Warming underpins community turnover in temperate freshwater and terrestrial communities.

Rising temperatures are leading to increased prevalence of warm-affinity species in ecosystems, known as thermophilisation. However, factors influencing variation in thermophilisation rates among taxa and ecosystems, particularly freshwater communities with high diversity and high population decline, remain unclear. We analysed compositional change over time in 7123 freshwater and 6201 terrestrial, mostly temperate communities from multiple taxonomic groups. Overall, temperature change was positively linked to thermophilisation in both realms. Extirpated species had lower thermal affinities in terrestrial communities but higher affinities in freshwater communities compared to those persisting over time. Temperature change's impact on thermophilisation varied with community body size, thermal niche breadth, species richness and baseline temperature; these interactive effects were idiosyncratic in the direction and magnitude of their impacts on thermophilisation, both across realms and taxonomic groups. While our findings emphasise the challenges in predicting the consequences of temperature change across communities, conservation strategies should consider these variable responses when attempting to mitigate climate-induced biodiversity loss.

Reducing contrast agent residuals in hospital wastewater: the GREENWATER study protocol.

The potential enviromental impact of iodinated (ICAs) and gadolinium-based contrast agents (GBCAs) have recently come under scrutiny, considering the current nonselective wastewater treatment. However, their rapid excretion after intravenous administration could allow their potential recovery by targeting hospital sewage. The GREENWATER study aims to appraise the effective quantities of ICAs and GBCAs retrievable from patients' urine collected after computed tomography (CT) and magnetic resonance imaging (MRI) exams, selecting ICA/GBCA per-patient urinary excretion and patients' acceptance rate as study endpoints. Within a prospective, observational, single-centre, 1-year framework, we will enrol outpatients aged ≥ 18 years, scheduled to perform contrast-enhanced CT or MRI, willing to collect post-examination urine in dedicated canisters by prolonging their hospital stay to 1 h after injection. Collected urine will be processed and partially stored in the institutional biobank. Patient-based analysis will be performed for the first 100 CT and 100 MRI patients, and then, all analyses will be conducted on the pooled urinary sample. Quantification of urinary iodine and gadolinium will be performed with spectroscopy after oxidative digestion. The evaluation of the acceptance rate will assess the "environmental awareness" of patients and will aid to model how procedures to reduce ICA/GBCA enviromental impact could be adapted in different settings. Key points • Enviromental impact of iodinated and gadolinium-based contrast agents represents a growing point of attention.• Current wastewater treatment is unable to retrieve and recycle contrast agents.• Prolonging hospital stay may allow contrast agents retrieval from patients' urine.• The GREENWATER study will assess the effectively retrievable contrast agents' quantities.• The enrolment acceptance rate will allow to evaluate patients' "green sensitivity".

Disruption of ecological networks in lakes by climate change and nutrient fluctuations.

Climate change interacts with local processes to threaten biodiversity by disrupting the complex network of ecological interactions. While changes in network interactions drastically affect ecosystems, how ecological networks respond to climate change, in particular warming and nutrient supply fluctuations, is largely unknown. Here, using an equation-free modelling approach on monthly plankton community data in ten Swiss lakes, we show that the number and strength of plankton community interactions fluctuate and respond nonlinearly to water temperature and phosphorus. While lakes show system-specific responses, warming generally reduces network interactions, particularly under high phosphate levels. This network reorganization shifts trophic control of food webs, leading to consumers being controlled by resources. Small grazers and cyanobacteria emerge as sensitive indicators of changes in plankton networks. By exposing the outcomes of a complex interplay between environmental drivers, our results provide tools for studying and advancing our understanding of how climate change impacts entire ecological communities.

Deep Learning Classification of Lake Zooplankton.

