### EPA/600/R-08/066F November 2008



# Predicting Future Introductions of Nonindigenous Species to the Great Lakes

National Center for Environmental Assessment Office of Research and Development U.S. Environmental Protection Agency Washington, DC 20460

### ii DISCLAIMER

This document has been reviewed in accordance with U.S. Environmental Protection Agency policy and approved for publication. Mention of trade names or commercial products

does not constitute endorsement or recommendation for use.

ABSTRACT

The Great Lakes of the United States have been subjected to adverse ecological and economic impacts from nonindigenous species (NIS). Ballast water from commercial shipping is the major means by which NIS have entered the Great Lakes. To help resource managers assess the future arrival and spread of invasive species, 58 species were initially identified as having a moderate or high potential to spread and cause ecological impacts to the Great Lakes. Using a species distribution model (the Genetic Algorithm for Rule-Set Production or GARP), areas within the Great Lakes where 14 of these 58 potential invasive species could find suitable habitat, were identified. Based on the model and species depth tolerances, all of Lake Erie and the shallow water areas of the other four Great Lakes are most vulnerable to invasion by the 14 modeled species. Analysis of ballast water discharge data of vessels entering the Great Lakes via the St. Lawrence Seaway revealed that the original source of most ballast water discharges came from Canada and Western Europe. The Great Lakes ports at greatest risk for invasion by the 14 modeled species from ballast water discharges are Toledo, Ashtabula and Sandusky, OH; Gary, IN; Duluth, MN; Milwaukee and Superior, WI; and Chicago, IL. Since early detection is critical in managing for NIS, these results should help focus monitoring activities on particular species at the most vulnerable Great Lakes ports. This assessment demonstrates that successful invasions are best predicted by knowing the propagule pressure (i.e., the number of larvae/individuals entering a new area) and habitat matching (i.e., how similar is the invaded area to the native range of the species).

Preferred citation: U.S. EPA (Environmental Protection Agency). (2008) Predicting future introductions of nonindigenous species to the Great Lakes. National Center for Environmental Assessment, Washington, DC; EPA/600/R-08/066F. Available from the National Technical Information Service, Springfield, VA, and http://www.epa.gov/ncea

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### ii i CONTENTS

DISCLAIMER ................................................................................................................................ ii ABSTRACT .................................................................................................................................... ii LIST OF TABLES .......................................................................................................................... v LIST OF FIGURES ....................................................................................................................... vi LIST OF ABBREVIATIONS AND ACRONYMS .................................................................... viii AUTHORS, CONTRIBUTORS, AND REVIEWERS ................................................................. ix

1. EXECUTIVE SUMMARY .................................................................................................... 1

2. INTRODUCTION

— NONINDIGENOUS SPECIES POSE A THREAT TO LAKE ECOSYSTEMS ...................................................................................................................... 4 2.1. NONINDIGENOUS SPECIES AND THE GREAT LAKES .................................... 5 2.1.1. Origin and Patterns of Species Invasions ........................................................ 6 2.1.2. Ballast Water and NIS ..................................................................................... 7 2.1.3. Measures to Control the Release of Ballast Water Containing NIS ............... 7 2.1.4. NOBOB Vessels and Species Invasions ......................................................... 9 2.1.5. Other Options for Controlling Species Invasions From Ballast Water ......... 10 2.2. CONCEPTUAL FRAMEWORK.............................................................................. 11 2.2.1. Propagule Pressure ........................................................................................ 11 2.2.2. Habitat Suitability ......................................................................................... 12 2.2.2.1. Species Distribution Modeling ....................................................... 12 2.2.2.2. Genetic Algorithms for Rule-Set Production (GARP) ................... 13 2.2.2.3. Modification of GARP for Aquatic Systems ................................. 14

