<i>Matching–centrality</i>
            decomposition and the forecasting of new links in networks

Rohr, Rudolf P.; Naisbit, Russell E.; Mazza, Christian; Bersier, Louis‐Félix

doi:10.1098/rspb.2015.2702

Cited by 35 publications

(66 citation statements)

References 51 publications

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“…Interactions were analysed following the matching‐centrality formalism (Rohr et al., ). Each species was represented by a “matching” and a “centrality” component.…”

Section: Methodsmentioning

confidence: 99%

“…accounts for their respective specificity/generality relative to the number of associated prey or predators. The probability of interaction P ( L ij = 1) based on a general linear model (GLM) follows a log‐linear model of the form (Rohr et al., ):

logit false(P (L_{i j} = 1) false) = - λ {(v_{i} - f_{j})}^{2} + {normalδ}_{i} v_{i} + {normalδ}_{2} f_{j}

where λ, δ 1 and δ 2 are parameters describing the importance of the “matching” ( v i − f j ) 2 and of the “centrality” of prey (

v_{i}^{*}

) and predators (

f_{i}^{*}

). Ecologically, the v term represents the vulnerability of the prey, while the f term represents the foraging ability of the predator (Rohr & Bascompte, ).…”

Section: Methodsmentioning

confidence: 99%

“…Although mixing both traits and phylogeny could be efficient to predict ecological interactions (Morales‐Castilla et al., ), food web models including them are descriptive only (Krasnov, Shenbrot, Khokhlova, & Degen, ; Rafferty & Ives, ; Rezende, Albert, Fortuna, & Bascompte, ; Rohr, Scherer, Kehrli, Mazza, & Bersier, ). The few existing predictive models are still based uniquely on trait matching (Gravel et al., ; Rohr, Naisbit, Mazza, & Bersier, ). The trait‐matching approach takes into account the probability of interactions between the traits of the consumer and the traits of the resource (Gravel, Albouy, & Thuiller, ; Rohr & Bascompte, ).…”

Section: Introductionmentioning

confidence: 99%

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Trait matching and phylogeny as predictors of predator–prey interactions involving ground beetles

2017

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Abstract1. With global change modifying species assemblages, our success in predicting ecosystem-level consequences of these new communities will depend, in part, on our ability to understand biotic interactions. Current food web theory considers interactions between numerous species simultaneously, but descriptive models are unable to predict interactions between newly co-occurring species. Incorporating proxies such as functional traits and phylogeny into models could help infer predator/prey interactions.2. Here, we used trait matching between predator feeding traits and prey vulnerability traits, along with phylogeny (used as a proxy for chemical defence and other traits difficult to document), to infer predatory interactions using ground beetles as model organisms.3. A feeding experiment was conducted involving 20 ground beetle and 115 prey species to determine which pair of species did or did not interact. Eight predator and four prey functional traits were measured directly on specimens. Then, using a modelling approach based on the matching-centrality formalism, we evaluated 511 predictive ecological models that tested different combinations of all predator and prey functional traits, and phylogenetic information.4. The most parsimonious model accurately predicted 81% of the observed realized and unrealized interactions, using phylogenetic information and the trait-matches predator biting force/prey cuticular toughness and predator/prey body size ratio.The best trait-based models predicted correctly >80% which species interact (realized interactions), but predict <58% of which species did not interact (unrealized interactions). Adding a phylogenetic term representing the evolutionary distance within each trophic level increased the ability to predict which species did not interact to >75%.5. The matching of predator biting force and prey cuticular toughness demonstrated a better predictive power than the commonly used predator/prey body size ratio.Our novel model combining both functional traits and phylogeny extends beyond existing descriptive approaches and could represent a valuable tool to predict consumer/resource interactions of newly introduced species and to resolve cryptic food webs. K E Y W O R D Sfood web, functional traits, matching-centrality formalism, networks, trophic interactions

