@article{KoussoroplisPincebourdeWacker2017,
  author    = {Koussoroplis, Apostolos-Manuel and Pincebourde, Sylvain and Wacker, Alexander},
  title     = {Understanding and predicting physiological performance of organisms in fluctuating and multifactorial environments},
  series = {Ecological monographs : a publication of the Ecological Society of America.},
  volume    = {87},
  journal   = {Ecological monographs : a publication of the Ecological Society of America.},
  publisher = {Wiley},
  address   = {Hoboken},
  issn      = {0012-9615},
  doi       = {10.1002/ecm.1247},
  pages     = {178 -- 197},
  year      = {2017},
  abstract  = {Understanding how variance in environmental factors affects physiological performance, population growth, and persistence is central in ecology. Despite recent interest in the effects of variance in single biological drivers, such as temperature, we have lacked a comprehensive framework for predicting how the variances and covariances between multiple environmental factors will affect physiological rates. Here, we integrate current theory on variance effects with co-limitation theory into a single unified conceptual framework that has general applicability. We show how the framework can be applied (1) to generate mathematically tractable predictions of the physiological effects of multiple fluctuating co-limiting factors, (2) to understand how each co-limiting factor contributes to these effects, and (3) to detect mechanisms such as acclimation or physiological stress when they are at play. We show that the statistical covariance of co-limiting factors, which has not been considered before, can be a strong driver of physiological performance in various ecological contexts. Our framework can provide powerful insights on how the global change-induced shifts in multiple environmental factors affect the physiological performance of organisms.},
  language  = {en}
}
@article{BaroniFrancke2020,
  author    = {Baroni, Gabriele and Francke, Till},
  title     = {An effective strategy for combining variance- and distribution-based global sensitivity analysis},
  series = {Environmental modelling \& software with environment data news},
  volume    = {134},
  journal   = {Environmental modelling \& software with environment data news},
  publisher = {Elsevier},
  address   = {Oxford},
  issn      = {1364-8152},
  doi       = {10.1016/j.envsoft.2020.104851},
  pages     = {14},
  year      = {2020},
  abstract  = {We present a new strategy for performing global sensitivity analysis capable to estimate main and interaction effects from a generic sampling design. The new strategy is based on a meaningful combination of varianceand distribution-based approaches. The strategy is tested on four analytic functions and on a hydrological model. Results show that the analysis is consistent with the state-of-the-art Saltelli/Jansen formula but to better quantify the interaction effect between the input factors when the output distribution is skewed. Moreover, the estimation of the sensitivity indices is much more robust requiring a smaller number of simulations runs. Specific settings and alternative methods that can be integrated in the new strategy are also discussed. Overall, the strategy is considered as a new simple and effective tool for performing global sensitivity analysis that can be easily integrated in any environmental modelling framework.},
  language  = {en}
}