DEAR EDITOR,Big cats,such as Amur tigers(Panthera tigris altaica)and Amur leopards(P.pardus orientalis),are apex predator and have evolved specialized traits for hunting and carnivory(Moya et al.,2022),thus playing a ...DEAR EDITOR,Big cats,such as Amur tigers(Panthera tigris altaica)and Amur leopards(P.pardus orientalis),are apex predator and have evolved specialized traits for hunting and carnivory(Moya et al.,2022),thus playing a crucial role in maintaining biodiversity and ecosystem integrity by regulating prey-predator dynamics.However,human-induced pressures,habitat fragmentation,and environmental alterations have restricted these species in small and isolated populations.Currently,all extant big cats are categorized as endangered or threatened according to their conservation status.Amur tigers and Amur leopards share overlapping geographic ranges,habitats,and certain prey species in the forests of Northeast Asia(Jiang et al.,2015).To reduce interspecies conflict,these carnivores exhibit differentiated dietary and temporal niches.Amur tigers predominantly prey on large ungulates,while Amur leopards hunt small to medium-sized animals(Sugimoto et al.,2016).Additionally,they occupy different temporal niches,with tigers being active at night and leopards more active during the day.Despite spatial and temporal niche partitioning,interspecific competition between these two species is inevitable.Tigers,benefiting from their greater size,have a competitive advantage over leopards,which can manifest in occasional leopard predation by tigers and declines in leopard populations with increasing tiger density(Jiang et al.,2015).Tigers also displace leopards from marginal habitats in nature reserves where they coexist.展开更多
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 iden...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 identifi-cation,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.展开更多
Male tigers(Panthera tigris altaica) in captivity copulate alternatively with an estrous female,suggesting a potential for heteropaternity as an effective reproductive strategy to maximize genetic diversity of offspri...Male tigers(Panthera tigris altaica) in captivity copulate alternatively with an estrous female,suggesting a potential for heteropaternity as an effective reproductive strategy to maximize genetic diversity of offspring.We analyzed microsatellites to test and compare the genetic output of multiple male mating(simultaneous polyandry) and single male mating(monogamy) with a female in a captive population.Simultaneous polyandry resulted in heteropaternity in 66.7% observed litters.No significant differences between parental populations and between offspring populations were detected in the number of alleles(A),expected heterozygosity(H e),number of effective alleles(N e) per locus and standard individual heterozygosity(SH)(P>0.05 for all 4 indexes).Comparisons showed no significant reduction of A,H o,H e and SH from parental population to offspring population for the two mating modes(P>0.05) except for SH in polyandrous families(P=0.029).However,such reduction was equivalent to single mating families when the influence of relatedness was eliminated using effective SH(E SH)(P>0.05).These results highlight an alternative strategy for managing captive populations of tiger and other wild felids in which animals are combined at one location allowing for copulation by multiple males to encourage heteropaternity in favor of maintained genetic diversity among offspring.展开更多
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...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.展开更多
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 combinat...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.展开更多
The Amur leopard,one of nine recently recognized subspecies of leopard,is still the most threatened by a stochastic procession of extinction.Evaluation of the potential danger to the conservation of the Amur leopard o...The Amur leopard,one of nine recently recognized subspecies of leopard,is still the most threatened by a stochastic procession of extinction.Evaluation of the potential danger to the conservation of the Amur leopard originating from disease urgently needs to be studied.Unfortunately,research on the potential risk to Amur leopards caused by disease is rare.In terms of parasitic diseases that affect this species,even basic data for parasitic fauna are absent.The aim of this study is to acquire this knowledge to improve the general understanding of Amur leopard parasites.Seven parasite species,including 3 nematodes(Toxocara cati,a capillarid-type parasite,and a Metastrongyloideatype parasite),2 cestodes(Spirometra sp.and Taenia sp.),1 trematode(Paragonimus sp.),and 1 protozoan(Cystoisospora felis),were found in this research.Toxocara cati occurred most frequently,followed by Spirometra sp.展开更多
基金supported by the Fundamental Research Funds for the Central Universities of China(2572022DQ03)National Natural Science Foundation of China(32170517)+1 种基金Guangdong Provincial Key Laboratory of Genome Read and Write(2017B030301011)supported by China National GeneBank(CNGB)。
文摘DEAR EDITOR,Big cats,such as Amur tigers(Panthera tigris altaica)and Amur leopards(P.pardus orientalis),are apex predator and have evolved specialized traits for hunting and carnivory(Moya et al.,2022),thus playing a crucial role in maintaining biodiversity and ecosystem integrity by regulating prey-predator dynamics.However,human-induced pressures,habitat fragmentation,and environmental alterations have restricted these species in small and isolated populations.Currently,all extant big cats are categorized as endangered or threatened according to their conservation status.Amur tigers and Amur leopards share overlapping geographic ranges,habitats,and certain prey species in the forests of Northeast Asia(Jiang et al.,2015).To reduce interspecies conflict,these carnivores exhibit differentiated dietary and temporal niches.Amur tigers predominantly prey on large ungulates,while Amur leopards hunt small to medium-sized animals(Sugimoto et al.,2016).Additionally,they occupy different temporal niches,with tigers being active at night and leopards more active during the day.Despite spatial and temporal niche partitioning,interspecific competition between these two species is inevitable.Tigers,benefiting from their greater size,have a competitive advantage over leopards,which can manifest in occasional leopard predation by tigers and declines in leopard populations with increasing tiger density(Jiang et al.,2015).Tigers also displace leopards from marginal habitats in nature reserves where they coexist.
