多亏了学生、工作人员、船长和船员, 他们在这些研究旅行和提供船只的业主身上航行。感谢 t Jaffarian 开发实验室数据收集程序, Electromechanica, 开发现场软件和设备, 并 CVision 咨询开发图像注释器程序。资助由 NOAA 奖 NA17NMF4540043、NA17NMF4540034 和 NA17NMF4540028 提供。此处所表达的观点是作者的意见, 不一定反映 NOAA 的意见。

1. 勘察设计
<ol><li>找到一个或多个离岸商业扇贝船可提供 6-8 天的时间间隔。</li>
	<li>建造一个内部框架的钢金字塔, 其中三摄像头, 灯, 和一个光缆接线盒可以安装 (图 1)。确保一台相机是高分辨率数码静止相机, 二是分辨率较低, 但仍高清晰度、摄像机。</li>
</ol><img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/57493/57493fig1.jpg"> 图 1: 在2017年使用用于数据收集的照相机和灯放置照相机测量金字塔.马萨诸塞州的达特茅斯大学, 海洋科学和技术学院, 在2017年将相机和灯用于数据收集的金字塔投影在一起。将照相机和光缆连接到光纤电缆的接线盒安装在两个带灯的条形之间, 不显示。<a href="/files/ftp_upload/57493/57493fig1large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
<ol start="3"><li>使用系统抽样设计, 将监测站5.6 公里, 除渔业使用的大多数地区和2.8 公里外, 在两个重要领域1 注: 两名科学家、一名船长和一名队友能够在2.8 公里网格上每24小时对5.6 公里网格和80个站进行大约50站的调查。因此, 需要大约5次调查旅行来完成调查。</li>
</ol>2. 数据收集
<ol><li>将设备装载到容器上。</li>
	<li>在船甲板上设置设备。<ol>
<li>安排金字塔, 一个压敏绞车与光纤电缆在一个鼓和连接到滑动环, 并吊在甲板上的船只。确保光纤电缆可以从绞车通过吊架和金字塔运行, 而不触及船只的绞车电缆。</li>
		<li>使用小, 临时焊缝, 以粘贴绞车, 吊架和吊架板到位。</li>
		<li>钢丝压敏绞车电力电缆进入容器的保险丝盒。</li>
		<li>将接线盒连接到金字塔。</li>
		<li>将照相机和灯连接到金字塔上。</li>
		<li>用照相机和光缆将照相机和灯连接到接线盒。</li>
		<li>通过滑轮运行光缆并连接到吊架上。</li>
		<li>将船用液压绞车电缆连接到金字塔。</li>
	</ol></li>
	<li>在船用舵手室设置设备。<ol>
<li>插入并保护台式计算机。</li>
		<li>将2台监视器连接到台式计算机。在计算机附近保护一个显示器, 并在该船的液压绞车的控制附近找到第二个监视器。</li>
		<li>通过USB 端口将全局定位系统 (GPS) 设备连接到台式计算机。</li>
		<li>插入并保护笔记本电脑, 并在船舵附近使用移动字段映射程序。在出发前将站位置加载到计算机中。</li>
		<li>通过串行端口将 GPS 设备连接到膝上型计算机。</li>
		<li>把相机和灯光连接到船上的舵手室。<ol>
<li>将光纤电缆的舵手运行的 "甲板端" 连接到压敏绞车上的光滑环上。</li>
			<li>将光纤光缆的舵手运行的 "舵手端" 连接到台式计算机和轻型电源插头上的光纤接口上。</li>
			<li>从台式计算机启动字段数据收集程序。确保所有安装在金字塔上并插入接线盒的摄像头自动显示为已连接。 注: 如果所有相机都不显示为已连接, 则在程序中复位摄像机连接, 或通过交换元件 (照相机、连接电缆、滑动环等) 系统地进行故障排除。</li>
		</ol>
</li>
	</ol></li>
	<li>在每个站捕获图像和记录数据。<ol>
<li>从笔记本电脑启动移动字段映射程序。</li>
		<li>选择 "标记" 工具, 并将目标图标拖到工作站上, 以提供对工作站的轴承。</li>
		<li>当车站到达时, 用船的液压绞车缓慢地将取样金字塔降低到海底, 而船只已经停止。 注: 这是由船的船员完成, 压力敏感绞车必须打开之前, 降低取样金字塔。</li>
		<li>从台式计算机启动现场数据收集程序, 而金字塔正被降低到海底。<ol>
<li>双击 "区域短名称" 框并输入该区域的名称。 注: 这只需要为一个地区的第一站做。</li>
			<li>单击 "启动船长凸轮" 按钮, 在液压绞车控制附近的监视器上显示照相机视图和其他信息。</li>
		</ol>
</li>
		<li>将电源切换到指示灯。</li>
		<li>一旦取样金字塔降落在海底, 捕获样方数据。<ol>
<li>在现场数据收集程序中, 单击 "启动站" 开始录制视频。 注意: 当视频录制时, 按钮会闪烁红色。</li>
			<li>当看到海底的清晰视图时, 单击 "拍摄快照", 然后单击 "全部保存", 同时从所有照相机视图中捕获静止图像。</li>
			<li>单击 "写入数据库" 按钮。 注意: 这将带来一个新的对话框, 其中有深度、位置、温度、站号、样方号、区域名称以及软件自动输入的唯一标识号。</li>
			<li>输入在 "扇贝计数" 框中的数字静止相机图像中看到的扇贝数, 并在 "注释" 框中键入任何注释。</li>
			<li>单击 "提交数据" 按钮, 将有关样方的数据作为一行写入字段数据库。</li>
		</ol>
</li>
		<li>举起金字塔, 直到海底再也看不见。</li>
		<li>慢慢降低金字塔到海底, 重复步骤2.4.6 和2.4.7。直到四样方的数据被捕获为止。确保金字塔漂流, 使海底的不同样方图像被捕获。</li>
		<li>将取样金字塔从海底提升到船只旁边的安全位置。</li>
		<li>在金字塔升起的时候结束车站。<ol>
<li>单击现场数据收集程序中的 "终端站", 结束视频录制, 并将计划提前到下一站。</li>
			<li>单击 "退出程序" 按钮以关闭程序。</li>
			<li>关灯电源。</li>
			<li>单击移动字段映射程序中的 "捕获站", 将工作站标记为已完成, 然后重复步骤2.4.2。</li>
		</ol>
</li>
		<li>重复2.4 节前面的步骤。直到所有的调查站都完成了。</li>
	</ol></li>
	<li>进行相机校准降。<ol>
<li>用电子卡尺测量导线网格中至少30个网格单元的长度。标记所测量的单元格。</li>
		<li>用钢丝绳或绳子将网格连接到取样金字塔的底座上。确保已测量的网格单元格位于照相机视图中。</li>
		<li>重复步骤2.4.3 到2.4.6.2 以捕获网格的图像。 注: 此校准通常在第一站前完成, 但可以在测试箱出发之前或在调查期间的任何时间进行。其目的是确定图像中样方尺寸和测量特征的 mm 与像素比值的长度。</li>
	</ol></li>
	<li>在数字静止相机样方图像中量化数据。 注: 在这个过程中使用其他相机和视频中的图像作为辅助工具。<ol>
<li>启动实验室数据收集程序并选择 "数字化" 配置文件。</li>
		<li>从下拉菜单中, 选择年份、区域、照相机、站和样方的兴趣。</li>
		<li>单击 "转到" 以根据在步骤2.6.2 中选择的条件来带来图像。到程序中。</li>
		<li>在 "基板" 部分, 单击存在的基板类型的框。有关基板类型及其分类的详细说明, 请参阅14 。</li>
		<li>量化大型底栖动物。 注: 栖的50个分类群被计数或指出为当前或缺席。在参考13中可以找到这些分类群及其被跟踪的完整列表。<ol>
<li>单击 "反转" 存在部分中存在的动物的框。</li>
			<li>输入在 "反转" 计数部分中观察到的每种动物的数量。</li>
			<li>单击红色的 "SC" 按钮, 并在图像中点缀每个扇贝。</li>
			<li>单击绿色的 "SF" 按钮, 并在图像中点缀每个海星。</li>
			<li>点击黑色的 "CL" 按钮, 点每个克拉 (扇贝已经死了, 但双方的外壳仍然连接到铰链) 在图像中。</li>
			<li>单击蓝色的 "FI" 按钮, 并在图像中点缀每条鱼。</li>
			<li>在 "鱼" 计数部分, 输入所观察到的每种鱼的数量。 注意: 对于其他虚线动物, 程序自动计算点数, 并将计数分配给适当的类别。对于鱼, 点是自动计数, 但用户必须确定哪种类型的鱼和多少。该程序计数的鱼点总数必须与用户输入的每种鱼类型的数量相匹配。</li>
		</ol>
</li>
		<li>单击 "提交" 按钮, 将图像的数据作为一行写入实验室数据库, 并创建图像的副本, 并将这些动物点缀在一起。</li>
		<li>执行2.6.4 步骤的质量控制。和2.6.5。<ol>
<li>将实验室数据收集程序中的配置文件更改为 "ImageCheck"。 注意: 这应该由一个不同的人完成, 这是一个完成步骤2.6.4 和2.6.5 的图像。</li>
			<li>重复步骤2.6.2 和2.6.3。加载原始图像、虚线图像, 并填写 "数字化" 用户配置文件输入的基板和动物数据。</li>
			<li>检查条目的准确性并进行必要的更改。</li>
			<li>选择 "提交" 按钮以覆盖 "数字化" 用户提交的图像的数据, 并将图像标记为实验室数据库中的质量控制。</li>
		</ol>
</li>
		<li>测量在图像中观察到的扇贝。 注: 扇贝部分可见 (由生长遮蔽, 部分在图像等) 或关闭海底不应该测量。<ol>
<li>启动图像注释器程序。</li>
			<li>从下拉菜单中选择 "文件", 然后 "加载图像目录"。导航到感兴趣的虚线图像并将图像加载到程序中。</li>
			<li>选择 "线条注释", 从扇贝的近几年来到扇贝壳的顶端画一条线。</li>
			<li>重复步骤2.6.8.3。对于图像中所有可测量的扇贝。</li>
			<li>选择 "文件", 然后 "保存批注" 以创建一个跨页测量。</li>
			<li>将测量从像素转换为毫米, 方法是使用从2.5 的平均像素到毫米的比率。</li>
		</ol>
</li>
	</ol></li>
</ol>3. 数据产品
<ol><li>计算扇贝密度和大小的空间特异性估计。<ol>
<li>使用绘图软件绘制测量站。</li>
		<li>通过扇贝区管理模拟器 (sam) 模型区划分测量站。 注: 在美国大西洋扇贝渔业中, 该模型被用来设计海扇贝丰度和着陆8。对每个 sam 区域都执行以下所有步骤。</li>
		<li>平均扇贝测量以获得扇贝的平均壳高度。</li>
		<li>计算扇贝的平均密度和标准误差。<ol>
<li>增加样方大小的扇贝的平均壳高度, 以适应部分可见的扇贝计数沿图像的边缘19。</li>
			<li>使用调整后的样方大小和公式计算密度, 用于对每个站20取样的多个样方进行2级取样设计; (1)<img alt="Equation 1" src="/files/ftp_upload/57493/57493eq1v2.jpg"> (2)<img alt="Equation 2" src="/files/ftp_upload/57493/57493eq2v2.jpg"> 其中 n = 主要样品单位 (驻地), m = 元素每个主要样品单位 (样方), <img alt="Equation 3" src="/files/ftp_upload/57493/57493eq3v2.jpg"> = 测量的价值 (扇贝数) 为元素 j 在第一个单位 i <img alt="Equation 4" src="/files/ftp_upload/57493/57493eq4v2.jpg"> , = 样品平均每元素 (样方) 在主要单位 i (驻地), <img alt="Equation 5" src="/files/ftp_upload/57493/57493eq5v2.jpg">和 =意味着在两个阶段。此平均值的标准误差为: (3)<img alt="Equation 6" src="/files/ftp_upload/57493/57493eq6v2.jpg"> 主要<img alt="Equation 7" src="/files/ftp_upload/57493/57493eq7v2.jpg">单位 (站) 之间的差异在哪里。</li>
		</ol>
</li>
	</ol>
</li>
	<li>计算总的和利用的生物量。<ol>
<li>将扇贝密度乘以所调查的总面积, 估计该地区扇贝的数量。</li>
		<li>创建一个外壳高度频率分布的扇贝测量与5毫米大小的箱子。</li>
		<li>乘以3.2.1。由3.2.2 的每个大小纸盒的扇贝频率。以获取区域内每个大小纸盒中的扇贝数。</li>
		<li>每大小纸盒中的扇贝数乘以每个5毫米大小纸盒中点的扇贝肉重量估计值。使用壳高的肉类重量回归指定的新英格兰渔业管理委员会的扇贝计划开发小组估计的扇贝重量在21克的大小。</li>
		<li>从3.2.4 中总结扇贝肉的重量。产生总扇贝生物量的估计。将扇贝生物量从克转换成公吨。</li>
		<li>从3.2.5 中分离扇贝肉重量的总和。由3.2.1 的扇贝总数。得到一个扇贝的平均重量。</li>
		<li>将每个大小纸盒中的扇贝数乘以3.2.3。用商业扇贝疏浚选择性方程估计可利用扇贝的数量22。</li>
		<li>重复步骤3.2.5。和3.2.6。与3.2.7 的利用扇贝的计数。估算可采大小扇贝的生物量及其平均肉重。</li>
	</ol>
</li>
	<li>创建扇贝分布图。<ol>
<li>划分扇贝计数的总和, 扇贝与壳高度少于75毫米, 和扇贝与贝壳高度超过100毫米在每个调查站由数字静止照相机 (9.2 m2) 在每个驻地观看的总面积分别计算整体扇贝, 幼扇贝, 和在每个站的利用扇贝密度。</li>
		<li>绘制每个站的每个密度, 分别映射整体、幼和可利用扇贝丰度的空间分布。</li>
	</ol>
</li>
</ol>

