Turnover, or change in the composition of species over space and time, is one of the primary ways to define beta diversity. Inferring what factors impact beta diversity is not only important for understanding biodiversity processes but also for conservation planning. At present, a popular approach to understanding the drivers of compositional turnover is through generalized dissimilarity modelling (GDM). We argue that the current GDM approach suffers several limitations and provide an alternative modelling approach that remedies these issues. We propose using generative spatial random effects models implemented in a Bayesian framework. We offer hierarchical specifications to yield full regression and spatial predictive inference, both with associated full uncertainties. The approach is illustrated by examining dissimilarity in three datasets: tree survey data from Panama's Barro Colorado Island (BCI), plant occurrence data from southwest Australia and plant abundance surveys from the Greater Cape Floristic Region (GCFR) of South Africa. We select a best model using out‐of‐sample predictive performance. We find that the form of the best model differs across the three datasets, but our models provide performance ranging from comparable to significant improvement over GDMs. Within the GCFR, the spatial random effects play a more important role in the modelling than all the environmental variables. We have proposed a model that provides several improvements to the current GDM framework. This includes advantages such as a flexible spatially varying mean function, spatial random effects that capture dependence unaccounted for by explanatory variables, and spatially heterogeneous variance structure. All these features are offered in a model that can adequately handle a large incidence of
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.
- Award ID(s):
- 1655344
- NSF-PAR ID:
- 10443345
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Global Ecology and Biogeography
- Volume:
- 31
- Issue:
- 4
- ISSN:
- 1466-822X
- Page Range / eLocation ID:
- p. 802-821
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract total dissimilarity through ‘one‐inflation’, as would be expected from highly biodiverse areas with steep turnover gradients. -
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Abstract Background Efforts to achieve improved student outcomes in STEM are critically reliant on the success of reform efforts associated with teaching and learning. Reform efforts include the transformation of course-based practices, community values, and the institutional policies and structures associated with teaching and learning in higher education. Enacting change is a complex process that can be guided by change theories that describe how and why a desired change takes place. We analyzed the utility of a theory-based change model applied in a higher education setting. Our results provide guidance for change efforts at other institutions.
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Abstract Background Ambitious initiatives to coordinate genome sequencing of Earth's biodiversity mean that the accumulation of genomic data is growing rapidly. In addition to cataloguing biodiversity, these data provide the basis for understanding biological function and evolution. Accurate and complete genome assemblies offer a comprehensive and reliable foundation upon which to advance our understanding of organismal biology at genetic, species, and ecosystem levels. However, ever-changing sequencing technologies and analysis methods mean that available data are often heterogeneous in quality. To guide forthcoming genome generation efforts and promote efficient prioritization of resources, it is thus essential to define and monitor taxonomic coverage and quality of the data.
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Spatial location and grain Global coverage (grain size: ocean basin), spanning eight ocean basins, including vents on 12 mid‐ocean ridges and 6 back‐arc spreading centres.
Time period and grain sFDvent includes information on deep‐sea vent species, and associated taxonomic updates, since they were first discovered in 1977. Time is not recorded. The database will be updated every 5 years.
Major taxa and level of measurement Deep‐sea hydrothermal‐vent fauna with species‐level identification present or in progress.
Software format .csv and MS Excel (.xlsx).
