Modeling zooplankton growth in Lake Washington: A mechanistic approach to physiology in a eutrophication model

Grazer-mediated regeneration of methylmercury, inorganic mercury, and other metals in freshwater

Whereas it is well established that zooplankton can transfer various pollutants such as mercury (Hg) from primary producers to higher trophic levels, less is known on the effects of their activities on the recycling of Hg in aquatic ecosystems. Here, the impact of zooplankton grazing efficiency and excretion/egestion processes on metal concentrations in freshwater was investigated. Isotopically labeled algae (²⁰⁰HgCl₂, Me¹⁹⁸HgCl) was used as a food source and the transfer of the selected isotopes to the culture medium during grazing was measured. In parallel, the potential for the recycling of major ions and metals, including selected essential and non-essential metals, was investigated for this could help to track the effects of sloppy feeding. To highlight the role of feeding behavior, a large filter-feeder (Daphnia magna) was compared to a smaller selective feeder zooplankton taxon (calanoid copepods), with the latter being expected to cause more sloppy feeding than the former. The experiments demonstrated that zooplankton grazing of both taxa significantly influenced the concentrations of the particulate portion of both inorganic Hg (IHg) and monomethylmercury (MeHg) in water. In contrast, only Daphnia significantly increased the concentration of dissolved IHg, whereas the concentration of dissolved MeHg was not affected by either grazer. The results also suggested that both taxa affected the concentrations of dissolved Fe, Zn, SO₄^2- and rare earth elements via sloppy feeding, whereas only Daphnia significantly increased the concentration of dissolved Cu via this mechanism. The effects of excretion/egestion were negligible except for dissolved IHg and Cu in Daphnia treatment. These results highlight a neglected pathway of IHg and MeHg recycling in the water column in freshwater ecosystems with potentially important consequences for trophic transfer.

Environmental seasonality on predator-prey systems under nutrient and toxicant constraints

Bioaccumulation of toxicants in aquatic food webs can pose risks to ecosystem function and human health. Toxicant models of aquatic ecosystems can be improved by incorporating realistic environmental impacts such as nutrient availability and seasonality. It is well known that the carrying capacity of predator-prey systems can vary seasonally due to environmental cycles resulting from natural and human activities. As such, incorporating seasonal variation in the carrying capacity of a predator-prey system provides a better understanding of the underlying population dynamics bioaccumulation of toxicants. Here, we develop a seasonally varied predator-prey model subject to concurrent nutrient and toxicant stressors. We investigate the effects of seasonality on population dynamics to increase understanding of the complex governing processes of the trophic transfer of nutrients, energy, and toxicants. We observe that the strength of seasonality can shift solutions from periodic to quasi-periodic and models that neglect environmental seasonality may be under-predicting adverse effects of toxicity.

Enhanced lake-eutrophication model combined with a fish sub-model using a microcosm experiment

Eutrophication models are effective tools for assessing aquatic environments. The lake ecosystem consists of at least three trophic levels: phytoplankton, zooplankton, and fish. However, only a few studies have included fish sub-models in existing eutrophication models. In addition, no specific value or range is available for certain parameters of the fish sub-model. In the present study, a lake microcosm experimental system was established to determine the range of fish sub-model parameters. A three-trophic-level eutrophication model was established by combining the fish sub-model and eutrophication model. The Bayesian Markov Chain Monte Carlo and genetic algorithm method was used to calibrate the parameters of the eutrophication model. The results show that the maximum relative errors were due to phosphate (5.31%), the minimum relative error was due to nitrate (1.94%), and the relative error of dissolved oxygen, ammonia N, zooplankton, and chlorophyll ranged from 3 to 4%. Compared with the two-trophic-level eutrophication model, the relative errors of ammonia nitrogen (4.17%), phosphate (- 5.31%), and nitrate (1.94%) in the three-trophic-level eutrophication model were lower than those in the two-trophic-level eutrophication model, indicating that the three-trophic-level eutrophication model can obtain highly accurate simulation results and provide a better understanding of eutrophication models for future use.

Evaluation of the complexity and performance of marine planktonic trophic models

Planktonic models represent a powerful tool for creating hypotheses and making predictions about the functioning of marine ecosystems. Their complexity varies according to the number of state variables and the choice of functional forms. We evaluated plankton models during the last 15 years (n =145) with the aims of understanding why they differ in complexity, evaluating model robustness, and describing studies of plankton modelling around the globe. We classified models into four groups: Nutrient-Phytoplankton-Zooplankton (NPZ), Nutrient-Phytoplankton-Zooplankton-Detritus (NPZD), Size-Structured (SS) and Plankton-Functional-Type (PFT). Our results revealed that the number of state variables varied according to the question being addressed: NPZ models were more frequently applied in physical-biological studies, while PFT models were more applied for investigating biogeochemical cycles. Most models were based on simple functional forms which neglect important feedback related to control of plankton dynamics. Modelling studies sometimes failed to describe sensitivity analysis, calibration and validation. The importance of testing different functional forms was commonly overlooked, and the lack of empirical data affected the verification of model robustness. Lastly, we highlight the need to develop modelling studies in the Southern Hemisphere, including Brazil, in order to provide predictions that assist the management of marine ecosystems.

A case study of an enhanced eutrophication model with stoichiometric zooplankton growth sub-model calibrated by Bayesian method

Urban lakes in China have suffered from severe eutrophication over the past several years, particularly those with relatively small areas and closed watersheds. Many efforts have been made to improve the understanding of eutrophication physiology with advanced mathematical models. However, several eutrophication models ignore zooplankton behavior and treat zooplankton as particles, which lead to the systematic errors. In this study, an eutrophication model was enhanced with a stoichiometric zooplankton growth sub-model that simulated the zooplankton predation process and the interplay among nitrogen, phosphorus, and oxygen cycles. A case study in which the Bayesian method was used to calibrate the enhanced eutrophication model parameters and to calculate the model simulation results was carried out in an urban lake in Tianjin, China. Finally, a water quality assessment was also conducted for eutrophication management. Our result suggests that (1) integration of the Bayesian method and the enhanced eutrophication model with a zooplankton feeding behavior sub-model can effectively depict the change in water quality and (2) the nutrients resulting from rainwater runoff laid the foundation for phytoplankton bloom.