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Analysis of Underwater Visual Data to Identify the Impact of Physical Disturbance on Horse Mussel (Modiolus modiolus) Beds

机译:水下视觉数据分析,以确定物理干扰对贻贝(Modiolus modiolus)床的影响

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摘要

Underwater visual data from video or still photography can provide immediate qualitative descriptions of in situ epibenthic communities. However, few studies have attempted statistical analysis of such data in order to quantitatively assess the sensitivity of epifauna to anthropogenic influences. This paper discusses the use of species-time techniques, which substitute time for area and produce estimates of relative abundance of species based on time. This paper adapts such a technique, the 'Visual Fast Count', to quantitatively assess the impact of trawling on horse mussel, Modiolus modiolus, communities. Direct counts of individuals at various taxonomic levels were made from the still photographic images. The potential role of such techniques in the management of epifaunal communities in wider marine pollution studies is discussed.
机译:来自视频或静态摄影的水下视觉数据可以提供对原位表皮动物群落的直接定性描述。但是,很少有研究尝试对此类数据进行统计分析,以定量评估表生动物对人为影响的敏感性。本文讨论了物种时间技术的使用,该技术可以用时间代替面积,并根据时间得出相对物种丰富度的估计值。本文采用了这种技术,即“视觉快速计数”,以定量评估拖网捕捞对贻贝,Mo蒲,群落的影响。从静止图像中直接分类各个分类级别的个人。在更广泛的海洋污染研究中,讨论了这种技术在表彰群落管理中的潜在作用。

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