survivors {divDyn} | R Documentation

## 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)

### 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 `NA`s, 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])

[Package divDyn version 0.8.0 Index]