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Why the World's Biggest Sharks Love Mafia Island.

A technique that combines lasers and photography provides a decent measurement of the largest fish in the world.

A technique that combines lasers and photography provides a decent measurement of the largest fish in the world.

The estimated period in which human colonization of Madagascar began has expanded recently to 5000-1000 y B.P., six times its range in 1990, prompting revised thinking about early migration sources, routes, maritime capability and environmental changes. Cited evidence of colonization age includes anthropogenic palaeoecological data 2500-2000 y B.P., megafaunal butchery marks 4200-1900 y B.P. and OSL dating to 4400 y B.P. of the Lakaton'i Anja occupation site. Using large samples of newly-excavated bone from sites in which megafaunal butchery was earlier dated >2000 y B.P. we find no butchery marks until ~1200 y B.P., with associated sedimentary and palynological data of initial human impact about the same time. Close analysis of the Lakaton'i Anja chronology suggests the site dates <1500 y B.P. Diverse evidence from bone damage, palaeoecology, genomic and linguistic history, archaeology, introduced biota and seafaring capability indicate initial human colonization of Madagascar 1350-1100 y B.P.

Researchers use 'biological passport' to monitor Earth's largest fish.

First study of world's largest marine stingray reveals long-distance migration.

Highly mobile marine megafauna species, while widely distributed and frequently threatened, often aggregate in distinct localized habitats. Implementation of local management initiatives within these hotspots is more achievable than developing effective conservation strategies that encompass their entire distributions. Such measures have the potential for disproportionate population-level benefits but rely on a detailed understanding of spatiotemporal habitat use. To that end, we examined the residency and small-scale habitat use of 51 whale sharks (Rhincodon typus) over 5 years at an aggregation site in Tanzania using passive acoustic telemetry. Whale sharks were highly resident within and across years, with a combined maximum residency index of 0.39. Although fewer sharks were detected from March to September, residency was high throughout the year. Ancillary photographic-identification data showed that individual residency persisted before and after tag attachment. Kernel utilization distributions (KUD) and movement networks both revealed the same spatiotemporal pattern of habitat use, with a small core habitat (50% KUD area for all sharks combined = 12.99 km2) that predictably changed on a seasonal basis. Activity spaces did not differ with time of day, sex, or size of the sharks, indicating a population-level pattern driven by prey availability. The small and predictable core habitat area at this site means that site-based management options to reduce shark injuries and mortality from boat strike and fishing gear entanglement can be spatially targeted for maximum effectiveness and compliance by human users.

Imagery has become a key tool for assessing deep-sea megafaunal biodiversity, historically based on physical sampling using fishing gears. Image datasets provide quantitative and repeatable estimates, small-scale spatial patterns and habitat descriptions. However, taxon identification from images is challenging and often relies on morphotypes without considering a taxonomic framework. Taxon identification is particularly challenging in regions where the fauna is poorly known and/or highly diverse. Furthermore, the efficiency of imagery and physical sampling may vary among habitat types. Here, we compared biodiversity metrics (alpha and gamma diversity, composition) based on physical sampling (dredging and trawling) and towed-camera still images (1) along the upper continental slope of Papua New Guinea (sedimented slope with wood-falls, a canyon and cold seeps), and (2) on the outer slopes of the volcanic islands of Mayotte, dominated by hard bottoms. The comparison was done on selected taxa (Pisces, Crustacea, Echinoidea, and Asteroidea), which are good candidates for identification from images. Taxonomic identification ranks obtained for the images varied among these taxa (e.g., family/order for fishes, genus for echinoderms). At these ranks, imagery provided a higher taxonomic richness for hard-bottom and complex habitats, partially explained by the poor performance of trawling on these rough substrates. For the same reason, the gamma diversity of Pisces and Crustacea was also higher from images, but no difference was observed for echinoderms. On soft bottoms, physical sampling provided higher alpha and gamma diversity for fishes and crustaceans, but these differences tended to decrease for crustaceans identified to the species/morphospecies level from images. Physical sampling and imagery were selective against some taxa (e.g., according to size or behavior), therefore providing different facets of biodiversity. In addition, specimens collected at a larger scale