Towards better modelling and decision support: Documenting model development, testing, and analysis using TRACE

Grimm, Volker; Augusiak, Jacqueline; Focks, Andreas; Frank, Béatrice; Gabsi, Faten; Johnston, Alice S. A.; Chun, Liu; Martin, Benjamin T.; Meli, Mattia; Radchuk, Viktoriia; Thorbek, Pernille; Railsback, Steven F.

doi:10.1016/j.ecolmodel.2014.01.018

Cited by 197 publications

(164 citation statements)

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“…There is no standard way of documenting AEMs, and different model developers have different methods to obtain and save their information. The existing ODD protocol (overview, design concepts and details) for individual-based models , the Earth System documentation project (http://es-doc.org) or the TRACE approach (TRAnsparent and Comprehensive Ecological modelling documentation) might be adopted by aquatic ecosystem modellers in the future (Grimm et al 2014 . For these models, we were able to obtain metadata from experts to categorize them (see Online Resource 3 for all the details and see Text Box 1 for an explanation of all the technical terms used below).…”

Section: Documenting Diversity In Aemsmentioning

confidence: 99%

Exploring, exploiting and evolving diversity of aquatic ecosystem models: a community perspective

et al. 2015

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Here, we present a community perspective on how to explore, exploit and evolve the diversity in aquatic ecosystem models. These models play an important role in understanding the functioning of aquatic ecosystems, filling in observation gaps and developing effective strategies for water quality management. In this spirit, numerous models have been developed since the 1970s. We set off to explore model diversity by making an inventory among 42 aquatic ecosystem modellers, by categorizing the resulting set of models and by analysing them for diversity. We then focus on how to exploit model diversity by comparing and combining different aspects of existing models. Finally, we discuss how model diversity came about in the past and could evolve in the future. Throughout our study, we use Handling Editor: Piet Spaak.Electronic supplementary material The online version of this article (doi:10.1007/s10452-015-9544-1) contains supplementary material, which is available to authorized users. 123Aquat ) 49:513-548 DOI 10.1007 analogies from biodiversity research to analyse and interpret model diversity. We recommend to make models publicly available through open-source policies, to standardize documentation and technical implementation of models, and to compare models through ensemble modelling and interdisciplinary approaches. We end with our perspective on how the field of aquatic ecosystem modelling might develop in the next 5-10 years. To strive for clarity and to improve readability for non-modellers, we include a glossary.

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Section: Documenting Diversity In Aemsmentioning

confidence: 99%

Exploring, exploiting and evolving diversity of aquatic ecosystem models: a community perspective

et al. 2015

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“…ECOBAS is designed to overcome the difficulties in writing model documentation and applying the documentation to any model language. Schmolke et al [30] and Grimm et al [31] propose 'transparent and comprehensive ecological modeling evaluation' (TRACE), a tool for planning, performing and documenting good modeling practice. The authors aim to establish expectations of what modelers should clearly communicate when presenting their model (e.g.…”

Section: Literature Reviewmentioning

confidence: 99%

Raising awareness in model-based energy scenario studies—a transparency checklist

et al. 2016

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Background: The focus of the paper is on scenario studies that examine energy systems. This type of studies is usually based on formal energy models, from which energy policy recommendations are derived. In order to be valuable for strategic decision-making, the comprehensibility of these complex scenario studies is necessary. We aim at highlighting and mitigating the problematic issue of lacking transparency in such model-based scenario studies.

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“…Validation of these various models is an important consideration and a topic of lively debate (which is beyond the scope of the present Focus article). However, using an iterative process that involves feedback between data collection and modeling in a so-called modeling cycle [31,32] as well as efforts to increase systematic testing, documentation, and evaluation of model outputs [33][34][35][36] will help to address validation issues. …”

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confidence: 99%

A framework for predicting impacts on ecosystem services from (sub)organismal responses to chemicals

Forbes¹,

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Birnir

et al. 2017

Enviro Toxic and Chemistry

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Abstract-Protection of ecosystem services is increasingly emphasized as a risk-assessment goal, but there are wide gaps between current ecological risk-assessment endpoints and potential effects on services provided by ecosystems. The authors present a framework that links common ecotoxicological endpoints to chemical impacts on populations and communities and the ecosystem services that they provide. This framework builds on considerable advances in mechanistic effects models designed to span multiple levels of biological organization and account for various types of biological interactions and feedbacks. For illustration, the authors introduce 2 case studies that employ well-developed and validated mechanistic effects models: the inSTREAM individual-based model for fish populations and the AQUATOX ecosystem model. They also show how dynamic energy budget theory can provide a common currency for interpreting organism-level toxicity. They suggest that a framework based on mechanistic models that predict impacts on ecosystem services resulting from chemical exposure, combined with economic valuation, can provide a useful approach for informing environmental management. The authors highlight the potential benefits of using this framework as well as the challenges that will need to be addressed in future work. Environ Toxicol Chem 2017;36:845-859. # 2017 SETAC Keywords-Ecological production function; Ecological risk assessment; Ecosystem service; Environmental management; Mechanistic effects model Challenges for Ecological Risk Assessment and ManagementThe primary goal of ecological risk assessment (ERA) of chemicals is to provide defensible science-based support for environmental management decisions. This involves making explicit connections between impacts on the benefits derived by people from ecosystems (so-called ecosystem services [1]) and the costs of managing the causes of those impacts. At the core of this approach is the need for relevant chemical exposure-response relationships. However, current ERA approaches often fall short in these regards because methods In This Issue: ET&C FOCUSFocus articles are part of a regular series intended to sharpen understanding of current and emerging topics of interest to the scientific community. for estimating and integrating exposure and effects are often based on overly simplistic assumptions [2,3]. For example, measures of organism-level toxicity (e.g., 50% effect concentrations) are used as indicators of population-level impacts of chemicals. A primary concern is that the kinds of information collected to support ERAs are far removed from the kinds of ecological entities (e.g., species or habitats) that are the targets of protection, which themselves are often only vaguely defined in legislation (e.g., European pesticides legislation refers to "no unacceptable effects on the environment"). In practice, protection goals for ecological systems are (implicitly or explicitly) often at the population, community, or ecosystem level (e.g., persistence or abundance of a p...

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Towards better modelling and decision support: Documenting model development, testing, and analysis using TRACE

Cited by 197 publications

References 18 publications

Exploring, exploiting and evolving diversity of aquatic ecosystem models: a community perspective

Exploring, exploiting and evolving diversity of aquatic ecosystem models: a community perspective

Raising awareness in model-based energy scenario studies—a transparency checklist

A framework for predicting impacts on ecosystem services from (sub)organismal responses to chemicals

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