Calculate CSR strategy for multiple plant species

Description

This function processes a dataframe containing leaf traits (LA, LDMC, SLA) and applies the Strate_CSR function to each row.

Usage

CSR(data)

Arguments

Value

A dataframe with the original input data and additional columns:

• C: Competitive strategy score

• S: Stress-tolerant strategy score

• R: Ruderal strategy score

• type: The dominant CSR strategy type

References

1. Grime, J.P. (1974). Vegetation classification by reference to strategies. Nature, 250, 26–31.

2. Pierce, S., Negreiros, D., Cerabolini, B.E.L., Kattge, J., Díaz, S., et al. (2017). A global method for calculating plant CSR ecological strategies applied across biomes world-wide. Funct Ecol, 31: 444-457.

Examples

LA <- c(369615.7, 11.8, 55.7, 36061.2, 22391.8, 30068.1, 31059.5, 29895.1)
LDMC <- c(25.2, 39.7, 13.3, 35.5, 33.2, 36.1, 35.2, 34.9)
SLA <- c(17.4, 6.6, 34.1, 14.5, 8.1, 12.1, 9.4, 10.9)
traits <- data.frame(LA, LDMC, SLA)
CSR(data = traits)

Create a ternary plot of CSR strategies

Description

This function creates a ternary plot of Competition-Stress-Ruderal (CSR) strategies using the ggtern package.

Usage

CSR_plot(
  data,
  point_size = 3,
  point_shape = 21,
  expand_margin = 1,
  custom_colors = c("#57C4AD", "#E6E1BC", "#EDA348", "#006165", "#F8E3EF", "#FFFF99",
    "#376CB1", "#B2589B", "#DC4325", "#F16C4E", "#85B97B", "#FCEB2D", "#04A89E",
    "#F49320", "#8F88C1", "#33B5D9", "#440153", "#B29BBD", "#28C865")
)

Arguments

Details

This function implements:

• Configurable point attributes (size, shape)

• Adjustable plot boundaries

• User-defined color schemes for categories

• Automated legend column optimization

• Standardized legend positioning

• Reference grid and directional indicators

Value

A ggplot object representing the ternary plot of CSR strategies.

References

1. Grime, J.P. (1974). Vegetation classification by reference to strategies. Nature, 250, 26–31.

2. Pierce, S., Negreiros, D., Cerabolini, B.E.L., Kattge, J., Díaz, S., et al. (2017). A global method for calculating plant CSR ecological strategies applied across biomes world-wide. Funct Ecol, 31: 444-457.

Examples

data(PFF)
head(PFF)
traits <- data.frame(LA=PFF$Leaf_area, LDMC=PFF$LDMC, SLA=PFF$SLA)
head(traits)
result <- CSR(data = traits)
head(result)
CSR_plot(data=result)

Calculate LHS (Leaf-Height-Seed) Strategy

Description

This function calculates the LHS strategy for plant species based on their SLA (Specific Leaf Area), Height, and Seed Mass values.

Usage

LHS(data)

Arguments

Details

The function calculates median values for SLA, Height, and SeedMass from the input data. It then determines the LHS strategy for each species based on whether its trait values are less than or equal to (S) or greater than (L) the median values.

The resulting strategy is a combination of three letters (S or L) representing Leaf (SLA), Height, and Seed (SeedMass) respectively, separated by hyphens.

Value

A data frame with an additional column 'LHS_strategy' containing the calculated LHS strategy.

References

1. Westoby, M. (1998). A leaf-height-seed (LHS) plant ecology strategy scheme. Plant and Soil, 199, 213–227. https://doi.org/10.1023/A:1004327224729

2. Yang, J., Wang, Z., Zheng, Y., & Pan, Y. (2022). Shifts in plant ecological strategies in remnant forest patches along urbanization gradients. Forest Ecology and Management, 524, 120540. https://doi.org/10.1016/j.foreco.2022.120540

Examples

data(PFF)
pff <- PFF[, c("SLA", "Height", "SeedMass")]
result <- LHS(pff)
head(result)

Generate a 3D scatterplot of plant traits

Description

This function creates a three-dimensional scatterplot of plant traits (Specific Leaf Area, Height, and Seed Mass) based on the Leaf-Height-Seed (LHS) plant ecology strategy scheme.