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E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Geostatistical+comparison+of+two+independent+video+surveys+of+sea+scallop+abundance+in+the+Elephant+Trunk+Closed+Area,+USA.">Geostatistical comparison of two independent video surveys of sea scallop abundance in the Elephant Trunk Closed Area, USA.</a> ICES J Mar Sci. 65, 995-1003 (2008).</li><li>CIE (Center for Independent Experts). <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Individual Peer Review Report. Review of Sea Scallop Survey Methodologies and their Integration for Stock Assessment and Fishery Management. , (2015).</li><li>. Research Set-Aside Program Available from: <a target="_blank" href="https://www.nefsc.noaa.gov/coopresearch/rsa_program.html">https://www.nefsc.noaa.gov/coopresearch/rsa_program.html</a> (2017)</li><li>Marino, M. C., O'Keefe, C. E., Jacobson, L. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Selectivity+and+efficiency+of+large+camera+video+data+from+the+SMAST+video+survey+during+2003+-+2006:+Appendix+B7.">Selectivity and efficiency of large camera video data from the SMAST video survey during 2003 - 2006: Appendix B7.</a> 45th Northeast Regional Stock Assessment Workshop (45th SAW) Assessment Report. , (2007).</li><li>Chang, J., Hart, D. R., Shank, B. V., Gallagher, S. M., Honig, P., York, A. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Combining+imperfect+automated+annotations+of+underwater+images+with+human+annotations+to+obtain+precise+and+unbiased+population+estimates.">Combining imperfect automated annotations of underwater images with human annotations to obtain precise and unbiased population estimates.</a> Methods Oceanogr. 17, 169-186 (2016).</li><li>Hennen, D. R., Hart, D. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Shell+height-to-weight+relationships+for+Atlantic+sea+scallops+(Placopecten+magellanicus)+in+offshore+U.S.+waters.">Shell height-to-weight relationships for Atlantic sea scallops (Placopecten magellanicus) in offshore U.S. waters.</a> J. Shellfish Res. 31 (4), 1133-1144 (2012).</li><li>Jacobson, L. D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Measurement+errors+in+body+size+of+sea+scallops+(Placopecten+magellanicus)+and+their+effect+on+stock+assessment+models.">Measurement errors in body size of sea scallops (Placopecten magellanicus) and their effect on stock assessment models.</a> Fish. Bull. 108, 233-247 (2010).</li><li>Mallet, D., Pelletier, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Underwater+video+techniques+for+observing+coastal+marine+biodiversity:+A+review+of+sixy+years+of+publications+(1952-2012).">Underwater video techniques for observing coastal marine biodiversity: A review of sixy years of publications (1952-2012).</a> Fish. Res. 154, 44-62 (2014).</li></ol>

