Structure of the Parasitoid Communities of Grass-Feeding Chalcid Wasps

Dawah, H. A.; Hawkins, Bradford A.; Claridge, M. F.

doi:10.2307/5850

Cited by 87 publications

(87 citation statements)

References 19 publications

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“…According to the AUC value, this algorithm outperforms the CN index for the terrorist association network [80] and the grassland species food web [81], while loses for the metabolic network of the spirochaete Treponema Pallidum [82]. The hierarchical structure model provides a smart way to predict missing links, and, maybe more significantly, it uncovers the hidden hierarchical organization of networks.…”

Section: Hierarchical Structure Modelmentioning

confidence: 99%

Link prediction in complex networks: A survey

Lü

Zhou

2011

Physica A: Statistical Mechanics and its Applications

2,289

1,621

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Link prediction in complex networks has attracted increasing attention from both physical and computer science communities. The algorithms can be used to extract missing information, identify spurious interactions, evaluate network evolving mechanisms, and so on. This article summaries recent progress about link prediction algorithms, emphasizing on the contributions from physical perspectives and approaches, such as the random-walk-based methods and the maximum likelihood methods. We also introduce three typical applications: reconstruction of networks, evaluation of network evolving mechanism and classification of partially labelled networks. Finally, we introduce some applications and outline future challenges of link prediction algorithms.

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Section: Hierarchical Structure Modelmentioning

confidence: 99%

Link prediction in complex networks: A survey

Lü

Zhou

2011

Physica A: Statistical Mechanics and its Applications

2,289

1,621

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show abstract

“…We used the published data set of Brose et al (2005), from which we extracted 12 highly resolved food webs: (1) Silwood Park , (2) grassland (Dawah et al 1995), (3) Sierra Lakes (Harper-Smith et al 2005), (4) Tuesday Lakes ), (5) Mill Stream (M. E. Ledger, F. Edwards, and G. Woodward, unpublished data), (6) Broom (Memmott et al 2000), (7) Celtic Sea (Pinnegar et al 2003), (8) Mulgrave River (T. S. Rayner, unpublished data), (9) Goettingen (Ulrich 1999), (10) Skipwith Pond (Warren 1989), (11) Sheffield (P. H. Warren, unpublished data), and (12) Broadstone Stream (Woodward et al 2005). We discarded webs for which trophic interactions were not determined from direct observations, as well as, for computational reasons, one very large web.…”

Section: Datamentioning

confidence: 99%

Modeling Food Webs: Exploring Unexplained Structure Using Latent Traits

Rohr

Scherer

Kehrli

et al. 2010

The American Naturalist

125

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Several stochastic models have tried to capture the architecture of food webs. This approach is interesting, but it is limited by the fact that different assumptions can yield similar results. To overcome this limitation, we develop a purely statistical approach. Body size in terms of an optimal ratio between prey and predator is used as explanatory variable. In 12 observed food webs, this model predicts, on average, 20% of interactions. To analyze the unexplained part, we introduce a latent term: each species is described by two latent traits, foraging and vulnerability, that represent nonmeasured characteristics of species once the optimal body size has been accounted for. The model now correctly predicts an average of 73% of links. The key features of our approach are that latent traits quantify the structure that is left unexplained by the explanatory variable and that this quantification allows a test of whether independent biological information, such as microhabitat use, camouflage, or phy-logeny, explains this structure. We illustrate this method with phylogeny and find that it is linked to one or both latent traits in nine of 12 food webs. Our approach opens the door to the formulation of more complex models that can be applied to any kind of biological network. abstract: Several stochastic models have tried to capture the architecture of food webs. This approach is interesting, but it is limited by the fact that different assumptions can yield similar results. To overcome this limitation, we develop a purely statistical approach. Body size in terms of an optimal ratio between prey and predator is used as explanatory variable. In 12 observed food webs, this model predicts, on average, 20% of interactions. To analyze the unexplained part, we introduce a latent term: each species is described by two latent traits, foraging and vulnerability, that represent nonmeasured characteristics of species once the optimal body size has been accounted for. The model now correctly predicts an average of 73% of links. The key features of our approach are that latent traits quantify the structure that is left unexplained by the explanatory variable and that this quantification allows a test of whether independent biological information, such as microhabitat use, camouflage, or phylogeny, explains this structure. We illustrate this method with phylogeny and find that it is linked to one or both latent traits in nine of 12 food webs. Our approach opens the door to the formulation of more complex models that can be applied to any kind of biological network.

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“…For the analysis of body masses, we use the large data set of Brose et al (2005), which encompasses 16,863 records of trophic interactions from 10 food-web studies (Warren, 1989;Dawah et al, 1995;Yodzis, 1998;Memmott et al, 2000;Cattin Blandenier, 2004;Woodward et al, 2005;Jonsson et al, 2005;Harper-Smith et al, 2005; Ledger, Edwards, and Woodward, unpublished data; Jacob, Brey, and Mintenbeck, unpublished data), supplemented by data for some specific groups (Andrassy, 1956;Hansen et al, 1994;Ulrich, 1999Ulrich, , 2001Scharf et al, 2000;Pinnegar et al, 2003;Cohen et al, 2005;Dell, unpublished data;Rayner, unpublished data;Ruess, unpublished data;Warren, unpublished data). For the purpose of comparison and to avoid pseudoreplication, we retain the 5103 records of Brose et al (2006b).…”

Section: Data Setmentioning

confidence: 99%