In order to determine the present state of recovery of whale populations, it is critical that we are able to accurately reconstruct the history of their exploitation. This is typically performed by fitting an abundance trajectory through three points in time; the first is prior to exploitation, when the population is assumed to have been at carrying capacity; the second is at the point of minimum abundance (the ‘bottleneck', PDF), which is generally constrained to be greater than zero; the third is provided by current abundance estimates from the recovering population. While the third point in the trajectory tends to be well characterized, little attention has been given to estimating the point of minimum abundance. We have described a general analytical framework for estimating the minimum size of a population bottleneck using current day genetic (mitochondrial haplotype) samples from the population. This framework was applied to the southern right whale (Jackson et al 2008) and to south Pacific humpbacks (Jackson et al 2006) in a more basic form.
For the southern right whales, we ran simulations over a variety of demographic scenarios, all of which suggested substantial loss of haplotype richness as a result of 19th century exploitation. We also discovered that the population growth rates used in previous assessment of this population by the International Whaling Commission predicted bottleneck sizes that were implausibly narrow, given present day genetic diversity. High remnant sequence diversity indicated pre-exploitation abundances (carrying capacities) much larger than predicted by the population dynamic model. This discrepancy may stem from underestimation of the historical catch record, which is known to be incomplete. Our results point to the need to better integrate evolutionary processes into population dynamic models to account for uncertainty in catch record, the influence of maternal fidelity on metapopulation dynamics and the potential for inverse density dependence (an ‘Allee effect') in severely depleted populations.
This research was presented to the Scientific Committee of the International Whaling Commission in Anchorage, Alaska (2007). While the above research used genetic information to provide a lower constraint, or floor, on the point of minimum abundance, we are currently working to incorporate genetic information into the Bayesian population dynamics model in order to provide a general prior distribution on the point of minimum abundance (Nmin).
Jackson, J.A., C. Olavarria and C. S. Baker. 2007. Estimating the minimum historical population size of Southern Hemisphere humpback whales using diversity of mtDNA haplotypes. 58th Annual Meeting of the International Whaling Commission, Document SC/58/SH22.
Jackson, J.A., A. Zerbini, P. Clapham, C. Garrigue, N. Hauser, M. Poole and C.S. Baker. 2006. A Bayesian assessment of humpback whales on breeding grounds of Eastern Australia and Oceania (IWC Stocks E, E1, E2 and F). Workshop for the Comprehensive Assessment of Southern Hemisphere Humpback Whales, Hobart. Document SC/A06/HW52.