Comparing population recovery after insecticide exposure for four aquatic invertebrate species using models of different complexity

Population models, in particular individual-based models (IBMs), are becoming increasingly important in chemical risk assessment. They can be used to assess recovery of spatially structured populations after chemical exposure that varies in time and space. The authors used an IBM coupled to a toxicokinetic-toxicodynamic model, the threshold damage model (TDM), to assess recovery times for 4 aquatic organisms, after insecticide application, in a nonseasonal environment and in 3 spatial settings (pond, stream, and ditch). The species had different life histories (e.g., voltinism, reproductive capacity, mobility). Exposure was derived from a pesticide fate model, following standard European Union scenarios. The results of the IBM-TDM were compared with results from simpler models: one in which exposure was linked to effects by means of concentration-effect relationships (IBM-CE) and one in which the IBM was replaced by a nonspatial, logistic growth model (logistic). For the first, exposure was based on peak concentrations only; for the second, exposure was spatially averaged as well. By using comparisons between models of different complexity and species with different life histories, the authors obtained an understanding of the role spatial processes play in recovery and the conditions under which the full time-varying exposure needs to be considered. The logistic model, which is amenable to an analytic approach, provided additional insights into the sensitivity of recovery times to density dependence and spatial dimensions.

[1]  Paul J. Van den Brink,et al.  Assessing aquatic population and community-level risks of pesticides. , 2013 .

[2]  Ettore Capri,et al.  Guidance on tiered risk assessment for plant protection products for aquatic organisms in edge-of-field surface waters , 2013 .

[3]  R. Stoks,et al.  Habitat isolation shapes the recovery of aquatic insect communities from a pesticide pulse , 2011 .

[4]  Roman Ashauer,et al.  Highly time‐variable exposure to chemicals—toward an assessment strategy , 2013, Integrated environmental assessment and management.

[5]  Roman Ashauer,et al.  Toxicokinetic–toxicodynamic modelling in an individual based context—Consequences of parameter variability , 2010 .

[6]  Valery E. Forbes,et al.  TOXICANT IMPACTS ON DENSITY‐LIMITED POPULATIONS: A CRITICAL REVIEW OF THEORY, PRACTICE, AND RESULTS , 2001 .

[7]  P. Calow,et al.  Is the per capita rate of increase a good measure of population‐level effects in ecotoxicology? , 1999 .

[8]  V E Forbes,et al.  Population-level impacts of pesticide-induced chronic effects on individuals depend more on ecology than toxicology. , 2009, Ecotoxicology and environmental safety.

[9]  Andreas Focks,et al.  Integrating chemical fate and population-level effect models for pesticides at landscape scale: New options for risk assessment , 2014 .

[10]  Geerten M. Hengeveld,et al.  Persistence of Aquatic Insects across Managed Landscapes: Effects of Landscape Permeability on Re-Colonization and Population Recovery , 2013, PloS one.

[11]  Roman Ashauer,et al.  Toxicokinetic variation in 15 freshwater arthropod species exposed to the insecticide chlorpyrifos , 2010, Environmental toxicology and chemistry.

[12]  Roman Ashauer,et al.  Modeling the contribution of toxicokinetic and toxicodynamic processes to the recovery of Gammarus pulex populations after exposure to pesticides , 2014, Environmental toxicology and chemistry.

[13]  Paul J van den Brink,et al.  Simulating population recovery of an aquatic isopod: Effects of timing of stress and landscape structure. , 2012, Environmental pollution.

[14]  Roman Ashauer,et al.  General unified threshold model of survival--a toxicokinetic-toxicodynamic framework for ecotoxicology. , 2011, Environmental science & technology.

[15]  Paulien Hogeweg,et al.  From population dynamics to ecoinformatics: Ecosystems as multilevel information processing systems , 2007, Ecol. Informatics.

[16]  Matthias Liess,et al.  Interspecific competition delays recovery of Daphnia spp. populations from pesticide stress , 2012, Ecotoxicology.

