A random encounter model for wildlife density estimation with vertically oriented camera traps DOI Creative Commons
Shuiqing He, J. Marcus Rowcliffe, Hao Lin

et al.

Remote Sensing in Ecology and Conservation, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 1, 2024

Abstract The random encounter model (REM) estimates animal densities from camera‐trap data by correcting capture rates for a set of biological variables the animals (average group size, speed and activity level) characteristics camera sensors. REM has been widely used setups in which cameras are mounted on trees or other structures aimed parallel to ground. Here, we modify formula accommodate an alternative field view acquired with vertically oriented traps, type deployment avoid theft damage. We show how calculations can be adapted account different detection zone minor modifications. find that effective area close rectangle dimensions influenced properties Fresnel lens camera's motion sensor, body mass species height camera. parameters remain same. tested modified (vREM) applying it wildlife collected traps Bardia National Park, Nepal. further validated was best approximated as shape using maximum likelihood estimation. Density obtained broadly matched independent density nine previous studies varying sizes four orders magnitude. conclude these modifications allow effectively mammal estimation wide range sizes, traps.

Language: Английский

Assessing trends in population size of three unmarked species: A comparison of a multi‐species N‐mixture model and random encounter models DOI Creative Commons
Martijn Bollen, Pablo Palencia, Joaquín Vicente

et al.

Ecology and Evolution, Journal Year: 2023, Volume and Issue: 13(10)

Published: Oct. 1, 2023

Estimation of changes in abundances and densities is essential for the research, management, conservation animal populations. Recently, technological advances have facilitated surveillance populations through adoption passive sensors, such as camera traps (CT). Several methods, including random encounter model (REM), been developed estimating unmarked but require additional information. Hierarchical abundance models, N-mixture (NMM), can estimate without performing fieldwork do not explicitly area effectively sampled. This obscures interpretation its requires users to focus on relative measures instead. Hence, main objective our study evaluate if REM NMM yield consistent results qualitatively. Therefore, we compare trends: (i) between species, (ii) years (iii) across obtained from annual density/abundance estimates three species (fox, wild boar red deer) central Spain monitored by a trapping network five consecutive winter periods. We reveal that provided density same order magnitude boar, foxes deer. Assuming Poisson detection process was important control inflation frequently detected species. Both methods consistently ranked (between trend), did always agree ranks yearly within single population nor linear trends (across trend). Our suggest are generally when range variability large, become inconsistent smaller.

Language: Английский

Citations

2

A random encounter model for wildlife density estimation with vertically oriented camera traps DOI Creative Commons
Shuiqing He, J. Marcus Rowcliffe, Hao Lin

et al.

Remote Sensing in Ecology and Conservation, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 1, 2024

Abstract The random encounter model (REM) estimates animal densities from camera‐trap data by correcting capture rates for a set of biological variables the animals (average group size, speed and activity level) characteristics camera sensors. REM has been widely used setups in which cameras are mounted on trees or other structures aimed parallel to ground. Here, we modify formula accommodate an alternative field view acquired with vertically oriented traps, type deployment avoid theft damage. We show how calculations can be adapted account different detection zone minor modifications. find that effective area close rectangle dimensions influenced properties Fresnel lens camera's motion sensor, body mass species height camera. parameters remain same. tested modified (vREM) applying it wildlife collected traps Bardia National Park, Nepal. further validated was best approximated as shape using maximum likelihood estimation. Density obtained broadly matched independent density nine previous studies varying sizes four orders magnitude. conclude these modifications allow effectively mammal estimation wide range sizes, traps.

Language: Английский

Citations

0