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Wang T, Wang H. Stoichiometric microplastics models in natural and laboratory environments. J Theor Biol 2024; 580:111733. [PMID: 38224853 DOI: 10.1016/j.jtbi.2024.111733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 12/27/2023] [Accepted: 01/08/2024] [Indexed: 01/17/2024]
Abstract
Microplastics pose a severe threat to marine ecosystems; however, relevant mathematical modeling and analysis are lacking. This paper formulates two stoichiometric producer-grazer models to investigate the interactive effects of microplastics, nutrients, and light on population dynamics under different settings. One model incorporates optimal microplastic uptake and foraging behavior based on nutrient availability for natural settings, while the other model does not include foraging in laboratory settings. We establish the well-posedness of the models and examine their long-term behaviors. Our results reveal that in natural environments, producers and grazers exhibit higher sensitivity to microplastics, and the system may demonstrate bistability or tristability. Moreover, the influences of microplastics, nutrients, and light intensity are highly intertwined. The presence of microplastics amplifies the constraints on grazer growth related to food quality and quantity imposed by extreme light intensities, while elevated phosphorus input enhances the system's resistance to intense light conditions. Furthermore, higher environmental microplastic levels do not always imply elevated microplastic body burdens in organisms, as organisms are also influenced by nutrients and light. We also find that grazers are more vulnerable to microplastics, compared to producers. If producers can utilize microplastics for growth, the system displays significantly greater resilience to microplastics.
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Affiliation(s)
- Tianxu Wang
- Interdisciplinary Lab for Mathematical Ecology & Epidemiology, Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, Alberta T6G 2G1, Canada
| | - Hao Wang
- Interdisciplinary Lab for Mathematical Ecology & Epidemiology, Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, Alberta T6G 2G1, Canada.
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2
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Peace A, Frost PC, Wagner ND, Danger M, Accolla C, Antczak P, Brooks BW, Costello DM, Everett RA, Flores KB, Heggerud CM, Karimi R, Kang Y, Kuang Y, Larson JH, Mathews T, Mayer GD, Murdock JN, Murphy CA, Nisbet RM, Pecquerie L, Pollesch N, Rutter EM, Schulz KL, Scott JT, Stevenson L, Wang H. Stoichiometric Ecotoxicology for a Multisubstance World. Bioscience 2021. [DOI: 10.1093/biosci/biaa160] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
Abstract
Nutritional and contaminant stressors influence organismal physiology, trophic interactions, community structure, and ecosystem-level processes; however, the interactions between toxicity and elemental imbalance in food resources have been examined in only a few ecotoxicity studies. Integrating well-developed ecological theories that cross all levels of biological organization can enhance our understanding of ecotoxicology. In the present article, we underline the opportunity to couple concepts and approaches used in the theory of ecological stoichiometry (ES) to ask ecotoxicological questions and introduce stoichiometric ecotoxicology, a subfield in ecology that examines how contaminant stress, nutrient supply, and elemental constraints interact throughout all levels of biological organization. This conceptual framework unifying ecotoxicology with ES offers potential for both empirical and theoretical studies to deepen our mechanistic understanding of the adverse outcomes of chemicals across ecological scales and improve the predictive powers of ecotoxicology.
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Affiliation(s)
- Angela Peace
- Department of Mathematics and Statistics, Texas Tech University, Lubbock, Texas, United States
| | - Paul C Frost
- Department of Biology, Trent University, Peterborough, Ontario, Canada
| | - Nicole D Wagner
- Center for Reservoir and Aquatic Systems Research, Baylor University, Waco, Texas, United States
| | | | - Chiara Accolla
- Department of Ecology, Evolution, and Behavior, University of Minnesota, Twin Cities, Minneapolis, Minnesota, United States
| | | | - Bryan W Brooks
- Department of Environmental Science, Baylor University, Waco, Texas, United States
| | - David M Costello
- Department of Biological Sciences, Kent State University, Kent, Ohio, United States
| | - Rebecca A Everett
- Department of Mathematics and Statistics, Haverford College, Haverford, Pennsylvania, United States
| | - Kevin B Flores
- Department of Mathematics and the Center for Research in Scientific Computation, North Carolina State University, Raleigh, North Carolina, United States
| | - Christopher M Heggerud
- Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, Alberta, Canada
| | - Roxanne Karimi
- School of Marine and Atmospheric Sciences, Stony Brook University, Stony Brook, New York, United States
| | - Yun Kang
- Arizona State University, Mesa, Arizona, United States
| | - Yang Kuang