作者没有什么可透露的。

调查设计协议是灵活的, 但在推广这些协议时, 考虑目标物种行为和调查目标至关重要。文献综述和初步或初步研究可用于将目标物种行为纳入调查设计。例如, 不到一个扇贝在12.5 米2 (0.08 扇贝/米2) 低于可持续的商业捕鱼密度23。因此, 通过抽样四样方每站, 空间站样本区是连接到检测扇贝的商业密度。此外, 海扇贝通常是聚集而不是随机分布在海底, 影响如何空间站间距影响密度估计精度24。几项研究使用平均值和方差数据从最初的研究审查了精确度并且确定5.6 公里是最大距离驻地应该被放置分开5,25,26。调查的系统抽样设计受调查目标的影响。在进行了21、27的调查之后, 该区的边界经常发生变化。系统抽样避免了在空间估计中影响随机分层或优化分配的测量设计20的边界后分层的严重问题。统一分配驻地也有助于探测新的扇贝招募和测绘海底沉积物和 macroinvertebrate 分布28。可能无法考虑目标物种行为和调查目标的一步是查明一艘调查船, 这就是议定书从这一步骤开始的原因。船只对海上取样是必不可少的, 并规定了调查设计的后续步骤。对于我们的议定书, 必须让商业捕鱼业促进调查方法的透明度和对调查结果的信心。使用商业渔船是一种有影响力的方式, 将工业纳入我们的方法, 以及允许大型、重型摄像设备和测量站在所需时间线内取样的船只的大小和能力。此外, 船主还负责所有与船只使用有关的费用, 并通过由国家海洋和大气管理局通过大西洋扇贝研究预留计划授予的扇贝镑的分配得到补偿。29. 虽然没有必要让工业界参与调查, 但在制定调查设计的其他方面之前, 必须考虑到现有船只的规模、能力和费用。
该协议的数据收集和处理方面具有最大的优势, 也是该方法的一个局限性。使用自定义软件和数据库来量化图像中的数据是一个很大的代价。然而, 使用这些产品的 SMAST 下降相机调查代表了一个程序的演变, 开始于 1999年, 并没有必要。例如, 当程序第一次启动时, 扇贝计数是用钢笔和纸制作的, 现在可用来测量图像中的自由软件。同样, 目前的数码静止相机被选中, 因为它能够检测所有大小的扇贝, 并允许约200% 放大, 而不损失图像质量 (图 3), 但低分辨率, 更便宜的相机早在调查中使用, 就能充分检测到商业规模30的扇贝。与调查设计协议一样, 摄像机的类型应与检测目标物种和实现调查目标所需的分辨率相关联。在每个工作站上捕获图像和录制视频比传统的调查方法提供了一个显著的优势, 它提供了持续的能力来重新访问样本, 并将分析扩展到分类或栖息地特征, 而不是最初跟踪或枚举。例如, SMAST 数据库中原先提到的沙美元和其他棘皮动物的图像被重新审视, 以便通过时间12来量化它们的丰度和生物量。相比之下, 从传统的调查方法, 如挖泥船或蚊帐的样本被丢弃在海上, 不能再被重新审视。但是, 允许大量图像被采集和存储的进展可能导致数以百万计的图像被收集, 只使用一小部分。这主要是由于时间和成本的限制, 因为需要人类进行数据抽取, 导致大量未用信息31。对动物和生境特性进行自动检测的进展可能有助于解决这一难题。
基于图像的测量方法可以为监测无脊椎动物和相关生境提供必要的数据, 但补充与采集生物样本的其他方法有关的协议是理想的。如果没有扇贝壳-高度肉重量关系, 由疏浚为基础的抽样创造, 生物量估计是不可能的。此外, 扇贝壳高度的肉类重量关系随时间和地点在乔治银行表明, 一贯更新的等式用来描述这种关系是有益的32。结合图像和物理样本技术也有助于探索每种方法的偏差和假设。用卡尺测量带相机图像的扇贝壳高度量化了与相机透镜曲率和距离从图像中心33相关的测量偏差。相反, 图像与疏浚拖曳的配对比较有助于确定海底扇贝的实际采集比例, 以及与扇贝大小6的比例变化。
水下成像技术已在海洋生态学领域应用了数十年17,34。然而, 高分辨率相机和数据存储的成本降低, 使得这种方法比以往更加实用。本文所描述的方法可以推广, 具有广泛的适用性, 有助于促进基于图像的调查的发展。更具体地说, 这些程序显示了如何利用结果来产生数据来帮助管理无柄无脊椎动物 (表 1-2), 并有助于更广泛地了解海洋环境7、9、10 ,11,12,13,14,15。

