Plastic debris in lakes and reservoirs.

Plastic debris is thought to be widespread in freshwater ecosystems globally1. However, a lack of comprehensive and comparable data makes rigorous assessment of its distribution challenging2,3. Here we present a standardized cross-national survey that assesses the abundance and type of plastic debris (>250 µm) in freshwater ecosystems. We sample surface waters of 38 lakes and reservoirs, distributed across gradients of geographical position and limnological attributes, with the aim to identify factors associated with an increased observation of plastics. We find plastic debris in all studied lakes and reservoirs, suggesting that these ecosystems play a key role in the plastic-pollution cycle. Our results indicate that two types of lakes are particularly vulnerable to plastic contamination: lakes and reservoirs in densely populated and urbanized areas and large lakes and reservoirs with elevated deposition areas, long water-retention times and high levels of anthropogenic influence. Plastic concentrations vary widely among lakes; in the most polluted, concentrations reach or even exceed those reported in the subtropical oceanic gyres, marine areas collecting large amounts of debris4. Our findings highlight the importance of including lakes and reservoirs when addressing plastic pollution, in the context of pollution management and for the continued provision of lake ecosystem services.

Widespread deoxygenation of temperate lakes.

The concentration of dissolved oxygen in aquatic systems helps to regulate biodiversity1,2, nutrient biogeochemistry3, greenhouse gas emissions4, and the quality of drinking water5. The long-term declines in dissolved oxygen concentrations in coastal and ocean waters have been linked to climate warming and human activity6,7, but little is known about the changes in dissolved oxygen concentrations in lakes. Although the solubility of dissolved oxygen decreases with increasing water temperatures, long-term lake trajectories are difficult to predict. Oxygen losses in warming lakes may be amplified by enhanced decomposition and stronger thermal stratification8,9 or oxygen may increase as a result of enhanced primary production10. Here we analyse a combined total of 45,148 dissolved oxygen and temperature profiles and calculate trends for 393 temperate lakes that span 1941 to 2017. We find that a decline in dissolved oxygen is widespread in surface and deep-water habitats. The decline in surface waters is primarily associated with reduced solubility under warmer water temperatures, although dissolved oxygen in surface waters increased in a subset of highly productive warming lakes, probably owing to increasing production of phytoplankton. By contrast, the decline in deep waters is associated with stronger thermal stratification and loss of water clarity, but not with changes in gas solubility. Our results suggest that climate change and declining water clarity have altered the physical and chemical environment of lakes. Declines in dissolved oxygen in freshwater are 2.75 to 9.3 times greater than observed in the world's oceans6,7 and could threaten essential lake ecosystem services2,3,5,11.

Decomposing predictability to identify dominant causal drivers in complex ecosystems.

Ecosystems are complex systems of various physical, biological, and chemical processes. Since ecosystem dynamics are composed of a mixture of different levels of stochasticity and nonlinearity, handling these data is a challenge for existing methods of time series-based causal inferences. Here, we show that, by harnessing contemporary machine learning approaches, the concept of Granger causality can be effectively extended to the analysis of complex ecosystem time series and bridge the gap between dynamical and statistical approaches. The central idea is to use an ensemble of fast and highly predictive artificial neural networks to select a minimal set of variables that maximizes the prediction of a given variable. It enables decomposition of the relationship among variables through quantifying the contribution of an individual variable to the overall predictive performance. We show how our approach, EcohNet, can improve interaction network inference for a mesocosm experiment and simulated ecosystems. The application of the method to a long-term lake monitoring dataset yielded interpretable results on the drivers causing cyanobacteria blooms, which is a serious threat to ecological integrity and ecosystem services. Since performance of EcohNet is enhanced by its predictive capabilities, it also provides an optimized forecasting of overall components in ecosystems. EcohNet could be used to analyze complex and hybrid multivariate time series in many scientific areas not limited to ecosystems.

Intermittent instability is widespread in plankton communities.

Chaotic dynamics appear to be prevalent in short-lived organisms including plankton and may limit long-term predictability. However, few studies have explored how dynamical stability varies through time, across space and at different taxonomic resolutions. Using plankton time series data from 17 lakes and 4 marine sites, we found seasonal patterns of local instability in many species, that short-term predictability was related to local instability, and that local instability occurred most often in the spring, associated with periods of high growth. Taxonomic aggregates were more stable and more predictable than finer groupings. Across sites, higher latitude locations had higher Lyapunov exponents and greater seasonality in local instability, but only at coarser taxonomic resolution. Overall, these results suggest that prediction accuracy, sensitivity to change and management efficacy may be greater at certain times of year and that prediction will be more feasible for taxonomic aggregates.

