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The insurance effect of biodiversity—that diversity stabilises aggregate ecosystem properties—is mechanistically underlain by inter- and intraspecific trait variation in organismal responses to the environment. This variation, termedresponse diversity, is therefore a potentially critical determinant of ecological stability. However, response diversity has yet to be widely quantified, possibly due to difficulties in its measurement. Even when it has been measured, approaches have varied.Here, we review methods for measuring response diversity and from them distil a methodological framework for quantifying response diversity from experimental and/or observational data, which can be practically applied in lab and field settings across a range of taxa.Previous empirical studies on response diversity most commonly invoke response traits as proxies aimed at capturing species’ ecological responses to the environment. Our approach, which is based on environment-dependent ecological responses to any biotic or abiotic environmental variable, is conceptually simple and robust to any form of environmental response, including nonlinear responses. Given its derivation from empirical data on species’ ecological responses, this approach should more directly reflect response diversity than the trait-based approach dominant in the literature.By capturing even subtle inter- or intraspecific variation in environmental responses, and environment-dependencies in response diversity, we hope this framework will motivate tests of the diversity-stability relationship from a new perspective, and provide an approach for mapping, monitoring, and conserving this critical dimension of biodiversity.

Games as a didactic tool (e. g., puzzles) are gaining recognition in environmental education to promote skill development, but also to develop a specific understanding of the natural world. However, a children’s puzzle containing representations of nature may unwillingly lead to “misconceptions” of biodiversity themes and processes, and an over-simplification of the relationship between people and nature. To solve this problem, positive connotations of biodiversity may prompt a conceptual change to a more nuanced, multifaceted conception of biodiversity.

The potential for forecasting the dynamics of ecological systems is currently unclear, with contrasting opinions regarding its feasibility due to ecological complexity. To investigate forecast skill within and across system complexity, we monitored a microbial system exposed to either constant or fluctuating temperatures in a five months long laboratory experiment. We tested how forecasting of species abundances depends on number and strength of interactions and on model size (number of predictors). We also tested how greater system complexity (i.e. the fluctuating temperatures) impacted these relations. We found that the more a species interacted, the weaker these interactions were and the better its abundance was predicted. Forecast skill increased with model size. Greater system complexity decreased forecast skill for three out of eight species. These insights into how abundance prediction depends on the embedding of the species within the system and on overall system complexity could improve species forecasting and monitoring.

Food web models explain and predict the trophic interactions in a food web, and they can infer missing interactions among the organisms. The allometric diet breadth model (ADBM) is a food web model based on the foraging theory. In the ADBM, the foraging parameters are allometrically scaled to body sizes of predators and prey. In Petchey et al. (

Non-consumptive effects (NCEs) of predators on prey, such as induced defensive strategies, are frequently neglected in the analysis of predator-prey interactions. Yet these effects can have demographic impacts as strong as consumption. As a counterpart to NCEs, resource-availability effects (RAEs) can prompt changes in predators as well, e.g., in their foraging behavior. We studied NCEs and RAEs in the ciliate predator-prey pairDidinium nasutumandParamecium caudatum. We examined the dependence of prey/predator swimming speed and body size on predator/prey presence. We also investigated prey spatial grouping behavior and the dependence of predator movement on local prey abundance. We collected individual movement and morphology data through videography of laboratory-based populations. We compared swimming speeds and body sizes based on their distributions. We used linear models to respectively quantify the effects of local prey abundance on predator displacements and of predator presence on prey grouping behavior. In the presence of prey, predator individuals swam more slowly, were bigger and made smaller displacements. Further, their displacements decreased with increasing local prey abundance. In contrast, in the presence of predators, proportionally more prey individuals showed a fast-swimming behavior and there was weak evidence for increased prey grouping. Trait changes entail energy expenditure shifts, which likely affect interspecific interactions and populations, as has been shown for NCEs. Less is known about the link between RAEs and demography, but it seems likely that the observed effects scale up to influence community and ecosystem stability, yet this remains largely unexplored.Significance StatementTo maximize their fitness, organisms balance investment in foraging and avoiding being eaten. The behaviors of prey and predators are thus expected to depend on the presence and absence of each other and serve either to boost the chances of predation evasion or to increase predation success. Here we provide an example of the co-dependence of behaviors in the predator-prey pairDidinium nasutumandParamecium caudatum. We show that the predator slows down and searches in smaller areas when prey are present, while the prey speeds up and possibly groups more as a response to the presence of predators. Such behavioral changes are likely to have a demographic and community impact that is not accounted for with common measures of predators-prey interactions.

