Using network analysis for making predictions about food web dynamics is one of the major challengesin systems ecology. Since there are several notoriously difficult methodological problems with foodwebs, only a comparative perspective can help. We study a standard database for trophic flow networksand analyse the correlation between structure and dynamics in strictly hierarchical food webs (directedacyclic graphs, DAGs). To characterize the structural information about trophic nodes in food webs (theirpositional importance), we use 8 topological indices that had been developed for quantifying DAGs (3indices related to status, s, and 5 indices related to the keystone index, K). For dynamics, we use the KS(keystoneness) index that quantifies the importance of trophic nodes in the food web, considering alsocarbon flows and biomass. We statistically compare the structural and dynamical importance of eachnetwork node and find that the K indices are much better predictors of KS than the s indices. Based onthese results, we suggest that functional studies have to consider both bottom-up and top-down effectsas well as indirect effects that are dampening with distance. We suggest that this kind of study can behelpful to better understand the relevance and applicability of network analysis, an otherwise popularresearch methodology with continuously increasing predictive power

Food web dynamics in trophic hierarchies

Libralato S;
2018

Abstract

Using network analysis for making predictions about food web dynamics is one of the major challengesin systems ecology. Since there are several notoriously difficult methodological problems with foodwebs, only a comparative perspective can help. We study a standard database for trophic flow networksand analyse the correlation between structure and dynamics in strictly hierarchical food webs (directedacyclic graphs, DAGs). To characterize the structural information about trophic nodes in food webs (theirpositional importance), we use 8 topological indices that had been developed for quantifying DAGs (3indices related to status, s, and 5 indices related to the keystone index, K). For dynamics, we use the KS(keystoneness) index that quantifies the importance of trophic nodes in the food web, considering alsocarbon flows and biomass. We statistically compare the structural and dynamical importance of eachnetwork node and find that the K indices are much better predictors of KS than the s indices. Based onthese results, we suggest that functional studies have to consider both bottom-up and top-down effectsas well as indirect effects that are dampening with distance. We suggest that this kind of study can behelpful to better understand the relevance and applicability of network analysis, an otherwise popularresearch methodology with continuously increasing predictive power
food webs; centrality; network analysis
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.14083/185
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