Usage

LHS_plot(
  data,
  colors = c("#30123BFF", "#4777EFFF", "#1BD0D5FF", "#62FC6BFF", "#D2E935FF",
    "#FE9B2DFF", "#DB3A07FF", "#7A0403FF"),
  log_transform = TRUE
)

Arguments

Details

The function performs the following steps: Checks if the input data contains the required columns. Converts LHS_strategy to a factor. Optionally log-transforms the data based on the log_transform parameter. Creates a 3D scatterplot using (potentially log-transformed) values of SLA, Height, and Seed Mass. Colors points based on LHS strategy. Adds a legend to identify different LHS strategies.

Value

A 3D scatterplot of SLA, Height, and Seed Mass (optionally log-transformed), with points colored by LHS strategy.

References

1. Westoby, M. (1998). A leaf-height-seed (LHS) plant ecology strategy scheme. Plant and Soil, 199, 213–227. https://doi.org/10.1023/A:1004327224729

2. Yang, J., Wang, Z., Zheng, Y., & Pan, Y. (2022). Shifts in plant ecological strategies in remnant forest patches along urbanization gradients. Forest Ecology and Management, 524, 120540. https://doi.org/10.1016/j.foreco.2022.120540

Examples

data(PFF)
pff <- PFF[, c("SLA", "Height", "SeedMass")]
result <- LHS(pff)
LHS_plot(result)
LHS_plot(result, log_transform = FALSE)

Create a table of Leaf-Height-Seed (LHS) strategy types

Description

This function generates a data frame containing different plant growth strategies based on the Leaf-Height-Seed (LHS) scheme. Each strategy is described by a combination of traits and their corresponding ecological interpretation.

Usage

LHS_strategy_scheme()

Value

A data frame with two columns:

type

Character vector of LHS strategy combinations (e.g., "L-L-L", "L-L-S", etc.)

strategy

Character vector describing the ecological strategy for each type

References

1. Westoby, M. (1998). A leaf-height-seed (LHS) plant ecology strategy scheme. Plant and Soil, 199, 213–227. https://doi.org/10.1023/A:1004327224729

2. Yang, J., Wang, Z., Zheng, Y., & Pan, Y. (2022). Shifts in plant ecological strategies in remnant forest patches along urbanization gradients. Forest Ecology and Management, 524, 120540. https://doi.org/10.1016/j.foreco.2022.120540

Examples

LHS_strategy_scheme()

Perform nested principal component analysis (PCA of PCAs) for ecological niche periodicity

Description

This function conducts a ‘PCA of PCAs’ to analyze ecological niche periodicity based on multiple trait dimensions.

Usage

NPT(data, dimension)

Arguments

Details

The function performs the following steps:

1. Checks and cleans input data, removing rows with NA values

2. Conducts first-level PCA for each trait dimension

3. Extracts PC1 and PC2 scores from each first-level PCA

4. Combines all PC scores and performs a second-level PCA

5. Returns summary statistics and results from both levels of analysis

Value

A list containing three elements:

References

1. Winemiller, K. O., Fitzgerald, D. B., Bower, L. M., & Pianka, E. R. (2015). Functional traits, convergent evolution, and periodic tables of niches. Ecology letters, 18(8), 737-751. https://doi.org/10.1111/ele.12462

2. Yu, R., Huang, J., Xu, Y., Ding, Y., & Zang, R. (2020). Plant functional niches in forests across four climatic zones: Exploring the periodic table of niches based on plant functional traits. Frontiers in Plant Science, 11, 841. https://doi.org/10.3389/fpls.2020.00841

Examples

data(PFF)
PFF[,3:20] <- log(PFF[,3:20])
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")
)
result <- NPT(data = PFF, dimension = traits_dimension)
result

Plot results from nested principal component analysis

Description

This function creates a visualization of the results from the nested principal component analysis (NPT function) for ecological niche periodicity.

Usage

NPT_plot(pca_obj, group = NULL)

Arguments

Details

The function creates a biplot that includes:

1. Sample points colored by group

2. Species scores represented as arrows

3. Species labels positioned using ggrepel to avoid overlapping

The plot also includes dashed lines at x=0 and y=0, and displays the percentage of variance explained by each principal component on the axes.

Value

A ggplot object representing a biplot of species scores and sample points.

