Dining in danger: Resolving adaptive fish behavior increases realism of modeled ecosystem dynamics.

Animals occupying higher trophic levels can have disproportionately large influence on ecosystem structure and functioning, owning to intricate behavioral responses to their environment, but the effects of behavioral adaptations on aquatic ecosystem dynamics are underrepresented, especially in model studies. Here, we explore how adaptive behavior of fish can affect the dynamics of aquatics ecosystems. We frame fish behavior in the context of the central trade-off between feeding and predation, calculating the optimal level of feeding determined by ambient food availability and predation risk. To explore whole-ecosystem consequences of fish behavior, we embed our behavioral model within the Water Ecosystems Tool (WET), a contemporary end-to-end aquatic ecosystem model. The principle of optimality provides a robust and mechanistic framework for representing animal behavior that is relevant for complex models, and can provide a stabilizing effect on model dynamics. The model predicts an emergent functional response similar to Holling type III, but with richer dynamics and a more rigorous theoretical foundation. We show how adaptive fish behavior works to stabilize food web dynamics compared to a control model with no optimal behavior, and how changing the strength of the underlying trade-off has profound effects on trophic control and food web structure. Furthermore, we demonstrate how including fish behavior allows for an overall more realistic response of the model system to environmental perturbation in the form of nutrient enhancement. We discuss the structuring effects of behavioral adaptations in real ecosystems, and how approaches like this one may benefit aquatic ecological modeling. Our study further highlights how a mechanistic approach based on concepts from theoretical ecology can be successfully implemented in complex operational models resulting in improved dynamics and descriptive power.

Behaviour of anadromous brown trout (Salmo trutta) in a hydropower regulated freshwater system.

Many Norwegian rivers and lakes are regulated for hydropower, which affects freshwater ecosystems and anadromous fish species, such as sea trout (Salmo trutta). Lakes are an important feature of many anadromous river systems. However, there is limited knowledge on the importance of lakes as habitat for sea trout and how hydropower affects the behaviour of sea trout in lakes. To investigate this, we conducted an acoustic telemetry study. A total of 31 adult sea trout (532 ± 93 mm total length) were captured by angling in river Aurlandselva, Norway, and tagged between July 20 and August 12, 2021. The tags were instrumented with accelerometer, temperature, and depth sensors, which provided information on the sea trout's presence and behaviour in lake Vassbygdevatnet. Our results indicate that there was a large prevalence of sea trout in the lake during the spawning migration, and that the sea trout were less active in the lake compared to the riverine habitats. An increase in activity of sea trout in the lake during autumn might indicate that sea trout spawn in the lake. However, the discharge from the high-head storage plant into the lake did not affect the depth use or activity of sea trout in the lake. Furthermore, the large prevalence of spawners in the lake during autumn will likely cause an underestimation of the size of the sea trout population in rivers with lakes during annual stock assessment. In conclusion, our results could not find evidence of a large impact of the discharge on the behaviour of sea trout in the lake.

Microplastic pollution in sediments of tropical shallow lakes.

Forty-eight tropical shallow lakes (depth ≤ 4 m) across a climatic gradient were assessed for microplastic (MPs; <5 mm) pollution based on MPs concentrations in archive samples from lake shore sediments. The MPs were classified by type (fragments or fibres), colour (yellow, black, red, green, blue, white, and transparent), size (0.55 to 4.93 mm), and polymer (polyester, polyethylene, chlorinated polyethylene, and polyamide). Sediments were predominantly medium sand, and all samples (144) contained MPs, consisting of 24 % fragments (6.3 ± 11.3 MPs·300 g-1) and 76 % fibres (21.25 ± 12.7 MPs·300 g-1). The lake climate (humid, transitional, or semi-arid), type of surrounding land use (urban, semi-arid, or rural), and distance from the shoreline (0, 5 or 10 m) did not explain the differences in MPs concentrations, partially refuting the initial hypothesis. The only significant difference was between the sample medians for the number of fragments based on the region (H = 7.586; p = 0.0481). The number of fragments in the lakes in the humid region was greater than that in the semi-arid region (p < 0.05). Poor sanitation, sewage effluents, and solid wastes reaching and accumulating in the lakes may be the primary and transversal conditioning factors for this small difference among diverse environments. Freshwater lakes are investigated in all continents, and the present study contributes to the first record of MPs in shallow lake sediments in eastern South America. The 48 shallow lakes assessed showed a relatively low concentration of MPs compared to other lake contaminants reported in the international literature. This information coincides with public policies issued, regarding the control and reduction of plastics and MPs in Brazil, and the study region.

