Standardizing indicators for the status and trends of biodiversity

Biodiversity assessments and policies rely on a multitude of indicators that measure diverse aspects of nature. These indicators are based on various underlying measurements, from in situ observations and ecological monitoring to remote sensing products and model outputs. Therefore, synthesizing and comparing indicators is a demanding task, challenged by mismatched temporal measurement cycles, scattered data sources and inconsistent formats, which can limit their use as robust impact metrics for conservation and business reporting.

Here we present indiGO (Indicators in Generalized Outputs), an open-source R package that standardizes and compares biodiversity indicators in a transparent and reproducible framework aligned with the standards of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES). The package includes around one hundred datasets that were integrated in the first IPBES Global Assessment on the status and trends of nature, such as Red List Indices, the Biodiversity Intactness Index and cumulative introduced alien species, among others. indiGO offers user-friendly functions to generalize metrics, for example to visualize global trajectories as change since 1970 or as deviation from a pristine world, consistent with the figures presented in the IPBES annex.

A key feature of indiGO is that users can integrate their own indicators by simply adding one text file with the data and one with the metadata, which allows them to display these indicators directly alongside other globally recognized metrics. The workflows are transparent, documented and accompanied by clear step-by-step tutorials, supporting both expert users and newcomers.

indiGO provides scientific infrastructure for emerging efforts towards standardization, governance and coordinated use of biodiversity indicators across scales and sectors. It aligns with FAIR principles through a traceable pathway from raw data to harmonized trends. Its flexible application for more recent and novel data supports upcoming assessments and policy frameworks, and strengthens open science and data-driven decision making for biodiversity conservation and sustainable development.