Plankton are effective indicators of environmental change and ecosystem health in freshwater habitats, but collection of plankton data using manual microscopic methods is extremely labor-intensive and expensive. Automated plankton imaging offers a promising way forward to monitor plankton communities with high frequency and accuracy in real-time. Yet, manual annotation of millions of images proposes a serious challenge to taxonomists. Deep learning classifiers have been successfully applied in various fields and provided encouraging results when used to categorize marine plankton images. Here, we present a set of deep learning models developed for the identification of lake plankton, and study several strategies to obtain optimal performances, which lead to operational prescriptions for users. To this aim, we annotated into 35 classes over 17900 images of zooplankton and large phytoplankton colonies, detected in Lake Greifensee (Switzerland) with the Dual Scripps Plankton Camera. Our best models were based on transfer learning and ensembling, which classified plankton images with 98% accuracy and 93% F1 score. When tested on freely available plankton datasets produced by other automated imaging tools (ZooScan, Imaging FlowCytobot, and ISIIS), our models performed better than previously used models. Our annotated data, code and classification models are freely available online.

Phytoplankton and cyanobacteria abundances in mid-21st century lakes depend strongly on future land use and climate projections.

Land use and climate change are anticipated to affect phytoplankton of lakes worldwide. The effects will depend on the magnitude of projected land use and climate changes and lake sensitivity to these factors. We used random forests fit with long-term (1971-2016) phytoplankton and cyanobacteria abundance time series, climate observations (1971-2016), and upstream catchment land use (global Clumondo models for the year 2000) data from 14 European and 15 North American lakes basins. We projected future phytoplankton and cyanobacteria abundance in the 29 focal lake basins and 1567 lakes across focal regions based on three land use (sustainability, middle of the road, and regional rivalry) and two climate (RCP 2.6 and 8.5) scenarios to mid-21st century. On average, lakes are expected to have higher phytoplankton and cyanobacteria due to increases in both urban land use and temperature, and decreases in forest habitat. However, the relative importance of land use and climate effects varied substantially among regions and lakes. Accounting for land use and climate changes in a combined way based on extensive data allowed us to identify urbanization as the major driver of phytoplankton development in lakes located in urban areas, and climate as major driver in lakes located in remote areas where past and future land use changes were minimal. For approximately one-third of the studied lakes, both drivers were relatively important. The results of this large scale study suggest the best approaches for mitigating the effects of human activity on lake phytoplankton and cyanobacteria will depend strongly on lake sensitivity to long-term change and the magnitude of projected land use and climate changes at a given location. Our quantitative analyses suggest local management measures should focus on retaining nutrients in urban landscapes to prevent nutrient pollution from exacerbating ongoing changes to lake ecosystems from climate change.

Underwater dual-magnification imaging for automated lake plankton monitoring.

The Dual Scripps Plankton Camera (DSPC) is a new approach for automated in-situ monitoring of phyto- and zooplankton communities based on a dual magnification dark-field imaging microscope. Here, we present the DSPC and its associated image processing while evaluating its capabilities in i) detecting and characterizing plankton species of different size and taxonomic categories and ii) measuring their abundance in both laboratory and field applications. In the laboratory, body size and abundance estimates by the DSPC significantly and robustly scaled with measurements derived by microscopy. In the field, a DSPC installed permanently at 3 m depth in Lake Greifensee (Switzerland) delivered images of plankton individuals, colonies, and heterospecific aggregates at hourly timescales without disrupting natural arrangements of interacting organisms, their microenvironment or their behavior. The DSPC was able to track the dynamics of taxa, mostly at the genus level, in the size range between ∼10 µm to ∼ 1 cm, covering many components of the planktonic food web (including parasites and potentially toxic cyanobacteria). Comparing data from the field-deployed DSPC to traditional sampling and microscopy revealed a general overall agreement in estimates of plankton diversity and abundances. The most significant disagreements between traditional methods and the DSPC resided in the measurements of zooplankton community properties. Our data suggest that the DSPC is better equipped to study the dynamics and demography of heterogeneously distributed organisms such as zooplankton, because high temporal resolution and continuous sampling offer more information and less variability in taxa detection and quantification than traditional sampling. Time series collected by the DSPC depicted ecological succession patterns, algal bloom dynamics and diel fluctuations with a temporal frequency and morphological resolution that was never observed by traditional methods. Access to high frequency, reproducible and real-time data of a large spectrum of the planktonic ecosystem expands our understanding of both applied and fundamental plankton ecology. We conclude the DSPC is robust for both research and water quality monitoring and suitable for stable long-term deployments.

