Why so many published sensitivity analyses are false: A systematic review of sensitivity analysis practices

Saltelli, Andrea; Aleksankina, Ksenia; Becker, William E.; Fennell, Pamela; Ferretti, Federico; Holst, Niels; Li, Sushan; Wu, Qing

doi:10.1016/j.envsoft.2019.01.012

Cited by 521 publications

(427 citation statements)

References 42 publications

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“…For other parameters, we performed a local sensitivity analysis where we computed a first‐order sensitivity index (Saltelli et al, ; Sobol & Saltelli, ) given the contribution of each parameter to the variance (in Figure for scenario D—intermediate amount of 7.5 kg AR at threshold density of 250 voles). Parameter values were moved within ±10% interval around the fixed value as defined in scenario D (see Table ).…”

Section: Resultsmentioning

confidence: 99%

“…Then, we performed a local sensitivity analysis to study changes in model outputs apportioned to small variation around model parameters input (Saltelli et al, ). We applied a first‐order Sobol's sensitivity index S i defined as

S_{i} = V false(E_{x \sim i} (y | x_{i}) false) / V false(y false)

, where

V (y)

is the variance of y when all factors are allowed to vary,

{double-struckE}_{x \sim i} false(y false| x_{i} false)

the mean of y when one factor is fixed (Sobol & Saltelli, ).…”

Section: Methodsmentioning

confidence: 99%

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Trophic transfer of pesticides: The fine line between predator–prey regulation and pesticide–pest regulation

Baudrot

Fernandez-de-Simon

Cœurdassier

et al. 2020

Journal of Applied Ecology

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Understanding pesticide impacts on populations of target/non‐target species and communities is a challenge to applied ecology. When predators that otherwise regulate pest densities ingest prey contaminated with pesticides, this can suppress predator populations by secondary poisoning. It is, however, unknown how species relationships and protocols of treatments (e.g. anticoagulant rodenticide [AR]) interact to affect pest regulation. To tackle this issue, we modelled a heuristic non‐spatialized system including montane water voles, specialist vole predators (stoats, weasels) and a generalist predator (red fox) which consumes voles, mustelids and other prey. By carrying out a broad‐range sensitivity analysis on poorly known toxicological parameters, we explored the impact of five farmer functional responses (defined by both AR quantity and threshold vole density above which AR spreading is prohibited) on predator–prey interactions, AR transfer across the trophic chain and population effects. Spreading AR to maintain low vole densities suppressed mustelid and fox populations, leading to vole population dynamics being entirely regulated by AR use. Such vole‐suppression treatment regimes inhibited predation ecosystem services and promoted pesticide dependence. Keeping vole density below acceptable bounds by spreading AR while maintaining sufficient voles as prey resources led to less AR being applied and extended periods without AR in the environment, benefiting predators while avoiding episodes with high vole density. This may meet farm production interests while minimizing the impact on mustelid and fox populations and associated ecosystem processes. These alternating phases of mustelids and farmer regulation highlight the consequence of intraguild relationship where mustelids may rescue foxes from poisoning. Both global and wide‐range sensitivity analysis illustrate the tightrope between predator–prey regulation and pesticide–pest regulation. Synthesis and applications. Different pesticide protocols lead to a rich variety of predator–prey dynamics in agro‐ecosystems. Our model reveals the need to maintain refuges with sufficient non‐poisoned voles for sustaining specialist mustelids, to conserve the predator community given the potential of secondary poisoning with rodenticides. We suggest that long periods without pesticide treatment are essential to maintain predator populations, and that practices of pesticides use that attempt to permanently suppress a pest over a large scale are counterproductive.