- Arizona State University, Tempe, Arizona, United States
| | - James H Larson
- US Geological Survey's Upper Midwest Environmental Sciences Center, La Crosse, Wisconsin, United States
| | - Teresa Mathews
- Environmental Sciences Division of Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States
| | - Gregory D Mayer
- Department of Environmental Toxicology, Texas Tech University, Lubbock, Texas, United States
| | - Justin N Murdock
- Department of Biology, Tennessee Tech University, Cookeville, Tennessee, United States
| | - Cheryl A Murphy
- Department of Fisheries and Wildlife, Michigan State University, East Lansing, Michigan, United States
| | - Roger M Nisbet
- Department of Ecology, Evolution, and Marine Biology, University of California, Santa Barbara, Santa Barbara, California, United States
| | - Laure Pecquerie
- Université de Brest, CNRS, IRD, Ifremer, LEMAR, Plouzane, France
| | - Nathan Pollesch
- University of Wisconsin's Aquatic Sciences Center and with the US Environmental Protection Agency's Great Lakes Toxicology and Ecology Division, Duluth, Minnesota, United States
| | - Erica M Rutter
- Department of Applied Mathematics, University of California, Merced, Merced, California, United States
| | - Kimberly L Schulz
- Department of Environmental and Forest Biology, State University of New York's College of Environmental Science and Forestry, Syracuse, New York, United States
| | - J Thad Scott
- Department of Biology, Baylor University, Waco, Texas, United States
| | - Louise Stevenson
- Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee; with the Department of Ecology, Evolution, and Marine Biology, University of California, Santa Barbara, Santa Barbara, California; and with the Department of Biological Sciences at Bowling Green State University, in Bowling Green, Ohio, United States
| | - Hao Wang
- Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, Alberta, Canada
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Hassan MN, Asik L, Kulik J, Long KR, Peace A. Environmental seasonality on predator-prey systems under nutrient and toxicant constraints. J Theor Biol 2019; 480:71-80. [PMID: 31386868 DOI: 10.1016/j.jtbi.2019.08.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2019] [Revised: 07/20/2019] [Accepted: 08/03/2019] [Indexed: 10/26/2022]
Abstract
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.
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Affiliation(s)
- Md Nazmul Hassan
- Department of Mathematics and Statistics, Texas Tech University, Lubbock, USA.
| | - Lale Asik
- Department of Mathematics and Statistics, Texas Tech University, Lubbock, USA
| | - Jackson Kulik
- Department of Mathematics and Statistics, Texas Tech University, Lubbock, USA
| | - K R Long
- Department of Mathematics and Statistics, Texas Tech University, Lubbock, USA
| | - Angela Peace
- Department of Mathematics and Statistics, Texas Tech University, Lubbock, USA
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Van den Brink PJ, Bracewell SA, Bush A, Chariton A, Choung CB, Compson ZG, Dafforn KA, Korbel K, Lapen DR, Mayer-Pinto M, Monk WA, O'Brien AL, Rideout NK, Schäfer RB, Sumon KA, Verdonschot RCM, Baird DJ. Towards a general framework for the assessment of interactive effects of multiple stressors on aquatic ecosystems: Results from the Making Aquatic Ecosystems Great Again (MAEGA) workshop. THE SCIENCE OF THE TOTAL ENVIRONMENT 2019; 684:722-726. [PMID: 30857726 DOI: 10.1016/j.scitotenv.2019.02.455] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2018] [Revised: 02/28/2019] [Accepted: 02/28/2019] [Indexed: 05/12/2023]
Abstract
A workshop was held in Wageningen, The Netherlands, in September 2017 to collate data and literature on three aquatic ecosystem types (agricultural drainage ditches, urban floodplains, and urban estuaries), and develop a general framework for the assessment of multiple stressors on the structure and functioning of these systems. An assessment framework considering multiple stressors is crucial for our understanding of ecosystem responses within a multiply stressed environment, and to inform appropriate environmental management strategies. The framework consists of two components: (i) problem identification and (ii) impact assessment. Both assessments together proceed through the following steps: 1) ecosystem selection; 2) identification of stressors and quantification of their intensity; 3) identification of receptors or sensitive groups for each stressor; 4) identification of stressor-response relationships and their potential interactions; 5) construction of an ecological model that includes relevant functional groups and endpoints; 6) prediction of impacts of multiple stressors, 7) confirmation of these predictions with experimental and monitoring data, and 8) potential adjustment of the ecological model. Steps 7 and 8 allow the assessment to be adaptive and can be repeated until a satisfactory match between model predictions and experimental and monitoring data has been obtained. This paper is the preface of the MAEGA (Making Aquatic Ecosystems Great Again) special section that includes three associated papers which are also published in this volume, which present applications of the framework for each of the three aquatic systems.
