Organizing and understanding a winter’s seagrass foodweb network through effective trophic levels

Christian, Robert R.; Luczkovich, Joseph J.

doi:10.1016/s0304-3800(99)00022-8

Cited by 219 publications

(80 citation statements)

References 29 publications

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“…The eight food webs that we analyzed represent a variety of aquatic and terrestrial commmunities (14) (SI Text). Each of the corresponding eight source articles (15)(16)(17)(18)(19)(20)(21)(22) contains information from which the diet matrix for the presence (1) and absence (0) of feeding linkage between nodes can be deduced. Cannibalism data (y ii ) are not modeled by Eq.…”

Section: Methodsmentioning

confidence: 99%

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A unifying approach for food webs, phylogeny, social networks, and statistics

Chiu

Westveld²

2011

Proc. Natl. Acad. Sci. U.S.A.

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A food web consists of nodes, each consisting of one or more species. The role of each node as predator or prey determines the trophic relations that weave the web. Much effort in trophic food web research is given to understand the connectivity structure, or the nature and degree of dependence among nodes. Social network analysis (SNA) techniques—quantitative methods commonly used in the social sciences to understand network relational structure—have been used for this purpose, although postanalysis effort or biological theory is still required to determine what natural factors contribute to the feeding behavior. Thus, a conventional SNA alone provides limited insight into trophic structure. Here we show that by using novel statistical modeling methodologies to express network links as the random response of within- and internode characteristics (predictors), we gain a much deeper understanding of food web structure and its contributing factors through a unified statistical SNA. We do so for eight empirical food webs: Phylogeny is shown to have nontrivial influence on trophic relations in many webs, and for each web trophic clustering based on feeding activity and on feeding preference can differ substantially. These and other conclusions about network features are purely empirical, based entirely on observed network attributes while accounting for biological information built directly into the model. Thus, statistical SNA techniques, through statistical inference for feeding activity and preference, provide an alternative perspective of trophic clustering to yield comprehensive insight into food web structure.

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Section: Methodsmentioning

confidence: 99%

“…Uneven aggregation is common in food web studies (15,22,30). For example, consider the following four nodes in the Benguela food web: Node "3" is identified as "bacteria"; "4," as "benthic carnivores"; "23," as "kob"; and "26," as "whales and dolphins."…”

Section: Methodsmentioning

confidence: 99%

A unifying approach for food webs, phylogeny, social networks, and statistics

Chiu

Westveld²

2011

Proc. Natl. Acad. Sci. U.S.A.

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“…This character should also exist in Lake Biandantang though we did not demonstrate the effects of consumers on the production of their food supply. However, like other energy flow webs (Raffaelli & Hall, 1996;Benke & Wallace, 1997;Christian & Luczkovich, 1999;Benke et al, 2001), our analysis showed that most energy flows concentrated in a few feeding pathways, and the amount of energy flowing through the rest was less than 1% of the total in Lake Biandantang. We do not think that consumers could have strong effects on the abundant food supply through such weak links in this lake.…”

Section: Discussionmentioning

confidence: 69%

Food web of macroinvertebrate community in a Yangtze shallow lake: trophic basis and pathways

2006

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No detailed food web research on macroinvertebrate community of lacustrine ecosystem was reported in China. The present study is the first attempt on the subject in Lake Biandantang, a macrophytic lake in Hubei Province. Food webs of the macroinvertebrate community were compiled bimonthly from March, 2002 to March, 2003. Dietary information was obtained from gut analysis. Linkage strength was quantified by combining estimates of energy flow (secondary production) with data of gut analysis. The macroinvertebrate community of Lake Biandantang was based heavily on detritus. Quantitative food webs showed the total ingestion ranged from 6930 to 36,340 mg dry mass m )2 bimonthly. The ingestion of macroinvertebrate community was higher in the months with optimum temperature than that in other periods with higher or lower temperature. Through comparison, many patterns in benthic food web of Lake Biandantang are consistent with other detritus-based webs, such as stream webs, but different greatly from those based on autochthonous primary production (e.g. pelagic systems). It suggests that the trophic basis of the web is essential in shaping food web structure.

