Grazing drives dominant and indicator plant species turnover on a mountainous communal rangeland
January 2025
Introduction
Methods
Vegetation, soil, grazing indicators data
Vegetation
Table 2 Paper REM
Plant community types
Soil
We collected three soil samples at 2, 5 and 8 meters of the 10-m-length transect. We did so for 62 vegetation transects done during spring 2022 and 2024.
We measured the following soil physico-chemical variables:
phvolume of stones (
v_stones) within the cylinders.bulk.density: Soil bulk density (\(\frac{g}{cm^{3}}\))depth: Soil depth (cm)HCL Test:Limestone : In cold condition, generalized effervescence under HCl
Dolomite : In cold condition, little or non effervescence under HCL
Four categories from 0 to 3 indicating effervescence under HCL
0 : No detectable reaction
1 : Weak, a few bubbles or murmur of effervescence
2 : Medium, bubbles in continuous layer
3 : Vivid, several superimposed layers of bubbles
Munsell soil colour :
Value: the lightness or the darkness of the colour
om: Percentage (%) of organic matter calculated after being in muffle furnace at 550 °C during 4 hours.N: Percentage (%) of nitrogen in a sample weighing ~ 35 mg. CHN analysis.C: Percentage (%) of carbon (organic + inorganic) in a sample weighing ~ 35 mg. CHN analysis.C:N ratios
Also we recorded litter, stones,
rock and moss in the field along the
vegetation transect.
Organic matter is highly correlated to Value and Nitrogen so we removed these two latter variables for the multivariate analysis to avoid collinearity. We used criterion of |r| > 0.7 for avoid collinearity (Dormann et al. 2013)
Proxies of grazing pressure
Field level grazing indicators
Indicators measured at the field level, i.e.nearby or along the 72 vegetation transects carried out during spring 2022 and 2023.
pl_density: total number of plant contacts in the transects. Plant density allows evaluating grazing effects (Castañeda et al., 2023).n_flower: total number of plant with flowers recorded in the transect. Flower abundance has been negatively associated with grazing pressure (Tadey et al. 2015).bare: Bare ground recorded in the transect. Bare ground can be an indicator of herbivores’ activity and is positively correlated with runoff and soil erosion (Pyke et al. 2002).dung: Dung measured at the beginning (dung_sp) and at the end (dung_au) of the grazing period. We employed quadrats of 50 x 50 cm to measure the dung amount. The quadrats were located adjacent to the 10m transect every 1 meter so carrying out 10 dung amount measurement. Dung can be a measure of grazing pressure on the transect scale (Jordan et al. 2022).Plant Utilization Rates (PUR): We followed the method described by Ruiz-Mirazo et al. (2011) which proposes qualitative categories of plant utilization ranging from 0 to 5, with 3 referring to intermediate utilization. PUR have been measured nearby the vegetation transect in 25 individuals of dominant species, i.e. Helianthemum cinereum, Helianthemum oelandicum, Helianthemum appenninum, Thymus serpylloides, Koeleria vallesiana, Seseli montanum, Festuca reverchonii, Teucrium aureum, Helictotrichon filifolium and Festuca segimonensis. Also we measured an overall Plant Utilisation Rate (PUR) of the vegetation
pur_RA, which was measured at each meter of the 10m transect.
For the statistical analysis we aggregated by life forms:
pur_CH : Chamaephytes (Helianthemum spp.,
Thymus serpylloides and Teucrium aureum)
pur_Fes: But we keep Festuca segimonensis alone
as it is the dominant plant of the target community
Individual/Unit grazing area level
Stocking Rate,which refers to the number of livestock per hectare over a specified time (Allen et al. 2011), is usually used to asses the effect of livestock on vegetation. Here we used animals*days per hectare (Scarnecchia 1985), which consider the number of days that the comarca is grazed. We gather such informations through the observations in field of Pau and Francisco and our observations during 2022 and 2023 fieldwork. Also, I check the transcriptions of the interviews done by me and Adrià during 2023.
\[\text{Stocking Rate} = \frac{Animals * Days}{Hectares}\]
However, this indicator may not be suitable in case of environmental and vegetation heterogeneity, also it does not take into account herding practices at the field scale (Genin and Hanafi 2010).
