@article{RadchukOppelGroeneveldetal.2016,
  author    = {Radchuk, Viktoriia and Oppel, Steffen and Groeneveld, Juergen and Grimm, Volker and Schtickzelle, Nicolas},
  title     = {Simple or complex: Relative impact of data availability and model purpose on the choice of model types for population viability analyses},
  series = {Ecological modelling : international journal on ecological modelling and engineering and systems ecolog},
  volume    = {323},
  journal   = {Ecological modelling : international journal on ecological modelling and engineering and systems ecolog},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {0304-3800},
  doi       = {10.1016/j.ecolmodel.2015.11.022},
  pages     = {87 -- 95},
  year      = {2016},
  abstract  = {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 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.},
  language  = {en}
}
@article{RadchukJohstGroeneveldetal.2013,
  author    = {Radchuk, Viktoriia and Johst, Karin and Gr{\"o}neveld, Juergen and Grimm, Volker and Schtickzelle, Nicolas},
  title     = {Behind the scenes of population viability modeling predicting butterfly metapopulation dynamics under climate change},
  series = {Ecological modelling : international journal on ecological modelling and engineering and systems ecolog},
  volume    = {259},
  journal   = {Ecological modelling : international journal on ecological modelling and engineering and systems ecolog},
  number    = {2},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {0304-3800},
  doi       = {10.1016/j.ecolmodel.2013.03.014},
  pages     = {62 -- 73},
  year      = {2013},
  abstract  = {Studies explaining the choice of model structure for population viability analysis (PVA) are rare and no such study exists for butterfly species, a focal group for conservation. Here, we describe in detail the development of a model to predict population viability of a glacial relict butterfly species, Boloria eunomia, under climate change. We compared four alternative formulations of an individual-based model, differing in the environmental factors acting on the survival of immature life stages: temperature (only temperature impact), weather (temperature, precipitation, and sunshine), temperature and parasitism, and weather and parasitism. Following pattern-oriented modeling, four observed patterns were used to contrast these models: one qualitative (response of population size to habitat parameters) and three quantitative ones describing population dynamics during eight years (mean and variability of population size, and magnitude of the temporal autocorrelation in yearly population growth rates). The four model formulations were not equally able to depict population dynamics under current environmental conditions; the model including only temperature was selected as the most parsimonious model sufficiently well reproducing the empirical patterns. We used all four model formulations to test a range of climate change scenarios that were characterized by changes in both mean and variability of the weather variables. All models predicted adverse effects of climate change and resulted in the same ranking of mean climate change scenarios. However, models differed in their absolute values of population viability measures, underlining the need to explicitly choose the most appropriate model formulation and avoid arbitrary usage of environmental drivers in a model. We conclude that further applications of pattern-oriented modeling to butterfly and other species are likely to help in identifying the key factors impacting the viability of certain taxa, which, ultimately, will aid and speed up informed management decisions for endangered species under climate change.},
  language  = {en}
}
@article{RadchukJohstGroeneveldetal.2014,
  author    = {Radchuk, Viktoriia and Johst, Karin and Groeneveld, J{\"u}rgen and Turlure, Camille and Grimm, Volker and Schtickzelle, Nicolas},
  title     = {Appropriate resolution in time and model structure for population viability analysis: Insights from a butterfly metapopulation},
  series = {: an international journal},
  volume    = {169},
  journal   = {: an international journal},
  publisher = {Elsevier},
  address   = {Oxford},
  issn      = {0006-3207},
  doi       = {10.1016/j.biocon.2013.12.004},
  pages     = {345 -- 354},
  year      = {2014},
  abstract  = {The importance of a careful choice of the appropriate scale for studying ecological phenomena has been stressed repeatedly. However, issues of spatial scale in metapopulation dynamics received much more attention compared to temporal scale. Moreover, multiple calls were made to carefully choose the appropriate model structure for Population Viability Analysis (PVA). We assessed the effect of using coarser resolution in time and model structure on population dynamics. For this purpose, we compared outcomes of two PVA models differing in their time step: daily individual-based model (dIBM) and yearly stage-based model (ySBM), loaded with empirical data on a well-known metapopulation of the butterfly Boloria eunomia. Both models included the same environmental drivers of population dynamics that were previously identified as being the most important for this species. Under temperature change scenarios, both models yielded the same qualitative scenario ranking, but they quite substantially differed quantitatively with dIBM being more pessimistic in absolute viability measures. We showed that these differences stemmed from inter-individual heterogeneity in dIBM allowing for phenological shifts of individual appearance. We conclude that a finer temporal resolution and an individual-based model structure allow capturing the essential mechanisms necessary to go beyond mere PVA scenario ranking. We encourage researchers to carefully chose the temporal resolution and structure of their model aiming at (1) depicting the processes important for (meta)population dynamics of the species and (2) implementing the environmental change scenarios expected for their study system in the future, using the temporal resolution at which such changes are predicted to operate.},
  language  = {en}
}