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Affiliation(s)
- Paul J Van den Brink
- Aquatic Ecology and Water Quality Management group, Wageningen University, P.O. Box 47, 6700 AA Wageningen, the Netherlands; Wageningen Environmental Research, P.O. Box 47, 6700 AA Wageningen, the Netherlands.
| | - Sally A Bracewell
- Aquatic Ecology and Water Quality Management group, Wageningen University, P.O. Box 47, 6700 AA Wageningen, the Netherlands
| | - Alex Bush
- Environment and Climate Change Canada @ Canadian Rivers Institute, Department of Biology, University of New Brunswick, Fredericton, NB. Canada
| | - Anthony Chariton
- Department of Biological Sciences, Macquarie University, NSW, Australia
| | - Catherine B Choung
- Environment and Climate Change Canada @ Canadian Rivers Institute, Department of Biology, University of New Brunswick, Fredericton, NB. Canada
| | - Zacchaeus G Compson
- Environment and Climate Change Canada @ Canadian Rivers Institute, Department of Biology, University of New Brunswick, Fredericton, NB. Canada
| | | | - Kathryn Korbel
- Department of Biological Sciences, Macquarie University, NSW, Australia
| | - David R Lapen
- Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, 960 Carling Ave., Ottawa, Ontario, K1A 0C6, Canada
| | - Mariana Mayer-Pinto
- E&ERC, School of Biological, Earth and Environmental Sciences, The University of New South Wales, Sydney, Australia
| | - Wendy A Monk
- Environment and Climate Change Canada, Canadian Rivers Institute, Faculty of Forestry and Environmental Management, University of New Brunswick, Fredericton, NB. Canada
| | | | - Natalie K Rideout
- Environment and Climate Change Canada @ Canadian Rivers Institute, Department of Biology, University of New Brunswick, Fredericton, NB. Canada
| | - Ralf B Schäfer
- Quantitative Landscape Ecology, Institute for Environmental Sciences, University Koblenz-Landau, Landau in der Pfalz, Germany
| | - Kizar A Sumon
- Department of Fisheries Management, Bangladesh Agricultural University, Mymensingh 2202, Bangladesh
| | - Ralf C M Verdonschot
- Wageningen Environmental Research, P.O. Box 47, 6700 AA Wageningen, the Netherlands
| | - Donald J Baird
- Environment and Climate Change Canada @ Canadian Rivers Institute, Department of Biology, University of New Brunswick, Fredericton, NB. Canada
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Wagner ND, Simpson AJ, Simpson MJ. Sublethal metabolic responses to contaminant mixture toxicity in Daphnia magna. ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY 2018; 37:2448-2457. [PMID: 29920755 DOI: 10.1002/etc.4208] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/13/2018] [Revised: 04/09/2018] [Accepted: 06/13/2018] [Indexed: 06/08/2023]
Abstract
Anthropogenic activity is increasing the presence of contaminants that enter waterways through wastewater effluent and urban and/or agricultural runoff, generally in complex mixtures. Depending on the mode of action of the individual contaminant within the mixture, toxicity can occur due to contaminants having similar or dissimilar modes of action. However, it is unknown how the metabolome responds to sublethal contaminant mixtures in the keystone genus Daphnia. In the present study we examined D. magna metabolic responses to acute sublethal exposure of propranolol, carbamazepine, and perfluorooctanesulfonic acid (PFOS) as well as in binary (propranolol-carbamazepine, propranolol-PFOS, carbamazepine-PFOS) and tertiary mixtures (carbamazepine-propranolol-PFOS), all at 10% of the median lethal concentration of the population (LC50). The metabolome was measured using 1 H nuclear magnetic resonance (NMR) and characterized using principal component analysis, regression analysis, and fold changes in metabolite relative to the unexposed (control) group. The averaged principal component analysis scores plots revealed that carbamazepine-PFOS and carbamazepine-propranolol-PFOS exposures were significantly different from the control treatment. After normalizing the toxicity of each contaminant, we found that some metabolites responded monotonically, whereas others displayed a nonmonotonic response with increasing toxicity units. The single contaminant exposures and 2 binary mixtures (propranolol-carbamazepine, and propranolol-PFOS) resulted in minimal changes in the identified metabolites, whereas the carbamazepine-PFOS and carbamazepine-propranolol-PFOS displayed increases in several amino acid metabolites and decreases in glucose. Overall, our results highlight the sensitivity of the metabolome to distinguish the composition of the contaminant mixtures, with some mixtures displaying heightened responses versus others. Environ Toxicol Chem 2018;37:2448-2457. © 2018 SETAC.
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Affiliation(s)
- Nicole D Wagner
- Environmental NMR Centre and Department of Physical and Environmental Sciences, University of Toronto, Toronto, Ontario, Canada
| | - André J Simpson
- Environmental NMR Centre and Department of Physical and Environmental Sciences, University of Toronto, Toronto, Ontario, Canada
| | - Myrna J Simpson
- Environmental NMR Centre and Department of Physical and Environmental Sciences, University of Toronto, Toronto, Ontario, Canada
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