First results
1. Which are the relationship between grazing indicators ?
Spearman correlation between indicators. Correlation matrix:
## stoking_rate_2 pl_density pur_CH pur_Fes pur_RA dung_au dung_sp
## stoking_rate_2 1
## pl_density -0.12 1
## pur_CH 0.17 0.22 1
## pur_Fes 0.04 0.01 -0.17 1
## pur_RA 0.27 -0.16 0.33 0.54 1
## dung_au 0.21 -0.02 0.2 -0.12 0 1
## dung_sp 0.01 -0.09 -0.01 0.15 0.16 0.42 1
## n_flower -0.1 0.17 -0.26 -0.05 -0.08 -0.22 -0.05
## bare 0 -0.53 -0.15 0.04 0.12 -0.13 0.03
## n_flower bare
## stoking_rate_2
## pl_density
## pur_CH
## pur_Fes
## pur_RA
## dung_au
## dung_sp
## n_flower 1
## bare -0.12 1The indicators are not highly correlated between them. As they are not highly correlated, we do not have the issue of multicollinearity so we can keep them all for the analysis.
Partial distance-based Redundancy Analysis (db-RDA): Modelling of vegetation data considering grazing indicators + soil variables
Geographical coordinates as covariate
# capscale() with raw (site by species) data (Borcard et al. 2018)
rda_soil_ind <- capscale(community_data_density_22_23.72_sin_cus_filt ~ ph+ bulk_density+ soil_depth+ hcl_category+ om+ v_stones+ C_N+ lit+ ston+ rock+ moss+ stoking_rate_2+ pl_density+ pur_CH+ pur_Fes+ pur_RA+ dung_au+ dung_sp+ n_flower+ bare +
Condition(scores(as.matrix(coord_22_23_st))), data = db_soil_ind_st, distance = "bray",add = "lingoes")Variability explained by the first three axes.
Figure: Species and variables
Backward selection
Using AIC criterion. Model selection used for the moment.
## capscale(formula = community_data_density_22_23.72_sin_cus_filt ~
## bulk_density + soil_depth + om + C_N + ston + moss + pl_density +
## pur_CH + pur_Fes + Condition(scores(as.matrix(coord_22_23_st))),
## data = db_soil_ind_st, distance = "bray", add = "lingoes")# Total variance explained by the model
Tot.var <- rda_bwd$tot.chi
# Constrained and unconstrained eigenvalues
eig.val <- c(rda_bwd$CCA$eig, rda_bwd$CA$eig)
# Relative eigenvalues of Y-hat
eig.val.rel <- eig.val / Tot.var
# Variability explained by the 1,2 and 3 of constrained RDA axes
100*eig.val.rel[1:3] ## CAP1 CAP2 CAP3
## 6.867502 5.807608 2.926120Testing the statistical significance of the model
## Permutation test for capscale under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: capscale(formula = community_data_density_22_23.72_sin_cus_filt ~ bulk_density + soil_depth + om + lit + moss + pl_density + pur_CH + pur_Fes + bare + Condition(scores(as.matrix(coord_22_23_st))), data = db_soil_ind_st, distance = "bray", add = "lingoes")
## Df SumOfSqs F Pr(>F)
## Model 9 4.9519 1.9631 0.001 ***
## Residual 50 14.0136
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Permutation test for capscale under reduced model
## Forward tests for axes
## Permutation: free
## Number of permutations: 999
##
## Model: capscale(formula = community_data_density_22_23.72_sin_cus_filt ~ bulk_density + soil_depth + om + lit + moss + pl_density + pur_CH + pur_Fes + bare + Condition(scores(as.matrix(coord_22_23_st))), data = db_soil_ind_st, distance = "bray", add = "lingoes")
