Losses and gains in species diversity affect ecological stability 1-7 and the sustainability of ecosystem functions and services 8-13. Experiments and models reveal positive, negative, and no effects of diversity on individual components of stability such as temporal variability, resistance, and resilience 2,3,6,11,12,14. How these stability components covary is poorly appreciated 15 , as are diversity effects on overall ecosystem stability 16 , conceptually akin to ecosystem multifunctionality 17,18. We observed how temporal variability, resistance, and overall ecosystem stability responded to diversity (i.e. species richness) in a large experiment involving 690 micro-ecosystems sampled 19 times over 40 days, resulting in 12939 samplings. Species richness increased temporal stability but decreased resistance to warming. Thus, two stability components negatively covaried along the diversity gradient. Previous biodiversity manipulation studies rarely reported such negative covariation despite general predictions of negative effects of diversity on individual stability components 3. Integrating our findings with the ecosystem multifunctionality concept revealed hump-and U-shaped effects of diversity on overall ecosystem stability. That is, biodiversity can increase overall ecosystem stability when biodiversity is low, and decrease it when biodiversity is high, or the opposite with a Ushaped relationship. Effects of diversity on ecosystem multifunctionality would also be hump-or U-shaped if diversity has positive effects on some functions and negative effects on others. Linking the ecosystem multifunctionality concept and ecosystem stability can transform perceived effects of diversity on ecological stability and may assist translation of this science into policy-relevant information. Ecological stability consists of numerous components including temporal variability, resistance to environmental change, and rate of recovery from disturbance 1,2,16. Effects of species losses and gains on these components are of considerable interest, not least due to potential effects on ecosystem functioning and hence the sustainable delivery of ecosystem services 1-13. A growing number of experimental studies reveal stabilising effects of diversity on individual stability components. In particular, higher diversity often, but not always, reduces temporal variability of biomass production 13. Positive effects of diversity on resistance are common, though neutral and negative effects on resistance and resilience also occur 9,13,19,20. While assessment of individual stability components is essential, a more integrative approach to ecological stability could lead to clearer conceptual understanding 15 and might improve policy guidance concerning ecological stability 16. Analogous to ecosystem multifunctionality 17,18 , a more integrative approach considers variation in multiple stability components, and the often-ignored covariation among stability components. The nature of this covariation is of paramount importance, as it defines whe...

Forecasts of ecological dynamics in changing environments are increasingly important, and are available for a plethora of variables, such as species abundance and distribution, community structure and ecosystem processes. There is, however, a general absence of knowledge about how far into the future, or other dimensions (space, temperature, phylogenetic distance), useful ecological forecasts can be made, and about how features of ecological systems relate to these distances. The ecological forecast horizon is the dimensional distance for which useful forecasts can be made. Five case studies illustrate the influence of various sources of uncertainty (e.g. parameter uncertainty, environmental variation, demographic stochasticity and evolution), level of ecological organisation (e.g. population or community), and organismal properties (e.g. body size or number of trophic links) on temporal, spatial and phylogenetic forecast horizons. Insights from these case studies demonstrate that the ecological forecast horizon is a flexible and powerful tool for researching and communicating ecological predictability. It also has potential for motivating and guiding agenda setting for ecological forecasting research and development.

Summary1. Laboratory microcosm experiments using protists as model organisms have a long tradition and are widely used to investigate general concepts in population biology, community ecology and evolutionary biology. Many variables of interest are measured in order to study processes and patterns at different spatiotemporal scales and across all levels of biological organization. This includes measurements of body size, mobility or abundance, in order to understand population dynamics, dispersal behaviour and ecosystem processes. Also, a variety of manipulations are employed, such as temperature changes or varying connectivity in spatial microcosm networks. 2. Past studies, however, have used varying methods for maintenance, measurement, and manipulation, which hinders across-study comparisons and meta-analyses, and the added value they bring. Furthermore, application of techniques such as flow cytometry, image and video analyses, and in situ environmental probes provide novel and improved opportunities to quantify variables of interest at unprecedented precision and temporal resolution. 3. Here, we take the first step towards a standardization of well-established and novel methods and techniques within the field of protist microcosm experiments. We provide a comprehensive overview of maintenance, measurement and manipulation methods. An extensive supplement contains detailed protocols of all methods, and these protocols also exist in a community updateable online repository. 4. We envision that such a synthesis and standardization of methods will overcome shortcomings and challenges faced by past studies and also promote activities such as meta-analyses and distributed experiments conducted simultaneously across many different laboratories at a global scale.

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Complex dynamics, such as population cycles, can arise when the individual members of a population become synchronized. However, it is an open question how readily and through which mechanisms synchronization-driven cycles can occur in unstructured microbial populations. In experimental chemostats we studied large populations (>10 9 cells) of unicellular phytoplankton that displayed regular, inducible and reproducible population oscillations. Measurements of cell size distributions revealed that progression through the mitotic cycle was synchronized with the population cycles. A mathematical model that accounts for both the cell cycle and population-level processes suggests that cycles occur because individual cells become synchronized by interacting with one another through their common nutrient pool. An external perturbation by direct manipulation of the nutrient availability resulted in phase resetting, unmasking intrinsic oscillations and producing a transient collective cycle as the individuals gradually drift apart. Our study indicates a strong connection between complex within-cell processes and population dynamics, where synchronized cell cycles of unicellular phytoplankton provide sufficient population structure to cause small-amplitude oscillations at the population level.