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Behavioral ecology: New technology enables a more holistic view of complex animal behavior

* E-mail: [email protected]

Affiliation Department of Evolution and Ecology, University of California, Davis, California, United States of America

• Gail L. Patricelli

Published: August 24, 2023

• https://doi.org/10.1371/journal.pbio.3002264
• Reader Comments

As any animal observer will tell you, behavior is complex. A more holistic view of this complexity is emerging as technological advances enable the study of spatiotemporal variability and expand the focus from single components to behavioral systems.

Citation: Patricelli GL (2023) Behavioral ecology: New technology enables a more holistic view of complex animal behavior. PLoS Biol 21(8): e3002264. https://doi.org/10.1371/journal.pbio.3002264

Copyright: © 2023 Gail L. Patricelli. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: The author received no specific funding for this work.

Competing interests: The author has declared that no competing interests exist.

This article is part of the PLOS Biology 20th Anniversary Collection.

Behavior is more than just a suite of traits; it is the crux where the inside of the organism meets and interacts with the external environment. On the inside of the organism, behavior emerges through an interaction of genetic, physiological, cognitive, and developmental processes, which can be affected, in turn, by that organism’s behavior and experience. Behavior is also how organisms respond to—and influence—the biotic and physical environment, which includes potential mates, rivals, offspring, group members, predators, prey, and pathogens—all with their own behaviors—interacting amidst changing seasons and climates. And behaviors may manifest at multiple scales, from individuals to swarms. For the past 20 years and going forward, many of the exciting frontiers in the study of animal behavior involve grappling with this complexity in a more holistic way, examining the causes and functions of variability in behavior over space and time, and scaling up from components to systems, examining interaction networks that function as a whole.

Many breakthroughs on key aspects of animal behavior (social behavior, mate choice, communication, predator–prey dynamics, foraging ecology, and migration) have been enabled by advances in technology that allow us to collect detailed and simultaneous data from many components of complex systems ( Box 1 ). These tools are made possible by increased computing power, rapid advances in machine learning, and the development of smaller, cheaper, and more powerful hardware. Such tools are pushing the study of behavior, like other fields of science, into the “big data” era.

Box 1. Technology allowing a more holistic view of animal behavior.

Advances in both hardware for collecting data and machine learning software to analyze those data are expanding the detail and the scale at which we can study behavior.

• Animal-borne telemetry tags, which collect or transmit data about movements and other measures, can be miniaturized to much less than a gram and provide precise locations with onboard GPS or data from sensors (e.g., accelerometers, physiological monitors, microphones, or light-level monitors). Tags may store or transmit data to other tags, land-based receiver arrays, or satellites (e.g., ICARUS or MOTUS). These tags reveal aspects of animal lives that were previously unobservable, helping to identify critical resources and habitats for protection (e.g., migration corridors and refueling sites), exposure and response to stressors (e.g., human activity, noise and light pollution), and cryptic behaviors (e.g., nocturnal movements, quiet communication, and visits to potential mates).
• Other key hardware includes synchronized microphone arrays to triangulate animal positions from the arrival time of their vocalizations [ 1 ], drones with imaging tools, terrestrial laser scanning (ground-based LiDAR) for detailed habitat measures, and Passive Integrated Transponders (PIT tags).

Machine learning

• Supervised machine learning, trained on human-annotated data sets, is automating tedious tasks and making detailed analysis of large datasets more feasible.
• Unsupervised machine learning can identify new patterns in movement tracks and other behavioral data, providing insights less limited by human biases and reducing (not eliminating) subjective decisions about which characteristics to measure.
• On videos, freely available software [ 2 ] uses machine learning to track position and orientation on multiple individuals, enabling the study of social networks and swarm dynamics. Machine learning can also be used for pose estimation by tracking the relative position of multiple body parts for biomechanical studies of behaviors (e.g., DEEPLabCut [ 3 ]).
• On audio recordings, machine learning is automating detection and identification of sounds from birds, bats, and other vocal animals, enabling acoustic monitoring over time, in remote locations, and at night [ 4 ] and increasing the feasibility of using synchronized microphone arrays to study vocal behavior and movements [ 1 ].

Spatiotemporal variability is a ubiquitous feature of animal behavior. By necessity, behaviors are often measured by choosing a few key characteristics that can be scored accurately and repeatably, often averaging multiple measures from consistent conditions. This allows behaviorists to examine, for example, the relationship between courtship rate and mating success, or dominance hierarchies in social groups. While important, there is increasing awareness that fascinating biology is being averaged away, such as differences among individuals in the ability to execute behaviors consistently or adapt to changing social and environmental situations, or variation among groups in the stability of social networks [ 5 – 7 ]. The past few decades have seen frameworks for understanding aspects of this behavioral variation, such as consistent individual differences (CIDs) and personality, behavioral reaction norms, and dynamic social network analyses, but the difficulty of collecting data has limited the scope of empirical work.

To capture and analyze variability itself, we need enough snapshots to make a movie, multiple measures of behaviors within and among individuals or groups, across time and context, so the patterns of change can be examined. New hardware and machine learning algorithms for tracking movements and recognizing patterns are opening exciting new opportunities for collecting such data [ 2 , 4 , 8 ].

For example, using GPS telemetry tags in the wild or overhead video in captive enclosures, it is increasingly feasible to study the causes and consequences of CIDs in behavior, such as activity level or aggressiveness, by tracking multiple individuals throughout development or among contexts. Patterns of behavioral variation can then be examined relative to genotype, epigenetics, experience, adult behavior (of the focal animal, their parents, and their offspring), and social group dynamics. In fish, for example, tracking has revealed that CIDs in behavior among clonal mollies raised in identical conditions are present from birth and strengthen over time [ 9 ] and that CIDs among sticklebacks in sociability and boldness can affect the movement and foraging performance of entire shoals [ 10 ].

Similar machine learning algorithms can track the position of body parts for pose estimation, automating frame-by frame analysis of biomechanics during courting, fighting, prey capture, locomotion, and other behaviors [ 3 , 8 ]. This can save time, expand the number of traits measured on focal or interacting individuals, and reduce subjectivity in analyses ( Box 1 ). These opportunities for high-resolution data collection will (I hope) inspire further development of theory in neglected areas, such as optimal tactics during courtship and other dynamic behavioral interactions [ 5 ].

New tracking tools are also helping us to scale up from spatiotemporal analyses of behavioral components to a systems-level view of the whole. The systems approach focuses on structure–function relationships, moving from cause-and-effect thinking to synergistic thinking, by emphasizing interactions, linkages, and integrated phenotypes [ 5 , 11 ].