Warming increases the metabolic demand of consumers1, strengthening their feeding interactions2. This could alter energy fluxes3–5 and even amplify extinction rates within the food web6–8. Such effects could simplify the structure and dynamics of ecological networks9,10, although an empirical test in natural systems has been lacking. Here, we tested this hypothesis by characterizing around 50,000 directly observed feeding interactions across 14 naturally heated stream ecosystems11–15. We found that higher temperature simplified food-web structure and shortened the pathways of energy flux between consumers and resources. A simple allometric diet breadth model10,16 predicted 68–82% of feeding interactions and the effects of warming on key food-web properties. We used model simulations to identify the underlying mechanism as a change in the relative diversity and abundance of consumers and their resources. This model shows how warming can reduce the stability of aquatic ecosystems by eroding the structural integrity of the food web. Given these fundamental drivers, such responses are expected to be manifested more broadly and could be predicted using our modelling framework and knowledge of how warming alters some routinely measured characteristics of organisms. Observations of feeding interactions show that warming simplifies the structure of food webs in stream ecosystems. Simulations show that consumer diversity and changes in abundance drive this simplification and can reduce ecosystem stability.

Warming and nutrient enrichment are major environmental factors shaping ecological dynamics. However, cross-scale investigation of their combined effects by linking theory and experiments is lacking. We collected data from aquatic microbial ecosystems investigating the interactive effects of warming (constant and rising temperatures) and enrichment across levels of organisation and contrasted them with community models based on metabolic theory. We found high agreement between our observations and theoretical predictions: we observed in many cases the predicted antagonistic effects of high temperature and high enrichment across levels of organisation. Temporal stability of total biomass decreased with warming but did not differ across enrichment levels. Constant and rising temperature treatments with identical mean temperature did not show qualitative differences. Overall, we conclude that model and empirical results are in broad agreement due to robustness of the effects of temperature and enrichment, that the mitigating effects of temperature on effects of enrichment may be common, and that models based on metabolic theory provide qualitatively robust predictions of the combined ecological effects of enrichment and temperature.

Aquatic ecosystems are often stratified, with cyanobacteria in oxic layers and phototrophic sulfur bacteria in anoxic zones. Changes in stratification caused by the global environmental change are an ongoing concern. Increasing understanding of how such aerobic and anaerobic microbial communities, and associated abiotic conditions, respond to multifarious environmental changes is an important endeavor in microbial ecology. Insights can come from observational and experimental studies of naturally occurring stratified aquatic ecosystems, theoretical models of ecological processes, and experimental studies of replicated microbial communities in the laboratory. Here, we demonstrate a laboratory‐based approach with small, replicated, and liquid‐dominated Winogradsky columns, with distinct oxic/anoxic strata in a highly replicable manner. Our objective was to apply simultaneous global change scenarios (temperature, nutrient addition) on this micro‐ecosystem to report how the microbial communities (full‐length 16S rRNA gene seq.) and the abiotic conditions (O2, H2S, TOC) of the oxic/anoxic layer responded to these environmental changes. The composition of the strongly stratified microbial communities was greatly affected by temperature and by the interaction of temperature and nutrient addition, demonstrating the need of investigating global change treatments simultaneously. Especially phototrophic sulfur bacteria dominated the water column at higher temperatures and may indicate the presence of alternative stable states. We show that the establishment of such a micro‐ecosystem has the potential to test global change scenarios in stratified eutrophic limnic systems., We used a modified and highly replicated Winogradsky column approach for analyzing simultaneous global change effects (temperature and nutrient addition) on aquatic stratified microbial communities. We found that these global change treatments can have interaction effects and can differ between the oxic and anoxic layers and the community within.

This and the next chapter involve us working through the process of getting insights from data. We have chosen a research subject that should have some interest to us all: food. More specifically, what bats eat. In this chapter you will experience a clearly specified set of research questions, learn how the study was performed and why it was done that way, and learn how to prepare your computer, R, and RStudio for the project, and how to read the dataset into R. The chapter also covers how you can clean, tidy, and manipulate the data in R. These are the solid foundations from which robust and reliable insights can grow.

