Behavioural patterns and functional responses of a generalist predator revealed using automated video tracking

2019 ◽  
Vol 75 (6) ◽  
pp. 1517-1526 ◽  
Author(s):  
Yi Feng ◽  
Yu‐Dan Li ◽  
Zhi‐Guo Liu ◽  
Xing‐Lin Yu ◽  
Guan‐Xiong Zhu ◽  
...  
Keyword(s):  
Video Tracking ◽  
Generalist Predator ◽  
Functional Responses ◽  
Behavioural Patterns ◽  
Automated Video Tracking

scholarly journals Automated video tracking and flight analysis show how bumblebees solve a pattern discrimination task using active vision

2021 ◽  
Author(s):  
HaDi MaBouDi ◽  
Mark Roper ◽  
Marie Guiraud ◽  
James A.R. Marshall ◽  
Lars Chittka
Keyword(s):  
High Speed ◽  
Discrimination Task ◽  
Active Vision ◽  
Pattern Discrimination ◽  
Relevant Information ◽  
Small Region ◽  
Video Tracking ◽  
Head Movements ◽  
Flight Behaviour ◽  
Automated Video Tracking

AbstractActive vision, the ability of the visual system to actively sample and select relevant information out of a visual scene through eye and head movements, has been explored in a variety of animal species. Small-brained animals such as insects might rely even more on sequential acquisition of pattern features since there might be less parallel processing capacity in their brains than in vertebrates. To investigate how active vision strategies enable bees to solve visual tasks, here, we employed a simple visual discrimination task in which individual bees were presented with a multiplication symbol and a 45° rotated version of the same pattern (“plus sign”). High-speed videography of unrewarded tests and analysis of the bees’ flight paths shows that only a small region of the pattern is inspected before successfully accepting a target or rejecting a distractor. The bees’ scanning behaviour of the stimuli differed for plus signs and multiplication signs, but for each of these, the flight behaviour was consistent irrespective of whether the pattern was rewarding or unrewarding. Bees typically oriented themselves at ~±30° to the patterns such that only one eye had an unobscured view of stimuli. There was a significant preference for initially scanning the left side of the stimuli. Our results suggest that the bees’ movement may be an integral part of a strategy to efficiently analyse and encode their environment.Summary statementAutomated video tracking and flight analysis is proposed as the next milestone in understanding mechanisms underpinning active vision and cognitive visual abilities of bees.

Parasites in space and time: a novel method to assess and illustrate host-searching behaviour of trematode cercariae

Parasitology ◽  
2018 ◽  
Vol 145 (11) ◽  
pp. 1469-1474 ◽  
Author(s):  
Christian Selbach ◽  
Robert Poulin
Keyword(s):  
Life Histories ◽  
Parasite Community ◽  
Video Tracking ◽  
Trematode Species ◽  
Host Searching ◽  
Novel Method ◽  
The Individual ◽  
Individual Strategies ◽  
Host Parasite ◽  
Automated Video Tracking

AbstractThe transmission from one host to another constitutes a challenging obstacle for parasites and is a key determinant of their fitness. Due to their complex life histories involving several different hosts, the free-living dispersal stages (cercariae) of digenean trematodes show a huge diversity in morphology and behaviour. On a finer scale, we still have an extremely limited understanding of the inter- and intraspecific variation in transmission strategies of many trematode species. Here, we present a novel method to study the movement patterns of cercariae of four New Zealand trematode species (Coitocaecum parvum, Maritrema poulini, Apatemon sp. and Aporocotylid sp. I.) via automated video tracking. This approach allows to quantify parameters otherwise not measurable and clearly illustrates the individual strategies of parasites to search for their respective target hosts. Cercariae that seek out an evasive fish target hosts showed higher swimming speeds (acceleration and velocity) and travelled further distances, compared with species searching for high-density crustacean hosts. Automated video tracking provides a powerful tool for such detailed analyses of parasites’ host-searching strategies and can enhance our understanding of complex host–parasite interactions, ranging from parasite community structure to the transmission of potential disease agents.

Automated Video Tracking To Assess Executive Aspects of Cancellation in Stroke (P02.031)

Neurology ◽  
2012 ◽  
Vol 78 (Meeting Abstracts 1) ◽  
pp. P02.031-P02.031
Author(s):  
V. Mark ◽  
L. Chasan
Keyword(s):  
Video Tracking ◽  
Automated Video Tracking

scholarly journals Optimizing multivariate behavioural syndrome models in locusts using automated video tracking

2012 ◽  
Vol 84 (4) ◽  
pp. 771-784 ◽  
Author(s):  
Darron A. Cullen ◽  
Gregory A. Sword ◽  
Stephen J. Simpson
Keyword(s):  
Video Tracking ◽  
Behavioural Syndrome ◽  
Automated Video Tracking

scholarly journals Anisotropic interaction and motion states of locusts in a hopper band

2021 ◽  
Author(s):  
Jasper Weinburd ◽  
Jacob Landsberg ◽  
Anna Kravtsova ◽  
Shanni Lam ◽  
Tarush Sharma ◽  
...  
Keyword(s):  
Collective Motion ◽  
Video Tracking ◽  
Low Density ◽  
Natural Setting ◽  
Trajectory Data ◽  
Novel Interactions ◽  
The Individual ◽  
Low Density Area ◽  
Australian Plague Locust ◽  
Automated Video Tracking

Swarming locusts present a quintessential example of animal collective motion. Juvenile locusts march and hop across the ground in coordinated groups called hopper bands. Composed of up to millions of insects, hopper bands exhibit coordinated motion and various collective structures. These groups are well-documented in the field, but the individual insects themselves are typically studied in much smaller groups in laboratory experiments. We present the first trajectory data that detail the movement of individual locusts within a hopper band in a natural setting. Using automated video tracking, we derive our data from footage of four distinct hopper bands of the Australian plague locust, Chortoicetes terminifera. We reconstruct nearly twenty-thousand individual trajectories composed of over 3.3 million locust positions. We classify these data into three motion states: stationary, walking, and hopping. Distributions of relative neighbor positions reveal anisotropies that depend on motion state. Stationary locusts have high-density areas distributed around them apparently at random. Walking locusts have a low-density area in front of them. Hopping locusts have low-density areas in front and behind them. Our results suggest novel interactions, namely that locusts change their motion to avoid colliding with neighbors in front of them.

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