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“…• Benguela: Marine ecosystem of Benguela off the southwest coast of South Africa [57]; Bridge Brook: Pelagic species from the largest of a set of 50 New York Adirondack lake food webs [47]; Canton Creek: Primarily invertebrates and algae in a tributary, surrounded by pasture, of the Taieri River in the South Island of New Zealand [48]; Chesapeake Bay: The pelagic portion of an eastern U.S. estuary, with an emphasis on larger fishes [49]; Coachella: Wide range of highly aggregated taxa from the Coachella Valley desert in southern California [50]; El Verde: Insects, spiders, birds, reptiles and amphibians in a rainforest in Puerto Rico [51]; Grassland: all vascular plants and all insects and trophic interactions found inside stems of plants collected from 24 sites distributed within England and Wales [52]; Little Rock: Pelagic and benthic species, particularly fishes, zooplankton, macroinvertebrates, and algae of the Little Rock Lake, Wisconsin, U.S. [53]; Reef Small: Caribbean coral reef ecosystem from the Puerto Rico-Virgin Island shelf complex [54]; Scotch Broom: Trophic interactions between the herbivores, parasitoids, predators and pathogens associated with broom, Cytisus scoparius, collected in Silwood Park, Berkshire, England, UK [55]; Shelf: Marine ecosystem on the northeast US shelf [56]; Skipwith: Invertebrates in an English pond [46]; St. Marks: Mostly macroinvertebrates, fishes, and birds associated with an estuarine seagrass community, Halodule wrightii, at St. Marks Refuge in Florida [58]; St. Martin: Birds and predators and arthropod prey of Anolis lizards on the island of St. Martin, which is located in the northern Lesser Antilles [59]; Stony Stream: Primarily invertebrates and algae in a tributary, surrounded by pasture, of the Taieri River in the South Island of New Zealand in native tussock habitat [60]; Ythan_1: Mostly birds, fishes, invertebrates, and metazoan parasites in a Scottish Estuary [61] ;Ythan_2: Reduced version of Ythan1 with no parasites [62].…”

Section: Ecological Networkmentioning

confidence: 99%

Exploring the “Middle Earth” of network spectra via a Gaussian matrix function

Estrada

Alhomaidhi

Al-Thukair

2017

Chaos: An Interdisciplinary Journal of Nonlinear Science

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We study a Gaussian matrix function of the adjacency matrix of artificial and real-world networks. In particular, we study the Gaussian Estrada index-an index characterizing the importance of eigenvalues close to zero. This index accounts for the information contained in the eigenvalues close to zero in the spectra of networks. Here we obtain bounds for this index in simple graphs, proving that it reaches its maximum for star graphs followed by complete bipartite graphs. We also obtain formulas for the Estrada Gaussian index of Erdős-Rï¿oenyi random graphs as well as for the Barabï¿oesi-Albert graphs. We also show that in real-world networks this index is related to the existence of important structural patterns, such as complete bipartite subgraphs (bicliques). Such bicliques appear naturally in many real-world networks as a consequence of the evolutionary processes giving rise to them. In general, the Gaussian matrix function of the adjacency matrix of networks characterizes important structural information not described in previously used matrix functions of graphs. 2The spectrum of a network-the set of its eigenvalues-provides important information about the structural and dynamical properties of the corresponding system. Most of the functions used to study network spectra give more weight to the largest modular eigenvalues. Then, the information contained in the eigenvalues close to the centre of the spectra, i.e, those close to zero, has remained totally unexplored in the study of graph spectra. Here we study a Gaussian matrix function that gives more weights to the eigenvalues closest to the centre of the spectrum of a network. Using this function we extract important structural information hidden in the spectra of networks, such as emergence of complete bipartite subgraphs (bicliques) which appear naturally in many real-world networks as a consequence of the evolutionary processes giving rise to them. These bicliques are also ubiquitous in random networks generated by preferential attachment mechanisms, such as the Barabï¿oesi-Albert model. In this work we provide a series of analytical results that pave the way for further analysis and uses of this Gaussian matrix function to understand network structure and dynamics.3

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Organizing and understanding a winter’s seagrass foodweb network through effective trophic levels

Cited by 219 publications

References 29 publications

A unifying approach for food webs, phylogeny, social networks, and statistics

A unifying approach for food webs, phylogeny, social networks, and statistics

Food web of macroinvertebrate community in a Yangtze shallow lake: trophic basis and pathways

Exploring the “Middle Earth” of network spectra via a Gaussian matrix function

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