Temperature, phytoplankton density and bacteria diversity drive the biotransformation of micropollutants in a lake ecosystem.

For most micropollutants (MPs) present in surface waters, such as pesticides and pharmaceuticals, the contribution of biotransformation to their overall removal from lake ecosystems is largely unknown. This study aims at empirically determining the biotransformation rate constants for 35 MPs at different periods of the year and depths of a meso-eutrophic lake. We then tested statistically the association of environmental parameters and microbial community composition with the biotransformation rate constants obtained. Biotransformation was observed for 14 out of 35 studied MPs for at least one sampling time. Large variations in biotransformation rate constants were observed over the seasons and between compounds. Overall, the transformation of MPs was mostly influenced by the lake's temperature, phytoplankton density and bacterial diversity. However, some individual MPs were not following the general trend or association with microorganism biomass. The antidepressant mianserin, for instance, was transformed in all experiments and depths, but did not show any relationship with measured environmental parameters, suggesting the importance of specific microorganisms in its transformation. The results presented here contribute to our understanding of the fate of MPs in surface waters and thus support improved risk assessment of contaminants in the environment.

Reconciling trait based perspectives along a trait-integration continuum.

Trait based ecology has developed fast in the last decades, aiming to both explain mechanisms of community assembly, and predict patterns in nature, such as the effects of biodiversity shifts on key ecosystem processes. This body of work has stimulated the development of several conceptual frameworks and analytical methods, as well as the production of trait databases covering a growing number of taxa and organizational levels (from individuals to guilds). However, this breeding ground of novel concepts and tools currently lacks a general and coherent framework, under which functional traits can help ecologists organize their research aims, and serve as the common currency to unify several scientific disciplines. Specifically, we see a need to bridge the gaps between community ecology, ecosystem ecology, and evolutionary biology, in order to address the most pressing environmental issues of our time. To achieve this integration goal, we define a trait-integration continuum, which reconciles alternative trait definitions and approaches in ecology. This continuum outlines a coherent progression of biological scales, along which traits interact and hierarchically integrate from genetic information, to whole organism fitness-related traits, to trait syndromes and functional groups. Our conceptual scheme proposes that lower-level trait integration is closer to the inference of ecoevolutionary mechanisms determining population and community properties, whereas higher-level trait integration is most suited to the prediction of ecosystem processes. Within these two extremes, trait integration varies on a continuous scale, which relates directly to the inductive-deductive loop that should characterize the scientific method. With our proposed framework, we aim to facilitate scientists in contextualising their research based on the trait-integration levels that matter most to their specific goals. Explicitly acknowledging the existence of a trait-integration continuum is a promising way for framing the appropriate questions, thus obtaining reliable answers and results that are comparable across studies and disciplines.

A brief guide for the measurement and interpretation of microbial functional diversity.

The importance of functional diversity for the functioning and behaviour of microbial communities is clear, yet the widespread incorporation of functional diversity measurements into environmental microbiology study designs remains surprisingly limited. This may, at least to some extent, be a consequence of the unique conceptual and methodological challenges to measuring functional diversity in microbial communities. To facilitate the increased incorporation of functional diversity measurements into environmental microbiology study designs, we review here the process and some key caveats for measuring functional diversity and provide specific examples. We highlight three main decision points and provide guidance to making these decisions based on the underlying mechanisms for how functional diversity relates to an ecosystem process or property of interest. We discuss the selection of an appropriate type of functional trait, selection of the specificity at which functional diversity will be measured, and selection of an appropriate metric for estimating functional diversity from quantitative measures of those traits. We further discuss decisions regarding the use of one- or multi-dimensional measures of functional diversity and how advances in the field of trait-based community ecology could be applied or adapted to address questions in environmental microbiology.

Second-order phase transition in phytoplankton trait dynamics.

