Both year results : Question 2
SP
2023-10-12
Explaining plant assemblies
Relationship between bare ground, litter, stone, rock, moss and plant community composition in Castril, Santiago and Pontones (CSP) pasturelands.
Soil variables
dbMEM Analysis
Distance-Based Moran’s Eigenvector Maps (dbMEM)
Construction of eigenvectors from latitude-longitude coordinates that makes possible to test spatial explanatory variable.
## Hellinger-transformed community data
community_data_density_22_23.h <- decostand(community_data_density_22_23, "hellinger")
## Computation of linearly detrended community data
csp.h.det <- resid(lm(as.matrix(community_data_density_22_23.h) ~ ., data = coord_22_23_b))
#csp.dbmem.quick <- quickMEM(community_data_density_22_23.h, coord_22_23_b)
#summary(csp.dbmem.quick)## Step 1. Construct the matrix of dbMEM variables
# argument of dbmem either a matrix of spatial coordinates or a distance matrix
csp.dbmem.tmp <- dbmem(coord_22_23_b, silent = FALSE)## Truncation level = 0.04710244
## Time to compute dbMEMs = 0.030000 seccsp.dbmem <- as.data.frame(csp.dbmem.tmp)
# Truncation distance used above:
thr <- give.thresh(dist(coord_22_23_b))
# Display and count the eigenvalues
#attributes(csp.dbmem.tmp)$values
#length(attributes(csp.dbmem.tmp)$values)
## Step 2. Run the global dbMEM analysis
(csp.dbmem.rda <- rda(csp.h.det ~., csp.dbmem))## Call: rda(formula = csp.h.det ~ MEM1 + MEM2 + MEM3 + MEM4 + MEM5 + MEM6
## + MEM7 + MEM8 + MEM9 + MEM10 + MEM11 + MEM12 + MEM13 + MEM14 + MEM15 +
## MEM16 + MEM17, data = csp.dbmem)
##
## Inertia Proportion Rank
## Total 0.38236 1.00000
## Constrained 0.09632 0.25190 17
## Unconstrained 0.28605 0.74810 55
## Inertia is variance
##
## Eigenvalues for constrained axes:
## RDA1 RDA2 RDA3 RDA4 RDA5 RDA6 RDA7 RDA8
## 0.021968 0.014454 0.011737 0.008997 0.006848 0.006101 0.005164 0.004413
## RDA9 RDA10 RDA11 RDA12 RDA13 RDA14 RDA15 RDA16
## 0.003495 0.002998 0.002727 0.002316 0.002033 0.001651 0.001151 0.000184
## RDA17
## 0.000084
##
## Eigenvalues for unconstrained axes:
## PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8
## 0.04951 0.02949 0.02246 0.01843 0.01441 0.01399 0.01099 0.01005
## (Showing 8 of 55 unconstrained eigenvalues)## Permutation test for rda under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: rda(formula = csp.h.det ~ MEM1 + MEM2 + MEM3 + MEM4 + MEM5 + MEM6 + MEM7 + MEM8 + MEM9 + MEM10 + MEM11 + MEM12 + MEM13 + MEM14 + MEM15 + MEM16 + MEM17, data = csp.dbmem)
## Df Variance F Pr(>F)
## Model 17 0.096318 1.0894 0.159
## Residual 55 0.286046## [1] 0.02067161Variance explained by the model is reduced (0.096318) and is not significant (p-value > 0.05). The adjusted R2 of the model is: 0.02. Thus, it seems that the eigenvectors, representing spatial explanatory variables, do not have an important influence on plant community heterogeneity.
Redundancy Analysis
Model with soil variables considering the latitude-longitude coordinates
Distance-based RDA as we use Bray-Curtis distance.
csp_rda_1_bis <- capscale(community_data_density_22_23 ~ bare + lit + ston + rock + moss + Condition(scores(coord_22_23_b)),
data = soil_22_23, distance = "bray")
csp_rda_1_bis## Call: capscale(formula = community_data_density_22_23 ~ bare + lit +
## ston + rock + moss + Condition(scores(coord_22_23_b)), data =
## soil_22_23, distance = "bray")
##
## Inertia Proportion Rank
## Total 13.89670 1.00000
## Conditional 0.92328 0.06644 2
## Constrained 1.87598 0.13499 5
## Unconstrained 12.47160 0.89745 45
## Imaginary -1.37417 -0.09888 27
## Inertia is squared Bray distance
## Species scores projected from 'community_data_density_22_23'
##
## Eigenvalues for constrained axes:
## CAP1 CAP2 CAP3 CAP4 CAP5
## 0.9251 0.4785 0.2092 0.1555 0.1077
##
## Eigenvalues for unconstrained axes:
## MDS1 MDS2 MDS3 MDS4 MDS5 MDS6 MDS7 MDS8
## 1.8772 1.5702 1.0563 0.8840 0.7126 0.6183 0.5751 0.5062
## (Showing 8 of 45 unconstrained eigenvalues)6% of inertia (i.e. variance) explained by the condition, i.e. geographical distance between transect.
13% of inertia is related to the explanatory variables, i.e. litter, stone, rock, moss, bare ground.
## CAP1 CAP2 CAP3 CAP4
## Eigenvalue 0.92510603 0.47846328 0.20922779 0.15552453
## Proportion Explained 0.06447816 0.03334799 0.01458279 0.01083977
## Cumulative Proportion 0.06447816 0.09782615 0.11240894 0.12324871Axes 1 and axes 2 explain 6.4% and 3.3% of variance, respectively. We keep just these two axes for the data visualization.
Axes one and two of RDA
s_csp_rda_1_bis = summary(csp_rda_1_bis)
sp_sel = s_csp_rda_1_bis$species[,1:3] %>% as.data.frame %>% mutate(position = seq(1:156)) %>% filter(if_any(c(CAP1,CAP2,CAP3), ~ .x >= 0.1)|if_any(c(CAP1,CAP2,CAP3), ~ .x <= -0.1)) %>% pull(position)
ggtriplotRDA(csp_rda_1_bis,site.sc = "wa", plot.sites = FALSE,plot.spe = FALSE,label.spe = FALSE, scaling = 2,plot.centr = FALSE) #select.spe = sp_sel)##
## No factor, hence levels cannot be plotted with symbols;
## 'plot.centr' is set to FALSE
Figure 1: Scaling 2 i.e angles between variables reflect their correlation
Regarding the coding, the initial letter C,S and P represent Castril, Santiago and Pontones, respectively. The following number (01 up to 12) is the transect number in each common. The letters A, B and C are the replicates in each pastoral unit. Replicates A were carried out during the spring 2022. Replicates B and C were carried out in 2023. The last number(1,2 or 3) represents the Tranhumance modality.
##
## No factor, hence levels cannot be plotted with symbols;
## 'plot.centr' is set to FALSE
Figure 2: Type Scaling 1 which focuses on the distance relationships among species
Contribution of each variables in each canonical axes:
## CAP1 CAP2
## bare -0.7545247 -0.1679387
## lit -0.1147567 0.6200539
## ston -0.3374754 -0.5627073
## rock 0.4747150 -0.4234487
## moss 0.7912226 -0.1000900Variance Inflation Factors (VIF)
Linear dependencies can be explored by computing the X variables’ Variance Inflation Factors (VIF). VIF greater than 10 should be avoided.
## scores(coord_22_23_b)x scores(coord_22_23_b)y bare
## 1.321748 1.421529 1.236206
## lit ston rock
## 1.307199 1.339330 1.210303
## moss
## 1.221457