## Henry Lee II 1



## Deborah A. Reusser 2



## Melanie Frazier 1


## Greg Ruiz 3

1: Western Ecology Division,

National Health and Environmenta l Effects Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency,

2111 SE Marine Science Drive,

Newport, OR 97365, U.S.A.

2: U.S. Geological Survey,

Western Fisheries Research Center,

2111 SE Marine Science Drive,

Newport, OR 97365, U.S.A.

3: Marine Invasion Research Laboratory,

Smithsonian Environmental Research Center, 647 Contees Wharf Road, P.O. Box 28, Edgewater, MD 21037, U.S.A. 

# Density Matters:

Review of Approaches to Setting Organism-Based Ballast Water Discharge Standards

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i 012 3 4 5 6 0. 00 0. 05 0. 10 0. 15 0. 20 0. 25 0. 30 0. 35Disclaimer: The information in this document ha s been funded in part by the U.S. Environmental Protection Agency (U.S. EPA). The publication was subjected to review by the

National Health and Environmental Effects Re search Laboratory’s Western Ecology Division and the U.S. Geological Survey (USGS), and is approved for publication. However, approval does not signify that the contents reflect the views of the U.S. EPA. The use of trade, firm, or corporation names in this publica tion is for the information and convenience of the reader; such

use does not constitute official endorsement or approval by the U.S. Department of Interior, the USGS, or the EPA of any product or service to th e exclusion of others that may be suitable.

Acknowledgements:

The authors wish to thank the fo llowing for their contributions to this report: M.S. Minton, P.W. Fofonoff, and A.W. Miller provide d data on ballast water discharg e and historical invasion rates and insights into their interpre tation. Richard Everett, John Li shman, and Ryan Albert provided guidance through the intri cacies of IMO, national, and stat e regulations; Cheryl A. Brown provided advice on mathematical issues; John Van Sickle provided advice on statistical issues. Richard Everett, John Lishman, Ryan Albert, Jo hn Van Sickle, Nathan H. Schumaker, Scott Smith, Jody Stecher, Katharine Marko, and Robert J. Ozretich reviewed an earlier version of this document and provided insightful suggestions. Deborah Reusser was partially funded through

AMI/GEOSS IAG #DW-14-92231501-0 from the U.S. EPA. Melanie Frazier was funded through AMI/GEOSS EP08D00051.

Preferred Citation: Lee II, H., Reusser, D.A., Frazier, M ., and Ruiz, G. 2010. Density Matters: Review of Approaches to Setti ng Organism-Based Ballast Water Discharge Standards. U.S. EPA, Office of Research and Development, National Health and Environmental Effects Research Laboratory, Western Ec ology Division. EPA/600/R-10/031.

iiwe address the statistical consid erations of the volume of water that needs to be sampled when estimating the concentrations of organi sms in ballast water discharges.

Niche or species distribution models that predic t the potential distribut ion of species based on

environmental conditions are not considered. While these models can be used to predict current

and future distributions of an individual non- native species after it has invaded (e.g., Peterson and Vieglais, 2001; Herborg et al., 2007; Reu sser and Lee, 2008), they do not address the likelihood of invasion via ballast water discharges.

Nonindigenous Species Background: Introductions of nonindigenous species (NIS) 1 , also known as aquatic nuisance species (ANS), are recognized as one of the major environmental stressors in freshwaterand marine/estuarine ecosystems. Examples of individual invasive species having deleterious impacts on aquatic systems include the zebra mussel ( Dreissena polymorpha ) in the Great Lakes and other freshwater systems (Drake and Bossenbroe k, 2004) and the European green crab ( Carcinus

maenas ) on both the Atlantic and Pacific coasts (C arlton et al., 2003). Other indicators of the prevalence of nonindigenous species are their dominance in benthic communities in the San Francisco Estuary (Lee et al., 2003) and the larg e number of invaders fo und on the East, Gulf, and Pacific coasts of the United States (e.g., Ruiz et al., 2000; Wonham and Carlton, 2005). It is beyond the scope of this document to review the effects of nonindige nous species on aquatic systems, and the reader is referred to previ ous reviews on the ecologi cal, human health, and economic impacts of invasive species (e.g., McMi chal and Bouma, 2000; Pimentel et al., 2005; Lodge et al., 2006). Additionally, implications of invasions of nonindigenous species on the ability of the EPA to achieve its environmental goals and mandates as of 2000 were reviewed by Lee and Chapman (2001).

Nonindigenous species can potentially invade a quatic systems through a variety of mechanisms (Ruiz and Carlton, 2003). Of these potential ro utes, shipping, including both ballast water discharges and hull fouling, is the primary vect or for biological invasi ons in the Great Lakes (Duggan et al., 2003) and most marine/estuarine ecosystems (Ruiz and Carlton, 2003; Hewitt et al., 2009) with the notable exception of the Medi terranean where Lessepsian invasions through the Suez canal is the major invasi on mechanism (Galil and Zenetos, 2002). In the past century, the increase in shipping traffic as well as the reduced time for transoceanic voyages has increased the number and abundance of nonindige nous species arriving in new environments around the world (Ruiz et al., 1997). This increasing propagule s upply appears to have increased

the rates of invasions in a num ber of aquatic ecosystems (e.g., Cohen and Carlton, 1998; Holeck et al., 2004; but see Costello and So low, 2003 and Drake et al., 2005).

Of the sub-vectors associated with shipping, ballast water is a major source of nonindigenous species in both the Great Lakes and most mari ne/estuarine environmen ts (Carlton and Geller 1993, Carlton 1996; Fofonoff et al. 2003a; Holeck et al., 2004; Hewitt et al., 2009). When ships

1 The terms “nonindigenous species” and “non-native species” are used to denote species that were introduced via anthropogenic vectors into a novel location with no speci fic connotation of ecological , human health, or economic impacts. “Invasive species” is used to denote a nonindi genous species for which there is evidence of an adverse impact on ecological, human health, or economic endpoints. (Executive Order 13112; http://www.invasivespeciesinfo.gov/laws/execorder.shtml ).

23 take on ballast water to compensate for changes in load, vast assemblages of aquatic organisms are collected and then discharged into subsequent ports. This inte rnational transfer of organisms is massive – untreated ballast discharges can contain thousands of organisms per cubic meter (Minton et al., 2005) and the total foreign ballas t discharged in the United States in 2004-2005 was over 73 million metric tons (Miller et al., 2007; also see National Ballast Information Clearinghouse (NBIC), http://invasions.si.edu/nbic ).

IMO Ballast Water Treaty and Proposed USCG Standards Background: The first approach to managing this vector was to implement mid-ocean ballast water exchange, where ballast was exchanged either through flow -through of the ballast or empty-and-refill. Ballast water exchange, and similar treatment for vessels declaring “No Ballast on Board” (NOBOBs), have been shown to reduce the numbe r of living organisms in ballast water tanks which are adapted to living in both freshwater and coastal/estuarine environments, thereby reducing the risk of invasion (Gray et al., 2007; Locke et al., 1993; McCollin et al., 2007; Ruiz and Reid, 2007; Cordell et al ., 2009; see summary of 4.3 in U. S. EPA, 2008a). Though ballast water exchange and saltwater flushing may reduce th e risk of invasion, a nu mber of studies have shown that ballast water exchange was not suffi ciently effective or c onsistent in reducing organism concentrations in ballast water espe cially in coastal/estuarine environments (e.g., Locke et al., 1991; see summary in Section 4.3. 2 of USCG, 2008). Additionally, in many cases,

it is not safe for vessels to conduct ballast wa ter exchange, given constraints of design and

construction.

In response to this concern, nati onal and international efforts bega n to evaluate other options for managing ballast water discharges. Ultimately, a key decision was made to base ballast water discharge standards on the concentr ation of organisms in discharged ballast water, rather than on the percentage of ballast water exchanged dur ing mid-ocean exchanges. Through the Marine

Environment Protection Committee (MEPC) of the International Maritime Organization (IMO),

an international ballast wate r treaty (“The International Convention for the Control and Management of Ships' Ballast Water and Sediment s, 2004”) was initiated to reduce the spread of nonindigenous species through ballast water tran sport (IMO, 2004a). An overview of the IMO

international convention can be found in Gollash et al. (2007). A key section of the IMO treaty (Regulation D-2) sets standard s for the maximum concentratio ns of organisms allowed in discharged ballast water based on different size groups of organi sms (Table 1). The treaty has not yet entered into force, and while recognized as a major step forward, the IMO standards are considerably above those proposed by the United States (<0.01 organisms m -3 ; IMO, 2004b). In response to concerns that the IM O standards were not sufficiently protective, a number of states, such as California and Wisconsin, have initiate d or passed more stringent discharge limits for

ballast water.

