Data from: Ant-plant specialisation influenced more by network types than by disturbance, elevation, or latitude
Data files
Oct 18, 2024 version files 481.25 KB
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Abund.csv
51.14 KB
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ants_final.nex
4.91 KB
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code.R
46.61 KB
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Effort.csv
55.87 KB
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Genus.to.Species.Tree.R
1 KB
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List_of_species.csv
247.13 KB
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Matrix_for_bipartite_plot.zip
2.12 KB
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Matrix_for_R_analysis.zip
64.96 KB
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predictors.csv
3.14 KB
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README.md
4.36 KB
Abstract
The ecological factors driving specialisation in species interaction networks along environmental gradients at large spatial scales are poorly understood. Although such drivers can have synergistic impacts, previous work has mainly assessed effects of network type and the abiotic environment separately. We conducted a meta-analysis of existing network data to assess the interactive effects and relative importance of these drivers of specialisation in ant-plant networks at global scales. We collated 74 ant plant networks from 1979–2023, categorised into four network types: plants that provide ants nesting sites (myrmecophytes); plants that provide only food sources (myrmecophiles); plants for which ants disperse seeds (myrmecochories); plants on which ants forage only (foraging). We explored how network specialisation varies between interaction types with elevation, latitude, and anthropogenic disturbance. We used a standard measure of network specialisation, tested whether standardising this against network null models influenced results, and measured phylogenetic network specialisation. We found that the unstandardised specialisation index was strongly affected by habitat disturbance, elevation and interaction type in a manner congruent with previous work, However, these effects are diluted after the index is standardised. These indicates that previous results may relate to species abundance and richness within the network rather than specialisation. This is supported by the existence of correlations between network species richness/weighted connectance and the unstandardized index. Phylogenetic network specialisation was greater for myrmecophytes than for other three network types. This probably relates to closer co-evolution between partners in myrmecophytic networks. Phylogenetic network specialisation also did not vary significantly with elevation, latitude or anthropogenic disturbance. Our results demonstrate that ant-plant network types, in this case relating to strength of mutualistic interaction, is more important is shaping network specialisation than geographical gradients.
Description of the data and file structure
We explored how network specialisation varies between interaction types with elevation, latitude, and anthropogenic disturbance. We used a standard measure of network specialisation (H2' index) which measure of specialisation of all species in the network, across upper and lower network levels. It is calculated based on the deviation of the observed number of interactions per species from the expected number of interactions, given the marginal total abundances of each species. However, since each network had a different species abundance, richness, and sampling effort, which might affect H2’ independently of species specialisation per se, we also generated 1000 null models for each network, recalculated H2’ for each, and calculated a standardised effect size (H2’ z-score) to assess deviation from random expectation. We then tested whether networks were specialised in terms of resource phylogenetic relatedness, while accounting for resource abundance by measuring phylogenetic network specialisation (dsi*). (i.e., concerning plant partner phylogenetic relatedness; dsiants) and plant specialisation on ants (i.e. concerning ant partner phylogenetic relatedness; dsiplants). It is also expected that ecological community data may experience some degree of under-sampling, which might create species richness unevenness and lead to bias. We tried to minimize this effect by calculating network completeness.
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files in "matrix for analysis" zip are main data source or interaction matrix for all calculation needed. The name of the of each files represent: Type of interaction, 1 = myrmecophytic, 2 = myrmecophilic, 3 = diaspore, 4 = co-occurrence; author name; published year.
a. column name: list of ant species found in the locality
row names: list of plant species found in locality
numbers indicate the number of interaction happened between the ant and plants
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files in "matrix for bipartite plot" zip are matrix used for figure 3. It is consist 4 matrix that can also be found in "matrix for analysis" but with column and row abbreviated to ensure readability in bipartite plot
column name is the abbreviated name of ant species, row names is abbreviated name of plant species. First letter represent genus and the rest represent species.
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List of species and ants_final.nex are dataset that are needed to build plant and ant phylogenetic tree to calculate dsi. Genus.to.Species.Tree.R is code that is needed to help building ant phylogenetic tree. List of species contained of corrected and updated species listed in the dataset.
network: file name of the dataset for each locality
family: the family of species listed in the network
genus: the genus of species listed in the network
species: the name of species listed in the network
old: the original name of the species in the network before it is corrected or updated
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abund is dataset that is needed to calculate dsi plants, and effort is needed to calculate dsi ants
network_id: file name of the dataset
species: list of species in the dataset
abund: total individual of each ant species observed for each dataset
effort: total of plant individual of each plant species observed for each datase.
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predictors are covariates of each matrix extracted from manuscript
NetworkID: file name of the dataset, each locality has different network ID
ID: random factor meaning one dataset may contain multiple locality, hence one ID
Latitude: Latitude of locality
Elevation: Elevation of locality
Disturbance: This explain, if the locality is disturbed or not
code.R file contains script to run with R studio
How to run the calculation:
a. To calculate H2', H2' Zscore, and network completeness, only matrix for analysis zip is needed.
b. To calculate dsi*, additional dataset will be needed which are Abund, effort, list of species, final ant.nex, and genus to species r file.
c. Predictors file will be needed when doing model selection.
Data was derived from the following sources:
- supplementary of published datasets
- authors of the study
Code/Software
all code are written in R.file and should be run by R studio
We collated existing ant-plant interaction network datasets by searching for publications on the Web of Science (WOS) online database supplemented by direct requests to the authors of some datasets. Data collection activities were carried out throughout 2021, with no date limits imposed. We searched Web of Science database using the keywords "myrmecophyte" OR "extrafloral nectaries” (EFN) OR "fruiting bodies" (FB), OR "myrmecophily" OR "ant-plant interaction" OR "ant-plant foraging," OR "myrmecochory". We selected networks that consisted of at least three plant species and at least three ant species. We excluded networks that consisted of presence/absence data only, as many network descriptors are sensitive to binary data.
- Zahra, Shafia; Jorge, Leonardo; Dáttilo, Wesley et al. (2024). Ant-plant specialisation depends on network type, but not disturbance, elevation, or latitude. [Preprint]. Authorea, Inc.. https://doi.org/10.22541/au.171500704.45910925/v1
