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Blue whales were brought to the edge of extinction by commercial whaling in the twentieth century and their recovery rate in the Southern Hemisphere has been slow; they remain endangered. Blue whales, although the largest animals on Earth, are difficult to study in the Southern Hemisphere, thus their population structure, distribution and migration remain poorly known. Fortunately, blue whales produce powerful and stereotyped songs, which prove an effective clue for monitoring their different 'acoustic populations.' The DGD-Chagos song has been previously reported in the central Indian Ocean. A comparison of this song with the pygmy blue and Omura's whale songs shows that the Chagos song are likely produced by a distinct previously unknown pygmy blue whale population. These songs are a large part of the underwater soundscape in the tropical Indian Ocean and have been so for nearly two decades. Seasonal differences in song detections among our six recording sites suggest that the Chagos whales migrate from the eastern to western central Indian Ocean, around the Chagos Archipelago, then further east, up to the north of Western Australia, and possibly further north, as far as Sri Lanka. The Indian Ocean holds a greater diversity of blue whale populations than thought previously.

Otoliths are commonly used to discriminate between fish stocks, through both elemental composition and otolith shape. Typical studies also have a large number of elemental compositions and shape measures relative to the number of otolith samples, with these measures exhibiting strong mean-variance relationships. These properties make otolith composition and shape data highly suitable for use within a multivariate generalised linear model (MGLM) framework, yet MGLMs have never been applied to otolith data. Here we apply both a traditional distance based permutational multivariate analysis of variance (PERMANOVA) and MGLMs to a case study of striped snakehead (Channa striata) in India. We also introduce the Tweedie and gamma distributions as suitable error structures for the MGLMs, drawing similarities to the properties of Biomass data. We demonstrate that otolith elemental data and combined otolith elemental and shape data violate the assumption of homogeneity of variance of PERMANOVA and may give misleading results, while the assumptions of the MGLM with Tweedie and gamma distributions are shown to be satisfied and are appropriate for both otolith shape and elemental composition data. Consistent differences between three groups of C. striata were identified using otolith shape, otolith chemistry and a combined otolith shape and chemistry dataset. This suggests that future research should be conducted into whether there are demographic differences between these groups which may influence management considerations. The MGLM method is widely applicable and could be applied to any multivariate otolith shape or elemental composition dataset.