Evidence for extensive population structure in the white-spotted eagle ray within the Indo-Pacific inferred from mitochondrial gene sequences

The white-spotted eagle ray Aetobatus narinari is a species complex that occurs circumglobally throughout warm-temperate waters. Aetobatus narinari is semi-pelagic and large (up to 300 cm disc width), suggesting high dispersal capabilities and gene flow on a wide spatial scale. Sequence data from two mitochondrial genes, cytochrome b (cytb) and NADH dehydrogenase subunit 4 (ND4), were used to determine the genetic variability within and among 18 sampling locations in the central Indo-Pacific biogeographical region. Populations in the Indo-Pacific were highly genetically structured with c. 70% of the total genetic variation found among three geographical regions (East China Sea, Southeast Asia and Australia). FST was 0.64 for cytb and 0.53 for ND4, with φST values being even larger, that is, 0.78 for cytb and 0.65 for ND4. This high-level genetic partitioning provides strong evidence against extensive gene flow in A. narinari. The degree of genetic population structuring in the Indo-Pacific was similar to that found on a global scale. Global FST was 0.63 for cytb and 0.57 for ND4, and global φST values were 0.94 for cytb and 0.82 for ND4. This suggests that the A. narinari complex may be more speciose than the two or three species proposed to date. Further sampling and genetic analyses are likely to uncover the ‘evolutionarily significant’ and ‘management’ units that are critical to determine the susceptibilities of individual populations to regional fishing pressures and to provide advice on management options. Network analyses showed a close genetic relationship between haplotypes from the central Indo-Pacific and South Africa, providing support for a proposed dispersal pathway from the possible centre of origin of the A. narinari species complex in the Indo-Pacific into the Atlantic Ocean.

A hidden Markov model approach for determining vessel activity from vessel monitoring system data.

Many fisheries worldwide have adopted vessel monitoring systems (VMS) for compliance purposes. An added benefit of these systems is that they collect a large amount of data on vessel locations at very fine spatial and temporal scales. This data can provide a wealth of information for stock assessment, research, and management. However, since most VMS implementations record vessel location at set time intervals with no regard to vessel activity, some methodology is required to determine which data records correspond to fishing activity. This paper describes a probabilistic approach, based on hidden Markov models (HMMs), to determine vessel activity. A HMM provides a natural framework for the problem and, by definition, models the intrinsic temporal correlation of the data. The paper describes the general approach that was developed and presents an example of this approach applied to the Queensland trawl fishery off the coast of eastern Australia. Finally, a simulation experiment is presented that compares the misallocation rates of the HMM approach with other approaches.