For example, a hot topic of research for more than 20 years has been why sexual selection frequently favors complex courtship displays with components in different sensory modalities, combining songs, dances, colors, scents, and vibrations [ 5 , 6 , 11 ]. A recent comparative analysis of the famously complex and spectacular displays of 40 species of birds-of-paradise utilized video and audio recordings, as well as color patterns from museum skins, finding positive relationships instead of trade-offs between complexity in the acoustic, color, and behavioral display components [ 12 ]. The authors argued that integrated suites of traits evolve as a courtship phenotype, with functional overlap and interdependency providing robustness and promoting diversification. Further research is needed to determine whether similar patterns emerge in the complex courtship displays of other clades of birds, as well as clades of reptiles, amphibians, fishes, insects, and spiders. With machine learning tools for automated data collection, such broad comparative analyses are becoming possible with growing online databases, such as libraries of audio and video recordings and 3D scans of museum specimens. Ultimately, to understand the evolution of complex courtship phenotypes, as in birds-of-paradise, we must also understand how male display components interact to stimulate the females’ sensory, cognitive, and motivational systems to influence their mate choice. In other words, a holistic approach is also required to understand the aesthetic experiences and complex preferences of the females these courtship displays evolved to impress. This interface between behavioral ecology and neuroethology promises exciting discoveries about the evolution of some of nature’s most beautiful spectacles.

Systems-level analyses of multicomponent social groups have been similarly insightful. Tracking of large groups of birds is revealing surprisingly complex, multilevel social systems, from families, to cohesive groups of unrelated individuals, to fission–fusion dynamics among groups, to structured flocks of interacting species [ 13 ]. Tracking is also allowing the detailed examination of collective behaviors [ 10 , 14 ], exploring how behavioral rules followed by individuals scale into emergent properties of groups, such as swarming behavior of locusts. For example, by modelling how group size and spacing affect individuals’ views through the crowd, researchers are learning how geometry affects swarm dynamics and collective decisions.

Along with benefits of new technology, come challenges. To name a few, minimizing the impacts of our technology on animal bearers, finding meaningful biology in the output of black box algorithms, and not letting data volume and high statistical power substitute for thoughtful experimental design and biologically relevant effect sizes. Downloading data from satellites is no substitute for time in the field or lab learning about natural history and carefully observing behaviors, which is essential to inspire creativity and anchor us to the real world. At its best, new technology complements existing methods and helps to reveal hidden dimensions of behavior. Moving into the big data era, animal behavior, like other fields, can minimize pitfalls by increasing transparency, standardization, and sharing of data, algorithms, and statistical code.

As the pace of urbanization, habitat loss, climate change, and other human impacts increase, behavior will often be the first response, either allowing animals to adjust to change, or not. Behavioral changes are often the first signs scientists can measure as evidence of human impacts. Behavior is also what often inspires public fascination and concern about wildlife. Therefore, in addition to addressing basic questions about behavioral evolution, new technology and a more holistic view of animal behavior is key to understanding, predicting, and mitigating human impacts on wildlife. For example, behaviorists are revealing how noise and light pollution impact social behaviors, improving methods for population monitoring and restoration, and reducing human–wildlife conflict. The next 20 years will bring increased opportunity and increased necessity for animal behaviorists to engage actively with conservationists, policy makers, stakeholders, and the public to find solutions to these complex problems.

Acknowledgments

The author apologizes to the hundreds of authors and ideas in the field of animal behavior that there was insufficient space to credit here.

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Editorial article, editorial: insights in animal behavior and welfare: 2021.

• 1 Faculty of Science, School of Agriculture and Food Sciences, The University of Queensland, St. Lucia, QLD, Australia
• 2 Centre for Animal Science, Queensland Alliance for Agriculture & Food Innovation, The University of Queensland, St. Lucia, QLD, Australia

Editorial on the Research Topic Insights in animal behavior and welfare: 2021

We are now entering the third decade of the twenty-first Century, and, especially in the last years, the achievements made by scientists have been exceptional, leading to major advancements in the fast-growing field of Animal Behavior and Welfare. In 2021, Frontiers organized a series of Research Topics to highlight the latest advancements in research across the field of Animal Behavior and Welfare, with articles from the members of our accomplished Editorial Board. This editorial initiative focusses on new insights, novel developments, current challenges, latest discoveries, recent advances, and future perspectives in the field of Animal Behavior and Welfare. The Research Topic solicited brief, forward-looking contributions from the editorial board members that describe the state of the art, outlining recent developments and major accomplishments that have been achieved and that need to occur to move the field forward.

The goal of this special edition Research Topic was to shed light on the progress made in the past decade in the Animal Behavior and Welfare field, and on its future challenges to provide a thorough overview of the field. This article collection will inspire, inform and provide direction and guidance to researchers in the field.

In 2021 Edition of this Topic, we show a collection of 6 peer reviewed articles which highlight the different advancements in the fields of animal welfare and behavior.

The first manuscript by Narayan et al. studied novel epigenetic markers, activity budget, physiological stress responses (wool cortisol) and wool quality of Merino sheep ( Ovis aries ) under single or twice annual shearing practice. Ewes managed under twice annual shearing expressed significantly lower levels of wool cortisol, 10% higher grazing activity and the lambs were born with better wool phenotype quality in terms of micron, spin fineness, and curvature. Novel epigenetic markers were discovered in the Merino ewes and lambs which can be evaluated further for improving genomic tools for sheep breeding and welfare programs.

In the second research, Sun et al. investigated the tongue rolling stereotypic behavior of Dairy Cows as an indicator of welfare. Researchers selected a cohort of 10 Holstein cows with or without tongue-rolling behavior and measured physical conditions, daily activity, rumen fermentation, and milk production. The results provided both physiological stress and metabolic differences in cows with or without tongue-rolling behavior. Cows with tongue-rolling behavior on average had high serum indicators of physiological stress. These cows also had higher energy metabolic status and they also showed more often drinking and lying behavior. The research provides baseline knowledge for further exploring the management of tongue-rolling behavior in dairy cows.

Three research articles were based on the welfare of dogs in various working environments, including animal-assisted interventions, police work, and dangerous fieldwork involving harmful chemicals.

Firstly, Miller et al. conducted a review of the welfare characteristics and temperament in working therapy dogs, with focus on positive affective state of the therapy dogs working in animal-assisted interventions. The researchers evaluated publications to determine the suitable biomarkers of the HPA axis, which can be used to evaluate positive welfare in therapy dogs. The review suggested that oxytocin could be used as an index of positive welfare as studies have shown that peripheral concentrations of oxytocin increases in dogs during positive social and affiliative interactions, including human-dog interaction. Aside from physiological and behavioral measures, the researchers also recommended that future studies of positive welfare assessments in therapy dogs should also consider the breed and temperament of the dog as influencing factors.

Gobbo and Šemrov conducted research on dogs to assess the self-control abilities under aggression reactivity. The study was based on police and privately owned dogs to study the associations between two aspects of inhibitory control in dogs, self-control, and cognitive inhibition. Police dogs showed higher aggression levels and poorer self-control than privately owned dogs, however no difference in cognitive inhibition. Researchers concluded that self-control or ability to tolerate delayed rewards, is key determinant of inhibitory control ability in police dogs.

Jarrett et al. carried out research on the working dogs that are exposed to dangerous work environments or harmful agent exposure. They evaluated the access to personal protective equipment and canine-specific field-use ready decontamination techniques and kits for use on working dogs, especially for exposure to harmful biologic or chemical agents.