大西洋扇贝 (Placopecten magellanicus) 是一种海洋双壳软体动物, 分布在西北大西洋的大陆架上, 从加拿大的圣劳伦斯湾到北卡罗来纳州的哈特勒斯角,1。在过去的十五年中, 美国的海扇贝渔业在着陆和价值方面经历了空前的增长, 并已成为该国价值最高的渔业之一, 其着陆价值约为4.4亿美元, 2015年2。尽管这种情况有所增加, 但在过去20年中, 扇贝捕鱼的努力已大大减少, 这是因为实施了一个区域轮换系统, 目的是保护带幼扇贝的地区, 并在高的扇贝区集中捕鱼。密度1。这种管理方法要求关于扇贝密度和大小的空间特定的信息, 这是由几项调查提供的, 包括马萨诸塞州大学达特茅斯学院的海洋科学和技术 (SMAST) 下降相机调查。
SMAST 的目标是为渔业资源管理人员、海洋科学家和渔业社区提供对美国海扇贝资源及其相关生境的独立评估。这项调查是与扇贝渔民合作开发的, 并应用了基于潜水研究3,4的样方取样技术。2000s 早期的初步调查集中于估算被称为乔治银行5的渔业生产区封闭部分海域扇贝的密度, 但这项调查扩大到覆盖美国和加拿大的扇贝资源的大部分。水 (≈100,000 公里2)6,7。调查的资料已通过股票评估讲习班过程纳入扇贝库存评估, 并可靠地提供给新英格兰渔业管理委员会, 以协助8年的扇贝收获分配。此外, SMAST 的数据从许多方面贡献了了解非扇贝物种的生态学7,9,10,11,12和底栖生境的特征13,14,15。这种广泛的适用性表明, 该方法可以推广并应用于各种无梗无脊椎动物, 有可能有助于缓解无脊椎动物渔业扩张的问题, 超过科学知识和政策需要成功地管理他们16。此外, 与传统的人口抽样方法相比, 基于图像的取样是无侵入性的, 而且由于高分辨率相机和数据存储17、18的成本降低, 因此越来越容易负担得起。在这里, SMAST 下降相机调查的2017方法, 用于扇贝管理在美国部分的乔治银行被介绍为例证的程序。我们讨论这一程序背后的基本原理, 以帮助其推广和应用到其他无梗无脊椎动物。

<table border="0" cellpadding="0" cellspacing="0" width="963">
	<tbody>
		<tr height="42">
			<td height="42" style="height:42px;width:299px;">Bobcat, 43.3mm, F-Mount, 6600x4400, 1.9/2.4 fps, Color, GigE Vision</td>
			<td style="width:275px;">Imperx</td>
			<td style="width:171px;">PoE-B6620C-TF00</td>
			<td style="width:220px;">Digital Still Camera</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:299px;">Ace - EV76C560, 1/1.8&#34;, C-Mount, 1280x1024, 60fps, Color, CMOS, GigE&#160;</td>
			<td style="width:275px;">Basler</td>
			<td style="width:171px;">acA1300-60g</td>
			<td>HD video camera</td>
		</tr>
		<tr height="63">
			<td height="63" style="height:63px;width:299px;">Stock MV 40-25 Housing. Black Anodized Aluminum, 5.3&#34; standard dome port, DBCR2008M connector&#160;&#160;</td>
			<td style="width:275px;">Sexton</td>
			<td style="width:171px;">MV 40-25</td>
			<td style="width:220px;">Underwater housing for digital still camera</td>
		</tr>
		<tr height="63">
			<td height="63" style="height:63px;width:299px;">Stock MV 25-25 Housing. Black Anodized Aluminum, 3.4&#34; standard dome port, DBCR2008M connector&#160;&#160;</td>
			<td style="width:275px;">Sexton</td>
			<td style="width:171px;">MV 25-25</td>
			<td style="width:220px;">Underwater housing for HD video camera</td>
		</tr>
		<tr height="63">
			<td height="63" style="height:63px;width:299px;">Optical Slip Ring</td>
			<td style="width:275px;">MOOG</td>
			<td style="width:171px;">180-2714-00</td>
			<td style="width:220px;">Transmission of power and electrical signals to rotating cable on winch</td>
		</tr>
		<tr height="63">
			<td height="63" style="height:63px;width:299px;">Fiber Optic Cable</td>
			<td style="width:275px;">Cortland</td>
			<td style="width:171px;">OCG0010</td>
			<td style="width:220px;">Transmission of power and electrical signals from junction box to vessel deck/wheelhouse</td>
		</tr>
		<tr height="84">
			<td height="84" style="height:84px;width:299px;">Wheelhouse Run&#160;</td>
			<td style="width:275px;">Electromechanica</td>
			<td style="width:171px;">EM0117-02</td>
			<td style="width:220px;">Segment of fiber optic wire adapted to plug into optical slip ring on one end and light power and computer on the other</td>
		</tr>
		<tr height="63">
			<td height="63" style="height:63px;width:299px;">Underwater Junction Box</td>
			<td style="width:275px;">Electromechanica</td>
			<td style="width:171px;">EM0117-01</td>
			<td style="width:220px;">Connection of power and electrical signals from camera and lights to hybrid cable</td>
		</tr>
		<tr height="63">
			<td height="63" style="height:63px;width:299px;">Camera Cable</td>
			<td style="width:275px;">SubConn</td>
			<td style="width:171px;">DIL8F/LS2000/10FT/LS2000/DIL8M</td>
			<td style="width:220px;">Transmission of power and electrical signals from camera to junction box</td>
		</tr>
		<tr height="63">
			<td height="63" style="height:63px;width:299px;">Light Cable</td>
			<td style="width:275px;">SEACON</td>
			<td>HRN-S0484</td>
			<td style="width:220px;">Transmission of power and electrical signals from lights to junction box</td>
		</tr>
		<tr height="63">
			<td height="63" style="height:63px;width:299px;">Desktop Computer</td>
			<td style="width:275px;">Various</td>
			<td style="width:171px;">Custom</td>
			<td style="width:220px;">Windows based operating system with fiber optic interface</td>
		</tr>
		<tr height="63">
			<td height="63" style="height:63px;width:299px;">Hydraulic Winch</td>
			<td style="width:275px;">Diversified Marine</td>
			<td style="width:171px;">Custom</td>
			<td style="width:220px;">Tension sensitive winch for deployment and retrieval of fiber optic cable</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:299px;">Steel Pyramid</td>
			<td style="width:275px;">Blue Fleet Welding</td>
			<td style="width:171px;">Custom</td>
			<td style="width:220px;">Apparatus for deploying cameras and lights</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:299px;">Steel Davit</td>
			<td style="width:275px;">Blue Fleet Welding</td>
			<td style="width:171px;">Custom</td>
			<td style="width:220px;">Suspends fiber optic cable over the side of the vessel</td>
		</tr>
		<tr height="63">
			<td height="63" style="height:63px;width:299px;">Fiberglass sheave in metal housing</td>
			<td style="width:275px;">Diversified Marine</td>
			<td style="width:171px;">Custom</td>
			<td style="width:220px;">Attaches to davit, guides fiber optic cable over the side of the vessel and into the water</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:299px;">Sealight Sphere 6500, Day Light White, Flood</td>
			<td style="width:275px;">DeepSea Power &#38; Light</td>
			<td style="width:171px;">712-045-201-0A-01</td>
			<td>Underwater LED light</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:299px;">GPSMAP 78</td>
			<td style="width:275px;">Garmin&#160;</td>
			<td style="width:171px;">01-00864-00</td>
			<td>Global Positioing System device</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:299px;">ArcPad 10.2&#160;</td>
			<td style="width:275px;">ESRI</td>
			<td style="width:171px;">N/A</td>
			<td>Mobile field mapping program</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Undersea Vision Acquisition System</td>
			<td style="width:275px;">Electromechanica</td>
			<td style="width:171px;">UVAS</td>
			<td>Field data collection program</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:299px;">Digitzer</td>
			<td style="width:275px;">University of Massachusetts, Dartmouth</td>
			<td style="width:171px;">N/A</td>
			<td>Lab data collection program</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">FishAnnotator</td>
			<td style="width:275px;">Cvision Consulting</td>
			<td style="width:171px;">0.3.0</td>
			<td>Image annotator program</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:299px;">ArcMap 10.4&#160;</td>
			<td style="width:275px;">ESRI</td>
			<td style="width:171px;">N/A</td>
			<td>Mapping software</td>
		</tr>
	</tbody>
</table>

methods for image-based surveys of benthic macroinvertebrates and their habitat exemplified by the drop camera survey for the atlantic sea scallop

