Dataset Information

A study on giant panda recognition based on images of a large proportion of captive pandas.

ABSTRACT: As a highly endangered species, the giant panda (panda) has attracted significant attention in the past decades. Considerable efforts have been put on panda conservation and reproduction, offering the promising outcome of maintaining the population size of pandas. To evaluate the effectiveness of conservation and management strategies, recognizing individual pandas is critical. However, it remains a challenging task because the existing methods, such as traditional tracking method, discrimination method based on footprint identification, and molecular biology method, are invasive, inaccurate, expensive, or challenging to perform. The advances of imaging technologies have led to the wide applications of digital images and videos in panda conservation and management, which makes it possible for individual panda recognition in a noninvasive manner by using image-based panda face recognition method.In recent years, deep learning has achieved great success in the field of computer vision and pattern recognition. For panda face recognition, a fully automatic deep learning algorithm which consists of a sequence of deep neural networks (DNNs) used for panda face detection, segmentation, alignment, and identity prediction is developed in this study. To develop and evaluate the algorithm, the largest panda image dataset containing 6,441 images from 218 different pandas, which is 39.78% of captive pandas in the world, is established.The algorithm achieved 96.27% accuracy in panda recognition and 100% accuracy in detection.This study shows that panda faces can be used for panda recognition. It enables the use of the cameras installed in their habitat for monitoring their population and behavior. This noninvasive approach is much more cost-effective than the approaches used in the previous panda surveys.

SUBMITTER: Chen P

PROVIDER: S-EPMC7141006 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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A study on giant panda recognition based on images of a large proportion of captive pandas.

Chen Peng P Swarup Pranjal P Matkowski Wojciech Michal WM Kong Adams Wai Kin AWK Han Su S Zhang Zhihe Z Rong Hou H

Ecology and evolution 20200310 7

As a highly endangered species, the giant panda (panda) has attracted significant attention in the past decades. Considerable efforts have been put on panda conservation and reproduction, offering the promising outcome of maintaining the population size of pandas. To evaluate the effectiveness of conservation and management strategies, recognizing individual pandas is critical. However, it remains a challenging task because the existing methods, such as traditional tracking method, discriminatio ...[more]

PMID: 32274009

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Project description:Captive breeding has been used as an effective approach to protecting endangered animals but its effect on the gut microbiome and the conservation status of these species is largely unknown. The giant panda is a flagship species for the conservation of wildlife. With integrated efforts including captive breeding, this species has been recently upgraded from "endangered" to "vulnerable" (IUCN 2016). Since a large proportion (21.8%) of their global population is still captive, it is critical to understand how captivity changes the gut microbiome of these pandas and how such alterations to the microbiome might affect their future fitness and potential impact on the ecosystem after release into the wild. Here, we use 16S rRNA (ribosomal RNA) marker gene sequencing and shotgun metagenomics sequencing to demonstrate that the fecal microbiomes differ substantially between wild and captive giant pandas. Fecal microbiome diversity was significantly lower in captive pandas, as was the diversity of functional genes. Additionally, captive pandas have reduced functional potential for cellulose degradation but enriched metabolic pathways for starch metabolism, indicating that they may not adapt to a wild diet after being released into the wild since a major component of their diet in the wild will be bamboo. Most significantly, we observed a significantly higher level of amylase activity but a lower level of cellulase activity in captive giant panda feces than those of wild giant pandas, shown by an in vitro experimental assay. Furthermore, antibiotic resistance genes and virulence factors, as well as heavy metal tolerance genes were enriched in the microbiomes of captive pandas, which raises a great concern of spreading these genes to other wild animals and ecosystems when they are released into a wild environment. Our results clearly show that captivity has altered the giant panda microbiome, which could have unintended negative consequences on their adaptability and the ecosystem during the reintroduction of giant pandas into the wild.

