Ecosystems are complex and sparsely observed making inference and prediction challenging. Empirical dynamic modelling (EDM) circumvents the need for a parametric model and complete observations of all system variables. Classical univariate approaches, which require time‐series observations of only a single focal variable, can produce verifiable out‐of‐sample forecasts; however, they can sometimes require long time series that may be difficult to obtain. More importantly, classical approaches limit the depth of mechanistic understanding that can be gained and the generalizability of forecasts to non‐analogue futures. We review the main ideas of EDM and more recent extensions that expand their capabilities for improving forecasts and understanding mechanism. Algorithms are now available that allow for missing data, unequal sampling intervals and combining short time series, which increase the number of datasets that can be used. Recent extensions of EDM to multivariate time series substantially expand the range of applications and mechanistic questions that can be addressed, including detecting causal coupling, tracking changing interactions in real time, leveraging short time series from information shared in coupled variables, modelling dynamically changing stability, scenario exploration, and management applications involving optimal control.

Experiments have revealed much about top‐down and bottom‐up control in ecosystems, but manipulative experiments are limited in spatial and temporal scale. To obtain a more nuanced understanding of trophic control over large scales, we explored long‐term time‐series data from 13 globally distributed lakes and used empirical dynamic modelling to quantify interaction strengths between zooplankton and phytoplankton over time within and across lakes. Across all lakes, top‐down effects were associated with nutrients, switching from negative in mesotrophic lakes to positive in oligotrophic lakes. This result suggests that zooplankton nutrient recycling exceeds grazing pressure in nutrient‐limited systems. Within individual lakes, results were consistent with a ‘seasonal reset’ hypothesis in which top‐down and bottom‐up interactions varied seasonally and were both strongest at the beginning of the growing season. Thus, trophic control is not static, but varies with abiotic conditions – dynamics that only become evident when observing changes over large spatial and temporal scales.

Climate change is increasingly exposing populations to rare and novel environmental conditions. Theory suggests that extreme conditions will expose cryptic phenotypes, with a concomitant increase in trait variation. Although some empirical support for this exists, it is also well established that physiological mechanisms (e.g. heat shock protein expression) change when organisms are exposed to constant versus fluctuating temperatures. To determine the effect of common, rare and novel temperatures on the release of hidden variation, we exposed fathead minnows, Pimephales promelas , to five fluctuating and four constant temperature regimes (constant treatments: 23.5, 25, 28.5 and 31°C; all fluctuating treatments shared a minimum temperature of 22°C at 00.00 and a maximum of 25, 28, 31, 34 or 37°C at 12.00). We measured each individual's length weekly over 60 days, critical thermal maximum (CT _max ), five morphometric traits (eye anterior–posterior distance, pelvic fin length, pectoral fin length, pelvic fin ray count and pectoral fin ray count) and fluctuating asymmetry (FA, absolute difference between left and right morphometric measurements; FA is typically associated with stress). Length-at-age in both constant and fluctuating conditions decreased with temperature, and this trait's variance decreased with temperature under fluctuating conditions but increased and then decreased in constant temperatures. CT _max in both treatments increased with increasing water temperature, while its variance decreased in warmer waters. No consistent pattern in mean or variance was found across morphometric traits or FA. Our results suggest that, for fathead minnows, variance can decrease in important traits (e.g. length-at-age and CT _max ) as the environment becomes more stressful, so it may be difficult to establish comprehensive rules for the effects of rarer or stressful environments on trait variation. This article is part of the theme issue ‘The role of plasticity in phenotypic adaptation to rapid environmental change’.

Chaos was proposed in the 1970s as an alternative explanation for apparently noisy fluctuations in population size. Although readily demonstrated in models, the search for chaos in nature proved challenging and led many to conclude that chaos is either rare or nigh impossible to detect. However, in the intervening half-century, it has become clear that ecosystems are replete with the enabling conditions for chaos. Chaos has been repeatedly demonstrated under laboratory conditions and has been found in field data using updated detection methods. Together, these developments indicate that the apparent rarity of chaos was an artifact of data limitations and overreliance on low-dimensional population models. We invite readers to reevaluate the relevance of chaos in ecology, and we suggest that chaos is not as rare or undetectable as previously believed.