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Chen P, Swarup P, Matkowski WM, Kong AWK, Han S, Zhang Z, Rong H. A study on giant panda recognition based on images of a large proportion of captive pandas. Ecol Evol 2020; 10:3561-3573. [PMID: 32274009 PMCID: PMC7141006 DOI: 10.1002/ece3.6152] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2019] [Revised: 01/19/2020] [Accepted: 02/14/2020] [Indexed: 11/10/2022] Open
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.
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Affiliation(s)
- Peng Chen
- Chengdu Research Base of Giant Panda Breeding Chengdu China
- Sichuan Key Laboratory of Conservation Biology for Endangered Wildlife Chengdu China
- Sichuan Academy of Giant Panda Chengdu China
| | - Pranjal Swarup
- School of Computer Science and Engineering Nanyang Technological University Singapore City Singapore
| | - Wojciech Michal Matkowski
- School of Computer Science and Engineering Nanyang Technological University Singapore City Singapore
| | - Adams Wai Kin Kong
- School of Computer Science and Engineering Nanyang Technological University Singapore City Singapore
| | - Su Han
- College of Computer Science Sichuan Normal University Chengdu China
| | - Zhihe Zhang
- Chengdu Research Base of Giant Panda Breeding Chengdu China
- Sichuan Key Laboratory of Conservation Biology for Endangered Wildlife Chengdu China
- Sichuan Academy of Giant Panda Chengdu China
| | - Hou Rong
- Chengdu Research Base of Giant Panda Breeding Chengdu China
- Sichuan Key Laboratory of Conservation Biology for Endangered Wildlife Chengdu China
- Sichuan Academy of Giant Panda Chengdu China
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Connor T, Viña A, Winkler JA, Hull V, Tang Y, Shortridge A, Yang H, Zhao Z, Wang F, Zhang J, Zhang Z, Zhou C, Bai W, Liu J. Interactive spatial scale effects on species distribution modeling: The case of the giant panda. Sci Rep 2019; 9:14563. [PMID: 31601927 PMCID: PMC6787011 DOI: 10.1038/s41598-019-50953-z] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2018] [Accepted: 09/19/2019] [Indexed: 11/08/2022] Open
Abstract
Research has shown that varying spatial scale through the selection of the total extent of investigation and the grain size of environmental predictor variables has effects on species distribution model (SDM) results and accuracy, but there has been minimal investigation into the interactive effects of extent and grain. To do this, we used a consistently sampled range-wide dataset of giant panda occurrence across southwest China and modeled their habitat and distribution at 4 extents and 7 grain sizes. We found that increasing grain size reduced model accuracy at the smallest extent, but that increasing extent negated this effect. Increasing extent also generally increased model accuracy, but the models built at the second-largest (mountain range) extent were more accurate than those built at the largest, geographic range-wide extent. When predicting habitat suitability in the smallest nested extents (50 km2), we found that the models built at the next-largest extent (500 km2) were more accurate than the smallest-extent models but that further increases in extent resulted in large decreases in accuracy. Overall, this study highlights the impacts of the selection of spatial scale when evaluating species' habitat and distributions, and we suggest more explicit investigations of scale effects in future modeling efforts.
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Affiliation(s)
- Thomas Connor
- Center for Systems Integration and Sustainability, Department of Fisheries and Wildlife, Michigan State University, East Lansing, MI, USA.
