Accurate forecasts of ecological dynamics are critical for ecosystem management and conservation, yet the drivers of forecastability are poorly understood. Environmental change and diversity are considered major challenges to ecological forecasting. This assumption, however, has never been tested experimentally because forecasts have high data requirements. In a long‐term microcosm experiment, we manipulated the species richness of 30 experimental protist communities and exposed them to constant or gradually decreasing light levels. We collected finely resolved time series (123 sampling dates over 41 weeks) of species abundances, community biomass, and oxygen concentrations. We then employed data‐driven forecasting methods to forecast these variables. We found that species richness and light had a weak interactive effect on forecasts of species abundances: richness tended to reduce forecast accuracy in constant light but tended to increase forecast accuracy in declining light. These effects could partially be explained by differences among time series in variability and autocorrelation. Forecasts of aggregate properties (community biomass, oxygen), however, were unaffected by richness and light and were not more accurate than those of species abundances. Our forecasts were based on time series that were detrended and standardized. Since real‐world forecasting applications require predictions at the original scale of the forecasted variable, it is important to note that the results were qualitatively identical when back‐transforming the forecasts to the original scale. Taken together, we found no strong evidence that higher diversity results in lower forecastability. Rather, our results imply that promoting diversity could make populations more predictable when environmental conditions change. From a conservation and management perspective, our findings suggest preliminary support that diversity conservation might have beneficial effects on decision‐taking by increasing the forecastability of species abundances in changing environments.

Facing climate change and biodiversity loss, it is critical that ecology advances so that processes, such as species interactions and dynamics, can be correctly estimated and skillfully forecasted. As different processes occur on different time scales, the sampling frequency used to record them should intuitively match these scales. Yet, the effect of data sampling frequency on ecological forecasting accuracy is understudied. Using a simple simulated dataset as a baseline and a more complex high‐frequency plankton dataset, we tested how different sampling frequencies impacted abundance forecasts of different plankton classes and the estimation of their interactions. We then investigated whether plankton growth rates and body sizes could be used to select the most appropriate sampling frequency. The simple simulated dataset showed that the optimal sampling frequency scaled positively with growth rate. This finding was not repeated in the analyses of the plankton time series, however. There, we found that a reduction in sampling frequency worsened forecasts and led us to both over‐ and underestimate plankton interactions. This suggests that forecasting can be used to determine the ideal sampling frequency in scientific and monitoring programs. A better study design will improve theoretical understanding of ecology and advance policy measures dealing with current global challenges.

Biodiversity may increase ecosystem resilience. However, we have limited understanding if this holds true for ecosystems that respond to gradual environmental change with abrupt shifts to an alternative state. We used a mathematical model of anoxic–oxic regime shifts and explored how trait diversity in three groups of bacteria influences resilience. We found that trait diversity did not always increase resilience: greater diversity in two of the groups increased but in one group decreased resilience of their preferred ecosystem state. We also found that simultaneous trait diversity in multiple groups often led to reduced or erased diversity effects. Overall, our results suggest that higher diversity can increase resilience but can also promote collapse when diversity occurs in a functional group that negatively influences the state it occurs in. We propose this mechanism as a potential management approach to facilitate the recovery of a desired ecosystem state.

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 systems, we monitored a microbial system exposed to either constant or fluctuating temperatures in a 5‐month‐long laboratory experiment. We tested how forecasting of species abundances depends on the 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 interactions a species had, 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 connectedness of the species within the system and on overall system complexity could improve species forecasting and monitoring.

Microbial communities in many ecosystems are facing a broad range of global change drivers, such as nutrient enrichment, chemical pollution, and temperature change. These drivers can cause changes in the abundance of taxa, the composition of communities, and the properties of ecosystems. While the influence of single drivers is already described in numerous studies, the effect and predictability of multiple drivers changing simultaneously is still poorly understood. In this study, we used 240 highly replicable oxic/anoxic aquatic lab microcosms and four drivers (fertilizer, glyphosate, metal pollution, antibiotics) in all possible combinations at three different temperatures (20, 24, and 28°C) to shed light into consequences of multiple drivers on different levels of organization, ranging from species abundance to community and ecosystem parameters. We found (i) that at all levels of ecological organization, combinations of drivers can change the biological consequence and direction of effect compared to single drivers, (ii) that effects of combinations are further modified by temperature, (iii) that a larger number of drivers occurring simultaneously is often quite closely related to their effect size, and (iv) that there is little evidence that any of these effects are associated with the level of ecological organization of the state variable. These findings suggest that, at least in this experimental ecosystem approximating a stratified aquatic ecosystem, there may be relatively little scope for predicting the effects of combinations of drivers from the effects of individual drivers, or by accounting for the level of ecological organization in question, though there may be some scope for prediction based on the number of drivers that are occurring simultaneous. A priority, though also a considerable challenge, is to extend such research to consider continuous variation in the magnitude of multiple drivers acting together.

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 (O 2 , H 2 S, 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.

The potential for climate change and temperature shifts to affect community stability remains relatively unknown. One mechanism by which temperature may affect stability is by altering trophic interactions. The functional response quantifies the per capita resource consumption by the consumer as a function of resource abundance and is a suitable framework for the description of nonlinear trophic interactions.

We studied the effect of temperature on a ciliate predator–prey pair ( Spathidium sp. and Dexiostoma campylum ) by estimating warming effects on the functional response and on the associated conversion efficiency of the predator.

We recorded prey and predator dynamics over 24 hr and at three temperature levels (15, 20 and 25°C). To these data, we fitted a population dynamic model including the predator functional response, such that the functional response parameters (space clearance rate, handling time and density dependence of space clearance rate) were estimated for each temperature separately. To evaluate the ecological significance of temperature effects on the functional response parameters, we simulated predator–prey population dynamics. We considered the predator–prey system to be destabilized, if the prey was driven extinct by the predator.

Effects of increased temperature included a transition of the functional response from a Type III to a Type II and an increase of the conversion efficiency of the predator. The simulated population dynamics showed a destabilization of the system with warming, with greater risk of prey extinction at higher temperatures likely caused by the transition from a Type III to a Type II functional response.

Warming‐induced shifts from a Type III to II are not commonly considered in modelling studies that investigate how population dynamics respond to warming. Future studies should investigate the mechanism and generality of the effect we observed and simulate temperature effects in complex food webs including shifts in the type of the functional response as well as consider the possibility of a temperature‐dependent conversion efficiency.

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 pair Didinium nasutum and Paramecium 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 Statement. To 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 pair Didinium nasutum and Paramecium 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.