In this chapter we go through some miscellaneous R topics, all of which you experienced briefly in the bat diet workflow demonstration. These include pipes, a mechanism for moving data from one operation to another; strings, how words and text are represented in computers using ‘stringr’; dates and times, until recently a proper pain anywhere on a computer, but now much simpler and straightforward to deal with, using the lubridate package; and pivoting, changing data from long to wide format, and wide to long, using the ‘pivot_longer’ and ‘pivot_wider’ functions in the tidyr package.

In the previous two chapters we experienced/demonstrated a data analysis workflow about variation in the diets of bats. In this and the next few chapters we will take a deeper dive into the details of R and of concepts. In this chapter, you will become much better acquainted with the wonderful world of the dplyr package. We look more carefully at the some of the core dplyr functions: ‘select’ (get some columns), ‘mutate’ (make a new column), ‘filter’ (get some rows), ‘arrange’ (order the rows), ‘group_by’ (add grouping information), and ‘summarise’ (calculate summary information about groups).

After reading this book, what comes next? There is a lot that we decided to leave out of the book that we would have loved to put in. This chapter includes some pointers about what we left out, and what you could now start exploring. We also include a section on reproducibility, which covers the basics of what reproducibility is, why we might care about making our work reproducible, and what practical moves we can make towards achieving this.

In the previous chapter we looked at individual variables; however, a sample may involve more than one variable. Moreover, data analysis is usually concerned with the relationships among two or more variables. These relationships might involve the same (e.g. numeric versus numeric) or different (e.g. numeric versus categorical) types of variable. In either case, we need to understand how the values of one variable relate to and/or depend on those of the other. Just as with single-variable analyses, we use both descriptive statistics and graphical summaries to explore such relationships. This chapter focuses on associations between variables. An association is any relationship between two variables that makes them dependent, i.e. knowing the value of one variable gives us some information about the possible values of the second variable. The main goal of this chapter is to show how to use descriptive statistics and visualizations to explore associations among different kinds of variables.

In this chapter, we gain our first real insights about the data. We use visualizations and summarize the data to show how it is distributed and, among other things, that female bats eat larger prey on average than males. We also look at the data from a different perspective and show that the composition of prey species taken differs among female and male bats. This chapter covers a lot of new R, for example splitting our data into groups for summary statistics, and many new concepts. These will be explored in more detail in the following chapters.

We have made quite a few graphs already but only briefly explained how we did so, specifically using ggplot. We need a deeper understanding… hence this chapter. We focus on making graphs with ggplot2. The ggplot2 package can help us to produce quite complex visualizations, with elements such as graphical keys, without the need to write lines and lines of R code. It is, nevertheless, still flexible enough for us to tweak the appearance of a figure so that it meets our specific needs. This chapter describes the underlying principles of the grammar used in ggplot2, and then demonstrates various kinds of plots and modifications to plots that ggplot2 can implement.

Data analysis is not just about physically performing the analyses. We also need to think carefully about our data, and various issues that they might have. In this chapter, we explore conceptual issues raised by the bat diet workflow demonstration. This chapter discusses statistical variables, populations and samples, independence and non-independence in data, and working with numeric and categorical variables. In the last two items of that list, we look both at quantitative summaries of variables and relationships and also at graphical summaries.

Knowledge of how to get useful information from data is essential in the life and environmental sciences. This book provides learners with knowledge, experience, and confidence about how to efficiently and reliably discover useful information from data. The content is developed from first- and second-year undergraduate-level courses taught by the authors. It charts the journey from question, to raw data, to clean and tidy data, to visualizations that provide insights. This journey is presented as a repeatable workflow fit for use with many types of question, study, and data. Readers discover how to use R and RStudio, and learn key concepts for drawing appropriate conclusions from patterns in data. The book focuses on providing learners with a solid foundation of skills for working with data, and for getting useful information from data summaries and visualizations. It focuses on the strength of patterns (i.e. effect sizes) and their meaning (e.g. correlation or causation). It purposefully stays away from statistical tests and p-values. Concepts covered include distribution, sample, population, mean, median, mode, variance, standard deviation, correlation, interactions, and non-independence. The journey from data to insight is illustrated by one workflow demonstration in the book, and three online. Each involves data collected in a real study. Readers can follow along by downloading the data, and learning from the descriptions of each step in the journey from the raw data to visualizations that show the answers to the questions posed in the original studies.

