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

So far,there has been no safe and convenient method to weigh the largefierce 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,ellipsefitting image,and rectanglefitting 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 rectanglefitting 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,wefitted 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.

一具东北虎尸体同时制作剥制和完整骨骼标本的试验

制作大型哺乳动物的剥制标本和完整的骨骼标本,通常都各需要一完整的个体为材料。本试验是以一具东北虎尸体为材料,探索出用同一具东北虎尸体制作剥制和完整的骨骼标本各一具的方法。

圈养亚成体及成体东北虎体尺参数及体重

为探究东北虎(Panthera tigris altaica)的生长发育特征,为东北虎的人工饲养以及野生东北虎的保护提供科学参考,测定了东北虎体重、胸围、腰围、肩高、臀高、体长、颈围和前后掌垫宽9个体尺参数,并对亚成体与成体东北虎体重及体尺参数的差异性及体重与体尺参数间的线性关系进行分析。结果表明:成体雄虎体重、体长、肩高、前掌垫宽及后掌垫宽均显著大于亚成体;成体雌虎臀高、胸围及腰围显著小于亚成体雌虎。4—5岁的东北虎,除体长、前掌垫宽和后掌垫宽以外,其他体尺参数均下降,5岁以后逐渐增长。在对体尺参数与体重的线性回归分析中,肩高与体重的拟合效果最好,并且肩高与体长也具有显著线性关系,因此肩高是评估东北虎体重和体长的重要参数。

Individual automatic detection and identification of big cats with the combination of different body parts

The development of facial recognition technology has become an increasingly powerful tool in wild animal indi-vidual recognition.In this paper,we develop an automatic detection and recognition method with the combinations of body features of big cats based on the deep convolutional neural network(CNN).We collected dataset including 12244 images from 47 individual Amur tigers(Panthera tigris altaica)at the Siberian Tiger Park by mobile phones and digital camera and 1940 images and videos of 12 individual wild Amur leopard(Panthera pardus orientalis)by infrared cameras.First,the single shot multibox detector algorithm is used to perform the automatic detection process of feature regions in each image.For the different feature regions of the image,like face stripe or spots,CNNs and multi-layer perceptron models were applied to automatically identify tiger and leopard individuals,in-dependently.Our results show that the identification accuracy of Amur tiger can reach up to 93.27%for face front,93.33%for right body stripe,and 93.46%for left body stripe.Furthermore,the combination of right face,left body stripe,and right body stripe achieves the highest accuracy rate,up to 95.55%.Consequently,the combination of different body parts can improve the individual identification accuracy.However,it is not the higher the number of body parts,the higher the accuracy rate.The combination model with 3 body parts has the highest accuracy.The identification accuracy of Amur leopard can reach up to 86.90%for face front,89.13%for left body spots,and 88.33%for right body spots.The accuracy of different body parts combination is lower than the independent part.For wild Amur leopard,the combination of face with body spot part is not helpful for the improvement of identification accuracy.The most effective identification part is still the independent left or right body spot part.It can be applied in long-term monitoring of big cats,including big data analysis for animal behavior,and be helpful for the individual identification of other wildlife species.