Distance sampling with camera traps

Howe, Eric J.; Buckland, Stephen T.; Després‐Einspenner, Marie‐Lyne; Kühl, Hjalmar S.

doi:10.1111/2041-210x.12790

Cited by 180 publications

(391 citation statements)

References 39 publications

(104 reference statements)

Supporting

Mentioning

383

Contrasting

Unclassified

Order By: Relevance

“…A more widely accessible and long-term economical alternative is the rigorous application of visual data acquisition, particularly remote camera trapping in combination with distance sampling [25] or capture-recapture models [35,58,67]. Remote camera trapping can effectively record all apes in a given region [35].…”

Section: Monitoring In Ecology Todaymentioning

confidence: 99%

See 1 more Smart Citation

Towards Automated Visual Monitoring of Individual Gorillas in the Wild

Brust

Burghardt

Groenenberg

et al. 2017

2017 IEEE International Conference on Computer Vision Workshops (ICCVW)

View full text Add to dashboard Cite

In this paper we report on the context and evaluation of a system for an automatic interpretation of sightings of individual western lowland gorillas (Gorilla gorilla gorilla) as captured in facial fieldVisual data acquisition in the field often captures sufficient information to establish encounters with individual gorillas. However, relevant information is locked within the pixel patterns measured, usually requiring expert knowledge and time-consuming efforts for identification. Computer vision can help to extract gorilla identities by performing automated species detection, followed by individual facial identification. We show that standard deep learning models combined with a traditional SVM classifier can be used for this task. To assist encounter processing, predictions can be presented graphically with known population information as shown.

show abstract

Section: Monitoring In Ecology Todaymentioning

confidence: 99%

“…In addition, once images are interpreted, statistical tools [25] applied to visual sighting data can be used to estimate abundance in a study area. However, the manual effort required to conduct such studies currently limits their application [12].…”

Section: Introductionmentioning

confidence: 99%

Towards Automated Visual Monitoring of Individual Gorillas in the Wild

Brust

Burghardt

Groenenberg

et al. 2017

2017 IEEE International Conference on Computer Vision Workshops (ICCVW)

View full text Add to dashboard Cite

show abstract

“…Dividing the extrapolated abundance estimates by the maximum number of detection events thus provides a 'snapshot' or 'frozen-intime' view of the number of animals in the area, similar to Distance sampling methods [26].…”

Section: Maximum Number Of Detectionsmentioning

confidence: 99%

Estimation of Species Area Abundance from Point Abundance Data, Using Effective Detection Areas from Camera Traps

Rademaker¹,

Ej²,

Mja³

2017

J Biodivers Endanger Species

View full text Add to dashboard Cite

Estimations of species abundance are a common goal of wildlife monitoring surveys, but debate remains as to which methods are theoretically and practically most useful. Abundance-induced heterogeneity (AIH) models developed in the early 2000s allowed estimation of point abundance from repeated presence-absence data (e.g. occupancy models), and advanced estimation of point abundances of unmarked species. AIH models, however, do not provide an estimate of the effective detection area sampled. Therefore the absolute number of individuals in a survey area cannot be estimated directly. Recently, methods have become available to determine the effective detection area sampled by camera traps. Our objective was to present a novel method to estimate the absolute number of individuals of a species in an area from point abundance data using effective detection areas from camera traps. This would make AIH models available for population estimates. We applied this newly developed Species Area Abundance (SAA) model to a 3-month camera trapping data set of Bawean warty pigs (Sus blouchi) from Indonesia, and compared the result to an independent Random Encounter Model (REM) estimate from the same data. Population sizes and uncertainties estimated by the SAA and the REM model were comparable. Differences in density estimations between the REM and SAA model were not significant when mean group size was included in the REM. The less restrictive assumptions regarding camera trap placement of the SAA model compared to the REM might make it more practical to study cryptic and unmarked animal populations. Further studies are needed to determine the accuracy and practicality of the SAA model using a range of differrent sampling designs and focus species.

show abstract

“…Scientists use cameras in animal biology allowing multiple research objectives to be addressed including ecology and behaviour (O'Connell et al, 2011;Trolliet et al, 2014;Howe et al, 2017). More recently, researchers increasingly use camera traps to study wildlife, including research on elephant behaviour (Ngama et al, 2016;Howe et al, 2017;Smit et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…More recently, researchers increasingly use camera traps to study wildlife, including research on elephant behaviour (Ngama et al, 2016;Howe et al, 2017;Smit et al, 2017). The aim of this study was to use camera trap to better understand how elephants react to chilli pepper used as a repellent.…”

Section: Introductionmentioning

confidence: 99%

Camera traps to study the forest elephant's (Loxodonta cyclotis) response to chilli pepper repellent devices in Gamba, Gabon

et al. 2018

View full text Add to dashboard Cite

In Central Africa, Gabon is a forested country with a rich biodiversity where conflict between wild animals and humans is common and causes innumerable damage to crops. The worst crop raiders are elephants, which can destroy an entire crop in a single night. These raids threaten people's livelihoods as well as elephants because angry farmers often retaliate with killing campaigns against crop raiding elephants. To keep elephants out of farms the use of chilli pepper is recommended as a non-lethal method. But only a few studies have tested methods to use chilli pepper to deter elephants in Gabon. Results from this study give a starting point for understanding how forest elephants react to devices using chilli pepper as a deterrent based on sequential camera trap photos. A chilli pepper device that resulted in splashing concentrate on the elephant face proved to be the most effective at deterring elephants. Surprisingly, chilli pepper concentrate directly applied to mango fruits did not deter elephants from eating the fruits, although it probably caused some discomfort. To make effective deterrent devices with chilli pepper future works need to focus on exploring practices to reach the elephant face with the least, safe quantity of chilli pepper and which will have enough strong deterrent effect.

show abstract

Distance sampling with camera traps

Cited by 180 publications

References 39 publications

Towards Automated Visual Monitoring of Individual Gorillas in the Wild

Towards Automated Visual Monitoring of Individual Gorillas in the Wild

Estimation of Species Area Abundance from Point Abundance Data, Using Effective Detection Areas from Camera Traps

Camera traps to study the forest elephant's (Loxodonta cyclotis) response to chilli pepper repellent devices in Gamba, Gabon

Contact Info

Product

Resources

About