应用激光雷达技术探究北京地区乌鸦夜栖地选择机制

doi:10.17520/biods.2026004

生物多样性 ›› 2026, Vol. 34 ›› Issue (5): 26004. DOI: 10.17520/biods.2026004 cstr: 32101.14.biods.2026004

• 研究报告: 动物多样性 • 上一篇下一篇

应用激光雷达技术探究北京地区乌鸦夜栖地选择机制

谢冰¹(), 杨海涛²(), 曹吉鑫³, 李进宇³, 王茂良³, 张微⁴, 李建强¹(), 徐基良¹^,^*()

¹ 北京林业大学生态与自然保护学院, 北京 100083
² 海南大学生态学院, 海口 570228
³ 北京市园林绿化科学研究院, 北京 100102
⁴ 北京市西山试验林场管理处, 北京 100093

收稿日期:2026-01-04 接受日期:2026-03-13 出版日期:2026-05-20 发布日期:2026-07-01
通讯作者: 徐基良
基金资助:
北京市科学技术委员会资助项目(Z221100005222015)

LiDAR-based investigation of the mechanisms governing nocturnal roost selection by crows in the Beijing urban area

Bing Xie¹(), Haitao Yang²(), Jixin Cao³, Jinyu Li³, Maoliang Wang³, Wei Zhang⁴, Jianqiang Li¹(), Jiliang Xu¹^,^*()

¹ School of Ecology and Nature Conservation, Beijing Forestry University, Beijing 100083, China
² School of Ecology, Hainan University, Haikou 570228, China
³ Beijing Academy of Forestry and Landscape Architecture, Beijing 100102, China
⁴ Administration of Xishan Mountain Experimental Forest Farm, Beijing 100093, China

Received:2026-01-04 Accepted:2026-03-13 Online:2026-05-20 Published:2026-07-01
Contact: Jiliang Xu
Supported by:
The Beijing Municipal Science and Technology Commission(Z221100005222015)

1. 附录.pdf(642KB)

摘要/Abstract

摘要：

城市鸟类夜栖地选择是其在人工环境中适应与生存的关键。现有研究多关注宏观生境或微生境, 对鸟类直接利用的栖枝信息缺乏精准的测量手段和研究。激光雷达技术的高精度数据测量能力使得对鸟类栖枝的三维结构研究成为可能。本研究以北京城区冬季夜栖乌鸦为研究对象, 于2023-2025年在北京市六环内乌鸦主要聚集区域开展调查。在采集36个夜栖样方和36个对照样方的18个生境因子数据的基础上, 进一步使用激光雷达技术测量了1,361个夜栖枝条和581个对照枝条的4个变量, 以分析影响乌鸦夜栖地选择的主要因子。结果表明: 夜栖样方的树高、郁闭度、冠幅、噪声和照度均显著大于对照样方; 夜栖枝条的高度、直径、长度均显著大于对照枝条, 角度显著小于对照枝条。广义线性混合模型和广义线性模型结果表明, 栖树树高和噪声及4个枝条变量均是影响北京城区乌鸦夜栖地选择的关键生境变量, 乌鸦倾向于选择树高更高、环境噪声更大的区域夜栖, 在栖枝上偏好高度较高, 角度平缓, 较短的粗枝条或较长的细枝条。研究揭示了北京城区乌鸦夜栖地选择机制, 为城市鸟类管理提供了一定的科学依据, 同时验证了通过激光雷达技术开展鸟类栖息地精细化研究的可行性。

