1
|
Rowe ZW, Robins JH, Rands SA. Red deer Cervus elaphus blink more in larger groups. Ecol Evol 2023; 13:e9908. [PMID: 36937074 PMCID: PMC10015368 DOI: 10.1002/ece3.9908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Revised: 02/19/2023] [Accepted: 02/28/2023] [Indexed: 03/17/2023] Open
Abstract
Most animals need to spend time being vigilant for predators, at the expense of other activities such as foraging. Group-living animals can benefit from the shared vigilance effort of other group members, with individuals reducing personal vigilance effort as group size increases. Behaviors like active scanning or head lifting are usually used to quantify vigilance but may not be accurate measures of this. We suggest that measuring an animal's blinking rate gives a meaningful measure of vigilance: increased blinking implies reduced vigilance, as the animal cannot detect predators when its eyes are closed. We describe an observational study of a captive population of red deer, where we measured the blinking rates of individual deer from groups of differing sizes (where mean group size ranged between 1 and 42.7 individuals). We demonstrate that as group size increases in red deer, individuals increase their blink rate, confirming the prediction that vigilance should decrease. Blinking is a simple non-invasive measure and offers a useful metric for assessing the welfare of animals experiencing an increase in perceived predation risk or other stressors.
Collapse
Affiliation(s)
- Zeke W. Rowe
- School of Biological SciencesUniversity of BristolBristolUK
- Department of Ecological SciencesVrije Universiteit AmsterdamAmsterdamNetherlands
| | | | - Sean A. Rands
- School of Biological SciencesUniversity of BristolBristolUK
| |
Collapse
|
2
|
Huang K, Yang Q, Han Y, Zhang Y, Wang Z, Wang L, Wei P. An Easily Compatible Eye-tracking System for Freely-moving Small Animals. Neurosci Bull 2022; 38:661-676. [PMID: 35325370 PMCID: PMC9206064 DOI: 10.1007/s12264-022-00834-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Accepted: 12/03/2021] [Indexed: 12/13/2022] Open
Abstract
Measuring eye movement is a fundamental approach in cognitive science as it provides a variety of insightful parameters that reflect brain states such as visual attention and emotions. Combining eye-tracking with multimodal neural recordings or manipulation techniques is beneficial for understanding the neural substrates of cognitive function. Many commercially-available and custom-built systems have been widely applied to awake, head-fixed small animals. However, the existing eye-tracking systems used in freely-moving animals are still limited in terms of their compatibility with other devices and of the algorithm used to detect eye movements. Here, we report a novel system that integrates a general-purpose, easily compatible eye-tracking hardware with a robust eye feature-detection algorithm. With ultra-light hardware and a detachable design, the system allows for more implants to be added to the animal's exposed head and has a precise synchronization module to coordinate with other neural implants. Moreover, we systematically compared the performance of existing commonly-used pupil-detection approaches, and demonstrated that the proposed adaptive pupil feature-detection algorithm allows the analysis of more complex and dynamic eye-tracking data in free-moving animals. Synchronized eye-tracking and electroencephalogram recordings, as well as algorithm validation under five noise conditions, suggested that our system is flexibly adaptable and can be combined with a wide range of neural manipulation and recording technologies.
