unstructured_pop | R Documentation |

This function simulates an unstructured population with temporally autocorrelated vital rates (survival and fertility). In other words, this function will show you the dynamics over time of a population whose survival and fertility is stochastic, but also correlated to the survival and fertility in the previous year, respectively. The assumptions of the simulation are that the population is asexually reproducing or female-only, survival and fertility are the same at all ages / stages, and that individuals continue to be reproductively capable until they die. The function includes demographic stochasticity as well as environmental stochasticity, and does not support density dependence at this time.

```
unstructured_pop(
start,
timesteps,
survPhi,
fecundPhi,
survMean,
survSd,
fecundMean,
fecundSd
)
```

`start` |
The starting population size. |

`timesteps` |
The number of timesteps you want to simulate. Individuals are added and killed off every timestep according to the survival and fertility rates. In ecological applications, timesteps are usually years, but theoretically they can be any length of time. |

`survPhi` |
The temporal autocorrelation of survival. 0 is white noise (uncorrelated), positive values are red noise (directly correlated) and negative values are blue noise (inversely correlated). |

`fecundPhi` |
The temporal autocorrelation of fecundity. As above. |

`survMean` |
The mean survival from timestep to timestep. Must be a value between 0 (all individuals die) and 1 (all individuals live). |

`survSd` |
The standard deviation of the survival from timestep to timestep. Must be a value between 0 and 1. |

`fecundMean` |
The mean fertility: mean offspring produced by each individual per timestep. |

`fecundSd` |
The standard deviation of the fertility. |

Be advised that not all combinations of values will work. If you set survival and fertility unrealistically high, the population size will tend toward infinity and the simulation will fail because the numbers are too large to handle. Use your common sense as a demographer / population biologist.

A data frame with four variables: timestep, population (total individuals alive at the start of the timestep), newborns (new individuals born this timestep), and survivors (individuals who survive this timestep).

```
series1 <- unstructured_pop(start = 20, timesteps = 10, survPhi = 0.7, fecundPhi = -0.1,
survMean = 0.6, survSd = 0.52, fecundMean = 1.2, fecundSd = 0.7)
head(series1)
```

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