The United States Coast Guard (USCG) res ponded to this concern by preparing a draft Programmatic Environmental Impact Statement on ballast water discharges (USCG, 2008) and then proposing a two phase implementation of di scharge standards (USCG, 2009). The proposed USCG Phase I standards are equivalent to the IMO D-2 standards, while Phase II is 1000-fold more stringent (Table 1). Additionally, Calif ornia and some other states have proposed alternative standards with the ultimate goal of “no detectable” discharges of organisms in ballast water (Table 1).4Table 1: Existing or proposed international and national ballast water discharge standards applicable to the waters of the United Statesand examples of state standards. All organism dimensions are for the “minimum dimension”. Standards for the >50 micron and >10 -<50 micron classes are for “viable” or “living” organisms. Notethat Phase II of the Coast Guard standard can be implementedincrementally. The date for the implementation of the final California standard is 2020. The California standards are from theCalifornia State Lands Commission (2009). The Wisconsin standards are from the Wisconsin Department of Natural Resources(2010). The California standards are instantaneous standards while those for Wisconsin are daily averages. NPRM = Notice ofproposed rule making. IMO = International Maritime Organization. cfu = “colony forming units”Organism ClassIMO D-2StandardUSCGNPRM Phase IUSCGNPRM Phase IIWIState StandardCAInterim StandardsCAFinal StandardOrganisms >50microns<10 per m3<10 per m3<1 per 100 m3(< 0.01 per m3)<1 per 10 m3(<0.1 per m3)“No detectableliving organisms”“Zero detectableliving organisms”Organisms >10 -<50 microns<10 per ml<10 per ml<1 per 100 ml(<0.01 per ml)<1 per 10 ml(<0.1 per ml)<1 per 100 ml(<0.01 per ml)“Zero detectableliving organisms”Organisms <10micronsNo standardNo standard<103bacteria/100 ml<104viruses/100 mlNo standard< 103bacteria/100ml< 104viruses/100 ml“Zero detectableliving organisms”Escherichia coli<250 cfu per100 ml<250 cfu per100 ml<126 cfu per100 ml<126 cfu per100 ml<126 cfu per100 ml“Zero detectableliving organisms”Intestinalenterococci<100 cfu per100 ml<100 cfu per100 ml<33 cfu per100 ml<33 cfu per 100ml<33 cfu per100 ml“Zero detectableliving organisms”ToxicogenicVibrio cholerae(serotypes O1 andO139)<1 cfu per 100 mlor1 cfu per g wet wt.zooplankton<1 cfu per100 ml<1 cfu per100 mlNo standard<1 cfu per 100 mlor<1 cfu per g wetzoological sample“Zero detectableliving organisms”U.S. EPA Regulatory Background: Under the Clean Water Act (CWA), the U.S. EPA has the responsibility for managing the National Pollutant Discharge Elimina tion System (NPDES) Program (see http://cfpub.epa.gov/npdes/home.cfm?program_id=45 ). Under the NPDES Program, all facilities that discharge pollutants from any point source into waters of the United States generally are required to obtain an NPDES permit (U.S. EP A, no date; U.S. EPA, 1996; see also CWA § 301(a)). Since the early 1970’s, EPA regulations (40 C.F.R. 122.3(a)) ha d excluded discharges incidental to the normal operation of vessels (including ballast wate r) from the need to obtain an NPDES permit (see http://www.epa.gov/owow/invasive _species/ballast_water.html ). However, that exclusion from the NPDES permitting program was successfully challenged in court, and as a result, was vacated (struck down) by the U.S. District Court for the Northern District of California ( Northwest Envtl Advocates et al. v. United States EPA, No. C 03–05760–SI (December 17, 2008) (vacatur of 40 C.F.R. 122.3(a) as of February 6, 2009)). For a further description of the lawsuit, see also, Northwest Envtl. Advocates v. EPA , 537 F.3d 1006 (9th Cir. 2008). In light of the court decision, in December 2008, EPA issued a general NPDES permit (known as the “Vessel General Permit” or “VGP”) that contains, among ot her things, effluent limits for ballast water discharges from non-recreational vessels (U.S. EPA, 2008b; http://cfpub.epa.gov/npdes/home.cfm?program_id=350 ). Specifically, “The 2008 Vessel General Permit (VGP) regulates discharges in cidental to the normal operation of vessels operating in a capacity as a means of transporta tion. The VGP includes general effluent limits applicable to all discharges; general effluent lim its applicable to 26 spec ific discharge streams; narrative water-quality based effluent limits ; inspection, monitori ng, recordkeeping, and reporting requirements; and additi onal requirements applicable to certain vessel types.

Recreational vessels as defined in section 502(25) of the Clean Wate r Act are not subject to this permit. In addition, with the exception of ballast water discha rges, non-recreational vessels less than 79 feet (24.08 meters) in length, and all comm ercial fishing vessels, regardless of length, are not subject to this permit.” (U.S. EPA, 2008b)

EPA currently is undertaking development of or ganism-based effluent limits (discharge standards) for ballast water discharges for use in the future reissuance of the VGP. To help ensure it uses a scientifically sound approach in that effort, EPA is seeking an objective and independent scientific opinion on approaches for de riving these standards. As part of the effort to achieve that objective, this document synthesi zes potential approaches to generating organismbased discharge standards. This synthesis is a component of the risk assessment process. As pointed out by EPA’s Office of the Science Adviso r, “The primary purpose of a risk assessment is to inform the risk manager’s decision ma king process. The primary purpose of a risk assessment is not to make or recommend any pa rticular decisions; rath er, it gives the risk manager information to consider along with ot her pertinent information.” (U.S. EPA, 2004a). Accordingly, it is not the purpose of this doc ument to propose specific discharge standards, which is a risk management decision that inco rporates additional fact ors potentially including existing laws, social factors, economics, and feasibility.

5

### IV. ZERO DETECTABLE DISCHARGE

Henry Lee II

Overview: The state of California and other states have adop ted or have proposed standards with the goal of “zero detectable living organisms” in ballast water di scharges. In this assessm ent of zero detectable organisms, we will focus on the California standards as they are the best documented of the efforts (see Dobroski et al., 2009a and http://www.slc.ca.gov/Spec_Pub/MFD/Balla st_Water/Ballast_Water_Default.html ). California’s standards will be adopted in tw o-phases, with an interim sta ndard of “no detectable living organisms” >50 microns for ships constructed in 2010 to 2012, depending upon vessel size (Table 1). The proposed final standard is “no de tectable discharge” of zooplankton, phytoplankton, protists, bacteria, or viruses fo r ships constructed beginning in 2020 (Table 1). While California considered the natural invasion rate approach (described in Section V), it was not used in establishing the standards and the “n o detectable discharge” standard is a special case of the expert opinion/management consensus approach discussed in Section III.

Rationale: The principal legal authority for California to set these standards is the Coastal Ecosystems

Protection Act of 2006. As noted in th e Notice of Proposed Regulatory Action ( www.slc.ca.gov/Spec_Pub/MFD/

.. ./Art_4-7_2009_NOPR.doc ), “Current California law requires that vessels manage ballast water to reduce the discharge of NIS into California waters. The performance standards for the discharge of ballast water prescribed by Article 4.7 are necessary to minimize the transport of NIS into and througho ut the waters of the State of California.” (Amendments to Article 4.7 entitled “Performance Standards for the Discharge of Ballast Water for Vessels Operating in California Waters”; Updated August 31, 2009).

The environmental rationale for the zero detectab le discharge standard given by the “Report and Recommendation of the California Advisory Pa nel on Ballast Water Performance Standards” ( www.slc.ca.gov/Spec_Pub/MFD/Ballast_Water/Documents/Appendix_A.doc ) was, “The scientific basis for a standard of discharging no exotic orga nisms is that exotic organisms, unlike conventional chemical pollutants, can reproduce and increase over time, persist indefinitely and spread over large regions. Thus, very large, widespread and long- term impacts could poten tially result from the discharge of a small number of individual organism s—in some cases as few as a single mated pair, or in the case of asexually-reprodu cing species, a single individual. Fr om this perspective, the only biologically safe standard is no di scharge of exotic organisms.” This rationale was also discussed by Cohen (2005 in Appendix 3 of the California Panel Re port). Note that this rationale implies that no organisms should be discharged (actual zero discharge), which can be substantially different than the “zero detectable discharge” of the California regul ation, which is entire ly dependent upon the sampling regime used.

