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Tian P, Wang L, Li Q, Liang C, She D, Liu S, Chen Y, Yao L, Wang W, Wang H, Wang W. Feasibility of urban bird occurrence and nest amount evaluation by the street-view image virtual survey. Proc Biol Sci 2023; 290:20230406. [PMID: 37072036 PMCID: PMC10113023 DOI: 10.1098/rspb.2023.0406] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Accepted: 03/21/2023] [Indexed: 04/20/2023] Open
Abstract
Bird observation mainly relies on field surveys, which are time-consuming and laborious. In this study, we explored using street-view images in the virtual survey of urban birds and nests. Using the coastal city of Qingdao as the study area, 47 201 seamless spherical photos at 2741 sites were collected using the Baidu street-view (BSV) map. Single-rater-all photo checks and seven-rater-metapopulation checks were used to find inter-rater repeatability, the best viewing layer for BSV collection, and possible environments affecting the results. We also collected community science data for comparison. The BSV time machine was used to assess the temporal dynamics. Kappa square test, generalized linear model, redundancy ordination and ArcMap were used in the analysis. Different rater repeatability was 79.1% in nest evaluations and 46.9% in bird occurrence. A re-check of the different-rating photos can increase them to 92% and 70%. Seven-rater statistics showed that more than 5% sampling ratio could produce a non-significant different bird and nest percentage of the whole data, and the higher sampling ratio could reduce the variation. The middle-viewing layer survey alone could produce 93% precision of the nest checks by saving 2/3 of the time used; in birds, selecting middle and upper-view photos could find 97% of bird occurrences. In the spatial distribution, the nest's hotspot areas from this method were much greater than the community science bird-watching sites. The BSV time machine made it possible to re-check nests in the same sites but challenging the re-check of bird occurrences. The nests and birds can be observed more in the leafless season, on wide, traffic-dense coastal streets with complex vertical structures of trees, and in the gaps of tall buildings dominated by road forests. Our results indicate that BSV photos could be used to virtually evaluate bird occurrence and nests from their numbers, spatial distribution and temporal dynamics. This method provides a pre-experimental and informative supplement to large-scale bird occurrence and nest abundance surveys in urban environments.
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Affiliation(s)
- Panli Tian
- Key Laboratory of Forest Plant Ecology, Northeast Forestry University, Harbin 150040, People's Republic of China
| | - Lei Wang
- Key Laboratory of Forest Plant Ecology, Northeast Forestry University, Harbin 150040, People's Republic of China
| | - Qi Li
- Key Laboratory of Forest Plant Ecology, Northeast Forestry University, Harbin 150040, People's Republic of China
| | - Chentao Liang
- Key Laboratory of Forest Plant Ecology, Northeast Forestry University, Harbin 150040, People's Republic of China
| | - Danqi She
- Key Laboratory of Forest Plant Ecology, Northeast Forestry University, Harbin 150040, People's Republic of China
| | - Siyu Liu
- State Key Laboratory of Subtropical Silviculture, College of Forestry and Biotechnology, Zhejiang A&F University, Hangzhou 311300, People's Republic of China
| | - Yuwen Chen
- Key Laboratory of Forest Plant Ecology, Northeast Forestry University, Harbin 150040, People's Republic of China
| | - Liuyang Yao
- Key Laboratory of Forest Plant Ecology, Northeast Forestry University, Harbin 150040, People's Republic of China
| | - Weiqi Wang
- School of Information Science and Technology, University of Science and Technology of China, Hefei 230026, People's Republic of China
| | - Huimei Wang
- State Key Laboratory of Subtropical Silviculture, College of Forestry and Biotechnology, Zhejiang A&F University, Hangzhou 311300, People's Republic of China
| | - Wenjie Wang
- Northeast Institute of Geography and Agroecology, Chinese Academy of Science, Changchun 130102, People's Republic of China
- State Key Laboratory of Subtropical Silviculture, College of Forestry and Biotechnology, Zhejiang A&F University, Hangzhou 311300, People's Republic of China
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