Main Functions
| Module | Function | Description |
|---|---|---|
| CSR Module | CSR() |
Classifies plant species’ CSR ecological strategies based on leaf traits (LA, LDMC, and SLA) using the Pierce et al. (2017) method. |
CSR_hodgson() |
Implements the Hodgson et al. (1999) method for CSR classification. | |
CSR_plot() |
Provides advanced ternary plot visualizations for CSR analysis to explore plant adaptation to different environments. | |
| LHS Module | LHS() |
Analyzes LHS strategies using SLA, plant height, and seed mass. |
LHS_plot() |
Visualizes three-dimensional trait distributions in LHS space. | |
LHS_strategy_scheme() |
Classifies strategies based on the LHS scheme. | |
| NPT Module | NPT_discrete() |
Supports discrete niche classification schemes. Performs PCA and clustering (CART or K-means) to group species within each dimension. |
NPT_discrete_plot() |
Visualizes the discrete niche classification scheme. | |
NPT_continuous() |
Supports continuous niche classification schemes. Performs a hierarchical PCA approach. | |
NPT_continuous_plot() |
Visualizes the results of the continuous niche classification. | |
| PTN Module | PTN_corr() |
Offers correlation computation and visualization for trait networks. |
PTN() |
Generates trait correlation networks. | |
PTN_metrics() |
Quantifies network metrics for trait networks. | |
PTN_plot() |
Visualizes the trait network graph. | |
| PTMN Module | PTMN() |
Constructs trait multilayer networks. |
PTMN_metrics() |
Calculates metrics for trait multilayer networks. | |
PTMN_plot() |
Visualizes trait multilayer networks. |
Example Datasets: PFF (Pine Forests Flora)
The package includes a built-in example dataset called PFF that can be loaded as follows:
## species genus family Height Leaf_area LDMC SLA
## 1 Achillea millefolium Achillea Asteraceae 14 240 27 8
## 2 Agoseris glauca Agoseris Asteraceae 12 520 17 20
## 3 Agropyron desertorum Agropyron Poaceae 26 290 42 9
## 4 Ambrosia psilostachya Ambrosia Asteraceae 44 490 20 16
## 5 Androsace septentrionalis Androsace Primulaceae 1 51 14 31
## 6 Antennaria parvifolia Antennaria Asteraceae 2 66 27 13
## SRL SeedMass FltDate FltDur k_value Leaf_Cmass Leaf_Nmass Leaf_CN Leaf_Pmass
## 1 35 0.140 197 152 1.70 41.16 1.92 22 0.36
## 2 56 2.530 213 183 1.10 42.62 1.72 25 0.39
## 3 99 0.388 198 91 NA 41.48 2.32 18 0.06
## 4 73 5.896 243 122 2.23 38.42 2.32 17 0.25
## 5 80 0.128 182 182 NA 40.83 1.95 22 0.29
## 6 27 0.061 182 122 0.54 44.07 1.53 30 0.18
## Leaf_NP Leaf_CP Root_Cmass Root_Nmass Root_CN
## 1 6 114 40.96 0.48 85
## 2 4 109 45.86 0.89 52
## 3 52 1021 41.46 1.61 26
## 4 14 224 35.69 1.29 28
## 5 7 131 46.46 1.23 38
## 6 9 252 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 (mm2)
LDMC: Leaf dry matter content (%)
SLA: Specific leaf area (mm2/mg)
SRL: Specific root length (m/g)
SeedMass: Seed mass (mg)
FltDate: Mean flowering date (Julian day)
FltDur: Mean flowering duration (days)
k_value: Decomposition decay constant, where proportion of original mass remaining = exp(- k-value*0.926)
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 (Competitor–Stress-Tolerator–Ruderal) strategy, initially conceptualized by Grime (1974), classifies plants based on their ecological strategies, which are influenced by environmental factors such as competition, stress, and disturbance. The MultiTraits package offers two approaches for CSR analysis: the method proposed by Hodgson et al. (1999) and the method developed by Pierce et al. (2017). The Pierce method, used here, employs leaf area (LA), leaf dry matter content (LDMC), and specific leaf area (SLA) to classify species. The CSR_plot() function visualizes the results in a ternary plot, showing species’ positions based on their relative C, S, and R strategy proportions. This aids in understanding plant functional classification and predicting responses to environmental changes.
