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Temporal accuracy is a fundamental characteristic of logging technology and is needed to correlate data streams. Single biologgers sensing animal movement (accelerometers, gyroscope, magnetometers, collectively inertial measurement unit; IMU) have been extensively used to study the ecology of animals. To better capture whole body movement and increase the accuracy of behavior classification, there is a need to deploy multiple loggers on a single individual to capture the movement of multiple body parts. Yet due to temporal drift, accurately aligning multiple IMU datasets can be problematic, especially as deployment duration increases. In this paper we quantify temporal drift and errors in commercially available IMU data loggers using a combination of robotic and animal borne experiments. The variance in drift rate within a tag is over an order of magnitude lower (σ = 0.001 s h⁻¹) than the variance between tags (σ = 0.015 s·h⁻¹), showing that recording frequency is a characteristic of each tag and not a random variable. Furthermore, we observed a large offset (0.54 ± 0.016 s·h⁻¹) between two groups of tags that had differing recording frequencies, and we observed three instances of instantaneous temporal jumps within datasets introducing errors into the data streams. Finally, we show that relative drift rates can be estimated even when deployed on animals displaying various behaviors without the tags needing to be simultaneously moved. For the tags used in this study, drift rates can vary significantly between tags, are repeatable, and can be accurately measured in the field. The temporal alignment of multiple tag datasets allows researchers to deploy multiple tags on an individual animal which will greatly increase our knowledge of movement kinematics and expand the range of movement characteristics that can be used for behavioral classification.

 

To understand the complexities of morphological evolution, we must understand the relationships between genes, morphology, performance, and fitness in complex traits. Genomicists have made tremendous progress in finding the genetic basis of many phenotypes, including a myriad of morphological characters. Similarly, field biologists have greatly advanced our understanding of the relationship between performance and fitness in natural populations. However, the connection from morphology to performance has primarily been studied at the interspecific level, meaning that in most cases we lack a mechanistic understanding of how evolutionarily relevant variation among individuals affects organismal performance. Therefore, functional morphologists need methods that will allow for the analysis of fine-grained intraspecific variation in order to close the path from genes to fitness. We suggest three methodological areas that we believe are well suited for this research program and provide examples of how each can be applied within fish model systems to build our understanding of microevolutionary processes. Specifically, we believe that structural equation modeling, biological robotics, and simultaneous multi-modal functional data acquisition will open up fruitful collaborations among biomechanists, evolutionary biologists, and field biologists. It is only through the combined efforts of all three fields that we will understand the connection between evolution (acting at the level of genes) and natural selection (acting on fitness).

 

Fish display a versatile array of swimming patterns, and frequently demonstrate the ability to switch between these patterns altering kinematics as necessary. Many hard and soft robotic systems have sought to understand a variety of aspects pertaining to undulatory swimming, but most have been built to focus solely on a subset of those swimming patterns. We have expanded upon a previous soft robotic model, the pneufish, so that it can now simulate a variety of swimming patterns, much like a real fish. We explore the performance space available for this longer soft robotic model, which we call the quad-pneufish, with particular attention to the effects on lateral forces and z-torques produced during locomotion. We show that the quad-pneufish is capable of achieving a variety of midline patterns—including more realistic, fish-like patterns—and introducing a slight amount of co-activation between the left and right sides maintains forward thrust while decreasing lateral forces, indicating an increase in swimming efficiency. Robotic systems that are capable of producing an array of swimming movement patterns hold promise as experimental platforms for studying the diversity of fish locomotor patterns.

 

Comparative biologists have typically used one or more of the following methods to assist in evaluating the proposed functional and performance significance of individual traits: comparative phylogenetic analysis, direct interspecific comparison among species, genetic modification, experimental alteration of morphology (for example by surgically modifying traits), and ecological manipulation where individual organisms are transplanted to a different environment. But comparing organisms as the endpoints of an evolutionary process involves the ceteris paribus assumption: that all traits other than the one(s) of interest are held constant. In a properly controlled experimental study, only the variable of interest changes among the groups being compared. The theme of this paper is that the use of robotic or mechanical models offers an additional tool in comparative biology that helps to minimize the effect of uncontrolled variables by allowing direct manipulation of the trait of interest against a constant background. The structure and movement pattern of mechanical devices can be altered in ways not possible in studies of living animals, facilitating testing hypotheses of the functional and performance significance of individual traits. Robotic models of organismal design are particularly useful in three arenas: (1) controlling variation to allow modification only of the trait of interest, (2) the direct measurement of energetic costs of individual traits, and (3) quantification of the performance landscape. Obtaining data in these three areas is extremely difficult through the study of living organisms alone, and the use of robotic models can reveal unexpected effects. Controlling for all variables except for the length of a swimming flexible object reveals substantial non-linear effects that vary with stiffness. Quantification of the swimming performance surface reveals that there are two peaks with comparable efficiency, greatly complicating the inference of performance from morphology alone. Organisms and their ecological interactions are complex, and dissecting this complexity to understand the effects of individual traits is a grand challenge in ecology and evolutionary biology. Robotics has great promise as a “comparative method,” allowing better-controlled comparative studies to analyze the many interacting elements that make up complex behaviors, ecological interactions, and evolutionary histories.