Another rationale for setti ng stringent standards is to “force” tec hnology development. In a letter to

California Lt. Governor Bustamante and the California State Lands Commission, The Ocean Conservancy (TOC) stated, “Dur ing the [Ballast Water] Comm ittee’s work, TOC sought higher

18standards because the existence of such standards – combined with a competitive marketplace for ballast water treatment products – would motivate the rapid development of technology appropriate for meeting them.” ( http://www.slc.ca.gov/Spec_Pub/MFD/ Ballast_Water/Documents/Appendix_C.pdf ).

California states that their ballas t water standards should be interp reted as instantaneous standards rather than averages over the entire discharge. As a result, if any individual sample from a discharge exceeds any of the standards, this w ould be grounds for finding non-compliance and it is unnecessary to show non-compliance in multiple sa mples or in mean values (Dobroski et al., 2009a). California justifies the instantaneous standards based on the 2005 draft MEPC G2 sampling guidelines. However, the final MEPC G2 guidelines were subsequently revised to instead suggest an average method (J. Lishman, pers. comm., Nove mber, 2009). It is wo rth noting that while California cites the earlier G2 guidelin es as a justification, any zero ba sed standard is inherently an

instantaneous standard since once any sample contains an organi sm the discharge has failed the

standard.

Assumptions and Limitations: A true zero discharge of all size groups eliminates the risk of invasion via ballast water, assuming perfect compliance and no equipment failures. However, perfect compliance and no failure is practically, if not theore tically, impossible, particularly for microbiological organisms unless ballast water is discharged into a land-based treatment facil ity or ships are redesigned to eliminate the need to discharge ballast water (see Gregg et al., 2009 for discussion of ballast-free ships). Thus, ignoring all the other issues mentione d below, there will still be some level of risk associated with the proposed California standards resulting from equipment and human failure.

A major limitation with “zero detect able” discharge standards is that they are undefined in the absence of a quantitative sampling protocol, and depending upon the sampling protocol, the actual risk may be considerably higher than that associated with othe r standards. For example, as discussed in Section X, when zer o organisms are detected in a 1 liter sample, the actual ballast

water concentration could be as high as 3,000 organisms m -3 based on the 95% confidence interval (1-tailed). For a 10 m 3 sample (=2641 gallons) with no detected organisms, the ballast water concentration could be as high as 0.3 organisms m -3 (see Table 15). Thus, with a small (1 liter) sample with no organisms, the actu al concentration could be as much as 300-fold higher than the IMO standard, while for a 10,000-fold larger samp le the actual concentrat ion could be 30-fold higher than the proposed USCG Ph ase II standard. Without a stat istically-robust sampling protocol to quantify the detection limits dur ing both testing of treatment sy stems and compliance monitoring, it is impossible to conclude that the zero detect able discharge standard is actually any more stringent than the other standards.

These problems with a zero detectable discharge st andard for ballast water have been previously identified by various expert panels. For example, the summary of the 2003 “International Workshop on Ballast Water Discharge Standard s” (MEPC, 2003a) included the following two points: “Experience following passage of the United States Clean Water Act showed that an absolute standard of “zero discharge” was an unrealistic/ unworkable concept – detection limits have always been a problem.”

19“Setting a specific detection limit means that an actual concentration will be allowed for the testing protocol, therefore it might be better to specify the (acceptable) concentration as determined by the selected test protocols, rather than to use the expression “zero detectable” in the standard. This concept could be specifi ed in the testing protocol guidelines.”

Other major challenges are whether the putative low concentration associated with the “zero detectable” standard is economically viable and/or technologically f easible given the constraints of ship operations. These challenges are likely to be especially acute meeting the standards for microbiological organisms. It is beyond the scope of this document to conduct a review of the technical approaches to ballast water treatment methodologies but it is worth noting the scope of the challenges such systems face. Large tankers can carry in excess of 200,000 m 3 of ballast and the rates of ballasting and deballas ting can be as high as 20,000 m 3 hr -1 (NRC, 1996; Wright, 2007). Treatment systems must fit within the confined sp aces available on ships, continue to operate under the demanding conditions of ocean voyages, not be so complex that the crew can not operate them effectively, and pose no risk to th e crew or environment. As pointed out by Gregg et al. (2009), “Effectively eliminating the risk of ballast wate r mediated invasions sti ll remains a monumental technological and economical challenge.”

In evaluating the practicality of the “zero detectable” standard, the State of California conducted a

review of ballast water treatment systems using available informati on (Dobroski et al., 2009a, b) in terms of whether they presently meet the Californi a standards or showed th e potential of meeting them. They conducted a review for the >50 micron group, 10 - 50 micron size group, E. coli

, intestinal Enterococci , and Vibrio chlorae . They initially included viru ses, but concluded that there was no widely accepted technique or proxy for en umerating them and dropped them from the evaluation. They noted that their review was hampered by the lack of detailed testing data, inconsistency in the testing met hodologies, and differences in the scale and location in which the tests were conducted (e.g., lab based vs. land based vs. shipboard). They also noted that much of the available data have not been subject to a re view by an independent scientific organization.

Based on the January 2009 review (D obroski et al., 2009a), 15 systems were considered to have the potential to meet the California standard of zero detectable discharge for the >50 micron size class (see Table 3). In an October 2009 update to this review (Dobroski et al ., 2009b), the MH Systems was also listed as having potential. However, it is critical to note th at California listed a system as having the “potential” to meet thei r standards if it had “at least one replicate in compliance with the performance standards” (Dobroski et al., 2009a). In other words, a system was considered to have “potential” as long as it did not fail the standard in 100% of the replicates , which is the least stringent criterion possible. Failure of these syst ems could be due to several factors, such as mechanical problems, inherent variability in the efficacy of the system, the system working in one test mode but not another (e.g., working in a land-b ased testing facility but failing on a ship), or

statistical variation in the resu lts based on a inadequate sampling regime. Regardless of the cause for failures, the California criterion for “potential” is much less stringent than the criteria to meet approval through the Marine Environment Protect ion Committee (MEPC) of the IMO. The G8 MEPC Guidelines for Approval of Ballast Water Ma nagement Systems provide that to obtain typeapproval, a system needs to satisfy the IMO Regul ation D-2 standards in three consecutive valid shipboard test cycles and five consecu tive valid land-based cycles (MEPC, 2008a).

20To gain a better insight into the performance of these treatment syst ems, we analyzed their failure rates among replicate trials as presented in Appendix B1 of Dobr oski et al. (2009a) (Table 3). It was not possible to conduct this an alysis with the more limited data presented in the October update (Dobroski et al., 2009b), so the MH Systems treatment system is not included. With the exception of SeaKleen®, all systems failed a moderate to high percentage of th e replicates and/or they were not tested in all three modes (l aboratory, land-based, and shipboard testing). While the results for SeaKleen® are promising, they are only based on one laboratory test, two land-based tests, and one ship-based test, which do not meet the minimu m G8 requirements menti oned above. Additionally, there are concerns over residual toxicity from Se aKleen®, as well as its effectiveness against bacteria, resistant resting stages, and sediment-d welling organisms (Gregg et al., 2009). SeaKleen® has not been registered by EPA for use in trea ting ballast water under th e Federal Insecticide, Fungicide, and Rodenticide Act (FIFRA), and thus is not currently approved for such use in the United States. Furthermore, it has not received final type approval under G8 requirements or final approval from IMO for use as an active substanc e. We identify these issues not criticize SeaKleen® but to point out the gap between identifying a system with “potential” and having an approved system. Finally, our analysis is based only on the percentage fa ilure as reported in Dobroski et al. (2009a); it would take a detailed stat istical review of the sampling protocols used in testing these systems to ascertain the statisti cal confidence of detec ting a zero discharge (see Section X) and thus what level of confiden ce to place in the reported system successes.

Dobroski et al. (2009a) concluded based on their review that “at l east two treatment systems have demonstrated the potential to comply with Calif ornia’s performance standards. Many additional systems are close to completing system performan ce verification testing and will soon have data

available for review. Commission sta ff expects that before 2010 seve ral systems will be ready to

meet California standards.” Our assessment is not as optimistic, especially since it is now 2010 and no systems meeting the California standards have b een approved by IMO. While predictions about technology development are littered with emba rrassing prognoses (Ken Olson founder of DEC
computers: “There is no reason anyone would want a computer in their home.” ), our view is that it is unlikely that any practical ballast water treatmen t system will approach an actual zero discharge of organisms, defined here as co ncentrations substantially less than the USCG Phase II standards, in the near term, in particular for microbes and viruse s. Of course it is pos sible to achieve a “zero detectable” standard simply by using an inadequate sampling protocol with insufficient statistical power. Again, this emphasizes the need to have quantitative sampling pr otocols with adequate sample volumes and replication to qu antitatively assess these systems.