Project description:BackgroundThe giant panda (Ailuropoda melanoleuca) is a vulnerable mammal herbivore living wild in central China. Viral infections have become a potential threat to the health of these endangered animals, but limited information related to these infections is available.MethodsUsing a viral metagenomic approach, we surveyed viruses in the feces, nasopharyngeal secretions, blood, and different tissues from a wild giant panda that died from an unknown disease, a healthy wild giant panda, and 46 healthy captive animals.ResultsThe previously uncharacterized complete or near complete genomes of four viruses from three genera in Papillomaviridae family, six viruses in a proposed new Picornaviridae genus (Aimelvirus), two unclassified viruses related to posaviruses in Picornavirales order, 19 anelloviruses in four different clades of Anelloviridae family, four putative circoviruses, and 15 viruses belonging to the recently described Genomoviridae family were sequenced. Reflecting the diet of giant pandas, numerous insect virus sequences related to the families Iflaviridae, Dicistroviridae, Iridoviridae, Baculoviridae, Polydnaviridae, and subfamily Densovirinae and plant viruses sequences related to the families Tombusviridae, Partitiviridae, Secoviridae, Geminiviridae, Luteoviridae, Virgaviridae, and Rhabdoviridae; genus Umbravirus, Alphaflexiviridae, and Phycodnaviridae were also detected in fecal samples. A small number of insect virus sequences were also detected in the nasopharyngeal secretions of healthy giant pandas and lung tissues from the dead wild giant panda. Although the viral families present in the sick giant panda were also detected in the healthy ones, a higher proportion of papillomaviruses, picornaviruses, and anelloviruses reads were detected in the diseased panda.ConclusionThis viral survey increases our understanding of eukaryotic viruses in giant pandas and provides a baseline for comparison to viruses detected in future infectious disease outbreaks. The similar viral families detected in sick and healthy giant pandas indicate that these viruses result in commensal infections in most immuno-competent animals.

Project description:BackgroundThe red panda (Ailurus fulgens) is a riddle of morphology, making it hard to tell whether it is an ursid, a procyonid, a mustelid, or a member of its own family. Previous genetic studies have given quite contradictory results as to its phylogenetic placement.ResultsA recently developed whole genome-based algorithm, the Whole Genome K-mer Signature algorithm was used to analyze the genomes of 28 species of Carnivora, including A. fulgens and several felid, ursid, mustelid, one mephitid species. This algorithm has the advantage of holistically using all the information in the genomes of these species. Being a genomics-based algorithm, it also reduces stochastic error to a minimum. Besides the whole genome, the mitochondrial DNA from 52 mustelids, mephitids, ursids, procyonids and A. fulgens were aligned to draw further phylogenetic inferences. The results from the whole genome study suggested that A. fulgens is a member of the mustelid clade (p = 9·10- 97). A. fulgens also separates from the mephitid Spilogala gracilis. The giant panda, Ailuropoda melanoleuca also clusters away from A. fulgens, together with other ursids (p = 1.2·10- 62). This could be due to the geographic isolation of A. fulgens from other mustelid species. However, results from the mitochondrial study as well as neighbor-joining methods based on the sequence identity matrix suggests that A. fulgens forms a monophyletic group. A Maximum Likelihood tree suggests that A. fulgens and Ursidae form a monophyletic group, although the bootstrap value is weak.ConclusionsThe main conclusion that we can draw from this study is that on a whole genome level A. fulgens possibly belongs to the mustelid clade, and not an ursid or a mephitid. This despite the fact that previously some researchers classified A. fulgens and A. melanoleuca as relatives. Since the genotype determines the phenotype, molecular-based classification takes precedence over morphological classifications. This affirms the results of some previous studies, which studied smaller portions of the genome. However, mitochondrial analyses based on neighbor-joining and maximum likelihood methods suggest otherwise.

Dataset Information

A study on giant panda recognition based on images of a large proportion of captive pandas.

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A study on giant panda recognition based on images of a large proportion of captive pandas.

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