## Df SumOfSqs F Pr(>F)
## CAP1 1 1.3803 4.9248 0.001 ***
## CAP2 1 1.1673 4.1648 0.001 ***
## CAP3 1 0.5881 2.0984 0.097 .
## CAP4 1 0.5028 1.7940 0.221
## CAP5 1 0.4018 1.4337 0.571
## CAP6 1 0.2929 1.0449 0.985
## CAP7 1 0.2380 0.8490 0.996
## CAP8 1 0.1957 0.6984 0.998
## CAP9 1 0.1851 0.6603 0.998
## Residual 50 14.0136
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Fitness of the model.Testing Variance Inflation Factor (VIF)
## scores(as.matrix(coord_22_23_st))x scores(as.matrix(coord_22_23_st))y
## 1.906537 1.705937
## bulk_density soil_depth
## 2.355096 1.451093
## om lit
## 2.527561 1.213699
## moss pl_density
## 1.546428 1.688496
## pur_CH pur_Fes
## 1.318621 1.400087
## bare
## 1.882438
Figure: Species and grazing indicators (blue) and soil variables (red). Scaling 2
Ellipses by Plant Community Types
Figure: Sites and Wards’ clusters found in Paper REM. Scaling 1
Ellipses by trashumance modalities
Figure: Transhumance modalities and Rda axes 1-2
Ellipses by community-based governance
Figure: Community-based governance modalities and Rda axes 1-2
Variance explained by variables
Variance explained by each explanatory variable considering all others variables as covariates Using first rda which include all variables.
anova(rda_soil_ind, by="mar", permutations=1000) ### "mar" to test statistical significance each variable## Permutation test for capscale under reduced model
## Marginal effects of terms
## Permutation: free
## Number of permutations: 1000
##
## Model: capscale(formula = community_data_density_22_23.72_sin_cus_filt ~ ph + bulk_density + soil_depth + hcl_category + om + v_stones + C_N + lit + ston + rock + moss + stoking_rate_2 + pl_density + pur_CH + pur_Fes + pur_RA + dung_au + dung_sp + n_flower + bare + Condition(scores(as.matrix(coord_22_23_st))), data = db_soil_ind_st, distance = "bray", add = "lingoes")
## Df SumOfSqs F Pr(>F)
## ph 1 0.2952 1.0939 0.290709
## bulk_density 1 0.3471 1.2865 0.125874
## soil_depth 1 0.3245 1.2026 0.184815
## hcl_category 1 0.3291 1.2195 0.192807
## om 1 0.3130 1.1601 0.223776
## v_stones 1 0.3798 1.4075 0.072927 .
## C_N 1 0.3365 1.2471 0.149850
## lit 1 0.3226 1.1957 0.182817
## ston 1 0.3721 1.3790 0.074925 .
## rock 1 0.3066 1.1362 0.233766
## moss 1 0.3383 1.2537 0.123876
## stoking_rate_2 1 0.2915 1.0803 0.322677
## pl_density 1 0.5343 1.9800 0.001998 **
## pur_CH 1 0.3193 1.1834 0.184815
## pur_Fes 1 0.3026 1.1215 0.245754
## pur_RA 1 0.2856 1.0585 0.322677
## dung_au 1 0.2991 1.1083 0.264735
## dung_sp 1 0.3274 1.2135 0.167832
## n_flower 1 0.3389 1.2559 0.136863
## bare 1 0.3332 1.2348 0.154845
## Residual 39 10.5234
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Appendix
Data summary of soil variable
Here we show the summary of the variables that were already averaged by replicates by transect. In brief, in the dataset we have 62 values of each soil variable.