基金the Fundamental Research Funds for the Central Universities(2572018BC07,2572017PZ14)the Heilongjiang postdoctoral project fund project(LBH-Z18003)+2 种基金Biodiversity Survey,Monitoring and Assessment Project of Ministry of Ecology and Environment,China(2019HB2096001006)the National Natural Science Foundation of China(NSFC 31872241,31572285)the Individual Identification Technological Research on Camera-trapping images of Amur tigers(NFGA 2017).
文摘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 identifi-cation,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.
基金supported by the Program for New Century Excellent Talents in University (NCET-10-0280)Technological Development Project of Longyan City (2011LY017)
文摘Male tigers(Panthera tigris altaica) in captivity copulate alternatively with an estrous female,suggesting a potential for heteropaternity as an effective reproductive strategy to maximize genetic diversity of offspring.We analyzed microsatellites to test and compare the genetic output of multiple male mating(simultaneous polyandry) and single male mating(monogamy) with a female in a captive population.Simultaneous polyandry resulted in heteropaternity in 66.7% observed litters.No significant differences between parental populations and between offspring populations were detected in the number of alleles(A),expected heterozygosity(H e),number of effective alleles(N e) per locus and standard individual heterozygosity(SH)(P>0.05 for all 4 indexes).Comparisons showed no significant reduction of A,H o,H e and SH from parental population to offspring population for the two mating modes(P>0.05) except for SH in polyandrous families(P=0.029).However,such reduction was equivalent to single mating families when the influence of relatedness was eliminated using effective SH(E SH)(P>0.05).These results highlight an alternative strategy for managing captive populations of tiger and other wild felids in which animals are combined at one location allowing for copulation by multiple males to encourage heteropaternity in favor of maintained genetic diversity among offspring.
基金funded by the National Natural Science Foundation of China(NSFC31872241 and 31702031)the National Key Programme of Research and Development,the Ministry of Science and Technology(2016YFC0503200)+2 种基金the Fundamental Research Funds for the Central Universities(2572017PZ14 and 2572020BC05)the Biodiversity Survey,Monitoring and Assessment Project of Ministry of Ecology and EnvironEnvironment,China(2019HB2096001006)the Heilongjiang postdoctoral project fund(LBH-Z18003).
文摘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.
基金funded by the Fundamental Research Funds for the Central Universities(2572020BC05)the Heilongjiang postdoctoral fund project(LBH-Z18003)+3 种基金the Biodiversity Survey,Monitoring and Assessment Project of Ministry of Ecology and Environment,China(2019HB2096001006)the National Natural Science Foundation of China(NSFC 31872241)the Individual Identification Technological Research on Cameratrapping images of Amur tigers(NFGA 2017)National Innovation and Entrepreneurship Training Program for College Student(S202010225022).
文摘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.
基金the following grants:National Key Research and Development Program(project 2017YFD0501702)Fundamental Research Funds for the Central Universities(2572020CG03)Surveillance of Wildlife Diseases from the State Forestry Administration of China(2020).
文摘The Amur leopard,one of nine recently recognized subspecies of leopard,is still the most threatened by a stochastic procession of extinction.Evaluation of the potential danger to the conservation of the Amur leopard originating from disease urgently needs to be studied.Unfortunately,research on the potential risk to Amur leopards caused by disease is rare.In terms of parasitic diseases that affect this species,even basic data for parasitic fauna are absent.The aim of this study is to acquire this knowledge to improve the general understanding of Amur leopard parasites.Seven parasite species,including 3 nematodes(Toxocara cati,a capillarid-type parasite,and a Metastrongyloideatype parasite),2 cestodes(Spirometra sp.and Taenia sp.),1 trematode(Paragonimus sp.),and 1 protozoan(Cystoisospora felis),were found in this research.Toxocara cati occurred most frequently,followed by Spirometra sp.