Finally, in the sixth article in this Research Topic, Marchetti et al. evaluated the use of the international classification of diseases (ICD-11) method for veterinary forensic pathology for coding the cause and manner of death in wildlife. The research included the manner and the cause of death of 167 wild animals of 16 different species. Researchers concluded that the use of the ICD-11 method, as a sort of summary of the autopsy report, was confirmed to be of great value for the clarity and simplicity of processing the data collected also by veterinary pathologists. This tool has potential for future research to evaluate the human impact on wildlife in a scientific and statistically usable way.

Overall, this Topic highlights some the recent developments in the fields of animal welfare and behavior.

Author contributions

The author confirms being the sole contributor of this work and has approved it for publication.

Acknowledgments

The editor thanks the Frontiers admin team for their wonderful support with the Research Topic as well as the numerous peer reviewers.

Conflict of interest

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Keywords: animal welfare, animal behavior, animal assisted interventions, animal therapy, stress, affective neuroscience

Citation: Narayan E (2022) Editorial: Insights in animal behavior and welfare: 2021. Front. Vet. Sci. 9:988463. doi: 10.3389/fvets.2022.988463

Received: 07 July 2022; Accepted: 14 July 2022; Published: 29 July 2022.

Edited and reviewed by:

Copyright © 2022 Narayan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Edward Narayan, e.narayan@uq.edu.au

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Insights in Animal Behavior and Welfare: 2021

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Analysis of activity rhythm and behavior pattern for plateau pika in degraded alpine meadow

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• Published: 27 April 2024
• Volume 6 , article number  226 , ( 2024 )

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This paper proposes a behavior observation method for plateau pika ( Ochotona curzoniae ) in degraded alpine meadow in the Source Zone of the Yellow River (SZYR) on the Qinghai–Tibet Plateau based on continuous monitoring videos to observe its activity rhythm. The video surveillance cameras are installed facing toward degraded alpine meadow in the SZYR and run from March 2021 to February 2022 to record time allocation of plateau pika behavior (i.e., digging, foraging, guarding, moving and other behaviors) and its daily, monthly, and seasonal activity rhythm. The results indicate that: (1) The most active ground activity time is between 10:00 and 16:00, meanwhile it is inactive at night, which proves it plateau pika is diurnal animals. (2) Digging and foraging are frequently occurring behaviors among its daily activities, which occurs from 9:00 to 12:00 and 16:00. (3) Compare to other season, winter sees relatively frequent occurrences of digging and foraging according to the seasonal analysis results. (4) From annual perspective, digging and foraging accounted for 66.13% of plateau pika activity, while guarding accounted for 13.35%, moving accounted for 11.64%, and other behaviors accounted for 8.88%. There is a significant difference in the frequency of each behavior between the two groups ( P  < 0.05). The behavior observation method using video surveillance camera contributes to behavioral ecology information of plateau pika.

Article Highlights

The most active ground activity time is between 10:00 and 16:00, meanwhile it is inactive at night.

Digging and foraging are the most frequent behavioral patterns of plateau pika, and it changes with seasons.

The most active plateau pika behavior is foraging which occurs in spring as vegetation green-up (in April).

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1 Introduction

Animal activity rhythm analysis is one of the important components in behavioral ecology [ 1 ]. Activity rhythm reveals not only periodic and regular changes of animals over time and space, but also animals’ adaption to complex environment during long-term evolution process [ 2 ]. Observing the activity rhythm of animals and studying their adaptation strategies are also helpful to understand the ecological behavior of animals, their ecological habits, the interactions between species and the environment under different pressure of animals. The relationship between animals and grassland ecosystem is critical for grassland ecosystem stability and protection [ 3 , 4 ]. This study clarifies plateau pika activity rhythm through video surveillance data and a scientific behavior observation method.

Line transect method, captivity method, observation method are conventional methods for animal activity rhythm study [ 5 , 6 ]. As escaping behavior is the first choice for wild animals when they are facing human activities [ 7 , 8 ], and highly-intensive human activities change their inherent behavior patterns [ 9 , 10 ]. The reasonable answer is video surveillance system in order to avoid stress reaction or interfere with their behavior during monitoring, plus video surveillance data obtain daily activity patterns of wild animals continuously without any disturbance as the equipment normally does not interfere with monitored animals. Therefore, It has been widely used for wildlife monitoring, investigation, rhythm and activity surveillance in animal ecology research [ 11 , 12 ].

Plateau pika is one of the important small mammals in alpine meadow and grassland on the Qinghai-Tibet Plateau. Their habitat lies at an altitude of 3000–5300 m, mainly distributed in western China and adjacent Nepal, Sikkim and other places nearby [ 13 ]. Plateau pika is regarded as an important indicator of wildlife species diversity on the Qinghai-Tibet Plateau and also considered a pest when its population reaches the outbreak proportion [ 14 , 15 ]. In the former role, they were the main source of food for almost all predators, and their abandoned caves could serve as nests for birds and reptiles. Their burrowing activities increase the infiltration rate of water, reduce surface flow on the Qinghai-Tibet Plateau, and provide key ecosystem services [ 16 ]. In the latter role, especially at a high density, they cause habitat fragmentation and the formation of many denudated patches in healthy grassland [ 17 ] and reduce grassland productivity and forage availability for livestock [ 18 ]. At the same time, plateau pika runway is also one of the reasons for the expansion of degraded patches in alpine meadows, which also accelerates the connectivity of degraded patches in alpine meadow [ 19 ]. Therefore, when the plateau pika population reaches a certain density, they damage grassland ecosystem. At present, research work on plateau pika has been carried out, including their plateau adaptation mechanism [ 20 ], population dynamics [ 21 ], diet analysis [ 22 ], habitat selection [ 23 ] and so on. For studying plateau pika activity rhythm, Zong et al. analyzed the effects of different intensity on the circadian activity rhythm and ground activity of plateau pika [ 24 ]. As few have analyzed the activity rhythm and behavior pattern of plateau pika, this work set up a video surveillance system in alpine meadows in the SZYR from March 2021 to February 2022 to study the activity rhythm and behavior pattern of plateau pika. The purpose of this study is to assess the ecological habits of the plateau pika as its activity rhythm and temporal pattern is crucial to analyze the adverse effect on grassland in settlements, and provide a detail behavioral ecology mechanism of plateau pika for scientific meadow ecosystem management.

2.1 Study area

The research area is located on the floodplain (101 47′ E, 34 44′ N) of the Keqi River Beach in Henan Mongolian Autonomous County, Huangnan Prefecture, Qinghai Province (Fig.  1 ), with an average altitude of 3,758 m. The terrain is complex, with mountains, valleys and alluvial plains adjacent each other. The Keqi River Beach measures about 10 km from east to west, 4 km wide from north to south, with an area of 35 km 2 , and 18 km away from the southeast of Henan county. The total vegetation coverage is less than 80%, mainly compose Kobresia humilis , Kobresia pygmaea , Elymus nutans, Poa pratensis , Ajania tenuifolia , etc., and the rest are denudated patches (20%).