The goal of Insights from Data with R is to help you learn the most important tools in R, and the most important concepts, to allow you gain insights from data. After reading this book, and working along with the methods, you will have the tools to tackle a wide variety of challenges involved in getting insights from data using R. This chapter describes what we mean by ‘insights’ and introduces our overall workflow for gaining insights from data using R. We also introduce features of datasets that influence the types of insight one can gain, and introduce four workflow demonstrations, one of which is in the book, and three of which are online.

1. Metadata plays an essential role in the long term preservation, reuse, and interoperability of data. Nevertheless, creating useful metadata can be sufficiently difficult and weakly-enough incentivised that many datasets may be accompanied by little or no metadata. One key challenge is, therefore, how to make metadata creation easier and more valuable. We present a solution that involves creating domain specific metadata schemes that are as complex as necessary and as simple as possible. These goals are achieved by co-development between a metadata expert and the researchers (i.e. the data creators). The final product is a bespoke metadata scheme into which researchers can enter information (and validate it) via the simplest of interfaces: a web browser application and a spreadsheet. 2.We provide the R package [‘dmdScheme‘](https://CRAN.R-project.org/package=dmdScheme) [@Krug2019] for creating a template domain specific scheme. We describe how to create a domain specific scheme from this template, including the iterative co-development process, and the simple methods for using the scheme, and simple methods for quality assessment, improvement, and validation. 3.The process of developing a metadata scheme following the outlined approach was successful, resulting in a metadata scheme which is used for the data generated in our research group. The validation quickly identifies forgotten metadata, as well as inconsistent metadata, therefore improving the quality of the metadata. Multiple output formats are available, including XML. 4. Making the provision of metadata easier while also ensuring high quality must be a priority for data curation initiatives. We show how both objectives are achieved by very close collaboration between metadata experts and researchers to create domain specific schemes. A near-future priority is to provide methods to interface domain specific schemes with general metadata schemes, such as the Ecological Metadata Language, to increase interoperability.

Aquatic ecosystems are often stratified, with cyanobacteria in oxic layers and phototrophic sulfur bacteria in anoxic ones. Changes in stratification caused by global environmental change are an ongoing concern. Increasing understanding how such aerobic and anaerobic microbial communities, and associated abiotic conditions, respond to multifarious environmental changes is an important endeavor in microbial ecology. Insights can come from observational and experimental studies of naturally occurring stratified aquatic ecosystems, from theoretical models of ecological processes, and from experimental studies of replicated microbial communities in the laboratory. Here we demonstrate a laboratory-based approach with small, replicated, and liquid dominated Winogradsky columns, with distinct oxic/anoxic strata in a highly replicable manner. Our objective is to apply simultaneous global change scenarios (temperature, nutrient addition) on this micro-ecosystem to report how the microbial communities (full-length 16SrRNA-seq.) and the abiotic conditions (O2, H2S, TOC) of the oxic/anoxic layer responded to these environmental changes. Composition of the strongly stratified microbial communities was greatly affected by temperature and by the interaction of temperature and nutrient addition, demonstrating the need of investigating global change treatments simultaneously. Especially phototrophic sulfur bacteria dominated the water column at higher temperatures, and may indicate the presence of alternative stable states. We show that the establishment of such a micro-ecosystem has potential to test global change scenarios in stratified eutrophic limnic systems.

Ecological communities face a variety of environmental and anthropogenic stressors acting simultaneously. Stressor impacts can combine additively or can interact, causing synergistic or antagonistic effects. Our knowledge of when and how interactions arise is limited, as most models and experiments only consider the effect of a small number of non-interacting stressors at one or few scales of ecological organization. This is concerning because it could lead to significant underestimations or overestimations of threats to biodiversity. Furthermore, stressors have been largely classified by their source rather than by the mechanisms and ecological scales at which they act (the target). Here, we argue, first, that a more nuanced classification of stressors by target and ecological scale can generate valuable new insights and hypotheses about stressor interactions. Second, that the predictability of multiple stressor effects, and consistent patterns in their impacts, can be evaluated by examining the distribution of stressor effects across targets and ecological scales. Third, that a variety of existing mechanistic and statistical modelling tools can play an important role in our framework and advance multiple stressor research.