Recommendations/Conclusions: As discussed above, “zero detect able” discharge standards are undefined in the absence of a quantitative sampling protocol, a nd depending on the sampling protoc ol, the actual risk may be considerable higher than that asso ciated with other standards. Th erefore, we recommend that it not be used at the national level as an approach for deriving environmentally protective limits on concentrations of living or ganisms in ballast water.

If zero detectable discharge standards are consider ed as a possible approach for national standards, part of the technical anal ysis should include an assessment of th e relative risk associated with the zero detectable discharge standa rds versus risk associated with the USCG Phase II standards

21Table 3: Failure rates of the sy stems listed as having the “potential” to achieve the California discharge standard for >50 micron organisms (Dobr oski et al., 2009a). Failure is defined as detection of organisms in a test sample. The fa ilure rate was calculated as the percent of the replicate tests that failed the cr iterion using the data in Appendix B1 of Dobroski et al. (2009a). The Hitachi system was listed as ach ieving the California standard in Table VI-1 of Dobroski et al. (2009a) but no results were given in Appendix B1. The RWO Marine Water Tech. was tested with

Artemia cysts only. Systems that had a 100% failure rate for the >50 micron organisms may have passed the standard for another size group. The stat us of the type and active substance approval are from Dobroski et al., 2009a, D obroski et al., 2009b, and Gregg et al. (2009). The commercial names of the systems are given in parentheses. NT = not tested. NA = approval for active substance not applicable. Note that the number of samples or sample volume used in the validation testing was not reported by D obroski et al. (2009a, b).

System Failure Rate for Laboratory Testing (# Tests) Failure Rate

for Landbased Testing (# Tests) Failure Rate for Shipboard Testing (# Tests) Type Approval 1 Active

Substance Approval 2

Alfa Laval (PureBallast) 100% (1) 25% (12) 40% (5) Yes Final

Ecochlor (Ecochlor BW

Treatment

System) 0% (2) NT 0% (1) No Basic

Greenship (Sedinox) NT 0% (5) NT No Final

Hamann Evonik Degussa

(SEDNA) * 0% (2) 16% (19) 20% (5) Yes (Germany) Final

Hitachi (Clearballast) ? ? ? No Final

Hyde Marine (Hyde Guardian) 100% (1) 50% (4) 100% (4) Yes Basic

MARENCO 33% (3) NT NT No NA

Mitsui Engineering (Special Pipe) NT 100% (4) 100% (1) No Basic

NEI (Venturi Oxygen Stripping (VOS)) NT 80% (5) 75% (4) Yes (Liberia) NA

Nutech 03 Inc. (SCX 2000, Mark III) 100% (3) 67% (3) 33% (3) No No

22System Failure Rate for Laboratory Testing (# Tests) Failure Rate

for Landbased Testing (# Tests) Failure Rate for Shipboard Testing (# Tests) Type Approval Active

Substance Approval

OceanSaver (OceanSaver BWMS) NT 86% (14) 67% (12) Yes (Norway) Final

OptiMarin (OptiMarin Ballast System) 100% (1) 38% (13) 100% (7) No NA

RWO Marine

Water Tech. (CleanBallast) 0% (1) NT NT No Final

SeaKleen 0% (1) 0% (2) 0% (1) No No

Severn Trent DeNora (BalPure) NT 40% (5) NT No No

TechCross (Electro-Cleen) NT 27% (11) 0% (3) Yes (Korea) Yes

1) Type Approval: Type approval is grante d by Flags states following successful equipment performance during land based and ship board testi ng in accordance with the IMO G8 Guidelines to verify treatment system efficacy, safety, desi gn, construction, operation, and function (MEPC, 2008a).

2) Active Substance Approval: Active Substance A pproval is granted by MEPC, not the Flag state, and is required by Convention Regulation D-3 for t hose treatment systems that make use of Active Substances (biocides) to comply with the C onvention’s Regulation D-2 standards. Active Substance Approval relates to the environmental and safety aspects of the sy stem's use of biocides

and is conducted in accordance with the IMO G9 Gu idelines (MEPC, 2008b). Approval typically is given in 2 stages, first “basic” approval, and then “final” approva l. Systems subject to Active Substance Approval also must undergo type-appr oval testing by Flag states under the IMO G8

Guidelines.

* : Harmann Ag ceased work on its ballast water tr eatment system after it was discovered that the

biocide Perclean was more toxic in cold waters and in freshwater than initially assumed (Lloyd’s List, February 9, 2010).

23(USCG, 2009) and/or standards derived from the per capita invasion approach (Section VIII). This analysis should include an assessment of th e number and impacts of historic invaders in

coastal waters and the Great Lakes that the USCG or PCIP standards might not have prevented, in particular asexual and parthe nogenic invaders, in comparison to the likelihood that the zero detectable standards would have prevented th eir introduction, assuming some practical sampling protocol for the zero detectable standards.

If zero detectable standards are cons idered at the national level, it is critical to define all aspects of the sampling protocols for verification of ba llast water treatment systems and for compliance monitoring. (see Section X).

24

### X. STATISTICAL CONSIDERATIO NS IN ESTIMATING THE

CONCENTRATION OF ORGANI SMS IN BALLAST WATER

DISCHARGES

Melanie Frazier, Henry Lee II, and Deborah A. Reusser

Overview: In the previous sections, we evaluated the potenti al utility and limitations of several approaches for generating ballast water discharge standards; he re we address the statistical issues associated with monitoring organisms at very low concentra tions. This is not an approach for setting standards; however these issues must be consider ed when assessing the practicality of verifying a discharge standard either in test facilities or as part of comp liance monitoring. The stringent discharge standards that have been proposed by various agencies will require estimating very small concentrations of organisms in ballast water. This will be challenging due to the inherent stochasticity of sampling when estimating concentr ations. Furthermore, at low densities, very large volumes of water must be sampled to find enough organisms to begin to estimate concentration. Understanding the limitations and requirements of sampling will help inform the development of protocols that ensure discharg e standards are adequately implemented.

Stringent discharge standards are environmentally appealing because they are very protective; however, they present cha llenges because it is difficult to estimate low concentrations through sampling. The U.S. Coast Guard recently pr oposed a Phase II discharge standard of 0.01 organisms m -3 for organisms >50 μm, a standard 1000 times more stringent than the IMO’s (USCG proposal is currently in the Federal Register, 74 Fe d. Reg. 44632, August 28, 2009). Some states, such as New York and Californi a, have proposed discharge standards of “zero detectable organisms” (Dobroski et al., 2009). We explore some of the statistical issues that must be considered for either ship-board testing in the field or type-approv al testing of treatment systems in controlled facilities. We do not address the logistics of sampling ship ballast water,

which are described in references such as Lemie ux et al. (2008) and Wri ght (2007). We also use a “best-case-scenario” approach: we limit our focu s to organisms > 50 μm in size, which are the easiest to quantify; we assume no human or equipm ent error, such that all organisms in a sample volume are counted; and for most scenarios, we assume organisms are randomly distributed in the ballast discharge.

Rationale: Currently, a great deal of effort is being devoted to selecting a discharge st andard that adequately protects against invasive species. An important aspect of testing whether ballast discharges meet these standards during either th e testing of treatment systems or compliance monitoring of individual ships is developing sa mpling protocols that are adequate for a discharge standard. For example, a standard of “zero detectable organism s” may seem very protec tive, but in reality, the degree of protection depends on th e sampling protocol. If a sma ll volume is used to evaluate whether the discharge meets a standard, the samp le may contain zero detectable organisms, but the true concentration of organisms may be quite high. For example, even with a relatively high concentration of 100 organisms m -3 , only about 10% of 1 L samples will contain one or more organisms. Furthermore, even if zero organisms are detected in a 1 L sample, the upper possible

72concentration, based on a 95% confiden ce interval, is about 3,000 organisms m -3 . More information about these calculations is presente d below. The general point is that more organisms may be released in ballast discharge using a stringent standa rd paired with a poor sampling protocol than a more le nient standard paired with a st ringent sampling protocol. For these reasons, some researchers claim that “par t of establishing the cr iteria is defining the required sampling plan” (Jarvis, 2000).

The current lack of consistent sampling protocols makes it diff icult to compare among existing and proposed standards. Even if a single disc harge standard is adopted, without consistent sampling protocols the outcome of different ballast management programs will vary dramatically. Furthermore, the efficacy of di fferent ballast treatment technologies cannot be compared without consistent samp ling protocols (Phillips, 2005). In this chapter, we explore some of the statistical aspects of estimating the concentration of organism s in ballast discharge using laboratory techniques which count the numb ers of living organisms. In Appendix C, we

describe some tools that can be used to develop statistically s ound sampling protocols.