The relationship between land cover and microbial community composition in European lakes.

Microbes are essential for element cycling and ecosystem functioning. However, many questions central to understanding the role of microbes in ecology are still open. Here, we analyze the relationship between lake microbiomes and the lakes' land cover. By applying machine learning methods, we quantify the covariance between land cover categories and the microbial community composition recorded in the largest amplicon sequencing dataset of European lakes available to date. Our results show that the aggregation of environmental features or microbial taxa before analysis can obscure ecologically relevant patterns. We observe a comparatively high covariation of the lakes' microbial community with herbaceous and open spaces surrounding the lake; nevertheless, the microbial covariation with land cover categories is generally lower than the covariation with physico-chemical parameters. Combining land cover and physico-chemical bioindicators identified from the same amplicon sequencing dataset, we develop analytical data structures that facilitate insights into the ecology of the lake microbiome. Among these, a list of the environmental parameters sorted by the number of microbial bioindicators we have identified for them points towards apparent environmental drivers of the lake microbial community composition, such as the altitude, conductivity, and area covered herbaceous vegetation surrounding the lake. Furthermore, the response map, a similarity matrix calculated from the Jaccard similarity of the environmental parameters' lists of bioindicators, allows us to study the ecosystem's structure from the standpoint of the microbiome. More specifically, we identify multiple clusters of highly similar and possibly functionally linked ecological parameters, including one that highlights the importance of the calcium-bicarbonate equilibrium for lake ecology. Taken together, we demonstrate the use of machine learning approaches in studying the interplay between microbial diversity and environmental factors and introduce novel approaches to integrate environmental molecular diversity into monitoring and water quality assessments.

Quantification of the covariation of lake microbiomes and environmental variables using a machine learning-based framework.

It is known that microorganisms are essential for the functioning of ecosystems, but the extent to which microorganisms respond to different environmental variables in their natural habitats is not clear. In the current study, we present a methodological framework to quantify the covariation of the microbial community of a habitat and environmental variables of this habitat. It is built on theoretical considerations of systems ecology, makes use of state-of-the-art machine learning techniques and can be used to identify bioindicators. We apply the framework to a data set containing operational taxonomic units (OTUs) as well as more than twenty physicochemical and geographic variables measured in a large-scale survey of European lakes. While a large part of variation (up to 61%) in many environmental variables can be explained by microbial community composition, some variables do not show significant covariation with the microbial lake community. Moreover, we have identified OTUs that act as "multitask" bioindicators, i.e., that are indicative for multiple environmental variables, and thus could be candidates for lake water monitoring schemes. Our results represent, for the first time, a quantification of the covariation of the lake microbiome and a wide array of environmental variables for lake ecosystems. Building on the results and methodology presented here, it will be possible to identify microbial taxa and processes that are essential for functioning and stability of lake ecosystems.

Microbial community changes during a toxic cyanobacterial bloom in an alkaline Hungarian lake.

