Résumé
Aims: Unraveling species–environment associations proves challenging in species‐rich tropical rainforests due to erroneous species identifications (observer error, OE ), which negatively affect multivariate analyses. OE occurs more frequently at species level—confusing one species with another—than at broader taxonomic depth (genus or family) and disproportionately affects rare species. Therefore, many studies broaden the taxonomic resolution (using genus or family as surrogates for species) or remove rare species prior to analysis. However, it remains unclear which approach best mitigates the effect of OE . Location: Gabon. Methods: We used a dataset comprising 19,287 trees in 99 forest plots across Gabon and introduced increasing proportions of OE at species, genus and family depth. We used redundancy analysis to quantify the overall strength of species–environment associations as adjusted R 2 and quantified the relative importance of predictors as ratios between partial R 2 for soil, climate, human activity and spatial predictors, at species, morphospecies, genus and family resolution. We modelled R 2 decline through exponential decay functions and tested for differences across depth and resolution using two‐way ANOVA . We compared R 2 decay after independently introducing OE among rare and common species. Results: R 2 declined consistently under OE . Models remained significant to 30%–60% error rate, depending on depth or taxonomic resolution. Relative predictor importance was altered only when error proportions exceeded 75%. Analysis at genus or family resolution caused R 2 decay to steepen. When introduced among rare species, R 2 decay was less pronounced. Paradoxically, rare species contributed little to R 2 despite having stronger associations with the environment. Conclusion: Moderate to high instances of observer error jeopardise our ability to detect significant species–environment associations. Broadening taxonomic resolution and removing rare species inflates R 2 due to dimensionality reduction. To mitigate the effect of OE , we recommend analysing floristic datasets at fine taxonomic resolutions (species or morphospecies) and retaining rare species.