DOI: 10.5061/dryad.8w9ghx3xs
title: Dandelion and vole interaction

description: Like many rodents, the water vole is able to reach high densities in
 meadows. During outbreaks, voles cause significant changes in plant
 communities. Although water voles consume a wide variety of plant species,
 dandelions have a unique position: they are selected by voles year-round
 and serve as a key resource during winter. Voles harvest all parts of the
 dandelion and store the roots in almost monospecific food stores. As
 dandelions are perennial plants that take years to grow, vole activity can
 significantly affect dandelion populations. Our aim was to estimate the
 influence of dandelion density on vole space use, particularly habitat
 selection during natal dispersal. We tested the hypothesis that voles
 select dandelion-rich plots for settlements. We also measured the
 variation in dandelion density due to new colony settlements to assess
 potential feedback effects. We hypothesized that voles decrease dandelion
 populations. To achieve that, we used a drone to monitor dandelions and
 voles over two years. We monitored 52 quadrats, each half a hectare, three
 times a year. We analysed each image using remote sensing to locate voles
 and dandelions, and then examined the interactions between their locations
 over time. We found that dandelion-rich plots were more likely to
 colonize. In plots with low dandelion density, areas denser than the plot
 average were also more likely to be colonised. We observed a decrease in
 the number of dandelions after colony settlement. Finally, we found
 evidence that existing burrows were more likely to be reused by new voles
 if dandelions were still present. This study demonstrates that dandelion
 density is a key criterion in habitat selection for water voles and that
 vole colonies rapidly deplete this resource after establishment. These
 findings provide insight into plant-herbivore interactions and offer
 valuable perspectives for further exploration of the plant hypothesis,
 particularly with respect to the dynamics of resource availability and its
 role in cyclic population fluctuations.
description: # Dandelion and vole interaction
 [https://doi.org/10.5061/dryad.8w9ghx3xs](https://doi.org/10.5061/dryad.8w9ghx3xs) ## Description of the data and file structure The aim of this study was to investigate the influence of dandelion density on vole space use, particularly on habitat selection during natal dispersal. Data were collected in the Massif Central (France) in 2021 and 2022 and in the Jura Mountains in 2022. 52 quadrats were monitored by drone 3 times per year to map the locations of water voles and dandelions from aerial pictures (in March and September for vole surface indices and in May for dandelions). Each image is divided into 2 m × 2 m tiles, resulting in a total of 1200 tiles per quadrat. In each tile, we compared the influence of dandelion density on the probability of colonization or reuse of an old colony. At a second time, we observed the depletion of dandelions by voles at broad and fine scales. ### Files and variables #### File: Dandelion_vole_interaction.R **Description:** Script used for the analysis. #### File: Depletion_colony_scale.txt **Description:** Database of information obtained by remote sensing in 46 subplots (20 x 20 m). Each mound were surrounded by 20 buffers at a distanc between 5 cm and 5 m. In each buffer the number of dandelion flowers was counted. This database is used for dandelion depletion by vole at fine scale analysis. ##### Variables * Modality: Code with the region of the quadrat and the year of the picture ( MC21 = Massif Central in 2021, MC22 = Massif Central in 2022, D22 = Doubs in 2022) * Quadrat: ID of the quadrat use for the extraction of the subplot * Extraction: ID of the extraction (number of the subplot) * Distance: radius of the buffer around the mound * Density: mean density of dandelion flower heads within the buffer area * Mean: mean density of flower heads of the subplot * Density_comp: comparison of the dandelion density whithin the buffer and the all subplot #### File: Colonisation_data.txt **Description:** Database for the analysis of the influence of dandelion density on new colony etablishment. This database obtained by remote sensing of 52 plots in 2021 and 2022 in Doubs and Massif central. Each plot was subdivided in 1200 tiles of 4 m². ##### Variables * Modality: Code with the region of the quadrat and the year of the picture ( MC21 = Massif Central in 2021, MC22 = Massif Central in 2022, D22 = Doubs in 