Planktonic functional diversity changes in synchrony with lake ecosystem state.

Managing ecosystems to effectively preserve function and services requires reliable tools that can infer changes in the stability and dynamics of a system. Conceptually, functional diversity (FD) appears as a sensitive and viable monitoring metric stemming from suggestions that FD is a universally important measure of biodiversity and has a mechanistic influence on ecological processes. It is however unclear whether changes in FD consistently occur prior to state responses or vice versa, with no current work on the temporal relationship between FD and state to support a transition towards trait-based indicators. There is consequently a knowledge gap regarding when functioning changes relative to biodiversity change and where FD change falls in that sequence. We therefore examine the lagged relationship between planktonic FD and abundance-based metrics of system state (e.g. biomass) across five highly monitored lake communities using both correlation and cutting edge non-linear empirical dynamic modelling approaches. Overall, phytoplankton and zooplankton FD display synchrony with lake state but each lake is idiosyncratic in the strength of relationship. It is therefore unlikely that changes in plankton FD are identifiable before changes in more easily collected abundance metrics. These results highlight the power of empirical dynamic modelling in disentangling time lagged relationships in complex multivariate ecosystems, but suggest that FD cannot be generically viable as an early indicator. Individual lakes therefore require consideration of their specific context and any interpretation of FD across systems requires caution. However, FD still retains value as an alternative state measure or a trait representation of biodiversity when considered at the system level.

Trophic control changes with season and nutrient loading in lakes.

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.

Long-term warming destabilizes aquatic ecosystems through weakening biodiversity-mediated causal networks.

Understanding how ecosystems will respond to climate changes requires unravelling the network of functional responses and feedbacks among biodiversity, physicochemical environments, and productivity. These ecosystem components not only change over time but also interact with each other. Therefore, investigation of individual relationships may give limited insights into their interdependencies and limit ability to predict future ecosystem states. We address this problem by analyzing long-term (16-39 years) time series data from 10 aquatic ecosystems and using convergent cross mapping (CCM) to quantify the causal networks linking phytoplankton species richness, biomass, and physicochemical factors. We determined that individual quantities (e.g., total species richness or nutrients) were not significant predictors of ecosystem stability (quantified as long-term fluctuation of phytoplankton biomass); rather, the integrated causal pathway in the ecosystem network, composed of the interactions among species richness, nutrient cycling, and phytoplankton biomass, was the best predictor of stability. Furthermore, systems that experienced stronger warming over time had both weakened causal interactions and larger fluctuations. Thus, rather than thinking in terms of separate factors, a more holistic network view, that causally links species richness and the other ecosystem components, is required to understand and predict climate impacts on the temporal stability of aquatic ecosystems.

Storm impacts on phytoplankton community dynamics in lakes.

In many regions across the globe, extreme weather events such as storms have increased in frequency, intensity, and duration due to climate change. Ecological theory predicts that such extreme events should have large impacts on ecosystem structure and function. High winds and precipitation associated with storms can affect lakes via short-term runoff events from watersheds and physical mixing of the water column. In addition, lakes connected to rivers and streams will also experience flushing due to high flow rates. Although we have a well-developed understanding of how wind and precipitation events can alter lake physical processes and some aspects of biogeochemical cycling, our mechanistic understanding of the emergent responses of phytoplankton communities is poor. Here we provide a comprehensive synthesis that identifies how storms interact with lake and watershed attributes and their antecedent conditions to generate changes in lake physical and chemical environments. Such changes can restructure phytoplankton communities and their dynamics, as well as result in altered ecological function (e.g., carbon, nutrient and energy cycling) in the short- and long-term. We summarize the current understanding of storm-induced phytoplankton dynamics, identify knowledge gaps with a systematic review of the literature, and suggest future research directions across a gradient of lake types and environmental conditions.

Bottom-up linkages between primary production, zooplankton, and fish in a shallow, hypereutrophic lake.