References

1. Winemiller, K. O., Fitzgerald, D. B., Bower, L. M., & Pianka, E. R. (2015). Functional traits, convergent evolution, and periodic tables of niches. Ecology letters, 18(8), 737-751. https://doi.org/10.1111/ele.12462

2. Yu, R., Huang, J., Xu, Y., Ding, Y., & Zang, R. (2020). Plant functional niches in forests across four climatic zones: Exploring the periodic table of niches based on plant functional traits. Frontiers in Plant Science, 11, 841. https://doi.org/10.3389/fpls.2020.00841

Examples

data(PFF)
PFF[,3:20] <- log(PFF[,3:20])
PFF <- na.omit(PFF)
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")
)
npt_result <- NPT(data = PFF, dimension = traits_dimension)
dev.new() # A window that is too small will interfere with the drawing.
          # Optionally, you can set the drawing window to pop up automatically.
NPT_plot(npt_result$result)
NPT_plot(npt_result$result, PFF$family)

Plant Functional Traits Dataset from Ponderosa Pine Forests Flora (PFF)

Description

A dataset containing functional traits for 133 plant species commonly found in southwestern USA Pinus ponderosa var. scopulorum P. & C. Lawson (ponderosa pine) forests.

Usage

PFF

Format

A data frame with 137 rows and 20 variables:

family

Plant family

species

Plant species

Height

Canopy height (cm)

Leaf_area

Leaf area (mm^2)

LDMC

Leaf dry matter content (%)

SLA

Specific leaf area (mm^2/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 (% dry mass)

Leaf_Nmass

Leaf nitrogen content (% dry mass)

Leaf_CN

Leaf carbon to nitrogen ratio

Leaf_Pmass

Leaf phosphorus content (% dry mass)

Leaf_NP

Leaf nitrogen to phosphorus ratio

Leaf_CP

Leaf carbon to phosphorus ratio

Root_Cmass

Root carbon content (% dry mass)

Root_Nmass

Root nitrogen content (% dry mass)

Root_CN

Root carbon to nitrogen ratio

Details

This dataset contains measurements of a core set of functional traits that reflect aspects of each species' ability to disperse, establish, acquire water and nutrients, and photosynthesize. Traits include specific leaf area (SLA), height, seed mass, specific root length (SRL), leaf and fine root nitrogen concentration, leaf phosphorus concentration, and leaf dry matter content (LDMC). Julian flowering date and flowering duration were also obtained for each species. Leaf litter decomposition rates were measured on 103 species.

Source

Laughlin, D. C., Leppert, J. J., Moore, M. M., & Sieg, C. H. (2010). A multi-trait test of the leaf-height-seed plant strategy scheme with 133 species from a pine forest flora. Functional Ecology, 24(3), 485-700.

Examples

data(PFF)
head(PFF)

Calculate CSR (Competition-Stress-Ruderal) Strategy

Description

This function calculates the CSR strategy based on leaf traits.

Usage

Strate_CSR(LA, LDMC, SLA)

Arguments

Value

A list containing:

• C: Proportion of competition strategy

• S: Proportion of stress-tolerance strategy

• R: Proportion of ruderal strategy

• type: Type of CSR strategy

References

1. Grime, J.P. (1974). Vegetation classification by reference to strategies. Nature, 250, 26–31.

2. Pierce, S., Negreiros, D., Cerabolini, B.E.L., Kattge, J., Díaz, S., et al. (2017). A global method for calculating plant CSR ecological strategies applied across biomes world-wide. Funct Ecol, 31: 444-457.

Examples

Strate_CSR(LA = 369615.7, LDMC = 25.2, SLA = 17.4)

Generate Plant Trait Network

Description

This function creates a network graph from a plant trait correlation matrix, applying thresholds for correlation strength and significance.

Usage

TN(traits_matrix, rThres = 0.2, pThres = 0.05, method = "pearson")

Arguments

Details

The function performs the following steps:

1. Calculates Pearson correlation coefficients and p-values for the trait matrix.

2. Applies correlation coefficient and p-value thresholds to filter relationships.

3. Constructs an unweighted undirected graph from the filtered correlation matrix.

4. Removes self-loops and isolated nodes from the graph.

5. Adds correlation coefficients as edge attributes.

Value

Returns an igraph object representing the trait network.

