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A workflow to integrate ecological monitoring data from different sources

Abstract : Programs and initiatives aiming to protect biodiversity and ecosystems have increased over the last decades in response to their decline. Most of these are based on monitoring data to quantitatively describe trends in biodiversity and ecosystems. The estimation of such trends, at large scales, requires the integration of numerous data from multiple monitoring sites. However, due to the high heterogeneity of data formats and the resulting lack of interoperability, the data integration remains sparsely used and synthetic analyses are often limited to a restricted part of the data available. Here we propose a workflow, comprising four main steps, from data gathering to quality control, to better integrate ecological monitoring data and to create a synthetic dataset that will make it possible to analyse larger sets of monitoring data, including unpublished data. The workflow was designed and applied in the production of the Status of Coral Reefs of the World: 2020 report, where more than two hundred individual datasets were integrated to assess the status and trends of hard coral cover at the global scale. The workflow was applied to two case studies and associated R codes, based on the experience acquired during the production of this report. The proposed workflow allows for the integration of datasets with different levels of taxonomic and spatial precision, with a high degree of reproducibility. It provides a conceptual and technical framework for the integration of ecological monitoring data, allowing for the estimation of temporal trends in biodiversity and ecosystems or to test ecological hypotheses at larger scales.
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https://hal-univ-perp.archives-ouvertes.fr/hal-03839113
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Submitted on : Friday, November 4, 2022 - 10:00:35 AM
Last modification on : Saturday, November 5, 2022 - 3:57:48 AM

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Jérémy Wicquart, Mishal Gudka, David Obura, Murray Logan, Francis Staub, et al.. A workflow to integrate ecological monitoring data from different sources. Ecological Informatics, 2022, 68, pp.101543. ⟨10.1016/j.ecoinf.2021.101543⟩. ⟨hal-03839113⟩

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