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量化河流生态系统中淡水野生动物大规模死亡事件的方法。

Methods of quantifying a mass mortality event in freshwater wildlife within the river ecosystem.

作者信息

Marchowski Dominik, Szlauer-Łukaszewska Agnieszka, Ławicki Łukasz, Engel Jacek, Drewniak Ewa, Ciężak Karol

机构信息

Ornithological Station, Museum and Institute of Zoology of the Polish Academy of Sciences, ul. Nadwiślańska 108, Gdańsk 80-680, Poland.

Institute of Marine and Environmental Sciences, Szczecin University, Szczecin, Poland.

出版信息

MethodsX. 2024 Jan 11;12:102567. doi: 10.1016/j.mex.2024.102567. eCollection 2024 Jun.

DOI:10.1016/j.mex.2024.102567
PMID:38287964
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10823132/
Abstract

This study introduces a comprehensive method for quantifying mass mortality events in freshwater wildlife, exemplified by the ecological disaster in the Odra River in 2022. Our approach integrates field observations, statistical analysis, and ecological assessment to measure the impact of such events on various aquatic species. Key steps include systematic counting of deceased organisms, assessing population declines, and evaluating the ecological repercussions of invasive species. Utilizing the R programming language, we developed a framework that is adaptable to similar ecological crises in different aquatic environments. This methodology facilitates a detailed understanding of the scale and implications of mass mortality events, thereby contributing to effective environmental management and conservation efforts. •The analysis and modeling methods of the disaster are presented in the R programming language.•Exclusively open-source software was used for the analysis.•The analysis includes detailed data on the disaster's impact on various species.

摘要

本研究介绍了一种量化淡水野生动物大规模死亡事件的综合方法,以2022年奥得河的生态灾难为例。我们的方法整合了实地观察、统计分析和生态评估,以衡量此类事件对各种水生物种的影响。关键步骤包括对死亡生物进行系统计数、评估种群数量下降以及评估入侵物种的生态影响。利用R编程语言,我们开发了一个适用于不同水生环境中类似生态危机的框架。这种方法有助于详细了解大规模死亡事件的规模和影响,从而为有效的环境管理和保护工作做出贡献。•灾难的分析和建模方法用R编程语言呈现。•分析仅使用开源软件。•分析包括灾难对各种物种影响的详细数据。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a7b4/10823132/1f04fd56dc04/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a7b4/10823132/ec5ad6c22144/ga1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a7b4/10823132/6589d0ed66f8/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a7b4/10823132/1f04fd56dc04/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a7b4/10823132/ec5ad6c22144/ga1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a7b4/10823132/6589d0ed66f8/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a7b4/10823132/1f04fd56dc04/gr2.jpg

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本文引用的文献

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2
A general deep learning model for bird detection in high-resolution airborne imagery.一种用于高分辨率航空影像中鸟类检测的通用深度学习模型。
Ecol Appl. 2022 Dec;32(8):e2694. doi: 10.1002/eap.2694. Epub 2022 Aug 10.
3
Drones, automatic counting tools, and artificial neural networks in wildlife population censusing.
野生动物种群普查中的无人机、自动计数工具和人工神经网络。
Ecol Evol. 2021 Nov 3;11(22):16214-16227. doi: 10.1002/ece3.8302. eCollection 2021 Nov.
4
Distance software: design and analysis of distance sampling surveys for estimating population size.距离软件:用于估计种群大小的距离抽样调查的设计与分析
J Appl Ecol. 2010 Feb;47(1):5-14. doi: 10.1111/j.1365-2664.2009.01737.x.