## species genus family Height Leaf_area LDMC SLA
## 1 Achillea millefolium Achillea Asteraceae 14 240 27 8
## 2 Agoseris glauca Agoseris Asteraceae 12 520 17 20
## 3 Agropyron desertorum Agropyron Poaceae 26 290 42 9
## 4 Ambrosia psilostachya Ambrosia Asteraceae 44 490 20 16
## 5 Androsace septentrionalis Androsace Primulaceae 1 51 14 31
## 6 Antennaria parvifolia Antennaria Asteraceae 2 66 27 13
## SRL SeedMass FltDate FltDur k_value Leaf_Cmass Leaf_Nmass Leaf_CN Leaf_Pmass
## 1 35 0.140 197 152 1.70 41.16 1.92 22 0.36
## 2 56 2.530 213 183 1.10 42.62 1.72 25 0.39
## 3 99 0.388 198 91 NA 41.48 2.32 18 0.06
## 4 73 5.896 243 122 2.23 38.42 2.32 17 0.25
## 5 80 0.128 182 182 NA 40.83 1.95 22 0.29
## 6 27 0.061 182 122 0.54 44.07 1.53 30 0.18
## Leaf_NP Leaf_CP Root_Cmass Root_Nmass Root_CN
## 1 6 114 40.96 0.48 85
## 2 4 109 45.86 0.89 52
## 3 52 1021 41.46 1.61 26
## 4 14 224 35.69 1.29 28
## 5 7 131 46.46 1.23 38
## 6 9 252 35.96 0.55 65# Create a data frame with the CSR traits
csr_traits <- data.frame(LA=PFF$Leaf_area, LDMC=PFF$LDMC, SLA=PFF$SLA)
rownames(csr_traits) <- PFF$species
head(csr_traits)## LA LDMC SLA
## Achillea millefolium 240 27 8
## Agoseris glauca 520 17 20
## Agropyron desertorum 290 42 9
## Ambrosia psilostachya 490 20 16
## Androsace septentrionalis 51 14 31
## Antennaria parvifolia 66 27 13## LA LDMC SLA C S R type
## Achillea millefolium 240 27 8 18.845244 81.154756 0.00000 S/CS
## Agoseris glauca 520 17 20 34.290705 4.542473 61.16682 R/CR
## Agropyron desertorum 290 42 9 12.118306 87.881694 0.00000 S
## Ambrosia psilostachya 490 20 16 30.293240 30.891427 38.81533 CSR
## Androsace septentrionalis 51 14 31 4.068212 0.000000 95.93179 R
## Antennaria parvifolia 66 27 13 5.131786 73.958953 20.90926 S/SR
LHS Strategy
The Leaf–Height–Seed (LHS) scheme, proposed by Westoby (1998), classifies plant species based on specific leaf area (SLA), canopy height at maturity, and seed mass. These three traits reflect trade-offs in resource acquisition, spatial occupation, and reproductive investment. The MultiTraits package implements the LHS scheme by positioning species in a three-dimensional trait space using log-transformed trait values. This continuous space is then divided into eight discrete strategy types (e.g., S–S–S, L–L–L) based on median trait values, representing different trade-offs in growth, competition, and survival. The LHS_plot() function visualizes these strategies in 3D space, while LHS_strategy_scheme() provides a reference key for interpreting the strategy types.
## species genus family Height Leaf_area LDMC SLA
## 1 Achillea millefolium Achillea Asteraceae 14 240 27 8
## 2 Agoseris glauca Agoseris Asteraceae 12 520 17 20
## 3 Agropyron desertorum Agropyron Poaceae 26 290 42 9
## 4 Ambrosia psilostachya Ambrosia Asteraceae 44 490 20 16
## 5 Androsace septentrionalis Androsace Primulaceae 1 51 14 31
## 6 Antennaria parvifolia Antennaria Asteraceae 2 66 27 13
## SRL SeedMass FltDate FltDur k_value Leaf_Cmass Leaf_Nmass Leaf_CN Leaf_Pmass
## 1 35 0.140 197 152 1.70 41.16 1.92 22 0.36
## 2 56 2.530 213 183 1.10 42.62 1.72 25 0.39
## 3 99 0.388 198 91 NA 41.48 2.32 18 0.06
## 4 73 5.896 243 122 2.23 38.42 2.32 17 0.25
## 5 80 0.128 182 182 NA 40.83 1.95 22 0.29
## 6 27 0.061 182 122 0.54 44.07 1.53 30 0.18
## Leaf_NP Leaf_CP Root_Cmass Root_Nmass Root_CN
## 1 6 114 40.96 0.48 85
## 2 4 109 45.86 0.89 52
## 3 52 1021 41.46 1.61 26
## 4 14 224 35.69 1.29 28
## 5 7 131 46.46 1.23 38
## 6 9 252 35.96 0.55 65# Create a data frame with the LHS traits
lhs_traits <- PFF[, c("SLA", "Height", "SeedMass")]
rownames(lhs_traits) <- PFF$species
head(lhs_traits)## SLA Height SeedMass
## Achillea millefolium 8 14 0.140
## Agoseris glauca 20 12 2.530
## Agropyron desertorum 9 26 0.388
## Ambrosia psilostachya 16 44 5.896
## Androsace septentrionalis 31 1 0.128
## Antennaria parvifolia 13 2 0.061## SLA Height SeedMass log_SLA log_Height log_SeedMass
## Achillea millefolium 8 14 0.140 2.079442 2.6390573 -1.9661129
## Agoseris glauca 20 12 2.530 2.995732 2.4849066 0.9282193
## Agropyron desertorum 9 26 0.388 2.197225 3.2580965 -0.9467499
## Ambrosia psilostachya 16 44 5.896 2.772589 3.7841896 1.7742742
## Androsace septentrionalis 31 1 0.128 3.433987 0.0000000 -2.0557250
## Antennaria parvifolia 13 2 0.061 2.564949 0.6931472 -2.7968814
## LHS_strategy
## Achillea millefolium S-S-S
## Agoseris glauca L-S-L
## Agropyron desertorum S-L-S
## Ambrosia psilostachya L-L-L
## Androsace septentrionalis L-S-S
## Antennaria parvifolia 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# Create a visualization plot of the LHS analysis results
LHS_plot(lhs_result, group = "LHS_strategy", show_cube = TRUE)
## type strategy
## 1 L-L-L Rapid growth, strong competitiveness and strong survivability
## 2 L-L-S Rapid growth, strong competitiveness and long-distance dispersal
## 3 L-S-L Rapid growth, weak competitiveness and strong survivability
## 4 L-S-S Rapid growth, weak competitiveness and long-distance dispersal
## 5 S-L-L Slow growth, strong competitiveness and strong survivability
## 6 S-L-S Slow growth, strong competitiveness and long-distance dispersal
## 7 S-S-L Slow growth, weak competitiveness and strong survivability
## 8 S-S-S Slow growth, weak competitiveness and long-distance dispersalNiche Periodicity Table
The Niche Periodic Table (NPT) framework systematically describes and predicts species functions by identifying recurring trait combinations driven by convergent evolution. Within the MultiTraits R package, the NPT module offers both discrete and continuous classification schemes. The NPT_discrete() function categorizes species into distinct niche groups using PCA and clustering, with NPT_discrete_plot() visualizing the results. For continuous classification, NPT_continuous() performs hierarchical PCA to create a two-dimensional ordination space, while NPT_continuous_plot() visualizes these integrated niche patterns.
