Ellenberg indicator values (EIVs) are a widely used metric in plant ecology comprising a semi‐quantitative description of species’ ecological requirements. Typically, point estimates of mean EIV scores are compared over space or time to infer differences in the environmental conditions structuring plant communities—particularly in resurvey studies where no historical environmental data are available. However, the use of point estimates as a basis for inference does not take into account variance among species EIVs within sampled plots and gives equal weighting to means calculated from plots with differing numbers of species. Traditional methods are also vulnerable to inaccurate estimates where only incomplete species lists are available.We present a set of multilevel (hierarchical) models—fitted with and without group‐level predictors (e.g., habitat type)—to improve precision and accuracy of plot mean EIV scores and to provide more reliable inference on changing environmental conditions over spatial and temporal gradients in resurvey studies. We compare multilevel model performance to GLMMs fitted to point estimates of mean EIVs. We also test the reliability of this method to improve inferences with incomplete species lists in some or all sample plots. Hierarchical modeling led to more accurate and precise estimates of plot‐level differences in mean EIV scores between time‐periods, particularly for datasets with incomplete records of species occurrence. Furthermore, hierarchical models revealed directional environmental change within ecological habitat types, which less precise estimates from GLMMs of raw mean EIVs were inadequate to detect. The ability to compute separate residual variance and adjusted R 2 parameters for plot mean EIVs and temporal differences in plot mean EIVs in multilevel models also allowed us to uncover a prominent role of hydrological differences as a driver of community compositional change in our case study, which traditional use of EIVs would fail to reveal. Assessing environmental change underlying ecological communities is a vital issue in the face of accelerating anthropogenic change. We have demonstrated that multilevel modeling of EIVs allows for a nuanced estimation of such from plant assemblage data changes at local scales and beyond, leading to a better understanding of temporal dynamics of ecosystems. Further, the ability of these methods to perform well with missing data should increase the total set of historical data which can be used to this end.

Compositional change is a ubiquitous response of ecological communities to environmental drivers of global change, but is often regarded as evidence of declining "biotic integrity" relative to historical baselines. Adaptive compositional change, however, is a foundational idea in evolutionary biology, whereby changes in gene frequencies within species boost population-level fitness, allowing populations to persist as the environment changes. Here, we present an analogous idea for ecological communities based on core concepts of fitness and selection. Changes in community composition (i.e., frequencies of genetic differences among species) in response to environmental change should normally increase the average fitnessof community members. We refer to compositional changes that improve the functional match, or "fit," between organisms' traits and their environment as adaptive community dynamics. Environmental change (e.g., land-use change) commonly reduces the fit between antecedent communities and new environments. Subsequent change in community composition in response to environmental changes, however, should normally increase community-level fit, as the success of at least some constituent species increases. We argue that adaptive community dynamics are likely to improve or maintain ecosystem function (e.g., by maintaining productivity). Adaptive community responses may simultaneously produce some changes that are considered societally desirable (e.g., increased carbon storage) and others that are undesirable (e.g., declines of certain species), just as evolutionary responses within species may be deemed desirable (e.g., evolutionary rescue of an endangered species) or undesirable (e.g., enhanced virulence of an agricultural pest).When assessing possible management interventions, it is important to distinguish between drivers of environmental change (e.g., undesired climate warming) and adaptive community responses, which may generate some desirable outcomes. Efforts to facilitate, accept, or resist ecological change require separate consideration of drivers and responses, and may highlight the need to reconsider preferences for historical baseline communities over communities that are better adapted to the new conditions.

Historical data on co-occurring taxa are extremely rare. As such, the extent to which distinct co-occurring taxa experience similar long-term patterns in species richness and compositional change (e.g., when exposed to a changing environment) is not clear.Using data from a diverse ecological community surveyed in the 1930s and resurveyed in the 2010s, we investigated whether local plant and insect assemblages displayed cross-taxon congruence-that is, spatiotemporal correlation in species richness and compositional change-across six co-occurring taxa: vascular plants, non-vascular plants, grasshoppers and crickets (Orthoptera), ants (Hymenoptera: Formicinae), hoverflies (Diptera: Syrphidae), and dragonflies and damselflies (Odonata). All taxa exhibited high levels of turnover across the ca. 80-year time period. Despite minimal observed changes at the level of the whole study system, species richness displayed widespread cross-taxon congruence (i.e., correlated temporal change) across local assemblages within the study system. Hierarchical logistic regression models suggest a role for shared responses to environmental change underlying cross-taxon correlations and highlight stronger correlations between vascular plants and their direct consumers, suggesting a possible role for biotic interactions between these groups.These results provide an illustration of cross-taxon congruence in biodiversity change using data unique in its combination of temporal and taxonomic scope, and highlight the potential for cascading and comparable effects of environmental change (abiotic and biotic) on co-occurring plant and insect communities. However, analyses of historical resurveys based on currently available data come with inherent uncertainties.As such, this study highlights a need for well-designed experiments, and monitoring programs incorporating co-occurring taxa, to determine the underlying mechanisms and prevalence of congruent biodiversity change as anthropogenic environmental change accelerates apace.