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Biodiversity indicators are used to inform decisions and measure progress toward global targets, such as the United Nations Sustainable Development Goals. Indicators aggregate and simplify complex information, so underlying information influencing its reliability and interpretation (e.g., variability in data and uncertainty in indicator values) can be lost. Communicating uncertainty is necessary to ensure robust decisions and limit misinterpretations of trends, yet variability and uncertainty are rarely quantified in biodiversity indicators. We developed a guide to representing uncertainty and variability in biodiversity indicators. We considered the key purposes of biodiversity indicators and commonly used methods for representing uncertainty (standard error, bootstrap resampling, and jackknife resampling) and variability (quantiles, standard deviation, median absolute deviation, and mean absolute deviation) with intervals. Using 3 high-profile biodiversity indicators (Red List Index, Living Planet Index, and Ocean Health Index), we tested the use, suitability, and interpretation of each interval method based on the formulation and data types underpinning the indicators. The methods revealed vastly different information; indicator formula and data distribution affected the suitability of each interval method. Because the data underpinning each indicator were not normally distributed, methods relying on normality or symmetrical spread were unsuitable. Quantiles, bootstrapping, and jackknifing provided useful information about the underlying variability and uncertainty. We built a decision tree to inform selection of the appropriate interval method to represent uncertainty or variation in biodiversity indicators, depending on data type and objectives. Our guide supports transparent and effective communication of biodiversity indicator trends to facilitate accurate interpretation by decision makers., (© 2021 Society for Conservation Biology.)

Global biodiversity indices are used to measure environmental change and progress toward conservation goals, yet few indices have been evaluated comprehensively for their capacity to detect trends of interest, such as declines in threatened species or ecosystem function. Using a structured approach based on decision science, we qualitatively evaluated 9 indices commonly used to track biodiversity at global and regional scales against 5 criteria relating to objectives, design, behavior, incorporation of uncertainty, and constraints (e.g., costs and data availability). Evaluation was based on reference literature for indices available at the time of assessment. We identified 4 key gaps in indices assessed: pathways to achieving goals (means objectives) were not always clear or relevant to desired outcomes (fundamental objectives); index testing and understanding of expected behavior was often lacking; uncertainty was seldom acknowledged or accounted for; and costs of implementation were seldom considered. These gaps may render indices inadequate in certain decision-making contexts and are problematic for indices linked with biodiversity targets and sustainability goals. Ensuring that index objectives are clear and their design is underpinned by a model of relevant processes are crucial in addressing the gaps identified by our assessment. Uptake and productive use of indices will be improved if index performance is tested rigorously and assumptions and uncertainties are clearly communicated to end users. This will increase index accuracy and value in tracking biodiversity change and supporting national and global policy decisions, such as the post-2020 global biodiversity framework of the Convention on Biological Diversity., (© 2020 Society for Conservation Biology.)

Synthesizing trait observations and knowledge across the Tree of Life remains a grand challenge for biodiversity science. Species traits are widely used in ecological and evolutionary science, and new data and methods have proliferated rapidly. Yet accessing and integrating disparate data sources remains a considerable challenge, slowing progress toward a global synthesis to integrate trait data across organisms. Trait science needs a vision for achieving global integration across all organisms. Here, we outline how the adoption of key Open Science principles-open data, open source and open methods-is transforming trait science, increasing transparency, democratizing access and accelerating global synthesis. To enhance widespread adoption of these principles, we introduce the Open Traits Network (OTN), a global, decentralized community welcoming all researchers and institutions pursuing the collaborative goal of standardizing and integrating trait data across organisms. We demonstrate how adherence to Open Science principles is key to the OTN community and outline five activities that can accelerate the synthesis of trait data across the Tree of Life, thereby facilitating rapid advances to address scientific inquiries and environmental issues. Lessons learned along the path to a global synthesis of trait data will provide a framework for addressing similarly complex data science and informatics challenges.

Biodiversity loss is a major challenge. Over the past century, the average rate of vertebrate extinction has been about 100-fold higher than the estimated background rate and population declines continue to increase globally. Birth and death rates determine the pace of population increase or decline, thus driving the expansion or extinction of a species. Design of species conservation policies hence depends on demographic data (e.g., for extinction risk assessments or estimation of harvesting quotas). However, an overview of the accessible data, even for better known taxa, is lacking. Here, we present the Demographic Species Knowledge Index, which classifies the available information for 32,144 (97%) of extant described mammals, birds, reptiles, and amphibians. We show that only 1.3% of the tetrapod species have comprehensive information on birth and death rates. We found no demographic measures, not even crude ones such as maximum life span or typical litter/clutch size, for 65% of threatened tetrapods. More field studies are needed; however, some progress can be made by digitalizing existing knowledge, by imputing data from related species with similar life histories, and by using information from captive populations. We show that data from zoos and aquariums in the Species360 network can significantly improve knowledge for an almost eightfold gain. Assessing the landscape of limited demographic knowledge is essential to prioritize ways to fill data gaps. Such information is urgently needed to implement management strategies to conserve at-risk taxa and to discover new unifying concepts and evolutionary relationships across thousands of tetrapod species., Competing Interests: The authors declare no conflict of interest., (Copyright © 2019 the Author(s). Published by PNAS.)