| | - Andrés Viña
- Center for Systems Integration and Sustainability, Department of Fisheries and Wildlife, Michigan State University, East Lansing, MI, USA
- Department of Geography, University of North Carolina, Chapel Hill, NC, USA
| | - Julie A Winkler
- Department of Geography, Environment, and Spatial Sciences, Michigan State University, East Lansing, MI, USA
| | - Vanessa Hull
- Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, FL, USA
| | - Ying Tang
- Department of Geography, Environment, and Spatial Sciences, Michigan State University, East Lansing, MI, USA
| | - Ashton Shortridge
- Department of Geography, Environment, and Spatial Sciences, Michigan State University, East Lansing, MI, USA
| | - Hongbo Yang
- Center for Systems Integration and Sustainability, Department of Fisheries and Wildlife, Michigan State University, East Lansing, MI, USA
| | - Zhiqiang Zhao
- Center for Systems Integration and Sustainability, Department of Fisheries and Wildlife, Michigan State University, East Lansing, MI, USA
| | - Fang Wang
- Department of Geography, University of North Carolina, Chapel Hill, NC, USA
| | - Jindong Zhang
- Key Laboratory of Southwest China Wildlife Resources Conservation, China West Normal University, Ministry of Education, Nanchong, China
| | - Zejun Zhang
- Key Laboratory of Southwest China Wildlife Resources Conservation, China West Normal University, Ministry of Education, Nanchong, China
| | - Caiquan Zhou
- Key Laboratory of Southwest China Wildlife Resources Conservation, China West Normal University, Ministry of Education, Nanchong, China
| | - Wenke Bai
- Key Laboratory of Southwest China Wildlife Resources Conservation, China West Normal University, Ministry of Education, Nanchong, China
| | - Jianguo Liu
- Center for Systems Integration and Sustainability, Department of Fisheries and Wildlife, Michigan State University, East Lansing, MI, USA
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Qiao M, Connor T, Shi X, Huang J, Huang Y, Zhang H, Ran J. Population genetics reveals high connectivity of giant panda populations across human disturbance features in key nature reserve. Ecol Evol 2019; 9:1809-1819. [PMID: 30847074 PMCID: PMC6392360 DOI: 10.1002/ece3.4869] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2018] [Revised: 10/30/2018] [Accepted: 11/30/2018] [Indexed: 11/23/2022] Open
Abstract
The giant panda is an example of a species that has faced extensive historical habitat fragmentation, and anthropogenic disturbance and is assumed to be isolated in numerous subpopulations with limited gene flow between them. To investigate the population size, health, and connectivity of pandas in a key habitat area, we noninvasively collected a total of 539 fresh wild giant panda fecal samples for DNA extraction within Wolong Nature Reserve, Sichuan, China. Seven validated tetra-microsatellite markers were used to analyze each sample, and a total of 142 unique genotypes were identified. Nonspatial and spatial capture-recapture models estimated the population size of the reserve at 164 and 137 individuals (95% confidence intervals 153-175 and 115-163), respectively. Relatively high levels of genetic variation and low levels of inbreeding were estimated, indicating adequate genetic diversity. Surprisingly, no significant genetic boundaries were found within the population despite the national road G350 that bisects the reserve, which is also bordered with patches of development and agricultural land. We attribute this to high rates of migration, with four giant panda road-crossing events confirmed within a year based on repeated captures of individuals. This likely means that giant panda populations within mountain ranges are better connected than previously thought. Increased development and tourism traffic in the area and throughout the current panda distribution pose a threat of increasing population isolation, however. Maintaining and restoring adequate habitat corridors for dispersal is thus a vital step for preserving the levels of gene flow seen in our analysis and the continued conservation of the giant panda meta-population in both Wolong and throughout their current range.
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Affiliation(s)
- Maiju Qiao
- Key Laboratory of Bio‐Resource and Eco‐Environment of Ministry of Education, College of Life SciencesSichuan UniversityChengduChina
- China Conservation and Research Center for the Giant PandaDujiangyanChina
| | - Thomas Connor
- Department of Fisheries and WildlifeMichigan State UniversityEast LansingMichigan
| | | | - Jie Huang
- Key Laboratory of Bio‐Resource and Eco‐Environment of Ministry of Education, College of Life SciencesSichuan UniversityChengduChina
| | - Yan Huang
- China Conservation and Research Center for the Giant PandaDujiangyanChina
| | - Hemin Zhang
- China Conservation and Research Center for the Giant PandaDujiangyanChina
| | - Jianghong Ran
- Key Laboratory of Bio‐Resource and Eco‐Environment of Ministry of Education, College of Life SciencesSichuan UniversityChengduChina
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Kang D, Li J. Role of nature reserves in giant panda protection. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2018; 25:4474-4478. [PMID: 29185223 DOI: 10.1007/s11356-017-0831-3] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2017] [Accepted: 11/23/2017] [Indexed: 06/07/2023]
Abstract
Giant panda (Ailuropoda melanoleuca) is a flagship species in nature conservation of the world; to protect this species, 67 nature reserves have been established in China. To evaluate the protection effect of giant panda nature reserves, we analyzed the variation of giant panda number and habitat area of 23 giant panda nature reserves of Sichuan province based on the national survey data released by State Forestry Administration and Sichuan Forestry Department. Results showed that from the third national survey to the fourth, giant panda number and habitat area of 23 giant panda nature reserves of Sichuan province failed to realize the significant increase. Furthermore, we found that the total population growth rate of 23 nature reserves in the last 12 years was lower than those of the province total of Sichuan and the national total of China, and the total habitat area of the 23 nature reserves was decreasing in the last 12 years, but the province total and national total were all increasing. We propose that giant panda protection should pay more attention to how to improve the protective effects of nature reserves.
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Affiliation(s)
- Dongwei Kang
- College of Forestry, Beijing Forestry University, No.35 East-Qinghua Road Haidian District, Beijing, 100083, People's Republic of China
| | - Junqing Li
- College of Forestry, Beijing Forestry University, No.35 East-Qinghua Road Haidian District, Beijing, 100083, People's Republic of China.
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