanners. Further, post-mitigation radio-tracking was compared to a pre-mitigation study. Squirrel gliders used all structure types to cross the freeway, while the unmitigated freeway remained a barrier to movement. However, movement was not restored to the levels observed at non-freeway sites. Nevertheless, based on the number and frequency of individuals crossing, mitigation is likely to provide some level of functional connectivity. The rate of crossing increased over several years as animals habituated to the structure. We also found that crossing rate can be a misleading indicator of effectiveness if the number of individuals crossing is not identified. Therefore, studies should employ long-term monitoring and identify individuals crossing if inferences about population connectivity are to be made from movement data alone.
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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.