# Load packages
library(MultiTraits)
# Load and prepare data
data(PFF)
rownames(PFF) <- PFF$species
PFF_traits <- PFF[, c("SLA", "SRL", "Leaf_Nmass", "Root_Nmass","Height",
"Leaf_CN", "Root_CN","SeedMass", "FltDate", "FltDur")]
# Perform log transformation of data and remove missing values
PFF_traits <- log(na.omit(PFF_traits))
head(PFF_traits)## SLA SRL Leaf_Nmass Root_Nmass Height
## Achillea millefolium 2.079442 3.555348 0.6523252 -0.7339692 2.6390573
## Agoseris glauca 2.995732 4.025352 0.5423243 -0.1165338 2.4849066
## Agropyron desertorum 2.197225 4.595120 0.8415672 0.4762342 3.2580965
## Ambrosia psilostachya 2.772589 4.290459 0.8415672 0.2546422 3.7841896
## Androsace septentrionalis 3.433987 4.382027 0.6678294 0.2070142 0.0000000
## Antennaria parvifolia 2.564949 3.295837 0.4252677 -0.5978370 0.6931472
## Leaf_CN Root_CN SeedMass FltDate FltDur
## Achillea millefolium 3.091042 4.442651 -1.9661129 5.283204 5.023881
## Agoseris glauca 3.218876 3.951244 0.9282193 5.361292 5.209486
## Agropyron desertorum 2.890372 3.258097 -0.9467499 5.288267 4.510860
## Ambrosia psilostachya 2.833213 3.332205 1.7742742 5.493061 4.804021
## Androsace septentrionalis 3.091042 3.637586 -2.0557250 5.204007 5.204007
## Antennaria parvifolia 3.401197 4.174387 -2.7968814 5.204007 4.804021# Define trait dimensions
dimension <- list(
Grow = c("SLA", "SRL", "Leaf_Nmass", "Root_Nmass"),
Survive = c("Height", "Leaf_CN", "Root_CN"),
Reproductive = c("SeedMass", "FltDate", "FltDur")
)
# Perform discrete niche scheme
set.seed(123)
discrete_result <- NPT_discrete(data = PFF_traits, dimension = dimension)## === PCA Analysis Results ===
## Dimension: Grow
## PC1 variance explained: 51.38 %
## PC2 variance explained: 31.01 %
##
## Dimension: Survive
## PC1 variance explained: 80.24 %
## PC2 variance explained: 15.45 %
##
## Dimension: Reproductive
## PC1 variance explained: 94.75 %
## PC2 variance explained: 4.5 %
##
## === CART Clustering Results ===
## Dimension Grow cluster count: 8
## Dimension Survive cluster count: 8
## Dimension Reproductive cluster count: 8
##
## === Niche Classification Results ===
## Total potential niches: 512
## Actual occupied niches: 99
## Niche occupancy rate: 19.34 %## $niche_classification
## species Grow Survive Reproductive niche_code
## 1 Achillea millefolium 7 1 2 7,1,2
## 2 Agoseris glauca 1 1 5 1,1,5
## 3 Agropyron desertorum 4 2 3 4,2,3
## 4 Ambrosia psilostachya 1 2 4 1,2,4
## 5 Androsace septentrionalis 4 8 6 4,8,6
## 6 Antennaria parvifolia 3 8 6 3,8,6
## 7 Antennaria rosulata 3 8 6 3,8,6
## 8 Arenaria fendleri 1 1 5 1,1,5
## 9 Arenaria lanuginosa 3 1 2 3,1,2
## 10 Aristida purpurea 7 5 5 7,5,5
## 11 Artemisia campestris 1 2 6 1,2,6
## 12 Artemisia carruthii 1 2 6 1,2,6
## 13 Artemisia dracunculus 4 6 6 4,6,6
## 14 Astragalus humistratus 3 7 2 3,7,2
## 15 Astragalus rusbyi 8 7 1 8,7,1
## 16 Bahia dissecta 4 6 3 4,6,3
## 17 Blepharoneuron tricholepis 7 5 1 7,5,1
## 18 Bouteloua curtipendula 4 5 5 4,5,5
## 19 Bouteloua gracilis 4 2 1 4,2,1
## 20 Bouteloua simplex 6 1 8 6,1,8
## 21 Bromus ciliatus 1 2 4 1,2,4
## 22 Bromus tectorum 3 1 8 3,1,8
## 23 Carex geophila 3 5 3 3,5,3
## 24 Castilleja austromontana 5 2 2 5,2,2
## 25 Castilleja integra 5 1 6 5,1,6
## 26 Ceanothus fendleri 1 1 4 1,1,4
## 27 Centaurea diffusa 1 2 3 1,2,3
## 28 Chaetopappa ericoides 1 1 5 1,1,5
## 29 Chamaesyce fendleri 4 5 6 4,5,6
## 30 Chamaesyce serpyllifolia 6 8 5 6,8,5
## 31 Chenopodium fremontii 8 6 1 8,6,1
## 32 Chenopodium graveolens 8 7 8 8,7,8
## 33 Cirsium wheeleri 5 6 7 5,6,7
## 34 Convolvulus arvensis 3 4 7 3,4,7
## 35 Coreopsis tinctoria 3 1 1 3,1,1
## 36 Cosmos parviflorus 8 3 4 8,3,4
## 37 Draba asperella 1 