References

1. He, N., Li, Y., Liu, C., et al. (2020). Plant trait networks: improved resolution of the dimensionality of adaptation. Trends in Ecology & Evolution, 35(10), 908-918. https://doi.org/10.1016/j.tree.2020.06.003

2. Li, Y., Liu, C., Sack, L., Xu, L., Li, M., Zhang, J., & He, N. (2022). Leaf trait network architecture shifts with species‐richness and climate across forests at continental scale. Ecology Letters, 25(6), 1442-1457. https://doi.org/10.1111/ele.14009

Examples

data(PFF)
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")]
PFF_traits <- na.omit(PFF_traits)
head(PFF_traits)
Tn_result <- TN(traits_matrix = PFF_traits, rThres = 0.2, pThres = 0.05, method = "pearson")
Tn_result

Calculate and Visualize Plant Trait Correlation Network

Description

This function calculates correlation coefficients for given plant traits and generates a correlation network plot.

Usage

TN_corr(traits_matrix, rThres = 0.2, pThres = 0.05, method = "pearson")

Arguments

Details

The function first calculates Pearson correlation coefficients between traits, then adjusts p-values using the FDR method. Finally, it plots the correlation network using the corrplot package. The plot displays only correlations that meet both the correlation coefficient and p-value thresholds.

Value

Returns a correlation network plot object.

References

1. He, N., Li, Y., Liu, C., et al. (2020). Plant trait networks: improved resolution of the dimensionality of adaptation. Trends in Ecology & Evolution, 35(10), 908-918. https://doi.org/10.1016/j.tree.2020.06.003

2. Li, Y., Liu, C., Sack, L., Xu, L., Li, M., Zhang, J., & He, N. (2022). Leaf trait network architecture shifts with species‐richness and climate across forests at continental scale. Ecology Letters, 25(6), 1442-1457. https://doi.org/10.1111/ele.14009

Examples

data(PFF)
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")]
PFF_traits <- na.omit(PFF_traits)
head(PFF_traits)
TN_corr(traits_matrix = PFF_traits, rThres = 0.3, pThres = 0.01,method = "pearson")

Calculate Node and Global Metrics for Trait Networks

Description

This function computes various node and global metrics for a trait network graph.

Usage

TN_metrics(graph)

Arguments

Value

A list containing two data frames:

References

1. He, N., Li, Y., Liu, C., et al. (2020). Plant trait networks: improved resolution of the dimensionality of adaptation. Trends in Ecology & Evolution, 35(10), 908-918. https://doi.org/10.1016/j.tree.2020.06.003

2. Li, Y., Liu, C., Sack, L., Xu, L., Li, M., Zhang, J., & He, N. (2022). Leaf trait network architecture shifts with species‐richness and climate across forests at continental scale. Ecology Letters, 25(6), 1442-1457. https://doi.org/10.1111/ele.14009

Examples

data(PFF)
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")]
PFF_traits <- na.omit(PFF_traits)
head(PFF_traits)
Tn_result <- TN(traits_matrix = PFF_traits, rThres = 0.2, pThres = 0.05)
TN_metrics(Tn_result)

Plot Trait Network Graph

Description

This function visualizes the trait network graph generated by the TN function.

Usage

TN_plot(graph, style = 1, vertex.size = 20, vertex.label.cex = 0.6)

Arguments

Details

The function uses the cluster_edge_betweenness algorithm to identify communities in the graph and assigns community membership to vertices. It offers two plotting styles:

• Style 1: Plots the community structure.

• Style 2: Plots the graph in a circular layout with vertex colors representing communities. The vertex size and label size can be customized using vertex.size and vertex.label.cex parameters respectively.

Value

An object of class igraph. This function generates a visualization of the trait network graph. When style = 1, it displays a community structure plot. When style = 2, it displays a circular layout plot where vertex colors represent community membership, edge thickness represents correlation strength, and edge color represents the sign of the correlation (black for positive, red for negative).

References

1. He, N., Li, Y., Liu, C., et al. (2020). Plant trait networks: improved resolution of the dimensionality of adaptation. Trends in Ecology & Evolution, 35(10), 908-918. https://doi.org/10.1016/j.tree.2020.06.003

2. Li, Y., Liu, C., Sack, L., Xu, L., Li, M., Zhang, J., & He, N. (2022). Leaf trait network architecture shifts with species‐richness and climate across forests at continental scale. Ecology Letters, 25(6), 1442-1457. https://doi.org/10.1111/ele.14009

Examples

data(PFF)
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")]
PFF_traits <- na.omit(PFF_traits)
head(PFF_traits)
Tn_result <- TN(traits_matrix = PFF_traits, rThres = 0.2, pThres = 0.05)
TN_plot(Tn_result, style = 1, vertex.size = 20, vertex.label.cex = 0.6)
TN_plot(Tn_result, style = 2, vertex.size = 20, vertex.label.cex = 0.6)