Common Biases in Density Estimation Based on Home Range Overlap with Reference to Pumas in Patagonia. Issue 1 (1st March 2014)

• Record Type: Journal Article
• Authors: Rinehart, Kurt A.; Elbroch, L. Mark; Wittmer, Heiko U.
• Is Part Of: Wildlife biology. Volume 20:Issue 1(2014)
• Page Start: 19
• Page End: 26
• Publication Date: 2014-03-01
• Subjects: Wildlife conservation; Wildlife management; Animal ecology; 590
• DOI: 10.2981/wlb.12100
• Languages: English
• ISSNs: 0909-6396

Abstract

Abstract : Density estimates are critical to proper population management and conservation, yet difficult to obtain for many wide‐ranging or cryptic species. One proven method used to quantify carnivore density, especially species difficult to individually identify from photos taken by camera traps, utilizes overlapping home ranges of individual animals in the study area. This method, however, may be particularly prone to residency and extrapolation biases. Residency bias occurs when the reference area for the density estimate is incorrect, and extrapolation bias occurs when scaling a density to a different spatial extent than that of the study area upon which the estimate was based. We used a simulation approach based on GPS locations to diagnose potential biases in published densities of pumas Puma concolor from Patagonia, where Franklin et al. 1999 (Biol. Conserv. 90: 33–40) reported 'minimum' densities of 6 and 30 pumas per 100 km 2, and Elbroch and Wittmer 2012a (Mammal. Biol. 77: 377–384) reported densities of 3.4 pumas per 100 km 2 . Using GPS data from the latter study we tested methods described in Franklin et al. (1999) and compared their outcomes. Our results showed that density estimates that do not account for residency bias resulted in severely inflated density estimates. Our findings also indicated that actual densities from Franklin et al. (1999) might have been an order of magnitude lower than reported, and therefore consistent with puma densities reported across the range of the species. Both studies introduced extrapolation bias by translating linearly their estimates to new spatial extents, although the magnitude of this bias was much less than that of residency bias. Our results underscore the need for rigorous accounting of residential space in estimating population density, and highlight the scale‐dependency of density estimates.