8 6 1,8,6
## 38 Drymaria leptophylla 6 8 8 6,8,8
## 39 Drymaria molluginea 7 8 8 7,8,8
## 40 Echeandia flavescens 4 1 8 4,1,8
## 41 Elymus elymoides 1 2 4 1,2,4
## 42 Elymus trachycaulus 2 5 4 2,5,4
## 43 Epilobium brachycarpum 4 3 8 4,3,8
## 44 Eragrostis mexicana 6 1 3 6,1,3
## 45 Erigeron divergens 3 1 6 3,1,6
## 46 Erigeron flavescens 1 8 6 1,8,6
## 47 Erigeron formosissimus 1 1 3 1,1,3
## 48 Erigeron speciosa 3 2 3 3,2,3
## 49 Eriogonum alatum 7 1 4 7,1,4
## 50 Eriogonum pharnaceoides 1 1 1 1,1,1
## 51 Eriogonum racemosum 2 4 2 2,4,2
## 52 Erodium cicutarium 8 8 2 8,8,2
## 53 Euphorbia sp. 5 1 4 5,1,4
## 54 Festuca arizonica 2 5 3 2,5,3
## 55 Gaillardia pinnatifida 1 4 2 1,4,2
## 56 Geranium caespitosum 5 1 5 5,1,5
## 57 Geranium richardsonii 1 1 5 1,1,5
## 58 Gutierrezia sarothrae 4 1 2 4,1,2
## 59 Heliomeris multiflora 4 3 5 4,3,5
## 60 Helianthella quinquinervis 5 2 7 5,2,7
## 61 Hesperostipa comata 7 5 7 7,5,7
## 62 Heterotheca villosa 1 1 5 1,1,5
## 63 Hieracium fendleri 2 4 1 2,4,1
## 64 Houstonia wrightii 3 8 8 3,8,8
## 65 Hymenoxys hoopsii 1 6 1 1,6,1
## 66 Hymenoxys richardsonii 1 1 2 1,1,2
## 67 Hymenopappus mexicanus 3 1 1 3,1,1
## 68 Ipomopsis aggregata 2 2 2 2,2,2
## 69 Ipomopsis multiflora 1 1 1 1,1,1
## 70 Iris missouriensis 5 2 7 5,2,7
## 71 Koeleria macrantha 2 1 6 2,1,6
## 72 Laennecia schiedeana 1 2 6 1,2,6
## 73 Lepidium densiflorum 8 3 2 8,3,2
## 74 Linum australe 1 1 3 1,1,3
## 75 Linum lewisii 3 2 5 3,2,5
## 76 Linaria dalmatica 3 6 8 3,6,8
## 77 Lithospermum multiflorum 5 2 4 5,2,4
## 78 Lotus wrightii 8 7 4 8,7,4
## 79 Lupinus argenteus 8 7 7 8,7,7
## 80 Lupinus kingii 8 7 7 8,7,7
## 81 Machaeranthera canescens 1 2 5 1,2,5
## 82 Machaeranthera gracilis 4 1 5 4,1,5
## 83 Mirabilis decipiens 8 3 4 8,3,4
## 84 Monroa squarrosa 4 4 2 4,4,2
## 85 Muhlenbergia minutissima 6 1 6 6,1,6
## 86 Muhlenbergia montana 7 5 3 7,5,3
## 87 Muhlenbergia ramulosa 7 1 8 7,1,8
## 88 Muhlenbergia rigens 7 5 6 7,5,6
## 89 Muhlenbergia wrightii 4 2 1 4,2,1
## 90 Nama dichotomum 8 3 8 8,3,8
## 91 Noccaea montana 1 8 5 1,8,5
## 92 Oenothera pubescens 4 1 1 4,1,1
## 93 Oxalis sp. 8 8 3 8,8,3
## 94 Oxytropis lambertii 8 7 5 8,7,5
## 95 Packera multilobata 3 1 5 3,1,5
## 96 Pascopyrum smithii 3 6 4 3,6,4
## 97 Penstemon barbatus 3 2 8 3,2,8
## 98 Penstemon linearis 7 5 1 7,5,1
## 99 Penstemon virgatus 3 2 1 3,2,1
## 100 Phlox speciosa 3 4 3 3,4,3
## 101 Pinus ponderosa 7 6 8 7,6,8
## 102 Plantago argyraea 4 1 1 4,1,1
## 103 Poa compressa 2 5 5 2,5,5
## 104 Poa fendleriana 7 5 2 7,5,2
## 105 Poa pratensis 1 2 6 1,2,6
## 106 Polygonum aviculare 4 7 5 4,7,5
## 107 Polygonum douglasii 4 1 1 4,1,1
## 108 Portulaca oleracea 6 8 6 6,8,6
## 109 Potentilla crinita 2 4 5 2,4,5
## 110 Potentilla hippiana 5 1 1 5,1,1
## 111 Potentilla subviscosa 2 8 1 2,8,1
## 112 Pseudognaphalium macounii 6 1 6 6,1,6
## 113 Cymopterus lemmonii 5 1 5 5,1,5
## 114 Quercus gambelii 5 6 8 5,6,8
## 115 Rosa woodsii 3 5 4 3,5,4
## 116 Salsola tragus 8 7 1 8,7,1
## 117 Schizachyrium scoparium 4 6 3 4,6,3
## 118 Senecio actinella 2 4 1 2,4,1
## 119 Senecio eremophilis 8 6 1 8,6,1
## 120 Solidago nana 4 5 3 4,5,3
## 121 Solidago velutina 3 2 2 3,2,2
## 122 Sporobolus interruptus 4 5 3 4,5,3
## 123 Symphyotrichum ascendens 1 1 1 1,1,1
## 124 Taraxacum officinale 8 3 5 8,3,5
## 125 Thalictrum fendleri 3 6 4 3,6,4
## 126 Townsendia exscapa 1 8 3 1,8,3
## 127 Tragopogon dubius 1 2 4 1,2,4
## 128 Trifolium longipes 8 4 2 8,4,2
## 129 Verbena bracteata 1 1 2 1,1,2
## 130 Verbascum thapsus 1 6 6 1,6,6
## 131 Vicia americana 8 7 7 8,7,7
## 132 Vicia pulchella 8 7 7 8,7,7
## 133 Viola canadensis 2 1 5 2,1,5
##
## $summary
## # A tibble: 99 × 3