International audience; Environmental change can alter species’ abundances within communities consistently; for example, increasing all abundances by the same percentage, or more idiosyncratically. Here, we show how comparing effects of temperature on species grown in isolation and when grown together helps our understanding of how ecological communities more generally respond to environmental change. In particular, we find that the shape of the feasibility domain (the parameter space of carrying capacities compatible with positive species’ abundances) helps to explain the composition of experimental microbial communities under changing environmental conditions. First, we introduce a measure to quantify the asymmetry of a community’s feasibility domain using the column vectors of the corresponding interaction matrix. These column vectors describe the effects each species has on all other species in the community (hereafter referred to as species’ multidimensional effects). We show that as the asymmetry of the feasibility domain increases the relationship between species’ abundance when grown together and when grown in isolation weakens. We then show that microbial communities experiencing different temperature environments exhibit patterns consistent with this theory. Specifically, communities at warmer temperatures show relatively more asymmetry; thus, the idiosyncrasy of responses is higher compared with that in communities at cooler temperatures. These results suggest that while species’ interactions are typically defined at the pairwise level, multispecies dynamics can be better understood by focusing on the effects of these interactions at the community level.

Understanding factors that maintain ecosystem stability is critical in the face of environmental change. Experiments simulating species loss from grassland have shown that losing biodiversity decreases ecosystem stability. However, as the originally sown experimental communities with reduced biodiversity develop, plant evolutionary processes or the assembly of interacting soil organisms may allow ecosystems to increase stability over time. We explored such effects in a long‐term grassland biodiversity experiment with plant communities with either a history of co‐occurrence (selected communities) or no such history (naive communities) over a 4‐yr period in which a major flood disturbance occurred. Comparing communities of identical species composition, we found that selected communities had temporally more stable biomass than naive communities, especially at low species richness. Furthermore, selected communities showed greater biomass recovery after flooding, resulting in more stable post‐flood productivity. In contrast to a previous study, the positive diversity–stability relationship was maintained after the flooding. Our results were consistent across three soil treatments simulating the presence or absence of co‐selected microbial communities. We suggest that prolonged exposure of plant populations to a particular community context and abiotic site conditions can increase ecosystem temporal stability and resilience due to short‐term evolution. A history of co‐occurrence can in part compensate for species loss, as can high plant diversity in part compensate for the missing opportunity of such adaptive adjustments.

Theory predicts that organism–environment feedbacks play a central role in how ecological communities respond to environmental change. Strong feedback causes greater nonlinearity between environmental change and ecosystem state, increases the likelihood of hysteresis in response to environmental change, and augments the possibility of alternative stable regimes. To illustrate these predictions and their dependence on a temporal scale, we simulated a minimal ecosystem model. To test the predictions, we manipulated the feedback strength between the metabolism and the dissolved oxygen concentration in an aquatic heterotrophic tri‐trophic community in microecosystems. The manipulation consisted of five levels, ranging from low to high feedback strength by altering the oxygen diffusivity: free gas exchange between the microcosm atmosphere and the external air (metabolism not strongly affecting environmental oxygen), with the regular addition of 200, 100, or 50 ml of air and no gas exchange. To test for nonlinearity and hysteresis in response to environmental change, all microecosystems experienced a gradual temperature increase from 15 to 25°C and then back to 15°C. We regularly measured the dissolved oxygen concentration, total biomass, and species abundance. Nonlinearity and hysteresis were higher in treatments with stronger organism–environment feedbacks. There was no evidence that stronger feedback increased the number of observed ecosystem states. These empirical results are in broad agreement with the theory that stronger feedback increases nonlinearity and hysteresis. They therefore represent one of the first direct empirical tests of the importance of feedback strength. However, we discuss several limitations of the study, which weaken confidence in this interpretation. Research demonstrating the causal effects of feedback strength on ecosystem responses to environmental change should be placed at the core of efforts to plan for sustainable ecosystems., The organism–environment feedbacks play a central role in how ecological community response to environmental change. We simulated the “minimal ecosystem model” and incorporated different rates of environmental change to establish new predictions on the observation of nonlinearity and hysteresis. We confirmed those predictions using an experiment that manipulated the metabolism‐dissolved oxygen concentration feedback strength in response to a gradual change of temperature.

