A guide to representing variability and uncertainty in biodiversity indicators

Trace metals in coastal marine sediments: Natural and anthropogenic sources, correlation matrices, and proxy potentials

Rapidly spreading industrialization since the 19th century has led to a drastic increase in trace metal deposition in coastal sediments. Provided that these trace metals have remained relatively immobile after deposition, their sedimentary enrichments can serve as records of local-regional pollution histories. Factors controlling this proxy potential include trace metal geochemistry (carrier-, and host phase affinity), and depositional environmental factors (redox variability, particulate shuttling, organic matter loading, bathymetry). Yet, the relative importance and interactions between these controls are still poorly understood, hampering the reliable use of trace metal-based environmental proxies. By summarizing nine site-specific correlation matrices of 16 metal (loid) s (Pb, Cd, Cu, Zn, Sb, Sn, Ni, As, Tl, V, Mo, U, Re, Fe, Mn, Al), total organic C, and S contents in short sediment cores into a single meta-matrix, we test a novel approach for quickly detecting common and contrasting trace metal enrichment patterns across different study locations. Our meta-matrix shows two trace metal groups, within which positive correlations of e.g., Pb, Cd, Zn, Cu, Sb suggest a primary "anthropogenically sourced" (group I) control, whereas known "redox-sensitive" (group II) trace metals (Mo, U, Re) are characterized by fewer positive correlations. However, some group I metals (Cd, Zn, Cu, Sb) also covary with group II metals, inferring that redox variability may obscure primary anthropogenic signals; Sb even shows advantages over Mo and U under oxic conditions. As a more robust pollution indicator we identified Pb; yet for reconstructing historical Pb atmospheric pollution signals (1970s Pb peak), it is crucial to consider the distance from shore. In near-shore environments, local (fluvial) pollution signals may overprint large-scale (atmospheric) signals. Our findings demonstrate that combining site-specific sedimentary correlation and distribution patterns with a meta-matrix considerably aids the understanding of trace metal sequestration in different coastal sedimentary environments, which thereby improves trace metal proxy reliability.

Synergies and complementarities between ecosystem accounting and the Red List of Ecosystems

Safeguarding biodiversity and human well-being depends on sustaining ecosystems. Two global standards for quantifying ecosystem change, the International Union for Conservation of Nature Red List of Ecosystems (RLE) and the United Nations System of Environmental-Economic Accounting Ecosystem Accounting (EA), underpin headline indicators for the Kunming-Montreal Global Biodiversity Framework. We analyse similarities and differences between the standards to understand their complementary roles in environmental policy and decision-making. The standards share key concepts, definitions of ecosystems and spatial data needs, meaning that similar data can be used in both. Their complementarities stem from their differing purposes and thus how data are analysed and interpreted. Although both record changes in ecosystem extent and condition, the RLE analyses the magnitude of change in terms of risk of ecosystem collapse and biodiversity loss, whereas EA links ecosystem change with the ecosystem's contributions to people and the economy. We recommend that the RLE and EA should not be treated as unrelated nor undertaken in isolation. Developing them in concert can exploit their complementarities while ensuring consistency in foundational data, in particular ecosystem classifications, maps and condition variables. Finding pathways for co-investment in foundational data, and for knowledge-sharing between people and organizations who undertake RLE assessments and accounting, will improve both processes and outcomes for biodiversity, ecosystems and people.

Accounting for Uncertainties in Biodiversity Estimations: A New Methodology and Its Application to the Mesopelagic Sound Scattering Layer of the High Arctic

Measures of biological diversity (biodiversity) are important for monitoring the state of ecosystems. Several indices and methods are used to describe biodiversity from field observations. Marine faunal biodiversity is often quantified based on analysis of samples collected using a trawl during research surveys. To monitor spatial and temporal changes in biodiversity between surveys, samples are generally collected from a series of stations. Inference regarding changes in biodiversity must account for uncertainties in the estimation of the values for the different biodiversity indices used. Estimation for a single station is affected by spatial-temporal variation in the species composition in the area and by uncertainty due to the finite sample size taken by the trawl. Therefore, variation between stations needs to be accounted for when estimating uncertainty for values of different indices during a survey as total or as mean for the survey. Herein, we present a method based on nested bootstrapping that accounts for uncertainties in the estimation of various indices and which can be used to infer changes in biodiversity. Application of this methodology is demonstrated using data collected in the mesopelagic sound scattering layer in the high Arctic.

Ecological equivalence assessment: The potential of genetic tools, remote sensing and metapopulation models to better apply the mitigation hierarchy

Within the framework of the mitigation hierarchy, biodiversity offsetting is the main tool promoted to reach No Net Loss. One of the determining factors of offsetting success is the evaluation of ecological equivalence. Various equivalence assessment methods (EAMs) have been developed to provide a framework to evaluate the balance between expected biodiversity losses and gains. In the context of achieving No Net Loss, EAMs must address challenges of Operationality, Currency, Uncertainty, Spatial scale and Time frame. In this study, we investigated the way the most widely used EAMs address these challenges, positing that certain tools from ecological science could limit the trade-offs between these challenges and improve the ecological assessment process. To this end, we analysed the risks and benefits associated with the inclusion of genetic tools (landscape genetics and eDNA), remote sensing and metapopulation models in selected EAMs. Our results revealed trade-offs between these five challenges, in particular between Operationality and Currency. The EAMs varied strongly in these two aspects, depending on the general assessment approach and the biodiversity component they focus on. To a lesser degree, Time frame and Spatial scale also differed between the methods. We identified that the integration of the different tools differs among them, being easier for remote sensing and metapopulation models than for the genetic tools. Nevertheless, the integration resulted in benefits compared to the current use of the methods - benefits that included improving the objectivation of the assessment and the automatization potential. The tools also show potential for automatization, which could have major benefits for operationality. In terms of risks, the integration of these tools increases the technical complexity of the methods, requiring new skills, and would change the overall approach of the ecological assessment.