## niche_code species_count species_list
## <chr> <int> <chr>
## 1 1,1,1 3 Eriogonum pharnaceoides, Ipomopsis multiflora, Symp…
## 2 1,1,2 2 Hymenoxys richardsonii, Verbena bracteata
## 3 1,1,3 2 Erigeron formosissimus, Linum australe
## 4 1,1,4 1 Ceanothus fendleri
## 5 1,1,5 5 Agoseris glauca, Arenaria fendleri, Chaetopappa eri…
## 6 1,2,3 1 Centaurea diffusa
## 7 1,2,4 4 Ambrosia psilostachya, Bromus ciliatus, Elymus elym…
## 8 1,2,5 1 Machaeranthera canescens
## 9 1,2,6 4 Artemisia campestris, Artemisia carruthii, Laenneci…
## 10 1,4,2 1 Gaillardia pinnatifida
## # ℹ 89 more rows
# Perform continuous niche scheme
continuous_result <- NPT_continuous(data = PFF_traits, dimension = dimension)
continuous_result ## $PCA_first
## pc1_percent pc1_major_eigenvector pc2_percent
## Grow 51.37787 SRL 31.00776
## Survive 80.23625 Height 15.44795
## Reproductive 94.74834 SeedMass 4.49701
## pc2_major_eigenvector
## Grow Leaf_Nmass
## Survive Root_CN
## Reproductive FltDur
##
## $PCA_second
## PC1 PC2
## pc1.Grow 0.1089647 -0.5590265
## pc2.Grow 0.6488371 0.1751767
## pc1.Survive -0.1162702 0.5380610
## pc2.Survive -0.6329334 -0.2313366
## pc1.Reproductive -0.3185430 0.5385830
## pc2.Reproductive 0.2270331 0.1539628
##
## $result
## Call: rda(X = P)
##
## -- Model Summary --
##
## Inertia Rank
## Total 0.04545
## Unconstrained 0.04545 6
##
## Inertia is variance
##
## -- Eigenvalues --
##
## Eigenvalues for unconstrained axes:
## PC1 PC2 PC3 PC4 PC5 PC6
## 0.015689 0.012709 0.008295 0.004579 0.003609 0.000573
Plant Trait Network
Plant Trait Networks (PTNs) provide a framework for understanding complex interdependencies among plant traits by treating traits as nodes and their associations as edges within a network. The PTN module, implemented as described by He et al. (2020), offers functions to calculate correlations (PTN_corr()), generate networks (PTN()), quantify network metrics (PTN_metrics()), and visualize networks (PTN_plot()). The package supports phylogenetic correction to account for species’ shared evolutionary history. Significant correlations are selected based on defined thresholds, and network metrics, including both node-level and network-level parameters, are used to analyze trait interdependence.
# Load packages
library(MultiTraits)
# Load and prepare data
data(PFF)
rownames(PFF) <- PFF$species
PFF_traits <- PFF[, c("Leaf_area","LDMC","SLA", "Leaf_Cmass","Leaf_Nmass",
"Leaf_CN","Leaf_Pmass", "Leaf_NP","Leaf_CP")]
# Perform log transformation of data and remove missing values
PFF_traits <- log(na.omit(PFF_traits))
head(PFF_traits)## Leaf_area LDMC SLA Leaf_Cmass Leaf_Nmass
## Achillea millefolium 5.480639 3.295837 2.079442 3.717467 0.6523252
## Agoseris glauca 6.253829 2.833213 2.995732 3.752324 0.5423243
## Agropyron desertorum 5.669881 3.737670 2.197225 3.725211 0.8415672
## Ambrosia psilostachya 6.194405 2.995732 2.772589 3.648578 0.8415672
## Androsace septentrionalis 3.931826 2.639057 3.433987 3.709417 0.6678294
## Antennaria parvifolia 4.189655 3.295837 2.564949 3.785779 0.4252677
## Leaf_CN Leaf_Pmass Leaf_NP Leaf_CP
## Achillea millefolium 3.091042 -1.0216512 1.791759 4.736198
## Agoseris glauca 3.218876 -0.9416085 1.386294 4.691348
## Agropyron desertorum 2.890372 -2.8134107 3.951244 6.928538
## Ambrosia psilostachya 2.833213 -1.3862944 2.639057 5.411646
## Androsace septentrionalis 3.091042 -1.2378744 1.945910 4.875197
## Antennaria parvifolia 3.401197 -1.7147984 2.197225 5.529429##
## Phylogenetic tree with 133 tips and 121 internal nodes.
##
## Tip labels:
## Hymenopappus mexicanus, Heliomeris multiflora, Bahia dissecta, Gaillardia pinnatifida, Hymenoxys richardsonii, Hymenoxys hoopsii, ...