3. METHODS ........................................................................................................................... 15 3.1. HABITAT SUITABILITY USING THE GARP MODEL ....................................... 15 3.1.1. Selection of Modeled Species ....................................................................... 15 3.1.2. Model Inputs and Environmental Data Layers ............................................. 16 3.1.2.1. Environmental Data Layers ............................................................ 16 3.1.2.2. Environmental Data Sources .......................................................... 18 3.1.3. Use of GARP model ...................................................................................... 19 3.1.4. Assumptions and Limitations ........................................................................ 20 3.1.4.1. Data Errors ..................................................................................... 20 3.1.4.2. Biological Errors ............................................................................ 22 3.1.5. Testing the GARP Model Performance ........................................................ 22 3.1.6. Determining GARP‘s Power to Predict ........................................................ 23 3.2. DETERMINING PROPAGULE PRESSURE USING BALLAST WATER DISCHARGE DATA AND VESSEL TRAFFIC PATTERNS ................................ 24 3.2.1. Analysis of Ballast Water Discharge Data .................................................... 24 3.2.2. Assumptions and Uncertainty ....................................................................... 26

### iv CONTENTS (continued)

4. RESULTS ............................................................................................................................. 27 4.1. COMPARISON OF THE GREAT LAKES TO THE PONTO-CASPIAN SEA ..... 27 4.1.1. Temperature .................................................................................................. 27 4.1.2. Chlorophyll a Concentrations ....................................................................... 27 4.1.3. Diffuse Attenuation Coefficient (K490)........................................................ 30 4.1.4. Normalized Water-Leaving Radiance ........................................................... 30 4.2. HABITAT SUITABILITY FOR MODELED SPECIES .......................................... 30 4.2.1. Blueback Herring .......................................................................................... 33 4.2.2. Quagga Mussel .............................................................................................. 35 4.2.3. Round Goby .................................................................................................. 35 4.2.4. Fishhook Waterflea ....................................................................................... 37 4.2.5. Zebra Mussel ................................................................................................. 37 4.2.6. Ruffe .............................................................................................................. 39 4.2.7. Monkey Goby ................................................................................................ 40 4.2.8. New Zealand Mud Snail ................................................................................ 40 4.2.9. Tubenose Goby ............................................................................................. 42 4.2.10. Rudd .............................................................................................................. 42 4.2.11. Corophium Curvispinum (an Amphipod) ..................................................... 44 4.2.12. Sand Goby ..................................................................................................... 45 4.2.13. Roach ............................................................................................................. 46 4.2.14. Tench ............................................................................................................. 46 4.3. VESSEL TRAFFIC AND GREAT LAKES PORTS ................................................ 47 4.3.1. Analysis of Vessels With Ballast on Board (BOB) ....................................... 47 4.3.2. Analysis of Vessels with No Ballast on Board (NOBOB) ............................ 51

5. DISCUSSION ....................................................................................................................... 54 5.1. PREDICTING THE SPREAD OF SPECIES ........................................................... 54 5.2. POTENTIAL MONITORING SITES BASED ON VESSEL TRAFFIC ................. 55

6. REFERENCES ..................................................................................................................... 59

APPENDIX A. LIST OF NONINDIGENOUS SPECIES THAT HAVE BEEN

REPORTED AS OCCURRING IN THE GREAT LAKES ............................ A-1 APPENDIX B. NONINDIGENOUS SPECIES THAT MAY SPREAD TO THE

GREAT LAKES ...............................................................................................B-1 APPENDIX C. GARP MODEL VALIDATION .....................................................................C-1 APPENDIX D. GARP POWER OF PREDICTION ANALYSIS ........................................... D-1 APPENDIX E. TABLES DEPICTING SOURCES AND DESTINATION OF

BALLAST WATER DISCHARGES IN U.S. GREAT LAKES ..................... E-1

### v LIST OF TABLES

1. Fourteen species modeled using GARP and the source of occurrence data ..................... 17

2. Environmental variables used to predict locations that would provide suitable habitat for the 14 modeled species in the Great Lakes ..................................................... 18

3. Composite results for 14 species modeled using GARP .................................................. 56

### vi LIST OF FIGURES

1. The five Great Lakes, some of the Great Lakes ports, and surrounding region ................. 8

2. Sources of Great Lakes species invasions from 1960

– 2006 (based on data provided in Appendix A, which are derived from NOAA, 2007a) .................................................... 8

