The Yangtze River dolphin or baiji (Lipotes vexillifer), an obligate freshwater odontocete known only from the middle-lower Yangtze River system and neighbouring Qiantang River in eastern China, has long been recognized as one of the world's rarest and most threatened mammal species. The status of the baiji has not been investigated since the late 1990s, when the surviving population was estimated to be as low as 13 individuals. An intensive six-week multi-vessel visual and acoustic survey carried out in November-December 2006, covering the entire historical range of the baiji in the main Yangtze channel, failed to find any evidence that the species survives. We are forced to conclude that the baiji is now likely to be extinct, probably due to unsustainable by-catch in local fisheries. This represents the first global extinction of a large vertebrate for over 50 years, only the fourth disappearance of an entire mammal family since AD 1500, and the first cetacean species to be driven to extinction by human activity. Immediate and extreme measures may be necessary to prevent the extinction of other endangered cetaceans, including the sympatric Yangtze finless porpoise (Neophocaena phocaenoides asiaeorientalis).

Brandon, J. R., Breiwick, J. M., Punt, A. E., and Wade, P. R. 2007. Constructing a coherent joint prior while respecting biological realism: application to marine mammal stock assessments. – ICES Journal of Marine Science, 64: 1085–1100. Bayesian estimation methods, employing the Sampling–Importance–Resampling algorithm, are currently used to perform stock assessments for several stocks of marine mammals, including the Bering–Chukchi–Beaufort Seas stock of bowhead whales (Balaena mysticetus) and walrus (Odobenus rosmarus rosmarus) off Greenland. However, owing to the functional relationships among parameters in deterministic age-structured population dynamics models, placing explicit priors on each life history parameter in addition to the population growth rate parameter results in an incoherent joint prior distribution (i.e. two different priors on the estimated parameters). One solution to constructing a coherent joint prior is to solve for juvenile survival analytically, using values generated from the prior distributions for the remaining parameters. However, certain combinations of model parameter values result in values for juvenile survival that are larger than adult survival, which is biologically implausible. Therefore, to respect biological realism, certain parameter values must be rejected for some or all the remaining parameters. This study investigates several alternative resampling schemes for obtaining a realistic joint prior distribution, given the constraint on survival rates. The sensitivity of assessment results is investigated for data-rich (bowhead) and data-poor (walrus) scenarios. The results based on limited data are especially sensitive to the choice of alternative resampling scheme.

No abstract

No abstract

Human-caused mortality due primarily to bycatch in fisheries is considered a major threat to some long-lived, slow-growing, and otherwise vulnerable marine species. Under many jurisdictions these species are designated as “protected”, and fisheries are subject to a management system that includes monitoring and assessment of bycatch impacts relative to management objectives. The US management system for marine mammals is one of the most sophisticated in the world, with a limit on human-caused mortality computed using the potential biological removal (PBR), formula. Fisheries are categorized according to their impact relative to PBR, and take reduction teams established to develop take reduction plans (TRPs) when bycatch exceeds PBR. The default values of the parameters of the PBR formula were selected in the late 1990s using management strategy evaluation (MSE), but the system, in particular the classification of fisheries, has yet to be evaluated in its entirety. A MSE framework is developed that includes the PBR formula, as well as the processes for evaluating whether a stock is “strategic”, assigning fisheries to categories, and implementing TRPs. The level of error associated with fisheries classification was found not to impact the ability to achieve the conservation objective established for a stock under the US Marine Mammal Protection Act (i.e. maintain or recover the stock to/at optimum sustainable population). However, this ability is highly dependent on the life history and absolute abundance of the species being managed, as well as on the premise that bycatch is reduced if bycatch is estimated to exceed the PBR. The probability of correctly classifying fisheries depends on both the coefficient of variations (CVs) of the estimates of bycatch and the marine mammal stock’s abundance because classification depends on the ratio of the estimate of bycatch by fishery-type to the stock’s PBR, and the precision of the former depends on the bycatch CV and the latter on the abundance estimate CV. Moreover, the probability of correctly classifying a fishery decreases for smaller populations, particularly when a fishery has low to moderate impact.