水下成像技术长期以来一直被应用于海洋生态学领域, 但高分辨率相机和数据存储成本的降低使得该方法比以往更加实用。基于图像的调查允许重新审查最初的样本, 与通常涉及蚊帐或挖泥船的传统调查方法相比, 它们是无侵入性的。基于图像的调查协议可能会有很大差异, 但应由目标物种行为和调查目标驱动。为了证明这一点, 我们描述了我们最近的方法为大西洋扇贝 (Placopecten magellanicus) 下落照相机调查提供一个程序例子和典型的结果。该过程分为三个关键步骤, 包括调查设计、数据收集和数据产品。然后, 在推广该方法的背景下, 讨论了扇贝行为的影响以及对美国海扇贝资源进行独立评估的调查目标。总的来说, 马萨诸塞州大学达特茅斯学院海洋科技 (SMAST) 的广泛适用性和灵活性表明, 该方法可以推广并应用于各种无梗无脊椎动物或栖息地重点研究。

调查站被抽样, 作为五项研究行程的一部分, 从7月中旬进行 (图 2)。由于能见度和天气问题, CL2-S-EXT 区的一大片车站没有取样, 其他地区的一些车站也在质量保证检查期间被丢弃。对于所有其他工作站, 捕获了四高质量的数字静止图像 (图 3)。对于这些站中的所有图像, 基质和大型底栖动物进行了量化, 并测量了扇贝。扇贝的计数和测量被划分了由 sam 区域允许丰富, 分布和生物量估计, 连同被审计的扇贝计数和测量的原始的数据, 将提供给东北渔场科学中心和新英格兰渔业管理理事会在8月1日之前列入年度扇贝分配进程 (表1和 2)。扇贝分布图是为所有扇贝、幼扇贝 (壳高小于75毫米) 和扇贝的开发规模 (外壳高度大于100毫米) 而创建 (图 4)。
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/57493/57493fig2.jpg"> 图 2: 2017年在乔治银行放置摄像站.各监测站均由该船只以调查日期显示, 并分层次地与2.8 公里外的监测站及所有其它与监测站相距5.6 公里的地区取样。黑色线条和标签标识扇贝区域管理模拟器模型区域, 用于项目海扇贝丰度和着陆。<a href="/files/ftp_upload/57493/57493fig2large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/57493/57493fig3.jpg"> 图 3: 从乔治银行2017滴相机调查中数字静止图像的示例.对于整个乔治银行的调查, 基质和大型底栖动物被量化, 扇贝的测量5216图像相似的质量。所有图像均可在 < http://bit.ly/scallopsurvey > 中查看。<a href="/files/ftp_upload/57493/57493fig3large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always"><tbody>
<tr>
<td>地区</td>
			<td>四</td>
			<td>站</td>
			<td>测量</td>
			<td>Sh</td>
			<td>每 m2</td>
			<td>硒</td>
			<td>扇贝</td>
		</tr>
<tr>
<td>CL1-NA-N</td>
			<td>2。6</td>
			<td>101</td>
			<td>858</td>
			<td>105</td>
			<td>0.98</td>
			<td>0.29</td>
			<td>761</td>
		</tr>
<tr>
<td>CL1-AC</td>
			<td>2。6</td>
			<td>155</td>
			<td>81</td>
			<td>106</td>
			<td>0.06</td>
			<td>0.01</td>
			<td>66</td>
		</tr>
<tr>
<td>CL1-NA-S</td>
			<td>--</td>
			<td>7</td>
			<td>0</td>
			<td>--</td>
			<td>< 0.02</td>
			<td>--</td>
			<td>--</td>
		</tr>
<tr>
<td>CL2-N-NA</td>
			<td>2。6</td>
			<td>16</td>
			<td>58</td>
			<td>87</td>
			<td>0.43</td>
			<td>0。2</td>
			<td>214</td>
		</tr>
<tr>
<td>CL2-S-AC</td>
			<td>2。6</td>
			<td>435</td>
			<td>556</td>
			<td>93。6</td>
			<td>0.14</td>
			<td>0.01</td>
			<td>465</td>
		</tr>
<tr>
<td>CL2-S-EXT</td>
			<td>2。5</td>
			<td>147</td>
			<td>660</td>
			<td>77。6</td>
			<td>0.48</td>
			<td>0.04</td>
			<td>545</td>
		</tr>
<tr>
<td>Nf</td>
			<td>2。6</td>
			<td>54</td>
			<td>13</td>
			<td>88</td>
			<td>0.02</td>
			<td>0.01</td>
			<td>39</td>
		</tr>
<tr>
<td>AC N</td>
			<td>2。7</td>
			<td>31</td>
			<td>72</td>
			<td>120</td>
			<td>0.27</td>
			<td>0。1</td>
			<td>260</td>
		</tr>
<tr>
<td>AC S</td>
			<td>2。5</td>
			<td>39</td>
			<td>2718</td>
			<td>72。7</td>
			<td>9。7</td>
			<td>3.09</td>
			<td>11676</td>
		</tr>
<tr>
<td>NLS-EXT</td>
			<td>2。6</td>
			<td>14</td>
			<td>170</td>
			<td>95。1</td>
			<td>2.24</td>
			<td>2.16</td>
			<td>966</td>
		</tr>
<tr>
<td>NLS-NA</td>
			<td>2。6</td>
			<td>42</td>
			<td>696</td>
			<td>99。1</td>
			<td>2</td>
			<td>0.83</td>
			<td>2597</td>
		</tr>
<tr>
<td>SCH</td>
			<td>2。5</td>
			<td>137</td>
			<td>138</td>
			<td>71。3</td>
			<td>0.15</td>
			<td>0.03</td>
			<td>631</td>
		</tr>
<tr>
<td>S f</td>
			<td>2。5</td>
			<td>126</td>
			<td>219</td>
			<td>74。4</td>
			<td>0.19</td>
			<td>0.03</td>
			<td>747</td>
		</tr>
</tbody></table>表 1: 从乔治银行2017滴相机调查的数字静止相机数据.结果由扇贝区管理模拟器模型区提出。表中包括调整后的样方面积 (四)、取样站数 (台)、所测量的扇贝壳高度 (测量值)、扇贝平均壳高度 (SH)、每 m2的扇贝平均数 (Sc)。2) 与相关标准误差 (SE) 和估计的扇贝数以百万计 (扇贝)。由于未观察到扇贝, CL1-NA-S 的结果无法产生。
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always"><tbody>
<tr>
<td></td>
			<td colspan="3">总生物量的估计</td>
			<td colspan="3">生物量的估算</td>
		</tr>
<tr>
<td>地区</td>
			<td>M w</td>
			<td>MT</td>
			<td>硒</td>
			<td>M w</td>
			<td>MT</td>
			<td>硒</td>
		</tr>
<tr>
<td>CL1-NA-N</td>
			<td>18.28</td>
			<td>13900</td>
			<td>4100</td>
			<td>23.85</td>
			<td>9900</td>
			<td>2950</td>
		</tr>
<tr>
<td>CL1-AC</td>
			<td>24.87</td>
			<td>1650</td>
			<td>350</td>
			<td>33.72</td>
			<td>1350</td>
			<td>300</td>
		</tr>
<tr>
<td>CL1-NA-S</td>
			<td>--</td>
			<td>--</td>
			<td>--</td>
			<td>--</td>
			<td>--</td>
			<td>--</td>
		</tr>
<tr>
<td>CL2-N-NA</td>
			<td>14.89</td>
			<td>3200</td>
			<td>1500</td>
			<td>26.51</td>
			<td>2100</td>
			<td>980</td>
		</tr>
<tr>
<td>CL2-S-AC</td>
			<td>15.84</td>
			<td>7360</td>
			<td>685</td>
			<td>23.47</td>
			<td>4600</td>
			<td>425</td>
		</tr>
<tr>
<td>CL2-S-EXT</td>
			<td>9.46</td>
			<td>5150</td>
			<td>440</td>
			<td>17。1</td>
			<td>1900</td>
			<td>165</td>
		</tr>
<tr>
<td>Nf</td>
			<td>16.26</td>
			<td>600</td>
			<td>260</td>
			<td>27.59</td>
			<td>500</td>
			<td>200</td>
		</tr>
<tr>
<td>AC N</td>
			<td>34.15</td>
			<td>8900</td>
			<td>3390</td>
			<td>38.02</td>
			<td>7800</td>
			<td>2990</td>
		</tr>
<tr>
<td>AC S</td>
			<td>8.49</td>
			<td>99100</td>
			<td>31590</td>
			<td>16.88</td>
			<td>24600</td>
			<td>7830</td>
		</tr>
<tr>
<td>NLS-EXT</td>
			<td>16.73</td>
			<td>16200</td>
			<td>15590</td>
			<td>19.54</td>
			<td>7600</td>
			<td>7310</td>
		</tr>
<tr>
<td>NLS-NA</td>
			<td>20。4</td>
			<td>5.3万</td>
			<td>22100</td>
			<td>25.13</td>
			<td>30700</td>
			<td>12800</td>
		</tr>
<tr>
<td>SCH</td>
			<td>10.45</td>
			<td>6600</td>
			<td>1260</td>
			<td>24.65</td>
			<td>3300</td>
			<td>620</td>
		</tr>
<tr>
<td>S f</td>
			<td>9。1</td>
			<td>6800</td>
			<td>1080</td>
			<td>17.33</td>
			<td>2400</td>
			<td>380</td>
		</tr>
</tbody></table>表 2: 2017 的乔治银行投照调查的总量和利用生物量估计数.结果由扇贝区管理模拟器模型区提出。表中包含的平均扇贝肉重量在 g (兆瓦), 总重量的扇贝在公吨 (MT) 和标准误差公吨。由于未观察到扇贝, CL1-NA-S 的结果无法产生。
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/57493/57493fig4.jpg"> 图 4: 2017 期间, 乔治银行的扇贝分布和丰度.在乔治银行的扇贝分布和丰度在2017期间, 所有扇贝 (顶部), 扇贝小于75毫米壳高度 (中间), 和扇贝大于100毫米壳高度 (底部) 从一滴相机调查。<a href="/files/ftp_upload/57493/57493fig4large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>