Sampling Ballast Water Discharges: Given current methodologies, it is not possible to count every single organism in a ballast tank or discharge (i.e., the “population”); consequently, we must use sampling techniques to estimate the

true concentration. Due to the stochastic natu re of sampling, multiple samples taken from the same population will have varying numbers of organisms due to random chance. However, if we know how organisms are distributed in their enviro nment, this uncertainty can be estimated and taken into consideration during the development of sampling protocols. A sample taken from a ship’s ballast discharge may have a higher concentration than the standard even though the true concentration of organisms is less than the standard. In this situation, if we failed to take into account the inherent stochasticity of sampling, sh ips that do not violate the standard would be unfairly penalized. Conversely, a sample may have a lower concentration than the discharge standard even though the true concentration of the discharge exceeds the standard. In this situation, if sampling protocols are inadequate then many ships th at exceed the standard would

not be detected.

Two questions that must be answered to devel op adequate sampling prot ocols, are 1) how many organisms must we observe in a sample before we feel reasonably confident that we can identify ships in violation of the standard ; and 2) how few organisms must we observe before we can feel reasonably confident that a ship does not violate the standard? The answers will depend on the size of the sample, the true concentration of organisms, the discharge standard, and the definition

of “reasonable confidence”.

The answer also depends on how organisms are dist ributed in the discharged ballast water. The best-case scenario, from a sampling perspective, is a random distributi on (Figure 9A), meaningthat organisms occur independently of one another. A random distribution will occur in wellmixed ballast water. This distribution may be unlikely because organisms are often aggregated

to some extent in their environm ent. There are several biological reasons for this phenomenon. Organisms may be responding to similar environmen tal cues, resources or physical forces, such as gravity (Figure 9B); they may be actively s eeking conspecifics (Figures 9B and C); or, for organisms with fast population growth rates, reproduction may occur at a faster rate than

73diffusion or convection away from each other (Fi gure 9C). Different mechanisms can lead to varying patterns of aggregation, which can have different consequences for sampling. Murphy and colleagues (2002) have shown that the abund ance of zooplankton varies with depth in the ballast tanks, indicating that at least one type of aggregation in ballast discharges is likely.

Figure 9: An example of a random distribution (A, Poisson) a nd two possible variations of aggregated distribut ions (B and C).

For most of the analyses in this report, we a ssume that organisms are randomly distributed in ballast tanks and the discharges. From a practic al perspective, this was the only option because we do not possess data that can be used to estimat e the degree of aggregation in ballast water. Furthermore, Elliott (1971) argues that assuming a random distribution is a reasonable starting point because the Poisson is the default, or nu ll, hypothesis, and theref ore, should be assumed until rejected by testing. Ellio tt also makes the point that for benthic organisms low density populations are effectively randomly distributed in regard to sa mpling, and therefore a random

distribution is often a suitable hypothesis. Whether this applies to organisms discharged in ballast water is unclear. The values presented in this chapter are probably optimistic because: 1) almost all organisms demonstrate at least some aggregation; and 2) for aggregated populations, larger volumes must be sampled to obt ain good estimates of concentration.

Further, we assume that the samples are “taken from the discharge line, as near to the point of discharge as practicable, during ballast water discharge whenever possible”, as recommended in the final MEPC G2 ballast water sampling guidelines (MEPC, 2008c), and as such are representative of the actual concentrations disc harged. Important aspects of developing sampling protocols may be determining the extent that organisms are aggreg ated in ballast discharges and whether samples of ballast water discharges are re presentative of the tota l number of organisms discharged. We discuss some aspects of sampling aggregated populations later in this chapter.

Sampling Poisson Distributions: For randomly distributed populations, the Poisson distribution can be used to determine the probability that a given number of individuals will occur in a sample given the true concentration of organisms (see Table 15 for definitions). This information provides th e statistical basis of sampling protocols for randomly distributed populat ions. A defining characteristic of the Poisson

distribution is that it is defined by a single parameter, λ , which describes both the mean and

74variance of the expected counts pe r unit of sampling effort, thus λ = μ = σ 2 . Lambda can be a real number, and in regard to ballast sampling, it can be interpreted as the true concentration of organisms in the ballast discharge.

For a randomly distributed population the variance increases at exactly the same rate as the

mean. This differs from the normal distribut ion which has two parameters, the mean and variance, which can vary independe ntly of one another. The expected mean and variance scale isometrically with sampling effort. If sample volume doubles, the expected mean and variance of the sample will also double. For these reasons, ten 1 m 3 samples do not provide more information than a single 10 m 3 sample when a population is ra ndomly distributed. However, the first sampling scenario (i.e., ten 1 m 3 samples) provides the data for independently estimating variance, which can be used to determine whet her a population is randomly distributed versus aggregated. If the population is randomly distri buted, then the mean and variance from multiple samples should not be significantl y different from one another (see Elliott, 1971, for more information). An increase in sampling effort, either by taking more sample units or increasing sample volume, improves the average estimate of λ . Ultimately, if all the discharged ballast water was sampled the concentration would equal λ

.

Given λ , the probability of N organisms occurring in a sampling unit (individual sample of ballast discharge) is

Equation 23: ! )( N e NP N λ λ

−

=

For example, for a true concentration of 15 organisms m -3 , the probability of getting 10 organisms in a sampling unit (i.e., 1 m 3 ) is

0486.0 3628800 176399

12345678910 )1077.5()1006.3(

!10 )15( )10( 11 7 1015

= =
××××××××× ×××

= = − − e P

Although the true concentrati on of organisms in the ballast tank is 15 organisms m -3 , there is a

4.9% chance that a 1 m 3 sample unit will contain 10 organisms (there is about a 12% chance that the sample unit will contain ≤ 10 organisms). The estimate of parameter λ is represented by the

statistic m , which, in this sample, equals 10 organisms m -3 . This estimate of λ is low given the true concentration of 15 organisms m -3 . As the sample volume increases, the sampling statistic,

m , will on average provide a better estimate of λ

.

In the above example, the probability of N even ts occurring in a sampling unit is determined by λ which represents the average number of events expected to occur per sampling unit. By assuming a constant sampling unit, λ represents an average count with no associated units. However, λ is often expressed as a concentration (organisms m -3 ) by dividing the average expected count by the volume of the sampling un it. An alternative parameterization of the

Poisson distribution can be used which expresses probabilities in terms of the number of events per sampling effort rather than sampling unit. In this case, λ is replaced by the true concentration of organisms, c , times the sample volume, v ,

75Equation 24: ! )( )( N cve NP Ncv

−

=

This equation is more flexible because it allows the volume of the sample to vary, and it emphasizes that even with a constant population density the Poisson dist ribution of counts can change by sampling larger or smaller volumes (Bolker, 2008).

Table 15: Definition of statistical terms. Population All the organisms in a population, in this case, all the >50 μm organisms in the discharged ballast water.

Sample A random sample of the population, in this case, a volume of the discharged ballast water in which all >50 μm organisms are counted.

Poisson A distribution that describes the probability of a given number of “events” (counts, individuals, arrivals, etc) occurri ng in a unit of time/ space if the events are independent of each other. A defining ch aracteristic of the Poisson is that the

mean and variance of the expected counts are equal ( λ = μ = σ 2 ). If organisms in a ballast tank are randomly distributed, then sampling probabilities can be modeled using the Poisson.

λ Lambda, the single parameter of the Po isson distribution, which is a rate describing the average number of events e xpected to occur per unit of time/space. In this case, it also describes the true c oncentration of organisms in the discharged ballast water. This parameter is a real number.

m A statistic estimating λ , calculated from the average number of events observed during sampling. In this case, m is an estimate of the true concentration of organisms in the discharged ballast water based on sampling.

Count Number of organisms in a sa mple. This value is an integer.

Negative Binomial A probability dist ribution often used to mode l aggregated populations ( σ 2 > μ).

θ Theta, dispersion parameter for the ne gative binomial dist ribution. Highly aggregated populations have smaller θ values, and as θ approaches infinity, the negative binomial approximates the Poisson distribution.

Taylor’s Power Law An alternative to the negative bi nomial for modeling aggregated populations

From equations 23 or 24, probability distributions can be obtained that describe the probability of a sample containing a specifi c number of organisms given λ and the sample volume (Figure 10). If 1 m 3 of ballast is sampled from the discharge with a concen tration of 10 organisms per

m 3 , the sample could theoretically contain any number of organisms from zero to positive

76Figure 10: Probability distributions for random samples of 1 m 2 for a randomly distributed population with 10 (A ), 1 (B), or 0.01 (C) organisms m -2 . Red squares represent random samples. The data are displayed in terms of area with units of m 2 , but the probabilities are the same for volumes. Plots on the right indicate the probability that a 1 m 2 sample will contain a given number of organisms. At low concentratio ns, the concentration of organisms is likely to be estimated as 0 organisms m -2 , unless very large volumes are sampled.

infinity (or in the case of a finite volume su ch as a ballast discharg e, the total number of organisms in the discharged ballast), but about 95% of samples will contain 4 to 17 organisms (Figure 10A).

