Paleontologists must confront the challenge of studying the forms and functions of extinct species for which data from preserved fossils are extremely limited, yielding only a fragmented picture of life in deep time. In response to this hurdle, we describe the nascent field of paleoinspired robotics, an innovative method that builds upon established techniques in bioinspired robotics, enabling the exploration of the biology of ancient organisms and their evolutionary trajectories. This Review presents ways in which robotic platforms can fill gaps in existing research using the exemplars of notable transitions in vertebrate locomotion. We examine recent case studies in experimental paleontology, highlighting substantial contributions made by engineering and robotics techniques, and further assess how the efficient application of robotic technologies in close collaboration with paleontologists and biologists can offer additional insights into the study of evolution that were previously unattainable.

Schooling is the collective movement of fishes characterized by high polarization and swimming synchronization of individuals within the group. This locomotor behavior is fundamental to the survival of many species of fish that exhibit schooling during some stage of their life. While collective motion has been a topic of extensive research, this article provides an overview of the mechanics and energetics of locomotion during schooling behavior, including the potential effects of climate change on this movement, and suggests future research directions.

Deep-sea fishes must overcome extremely large nearest-neighbour distances and darkness to find mates. Sexual dimorphism in the size of luminescent structures in many deep-sea taxa, including dragonfishes (family Stomiidae), indicates reproductive behaviours may be mediated by visual signalling. This presents a paradox: if male photophores are larger, females may find males at shorter distances than males find females. Solutions to this gap may include females closing this gap or by males gathering more photons with a larger eye. We examine the eye size of two species of dragonfishes (Malacosteus niger and Phostomias guernei) for sexual dimorphism and employ a model of detection distance to evaluate the potential for such dimorphism to bridge the detection gap. This model incorporates the flux of sexually dimorphic postorbital photophores and eye lens size to predict detection distances. In both species, we found a significant visual detection gap in which females find males before males find females and that male lens size is larger, marking the second known case of size dimorphism in the actinopterygian visual system. Our results indicate the larger eye affords males a significant improvement in detection distance. We conclude that this dimorphic phenotype may have evolved to close the detection gap.

As climate change redirects migration patterns of marine species towards the extremes of their geographic range, sharks find themselves stunned by rising cold upwelling currents.

This paper develops a tree-topological local mesh refinement (TLMR) method on Cartesian grids for the simulation of bio-inspired flow with multiple moving objects. The TLMR nests refinement mesh blocks of structured grids to the target regions and arrange the blocks in a tree topology. The method solves the time-dependent incompressible flow using a fractional-step method and discretizes the Navier-Stokes equation using a finite-difference formulation with an immersed boundary method to resolve the complex boundaries. When iteratively solving the discretized equations across the coarse and fine TLMR blocks, for better accuracy and faster convergence, the momentum equation is solved on all blocks simultaneously, while the Poisson equation is solved recursively from the coarsest block to the finest ones. When the refined blocks of the same block are connected, the parallel Schwarz method is used to iteratively solve both the momentum and Poisson equations. Convergence studies show that the algorithm is second-order accurate in space for both velocity and pressure, and the developed mesh refinement technique is benchmarked and demonstrated by several canonical flow problems. The TLMR enables a fast solution to an incompressible flow problem with complex boundaries or multiple moving objects. Various bio-inspired flows of multiple moving objects show that the solver can save over 80% computational time, proportional to the grid reduction when refinement is applied.