Ongoing ecosystem degradation and transformation are major threats to biodiversity. Measuring ecosystem change toward collapse relies on monitoring indicators that quantify key ecological processes. Yet little guidance is available on selection and use of indicators for ecosystem risk assessment. We reviewed indicator use in ecological studies of ecosystem collapse in marine pelagic and temperate forest ecosystems. We examined indicator-selection methods, indicator types (geographic distribution, abiotic, biotic), methods of assessing multiple indicators, and temporal quality of time series. We compared how these factors were applied in the ecological studies with how they were applied in risk assessments by using the International Union for Conservation of Nature's Red List of Ecosystems (RLE), for which indicators are used to estimate risk of ecosystem collapse. Ecological studies and RLE assessments rarely reported how indicators were selected, particularly in terrestrial ecosystems. Few ecological studies and RLE assessments quantified ecosystem change based on all 3 indicator types, and indicators types used differed between marine and terrestrial ecosystems. Several studies used indices or multivariate analyses to assess multiple indicators simultaneously, but RLE assessments did not because as RLE guidelines advise against them. Most studies and RLE assessments used time-series data that spanned at least 30 years, which increases the probability of reliably detecting change. Limited use of indicator-selection protocols and infrequent use of all 3 indicator types may hamper accurate detection of change. To improve the value of risk assessments for informing policy and management, we recommend using explicit protocols, including conceptual models, to identify and select indicators; a range of indicators spanning distributional, abiotic, and biotic features; indices and multivariate analyses with extreme care until guidelines are developed; time series with sufficient data to increase ability to accurately diagnose directional change; data from multiple sources to support assessments; and explicitly reporting steps in the assessment process., (© 2018 Society for Conservation Biology.)

Conservation targets perform beneficial auxiliary functions that are rarely acknowledged, including raising awareness, building partnerships, promoting investment, and developing new knowledge. Building on these auxiliary functions could enable more rapid progress towards current targets and inform the design of future targets., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

The Convention on Biological Diversity and its Strategic Plan for Biodiversity 2011-2020 form the central pillar of the world's conservation commitment, with 196 signatory nations; yet its capacity to reign in catastrophic biodiversity loss has proved inadequate. Indicators suggest that few of the Convention on Biological Diversity's Aichi targets that aim to reduce biodiversity loss will be met by 2020. While the indicators have been criticized for only partially representing the targets, a bigger problem is that the indicators do not adequately draw attention to and measure all of the drivers of the biodiversity crisis. Here, we show that many key drivers of biodiversity loss are either poorly evaluated or entirely lacking indicators. We use a biodiversity-crisis hierarchy as a conceptual model linking drivers of change to biodiversity loss to evaluate the scope of current indicators. We find major gaps related to monitoring governments, human population size, corruption and threat-industries. We recommend the hierarchy is used to develop an expanded set of indicators that comprehensively monitor the human behaviour and institutions that drive biodiversity loss and that, so far, have impeded progress towards achieving global biodiversity targets.

The current set of global conservation targets requires methods for monitoring the changing status of ecosystems. Protocols for ecosystem risk assessment are uniquely suited to this task, providing objective syntheses of a wide range of data to estimate the likelihood of ecosystem collapse. Satellite remote sensing can deliver ecologically relevant, long-term datasets suitable for analysing changes in ecosystem area, structure and function at temporal and spatial scales relevant to risk assessment protocols. However, there is considerable uncertainty about how to select and effectively utilise remotely sensed variables for risk assessment. Here, we review the use of satellite remote sensing for assessing spatial and functional changes of ecosystems, with the aim of providing guidance on the use of these data in ecosystem risk assessment. We suggest that decisions on the use of satellite remote sensing should be made a priori and deductively with the assistance of conceptual ecosystem models that identify the primary indicators representing the dynamics of a focal ecosystem., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Effective ecosystem risk assessment relies on a conceptual understanding of ecosystem dynamics and the synthesis of multiple lines of evidence. Risk assessment protocols and ecosystem models integrate limited observational data with threat scenarios, making them valuable tools for monitoring ecosystem status and diagnosing key mechanisms of decline to be addressed by management. We applied the IUCN Red List of Ecosystems criteria to quantify the risk of collapse of the Meso-American Reef, a unique ecosystem containing the second longest barrier reef in the world. We collated a wide array of empirical data (field and remotely sensed), and used a stochastic ecosystem model to backcast past ecosystem dynamics, as well as forecast future ecosystem dynamics under 11 scenarios of threat. The ecosystem is at high risk from mass bleaching in the coming decades, with compounding effects of ocean acidification, hurricanes, pollution and fishing. The overall status of the ecosystem is Critically Endangered (plausibly Vulnerable to Critically Endangered), with notable differences among Red List criteria and data types in detecting the most severe symptoms of risk. Our case study provides a template for assessing risks to coral reefs and for further application of ecosystem models in risk assessment., (© 2017 The Authors.)

