Controlling biases in targeted plant removal experiments

© 2023 The Authors. New Phytologist © 2023 New Phytologist Foundation.

Bibliographische Detailangaben
Veröffentlicht in:The New phytologist. - 1979. - 242(2024), 4 vom: 13. Mai, Seite 1835-1845
1. Verfasser: Monteux, Sylvain (VerfasserIn)
Weitere Verfasser: Blume-Werry, Gesche, Gavazov, Konstantin, Kirchhoff, Leah, Krab, Eveline J, Lett, Signe, Pedersen, Emily P, Väisänen, Maria
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2024
Zugriff auf das übergeordnete Werk:The New phytologist
Schlagworte:Journal Article Research Support, Non-U.S. Gov't Monte Carlo simulations biomass removal gradient disturbance bias ectomycorrhizal plant ericoid mycorrhizal plant plant removal experiment shrubification
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520 |a 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. 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. 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. 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 
650 4 |a Journal Article 
650 4 |a Research Support, Non-U.S. Gov't 
650 4 |a Monte Carlo simulations 
650 4 |a biomass removal gradient 
650 4 |a disturbance bias 
650 4 |a ectomycorrhizal plant 
650 4 |a ericoid mycorrhizal plant 
650 4 |a plant removal experiment 
650 4 |a shrubification 
700 1 |a Blume-Werry, Gesche  |e verfasserin  |4 aut 
700 1 |a Gavazov, Konstantin  |e verfasserin  |4 aut 
700 1 |a Kirchhoff, Leah  |e verfasserin  |4 aut 
700 1 |a Krab, Eveline J  |e verfasserin  |4 aut 
700 1 |a Lett, Signe  |e verfasserin  |4 aut 
700 1 |a Pedersen, Emily P  |e verfasserin  |4 aut 
700 1 |a Väisänen, Maria  |e verfasserin  |4 aut 
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773 1 8 |g volume:242  |g year:2024  |g number:4  |g day:13  |g month:05  |g pages:1835-1845 
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