What question did this study set out to answer?

This research aims to understand how observer error impacts species-environment associations in tropical rainforests and to identify the best analytical approaches to mitigate these effects.

February 19, 2026

How to Mitigate the Effect of Observer Error When Unraveling Species‐Environment Associations? A Case Study With Tropical Tree Communities From Western Central Africa

Key Points

This research aims to understand how observer error impacts species-environment associations in tropical rainforests and to identify the best analytical approaches to mitigate these effects.
Analyzed a dataset of 19,287 trees from 99 forest plots in Gabon.
Introduced varying levels of observer error at species, genus, and family levels.
Used redundancy analysis to assess species-environment associations and quantified predictor importance.
Modelled the decline of R2 through exponential decay functions and conducted two-way ANOVA to compare effects across taxonomic resolutions.
Examined R2 decay in rare versus common species under observer error conditions.
R2 consistently declined under increased observer error rates.
Models detected significant associations at 30%-60% error rates, depending on taxonomic depth.
Relative predictor importance changed significantly only beyond 75% observer error.
Using broader taxonomic levels heightened R2 decline compared to species-level analysis.
Rare species showed less pronounced R2 decay but had stronger environmental associations.

Abstract

ABSTRACT 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.

Bookmark

Cite This Study

Klein et al. (Thu,) studied this question.

synapsesocial.com/papers/6996a83eecb39a600b3eecaa https://doi.org/https://doi.org/10.1111/jvs.70106

Bookmark