Watch this Scientific Journal Video about Methods for Image-based Surveys of Benthic Macroinvertebrates and Their Habitat Exemplified by the Drop Camera Survey for the Atlantic Sea Scallop at JoVE.com

Methods for Image-based Surveys of Benthic Macroinvertebrates and Their Habitat Exemplified by the Drop Camera Survey for the Atlantic Sea Scallop

基于图像的测量是一种越来越实用、无侵入性的海洋环境取样方法。我们提出了一个下降相机调查的协议, 估计大西洋扇贝 (Placopecten magellanicus) 的丰度和分布。我们讨论如何将该协议推广到其他底栖无脊椎动物的应用。

基于图像的海底无脊椎动物及其栖息地调查方法大西洋扇贝落相机调查

Underwater imaging has long been used in the field of marine ecology but decreasing costs of high-resolution cameras and data storage have made the ...

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SCIENTISTS IN ACTION

9.4K 次观看.  University of Massachusetts Dartmouth. 基于图像的测量是一种越来越实用、无侵入性的海洋环境取样方法。我们提出了一个下降相机调查的协议, 估计大西洋扇贝 (Placopecten magellanicus) 的丰度和分布。我们讨论如何将该协议推广到其他底栖无脊椎动物的应用。

视频: Methods for Image-based Surveys of Benthic Macroinvertebrates and Their Habitat Exemplified by the Drop Camera Survey for the Atlantic Sea Scallop

Optimized Negative Staining: a High-throughput Protocol for Examining Small and Asymmetric Protein Structure by Electron Microscopy

Structural determination of proteins is rather challenging for proteins with molecular masses between 40 - 200 kDa. Considering that more than half of natural proteins have a molecular mass between 40 - 200 kDa1,2, a robust and high-throughput method with a nanometer resolution capability is needed. Negative staining (NS) electron microscopy (EM) is an easy, rapid, and qualitative approach which has frequently been used in research laboratories to examine protein structure and protein-protein interactions. Unfortunately, conventional NS protocols often generate structural artifacts on proteins, especially with lipoproteins that usually form presenting rouleaux artifacts. By using images of lipoproteins from cryo-electron microscopy (cryo-EM) as a standard, the key parameters in NS specimen preparation conditions were recently screened and reported as the optimized NS protocol (OpNS), a modified conventional NS protocol 3 . Artifacts like rouleaux can be greatly limited by OpNS, additionally providing high contrast along with reasonably high&#8208;resolution (near 1 nm) images of small and asymmetric proteins. These high-resolution and high contrast images are even favorable for an individual protein (a single object, no average) 3D reconstruction, such as a 160 kDa antibody, through the method of electron tomography4,5. Moreover, OpNS can be a high&#8208;throughput tool to examine hundreds of samples of small proteins. For example, the previously published mechanism of 53 kDa cholesteryl ester transfer protein (CETP) involved the screening and imaging of hundreds of samples 6. Considering cryo-EM rarely successfully images proteins less than 200 kDa has yet to publish any study involving screening over one hundred sample conditions, it is fair to call OpNS a high-throughput method for studying small proteins. Hopefully the OpNS protocol presented here can be a useful tool to push the boundaries of EM and accelerate EM studies into small protein structure, dynamics and mechanisms.

More than half of proteins are small proteins (molecular mass &#60;200 kDa) that are challenging for both electron microscope imaging and three-dimensional reconstructions. Optimized negative staining is a robust and high-throughput protocol to obtain high contrast and relatively high resolution (~1 nm) images of small asymmetric proteins or complexes under different physiological conditions.

优化负染:通过电子显微镜检查小和不对称蛋白结构的高通量协议

Structural determination of proteins is rather challenging for proteins with molecular masses between 40 - 200 kDa. Considering that more than half of ...

