Controlling biases in targeted plant removal experiments
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Controlling biases in targeted plant removal experiments. / Monteux, Sylvain; Blume-Werry, Gesche; Gavazov, Konstantin; Kirchhoff, Leah; Krab, Eveline J.; Lett, Signe; Pedersen, Emily P.; Väisänen, Maria.
In: New Phytologist, Vol. 242, No. 4, 2024, p. 1835-1845.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Controlling biases in targeted plant removal experiments
AU - Monteux, Sylvain
AU - Blume-Werry, Gesche
AU - Gavazov, Konstantin
AU - Kirchhoff, Leah
AU - Krab, Eveline J.
AU - Lett, Signe
AU - Pedersen, Emily P.
AU - Väisänen, Maria
N1 - Publisher Copyright: © 2023 The Authors. New Phytologist © 2023 New Phytologist Foundation.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - biomass removal gradient
KW - disturbance bias
KW - ectomycorrhizal plant
KW - ericoid mycorrhizal plant
KW - Monte Carlo simulations
KW - plant removal experiment
KW - shrubification
U2 - 10.1111/nph.19386
DO - 10.1111/nph.19386
M3 - Journal article
AN - SCOPUS:85178479833
VL - 242
SP - 1835
EP - 1845
JO - New Phytologist
JF - New Phytologist
SN - 0028-646X
IS - 4
ER -
ID: 375722943