木村 敏文

Self-grooming of the antennae is frequently observed in ants. This antennal maintenance behavior is presumed to be essential for effective chemical communication but, to our knowledge, this has not yet been well studied. When we removed the antenna-cleaning apparatuses of the Japanese carpenter ant (C. japonicus) to limit the self-grooming of the antennae, the worker ants demonstrated the self-grooming gesture as usual, but the antennal surface could not be sufficiently cleaned. By using scanning electron microscopy with NanoSuit, we observed the ants’ antennae for up to 48 h and found that the antennal surfaces gradually became covered with self-secreted surface material. Concurrently, the self-grooming-limited workers gradually lost their behavioral responsiveness to undecane—the alarm pheromone. Indeed, their locomotive response to the alarm pheromone diminished for up to 24 h after the antenna cleaner removal operation. In addition, the self-grooming-limited workers exhibited less frequent aggressive behavior toward non-nestmate workers, and 36 h after the operation, approximately half of the encountered non-nestmate workers were accepted as nestmates. These results suggest that the antennal sensing system is affected by excess surface material; hence, their proper function is prevented until they are cleaned.

Three-dimensional (3D) root system architecture (RSA) is a predominant factor in anchorage failure in trees. Only a few studies have used 3D laser scanners to evaluate RSA, but they do not check the accuracy of measurements. 3D laser scanners can quickly obtain RSA data, but the data are collected as a point cloud with a large number of points representing surfaces. The point cloud data must be converted into a set of interconnected axes and segments to compute the root system traits. The purposes of this study were: (i) to propose a new method for easily obtaining root point data as 3D coordinates and root diameters from point cloud data acquired by 3D laser scanner measurement; and (ii) to compare the accuracy of the data from main roots with intensive manual measurement. We scanned the excavated root systems of two Pinus thunbergii Parl. trees using a 3D laser scanner and neuTube software, which was developed for reconstructing the neuronal structure, to convert the point cloud data into root point data for reconstructing RSA. The reconstruction and traits of the RSA calculated from point cloud data were similar in accuracy to intensive manual measurements. Roots larger than 7 mm in diameter were accurately measured by the 3D laser scanner measurement. In the proposed method, the root point data were connected as a frustum of cones, so the reconstructed RSAs were simpler than the 3D root surfaces. However, the frustum of cones still showed the main coarse root segments correctly. We concluded that the proposed method could be applied to reconstruct the RSA and calculate traits using point cloud data of the root system, on the condition that it was possible to model both the stump and ovality of root sections.

<p>In recent ethological studies, the behaviors and interactions of animals have been recorded by digital video cameras and webcams, which provide high functionality at reasonable cost. However, extracting the behavioral data from these videos is a laborious and time-consuming manual task. We recently proposed a novel method for tracking unmarked multiple honeybees in a flat arena, and developed a prototype software named "K-Track". The K-Track algorithm successfully resolved nearly 90% of cases involving overlapped or interacted insects, but failed when such events happened near an edge of a circular arena, which is commonly employed in experiments. In the present study, we improved our K-Track algorithm by comparing the interaction trajectories obtained from forward and backward playing of video episodes. If the tracking results differed between the forward and backward episodes, the trajectory with lower maximum moving distance per frame is chosen. Based on this concept, we developed a new software, "K-Track-kai", and compared the performances of K-Track and K-Track-kai in honeybee tracking experiments. In the cases of 6 and 16 honeybees, K-Track-kai improved the tracking accuracy from 91.7% to 96.4% and from 94.4% to 96.7%, respectively.</p>

© 2017 IEEE. In recent studies, researchers can easily record the behaviors of animals by digital video cameras, which provide high functionality at reasonable cost. However, it is a laborious and time-consuming manual task for them to extract the useful behavioral data from these videos. We recently proposed a tracking method for unmarked multiple honeybees in a flat arena, named the 'K-Track' algorithm. The algorithm can successfully identify and track nearly 90% of interaction cases of targets. In this study, we proposed an improved method for the existing K-Track algorithm by tracking results using backward-play movie. If the tracking results differed between the forward and backward episodes, one of them had probably resulted from correct tracking. Therefore, by comparing the forward and backward trajectories of the same interaction, we assumed that there is the potential for an increase in tracking accuracy. In the experiments, K-Track using backward movies successfully tracked four out of five situations that was failed by original K-Track and we confirmed that the method has the potential of improved tracking accuracy.

