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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.

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.

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.

The interaction of land use with local versus regional processes driving biological homogenization (β‐diversity loss) is poorly understood. We explored: (a) stream β‐diversity responses to land cover (forest versus agriculture) in terms of physicochemistry and physicochemical heterogeneity; (b) whether these responses were constrained by the regional species pool, i.e. γ‐diversity, or local assembly processes through local (α) diversity; (c) whether local assembly operated through the regional species abundance distribution (SAD) or intraspecific spatial aggregation; and (d) the dependence on body size, dispersal capacity and trophic level (producer versus consumer).

USA, Canada and France.

1993–2012.

Stream diatoms, insects and fish.

We analysed six datasets totalling 1,225 stream samples. We compared diversity responses to eutrophication and physicochemical heterogeneity in forested versus agricultural streams with regression methods. Null models quantified the contribution of local assembly to β‐diversity (β‐deviance, β_DEV) for both types of land covers and partitioned it into fractions explained by the regional SAD (β_SAD) versus aggregation (β_AGG).

Eutrophication explained homogenization and more uneven regional SADs across groups, but local and regional biodiversity responses differed across taxa. The β_DEVwas insensitive to land use. The β_SADlargely exceeded β_AGGand was higher in agriculture.

Eutrophication but not physicochemical heterogeneity of agricultural streams underlay β‐diversity loss in diatoms, insects and fish. Agriculture did not constrain the magnitude of local versus regional effects on β‐diversity but controlled the local assembly mechanisms. Although the SAD fraction dominated in both land covers, it increased further in agriculture at the expense of aggregation. Notably, the regional SADs were more uneven in agriculture, exhibiting excess common species or stronger dominance. Diatoms and insects diverged from fish in terms of biodiversity, SAD shape and β_DEVpatterns, suggesting an overriding role of body size and/or dispersal capacity compared with trophic position.

We may be able to buffer biodiversity against the effects of ongoing climate change by prioritizing the protection of habitat with diverse physical features (high geodiversity) associated with ecological and evolutionary mechanisms that maintain high biodiversity. Nonetheless, the relationships between biodiversity and habitat vary with spatial and biological context. In this study, we compare how well habitat geodiversity (spatial variation in abiotic processes and features) and climate explain biodiversity patterns of birds and trees. We also evaluate the consistency of biodiversity–geodiversity relationships across ecoregions.

Contiguous USA.

2007–2016.

Birds and trees.

We quantified geodiversity with remotely sensed data and generated biodiversity maps from the Forest Inventory and Analysis and Breeding Bird Survey datasets. We fitted multivariate regressions to alpha, beta and gamma diversity, accounting for spatial autocorrelation among Nature Conservancy ecoregions and relationships among taxonomic, phylogenetic and functional biodiversity. We fitted models including climate alone (temperature and precipitation), geodiversity alone (topography, soil and geology) and climate plus geodiversity.

A combination of geodiversity and climate predictor variables fitted most forms of bird and tree biodiversity with < 10% relative error. Models using geodiversity and climate performed better for local (alpha) and regional (gamma) diversity than for turnover‐based (beta) diversity. Among geodiversity predictors, variability of elevation fitted biodiversity best; interestingly, topographically diverse places tended to have higher tree diversity but lower bird diversity.

Although climatic predictors tended to have larger individual effects than geodiversity, adding geodiversity improved climate‐only models of biodiversity. Geodiversity was correlated with biodiversity more consistently than with climate across ecoregions, but models tended to have a poor fit in ecoregions held out of the training dataset. Patterns of geodiversity could help to prioritize conservation efforts within ecoregions. However, we need to understand the underlying mechanisms more fully before we can build models transferable across ecoregions.