关键词: 夜栖地选择, 地基激光雷达, 乌鸦, 栖枝结构, 城市鸟类

Abstract

Aims: The selection of nocturnal roosting sites is a critical aspect of urban birds’ adaptation and survival within built environments. Existing studies have predominantly focused on macrohabitat or microhabitat characteristics, yet lack precise measurement methods and research on the perching branch information directly utilized by birds. LiDAR technology, with its high-precision data measurement capabilities, makes it possible to study the three-dimensional structure of avian perching branches. As a typical urban-adapted species, crows often form large-scale aggregated roosting groups in cities in winter, triggering human-wildlife conflict issues. To explore the key factors influencing their nocturnal roost selection and verify the feasibility of LiDAR in the refined research of bird habitats, this study took winter roosting crows in urban Beijing as the focal species and conducted surveys in the main crow aggregation areas within the 6th Ring Road of Beijing from 2023 to 2025.
Methods: Based on data collected on 18 habitat factors from 36 roosting quadrats and 36 control quadrats, we further employed terrestrial laser scanning to measure four variables for 1,361 roosting branches and 581 control branches. Among the control branches, 349 were from roosting trees and 232 from non-roosting trees. The Mann-Whitney U test was used to compare differences in the 18 habitat factors between roosting and control quadrats. The Kruskal-Wallis test was applied to examine pairwise differences among roosting branches, control branches on roosting trees, and control branches on non-roosting trees. Further, generalized linear mixed models (GLMMs) and generalized linear models (GLMs) were adopted to identify the key variables affecting crow nocturnal roost selection.
Results: The results indicated that tree height, canopy closure, crown width, noise, and illuminance were significantly higher in roosting quadrats compared to control quadrats. Furthermore, the height, diameter, and length of nocturnal roosting branches were significantly greater than those of both control branch groups, while the branch angle was significantly smaller. Between the two control groups, only branch height showed a significant difference. Analyses using GLMM revealed that tree height and noise were key habitat factors affecting nocturnal roost selection. GLM analyses revealed that branch angle, the quadratic term of branch height, and the interaction between branch diameter and branch length all had significant effects on the nocturnal roosting site selection by crows.
Conclusion: Crows exhibited a clear preference for nocturnal roosting sites characterized by taller trees and higher ambient noise levels. At the branch scale, they selectively perched on branches positioned at greater heights with shallower angles, which were typically either long and moderately thick or short and thick. This study elucidates the nocturnal roosting selection mechanism of crows in urban Beijing and offers a scientific basis for evidence-based urban bird management. Furthermore, it demonstrates the feasibility and distinct advantages of employing LiDAR technology in fine-scale habitat research, highlighting its potential to advance urban ornithological studies.

Key words: nocturnal roost selection, terrestrial laser scanner, crows, roosting branch architecture, urban birds

谢冰, 杨海涛, 曹吉鑫, 李进宇, 王茂良, 张微, 李建强, 徐基良 (2026) 应用激光雷达技术探究北京地区乌鸦夜栖地选择机制. 生物多样性, 34, 26004. DOI: 10.17520/biods.2026004.

Bing Xie, Haitao Yang, Jixin Cao, Jinyu Li, Maoliang Wang, Wei Zhang, Jianqiang Li, Jiliang Xu (2026) LiDAR-based investigation of the mechanisms governing nocturnal roost selection by crows in the Beijing urban area. Biodiversity Science, 34, 26004. DOI: 10.17520/biods.2026004.

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链接本文: https://www.biodiversity-science.net/CN/10.17520/biods.2026004

https://www.biodiversity-science.net/CN/Y2026/V34/I5/26004

图/表 9

图1 北京城区乌鸦主要集群夜栖区域。THU: 清华大学及其周边; BNU: 北京师范大学及其周边; XD: 西单。

Fig. 1 Major aggregated nocturnal roosting areas of crows in the urban area of Beijing. THU, Tsinghua University and its surrounding area; BNU, Beijing Normal University and its surrounding area; XD, Xidan.