2022) * Quadrat: ID of the quadrat * num_tile: ID of the tile after the 4 m² grid was applied * Plot_yellow_rate: rate of yellow pixel in the plot in May * Plot_brown_rate_march: rate of brown/ground color pixel in the plot in March * Sum_occ\_ tile_march: Number of tiles considered as colonized by vole in March * Stade_physio: mean number of flower heads of dandelion by plant * Plot_quality_march: degree of confidence of the observation in March from A = the best to C= the worst * Plot_quality_may: degree of confidence of the observation in May from A = the best to C= the worst * Tile_brown_rate_march: rate of brown/ground color pixel in the tile in March * Tile_yellow_rate: rate of yellow pixel in the tile in May * Plot_brown_rate_sept: rate of brown/ground color pixel in the plot in September * Sum_occ_tile_sept: Number of tiles considered as colonized by vole in September * Plot_quality_sept: Degree of confidence of the observation in September from A = the best to C= the worst * Tile_brown_rate_sept: rate of brown/ground color pixel in the tile in September #### File: Quadrat_position.csv **Description:** Geographical coordinates of the centroids of each plot ##### Variables * Name_Quadrat: Quadrat ID * Coord_X: X coordinate * Coord_Y: Y coordinate #### File: Depletion_and_persistence.txt **Description:** Database for the analysis of the influence of dandelion density on persistence and depletion (broad scale analysis). This database is a time serie 2021 and 2022 of the 35 plots in Massif central. Each quadrat was subdivided in 1200 tiles of 4 m². ##### Variables * Quadrat: ID of the quadrat * num_tile: ID of the tile after the 4m² grid was applied * Plot_yellow_rate_21: rate of yellow pixel in the plot in May 2021 * Plot_brown_rate_march_21:rate of brown/ground color pixel in the plot in March 2021 * Sum_occ_tile_march_21: Number of tiles considered as colonized by vole in March 2021 * Stade_physio_21: mean number of flower heads of dandelion by plant in May 2021 * Plot_quality_march_21: degree of confidence of the observation in March 2021 from A = the best to C= the worst * Plot_quality_may_21: degree of confidence of the observation in May 2021 from A = the best to C= the worst * Tile_brown_rate_march_21: rate of brown/ground color pixel in the tile in March 2021 * Tile_yellow_rate_21: rate of yellow pixel in the tile in May 2021 * Plot_brown_rate_sept_21: rate of brown/ground color pixel in the plot in September 2021 * Sum_occ_tile_sept_21: Number of tiles considered as colonized by vole in September 2021 * Plot_quality_sept_21: Degree of confidence of the observation in September 2021 from A = the best to C= the worst * Tile_brown_rate_sept_21:rate of brown/ground color pixel in the tile in September 2021 * Plot_brown_rate_march_22: rate of brown/ground color pixel in the plot in March 2022 * Sum_occ_tile_march_22: Number of tiles considered as colonized by vole in March 2022 * Plot_quality_march_22: degree of confidence of the observation in March 2022 from A = the best to C= the worst * Tile_brown_rate_march_22: rate of brown/ground color pixel in the tile in March 2022 * Plot_yellow_rate_22: rate of yellow pixel in the plot in May 2022 * Stade_physio_22: mean number of flower heads of dandelion by plant in May 2022 * Plot_quality_may_22: degree of confidence of the observation in May 2022 from A = the best to C= the worst * Tile_yellow_rate_22: rate of yellow pixel in the tile in May 2022 * Plot_brown_rate_sept_22: rate of brown/ground color pixel in the plot in September 2022 * Sum_occ_tile_sept_22 : Number of tiles considered as colonized by vole in September 2022 * Plot_quality_sept_22: Degree of confidence of the observation in September 2022 from A = the best to C= the worst * Tile_brown_rate_sept_22: rate of brown/ground color pixel in the tile in September 2022 ## Code/software All analyses were performed using the R environment (version 4.2.2). Packages to download to use the script : library(lme4) library(MuMIn) library(boot) library(tidyverse) library(ggpubr) library(mgcv) library(itsadug) library(lmerTest) library(visreg) library(extrafont) library(wesanderson) The script is divided into 4 parts and each part contains: \- importating and formating data \- checking the data \- the models + choice of reference quadrat \- creating graphics Run the part # COLONISATION to find the figure 3A Run the part # REUSE to find the figure 3B Run the part # DEPLETION: population scale ---- to find the figure 4A Run the part # DEPLETION: subplot scale ---- to find the figure 4B All model are summarize in table 1