Human modification of natural systems has typically enhanced provisioning ecosystem services (ES), such as agriculture, at the expense of biodiversity and other types of services. Long-term sustainability requires a balance of ES flows to both maintain human wellbeing, while preserving biodiversity. In river deltas this balance is critical – fertile flat land, well stocked fisheries, and water for use and navigation have promoted rapid population growth and development. Yet the same development degrades the key ES protecting these deltas from hazards and pollution. Many deltas face a critical juncture, at risk or already ‘locked-in’ to the need for engineered solutions to global change problems, unsustainable globally and in the long-term.We created a global dataset of 237 deltas and collected indicators of the extent each was modified from its natural state, and its ES supply. Several types of modification to the most important aspects of delta systems were considered – overall human impact (human footprint), pressure and demand on ES (population density), modification of water systems (flow disruption) and modification of natural productivity (human appropriation of net primary productivity); grouping deltas by modification state. The impacts of this human modification on over 50 robust biodiversity and ES indicators were then analysed.Firstly, we attempted to create bundles of commonly associated ES in delta areas. Hierarchical clustering highlighted several logical clusters related to crops, fisheries, water, species richness, biodiversity intactness and NPP. Secondly, we examined synergies and trade-offs between different ES. Provisioning services all showed clear correlations with one another, but clear trade-offs with supporting services or biodiversity. There were weaker synergies within and between regulating and supporting ES. Finally, the relationship between each type of modification and the ES was classified using six typologies fitted by applying a decision tree to their LOESS regression curves. Crop indicators typically had an inverted-U relationship, increasing in moderately modified deltas, but decreasing in the most modified, presumably pushed out by other land uses. While many other ES declined with modification, interestingly, species richness and intactness both began to increase again in the most modified deltas. In summary, this global analysis is the first to illustrate how ES vary along a gradient of development in deltas, and highlights the need to balance further modification against these critical services.

With an increasing call for FAIR data, metadata, and it's quality, plays an ever increasing role. Nevertheless, providing metadata is not easily accomplished. One of the reasons for this is the cumbersome way of entering metadata in formats or webforms needed by repositories. Also, there is little or no quality control. Finally, the metadata schemes used by the repositories are either restricted to bibliometric metadata (e.g. DataCite) or are using metadata schemes which have to cover a vast diversity of different data (e.g. EML or Darwin Core). I will present an approach which uses domain specific metadata schemes to overcome these problems, by developing these schemes together with scientists of their domain. It can be employed without detailed technical knowledge, e.g. using spreadsheets and a web browser. This will help researchers recognise the value of metadata for their own research and sharing. The provided validation tool increases quality of metadata.

Understanding and monitoring pressures on ecosystems and their consequences for ecosystem services (ES) is essential for management decisions and verification of progress towards national and international policies (e.g. Aichi Biodiversity Targets, Sustainable Development Goals). Remote sensing (RS) offers a unique capability to assess ES systematically and regularly across spatial and temporal scales. We aim to evaluate the benefits of RS to monitor spatio-temporal variations of ES by assessing several ES in Switzerland between 2004 and 2014. We coupled mechanistic ES models and RS data to estimate time series of three regulating (i.e. carbon dioxide regulation (CO₂R), soil erosion prevention (SEP), and air quality regulation (AQR)) and one cultural ES (re- creational hiking (RH)). The resulting ES were used to assess spatial and temporal changes, trade-offs and sy- nergies of ES potential supply and flow in Switzerland between 2004 and 2014. Resulting ES trends showed diverse spatial patterns across Switzerland with largest changes in CO₂R and AQR. ES interactions revealed a scale and elevation dependency. We identified weak to strong synergies between all ES combinations except for trade-offs between CO₂R–AQR and AQR–RH at Swiss scale. Spatially, all ES interactions revealed a hetero- geneous mix of synergies and trade-offs within Switzerland. Our results demonstrate the strength of RS for systematic and regular spatio-temporal ES monitoring and contribute insights to the large potential of RS, which will be extended with future Earth observation missions. Derived spatially explicit ES information will facilitate decision-making in landscape planning and conservation and will allow examining progress towards environmental policies.

