Key Laboratory of Forest Plant Ecology, Northeast Forestry University, Harbin 150040, 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.
Proc Biol Sci. 2023 Apr 26;290(1997):20230406. doi: 10.1098/rspb.2023.0406. Epub 2023 Apr 19.
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.
鸟类观测主要依赖于实地调查,这种方法既耗时又费力。在本研究中,我们探索了利用街景图像进行城市鸟类和鸟巢的虚拟调查。以沿海城市青岛为研究区域,使用百度街景(BSV)地图采集了 2741 个点位的 47201 张无缝球形照片。使用单一评级员-全部照片检查和七评级员-复合种群检查来确定评级员之间的可重复性、BSV 采集的最佳观测层以及可能影响结果的环境。我们还收集了社区科学数据进行比较。使用 BSV 时间机器评估时间动态。卡帕平方检验、广义线性模型、冗余排序和 ArcMap 用于分析。巢评估的不同评级员重复性为 79.1%,鸟类出现的重复性为 46.9%。对不同评级照片的重新检查可以将其分别提高到 92%和 70%。七评级员统计数据显示,超过 5%的采样比例就可以产生与整个数据无显著差异的鸟类和巢百分比,并且更高的采样比例可以减少变异。单独使用中视角层调查可以节省 2/3 的时间,产生 93%的巢检查精度;在鸟类方面,选择中视角和上视角照片可以发现 97%的鸟类出现。在空间分布方面,这种方法得出的鸟巢热点区域比社区科学鸟类观测点大得多。BSV 时间机器使得在同一地点重新检查鸟巢成为可能,但对鸟类重新检查具有挑战性。在无叶季节、在宽阔、交通密集的沿海街道上,在树木垂直结构复杂的地方,以及在以道路林为主的高楼大厦的缝隙中,可以观察到更多的鸟巢和鸟类。研究结果表明,BSV 照片可以用于从数量、空间分布和时间动态方面来虚拟评估鸟类的出现和鸟巢。这种方法为城市环境中的大规模鸟类出现和巢丰富度调查提供了实验前的、信息丰富的补充。