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Matching biodiversity indicators to policy needs.

Simone L Stevenson1, Kate Watermeyer1, Giovanni Caggiano2

  • 1Centre for Integrative Ecology, School of Life and Environmental Sciences, Deakin University, 221 Burwood Highway, Burwood, VIC, 3125, Australia.

Conservation Biology : the Journal of the Society for Conservation Biology
|June 20, 2020
PubMed
Summary

Biodiversity indicators can now be classified as leading, coincident, or lagging to better inform conservation decisions and policy. This framework links indicator function to specific management goals for proactive and evaluative action.

Keywords:
Aichi TargetsConvenio sobre la Diversidad BiológicaConvention on Biological DiversityIUCN red list indexIUCN 红色名录指数Objetivos de Aichibiodiversity indicatorsdinámicas poblacionalesecosystem based fisheries managementextinciónextinctionindicadores de biodiversidadmanejo de pesquerías basado en el ecosistemaobjetivos de desarrollo sustentablepopulation dynamicssustainable development goalsíndice de la lista roja de la UICN《生物多样性公约》可持续发展目标基于生态系统的渔业管理灭绝爱知目标生物多样性指标种群动态

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Area of Science:

  • Ecology
  • Conservation Science
  • Environmental Policy

Background:

  • Global biodiversity indicators are often used for general monitoring but lack explicit links to decision-making.
  • The distinct functions of indicators (predictive vs. evaluative) are seldom clarified, limiting their policy impact.
  • Existing indicator frameworks do not consistently differentiate between leading, coincident, and lagging measures.

Purpose of the Study:

  • To develop a conceptual model for classifying biodiversity indicators based on their predictive or evaluative functions.
  • To link indicator classification to specific management objectives and policy cycles.
  • To enhance the utility of biodiversity indicators for proactive conservation decision-making.

Main Methods:

  • Developed a conceptual model adapting an economic approach to classify indicators as leading, coincident, or lagging.
  • Applied the model to ecological theory on biodiversity response times and management goals.
  • Classified indicators within two case studies: global species extinction and marine ecosystem collapse.

Main Results:

  • Indicators of abundance (e.g., Living Planet Index) were identified as leading indicators for species extinction, signaling preventative action needs.
  • Extinction-specific indicators were classified as lagging, suitable for evaluating past conservation efforts.
  • In marine ecosystems, indicators of fishing impact were deemed leading, while ecosystem collapse indicators were considered lagging.

Conclusions:

  • Classifying biodiversity indicators by function (leading, coincident, lagging) clarifies their role in the policy cycle.
  • This classification establishes an explicit link between indicators and preventative decision-making.
  • The framework supports proactive conservation actions by defining how indicators inform policy and management evaluations.