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航空图像并不会排除在估算鸟类聚居地规模时出现的可探测性问题。

Airborne imagery does not preclude detectability issues in estimating bird colony size.

机构信息

CEFE, IRD, CNRS, University of Montpellier, EPHE-PSL University, Montpellier, France.

GEPOMAY, Groupe d'Études et de Protection des Oiseaux de Mayotte, 4 Impasse Tropina, Miréréni, Tsingoni, Mayotte, France.

出版信息

Sci Rep. 2024 Feb 14;14(1):3673. doi: 10.1038/s41598-024-53961-w.

DOI:10.1038/s41598-024-53961-w
PMID:38351024
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10864377/
Abstract

Aerial images obtained by drones are increasingly used for ecological research such as wildlife monitoring. Yet detectability issues resulting from animal activity or visibility are rarely considered, although these may lead to biased population size and trend estimates. In this study, we investigated detectability in a census of Malagasy pond heron Ardeola idae colonies on the island of Mayotte. We conducted repeated drone flights over breeding colonies in mangrove habitats during two breeding seasons. We then identified individuals and nests in the images and fitted closed capture-recapture models on nest-detection histories. We observed seasonal variation in the relative abundance of individuals, and intra-daily variation in the relative abundance of individuals-especially immature birds-affecting the availability of nests for detection. The detection probability of nests estimated by capture-recapture varied between 0.58 and 0.74 depending on flyover days and decreased 25% from early to late morning. A simulation showed that three flyovers are necessary to detect a 5-6% decline in colonies of 50 to 200 nests. These results indicate that the detectability of nests of forest-canopy breeding species from airborne imagery can vary over space and time; we recommend the use of capture-recapture methods to control for this bias.

摘要

无人机获取的航空图像越来越多地用于野生动物监测等生态研究。然而,由于动物活动或能见度导致的可检测性问题很少被考虑到,尽管这些问题可能导致对种群规模和趋势的估计产生偏差。在这项研究中,我们调查了在马约特岛的马达加斯加池鹭 Ardeola idae 繁殖地进行普查时的可检测性。我们在两个繁殖季节在红树林生境中的繁殖地进行了多次无人机飞行。然后,我们在图像中识别个体和巢穴,并根据巢检测历史拟合封闭的捕获-再捕获模型。我们观察到个体的相对丰度存在季节性变化,以及个体的日内变化——特别是未成年鸟类——影响了巢穴的可检测性。捕获-再捕获估计的巢穴检测概率在不同的飞越日之间变化在 0.58 到 0.74 之间,并从清晨到上午晚些时候下降了 25%。模拟结果表明,需要进行三次飞越才能检测到 50 到 200 个巢穴的繁殖地中 5-6%的下降。这些结果表明,从航空图像中检测林冠繁殖物种的巢穴的可检测性可能会随空间和时间而变化;我们建议使用捕获-再捕获方法来控制这种偏差。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7289/10864377/a02d1b0d32e7/41598_2024_53961_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7289/10864377/e37bbb14a126/41598_2024_53961_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7289/10864377/49efef718775/41598_2024_53961_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7289/10864377/cb1486c3d86f/41598_2024_53961_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7289/10864377/c770af71b8a1/41598_2024_53961_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7289/10864377/21fadead7598/41598_2024_53961_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7289/10864377/a02d1b0d32e7/41598_2024_53961_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7289/10864377/e37bbb14a126/41598_2024_53961_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7289/10864377/49efef718775/41598_2024_53961_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7289/10864377/cb1486c3d86f/41598_2024_53961_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7289/10864377/c770af71b8a1/41598_2024_53961_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7289/10864377/21fadead7598/41598_2024_53961_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7289/10864377/a02d1b0d32e7/41598_2024_53961_Fig6_HTML.jpg

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