1 Updates

21-01-2025

  • The dataset is updated to December 2024.

  • Checked the database (urban_review_13_11_24; previous versions in the “archive data” folder) https://docs.google.com/spreadsheets/d/1RQYPN_h9fll4WWYf3i59G-NWt4AgDLax/edit?gid=891108749#gid=891108749

    • Checked total abundance, EC abundance, EC biomass, total biomass, and species richness
    • Ensured that when TOTabundance = 0, other columns like ECabundance, ECbiomass, TOTbiomass, and TOTrichness were also set to 0 (and vice versa)
    • Ensured species presence matched species richness
    • Ensured species abundance matched ECabundance (see ecological categories in “EW Ecological Groups” sheet)
    • Corrected some incorrect values
    • …

  • Some authors exclude juveniles in EC abundance calculations, causing differences between sum ECabundance and TOTabundance. While this introduces a bias, i think that we have to work within these limitations.

  • Different sampling methods (physical and/or chemical) introduce biases that random effects cannot fully address, but i think that we have to work within these limitations.

  • Aggregated data (site or modality averages, and between-site or between-modality averages) are present. Random effects help account for these biases.

  • We’ve selected/removed articles in about three stages:

    • The first selection/removal was done in Montpellier. => do we have any records of this ?
    • The second removal occurred when each of us reviewed our own papers. => do we have any records of this ?
    • The third removal, includes articles or lines excluded for reasons such as lack of available GPS coordinates (and inability to accurately create centroids), inappropriate sampling protocols, temporal duplicates (same site sampled multiple times), etc. These lines, removed “post-hoc” are still visible in the dataset (column A, not kept), with the reason for their removal indicated.

  • Experimental sites in rural areas will be excluded for descriptive and inferential analyses.

  • Urban/rural classifications are based on explicit descriptors in the dataset, so coming from the authors (e.g., “urban park” or “urban roadside”). This goes beyond interpretation. GPS coordinates were used in ~10% of ambiguous cases.

  • For describing earthworm communities and environmental variables, I think it’s essential to focus exclusively on “strictly urban areas”.

Attention article 39 => suppression de la richesse => voir HP

2 Data exploration

2.1 Earthworm variables

The datamset contains 726 available rows.

2.1.1 Map

2.1.2 Data composition

##         publication_name count percentage
## 1     Tresch et al._2019   160  22.038567
## 2   Francini et al._2018    89  12.258953
## 3  Audusseau et al._2020    87  11.983471
## 4        Xie et al._2018    39   5.371901
## 5     Pelosi et al._2021    36   4.958678
## 6 Richardson et al._2019    28   3.856749

2.1.3 Sampling resolution

## # A tibble: 3 × 2
##   samplingresolution num_publications
##   <fct>                         <int>
## 1 1                                 7
## 2 2                                25
## 3 3                                 9
##   samplingresolution   n
## 1                  1 343
## 2                  2 328
## 3                  3  55

2.1.4 Collection year

1990s: First studies in strictly urban environments by Pizl, Josens, Tiho, etc.

2.1.5 Descriptive analysis

Variable Min Max Mean Median SD n NAs
totalRichness 0 14.0 3.6 3.0 2.3 534 132
totalAbundance 0 1177.8 148.6 103.7 152.3 502 164
totalBiomass 0 608.9 75.6 50.7 81.9 427 239
ab_anecic 0 188.9 19.5 11.1 27.2 377 289
ab_endogeic 0 611.1 56.5 28.0 83.0 377 289
ab_epigeic 0 173.8 9.8 0.0 23.1 377 289
biom_anecics 0 533.3 68.5 56.1 66.4 258 408
biom_endogeics 0 170.0 26.6 16.7 31.2 258 408
biom_epigeics 0 53.1 1.5 0.0 5.9 258 408

How to analyze earthworm abundance/biomass/richness datam collected at different observation scales?