Nutrient supply is a key bottom-up control of phytoplankton primary production in lake ecosystems. Top-down control via grazing pressure by zooplankton also constrains primary production and primary production may simultaneously affect zooplankton. Few studies have addressed these bidirectional interactions. We used convergent cross-mapping (CCM), a numerical test of causal associations, to quantify the presence and direction of the causal relationships among environmental variables (light availability, surface water temperature, NO3 -N, and PO4 -P), phytoplankton community composition, primary production, and the abundances of five functional zooplankton groups (large cladocerans, small cladocerans, rotifers, calanoids, and cyclopoids) in Lake Kasumigaura, a shallow, hypereutrophic lake in Japan. CCM suggested that primary production was causally influenced by NO3 -N and phytoplankton community composition; there was no detectable evidence of a causal effect of zooplankton on primary production. Our results also suggest that rotifers and cyclopoids were forced by primary production, and cyclopoids were further influenced by rotifers. However, our CCM suggested that primary production was weakly influenced by rotifers (i.e., bidirectional interaction). These findings may suggest complex linkages between nutrients, primary production, and rotifers and cyclopoids, a pattern that has not been previously detected or has been neglected. We used linear regression analysis to examine the relationships between the zooplankton community and pond smelt (Hypomesus nipponensis), the most abundant planktivore and the most important commercial fish species in Lake Kasumigaura. The relative abundance of pond smelt was significantly and positively correlated with the abundances of rotifers and cyclopoids, which were causally influenced by primary production. This finding suggests that bottom-up linkages between nutrient, primary production, and zooplankton abundance might be a key mechanism supporting high planktivore abundance in eutrophic lakes. Because increases in primary production and cyanobacteria blooms are likely to occur simultaneously in hypereutrophic lakes, our study highlights the need for ecosystem management to resolve the conflict between good water quality and high fishery production.

Trends and stability of inland fishery resources in Japanese lakes: introduction of exotic piscivores as a driver.

Although many studies have focused on marine resources, few studies have considered the resources of inland fisheries. Inland fishery resources are typically either monitored on the basis of catch data alone or are not assessed quantitatively at all, despite their social, economic, and ecological importance. Because freshwater ecosystems have been severely degraded by human activities, evaluating the trends and current status of fishery resources and assessing their drivers are urgent tasks. We compiled long-term data on the annual catch, fishing effort, and fishing power of 23 Japanese lakes, using two sets of government statistics that date back to the 1950s, which were previously neglected because of the large number of missing values. Using Bayesian state-space models, we examined the trajectories of the catch per unit effort (CPUE) of entire communities, considering changes in fishing effort and fishing power, and quantified both changes in the CPUE over the 10-, 20-, and 30-year periods preceding 2008 and the temporal detrended stability of the CPUE over the three periods. We also investigated the relationships among the CPUE changes and stability, anthropogenic drivers, and lake morphometric characteristics. The CPUE declined in 17, 19, and 15 of the 23 lakes over the past 10-, 20-, and 30-year periods, respectively. Our macroecological analyses demonstrate that the functional group richness of exotic piscivores was the most important predictor of changes in the CPUE among the drivers we considered. The stability of the CPUE was positively related to lake area; larger lakes have more stable CPUE. The functional group richness of exotic piscivores also negatively affected the stability of the CPUE. The effect of overfishing was considered to be small because both fishing effort and power declined in almost all of the lakes. Thus, our findings suggest that increasing exotic piscivore species may diminish the resources and their stability, particularly in Japanese lakes where native piscivores are rare. This might lead to a substantial decline in ecosystem services. Our study highlights the importance of assessing inland fishery resources in a comprehensive manner and the need for restoration strategies to mitigate the effects of exotic piscivores.

Robustness of trait distribution metrics for community assembly studies under the uncertainties of assembly processes.

Numerous studies have revealed the existence of nonrandom trait distribution patterns as a sign of environmental filtering and/or biotic interactions in a community assembly process. A number of metrics with various algorithms have been used to detect these patterns without any clear guidelines. Although some studies have compared their statistical powers, the differences in performance among the metrics under the conditions close to actual studies are not clear. Therefore, the performances of five metrics of convergence and 16 metrics of divergence under alternative conditions were comparatively analyzed using a suite of simulated communities. We focused particularly on the robustness of the performances to conditions that are often uncertain and uncontrollable in actual studies; e.g., atypical trait distribution patterns stemming from the operation of multiple assembly mechanisms, a scaling of trait-function relationships, and a sufficiency of analyzed traits. Most tested metrics, for either convergence or divergence, had sufficient statistical power to distinguish nonrandom trait distribution patterns without uncertainty. However, the performances of the metrics were considerably influenced by both atypical trait distribution patterns and other uncertainties. Influences from these uncertainties varied among the metrics of different algorithms and their performances were often complementary. Therefore, under the uncertainties of an assembly process, the selection of appropriate metrics and the combined use of complementary metrics are critically important to reliably distinguish nonrandom patterns in a trait distribution. We provide a tentative list of recommended metrics for future studies.

Contrasting radiocesium transfer in the river and lake food webs: Importance of trophic level and food source.

