School of BioSciences - Research Publications

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    A comparison of joint species distribution models for presence-absence data
    Wilkinson, DP ; Golding, N ; Guillera-Arroita, G ; Tingley, R ; McCarthy, MA ; Peres‐Neto, P (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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    Statistical approaches to account for false-positive errors in environmental DNA samples
    Lahoz-Monfort, JJ ; Guillera-Arroita, G ; Tingley, R (WILEY, 2016-05-01)
    Environmental DNA (eDNA) sampling is prone to both false-positive and false-negative errors. We review statistical methods to account for such errors in the analysis of eDNA data and use simulations to compare the performance of different modelling approaches. Our simulations illustrate that even low false-positive rates can produce biased estimates of occupancy and detectability. We further show that removing or classifying single PCR detections in an ad hoc manner under the suspicion that such records represent false positives, as sometimes advocated in the eDNA literature, also results in biased estimation of occupancy, detectability and false-positive rates. We advocate alternative approaches to account for false-positive errors that rely on prior information, or the collection of ancillary detection data at a subset of sites using a sampling method that is not prone to false-positive errors. We illustrate the advantages of these approaches over ad hoc classifications of detections and provide practical advice and code for fitting these models in maximum likelihood and Bayesian frameworks. Given the severe bias induced by false-negative and false-positive errors, the methods presented here should be more routinely adopted in eDNA studies.
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    Dealing with false-positive and false-negative errors about species occurrence at multiple levels
    Guillera-Arroita, G ; Lahoz-Monfort, JJ ; van Rooyen, AR ; Weeks, AR ; Tingley, R ; McCrea, R (Wiley, 2017-09-01)
    Summary 1. Accurate knowledge of species occurrence is fundamental to a wide variety of ecological, evolutionary and conservation applications. Assessing the presence or absence of species at sites is often complicated by imperfect detection, with different mechanisms potentially contributing to false‐negative and/or false‐positive errors at different sampling stages. Ambiguities in the data mean that estimation of relevant parameters might be confounded unless additional information is available to resolve those uncertainties. 2. Here, we consider the analysis of species detection data with false‐positive and false‐negative errors at multiple levels. We develop and examine a two‐stage occupancy‐detection model for this purpose. We use profile likelihoods for identifiability analysis and estimation, and study the types of additional data required for reliable estimation. We test the model with simulated data, and then analyse data from environmental DNA (eDNA) surveys of four Australian frog species. In our case study, we consider that false positives may arise due to contamination at the water sample and quantitative PCR‐sample levels, whereas false negatives may arise due to eDNA not being captured in a field sample, or due to the sensitivity of laboratory tests. We augment our eDNA survey data with data from aural surveys and laboratory calibration experiments. 3. We demonstrate that the two‐stage model with false‐positive and false‐negative errors is not identifiable if only survey data prone to false positives are available. At least two sources of extra information are required for reliable estimation (e.g. records from a survey method with unambiguous detections, and a calibration experiment). Alternatively, identifiability can be achieved by setting plausible bounds on false detection rates as prior information in a Bayesian setting. The results of our case study matched our simulations with respect to data requirements, and revealed false‐positive rates greater than zero for all species. 4. We provide statistical modelling tools to account for uncertainties in species occurrence survey data when false negatives and false positives could occur at multiple sampling stages. Such data are often needed to support management and policy decisions. Dealing with these uncertainties is relevant for traditional survey methods, but also for promising new techniques, such as eDNA sampling.
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    Is my species distribution model fit for purpose? Matching data and models to applications
    Guillera-Arroita, G ; Lahoz-Monfort, JJ ; Elith, J ; Gordon, A ; Kujala, H ; Lentini, PE ; McCarthy, MA ; Tingley, R ; Wintle, BA (WILEY, 2015-03-01)