Meyer, Katrin Mareike

[["dc.bibliographiccitation.firstpage","1854"],["dc.bibliographiccitation.issue","11"],["dc.bibliographiccitation.journal","Methods in Ecology and Evolution"],["dc.bibliographiccitation.lastpage","1863"],["dc.bibliographiccitation.volume","10"],["dc.contributor.author","Salecker, Jan"],["dc.contributor.author","Sciaini, Marco"],["dc.contributor.author","Meyer, Katrin M."],["dc.contributor.author","Wiegand, Kerstin"],["dc.date.accessioned","2020-12-10T18:26:41Z"],["dc.date.available","2020-12-10T18:26:41Z"],["dc.date.issued","2019"],["dc.description.abstract","1.Agent-based models find wide application in all fields of science where large scale patterns emerge from properties of individuals. Due to increasing capacities of computing resources it was possible to improve the level of detail and structural realism of next-generation models in recent years. However, this is at the expense of increased model complexity, which requires more efficient tools for model exploration, analysis and documentation that enable reproducibility, repeatability and parallelisation. NetLogo is a widely used environment for agent-based model development, but it does not provide sufficient built-in tools for extensive model exploration, such as sensitivity analyses. One tool for controlling NetLogo externally is the R-package RNetLogo. However, this package is not suited for efficient, reproducible research as it has stability and resource allocation issues, is not straightforward to be setup and used on high performance computing clusters and does not provide utilities, such as storing and exchanging metadata, in an easy way. 2.We present the R-package nlrx, which overcomes stability and resource allocation issues by running NetLogo simulations via dynamically created XML experiment files. Class objects make setting up experiments more convenient and helper functions provide many parameter exploration approaches, such as Latin Hypercube designs, Sobol sensitivity analyses or optimization approaches. Output is automatically collected in user-friendly formats and can be post-processed with provided utility functions. nlrx enables reproducibility by storing all relevant information and simulation output of experiments in one R object which can conveniently be archived and shared. 3.We provide a detailed description of the nlrx package functions and the overall workflow. We also present a use case scenario using a NetLogo model, for which we performed a sensitivity analysis and a genetic algorithm optimization. 4.The nlrx package is the first framework for documentation and application of reproducible NetLogo simulation model analysis."],["dc.description.sponsorship","Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659"],["dc.identifier.doi","10.1111/2041-210X.13286"],["dc.identifier.issn","2041-210X"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/16518"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/76155"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.notes.intern","Merged from goescholar"],["dc.relation","SFB 990: Ökologische und sozioökonomische Funktionen tropischer Tieflandregenwald-Transformationssysteme (Sumatra, Indonesien)"],["dc.relation","SFB 990 | B | B10: Landschaftsbezogene Bewertung der ökologischen und sozioökonomischen Funktionen von Regenwald- Transformationssystemen in Sumatra (Indonesien)"],["dc.relation.issn","2041-210X"],["dc.relation.orgunit","Zentrum für Biodiversität und Nachhaltige Landnutzung"],["dc.relation.orgunit","Abteilung Ökosystemmodellierung"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.subject.gro","agent-based modelling"],["dc.subject.gro","individual-based modelling"],["dc.subject.gro","reproducible workflow"],["dc.subject.gro","R package"],["dc.subject.gro","NetLogo"],["dc.subject.gro","sfb990_journalarticles"],["dc.title","The nlrx r package: A next‐generation framework for reproducible NetLogo model analyses"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","e0222949"],["dc.bibliographiccitation.issue","9"],["dc.bibliographiccitation.journal","PLoS One"],["dc.bibliographiccitation.volume","14"],["dc.contributor.author","Salecker, Jan"],["dc.contributor.author","Dislich, Claudia"],["dc.contributor.author","Wiegand, Kerstin"],["dc.contributor.author","Meyer, Katrin M."],["dc.contributor.author","Pe Er, Guy"],["dc.date.accessioned","2020-12-08T07:41:51Z"],["dc.date.available","2020-12-08T07:41:51Z"],["dc.date.issued","2019"],["dc.description.abstract","Spatially-explicit simulation models are commonly used to study complex ecological and socio-economic research questions. Often these models depend on detailed input data, such as initial land-cover maps to set up model simulations. Here we present the landscape generator EFFortS-LGraf that provides artificially-generated land-use maps of agricultural landscapes shaped by small-scale farms. EFForTS-LGraf is a process-based landscape generator that explicitly incorporates the human dimension of land-use change. The model generates roads and villages that consist of smallholder farming households. These smallholders use different establishment strategies to create fields in their close vicinity. Crop types are distributed to these fields based on crop fractions and specialization levels. EFForTS-LGraf model parameters such as household area or field size frequency distributions can be derived from household surveys or geospatial data. This can be an advantage over the abstract parameters of neutral landscape generators. We tested the model using oil palm and rubber farming in Indonesia as a case study and validated the artificially-generated maps against classified satellite images. Our results show that EFForTS-LGraf is able to generate realistic land-cover maps with properties that lie within the boundaries of landscapes from classified satellite images. An applied simulation experiment on landscape-level effects of increasing household area and crop specialization revealed that larger households with higher specialization levels led to spatially more homogeneous and less scattered crop type distributions and reduced edge area proportion. Thus, EFForTS-LGraf can be applied both to generate maps as inputs for simulation modelling and as a stand-alone tool for specific landscape-scale analyses in the context of ecological-economic studies of smallholder farming systems."],["dc.identifier.doi","10.1371/journal.pone.0222949"],["dc.identifier.pmid","31560726"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/16510"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/69449"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.relation","SFB 990: Ökologische und sozioökonomische Funktionen tropischer Tieflandregenwald-Transformationssysteme (Sumatra, Indonesien)"],["dc.relation","SFB 990 | B | B10: Landschaftsbezogene Bewertung der ökologischen und sozioökonomischen Funktionen von Regenwald- Transformationssystemen in Sumatra (Indonesien)"],["dc.relation.eissn","1932-6203"],["dc.relation.orgunit","Abteilung Ökosystemmodellierung"],["dc.relation.orgunit","Zentrum für Biodiversität und Nachhaltige Landnutzung"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.subject.gro","landscape generator"],["dc.subject.gro","agent-based model"],["dc.subject.gro","ABM"],["dc.subject.gro","NetLogo"],["dc.subject.gro","process-based"],["dc.subject.gro","Indonesia"],["dc.subject.gro","sfb990_journalarticles"],["dc.title","EFForTS-LGraf: A landscape generator for creating smallholder-driven land-use mosaics"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]