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Section: Resultsmentioning

confidence: 99%

S_{i} = V false(E_{x \sim i} (y | x_{i}) false) / V false(y false)

, where

V (y)

is the variance of y when all factors are allowed to vary,

{double-struckE}_{x \sim i} false(y false| x_{i} false)

the mean of y when one factor is fixed (Sobol & Saltelli, ).…”

Section: Methodsmentioning

confidence: 99%

Trophic transfer of pesticides: The fine line between predator–prey regulation and pesticide–pest regulation

Baudrot

Fernandez-de-Simon

Cœurdassier

et al. 2020

Journal of Applied Ecology

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“…Uncertainty analysis quantifies the uncertainty, or variance, in a model's outputs based on uncertainties in the input parameters 1 . It should be performed in conjunction with sensitivity analysis to identify how much each input uncertainty contributes to the output uncertainty 2 . In chemical kinetics, these input parameters may include initial concentrations, temperature, pressure, rate parameters, or thermodynamic quantities.…”

Section: Introductionmentioning

confidence: 99%

“…1 It should be performed in conjunction with sensitivity analysis to identify how much each input uncertainty contributes to the output uncertainty. 2 In chemical kinetics, these input parameters may include initial concentrations, temperature, pressure, rate parameters, or thermodynamic quantities. In general, combustion and pyrolysis systems are highly nonlinear; often uncertainties in a few rate coefficients can dominate over the remainder.…”

Section: Introductionmentioning

confidence: 99%

“…10 The typical model construction workflow within RMG is an iterative cycle. Once the user decides on the model conditions and input species, they would (1) generate a model automatically; (2) conduct sensitivity analysis for key observables to identify the most sensitive input parameters; (3) refine the most sensitive parameters by updating the RMG database, usually via quantum chemistry calculations, then regenerate the model via step (1) and repeat the cycle until a satisfactory model is generated. However, efforts to refine an input parameter are only worthwhile if the model is both sensitive to the parameter and if the uncertainty of the parameter is significant.…”

Section: Introductionmentioning

confidence: 99%

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Uncertainty analysis of correlated parameters in automated reaction mechanism generation

Gao

Liu

Green

2020

Int J of Chemical Kinetics

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Uncertainty analysis is a useful tool for inspecting and improving detailed kinetic mechanisms because it can identify the greatest sources of model output error. Owing to the very nonlinear relationship between kinetic and thermodynamic parameters and computed concentrations, model predictions can be extremely sensitive to uncertainties in some parameters while uncertainties in other parameters can be irrelevant. Error propagation becomes even more convoluted in automatically generated kinetic models, where input uncertainties are correlated through kinetic rate rules and thermodynamic group values. Local and global uncertainty analyses were implemented and used to analyze error propagation in Reaction Mechanism Generator (RMG), an open‐source software for generating kinetic models. A framework for automatically assigning parameter uncertainties to estimated thermodynamics and kinetics was created, enabling tracking of correlated uncertainties. Local first‐order uncertainty propagation was implemented using sensitivities computed natively within RMG. Global uncertainty analysis was implemented using adaptive Smolyak pseudospectral approximations as implemented in the MIT Uncertainty Quantification Library to efficiently compute and construct polynomial chaos expansions to approximate the dependence of outputs on a subset of uncertain inputs. Cantera was used as a backend for simulating the reactor system in the global analysis. Analyses were performed for a phenyldodecane pyrolysis model. Local and global methods demonstrated similar trends; however, many uncertainties were significantly overestimated by the local analysis. Both local and global analyses show that correlated uncertainties based on kinetic rate rules and thermochemical groups drastically reduce a model's degrees of freedom and have a large impact on the determination of the most influential input parameters. These results highlight the necessity of incorporating uncertainty analysis in the mechanism generation workflow.

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Digitalisierung in der Versuchsführung und Monitoring von Bauwerken

Braml,

Haslbeck,

Wimmer

2023

2024 BetonKalender

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Why so many published sensitivity analyses are false: A systematic review of sensitivity analysis practices

Cited by 521 publications

References 42 publications

Trophic transfer of pesticides: The fine line between predator–prey regulation and pesticide–pest regulation

Trophic transfer of pesticides: The fine line between predator–prey regulation and pesticide–pest regulation

Uncertainty analysis of correlated parameters in automated reaction mechanism generation

Digitalisierung in der Versuchsführung und Monitoring von Bauwerken

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