2018
DOI: 10.1111/jbi.13367
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Spatial distance and climate determine modularity in a cross‐biomes plant–hummingbird interaction network in Brazil

Abstract: Aim We examined the effects of space, climate, phylogeny and species traits on module composition in a cross‐biomes plant–hummingbird network. Location Brazil, except Amazonian region. Methods We compiled 31 local binary plant–hummingbird networks, combining them into one cross‐biomes metanetwork. We conducted a modularity analysis and tested the relationship between species’ module membership with traits, geographical location, climatic conditions and range sizes, employing random forest models. We fitted red… Show more

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Cited by 36 publications
(33 citation statements)
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“…For instance, the approach can be used to describe species' functions that are difficult to assess indirectly via species' traits (e.g., physiological and metabolic processes) or to describe the environmental conditions under which species can exist, i.e., their Grinnellian niches (Dehling and Stouffer 2018). Taking into account species' functional roles could prove to be especially useful for comparisons of networks and ecological processes between sites that have few or no species in common, e.g., in comparisons on large spatial scales, across habitats, and in regional meta-networks (Araujo et al 2018, Dugger et al 2019). However, while the method we used allows the direct comparison of all species that contribute to an ecological process, independent of their taxonomic relationships, most networks have so far been sampled for single taxa in the upper trophic level (e.g., bird-plant or bat-plant networks).…”
Section: Discussionmentioning
confidence: 99%
“…For instance, the approach can be used to describe species' functions that are difficult to assess indirectly via species' traits (e.g., physiological and metabolic processes) or to describe the environmental conditions under which species can exist, i.e., their Grinnellian niches (Dehling and Stouffer 2018). Taking into account species' functional roles could prove to be especially useful for comparisons of networks and ecological processes between sites that have few or no species in common, e.g., in comparisons on large spatial scales, across habitats, and in regional meta-networks (Araujo et al 2018, Dugger et al 2019). However, while the method we used allows the direct comparison of all species that contribute to an ecological process, independent of their taxonomic relationships, most networks have so far been sampled for single taxa in the upper trophic level (e.g., bird-plant or bat-plant networks).…”
Section: Discussionmentioning
confidence: 99%
“…Indeed, a regional network (or metaweb) characterizes all potential interactions among all species that are susceptible to both co-occur and interact at the scale considered (Dunne 2006, Gravel et al 2013, Morales-Castilla et al 2015, Redhead et al 2018). Similar to variations in regional species diversity across geographical gradients, variation in regional network structure can occur, for example, because of the presence of more specialized species in certain regions or due to habitat heterogeneity that can promote the emergence of network modules at the regional scale (Araujo et al 2018). The motivation for comparing local and regional networks is thus similar to the motivation for comparing local and regional diversity: what and how spatial processes affect the changes of network properties.…”
Section: Introductionmentioning
confidence: 99%
“…Modularity is a network property that emerges when groups of species are densely connected and have sparser connections to other groups of interacting species. The organization of interactions into modules may reflect similarity of traits, phylogeny, biogeography and climate among species, providing information on how the interactions are partitioned in the community (Maruyama et al 2014, Araujo et al 2018. We analyzed modularity using the recently implemented LPAwb + algorithm (Liu andMurata 2010, Beckett 2016).…”
Section: Measuring Network Metricsmentioning
confidence: 99%