Location of the study area ( A Qinghai-Tibet Plateau; B SZYR; C Henan county)

2.2 Experimental design

Three experimental plots of 25 m × 30 m are set up on degraded alpine meadow [ 25 , 26 ]. In order to prevent plateau pika from disturbance these plots, the underground boundary of each plot is sealed with a mesh diameter of 2.5 cm × 2.5 cm, a custom wire mesh fence of 50 cm height, a rat-proof iron fence with of 50 cm sealed the above-ground part, and a steel gauze net and galvanized iron sheet are respectively connected with 10 cm rivets to prevent the plateau pika from passing through the gap between the steel screen and galvanized iron. Based on literature data and actual field observation [ 19 ], the density test areas of different plateau pika are designed according to high density active areas and has 200 pika per hectare. Each plot was recorded as an independent experimental group with 7 plateau pika (3 males and 4 females) for one year (from March 2021 to February 2022), A mousetrap was placed in each community, and the captured plateau pika were investigated in number and sex, and then released according to the experimental design.

2.3 Installation of video image acquisition equipment

After the plots have been set up properly, monitoring equipment is installed at the plot corner to collect video image data of plateau pika activity. The installation scheme is determined according to the monitoring camera equipment parameters (resolution, rotation angle, infrared distance, power supply, etc.), as well as experimental objectives. A Haikangwei Field Surveillance camera (DS-2XS3T26-IHGLE/CH20S80) is fixed on a 2 m high bracket (Fig.  2 A). After the installation is completed, the video data of plateau pika activities are captured and observed to verify that its performance meets the experimental of the application. The resolution of the captured video image is 1920 * 1080, and the moving target is less than 30 × 60 in the whole image. When the verification conditions are met, the observation time is adjusted according to the experimental requirements (Fig.  2 B).

Image acquisition equipment and video shooting effect display

2.4 Behavior observation and classification of Ochotona curzoniae

According to the monitoring video data, the diurnal aboveground activity of plateau pika is observed on the recorded photographs directly. According to Reference [ 27 ], behaviors of plateau pika can be divided into four categories: digging and feeding, guarding, moving, and others (sitting, mowing, ground eyes closed rest) (Table  1 ). These behaviors (digging, foraging, guarding, moving, and others) are studied from the recorded photographs. The activity of plateau pika is continuously recorded for 24 h at hourly intervals from March 2021 to February 2022 in the middle of each month, screening 3 typical sunny days conducive to observing plateau pika normal activities.

2.5 Data analysis

The monitoring camera record 108 sunny days, in the meantime plateau pika behaves normally (when they digging and foraging, guarding, moving, etc.). The rodent activities data for further analysis duration is 2592 h and transformed into a continuous image sequence based on MATLAB software. The target plateau pika is identified based on the difference of the image sequence per second, and the time when the target appears is recorded times. Finally, its behavior pattern is observed visually. Images with plateau pika are marked as valid data. Then a database is established based on acquired image data information. The data processing flow is shown in Fig.  3 .

Data process flow

Data statistics and analysis is processed based on MATLAB, and Origin 2021 is used for plotting. The rhythm activity is studied via the time-period relative abundance index (TRAI) and behavioral frequency. The collected data were analyzed for different activity rhythm and activity pattern of plateau pika.

(1) Activity rhythm by time-period relative abundance index is:

where TRAI represents the time-period relative abundance index. T is the number of valid image sequences of plateau pika per hour, and N is the total number of image sequences per hour.

Behavioral frequency is the frequency of various behaviors under different spatio-temporal scales that range from daily to monthly and to seasonally. The calculation formula is as follows:

where i is the time period (e.g., month and season); k represents the activity behavior of the Ochotona curzoniae (such as digging, feeding, moving, vigilance, etc.); T i is the frequency of the corresponding behavior in the i th period. B k is the total number of k behavior activities; P i represents the frequency of the corresponding behavior in the i th period. Higher P i sees more frequent behavior.

3.1 Daily activity rhythm

The rhythmic behavior data are analyzed hourly. As can be seen from Fig.  4 , plateau pika is more active during the observation period compare with other time, and the relative richness curve daily presented as M-shape. Their aboveground activity time is from 6:00 to 19:00, meanwhile no activity is seen during night (20:00-06:00). Therefore plateau pika is diurnal animal that has two obvious peaks in daily activities at 10:00 and 16:00 respectively.

Relative richness index of daily activity time of plateau pika

The main daily behavior of plateau pika is digging and foraging, followed by guarding and moving (Fig.  5 ). Daily behavior rhythm shows that foraging behavior is at a high level, and digging and foraging occurred between 9:00 and 12:00 and at 16:00 with the highest frequency. The foraging behavior accounted for the largest proportion in each time period, while guard, movement and others remains low and do not see significant change.

Frequency of Sun-Moon Behavior of Plateau pika

3.2 Monthly activity rhythm of Ochotona curzoniae

The behavioral frequency distribution of plateau pika varies monthly (Fig.  5 ). The digging and foraging activity frequency reaches their highest level in April (71.62%) and lowest level in October (60.52%). The activity frequency of guarding behavior reaches the highest level in August (16.37%) and lowest level in December (10.80%). Moving behavior reaches the highest level in October (13.23%) and lowest level in January (9.77%). The activity frequency of other behaviors reaches the highest level in July (10.45%) and lowest level in April (7.21%).

3.3 Seasonal activity rhythm of Ochotona curzoniae

The digging, foraging, guarding, moving and other behaviors of plateau pika are statistically analyzed seasonal. Behavioral differences of Plateau pikas is seen in each season, mainly are foraging, vigilance, movement and other behaviors (Figs. 6 and 7 ). The average frequency of digging and foraging reaches the highest level in winter (70.06%), followed by spring (69.62%), autumn (64.12%) and summer (61.37%). The digging and foraging frequency is significantly higher during spring and winter, compare with summer and autumn. Meanwhile, foraging frequency is significantly higher in spring and winter compare with summer and autumn caused by lack of food (P < 0.05). The average frequency of guarding behavior in summer (15.41%) and autumn (14.54%) is higher, compare with spring (11.66%) and winter (11.21%) (P < 0.05). The season sorted by average frequency of moving behavior is: autumn > summer > spring > winter, without obvious difference. The season sorted by average frequency of other behaviors is: summer > autumn > winter > spring, where a highest number is seen in summer (P < 0.05).

Changes of behavior frequency of plateau pika in different seasons ( a digging and foraging; b guarding; c moving; d others)

3.4 Average annual activity rhythm of Ochotona curzoniae

Digging and foraging accounted for 66.13% of the total behavior distribution, 13.35% for guarding, 11.64% for moving, and 8.88% for other behaviors. The results of one-way ANOVA shows that the average annual behavioral frequency of plateau pika is as follows: digging foraging > guarding > moving > others, and there was significant difference between the two groups ( P  < 0.05) (Fig.  8 ).

Distribution of behavior time of plateau pika throughout the year

4 Discussion

The activity patterns of animals are related to aboveground biomass, interspecific competition, predation risk, solar radiation, light level, ambient temperature, wind speed and other factors. The plateau pika behavioral differences indicate that animal behavior patterns changes in different seasons. Vegetation traits is one of the most important environmental factors affecting plateau pikas behavior patterns, because vegetation not only determines the richness and quality of their food sources, but also affects other habitat factors like risk aversion etc. These are inevitable results of long-term co-evolution for animals and environment [ 28 , 29 ]. In this study, the average daily ground activity of plateau pikas lasted from 6: 00 a. m. to 19: 00 p. m. The relative abundance of daily activity time has two peaks, the first activity peak time is at 10: 00 in the morning, while the second activity peak time is at 14: 00 in the afternoon. The solar radiation is strong due to the direct sunlight at noon, so the activity frequency of plateau pika decreased.

