An energy budget agent-based model of earthworm populations and its application to study the effects of pesticides

Johnston, Alice S. A.; Hodson, Mark E.; Thorbek, Pernille; Alvarez, Tania; Sibly, Richard M.

doi:10.1016/j.ecolmodel.2013.09.012

Cited by 61 publications

(53 citation statements)

References 72 publications

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“…Incorporating the effects of soil biology into models of soil function and evolution requires an understanding of the causes of these effects but also an understanding of the distribution and behavior of soil organisms. In recognition of this fact we are currently developing agent based models that allow us to predict the behavior and distribution of earthworms in soils 23 .…”

Section: Discussionmentioning

confidence: 99%

Biology as an Agent of Chemical and Mineralogical Change in Soil

Hodson

Brinza

Carpenter

et al. 2014

Procedia Earth and Planetary Science

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Section: Discussionmentioning

confidence: 99%

Biology as an Agent of Chemical and Mineralogical Change in Soil

Hodson

Brinza

Carpenter

et al. 2014

Procedia Earth and Planetary Science

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“…In each case, 5-10 earthworms were placed in small containers filled with cattle manure for food (Reinecke and Viljoen 1990, Gunadi et al 2002, Gunadi and Edwards 2003. Earthworms wriggle around randomly as they forage, and allocate assimilated energy to maintenance, growth, reproduction, and reserves in a fixed order of priority; see Johnston et al (2014). Earthworms wriggle around randomly as they forage, and allocate assimilated energy to maintenance, growth, reproduction, and reserves in a fixed order of priority; see Johnston et al (2014).…”

Section: Applicationsmentioning

confidence: 99%

“…Earthworms wriggle around randomly as they forage, and allocate assimilated energy to maintenance, growth, reproduction, and reserves in a fixed order of priority; see Johnston et al (2014). The priors for all parameters were lognormal, with means equal to previously determined literature values (see Johnston et al [2014]) and standard deviations equal to 0.3536. The priors for all parameters were lognormal, with means equal to previously determined literature values (see Johnston et al [2014]) and standard deviations equal to 0.3536.…”

Section: Applicationsmentioning

confidence: 99%

“…We illustrate the use of this new algorithm by analyzing both a toy example and a complex computer simulation of earthworms (Johnston et al 2014), which was developed for the purpose of pesticide risk assessment. We illustrate the use of this new algorithm by analyzing both a toy example and a complex computer simulation of earthworms (Johnston et al 2014), which was developed for the purpose of pesticide risk assessment.…”

Section: Introductionmentioning

confidence: 99%

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Taking error into account when fitting models using Approximate Bayesian Computation

Vaart

Prangle

Sibly

2018

Ecological Applications

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Stochastic computer simulations are often the only practical way of answering questions relating to ecological management. However, due to their complexity, such models are difficult to calibrate and evaluate. Approximate Bayesian Computation (ABC) offers an increasingly popular approach to this problem, widely applied across a variety of fields. However, ensuring the accuracy of ABC's estimates has been difficult. Here, we obtain more accurate estimates by incorporating estimation of error into the ABC protocol. We show how this can be done where the data consist of repeated measures of the same quantity and errors may be assumed to be normally distributed and independent. We then derive the correct acceptance probabilities for a probabilistic ABC algorithm, and update the coverage test with which accuracy is assessed. We apply this method, which we call error-calibrated ABC, to a toy example and a realistic 14-parameter simulation model of earthworms that is used in environmental risk assessment. A comparison with exact methods and the diagnostic coverage test show that our approach improves estimation of parameter values and their credible intervals for both models.

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“…For complex systems in ecology and the nature-farming interface, ABM allows for the testing and observation of virtual policy interventions ex-ante of actual funding and implementation. For example, ABM has been used to simulate the effect of earthworms on soil structure (Blanchart et al 2009) and earthworm responses to pesticide applications (Johnston et al 2014). In these two examples, the behaviour of the earthworms interact with their environment according to a set of rules, which apply themselves to earth structure in the first example, or as in the second example, a reaction to an intervention.…”

Section: An Agent Based Modelling Approachmentioning

confidence: 99%

Combining micro-bottom-up and macro-top-down modelling responses to nutrient cycles in complex agricultural systems

Burdock

Crawford

2015

Nutr Cycl Agroecosyst

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Concerns over the long-term sustainability of the food production system and the nutritional content of food from mineral depleted soils have encouraged a policy shift to sustainable agricultural practices where soil health supports nutrition-sensitive agriculture. Interventions at the micro scale have the ability to affect the entire system, forcing an examination at the whole of systems level. Modelling plays an important role in determining the outcomes of policy intervention combinations, particularly when systems are identified as being complex. This study begins with a systems map tracing the nutrient cycling process between natural ecosystem processes and farm practices from the bottom-up within a top-down framework. Soil is at the centre of this approach, expanding links to other influences within the framework in order to understand the relationships between elements in an environmental-agricultural system. Moving to a generic model from a broad conceptual system map is problematic when crop types, plant mineral absorption rates, soil and geographic differences are to be accommodated. Work has been done to develop top-down framework models integrating bottom-up component models, capable of being used in different scenarios. Whether existing models are used or new models are created, this study recommends that an appropriate modelling response require examination of systems and policy interventions both holistically and in detail encompassing situational specifics.

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An energy budget agent-based model of earthworm populations and its application to study the effects of pesticides

Cited by 61 publications

References 72 publications

Biology as an Agent of Chemical and Mineralogical Change in Soil

Biology as an Agent of Chemical and Mineralogical Change in Soil

Taking error into account when fitting models using Approximate Bayesian Computation

Combining micro-bottom-up and macro-top-down modelling responses to nutrient cycles in complex agricultural systems

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