Saccadic movement strategy in a semiaquatic species – the harbour seal (<i>Phoca vitulina</i>)

Geurten, Bart R. H.; Niesterok, Benedikt; Dehnhardt, Guido; Hanke, Frederike D.

doi:10.1242/jeb.150763

Cited by 25 publications

(2 citation statements)

References 61 publications

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“…Figure 7 shows an example of how LACE traces the trajectory of a freely moving fish for 30 seconds. Like many other animals (Kramer and McLaughlin, 2001 ; Geurten et al, 2017 ; Helmer et al, 2017 ), zebrafish move intermittently (compare Figure 7C ). Intermittent motion alternates between phases of active propulsion and gliding, which seems to be energy efficient (Kramer and McLaughlin, 2001 ).…”

Section: Resultsmentioning

confidence: 59%

A Markerless Pose Estimator Applicable to Limbless Animals

Garg

André

Giraldo

et al. 2022

Front. Behav. Neurosci.

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The analysis of kinematics, locomotion, and spatial tasks relies on the accurate detection of animal positions and pose. Pose and position can be assessed with video analysis programs, the “trackers.” Most available trackers represent animals as single points in space (no pose information available) or use markers to build a skeletal representation of pose. Markers are either physical objects attached to the body (white balls, stickers, or paint) or they are defined in silico using recognizable body structures (e.g., joints, limbs, color patterns). Physical markers often cannot be used if the animals are small, lack prominent body structures on which the markers can be placed, or live in environments such as aquatic ones that might detach the marker. Here, we introduce a marker-free pose-estimator (LACE Limbless Animal traCkEr) that builds the pose of the animal de novo from its contour. LACE detects the contour of the animal and derives the body mid-line, building a pseudo-skeleton by defining vertices and edges. By applying LACE to analyse the pose of larval Drosophila melanogaster and adult zebrafish, we illustrate that LACE allows to quantify, for example, genetic alterations of peristaltic movements and gender-specific locomotion patterns that are associated with different body shapes. As illustrated by these examples, LACE provides a versatile method for assessing position, pose and movement patterns, even in animals without limbs.

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Section: Resultsmentioning

confidence: 59%

A Markerless Pose Estimator Applicable to Limbless Animals

Garg

André

Giraldo

et al. 2022

Front. Behav. Neurosci.

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“…The upper point for the manta is the maximum turning rate recorded, whereas the lower point for the manta represents the lowest turning rate of the highest 20% of the data for the manta. Data from Webb (1976Webb ( , 1983, Hui (1985), Foyle and O'Dor (1988), Miller (1991), Blake et al (1995), Gerstner (1999), Walker (2000), Frey and Salisbury (2001), Fish (1997Fish ( , 2002, Fish and Nicastro (2003), , Kajiura et al (2003), Domenici et al (2004), Rivera et al (2006), Parson et al (2011), Jastrebsky et al (2016Jastrebsky et al ( , 2017, Helmer et al (2016) and Geurten et al (2017).…”

Section: Comparative Turning Performancementioning

confidence: 99%

Kinematics of swimming of the manta ray: three-dimensional analysis of open water maneuverability

Fish

Kolpas

Crossett

et al. 2018

Journal of Experimental Biology

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For aquatic animals, turning maneuvers represent a locomotor activity that may not be confined to a single coordinate plane, making analysis difficult, particularly in the field. To measure turning performance in a three-dimensional space for the manta ray (), a large open-water swimmer, scaled stereo video recordings were collected. Movements of the cephalic lobes, eye and tail base were tracked to obtain three-dimensional coordinates. A mathematical analysis was performed on the coordinate data to calculate the turning rate and curvature (1/turning radius) as a function of time by numerically estimating the derivative of manta trajectories through three-dimensional space. Principal component analysis was used to project the three-dimensional trajectory onto the two-dimensional turn. Smoothing splines were applied to these turns. These are flexible models that minimize a cost function with a parameter controlling the balance between data fidelity and regularity of the derivative. Data for 30 sequences of rays performing slow, steady turns showed the highest 20% of values for the turning rate and smallest 20% of turn radii were 42.65±16.66 deg s and 2.05±1.26 m, respectively. Such turning maneuvers fall within the range of performance exhibited by swimmers with rigid bodies.

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