Main Functions
| Module | Function | Description |
|---|---|---|
| CSR | CSR_strategy() | Computes C-S-R ecological strategy coordinates for individual plant species based on key functional traits |
| CSR() | Performs batch calculation of C-S-R strategies across multiple plant species simultaneously | |
| CSR_plot() | Generates publication-quality ternary plots for visualizing C-S-R strategy distributions | |
| LHS | LHS() | Analyzes and classifies plant species into ecological strategy types based on the Leaf-Height-Seed (LHS) trait spectrum |
| LHS_plot() | Renders three-dimensional visualization of species distribution in LHS strategy space | |
| LHS_strategy_scheme() | Generates standardized classification scheme for LHS ecological strategies ^ | |
| NPT | NPT() | Implements hierarchical principal component analysis to identify ecological niche periodicity patterns |
| NPT_plot() | Visualizes results of niche periodicity analysis through ordination plots | |
| TN | TN_corr() | Analyzes and visualizes correlation structure within plant trait networks |
| TN() | Constructs functional trait networks based on specified correlation thresholds | |
| TN_metrics() | Calculates network centrality metrics and topological properties | |
| TN_plot() | Renders customizable network graphs highlighting trait relationships |
Example Datasets: PFF (Pine Forests Flora)
The package includes a built-in example dataset called PFF that can be loaded as follows:
## Loading required package: ggplot2## Registered S3 methods overwritten by 'ggtern':
## method from
## grid.draw.ggplot ggplot2
## plot.ggplot ggplot2
## print.ggplot ggplot2## family species Height Leaf_area LDMC SLA SRL SeedMass
## 1 Asteraceae Achillea millefolium 14 240 27 8 35 0.140
## 2 Asteraceae Agoseris glauca 12 520 17 20 56 2.530
## 3 Poaceae Agropyron desertorum 26 290 42 9 99 0.388
## 4 Asteraceae Ambrosia psilostachya 44 490 20 16 73 5.896
## 5 Primulaceae Androsace septentrionalis 1 51 14 31 80 0.128
## 6 Asteraceae Antennaria parvifolia 2 66 27 13 27 0.061
## FltDate FltDur k_value Leaf_Cmass Leaf_Nmass Leaf_CN Leaf_Pmass Leaf_NP
## 1 197 152 1.70 41.16 1.92 22 0.36 6
## 2 213 183 1.10 42.62 1.72 25 0.39 4
## 3 198 91 NA 41.48 2.32 18 0.06 52
## 4 243 122 2.23 38.42 2.32 17 0.25 14
## 5 182 182 NA 40.83 1.95 22 0.29 7
## 6 182 122 0.54 44.07 1.53 30 0.18 9
## Leaf_CP Root_Cmass Root_Nmass Root_CN
## 1 114 40.96 0.48 85
## 2 109 45.86 0.89 52
## 3 1021 41.46 1.61 26
## 4 224 35.69 1.29 28
## 5 131 46.46 1.23 38
## 6 252 35.96 0.55 65The PFF dataset contains trait measurements for plant species found in pine forests, with the following variables:
Height: Maximum plant height (cm)
Leaf_area: Leaf area (cm²)
LDMC: Leaf dry matter content (mg/g)
SLA: Specific leaf area (mm²/mg)
SRL: Specific root length
SeedMass: Seed mass
FltDate: Flowering date
FltDur: Flowering duration
k_value: Decomposition rate
Leaf_Cmass: Leaf carbon content
Leaf_Nmass: Leaf nitrogen content
Leaf_CN: Leaf carbon/nitrogen ratio
Leaf_Pmass: Leaf phosphorus content
Leaf_NP: Leaf nitrogen/phosphorus ratio
Leaf_CP: Leaf carbon/phosphorus ratio
Root_Cmass: Root carbon content
Root_Nmass: Root nitrogen content
Root_CN: Root carbon/nitrogen ratio
CSR Strategy
The CSR strategy analysis is based on Pierce et al.’s (2017) global CSR methodology, which uses three key leaf traits: Leaf Area (LA), Leaf Dry Matter Content (LDMC), and Specific Leaf Area (SLA). Using the MultiTraits package, we analyze these traits for 133 plant species from the PFF dataset. The CSR() function calculates the C, S, and R scores for each species, while CSR_plot() creates a ternary plot showing the distribution of species along the three strategy axes. This visualization helps identify whether species tend toward competitive (C), stress-tolerant (S), or ruderal (R) life strategies.
