Scripts from: Performance of generalized distance sampling models with temporary emigration: a simulation study

Generalized distance sampling (GDS) models are the distance sampling equivalent of temporary emigration N-mixture models. In addition to density and the perceptibility component of detection, both contain an additional parameter for availability for detection which becomes estimable when data from repeated 'visits' are available. GDS models thus account for open populations. This makes them more robust, since natural populations are hardly ever perfectly closed, arguably even over the course of a single breeding season. However, the performance of these models has not been tested thoroughly, and prior (unpublished) analyses suggested that biased estimates, especially for density (high) and availability (low), may typically occur under certain conditions. We conducted three simulation studies and found that bias arises in low-information scenarios, particularly with low sample sizes and low parameter values. Our simulations enable us to determine \"estimation frontiers\", which separate sa..., , # Title of Dataset: Performance of generalized distance sampling models with temporary emigration: a simulation study

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## Description of the data

The study was not based on real data. All data used in the study were generated using simulation code.

## Code/Software

The dataset contains four R files with simulation codes:

1. Code_1_simGDS_function.R- R code with data simulation function;
2. Code_2_gds_Sim1.R - R code to perform Simulation 1 with varying number of sites (20–500) and of surveys (2–10);
3. Code_3_gds_Sim2.R - R code to perform Simulation 2 with varying number of sites (20–500), surveys (2–10), density λ (0.01-2 individuals per hectare), availability ϕ (0.01-1), and the parameter that governs the decline of the detection function over distance σ (20-200 meters);
4. Code_4_gds_Sim3.R - R code to perform Simulation 3 with effects of three continuous covariates on all the three parameters (λ,ϕ,σ).

First, run Code_1. The other codes are independent, but the first simul...,