Biological insurance theory predicts that, in a variable environment, aggregate ecosystem properties will vary less in more diverse communities because declines in the performance or abundance of some species or phenotypes will be offset, at least partly, by smoother declines or increases in others. During the past two decades, ecology has accumulated strong evidence for the stabilising effect of biodiversity on ecosystem functioning. As biological insurance is reaching the stage of a mature theory, it is critical to revisit and clarify its conceptual foundations to guide future developments, applications and measurements. In this review, we first clarify the connections between the insurance and portfolio concepts that have been used in ecology and the economic concepts that inspired them. Doing so points to gaps and mismatches between ecology and economics that could be filled profitably by new theoretical developments and new management applications. Second, we discuss some fundamental issues in biological insurance theory that have remained unnoticed so far and that emerge from some of its recent applications. In particular, we draw a clear distinction between the two effects embedded in biological insurance theory, i.e. the effects of biodiversity on the mean and variability of ecosystem properties. This distinction allows explicit consideration of trade-offs between the mean and stability of ecosystem processes and services. We also review applications of biological insurance theory in ecosystem management. Finally, we provide a synthetic conceptual framework that unifies the various approaches across disciplines, and we suggest new ways in which biological insurance theory could be extended to address new issues in ecology and ecosystem management. Exciting future challenges include linking the effects of biodiversity on ecosystem functioning and stability, incorporating multiple functions and feedbacks, developing new approaches to partition biodiversity effects across scales, extending biological insurance theory to complex interaction networks, and developing new applications to biodiversity and ecosystem management.

Summary Differences between species in their response to environmental fluctuations cause asynchronized growth series, suggesting that species diversity may help communities buffer the effects of environmental fluctuations. However, within‐species variability of responses may impact the stabilizing effect of growth asynchrony. We used tree ring data to investigate the diversity–stability relationship and its underlying mechanisms within the temperate and boreal mixed woods of Eastern Canada. We worked at the individual tree level to take into account the intraspecific variability of responses to environmental fluctuations. We found that species diversity stabilized growth in forest ecosystems. The asynchrony of species’ response to climatic fluctuations and to insect outbreaks explained this effect. We also found that the intraspecific variability of responses to environmental fluctuations was high, making the stabilizing effect of diversity highly variable. Synthesis. Our results are consistent with previous studies suggesting that the asynchrony of species’ response to environmental fluctuations drives the stabilizing effect of diversity. The intraspecific variability of these responses modulates the stabilizing effect of species diversity. Interactions between individuals, variation in tree size and spatial heterogeneity of environmental conditions could play a critical role in the stabilizing effect of diversity.

Forest models are widely used to assess the impacts of changing environmental conditions such as climate, atmospheric CO 2 concentration and nitrogen deposition on forest functioning, dynamics and structure (e.g., Reyer et al., 2013). Yet, because of our incomplete understanding of forest ecosystems and computational constraints, these models differ in the way specific processes are represented, leading to differences in their predictions (Bugmann et al., 2019;Collalti et al., 2019;Huber et al., 2021). Hence, models need to be comprehensively evaluated using different data types at different spatio-temporal scales before we can judge their structural uncertainties and suitability for answering specific questions (Marechaux et al., 2021;Oberpriller et al., 2021).Model simulations need to be in adequate agreement with independent observations. Moreover, models have to be sensitive to environmental drivers to ensure that system responses are realistically predicted under a wide range of environmental and climatic