Plant-Pollinator Networks in the Tropics: A Review

Vizentin‐Bugoni, Jeferson; Maruyama, Pietro Kiyoshi; Souza, Camila Silveira; Ollerton, Jeff; Rech, André Rodrigo; Sazima, Marlies

doi:10.1007/978-3-319-68228-0_6

Cited by 105 publications

(128 citation statements)

References 76 publications

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“…Indeed, modular patterns in plant–hummingbird networks depend on sampling at the landscape scale, since modules emerge from the match of the habitats used by subsets of partners (Maruyama et al., ). Moreover, pollination and seed dispersal networks are more strongly constrained by morphological barriers than ant–plant interactions (Vázquez et al., ); these barriers create myriad forbidden links in these systems, especially in tropical areas (Jordano, ; Vizentin‐Bugoni, Maruyama, & Sazima, ; Vizentin‐Bugoni et al., ). Further, we found that most network descriptors calculated from both random assembly and adjacent assembly produced the same deviation from the mean, even with the accumulation of few subplots or plots.…”

Section: Discussionmentioning

confidence: 99%

The influence of spatial sampling scales on ant–plant interaction network architecture

Dáttilo

Vizentin‐Bugoni

Debastiani

et al. 2019

Journal of Animal Ecology

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Despite great interest in metrics to quantify the structure of ecological networks, the effects of sampling and scale remain poorly understood. In fact, one of the most challenging issues in ecology is how to define suitable scales (i.e., temporal or spatial) to accurately describe and understand ecological systems. Here, we sampled a series of ant–plant interaction networks in the southern Brazilian Amazon rainforest in order to determine whether the spatial sampling scale, from local to regional, affects our understanding of the structure of these networks. To this end, we recorded ant–plant interactions in adjacent 25 × 30 m subplots (local sampling scale) nested within twelve 250 × 30 m plots (regional sampling scale). Moreover, we combined adjacent or random subplots and plots in order to increase the spatial sampling scales at the local and regional levels. We then calculated commonly used binary and quantitative network‐level metrics for both sampling scales (i.e., number of species and interactions, nestedness, specialization and modularity), all of which encompass a wide array of structural patterns in interaction networks. We observed increasing species and interactions across sampling scales, and while most network descriptors remained relatively constant at the local level, there was more variation at the regional scale. Among all metrics, specialization was most constant across different spatial sampling scales. Furthermore, we observed that adjacent assembly did not generate more variation in network descriptor values compared to random assembly. This finding indicates that the spatially aggregated distribution of species/individuals and abiotic conditions does not affect the organization of these interacting assemblages. Our results have a direct impact on our empirical and theoretical understanding of the ecological dynamics of species interactions by demonstrating that small spatial sampling scales should suffice to record some patterns commonly found in ant–plant interaction networks in a highly diverse tropical rainforest.

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Section: Discussionmentioning

confidence: 99%

The influence of spatial sampling scales on ant–plant interaction network architecture

Dáttilo

Vizentin‐Bugoni

Debastiani

et al. 2019

Journal of Animal Ecology

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“…Ecological specialization is a multifaceted concept strongly related to recurrent patterns of interactions among species within communities (Fox and Morrow , Waser et al , Blüthgen et al , Poisot et al , Dalsgaard et al , Vizentin‐Bugoni et al ). In most ecological systems, species specialization spans a continuum from highly specialized to highly generalized species (Waser et al , Blüthgen et al ).…”

Section: Introductionmentioning

confidence: 99%

“…This variation defines the extent of niche partitioning among coexisting species and is driven by multiple non‐exclusive ecological, evolutionary and historical processes that influence species phenotypes (e.g. traits), spatio–temporal distributions and abundances (Lewinsohn et al , Vázquez et al , Poisot et al , Dormann et al , Vizentin‐Bugoni et al ). Teasing apart the drivers of specialization and niche partitioning and how they translate into patterns of interactions is a persistent challenge in ecology (Dormann et al ).…”

Section: Introductionmentioning

confidence: 99%

“…These processes include constraints related to physiological barriers (e.g. plant defenses against herbivory), morphological mismatches, limited overlap in temporal (phenological) and spatial distributions as well as changes in the encounter probabilities governed by species abundances (Burkepile and Parker , Vizentin‐Bugoni et al ). Nonetheless, such potential drivers of interactions have not yet been investigated for florivore–plant interaction networks.…”

Section: Introductionmentioning

confidence: 99%

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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

Cordeiro

Oliveira

Schmitz

et al. 2020

Oikos

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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.

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“…The identification of areas that could benefit from såli seed dispersal on Guam is driven primarily by the location of intact forest, which serves as a seed source for all other forest types. Since seed dispersers do not distinguish between native and non-native species [65], and sometimes even prefer non-native fruits [66], adding seed dispersers to parts of the island where såli could persist but that are far from the intact forest could be counter-productive. Såli would probably disperse non-native species due to a lack of native seed sources in their home range, perpetuating the dominance of introduced forest [67].…”

Section: Discussionmentioning

confidence: 99%

Where to rewild? A conceptual framework to spatially optimize ecological function

Thierry

Rogers

2020

Proc. R. Soc. B.

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Rewilding is an approach aiming at restoring ecosystems to a self-sustaining state by restoring ecological function through active reintroductions or passive management. Locations for most rewilding-through-reintroduction efforts today are selected based on the suitability of the habitat for the reintroduced species, often with little consideration of where the ecological function is most needed. We developed the Spatial Planning of Rewilding Effort (Spore) framework to identify priority locations for rewilding projects through simultaneous consideration of habitat suitability and provisioning of ecological function. We use the island of Guam as a case study for a potential rewilding project, as the island has functionally lost all native seed dispersers as a result of the invasive brown treesnake (Boiga irregularis). The Såli (Micronesian starling, Aplonis opaca) is a good candidate for rewilding to restore ecological function, because it is an effective seed disperser with a localized remnant population. Using Spore, we identify three priority areas for the restoration of seed dispersal, each subdivided into management units. By recognizing the influence of landscape structure and the behaviour of the reintroduced species on the spatial pattern of the function provided by that species, this approach should lead to improved ecological outcomes.royalsocietypublishing.org/journal/rspb Proc. R. Soc. B 287: 20193017

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Plant-Pollinator Networks in the Tropics: A Review

Cited by 105 publications

References 76 publications

The influence of spatial sampling scales on ant–plant interaction network architecture

The influence of spatial sampling scales on ant–plant interaction network architecture

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

Where to rewild? A conceptual framework to spatially optimize ecological function

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