Lucie M. Bland

A practical guide to the application of the IUCN Red List of Ecosystems criteria

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.

Cost‐effective assessment of extinction risk with limited information

1. Cost-effective reduction of uncertainty in global biodiversity indicators is a central goal of conservation. Comprising a sixth of the 74,000+ species currently on the IUCN Red List, Data Deficient species contribute to considerable uncertainty in estimates of extinction risk. Estimating levels of risk in Data Deficient species will require large resources given the costs of surveys and Red List assessments. Predicting extinction risk from species traits and geographical information could provide a cheaper approach for determining the proportion of Data Deficient species at risk of extinction. 2. We use double sampling theory to compare the cost-effectiveness of predictive models and IUCN Red List assessments for estimating risk levels in Data Deficient terrestrial mammals, amphibians, reptiles and crayfish. For each group, we calibrate Machine Learning models of extinction risk on species of known conservation status, and assess their cost and reliability relative to field surveys followed by Red List assessments. 3. We show that regardless of model type used or species group examined, it is always more cost-effective to determine the conservation status of all species with models and assess a small proportion of species with IUCN criteria (double sampling), rather than spend the same resources on field surveys and Red List assessments alone (single sampling). 4. We estimate that surveying and re-assessing all Data Deficient species currently listed on the IUCN Red List (12,206 species) with IUCN criteria would cost a minimum of US

Toward reassessing data‐deficient species

323 million. Double sampling reduces the cost of determining the proportion of Data Deficient species at risk of extinction by up to 68%, because less than 6% of Data Deficient species would need to be surveyed and assessed with IUCN criteria. 5. Synthesis and applications. Double sampling with models cost-effectively estimates extinction risk levels in poorly-known species, and can be used to reduce the impact of uncertainty in the Red List and Red List Index. We provide recommendations for uptake by managers and a sampling planner spreadsheet. Double sampling could be applied more widely in ecology and conservation to formally compare the cost-effectiveness of sampling methods differing in cost and reliability.

The role of satellite remote sensing in structured ecosystem risk assessments

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.

Using multiple lines of evidence to assess the risk of ecosystem collapse

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.

A biodiversity-crisis hierarchy to evaluate and refine conservation indicators

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.

Expanding the Role of Targets in Conservation Policy

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.A conceptual model linking drivers of change to biodiversity loss identifies major gaps in the Aichi targets of the Convention on Biological Diversity, and provides a mechanism for developing new indicators.

Selecting and applying indicators of ecosystem collapse for risk assessments: Indicators of Ecosystem Collapse

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.

A Computational Pipeline for the IUCN Risk Assessment for Meso-American Reef Ecosystem

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 Natures 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.

Predicting the conservation status of data-deficient species.

Coral reefs are of global economic and biological significance but are subject to increasing threats. As a result, it is essential to understand the risk of coral reef ecosystem collapse and to develop assessment process for those ecosystems. The International Union for Conservation of Nature (IUCN) Red List of Ecosystem (RLE) is a framework to assess the vulnerability of an ecosystem. Importantly, the assessment processes need to be repeatable as new monitoring data arises. The repeatability will also enhance transparency. In this paper, we discuss the evolution of a computational pipeline for risk assessment of the Meso-American reef ecosystem, a diverse reef ecosystem located in the Caribbean, with the focus on improving the execution time starting from sequential and parallel implementation and finally using Apache Spark. The final form of the pipeline is a scientific workflow to improve its repeatability and reproducibility.