Summary
Abstract Aim Generalized dissimilarity modelling (GDM) is a powerful and unique method for characterizing and predicting beta diversity, the change in biodiversity over space, time and environmental gradients. The number of studies applying GDM is expanding, with increasing recognition of its value in improving our understanding of the drivers of biodiversity patterns and in implementing a wide variety of spatial assessments relevant to biodiversity conservation. However, apart from the original presentation of the GDM technique, there has been little guidance available to users on applying GDM to different situations or on the key modelling decisions required. Innovation We present an accessible working guide to GDM. We describe the context for the development of GDM, present a simple statistical explanation of how model fitting works, and step through key considerations involved in data preparation, model fitting, refinement and assessment. We then describe how several novel spatial biodiversity analyses can be implemented using GDM, with code to support broader implementation. We conclude by providing an overview of the range of GDM‐based analyses that have been undertaken to date and identify priority areas for future research and development. Main conclusions Our vision is that this working guide will facilitate greater and more rigorous use of GDM as a powerful tool for undertaking biodiversity analyses and assessments.
Outcomes reported
Referenced by Nature Communications British biodiversity scenarios as citation 26; likely supports topic area: biodiversity / conservation; methods / modelling / statistics. Topics: biodiversity / conservation; methods / modelling / statistics Evidence type: Modelling / projection Source report: Nature Communications British biodiversity scenarios Ref#: Nature Communications British biodiversity scenarios #26 Original: Mokany, K., Ware, C., Woolley, S. N. C., Ferrier, S. & Fitzpatrick, M. C. A working guide to harnessing generalized dissimilarity modelling for biodiversity analysis and conservation assessment. Glob. Ecol. Biogeogr. 31, 802-821 (2022).
Dig deeper with Pulse AI.
Pulse AI has read the whole catalogue. Ask about this record, its theme, or how the findings apply to UK farming and policy — every answer cites the underlying studies.