  • Currently, the datamset is biased, mixing individual samples with averages from multiple observations.
  • Possible approaches:
    • Leave the datam as is. => decision of 19/11
    • Impute missing values based on SE or SD, will modify the mean and SD of our study (this approach is highly biased).
    • Correct the error value (SD) of our study based on the error value coming from the papers (SE or SD). Probl : we don’t have always the SE or SD or number of observation to correct the error value.
    • Apply a coefficient to correct the SD of our study based on the error value coming from the papers (SE or SD), larger errors = lower weight. Probl : we don’t have always the SE or SD or number of observation to correct the error value.

Species richness is affected by sampling resolution, meaning that more samples generally lead to higher richness.

  • do you agree with homogenizing the sampling resolution for species richness?
    • We could set sampling_resolution = [1 and 2] across all observations.=> decision of 19/11
    • Alternatively, we could use rarefaction to adjust for differences in sampling protocol and resolution. Probl : we don’t have always all the information to correct the species richness.
samplingresolution n
1 295
2 322
3 49

1: replicate (1 sampling point)

2: site or modality average (default)

3: average between sites or between modalities of the same site (e.g. with same land use)

2.1.5.1 Richness with spatial resolution = 1

names(datam)

Variable Min Max Mean Median SD n NAs
totalRichness 0 8 3 3 1.8 287 8

2.1.5.2 Richness with spatial resolution = 1 and 2

Variable Min Max Mean Median SD n NAs
totalRichness 0 12 3.4 3 2.1 500 117

2.1.6 Ecological category structure

###Common species (n=666)

##                     species percentage
## 1        Aporrectodea_rosea   42.08038
## 2  Allolobophora_chlorotica   40.42553
## 3      Lumbricus_terrestris   39.00709
## 4   Aporrectodea_caliginosa   34.75177
## 5        Aporrectodea_longa   32.62411
## 6       Lumbricus_castaneus   25.53191
## 7    Allolobophora_icterica   22.22222
## 8        Octalasion_lacteum   15.13002
## 9        Lumbricus_rubellus   15.13002
## 10    Aporrectodea_nocturna   11.82033

2.2 Environmental variables

The dataset contains 726 available rows.

2.2.1 Descriptive analysis

The distance to the urban core metric (‘dist_area_ratio’ variable), unit : km ?

The human population density (‘HPD_final’ variable), unit : inhabitants .km² ?

The density of the roads around the site (‘road_density’) unit : Kilometers of road per km² ?

The proportion of urban habitat within a 1km grid (‘urban_1km’) in percentage

The proportion of urban habitat within a 25km grid (‘urban_25km’) in percentage

Variable Min Max Mean Median SD n NAs
phwater 3.1 8.8 7.0 7.2 1.1 504 162
sand 4.3 85.0 40.4 41.1 14.4 258 408
silt 7.8 86.5 39.3 34.8 15.2 258 408
clay 1.1 49.5 19.9 21.1 9.6 258 408
om 0.0 34.9 7.4 7.0 4.4 447 219
corg 0.0 20.2 4.3 4.1 2.5 432 234

Many NAs were present for soil properties in the datamset. In the last meeting, we decided to retain pH, texture, and C_organic for further analyses.

2.3 Correlation matrix

These variables : silt, sand, corg, om, ab_endogeic, totalBiomass, biom_endogeics, biom_anecics, totalAbundance, biom_epigeics, ab_anecic, ab_epigeic are highly correlated