The pervasive influence of human induced global environmental change affects biodiversity across the globe, and there is great uncertainty as to how the biosphere will react on short and longer time scales. To adapt to what the future holds and to manage the impacts of global change, scientists need to predict theexpected effects with some confidence and communicate these predictions to policy makers. However, recent reviews found that we currently lack a clear understanding of how predictable ecology is, with views seeing it as mostly unpredictable to potentially predictable, at least over short time frames. However, in applied, ecology-related fields predictions are more commonly formulated and reported, as well as evaluated in hindsight, potentially allowing one to define baselines of predictive proficiency in these fields. We searched the literature for representative case studies in these fields and collectedinformation about modeling approaches, target variables of prediction, predictive proficiency achieved, as well as the availability of data to parameterize predictive models. We find that some fields such as epidemiology achieve high predictive proficiency, but even in the more predictive fields proficiency isevaluated in different ways. Both phenomenological and mechanistic approaches are used in most fields, but differences are often small, with no clear superiority of one approach over the other. Data availability is limiting in most fields, with long-term studies being rare and detailed data for parameterizing mechanistic models being in short supply. We suggest that ecologists adopt a more rigorous approach to report and assess predictive proficiency, and embrace the challenges of real world decision making to strengthen the practice of prediction in ecology.

Global environmental change and biodiversity loss are closely linked through different feedback mechanisms. The University of Zurich Research Priority Programme on ‘Global Change and Biodiversity’ approach is to work with interdisciplinarity and transdisciplinarity to integrate mechanisms of interactions, feedback and scale and improve our understanding of the feedbacks between global change and biodiversity effects. Such work across research disciplines is not without its challenges. Here we share some of the questions that arose from our research approach over the last five years and how we addressed these challenges. First, our transdisciplinary approach allows combining different disciplines into a more holistic perspective towards integrative research, but demands collaborative work to establish common terminology, concepts, and metrics. Second, the research theme's common perspective (biodiversity is desirable, global change is not) may also induce a confirmation bias from preconceived ideas. Third, new challenges emerge from scaling mechanisms and feedbacks at different spatial and temporal scales. Fourth, we investigate how to relate biodiversity, global change, ecosystem services and functions using interdisciplinary approaches. Fifth, we identify gaps between existing experiments and data requirements, and propose the definition of new experimental setups by linking processes and performing experiments at typical experimental scales as well as at larger scales. We conclude by emphasising the necessity to integrate theory, experiments, modelling and simulation, high performance computing and big data to understand feedbacks between biodiversity loss and processes of global change.

Global change affects ecosystem functioning both directly by modifications in physicochemical processes, and indirectly, via changes in biotic metabolism and interactions. Unclear, however, is how multiple anthropogenic drivers affect different components of community structure and the performance of multiple ecosystem functions (ecosystem multifunctionality). We manipulated small natural freshwater ecosystems to investigate how warming and top predator loss affect seven ecosystem functions representing two major dimensions of ecosystem functioning, productivity and metabolism. We investigated their direct and indirect effects on community diversity and standing stock of multitrophic macro and microorganisms. Warming directly increased multifunctional ecosystem productivity and metabolism. In contrast, top predator loss indirectly affected multifunctional ecosystem productivity via changes in the diversity of detritivorous macroinvertebrates, but did not affect ecosystem metabolism. In addition to demonstrating how multiple anthropogenic drivers have different impacts, via different pathways, on ecosystem multifunctionality components, our work should further spur advances in predicting responses of ecosystems to multiple simultaneous environmental changes.

Environmental change can alter species' abundances within communities consistently; for example, increasing all abundances by the same percentage, or more idiosyncratically. Here, we show how comparing effects of temperature on species grown in isolation and when grown together helps our understanding of how ecological communities more generally respond to environmental change. In particular, we find that the shape of the feasibility domain (the parameter space of carrying capacities compatible with positive species' abundances) helps to explain the composition of experimental microbial communities under changing environmental conditions. First, we introduce a measure to quantify the asymmetry of a community's feasibility domain using the column vectors of the corresponding interaction matrix. These column vectors describe the effects each species has on all other species in the community (hereafter referred to as species' multidimensional effects). We show that as the asymmetry of the feasibility domain increases the relationship between species' abundance when grown together and when grown in isolation weakens. We then show that microbial communities experiencing different temperature environments exhibit patterns consistent with this theory. Specifically, communities at warmer temperatures show relatively more asymmetry; thus, the idiosyncrasy of responses is higher compared with that in communities at cooler temperatures. These results suggest that while species' interactions are typically defined at the pairwise level, multispecies dynamics can be better understood by focusing on the effects of these interactions at the community level.

