simMHB | R Documentation |

Function simulates MHB (Monitoring häufige Brutvögel) lookalike data. MHB is the Swiss breeding bird survey that is the source of many classic data sets (e.g. Kéry & Royle, 2016, 2021). This survey was launched in 1999 and for a total of 267 1km2 quadrats laid out in an approximate grid over Switzerland. 2 or 3 surveys are conducted in each breeding season (mid-April to early July) on a quadrat-specific, constant route averaging 4-6 km and all birds detected are mapped, thus yielding replicated counts of unmarked individuals.

The data are simulated under the assumptions of a binomial N-mixture model where for `lambda`

we can specify a log-linear trend over the years and we can account for site-level random effects in both the intercept and the slopes of the log-linear model.

For detection probability we have currently a constant average or a logit-linear trend over the years, with no further heterogeneity.

simMHB(nsites = 267, nsurveys = 3, nyears = 25, mean.lam = 1, mean.beta = 0.03, sd.lam = c(0.5, 0.05), mean.p = 0.6, beta.p = 0.1, show.plot = TRUE)

`nsites` |
number of sites included in the survey. |

`nsurveys` |
number of replicate surveys at each site in each year. |

`nyears` |
number of years. |

`mean.lam` |
intercept of expected abundance. |

`mean.beta` |
average slope of log(lambda) on year. |

`sd.lam` |
a length 2 vector, the SDs of the Normal distribution from which random site effects for the intercept and the slope in the log-linear model for lambda are drawn randomly. |

`mean.p` |
value of constant detection probability per survey (or intercept of the logit-linear model for p). |

`beta.p` |
slope of the logit(p) in year. |

`show.plot` |
if TRUE, the output will be displayed graphically. |

A list with the arguments used and the following components:

`alpha ` |
vector with intercept used for the log-linear model for lambda for each site. |

`beta ` |
vector with slope used for the log-linear model for lambda for each site. |

`lam ` |
sites x years matrix with the expected number of animals at each site. |

`N ` |
sites x years matrix with the realized number of animals at each site. |

`totalN ` |
vector with the total number of animals in each year. |

`p ` |
vector with the probability of detection in each year. |

`C ` |
sites x surveys x years array with the counts of animals detected. |

Marc Kéry

Kéry, M., Royle, A. (2016) *Applied Hierarchical Modeling in Ecology* Vol 1, Academic Press.

Kéry, M., Royle, A. (2021) *Applied Hierarchical Modeling in Ecology* Vol 2, Academic Press.

Schaub, M., Kéry, M. (2022) *Integrated Population Models*, Academic Press, section 4.3.3.

# Explicit default values for all function arguments str(dat <- simMHB(nsites=267, nsurveys=3, nyears=25, mean.lam=1, mean.beta=0.03, sd.lam=c(0.5, 0.05), mean.p=0.6, beta.p=0.1, show.plot=TRUE)) str(dat <- simMHB()) # Same, implicit str(dat <- simMHB(nsites=1000)) # More sites str(dat <- simMHB(nsurveys=10)) # More surveys str(dat <- simMHB(nyears=50)) # More years str(dat <- simMHB(mean.lam=5)) # Higher mean abundance str(dat <- simMHB(mean.beta=-0.03)) # Population declines str(dat <- simMHB(sd.lam=c(0, 0))) # No site variability in lambda str(dat <- simMHB(mean.p=1)) # Perfect detection str(dat <- simMHB(mean.p=0.6, beta.p=0)) # Constant p = 0.6 str(dat <- simMHB(mean.p=0.6, beta.p=-0.2)) # Declining p str(dat <- simMHB(show.plot=FALSE)) # No plots (when used in simulations)

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