DOI: 10.5061/dryad.vdncjsz17
title: Data from: Ant-plant specialisation influenced more by network types than by disturbance, elevation, or latitude

description: 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: 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.
description: RStudio 2023.03.1
description: # Data from: Ant-plant specialisation influenced more by network types
 than by disturbance, elevation, or latitude ## 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; dsi*ants) and plant specialisation on
 ants (i.e. concerning ant partner phylogenetic relatedness; dsi*plants).
 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. 1. 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 2. 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.  3. 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 4. 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. 5.
 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