Evidence-based alignment of conservation policies with remote sensing-enabled essential biodiversity variables

M.C. Lock*, A.K. Skidmore, I. van Duren, C.A. Mücher

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Nature conservation policies require up-to-date and accurate biodiversity monitoring. Innovative synoptic information products such as Remote Sensing-enabled Essential Biodiversity Variables (RS-enabled EBVs) could complement field observations in biodiversity monitoring. It is not clear however, how these scientific remote sensing products can be utilized for policy reporting. Agreement on the monitored geographic extent (area size and scale), as well as biodiversity attributes (composition, structure, and function), may provide a common'point of departure' for policymakers and the scientific community to develop and further improve monitoring. In this study, biodiversity indicators of 10 nature conservation policies and 50 RS-enabled EBVs were compared using non-parametric tests (chi-square and Mann-Whitney U). Our main finding is that policy indicators and RS-enabled EBVs are very similar in the spatial extent they address (mapping scale). However, most policy indicators are related to ecosystem structure while most of the RS-enabled EBVs are related to ecosystem function and ecosystem structure. RS-enabled EBVs have added value in monitoring of biodiversity, especially when looking at ecosystem functioning. Information on ecosystem functioning and structure provides evidence needed as input for policy development and management of biodiversity. However, to make this happen, a stronger focus on ecosystem functioning and structure with appropriate variables is needed, in policy requirements and targets

Original languageEnglish
Article number108272
JournalEcological Indicators
Volume132
DOIs
Publication statusPublished - Dec 2021

Keywords

  • Biodiversity monitoring
  • Conservation policies
  • Ecological indicators
  • Essential biodiversity variables
  • Remote sensing

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