In this study, differences in the behavior rhythm and pattern of plateau pikas between different seasons is studied. The proportions and time of digging and foraging behaviors among plateau pikas behavior rhythm reaches 66.13%, which is significantly higher than other behavior. For the daily activities of plateau pikas, digging foraging account for the highest proportion of time. Plants are in a withered period in spring and winter, as plateau pikas facing food resources shortage, they spend longer time daily inevitably, resulting in higher proportion of digging and foraging behaviors and moving time. Plants enter growth period in summer, gives abundant food resources, and the proportion of foraging behavior of plateau pikas decrease consequently. At the same time, during the peak period of plant growth in summer, with the increase of vegetation height, the frequency of vigilance behavior of plateau pikas increase successively, and the proportion of vigilance behavior is also the highest throughout the year.

Based on the monthly activity frequency analysis, the highest and lowest frequency of different behaviors of plateau pika are different monthly. The digging and foraging behaviors frequency reach the highest level in April. Plateau pika begin to breed from March and temperature rising at the beginning of April [ 30 , 31 , 32 ], when is the early stage of plant growth with few above-ground biomass. The plateau pika increase their ground activity intensity and excavate more regreening seedlings. Therefore digging and foraging behaviors are found more frequently in April other than other months, and corresponding alert and mobile behavior frequency are relatively low. Plateau pikas need more energy because their reproduction, so the frequency of feeding activity is higher than that in other months. The regreening period of forage is the most important period of grassland ecosystem in the SZYR. Once the regreening seedlings are eaten, their later growth and development will be seriously affected. Therefore, in order to slow down degraded alpine meadow, the measurement such as rodent control should be carried out during March.

Plant growth period in grassland in the SZYR is between May and August, gives abundant food resources to plateau pika, their digging and foraging activities decline for this reason. However, vegetation height increase leads to higher predation risk and vigilance activities. Therefore, the corresponding frequency of guarding and moving behaviors is low as well. Therefore, in order to reduce the occurrence of large areas of alpine meadows, plateau pika control measures should be carried out before March. The frequency of guarding behavior reaches the highest level in August, because of the plant growth peak, as well as the vegetation height. Some studies shows a positive correlation between plateau pika guarding behavior and vegetation height [ 33 ]. The plateau pika's line of sight is blocked by tall vegetation, which is not conducive to the discovery of natural enemies [ 34 , 35 ]. Therefore, the frequency of vigilance behavior reaches the highest level in August. Ground moving frequency is generally considered as animal activity intensity in the field [ 36 ]. The temperature decline in September, plants enter withered period gradually, so the movement behavior of plateau pikas increase consequently, overwintering food storage of plateau pika is another reasonable cause. Temperature plummets and sunshine time is shorter in winter, so plateau pikas reduce their ground activity time, with digging and foraging behaviors occupy the largest proportion. This indicates that plateau pikas face serious food resources shortage due to lower above-ground biomass in winter, so they spend more time on digging.

Future research should focus on their activity rhythm, especial its temporal pattern to reduce their adverse effects on grassland health and to prevent grassland degradation. Focusing on the analysis of activity rhythm and behavior pattern of plateau pika under different grazing intensity and different density of pika could provide scientific basis for the formulation of prevention and control strategies and methods of plateau pika damage in alpine meadow with different plateau pikas density.

5 Conclusion

The plateau Pika activity rhythm is a periodic and regular change of long-term adaptation to complex environment of alpine meadow. The study results indicate that: 1. Digging and foraging behaviors in the behavior rhythm of plateau pikas accounts for 66.13%. Considering the above-ground vegetation growth status, plateau pika are facing food shortage. 2. The foraging behavior frequency of plateau pika reach the highest level in April, at the same time, they are also in breeding period. In order to accurately evaluate the impact of plateau pika activities on alpine meadow, pika distribution is investigated in April rather than August.

Data availability

The data that support the findings of this study are available in the Additional files of this article.

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This study was funded by the National Natural Science Foundation of China (U23A20159, U21A20191), the Scientific Research Fund of Young Teachers in Qinghai University (2021-QNY-12), the 111 Project of China (D18013), Qinghai Science and Technology Innovation and Enterpreneuship Team Project titled 'Sanjiangyuan Ecological Evaolution and Management Innovation Team' and the project of ecosystem succession and management direction in the world-class discipline of ecology at Qinghai University.

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XS contributed significantly to analysis and manuscript preparation; XL contributed to the conception of the study; HS, ZS helped perform the analysis with constructive discussions.

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All animal procedures were approved by the local ethical committee in accordance with the “Institute Ethical Committee Guidelines” for animal experimentation and care.All animal experiments were approved by the Ethics Committee of Qinghai University. All animal handling procedures were conducted strictly in accordance with the regulations of the P.R. China on the use and care of laboratory animals. All protocols for experiments were approved by the Institutional Animal Care and Ethics Committee of Qinghai University.

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Video Recording of Animals Use in Research and Teaching. Three experimental plots of 25 m × 30 m were set up on degraded alpine meadow. A mousetrap was placed in each community, and the captured plateau pika were investigated in number and sex, and then released according to the experimental design. which were put into the plot with a sex ratio of 1:1. Each plot was recorded as an independent experimental group with 7 plateau pika (3 males and 4 females) for one year (from March 2021 to February 2022). All plateau pika were habituated to the plots and had ad libitum access to food and water.

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Su, X., Li, X., Sun, H. et al. Analysis of activity rhythm and behavior pattern for plateau pika in degraded alpine meadow. Discov Appl Sci 6 , 226 (2024). https://doi.org/10.1007/s42452-024-05852-y

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Test reveals mice think like babies

'surprisingly strategic' behavior deepens our understanding of animal cognition.

Are mice clever enough to be strategic?

Kishore Kuchibhotla, a Johns Hopkins University neuroscientist who studies learning in humans and animals, and who has long worked with mice, wondered why rodents often performed poorly in tests when they knew how to perform well. With a simple experiment, and by acting as "a little bit of a mouse psychologist," he and his team figured it out.

"It appears that a big part of this gap between knowledge and performance is that the animal is engaging in a form of exploration -- what the animal is doing is very clever," he said. "It's hard to say animals are making hypotheses, but our view is that animals, like humans, can make hypotheses and they can test them and may use higher cognitive processes to do it."

The work, which deepens our understanding of animal cognition, and could lead to identifying the neural basis for strategizing, published in Current Biology .

Kuchibhotla's lab previously found that animals know a lot more about tasks than they demonstrate in tests. The team had two theories about what could be behind this gap. Either the mice were making mistakes because they were stressed, or they were doing something more purposeful: exploring and testing their knowledge.

