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 -