3. Conceptual framework for predicting future introductions of nonindigenous species into the Great Lakes of the United States ......................................................................... 12

4. Maximum monthly mean temperature (MMT) (°C) as determined by AVHRR sensor (1985

– 2001) ...................................................................................................................... 28

5. Average chlorophyll a concentration (mg/m 3 

### ) as determined by MODIS (2001

– 2005) ...................................................................................................................... 29

6. Average diffuse attenuation coefficient (m - 1 

### ) at 490 nm as determined by MODIS (2001

– 2005) ...................................................................................................................... 31

7. Average normalized water leaving radian ce mW /( cm 2 

### μm sr) as determined by MODIS (2001

– 2005) ...................................................................................................................... 32

8. GARP-predicted habitat suitability of blueback herring ( Alosa aestivalis ) in the Great Lakes ....................................................................................................................... 34

9. GARP-predicted habitat suitability of quagga mussel ( Dreissena bugensis ) in the Great Lakes ....................................................................................................................... 36

10. GARP-predicted habitat suitability of round goby ( Neogobius melanstromus ) in the Great Lakes ....................................................................................................................... 36

11. GAR P-predicted habitat suitability of fishhook waterflea ( Cercopagis pengoi ) in the Great Lakes ....................................................................................................................... 38

12. GARP-predicted habitat suitability of zebra mussel ( Dreissena polymorpha ) in the Great Lakes ....................................................................................................................... 38

13. GARP-predicted habitat suitability of ruffe ( Gymnocephalus cernuus ) in the Great Lakes ................................................................................................................................. 40

14. GARP-predicted habitat suitability of monkey goby ( Neogobius fluviatilis ) in the Great Lakes ....................................................................................................................... 41

15. GARP predicted habitat suitability of New Zealand mud snail ( Potamopyrgus antipodarum ) in the Great Lakes ...................................................................................... 41

16. GARP-predicted habitat suitability of tubenose goby ( Proterorhinus marmoratus ) in the Great Lakes ............................................................................................................. 43

### vii LIST OF FIGURES (continued)

17. GARP-predicted habitat suitability of rudd ( Scardinius erythrophthalmus ) in the Great Lakes ....................................................................................................................... 43

18. GARP-predicted habitat suitability of Corophium curvispinum (no common name reported) in the Great Lakes ............................................................................................. 44

19. GARP-predicted habitat suitability of sand goby ( Potamoschistus minutus ) in the Great Lakes ....................................................................................................................... 45

20. GAR P-predicted habitat suitability of roach ( Rutilus rutilus ) in the Great Lakes ............ 46

21. GARP-predicted habitat suitability of tench ( Tinca tinca ) in the Great Lakes ................. 47

22. Location of the original source of ballast water taken-on prior to ballast water exchange and discharge in the U.S. Great Lakes .............................................................. 49

23. Location of the source of ballast water taken-on from Canadian ports in or near the Gulf of St. Lawrence prior to ballast water exchange and discharges in the U.S. Great Lakes ............................................................................................................... 49

24. Location of the source of ballast water taken-on from European ports prior to ballast water exchange and discharges in the U.S. Great Lakes ....................................... 50

25. Frequency, volume, and original source of ballast water (prior to ballast water exchange) discharged into U.S. Great Lakes ports, from sources outside the Great Lakes ....................................................................................................................... 50

26. Frequency and volume of ballast water discharges (after ballast water exchange

at sea) from ballast on board vessels, when the original source of ballast water came from outside the Great Lakes .................................................................................. 51

27. Possible source locations of residual materials discharged from vessels that entered the St. Lawrence Seaway with no ballast on board, based on last five ports of call in 2006 .......................................................................................................... 52