[17]  P. J. Van den Brink,et al.  Potential application of ecological models in the European environmental risk assessment of chemicals I: Review of protection goals in EU directives and regulations , 2010, Integrated environmental assessment and management.

[18]  Paul J. Van den Brink,et al.  Effects of the insecticide dursban® 4E (active ingredient chlorpyrifos) in outdoor experimental ditches: I. Comparison of short‐term toxicity between the laboratory and the field , 1996 .

[19]  W.H.J. Beltman,et al.  Manual of FOCUS_TOXSWA Version 2.2.1 , 2006 .

[20]  J. M. Baveco,et al.  Assessing the impact of pesticides on lumbricid populations: an individual-based modelling approach , 1996 .

[21]  M. Breitholtz,et al.  An individual‐based modeling approach for evaluation of endpoint sensitivity in harpacticoid copepod life‐cycle tests and optimization of test design , 2011, Environmental toxicology and chemistry.

[22]  John D Stark,et al.  A comparison of simple and complex population models to reduce uncertainty in ecological risk assessments of chemicals: example with three species of Daphnia , 2011, Ecotoxicology.

[23]  J. Gore,et al.  Island biogeographical theory: Can it be used to predict lotic recovery rates? , 1990 .

[24]  Peter Chapman,et al.  Ecological models and pesticide risk assessment: Current modeling practice , 2010, Environmental toxicology and chemistry.

[25]  Theo C M Brock,et al.  Macroinvertebrate responses to insecticide application between sprayed and adjacent nonsprayed ditch sections of different sizes , 2010, Environmental toxicology and chemistry.

[26]  Roman Ashauer,et al.  Simulating toxicity of carbaryl to Gammarus pulex after sequential pulsed exposure. , 2007, Environmental science & technology.

[27]  V E Forbes,et al.  Conceptual model for improving the link between exposure and effects in the aquatic risk assessment of pesticides. , 2007, Ecotoxicology and environmental safety.

[28]  Jana Verboom,et al.  An individual‐based approach to model spatial population dynamics of invertebrates in aquatic ecosystems after pesticide contamination , 2007, Environmental toxicology and chemistry.

[29]  P. J. Van den Brink,et al.  Potential application of population models in the European ecological risk assessment of chemicals II: Review of models and their potential to address environmental protection aims , 2010, Integrated environmental assessment and management.

[30]  S. Raimondo Density dependent functional forms drive compensation in populations exposed to stressors , 2013 .

[31]  Roman Ashauer,et al.  New ecotoxicological model to simulate survival of aquatic invertebrates after exposure to fluctuating and sequential pulses of pesticides. , 2007, Environmental science & technology.

[32]  Matthias Liess,et al.  Intraspecific competition delays recovery of population structure. , 2010, Aquatic toxicology.

[33]  Malgorzata Lagisz,et al.  Spatial Dynamic Factors Affecting Population-Level Risk Assessment for a Terrestrial Arthropod: An Agent-Based Modeling Approach , 2012 .

[34]  Volker Grimm,et al.  Home range dynamics and population regulation: An individual-based model of the common shrew Sorex araneus , 2007 .

[35]  Roman Ashauer,et al.  CREAM: a European project on mechanistic effect models for ecological risk assessment of chemicals , 2009, Environmental science and pollution research international.

[36]  Lawrence W Barnthouse,et al.  Quantifying population recovery rates for ecological risk assessment , 2004, Environmental toxicology and chemistry.

[37]  R. Schulz,et al.  Regulatory FOCUS surface water models fail to predict insecticide concentrations in the field. , 2012, Environmental science & technology.

[38]  J. Gareth Polhill,et al.  The ODD protocol: A review and first update , 2010, Ecological Modelling.

[39]  Pernille Thorbek,et al.  Linking pesticide exposure and spatial dynamics: An individual-based model of wood mouse (Apodemus sylvaticus) populations in agricultural landscapes , 2013 .