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iTag

About the Project:

Squid are ecologically important marine invertebrates that play a key role in many aquatic ecosystems and fisheries. Quantifying in situ squid movements, energetics, and habitat use provides important insight into the ecology of these key taxa. However, due in part to the inherent challenges of monitoring squid in their natural marine environment, fine-scale behavioral observations of these pelagic, soft-bodied animals are rare. Bio-logging tags provide an emerging way to remotely study squid behavior in their natural environments. Our lab, in collaboration with the University of Michigan and Monterey Bay Aquarium Research Institute, developed a novel, high-resolution bio-logging tag (iTag), for soft-bodied invertebrates, that measures temperature, light, depth, acceleration, and orientation. This combination of sensing, classification, and estimation will enable the quantification of organismal activity patterns in the wild to provide new biological information, such as identification of behavioral states, temporal patterns, habitat requirements, energy expenditure, and community interactions.

In 2017-18 we tested this tag in the lab and the field, tagging local longfin squid. In May 2019, our team went to Faial, Azores to deploy our the iTag bio-logging tags on captive and wild L. forbesi. In total, 11 wild L. forbesi were tagged, and every tag was retrieved.  We collected novel data showing diel movements, including fine-scale vertical movement data depicting how squid compensate for their negative buoyancy.  Multiple animals were also tagged in the Flying Sharks Aquarium, helping inform tag attachment methods and ground-truth the field data.

Project Media: Video Feature & Pictures

Related Publications

  • Mooney, T.A., Katija, K., Shorter, K.A. et al. ITAG: an eco-sensor for fine-scale behavioral measurements of soft-bodied marine invertebrates. Anim Biotelemetry 3, 31 (2015) doi:10.1186/s40317-015-0076-1
  • Flaspohler GE, Caruso F, Mooney TA, Katija K, Fontes J, Afonso P, Shorter KA. Quantifying the swimming gaits of veined squid (Loligo forbesi) using bio-logging tags. Journal of Experimental Biology. 2019 Jan 1:jeb-198226. 
  • Fannjiang C, Mooney TA, Cones S, Mann D, Shorter KA, Katija K. Augmenting biologging with supervised machine learning to study in situ behavior of the medusa Chrysaora fuscescens. bioRxiv. 2019 Jan 1:657684. 
  • Fossette S, Katija K, Goldbogen JA, Bograd S, Patry W, Howard MJ, Knowles T, Haddock SH, Bedell L, Hazen EL, Robison BH. How to tag a jellyfish? A methodological review and guidelines to successful jellyfish tagging. Journal of Plankton Research. 2016 Oct 15;38(6):1347-63.  doi.org/10.1093/plankt/fbw073

Funding Agencies

This project was funded by NSF and a WHOI Innovative Technology Award.

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Future Project Goals:

  • Examine seasonal differences in swimming behavior. By noting swimming behavior changes under different temperature regimes, we may be able to predict how the local squid population will be impacted by climate change.
  • Record and quantify in situ feeding rates. To do so, we will acquire concurrent video and tag data of squid feeding in a captive setting. After, we will classify and generate probabilistic models from captive squid’s acceleration during feeding to identify the unique acceleration signature in wild forbesi.
  • Deploy tags with newly developed sensors on wild forbesi. This month, our team constructed a tag with an oxygen sensor, and we pursuing salinity and speeds sensors as well. These sensors provide two key capabilities. 1- Salinity, temperature, and oxygen sensors allow predictions of ocean pH. 2- Speed sensors coupled with IMUs can generate 3D paths taken by tagged animals. One application of a 3D paths is to identify areas where squid ascend vertically. Webber et al. (2000) hypothesized squid leverage areas of upwelling to vertically ascend to mitigate the energetic cost for negatively buoyant squid; we hope to test their theory on L. forbesi.