图2 基于点云数据的枝条变量测量示意图

Fig. 2 Schematic of point cloud-based branch variable measurement

表1 乌鸦夜栖样方和对照样方生境比较

Table 1 Comparison of habitat variables between roosting plots and control plots

变量 Variables	夜栖样方 Roosting plot (n = 36)	对照样方 Control plot (n = 36)	T/Z	χ²	P
树高 Tree height (m)	15.58 ± 0.69	12.18 ± 0.47	4.18		< 0.001
胸径 Diameter at breast height (cm)	44.76 ± 19.38	34.92 ± 12.98	-1.76		0.078
冠幅 Crown width (m)	11.26 ± 0.52	9.63 ± 0.52	2.04		0.049
郁闭度 Canopy closure	2.31 ± 0.87	1.83 ± 0.85	-2.51		0.012
噪声 Noise (dB)	70.89 ± 1.31	64.23 ± 1.50	4.24		< 0.001
照度 Illuminance (lx)	6.67 ± 11.44	2.71 ± 2.56	-2.58		0.010
温度 Temperature (℃)	1.90 ± 0.37	1.73 ± 0.34	0.89		0.382
风速 Wind speed (m/s)	0.82 ± 0.67	0.97 ± 0.78	-1.01		0.314
到最近水体距离 Distance to the nearest water (m)	1,329.80 ± 71.54	1,353.85 ± 75.13	-0.80		0.429
人流量 Pedestrian flow (person/min)	13.47 ± 16.34	9.49 ± 10.08	-0.87		0.386
车流量 Traffic flow (vehicle/min)	40.09 ± 38.51	30.77 ± 34.55	-1.27		0.204
到最近道路距离 Distance to the nearest road (m)	3.03 ± 4.43	2.30 ± 4.03	-0.16		0.867
最近道路宽度 Width of the nearest road (m)	17.86 ± 13.00	15.48 ± 12.27	-0.75		0.456
建筑密度 Building side count (count)	1.72 ± 0.78	1.72 ± 0.85	-0.08		0.938
到最近建筑距离 Distance to the nearest building (m)	15.63 ± 9.95	17.70 ± 14.20	-0.58		0.561
最近建筑高度 Height of the nearest building (m)	30.83 ± 17.35	29.18 ± 25.88	-0.66		0.509
街道走向 Street orientation	-	-		1.89	0.596
最近建筑是否高于栖树 Whether the nearest building is higher than the roosting tree	-	-		1.69	0.242

表2 乌鸦夜栖地选择关键生境因子的模型选择结果

Table 2 Results of model selection for key habitat factors affecting the nocturnal habitat selection of crows

模型参数 Model parameters	df	Log(L)	AIC_c	ΔAIC_c	权重 Weight
TH + SO(N-S) + N + IL	8	-29.89	78.07	0	0.23
TH + DBH + SO(N-S) + N + IL	9	-29.10	79.11	1.03	0.14
TH + N + IL	5	-34.52	79.94	1.87	0.09

表3 ΔAICc < 2的广义线性混合模型中各保留变量的显著性

Table 3 Significance of retained variables in generalized linear mixed models with ΔAICc < 2

变量 Variables	估计值 Estimate	标准误 Standard error	Wald Z	相对重要性 Relative importance	P
树高 Tree height	1.23	0.46	2.70	0.95	0.007
噪声 Noise	1.04	0.42	2.46	0.93	0.014
照度 Illuminance	1.41	0.83	1.70	0.83	0.089
街道走向(南北走向) Street orientation (north-south)	-0.21	0.73	0.29	0.72	0.772
胸径 Diameter at breast height	0.56	0.53	1.06	0.37	0.290

图3 显著生境变量与夜栖选择概率的回归曲线。灰色部分为95%的置信区间。

Fig. 3 Regression curves of significant habitat variables on roost-site selection probability. Gray shading indicates 95% confidence interval.

图4 夜栖枝条与不同来源的对照枝条间的比较。RB: 夜栖枝条; RCB: 夜栖树上的对照枝条; NRCB: 非夜栖树上的对照枝条。** P < 0.01; *** P < 0.001。

Fig. 4 Comparison between nocturnal roosting branches and control branches from different sources. RB, Roosting branches; RCB, Control branches from roosting trees; NRCB, Control branches from non-roosting trees. ** P < 0.01; *** P < 0.001.

图5 乌鸦夜栖枝条选择影响因子的效应值森林图

Fig. 5 Forest plot of effect sizes for factors influencing crow roosting branch selection

图6 基于模型1 (纳入夜栖枝条和夜栖树上的对照枝条)的枝条变量与夜栖选择概率的响应曲线。灰色部分为95%的置信区间。OR: 优势比。

Fig. 6 Response curves of branch variables on roosting probability based on Model 1 (including roosting branches and control branches on the roosting trees). Gray shading indicates 95% confidence interval. OR, Odds ratio.

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[47]	Zhao YX, Zhang JY, Li ZH, Xie Q, Deng X, Zhang CX, Wang N (2024) Use of evergreen and deciduous plants by nocturnal-roosting birds: A case study in Beijing. Avian Research, 15, 100177. DOI URL

应用激光雷达技术探究北京地区乌鸦夜栖地选择机制

LiDAR-based investigation of the mechanisms governing nocturnal roost selection by crows in the Beijing urban area

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