3 Multivariate analysis on soil properties and EW com.

3.1 Data available

3.1.1 All combinations

Top 80 des combinaisons de variables
env_vars com_vars n_observations n_articles env_var_count com_var_count
phwater totalAbundance 386 21 1 1
phwater totalBiomass 375 14 1 1
phwater totalRichness 366 18 1 1
phwater totalAbundance, totalRichness 366 18 1 2
om totalBiomass 324 10 1 1
phwater, om totalBiomass 324 10 2 1
om totalAbundance 322 16 1 1
phwater, om totalAbundance 322 16 2 1
om totalRichness 314 14 1 1
om totalAbundance, totalRichness 302 13 1 2
phwater, om totalRichness 302 13 2 1
phwater, om totalAbundance, totalRichness 302 13 2 2
phwater totalAbundance, totalBiomass 289 13 1 2
phwater ab_anecic 285 14 1 1
phwater ab_endogeic 285 14 1 1
phwater ab_epigeic 285 14 1 1
phwater totalAbundance, ab_anecic 285 14 1 2
phwater totalAbundance, ab_endogeic 285 14 1 2
phwater totalAbundance, ab_epigeic 285 14 1 2
phwater ab_anecic, ab_endogeic 285 14 1 2
phwater ab_anecic, ab_epigeic 285 14 1 2
phwater ab_endogeic, ab_epigeic 285 14 1 2
phwater totalAbundance, ab_anecic, ab_endogeic 285 14 1 3
phwater totalAbundance, ab_anecic, ab_epigeic 285 14 1 3
phwater totalAbundance, ab_endogeic, ab_epigeic 285 14 1 3
phwater ab_anecic, ab_endogeic, ab_epigeic 285 14 1 3
phwater totalAbundance, ab_anecic, ab_endogeic, ab_epigeic 285 14 1 4
phwater totalBiomass, totalRichness 279 11 1 2
phwater totalAbundance, totalBiomass, totalRichness 279 11 1 3
phwater ab_anecic, totalRichness 275 13 1 2
phwater ab_endogeic, totalRichness 275 13 1 2
phwater ab_epigeic, totalRichness 275 13 1 2
phwater totalAbundance, ab_anecic, totalRichness 275 13 1 3
phwater totalAbundance, ab_endogeic, totalRichness 275 13 1 3
phwater totalAbundance, ab_epigeic, totalRichness 275 13 1 3
phwater ab_anecic, ab_endogeic, totalRichness 275 13 1 3
phwater ab_anecic, ab_epigeic, totalRichness 275 13 1 3
phwater ab_endogeic, ab_epigeic, totalRichness 275 13 1 3
phwater totalAbundance, ab_anecic, ab_endogeic, totalRichness 275 13 1 4
phwater totalAbundance, ab_anecic, ab_epigeic, totalRichness 275 13 1 4
phwater totalAbundance, ab_endogeic, ab_epigeic, totalRichness 275 13 1 4
phwater ab_anecic, ab_endogeic, ab_epigeic, totalRichness 275 13 1 4
phwater totalAbundance, ab_anecic, ab_endogeic, ab_epigeic, totalRichness 275 13 1 5
sand totalAbundance 252 8 1 1
sand totalRichness 252 8 1 1
clay totalAbundance 252 8 1 1
clay totalRichness 252 8 1 1
sand totalAbundance, totalRichness 252 8 1 2
clay totalAbundance, totalRichness 252 8 1 2
phwater, sand totalAbundance 252 8 2 1
phwater, sand totalRichness 252 8 2 1
phwater, clay totalAbundance 252 8 2 1
phwater, clay totalRichness 252 8 2 1
sand, clay totalAbundance 252 8 2 1
sand, clay totalRichness 252 8 2 1
sand, om totalAbundance 252 8 2 1
sand, om totalRichness 252 8 2 1
clay, om totalAbundance 252 8 2 1
clay, om totalRichness 252 8 2 1
phwater, sand totalAbundance, totalRichness 252 8 2 2
phwater, clay totalAbundance, totalRichness 252 8 2 2
sand, clay totalAbundance, totalRichness 252 8 2 2
sand, om totalAbundance, totalRichness 252 8 2 2
clay, om totalAbundance, totalRichness 252 8 2 2
phwater, sand, clay totalAbundance 252 8 3 1
phwater, sand, clay totalRichness 252 8 3 1
phwater, sand, om totalAbundance 252 8 3 1
phwater, sand, om totalRichness 252 8 3 1
phwater, clay, om totalAbundance 252 8 3 1
phwater, clay, om totalRichness 252 8 3 1
sand, clay, om totalAbundance 252 8 3 1
sand, clay, om totalRichness 252 8 3 1
phwater, sand, clay totalAbundance, totalRichness 252 8 3 2
phwater, sand, om totalAbundance, totalRichness 252 8 3 2
phwater, clay, om totalAbundance, totalRichness 252 8 3 2
sand, clay, om totalAbundance, totalRichness 252 8 3 2
phwater, sand, clay, om totalAbundance 252 8 4 1
phwater, sand, clay, om totalRichness 252 8 4 1
phwater, sand, clay, om totalAbundance, totalRichness 252 8 4 2
om totalAbundance, totalBiomass 238 9 1 2

