How few whales were there after whaling? Inference from contemporary mtDNA diversity

Authors

  • J. A. JACKSON,

    1. School of Biological Sciences, University of Auckland, Private Bag 92019, Auckland, New Zealand,
    2. Marine Mammal Institute and Department of Fisheries and Wildlife, Hatfield Marine Science Center, Oregon State University, Newport, Oregon 97365, USA
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  • N. J. PATENAUDE,

    1. School of Biological Sciences, University of Auckland, Private Bag 92019, Auckland, New Zealand,
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  • E. L. CARROLL,

    1. School of Biological Sciences, University of Auckland, Private Bag 92019, Auckland, New Zealand,
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  • C. SCOTT BAKER

    1. School of Biological Sciences, University of Auckland, Private Bag 92019, Auckland, New Zealand,
    2. Marine Mammal Institute and Department of Fisheries and Wildlife, Hatfield Marine Science Center, Oregon State University, Newport, Oregon 97365, USA
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Errata

This article is corrected by:

  1. Errata: Erratum Volume 18, Issue 19, 4134–4135, Article first published online: 15 September 2009

  • Box 1 The IWC, NMP, RMP, CLA and RMS

    For assessing the status of whale stocks, the Scientific Committee of the International Whaling Commission (IWC) has generally adopted an age- and sex-structured, density-dependent, deterministic logistic population model, referred to as BALEEN II (de la Mare 1986; Punt 1999). The model is currently implemented in two programs, ‘hitter’ and ‘fitter’. The former shoots a population trajectory through a population estimate for a specified year, while the latter uses several years of whaling catches and catch effort data together with abundance estimates to estimate past changes in population size and resilience (i.e. the ability of the stock to recover from depletion). BALEEN II has been used with differing degrees of complexity depending on available data and has incorporated both maximum likelihood and Bayesian estimators (e.g. Raftery et al. 1995; Wade 2002; Whitehead 2002).

    A simple age- and sex-aggregated logistic population model describes the relationship between the following variables

    image

    where Ny represents the population abundance in year y, K the population initial carrying capacity (Ny in year 0), z the density dependent exponent and Cy the total catch in year y; r represents the intrinsic growth rate of the population.

    In belated recognition of overexploitation of most whale stocks, the IWC agreed in 1982 to a moratorium on commercial hunting to take effect in 1986. The agreement called for the Comprehensive Assessment of depleted whale populations (including an historical reconstruction of population trends and abundance) and the development of a revised procedure to calculate catch limits for any future commercial whaling. Prior to the moratorium, the so-called New Management Procedure (NMP) was designed to provide maximum catches by maintaining populations at the level of maximum sustainable yield level (MSYL) but failed to account for uncertainty in many population parameters. The NMP is nominally still in force but has been in abeyance during the moratorium. The Revised Management Procedure (RMP), developed since the moratorium to replace the NMP, was designed to provide for reasonable levels of exploitation while minimizing the risk of population depletion or extinction. At the core of the RMP is the Catch Limit Algorithm (CLA) which requires relatively straightforward information on population size (with an associated coefficient of variation), past and present catches and population structure (Donovan 2002). In brief, the CLA works by fitting the basic logistic model to a time series of abundance data and incorporates feedback from new data (e.g. abundance estimates or new catches) to refine estimates of the required parameters (Cooke 1999). Uncertainty is accounted for using a quasi-Bayesian approach to generate a posterior probability distribution of parameter values. The recommended catch limit is a chosen percentile (less than the median) of the posterior distribution of a specified function of the estimated stock level and the model parameters. The nominal population level below which catches are prohibited is set to 54% of K for consistency with the IWC's previous management procedure. The actual fraction of K at which exploitation can resume is expected to be in the range 50–90% of K, depending on the history of exploitation and the values of the unknown parameters, particularly rmax. Unlike its predecessor, the RMP does not attempt to hold a stock to a particular target level, but is designed such that depletion of a stock to less than 50% of K would be very unlikely. Before hunting can be initiated, extensive computer-based simulations, referred to as Implementation Simulation Trials, are designed to evaluate the performance of the RMP for a particular stock under a range of plausible scenarios including uncertainty (usually in terms of fixed values) in current abundance, pre-exploitation abundance, catch history, stock structure, MSYL and MSYR (IWC 1999). Although the RMP has been tested extensively in silico and found to be robust to a wide range of uncertainty in most assumptions (Cooke 1999), it will not be implemented until the IWC agrees to a Revised Management Scheme (RMS). This Scheme is intended to include the many conditions required for robust and transparent observation and inspection [such as a DNA register to verify all catches records (Baker et al. 2000)]. A new procedure for management of aboriginal subsistence whaling (the AWNP) is under development by the Scientific Committee. Whaling programmes conducted under provisions for scientific research are not subject to the conditions of the RMP or the CLA (Clapham et al. 2003).

C. Scott Baker, Fax: +1-541-867-0345; E-mail: scott.baker@oregonstate.edu

Abstract

Reconstructing the history of exploited populations of whales requires fitting a trajectory through at least three points in time: (i) prior to exploitation, when abundance is assumed to be at the maximum allowed by environmental carrying capacity; (ii) the point of minimum abundance or ‘bottleneck’, usually near the time of protection or the abandonment of the hunt; and (iii) near the present, when protected populations are assumed to have undergone some recovery. As historical abundance is usually unknown, this trajectory must be extrapolated according to a population dynamic model using catch records, an assumed rate of increase and an estimate of current abundance, all of which have received considerable attention by the International Whaling Commission (IWC). Relatively little attention has been given to estimating minimum abundance (Nmin), although it is clear that genetic and demographic forces at this point are critical to the potential for recovery or extinction of a local population. We present a general analytical framework to improve estimates of Nmin using the number of mtDNA haplotypes (maternal lineages) surviving in a contemporary population of whales or other exploited species. We demonstrate the informative potential of this parameter as an a posteriori constraint on Bayesian logistic population dynamic models based on the IWC Comprehensive Assessment of the intensively exploited southern right whales (Eubalaena australis) and published surveys of mtDNA diversity for this species. Estimated historical trajectories from all demographic scenarios suggested a substantial loss of mtDNA haplotype richness as a result of 19th century commercial whaling and 20th century illegal whaling by the Soviet Union. However, the relatively high rates of population increase used by the IWC assessment predicted a bottleneck that was implausibly narrow (median, 67 mature females), given our corrected estimates of Nmin. Further, high levels of remnant sequence diversity (theta) suggested that pre-exploitation abundance was larger than predicted by the logistic model given the catch record, which is known to be incomplete. Our results point to a need to better integrate evolutionary processes into population dynamic models to account for uncertainty in catch records, the influence of maternal fidelity on metapopulation dynamics, and the potential for inverse density dependence (an ‘Allee effect’) in severely depleted populations.

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