77The shape of the Poisson probability distributio n, for a fixed sample volume, changes with λ

. For a concentration of 10 organisms m -3 and a sample volume of 1 m 3 , the probability distribution for the number of organisms in a ra ndom sample is very similar to a normal distribution (Figure 10A, right). However, there are some key differences between the Poisson

and normal distribution. For the Poisson distribu tion: 1) the model is bounded at 0, indicating there is zero probability of a negative count; 2) when the mean number of organisms in a sample is small (less than 10 or so organisms) due to low concentrations or relatively small sample volumes, the frequency distribution is skewed, with a tail to the right; and 3) the variance can not vary independently of the mean. As the con centration decreases, th e frequency distribution becomes increasingly skewed (Figure 10 from A to C) and the probability of obtaining a sample with zero organisms becomes very likely. For concentrations of 1 organism per m 3 , the probability of a 1 m 3 sample volume containing 0 organi sms is 36.8% (Figure 10B). For a concentration of 0.01 organisms per m 3 , the probability is about 99% (Figure 10C). One of the general challenges of sampling at low concentrations is the large number of samples that will have zero detects. In these cases, the estimated concentration is zero, and enormous volumes must be sampled to obtain a better estimate of the true concentration.

Some Sampling Scenarios: In this section, we translate the information fr om the theoretical probab ility distributions into specific sampling scenarios. We hope to illustrate some of the challenges inherent in sampling, as well as to aid in the development of sampli ng protocols that meet th e goals of regulatory agencies. For the following analyses, we use the conditions pres ented in Table 16.

Table 16: Conditions for ballast water samp ling scenarios (unless otherwise stated). 1) In these analyses, we assume disc harge standards directly regulate the concentration of organisms in ballast discharge. If so, the purpose of sampling is to estimate the true concentration of the ballast discharge, referred to as “average based sampling”. An

alternative is “maximum instantaneous” discharge standards which establish the maximum number of organisms that can occu r in a random sample. An instantaneous discharge standard of <10 organisms sample -1 (with a sample unit is 1 m 3 ) does not equal a concentration based standard of <10 organisms m -3 in terms of the allowable concentration of organisms in ballast discha rges. The two types of standards have the same outcome only when the discharge standard is 0 detectable organisms. The sampling protocols for instantaneous discharge standa rds must consider a dditional statistical factors because the results are very sensitiv e to the number and volume of the samples. 2) Organisms are randomly distributed in the ba llast discharges and can thus be modeled using the Poisson distribution. 3) We assume ALL organisms in a sample are co unted with no human or equipment error. Therefore any variation among the samples from a single population is due to the natural stochasticity of sampling. 4) For current ballast sampling techniques, th e “sample volume” can be obscured by the steps required to collect and c ount the organisms. The sample volume must be calculated

from the total volume of ballast that is f iltered (i.e., concentrated) and the volume of filtrate that is subsampled. The specific steps for sampling can vary, but one technique (Lemieux, 2008) involves filtering a known quan tity of ballast water through a net to capture > 50 μm organisms (Gollasch, 2006). The organisms are then rinsed from the net

78and resuspended in 1 L of water. From this diluted filtrate, several aliquots of 1 mL are

collected to enumerate the num ber of organisms. If 100 m 3 of ballast is filtered, then the filtrate is diluted with 1 L of water, and the organisms from 20 – 1 mL aliquots are counted, then the total sample volume is 2 m 3 (20 mL of aliquot /1000 mL filtrate × 100

m 3 filtered ballast = 2 m 3 ), not 100 m 3

. 5) Sometimes we report organism counts and samp le volumes rather than concentration. These can be converted to concentration by di viding the total number of organisms by the total volume of the sample.

One of the primary problems of sampling low density populations is that large volumes of ballast must be sampled to have a reasonable probability of detecting any organisms. From equation 24, the probability of getting 0 organisms in a sample is e -cv , and therefore, the probability of getting 1 or more organisms is 1- e -cv . We used this expression to calculate the probability of detecting

≥ 1 organism for a series of concentrations and sa mple volumes (Table 17). For a concentration of 0.01 organisms m -3 about 300 m 3 of ballast must be sampled to have a 95% probability of detecting at least one organism. For relati vely small sample volumes, the probability of detecting an organism is low even at relatively high concentrations. If a 1 L sample is taken from a population with a con centration of 100 organisms m -3 , organisms will be detected in fewer than 10% of the samples.

Table 17: Probability of detecting ≥ 1 organism for various samp le volumes (100 mL to 100 m3)

and ballast water concentra tions (0 to 100 organisms m -3 ). Gray boxes indi cate probabilities of

detection ≥ 0.95. True concentration (organisms per m 3 ) Sample volume, m 3 0 0.001 0.01 0.1 1 10 100

0.0001 (100 mL) 0 <0.001 <0.001 <0.001 <0.001 0.001 0.01 0.001 (1 L) 0 <0.001 <0.001 <0.001 0.001 0.01 0.095 0.01 (10 L) 0 <0.001 <0.001 0.001 0.01 0.095 0.632 0.1 (100 L) 0 <0.001 0.001 0.01 0.095 0.632 >0.99

1 0 0.001 0.01 0.095 0.632 >0.99 >0.99

5 0 0.005 0.049 0.393 >0.99 >0.99 >0.99

10 0 0.010 0.095 0.632 >0.99 >0.99 >0.99

25 0 0.025 0.221 0.918 >0.99 >0.99 >0.99

50 0 0.049 0.393 >0.99 >0.99 >0.99 >0.99

100 0 0.095 0.632 >0.99 >0.99 >0.99 >0.99

300 0 0.259 0.950 >0.99 >0.99 >0.99 >0.99

This analysis demonstrates that even when 0 organisms are detected in a sample, the true concentration may be large. A discharge standa rd of “zero detectable organisms” may appear very protective; however, the true degree of protection depends on the sample volume. From the Poisson distribution, the upper possible concentr ation (UPC, upper 95% c onfidence interval) of organisms can be estimated based on the numbe r of organisms in a sample volume. We

calculated the UPC when zero or ganisms were detected in sa mple volumes ranging from 100 mL

to 100 m 3 (Table 18). We primarily focus on conf idence intervals from 2-tailed sampling probabilities, but in the case of zero detects, th e lower estimate is always zero which is not very informative. For this reason, confidence interv als based on 1-tailed sampling probabilities may

79be preferred when there are zero detects. If zero organisms are detected in 1 m 3 of ballast, the

true concentration could be as high as 3.7 organisms m -3 . Given the inherent challenges of sampling ballast water, especially on board a ship, a more realistic sample volume may be around 1 L. For a 1 L sample, the upper c oncentration could be >3,500 organisms m -3 even with

zero detects.

Table 18: Upper possible concentration (UPC) of organisms based on one and two tailed 95% exact confidence intervals when zero organisms are detected in a range of sample volumes. Upper possible concentration, org m -3

Sample volume, m 3 one-tailed two-tailed

0.0001 m 3 (100 mL) 29,960 36,890

0.001 m 3 (1 L) 2,996 3,689

0.01 m 3 (10 L) 299.6 368.9

0.1 m 3 (100 L) 29.96 36.89

0.5 m 3 (500 L) 5.992 7.378

1 m 3 2.996 3.689

10 m 3 0.300 0.369

100 m 3 0.030 0.037

In an ideal world, we would alwa ys detect ballast water with c oncentrations of organisms that exceed the discharge standard. In reality, this is not possible with cu rrent methodologies. The probability of detecting an exceedance depends on: 1) the volume of ballast that is sampled; 2) the stringency of the discharge standard; and, 3) the magnitude of the exceedance. To demonstrate the relationship am ong these variables, we estimated the likelihood of detecting an exceedance for a discharge sta ndard of <0.01 organism m -3 when the sample volume ranged

from 1-50 m 3 and the true concentration ranged from 0.01 to 1 organism m -3 . For each combination, we simulated 10,000 random samples ( rpois function, R statistical program, R Development Core Team 2008) and calculated the percentage of samples that were correctly identified as exceeding the discharge standard (Figure 11). Ideally, none of the samples would pass inspection because in all cases the con centration of organisms exceeds the discharge standard. However, as the concentration approa ches the discharge standard, increasingly large

volumes of ballast must be tested to confid ently detect an exceedance. When the true concentration of organisms is 0.75 m -3 (75x the proposed U.S. Coast Guard Phase II standard) approximately 4 m 3 of ballast water must be sampled to detect this exceedance 95% of the time (Figure 11A). When the true con centration of organisms is 0.1 m -3 (10x the standard) approximately 30 m 3 of ballast water must be sampled (Figure 11B). For perspective on the magnitude of these sample volumes, a Volksw agen Transporter bus has a volume about 14 m 3

.

