For animals that harbor photosynthetic symbionts within their tissues, such as corals, the different relative contributions of autotrophy versus heterotrophy to organismal energetic requirements have direct impacts on fitness. This is especially true for facultatively symbiotic corals, where the balance between host‐caught and symbiont‐produced energy can be altered substantially to meet the variable demands of a shifting environment. In this study, we utilized a temperate coral–algal system (the northern star coral, Astrangia poculata, and its photosynthetic endosymbiont, Symbiodinium psygmophilum) to explore the impacts of nutritional sourcing on the host's health and ability to regenerate experimentally excised polyps. For fed and starved colonies, wound healing and total colony tissue cover were differentially impacted by heterotrophy versus autotrophy. There was an additive impact of positive nutritional and symbiotic states on a coral's ability to initiate healing, but a greater influence of symbiont state on the recovery of lost tissue at the lesion site and complete polyp regeneration. On the other hand, regardless of symbiont state, fed corals maintained a higher overall colony tissue cover, which also enabled more active host behavior (polyp extension) and endosymbiont behavior (photosynthetic ability of Symbiondinium). Overall, we determined that the impact of nutritional state and symbiotic state varied between biological functions, suggesting a diversity in energetic sourcing for each of these processes.

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Water temperature forecasting in lakes and reservoirs is a valuable tool to manage crucial freshwater resources in a changing and more variable climate, but previous efforts have yet to identify an optimal modelling approach. Here, we demonstrate the first multi-model ensemble (MME) reservoir water temperature forecast, a forecasting method that combines individual model strengths in a single forecasting framework. We developed two MMEs: a three-model process-based MME and a five-model MME that includes process-based and empirical models to forecast water temperature profiles at a temperate drinking water reservoir. Our results showed that the five-model MME improved forecast performance by 8-30% relative to individual models and the process-based MME, as quantified using an aggregated probabilistic skill score. This increase in performance was due to large improvements in forecast bias in the five-model MME, despite increases in forecast uncertainty. High correlation among the process-based models resulted in little improvement in forecast performance in the process-based MME relative to the individual process-based models. The utility of MMEs is highlighted by two results: 1) no individual model performed best at every depth and horizon (days in the future), and 2) MMEs avoided poor performances by rarely producing the worst forecast for any single forecasted period (<6% of the worst ranked forecasts over time). This work presents an example of how existing models can be combined to improve water temperature forecasting in lakes and reservoirs and discusses the value of utilising MMEs, rather than individual models, in operational forecasts.

Ecosystems around the globe are experiencing increased variability due to land use and climate change. In response, ecologists are increasingly using near-term, iterative ecological forecasts to predict how ecosystems will change in the future. To date, many near-term, iterative forecasting systems have been developed using high temporal frequency (minute to hourly resolution) data streams for assimilation. However, this approach may be cost-prohibitive or impossible for forecasting ecological variables that lack high-frequency sensors or have high data latency (i.e., a delay before data are available for modeling after collection). To explore the effects of data assimilation frequency on forecast skill, we developed water temperature forecasts for a eutrophic drinking water reservoir and conducted data assimilation experiments by selectively withholding observations to examine the effect of data availability on forecast accuracy. We used  in-situ sensors, manually collected data, and a calibrated water quality ecosystem model driven by forecasted weather data to generate future water temperature forecasts using FLARE (Forecasting Lake And Reservoir Ecosystems), an open-source water quality forecasting system. We tested the effect of daily, weekly, fortnightly, and monthly data assimilation on the skill of 1 to 35-day-ahead water temperature forecasts. We found that forecast skill varied depending on the season, forecast horizon, depth, and data assimilation frequency, but overall forecast performance was high, with a mean 1-day-ahead forecast root mean square error (RMSE) of 0.94°C, mean 7-day RMSE of 1.33°C, and mean 35-day RMSE of 2.15°C. Aggregated across the year, daily data assimilation yielded the most skillful forecasts at 1-7-day-ahead horizons, weekly data assimilation resulted in the most skillful forecasts at 8-35-day-ahead horizons. Within a year, daily to fortnightly data assimilation substantially outperformed monthly data assimilation in the stratified summer period, whereas all data assimilation frequencies resulted in skillful forecasts across depths in the mixed spring/autumn periods for shorter forecast horizons. Our results suggest that lower-frequency data (i.e., weekly) may be adequate for developing accurate forecasts in some applications, further enabling the development of forecasts broadly across ecosystems and ecological variables without high-frequency sensor data.