## family species Height Leaf_area LDMC SLA SRL SeedMass
## 1 Asteraceae Achillea millefolium 14 240 27 8 35 0.140
## 2 Asteraceae Agoseris glauca 12 520 17 20 56 2.530
## 3 Poaceae Agropyron desertorum 26 290 42 9 99 0.388
## 4 Asteraceae Ambrosia psilostachya 44 490 20 16 73 5.896
## 5 Primulaceae Androsace septentrionalis 1 51 14 31 80 0.128
## 6 Asteraceae Antennaria parvifolia 2 66 27 13 27 0.061
## FltDate FltDur k_value Leaf_Cmass Leaf_Nmass Leaf_CN Leaf_Pmass Leaf_NP
## 1 197 152 1.70 41.16 1.92 22 0.36 6
## 2 213 183 1.10 42.62 1.72 25 0.39 4
## 3 198 91 NA 41.48 2.32 18 0.06 52
## 4 243 122 2.23 38.42 2.32 17 0.25 14
## 5 182 182 NA 40.83 1.95 22 0.29 7
## 6 182 122 0.54 44.07 1.53 30 0.18 9
## Leaf_CP Root_Cmass Root_Nmass Root_CN
## 1 114 40.96 0.48 85
## 2 109 45.86 0.89 52
## 3 1021 41.46 1.61 26
## 4 224 35.69 1.29 28
## 5 131 46.46 1.23 38
## 6 252 35.96 0.55 65# Select required traits for CSR analysis
traits <- data.frame(LA=PFF$Leaf_area, LDMC=PFF$LDMC, SLA=PFF$SLA)
head(traits)## LA LDMC SLA
## 1 240 27 8
## 2 520 17 20
## 3 290 42 9
## 4 490 20 16
## 5 51 14 31
## 6 66 27 13## LA LDMC SLA C S R type
## 1 240 27 8 18.845244 81.154756 0.00000 S/CS
## 2 520 17 20 34.290705 4.542473 61.16682 R/CR
## 3 290 42 9 12.118306 87.881694 0.00000 S
## 4 490 20 16 30.293240 30.891427 38.81533 CSR
## 5 51 14 31 4.068212 0.000000 95.93179 R
## 6 66 27 13 5.131786 73.958953 20.90926 S/SR
LHS Strategy
The LHS (Leaf-Height-Seed) strategy analysis is based on three key plant functional traits: specific leaf area (SLA), plant height, and seed mass. The LHS() function in the MultiTraits package classifies plants into eight ecological strategy types, reflecting trade-offs in resource acquisition, competitive ability, and reproductive strategies. The analysis results can be visualized in three-dimensional space using the LHS_plot() function, while LHS_strategy_scheme() illustrates the ecological significance of the eight strategy types. This analytical approach helps understand how plants adapt to environments through different trait combinations.
# Load the PFF dataset
data(PFF)
# Select specific columns (SLA, Height, SeedMass) from the PFF dataset
pff <- PFF[, c("SLA", "Height", "SeedMass")]
head(pff)## SLA Height SeedMass
## 1 8 14 0.140
## 2 20 12 2.530
## 3 9 26 0.388
## 4 16 44 5.896
## 5 31 1 0.128
## 6 13 2 0.061## SLA Height SeedMass LHS_strategy
## 1 8 14 0.140 S-S-S
## 2 20 12 2.530 L-S-L
## 3 9 26 0.388 S-L-S
## 4 16 44 5.896 L-L-L
## 5 31 1 0.128 L-S-S
## 6 13 2 0.061 S-S-S##
## L-L-L L-L-S L-S-L L-S-S S-L-L S-L-S S-S-L S-S-S
## 16 18 11 21 20 9 19 19
## type strategy
## 1 L-L-L Rapid growth, strong survivability and competitiveness
## 2 L-L-S Rapid growth, strong survivability and weak competitiveness
## 3 L-S-L Rapid growth, long-distance dispersal and strong competitiveness
## 4 L-S-S Rapid growth, long-distance dispersal and weak competitiveness
## 5 S-L-L Slow growth, strong survivability and competitiveness
## 6 S-L-S Slow growth, strong survivability and weak competitiveness
## 7 S-S-L Slow growth, long-distance dispersal and strong competitiveness
## 8 S-S-S Slow growth, long-distance dispersal and weak competitivenessNiche Periodicity Table
The Niche Periodicity Table (NPT) analysis employs a “PCA of PCAs” approach to explore how plant functional traits are distributed across environmental gradients. While traditionally organized into three fundamental dimensions - growth, survival, and reproduction - these dimensions are not fixed and can be customized according to specific research questions and hypotheses. The NPT function processes these trait dimensions (whether three or more) to create a comprehensive view of plant ecological strategies, while NPT_plot generates visualizations that can be enhanced by adding taxonomic information such as plant family classifications.