## Node labels:
## Spermatophyta, mrcaott2ott121, eudicotyledons, Pentapetalae, mrcaott248ott557, mrcaott248ott650, ...
##
## Rooted; includes branch length(s).# Calculate trait correlations using specified thresholds
PTN_corr(traits_matrix=PFF_traits, rThres = 0.2, pThres = 0.05,method = "pearson",
phylo_correction = TRUE,phylo_tree = PFF_tree)## Warning: Phylogenetic tree is not binary. Converting to binary tree using multi2di()...
## Tree converted to binary successfully.
## $corr
## Leaf_area Leaf_Pmass SLA Leaf_Nmass Leaf_CN
## Leaf_area 1.0000000 0.43912732 0.07106310 0.29303640 -0.2770506
## Leaf_Pmass 0.4391273 1.00000000 0.12060634 0.18114586 -0.1622283
## SLA 0.0710631 0.12060634 1.00000000 0.26196776 -0.3165084
## Leaf_Nmass 0.2930364 0.18114586 0.26196776 1.00000000 -0.9761786
## Leaf_CN -0.2770506 -0.16222829 -0.31650840 -0.97617860 1.0000000
## Leaf_NP -0.1850404 -0.81553086 -0.02961639 0.26587312 -0.2712585
## Leaf_CP -0.3384473 -0.90624588 -0.17882705 -0.18159881 0.1847273
## LDMC -0.0586106 -0.21033983 -0.53223724 -0.06763656 0.1264002
## Leaf_Cmass 0.1708826 -0.05369696 -0.26471993 0.32997265 -0.1559343
## Leaf_NP Leaf_CP LDMC Leaf_Cmass
## Leaf_area -0.18504037 -0.3384473 -0.05861060 0.17088257
## Leaf_Pmass -0.81553086 -0.9062459 -0.21033983 -0.05369696
## SLA -0.02961639 -0.1788270 -0.53223724 -0.26471993
## Leaf_Nmass 0.26587312 -0.1815988 -0.06763656 0.32997265
## Leaf_CN -0.27125855 0.1847273 0.12640024 -0.15593428
## Leaf_NP 1.00000000 0.8903621 0.19732960 0.24660715
## Leaf_CP 0.89036211 1.0000000 0.28270267 0.17686543
## LDMC 0.19732960 0.2827027 1.00000000 0.37675591
## Leaf_Cmass 0.24660715 0.1768654 0.37675591 1.00000000
##
## $corrPos
## xName yName x y corr p.value
## 1 Leaf_area Leaf_Pmass 1 8 0.43912732 0
## 2 Leaf_area SLA 1 7 0.07106310 1
## 3 Leaf_area Leaf_Nmass 1 6 0.29303640 0
## 4 Leaf_area Leaf_CN 1 5 -0.27705059 0
## 5 Leaf_area Leaf_NP 1 4 -0.18504037 1
## 6 Leaf_area Leaf_CP 1 3 -0.33844728 0
## 7 Leaf_area LDMC 1 2 -0.05861060 1
## 8 Leaf_area Leaf_Cmass 1 1 0.17088257 1
## 9 Leaf_Pmass SLA 2 7 0.12060634 1
## 10 Leaf_Pmass Leaf_Nmass 2 6 0.18114586 1
## 11 Leaf_Pmass Leaf_CN 2 5 -0.16222829 1
## 12 Leaf_Pmass Leaf_NP 2 4 -0.81553086 0
## 13 Leaf_Pmass Leaf_CP 2 3 -0.90624588 0
## 14 Leaf_Pmass LDMC 2 2 -0.21033983 0
## 15 Leaf_Pmass Leaf_Cmass 2 1 -0.05369696 1
## 16 SLA Leaf_Nmass 3 6 0.26196776 0
## 17 SLA Leaf_CN 3 5 -0.31650840 0
## 18 SLA Leaf_NP 3 4 -0.02961639 1
## 19 SLA Leaf_CP 3 3 -0.17882705 1
## 20 SLA LDMC 3 2 -0.53223724 0
## 21 SLA Leaf_Cmass 3 1 -0.26471993 0
## 22 Leaf_Nmass Leaf_CN 4 5 -0.97617860 0
## 23 Leaf_Nmass Leaf_NP 4 4 0.26587312 0
## 24 Leaf_Nmass Leaf_CP 4 3 -0.18159881 1
## 25 Leaf_Nmass LDMC 4 2 -0.06763656 1
## 26 Leaf_Nmass Leaf_Cmass 4 1 0.32997265 0
## 27 Leaf_CN Leaf_NP 5 4 -0.27125855 0
## 28 Leaf_CN Leaf_CP 5 3 0.18472730 1
## 29 Leaf_CN LDMC 5 2 0.12640024 1
## 30 Leaf_CN Leaf_Cmass 5 1 -0.15593428 1
## 31 Leaf_NP Leaf_CP 6 3 0.89036211 0
## 32 Leaf_NP LDMC 6 2 0.19732960 1
## 33 Leaf_NP Leaf_Cmass 6 1 0.24660715 0
## 34 Leaf_CP LDMC 7 2 0.28270267 0
## 35 Leaf_CP Leaf_Cmass 7 1 0.17686543 1
## 36 LDMC Leaf_Cmass 8 1 0.37675591 0
##
## $arg
## $arg$type
## [1] "lower"# Perform Trait Network (TN) analysis
PTN_phylo_result <- PTN(traits_matrix = PFF_traits,
rThres = 0.2,
pThres = 0.05,
method = "pearson",
phylo_correction = TRUE,
phylo_tree = PFF_tree)## Warning: Phylogenetic tree is not binary. Converting to binary tree using multi2di()...