Key traits of unicellular species, such as cell size, often follow scale-free or self-similar distributions, hinting at the possibility of an underlying critical process. However, linking such empirical scaling laws to the critical regime of realistic individual-based model classes is difficult. Here, we reveal new empirical scaling evidence associated with a transition in the population and the chlorophyll dynamics of phytoplankton. We offer a possible explanation for these observations by deriving scaling laws in the vicinity of the critical point of a new universality class of non-local cell growth and division models. This "criticality hypothesis" can be tested through new scaling predictions derived for our model class, for the response of chlorophyll distributions to perturbations. The derived scaling laws may also be generalized to other cellular traits and environmental drivers relevant to phytoplankton ecology.

On biological evolution and environmental solutions.

Drawing insights from multiple disciplines is essential for finding integrative solutions that are required to tackle complex environmental problems. Human activities are causing unprecedented influence on global ecosystems, culminating in the loss of species and fundamental changes in the selective environments of organisms across the tree of life. Our collective understanding about biological evolution can help identify and mitigate many of the environmental problems in the Anthropocene. To this end, we propose a stronger integration of environmental sciences with evolutionary biology.

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.

Resilience of Natural Phytoplankton Communities to Pulse Disturbances from Micropollutant Exposure and Vertical Mixing.

Freshwaters are increasingly exposed to complex mixtures of pharmaceutical and personal care products (PPCPs) from municipal wastewater, which are known to alter freshwater communities' structure and functioning. However, their interaction with other disturbances and whether their combined effects can impact ecological resilience (i.e., the ability of a system to tolerate disturbances without altering the system's original structure and processes) remain unexplored. Using in situ mesocosms in 2 lakes with different nutrient levels (mesotrophic and eutrophic), we assessed whether a pulse exposure to sublethal concentrations of 12 PPCPs affects the ecological resilience of natural phytoplankton communities that experienced an abrupt environmental change involving the destabilization of the water column through mixing. Such mixing events are predicted to increase as the effects of climate change unfold, leading to more frequent storms, which disrupt stratification in lakes and force communities to restructure. We assessed their combined effects on community metrics (biomass, species richness, and composition) and their relative resilience using 4 indicators (cross-scale, within-scale, aggregation length, and gap length), inferred from phytoplankton communities by discontinuity analysis. The mixing disturbance alone had negligible effects on the community metrics, but when combined with chemical contaminants significant changes were measured: reducing total biomass, species richness, and altered community composition of phytoplankton. Once these changes occurred, they persisted until the end of the experiment (day 20), when the communities' structures from the 2 highest exposure levels diverged from the controls. The resilience indicators were not affected by PPCPs but differed significantly between lakes, with lower resilience found in the eutrophic lake. Thus, PPCPs can significantly alter community structures and reinforce mechanisms that maintain ecosystems in a "degraded state." Environ Toxicol Chem 2019;38:2197-2208. © 2019 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals, Inc. on behalf of SETAC.

A single pulse of diffuse contaminants alters the size distribution of natural phytoplankton communities.

The presence of a multitude of bioactive organic pollutants collectively classified as pharmaceuticals and personal care products (PPCPs) in freshwaters is of concern, considering that ecological assessments of their potential impacts on natural systems are still scarce. In this field experiment we tested whether a single pulse exposure to a mixture of 12 pharmaceuticals and personal care products, which are commonly found in European inland waters, can influence the size distributions of natural lake phytoplankton communities. Size is one of the most influential determinants of community structure and functioning, particularly in planktonic communities and food webs. Using an in-situ microcosm approach, phytoplankton communities in two lakes with different nutrient levels (mesotrophic and eutrophic) were exposed to a concentration gradient of the PPCPs mixture at five levels. We tested whether sub-lethal PPCPs doses affect the scaling of organisms' abundances with their size, and the slope of these size spectra, which describe changes in the abundances of small relative to large phytoplankton. Our results showed that a large proportion (approximately 80%) of the dataset followed a power-law distribution, thus suggesting evidence of scale invariance of abundances, as expected in steady state ecosystems. PPCPs were however found to induce significant changes in the size spectra and community structure of natural phytoplankton assemblages. The two highest treatment levels of PPCPs were associated with decreased abundance of the most dominant size class (nano-phytoplankton: 2-5 µm), leading to a flattening of the size spectra slope. These results suggest that a pulse exposure to PPCPs induce changes that potentially lead to unsteady ecosystem states and cascading effects in the aquatic food webs, by favoring larger non-edible algae at the expense of small edible species. We propose higher susceptibility due to higher surface to volume ratio in small species as the likely cause of these structural changes.

