ABSTRACT Navigating across light gradients is essential for survival for many animals. However, we still have a poor understanding of the algorithms that underlie such behaviors. Here, we developed a novel closed-loop phototaxis assay for Drosophila larvae in which light intensity is always spatially uniform but updates depending on the location of the animal in the arena. Even though larvae can only rely on temporal cues during runs, we find that they are capable of finding preferred areas of low light intensity. Further detailed analysis of their behavior reveals that larvae turn more frequently and that heading angle changes increase when they experience brightness increments over extended periods of time. We suggest that temporal integration of brightness change during runs is an important – and so far largely unexplored – element of phototaxis.

ABSTRACT Mantis shrimp commonly inhabit seafloor environments with an abundance of visual features including conspecifics, predators, prey and landmarks used for navigation. Although these animals are capable of discriminating color and polarization, it is unknown what specific attributes of a visual object are important during recognition. Here, we show that mantis shrimp of the species Neogonodactylus oerstedii are able to learn the shape of a trained target. Further, when the shape and color of a target that they had been trained to identify were placed in conflict, N. oerstedii tended to choose the target of the trained shape over the target of the trained color. Thus, we conclude that the shape of the target was more salient than its color during recognition by N. oerstedii , suggesting that the shapes of objects, such as landmarks or other animals, are important for their identification by the species.

ABSTRACT Mantis shrimp of the species Neogonodactylus oerstedii occupy small burrows in shallow waters throughout the Caribbean. These animals use path integration, a vector-based navigation strategy, to return to their homes while foraging. Here, we report that path integration in N. oerstedii is prone to error accumulated during outward foraging paths and we describe the search behavior that N. oerstedii employs after it fails to locate its home following the route provided by its path integrator. This search behavior forms continuously expanding, non-oriented loops that are centered near the point of search initiation. The radius of this search is scaled to the animal's positional uncertainty during path integration, improving the effectiveness of the search. The search behaviors exhibited by N. oerstedii bear a striking resemblance to search behaviors in other animals, offering potential avenues for the comparative examination of search behaviors and how they are optimized in disparate taxa.

ABSTRACT Field studies on freely behaving animals commonly require tagging and often are focused on single species. Weakly electric fish generate a species- and individual-specific electric organ discharge (EOD) and therefore provide a unique opportunity for individual tracking without tagging. Here, we present and test tracking algorithms based on recordings with submerged electrode arrays. Harmonic structures extracted from power spectra provide fish identity. Localization of fish based on weighted averages of their EOD amplitudes is found to be more robust than fitting a dipole model. We apply these techniques to monitor a community of three species, Apteronotus rostratus , Eigenmannia humboldtii and Sternopygus dariensis , in their natural habitat in Darién, Panama. We found consistent upstream movements after sunset followed by downstream movements in the second half of the night. Extrapolations of these movements and estimates of fish density obtained from additional transect data suggest that some fish cover at least several hundreds of meters of the stream per night. Most fish, including E . humboldtii , were traversing the electrode array solitarily. From in situ measurements of the decay of the EOD amplitude with distance of individual animals, we estimated that fish can detect conspecifics at distances of up to 2 m. Our recordings also emphasize the complexity of natural electrosensory scenes resulting from the interactions of the EODs of different species. Electrode arrays thus provide an unprecedented window into the so-far hidden nocturnal activities of multispecies communities of weakly electric fish at an unmatched level of detail.

ABSTRACT Among-individual behavioral differences (i.e. animal personality) are commonly observed across taxa, although the underlying, causal mechanisms of such differences are poorly understood. Animal personality has been correlated with physiological functions as well as fitness-related traits. Variation in many aspects of monoamine systems, such as metabolite levels and gene polymorphisms, has been linked to behavioral variation. Therefore, here we experimentally investigated the potential role of monoamines in explaining individual variation in personality, using two common pharmaceuticals that respectively alter the levels of serotonin and dopamine in the brain: fluoxetine and ropinirole. We exposed three-spined sticklebacks, a species that shows animal personality, to either chemical alone or to a combination of the two chemicals, for 18 days. During the experiment, fish were assayed at four time points for the following personality traits: exploration, boldness, aggression and sociability. To quantify brain gene expression on short- and longer-term scales, fish were sampled at two time points. Our results show that monoamine manipulations influence fish behavior. Specifically, fish exposed to either fluoxetine or ropinirole were significantly bolder, and fish exposed to the two chemicals together tended to be bolder than control fish. Our monoamine manipulations did not alter the gene expression of monoamine or stress-associated neurotransmitter genes, but control, untreated fish showed covariation between gene expression and behavior. Specifically, exploration and boldness were predicted by genes in the dopaminergic, serotonergic and stress pathways, and sociability was predicted by genes in the dopaminergic and stress pathways. These results add further support to the links between monoaminergic systems and personality, and show that exposure to monoamines can causally alter animal personality.

