Components of Phylogenetic Signal in Antagonistic and Mutualistic Networks

Rohr, Rudolf P.; Bascompte, Jordi

doi:10.1086/678234

Cited by 40 publications

(48 citation statements)

References 33 publications

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“…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, ). Rohr et al.…”

Section: Methodsmentioning

confidence: 99%

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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“…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, ). Rohr et al.…”

Section: Methodsmentioning

confidence: 99%

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

2017

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“…, Krasnov et al. , Rohr and Bascompte , Schleuning et al. ), implying that some individual modules within networks may be better characterized by cophylogenetic signal than others (Fig.…”

Section: Introductionmentioning

confidence: 96%

Cophylogenetic signal is detectable in pollination interactions across ecological scales

Hutchinson

Cagua

Stouffer

2017

Ecology

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That evolutionary history can influence the way that species interact is a basic tenet of evolutionary ecology. However, when the role of evolution in determining ecological interactions is investigated, focus typically centers on just one side of the interaction. A cophylogenetic signal, the congruence of evolutionary history across both sides of an ecological interaction, extends these previous explorations and provides a more complete picture of how evolutionary patterns influence the way species interact. To date, cophylogenetic signal has most typically been studied in interactions that occur between fine taxonomic clades that show high intimacy. In this study, we took an alternative approach and made an exhaustive assessment of cophylogeny in pollination interactions. To do so, we assessed the strength of cophylogenetic signal at four distinct scales of pollination interaction: (1) across plant–pollinator associations globally, (2) in local pollination communities, (3) within the modular structure of those communities, and (4) in individual modules. We did so using a globally distributed dataset comprised of 54 pollination networks, over 4000 species, and over 12,000 interactions. Within these data, we detected cophylogenetic signal at all four scales. Cophylogenetic signal was found at the level of plant–pollinator interactions on a global scale and in the majority of pollination communities. At the scale defined by the modular structure within those communities, however, we observed a much weaker cophylogenetic signal. Cophylogenetic signal was detectable in a significant proportion of individual modules and most typically when within‐module phylogenetic diversity was low. In sum, the detection of cophylogenetic signal in pollination interactions across scales provides a new dimension to the story of how past evolution shapes extant pollinator–angiosperm interactions.

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“…; Parker et al. ) and patterns of mutualistic association (Rohr and Bascompte ). Phylogenetic information for species involved in plant–rhizobial interactions may therefore help to understand how variation in mutualistic benefit emerges and is subsequently maintained.…”

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confidence: 99%

Evolutionary history shapes patterns of mutualistic benefit in Acacia –rhizobial interactions

et al. 2016

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The ecological and evolutionary factors that drive the emergence and maintenance of variation in mutualistic benefit (i.e., the benefits provided by one partner to another) in mutualistic symbioses are not well understood. In this study, we evaluated the role that host and symbiont phylogeny might play in determining patterns of mutualistic benefit for interactions among nine species of Acacia and 31 strains of nitrogen-fixing rhizobial bacteria. Using phylogenetic comparative methods we compared patterns of variation in mutualistic benefit (host response to inoculation) to rhizobial phylogenies constructed from housekeeping and symbiosis genes; and a multigene host phylogeny. We found widespread genotype-by-genotype variation in patterns of plant growth. A relatively large component of this variation (21-28%) was strongly influenced by the interacting evolutionary histories of both partners, such that phylogenetically similar host species had similar growth responses when inoculated with phylogenetically similar rhizobia. We also found a relatively large nonphylogenetic effect for the average mutualistic benefit provided by rhizobia to plants, such that phylogenetic relatedness did not predict the overall benefit provided by rhizobia across all hosts. We conclude that phylogenetic relatedness should frequently predict patterns of mutualistic benefit in acacia-rhizobial mutualistic interactions; but that some mutualistic traits also evolve independently of the phylogenies.

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Components of Phylogenetic Signal in Antagonistic and Mutualistic Networks

Cited by 40 publications

References 33 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

Cophylogenetic signal is detectable in pollination interactions across ecological scales

Evolutionary history shapes patterns of mutualistic benefit in Acacia –rhizobial interactions

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