Collapse
Affiliation(s)
- Kang Huang
- Shenzhen Key Lab of Neuropsychiatric Modulation and Collaborative Innovation Center for Brain Science, Guangdong Provincial Key Laboratory of Brain Connectome and Behavior, CAS Center for Excellence in Brain Science and Intelligence Technology, Brain Cognition and Brain Disease Institute, Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Qin Yang
- Shenzhen Key Lab of Neuropsychiatric Modulation and Collaborative Innovation Center for Brain Science, Guangdong Provincial Key Laboratory of Brain Connectome and Behavior, CAS Center for Excellence in Brain Science and Intelligence Technology, Brain Cognition and Brain Disease Institute, Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Yaning Han
- Shenzhen Key Lab of Neuropsychiatric Modulation and Collaborative Innovation Center for Brain Science, Guangdong Provincial Key Laboratory of Brain Connectome and Behavior, CAS Center for Excellence in Brain Science and Intelligence Technology, Brain Cognition and Brain Disease Institute, Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Yulin Zhang
- Shenzhen Key Lab of Neuropsychiatric Modulation and Collaborative Innovation Center for Brain Science, Guangdong Provincial Key Laboratory of Brain Connectome and Behavior, CAS Center for Excellence in Brain Science and Intelligence Technology, Brain Cognition and Brain Disease Institute, Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Zhiyi Wang
- Harbin Institute of Technology Shenzhen, Shenzhen, 518055, China
| | - Liping Wang
- Shenzhen Key Lab of Neuropsychiatric Modulation and Collaborative Innovation Center for Brain Science, Guangdong Provincial Key Laboratory of Brain Connectome and Behavior, CAS Center for Excellence in Brain Science and Intelligence Technology, Brain Cognition and Brain Disease Institute, Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Pengfei Wei
- Shenzhen Key Lab of Neuropsychiatric Modulation and Collaborative Innovation Center for Brain Science, Guangdong Provincial Key Laboratory of Brain Connectome and Behavior, CAS Center for Excellence in Brain Science and Intelligence Technology, Brain Cognition and Brain Disease Institute, Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
| |
Collapse
|
3
|
Rands SA. Phylogenetically-controlled correlates of primate blinking behaviour. PeerJ 2021; 9:e10950. [PMID: 33643718 PMCID: PMC7896502 DOI: 10.7717/peerj.10950] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2020] [Accepted: 01/26/2021] [Indexed: 11/20/2022] Open
Abstract
Eye blinking is an essential maintenance behaviour for many terrestrial animals, but is also a risky behaviour as the animal is unable to scan the environment and detect hazards while its eyes are temporarily closed. It is therefore likely that the length of time that the eyes are closed and the length of the gap between blinks for a species may reflect aspects of the ecology of that species, such as its social or physical environment. An earlier published study conducted a comparative study linking blinking behaviour and ecology, and detailed a dataset describing the blinking behaviour of a large number of primate species that was collected from captive animals, but the analysis presented did not control for the nonindependence of the data due to common evolutionary history. In the present study, the dataset is reanalysed using phylogenetic comparative methods, after reconsideration of the parameters describing the physical and social environments of the species. I find that blink rate is best described by the locomotion mode of a species, where species moving through arboreal environments blink least, ground-living species blink most, and species that use both environments show intermediate rates. The duration of a blink was also related to locomotion mode, and positively correlated with both mean species group size and mean species body mass, although the increase in relation to group size is small. How a species moves through the environment therefore appears to be important for determining blinking behaviour, and suggests that complex arboreal environments may require less interruption to visual attention. Given that the data were collected with captive individuals, caution is recommended for interpreting the correlations found.
Collapse
Affiliation(s)
- Sean A. Rands
- School of Biological Sciences, University of Bristol, Bristol, UK
| |
Collapse
|
4
|
A songbird strategically modifies its blinking behavior when viewing human faces. Anim Cogn 2021; 24:787-801. [PMID: 33501597 DOI: 10.1007/s10071-021-01476-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2020] [Revised: 01/04/2021] [Accepted: 01/08/2021] [Indexed: 10/22/2022]
Abstract
Even though blinking is necessary to maintain clear vision in many species, blinking is likely costly because it temporarily impairs vision. Given this cost, individuals can strategically modify their blinking behavior to minimize information loss. We tested whether a songbird species modifies its blinking behavior when viewing potential threats (human faces). We recorded the blinking behavior of captive great-tailed grackles (Quiscalus mexicanus) before, during, and after they viewed human face stimuli or control stimuli (tree bark as well as scrambled versions of human faces and tree bark). We found that the birds inhibited their blinking behavior the most when viewing human faces versus controls. In addition, they inhibited their blinking behavior more when viewing human faces that were directed rather than averted. Furthermore, when viewing the human faces, their blinking behavior was modified based on reactivity. These results suggest that a songbird can strategically modify its blinking behavior based on its perceived level of risk.
Collapse
|