Modeling sampling strategies for determination of zooplankton abundance in ballast water. (15th February 2017)

• Record Type: Journal Article
• Authors: Hernandez, Marco R.; Johansson, Mattias L.; Xiao, Yanyu; Lewis, Mark A.; MacIsaac, Hugh J.
• Is Part Of: Marine pollution bulletin. Volume 115:Number 1/2(2017)
• Page Start: 80
• Page End: 85
• Publication Date: 2017-02-15
• Subjects: Ballast water -- Zooplankton -- IMO D-2 standard -- Modeling -- Probability density function -- Sampling effort; Marine pollution -- Periodicals; Marine Biology -- Periodicals; Water Pollution -- Periodicals; Mer -- Pollution -- Périodiques; Publications périodiques; Pollution des mers; Lutte antipollution; Electronic journals; 363.7394
• DOI: 10.1016/j.marpolbul.2016.11.050
• Languages: English
• ISSNs: 0025-326X

Abstract

Abstract: Ballast water has been a major source of non-indigenous species introductions. The International Maritime Organization has proposed performance standard that will establish an upper limit for viable organisms in discharged ballast. Here we test different sampling efforts for zooplankton in ballast water on a commercial vessel. We fit different probability density functions to find the most representative and evaluated sampling efforts necessary to achieve error rates (α, β) of < 0.05. Our tests encompassed four seasonal trials and five sample volumes. To estimate error rates, we performed simulations which drew from 1 to 30 replicates of each volume (0.10–3.00m 3 ) for mean densities ranging between 1 and 20 organisms m − 3 . Fieldwork and simulations suggested that > 0.5 m 3 samples had the best accuracy and precision, and that the Poisson distribution fit these communities best. This study provides the first field test of a sampling strategy to assess compliance with the future IMO standard for large vessels. Graphical abstract: Highlights: Field sampling and simulations used to assess sampling for compliance with IMO-D2. Multiport sampling inlets effectively smooth within-tank variability from aggregation. Larger samples/more replicates result in lower false-positive or false-negative rates. Best tradeoff between accuracy, precision, and workload achieved with 1.00 m 3 samples.