# Load the PFF dataset
data(PFF)
# Log-transform columns 3-20 of the dataset
PFF[,3:20] <- log(PFF[,3:20])
# Remove rows with missing values (NA)
PFF <- na.omit(PFF)
head(PFF)## family species Height Leaf_area LDMC SLA
## 1 Asteraceae Achillea millefolium 2.6390573 5.480639 3.295837 2.079442
## 2 Asteraceae Agoseris glauca 2.4849066 6.253829 2.833213 2.995732
## 4 Asteraceae Ambrosia psilostachya 3.7841896 6.194405 2.995732 2.772589
## 6 Asteraceae Antennaria parvifolia 0.6931472 4.189655 3.295837 2.564949
## 7 Asteraceae Antennaria rosulata 0.0000000 3.401197 3.367296 2.944439
## 8 Caryophyllaceae Arenaria fendleri 2.0794415 3.465736 3.295837 2.564949
## SRL SeedMass FltDate FltDur k_value Leaf_Cmass Leaf_Nmass
## 1 3.555348 -1.9661129 5.283204 5.023881 0.53062825 3.717467 0.6523252
## 2 4.025352 0.9282193 5.361292 5.209486 0.09531018 3.752324 0.5423243
## 4 4.290459 1.7742742 5.493061 4.804021 0.80200159 3.648578 0.8415672
## 6 3.295837 -2.7968814 5.204007 4.804021 -0.61618614 3.785779 0.4252677
## 7 3.401197 -2.6172958 5.204007 4.804021 -0.43078292 3.731939 0.4637340
## 8 3.912023 -1.6928195 5.204007 5.204007 0.03922071 3.768845 0.5538851
## Leaf_CN Leaf_Pmass Leaf_NP Leaf_CP Root_Cmass Root_Nmass Root_CN
## 1 3.091042 -1.0216512 1.791759 4.736198 3.712596 -0.73396918 4.442651
## 2 3.218876 -0.9416085 1.386294 4.691348 3.825593 -0.11653382 3.951244
## 4 2.833213 -1.3862944 2.639057 5.411646 3.574871 0.25464222 3.332205
## 6 3.401197 -1.7147984 2.197225 5.529429 3.582407 -0.59783700 4.174387
## 7 3.258097 -1.7719568 2.302585 5.583496 3.723522 -0.89159812 4.615121
## 8 3.218876 -2.0402208 2.639057 5.831882 3.795714 -0.04082199 3.850148# Define trait dimensions for NPT analysis
traits_dimension <-list(
grow = c("SLA","Leaf_area","LDMC","SRL","Leaf_Nmass","Leaf_Pmass","Root_Nmass"),
survive = c("Height","Leaf_Cmass","Root_Cmass","Leaf_CN","Leaf_NP","Leaf_CP","Root_CN"),
reproductive = c("SeedMass","FltDate","FltDur"))
# Perform NPT analysis using the defined dimensions
npt_result <- NPT(data = PFF, dimension = traits_dimension)
npt_result## $PCA_first
## pc1_percent pc1_major_eigenvector pc2_percent
## grow 61.05240 Leaf_area 15.460864
## survive 48.69647 Height 35.885156
## reproductive 95.17637 SeedMass 4.170238
## pc2_major_eigenvector
## grow Leaf_Pmass
## survive Leaf_CP
## reproductive FltDur
##
## $PCA_second
## PC1 PC2
## pc1.grow -0.4421504 0.29926226
## pc2.grow -0.2646905 -0.47439875
## pc1.survive -0.4575448 0.19461245
## pc2.survive 0.1929222 0.51719484
## pc1.reproductive -0.5001406 0.02455148
## pc2.reproductive 0.1055980 0.07855093
##
## $result
## Call: rda(X = P)
##
## Inertia Rank
## Total 0.05941
## Unconstrained 0.05941 6
## Inertia is variance
##
## Eigenvalues for unconstrained axes:
## PC1 PC2 PC3 PC4 PC5 PC6
## 0.018757 0.015200 0.011163 0.005438 0.004725 0.004123
# Create a visualization of NPT results colored by plant family
NPT_plot(npt_result$result, PFF$family)
Trait Network
The Trait Network (TN) analysis module enables exploration of complex relationships between multiple plant traits. Using correlation analysis (Pearson or Spearman), it creates a network where traits are nodes and significant correlations form edges. The TN() function analyzes relationships, TN_corr() visualizes correlations, TN_metrics() calculates network properties, and TN_plot() offers two visualization styles. In the example, 17 key plant traits from the PFF dataset are analyzed, with correlations filtered by significance thresholds (rThres=0.2, pThres=0.05) after log transformation and NA removal.
