Ecosystem flow networks: Loaded dice?

Ulanowicz, Robert E.; Wolff, Wilfried F.

doi:10.1016/0025-5564(91)90090-6

Cited by 94 publications

(67 citation statements)

References 18 publications

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“…Link width represents interaction strength. There are a few strong interaction strengths distributed within a matrix of weak interactions, confirming previous results (1)(2)(3)(4)(5)(6). The frequency distribution of per-capita interaction strengths fits a lognormal distribution with marginal significance (P ϭ 0.06, Lilliefors' test) (Fig.…”

Section: (Q͞b) J Is Essentially Identical To the Maximum Ingestion Ratesupporting

confidence: 88%

Interaction strength combinations and the overfishing of a marine food web

Bascompte

Melián

Sala

2005

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

537

475

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The stability of ecological communities largely depends on the strength of interactions between predators and their prey. Here we show that these interaction strengths are structured nonrandomly in a large Caribbean marine food web. Specifically, the cooccurrence of strong interactions on two consecutive levels of food chains occurs less frequently than expected by chance. Even when they occur, these strongly interacting chains are accompanied by strong omnivory more often than expected by chance. By using a food web model, we show that these interaction strength combinations reduce the likelihood of trophic cascades after the overfishing of top predators. However, fishing selectively removes predators that are overrepresented in strongly interacting chains. Hence, the potential for strong community-wide effects remains a threat.community stability ͉ omnivory ͉ trophic chain ͉ trophic cascade ͉ quantitative networks Q uantification of the strength of interactions between species is essential for understanding how ecological communities are organized and how they respond to human exploitation. Food webs are characterized by many weak interactions and a few strong interactions (1-6), which appears to promote community persistence and stability (7-9). However, little is known about how interaction strengths are combined to form the simplest components of complex food webs (10, 11). An example of such a component is a tritrophic food chain (TFC) in which a top predator P eats a consumer C, which in turn eats a resource R (Fig. 1).The cooccurrence of strong interactions on two consecutive levels of a trophic chain has the potential to modify the structure and dynamics of entire food webs through trophic cascades (12-16). Trophic cascades are predator-prey effects that alter biomass or abundance of a species across more than one trophic link (12,16). Reductions in the abundance of a predator through fishing would propagate through the food chain resulting in increased consumer abundance and fewer resources (13).The role of omnivory (the top predator also feeds on the resource) (Fig. 1b) in food web stability has been debated for decades. Although previous results concluded that omnivory destabilizes food webs (17), recent studies have shown the opposite trend (18,19). It is unclear whether omnivory can mitigate the effect of trophic cascades when top predators and consumers are strong interactors (20). Thus, although the overfishing of top predators has the potential to cause trophic cascades, we still do not know how general the community-wide effects of overfishing are.Here we analyze a real, large food web to describe the patterns of interaction strength combinations and explore their implications for food web dynamics. Specifically, we first quantified per-capita interaction strengths (interaction strengths hereafter) between all predator-prey links. Then, we determined how strong interactions are combined within TFCs with and without omnivory. Finally, we linked structure and dynamics by using a biologically para...

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Section: (Q͞b) J Is Essentially Identical To the Maximum Ingestion Ratesupporting

confidence: 88%

Interaction strength combinations and the overfishing of a marine food web

Bascompte

Melián

Sala

2005

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

537

475

View full text Add to dashboard Cite

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“…The choice of the geometric series distribution in the second step of the procedure arises from observations of the magnitudes of trophic transfers in real ecosystems (Kenny and Loehle 1991;Ulanowicz and Wolff 1991). However, the suitability of this particular distribution has been questioned (PahlWostl 1992; Ulanowicz and Wolff 1992), and a log-normal distribution has been reported to fit the empirical distribution of link frequencies for the St. Martin Island food web (Goldwasser and Roughgarden 1993). Here, we continue to use the geometric series both for simplicity and because other decreasing distributions (e.g., log normal) lead to qualitatively the same results.…”

Section: Models Of Sampling Biasmentioning

confidence: 99%

Scale-Invariant or Scale-Dependent Behavior of the Link Density Property in Food Webs: A Matter of Sampling Effort?

Bersier¹,

Dixon²,

Sugihara³

1999

The American Naturalist

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“…Over last few decades, Ulanowicz has established a body of work that uses information theory to describe the organization of ecological networks (Ulanowicz and Wolff 1991;Ulanowicz and Hannon 1987;Ulanowicz 2002;Ulanowicz et al 2009;Ho and Ulanowicz 2005;Goerner et al 2009). The value of this work is that it provides a method to understand the structure and organization of flows within networked systems from the standpoint of the trade-off between redundancy (via diversity of options) versus efficiency.…”

Section: Information Theory To Quantify Structure Of Network Flowsmentioning

confidence: 99%

“…For terminology and derivation of the information theory metrics in ''Information Theory Mathematics Background'' section, see particularly Chapters 2 and 8 of (MacKay 2003). For Ulanowicz's definitions and use of the metrics (as applied to ecosystems) as in ''Closed Network Model Mathematics'' and ''Open Network Model Mathematics'' sections, see (Ulanowicz and Wolff 1991;Ulanowicz 2004;Ulanowicz et al 2009). Kharrazi's (2013) perspectives on information theory for network analysis of economic flows are also useful.…”

Section: Information Theory To Quantify Structure Of Network Flowsmentioning

confidence: 99%

Information Theory to Assess Relations Between Energy and Structure of the U.S. Economy Over Time

King

2016

Biophys Econ Resour Qual

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This paper describes the changing structure of the United States' (U.S.) domestic economy by applying information theory-based metrics to the U.S. input-output (I-O) tables from 1947 to 2012. Here the I-O tables are an economic network where the sectors are the nodes. The value of these metrics is that they describe the balance or trade-off between efficiency and redundancy of network flows as well as equality and hierarchy of flows through nodes in a network. I relate these metrics to the U.S. gross power consumption and annual intermediate spending by the food and energy sectors, the latter being a proxy for the inverse of the net power ratio (or net energy) of the economy, to test hypotheses of energy-economy structural linkages. The results of this paper show that increasing gross power consumption, as well as a decreasing share of intermediate expenditures of the food and energy sectors, correlates with increased distribution of money among economic sectors, and vice versa. The information theory metrics indicate two time periods at which major structural shifts occurred. The first was between 1967 and 1972, and the second was around the turn of the twenty-first century when food and energy expenditures no longer continued to decrease after 2002. In response to the latter, it is clear that the U.S. economy did trade off structural reserves (e.g., decreasing metrics of conditional entropy, redundancy, and equality) for structural efficiency (e.g., increasing metrics of efficiency, mutual constraint, and hierarchy) after food and energy expenditures increased post-2002. I also test the structural trends with increasingly simpler (e.g., fewer sectors) representations of the I-O tables, and the results are more consistent for I-O representations that account for inputs and outputs (e.g., value added and gross domestic product) rather than only the intermediate transactions among sectors. The findings of this paper have important implications for economic modeling in at least two ways. First, the paper helps explain how fundamental shifts in resources costs relate to economic structure and economic growth. Second, the paper shows that the number of sectors used to represent economic transactions influences the systemic metrics themselves.

show abstract

Ecosystem flow networks: Loaded dice?

Cited by 94 publications

References 18 publications

Interaction strength combinations and the overfishing of a marine food web

Interaction strength combinations and the overfishing of a marine food web

Scale-Invariant or Scale-Dependent Behavior of the Link Density Property in Food Webs: A Matter of Sampling Effort?

Information Theory to Assess Relations Between Energy and Structure of the U.S. Economy Over Time

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