Bias in the detection of negative density dependence in plant communities

Matteo Detto, Marco D. Visser, S. Joseph Wright, Stephen W. Pacala

Research output: Contribution to journalReview articlepeer-review

72 Scopus citations


Regression dilution is a statistical inference bias that causes underestimation of the strength of dependency between two variables when the predictors are error-prone proxies (EPPs). EPPs are widely used in plant community studies focused on negative density-dependence (NDD) to quantify competitive interactions. Because of the nature of the bias, conspecific NDD is often overestimated in recruitment analyses, and in some cases, can be erroneously detected when absent. In contrast, for survival analyses, EPPs typically cause NDD to be underestimated, but underestimation is more severe for abundant species and for heterospecific effects, thereby generating spurious negative relationships between the strength of NDD and the abundances of con- and heterospecifics. This can explain why many studies observed rare species to suffer more severely from conspecific NDD, and heterospecific effects to be disproportionally smaller than conspecific effects. In general, such species-dependent bias is often related to traits associated with likely mechanisms of NDD, which creates false patterns and complicates the ecological interpretation of the analyses. Classic examples taken from literature and simulations demonstrate that this bias has been pervasive, which calls into question the emerging paradigm that intraspecific competition has been demonstrated by direct field measurements to be generally stronger than interspecific competition.

Original languageEnglish (US)
Pages (from-to)1923-1939
Number of pages17
JournalEcology letters
Issue number11
StatePublished - Nov 1 2019

All Science Journal Classification (ASJC) codes

  • Ecology, Evolution, Behavior and Systematics


  • Errors in predictors
  • measurement error
  • negative density dependence
  • plant community
  • regression dilution


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