 

Many aquatic animals swim by undulatory body movements and understanding the diversity of these movements could unlock the potential for designing better underwater robots. Here, we analyzed the steady swimming kinematics of a diverse group of fish species to investigate whether their undulatory movements can be represented using a series of interconnected multi-segment models, and if so, to identify the key factors driving the segment configuration of the models. Our results show that the steady swimming kinematics of fishes can be described successfully using parsimonious models, 83% of which had fewer than five segments. In these models, the anterior segments were significantly longer than the posterior segments, and there was a direct link between segment configuration and swimming kinematics, body shape, and Reynolds number. The models representing eel-like fishes with elongated bodies and fishes swimming at high Reynolds numbers had more segments and less segment length variability along the body than the models representing other fishes. These fishes recruited their anterior bodies to a greater extent, initiating the undulatory wave more anteriorly. Two shape parameters, related to axial and overall body thickness, predicted segment configuration with moderate to high success rate. We found that head morphology was a good predictor of its segment length. While there was a large variation in head segments, the length of tail segments was similar across all models. Given that fishes exhibited variable caudal fin shapes, the consistency of tail segments could be a result of an evolutionary constraint tuned for high propulsive efficiency. The bio-inspired multi-segment models presented in this study highlight the key bending points along the body and can be used to decide on the placement of actuators in fish-inspired robots, to model hydrodynamic forces in theoretical and computational studies, or for predicting muscle activation patterns during swimming.

 

Fishes exhibit an astounding diversity of locomotor behaviors from classic swimming with their body and fins to jumping, flying, walking, and burrowing. Fishes that use their body and caudal fin (BCF) during undulatory swimming have been traditionally divided into modes based on the length of the propulsive body wave and the ratio of head:tail oscillation amplitude: anguilliform, subcarangiform, carangiform, and thunniform. This classification was first proposed based on key morphological traits, such as body stiffness and elongation, to group fishes based on their expected swimming mechanics. Here, we present a comparative study of 44 diverse species quantifying the kinematics and morphology of BCF-swimming fishes. Our results reveal that most species we studied share similar oscillation amplitude during steady locomotion that can be modeled using a second-degree order polynomial. The length of the propulsive body wave was shorter for species classified as anguilliform and longer for those classified as thunniform, although substantial variability existed both within and among species. Moreover, there was no decrease in head:tail amplitude from the anguilliform to thunniform mode of locomotion as we expected from the traditional classification. While the expected swimming modes correlated with morphological traits, they did not accurately represent the kinematics of BCF locomotion. These results indicate that even fish species differing as substantially in morphology as tuna and eel exhibit statistically similar two-dimensional midline kinematics and point toward unifying locomotor hydrodynamic mechanisms that can serve as the basis for understanding aquatic locomotion and controlling biomimetic aquatic robots. 

Integration of mechanoelectrical signaling and cardiac automaticity enables an autonomously swimming biohybrid fish analog. 

ABSTRACT Whereas many fishes swim steadily, zebrafish regularly exhibit unsteady burst-and-coast swimming, which is characterized by repeated sequences of turns followed by gliding periods. Such a behavior offers the opportunity to investigate the hypothesis that negative mechanical work occurs in posterior regions of the body during early phases of the turn near the time of maximal body curvature. Here, we used a modified particle image velocimetry (PIV) technique to obtain high-resolution flow fields around the zebrafish body during turns. Using detailed swimming kinematics coupled with body surface pressure computations, we estimated fluid–structure interaction forces and the pattern of forces and torques along the body during turning. We then calculated the mechanical work done by each body segment. We used estimated patterns of positive and negative work along the body to evaluate the hypothesis (based on fish midline kinematics) that the posterior body region would experience predominantly negative work. Between 10% and 20% of the total mechanical work was done by the fluid on the body (negative work), and negative work was concentrated in the anterior and middle areas of the body, not along the caudal region. Energetic costs of turning were calculated by considering the sum of positive and negative work and were compared with previous metabolic estimates of turning energetics in fishes. The analytical workflow presented here provides a rigorous way to quantify hydrodynamic mechanisms of fish locomotion and facilitates the understanding of how body kinematics generate locomotor forces in freely swimming fishes. 

Finlets are a series of small non-retractable fins common to scombrid fishes (mackerels, bonitos and tunas), which are known for their high swimming speed. It is hypothesized that these small fins could potentially affect propulsive performance. Here, we combine experimental and computational approaches to investigate the hydrodynamics of finlets in yellowfin tuna ( Thunnus albacares ) during steady swimming. High-speed videos were obtained to provide kinematic data on the in vivo motion of finlets. High-fidelity simulations were then carried out to examine the hydrodynamic performance and vortex dynamics of a biologically realistic multiple-finlet model with reconstructed kinematics. It was found that finlets undergo both heaving and pitching motion and are delayed in phase from anterior to posterior along the body. Simulation results show that finlets were drag producing and did not produce thrust. The interactions among finlets helped reduce total finlet drag by 21.5%. Pitching motions of finlets helped reduce the power consumed by finlets during swimming by 20.8% compared with non-pitching finlets. Moreover, the pitching finlets created constructive forces to facilitate posterior body flapping. Wake dynamics analysis revealed a unique vortex tube matrix structure and cross-flow streams redirected by the pitching finlets, which supports their hydrodynamic function in scombrid fishes. Limitations on modelling and the generality of results are also discussed.