School of BioSciences - Research Publications
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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.
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ItemNo Preview AvailableIs my species distribution model fit for purpose? Matching data and models to applications(WILEY, 2015-03)Abstract Species distribution models (SDMs) are used to inform a range of ecological, biogeographical and conservation applications. However, users often underestimate the strong links between data type, model output and suitability for end‐use. We synthesize current knowledge and provide a simple framework that summarizes how interactions between data type and the sampling process (i.e. imperfect detection and sampling bias) determine the quantity that is estimated by a SDM. We then draw upon the published literature and simulations to illustrate and evaluate the information needs of the most common ecological, biogeographical and conservation applications of SDM outputs. We find that, while predictions of models fitted to the most commonly available observational data (presence records) suffice for some applications, others require estimates of occurrence probabilities, which are unattainable without reliable absence records. Our literature review and simulations reveal that, while converting continuous SDM outputs into categories of assumed presence or absence is common practice, it is seldom clearly justified by the application's objective and it usually degrades inference. Matching SDMs to the needs of particular applications is critical to avoid poor scientific inference and management outcomes. This paper aims to help modellers and users assess whether their intended SDM outputs are indeed fit for purpose.
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ItemAssessing the cost-efficiency of environmental DNA sampling(WILEY, 2016-11)Summary Environmental DNA (eDNA) sampling can be a highly sensitive method for detecting aquatic taxa; however, the cost‐efficiency of this technique relative to traditional methods has not been rigorously assessed. We show how methods that account for imperfect and stochastic detection can be used to (i) determine the optimal allocation of survey effort with eDNA sampling for a fixed budget (i.e. identify the optimal combination of water samples vs. site visits), and (ii) assess the cost‐efficiency of eDNA sampling relative to traditional survey techniques. We illustrate this approach by comparing eDNA sampling and bottle‐trapping for an exotic newt species (Lissotriton v. vulgaris) recently detected in Melbourne, Australia. Bottle traps produced much lower detection rates than eDNA sampling, but the cost‐efficiency of the two methods can be similar because bottle‐trapping is cheaper per sample. The relative cost‐efficiency of the two sampling methods was sensitive to the available survey budget, the costs of eDNA primer/probe development and sample processing and the number of positive quantitative PCR assays (qPCRs) used to designate a water sample as positive for newt DNA. Environmental DNA sampling was more cost‐efficient than bottle‐trapping for small to intermediate budgets when primer/probe development and sample processing costs were low, and 1/4 or 2/4 positive qPCRs were used to label a water sample as positive for newt eDNA. However, bottle traps were generally more cost‐efficient than eDNA sampling when primer/probe development and sample processing costs were high, regardless of qPCR threshold or survey budget. Traditional sampling methods may achieve lower detection probabilities compared to eDNA sampling, but the totality of costs can make eDNA sampling less efficient than traditional techniques in some circumstances. Our approach provides a quantitative framework for determining how many water samples and site visits are required to maximize detection probabilities with eDNA sampling, and can calculate the cost‐efficiency of any sampling method.