The genetic status and rescue measure for a geographically isolated population of Amur tigers.

The Amur tiger is currently confronted with challenges of anthropogenic development, leading to its population becoming fragmented into two geographically isolated groups: smaller and larger ones. Small and isolated populations frequently face a greater extinction risk, yet the small tiger population's genetic status and survival potential have not been assessed. Here, a total of 210 samples of suspected Amur tiger feces were collected from this small population, and the genetic background and population survival potentials were assessed by using 14 microsatellite loci. Our results demonstrated that the mean number of alleles in all loci was 3.7 and expected heterozygosity was 0.6, indicating a comparatively lower level of population genetic diversity compared to previously reported studies on other subspecies. The genetic estimates of effective population size (Ne) and the Ne/N ratio were merely 7.6 and 0.152, respectively, representing lower values in comparison to the Amur tiger population in Sikhote-Alin (the larger group). However, multiple methods have indicated the possibility of genetic divergence within our isolated population under study. Meanwhile, the maximum kinship recorded was 0.441, and the mean inbreeding coefficient stood at 0.0868, both of which are higher than those observed in other endangered species, such as the African lion and the grey wolf. Additionally, we have identified a significant risk of future extinction if the lethal equivalents were to reach 6.26, which is higher than that of other large carnivores. Further, our simulation results indicated that an increase in the number of breeding females would enhance the prospects of this population. In summary, our findings provide a critical theoretical basis for further bailout strategies concerning Amur tigers.

Combination of facial and nose features of Amur tigers to determine age.

We found that the area of black round or irregular-shaped spots on the tiger's nose increased with age, indicating a positive relationship between age and nose features. We used the deep learning model to train the facial and nose image features to identify the age of Amur tigers, using a combination of classification and prediction methods to achieve age determination with an accuracy of 87.81%.

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 individual 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 12 244 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, independently. 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.

Impacts of top predators and humans on the mammal communities of recovering temperate forest regions.

Top predators are important drivers in shaping ecological community structure via top-down effects. However, the ecological consequences and mechanisms of top predator loss under accelerated human impacts have rarely been quantitatively assessed due to the limited availability of long-term community data. With increases in top predator populations in northern China over the past two decades, forests with varying densities of top predators and humans provide an opportunity to study their ecological effects on mammal communities. We hypothesized a priori of conceptual models and tested these using structural equation models (SEMs) with multi-year camera trap data, aiming to reveal the underlying independent ecological effects of top predators (tigers, bears, and leopards) and humans on mammal communities. We used random forest models and correlations among species pairs to validate results. We found that top predator reduction could be related to augmented populations of large ungulates ("large ungulate release") and mesopredators ("mesopredator release"), consistent with observations of mammal communities in other ecosystems. Additionally, top predator reduction could be related to reduced small mammal abundance. Hierarchical SEMs identified three bottom-up pathways from forest quality to human activities, large ungulates, and some small mammals, and five top-down pathways from human activities and top predators to some small mammals, large ungulates, and mesopredators. Furthermore, our results suggest that humans showed predominant top-down effects on multiple functional groups, partially replacing the role of top predators, rather than be mediated by them; effects of humans and top predators appeared largely independent. Effects of humans on top predators were non-significant. This study provides novel insights into the effects of top predators and humans as super-predators on mammal communities in forest ecosystems and presents cues of bottom-up effects that can be translated into actionable management plans for improving forest quality, thereby supporting top predator recovery and work/life activities of local people.

Temporal activity patterns of North China leopards and their prey in response to moonlight and habitat factors.

The nocturnal activities of predators and prey are influenced by several factors, including physiological adaptations, habitat quality and, we suspect, corresponds to changes in brightness of moonlight according to moon phase. In this study, we used a dataset from 102 camera traps to explore which factors are related to the activity pattern of North China leopards (Panthera pardus japonensis) in Shanxi Tieqiaoshan Provincial Nature Reserve (TPNR), China. We found that nocturnal activities of leopards were irregular during four different lunar phases, and while not strictly lunar philic or lunar phobic, their temporal activity was highest during the brighter moon phases (especially the last quarter) and lower during the new moon phase. On the contrary, roe deer (Capreolus pygargus) exhibited lunar philic activity, while wild boar (Sus scrofa) and tolai hare (Lepus tolai) were evidently lunar phobic, with high and low temporal activity during the full moon, respectively. In terms of temporal overlap, there was positive overlap between leopards and their prey species, including roe deer and tolai hare, while leopard activity did not dip to the same low level of wild boar during the full moon phase. Human activities also more influenced the temporal activity of leopards and wild boar than other species investigated. Generally, our results suggested that besides moonlight risk index (MRI), cloud cover and season have diverse effects on leopard and prey nocturnal activity. Finally, distinct daytime and nighttime habitats were identified, with leopards, wild boar, and tolai hare all using lower elevations at night and higher elevations during the day, while leopards and roe deer were closer to secondary roads during the day than at night.

Amur tiger stripes: individual identification based on deep convolutional neural network.

The automatic individual identification of Amur tigers (Panthera tigris altaica) is important for population monitoring and making effective conservation strategies. Most existing research primarily relies on manual identification, which does not scale well to large datasets. In this paper, the deep convolution neural networks algorithm is constructed to implement the automatic individual identification for large numbers of Amur tiger images. The experimental data were obtained from 40 Amur tigers in Tieling Guaipo Tiger Park, China. The number of images collected from each tiger was approximately 200, and a total of 8277 images were obtained. The experiments were carried out on both the left and right side of body. Our results suggested that the recognition accuracy rate of left and right sides are 90.48% and 93.5%, respectively. The accuracy of our network has achieved the similar level compared to other state of the art networks like LeNet, ResNet34, and ZF_Net. The running time is much shorter than that of other networks. Consequently, this study can provide a new approach on automatic individual identification technology in the case of the Amur tiger.