Data from: Patterns in vertical distribution and their potential effects on transport of larval benthic invertebrates in a shallow embayment
Lloyd, Michelle J.; Metaxas, Anna; deYoung, Brad; Lloyd, MJ (2013), Data from: Patterns in vertical distribution and their potential effects on transport of larval benthic invertebrates in a shallow embayment, Dryad, Dataset, https://doi.org/10.5061/dryad.9js2m
Measurements of larval vertical distributions at high temporal and spatial resolutions as well as larval behavioural responses to environmental characteristics are needed to parameterize bio-physical models of larval dispersal or transport. We studied larval vertical distribution for 7 taxonomic groups (gastropods, bivalves, polychaetes, bryozoans, asteroids, carideans and brachyurans), with different morphology, swimming abilities and life-history strategies, and examined whether these vary with physical or biological factors and periodic cycles (diel period and tidal state) in the field. Using a pump, we collected plankton samples at 6 depths (3, 6, 9, 12, 18 and 24 m), over a 36 and a 26 h period. Temperature, salinity, fluorescence and current velocity were measured concurrently. Larval vertical distribution varied among taxonomic groups, but 4 patterns could be distinguished: (1) larvae exclusively in the mixed layer (asteroids), (2) larvae predominantly below the thermocline, halocline and pycnocline (gastropods, bivalves, polychaetes), (3) larvae associated predominantly with the fluorescence maximum (bryozoans and carideans) and (4) larval distribution varying dielly (gastropods, polychaetes, carideans and brachyurans). Based on flow velocities and depending on distribution, asteroid larvae were likely to be transported farther than those of bryozoans and carideans, while direction and magnitude of transport varied for the other larvae. For most taxonomic groups, behaviour observed in the field agreed with measured laboratory responses to relevant cues. For asteroids and bivalves, simple behavioural parameters can be generated that can be utilized to improve the accuracy of biophysical models.
St. Georges's Bay