The examples thus far have been theoretical b ecause we begin with a kno wn concentration that exceeds the discharge standard and we calculate the probability of detecting the exceedance given a specific sample volume. These examples are useful because they demonstrate the power and limitations of specific sampling protocols. They are less useful from the perspective of actual sampling, because in reality the true c oncentration is unknown and must be estimated using sampling techniques. Ultimately, the goal of sampling is to determine whether a ballast discharge exceeds or meets the discharge standard with some pre-established degree of

80confidence. Two obvious results of sampling are: 1) Fail : The number of organisms in the sample is large enough that the true concentrat ion likely exceeds the di scharge standard; 2) Pass : The number of organisms in the sample is small enough that the true con centration likely meets the discharge standard. There is also a third, indeterminate category due to the inherent stochasticity of sampling. A ra ndom sample from a ballast disc harge may have a concentration

that

Figure 11: Probability of de tecting an exceedance for sample volumes between 1 and 50 m 3 and a discharge standard of 0.01 organisms m -3 . The true concentration ranges from (A) 0.01 to 1 organisms m -3 or, (B) 0.01 to 0.25 organisms m -3 , all of which fail to comply with the discharge standard. The legend describes the proportion of samples in which the exceedance is detected: white regions of plot indicate a >95% probability of detecting the exceedance; the darkest red regions indicate a <25% probability of detecting th e exceedance. See text for information about specific examples identified by the blue lines.

exceeds the discharge standard, but the true concen tration may actually be less than the discharge standard. For example, if the true concentration of the discharged ballas t water is 7 organisms

m -3 and a volume of 1 m 3 is sampled, about 17% of samples will have 10 or more organisms, and will appear to exceed the current IMO standard of ≤ 10 organisms m -3 . The possibility of getting an indeterminate result increases as the sample volume decreases and as the desired level of certainty increases.

We calculated the absolute number of organisms that must be observed in a range of sample volumes to determine – using two-tailed 95 % confidence intervals – whether the true concentration exceeds or meets a discharge st andard of either 0.01 or 10 organisms m -3 (Figure 12). For a very stringent discharge st andard, such as the <0.01 organisms m -3 proposed by the U.S. Coast Guard, only a few organisms must be obs erved before a discharge can be classified as exceeding the standard (i.e., concentr ation > discharge standard). If a single organism is detected in a sample volume of ≤ 2 m 3 then we can be confident that the standard has been exceeded

81(Figure 12A, red region) given that the lower value of the 95% confidence interval estimate (0.0125 to 2.786 organisms m -3 ) is greater than the standard. On the other hand, very large amounts of water must be sampled before a sample can be classified as meeting the standard with the same confidence (i.e. concentration < di scharge standard). For a discharge standard of 0.01 organisms m -3 , approximately 370 m 3 of ballast water must be sampled, with zero detects, before we can be confident that the tank meets the standard. In this case, the upper value of the 95% confidence interval estimate (0 to 0.00997 organisms m -3 ) is less than the discharge standard (Figure 12A, green region) . Another way to think about th is is that the probability of detecting 0 organisms in a 370 m 3 sample must be <2.5% (based on two-tailed test) to meet the discharge standard. Of course, the di scovery of a single organism in a 370 m 3 sample does not suggest the true concentration exceeds the standard , in fact the ballast discharge in question is still more likely to meet the standard than not. Ra ther, based on this result we can not distinguish

within our desired confidence whether the discha rge standard is met given the 95% confidence interval of 0.000068 to 0.015 organisms m -3 (Figure 12A, white region). For the IMO discharge standard of <10 organisms m -3 (Figure 12B) nearly 0.4 m 3 of ballast discharge must be sampled before the ballast discharge can be cl assified as passing the standard.

Figure 12: Determining whether ballast water discha rge exceeds or meets a discharge standard of <0.01 (A) and <10 (B) organisms m -3 (note: axes have different sc ales). Red regions indicate total organism counts that exceed the standard. Gr een regions indicate total organism counts that meet the standard. White regions indicate indeterminat e results; counts in th is region do not pass or fail inspection based on two-ta iled 95% confidence intervals.

Aggregated Populations: Sampling aggregated populations, also known as clumped or contagious populations, is more complicated than sampling randomly distributed popul ations. One of the defining characteristics of aggregated populations is that the variance is greater than the mean ( σ 2 > μ, recall that for

Poisson distributions σ 2 = μ). As variance increases, the tr ue concentration becomes increasingly difficult to accurately estimate because the num ber of organisms in a random sample becomes

82increasingly unpredictable. Consequently, ag gregated populations must be sampled more intensively to estimate concentration confidently.

Aggregation results from many di fferent ecological and physical pr ocesses, making it difficult to apply a single probability distribut ion to the diverse array of possi ble patterns of aggregation. Although many distributions have been used to model aggregated populations, the negative binomial is probably the most useful of th ese models (Elliott, 1971). Like the Poisson distribution, the negative binomial can be used to predict the probability of observing a specific number of organisms in a sample. Unlike the Poisson distribution, the negative binomial is defined by two parameters, the mean (μ) and the dispersion ( θ , also called the size parameter). The dispersion parameter is related to the spatial distribution of organisms in their environment. More aggregated populations have smaller disper sion parameters. As the dispersion factor approaches positive infinity, the negative binomial approximates the Poisson distribution. The dispersion parameter of the negative binomial is related to both the mean and the variance (Bolker, 2008). An approximate es timate of this relationship is: θ = μ 2 /( σ 2 - μ).

As mentioned, one of the general challenges of sampling at low concentrations is the fact that a large number of samples will contain zero organisms. This problem can be compounded when organisms are aggregated. For example, if a 1 m 3 sample of ballast is taken from a randomly distributed population with a true concentration of 1 organism m -3 , about 37% of the samples will contain 0 organisms. In contrast, for an aggregated population with a dispersion parameter of 0.1, about 79% of samples will contain 0 or ganisms (Figure 13). Conversely, the probability of obtaining samples with large numbers of organism s, relative to the true concentration, also increases. For the randomly distributed population, the probability of a sample unit containing > 3 organisms is 1.9%, whereas, for the aggregated population, the probabili ty is 8.3%. If large sample volumes are not taken from aggregated populations, then estimates of concentration are likely to be much lower or higher than the true concentration.

Figure 13: Comparison of sample probabilities from a randomly distributed (Poisson) population vs. an aggregated population with a dispersion para meter of 0.1 (negative binomial) for a sample

volume of 1 m 3 and concentration of 1 organism m -3 . The probability that a sample will contain 0 organisms is greater for the aggregated population than for the Poisson distributed population.

83There are several ways to determine whether a population is aggregated , all of which require multiple sample units from a popula tion to estimate both paramete rs of the negative binomial distribution (see Elliott, 1971). Th is is complicated by the fact that estimates of aggregation depend upon the scale of the aggreg ation pattern relative to the size of the sampling unit (Figure 14). One pattern of aggrega tion occurs when organisms form clumps that are randomly distributed throughout the environmen t. In this case, the population can be highly aggregated but if the sample volume is relatively small, such that most sample units contain 0 or 1 organisms, then the population will appear randomly distribute d or only slightly aggregated. As the volume of the sample unit increases, the va riation in the number of organism s will increase relative to the mean, peaking at the point when the sample vol ume is about equal to the volume of a single cluster. As sample volume increases beyond this point, the variance will decline relative to the mean because a sample unit will include severa l clusters. Given these and other issues, the Taylor power law (Taylor, 1961) is an alternative to the negative binomial for modeling aggregated populations that may be applicable for a wider range of distributions than the negative binomial (Elliott, 1971; Downing et al., 1987).

Figure 14: Theoretical example of how the appa rent aggregation in th e population will differ based on the scale of aggregation relativ e to the size of the sample unit.

Recommendations/Conclusions: Instituting standardized sampling protocols is a critical component of implementing ballast discharge standards.

