"Which camera trap type and how many do I need?" A review of camera features and study designs for a range of wildlife research applications

By F. Rovero, F. Zimmermann, D. Berzi, Paul D. Meek. 2013

435 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/e827171948ebea5802e30be37cacd8484bdb9cc5

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ecological Modeling

1. Timm Haucke
2. Volker Steinhage
3. Patrick A. Jansen
4. Hjalmar S. Kühl
5. T. Hofmeester
6. Paul D. Meek
7. S. T. Buckland
8. Marie-Lyne Després-Einspenner

In Ecology

1. Volker Steinhage
2. Timm Haucke
3. Hjalmar S. Kühl
4. Patrick A. Jansen
5. T. Hofmeester
6. J. Marcus Rowcliffe
7. E. Howe
8. Marie-Lyne Després-Einspenner

In Molecular Biology

1. Timm Haucke
2. Volker Steinhage
3. Hjalmar S. Kühl

Citations in the corpus

• A simple method for estimating the effective detection distance of camera traps (2017, background): “Furthermore, most PIR sensors are more sensitive in the middle of the field of view (FOV) than at the edges (Rovero et al. 2013), which also results in differences in detectability between large and small species (Rowcliffe et al.”
• A review of factors to consider when using camera traps to study animal behavior to inform wildlife ecology and conservation (2020, neutral): “For example, CTs vary in the sensitivity of their passive infrared detection band, leading to marked differences in how animals are detected given their speed, position, time of day, and ambient temperature (Rovero et al., 2013; Rovero & Zim-mermann, 2016).”
• Distance sampling with camera traps (2017, background): “…of the trigger speed, the effective horizontal angle of the sensor relative to the AOV of the camera (which varies among CT models) and possibly reduced sensitivity of the sensor at the periphery of its horizontal range (Rowcliffe et al. 2011; Rovero et al. 2013; Rovero & Zimmermann 2016).”
• The history of wildlife camera trapping as a survey tool in Australia (2015, background): “Current researchers have access to a diverse range of camera traps and equipment, providing better tools for the collection and analysis of image-based data (Rovero et al. 2013; Meek et al. 2014b).”
• Exploiting Depth Information for Wildlife Monitoring (2021, background): “…RGB-D camera traps and RGB-D instance segmentation to wildlife and animal monitoring (cf. surveys of Kays et al. (2020), Caravaggi et al. (2020), Wearn and Glover-Kapfer (2019), Schneider et al. (2018), Scotson et al. (2017), Li et al. (2014), Rovero et al. (2013), Rowcliffe and Carbone (2008)).”