@article{fdi:010092604,
  title = {{A} protocol for implementing parameter sensitivity analyses in complex ecosystem models},
  author = {{L}uján, {C}. and {S}hin, {Y}unne-{J}ai and {B}arrier, {N}icolas and {L}eadley, {P}. and {O}liveros-{R}amos, {R}.},
  editor = {},
  language = {{ENG}},
  abstract = {{S}ystematic analysis of uncertainty is critical for consolidating ecosystem model projections. {S}ensitivity analysis is an essential step in understanding model uncertainty, but there are many challenges when dealing with complex models. {O}ne of these is related to the quantification of uncertainty in model input parameters, which is especially problematic when limited data leads to the use of arbitrary fixed ranges of variation to define parameter uncertainty. {W}e show the drawbacks of this practice and propose an alternative approach based on the parameter reliability criterion. {T}his criterion helps to classify the model parameters according to the source of information used to estimate their values and calculate the ranges of variability for each parameter used in a sensitivity analysis. {O}ur proposed approach presented in this protocol is illustrated by implementing a sensitivity analysis of the {OSMOSE} marine ecosystem modelling platform applied to the northern {P}eru {C}urrent ecosystem. {W}e compare the results from the sensitivity analysis based on the parameter reliability criterion with those obtained using fixed ranges of variation. {W}e find that using arbitrary uncertainty ranges can produce different conclusions compared to alternative approaches, such as the one based on the reliability of the parameters. {T}he parameter reliability criterion can be helpful in situations where direct quantification of uncertainty in model inputs is not available.},
  keywords = {{S}ensitivity analysis ; {P}arameter uncertainty ; {E}cosystem models ; {OSMOSE} ; model},
  booktitle = {},
  journal = {{E}cological {M}odelling},
  volume = {501},
  numero = {},
  pages = {110990 [13 p.]},
  ISSN = {0304-3800},
  year = {2025},
  DOI = {10.1016/j.ecolmodel.2024.110990},
  URL = {https://www.documentation.ird.fr/hor/fdi:010092604},
}