Data from: Emergence of structure in plant-pollinator networks: Low floral resource constrains network specialisation
Cite this dataset
Yahaya, Mukhtar Muhammed; Rodger, James; Landi, Pietro; Hui, Cang (2024). Data from: Emergence of structure in plant-pollinator networks: Low floral resource constrains network specialisation [Dataset]. Dryad. https://doi.org/10.5061/dryad.jq2bvq8hm
Abstract
Specialisation enhances the efficiency of plant-pollinator networks through the exchange of conspecific pollen transfer for floral resources. Floral resources form the currency of plant-pollinator interactions, but the understanding of how floral resources affect the structure of plant-pollinator networks remains modest. Previous theory predicts that optimally foraging animal species will specialise to improve resource acquisition under high resource availability. Although floral resource availability depends on both the plant production and animal consumption of the resources, previous work has assumed that production and availability to be equivalent. This potentially may have led to erroneous inferences on the effect of resource availability on specialisation. We develop a mutualistic Lotka-Volterra consumer-resource model to investigate the influence of floral resource availability on plant-pollinator network structure. The model incorporates animal adaptive foraging behaviour, floral resource dynamics, and density-dependent dynamics. Specialisation, nestedness and modularity of simulated networks generated from the model under a wide range of parameters were explained using the Generalised Linear Model. We found that the distinction between floral resource dynamics and plant density dynamics was necessary for partial specialisation of plant-pollinator networks. This is because floral resource dynamics constraint animal preference due to its depletion by animal species. Floral resource abundance had a positive effect on network specialisation, but animal density had a negative effect on network specialisation. Floral resource dynamics thus play key roles on the structure of plant-pollinator network, distinctive from plant species density dynamics.
README: Yahaya, M.M., Rodger, J.G., Landi, P., Hui. C., (2024) Emergence of structure in plant-pollinator networks: Low floral resource contrains network specialisation.
DOI:10.1111/oik.10533
Data files
modelledNet.csv - Speedsheet
NetModel.R - R script
NetStructurePar.R - R script
PostModelAnalysis.R - R script
Meaning of variables in the simulated data and R scripts
[1] "P" - Number of plant species
[2] "A" - Number of animal species
[3] "H2" - Specialisation
[4] "WNODA" - Nestedness
[5] "modularity" - Modularity
[6] "mean_XP0" - Mean of plant density (initial)
[7] "mean_XA0" - Mean of animal density (initial)
[8] "mean_rP" - Mean of plant intrinsic growth rate
[9] "mean_rA" - Mean of animal intrinsic growth rate
[10] "mean_CP" - Mean of plant density dependence
[11] "mean_CA" - Mean of animal density dependence
[12] "mean_a" - Mean of floral resource production
[13] "mean_w" - Mean of floral resource decay rate
[14] "mean_Fi0" - Mean of floral resource abundance (initial)
[15] "mean_c_sigma_P" - Mean of plant conversion efficiency
[16] "mean_c_sigma_A" - Mean of animal conversion efficiency
[17] "mean_XPF" - Mean of plant density (final)
[18] "mean_XAF" - Mean of animal density (final)
[19] "mean_FiF" - Mean of floral resource abundance (final)
[20] "mean_G" - Mean of animal adaptaion rate
[21] "mean_FA" - Mean of encounter rate (between animal and floral resource)
[22] "var_XP0" - Variance of plant density (initial)
[23] "var_XA0" - Variance of animal density (initial)
[24] "var_rP" - Variance of plant intrinsic growth rate
[25] "var_rA" - Variance of animal intrinsic growth rate
[26] "var_CP" - Variance of plant density dependence
[27] "var_CA" - Variance of animal density dependence
[28] "var_a" - Variance of floral resource production rate
[29] "var_w" - Variance of floral resource decay rate
[30] "var_Fi0" - Variance of floral resource abundance (initial)
[31] "var_c_sigma_P" - Variance of plant conversion efficiency
[32] "var_c_sigma_A" - Vaariance of animal conversion efficiency
[33] "var_XPF" - Variance of plant density (final)
[34] "var_XAF" - Variance of animal density (final)
[35] "var_FiF" - Variance of floral resource abundance (final)
[36] "var_G" - Variance of animal adaption rate
[37] "var_FA" - Variance of encounter rate
[38] "S" - Specie richness
[39] "P_div_A" - Ratio of plant to animal specie richness
Brief description of data
modelleNet: A spreadsheet containing 3812 simulated plan-pollinator communities. Each row correspond to single plant-pollinator community
NetModel: R code for simulation of plant-pollinator model
NetStructurePar: R code for simulation of network structure and species characteristics using High Performance Computing
PostModelAnalysis: R code for post-model statistical analyses
Software version
R: R version 4.3.2 (2023-10-31 ucrt) "Eye Holes"
RStudio: RStudio 2023.09.1+494 "Desert Sunflower" Release (cd7011dce393115d3a7c3db799dda4b1c7e88711, 2023-10-16) for windows
Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) RStudio/2023.09.1+494 Chrome/116.0.5845.190 Electron/26.2.4 Safari/537.36
Methods
This data was simulated with theoretical model which reperesent plant-pollinator community. Parameter are generated abitrarily for each networks which correspond to each row in the spread sheet. Network structure and species' characteristics were estimated and stored for each network. The simulation was carried out with High Performing Computing. The simulations were conducted using R language.
Funding
German Academic Exchange Service, Award: 57588587
National Research Foundation, Award: 89967