Effective trophic positions in ecological acyclic networks

Scotti, Marco; Allesina, Stefano; Bondavalli, Cristina; Bodini, Antonio; Abarca-Arenas, Luis Gerardo

doi:10.1016/j.ecolmodel.2006.06.005

Cited by 17 publications

(10 citation statements)

References 46 publications

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“…Although it has been said that the number of links in a minimal spanning tree gives the trophic level of each node (Garlaschelli et al ), we reiterate that the way we measured the length of these trees should not be used to infer about trophic levels. In fact, many links that contribute to define species’ trophic level are excluded when trees are constructed (Scotti et al ).…”

Section: Discussionmentioning

confidence: 99%

Food web's backbones and energy delivery in ecosystems

Bellingeri

Bodini

2015

Oikos

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A food web can be seen as a collection of pathways distributing energy to the ecological community. Because of thermodynamical as well as ecological constraints, some pathways are more important than others, as they carry a much larger share of the energy flow. Such pathways, composed of 'strong' links, might be hidden in the tangled network of trophic interactions. Due to thermodynamic efficiency, leading to considerable loss at every passage, we expect these important pathways to be short -i.e. of minimal length. Minimum length spanning trees, which are trees formed by the minimum number of links to keep food webs connected, have been indicated as main energy routes. To test this hypothesis, we analyzed 30 well-resolved, weighted empirical food webs. From each food web, we extracted the minimum length spanning tree (MLST), and compared it to the maximum weight spanning tree (MWST) -a tree composed of connections of maximum strength. To have a robust comparison, we also computed the minimum weight spanning tree (MinWST), composed of the links of minimal strength. Finally, we extracted from each web 1000 random weighted spanning trees (RWSTs) to serve as a null model. We contrasted the different tree structures, and found that MWSTs are significantly shorter than random trees and MinWSTs, but always longer than MLSTs. In addition, MinWSTs are significantly longer than what expected from random trees. Our results show that in food webs, the bulk of energy travels along pathways that tend to be short, although they never coincide with the minimum length spanning trees.

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Section: Discussionmentioning

confidence: 99%

Food web's backbones and energy delivery in ecosystems

Bellingeri

Bodini

2015

Oikos

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“…For a secondary consumer feeding on only one primary consumer, this corresponds to an effective trophic level of 3 (abiotic environment (TL 0) → primary producer (TL 1) → primary consumer (TL 2) → secondary consumer (TL 3)). With mixed diets, one calculates a weighted average for each compartment in the food web matrix, e.g., ref and . For each species or population i with a diet consisting of G other species according to the fraction F ij , effective trophic level is then calculated as normalT normalL i = 1 + prefix∑ j ∈ G F i j T L j Or equivalently in matrix notation for the vector of trophic levels: boldT boldL = prefix∑ ( I − F ) − 1 where I is the identity matrix and F is the dietary matrix describing the food web.…”

Section: Theory and Methodsmentioning

confidence: 99%

Estimating Trophic Levels and Trophic Magnification Factors Using Bayesian Inference

Starrfelt

Borgå

Ruus

et al. 2013

Environ. Sci. Technol.

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Food web biomagnification is increasingly assessed by estimating trophic magnification factors (TMF) where solvent (often lipid) normalized contaminant concentration is regressed onto the trophic level, and TMFs are represented by the slope of the relationship. In TMF regressions, the uncertainty in the contaminant concentrations is appreciated, whereas the trophic levels are assumed independent and not associated with variability or uncertainty pertaining to e.g. quantification. In reality, the trophic levels may vary due to measurement error in stable isotopes of nitrogen (δ(15)N) of each sample, in δ(15)N in selected reference baseline trophic level, and in the enrichment factor of δ(15)N between two trophic levels (ΔN), which are all needed to calculate trophic levels. The present study used a Markov Chain Monte Carlo method, with knowledge about the food web structure, which resulted in a dramatic increase in the precision in the TMF estimates. This also lead to a better understanding of the uncertainties in bioaccumulation measures; instead of using point estimates of TMF, the uncertainty can be quantified (i.e., TMF >1, namely positive biomagnification, with an estimated X % probability).

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“…Conceptually, trophic position (TP) comes out from apportioning a species feeding activity to a series of discrete trophic levels sensu Lindeman (1942) and summing up these fractions. Its computation is made possible by a suite of different techniques that are essentially based on matrix manipulations; in this paper we used the following three methods: a) the canonical trophic aggregation (C, Ulanowicz and Kemp 1979, Scotti et al 2006); b) the network unfolding approach (H, Higashi et al 1989) and c) the path‐based network unfolding algorithm (W, Whipple 1998). The main difference between the three methods lies in that in the C algorithm TP is computed using only trophic links; the other two make use also of non‐trophic flows (i.e.…”

Section: Methodsmentioning

confidence: 99%

Using trophic hierarchy to understand food web structure

Scotti¹,

Bondavalli²,

Bodini³

et al. 2009

Oikos

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Link arrangement in food webs is determined by the species’ feeding habits. This work investigates whether food web topology is organized in a gradient of trophic positions from producers to consumers. To this end, we analyzed 26 food webs for which the consumption rate of each species was specified. We computed the trophic positions and the link densities of all species in the food webs. Link density measures how much each species contributes to the distribution of energy in the system. It is expressed as the number of links species establish with other nodes, weighted by their magnitude. We computed these two metrics using various formulations developed in the ecological network analysis framework. Results show a positive correlation between trophic position and link density across all the systems, regardless the specific formulas used to measure the two quantities. We performed the same analysis on the corresponding binary matrices (i.e. removing information about rates). In addition, we investigated the relation between trophic position and link density in: a) simulated binary webs with same connectance as the original ones; b) weighted webs with constant topology but randomized link strengths and c) weighted webs with constant connectance where both topology and link strengths are randomized. The correlation between the two indices attenuates, vanishes or becomes negative in the case of binary food webs and simulated data (weighted and unweighted). According to our analysis, link density in food webs decreases with trophic position so that it is greatly reduced toward the top of the trophic hierarchy. This outcome, that seems to challenge previous conclusions based on null models, strongly depends on link quantification. Including interaction strengths may improve substantially our understanding of food web organization, and possibly contradict results based on the analysis of binary webs.

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Effective trophic positions in ecological acyclic networks

Cited by 17 publications

References 46 publications

Food web's backbones and energy delivery in ecosystems

Food web's backbones and energy delivery in ecosystems

Estimating Trophic Levels and Trophic Magnification Factors Using Bayesian Inference

Using trophic hierarchy to understand food web structure

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