3.1.2 Top combinations

Combinaisons maximisant les variables environnementales et de communauté
env_vars com_vars n_observations n_articles
phwater, sand, clay, om totalAbundance 252 8
phwater, sand, clay, om totalRichness 252 8
phwater, sand, clay, om totalAbundance, totalRichness 252 8
phwater totalAbundance, totalBiomass, ab_anecic, ab_endogeic, ab_epigeic, totalRichness 221 8
phwater, sand, clay, om totalBiomass 205 4
phwater, sand, clay, om totalAbundance, totalBiomass 205 4
phwater, sand, clay, om totalBiomass, totalRichness 205 4
phwater, sand, clay, om totalAbundance, totalBiomass, totalRichness 205 4
phwater, sand, clay, om ab_anecic 191 6
phwater, sand, clay, om ab_endogeic 191 6
phwater, sand, clay, om ab_epigeic 191 6
phwater, sand, clay, om totalAbundance, ab_anecic 191 6
phwater, sand, clay, om totalAbundance, ab_endogeic 191 6
phwater, sand, clay, om totalAbundance, ab_epigeic 191 6
phwater, sand, clay, om ab_anecic, ab_endogeic 191 6
phwater, sand, clay, om ab_anecic, ab_epigeic 191 6
phwater, sand, clay, om ab_anecic, totalRichness 191 6
phwater, sand, clay, om ab_endogeic, ab_epigeic 191 6
phwater, sand, clay, om ab_endogeic, totalRichness 191 6
phwater, sand, clay, om ab_epigeic, totalRichness 191 6
phwater, sand, clay, om totalAbundance, ab_anecic, ab_endogeic 191 6
phwater, sand, clay, om totalAbundance, ab_anecic, ab_epigeic 191 6
phwater, sand, clay, om totalAbundance, ab_anecic, totalRichness 191 6
phwater, sand, clay, om totalAbundance, ab_endogeic, ab_epigeic 191 6
phwater, sand, clay, om totalAbundance, ab_endogeic, totalRichness 191 6
phwater, sand, clay, om totalAbundance, ab_epigeic, totalRichness 191 6
phwater, sand, clay, om ab_anecic, ab_endogeic, ab_epigeic 191 6
phwater, sand, clay, om ab_anecic, ab_endogeic, totalRichness 191 6
phwater, sand, clay, om ab_anecic, ab_epigeic, totalRichness 191 6
phwater, sand, clay, om ab_endogeic, ab_epigeic, totalRichness 191 6
phwater, sand, clay, om totalAbundance, ab_anecic, ab_endogeic, ab_epigeic 191 6
phwater, sand, clay, om totalAbundance, ab_anecic, ab_endogeic, totalRichness 191 6
phwater, sand, clay, om totalAbundance, ab_anecic, ab_epigeic, totalRichness 191 6
phwater, sand, clay, om totalAbundance, ab_endogeic, ab_epigeic, totalRichness 191 6
phwater, sand, clay, om ab_anecic, ab_endogeic, ab_epigeic, totalRichness 191 6
phwater, sand, clay, om totalAbundance, ab_anecic, ab_endogeic, ab_epigeic, totalRichness 191 6
om totalAbundance, totalBiomass, ab_anecic, ab_endogeic, ab_epigeic, totalRichness 170 4
phwater, om totalAbundance, totalBiomass, ab_anecic, ab_endogeic, ab_epigeic, totalRichness 170 4
sand totalAbundance, totalBiomass, ab_anecic, ab_endogeic, ab_epigeic, totalRichness 164 3
clay totalAbundance, totalBiomass, ab_anecic, ab_endogeic, ab_epigeic, totalRichness 164 3
phwater, sand totalAbundance, totalBiomass, ab_anecic, ab_endogeic, ab_epigeic, totalRichness 164 3
phwater, clay totalAbundance, totalBiomass, ab_anecic, ab_endogeic, ab_epigeic, totalRichness 164 3