# Load the PFF dataset
data(PFF)
# Select specific trait columns for analysis
PFF_traits <- PFF[, c("Height", "Leaf_area","LDMC","SLA","SRL","SeedMass","FltDate","FltDur","Leaf_Cmass","Leaf_Nmass",
"Leaf_CN","Leaf_Pmass","Leaf_NP","Leaf_CP","Root_Cmass","Root_Nmass","Root_CN") ]
# Perform log transformation of data and remove missing values
PFF_traits <- log(na.omit(PFF_traits))
head(PFF_traits)## Height Leaf_area LDMC SLA SRL SeedMass FltDate FltDur
## 1 2.6390573 5.480639 3.295837 2.079442 3.555348 -1.9661129 5.283204 5.023881
## 2 2.4849066 6.253829 2.833213 2.995732 4.025352 0.9282193 5.361292 5.209486
## 3 3.2580965 5.669881 3.737670 2.197225 4.595120 -0.9467499 5.288267 4.510860
## 4 3.7841896 6.194405 2.995732 2.772589 4.290459 1.7742742 5.493061 4.804021
## 5 0.0000000 3.931826 2.639057 3.433987 4.382027 -2.0557250 5.204007 5.204007
## 6 0.6931472 4.189655 3.295837 2.564949 3.295837 -2.7968814 5.204007 4.804021
## Leaf_Cmass Leaf_Nmass Leaf_CN Leaf_Pmass Leaf_NP Leaf_CP Root_Cmass
## 1 3.717467 0.6523252 3.091042 -1.0216512 1.791759 4.736198 3.712596
## 2 3.752324 0.5423243 3.218876 -0.9416085 1.386294 4.691348 3.825593
## 3 3.725211 0.8415672 2.890372 -2.8134107 3.951244 6.928538 3.724729
## 4 3.648578 0.8415672 2.833213 -1.3862944 2.639057 5.411646 3.574871
## 5 3.709417 0.6678294 3.091042 -1.2378744 1.945910 4.875197 3.838592
## 6 3.785779 0.4252677 3.401197 -1.7147984 2.197225 5.529429 3.582407
## Root_Nmass Root_CN
## 1 -0.7339692 4.442651
## 2 -0.1165338 3.951244
## 3 0.4762342 3.258097
## 4 0.2546422 3.332205
## 5 0.2070142 3.637586
## 6 -0.5978370 4.174387# Calculate trait correlations using specified thresholds
TN_corr(traits_matrix=PFF_traits, rThres = 0.2, pThres = 0.05,method = "pearson")
## $corr
## Leaf_Cmass SeedMass Height Leaf_area SLA
## Leaf_Cmass 1.00000000 0.22978000 0.384636473 0.206554780 -0.18998237
## SeedMass 0.22978000 1.00000000 0.361121985 0.416619763 -0.15940285
## Height 0.38463647 0.36112198 1.000000000 0.452299592 -0.16412921
## Leaf_area 0.20655478 0.41661976 0.452299592 1.000000000 0.13682566
## SLA -0.18998237 -0.15940285 -0.164129207 0.136825665 1.00000000
## Leaf_Pmass -0.05970399 -0.02079289 0.075885741 0.203112601 0.30203299
## FltDur -0.04153009 -0.02443231 -0.211012698 0.108552587 0.13033932
## Leaf_Nmass 0.08286743 0.22882492 0.067455833 0.269333000 0.51747515
## Root_Nmass -0.14998887 0.09101701 -0.016426940 0.050510285 0.29408580
## Root_Cmass 0.04530142 -0.04710585 0.034900117 0.039292500 -0.07639932
## LDMC 0.30307272 0.10964199 0.301659012 -0.003080587 -0.47896555
## Leaf_CN 0.13416706 -0.20052995 0.009434968 -0.223032542 -0.53766219
## Root_CN 0.14757958 -0.10071061 0.030360449 -0.040347331 -0.29119563
## Leaf_NP 0.12258834 