Incidence, or compositional, matrices are generated for a broad range of research applications in biology. Zeta diversity provides a common currency and conceptual framework that links incidence‐based metrics with multiple patterns of interest in biology, ecology, and biodiversity science. It quantifies the variation in species (or OTU) composition of multiple assemblages (or cases) in space or time, to capture the contribution of the full suite of narrow, intermediate, and wide‐ranging species to biotic heterogeneity. Here we provide a conceptual framework for the application and interpretation of patterns of continuous change in compositional diversity using zeta diversity. This includes consideration of the survey design context, and the multiple ways in which zeta diversity decline and decay can be used to examine and test turnover in the identity of elements across space and time. We introduce the zeta ratio–based retention rate curve to quantify rates of compositional change. We illustrate these applications using 11 empirical data sets from a broad range of taxa, scales, and levels of biological organization—from DNA molecules and microbes to communities and interaction networks—including one of the original data sets used to express compositional change and distance decay in ecology. We show (1) how different sample selection schemes used during the calculation of compositional change are appropriate for different data types and questions, (2) how higher orders of zeta may in some cases better detect shifts and transitions, and (3) the relative roles of rare vs. common species in driving patterns of compositional change. By exploring the application of zeta diversity decline and decay, including the retention rate, across this broad range of contexts, we demonstrate its application for understanding continuous turnover in biological systems.
Biodiversity data are being collected at unprecedented rates. Such data often have significant value for purposes beyond the initial reason for which they were collected, particularly when they are combined and collated with other data sources. In the field of invasion ecology, however, integrating data represents a major challenge due to the notorious lack of standardisation of terminologies and categorisations, and the application of deviating concepts of biological invasions. Here, we introduce the SInAS workflow, short for Standardising and Integrating Alien Species data. The SInAS workflow standardises terminologies following Darwin Core, location names using a proposed translation table, taxon names based on the GBIF backbone taxonomy, and dates of first records based on a set of predefined rules. The output of the SInAS workflow provides various entry points that can be used both to improve coherence among the databases and to check and correct the original data. The workflow is flexible and can be easily adapted and extended to the needs of different users. We illustrate the workflow using a case-study integrating five widely used global databases of information on biological invasions. The comparison of the standardised databases revealed a surprisingly low degree of overlap, which indicates that the amount of data may currently not be fully exploited in the original databases. We highly recommend the use and development of publicly available workflows to ensure that the integration of databases is reproducible and transparent. Workflows, such as SInAS, ultimately increase trust in data, study results, and conclusions.
Monitoring the progress parties have made toward meeting global biodiversity targets requires appropriate indicators. The recognition of invasive alien species (IAS) as a biodiversity threat has led to the development of specific targets aiming at reducing their prevalence and impact. However, indicators for adequately monitoring and reporting on the status of biological invasions have been slow to emerge, with those that exist being arguably insufficient. We performed a systematic review of the peer-reviewed literature to assess the adequacy of existing IAS indicators against a range of policy-relevant and scientifically valid properties. We found that very few indicators have most of the desirable properties and that existing indicators are unevenly spread across the components of the Driver-Pressure-State-Response and Theory of Change frameworks. We provide three possible reasons for this: (i) inadequate attention paid to the requirements of an effective IAS indicator, (ii) insufficient data required to populate and inform policy-relevant, scientifically robust indicators, or (iii) deficient investment in the development and maintenance of IAS indicators. This review includes an analysis of where current inadequacies in IAS indicators exist and provides a roadmap for the future development of indicators capable of measuring progress made toward mitigating and halting biological invasions.
Summary Pest risk assessment (PRA) comprises a set of quantitative and qualitative tools to protect productive ecosystems from the impacts of unwanted biological invasions. Self‐organizing maps for pest profile analysis (SOM PPA) is a methodological approach aimed to support PRA. It is based on cluster analysis and extracts information out of current distributions of insect crop pests world‐wide, allowing the analyst to generate a list of potential risk species for a target region. Self‐organizing maps for pest profile analysis currently lacks of a measure of performance able to provide a level of confidence for its outputs. In this study, we investigate ζ diversity as an ecologically meaningful and generalizable metric of similarity. The application of ζ allowed us to quantify and thus reveal different levels of similarity across pest profiles. The use of ζ diversity applied to the SOM PPA provides an informative measure of uncertainty for the output of SOM PPA, thus adding major improvements to the methodology while only marginally increasing its complexity.
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