sand, clay totalAbundance, totalBiomass, ab_anecic, ab_endogeic, ab_epigeic, totalRichness 164 3
sand, om totalAbundance, totalBiomass, ab_anecic, ab_endogeic, ab_epigeic, totalRichness 164 3
clay, om totalAbundance, totalBiomass, ab_anecic, ab_endogeic, ab_epigeic, totalRichness 164 3
phwater, sand, clay totalAbundance, totalBiomass, ab_anecic, ab_endogeic, ab_epigeic, totalRichness 164 3
phwater, sand, om totalAbundance, totalBiomass, ab_anecic, ab_endogeic, ab_epigeic, totalRichness 164 3
phwater, clay, om totalAbundance, totalBiomass, ab_anecic, ab_endogeic, ab_epigeic, totalRichness 164 3
sand, clay, om totalAbundance, totalBiomass, ab_anecic, ab_endogeic, ab_epigeic, totalRichness 164 3
phwater, sand, clay, om totalBiomass, ab_anecic 164 3
phwater, sand, clay, om totalBiomass, ab_endogeic 164 3
phwater, sand, clay, om totalBiomass, ab_epigeic 164 3
phwater, sand, clay, om totalAbundance, totalBiomass, ab_anecic 164 3
phwater, sand, clay, om totalAbundance, totalBiomass, ab_endogeic 164 3
phwater, sand, clay, om totalAbundance, totalBiomass, ab_epigeic 164 3
phwater, sand, clay, om totalBiomass, ab_anecic, ab_endogeic 164 3
phwater, sand, clay, om totalBiomass, ab_anecic, ab_epigeic 164 3
phwater, sand, clay, om totalBiomass, ab_anecic, totalRichness 164 3
phwater, sand, clay, om totalBiomass, ab_endogeic, ab_epigeic 164 3
phwater, sand, clay, om totalBiomass, ab_endogeic, totalRichness 164 3
phwater, sand, clay, om totalBiomass, ab_epigeic, totalRichness 164 3
phwater, sand, clay, om totalAbundance, totalBiomass, ab_anecic, ab_endogeic 164 3
phwater, sand, clay, om totalAbundance, totalBiomass, ab_anecic, ab_epigeic 164 3
phwater, sand, clay, om totalAbundance, totalBiomass, ab_anecic, totalRichness 164 3
phwater, sand, clay, om totalAbundance, totalBiomass, ab_endogeic, ab_epigeic 164 3
phwater, sand, clay, om totalAbundance, totalBiomass, ab_endogeic, totalRichness 164 3
phwater, sand, clay, om totalAbundance, totalBiomass, ab_epigeic, totalRichness 164 3
phwater, sand, clay, om totalBiomass, ab_anecic, ab_endogeic, ab_epigeic 164 3
phwater, sand, clay, om totalBiomass, ab_anecic, ab_endogeic, totalRichness 164 3
phwater, sand, clay, om totalBiomass, ab_anecic, ab_epigeic, totalRichness 164 3
phwater, sand, clay, om totalBiomass, ab_endogeic, ab_epigeic, totalRichness 164 3
phwater, sand, clay, om totalAbundance, totalBiomass, ab_anecic, ab_endogeic, ab_epigeic 164 3
phwater, sand, clay, om totalAbundance, totalBiomass, ab_anecic, ab_endogeic, totalRichness 164 3
phwater, sand, clay, om totalAbundance, totalBiomass, ab_anecic, ab_epigeic, totalRichness 164 3
phwater, sand, clay, om totalAbundance, totalBiomass, ab_endogeic, ab_epigeic, totalRichness 164 3
phwater, sand, clay, om totalBiomass, ab_anecic, ab_endogeic, ab_epigeic, totalRichness 164 3
phwater, sand, clay, om totalAbundance, totalBiomass, ab_anecic, ab_endogeic, ab_epigeic, totalRichness 164 3

