Bivalve populations inhabiting hydrothermal vents on submarine volcanoes: Using size frequency distributions to infer potential regulatory factors

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Abstract

In this study, I used length frequency distributions of bivalve populations at hydrothermal vents on submarine volcanoes at the South Tonga Arc and the Valu Fa Ridge to infer processes that may be responsible for the observed population structures. On Volcano 19, the lucinid Bathyaustriella thionipta co-occurred with the sea star Rumbleaster eructans at two sedimented sites. At one site, live clams were significantly smaller (in shell length, SL) than dead ones (showing signs of predation by sea stars), whereas at the other site, both, live and dead clams had similarly small SL, suggesting a size refuge from predation. At Volcano 1, mussel beds of Bathymodiolus sp. were present in two areas: one with vigorous hydrothermal discharge ('Bubbles'), and another with some discharge but also in the photic zone ('Barracuda'). Mussels were smaller in SL at 'Bubbles' than at 'Barracuda', a consequence of either a younger population or a nutritionally less favourable environment at 'Bubbles'. At 'Barracuda', mussels utilized both chemo- and photosynthetically derived carbon, possibly allowing them to reach larger sizes. At Hine Hina on Valu Fa Ridge, a population of mussels was composed mainly of large individuals (>55 mm SL), estimated to be on average 7-13 years old. The combination of old mussels, absence of recruits, and presence of abundant empty mussel shells and of dead chimneys suggests that this is a waning hydrothermal system. My results indicate that the population structure of bivalves at hydrothermal vents can provide information on the processes that regulate these populations, which in turn can be used to generate testable hypotheses for the same location, should return visits occur, or for other locations with similarly structured populations.

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Metaxas, A. (2015). Bivalve populations inhabiting hydrothermal vents on submarine volcanoes: Using size frequency distributions to infer potential regulatory factors. Marine Ecology, 36(S1), 62–70. https://doi.org/10.1111/maec.12248

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