## ph bulk_density soil_depth hcl_category
## Min. :6.480 Min. :0.5000 Min. : 5.80 Min. :0.0000
## 1st Qu.:6.822 1st Qu.:0.9000 1st Qu.: 11.70 1st Qu.:0.0000
## Median :7.015 Median :1.0000 Median : 16.50 Median :0.7000
## Mean :7.054 Mean :0.9887 Mean : 23.99 Mean :0.6806
## 3rd Qu.:7.265 3rd Qu.:1.1000 3rd Qu.: 25.45 3rd Qu.:1.0000
## Max. :7.710 Max. :1.6000 Max. :101.00 Max. :3.0000
## value N om v_stones
## Min. :2.000 Min. :0.1930 Min. : 5.470 Min. : 1.00
## 1st Qu.:3.000 1st Qu.:0.3415 1st Qu.: 9.153 1st Qu.: 7.30
## Median :3.000 Median :0.4535 Median :11.521 Median : 10.85
## Mean :3.319 Mean :0.5059 Mean :12.691 Mean : 24.72
## 3rd Qu.:4.000 3rd Qu.:0.7000 3rd Qu.:17.019 3rd Qu.: 41.83
## Max. :5.700 Max. :1.0970 Max. :26.191 Max. :123.70
## C_N lit ston rock
## Min. : 9.53 Min. : 4.00 Min. : 0.00 Min. : 0.000
## 1st Qu.:10.62 1st Qu.:14.00 1st Qu.:10.25 1st Qu.: 1.000
## Median :11.18 Median :17.00 Median :14.50 Median : 4.000
## Mean :14.27 Mean :18.94 Mean :16.11 Mean : 5.258
## 3rd Qu.:12.29 3rd Qu.:22.75 3rd Qu.:21.00 3rd Qu.: 9.000
## Max. :53.33 Max. :37.00 Max. :42.00 Max. :20.000
## moss
## Min. : 0.000
## 1st Qu.: 1.000
## Median : 3.500
## Mean : 6.129
## 3rd Qu.:10.500
## Max. :24.000Soil correlation matrix
Spearman correlation
## ph bulk_density soil_depth hcl_category value N om
## ph 1
## bulk_density 0.42 1
## soil_depth 0.13 -0.04 1
## hcl_category 0.48 0.34 0.01 1
## value 0.27 0.44 0.17 0.1 1
## N -0.26 -0.49 -0.37 -0.01 -0.7 1
## om -0.27 -0.53 -0.33 -0.06 -0.71 0.98 1
## v_stones -0.03 -0.18 -0.32 -0.06 -0.03 0.2 0.16
## C_N 0.49 0.38 0.21 0.34 0.43 -0.5 -0.46
## lit -0.04 -0.07 -0.09 -0.21 -0.02 -0.05 -0.07
## ston 0.26 0.17 -0.25 0.37 0.02 0.18 0.17
## rock -0.3 -0.38 0.02 -0.43 -0.02 0.11 0.12
## moss -0.32 -0.44 -0.04 -0.39 -0.01 0.04 0.08
## v_stones C_N lit ston rock moss
## ph
## bulk_density
## soil_depth
## hcl_category
## value
## N
## om
## v_stones 1
## C_N -0.03 1
## lit 0.03 0.03 1
## ston 0.43 0.01 -0.34 1
## rock 0.24 -0.26 0.05 -0.14 1
## moss 0.2 -0.23 0.07 -0.3 0.26 1PCA of soil variables
## $importance
## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6 PC7
## Eigenvalue 3.1169 1.8377 1.2134 1.07108 0.92277 0.86730 0.6105
## Proportion Explained 0.2834 0.1671 0.1103 0.09737 0.08389 0.07885 0.0555
## Cumulative Proportion 0.2834 0.4504 0.5607 0.65810 0.74199 0.82084 0.8763
## PC8 PC9 PC10 PC11
## Eigenvalue 0.49980 0.3729 0.2585 0.22909
## Proportion Explained 0.04544 0.0339 0.0235 0.02083
## Cumulative Proportion 0.92177 0.9557 0.9792 1.00000Principal component one explains 28.3%, component two explains 16.7%, and component three explains 11.1% of the variance.