0.14314473 -0.011682975 -0.011125417 0.04562234
## Leaf_CP 0.19999186 0.03455441 -0.006257259 -0.143819859 -0.27574480
## SRL -0.26965166 -0.42937100 -0.226394473 -0.390629158 0.26116253
## FltDate 0.03411447 -0.29470135 0.176465902 -0.030128255 0.12864332
## Leaf_Pmass FltDur Leaf_Nmass Root_Nmass Root_Cmass
## Leaf_Cmass -0.05970399 -0.04153009 0.08286743 -0.14998887 0.04530142
## SeedMass -0.02079289 -0.02443231 0.22882492 0.09101701 -0.04710585
## Height 0.07588574 -0.21101270 0.06745583 -0.01642694 0.03490012
## Leaf_area 0.20311260 0.10855259 0.26933300 0.05051028 0.03929250
## SLA 0.30203299 0.13033932 0.51747515 0.29408580 -0.07639932
## Leaf_Pmass 1.00000000 0.11851692 0.26074898 0.04890303 -0.04630048
## FltDur 0.11851692 1.00000000 0.13238657 0.13890508 -0.01082022
## Leaf_Nmass 0.26074898 0.13238657 1.00000000 0.52876638 -0.15697014
## Root_Nmass 0.04890303 0.13890508 0.52876638 1.00000000 -0.08359383
## Root_Cmass -0.04630048 -0.01082022 -0.15697014 -0.08359383 1.00000000
## LDMC -0.21754303 0.04461883 -0.43485621 -0.32745254 0.09934752
## Leaf_CN -0.25465544 -0.13259233 -0.97164631 -0.55088650 0.16825407
## Root_CN -0.06412254 -0.13357624 -0.52910745 -0.96572894 0.33066731
## Leaf_NP -0.70514044 -0.05874797 0.35059829 0.22562513 0.02677610
## Leaf_CP -0.88553166 -0.14723135 -0.20526729 -0.07985607 0.10018531
## SRL -0.03387244 -0.14561437 -0.04037146 0.06146170 -0.08854235
## FltDate -0.01149970 -0.03573571 0.04398669 0.12121725 -0.02448182
## LDMC Leaf_CN Root_CN Leaf_NP Leaf_CP
## Leaf_Cmass 0.303072716 0.134167058 0.14757958 0.12258834 0.199991857
## SeedMass 0.109641991 -0.200529948 -0.10071061 0.14314473 0.034554412
## Height 0.301659012 0.009434968 0.03036045 -0.01168298 -0.006257259
## Leaf_area -0.003080587 -0.223032542 -0.04034733 -0.01112542 -0.143819859
## SLA -0.478965555 -0.537662192 -0.29119563 0.04562234 -0.275744804
## Leaf_Pmass -0.217543031 -0.254655437 -0.06412254 -0.70514044 -0.885531661
## FltDur 0.044618830 -0.132592326 -0.13357624 -0.05874797 -0.147231348
## Leaf_Nmass -0.434856211 -0.971646305 -0.52910745 0.35059829 -0.205267290
## Root_Nmass -0.327452541 -0.550886498 -0.96572894 0.22562513 -0.079856074
## Root_Cmass 0.099347516 0.168254073 0.33066731 0.02677610 0.100185313
## LDMC 1.000000000 0.485062621 0.33012948 -0.07020455 0.215033041
## Leaf_CN 0.485062621 1.000000000 0.55169993 -0.33396609 0.234136224
## Root_CN 0.330129481 0.551699928 1.00000000 -0.19803730 0.102393514
## Leaf_NP -0.070204551 -0.333966095 -0.19803730 1.00000000 0.829726245
## Leaf_CP 0.215033041 0.234136224 0.10239351 0.82972624 1.000000000
## SRL -0.190866435 -0.004185425 -0.07054222 0.02164472 0.032052800
## FltDate -0.104362513 -0.024640552 -0.11654247 0.07958522 0.079064142