High dispersal levels and lake warming are emergent drivers of cyanobacterial community assembly in peri-Alpine lakes.

Disentangling the relative importance of deterministic and stochastic processes in shaping natural communities is central to ecology. Studies about community assembly over broad temporal and spatial scales in aquatic microorganisms are scarce. Here, we used 16S rDNA sequence data from lake sediments to test for community assembly patterns in cyanobacterial phylogenies across ten European peri-Alpine lakes and over a century of eutrophication and climate warming. We studied phylogenetic similarity in cyanobacterial assemblages over spatial and temporal distance, and over environmental gradients, comparing detected patterns with theoretical expectations from deterministic and stochastic processes. We found limited evidence for deviation of lake communities from a random assembly model and no significant effects of geographic distance on phylogenetic similarity, suggesting no dispersal limitation and high levels of stochastic assembly. We detected a weak influence of phosphorus, but no significant effect of nitrogen levels on deviation of community phylogenies from random. We found however a significant decay of phylogenetic similarity for non-random communities over a gradient of air temperature and water column stability. We show how phylogenetic data from sedimentary archives can improve our understanding of microbial community assembly processes, and support previous evidence that climate warming has been the strongest environmental driver of cyanobacterial community assembly over the past century.

Biodiversity Drives Micropollutant Biotransformation in Freshwater Phytoplankton Assemblages.

Biotransformation of chemical pollutants is an ecological process requiring multifunctionality (multiple metabolic pathways) and, potentially, high biodiversity. Phytoplankton communities are highly diverse functionally and taxonomically and co-occur with complex mixtures of organic pollutants in aquatic environments. Here, we investigated how phytoplankton species richness (SR) and class richness (CR) determine the biotransformation of a mixture of 37 structurally diverse pollutants using laboratory experiments and analysis of high-resolution mass spectrometry data. Laboratory phytoplankton communities were assembled from pure cultures by creating a gradient from one to five taxonomic groups, and 5 to 11 total species, in defined medium. The biotransformation of pollutants over 6 days and the total number of transformed chemicals increased with CR for 13 considerably transformed compounds. The total number of transformation products (TPs, up to 42) was positively affected by both CR and SR: CR had a positive effect on stable TPs found, and SR led to more transient TPs. Our data indicate that both taxonomic and functional diversity are important for biotransformation of anthropogenic chemicals in phytoplankton and suggest that plankton biodiversity could play a role in the remediation of pollutant loads in aquatic ecosystems.

Light limitation increases multidimensional trait evenness in phytoplankton populations.

Individual-level variation arising from responses to environmental gradients influences population and community dynamics. How such responses empirically relate to the mechanisms that govern species coexistence is, however, poorly understood. Previous results from l ake phytoplankton communities suggested that the evenness of organismal traits in multiple dimensions increases with resource limitation, possibly due to resource partitioning at the individual level. Here we experimentally tested the emergence of this pattern by growing two phytoplankton species (Pseudokirchneriella subcapitata and Microcystis aeruginosa) under a gradient of light intensity, in monoculture and jointly. Under low light (resource) conditions, the populations diversified into a wide range of phenotypes, which were evenly distributed in multidimensional trait space (defined by four pigment-related trait dimensions), consistent with the observed field pattern. Our interpretation is that under conditions of light limitation, individual phytoplankton cells alter photosynthetic traits to reduce overlap in light acquisition, acquiring unexploited resources and thereby likely maximising individual success. Our results provide prime experimental evidence that resource limitation increases the evenness of conspecific and heterospecific microbial phenotypes along trait axes, advancing our understanding of trait-based coexistence.

