School of BioSciences - Research Publications
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Itemsteps: Software for spatially and temporally explicit population simulations(WILEY, 2020-02-25)Species population dynamics are driven by spatial and temporal changes in the environment, anthropogenic activities and conservation management actions. Understanding how populations will change in response to these drivers is fundamental to a wide range of ecological applications, but there are few open-source software options accessible to researchers and managers that allow them to predict these changes in a flexible and transparent way. We introduce an open-source, multi-platform r package, steps, that models spatial changes in species populations as a function of drivers of distribution and abundance, such as climate, disturbance, landscape dynamics and species ecological and physiological requirements. To illustrate the functionality of steps, we model the population dynamics of the greater glider Petauroides volans, an arboreal Australian mammal. We demonstrate how steps can be used to simulate population responses of the glider to forest dynamics and management with the types of data commonly used in ecological analyses. steps expands on the features found in existing software packages, can easily incorporate a range of spatial layers (e.g. habitat suitability, vegetation dynamics and disturbances), facilitates integrated and transparent analyses within a single platform and produces interpretable outputs of changes in species' populations through space and time. Further, steps offers both ready-to-use, built-in functionality, as well as the ability for advanced users to define their own modules for custom analyses. Thus, we anticipate that steps will be of significant value to environment and wildlife managers and researchers from a broad range of disciplines.
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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.