Earthworms are important soil macrofauna inhabiting almost all ecosystems. Their biomass is large and their burrowing and ingestion of soils alters soil physicochemical properties. Because of their large biomass, earthworms are regarded as an indicator of "soil heath". However, primarily because the difficulties in quantifying their behavior, the extent of their impact on soil material flow dynamics and soil health is poorly understood. Image data, with the aid of image processing tools, are a powerful tool in quantifying the movements of objects. Image data sets are often very large and time-consuming to analyze, especially when continuously recorded and manually processed. We aimed to develop a system to quantify earthworm movement from video recordings. Our newly developed program successfully tracked the two-dimensional positions of three separate parts of the earthworm and simultaneously output the change in its body length. From the output data, we calculated the velocity of the earthworm's movement. Our program processed the image data three times faster than the manual tracking system. To date, there are no existing systems to quantify earthworm activity from continuously recorded image data. The system developed in this study will reduce input time by a factor of three compared with manual data entry and will reduce errors involved in quantifying large data sets. Furthermore, it will provide more reliable measured values, although the program is still a prototype that needs further testing and improvement. Combined with other techniques, such as measuring metabolic gas emissions from earthworm bodies, this program could provide continuous observations of earthworm behavior in response to environmental variables under laboratory conditions. In the future, this standardized method will be applied to other animals, and the quantified earthworm movement will be incorporated into models of soil material flow dynamics or behavior in response to chemical substances present in the soil. © 2014 Kodama et al.

The honeybee waggle dance communicates the location of profitable food sources, usually with a certain degree of error in the directional information ranging from 10-15° at the lower margin. We simulated one-day colonial foraging to address the biological significance of information error in the waggle dance. When the error was 30° or larger, the waggle dance was not beneficial. If the error was 15°, the waggle dance was beneficial when the food sources were scarce. When the error was 10° or smaller, the waggle dance was beneficial under all the conditions tested. Our simulation also showed that precise information (0-5° error) yielded great success in finding feeders, but also caused failures at finding new feeders, i.e., a high-risk high-return strategy. The observation that actual bees perform the waggle dance with an error of 10-15° might reflect, at least in part, the maintenance of a successful yet risky foraging trade-off.

A computer program that tracks animal behavior, thereby revealing various features and mechanisms of social animals, is a powerful tool in ethological research. Because honeybee colonies are populated by thousands of bees, individuals co-exist in high physical densities and are difficult to track unless specifically tagged, which can affect behavior. In addition, honeybees react to light and recordings must be made under special red-light conditions, which the eyes of bees perceive as darkness. The resulting video images are scarcely distinguishable. We have developed a new algorithm, K-Track, for tracking numerous bees in a flat laboratory arena. Our program implements three main processes: (A) The object (bee's) region is detected by simple threshold processing on gray scale images, (B) Individuals are identified by size, shape and spatiotemporal positional changes, and (C) Centers of mass of identified individuals are connected through all movie frames to yield individual behavioral trajectories. The tracking performance of our software was evaluated on movies of mobile multi-artificial agents and of 16 bees walking around a circular arena. K-Track accurately traced the trajectories of both artificial agents and bees. In the latter case, K-track outperformed Ctrax, well-known software for tracking multiple animals. To investigate interaction events in detail, we manually identified five interaction categories; 'crossing', 'touching', 'passing', 'overlapping' and 'waiting', and examined the extent to which the models accurately identified these categories from bee's interactions. All 7 identified failures occurred near a wall at the outer edge of the arena. Finally, K-Track and Ctrax successfully tracked 77 and 60 of 84 recorded interactive events, respectively. K-Track identified multiple bees on a flat surface and tracked their speed changes and encounters with other bees, with good performance. © 2014 Kimura et al.

In this work we investigate spatial collective decision-making in a swarm of microrobots, inspired by the thermotactic aggregation behavior of honeybees. The sensing and navigation capabilities of these robots are intentionally limited; no digital sensor data processing and no direct communication are allowed. In this way, we can approximate the features of smaller mesoscopic-scale systems and demonstrate that even such a limited swarm is nonetheless able to exhibit simple forms of intelligent and adaptive collective behavior. © The Author(s) 2012.

Information related to the growth of fine roots is important for understanding C allocation in trees and the mechanisms of C cycling in ecosystems. Observations using a camera or scanner embedded in the soil enabled us to obtain continuous images of fine-root-growth dynamics. However, these methods are still labor-intensive because the image analysis has to be conducted manually. We developed an automated method for tracking movement or elongation of fine roots using a sequence of scanner images. We also show how data obtained with these methods can be used for calculating fine-root behavior. Two A4-size scanners were buried in a mixed forest in Japan and images were taken continuously from within the soil. We preprocessed these images by extracting the fine-root area from the images and developed an automated calculation plug-in we named A-root for tracking growth movement of the tips of fine roots. A-root and manual-tracking results were compared using the same images. The results show the A-root and manual-tracking methods yielded similar levels of accuracy. The average growth rate of 17 fine roots tracked using the program was 0.16mm h-1. The observation of the direction of growth in fine roots showed the direction may be influenced by the original root's growth where the fine roots branched, distribution of soil particles, other roots, and the force of gravity. The A-root analysis also suggested there may be an interaction between speed of growth and changes in direction of growing fine roots. © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

