{"paper_id":"0d46b52e-c0cd-41bf-87a2-ad04df1d722a","body_text":"This is a Preprint and has not been peer reviewed. This is version 1 of this Preprint.\nYou must log in to post a comment.\nThere are no comments or no comments have been made public for this article.\nThis is a Preprint and has not been peer reviewed. This is version 1 of this Preprint.\nAdd a Comment\nYou must log in to post a comment.\nComments\nThere are no comments or no comments have been made public for this article.\nIn face of the ongoing nature crisis, the international community is setting targets and deciding on actions to combat the current biodiversity crises. For this to be effective they need tools to accurately describe the current situation and to monitor trends in ecosystems over time. Ecosystem condition accounts (ECA) is one such tool that use variables and indicators to describe key ecosystem characteristics, reflecting their condition and deviations from a reference condition. Because the purpose is to inform decisions at relatively high political levels, these metrics are often spatially aggregated to represent larger areas, such as countries. However, spatial aggregation of information has the potential to alter the descriptive and normative interpretations one can make from these metrics. For example, aggregation displacement causes the information\nheld in variables and indicators to diverge when these are aggregated spatially. This process is influenced also by the order of steps involved in normalising and aggregating variables, i.e. the aggregation pathway. Although aggregation displacement and the type of aggregation pathway chosen for the indicator clearly impact both the indicator values and their interpretation, there are no clear guidelines or deliberation on these topics in the SEEA EA standard for ecosystem accounting. This paper outlines the consequences of\ndifferent aggregation pathways, emphasising their impact on the credibility of ECAs, and how these are interpreted by users. We introduce a standardised terminology for aggregation pathways specific to ecosystem condition indicators following the SEEA EA standard and provide recommendations for selecting appropriate pathways in various contexts. Our discussion of this topic is aimed at raising the general awareness of\nspatial aggregation issues and to guide indicator developers in choosing and reporting spatial aggregation methods.\nhttps://doi.org/10.32942/X2WT0Z\nApplied Statistics, Ecology and Evolutionary Biology, Environmental Indicators and Impact Assessment, Environmental Monitoring, Life Sciences, Other Life Sciences\nSEEA EA, ecosystem condition, ecosystem accounting, indicators, aggregation bias, aggregation error, aggregation displacement, upscaling\nPublished: 2025-12-03 04:52\nLast Updated: 2025-12-03 04:52\nCC BY Attribution 4.0 International\nData and Code Availability Statement:\nhttps://github.com/anders-kolstad/aggregationPathways\nLanguage:\nEnglish","source_license":"CC-BY-4.0","license_restricted":false}