From the lab to the field: studying fish locomotor dynamics using animal-borne dataloggers

Abstract

Understanding the mechanics and efficiencies associated with how animals move is essential to understand links between form and function and ultimately understand where and why animals move. However, most current studies of animal biomechanics are focused on stereotyped behaviors and dominated by small-bodied animals due to logistical constraints of laboratory experiments. Instead of bringing an animal into the lab and recording its movements with a camera, we can directly measure the body movement of animals using small acceleration, angular velocity, and magnetic field data loggers. This method allows us to tag large animals and collect spatio-temporally unbiased estimates of their kinematics in both lab conditions and in the wild. In my dissertation, I identify the problems, solutions, and benefits to using this method and I show how we can expand our knowledge of in situ kinematics that would be impossible to document using traditional methods. In Chapter 1, I demonstrate that individual dataloggers temporally drift apart from each other at a rate that meaningfully disrupts their use for kinematics. I show that these errors are quantifiable in both the lab and field and how to correct them. In Chapter 2, I deploy multiple dataloggers on a soft robotic system and three diverse species of fish (an agnathan, an elasmobranch, and a teleost), show the diverse metrics that can be generated using multiple dataloggers during both routine and high performance swimming in near-field conditions, and evaluate these metrics against traditional video data. In Chapters 3 and 4, I develop a novel multitag package and deploy it on smooth dogfish in the wild. In Chapter 3, I use this dataset to test a previous hypothesis that suggested that the anterior and posterior body regions of sharks oscillate at different frequencies. I observed no evidence of different oscillation frequencies between the head and tail and suggest these previous observations are due to short monitoring periods and small errors in location and frequency estimation. In Chapter 4, I use this multi datalogger data set to recreate the kinematics of sharks in the wild using five smooth dogfish released into Massachusetts Bay. I use data from three data loggers to measure how different body parts move and demonstrate how movement along the body varies with tailbeat frequency during routine swimming. Additionally, I document maximal performance and unsteady behaviors such as turns and show that unsteady behaviors are a significant part of a wild animal’s behavior that are poorly captured in laboratory studies. Overall, this dissertation demonstrates that important biomechanical metrics can be calculated in situ from multiple data loggers attached to animals in the field, and opens the door for documentation of new behaviors, in new species. This work advances and directly links the biomechanics, behavior and ecology of animals.