Identification of the Anteroposterior and Mediolateral Position of Lion Paws and Tracks Using 3D Geometric Morphometrics

Marchal, Antoine; Lejeune, Philippe; Bruyn, P. J. Nico de

doi:10.3957/056.047.0106

Cited by 2 publications

(1 citation statement)

References 16 publications

(22 reference statements)

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“…This also probably contributed to prediction error. So, further studies may find it necessary to develop software technology to automatically extract the body measurements of Amur tigers, or to develop new methodology to eliminate parallax error, such as 3D geometric morphometrics (Marchal et al 2016(Marchal et al , 2017.…”

Section: Discussionmentioning

confidence: 99%

Estimation of body weight in captive Amur tigers (Panthera tigris altaica)

Wang

Shi²,

Liú³

et al. 2021

Integr. Zool.

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So far, there has been no safe and convenient method to weigh the large fierce animals, like Amur tigers. To address this problem, we built models to predict the body weight of Amur tigers based on the fact that body weight is proportional to body measurements or age. Using the method of body measurements, we extracted the body measurements from 4 different kinds of the lateral body image of tigers, that is, total lateral image, central lateral image, ellipse fitting image, and rectangle fitting image, and then we respectively used artificial neural network (ANN) and power regression model to analyze the predictive relationships between body weight and body measurements. Our results demonstrated that, among all ANN models, the model built with rectangle fitting image had the smallest mean square error. Comparatively, we screened power regression models which had the smallest Akakai information criteria (AIC). In addition, using the method of age, we fitted nonlinear regression models for the relationship between body weight and age and found that, for male tigers, logistic model had the smallest AIC. For female tigers, Gompertz model had the smallest AIC. Consequently, this study could be applied to estimate body weight of captive, or even wild, Amur tigers safely and conveniently, helping to monitor individual health and growth of the Amur tiger populations.

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Section: Discussionmentioning

confidence: 99%

Estimation of body weight in captive Amur tigers (Panthera tigris altaica)

Wang

Shi²,

Liú³

et al. 2021

Integr. Zool.

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Individual Identification of Cheetah (Acinonyx jubatus) Based on Close-Range Remote Sensing: First Steps of a New Monitoring Technique

Baralle

Marchal²,

Lejeune

et al. 2021

Remote Sensing

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Wildlife monitoring is an important part of the conservation strategies for certain endangered species. Non-invasive methods are of significant interest because they preserve the studied animal. The study of signs, especially tracks, seems to be a valuable compromise between reliability, simplicity and feasibility. The main objective of this study is to develop and test an algorithm that can identify individual cheetahs based on 3D track modelling using proximal sensing with an off-the-shelf camera. More specifically, we propose a methodological approach allowing the identification of individuals, their sex and their foot position (i.e., left/right and hind/front). In addition, we aim to compare different track recording media: 2D photo and 3D photo models. We sampled 669 tracks from eight semi-captive cheetahs, corresponding to about 20 tracks per foot. We manually placed on each track 25 landmarks: fixed points representing the geometry of an object. We also automatically placed 130 semi-landmarks, landmark allowed to move on the surface, per track on only the 3D models. Geometric morphometrics allowed the measurement of shape variation between tracks, while linear discriminant analysis (LDA) with jack-knife prediction enabled track discrimination using the information from their size and shape. We tested a total of 82 combinations of features in terms of recording medium, landmarks configuration, extracted information and template used. For foot position identification, the best combination correctly identified 98.2% of the tracks. Regarding those results, we also ran an identification algorithm on a dataset containing only one kind of foot position to highlight the differences and mimic an algorithm identifying the foot position first and then an individual factor (here, sex and identity). This led to accuracy of 94.8 and 93.7%, respectively, for sex and individual identification. These tools appear to be effective in discriminating foot position, sex and individual identity from tracks. Future works should focus on automating track segmentation and landmark positioning for ease of use in conservation strategies.

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Identification of the Anteroposterior and Mediolateral Position of Lion Paws and Tracks Using 3D Geometric Morphometrics

Cited by 2 publications

References 16 publications

Estimation of body weight in captive Amur tigers (Panthera tigris altaica)

Estimation of body weight in captive Amur tigers (Panthera tigris altaica)

Individual Identification of Cheetah (Acinonyx jubatus) Based on Close-Range Remote Sensing: First Steps of a New Monitoring Technique

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