ABSTRACT Biologists commonly visualize different features of an organism using distinct sources of illumination. Such multichannel imaging has largely not been applied to behavioral studies because of the challenges posed by a moving subject. We address this challenge with the technique of multichannel stroboscopic videography (MSV), which synchronizes multiple strobe lights with video exposures of a single camera. We illustrate the utility of this approach with kinematic measurements of a walking cockroach ( Gromphadorhina portentosa ) and calculations of the pressure field around a swimming fish ( Danio rerio ). In both, transmitted illumination generated high-contrast images of the animal's body in one channel. Other sources of illumination were used to visualize the points of contact for the feet of the cockroach and the water flow around the fish in separate channels. MSV provides an enhanced potential for high-throughput experimentation and the capacity to integrate changes in physiological or environmental conditions in freely-behaving animals.

ABSTRACT Recent developments in machine vision methods for automatic, quantitative analysis of social behavior have immensely improved both the scale and level of resolution with which we can dissect interactions between members of the same species. In this paper, we review these methods, with a particular focus on how biologists can apply them to their own work. We discuss several components of machine vision-based analyses: methods to record high-quality video for automated analyses, video-based tracking algorithms for estimating the positions of interacting animals, and machine learning methods for recognizing patterns of interactions. These methods are extremely general in their applicability, and we review a subset of successful applications of them to biological questions in several model systems with very different types of social behaviors.

ABSTRACT Activity budgets in wild animals are challenging to measure via direct observation because data collection is time consuming and observer effects are potentially confounding. Although tri-axial accelerometers are increasingly employed for this purpose, their application in small-bodied animals has been limited by weight restrictions. Additionally, accelerometers engender novel complications, as a system is needed to reliably map acceleration to behaviors. In this study, we describe newly developed, tiny acceleration-logging devices (1.5–2.5 g) and use them to characterize behavior in two chipmunk species. We collected paired accelerometer readings and behavioral observations from captive individuals. We then employed techniques from machine learning to develop an automatic system for coding accelerometer readings into behavioral categories. Finally, we deployed and recovered accelerometers from free-living, wild chipmunks. This is the first time to our knowledge that accelerometers have been used to generate behavioral data for small-bodied (<100 g), free-living mammals.

Summary Integrating biomechanics, behavior and ecology requires a mechanistic understanding of the processes producing the movement of animals. This calls for contemporaneous biomechanical, behavioral and environmental data along movement pathways. A recently formulated unifying movement ecology paradigm facilitates the integration of existing biomechanics, optimality, cognitive and random paradigms for studying movement. We focus on the use of tri-axial acceleration (ACC) data to identify behavioral modes of GPS-tracked free-ranging wild animals and demonstrate its application to study the movements of griffon vultures ( Gyps fulvus , Hablizl 1783). In particular, we explore a selection of nonlinear and decision tree methods that include support vector machines, classification and regression trees, random forest methods and artificial neural networks and compare them with linear discriminant analysis (LDA) as a baseline for classifying behavioral modes. Using a dataset of 1035 ground-truthed ACC segments, we found that all methods can accurately classify behavior (80–90%) and, as expected, all nonlinear methods outperformed LDA. We also illustrate how ACC-identified behavioral modes provide the means to examine how vulture flight is affected by environmental factors, hence facilitating the integration of behavioral, biomechanical and ecological data. Our analysis of just over three-quarters of a million GPS and ACC measurements obtained from 43 free-ranging vultures across 9783 vulture-days suggests that their annual breeding schedule might be selected primarily in response to seasonal conditions favoring rising-air columns (thermals) and that rare long-range forays of up to 1750 km from the home range are performed despite potentially heavy energetic costs and a low rate of food intake, presumably to explore new breeding, social and long-term resource location opportunities.