Test if two variables jointly control changes in fossil data

Generates synthetic variables with different proportion of two environmental variables, and tests how much variance in the fossil data reconstructions of these synthetic variables explain.

Usage

jointsig(spp, fos, var1, var2, method = "randomTF", n = 99, r = 32, ...)

# S3 method for class 'js'
plot(x, names.v1, names.v2, ...)

Arguments

spp: Data frame of modern training set species data, transformed as required, for example with sqrt
fos: Data frame of fossil species data, with same species codes and transformations as spp
var1: Training set environmental variable 1
var2: Training set environmental variable 2
method: Which significance test to use. Current option are randomTF and obs.cor. The latter may give strange results - use with caution.
n: number of random training sets used to generate the null model
r: How many synthetic variables to make. More is better but slower
...: Other arguments to plot
x: Output from jointsig
names.v1: Vector length 2 with names of the end members of the first environmental variable, e.g., c("cold", "warm") for temperature.
names.v2: Ditto for the second variable.

Value

A list with components

PCA: The unconstrained ordination of the fossil data.
preds: A list of the containing the reconstructions for each environmental variable.
MAX: Proportion of the variance explained by the first axis of the unconstrained ordination. This is the maximum amount that a reconstruction of a single variable can explain.
EX: The proportion of the variance in the fossil data explained by each reconstruction.
sim.ex: The proportion of variance explained by each of the random environmental variables.
sig: The p-value of each reconstruction.

Details

With method="randomTF", the function calculates the proportion of variance in the fossil data explained by transfer function reconstructions of synthetic variables. The synthetic variables are composed of two environmental variables, weighted between -1 and +1, so to represent a circle. This is compared with a null distribution of the proportion of variance explained by reconstructions based on random environmental variables. Any transfer function in the rioja library can be used. With method="obs.cor", the aim is the same, but the function reports the correlation between the species weighted average optima on the synthetic variables and the species first axis scores. This option has some pathological behaviour and should probably be avoided.

Functions

plot(js): Plot js object

References

Unpublished method - use with caution. Can give spurious results with weighted averaging.

Author

Richard Telford richard.telford@bio.uib.no

Examples

require(rioja)
data(SWAP)
data(RLGH)

rlgh.js <- jointsig(
  spp = sqrt(SWAP$spec),
  fos = sqrt(RLGH$spec),
  var1 = SWAP$pH,
  var2 = sample(SWAP$pH),
  method = "randomTF",
  n = 49, r = 32, fun = WA, col = 1
)
# nonsense second variable

plot(rlgh.js, c("acid", "alkaline"), c("down", "up"))