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Trajectory plots

Source: R/trajectoryPlots.R
trajectoryPlot.Rd

Set of plotting functions for Ecological Trajectory Analysis:

Usage

trajectoryPCoA(
  x,
  traj.colors = NULL,
  axes = c(1, 2),
  survey.labels = FALSE,
  time.labels = FALSE,
  ...
)

trajectoryPlot(
  coords,
  sites,
  surveys = NULL,
  times = NULL,
  traj.colors = NULL,
  axes = c(1, 2),
  survey.labels = FALSE,
  time.labels = FALSE,
  ...
)

Arguments

x

An object of class trajectories.

traj.colors

A vector of colors (one per site). If selection != NULL the length of the color vector should be equal to the number of sites selected.

axes

The pair of principal coordinates to be plotted.

survey.labels

A boolean flag to indicate whether surveys should be added as text next to arrow endpoints

time.labels

A boolean flag to indicate whether times should be added as text next to arrow endpoints

...

Additional parameters for function arrows.

coords

A data.frame or matrix where rows are ecosystem states and columns are coordinates in an arbitrary space

sites

A vector indicating the site corresponding to each ecosystem state.

surveys

A vector indicating the survey corresponding to each ecosystem state (only necessary when surveys are not in order).

times

A numeric vector indicating survey times.

Value

Function trajectoryPCoA returns the result of calling cmdscale.

Details

  • Function trajectoryPCoA performs principal coordinates analysis (cmdscale) and draws trajectories in the ordination scatterplot.

  • Function trajectoryPlot draws trajectories in a scatter plot corresponding to the input coordinates.

References

De Cáceres M, Coll L, Legendre P, Allen RB, Wiser SK, Fortin MJ, Condit R & Hubbell S. (2019). Trajectory analysis in community ecology. Ecological Monographs 89, e01350.

See also

trajectoryMetrics, transformTrajectories, cmdscale, cyclePCoA

Author

Miquel De Cáceres, CREAF

Anthony Sturbois, Vivarmor nature, Réserve Naturelle nationale de la Baie de Saint-Brieuc

Examples

#Description of sites and surveys
sites <- c("1","1","1","2","2","2")
surveys <- c(1,2,3,1,2,3)
  
#Raw data table
xy<-matrix(0, nrow=6, ncol=2)
xy[2,2]<-1
xy[3,2]<-2
xy[4:6,1] <- 0.5
xy[4:6,2] <- xy[1:3,2]
xy[6,1]<-1

#Define trajectory data
x <- defineTrajectories(dist(xy), sites, surveys)
  
#Draw trajectories using original coordinates
trajectoryPlot(xy, sites, surveys, 
               traj.colors = c("black","red"), lwd = 2)


#Draw trajectories in a PCoA
trajectoryPCoA(x, 
               traj.colors = c("black","red"), lwd = 2)   

  
#Should give the same results if surveys are not in order 
#(here we switch surveys for site 2)
temp <- xy[5,]
xy[5,] <- xy[6,]
xy[6,] <- temp
surveys[5] <- 3
surveys[6] <- 2
  
trajectoryPlot(xy, sites, surveys, 
               traj.colors = c("black","red"), lwd = 2)   

 
x <- defineTrajectories(dist(xy), sites, surveys)
trajectoryPCoA(x, 
               traj.colors = c("black","red"), lwd = 2)