Patterns of toxicological effects in ecosystems: A modeling study

O’Neill, Robert V.; Bartell, Steven M.; Gardner, R. H.

doi:10.1002/etc.5620020410

Cited by 25 publications

(12 citation statements)

References 4 publications

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“…The resulting CASM for atrazine (CASM ATZ ) simulates 365 daily biomass (grams of carbon per square meter) values for each modeled population that change as nonlinear functions of physical–chemical factors, competitive interactions, grazing, predation, and atrazine exposure. Theoretical foundations and practical applications in modeling ecological production dynamics, simulating toxic effects, and estimating ecological impacts in aquatic ecosystems using the CASM derive from previous similar aquatic ecosystem modeling .…”

Section: Experimental Designmentioning

confidence: 99%

Modeling the potential effects of atrazine on aquatic communities in midwestern streams

Bartell¹,

Brain

Hendley

et al. 2013

Enviro Toxic and Chemistry

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The comprehensive aquatic systems model for atrazine (CASM(ATZ)) estimates the potential toxic effects of atrazine on populations of aquatic plants and consumers in a generic lower-order midwestern stream. The CASM(ATZ) simulates the daily production of 20 periphyton and 6 aquatic vascular plant species. The modeled consumer community consists of 17 functionally defined species of zooplankton, benthic invertebrates, bacteria, and fish. Daily values of population biomass (grams of carbon per square meter) are calculated as nonlinear functions of population bioenergetics, physical-chemical environmental parameters, grazing/predator-prey interactions, and population-specific direct and indirect responses to atrazine. The CASM(ATZ) uses Monte Carlo methods to characterize the implications of phenotypic variability, environmental variability, and uncertainty associated with atrazine toxicity data in estimating the potential impacts of time-varying atrazine exposures on population biomass and community structure. Comparisons of modeled biomass values for plants and consumers with published data indicate that the generic reference simulation realistically describes ecological production in lower-order midwestern streams. Probabilistic assessments were conducted using the CASM(ATZ) to evaluate potential modeled changes in plant community structure resulting from measured atrazine exposure profiles in 3 midwestern US streams representing watersheds highly vulnerable to runoff. Deviation in the median values of maximum 30-d average Steinhaus similarity index ranged from 0.09% to 2.52% from the reference simulation. The CASM(ATZ) could therefore be used for the purposes of risk assessment by comparison of site monitoring-based model output to a biologically relevant Steinhaus similarity index level of concern. Used as a generic screening technology or in site-specific applications, the CASM(AT) provides an effective, coherent, and transparent modeling framework for assessing ecological risks posed by pesticides in lower-order streams.

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Section: Experimental Designmentioning

confidence: 99%

Modeling the potential effects of atrazine on aquatic communities in midwestern streams

Bartell¹,

Brain

Hendley

et al. 2013

Enviro Toxic and Chemistry

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“…The use of Monte Carlo simulation, either simply to calculate the distributions (variability) of water quality characteristics that would result from alternative management scenarios [Whitehead and Young, 1979 ;O'Neill et al, 1982cO'Neill et al, , 1983 or to assess the risk of violating a prescribed standard [Fontaine, 1984 ;Chapra and Reckhow, 1983].…”

Section: Decision Making Under Uncertaintymentioning

confidence: 99%

Water quality modeling: A review of the analysis of uncertainty

Beck¹

1987

Water Resources Research

863

431

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This paper reviews the role of uncertainty in the identification of mathematical models of water quality and in the application of these models to problems of prediction. More specifically, four problem areas are examined in detail: uncertainty about model structure, uncertainty in the estimated model parameter values, the propagation of prediction errors, and the design of experiments in order to reduce the critical uncertainties associated with a model. The review is rather lengthy, and it has therefore been prepared in effect as two papers. There is a shorter, largely nontechnical version, which gives a quick impression of the current and future issues in the analysis of uncertainty in water quality modeling. Enclosed by this shorter discussion is the main body of the review dealing in turn with (1) identifiability and experimental design, (2) the generation of preliminary model hypotheses under conditions of sparse, grossly uncertain field data, (3) the selection and evaluation of model structure, (4) parameter estimation (model calibration), (5) checks and balances on the identified model, i.e., model “verification” and model discrimination, and (6) prediction error propagation. Much time is spent in discussing the algorithms of system identification, in particular, the methods of recursive estimation, and in relating these algorithms and the subject of identification to the problems of prediction uncertainty and first‐order error analysis. There are two obvious omissions from the review. It is not concerned primarily with either the development and solution of stochastic differential equations or the issue of decision making under uncertainty, although clearly some reference must be made to these topics. In brief, the review concludes (not surprisingly) that much work has been done on the analysis of uncertainty in the development of mathematical models of water quality, and much remains to be done. A lack of model identifiability has been an outstanding difficulty in the interpretation and explanation of past observed system behavior, and there is ample evidence to show that the “larger,” more “comprehensive” models are easily capable of generating highly uncertain predictions of future behavior. For the future of the subject, it is speculated that there is the possibility of progress in the development of novel algorithms for model structure identification, a need for new questions to be posed in the problem of prediction, and a distinct challenge to the conventional views of this review in the new forms of knowledge representation and manipulation now emerging from the field of artificial intelligence.

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“…The models, including the CASM AZT , constitute a flexible quantitative framework (e.g., Figure 1) that provides for the efficient incorporation of new ecological and toxicological data to support pesticide risk assessment. Uncertain risk estimates associated with mapping toxicity assay results to modeled populations have also been examined (O'Neill et al 1983). Food web and ecosystem modeling remains inherently uncertain .…”

Section: Discussionmentioning

confidence: 99%

Modeling the effects of thiamethoxam on Midwestern farm ponds and emergent wetlands

Bartell

Nair

Grant

et al. 2017

Enviro Toxic and Chemistry

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Potential toxic effects of thiamethoxam on nontarget organisms and the community structure of a generic Midwestern farm pond and emergent wetland were assessed using 2 versions of the comprehensive aquatic system model: CASM , a generic farm pond model, and CASM , a generic wetland model. The CASM and CASM are integrated bioenergetics-based and habitat quality models that describe the daily biomass values of selected producer and consumer populations representative of generalized Midwestern farm ponds and emergent wetlands. The CASM demonstrated the ability to reproduce values of population biomass reported for Midwestern (and other) pond ecosystems; the CASM provided a similar modeling capability for Midwestern emergent wetlands. Lethal and sublethal effects of thiamethoxam were modeled as extrapolations of laboratory toxicity assays using the CASM and the CASM . Time series of daily environmental concentrations of thiamethoxam constructed for 6 regional pesticide applications across the United States failed to produce any calculated impacts on modeled population biomass or changes in community structure of modeled trophic guilds in the CASM or the CASM . However, evaluation of systematically increased daily concentrations demonstrated the ability of both models to simulate direct and indirect toxic effects of this pesticide. The present model study suggests that process-based food web/ecosystem models can be used to characterize the potential ecological effects of thiamethoxam on generalized farm pond and emergent wetland ecosystems. Environ Toxicol Chem 2018;37:738-754. © 2017 SETAC.

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Patterns of toxicological effects in ecosystems: A modeling study

Cited by 25 publications

References 4 publications

Modeling the potential effects of atrazine on aquatic communities in midwestern streams

Modeling the potential effects of atrazine on aquatic communities in midwestern streams

Water quality modeling: A review of the analysis of uncertainty

Modeling the effects of thiamethoxam on Midwestern farm ponds and emergent wetlands

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