3.1.3 Number of variables available for multivariate analysis

phwater sand clay om totalAbundance totalBiomass ab_anecic ab_endogeic ab_epigeic totalRichness
phwater 543 289 289 471 426 413 317 317 317 421
sand 289 289 289 289 289 210 223 223 223 284
clay 289 289 289 289 289 210 223 223 223 284
om 471 289 289 486 365 357 266 266 266 372
totalAbundance 426 289 289 365 537 315 411 411 411 506
totalBiomass 413 210 210 357 315 456 227 227 227 316
ab_anecic 317 223 223 266 411 227 411 411 411 401
ab_endogeic 317 223 223 266 411 227 411 411 411 401
ab_epigeic 317 223 223 266 411 227 411 411 411 401
totalRichness 421 284 284 372 506 316 401 401 401 573

3.1.4 Numerical variables available for multivariate analysis with Landintensity and NA’s

LocalLand phwater sand clay om totalAbundance totalBiomass ab_anecic ab_endogeic ab_epigeic totalRichness
arable - high intensity 72 71 71 72 72 75 70 70 70 74
arable - medium intensity 23 23 23 23 23 23 22 22 22 23
grass - high intensity 181 109 109 172 152 128 77 77 77 164
grass_mediumlow 124 74 74 112 218 94 182 182 182 227
ruderal 7 0 0 6 2 7 1 1 1 8
trees 136 12 12 101 70 129 59 59 59 77

3.1.5 Numerical variables available for multivariate analysis with Landintensity and without NA’s

localLandCover CLC_reduced LocalLand phwater sand clay om totalAbundance totalBiomass ab_anecic ab_endogeic ab_epigeic totalRichness
arable 100s arable - high intensity 68 68 68 68 68 68 68 68 68 68
arable 100s arable - medium intensity 22 22 22 22 22 22 22 22 22 22
grass 100s grass - high intensity 50 50 50 50 50 50 50 50 50 50
grass 100s grass_mediumlow 20 20 20 20 20 20 20 20 20 20
trees 100s trees 4 4 4 4 4 4 4 4 4 4

3.1.6 Total data available

localLandCover CLC_reduced LocalLand phwater sand clay om totalAbundance totalBiomass ab_anecic ab_endogeic ab_epigeic totalRichness
arable 100s arable - high intensity 69 69 69 69 69 69 68 68 68 71
arable 100s arable - medium intensity 23 23 23 23 23 23 22 22 22 23
arable 200s arable - high intensity 3 2 2 3 3 6 2 2 2 3
grass 100s grass - high intensity 180 109 109 171 152 122 77 77 77 164
grass 100s grass_mediumlow 96 49 49 87 190 81 154 154 154 195
grass 200s grass - high intensity 0 0 0 0 0 5 0 0 0 0
grass 200s grass_mediumlow 18 17 17 17 18 11 18 18 18 18
grass 300s grass - high intensity 1 0 0 1 0 1 0 0 0 0
grass 300s grass_mediumlow 10 8 8 8 10 2 10 10 10 14
ruderal 100s ruderal 6 0 0 5 1 6 1 1 1 7
ruderal 300s ruderal 1 0 0 1 1 1 0 0 0 1
trees 100s trees 108 6 6 77 48 102 48 48 48 52
trees 200s trees 3 0 0 3 3 0 3 3 3 1
trees 300s trees 25 6 6 21 19 27 8 8 8 24 
##   Arable Lawn Grass Ruderal Trees
## 1     90    0    70       0     4
##   Agricultural Urban Natural
## 1            0   164       0
##   grass_low grass_high arable_medium arable_high
## 1        20         50            22          68

The datamset contains 164 available rows.

3.2 RDA on earthworms and env. variables

com variables = “totalAbundance”, “totalRichness”

env variables =“phwater”,“om”,“grass_medium”,“grass_low”, arable_high” ,“grass_high”,“arable_medium”

3.3 RDA on earthworms and env. variables

ew variables = “totalAbundance”, “totalRichness”,“ab_anecic”, “ab_endogeic”, “ab_epigeic”

env variables = “phwater”,“om”,“sand”, “clay”,“grass_medium”,“grass_low”,“arable_high”,“grass_high”,“arable_medium”

3.4 RDA on earthworms and env. variables

com variables = “totalBiomass”, “totalRichness”, “totalAbundance”, “ab_anecic”, “ab_endogeic”, “ab_epigeic”

env variables =“phwater”,“om”,“sand”, “clay”

