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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
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Article . 2020 . Peer-reviewed
License: Wiley Online Library User Agreement
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Article . 2020
Data sources: Datacite
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Article . 2020
Data sources: Datacite
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High niche partitioning promotes highly specialized, modular and non‐nested florivore–plant networks across spatial scales and reveals drivers of specialization

Authors: Juliana Cordeiro; João H. F. de Oliveira; Hermes J. Schmitz; Jeferson Vizentin‐Bugoni;

High niche partitioning promotes highly specialized, modular and non‐nested florivore–plant networks across spatial scales and reveals drivers of specialization

Abstract

Understanding patterns of ecological specialization, the processes underlying niche partitioning and how they translate into the structure of interaction networks is a persistent challenge in ecology. Advances on this regard are limited by the prevalent focus on single spatial scales, lack of tests of the mechanisms underlying specialization, and scarce investigation of some types of interactions. Here we investigated the patterns of interaction between plants and florivores (flower‐breeding drosophilids, FBD) at species‐ and community‐level and at local and regional scales, and tested the relative importance of multiple potential drivers of frequencies of interactions in a local network. First, based on a year‐round collection of 42 322 flowers belonging to 82 plant species, we investigated species specialization and network structure and tested whether frequencies of interactions were related to plant–consumer temporal overlap and resource availability. Second, we built a regional florivore–plant meta‐network for the Neotropical region and tested its structure for nestedness, modularity and overall specialization. Our findings revealed that although FBD species span a broad range of degrees of specialization, most species were highly specialized. At both local and regional scales, network structure was highly modular and non‐nested, presenting high complementary specialization. Moreover, phenological overlap between FBD and their hosts was the most influential driver of frequency of interactions, in comparison to abundance and traits. By describing the structure of FBD–plant networks, our results illustrate how a highly diverse and specialized system retains interaction patterns found in other types of interaction networks which are often driven by different processes. Furthermore, despite undersampling of interactions in the meta‐network caused by the lack of studies on this system and the high diversity of Neotropical FBD's, the high modularity and consistency of some clade–clade modules across spatial scales suggests the importance of evolutionary history and physiological constraints in shaping interactions in this system.

Keywords

Insecta, Arthropoda, fruit flies, Diptera, flies, Animalia, Biodiversity, Taxonomy

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
26
Top 10%
Average
Top 10%
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