Population modeling for pesticide risk assessment of threatened species—A case study of a terrestrial plant, <i>Boltonia decurrens</i>

Schmolke, Amélie; Brain, Richard A.; Thorbek, Pernille; Perkins, D. B.; Forbes, Valery E.

doi:10.1002/etc.3576

Cited by 16 publications

(24 citation statements)

References 43 publications

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“…This suggests that relying on individual-level responses as proxies of impacts of multiple stressors could seriously underestimate the risks for populations and ecosystems. Interestingly, many modelling studies evaluating impacts of individual stressors found that population responses are the same or lower than those at the level of individuals, usually due to density-dependent buffering mechanisms (Forbes et al 2001;Galic et al 2017;Schmolke et al 2017), but see Gergs et al (2013).…”

Section: Responses To Stressors At Different Levels Of Biological Orgmentioning

confidence: 99%

“…Assessing impacts on populations based on individual-level responses can result in errors because of many compensatory or depensatory processes and feedbacks across levels of organisation. Previous studies have shown that effects of isolated stressors on populations are not necessarily proportional to those on individuals, and can be of lesser (Galic et al 2017;Schmolke et al 2017) or greater magnitude (Gergs et al 2013).…”

Section: Introductionmentioning

confidence: 99%

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When things don't add up: quantifying impacts of multiple stressors from individual metabolism to ecosystem processing

et al. 2018

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Ecosystems are exposed to multiple stressors which can compromise functioning and service delivery. These stressors often co-occur and interact in different ways which are not yet fully understood. Here, we applied a population model representing a freshwater amphipod feeding on leaf litter in forested streams. We simulated impacts of hypothetical stressors, individually and in pairwise combinations that target the individuals' feeding, maintenance, growth and reproduction. Impacts were quantified by examining responses at three levels of biological organisation: individual-level body sizes and cumulative reproduction, population-level abundance and biomass and ecosystem-level leaf litter decomposition. Interactive effects of multiple stressors at the individual level were mostly antagonistic, that is, less negative than expected. Most population- and ecosystem-level responses to multiple stressors were stronger than expected from an additive model, that is, synergistic. Our results suggest that across levels of biological organisation responses to multiple stressors are rarely only additive. We suggest methods for efficiently quantifying impacts of multiple stressors at different levels of biological organisation.

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Section: Responses To Stressors At Different Levels Of Biological Orgmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

When things don't add up: quantifying impacts of multiple stressors from individual metabolism to ecosystem processing

et al. 2018

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“…Relative effects of stressors on populations may differ depending on whether the population is stable, declining, or increasing (Calow et al ; Salice ; Schmolke et al ). For the main simulation results given in the present study, we assumed stable populations in the untreated controls.…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, population models can be used to represent available information about the life history and ecology of a species and combined with organism‐level exposure–effects relationships from surrogate species to assess potential population‐level effects of pesticide exposures over extended time periods (Forbes et al ; Schmolke et al ). Species‐specific population models developed for pesticide risk assessments can be used as platforms to assess potential risks from various pesticides or other stressors, rather than being specific to the risk assessment of a single compound (Schmolke et al ). However, population models specifically developed for and applied to pesticide risk assessments are still relatively rare (Forbes et al ).…”

Section: Introductionmentioning

confidence: 99%

Adapting population models for application in pesticide risk assessment: A case study with Mead's milkweed

Schmolke

Roy

Brain

et al. 2018

Enviro Toxic and Chemistry

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Population models can facilitate assessment of potential impacts of pesticides on populations or species rather than individuals and have been identified as important tools for pesticide risk assessment of nontarget species including those listed under the Endangered Species Act. Few examples of population models developed for this specific purpose are available; however, population models are commonly used in conservation science as a tool to project the viability of populations and the long-term outcomes of management actions. We present a population model for Mead's milkweed (Asclepias meadii), a species listed as threatened under the Endangered Species Act throughout its range across the Midwestern United States. We adapted a published population model based on demographic field data for application in pesticide risk assessment. Exposure and effects were modeled as reductions of sets of vital rates in the transition matrices, simulating both lethal and sublethal effects of herbicides. Two herbicides, atrazine and mesotrione, were used as case study examples to evaluate a range of assumptions about potential exposure-effects relationships. In addition, we assessed buffers (i.e., setback distances of herbicide spray applications from the simulated habitat) as hypothetical mitigation scenarios and evaluated their influence on population-level effects in the model. The model results suggest that buffers can be effective at reducing risk from herbicide drift to plant populations. These case studies demonstrate that existing population models can be adopted and integrated with exposure and effects information for use in pesticide risk assessment. Environ Toxicol Chem 2018;37:2235-2245. © 2018 SETAC.

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“…For example, one can simulate temperature and/or food fluctuations and calculate their effects on individual metabolism at each time step (Accolla et al 2019). Other environmental drivers can be hydrological variability and water turbidity (Railsback et al 2009; Focks, ter Horst et al 2014), flooding (Schmolke, Brain et al 2017), or flowering periods that affect pollinator foraging (Becher et al 2014). Alternatively, states of agents may vary according to a pattern defined by the day of the year or season.…”

Section: Key Features To Consider In Model Development and Evaluationmentioning

confidence: 99%

A Review of Key Features and Their Implementation in Unstructured, Structured, and Agent‐Based Population Models for Ecological Risk Assessment

Accolla

Vaugeois

Grimm

et al. 2020

Integr Envir Assess & Manag

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Population models can provide valuable tools for ecological risk assessment (ERA). A growing amount of work on model development and documentation is now available to guide modelers and risk assessors to address different ERA questions. However, there remain misconceptions about population models for ERA, and communication between regulators and modelers can still be hindered by a lack of clarity in the underlying formalism, implementation, and complexity of different model types. In particular, there is confusion about differences among types of models and the implications of including or ignoring interactions of organisms with each other and their environment. In this review, we provide an overview of the key features represented in population models of relevance for ERA, which include density dependence, spatial heterogeneity, external drivers, stochasticity, life‐history traits, behavior, energetics, and how exposure and effects are integrated in the models. We differentiate 3 broadly defined population model types (unstructured, structured, and agent‐based) and explain how they can represent these key features. Depending on the ERA context, some model features will be more important than others, and this can inform model type choice, how features are implemented, and possibly the collection of additional data. We show that nearly all features can be included irrespective of formalization, but some features are more or less easily incorporated in certain model types. We also analyze how the key features have been used in published population models implemented as unstructured, structured, and agent‐based models. The overall aim of this review is to increase confidence and understanding by model users and evaluators when considering the potential and adequacy of population models for use in ERA. Integr Environ Assess Manag 2021;17:521–540. © 2020 SETAC

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Population modeling for pesticide risk assessment of threatened species—A case study of a terrestrial plant, Boltonia decurrens

Cited by 16 publications

References 43 publications

When things don't add up: quantifying impacts of multiple stressors from individual metabolism to ecosystem processing

When things don't add up: quantifying impacts of multiple stressors from individual metabolism to ecosystem processing

Adapting population models for application in pesticide risk assessment: A case study with Mead's milkweed

A Review of Key Features and Their Implementation in Unstructured, Structured, and Agent‐Based Population Models for Ecological Risk Assessment

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