3.5 RDA on earthworms and env. variables

ew variables = “totalAbundance”, “ab_anecic”, “ab_endogeic”, “ab_epigeic”, “totalRichness”, “totalBiomass”

env variables = “phwater”,“om”, “sand”, “clay”, “grass_medium”, “arable_high”, “grass_high”, “arable_medium”

3.6 Coinertia on earthworms and env. variables

com variables = “totalAbundance”, “totalRichness”

env variables =“phwater”,“om”,“grass_medium”,“arable_high”,“grass_high”,“arable_medium”

##           trees grass_mediumlow     arable_high         ruderal            lawn 
##              11              40              69               0               0 
##      grass_high   arable_medium 
##             109              23
## Coinertia analysis
## 
## Class: coinertia dudi
## Call: coinertia(dudiX = env_pca, dudiY = comm_pca, scannf = FALSE)
## 
## Total inertia: 0.5962
## 
## Eigenvalues:
##     Ax1     Ax2 
##  0.4259  0.1703 
## 
## Projected inertia (%):
##     Ax1     Ax2 
##   71.44   28.56 
## 
## Cumulative projected inertia (%):
##     Ax1   Ax1:2 
##   71.44  100.00 
## 
## Eigenvalues decomposition:
##         eig     covar     sdX       sdY      corr
## 1 0.4259341 0.6526363 1.47335 1.1130442 0.3979723
## 2 0.1702717 0.4126399 1.11156 0.8724291 0.4255085
## 
## Inertia & coinertia X (env_pca):
##     inertia      max     ratio
## 1  2.170761 2.307192 0.9408669
## 12 3.406326 3.567756 0.9547530
## 
## Inertia & coinertia Y (comm_pca):
##     inertia     max     ratio
## 1  1.238867 1.47161 0.8418447
## 12 2.000000 2.00000 1.0000000
## 
## RV:
##  0.1193925
## Monte-Carlo test
## Call: randtest.coinertia(xtest = coi_result, nrepet = 999)
## 
## Observation: 0.1193925 
## 
## Based on 999 replicates
## Simulated p-value: 0.001 
## Alternative hypothesis: greater 
## 
##      Std.Obs  Expectation     Variance 
## 1.894260e+01 1.109363e-02 3.268656e-05
## [1] "Inertie Axe 1: 0.425934078088838 (71.44%)"
## [1] "Inertie Axe 2: 0.170271696670797 (28.56%)"

##                         CS1        CS2
## phwater         -0.49348102 -0.3416676
## om               0.49372383  0.2292508
## trees            0.28320705 -0.7364955
## grass_mediumlow  0.14073516 -0.3632343
## arable_high      0.32480591  0.2670442
## grass_high      -0.55048300  0.2201331
## arable_medium    0.06457734  0.1912364

3.7 Coinertia on earthworms and env. variables

com variables = “totalRichness”, “totalAbundance”

env variables =“phwater”,“om”,“sand”, “clay”, “grass_medium”,“arable_high”,“grass_high”,“arable_medium”