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Spotting Cheetahs: Identifying Individuals by Their Footprints

The cheetah (Acinonyx jubatus) is Africa's most endangered large felid and listed as Vulnerable with a declining population trend by the IUCN1. It ranges widely over sub-Saharan Africa and in parts of the Middle East. Cheetah conservationists face two major challenges, conflict with landowners over the killing of domestic livestock, and concern over range contraction. Understanding of the latter remains particularly poor2. Namibia is believed to support the largest number of cheetahs of any range country, around 30%, but estimates range from 2,9053 to 13,5204. The disparity is likely a result of the different techniques used in monitoring.
Current techniques, including invasive tagging with VHF or satellite/GPS collars, can be costly and unreliable. The footprint identification technique5 is a new tool accessible to both field scientists and also citizens with smartphones, who could potentially augment data collection. The footprint identification technique analyzes digital images of footprints captured according to a standardized protocol. Images are optimized and measured in data visualization software. Measurements of distances, angles, and areas of the footprint images are analyzed using a robust cross-validated pairwise discriminant analysis based on a customized model. The final output is in the form of a Ward's cluster dendrogram. A user-friendly graphic user interface (GUI) allows the user immediate access and clear interpretation of classification results.
The footprint identification technique algorithms are species specific because each species has a unique anatomy. The technique runs in a data visualization software, using its own scripting language (jsl) that can be customized for the footprint anatomy of any species. An initial classification algorithm is built from a training database of footprints from that species, collected from individuals of known identity. An algorithm derived from a cheetah of known identity is then able to classify free-ranging cheetahs of unknown identity. The footprint identification technique predicts individual cheetah identity with an accuracy of &gt;90%.

The cheetah (Acinonyx jubatus) is an iconic, endangered species, but conservation efforts are challenged by habitat shrinkage and conflict with commercial farmers. The footprint identification technique, a robust, accurate and cost-effective image classification system, is a new approach to monitoring cheetahs.

通过他们的足迹识别个体:合模猎豹

The cheetah (Acinonyx jubatus) is Africa's most endangered large felid and listed as Vulnerable with a declining population trend by the IUCN1. It ranges ...

Development of New Methods for Quantifying Fish Density Using Underwater Stereo-video Tools

The use of video camera systems in ecological studies of fish continues to gain traction as a viable, non-extractive method of measuring fish lengths and estimating fish abundance. We developed and implemented a rotating stereo-video camera tool that covers a full 360 degrees of sampling, which maximizes sampling effort compared to stationary camera tools. A variety of studies have detailed the ability of static, stereo-camera systems to obtain highly accurate and precise measurements of fish; the focus here was on the development of methodological approaches to quantify fish density using rotating camera systems. The first approach was to develop a modification of the metric MaxN, which typically is a conservative count of the minimum number of fish observed on a given camera survey. We redefine MaxN to be the maximum number of fish observed in any given rotation of the camera system. When precautions are taken to avoid double counting, this method for MaxN may more accurately reflect true abundance than that obtained from a fixed camera. Secondly, because stereo-video allows fish to be mapped in three-dimensional space, precise estimates of the distance-from-camera can be obtained for each fish. By using the 95% percentile of the observed distance from camera to establish species-specific areas surveyed, we account for differences in detectability among species while avoiding diluting density estimates by using the maximum distance a species was observed. Accounting for this range of detectability is critical to accurately estimate fish abundances. This methodology will facilitate the integration of rotating stereo-video tools in both applied science and management contexts.

We describe a new method for counting fishes, and estimating relative abundance (MaxN) and fish density using rotating stereo-video camera systems. We also demonstrate how to use distance from camera (Z distance) to estimate species-specific detectability.

利用水下立体视频工具量化鱼类密度的新方法研究进展

The use of video camera systems in ecological studies of fish continues to gain traction as a viable, non-extractive method of measuring fish lengths and ...

Design and Use of an Apparatus for Quantifying Bivalve Suspension Feeding at Sea

As shellfish aquaculture moves from coastal embayments and estuaries to offshore locations, the need to quantify ecosystem interactions of farmed bivalves (i.e., mussels, oysters, and clams) presents new challenges. Quantitative data on the feeding behavior of suspension-feeding mollusks is necessary to determine important ecosystem interactions of offshore shellfish farms, including their carrying capacity, the competition with the zooplankton community, the availability of trophic resources at different depths, and the deposition to the benthos. The biodeposition method is used to quantify feeding variables in suspension-feeding bivalves in a natural setting and represents a more realistic proxy than laboratory experiments. This method, however, relies upon a stable platform to satisfy the requirements that water flow rates supplied to the shellfish remain constant and the bivalves are undisturbed. A flow-through device and process for using the biodeposition method to quantify the feeding of bivalve mollusks were modified from a land-based format for shipboard use by building a two-dimensional gimbal table around the device. Planimeter data reveal a minimal pitch and yaw of the chambers containing the test shellfish despite boat motion, the flow rates within the chambers remain constant, and operators are able to collect the biodeposits (feces and pseudofeces) with sufficient consistency to obtain accurate measurements of the bivalve clearance, filtration, selection, ingestion, rejection, and absorption at offshore shellfish aquaculture sites.

A flow-through device for using the biodeposition method to quantify filtration and feeding behavior of bivalve mollusks was modified for shipboard use. A two-dimensional gimbal table built around the device isolates the apparatus from boat motion, thereby allowing the accurate quantification of bivalve filtration variables at offshore shellfish aquaculture sites.

海上双壳贝类悬浮饲料定量装置的设计与应用

As shellfish aquaculture moves from coastal embayments and estuaries to offshore locations, the need to quantify ecosystem interactions of farmed bivalves ...

Modeling the Size Spectrum for Macroinvertebrates and Fishes in Stream Ecosystems

The size spectrum is an inverse, allometric scaling relationship between average body mass (M) and the density (D) of individuals within an ecological community or food web. Importantly, the size spectrum assumes that individual size, rather than species&#8217; behavioral or life history characteristics, is the primary determinant of abundance within an ecosystem. Thus, unlike traditional allometric relationships that focus on species-level data (e.g., mean species&#8217; body size vs. population density), size spectra analyses are &#8216;ataxic&#8217; &#8211; individual specimens are identified only by their size, without consideration of taxonomic identity. Size spectra models are efficient representations of traditional, complex food webs and can be used in descriptive as well as predictive contexts (e.g., predicting responses of large consumers to changes in basal resources). Empirical studies from diverse aquatic ecosystems have also reported moderate to high levels of similarity in size spectra slopes, suggesting that common processes may regulate the abundances of small and large organisms in very different settings. This is a protocol to model the community-level size spectrum in wadable streams. The protocol consists of three main steps. First, collect quantitative benthic fish and invertebrate samples that can be used to estimate local densities. Second, standardize the fish and invertebrate data by converting all individuals to ataxic units (i.e., individuals identified by size, irrespective of taxonomic identity), and summing individuals within log2 size bins. Third, use linear regression to model the relationship between ataxic M and D estimates. Detailed instructions are provided herein to complete each of these steps, including custom software to facilitate D estimation and size spectra modeling.

This is a protocol to model the size spectrum (scaling relationship between individual mass and population density) for combined fish and invertebrate data from wadable streams and rivers. Methods include: field techniques to collect quantitative fish and invertebrate samples; lab methods to standardize the field data; and statistical data analysis.

对河流生态系统中巨脊椎动物和鱼类的大小谱进行建模

The size spectrum is an inverse, allometric scaling relationship between average body mass (M) and the density (D) of individuals within an ecological ...

