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A generalised volumetric method to estimate the biomass of photographically surveyed benthic megafauna

机译:估计摄影底栖大型动物生物量的广义体积法

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Biomass is a key variable for understanding the stocks and flows of carbon and energy in the environment. The quantification of megabenthos biomass (body size a >= 1 cm) has been limited by their relatively low abundance and the difficulties associated with quantitative sampling. Developments in robotic technology, particularly autonomous underwater vehicles, offer an enhanced opportunity for the quantitative photographic assessment of the megabenthos. Photographic estimation of biomass has typically been undertaken using taxon-specific length-weight relationships (LWRs) derived from physical specimens. This is problematic where little or no physical sampling has occurred and/or where key taxa are not easily sampled. We present a generalised volumetric method (GVM) for the estimation of biovolume as a predictor of biomass. We validated the method using fresh trawl-caught specimens from the Porcupine Abyssal Plain Sustained Observatory (northeast Atlantic), and we demonstrated that the GVM has a higher predictive capability and a lower standard error of estimation than the LWR method. GVM and LWR approaches were tested in parallel on a photographic survey in the Celtic Sea. Among the 75% of taxa for which LWR estimation was possible, highly comparable biomass values and distribution patterns were determined by both methods. The biovolume of the remaining 25% of taxa increased the total estimated standing stock by a factor of 1.6. Additionally, we tested inter-operator variability in the application of the GVM, and we detected no statistically significant bias. We recommend the use of the GVM where LWRs are not available, and more generally given its improved predictive capability and its independence from the taxonomic, temporal, and spatial, dependencies known to impact LWRs.
机译:生物质是了解环境中碳和能源的存量和流量的关键变量。巨型底栖生物的定量(a> = 1 cm的大小)受到其相对较低的丰度和定量采样相关困难的限制。机器人技术的发展,尤其是自动水下机器人,为大型底栖动物的定量摄影评估提供了更多的机会。通常使用衍生自物理样本的分类单元特定的长度重量关系(LWR)进行生物量的照相估计。在很少或没有物理采样发生和/或关键分类单元不容易采样的情况下,这是有问题的。我们提出了一种用于估计生物体积作为生物量预测因子的广义体积法(GVM)。我们使用豪猪深渊平原持续观测站(东北大西洋)的新鲜拖网捕获标本对方法进行了验证,并且证明了与LWR方法相比,GVM具有更高的预测能力和更低的估计标准误。 GVM和LWR方法在凯尔特海的摄影调查中进行了平行测试。在可能进行LWR估算的75%的分类单元中,通过两种方法都确定了高度可比的生物量值和分布模式。剩余25%的分类单元的生物量使总估计存量增加了1.6倍。此外,我们在GVM的应用中测试了操作员之间的差异性,并且没有发现统计学上的显着偏差。我们建议在LWR不可用的情况下使用GVM,更普遍的情况是考虑到GVM的改进的预测能力以及与已知影响LWR的分类,时间和空间依赖性的独立性。

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