To figure it out Kuchibhotla and Ziyi Zhu, a graduate student studying neuroscience, came up with a new experiment.

Mice heard two sounds. For one sound they were supposed to turn a wheel to the left. For the other sound, they'd turn the wheel to the right. When the mice performed correctly they were rewarded.

The researchers observed how upon hearing either sound over consecutive trials, the mice would turn the wheel left for a bit, then switch to turning it right, seemingly making mistakes but actually being purposeful.

"We find that when the animal is exploring, they engage in a really simple strategy, which is, 'I'm going to go left for a while, figure things out, and then I'm going to switch and go right for a while,'" Kuchibhotla said. "Mice are more strategic than some might believe."

Zhu added, "Errors during animal learning are often considered as mistakes. Our work brings new insight that not all errors are the same."

The team learned even more about the rodents' actions by taking the reward out of the equation.

When a mouse performed correctly and wasn't rewarded, it immediately doubled down on the correct response when retested.

"If the animal has an internal model of the task, the lack of reward should violate its expectation. And if that's the case, it should affect the behavior on subsequent trials. And that's exactly what we found. On subsequent trials the animal just does a lot better," Kuchibhotla said. "The animal is like, 'Hey, I was expecting to be rewarded, I wasn't, so let me test my knowledge, let me use the knowledge I have and see if it's correct.'"

If the animal didn't have an internal model of the task, there would be no expectations to violate and the mice would keep performing poorly.

"At a very early time in learning the animal has an expectation and when we violate it, it changes its strategy," Kuchibhotla said. "It was surprisingly strategic."

This mouse strategizing is comparable to how nonverbal human babies learn. Both are highly exploratory and both may test hypotheses in various ways, Kuchibhotla said.

During the experiments Kuchibhotla said he became "a little bit of a mouse psychologist" to interpret their behavior. Like working with a nonverbal infant, he and Zhu had to infer the underlying mental processes from the behavior alone.

"That's what was really fun in this project, trying to figure out what the mouse is thinking," he said. "You have to think about it from the perspective of the animal."

Next the team hopes to determine the neural basis for strategic thinking, and how those strategies might compare across different animals.

• Intelligence
• Educational Psychology
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Materials provided by Johns Hopkins University . Original written by Jill Rosen. Note: Content may be edited for style and length.

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• Ziyi Zhu, Kishore V. Kuchibhotla. Performance errors during rodent learning reflect a dynamic choice strategy . Current Biology , 2024; DOI: 10.1016/j.cub.2024.04.017

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162 Best Animal Research Topics To Nail Your Paper In 2023

The world is filled with living things. There are some animals that we know about, some that we will discover, and there are many that we might never know about. All our knowledge about animals is mostly dependant on researchers. Well, we are rooting for you to be the next great researcher. Be it zoology, veterinary, or live wild stock, your study needs a research topic. If you’re looking for the best animal research topics to nail this year, we’re here with your help.

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• Why do people kill animals? The psyche behind animal cruelty
• Evaluation of the growth performance of three sheep breeds
• Study on the protection of terrestrial ecosystems
• Ecology of African dung beetles
• Effects of road infrastructure on wildlife in developing countries
• Analysis of the consequences of climate change related to pastoral livestock
• Strategies for management in the animal feed sector
• The feeding behavior of mosquitoes
• Bee learning and memory
• Immediate response to the animal cruelty
• Study of mass migration of land birds over the ocean
• A study of crocodile evolution
• The cockroach escape system
• The resistance of cockroaches against radiation: Myth or fact?
• Temperature regulation in the honey bee swarm
• Irresponsible dog breeding can often lead to an excess of stray dogs and animal cruelty
• Reliable communication signals in birds

Also see:  How to Write an 8 Page Research Paper ?

Animal Research Topics For University

• Color patterns of moths and moths
• Mimicry in the sexual signals of fireflies
• Ecophysiology of the garter snake
• Memory, dreams regarding cat neurology
• Spatiotemporal variation in the composition of animal communities
• Detection of prey in the sand scorpion
• Internal rhythms in bird migration
• Genealogy: Giant Panda
• Animal dissection: Severe type of animal cruelty and a huge blow to animal rights
• Cuckoo coevolution and patterns
• Use of plant extracts from Amazonian plants for the design of integrated pest management
• Research on flying field bug
• The usefulness of mosquitoes in biological control serves to isolate viruses
• Habitat use by the Mediterranean Ant
• Genetic structure of the  African golden wolf  based on its habitat
• Birds body odor on their interaction with mosquitoes and parasites
• The role of ecology in the evolution of coloration in owls
• The invasion of the red swamp crayfish
• Molecular taxonomy and biogeography of caprellids
• Bats of Mexico and United States
• What can animal rights NGOs do in case of animal cruelty during animal testing initiatives?

Or you can try 297 High School Research Paper Topics to Top The Class

Controversial Animal Research Topics

• Is it okay to adopt an animal for experimentation?
• The authorization procedures on animals for scientific experiments
• The objective of total elimination of animal testing
• Are there concrete examples of successful scientific advances resulting from animal experimentation?
• Animal rights for exotic animals: Protection of forests and wildlife
• How can animal rights help the endangered animals
• Animal experimentations are a type of animal cruelty: A detailed analysis
• Animal testing: encouraging the use of alternative methods
• Use of animals for the evaluation of chemical substances
• Holding seminars on the protection of animals
• Measures to take against animal cruelty
• Scientific research on marine life
• Scientific experiments on animals for medical research
• Experimentation on great apes
• Toxicological tests and other safety studies on chemical substances
• Why isn’t research done directly on humans rather than animals?
• Are animals necessary to approve new drugs and new medical technologies?
• Are the results of animal experiments transferable to humans?
• Humans are not animals, which is why animal research is not effective
• What medical advances have been made possible by animal testing?
• Animals never leave laboratories alive
• Scientific interest does not motivate the use of animal research
• Animal research is torture 
• How can a layperson work against the animal testing?

Every crime is a controversy too, right? Here are some juicy  Criminal Justice Research Paper Topics  as well.

Animal Research Topics: Animal Rights

• Growing awareness of the animal suffering generated by these experiments
• What are the alternatives to animal testing?
• Who takes care of animal welfare?
• Major global organizations working for animal rights
• Animal rights in developing countries
• International animal rights standards to work against animal cruelty
• Animal cruelty in developing countries
• What can a layperson do when seeing animal cruelty
• Role of society in the prevention of animal cruelty
• Animal welfare and animal rights: measures taken against animal cruelty in developing countries
• Animal cruelty in the name of science
• How can we raise a better, empathetic and warm-hearted children to put a stop to animal cruelty
• Ethical animal testing methods with safety
• Are efforts being made to reduce the number of animals used?
• The welfare of donkeys and their socioeconomic roles in the subcontinent
• Animal cruelty and superstitious conceptions of dogs, cats, and donkeys in subcontinent
• Efforts made by international organizations against the tragedy of animal cruelty
• International organizations working for animal welfare
• Animal abuse: What are the immediate measures to take when we see animal cruelty
• Efforts to stop animal abuse in South Asian Countries
• Animal abuse in the name of biomedical research

