Opening the black box—Development, testing and documentation of a mechanistically rich agent-based model

Topping, Chris; Høye, Toke T.; Olesen, Carsten Riis

doi:10.1016/j.ecolmodel.2009.09.014

Cited by 80 publications

(61 citation statements)

References 39 publications

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“…This documentation follows ODdox format (Topping et al, 2010), combining model description with doxygen (van Heesch, 1997) code documentation. The animal models comprising ALMaSS have been tested using a pattern-oriented approach (Grimm et al, 2005;Topping et al, 2010) to maximize confidence in their structure and function. The models are quite detailed in their behaviour and hence run times for ALMaSS can be long, usually measured in hours or even days.…”

Section: The Model System (Almass)mentioning

confidence: 99%

Towards a landscape scale management of pesticides: ERA using changes in modelled occupancy and abundance to assess long-term population impacts of pesticides

Topping

Craig

Jong

et al. 2015

Science of The Total Environment

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Citation for published item:oppingD ghris tF nd grigD eter F nd de tongD prnk nd uleinD wihel nd vskowskiD yszrd nd wnhiniD frr nd ieperD ilvi nd mithD o nd ousD tos¡ e ulo nd treisslD prnz nd wrowskyD ulus nd iktkD eldrik nd vn der vindenD on @PHISA 9owrds lndspe sle mngement of pestiides X ie using hnges in modelled oupny nd undne to ssess longEterm popultion impts of pestiidesF9D iene of the totl environmentFD SQU F ppF ISWEITWF Further information on publisher's website: Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. AbstractPesticides are regulated in Europe and this process includes an environmental risk assessment (ERA) for non-target arthropods (NTA). Traditionally a non-spatial or field trial assessment is used. In this study we exemplify the introduction of a spatial context to the ERA as well as suggest a way in which the results of complex models, necessary for proper inclusion of spatial aspects in the ERA, can be presented and evaluated easily using abundance and occupancy ratios (AOR). We used an agent-based simulation system and an existing model for a widespread carabid beetle (Bembidion lampros), to evaluate the impact of a fictitious highly-toxic pesticide on population density and the distribution of beetles in time and space. Landscape structure and field margin management were evaluated by comparing scenario-based ERAs for the beetle. Source-sink dynamics led to an off-crop impact even when no pesticide was present off-crop. In addition, the impacts increased with multi-year application of the pesticide whereas current ERA considers only maximally one year. These results further indicated a complex interaction between landscape structure and pesticide effect in time, both in-crop and off-crop, indicating the need for NTA ERA to be conducted at landscape-and multi-season temporal-scales. Use of AOR indices to compare ERA outputs facilitated easy comparison of scenarios, allowing simultaneous evaluation of impacts and planning of mitigation measures. The landscape and population ERA approach also demonstrates that there is a potential to change from regulation of a pesticide in isolation, towards the consideration of pesticide management at landscape scales and provision of biodiversity benefits via inclusion and testing of mitigation measures in authorisation procedures.

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Section: The Model System (Almass)mentioning

confidence: 99%

Towards a landscape scale management of pesticides: ERA using changes in modelled occupancy and abundance to assess long-term population impacts of pesticides

Topping

Craig

Jong

et al. 2015

Science of The Total Environment

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“…In this respect, comparisons of mean time to extinction or intrinsic mean time to extinction are useful to estimate the persistence of populations subject to different environmental conditions (Grimm and Wissel, 2004). However, extracting the frequency distribution of time to extinction populations of abundant species simulated by complex agent-based models considering temporal changes in drivers dynamically would be computationally demanding and require much longer time than assessments based on the AORindex (Topping et al, 2010b). For instance, populations of the model species integrated in ALMaSS tend to be rather stable and will typically not die out at century-long time scales (Høye unpublished results).…”