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“…Interactions were analysed following the matching‐centrality formalism (Rohr et al., ). Each species was represented by a “matching” and a “centrality” component.…”

Section: Methodsmentioning

confidence: 99%

logit false(P (L_{i j} = 1) false) = - λ {(v_{i} - f_{j})}^{2} + {normalδ}_{i} v_{i} + {normalδ}_{2} f_{j}

where λ, δ 1 and δ 2 are parameters describing the importance of the “matching” ( v i − f j ) 2 and of the “centrality” of prey (

v_{i}^{*}

) and predators (

f_{i}^{*}

). Ecologically, the v term represents the vulnerability of the prey, while the f term represents the foraging ability of the predator (Rohr & Bascompte, ).…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Trait matching and phylogeny as predictors of predator–prey interactions involving ground beetles

2017

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show abstract

“…; Rohr et al . ). First, field observations are used to establish which species interact, usually based on direct observation of interactions or proxies of species visitation.…”

Section: Introductionmentioning

confidence: 97%

Towards a predictive model of species interaction beta diversity

Graham

2018

Ecology Letters

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Species interactions are fundamental to community dynamics and ecosystem processes. Despite significant progress in describing species interactions, we lack the ability to predict changes in interactions across space and time. We outline a Bayesian approach to separate the probability of species co‐occurrence, interaction and detectability in influencing interaction betadiversity. We use a multi‐year hummingbird–plant time series, divided into training and testing data, to show that including models of detectability and occurrence improves forecasts of mutualistic interactions. We then extend our model to explore interaction betadiversity across two distinct seasons. Despite differences in the observed interactions among seasons, there was no significant change in hummingbird occurrence or interaction frequency between hummingbirds and plants. These results highlight the challenge of inferring the causes of interaction betadiversity when interaction detectability is low. Finally, we highlight potential applications of our model for integrating observations of local interactions with biogeographic and evolutionary histories of co‐occurring species. These advances will provide new insight into the mechanisms that drive variation in patterns of biodiversity.

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“…We use a resampling procedure that is able to generate a large gradient of inter-agent resource-competition networks while preserving the total number of interactions in the network [29].…”

Section: Appendix B Alternative Inter-agent Resourcecompetition Netwmentioning

confidence: 99%

How structurally stable are global socioeconomic systems?

Saavedra

Rohr

Gilarranz

et al. 2014

J. R. Soc. Interface.

Self Cite

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The stability analysis of socioeconomic systems has been centred on answering whether small perturbations when a system is in a given quantitative state will push the system permanently to a different quantitative state. However, typically the quantitative state of socioeconomic systems is subject to constant change. Therefore, a key stability question that has been under-investigated is how strongly the conditions of a system itself can change before the system moves to a qualitatively different behaviour, i.e. how structurally stable the systems is. Here, we introduce a framework to investigate the structural stability of socioeconomic systems formed by a network of interactions among agents competing for resources. We measure the structural stability of the system as the range of conditions in the distribution and availability of resources compatible with the qualitative behaviour in which all the constituent agents can be self-sustained across time. To illustrate our framework, we study an empirical representation of the global socioeconomic system formed by countries sharing and competing for multinational companies used as proxy for resources. We demonstrate that the structural stability of the system is inversely associated with the level of competition and the level of heterogeneity in the distribution of resources. Importantly, we show that the qualitative behaviour of the observed global socioeconomic system is highly sensitive to changes in the distribution of resources. We believe that this work provides a methodological basis to develop sustainable strategies for socioeconomic systems subject to constantly changing conditions.

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Matching–centrality decomposition and the forecasting of new links in networks

Cited by 35 publications

References 51 publications

Trait matching and phylogeny as predictors of predator–prey interactions involving ground beetles

Trait matching and phylogeny as predictors of predator–prey interactions involving ground beetles

Towards a predictive model of species interaction beta diversity

How structurally stable are global socioeconomic systems?

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