Interacting Temperature, Nutrients and Zooplankton Grazing Control Phytoplankton Size-Abundance Relationships in Eight Swiss Lakes.

Biomass distribution among size classes follows a power law where the Log-abundance of taxa scales to Log-size with a slope that responds to environmental abiotic and biotic conditions. The interactions between ecological mechanisms controlling the slope of locally realized size-abundance relationships (SAR) are however not well understood. Here we tested how warming, nutrient levels, and grazing affect the slope of phytoplankton community SARs in decadal time-series from eight Swiss lakes of the peri-alpine region, which underwent environmental forcing due to climate change and oligotrophication. We expected rising temperature to have a negative effect on slope (favoring small phytoplankton), and increasing nutrient levels and grazing pressure to have a positive effect (benefiting large phytoplankton). Using a random forest approach to extract robust patterns from the noisy data, we found that the effects of temperature (direct and indirect through water column stability), nutrient availability (phosphorus and total biomass), and large herbivore (copepods and daphnids) grazing and selectivity on slope were non-linear and interactive. Increasing water temperature or total grazing pressure, and decreasing phosphorus levels, had a positive effect on slope (favoring large phytoplankton, which are predominantly mixotrophic in the lake dataset). Our results therefore showed patterns that were opposite to the expected long-term effects of temperature and nutrient levels, and support a paradigm in which (i) small phototrophic phytoplankton appear to be favored under high nutrients levels, low temperature and low grazing, and (ii) large mixotrophic algae are favored under oligotrophic conditions when temperature and grazing pressure are high. The effects of temperature were stronger under nutrient limitation, and the effects of nutrients and grazing were stronger at high temperature. Our study shows that the phytoplankton local SARs in lakes respond to both the independent and the interactive effects of resources, grazing and water temperature in a complex, unexpected way, and observations from long-term studies can deviate significantly from general theoretical expectations.

Quantifying cell densities and biovolumes of phytoplankton communities and functional groups using scanning flow cytometry, machine learning and unsupervised clustering.

Scanning flow cytometry (SFCM) is characterized by the measurement of time-resolved pulses of fluorescence and scattering, enabling the high-throughput quantification of phytoplankton morphology and pigmentation. Quantifying variation at the single cell and colony level improves our ability to understand dynamics in natural communities. Automated high-frequency monitoring of these communities is presently limited by the absence of repeatable, rapid protocols to analyse SFCM datasets, where images of individual particles are not available. Here we demonstrate a repeatable, semi-automated method to (1) rapidly clean SFCM data from a phytoplankton community by removing signals that do not belong to live phytoplankton cells, (2) classify individual cells into trait clusters that correspond to functional groups, and (3) quantify the biovolumes of individual cells, the total biovolume of the whole community and the total biovolumes of the major functional groups. Our method involves the development of training datasets using lab cultures, the use of an unsupervised clustering algorithm to identify trait clusters, and machine learning tools (random forests) to (1) evaluate variable importance, (2) classify data points, and (3) estimate biovolumes of individual cells. We provide example datasets and R code for our analytical approach that can be adapted for analysis of datasets from other flow cytometers or scanning flow cytometers.

The predictability of a lake phytoplankton community, over time-scales of hours to years.

Forecasting changes to ecological communities is one of the central challenges in ecology. However, nonlinear dependencies, biotic interactions and data limitations have limited our ability to assess how predictable communities are. Here, we used a machine learning approach and environmental monitoring data (biological, physical and chemical) to assess the predictability of phytoplankton cell density in one lake across an unprecedented range of time-scales. Communities were highly predictable over hours to months: model R2 decreased from 0.89 at 4 hours to 0.74 at 1 month, and in a long-term dataset lacking fine spatial resolution, from 0.46 at 1 month to 0.32 at 10 years. When cyanobacterial and eukaryotic algal cell densities were examined separately, model-inferred environmental growth dependencies matched laboratory studies, and suggested novel trade-offs governing their competition. High-frequency monitoring and machine learning can set prediction targets for process-based models and help elucidate the mechanisms underlying ecological dynamics.