Variables and principal components 1-2
Soil variables accros CSP commons
Commons and principal Components 1-2
PERMANOVA
## Permutation test for adonis under reduced model
## Permutation: free
## Number of permutations: 999
##
## adonis2(formula = euclidean_dist_soil ~ common, data = inv_scores_c, permutations = 999)
## Df SumOfSqs R2 F Pr(>F)
## Model 2 8253 0.09812 3.2095 0.007 **
## Residual 59 75853 0.90188
## Total 61 84106 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## $parent_call
## [1] "euclidean_dist_soil ~ common , strata = Null , permutations 999"
##
## $C_vs_P
## Df SumOfSqs R2 F Pr(>F)
## Model 1 5518 0.12094 4.4025 0.007 **
## Residual 32 40109 0.87906
## Total 33 45627 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $C_vs_S
## Df SumOfSqs R2 F Pr(>F)
## Model 1 6485 0.09554 4.6478 0.009 **
## Residual 44 61396 0.90446
## Total 45 67882 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $P_vs_S
## Df SumOfSqs R2 F Pr(>F)
## Model 1 584 0.01151 0.489 0.662
## Residual 42 50201 0.98849
## Total 43 50786 1.00000
##
## attr(,"class")
## [1] "pwadstrata" "list"Bonferroni adjustment of p-values
p_value = c(pairwise.c_2$C_vs_P$`Pr(>F)`[1],pairwise.c_2$C_vs_S$`Pr(>F)`[1],pairwise.c_2$P_vs_S$`Pr(>F)`[1])
p.adjust(p_value, method = "bonferroni")## [1] 0.021 0.027 1.000Significant statistical differences between Castril-Pontones and Castril-Santiago.
Statistical differences by each variable accros commons
Differences of organic matter (om) between Castril-Santiago
##
## Kruskal-Wallis rank sum test
##
## data: om by common
## Kruskal-Wallis chi-squared = 7.5298, df = 2, p-value = 0.02317## # A tibble: 3 × 9
## .y. group1 group2 n1 n2 statistic p p.adj p.adj.signif
## * <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
## 1 om C P 18 16 1.71 0.0882 0.176 ns
## 2 om C S 18 28 2.73 0.00636 0.0191 *
## 3 om P S 16 28 0.761 0.447 0.447 ns
Differences in litter between Castril-Santiago
##
## Kruskal-Wallis rank sum test
##
## data: lit by common
## Kruskal-Wallis chi-squared = 6.2081, df = 2, p-value = 0.04487## # A tibble: 3 × 9
## .y. group1 group2 n1 n2 statistic p p.adj p.adj.signif
## * <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
## 1 lit C P 18 16 1.64 0.101 0.203 ns
## 2 lit C S 18 28 2.46 0.0138 0.0414 *
## 3 lit P S 16 28 0.578 0.563 0.563 ns
Nitrogen difference between Castril-Santiago
##
## Kruskal-Wallis rank sum test
##
## data: N by common
## Kruskal-Wallis chi-squared = 8.1212, df = 2, p-value = 0.01724## # A tibble: 3 × 9
## .y. group1 group2 n1 n2 statistic p p.adj p.adj.signif
## * <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
## 1 N C P 18 16 1.89 0.0592 0.118 ns
## 2 N C S 18 28 2.81 0.00489 0.0147 *
## 3 N P S 16 28 0.644 0.519 0.519 ns
soil depth differences between Castril-Santiago and Castril-Pontones.
##
## Kruskal-Wallis rank sum test
##
## data: soil_depth by common
## Kruskal-Wallis chi-squared = 14.291, df = 2, p-value = 0.0007883## # A tibble: 3 × 9
## .y. group1 group2 n1 n2 statistic p p.adj p.adj.signif
## * <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
## 1 soil_depth C P 18 16 -3.48 0.000503 0.00151 **
## 2 soil_depth C S 18 28 -3.11 0.00186 0.00373 **
## 3 soil_depth P S 16 28 0.816 0.415 0.415 ns
No differences in other soil variables.
