Abstract
As ecology becomes a more predictive discipline, identifying the intrinsic predictability, or stochasticity, of ecosystem variables across space and time is needed to help guide the development of ecological models and forecasts. For example, if an ecological time series has high intrinsic predictability, then a high‐performing model should presumably be able to replicate its dynamics. Conversely, if an ecological variable has low intrinsic predictability, then no model—regardless of its performance—will be able to replicate its dynamics. However, despite the proliferation of ecological models and forecasts, the intrinsic predictability of ecological variables remains largely unknown. To bridge this gap, we analyzed a >4‐year time series of high‐frequency sensor data collected from replicate freshwater ecosystems to determine how intrinsic predictability (quantified as permutation entropy) differs among ecological variables, seasons, and ecosystems. We observed greater differences in predictability among ecological variables and days of year than between ecosystems. Although intrinsic predictability was generally low for all variables, it was still significantly higher than white noise, indicating complex yet predictable dynamics. We observed the highest predictability for physical ecosystem variables (e.g., water temperature) and the lowest predictability for biological variables (e.g., phytoplankton biomass), with chemical variables (e.g., dissolved oxygen) intermediate. We observed substantial seasonal differences in predictability among variables: surface water temperature and dissolved organic matter exhibited their highest levels of predictability in autumn, whereas surface chlorophyll and bottom‐layer dissolved oxygen and temperature exhibited highest predictability in summer. Periods of anoxia (low oxygen) were associated with the highest levels of predictability in dissolved oxygen over the time series. Altogether, our analysis highlights how intrinsic predictability data can both guide ecological model development and improve our understanding of how ecological predictability varies across space and time.