Radiocesium (137Cs) contamination of the freshwater ecosystems adjacent to the Fukushima Daiichi Nuclear Power Plant (FDNPP) in Japan has persisted long after the accident that occurred at the facility in March 2011. It is necessary to elucidate the dynamics of 137Cs in various aquatic ecosystems to predict 137Cs concentrations in fish and manage freshwater fisheries in the vicinity of FDNPP. To these ends, we applied stable isotope analysis to evaluate changes in 137Cs levels through trophic positions and the relative importance of the 137Cs sources at the trophic bases of two rivers and two lakes in Fukushima. The δ15N analyses disclosed that 137Cs decreases from primary producers to fish consumers in the river food web and 137Cs increases among fish consumers with increasing trophic position in the lake food web. The δ13C analysis revealed that autochthonous 137Cs contributed to fish contamination. The periphyton-dependent and zooplankton-dependent fish had comparatively higher 137Cs concentrations in the rivers and lakes, respectively. Cesium-137 supply from the pelagic food web was observed to contribute to greater 137Cs levels in the fish consumers inhabiting the lakes. The results of this study show that stable isotope analysis may help clarify 137Cs dynamics in freshwater food webs and identify the important 137Cs sources in the food web. Identifying important 137Cs sources and trophic transfers depending on the ecosystem help guide regulatory and management frameworks to establish profitability of the food fish stocks there and maintain food security.

Tipping points of nitrogen use efficiency in freshwater phytoplankton along trophic state gradient.

Eutrophication and harmful algal blooms have severe effects on water quality and biodiversity in lakes and reservoirs. Ecological regime shifts of phytoplankton blooms are generally thought to be driven by the rapidly rising nutrient use efficiency of bloom-forming species over short periods, and often exhibit nonlinear dynamics. Regime shifts of trophic state, eutrophication, stratification, and clear or turbid waters are well-studied topics in aquatic ecology. However, information on the prevalence of regime shifts in relationships between trophic states and phytoplankton resource transfer efficiencies in ecosystems is still lacking. Here, we provided a first insight into regime shifts in nitrogen use efficiency of phytoplankton along the trophic state gradient. We explored the regime shifts of phytoplankton resource use efficiency and detected the tipping points by combining four temporal or spatial datasets from tropical to temperate zones in Asia and Europe. We first observed significant abrupt transitions (abruptness > 1) in phytoplankton nitrogen use efficiency along the trophic state gradient. The tipping point values were lower in subtropical/tropical waterbodies (mesotrophic states; TSIc: around 50) than those in temperate zones (eutrophic states; TSIc: 60-70). The regime shifts significantly reduced the primary production transfer efficiency via zooplankton (from 0.15 ± 0.03 to 0.03 ± 0.01; mean ± standard error) in the aquatic food web. Nitrogen-fixing filamentous cyanobacteria can drive eutrophication under mesotrophic state. Our findings imply that the time-window of opportunity for harmful algae prevention and control in lakes and reservoirs is earlier in subtropical/tropical regions.

Identifying the true drivers of abrupt changes in ecosystem state with a focus on time lags: Extreme precipitation can determine water quality in shallow lakes.

A better understanding of abrupt ecosystem changes is needed to improve prediction of future ecosystem states under climate change. Chronological analysis based on long-term monitoring data is an effective way to estimate the frequency and magnitude of abrupt ecosystem changes. In this study, we used abrupt-change detection to differentiate changes of algal community composition in two Japanese lakes and to identify the causes of long-term ecological transitions. Additionally, we focused on finding statistically significant relationships between abrupt changes to aid with factor analysis. To estimate the strengths of driver-response relationships underlying abrupt algal transitions, the timing of the algal transitions was compared to that of abrupt changes in climate and basin characteristics to identify any synchronicities between them. The timing of abrupt algal changes in the two study lakes corresponded most closely to that of heavy runoff events during the past 30-40 years. This strongly suggests that changes in the frequency of extreme events (e.g., heavy rain, prolonged drought) have a greater effect on lake chemistry and community composition than do shifts in the means of climate and basin factors. Our analysis of synchronicity (with a focus on time lags) could provide an easy method to identify better adaptative strategies for future climate change.

Early warning signals have limited applicability to empirical lake data.