## Tree converted to binary successfully.## $node
## degree closeness betweenness clustering_coefficient
## Leaf_area 4 0.08333333 2.000000 0.3333333
## LDMC 4 0.08333333 3.000000 0.3333333
## SLA 4 0.08333333 1.833333 0.5000000
## Leaf_Cmass 4 0.08333333 1.166667 0.5000000
## Leaf_Nmass 5 0.09090909 2.416667 0.5000000
## Leaf_CN 4 0.08333333 1.083333 0.5000000
## Leaf_Pmass 4 0.08333333 1.083333 0.5000000
## Leaf_NP 5 0.09090909 3.333333 0.3000000
## Leaf_CP 4 0.08333333 1.083333 0.5000000
##
## $global
## edge_density diameter avg_path_length avg_clustering_coefficient modularity
## 1 0.5277778 2 1.472222 0.4354839 0.1717452set.seed(22)
# Create visualization plots of the trait network
PTN_plot(PTN_phylo_result, style = 1, vertex.size = 20, vertex.label.cex = 0.6)
Plant Trait Multilayer Network
Plant Trait Multilayer Networks (PTMNs) provide an innovative framework for understanding multidimensional plant trait coordination, addressing the limitations of traditional, single-layer plant trait networks (PTNs). As outlined by He et al. (2026), this approach systematically organizes traits into distinct layers based on organs or functional systems (e.g., leaves, stems, roots). A key strength of PTMNs is the ability to differentiate between intralayer edges, which represent relationships within a single system, and interlayer edges, which capture the crucial coordination between different systems. The MultiTraits PTMN module implements this framework with functions to construct networks (PTMN()), calculate specialized multilayer metrics such as interlayer degree and module association (PTMN_metrics()), and create visualizations that clearly distinguish layer structure (PTMN_plot()). This methodology is powerful for identifying key traits that integrate whole-plant functioning and for revealing complex strategies of plant adaptation.
# Load packages
library(MultiTraits)
# Load and prepare data
data(forest_invader_traits)
traits <- forest_invader_traits[, 6:73]
# Define the layers, grouping traits by organ or functional system
layers <- list(
shoot_dynamics = c("LeafDuration", "LeafFall50", "LeafRate_max",
"Chl_shade50", "LAgain", "FallDuration",
"LeafOut", "Chl_sun50", "EmergeDuration",
"LeafTurnover"),
leaf_structure = c("PA_leaf", "Mass_leaf", "Lifespan_leaf",
"Thick_leaf", "SLA", "Lobe", "LDMC",
"Stomate_size", "Stomate_index"),
leaf_metabolism = c("J_max", "Vc_max", "Asat_area", "CC_mass",
"LSP", "AQY", "CC_area", "Rd_area",
"Asat_mass", "WUE", "Rd_mass", "PNUE"),
leaf_chemistry = c("N_area", "Chl_area", "DNA", "Phenolics",
"Cellulose", "N_mass", "N_litter", "Chl_ab",
"Chl_mass", "N_res", "C_litter", "C_area",
"C_mass", "Ash", "Lignin", "Solubles",
"Decomp_leaf", "Hemi"),
root = c("NPP_root", "SS_root", "SRL", "RTD", "RDMC",
"NSC_root", "Decomp_root", "Starch_root",
"C_root", "N_root", "Lignin_root"),
stem = c("Latewood_diam", "Metaxylem_diam", "Earlywood_diam",
"NSC_stem", "Vessel_freq", "SS_stem", "Cond_stem",
"Starch_stem")
)
# Construct the Plant Trait Multilayer Network (PTMN)
ptmn_edges <- PTMN(traits, layers_list = layers, method = "pearson")
# Calculate network metrics for the PTMN
PTMN_metrics(ptmn_edges)## $node