Section: Discussionmentioning

confidence: 99%

“…The ALMaSS model framework is implemented for six model animal species resembling six real-world species: a carabid beetle (Bembidion lampros) (Bilde and Topping, 2004), a sheet web spider (Erigone atra) (Thorbek and Topping, 2005), the grey partridge (Perdix perdix) (Topping et al, 2010a), the skylark (Alauda arvensis) (Topping and Odderskaer, 2004), the European brown hare (Lepus europaeus) (Topping et al, 2010b) and the field vole (Microtus agrestis) (Dalkvist et al, 2009). These species were chosen because they are common in agricultural ecosystems and their ecology is well understood which allowed the construction of detailed behavioural rules in the species models.…”

Section: Model Speciesmentioning

confidence: 99%

Interpreting outputs of agent-based models using abundance–occupancy relationships

Høye

Skov

Topping

2012

Ecological Indicators

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a b s t r a c tReliable assessments of how human activities affect wild populations are essential for effective natural resource management. Agent-based models provide a powerful tool for integration of multiple drivers of ecological systems, but selecting and interpreting their output is often challenging. Here, we develop an indicator (the AOR-index) based on the abundance-occupancy relationship to facilitate the interpretation of agent-based model outputs. The AOR-index is based on the distribution of individuals in the landscape translated into the number of individuals in each cell of a regular grid. The proportion of grid cells with at least one individual is used to quantify occupancy and the mean number of individuals in occupied cells is used to quantify abundance. The AOR-index is a two-dimensional index giving the relative change in abundance and occupancy in response to a scenario (e.g. a change in land use or climate). We systematically modify a digital version of a real landscape to produce a set of artificial landscapes differing only in the degree of landscape fragmentation. We test how these different landscapes affect the AOR-index of six model animal species in four different land use scenarios using an agent-based model framework (ALMaSS). Our results suggest that the AOR-index is a sensitive tool to demonstrate how different species respond to particular land-use scenarios. The bird and mammal species generally showed larger responses than the invertebrates and changes in abundance and occupancy were often of different magnitude. The different responses are caused by species-specific habitat requirements and dispersal abilities, but the importance of such life history traits depend on landscape structure. Hence, predictions of species-specific responses to land-use changes in terms of abundance and occupancy are greatly improved by incorporation in a model framework taking spatial and temporal dynamics into account. The AOR-index facilitates the evaluation of multiple scenarios and allows for multi-species assessments. Its use, however, still requires identified management goals in order to evaluate scenario responses.

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“…In part, this is due to the complexities involved in scaling-up and scaling-down existing models [17], and in particular the modelling of institutional agents. Another issue is the complexity of dealing with calibration and validation of ABMs; while some form of sensitivity analysis is already used in ABM assessment [30][31][32], the type of global sensitivity analysis [33] necessary to properly engage with these models is still an active research area, often limited by the computational cost of the necessary model runs [34]. The FuturICT Living Earth Simulator will provide a computational environment that supports this kind of structured large-scale investigation of complex model behaviour.…”

Section: Proliferating Models Based On More and More Datamentioning

confidence: 99%

Data and models for exploring sustainability of human well-being in global environmental change

Deffuant

Alvarez

Barreteau

et al. 2012

Eur. Phys. J. Spec. Top.

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Abstract. This position paper proposes a vision for the research activity about sustainability in global environmental change (GEC) taking place in the FuturICT flagship project. This activity will be organised in an "Exploratory", gathering a core network of European scientists from ICT, social simulation, complex systems, economics, demographics, Earth system science. These research teams will collaborate in building a self-organising network of data sources and models about GEC and in using new facilities fostering stakeholder participation. We develop examples of concrete directions for this research: world wide virtual population with demographic and some economic descriptors, ecosystem services production and distribution, governance systems at various scales.

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Opening the black box—Development, testing and documentation of a mechanistically rich agent-based model

Cited by 80 publications

References 39 publications

Towards a landscape scale management of pesticides: ERA using changes in modelled occupancy and abundance to assess long-term population impacts of pesticides

Towards a landscape scale management of pesticides: ERA using changes in modelled occupancy and abundance to assess long-term population impacts of pesticides

Interpreting outputs of agent-based models using abundance–occupancy relationships

Data and models for exploring sustainability of human well-being in global environmental change

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