Statistical techniques exist for inferring community assembly processes from community patterns. Habitat filtering, competition, and biogeographical effects have, for example, been inferred from signals in phenotypic and phylogenetic data. The usefulness of current inference techniques is, however, debated as a mechanistic and causal link between process and pattern is often lacking, and evolutionary processes and trophic interactions are ignored. Here, we revisit the current knowledge on community assembly across scales and, in line with several reviews that have outlined challenges associated with current inference techniques, we identify a discrepancy between the current paradigm of eco-evolutionary community assembly and current inference techniques that focus mainly on competition and habitat filtering. We argue that trait-based dynamic eco-evolutionary models in combination with recently developed model fitting and model evaluation techniques can provide avenues for more accurate, reliable, and inclusive inference. To exemplify, we implement a trait-based, spatially explicit eco-evolutionary model and discuss steps of model modification, fitting, and evaluation as an iterative approach enabling inference from diverse data sources. Through a case study on inference of prey and predator niche width in an eco-evolutionary context, we demonstrate how inclusive and mechanistic approaches—eco-evolutionary modelling and Approximate Bayesian Computation (ABC)—can enable inference of assembly processes that have been largely neglected by traditional techniques despite the ubiquity of such processes. Much literature points to the limitations of current inference techniques, but concrete solutions to such limitations are few. Many of the challenges associated with novel inference techniques are, however, already to some extent resolved in other fields and thus ready to be put into action in a more formal way for inferring processes of community assembly from signals in various data sources.

International audience; Human actions challenge nature in many ways. Ecological responses are ineluctably complex, demanding measures that describe them succinctly. Collectively, these measures encapsulate the overall ‘stability’ of the system. Many international bodies, including the Intergovernmental Science‐Policy Platform on Biodiversity and Ecosystem Services, broadly aspire to maintain or enhance ecological stability. Such bodies frequently use terms pertaining to stability that lack clear definition. Consequently, we cannot measure them and so they disconnect from a large body of theoretical and empirical understanding. We assess the scientific and policy literature and show that this disconnect is one consequence of an inconsistent and one‐dimensional approach that ecologists have taken to both disturbances and stability. This has led to confused communication of the nature of stability and the level of our insight into it. Disturbances and stability are multidimensional. Our understanding of them is not. We have a remarkably poor understanding of the impacts on stability of the characteristics that define many, perhaps all, of the most important elements of global change. We provide recommendations for theoreticians, empiricists and policymakers on how to better integrate the multidimensional nature of ecological stability into their research, policies and actions.

Losses and gains in species diversity affect ecological stability1–7 and the sustainability of ecosystem functions and services8–13. Experiments and models have revealed positive, negative and no effects of diversity on individual components of stability, such as temporal variability, resistance and resilience2,3,6,11,12,14. How these stability components covary remains poorly understood15. Similarly, the effects of diversity on overall ecosystem stability16, which is conceptually akin to ecosystem multifunctionality17,18, remain unknown. Here we studied communities of aquatic ciliates to understand how temporal variability, resistance and overall ecosystem stability responded to diversity (that is, species richness) in a large experiment involving 690 micro-ecosystems sampled 19 times over 40 days, resulting in 12,939 samplings. Species richness increased temporal stability but decreased resistance to warming. Thus, two stability components covaried negatively along the diversity gradient. Previous biodiversity manipulation studies rarely reported such negative covariation despite general predictions of the negative effects of diversity on individual stability components3. 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 U-shaped relationship. The effects of diversity on ecosystem multifunctionality would also be hump- or U-shaped if diversity had positive effects on some functions and negative effects on others. Linking the ecosystem multifunctionality concept and ecosystem stability can transform the perceived effects of diversity on ecological stability and may help to translate this science into policy-relevant information. Species richness was found to increase temporal stability but decrease resistance to warming in an experiment involving 690 micro-ecosystems consisting of 1 to 6 species of bacterivorous ciliates that were sampled over 40 days.