RT Journal Article
SR Electronic
T1 Quantifying the swimming gaits of veined squid (<em>Loligo forbesi</em><em>i</em>) using bio-logging tags
JF The Journal of Experimental Biology
JO J. Exp. Biol.
FD The Company of Biologists Ltd
SP jeb198226
DO 10.1242/jeb.198226
VO 222
IS 24
A1 Flaspohler, Genevieve E.
A1 Caruso, Francesco
A1 Mooney, T. Aran
A1 Katija, Kakani
A1 Fontes, Jorge
A1 Afonso, Pedro
A1 Shorter, K. Alex
YR 2019
UL http://jeb.biologists.org/content/222/24/jeb198226.abstract
AB Squid are mobile, diverse, ecologically important marine organisms whose behavior and habitat use can have substantial impacts on ecosystems and fisheries. However, as a consequence in part of the inherent challenges of monitoring squid in their natural marine environment, fine-scale behavioral observations of these free-swimming, soft-bodied animals are rare. Bio-logging tags provide an emerging way to remotely study squid behavior in their natural environments. Here, we applied a novel, high-resolution bio-logging tag (ITAG) to seven veined squid, Loligo forbesii, in a controlled experimental environment to quantify their short-term (24 h) behavioral patterns. Tag accelerometer, magnetometer and pressure data were used to develop automated gait classification algorithms based on overall dynamic body acceleration, and a subset of the events were assessed and confirmed using concurrently collected video data. Finning, flapping and jetting gaits were observed, with the low-acceleration finning gaits detected most often. The animals routinely used a finning gait to ascend (climb) and then glide during descent with fins extended in the tank's water column, a possible strategy to improve swimming efficiency for these negatively buoyant animals. Arms- and mantle-first directional swimming were observed in approximately equal proportions, and the squid were slightly but significantly more active at night. These tag-based observations are novel for squid and indicate a more efficient mode of movement than suggested by some previous observations. The combination of sensing, classification and estimation developed and applied here will enable the quantification of squid activity patterns in the wild to provide new biological information, such as in situ identification of behavioral states, temporal patterns, habitat requirements, energy expenditure and interactions of squid through space–time in the wild.