Ontogeny of swimming capacity in plaice (Pleuronectes platessa) larvae

Little is known regarding the swimming ability of the larvae of European plaice (Pleuronectes platessa) in relation to changes in total length (TL), dry weight (DW) and developmental stage, which is surprising given the importance of transport processes to the recruitment dynamics of this species in the North Sea and elsewhere. We investigated ontogenetic changes in the critical swimming speed (Ucrit) of plaice from hatching to the onset of metamorphosis (50 days post-hatch, dph) at 8 °C. The mean (±SD) TL and DW growth rates were 1.59 ± 0.81 and 7.7 ± 0.35 % d−1, respectively. Larvae were unable to swim at against a minimum current speed of <0.5 cm s−1 until 10 dph (7 mm TL), after which Ucrit significantly increased with increasing TL until the onset of metamorphosis and subsequent settlement. Mean (±SD) Ucrit was 0.38(0.35), 1.59(0.54), 2.27(0.49) and 2.99(0.37) cm s−1 for stage I (6.61 ± 2.64 mm TL), stage II (7.75 ± 0.60 mm TL), stage III (9.10 ± 1.00 mm TL) and stage IV (11.59 ± 0.85 mm TL) larvae, respectively. Larval TL, DW, DNA content, RNA content and Ucrit significantly increased, whereas sRD significantly declined as larvae developed from stage I to V. Although inter-individual differences in Ucrit (coefficient of variation, CV = 33 %) were as large as those in biochemical and morphological condition (CV’s of 21–42 %), differences in Ucrit were not significantly related to those in nutritional condition and larvae with lower DNA/DW had also better swimming abilities. These estimates should be useful to ongoing efforts to create individual- based models of the transport, foraging and growth of plaice larvae in the North Sea.

Call recognition and individual identification of fish vocalizations based on automatic speech recognition: An example with the Lusitanian toadfish

The study of acoustic communication in animals often requires not only the recognition of species specific acoustic signals but also the identification of individual subjects, all in a complex acoustic background. Moreover, when very long recordings are to be analyzed, automatic recognition and identification processes are invaluable tools to extract the relevant biological information. A pattern recognition methodology based on hidden Markov models is presented inspired by successful results obtained in the most widely known and complex acoustical communication signal: human speech. This methodology was applied here for the first time to the detection and recognition of fish acoustic signals, specifically in a stream of round-the-clock recordings of Lusitanian toadfish (Halobatrachus didactylus) in their natural estuarine habitat. The results show that this methodology is able not only to detect the mating sounds (boatwhistles) but also to identify individual male toadfish, reaching an identification rate of ca. 95%. Moreover this method also proved to be a powerful tool to assess signal durations in large data sets. However, the system failed in recognizing other sound types.