## node layer interlayer_degree interlayer_closeness
## 5 CC_area leaf_metabolism 23 0.5957447
## 14 Chl_mass leaf_chemistry 23 0.6410256
## 26 LAgain shoot_dynamics 23 0.5979381
## 27 LDMC leaf_structure 22 0.5959596
## 40 Metaxylem_diam stem 22 0.6000000
## 46 N_mass leaf_chemistry 22 0.6329114
## 25 J_max leaf_metabolism 21 0.6021505
## 44 N_area leaf_chemistry 21 0.6097561
## 65 Thick_leaf leaf_structure 21 0.5959596
## 3 Asat_mass leaf_metabolism 20 0.6021505
## 6 CC_mass leaf_metabolism 20 0.6021505
## 66 Vc_max leaf_metabolism 20 0.6021505
## 2 Asat_area leaf_metabolism 19 0.5957447
## 7 C_area leaf_chemistry 19 0.5952381
## 33 LeafRate_max shoot_dynamics 19 0.5858586
## 55 Rd_mass leaf_metabolism 19 0.5773196
## 13 Chl_area leaf_chemistry 17 0.5813953
## 21 Earlywood_diam stem 17 0.5555556
## 22 EmergeDuration shoot_dynamics 17 0.5576923
## 30 LeafDuration shoot_dynamics 17 0.5686275
## 48 N_root root 17 0.5588235
## 43 NSC_stem stem 16 0.5607477
## 56 SLA leaf_structure 16 0.5514019
## 58 SS_root root 16 0.5533981
## 23 FallDuration shoot_dynamics 15 0.5686275
## 31 LeafFall50 shoot_dynamics 15 0.5471698
## 59 SS_stem stem 15 0.5454545
## 35 Lifespan_leaf leaf_structure 14 0.5221239
## 57 SRL root 14 0.5327103
## 67 Vessel_freq stem 14 0.5263158
## 1 AQY leaf_metabolism 13 0.5233645
## 50 PNUE leaf_metabolism 13 0.5185185
## 24 Hemi leaf_chemistry 12 0.5376344
## 15 Chl_shade50 shoot_dynamics 11 0.5087719
## 49 PA_leaf leaf_structure 11 0.5086207
## 51 Phenolics leaf_chemistry 11 0.5319149
## 53 RTD root 11 0.5229358
## 32 LeafOut shoot_dynamics 10 0.5043478
## 38 Lobe leaf_structure 9 0.5086207
## 45 N_litter leaf_chemistry 9 0.5319149
## 29 Latewood_diam stem 8 0.4838710
## 34 LeafTurnover shoot_dynamics 8 0.4957265
## 12 Chl_ab leaf_chemistry 7 0.5050505
## 47 N_res leaf_chemistry 7 0.4854369
## 4 Ash leaf_chemistry 6 0.4629630
## 10 C_root root 6 0.4596774
## 61 Starch_root root 6 0.4789916
## 68 WUE leaf_metabolism 6 0.4869565
## 8 C_litter leaf_chemistry 5 0.4807692
## 16 Chl_sun50 shoot_dynamics 5 0.4566929
## 19 Decomp_leaf leaf_chemistry 5 0.4716981
## 39 Mass_leaf leaf_structure 5 0.4796748
## 42 NSC_root root 5 0.4789916
## 62 Starch_stem stem 5 0.4615385
## 17 Cond_stem stem 4 0.4255319
## 36 Lignin leaf_chemistry 4 0.4347826
## 9 C_mass leaf_chemistry 3 0.4716981
## 18 DNA leaf_chemistry 3 0.4065041
## 52 RDMC root 3 0.4384615
## 60 Solubles leaf_chemistry 3 0.4273504
## 63 Stomate_index leaf_structure 3 0.4041096
## 64 Stomate_size leaf_structure 3 0.4125874
## 54 Rd_area leaf_metabolism 2 0.4409449
## 11 Cellulose leaf_chemistry 1 0.3875969
## 28 LSP leaf_metabolism 1 0.3612903
## 37 Lignin_root root 1 0.4014085
## 41 NPP_root root 1 0.3800000
## 20 Decomp_root root 0 0.3630573
## interlayer_clustering_coefficient
## 5 0.3794466
## 14 0.5256917
## 26 0.4071146
## 27 0.4242424
## 40 0.5194805
## 46 0.4848485
## 25 0.4714286
## 44 0.3619048
## 65 0.3761905
## 3 0.4736842
## 6 0.3473684
## 66 0.4473684
## 2 0.4795322
## 7 0.5146199
## 33 0.4619883
## 55 0.4619883
## 13 0.5147059
## 21 0.4411765
## 22 0.4411765
## 30 0.5441176
## 48 0.4558824
## 43 0.5083333
## 56 0.5500000
## 58 0.6083333
## 23 0.5904762
## 31 0.6000000
## 59 0.5238095
## 35 0.5054945
## 57 0.4065934
## 67 0.6263736
## 1 0.6153846
## 50 0.4615385
## 24 0.6818182
## 15 0.5454545
## 49 0.4363636
## 51 0.5818182
## 53 0.6545455
## 32 0.3333333
## 38 0.8333333
## 45 0.7777778
## 29 0.3571429
## 34 0.6785714
## 12 0.1904762
## 47 0.6666667
## 4 0.4000000
## 10 0.4000000
## 61 0.3333333
## 68 0.6000000
## 8 0.3000000
## 16 0.9000000
## 19 0.6000000
## 39 0.2000000
## 42 0.5000000
## 62 0.5000000
## 17 1.0000000
## 36 0.5000000
## 9 0.3333333
## 18 0.3333333
## 52 0.3333333
## 60 0.3333333
## 63 1.0000000
## 64 0.3333333
## 54 1.0000000
## 11 0.0000000
## 28 0.0000000
## 37 0.0000000
## 41 0.0000000
## 20 0.0000000
##
## $global
## Interlayer_edge_density Average_interlayer_path_length
## 1 0.2058047 1.98153
## Average_interlayer_clustering_coefficient Module_interlayer_association
## 1 0.4730529 0.8167989set.seed(42)
# Create a visualization of the PTMN
PTMN_plot(ptmn_edges, style = 1, vertex.size = 8,
vertex.label.cex = 0.5, edge.width = 2,
show.legend = FALSE)