Javascript must be enabled for the correct page display

Correction of camera-trap capture rates for detection bias, based on body mass and diet

He, Shuiqing (2021) Correction of camera-trap capture rates for detection bias, based on body mass and diet. Master's Research Project 2, Ecology and Evolution.

[img]
Preview
Text
mEE_2019_ShuiqingHe.pdf

Download (1MB) | Preview
[img] Text
toestemming.pdf
Restricted to Registered users only

Download (117kB)

Abstract

Camera traps have become an important tool for estimating species abundance and density in medium- to large-sized mammals. However, though the camera trap rate should contain information about the density of a species, the probability of detection may differ between species hence may not reflect the relative abundance within communities. The aim of this study is to create a simple method for assessing an unbiased estimate of relative abundance of species within mammal communities from camera-trap data. Regression equations between three components of detectability – effective detection distance (EDD), speed and activity level, and body mass were used to estimate scaling relationships which were then combined to correct the camera trap rate. The camera trap rates before and after the correction were compared with independently estimated density for a number of mammal communities to see if the corrected capture rate improved the fit to the density data. The correction model in this study made a better estimation of the relative abundance of the species within communities. However, the correction performed better in herbivores than in faunivores. Several improvements can be included in future work. It may be better to divide faunivores into separate carnivore and omnivore diet groups. More factors that might affect the scaling relationships between body mass and related components can be considered.

Item Type: Thesis (Master's Research Project 2)
Supervisor name: Olff, H.
Degree programme: Ecology and Evolution
Thesis type: Master's Research Project 2
Language: English
Date Deposited: 26 Jan 2022 08:41
Last Modified: 26 Jan 2022 08:42
URI: https://fse.studenttheses.ub.rug.nl/id/eprint/26394

Actions (login required)

View Item View Item