@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}
}
@misc{LaitinenNikoloski2018,
  author    = {Laitinen, Roosa A. E. and Nikoloski, Zoran},
  title     = {Genetic basis of plasticity in plants},
  series = {Journal of experimental botany},
  volume    = {70},
  journal   = {Journal of experimental botany},
  number    = {3},
  publisher = {Oxford Univ. Press},
  address   = {Oxford},
  issn      = {0022-0957},
  doi       = {10.1093/jxb/ery404},
  pages     = {739 -- 745},
  year      = {2018},
  abstract  = {The ability of an organism to change its phenotype in response to different environments, termed plasticity, is a particularly important characteristic to enable sessile plants to adapt to rapid changes in their surroundings. Plasticity is a quantitative trait that can provide a fitness advantage and mitigate negative effects due to environmental perturbations. Yet, its genetic basis is not fully understood. Alongside technological limitations, the main challenge in studying plasticity has been the selection of suitable approaches for quantification of phenotypic plasticity. Here, we propose a categorization of the existing quantitative measures of phenotypic plasticity into nominal and relative approaches. Moreover, we highlight the recent advances in the understanding of the genetic architecture underlying phenotypic plasticity in plants. We identify four pillars for future research to uncover the genetic basis of phenotypic plasticity, with emphasis on development of computational approaches and theories. These developments will allow us to perform specific experiments to validate the causal genes for plasticity and to discover their role in plant fitness and evolution.},
  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}
}