paper/paper.md

In arcaravaggi/remBoot: Random Encounter Modelling for Density Estimation and Variance

title: 'remBoot: An R package for Random Encounter Modelling' tags: - Random Encounter Model - REM - density estimates - camera trap - ecology - bootstrapping authors: - name: Anthony Caravaggi orcid: 0000-0002-1763-8970 affiliation: 1 affiliations: - name: School of Biological Sciences, Queen's University Belfast, Belfast BT9 7BL, UK index: 1 date: 10 January 2017 bibliography: paper.bib

Summary

The Random Encounter Model (REM) allows researchers to calculate population densities from camera trap data for species which do not exhibit individually-identifiable markings [@Rowcliffe2008] such as tapir (Tapirus terrestris; [@OliveiraSantos2010], pine marten (Martes martes; [@Manzo2012], and hares (Lepus sp.; [@Caravaggi2016]).

Density (D) is linearly scaled with trapping rate, based on two biological variables and two camera characteristics: g = average animal group size; y = number of detections; t = survey effort (i.e. camera hours); v = average distance travelled by the species in 24 hours; r = radial distance to the animal; and, theta = zone of detection (Fig. 1; [@Rowcliffe2008]).

remBoot is the first package to implement REM calculations in R. The package also contains functions which allow the calculation of variance (standard deviation [SD] and/or 95% confidence intervals [CI]; Fig. 2). These calculations are computationally inexpensive and can be applied to datasets of considerable size. Densities and associated variances can be calculated for one or more sites concurrently, streamlining the analytical process.

Acknowledgments

Thanks to Kevin Keenan for his work on implementing REMs in R early on, when the language was still very new to me, and to Bryce Mecum (@amoeba) for his efforts in reviewing this package.

Figure 1. Data inputs (y and t) and parameters (r, v and theta) required for the calculation of population density estimates from camera trap data by the Random Encounter Model (REM). g = average animal group size; y = number of detections; t = survey effort (i.e. camera hours); v = average distance travelled by the species in 24 hours; r = radial distance to the animal; and, theta = zone of detection.

Figure 2. Animal densities (individuals.km2) with associated variances (SD) calculated via Random Encounter Models, using remBoot.

References

arcaravaggi/remBoot documentation built on May 10, 2019, 12:49 p.m.