One in 6 species (13,465 species) on the International Union for Conservation of Nature (IUCN) Red List is classified as data deficient due to lack of information on their taxonomy, population status, or impact of threats. Despite the chance that many are at high risk of extinction, data-deficient species are typically excluded from global and local conservation priorities, as well as funding schemes. The number of data-deficient species will greatly increase as the IUCN Red List becomes more inclusive of poorly known and speciose groups. A strategic approach is urgently needed to enhance the conservation value of data-deficient assessments. To develop this, we reviewed 2879 data-deficient assessments in 6 animal groups and identified 8 main justifications for assigning data-deficient status (type series, few records, old records, uncertain provenance, uncertain population status or distribution, uncertain threats, taxonomic uncertainty, and new species). Assigning a consistent set of justification tags (i.e., consistent assignment to assessment justifications) to species classified as data deficient is a simple way to achieve more strategic assessments. Such tags would clarify the causes of data deficiency; facilitate the prediction of extinction risk; facilitate comparisons of data deficiency among taxonomic groups; and help prioritize species for reassessment. With renewed efforts, it could be straightforward to prevent thousands of data-deficient species slipping unnoticed toward extinction., (© 2016 Society for Conservation Biology.)

The newly developed IUCN Red List of Ecosystems is part of a growing toolbox for assessing risks to biodiversity, which addresses ecosystems and their functioning. The Red List of Ecosystems standard allows systematic assessment of all freshwater, marine, terrestrial and subterranean ecosystem types in terms of their global risk of collapse. In addition, the Red List of Ecosystems categories and criteria provide a technical base for assessments of ecosystem status at the regional, national, or subnational level. While the Red List of Ecosystems criteria were designed to be widely applicable by scientists and practitioners, guidelines are needed to ensure they are implemented in a standardized manner to reduce epistemic uncertainties and allow robust comparisons among ecosystems and over time. We review the intended application of the Red List of Ecosystems assessment process, summarize 'best-practice' methods for ecosystem assessments and outline approaches to ensure operational rigour of assessments. The Red List of Ecosystems will inform priority setting for ecosystem types worldwide, and strengthen capacity to report on progress towards the Aichi Targets of the Convention on Biological Diversity. When integrated with other IUCN knowledge products, such as the World Database of Protected Areas/Protected Planet, Key Biodiversity Areas and the IUCN Red List of Threatened Species, the Red List of Ecosystems will contribute to providing the most complete global measure of the status of biodiversity yet achieved., (© 2015 The Author(s) Published by the Royal Society. All rights reserved.)

There is little appreciation of the level of extinction risk faced by one-sixth of the over 65,000 species assessed by the International Union for Conservation of Nature. Determining the status of these data-deficient (DD) species is essential to developing an accurate picture of global biodiversity and identifying potentially threatened DD species. To address this knowledge gap, we used predictive models incorporating species' life history, geography, and threat information to predict the conservation status of DD terrestrial mammals. We constructed the models with 7 machine learning (ML) tools trained on species of known status. The resultant models showed very high species classification accuracy (up to 92%) and ability to correctly identify centers of threatened species richness. Applying the best model to DD species, we predicted 313 of 493 DD species (64%) to be at risk of extinction, which increases the estimated proportion of threatened terrestrial mammals from 22% to 27%. Regions predicted to contain large numbers of threatened DD species are already conservation priorities, but species in these areas show considerably higher levels of risk than previously recognized. We conclude that unless directly targeted for monitoring, species classified as DD are likely to go extinct without notice. Taking into account information on DD species may therefore help alleviate data gaps in biodiversity indicators and conserve poorly known biodiversity., (© 2014 Society for Conservation Biology.)