## SRL FltDate
## Leaf_Cmass -0.269651660 0.03411447
## SeedMass -0.429370998 -0.29470135
## Height -0.226394473 0.17646590
## Leaf_area -0.390629158 -0.03012826
## SLA 0.261162529 0.12864332
## Leaf_Pmass -0.033872442 -0.01149970
## FltDur -0.145614371 -0.03573571
## Leaf_Nmass -0.040371456 0.04398669
## Root_Nmass 0.061461700 0.12121725
## Root_Cmass -0.088542350 -0.02448182
## LDMC -0.190866435 -0.10436251
## Leaf_CN -0.004185425 -0.02464055
## Root_CN -0.070542221 -0.11654247
## Leaf_NP 0.021644716 0.07958522
## Leaf_CP 0.032052800 0.07906414
## SRL 1.000000000 0.39428523
## FltDate 0.394285228 1.00000000
##
## $corrPos
## xName yName x y corr p.value
## 1 Leaf_Cmass SeedMass 1 16 0.229780000 0
## 2 Leaf_Cmass Height 1 15 0.384636473 0
## 3 Leaf_Cmass Leaf_area 1 14 0.206554780 0
## 4 Leaf_Cmass SLA 1 13 -0.189982370 1
## 5 Leaf_Cmass Leaf_Pmass 1 12 -0.059703994 1
## 6 Leaf_Cmass FltDur 1 11 -0.041530095 1
## 7 Leaf_Cmass Leaf_Nmass 1 10 0.082867433 1
## 8 Leaf_Cmass Root_Nmass 1 9 -0.149988873 1
## 9 Leaf_Cmass Root_Cmass 1 8 0.045301417 1
## 10 Leaf_Cmass LDMC 1 7 0.303072716 0
## 11 Leaf_Cmass Leaf_CN 1 6 0.134167058 1
## 12 Leaf_Cmass Root_CN 1 5 0.147579584 1
## 13 Leaf_Cmass Leaf_NP 1 4 0.122588338 1
## 14 Leaf_Cmass Leaf_CP 1 3 0.199991857 1
## 15 Leaf_Cmass SRL 1 2 -0.269651660 0
## 16 Leaf_Cmass FltDate 1 1 0.034114473 1
## 17 SeedMass Height 2 15 0.361121985 0
## 18 SeedMass Leaf_area 2 14 0.416619763 0
## 19 SeedMass SLA 2 13 -0.159402850 1
## 20 SeedMass Leaf_Pmass 2 12 -0.020792890 1
## 21 SeedMass FltDur 2 11 -0.024432307 1
## 22 SeedMass Leaf_Nmass 2 10 0.228824916 0
## 23 SeedMass Root_Nmass 2 9 0.091017014 1
## 24 SeedMass Root_Cmass 2 8 -0.047105850 1
## 25 SeedMass LDMC 2 7 0.109641991 1
## 26 SeedMass Leaf_CN 2 6 -0.200529948 1
## 27 SeedMass Root_CN 2 5 -0.100710610 1
## 28 SeedMass Leaf_NP 2 4 0.143144732 1
## 29 SeedMass Leaf_CP 2 3 0.034554412 1
## 30 SeedMass SRL 2 2 -0.429370998 0
## 31 SeedMass FltDate 2 1 -0.294701349 0
## 32 Height Leaf_area 3 14 0.452299592 0
## 33 Height SLA 3 13 -0.164129207 1
## 34 Height Leaf_Pmass 3 12 0.075885741 1
## 35 Height FltDur 3 11 -0.211012698 0
## 36 Height Leaf_Nmass 3 10 0.067455833 1
## 37 Height Root_Nmass 3 9 -0.016426940 1
## 38 Height Root_Cmass 3 8 0.034900117 1
## 39 Height LDMC 3 7 0.301659012 0
## 40 Height Leaf_CN 3 6 0.009434968 1
## 41 Height Root_CN 3 5 0.030360449 1
## 42 Height Leaf_NP 3 4 -0.011682975 1
## 43 Height Leaf_CP 3 3 -0.006257259 1
## 44 Height SRL 3 2 -0.226394473 0
## 45 Height FltDate 3 1 0.176465902 1
## 46 Leaf_area SLA 4 13 0.136825665 1
## 47 Leaf_area Leaf_Pmass 4 12 0.203112601 1
## 48 Leaf_area FltDur 4 11 0.108552587 1
## 49 Leaf_area Leaf_Nmass 4 10 0.269333000 0
## 50 