Global indicators of the environmental impacts of invasive alien species and their information adequacy.

Monitoring the extent to which invasive alien species (IAS) negatively impact the environment is crucial for understanding and mitigating biological invasions. Indeed, such information is vital for achieving Target 6 of the Kunming-Montreal Global Biodiversity Framework. However, to-date indicators for tracking the environmental impacts of IAS have been either lacking or insufficient. Capitalizing on advances in data availability and impact assessment protocols, we developed environmental impact indicators to track realized and potential impacts of IAS. We also developed an information status indicator to assess the adequacy of the data underlying the impact indicators. We used data on 75 naturalized amphibians from 82 countries to demonstrate the indicators at a global scale. The information status indicator shows variation in the reliability of the data and highlights areas where absence of impact should be interpreted with caution. Impact indicators show that growth in potential impacts are dominated by predatory species, while potential impacts from both predation and disease transmission are distributed worldwide. Using open access data, the indicators are reproducible and adaptable across scales and taxa and can be used to assess global trends and distributions of IAS, assisting authorities in prioritizing control efforts and identifying areas at risk of future invasions. This article is part of the theme issue 'Ecological novelty and planetary stewardship: biodiversity dynamics in a transforming biosphere'.

A multi-site method to capture turnover in rare to common interactions in bipartite species networks.

Ecological network structure is maintained by a generalist core of common species. However, rare species contribute substantially to both the species and functional diversity of networks. Capturing changes in species composition and interactions, measured as turnover, is central to understanding the contribution of rare and common species and their interactions. Due to a large contribution of rare interactions, the pairwise metrics used to quantify interaction turnover are, however, sensitive to compositional change in the interactions of, often rare, peripheral specialists rather than common generalists in the network. Here we expand on pairwise interaction turnover using a multi-site metric that enables quantifying turnover in rare to common interactions (in terms of occurrence of interactions). The metric further separates this turnover into interaction turnover due to species turnover and interaction rewiring. We demonstrate the application and value of this method using a host-parasitoid system sampled along gradients of environmental modification. In the study system, both the type and amount of habitat needed to maintain interaction composition depended on the properties of the interactions considered, that is, from rare to common. The analyses further revealed the potential of host switching to prevent or delay species loss, and thereby buffer the system from perturbation. Multi-site interaction turnover provides a comprehensive measure of network change that can, for example, detect ecological thresholds to habitat loss for rare to common interactions. Accurate description of turnover in common, in addition to rare, species and their interactions is particularly relevant for understanding how network structure and function can be maintained.

Measuring continuous compositional change using decline and decay in zeta diversity.

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.

The effect of network size and sampling completeness in depauperate networks.

The accurate estimation of interaction network structure is essential for understanding network stability and function. A growing number of studies evaluate under-sampling as the degree of sampling completeness (proportional richness observed). How the relationship between network structural metrics and sampling completeness varies across networks of different sizes remains unclear, but this relationship has implications for the within- and between-system comparability of network structure. Here, we test the combined effects of network size and sampling completeness on the structure of spatially distinct networks (i.e., subwebs) in a host-parasitoid model system to better understand the within-system variability in metric bias. Richness estimates were used to quantify a gradient of sampling completeness of species and interactions across randomly subsampled subwebs. The combined impacts of network size and sampling completeness on the estimated values of twelve unweighted and weighted network metrics were tested. The robustness of network metrics to under-sampling was strongly related to network size, and sampling completeness of interactions were generally a better predictor of metric bias than sampling completeness of species. Weighted metrics often performed better than unweighted metrics at low sampling completeness; however, this was mainly evident at large rather than small subweb size. These outcomes highlight the significance of under-sampling for the comparability of both unweighted and weighted network metrics when networks are small and vary in size. This has implications for within-system comparability of species-poor networks and, more generally, reveals problems with under-sampling ecological networks that may otherwise be difficult to detect in species-rich networks. To mitigate the impacts of under-sampling, more careful considerations of system-specific variation in metric bias are needed.