The Carpathian Basin is a lowland plain located mainly in Hungary. Due to the nature of the bedrock, alluvial deposits, and a bowl shape, many lakes and ponds of the area are characterized by high alkalinity. In this study, we characterized temporal changes in eukaryal and bacterial community dynamics with high throughput sequencing and relate the changes to environmental conditions in Lake Velence located in Fejér county, Hungary. The sampled Lake Velence microbial populations (algal and bacterial) were analyzed to identify potential correlations with other community members and environmental parameters at six timepoints over 6 weeks in the Spring of 2012. Correlations between community members suggest a positive relationship between certain algal and bacterial populations (e.g. Chlamydomondaceae with Actinobacteria and Acidobacteria), while other correlations allude to changes in these relationships over time. During the study, high nitrogen availability may have favored non-nitrogen fixing cyanobacteria, such as the toxin-producing Microcystis aeruginosa, and the eutrophic effect may have been exacerbated by high phosphorus availability as well as the high calcium and magnesium content of the Carpathian Basin bedrock, potentially fostering exopolymer production and cell aggregation. Cyanobacterial bloom formation could have a negative environmental impact on other community members and potentially affect overall water quality as well as recreational activities. To our knowledge, this is the first prediction for relationships between photoautotrophic eukaryotes and bacteria from an alkaline, Hungarian lake.

Modelling ROS formation in boreal lakes from interactions between dissolved organic matter and absorbed solar photon flux.

Concentrations of dissolved organic matter (DOM) are increasing in a large number of lakes across the Northern hemisphere. This browning serves a dual role for biota by protecting against harmful ultraviolet radiation, while also absorbing photosynthetically active radiation. The photochemical activation of DOM and subsequent formation of reactive oxygen species (ROS) is a potentially harmful side effect, but can be difficult to measure directly in situ. In this study, we combine a data set of physico-chemical properties from 71 Nordic lakes with in vitro ROS formation quantum yields to predict ROS formations across a representative boreal ecosystem gradient. For the upper centimeter of the water column, we calculate ROS formations in the range of 7.93-12.56 µmol L-1 h-1. In the first meter, they range between 1.69 and 6.69 µmol L-1 h-1 and in the remaining depth the range is 0.01-0.46 µmol L-1 h-1. These ROS formations are comparable with previously field-measured hydrogen peroxide formation rates and likely affect both phyto- and zooplankton, as well as lake chemistry. Interestingly, wavelengths of the visible spectrum (>400 nm) contribute more than half of the overall ROS formation in surface-near water layers. The association between DOM and ROS formation was found to be two-fold. While DOM promotes ROS formation in the first centimeters of the water column, the shading effect of light attenuation overpowers this with increasing depth. In the context of water browning, our results indicate the emergence of an underestimated oxidative stress environment for lake biota in the upper centimeters of the water column.

Temporal variation in methane emissions in a shallow lake at a southern mid latitude during high and low rainfall periods.

The global methane (CH4) emission of lakes is estimated at between 6 and 16 % of total natural CH4 emissions. However, these values have a high uncertainty due to the wide variety of lakes with important differences in their morphological, biological, and physicochemical parameters and the relatively scarse data from southern mid-latitude lakes. For these reasons, we studied CH4 fluxes and CH4 dissolved in water in a typical shallow lake in the Pampean Wetland, Argentina, during four periods of consecutive years (April 2011-March 2015) preceded by different rainfall conditions. Other water physicochemical parameters were measured and meteorological data were reported. We identified three different states of the lake throughout the study as the result of the irregular alternation between high and low rainfall periods, with similar water temperature values but with important variations in dissolved oxygen, chemical oxygen demand, water turbidity, electric conductivity, and water level. As a consequence, marked seasonal and interannual variations occurred in CH4 dissolved in water and CH4 fluxes from the lake. These temporal variations were best reflected by water temperature and depth of the Secchi disk, as a water turbidity estimation, which had a significant double correlation with CH4 dissolved in water. The mean CH4 fluxes values were 0.22 and 4.09 mg/m2/h for periods with low and high water turbidity, respectively. This work suggests that water temperature and turbidity measurements could serve as indicator parameters of the state of the lake and, therefore, of its behavior as either a CH4 source or sink.