Research aimed at identifying indicators of persistent abrupt shifts in ecological communities, a.k.a regime shifts, has led to the development of a suite of early warning signals (EWSs). As these often perform inaccurately when applied to real-world observational data, it remains unclear whether critical transitions are the dominant mechanism of regime shifts and, if so, which EWS methods can predict them. Here, using multi-trophic planktonic data on multiple lakes from around the world, we classify both lake dynamics and the reliability of classic and second generation EWSs methods to predict whole-ecosystem change. We find few instances of critical transitions, with different trophic levels often expressing different forms of abrupt change. The ability to predict this change is highly processing dependant, with most indicators not performing better than chance, multivariate EWSs being weakly superior to univariate, and a recent machine learning model performing poorly. Our results suggest that predictive ecology should start to move away from the concept of critical transitions, developing methods suitable for predicting resilience loss not limited to the strict bounds of bifurcation theory.

Relationships of temperature and biodiversity with stability of natural aquatic food webs.

Temperature and biodiversity changes occur in concert, but their joint effects on ecological stability of natural food webs are unknown. Here, we assess these relationships in 19 planktonic food webs. We estimate stability as structural stability (using the volume contraction rate) and temporal stability (using the temporal variation of species abundances). Warmer temperatures were associated with lower structural and temporal stability, while biodiversity had no consistent effects on either stability property. While species richness was associated with lower structural stability and higher temporal stability, Simpson diversity was associated with higher temporal stability. The responses of structural stability were linked to disproportionate contributions from two trophic groups (predators and consumers), while the responses of temporal stability were linked both to synchrony of all species within the food web and distinctive contributions from three trophic groups (predators, consumers, and producers). Our results suggest that, in natural ecosystems, warmer temperatures can erode ecosystem stability, while biodiversity changes may not have consistent effects.

Inferring causal impacts of extreme water-level drawdowns on lake water clarity using long-term monitoring data.

Although long-term ecosystem monitoring provides essential knowledge for practicing ecosystem management, analyses of the causal effects of ecological impacts from large-scale observational data are still in an early stage of development. We used causal impact analysis (CIA)-a synthetic control method that enables estimation of causal impacts from unrepeated, long-term observational data-to evaluate the causal impacts of extreme water-level drawdowns during summer on subsequent water quality. We used more than 100 years of transparency and water level monitoring data from Lake Biwa, Japan. The results of the CIA showed that the most extreme drawdown in recorded history, which occurred in 1994, had a significant positive effect on transparency (a maximum increase of 1.75 m on average over the following year) in the north basin of the lake. The extreme drawdown in 1939 was also shown to be a trigger for an increase in transparency in the north basin, whereas that in 1984 had no significant effects on transparency. In the south basin, contrary to the pattern in the north basin, the extreme drawdown had a significant negative effect on transparency shortly after the extreme drawdown. These different impacts of the extreme drawdowns were considered to be affected by the timing and magnitude of the extreme drawdowns and the depths of the basins. Our approach of inferring the causal impacts of past events on ecosystems will be helpful in implementing water-level management for ecosystem management and improving water quality in lakes.

Long-term ice phenology records spanning up to 578 years for 78 lakes around the Northern Hemisphere.

In recent decades, lakes have experienced unprecedented ice loss with widespread ramifications for winter ecological processes. The rapid loss of ice, resurgence of winter biology, and proliferation of remote sensing technologies, presents a unique opportunity to integrate disciplines to further understand the broad spatial and temporal patterns in ice loss and its consequences. Here, we summarize ice phenology records for 78 lakes in 12 countries across North America, Europe, and Asia to permit the inclusion and harmonization of in situ ice phenology observations in future interdisciplinary studies. These ice records represent some of the longest climate observations directly collected by people. We highlight the importance of applying the same definition of ice-on and ice-off within a lake across the time-series, regardless of how the ice is observed, to broaden our understanding of ice loss across vast spatial and temporal scales.

Causal networks of phytoplankton diversity and biomass are modulated by environmental context.

Untangling causal links and feedbacks among biodiversity, ecosystem functioning, and environmental factors is challenging due to their complex and context-dependent interactions (e.g., a nutrient-dependent relationship between diversity and biomass). Consequently, studies that only consider separable, unidirectional effects can produce divergent conclusions and equivocal ecological implications. To address this complexity, we use empirical dynamic modeling to assemble causal networks for 19 natural aquatic ecosystems (N24◦~N58◦) and quantified strengths of feedbacks among phytoplankton diversity, phytoplankton biomass, and environmental factors. Through a cross-system comparison, we identify macroecological patterns; in more diverse, oligotrophic ecosystems, biodiversity effects are more important than environmental effects (nutrients and temperature) as drivers of biomass. Furthermore, feedback strengths vary with productivity. In warm, productive systems, strong nitrate-mediated feedbacks usually prevail, whereas there are strong, phosphate-mediated feedbacks in cold, less productive systems. Our findings, based on recovered feedbacks, highlight the importance of a network view in future ecosystem management.