In an era of increasingly data-driven ecology, where large-scale biodiversity datasets are fundamental to informing conservation and management, the treatment of missing data has profound implications for the integrity of ecological inferences. This study is both timely and critical, as it scrutinizes the consequences of a common yet underexamined practice: the imputation of gaps in long-term biodiversity monitoring datasets.

In To fill or not to fill: Comparing imputation methods for improved riverine long-term biodiversity monitoring (Haubrock et al., 2025), published in the Journal of Applied Ecology, the authors employed more than two decades of river macroinvertebrate monitoring data from Denmark, the Netherlands, and Sweden to systematically assess how missing data—and efforts to fill these gaps—alter biodiversity trend estimates. By introducing artificial gaps and applying multiple imputation strategies, including Predictive Mean Matching, Random Forest, and Random Sampling from observed values, they revealed that even modest levels of missing or imputed data could dramatically distort perceived biodiversity trends. Notably, popular methods such as Predictive Mean Matching sometimes amplified deviations, while Random Forest and simple random sampling approaches were more conservative.

This work highlights an urgent methodological concern: imputation, if misapplied or undertaken without careful evaluation, can generate misleading narratives about biodiversity change, potentially steering policy and management decisions astray. The authors argue for a cautious, context-sensitive approach that rigorously weighs whether to fill data gaps at all, emphasizing transparency about the uncertainties that imputation introduces. By providing a nuanced comparative framework, this study advances more responsible practices in handling ecological time-series data, ultimately supporting more robust and credible biodiversity assessments in an increasingly data-dependent conservation landscape.