School of BioSciences - Research Publications
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ItemOptimal surveillance strategy for invasive species management when surveys stop after detection(WILEY, 2014-05)Invasive species are a cause for concern in natural and economic systems and require both monitoring and management. There is a trade-off between the amount of resources spent on surveying for the species and conducting early management of occupied sites, and the resources that are ultimately spent in delayed management at sites where the species was present but undetected. Previous work addressed this optimal resource allocation problem assuming that surveys continue despite detection until the initially planned survey effort is consumed. However, a more realistic scenario is often that surveys stop after detection (i.e., follow a "removal" sampling design) and then management begins. Such an approach will indicate a different optimal survey design and can be expected to be more efficient. We analyze this case and compare the expected efficiency of invasive species management programs under both survey methods. We also evaluate the impact of mis-specifying the type of sampling approach during the program design phase. We derive analytical expressions that optimize resource allocation between monitoring and management in surveillance programs when surveys stop after detection. We do this under a scenario of unconstrained resources and scenarios where survey budget is constrained. The efficiency of surveillance programs is greater if a "removal survey" design is used, with larger gains obtained when savings from early detection are high, occupancy is high, and survey costs are not much lower than early management costs at a site. Designing a surveillance program disregarding that surveys stop after detection can result in an efficiency loss. Our results help guide the design of future surveillance programs for invasive species. Addressing program design within a decision-theoretic framework can lead to a better use of available resources. We show how species prevalence, its detectability, and the benefits derived from early detection can be considered.
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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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ItemTraits influence detection of exotic plant species in tropical forests(PUBLIC LIBRARY SCIENCE, 2018-08-22)Detecting exotic plant species is essential for invasive species management. By accounting for factors likely to affect species' detection rates (e.g. survey conditions, observer experience), detectability models can help choose search methods and allocate search effort. Integrating information on species' traits can refine detectability models, and might be particularly valuable if these traits can help improve estimates of detectability where data on particular species are rare. Analysing data collected during line transect distance sampling surveys in Indonesia, we used a multi-species hierarchical distance sampling model to evaluate how plant height, leaf size, leaf shape, and survey location influenced plant species detectability in secondary tropical rainforests. Detectability of the exotic plant species increased with plant height and leaf size. Detectability varied among the different survey locations. We failed to detect a clear effect of leaf shape on detectability. This study indicates that information on traits might improve predictions about exotic species detection, which can then be used to optimise the allocation of search effort for efficient species management. The innovation of the study lies in the multi-species distance sampling model, where the distance-detection function depends on leaf traits and height. The method can be applied elsewhere, including for different traits that may be relevant in other contexts. Trait-based multispecies distance sampling can be a practical approach for sampling exotic shrubs, herbs, or grasses species in the understorey of tropical forests.
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ItemThreatened species impact assessments: survey effort requirements based on criteria for cumulative impacts(WILEY, 2015-06)Abstract Aim Environmental impact assessments (EIAs) often involve establishing whether a species of concern is present at the site considered for development. When surveys falsely conclude that sites are unoccupied, species prevalence in the region is cumulatively reduced. We argue that setting an acceptable level of induced decline in species occurrence provides a defensible strategy to determine minimum survey effort requirements. We investigate methods for setting such requirements. Location Eastern Australia, although we demonstrate methods applicable wherever species detection data are available to inform survey design. Methods We use probability theory to investigate required survey effort when aiming to limit decline in species occurrence. We use optimization tools to provide a method that, in addition, minimizes overall survey costs. We demonstrate the methods using data for an Australian gliding marsupial. Results A method based on ensuring a constant probability of occupied site misclassification directly links with a prescribed acceptable decline in occurrence. Optimization results indicate that, under particular conditions, a cost‐efficient survey effort allocation can be achieved by setting a constant posterior probability of occupancy at sites where the species is not detected, provided the target level is set in accordance with the acceptable decline in occurrence. Our results provide a critical examination of the approach recently proposed by Wintle et al. (2012) for determining minimum survey effort requirements. Main conclusions EIA survey effort requirements should explicitly link uncertainty in establishing species absence with the broader consequences of failing to detect species presence in places subject to proposed impacts. A direct method, which involves keeping a constant probability of occupied site misclassification, only requires information about species detectability. Alternatively, a method that minimizes overall survey costs can be used. This approach also requires occupancy probability estimates so its performance relies on availability of an informative species distribution model.
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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.