Estimating animal density using camera traps without the need for individual recognition

Abstract

1.Density estimation is of fundamental importance in wildlife management. The use of camera traps to estimate animal density has so far been restricted to capture-recapture analysis of species with individually identifiable markings. This study developed a method that eliminates the requirement for individual recognition of animals by modelling the underlying process of contact between animals and cameras. 2. The model provides a factor that linearly scales trapping rate with density, depending on two key biological variables (average animal group size and day range) and two characteristics of the camera sensor (distance and angle within which it detects animals). 3. We tested the approach in an enclosed animal park with known abundances of four species, obtaining accurate estimates in three out of four cases. Inaccuracy in the fourth species was because of biased placement of cameras with respect to the distribution of this species. 4. Synthesis and applications. Subject to unbiased camera placement and accurate measurement of model parameters, this method opens the possibility of reduced labour costs for estimating wildlife density and may make estimation possible where it has not been previously. We provide guidelines on the trapping effort required to obtain reasonably precise estimates.

Scatter hoarding by the Central American agouti: a test of optimal cache spacing theory

“…Agouti abundance was estimated within each plot with camera trapping. Because we could not recognize individual agoutis in the photographs, we followed the principles outlined in Rowcliffe et al (2008); we placed cameras in randomized locations and presumed that the difference in photo rates reflects differences in the densities of animals. We used two motion-triggered camera traps (Reconyx RC55 Rapid Fire IR, Reconyx, Inc., Holmen, WI, U.S.A.) within each plot, and moved them to a new random location every 8 days.…”

Influence of camera-trap sampling design on mammal species capture rates and community structures in southeastern Brazil

“…Despite these limitations and recommendations, the index of relative abundance, without calibration, obtained from the rate or frequency of capture has been widely used in camera-trap studies (e.g., Jácomo et al 2004, O'Brien et al 2003, Silveira et al 2003, Trolle & Kéry 2005, Weckel et al 2006, Kasper et al 2007, Rowcliffe et al 2008, Rovero & Marshall 2009, without consideration of the variations in species capture probability (Gu & Swihart 2004). These issues are relevant and need to be better understood because camera-trap data have been used in management and conservation studies around the world, including ratings of the distribution and monitoring of threatened species.…”

“…the use of camera-trap records as surrogates for abundance and population density of species whose individuals cannot be identified (Carbone et al 2001, Jennelle et al 2002, Yasuda 2004, Larrucea et al 2007, Srbek-Araujo & Chiarello 2007, Rowcliffe et al 2008, Rovero & Marshall 2009, Harmsen et al 2010. Despite these limitations and recommendations, the index of relative abundance, without calibration, obtained from the rate or frequency of capture has been widely used in camera-trap studies (e.g., Jácomo et al 2004, O'Brien et al 2003, Silveira et al 2003, Trolle & Kéry 2005, Weckel et al 2006, Kasper et al 2007, Rowcliffe et al 2008, Rovero & Marshall 2009, without consideration of the variations in species capture probability (Gu & Swihart 2004).…”

Monitoring wild pig populations: a review of methods

“…Size-wise, camera stations are probably most similar to the short-length tracking plots. Despite relatively high initial costs for cameras, this method opens the possibility of reduced labor cost for monitoring wildlife abundance (Bengsen et al 2011;Silveira et al 2003;Rowcliffe et al 2008). The use of cameras has obvious advantages in efficiency over conventional survey techniques (like tracking plots): fewer field visits and reduced staff labor time and more data resulting from continued activation and reduced downtime from adverse weather conditions (Minta and Mangel 1989;De Bondi et al 2010;Meek et al 2012).…”