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Simple or complex: Relative impact of data availability and model purpose on the choice of model types for population viability analyses

  • Population viability analysis (PVA) models are used to estimate population extinction risk under different scenarios. Both simple and complex PVA models are developed and have their specific pros and cons; the question therefore arises whether we always use the most appropriate model type. Generally, the specific purpose of a model and the availability of data are listed as determining the choice of model type, but this has not been formally tested yet. We quantified the relative importance of model purpose and nine metrics of data availability and resolution for the choice of a PVA model type, while controlling for effects of the different life histories of the modelled species. We evaluated 37 model pairs: each consisting of a generally simpler, population-based model (PBM) and a more complex, individual-based model (IBM) developed for the same species. The choice of model type was primarily affected by the availability and resolution of demographic, dispersal and spatial data. Low-resolution data resulted in the development of lessPopulation viability analysis (PVA) models are used to estimate population extinction risk under different scenarios. Both simple and complex PVA models are developed and have their specific pros and cons; the question therefore arises whether we always use the most appropriate model type. Generally, the specific purpose of a model and the availability of data are listed as determining the choice of model type, but this has not been formally tested yet. We quantified the relative importance of model purpose and nine metrics of data availability and resolution for the choice of a PVA model type, while controlling for effects of the different life histories of the modelled species. We evaluated 37 model pairs: each consisting of a generally simpler, population-based model (PBM) and a more complex, individual-based model (IBM) developed for the same species. The choice of model type was primarily affected by the availability and resolution of demographic, dispersal and spatial data. Low-resolution data resulted in the development of less complex models. Model purpose did not affect the choice of the model type. We confirm the general assumption that poor data availability is the main reason for the wide use of simpler models, which may have limited predictive power for population responses to changing environmental conditions. Conservation biology is a crisis discipline where researchers learned to work with the data at hand. However, for threatened and poorly-known species, there is no short-cut when developing either a PBM or an IBM: investments to collect appropriately detailed data are required to ensure PVA models can assess extinction risk under complex environmental conditions. (C) 2015 Elsevier B.V. All rights reserved.show moreshow less

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Metadaten
Author:Viktoriia Radchuk, Steffen Oppel, Juergen Groeneveld, Volker Grimm, Nicolas Schtickzelle
DOI:https://doi.org/10.1016/j.ecolmodel.2015.11.022
ISSN:0304-3800
ISSN:1872-7026
Parent Title (English):Ecological modelling : international journal on ecological modelling and engineering and systems ecolog
Publisher:Elsevier
Place of publication:Amsterdam
Document Type:Article
Language:English
Year of first Publication:2016
Year of Completion:2016
Release Date:2020/03/22
Tag:Individual-based model; Matrix model; Model complexity; Population-based model; Stage-based model; Structured population model
Volume:323
Pagenumber:9
First Page:87
Last Page:95
Funder:FRIA fund
Organizational units:Mathematisch-Naturwissenschaftliche Fakultät / Institut für Biochemie und Biologie
Peer Review:Referiert