The degree of statistical certainty desired for ball ast testing may differ accor ding to the situation. For stringent discharge standards, such as the <0.01 standard pr oposed by the U.S. Coast Guard, a large quantity of water must be sampled to know with 95% confidence that the true concentration of organisms is less than the di scharge standard (Figure 12A, counts in green region are acceptable, counts in red regions are unacceptable, and because counts in white regions are ambiguous they may be classified as unacceptable if a high degree of confidence is desired). This level of certaint y is important when testing the performance of ballast treatment systems; however, it may be less critical during co mpliance monitoring, especially if the primary goals are to detect gross failures or to generate compliance records for individual ships so as to flag those that appear to have poor compliance. For compliance monitoring, one approach may

84be to employ a three class sampling criteria, such that ships with organi sm counts in the green region of Figure 12 are acceptable, counts in the red region are unacceptable, and counts in the white region are marginally acceptable.

In situations where indeterminate results are classi fied as acceptable or marginally acceptable it

is critical to ensure that th e upper possible concentr ation of organisms is reasonable from a regulatory perspective. For samples with counts that fall in the intermediate category, the possible concentration of organisms could be qui te high depending on the sample volume. This can be true even if zero organisms are detect ed during sampling (Table s 17 and 18). If zero organisms are detected in a 1 L sample, the upper possible concentration could be nearly as high as 3,000 organisms m -3 based on the upper 1-tailed 95% conf idence interval. Given this, for ballast discharge standards of “zero detectable organisms” the sample volume must be large enough to ensure that the detection limit is ecologically protective.

Given the current challenges of ship-board tes ting and the stringency of the current and proposed discharge standards, it will be difficult to sample large enough volumes of ballast water to detect ballast discharges that do not m eet the standard, even if the co ncentration of organisms is 1-3 orders of magnitude greater than the discharge standard. Conse quently, the quality control to

assure that ballast treatment systems are designed to adequate ly control the introduction of nonindigenous species may be best achieved primarily through ri gorous type-approval of ballast water treatment systems in controll ed testing facilities, rather th an from after-the-fact compliance ship-board sampling.

Despite the limitations of compliance monitoring in the field this technique may still play an important role in the regulation of ballast discharge standards. Th is type of testing can detect gross exceedences of the discharge standard, whic h can be used to identify treatment system failures and problematic ships. Until new samp ling technologies are developed, however, ship board testing using existing sampling methods ar e likely to be inad equate for accurately distinguishing among concentrations in the range of 0.001 to 10 organisms m -3 . However, it

would be useful to institute a global repository of compliance te st results for individual ships through the IMO in order to increase the probability of de tecting troublesome patterns. For a discharge standard of <10 organisms m -3 , a single 1 m 3 sample containing 15 organisms does not necessarily indicate a ship’s treatme nt system is failing to meet the discharge standard (95% CI: 8.4 to 24.8 organisms m -3 ). If, however, the same pattern is observed at subs equent discharge events, there is mounting evidence that the treatm ent system may not be adequately reducing the concentration of organisms in the ballast discharge.

Aggregation may be a significant source of error in many sampling protocols, and estimating the extent of aggregation could be an important aspe ct of accurately estimating the concentration of organisms in ballast discharge. The extent that a population is aggregated must be determined empirically by taking many replicate samples. The problem is mitigated in the land-based testing facility in Key West, Florida (Lemieux et al., 2008) by continuously sampling throughout the ballast tank. This is achieved by removing all the water from the ballast tank while continuously diverting a relatively small portion of it for testing. This appr oach is currently not practical for ship-board testing.

85In these analyses, we assume the goal of disc harge standards is to directly regulate the concentration of organisms in ba llast discharges using “average based sampling”. However, if “maximum instantaneous” discharg e standards are used instead then additional statistical factors must be considered because the results will be very sensitive to the sample number and volume. For example, if an IMO discharg e standard of <10 organisms m -3 is enforced using instantaneous sampling, then a ship discharging ballast wi th a true concentration of 5 organisms m -3 will fail about 3.2% of the time based on a single sample of 1 m 3 . However, as the number of samples increases the probability of a false failure increas es, assuming failure is defined as one or more

of the 1 m 3 samples having a concentration of ≥ 10 organisms m -3 . If five 1 m 3 samples are taken from a ballast discharge event, about 15% of th e ships will have at least one sample with ≥ 10 organisms m -3

.

86

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104

### Appendix C: R Statistical Tools to De velop/Evaluate Ballast Water Sampling

Protocols

Melanie Frazier

Statistical Tools Using R:

Here we describe some tools that can be used to develop and evaluate sampling protocols. For these examples we use the statistical program R (R Development Core Team 2008) because it is widely available, free, and the preferred program for analysis of many researchers. We also suggest working with a data editor such as Tinn- R (Faria 2009). We do not attempt to provide an overview of R; however, some excellent introductory materials can be found at http://cran.rproject.org by following the “Contributed” link. Input and output from R are represented with

courier font, and input is preceded by “>”. The symbol “ <-“ indicates the assignment of a

variable name to a variable. R will not r ead input preceded by “# ”, which is for user

documentation.

Random distributions

To calculate the confidence interval around a con centration based on the results from sampling,

the ci.poisson function from the epicalc package (Chonsuvivatwong 2008) can be used. This function calculates the possible range of concentr ations based on the desired confidence interval (alpha = significance level = 1 – CI/100), the total number of observed organisms (events), and the total sample volume (person.time). For ex ample, if 0 organisms are observed in a 0.1 m 3
sample volume, the true concentration may be as high as 36.89 organisms m -3 based on the twotailed 95% confidence interval:

> ci.poisson(0, 0.1, alpha=0.05) events person.time incidence se exact.lower95ci exact.upper95ci

0 0.1 0 0 0 36.89

If 10 organisms are observed in a 1m 3 sample volume, the concentration is estimated to be between 4.8 and 18.4 organisms based on the 95% confidence interval:

> ci.poisson(10, 1, alpha=0.05) events person.time incidence se exact.lower95ci exact.upper95ci

10 1 10 3.162 4.79 18.4

The Poisson distribution can be further explored usi ng dpois, ppois, qpois, a nd rpois functions. These functions allow lots of flexibility for evaluating and developing sampling protocols.

Poisson distributions are desc ribed by a single parameter, λ , which equals both the mean and the variance ( λ = μ = σ 2 ). As the mean of a Poisson dist ribution increases, the variance also increases. For ballast water analyses, λ represents the concentration of organisms in the ballast

water. For more information about these func tions, type the function name preceded by a “?” (i.e., ?dpois) into the R console. For the follo wing examples, we assume a sample volume of 1

m 3

.

112The dpois function is the probability density function for a Poisson distribution. It calculates the probability of obtaining a specific number of organisms in a sample unit based on λ . For example, if a ballast tank contains 1 organism m -3 the probability of a 1 m 3 sample volume containing zero organisms is 36.8%:

> dpois(0, lambda=1)

0.3678794

A plot of the probability distribut ion can be created (see Fig. 9): > counts <- c(0,1,2,3,4,5,6) > poissonDist <- dpois(counts, lambda=1) > barplot(poissonDist, ylab="Probability", xlab="Count of organisms in sample", names.arg=counts)

The ppois function is the cumulative distribution function. This is used to calculate the probability of a sample containing ≤ a specified number of organism s. The probability of a 1 m 3
sample volume containing ≤ 3 organisms when the con centration is 1 organism m -3 is 98.1%:

> ppois(3, lambda=1)

0.9810118

The probability that a sample will contain >3 organisms is 1.9%: > 1 - ppois(3, lambda=1)

0.01898816

or, alternatively: > ppois(3, lambda=1, lower.tail=FALSE)

0.01898816

The qpois function is the quantile function and returns the number of organisms predicted to be in a sample for a given quantile of data (the i nverse of ppois). For example, if the concentration of organisms in ballast is 10 m -3 , about 95% of 1 m 3 samples will contain 15 or fewer organisms.

> qpois(0.950, lambda=10)

15

The rpois function generates random values from a Poisson distribution wi th a specified lambda.

To obtain ten 1 m 3 random samples from a population with concentration 1 organism m 3 :

> rpois(10, lambda=1)

1 0 3 1 2 1 0 0 3 2

Aggregated distributions
One way to determine whether a pop ulation is aggregated is to compare the observed distribution of sample data with an expect ed distribution derived from the mean and variance of the sample data. A chi-square test can then be used to compare the observed and expected values (this and other methods are described in Jarvis, 2000). Ne gative binomial distributions are described by two parameters, μ (mean) and θ (dispersion factor, referred to as “size” in R). These parameters

can be estimated with maximum likelihood techni ques using the fitdistr function from the MASS

113package from replicate samples taken from a population. Once the parameters have been estimated, the probability distribut ion for the negative binomial can be used to develop sampling protocols for aggregated populations. The nega tive binomial functions in R are: dnbinom, pnbinom, qnbinom, and rnbinom.

114