Combining centrality indices: Maximizing the predictability of keystone species in food webs

Network analysis offers a rich toolkit to study various graph models in biology. In ecology, centrality indices have been suggested to indicate keystone species in interaction networks and to quantify their importance in an ecosystem. There is a large number of centrality indices, however, and it is...

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Autores principales:	Catarina Gouveia, Ágnes Móréh, Ferenc Jordán
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Food web Keystone species Centrality Multiple indices Simulation Machine learning Ecology QH540-549.5
Acceso en línea:	https://doaj.org/article/18753159843749f7bae41a7dacb39518
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id	oai:doaj.org-article:18753159843749f7bae41a7dacb39518
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spelling	oai:doaj.org-article:18753159843749f7bae41a7dacb395182021-12-01T04:49:35ZCombining centrality indices: Maximizing the predictability of keystone species in food webs1470-160X10.1016/j.ecolind.2021.107617https://doaj.org/article/18753159843749f7bae41a7dacb395182021-07-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1470160X2100282Xhttps://doaj.org/toc/1470-160XNetwork analysis offers a rich toolkit to study various graph models in biology. In ecology, centrality indices have been suggested to indicate keystone species in interaction networks and to quantify their importance in an ecosystem. There is a large number of centrality indices, however, and it is often unclear what is their precise biological meaning, how are they related to each other and which one is the “best” predicting the functioning of the modelled biological system. It is a major challenge to use simple structural indicators in order to predict the outcome of much more complicated dynamical simulations. The question is which one is the most preidictive one and what is the meaning of particular structural indices. Here we use machine learning techniques to combine k centrality indices out of n in such a way that the gained combined index (a “cocktail” of single indices) correlates better with simulated dynamics. In particular, we are interested in rank correlations between single-node and multi-node centrality and simulated node importance. We identify index families based on similarity. The best single-index correlations (weighted degree centrality) can predict simulated food web dynamics with an accuracy up to 70.06%. This accuracy can be raised reasonably, using the best cocktail, up to 78.42%. This is a combination of node degree (D) and 5-step-long weighted importance index (WI5). Since they have completely different properties (the former is local and binary, the latter is meso-scale and weighted), we can demonstrate that a good cocktail has to combine indices from different families in order to best improve predictions. If one needs to predict dynamics from structure, there is a way to use wise proxies of simple topological indices – instead of performing complicated simulations.Catarina GouveiaÁgnes MóréhFerenc JordánElsevierarticleFood webKeystone speciesCentralityMultiple indicesSimulationMachine learningEcologyQH540-549.5ENEcological Indicators, Vol 126, Iss , Pp 107617- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Food web Keystone species Centrality Multiple indices Simulation Machine learning Ecology QH540-549.5
spellingShingle	Food web Keystone species Centrality Multiple indices Simulation Machine learning Ecology QH540-549.5 Catarina Gouveia Ágnes Móréh Ferenc Jordán Combining centrality indices: Maximizing the predictability of keystone species in food webs
description	Network analysis offers a rich toolkit to study various graph models in biology. In ecology, centrality indices have been suggested to indicate keystone species in interaction networks and to quantify their importance in an ecosystem. There is a large number of centrality indices, however, and it is often unclear what is their precise biological meaning, how are they related to each other and which one is the “best” predicting the functioning of the modelled biological system. It is a major challenge to use simple structural indicators in order to predict the outcome of much more complicated dynamical simulations. The question is which one is the most preidictive one and what is the meaning of particular structural indices. Here we use machine learning techniques to combine k centrality indices out of n in such a way that the gained combined index (a “cocktail” of single indices) correlates better with simulated dynamics. In particular, we are interested in rank correlations between single-node and multi-node centrality and simulated node importance. We identify index families based on similarity. The best single-index correlations (weighted degree centrality) can predict simulated food web dynamics with an accuracy up to 70.06%. This accuracy can be raised reasonably, using the best cocktail, up to 78.42%. This is a combination of node degree (D) and 5-step-long weighted importance index (WI5). Since they have completely different properties (the former is local and binary, the latter is meso-scale and weighted), we can demonstrate that a good cocktail has to combine indices from different families in order to best improve predictions. If one needs to predict dynamics from structure, there is a way to use wise proxies of simple topological indices – instead of performing complicated simulations.
format	article
author	Catarina Gouveia Ágnes Móréh Ferenc Jordán
author_facet	Catarina Gouveia Ágnes Móréh Ferenc Jordán
author_sort	Catarina Gouveia
title	Combining centrality indices: Maximizing the predictability of keystone species in food webs
title_short	Combining centrality indices: Maximizing the predictability of keystone species in food webs
title_full	Combining centrality indices: Maximizing the predictability of keystone species in food webs
title_fullStr	Combining centrality indices: Maximizing the predictability of keystone species in food webs
title_full_unstemmed	Combining centrality indices: Maximizing the predictability of keystone species in food webs
title_sort	combining centrality indices: maximizing the predictability of keystone species in food webs
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/18753159843749f7bae41a7dacb39518
work_keys_str_mv	AT catarinagouveia combiningcentralityindicesmaximizingthepredictabilityofkeystonespeciesinfoodwebs AT agnesmoreh combiningcentralityindicesmaximizingthepredictabilityofkeystonespeciesinfoodwebs AT ferencjordan combiningcentralityindicesmaximizingthepredictabilityofkeystonespeciesinfoodwebs
_version_	1718405710660763648

Combining centrality indices: Maximizing the predictability of keystone species in food webs

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