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Abstract Zooplankton play an integral role as indicators of water quality in freshwater ecosystems, but exhibit substantial variability in their density and community composition over space and time. This variability in zooplankton community structure may be driven by multiple factors, including taxon-specific migration behavior in response to environmental conditions. Many studies have highlighted substantial variability in zooplankton communities across spatial and temporal scales, but the relative importance of space vs. time in structuring zooplankton community dynamics is less understood. In this study, we quantified spatial (a littoral vs. a pelagic site) and temporal (hours to years) variability in zooplankton community structure in a eutrophic reservoir in southwestern Virginia, USA. We found that zooplankton community structure was more variable among sampling dates over 3 years than among sites or hours of the day, which was associated with differences in water temperature, chlorophyll a, and nutrient concentrations. Additionally, we observed high variability in zooplankton migration behavior, though a slightly greater magnitude of DHM vs. DVM during each sampling date, likely due to changing environmental conditions. Ultimately, our work underscores the need to continually integrate spatial and temporal monitoring to understand patterns of zooplankton community structure and behavior in freshwater ecosystems. 

Abstract Temperate reservoirs and lakes worldwide are experiencing decreases in ice cover, which will likely alter the net balance of gross primary production (GPP) and respiration (R) in these ecosystems. However, most metabolism studies to date have focused on summer dynamics, thereby excluding winter dynamics from annual metabolism budgets. To address this gap, we analyzed 6 years of year‐round high‐frequency dissolved oxygen data to estimate daily rates of net ecosystem production (NEP), GPP, and R in a eutrophic, dimictic reservoir that has intermittent ice cover. Over 6 years, the reservoir exhibited slight heterotrophy during both summer and winter. We found winter and summer metabolism rates to be similar: summer NEP had a median rate of −0.06 mg O₂L⁻¹ day⁻¹(range: −15.86 to 3.20 mg O₂L⁻¹ day⁻¹), while median winter NEP was −0.02 mg O₂L⁻¹ day⁻¹(range: −8.19 to 0.53 mg O₂L⁻¹ day⁻¹). Despite large differences in the duration of ice cover among years, there were minimal differences in NEP among winters. Overall, the inclusion of winter data had a limited effect on annual metabolism estimates in a eutrophic reservoir, likely due to short winter periods in this reservoir (ice durations 0–35 days), relative to higher‐latitude lakes. Our work reveals a smaller difference between winter and summer NEP than in lakes with continuous ice cover. Ultimately, our work underscores the importance of studying full‐year metabolism dynamics in a range of aquatic ecosystems to help anticipate the effects of declining ice cover across lakes worldwide. 

Abstract Models have become a key component of scientific hypothesis testing and climate and sustainability planning, as enabled by increased data availability and computing power. As a result, understanding how the perceived ‘complexity’ of a model corresponds to its accuracy and predictive power has become a prevalent research topic. However, a wide variety of definitions of model complexity have been proposed and used, leading to an imprecise understanding of what model complexity is and its consequences across research studies, study systems, and disciplines. Here, we propose a more explicit definition of model complexity, incorporating four facets—model class, model inputs, model parameters, and computational complexity—which are modulated by the complexity of the real‐world process being modelled. We illustrate these facets with several examples drawn from ecological literature. Overall, we argue that precise terminology and metrics of model complexity (e.g., number of parameters, number of inputs) may be necessary to characterize the emergent outcomes of complexity, including model comparison, model performance, model transferability and decision support. 

Abstract Near‐term, iterative ecological forecasts can be used to help understand and proactively manage ecosystems. To date, more forecasts have been developed for aquatic ecosystems than other ecosystems worldwide, likely motivated by the pressing need to conserve these essential and threatened ecosystems and increasing the availability of high‐frequency data. Forecasters have implemented many different modeling approaches to forecast freshwater variables, which have demonstrated promise at individual sites. However, a comprehensive analysis of the performance of varying forecast models across multiple sites is needed to understand broader controls on forecast performance. Forecasting challenges (i.e., community‐scale efforts to generate forecasts while also developing shared software, training materials, and best practices) present a useful platform for bridging this gap to evaluate how a range of modeling methods perform across axes of space, time, and ecological systems. Here, we analyzed forecasts from the aquatics theme of the National Ecological Observatory Network (NEON) Forecasting Challenge hosted by the Ecological Forecasting Initiative. Over 100,000 probabilistic forecasts of water temperature and dissolved oxygen concentration for 1–30 days ahead across seven NEON‐monitored lakes were submitted in 2023. We assessed how forecast performance varied among models with different structures, covariates, and sources of uncertainty relative to baseline null models. A similar proportion of forecast models were skillful across both variables (34%–40%), although more individual models outperformed the baseline models in forecasting water temperature (10 models out of 29) than dissolved oxygen (6 models out of 15). These top performing models came from a range of classes and structures. For water temperature, we found that forecast skill degraded with increases in forecast horizons, process‐based models, and models that included air temperature as a covariate generally exhibited the highest forecast performance, and that the most skillful forecasts often accounted for more sources of uncertainty than the lower performing models. The most skillful forecasts were for sites where observations were most divergent from historical conditions (resulting in poor baseline model performance). Overall, the NEON Forecasting Challenge provides an exciting opportunity for a model intercomparison to learn about the relative strengths of a diverse suite of models and advance our understanding of freshwater ecosystem predictability. 

Abstract Declining oxygen concentrations in the deep waters of lakes worldwide pose a pressing environmental and societal challenge. Existing theory suggests that low deep‐water dissolved oxygen (DO) concentrations could trigger a positive feedback through which anoxia (i.e., very low DO) during a given summer begets increasingly severe occurrences of anoxia in following summers. Specifically, anoxic conditions can promote nutrient release from sediments, thereby stimulating phytoplankton growth, and subsequent phytoplankton decomposition can fuel heterotrophic respiration, resulting in increased spatial extent and duration of anoxia. However, while the individual relationships in this feedback are well established, to our knowledge, there has not been a systematic analysis within or across lakes that simultaneously demonstrates all of the mechanisms necessary to produce a positive feedback that reinforces anoxia. Here, we compiled data from 656 widespread temperate lakes and reservoirs to analyze the proposed anoxia begets anoxia feedback. Lakes in the dataset span a broad range of surface area (1–126,909 ha), maximum depth (6–370 m), and morphometry, with a median time‐series duration of 30 years at each lake. Using linear mixed models, we found support for each of the positive feedback relationships between anoxia, phosphorus concentrations, chlorophyllaconcentrations, and oxygen demand across the 656‐lake dataset. Likewise, we found further support for these relationships by analyzing time‐series data from individual lakes. Our results indicate that the strength of these feedback relationships may vary with lake‐specific characteristics: For example, we found that surface phosphorus concentrations were more positively associated with chlorophyllain high‐phosphorus lakes, and oxygen demand had a stronger influence on the extent of anoxia in deep lakes. Taken together, these results support the existence of a positive feedback that could magnify the effects of climate change and other anthropogenic pressures driving the development of anoxia in lakes around the world.