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dc.contributor.authorMonteux, Sylvain
dc.contributor.authorBlume-Werry, Gesche
dc.contributor.authorGavazov, Konstantin
dc.contributor.authorKirchhoff, Leah
dc.contributor.authorKrab, Eveline J.
dc.contributor.authorLett, Signe
dc.contributor.authorPedersen, Emily P.
dc.contributor.authorVäisänen, Maria
dc.date.accessioned2024-01-22T12:55:11Z
dc.date.available2024-01-22T12:55:11Z
dc.date.issued2023-12-30
dc.description.abstract<uli> <li>Targeted removal experiments are a powerful tool to assess the effects of plant species or (functional) groups on ecosystem functions. However, removing plant biomass in itself can bias the observed responses. This bias is commonly addressed by waiting until ecosystem recovery, but this is inherently based on unverified proxies or anecdotal evidence. Statistical control methods are efficient, but restricted in scope by underlying assumptions.</li> <li>We propose accounting for such biases within the experimental design, using a gradient of biomass removal controls. We demonstrate the relevance of this design by presenting (1) conceptual examples of suspected biases and (2) how to observe and control for these biases.</li> <li>Using data from a mycorrhizal association-based removal experiment, we show that ignoring biomass removal biases (including by assuming ecosystem recovery) can lead to incorrect, or even contrary conclusions (e.g. false positive and false negative). Our gradient design can prevent such incorrect interpretations, regardless of whether aboveground biomass has fully recovered.</li> <li>Our approach provides more objective and quantitative insights, independently assessed for each variable, than using a proxy to assume ecosystem recovery. Our approach circumvents the strict statistical assumptions of, for example, ANCOVA and thus offers greater flexibility in data analysis.</li> </uli>en_US
dc.identifier.citationMonteux, Blume-Werry, Gavazov, Kirchhoff, Krab, Lett, Pedersen, Väisänen. Controlling biases in targeted plant removal experiments. New Phytologist. 2023
dc.identifier.cristinIDFRIDAID 2215521
dc.identifier.doi10.1111/nph.19386
dc.identifier.issn0028-646X
dc.identifier.issn1469-8137
dc.identifier.urihttps://hdl.handle.net/10037/32665
dc.language.isoengen_US
dc.publisherWileyen_US
dc.relation.journalNew Phytologist
dc.rights.holderCopyright 2023 The Author(s)en_US
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0en_US
dc.rightsAttribution-NonCommercial 4.0 International (CC BY-NC 4.0)en_US
dc.titleControlling biases in targeted plant removal experimentsen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
Except where otherwise noted, this item's license is described as Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)