Shoaling behavior is known to increase survival rates during attacks from predators, minimize foraging time, favor mating, and potentially increase locomotor efficiency. The onset of shoaling typically occurs during the larval phase, but it is unclear how it may improve across ontogenetic stages in forage fishes. Warming is known to increase metabolic rates during locomotion in solitary fish, and shoaling species may adjust their collective behavior to offset the elevated costs of swimming at higher temperatures. In this study, we quantified the effects of warming on shoaling performance across the ontogeny of a small forage fish, zebrafish (Danio rerio) at different speeds. Shoals of larval, juvenile, and adult zebrafish were acclimated at two temperatures (28°C and 32°C), and metabolic rates were quantified prior to and following nonexhaustive exercise at high speed. Shoals of five individuals were filmed in a flow tank to analyze the kinematics of collective movement. We found that zebrafish improve shoaling swimming performance from larvae to juveniles to adults. In particular, shoals become more cohesive, and both tail beat frequency (TBF) and head-to-tail amplitude decrease with ontogeny. Early life stages have higher thermal sensitivity in metabolic rates and TBF especially at high speeds, when compared to adults. Our study shows that shoaling behavior and thermal sensitivity improve as zebrafish shift from larval to juvenile to adult stages. 

The leading-edge vortex (LEV) formation on the caudal fin (CF) has been identified as playing a key role in efficient lift-based thrust production of fish-like propulsion. The enhancement of the CF LEV through its interaction with vortices formed upstream due to a median fin with a distinct shape is the focus of this paper. High-speed, high-fidelity videos and particle imaging velocimetry (PIV) were obtained from rainbow trout during steady forward swimming to visualize the undulatory kinematics and two-dimensional flow behavior. Body kinematics are quantified using a traveling-wave formulation that is used to prescribe the motion of a high-fidelity three-dimensional surface model of the fish body for a computational fluid dynamics (CFD) study. The pressure field of the CFD result is compared and validated with the PIV result from the experiment. Using CFD, the vortex forming and shedding behaviors of the anal fin (AF) and their capturing and interaction with the trunk (TK) and the CF are visualized and examined. Coherent AF-bound LEVs are found to form periodically, leading to thrust production of the AF. The vortices subsequently shed from the AF are found to help stabilize and reinforce the LEV formation on the CF by aiding LEV initiation at stroke reversal and enhancing LEV during a tail stroke, which leads to enhancement of lift-based thrust production. The CF is found to shed vortex tubes (VTs) that create backward-facing jets, and the ventral-side VT and the associated backward jets are both strengthened by vortices shed by the AF. An additional benefit of the AF is found to be reduction of body drag by reducing the lateral crossflow that leads to loss of beneficial pressure gradient across the body. Through varying AF-CF spacing and AF height, we find that CF thrust enhancement and TK drag reduction due to the AF are both affected by the position and size of the AF. The position and area of the AF that led to the most hydrodynamic benefit are found to be the original, anatomically accurate position and size. In this paper, we demonstrate the important effect of vortex interaction among propulsive surfaces in fish-like propulsion.

Ecological physiologists and biomechanists have investigated swimming performance in a diversity of fishes; however, the connection between form, function, and energetics of locomotion has been rarely evaluated in the same system and under climate change scenarios. In this perspective, I argue that working within the framework of “EcoPhysioMechanics,” i.e. integrating energetics and biomechanics tools, to measure locomotor performance and behavior under different abiotic factors, improves our understanding of the mechanisms, limits and costs of movement. To demonstrate how EcoPhysioMechanics can be applied to locomotor studies, I outline how linking biomechanics and physiology allows us to understand how fishes may modulate their movement to achieve high speeds or reduce the costs of locomotion. I also discuss how the framework is necessary to quantify swimming capacity under climate change scenarios. Finally, I discuss current dearth of integrative studies and gaps in empirical datasets that are necessary to understand fish swimming under changing environments. 

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.

Estimating the pose of multiple animals is a challenging computer vision problem: frequent interactions cause occlusions and complicate the association of detected keypoints to the correct individuals, as well as having highly similar looking animals that interact more closely than in typical multi-human scenarios. To take up this challenge, we build on DeepLabCut, an open-source pose estimation toolbox, and provide high-performance animal assembly and tracking—features required for multi-animal scenarios. Furthermore, we integrate the ability to predict an animal’s identity to assist tracking (in case of occlusions). We illustrate the power of this framework with four datasets varying in complexity, which we release to serve as a benchmark for future algorithm development.