Are network indices robust indicators of food web functioning? A Monte Carlo approach

Kones, Julius K.; Soetaert, Karline; Oevelen, Dick van; Owino, JO

doi:10.1016/j.ecolmodel.2008.10.012

Cited by 160 publications

(134 citation statements)

References 38 publications

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“…This finding indicates that the average of synthetic solutions, which was presented in detail in the previous section, provides a picture of the ecosystem flows at steady state which is nearly insensitive to the constraints and, therefore, does not heavily depend on our choice of the ranges for the inequalities. Therefore, the combination of the MN criterion with the perturbation method proved to be effective for obtaining a meaningful representation of the flows, even though alternative Monte-Carlo based methods, such as that proposed in Kones et al (2009), could represent a better alternative an certainly deserves to be tested.…”

Section: Performance Of Im Methodsmentioning

confidence: 99%

An inverse model for the analysis of the Venice lagoon food web

Brigolin

Savenkoff

Zucchetta

et al. 2011

Ecological Modelling

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Section: Performance Of Im Methodsmentioning

confidence: 99%

An inverse model for the analysis of the Venice lagoon food web

Brigolin

Savenkoff

Zucchetta

et al. 2011

Ecological Modelling

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“…To assess errors associated with our choice of minimization scheme, we used an RW algorithm, inspired by Kones et al (2009), to sample the solution space of our inverse problem. We drew random samples from a uniform distribution of the orthonormal set of vectors created by the SVD and added them to our minimum norm solution.…”

Section: Methodsmentioning

confidence: 99%

Contribution of picophytoplankton to carbon export in the equatorial Pacific: A reassessment of food web flux inferences from inverse models

Stukel

Landry

2010

Limnology & Oceanography

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The paradigm that carbon export is derived almost exclusively from the primary production of large phytoplankton has been challenged by inverse ecosystem modeling studies that suggest that most carbon export in the open ocean is fueled by picophytoplankton. To readdress this hypothesis, we use an inverse model to synthesize the planktonic rate measurements from a pair of recent cruises in the equatorial Pacific. The analysis based on this new experimental data, which crucially include vertically integrated taxon-specific production and grazing estimates, largely resolve the unexpected results of the previous inverse studies, including unbalanced growth and grazing processes and the dominance of production by picophytoplankton. While this very small size class does not produce the majority of phytoplankton carbon that is eventually exported to depth (only 23%, vs. 73% from a previous analysis of Joint Global Ocean Flux Study Equatorial Pacific data), our base model supports the conclusion that the role of picophytoplankton in vertical carbon flux is largely proportional to their contribution to net primary productivity (though neither is proportional to biomass). We show, however, that export-production proportionality is sensitive to the model representation of the detrital pool such that the relative export role of picophytoplankton declines substantially for an alternate model with size-structured detritus. A definitive assessment of the role of picoplankton in vertical carbon flux will thus require detailed experimental examination of the origin, composition, and fate of euphotic zone detrital material.

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“…Although it is recommended to calculate network indices on the median values from the likelihood approach (Kones et al 2009), in the present study, this was not feasible due to the large number of compartments in the inverse food web models. The use of the parsimony approach in this study typically provides low estimates of the network indices of the food webs (Kones et al 2009).…”

Section: Network Characteristicsmentioning

confidence: 92%

“…Network indices are a convenient way to represent certain aspects of food webs and to facilitate comparisons among them (Ulanowicz 2004, Kones et al 2009). Although it is recommended to calculate network indices on the median values from the likelihood approach (Kones et al 2009), in the present study, this was not feasible due to the large number of compartments in the inverse food web models.…”

Section: Network Characteristicsmentioning

confidence: 99%