Leaf_area Root_Nmass 4 9 0.050510285 1
## 51 Leaf_area Root_Cmass 4 8 0.039292500 1
## 52 Leaf_area LDMC 4 7 -0.003080587 1
## 53 Leaf_area Leaf_CN 4 6 -0.223032542 0
## 54 Leaf_area Root_CN 4 5 -0.040347331 1
## 55 Leaf_area Leaf_NP 4 4 -0.011125417 1
## 56 Leaf_area Leaf_CP 4 3 -0.143819859 1
## 57 Leaf_area SRL 4 2 -0.390629158 0
## 58 Leaf_area FltDate 4 1 -0.030128255 1
## 59 SLA Leaf_Pmass 5 12 0.302032989 0
## 60 SLA FltDur 5 11 0.130339323 1
## 61 SLA Leaf_Nmass 5 10 0.517475153 0
## 62 SLA Root_Nmass 5 9 0.294085800 0
## 63 SLA Root_Cmass 5 8 -0.076399318 1
## 64 SLA LDMC 5 7 -0.478965555 0
## 65 SLA Leaf_CN 5 6 -0.537662192 0
## 66 SLA Root_CN 5 5 -0.291195632 0
## 67 SLA Leaf_NP 5 4 0.045622342 1
## 68 SLA Leaf_CP 5 3 -0.275744804 0
## 69 SLA SRL 5 2 0.261162529 0
## 70 SLA FltDate 5 1 0.128643321 1
## 71 Leaf_Pmass FltDur 6 11 0.118516920 1
## 72 Leaf_Pmass Leaf_Nmass 6 10 0.260748983 0
## 73 Leaf_Pmass Root_Nmass 6 9 0.048903029 1
## 74 Leaf_Pmass Root_Cmass 6 8 -0.046300479 1
## 75 Leaf_Pmass LDMC 6 7 -0.217543031 0
## 76 Leaf_Pmass Leaf_CN 6 6 -0.254655437 0
## 77 Leaf_Pmass Root_CN 6 5 -0.064122544 1
## 78 Leaf_Pmass Leaf_NP 6 4 -0.705140444 0
## 79 Leaf_Pmass Leaf_CP 6 3 -0.885531661 0
## 80 Leaf_Pmass SRL 6 2 -0.033872442 1
## 81 Leaf_Pmass FltDate 6 1 -0.011499702 1
## 82 FltDur Leaf_Nmass 7 10 0.132386569 1
## 83 FltDur Root_Nmass 7 9 0.138905079 1
## 84 FltDur Root_Cmass 7 8 -0.010820221 1
## 85 FltDur LDMC 7 7 0.044618830 1
## 86 FltDur Leaf_CN 7 6 -0.132592326 1
## 87 FltDur Root_CN 7 5 -0.133576245 1
## 88 FltDur Leaf_NP 7 4 -0.058747966 1
## 89 FltDur Leaf_CP 7 3 -0.147231348 1
## 90 FltDur SRL 7 2 -0.145614371 1
## 91 FltDur FltDate 7 1 -0.035735706 1
## 92 Leaf_Nmass Root_Nmass 8 9 0.528766380 0
## 93 Leaf_Nmass Root_Cmass 8 8 -0.156970145 1
## 94 Leaf_Nmass LDMC 8 7 -0.434856211 0
## 95 Leaf_Nmass Leaf_CN 8 6 -0.971646305 0
## 96 Leaf_Nmass Root_CN 8 5 -0.529107450 0
## 97 Leaf_Nmass Leaf_NP 8 4 0.350598288 0
## 98 Leaf_Nmass Leaf_CP 8 3 -0.205267290 1
## 99 Leaf_Nmass SRL 8 2 -0.040371456 1
## 100 Leaf_Nmass FltDate 8 1 0.043986687 1
## 101 Root_Nmass Root_Cmass 9 8 -0.083593831 1
## 102 Root_Nmass LDMC 9 7 -0.327452541 0
## 103 Root_Nmass Leaf_CN 9 6 -0.550886498 0
## 104 Root_Nmass Root_CN 9 5 -0.965728939 0
## 105 Root_Nmass Leaf_NP 9 4 0.225625127 0
## 106 Root_Nmass Leaf_CP 9 3 -0.079856074 1
## 107 Root_Nmass SRL 9 2 0.061461700 1
## 108 Root_Nmass FltDate 9 1 0.121217248 1
## 109 Root_Cmass LDMC 10 7 0.099347516 1
## 110 Root_Cmass Leaf_CN 10 6 0.168254073 1
## 111 Root_Cmass Root_CN 10 5 0.330667308 0
## 112 Root_Cmass Leaf_NP 10 4 0.026776102 1
## 113 Root_Cmass Leaf_CP 10 3 0.100185313 1
## 114 Root_Cmass SRL 10 2 -0.088542350 1
## 115 Root_Cmass