##           trees grass_mediumlow     arable_high         ruderal            lawn 
##              11              40              69               0               0 
##      grass_high   arable_medium 
##             109              23
## Coinertia analysis
## 
## Class: coinertia dudi
## Call: coinertia(dudiX = env_pca, dudiY = comm_pca, scannf = FALSE)
## 
## Total inertia: 0.7689
## 
## Eigenvalues:
##     Ax1     Ax2 
##  0.5674  0.2015 
## 
## Projected inertia (%):
##     Ax1     Ax2 
##   73.79   26.21 
## 
## Cumulative projected inertia (%):
##     Ax1   Ax1:2 
##   73.79  100.00 
## 
## Eigenvalues decomposition:
##         eig     covar      sdX       sdY      corr
## 1 0.5673980 0.7532583 1.586341 1.1015549 0.4310634
## 2 0.2015418 0.4489341 1.173134 0.8868916 0.4314838
## 
## Inertia & coinertia X (env_pca):
##     inertia     max     ratio
## 1  2.516479 2.73918 0.9186977
## 12 3.892722 4.07652 0.9549130
## 
## Inertia & coinertia Y (comm_pca):
##     inertia     max     ratio
## 1  1.213423 1.47161 0.8245547
## 12 2.000000 2.00000 1.0000000
## 
## RV:
##  0.1310568
## Monte-Carlo test
## Call: randtest.coinertia(xtest = coi_result, nrepet = 999)
## 
## Observation: 0.1310568 
## 
## Based on 999 replicates
## Simulated p-value: 0.001 
## Alternative hypothesis: greater 
## 
##      Std.Obs  Expectation     Variance 
## 2.321956e+01 1.178798e-02 2.638429e-05
## [1] "Vergnes et al._2017"  "Pelosi et al._2021"   "Maréchal et al._2021"
## [4] "Tiho and Josens_2000" "Amossé et al._2016"   "Tresch et al._2019"  
## [7] "Xie et al._2018"      "Maréchal et al._2024"
## [1] "Inertie Axe 1: 0.567398049148044 (73.79%)"
## [1] "Inertie Axe 2: 0.201541797900794 (26.21%)"
## [1] 2 2

##                         CS1        CS2
## phwater         -0.43311298 -0.2918407
## om               0.43142991  0.1885502
## sand            -0.19282776  0.3414985
## clay             0.46104651  0.1932203
## trees            0.23285534 -0.6892914
## grass_mediumlow  0.11576009 -0.3400009
## arable_high      0.28578216  0.2308216
## grass_high      -0.47301516  0.2268448
## arable_medium    0.05914534  0.1728065
## [1] 2 2
##                    Axis1      Axis2
## totalRichness  0.1752492 -0.4366150
## totalAbundance 0.7325884  0.1044467

3.8 Coinertia on earthworms and env. variables

ew variables = “totalAbundance”, “ab_anecic”, “ab_endogeic”, “ab_epigeic”, “totalRichness”

env variables = “phwater”,“om”,“sand”, “clay”,“grass_medium”,“grass_low”,“arable_high”,“grass_high”,“arable_medium”

##           trees grass_mediumlow     arable_high         ruderal            lawn 
##              11              40              68               0               0 
##      grass_high   arable_medium 
##              50              22
## Coinertia analysis
## 
## Class: coinertia dudi
## Call: coinertia(dudiX = env_pca, dudiY = comm_pca, scannf = FALSE)
## 
## Total inertia: 1.23
## 
## Eigenvalues:
##      Ax1      Ax2      Ax3      Ax4      Ax5 
## 0.945336 0.200672 0.049102 0.031449 0.003244 
## 
## Projected inertia (%):
##     Ax1     Ax2     Ax3     Ax4     Ax5 
## 76.8689 16.3174  3.9926  2.5573  0.2638 
## 
## Cumulative projected inertia (%):
##     Ax1   Ax1:2   Ax1:3   Ax1:4   Ax1:5 
##   76.87   93.19   97.18   99.74  100.00 
## 
## Eigenvalues decomposition:
##         eig     covar      sdX       sdY      corr
## 1 0.9453357 0.9722838 1.201010 1.4944964 0.5416908
## 2 0.2006721 0.4479644 1.290618 0.9438584 0.3677383
## 
## Inertia & coinertia X (env_pca):
##     inertia      max     ratio
## 1  1.442426 1.805021 0.7991185
## 12 3.108121 3.446387 0.9018492
## 
## Inertia & coinertia Y (comm_pca):
##     inertia      max     ratio
## 1  2.233520 2.610407 0.8556213
## 12 3.124388 3.589751 0.8703634
## 
## RV:
##  0.1203808
## Monte-Carlo test
## Call: randtest.coinertia(xtest = coi_result, nrepet = 999)
## 
## Observation: 0.1203808 
## 
## Based on 999 replicates
## Simulated p-value: 0.001 
## Alternative hypothesis: greater 
## 
##      Std.Obs  Expectation     Variance 
## 1.255959e+01 2.355875e-02 5.942888e-05
## [1] "Inertie Axe 1: 0.94533571488998 (76.87%)"
## [1] "Inertie Axe 2: 0.20067213581569 (16.32%)"