Modeling the interaction between the exotic invasive aquatic macrophyte Myriophyllum spicatum and the native biocontrol agent Euhrychiopsis lecontei to improve augmented management programs

Miller, Judy Z.; Roketenetz, Lara; Garris, Heath W.

doi:10.1007/s10526-011-9371-9

Cited by 5 publications

(5 citation statements)

References 23 publications

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“…Terrestrial plants (dicots, monocots, and nonangiosperms combined), mammals, and birds were the most modeled taxonomic groups, followed by fish, aquatic invertebrates, and terrestrial invertebrates. There were relatively few models of amphibians and reptiles and only 2 models of aquatic plants (Figure A).…”

Section: Results Of the Literature Reviewmentioning

confidence: 99%

Assessing the risks of pesticides to threatened and endangered species using population modeling: A critical review and recommendations for future work

Forbes

Galić

Schmolke

et al. 2016

Enviro Toxic and Chemistry

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United States legislation requires the US Environmental Protection Agency to ensure that pesticide use does not cause unreasonable adverse effects on the environment, including species listed under the Endangered Species Act (ESA; hereafter referred to as listed species). Despite a long history of population models used in conservation biology and resource management and a 2013 report from the US National Research Council recommending their use, application of population models for pesticide risk assessments under the ESA has been minimal. The pertinent literature published from 2004 to 2014 was reviewed to explore the availability of population models and their frequency of use in listed species risk assessments. The models were categorized in terms of structure, taxonomic coverage, purpose, inputs and outputs, and whether the models included density dependence, stochasticity, or risk estimates, or were spatially explicit. Despite the widespread availability of models and an extensive literature documenting their use in other management contexts, only 2 of the approximately 400 studies reviewed used population models to assess the risks of pesticides to listed species. This result suggests that there is an untapped potential to adapt existing models for pesticide risk assessments under the ESA, but also that there are some challenges to do so for listed species. Key conclusions from the analysis are summarized, and priorities are recommended for future work to increase the usefulness of population models as tools for pesticide risk assessments. Environ Toxicol Chem 2016;35:190435: -191335: . # 2016

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Section: Results Of the Literature Reviewmentioning

confidence: 99%

Assessing the risks of pesticides to threatened and endangered species using population modeling: A critical review and recommendations for future work

Forbes

Galić

Schmolke

et al. 2016

Enviro Toxic and Chemistry

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“…These authors attribute wave shelter, higher temperature, predation refuge (but not light), are likely contributing factors to weevil success in smaller lakes. In addition, the modeling work by Miller et al (Miller et al, 2011) has shown diminishing returns between single‐season stockings of weevils and EWM control efficacy. That is, past 50 weevils added per season, the reduction in biomass as described by the models simulated biomass peak at the end of season is negligible.…”

Section: Discussionmentioning

confidence: 99%

“…Previous mathematical models of aquatic macrophyte growth have been developed to study both seasonal (Herb & Stefan, 2003; Miller et al, 2011) and yearly plant growth (Best et al, 2001). These predictive models are mechanistic in nature, taking into account how macrophytes are dependent on lake characteristics such as water clarity, available nutrients, water temperature, as well as the metabolic properties of the plant itself.…”

Section: Introductionmentioning

confidence: 99%

“…These predictive models are mechanistic in nature, taking into account how macrophytes are dependent on lake characteristics such as water clarity, available nutrients, water temperature, as well as the metabolic properties of the plant itself. Miller et al's model focuses on EWM growth, while also taking into account the effect of milfoil weevils on season biomass (Miller et al, 2011). Other models take geospatial and field data into account (plant location and lake boundary), merging this with other model variables that characterize the growth characteristics of invasive macrophytes, like EWM (Buchan & Padilla, 2000; Olson et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

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A machine‐learning approach to predict success of a biocontrol for invasive Eurasian watermilfoil reduction

White

Antoniou

Martin

et al. 2022

Ecological Applications

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Myriophyllum spicatum, more commonly known as Eurasian watermilfoil (EWM), is one of the most invasive aquatic plants in North America, causing negative ecological and economic impacts in ecosystems where it proliferates. Many control strategies have been developed and implemented to mitigate EWM growth and spread, although the results are mixed and there is no consensus on lake‐specific strategies. Here, we describe the development of a predictive model using a support vector technique, that predicts the success of biological pest control using Euhrychiopsis lecontei (the milfoil weevil), a milfoil specialist, to reduce EWM in lakes. Such a model is informed by lake characteristics (limnological and landscape) and augmentation strategies. To develop our predictive model, we performed a metadata analysis from 133 published peer‐reviewed literature and professional reports of milfoil weevil augmentation field experiments that contained information on lake characteristics. The predictive model's algorithm uses a support vector machine (SMV) to learn patterns among lake characteristics, along with the recorded augmentation strategy and the reported success of each study, where success is a measure of EWM change over a season and is recorded in a variety of ways (e.g., EWM biomass change, EWM percent change, EWM visual change, etc.,). Overall, the model results suggests that shallower lakes, more frequent weevil augmentations, and larger weevil overwintering habitat are the most important predictors for EWM reduction success by weevil augmentation. Although watermilfoil weevil augmentation is a promising mitigation strategy, it may not work for all lakes. However, in terms of suggesting weevil augmentation, our model is a valuable tool for lake stakeholders and resource managers, who can use it to determine whether milfoil weevil augmentation, which can be very costly due to the difficulties in finding and raising milfoil weevils, will be a useful and sustainable approach to control EWM in their lake community.

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“…Energy uptake and expenditure can be modeled as a function of age‐specific or stage‐specific body mass or age. Energy therefore drives the development of each class (Miller et al 2011; Pachzelt et al 2013). Other studies link more complicated energy budget models, such as DEB theory, to structured model parameters (Klanjscek et al 2006; Klok et al 2007).…”

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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Modeling the interaction between the exotic invasive aquatic macrophyte Myriophyllum spicatum and the native biocontrol agent Euhrychiopsis lecontei to improve augmented management programs

Cited by 5 publications

References 23 publications

Assessing the risks of pesticides to threatened and endangered species using population modeling: A critical review and recommendations for future work

Assessing the risks of pesticides to threatened and endangered species using population modeling: A critical review and recommendations for future work

A machine‐learning approach to predict success of a biocontrol for invasive Eurasian watermilfoil reduction

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

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