School of BioSciences - Research Publications
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ItemTwo-step adaptive management for choosing between two management actions(Ecological Society of America, 2017-06-01)Adaptive management is widely advocated to improve environmental management. Derivations of optimal strategies for adaptive management, however, tend to be case specific and time consuming. In contrast, managers might seek relatively simple guidance, such as insight into when a new potential management action should be considered, and how much effort should be expended on trialing such an action. We constructed a two‐time‐step scenario where a manager is choosing between two possible management actions. The manager has a total budget that can be split between a learning phase and an implementation phase. We use this scenario to investigate when and how much a manager should invest in learning about the management actions available. The optimal investment in learning can be understood intuitively by accounting for the expected value of sample information, the benefits that accrue during learning, the direct costs of learning, and the opportunity costs of learning. We find that the optimal proportion of the budget to spend on learning is characterized by several critical thresholds that mark a jump from spending a large proportion of the budget on learning to spending nothing. For example, as sampling variance increases, it is optimal to spend a larger proportion of the budget on learning, up to a point: if the sampling variance passes a critical threshold, it is no longer beneficial to invest in learning. Similar thresholds are observed as a function of the total budget and the difference in the expected performance of the two actions. We illustrate how this model can be applied using a case study of choosing between alternative rearing diets for hihi, an endangered New Zealand passerine. Although the model presented is a simplified scenario, we believe it is relevant to many management situations. Managers often have relatively short time horizons for management, and might be reluctant to consider further investment in learning and monitoring beyond collecting data from a single time period.
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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 AvailableImproving the transparency of statistical reporting in Conservation Letters(WILEY, 2018-03-01)
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ItemPlan S and publishing: reply to Lehtomaki et al. 2019(WILEY, 2019-10)
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ItemSimultaneous-count models to estimate abundance from counts of unmarked individuals with imperfect detection(WILEY, 2019-06)We developed a method to estimate population abundance from simultaneous counts of unmarked individuals over multiple sites. We considered that at each sampling occasion, individuals in a population could be detected at 1 of the survey sites or remain undetected and used either multinomial or binomial simultaneous-count models to estimate abundance, the latter being equivalent to an N-mixture model with one site. We tested model performance with simulations over a range of detection probabilities, population sizes, growth rates, number of years, sampling occasions, and sites. We then applied our method to 3 critically endangered vulture species in Cambodia to demonstrate the real-world applicability of the model and to provide the first abundance estimates for these species in Cambodia. Our new approach works best when existing methods are expected to perform poorly (i.e., few sites and large variation in abundance among sites) and if individuals may move among sites between sampling occasions. The approach performed better when there were >8 sampling occasions and net probability of detection was high (>0.5). We believe our approach will be useful in particular for simultaneous surveys at aggregation sites, such as roosts. The method complements existing approaches for estimating abundance of unmarked individuals and is the first method designed specifically for simultaneous counts.
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ItemOpen access and academic imperialism(WILEY, 2019-02)
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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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ItemPhylogenetic diversity meets conservation policy: small areas are key to preserving eucalypt lineages(ROYAL SOC, 2015-02-19)Evolutionary and genetic knowledge is increasingly being valued in conservation theory, but is rarely considered in conservation planning and policy. Here, we integrate phylogenetic diversity (PD) with spatial reserve prioritization to evaluate how well the existing reserve system in Victoria, Australia captures the evolutionary lineages of eucalypts, which dominate forest canopies across the state. Forty-three per cent of remaining native woody vegetation in Victoria is located in protected areas (mostly national parks) representing 48% of the extant PD found in the state. A modest expansion in protected areas of 5% (less than 1% of the state area) would increase protected PD by 33% over current levels. In a recent policy change, portions of the national parks were opened for development. These tourism development zones hold over half the PD found in national parks with some species and clades falling entirely outside of protected zones within the national parks. This approach of using PD in spatial prioritization could be extended to any clade or area that has spatial and phylogenetic data. Our results demonstrate the relevance of PD to regional conservation policy by highlighting that small but strategically located areas disproportionally impact the preservation of evolutionary lineages.
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ItemA simple framework for a complex problem? Predicting wildlife-vehicle collisions(WILEY, 2016-09)Collisions of vehicles with wildlife kill and injure animals and are also a risk to vehicle occupants, but preventing these collisions is challenging. Surveys to identify problem areas are expensive and logistically difficult. Computer modeling has identified correlates of collisions, yet these can be difficult for managers to interpret in a way that will help them reduce collision risk. We introduce a novel method to predict collision risk by modeling hazard (presence and movement of vehicles) and exposure (animal presence) across geographic space. To estimate the hazard, we predict relative traffic volume and speed along road segments across southeastern Australia using regression models based on human demographic variables. We model exposure by predicting suitable habitat for our case study species (Eastern Grey Kangaroo Macropus giganteus) based on existing fauna survey records and geographic and climatic variables. Records of reported kangaroo-vehicle collisions are used to investigate how these factors collectively contribute to collision risk. The species occurrence (exposure) model generated plausible predictions across the study area, reducing the null deviance by 30.4%. The vehicle (hazard) models explained 54.7% variance in the traffic volume data and 58.7% in the traffic speed data. Using these as predictors of collision risk explained 23.7% of the deviance in incidence of collisions. Discrimination ability of the model was good when predicting to an independent dataset. The research demonstrates that collision risks can be modeled across geographic space with a conceptual analytical framework using existing sources of data, reducing the need for expensive or time-consuming field data collection. The framework is novel because it disentangles natural and anthropogenic effects on the likelihood of wildlife-vehicle collisions by representing hazard and exposure with separate, tunable submodels.
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ItemA comparison of joint species distribution models for presence-absence data(WILEY, 2019-02-01)1. Joint species distribution models (JSDMs) account for biotic interactions and missing environmental predictors in correlative species distribution models. Several different JSDMs have been proposed in the literature, but the use of different or conflicting nomenclature and statistical notation potentially obscures similarities and differences among them. Furthermore, new JSDM implementations have been illustrated with different case studies, preventing direct comparisons of computational and statistical performance. 2. We aim to resolve these outstanding issues by (a) highlighting similarities among seven presence–absence JSDMs using a clearly defined, singular notation; and (b) evaluating the computational and statistical performance of each JSDM using six datasets that vary widely in numbers of sites, species, and environmental covariates considered. 3. Our singular notation shows that many of the JSDMs are very similar, and in turn parameter estimates of different JSDMs are moderate to strongly, positively correlated. In contrast, the different JSDMs clearly differ in computational efficiency and memory limitations. 4. Our framework will allow ecologists to make educated decisions about the JSDM that best suits their objective, and enable wider uptake of JSDM methods among the ecological community.