divDyn (version 0.8.3)

survivors: Proportions of survivorship

Description

This function will calculate both forward and backward survivorship proportions from a given occurrence dataset or FAD-LAD matrix.

Usage

survivors(
  x,
  tax = "genus",
  bin = "stg",
  method = "forward",
  noNAStart = FALSE,
  fl = NULL
)

Value

A numeric matrix of survivorship probabilities.

Arguments

x

(data.frame) The data frame containing fossil occurrences.

tax

(character) The variable name of the occurring taxa (variable type: factor or character).

bin

(character) The variable name of the time slice numbers of the particular occurrences (variable type: numeric). Bin numbers should be in ascending order,can contain NAs, it can start from a number other than 1 and must not start with 0.

method

(character) Either "forward" or "backward".

noNAStart

(logical) Useful when the dataset does not start from bin 1. Then noNAStart=TRUE will cut the first part of the resulting table, so the first row will contain the estimates for the lowest bin number.

fl

(matrix or data.frame). If so desired, the function can be run on an FAD-LAD dataset, output by the fadlad function.

Details

Proportions of survivorship are great tools to visualize changes in the composition of a group over time (Raup, 1978). The curves show how a once coexisting set of taxa, called a cohort, loses its participants (forward survivorship) as time progress, or gains its elements as time is analyzed backwards. Each value corresponds to a cohort in a bin (a) and one other bin (b). The value expresses what proportion of the analyzed cohort (present together in bin a) is present in bin b.

References:

Raup, D. M. (1978). Cohort analysis of generic survivorship. Paleobiology, 4(1), 1-15.

Examples

data(corals)
surv<-survivors(corals, tax="genus", bin="stg", method="forward")

# plot
data(stages)
tsplot(stages, shading="series", boxes="sys", xlim=c(260,0), 
  ylab="proportion of survivors present", ylim=c(0.01,1),plot.args=list(log="y"))
  
for(i in 1:ncol(surv)) lines(stages$mid, surv[,i])