School of BioSciences - Research Publications

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    Data Integration for Large-Scale Models of Species Distributions
    Isaac, NJB ; Jarzyna, MA ; Keil, P ; Dambly, LI ; Boersch-Supan, PH ; Browning, E ; Freeman, SN ; Golding, N ; Guillera-Arroita, G ; Henrys, PA ; Jarvis, S ; Lahoz-Monfort, J ; Pagel, J ; Pescott, OL ; Schmucki, R ; Simmonds, EG ; O'Hara, RB (ELSEVIER SCIENCE LONDON, 2020-01-01)
    With the expansion in the quantity and types of biodiversity data being collected, there is a need to find ways to combine these different sources to provide cohesive summaries of species' potential and realized distributions in space and time. Recently, model-based data integration has emerged as a means to achieve this by combining datasets in ways that retain the strengths of each. We describe a flexible approach to data integration using point process models, which provide a convenient way to translate across ecological currencies. We highlight recent examples of large-scale ecological models based on data integration and outline the conceptual and technical challenges and opportunities that arise.
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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.