Ground-level Unmanned Aerial System Imagery Coupled with Spatially Balanced Sampling and Route Optimization to Monitor Rangeland Vegetation

Rangeland ecosystems cover 3.6 billion hectares globally with 239 million hectares located in the United States. These ecosystems are critical for maintaining global ecosystem services. Monitoring vegetation in these ecosystems is required to assess rangeland health, to gauge habitat suitability for wildlife and domestic livestock, to combat invasive weeds, and to elucidate temporal environmental changes. Although rangeland ecosystems cover vast areas, traditional monitoring techniques are often time-consuming and cost-inefficient, subject to high observer bias, and often lack adequate spatial information. Image-based vegetation monitoring is faster, produces permanent records (i.e., images), may result in reduced observer bias, and inherently includes adequate spatial information. Spatially balanced sampling designs are beneficial in monitoring natural resources. A protocol is presented for implementing a spatially balanced sampling design known as balanced acceptance sampling (BAS), with imagery acquired from ground-level cameras and unmanned aerial systems (UAS). A route optimization algorithm is used in addition to solve the &#8216;travelling salesperson problem&#8217; (TSP) to increase time and cost efficiency. While UAS images can be acquired 2&#8211;3x faster than handheld images, both types of images are similar to each other in terms of accuracy and precision. Lastly, the pros and cons of each method are discussed and examples of potential applications for these methods in other ecosystems are provided.

The protocol presented in this paper utilizes route optimization, balanced acceptance sampling, and ground-level and unmanned aircraft system (UAS) imagery to efficiently monitor vegetation in rangeland ecosystems. Results from images obtained from ground-level and UAS methods are compared.

Rangeland ecosystems cover 3.6 billion hectares globally with 239 million hectares located in the United States. These ecosystems are critical for ...

Assessing Intertidal Populations of the Invasive European Green Crab

Invasive species have caused major disruptions to ecosystems worldwide. The European green crab invaded North America in the 1800s and is considered one of the world&#8217;s 100 worst invaders by the IUCN. Observations of spatiotemporal green crab population dynamics are essential for predicting and managing the ecological and economic impacts of this harmful invasive species. This protocol was developed in an effort to create a standardized method for assessing green crab population dynamics in the rocky intertidal zone of New England and Atlantic Canada. The protocol was designed to be accessible to multiple users including researchers, educators, students, and citizen scientists. Although it was designed for surveying crab populations, this protocol is easy to adapt and could be used for any number of intertidal species. The resulting data collected using this protocol has a wide range of uses, including to inform ecological research, conservation efforts, mitigation strategies, and fishery development, as well as for educational outreach purposes.

Understanding spatiotemporal patterns in green crab population dynamics is essential for predicting and managing the ecological and economic impacts of this harmful invasive species. This protocol was developed in an effort to create a standardized method for assessing green crab populations in the rocky intertidal zone of the Northwest Atlantic.

评估入侵的欧洲绿蟹的养殖种群

Invasive species have caused major disruptions to ecosystems worldwide. The European green crab invaded North America in the 1800s and is considered one ...

A Field Primer for Monitoring Benthic Ecosystems Using Structure-From-Motion Photogrammetry

Structure-from-motion (SfM) photogrammetry is a technique used to generate three-dimensional (3D) reconstructions from a sequence of two-dimensional (2D) images. SfM methods are becoming increasingly popular as a noninvasive way to monitor many systems, including anthropogenic and natural landscapes, geologic structures, and both terrestrial and aquatic ecosystems. Here, a detailed protocol is provided for collecting SfM imagery to generate 3D models of benthic habitats. Additionally, the cost, time efficiency, and output quality of employing a Digital Single Lens Reflex (DSLR) camera versus a less expensive action camera have been compared. A tradeoff between computational time and resolution was observed, with the DSLR camera producing models with more than twice the resolution, but taking approximately 1.4-times longer to produce than the action camera. This primer aims to provide a thorough description of the steps necessary to collect SfM data in benthic habitats for those who are unfamiliar with the technique as well as for those already using similar methods.

We provide a detailed protocol for conducting underwater structure-from-motion photogrammetry surveys to generate 3D models and orthomosaics.

利用运动结构摄影测量法监测底栖生态系统的野外入门书

Structure-from-motion (SfM) photogrammetry is a technique used to generate three-dimensional (3D) reconstructions from a sequence of two-dimensional (2D) ...

Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates

Body size is an important functional trait that can be used as a bioindicator to assess the impacts of perturbations in natural communities. Community size structure responds to biotic and abiotic gradients, including anthropogenic perturbations across taxa and ecosystems. However, the manual measurement of small-bodied organisms such as benthic macroinvertebrates (e.g., &gt;500 &#181;m to a few centimeters long) is time-consuming. To expedite the estimation of community size structure, here, we developed a protocol to semi-automatically measure the individual body size of preserved river macroinvertebrates, which are one of the most commonly used bioindicators for assessing the ecological status of freshwater ecosystems. This protocol is adapted from an existing methodology developed to scan marine mesozooplankton with a scanning system designed for water samples. The protocol consists of three main steps: (1) scanning subsamples (fine and coarse sample size fractions) of river macroinvertebrates and processing the digitized images to individualize each detected object in each image; (2) creating, evaluating, and validating a learning set through artificial intelligence to semi-automatically separate the individual images of macroinvertebrates from detritus and artifacts in the scanned samples; and (3) depicting the size structure of the macroinvertebrate communities. In addition to the protocol, this work includes the calibration results and enumerates several challenges and recommendations to adapt the procedure to macroinvertebrate samples and to consider for further improvements. Overall, the results support the use of the presented scanning system for the automatic body size measurement of river macroinvertebrates and suggest that the depiction of their size spectrum is a valuable tool for the rapid bioassessment of freshwater ecosystems.

The article is based on the creation of an adapted protocol to scan, detect, sort, and identify digitized objects corresponding to benthic river macroinvertebrates using a semi-automatic imaging procedure. This procedure allows the acquisition of the individual size distributions and size metrics of a macroinvertebrate community in about 1 h.

自动图像处理确定河流大型无脊椎动物群落大小结构

Body size is an important functional trait that can be used as a bioindicator to assess the impacts of perturbations in natural communities. Community ...

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

Digital imaging and processing technology have evolved to facilitate the expansion of large-area imaging surveys, which increase our capacity to study the status, trends, and dynamics of organisms living in subtidal habitats. By creating photo-realistic digital twins for ex situ analyses, these approaches allow small field teams to collect substantially more data than was previously possible. Here we present a four-step, large-area imaging survey pipeline and analysis methodology, including image collection, model construction, ecological analysis, and data curation, that has been developed and refined through experimentation over the past decade. Each step described has a consistent focus on the unique value of the original source imagery. While types of data extracted from large-area image surveys are vast, we include here workflows to extract ecological data for structural complexity, community composition, and demographic analyses valuable for monitoring and hypothesis-driven efforts. We additionally include recommendations for metadata standards, which complement the collection of large-area imaging data and support archival efforts facilitating transparency and collaboration between research groups.

This protocol covers a four-step large-area imaging survey methodology used to extract metrics of structural complexity, community composition, and population demographics for coral reef communities. The quality of imagery collected and integrated access to the source imagery are prioritized within each step of the protocol.

Digital imaging and processing technology have evolved to facilitate the expansion of large-area imaging surveys, which increase our capacity to study the ...