Soil variables accros transhumance modalities
Transhumance modalities and principal Components 1-2
PERMANOVA
## Permutation test for adonis under reduced model
## Permutation: free
## Number of permutations: 999
##
## adonis2(formula = euclidean_dist_soil ~ thr_cat2, data = inv_scores_c, permutations = 999)
## Df SumOfSqs R2 F Pr(>F)
## Model 2 2725 0.03239 0.9876 0.429
## Residual 59 81381 0.96761
## Total 61 84106 1.00000There are not significant statistical differences concerning transhumance modalities.
Yet, slight difference concerning organic matter between LDT and SDT.
## # A tibble: 3 × 9
## .y. group1 group2 n1 n2 statistic p p.adj p.adj.signif
## * <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
## 1 om LDT SDT 22 30 -2.18 0.0296 0.0889 ns
## 2 om LDT SDT/LDT 22 10 -0.317 0.751 0.751 ns
## 3 om SDT SDT/LDT 30 10 1.34 0.180 0.360 ns
Grazing indicators
PCA of grazing indicators
# do not subset db to keep rownames
#soil_ind_pca_1 <- rda(db_soil_ind, scale = TRUE) #scale=TRUE calls for a standardization of the variables
#sum_2 = summary(soil_ind_pca_1) # Default scaling 2; #sum_2$cont
ind_pca_1 <- rda(df_grazing_ind_v2, scale = TRUE) #scale=TRUE calls for a standardization of the variables
sum_2 = summary(ind_pca_1) # Default scaling 2
sum_2$cont## $importance
## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8
## Eigenvalue 1.9793 1.5978 1.3363 1.2860 0.9316 0.65651 0.51731 0.4455
## Proportion Explained 0.2199 0.1775 0.1485 0.1429 0.1035 0.07295 0.05748 0.0495
## Cumulative Proportion 0.2199 0.3975 0.5459 0.6888 0.7923 0.86527 0.92275 0.9723
## PC9
## Eigenvalue 0.24972
## Proportion Explained 0.02775
## Cumulative Proportion 1.00000Principal component one explains 22.0%, component two explains 17.8%, component three explains 14.9% of the variance.
Variables and principal components 1-2
Grazing indicators accros CSP commons
Commons and principal Components 1-2
Permanova
## Permutation test for adonis under reduced model
## Permutation: free
## Number of permutations: 999
##
## adonis2(formula = euclidean_dist_ind ~ common, data = inv_scores_c_ind, permutations = 999)
## Df SumOfSqs R2 F Pr(>F)
## Model 2 493320 0.14688 5.9399 0.002 **
## Residual 69 2865308 0.85312
## Total 71 3358628 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## $parent_call
## [1] "euclidean_dist_ind ~ common , strata = Null , permutations 999"
##
## $C_vs_P
## Df SumOfSqs R2 F Pr(>F)
## Model 1 46574 0.04412 1.6616 0.167
## Residual 36 1009084 0.95588
## Total 37 1055658 1.00000
##
## $C_vs_S
## Df SumOfSqs R2 F Pr(>F)
## Model 1 220793 0.08553 4.6762 0.027 *
## Residual 50 2360792 0.91447
## Total 51 2581585 1.00000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $P_vs_S
## Df SumOfSqs R2 F Pr(>F)
## Model 1 412370 0.1487 9.0833 0.001 ***
## Residual 52 2360740 0.8513
## Total 53 2773110 1.0000
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## attr(,"class")