FltDate 10 1 -0.024481823 1
## 116 LDMC Leaf_CN 11 6 0.485062621 0
## 117 LDMC Root_CN 11 5 0.330129481 0
## 118 LDMC Leaf_NP 11 4 -0.070204551 1
## 119 LDMC Leaf_CP 11 3 0.215033041 0
## 120 LDMC SRL 11 2 -0.190866435 1
## 121 LDMC FltDate 11 1 -0.104362513 1
## 122 Leaf_CN Root_CN 12 5 0.551699928 0
## 123 Leaf_CN Leaf_NP 12 4 -0.333966095 0
## 124 Leaf_CN Leaf_CP 12 3 0.234136224 0
## 125 Leaf_CN SRL 12 2 -0.004185425 1
## 126 Leaf_CN FltDate 12 1 -0.024640552 1
## 127 Root_CN Leaf_NP 13 4 -0.198037303 1
## 128 Root_CN Leaf_CP 13 3 0.102393514 1
## 129 Root_CN SRL 13 2 -0.070542221 1
## 130 Root_CN FltDate 13 1 -0.116542472 1
## 131 Leaf_NP Leaf_CP 14 3 0.829726245 0
## 132 Leaf_NP SRL 14 2 0.021644716 1
## 133 Leaf_NP FltDate 14 1 0.079585223 1
## 134 Leaf_CP SRL 15 2 0.032052800 1
## 135 Leaf_CP FltDate 15 1 0.079064142 1
## 136 SRL FltDate 16 1 0.394285228 0
##
## $arg
## $arg$type
## [1] "lower"# Perform Trait Network (TN) analysis
Tn_result <- TN(traits_matrix = PFF_traits, rThres = 0.2, pThres = 0.05,method = "pearson")
Tn_result## IGRAPH a50d487 UNW- 17 48 --
## + attr: name (v/c), weight (e/n), correlation (e/n)
## + edges from a50d487 (vertex names):
## [1] Height --Leaf_area Height --LDMC Height --SRL
## [4] Height --SeedMass Height --FltDur Height --Leaf_Cmass
## [7] Leaf_area--SRL Leaf_area--SeedMass Leaf_area--Leaf_Cmass
## [10] Leaf_area--Leaf_Nmass Leaf_area--Leaf_CN LDMC --SLA
## [13] LDMC --Leaf_Cmass LDMC --Leaf_Nmass LDMC --Leaf_CN
## [16] LDMC --Leaf_Pmass LDMC --Leaf_CP LDMC --Root_Nmass
## [19] LDMC --Root_CN SLA --SRL SLA --Leaf_Nmass
## [22] SLA --Leaf_CN SLA --Leaf_Pmass SLA --Leaf_CP
## + ... omitted several edges## $node
## degree closeness betweenness clustering_coefficient
## Height 6 0.11869962 16 0.4666667
## Leaf_area 6 0.11573501 8 0.5333333
## LDMC 9 0.13922700 26 0.4722222
## SLA 8 0.12842194 14 0.5714286
## SRL 6 0.12375593 12 0.4666667
## SeedMass 6 0.11342243 11 0.5333333
## FltDate 2 0.08277376 0 1.0000000
## FltDur 1 0.08628264 0 0.0000000
## Leaf_Cmass 5 0.12558547 4 0.7000000
## Leaf_Nmass 9 0.12265464 7 0.5000000
## Leaf_CN 9 0.11545889 9 0.5833333
## Leaf_Pmass 6 0.12327490 0 0.8000000
## Leaf_NP 5 0.09462849 0 0.7000000
## Leaf_CP 5 0.11396509 2 0.8000000
## Root_Cmass 1 0.06839341 0 0.0000000
## Root_Nmass 6 0.10360997 7 0.8000000
## Root_CN 6 0.10350594 15 0.6666667
##
## $global
## edge_density diameter avg_path_length avg_clustering_coefficient modularity
## 1 0.3529412 1.277311 0.583704 0.5845588 0.2777778# Create visualization plots of the trait network
par(mfrow=c(1,2))
# Style 1
TN_plot(Tn_result, style = 1,vertex.size = 10,vertex.label.cex = 0.6)
# Style 2
TN_plot(Tn_result, style = 2,vertex.size = 20,vertex.label.cex = 0.6)