Genetic Monitoring for Managers


Type of GEM

Category 1a GeM Project Example: Estimating population growth rates

Monitoring of grizzly bear population trends and demography using DNA mark-recapture methods in the Owikeno Lake area of British Columbia - Boulanger et al. 2004

One of the best examples of estimating trends using molecular markers and Category I monitoring is the study by Boulanger et al. (2004) on the influence of local salmon availability on grizzly bear (Ursus arctos) abundance in the Owikeno Lake area of British Columbia, Canada. Traditionally, estimating bear abundance trend was limited by the short sampling seasons, cost, the inability to capture and follow or recapture sufficient numbers of animals, and the risk in conducting such work (Boulanger and McLellan 2001). Boulanger et al. (2004) used DNA obtained from hair snares to mark and track individual bears, while simultaneously using traditional methods to monitor salmon availability in multiple parts of their study area. Although using DNA-based CMR to estimate single year abundance is becoming commonplace with bears (Paetkau 2003, Proctor et al. 2005), Boulanger et al.'s approach was unique in that they had a five year data set (1998-2002) and could use the open mark-recapture model of Pradel (1996) with the entire data set to estimate apparent survival, rates of recapture, additions (immigration and births) and changes in abundance (lambda).

lambda fig 1

The authors found that, in all three sampling areas, bears had significant negative population growth in the first two years of the study, followed by positive growth in the next two. The combined experimental design enabled the authors to elucidate the underlying mechanisms behind this trend: apparent survival and rates of addition were both related to salmon availability.

lambda fig 2
Model-averaged estimates of the rate of emigration/deaths (apparent survival), births/immigration (addition), and lambda from the reparameterized Pradel model. The width of the circles is proportion to the availability of salmon for a given year. Estimates from the Chuckwalla–Ambach (broken circles), Neechanz–Genesee (shaded circles), and Washwash–Inziana (solid circles) sampling areas are shown. Error bars represent unconditional SE estimates. [from Boulanger et al. 2004]

Beyond investigating the role of salmon escapement levels on grizzly bear abundance, these models allow use of covariates describing sampling conditions (e.g., effort) and allowing for the heterogeneity in capture probabilities that likely exists in such datasets (Boulanger et al. 2002). This study demonstrates how genetic sampling can be used in a formal modeling approach, over multiple years, to make inferences regarding changes in populations that may not be possible with simple count indices that remain common in wildlife research.