Species-specific abundance of bivalve larvae in relation to biological and physical conditions in a Cape Cod estuary : Waquoit Bay, Massachusetts (USA)
Thompson, Christine M.
York, Richard H.
Gallager, Scott M.
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Physical and biological conditions impact recruitment and adult population structure of 34 marine invertebrates by affecting early life history processes from spawning to post-settlement. We investigated how temperature, salinity and phytoplankton influenced larval abundance and larval size structure for three species of bivalves over two non-consecutive years in Waquoit Bay, MA. Abundance and size of Mercenaria mercenaria (quahog), Anomia simplex (jingle clam), and Geukensia demissa (ribbed mussel) larvae were compared between locations in the bay and with environmental conditions. Shell birefringence patterns using polarized light microscopy were used to distinguish species. Larval abundances for all three species were higher in 2009 than in 2007 and were positively correlated with temperature in both years. Differences in larval abundance and size structure between bay sites were attributed to salinity tolerances and potential source locations. Higher survival in 2009 than in 2007, as determined by number of pediveligers, was likely due to higher temperatures and greater food availability during the peak abundance months of July and August in 2009. Yearly differences in larval growth and survival can have a large impact on recruitment. Knowing the optimal periods and locations for larval abundance and survival can be useful for isolating species-specific patterns in larval dispersal and to aid resource managers in enhancing or restoring depleted populations.
Author Posting. © The Author(s), 2012. This is the author's version of the work. It is posted here by permission of Inter-Research for personal use, not for redistribution. The definitive version was published in Marine Ecology Progress Series 469 (2012): 53-69, doi:10.3354/meps09998.
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Thompson, Christine M.; Hare, Matthew P.; Gallager, Scott M. (Association for the Sciences of Limnology and Oceanography, 2012-07)Machine-learning methods for identifying planktonic organisms are becoming well-established. Although similar morphologies among species make traditional image identification methods difficult for larval bivalves, ...
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