## [1] "pwadstrata" "list"Univariate analyses between governance and grazing indicators
df_grazing_ind_v2_st_c = left_join(df_grazing_ind_v2%>% rownames_to_column(var = "transect") ,sites_density_coding%>%select(transect,common),by="transect")
# Stocking rate
df_grazing_ind_v2_st_c %>% summarise(statistic = kruskal.test(stoking_rate_2~ common)$statistic,p.value = kruskal.test(stoking_rate_2~ common)$p.value)## statistic p.value
## 1 10.08463 0.006458795## # A tibble: 3 × 9
## .y. group1 group2 n1 n2 statistic p p.adj p.adj.signif
## * <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
## 1 stoking_rate… C P 18 20 -0.766 0.444 0.444 ns
## 2 stoking_rate… C S 18 34 2.05 0.0407 0.0814 ns
## 3 stoking_rate… P S 20 34 3.00 0.00270 0.00810 **# Dung in the autumn
df_grazing_ind_v2_st_c %>% summarise(statistic = kruskal.test(dung_au~ common)$statistic,p.value = kruskal.test(dung_au~ common)$p.value)## statistic p.value
## 1 7.114342 0.0285194## # A tibble: 3 × 9
## .y. group1 group2 n1 n2 statistic p p.adj p.adj.signif
## * <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
## 1 dung_au C P 18 20 2.27 0.0229 0.0459 *
## 2 dung_au C S 18 34 2.46 0.0138 0.0414 *
## 3 dung_au P S 20 34 -0.0751 0.940 0.940 nsGrazing indicators accros transhumance modalities
Transhumance modalities and principal Components 1-2
Permanova
## Permutation test for adonis under reduced model
## Permutation: free
## Number of permutations: 999
##
## adonis2(formula = euclidean_dist_ind ~ thr_cat2, data = inv_scores_c_ind, permutations = 999)
## Df SumOfSqs R2 F Pr(>F)
## Model 2 74651 0.02223 0.7842 0.479
## Residual 69 3283977 0.97777
## Total 71 3358628 1.00000Univariate analyses trashumance and grazing indicators
#st stadistique
df_grazing_ind_v2_st = left_join(df_grazing_ind_v2%>% rownames_to_column(var = "transect") ,sites_density_coding%>%select(transect,thr_cat2),by="transect")
#rda_sites_thr <- within(rda_sites_thr, {thr_cat2<-factor(thr_cat2)})
# normality Fes
df_grazing_ind_v2_st %>% summarise(statistic = shapiro.test(pur_Fes)$statistic,p.value = shapiro.test(pur_Fes)$p.value) ## statistic p.value
## 1 0.8977121 2.481058e-05# pur_Fes
df_grazing_ind_v2_st %>% summarise(statistic = kruskal.test(pur_Fes~ thr_cat2)$statistic,p.value = kruskal.test(pur_Fes~ thr_cat2)$p.value)## statistic p.value
## 1 17.87338 0.0001314756## # A tibble: 3 × 9
## .y. group1 group2 n1 n2 statistic p p.adj p.adj.signif
## * <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
## 1 pur_Fes LDT SDT 29 31 4.19 0.0000280 0.0000839 ****
## 2 pur_Fes LDT SDT/LDT 29 12 1.11 0.268 0.268 ns
## 3 pur_Fes SDT SDT/LDT 31 12 -2.07 0.0388 0.0776 ns# pur_RA
df_grazing_ind_v2_st %>% summarise(statistic = kruskal.test(pur_RA~ thr_cat2)$statistic,p.value = kruskal.test(pur_RA~ thr_cat2)$p.value)## statistic p.value
## 1 7.410873 0.02458948## # A tibble: 3 × 9
## .y. group1 group2 n1 n2 statistic p p.adj p.adj.signif
## * <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
## 1 pur_RA LDT SDT 29 31 2.70 0.00696 0.0209 *
## 2 pur_RA LDT SDT/LDT 29 12 0.721 0.471 0.471 ns
## 3 pur_RA SDT SDT/LDT 31 12 -1.32 0.186 0.372 ns