A honeybee informs her nestmates about the location of a profitable food source that she has visited by means of a waggle dance: a round dance and a figure-of-eight dance for a short- and long-distance food source, respectively. Consequently, the colony achieves an effective collection of food. However, it is still not fully understood how much effect the dance behavior has on the food collection, because most of the relevant experiments have been performed only in limited locations under limited experimental conditions. Here, we examined the efficacy of the waggle dances by physically preventing bees from dancing and then analyzing the changes in daily mass of the hive as an index of daily food collection. To eliminate place- and year-specific effects, the experiments were performed under fully natural conditions in three different cities in Japan from mid September to early October in three different years. Because the experiments were performed in autumn, all six of the tested colonies lost mass on most of the experimental days. When the dance was prevented, the daily reduction in mass change was greater than when the dance was allowed, i.e. the dance inhibited the reduction of the hive mass. This indicates that dance is effective for food collection. Furthermore, clear inhibition was observed on the first two days of the experiments; after that, inhibition was no longer evident. This result suggests that the bee colony adapted to the new environment. © 2012. Published by The Company of Biologists Ltd.

Social insects interact extensively with their mates. To reveal the mechanism of their sociality, it is important to observe the behavior of each individual in a colony and to analyze their social contexts. Recently, digital video cameras have become less expensive despite increases in their performance, enabling researchers to record the behavior in various places easily. However, much labor and time is needed to analyze the social behavior manually from the video footage. In this study, we propose a new method to detect and track multiple bees moving on a flat surface. Our proposed method consists of three core processes: (i) detecting the bee candidate regions using a background subtraction method and binarization, (ii) identifying individuals by a combination of overlapping information in temporal changes of position and simple prediction of the candidate regions based on the bee's movement, and (iii) outputting the locations and trajectories of the identified bees. Our system succeeds in processing a video of sixteen bees moving freely on a flat arena for three minutes. More than 95% of the bees' central points were successfully extracted and their trajectories precisely traced. © 2012 IEEE.

Social activities are among the most striking of animal behaviors, and the clarification of their mechanisms is a major subject in ethology. Honeybees are a good model for revealing these mechanisms because they display various social behaviors, such as division of labor, in their colonies. Image processing is a precise and convenient tool for obtaining animal behavior data, but even recent methods are inadequate for the identification or description of honeybee behavior. This is because of the difficulty distinguishing between the large number of individuals in a small hive and their multiple movements. The present study developed a new computer-aided system, using a vector quantization method, for the identification and behavioral tracking of individual honeybees. The vector quantization method enabled separation of honeybee bodies in photographs recorded as a movie. This system succeeded in analyzing a huge number of frames quickly and can thus save both time and labor. Moreover, the system identified more than 72% of the bees in a hive and found and determined the active areas in the hive by extracting the trajectories of walking bees. In addition, useful behavioral data on the honeybee waggle dance were obtained using the present system. © INRA, DIB-AGIB and Springer Science+Business Media B.V., 2011.

It is well established mat honeybees inform their nestmates of the location of flowers they have visited by a unique behavior called a "waggle dance". To evaluate the efficacy of this dance in terms of food collection, we constructed a Markov model for foraging behavior. The computer simulation showed that our model faithfully expressed actual bee behavior; revealed that a colony in which honeybees danced and correctly transferred information made 2.15 times more successful visits to food sources compared to a colony with non-accurate information transfer; and suggested that the waggle dance is effective only under particular conditions. © 2010 International Information Institute.

The honeybee can control its hive environment to survive drastic changes in the field environment. To study the control of multiple environmental factors by honeybees, in this experiment, we developed a continual and simultaneous monitoring system for the temperature, moisture, and carbon dioxide (CO2) concentration in a honeybee hive. Changes in hive weight, CO2 production rate, and honeybee behavior were also monitored to estimate energy costs and behavioral activity for the environmental regulation. Measurements were conducted in August 2008. We found that the honeybee hive has a microclimate different from the ambient climate, and that the difference was partly accompanied by changes in honeybee activity. Our results also suggest that hive tem-perature, humidity, and CO2 concentrations are controlled by different mechanisms. Additional monitoring of the hive environment and honeybee behavior for longer periods would enable us to understand the mechanisms of environmental control by honeybees, which is one of the behaviors that define honeybees as social insects. © 2009 The Psychonomic Society, Inc.

Digital video cameras have become popular consumer items, and users are now able to create and view a lot of video contents. However, there is not an environment to view and edit them efficiently. That is, the editing environments available today are not easy enough for a general user to use all the tools effectively. Yet it seems logical that if people could edit video easily, they would produce more and more edited video. Thus, an easy-to-use editing environment would have mass appeal. In this paper, we propose an easy method of editing video contents by using the user's gaze when viewing a video. There are three factors with regard to editing video in it. With them we transform raw video contents together with information on multiple users' intentions into a semi-edited video. The semi-edited video is not a completely edited, but is ready to be easily edited by a method that can be extensively varied by repeated viewing. The paper discusses a prototype system that supports viewing such video and describes the method to recognize users' intentions. © 2005 IEEE.