Using decision science to evaluate global biodiversity indices

Abstract

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.

Annual changes in the Biodiversity Intactness Index in tropical and subtropical forest biomes, 2001–2012

“…Clade-specific responses to anthropogenic pressures, which average out in the modelling because of the taxonomic representativeness of the PREDICTS database 19 , preclude using observed trends for particular taxa to test hindcasts. Robust assessment of skill is an important future challenge for many ecological models 91 : no global biodiversity indicators have yet been tested thoroughly in this way 92 .…”

Toward monitoring forest ecosystem integrity within the post‐2020 Global Biodiversity Framework

“…Measurement and monitoring of components of EI has been somewhat successfully done at local to regional spatial scales largely with ground‐based methods, but not at global scales. Multiple global metrics of biodiversity have been developed in recent years and there is now considerable confusion among scientists and policy makers as to the utility and reliability of these metrics (Watermeyer et al., 2020). Also, yet to be established are the baseline conditions by which success in increasing EI will be judged.…”

Ecosystem services in connected catchment to coast ecosystems: monitoring to detect emerging trends

“…The leading international framework for developing environmental economic accounts is the System of Environmental Economic Accounting -Ecosystem Accounting (SEEA-EA) which has been adopted by the UN Statistical Commission and many countries (United Nations 2021). While ecosystem extent indicators have been widely studied, there has been limited testing of how effectively condition indicators in SEEA-EA (Farrell et al 2021), and biodiversity indicators more generally (Watermeyer et al 2021;Nicholson et al 2021), can inform management decisions.…”

“…Classification of the appropriate applications for indicators should ideally be informed by quantitative performance evaluation, but such evaluations are rarely conducted (Fulton et al 2005;Rowland et al 2020;Watermeyer et al 2021). Performance evaluations may be particularly important in ecosystems with high levels of natural variation and interconnectedness among intermediate and final ecosystem services.…”

“…Therefore, it is imperative that we quantify the sensitivity of condition indicators to human influences so we know if -and how quickly -such indicators can detect changes in cross-catchment processes. In particular, as SEEA EA is becoming a dominant framework for environmental economic accounts, tests of how well condition indicators in this framework can capture changes in cross-system interactions are needed (Watermeyer et al 2021;Nicholson et al 2021).…”