Talking about social causes, let’s have a look at social work topics too: 206  Social Work Research Topics

Interesting Animal Research Topics

• The urbanization process and its effect on the dispersal of birds:
• Patterns of diversification in Neotropical amphibians
• Interactions between non-native parrot species
• Impact of landscape anthropization dynamics and wild birds’ health
• Habitat-driven diversification in small mammals
• Seasonal fluctuations and life cycles of amphipods
• Animal cruelty in African countries
• Evolution of the environmental niche of amphibians
• Biological studies on Louisiana crawfish
• Biological studies on Pink bollworm
• Biological studies on snails
• Biological studies on Bush Crickets
• Biological studies on Mountain Gorillas
• Biological studies on piranha
• Consequences of mosquito feeding
• Birds as bioindicators of environmental health
• Biological studies on victoria crowned pigeon
• Biological studies on black rhinoceros
• Biological studies on European spider
• Biological studies on dumbo octopus
• Biological studies on markhor
• Study of genetic and demographic variation in amphibian populations
• Ecology and population dynamics of the blackberry turtle
• Small-scale population differentiation in ecological and evolutionary mechanisms
• Challenges in vulture conservation

Also interesting: 232  Chemistry Research Topics  To Make Your Neurochemicals Dance

Submarine Animals Research Topics

• The physiology behind the luminous fish
• A study of Fish population dynamics
• Study of insects on the surface of the water
• Structure and function of schools of fish
• Physiological ecology of whales and dolphins
• Form and function in fish locomotion
• Why do whales and dolphins jump?
• Impact of Noise on Early Development and Hearing in Zebrafish
• Animal cruelty against marine life on the hand of fishermen

Read More:  Accounting Research Topics

Animal Biology Research Topics

• Systematic and zoogeographical study of the ocellated lizards
• Morphological study of neuro histogenesis in the diencephalon of the chick embryo
• Anatomical study of three species of Nudibranch
• The adaptive strategy of two species of lagomorphs
• The Black vulture: population, general biology, and interactions with other birds
• Ocellated lizards: their phylogeny and taxonomy
• Studies on the behavior of ocellated lizards in captivity
• Comparative studies of the egg-laying and egg-hatching methods of ocellated lizards
• Studies on the ecology and behavior of ocellated lizards
• The taxonomic and phylogenetic implications of ocellated lizards
• Research on the egg-laying and egg-hatching methods of ocellated lizards
• Studies on the ecology and behavior of ocellated lizards in their natural environment
• Comparative studies of the egg-laying and egg-hatching methods of ocellated lizards in different countries
• Studies on the ecology and behavior of ocellated lizards in their natural environment in the light of evolutionary and ecological insights

Animal research topics are not hard to find for you anymore. As you have already read a load of them. You can use any of them and ace your research paper, and you don’t even need to ask permission. If you are looking for a research paper writing service , be it animal research, medical research, or any sort of research, you can contact us 24/7.

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Assoc Prof in UW ECE

Why can animals outrun robots.

It is obvious that animals outperform robots at running – really, any legged locomotion task involving significant momentum. But what causes this performance gap? Could it be better actuators? sensors? “compute”-ors? The answer to this question is important for determining the most fruitful lines of research for roboticists interested in closing the performance gap. This observation motivated my co-authors and I to write a review paper that definitively answers the question .

Spoiler: it’s not the parts that gives animals the advantage – it must be something about how the parts assemble into the whole.

For historical accuracy, I should point out that the observation initially motivated two of the co-authors to take up the challenge: Tom Libby and Max Donelan . Max was on sabbatical in Berkeley in 2014, so he had time to think big thoughts. Tom was a PhD student and Director of the CiBER center at the time, so he had the motivation to go after weighty problems. These two intellectual heavyweights set themselves on the monumental task of systematically comparing biological and engineering technologies at the level of every individual component but also subsystem and whole-system levels across scales spanning ants and cockroaches to cheetahs and elephants. The original vision was to create a “datasheet” containing a comprehensive comparison of every known metric – plus definitions of new, better metrics and corresponding experiments to characterize them in biomechanical and electromechanical locomotors.

Aaaand .. it went about as well as could be expected.

Which is to say: it went sloooowly. And dauntingly. Overwhelmingly humblingly challengingly. Ego-crushing panic-inducing existentially dreadfully.

That may be overstating the sitch a lil bit. I’m definitely projecting more than a lil, as those were the feels I personally felt once I weaseled my way into the project years later. But you get the idea: it was a big project that required a tremendous amount from its workers.

But back to my weaseling. I was lucky enough to recruit Tom as a postdoc through a (now sadly defunct) Institute for Neuroengineering (called “UWIN” :) in 2017. The “AvM” project (“animals v. machines”) was still in the mix, but so were a half dozen other wonderfullly fascinating projects – both old and new – that were on his plate. In the intervening years, the AvM project scope had been dialed back to “merely” a mega-review, rather than a mega-review-plus-half-dozen-PhD-theses as originally conceived. But it was still too much ground for a pair of researchers to cover.

To put a finer point on it, by this time the scope had been dialed down to focus on 5 subsystems deemed critical for running: power , frame , sensing , actuation , and control . So all that was needed was expertise in5 different Departments: energy systems, material science, sensory neuroscience, kinesiology / biomechanics, and control theory. I genuinely believe Tom has such astonishing breadth that he could have covered all this ground himself. But doing so to the degree of rigor sought by the team would require review of hundreds of papers to convince onesself that you weren’t missing some critical detail that would invalidate the paper’s whole premise.

Observing Tom grapple with all this from the perspective of a postdoc ( co- )advisor, I made the very sage and selfless observation that what they were missing was … me! In particular, I felt I could offer two key benefits: I could handle the control subsystem, and I could lower their standards help enforce a reasonable project scope and timeline.

Given that this was in 2019 and you, dear reader, are being regaled with this delightful tale in or after the year 2024, I clearly delivered on no more than half of my promises.

I think my real contribution to the project occured two years of frustrated false starts later when I declared that what we were really missing was … more experts! I had actually made this suggestion many times before. In fact, I’d suggested it to Tom before I joined the project, which is in all likelihood the only reason I have the privilege of writing this today. But – to my recollection – Max resisted bringing even more people in for the longest time . (Probably because he regretted the mistake he and Tom already made with bringing in someone new …)

But after Max became the BPK Chair, he had to acknowledge that something needed to change if we were ever to release this monster into the world. So after a little deliberation we agreed that what was missing was … our friends! We had decided that assigning one expert to each subsystem would be most effective. Between the three of us, Max had power covered, Tom could handily handle actuation , and I could muddle through control . So we were missing frame and sensing . Fortunately for us, our numero uno choices for each subsystem readily agreed to join the project, so we now had Kaushik Jayaram on frame and Simon Sponberg on sensing . An interesting historical note is that we all had a strong connection to the biomechanics group at Berkeley, and in particular with Bob Full , a towering figure in the integrative study of movement: Bob was Max’s sabbatical host, the founder of the center Tom directed, the PhD advisor to Kaushik and Simon, and a cherished mentor and collaborator to me. This project is built on Bob’s shoulders.

With this fresh injection of energy and renewed purpose, we made rapid progress … until we didn’t. Although we’d significantly decreased the workload on each of us individually, the mammoth scope of the endeavor continued to conflict with our many other obligations. It was just too hard to squeeze in thinking such big thoughts and making such sweeping claims among teaching, advising, grantwriting, service, and life.

It’s at this point where the story gets a lot less interesting and therefore quickly wraps up. The project had lain dormant for many months when I received an email notice about a Special Issue on Legged Locomotion in Science Robotics with a deadline 6 weeks out. We’d been targeting SciRob since getting positive feedback on a pre-submission inquiry 5 years prior (lol). And putting these ideas into a Special Issue that the community would be more likely to see was an opportunity we couldn’t afford to miss. I happened to have the good fortune of being on sabbatical at that moment, so I had the time in addition to the motivation to close . So we made it happen.

It’s amazing what a time constraint can do :)

It’s also amazing what a space constraint can do: the original conception was a 10,000 word, 200 cite monolith, but Science Robotics advises a svelte 5,000 words and 75 cites. Not wanting to antagonize the editor or reviewers, we brought our S-tier pithiness to the problem. I regard brevity as my gift, so it was a delightful challenge to boil the ideas down to their bones and serve up only the delicious marrow from with- .. this analogy is getting a little thin and macabre, so let’s move on …

I want to talk a bit informally about the ideas in the paper and give context for some of the decisions and considerations that went into the final product. I’ll work through the sections in order.

When considering System Performance, the original conception was to commit to a specific set of metrics and quantify performance of a suite of robot and animal runners – to create a “datasheet” of sorts that the community could continue to build out over the years. However, there two major problems with this idea: one scientific, and one sociological.

The scientific challenge is that the metrics we have for concepts like range , agility , and robustness are inadequate to capture what seems intuitively clear. One grand idea we tossed around was the conjecture that any metric for these concepts could be computed from the reachable set , that is, the set of states that can be achieved by a control system through an admissible input signal in a given distribution of environments and a given parameterization of designs. We ultimately abandoned mentioning this idea because, although potentially interesting, there’s no currently practical way to compute this set (and Bellman tells us there can’t be in general).

The sociological challenge is that we did not want to dunk on our colleagues, or get into endless debates about why we chose the specific metric we did and why their robot did so poorly with respect to it. We figured that no reasonable person would challenge the assertion that animals outperform robots in their range, agility, and robustness (however you define these terms) – what would surely be controversial is how existing robots stack up relative to one another. So we opted for the qualitative / coarsely-quantized comparison in the first Figure.

Regarding the central conclusion, that the difference in performance of parts does not explain the difference in performance of wholes, there are some caveats.

If you were building a cyborg to run as far as possible completely power autonomous, using metabolism would give an order-of-magnitude advantage in range over gas power (nearly two orders-of-magnitude w.r.t. batteries). So along that solitary dimension, defined in that specific way, the difference in the part does explain the difference in the whole. But as soon as you allow that there may be gas stations or electrical outlets along the way, this advantage disappears.

The biological distribution of sensors throughout a body is quite compelling from an agility and robustness perspective: richly sensing terrain or other interactions with the environment could be a real boon for those dimensions of performance. But the “simulated cyborg” thought experiment from the Discussion convinces us that, even in the presence of perfect state information about the locomotor and environment, we still lack the tools to integrate that information to make a high-performing runner.

Finally, there are a couple of points to make about biological and engineered controllers. To make the most apples-to-apples comparison, we looked at natural and artificial spiking neural networks. Of course robot controllers can be implemented using conventional von Neumann architectures. But there are no proof-of-concept high-performing controllers in that paradigm to compare to those in animals, and the comparison is difficult to make at a component level: although we can pack upwards of hundreds of billions of transistors into a chip (comparable to the number of neurons in the human brain), it seems clear that a single transistor has less computational power than an individual neuron, and we are not aware of any rigorous attempt to quantify their relative computational power. Even the comparison between natural and artificial spiking neural networks is probably unfair in the sense that ANN dynamics are vastly simpler (e.g. piecewise-linear) than their biological counterparts (NNN?). But it’s the best comparison we can make at present, and including these factors would only tip the outcome even further in biology’s favor.

HOWever, even allowing that brains can, in principle, implement vastly more complex transformations than chips (at any scale – cockroaches have more neurons and synapses than the biggest neural ICs), it is important to remember that the brain is doing a whole lot more than locomotion. I keep returning to the example we cite in the paper (citation 90) of a parasitic wasp that lyses more than 7000 of its approximately 7400 neurons during pupation. The upshot is that there are autonomous flyers that can identify and infect hosts using fewer than 400 neurons !!! If you gave me 400 neurons, I think I’d struggle to invert a pendulum ..

My takeaway from this example is that we could be doing a lot more (robust and agile behavior) with a lot less (computational power) if only (a) we had the right bodies and (b) we knew what to do with them.

The Discussion covers a lot of ground that doesn’t need to be retrod here. But there is one point I want to dwell on a bit more, because I personally find it very interesting and compelling: the need for better metrics. This problem came up a few paragraphs ago when I discussed the challenge in defining what we mean by “agility” and “robustness”. One way to view the results of our paper is that we are focusing on the wrong metrics when we evaluate performance at the subsystem level, as these are evidently not predictive of system performance. What’s needed are metrics for the integration of multiple components or subsystems – and these metrics must capture something about the whole-system behavior we seek. The reason good metrics could be so powerful is that the endeavor of engineering is driven by “specs”, i.e. performance criteria. Once you tell me how my artifact is going to be evaluated, I can bring the powerful machinery of prototyping, optimization, learning, et al. to bear on squeezing that metric for everything it’s got. In the absence of metrics, engineering becomes art.

As a final note for the history books, I want to acknowledge where this paper fits in my intellectual and academic trajectory. I got my start in research in the summer before my first year of undergrad working with Eric Klavins , who began his career in robotics before switching to synbio. In fact, Eric got his PhD with a luminary in legged robotics, Dan Koditschek , and it was through this connection (certainly not merit) that I had the tremendous good fortune to do an REU at UPenn the summer after my sophomore year. The REU was my first exposure to the interdisciplinary world of legged locomotion, and I was completely enraptured. (Actually, for historical accuracy, I have to acknowledge that my very first exposure to this world was as a high school student when I was part of the inaugural cohort of students at the Summer Institute for Mathematics at the University of Washington , where the inimitable Tom Daniel gave an afternoon lecture on biolocomotion that included a very memorable demo on passive dynamic walking . So I suppose I was primed to become enraptured.)

Biolocomotion was the driver behind my applications to grad school and fellowships, and legged locomotion in particular ended up as the focus of my PhD thesis . My postdoc took me in a completely new direction – human-in-the-loop control – so when I started my faculty position there were two main areas of focus. Over time, legged locomotion has shrunk from the dominant theme at the beginning to now, where I have only one PhD student in this area, and they will graduate in six months. So this review represents the closure of a major chapter in my career – a very satisfying closure to be sure, but bittersweet nonetheless.

With that, I’ll stop – this commentary has already run almost half as many words as you’ll find in the paper :)

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