28. Frequency and volume of discharges in 2006 from NOBOB-RM vessels ....................... 53

### viii LIST OF ABBREVIATIONS AND ACRONYMS

AVHRR Advanced Very High Resolution Radiometer

BOB ballast on board BWD ballast water discharge(s) BWE ballast water exchange CFR Code of Federal Regulations EPA Environmental Protection Agen cy GARP Genetic Algorithm for Rule-Set Production GBIF Global Biodiversity Information Facility GIS geographic information systems

K490 diffuse attenuation coefficient at 490 nm

MMT mean monthly water surface temperature

MODIS Moderate Resolution Ima ging Spectroradiometer

NBIC National Ballast Information Clearinghouse

NIS n on i ndigenous species

NOAA National Oceanic and Atmospheric Administration

NOBOB n o b allast on b oard

NOBOB

- RM no ballast on board but the vessel contains residual material

NVMC National Vessel Movement Center

nLW n ormalized w ater

- l eaving r adiance

ppt parts per thousand

sr steradian (units of a solid angle and can also be called a squared radian)

US CG United States Coast Guard

USGLP U.S. Great Lakes ports (or ports of call)

### ix AUTHORS, CONTRIBUTORS, AND REVIEWERS

The National Center for Environmental Assessment within the Office of Research and
Development prepared this document. TN & Associates, Oak Ridge, TN, under contract
numbers 68-C-04-004 and EP-D-06-072, performed the modeling and analysis, most of the
literature review, and helped prepare this report.

Contract Officer Representative: Vic Serveiss, U.S. EPA, National Center for Environmental Assessment, Washington, DC

Authors: Vic Serveiss, U.S. EPA, National Center for Environmental Assessment, Washington, DC Don Catanzaro, TN & Associates, Inc., Oak Ridge, TN Matt Fitzpatrick, TN & Associates, Inc., Oak Ridge, TN William Hargrove, U.S. Forest Service, Asheville, NC Art Stewart, TN & Associates, Inc., Oak Ridge, TN David Eskew, TN & Associates, Inc., Oak Ridge, TN

Contributors of Data and Technical Input: Jean Mayo, Jennifer Hendricks, Robert V. O‘Neill, TN & Associates, Inc., Oak Ridge, TN David Reid, National Oceanic and Atmospheric Administration, Ann Arbor, MI Paul Bunje, American Association for the Advancement of Science, Washington, DC Betsy vonHolle, University of Central Florida, Orlando, FL Sarah Bailey, Fisheries and Oceans Canada, Burlington, Ontario, Canada Barry Burns, Michigan Department of Environmental Quality, Lansing MI,

Formatting and Editing Contributors: Danelle Haake, TN & Associates, Inc., Oak Ridge, TN Cris Broyles, Intellitech Systems, Inc., Cincinnati, OH

U.S. EPA Reviewers: Henry Lee, National Health and Environmental Effects Research Laboratory, Newport, OR Britta Bierwagen, National Center for Environmental Assessment, Washington, DC Marilyn Katz, Office of Water, Washington, DC Jeffrey B. Frithsen, National Center for Environmental Assessment, Washington, DC Michael Slimak, National Center for Environmental Assessment, Washington, DC Elizabeth Murphy, Great Lakes National Program Office, Chicago, IL Marc Tuchmann, Great Lakes National Program Office, Chicago, IL

External Reviewers: Becky Cudmore, Fisheries and Oceans Canada, Burlington, Ontario, Canada John M. Drake, Odum School of Ecology, Athens, GA Edward L. Mills, Cornell University Biological Field Station, Bridgeport, NY

### x

Acknowledgements:
The authors thank James Andreasen and Dan Kluza for the idea of applying the Genetic
Algorithm for Rule-Set Production (GARP) model, originally developed for predicting
movements of endangered species in terrestrial environments, to aquatic systems; Michael
Slimak for the suggestion to apply GARP to the Great Lakes; and Anne Sergeant who served as
Project Officer from 2005

– 2006 and provided technical direction for the literature review and
initial species distribution models. The authors also thank Richard Everett, Stuart White, Bivan
Patnaik, and Keith Donohue of the U. S. Coast Guard (USCG) for explaining USCG regulations,
helping us interpret the National Ballast Information Clearinghouse database, and enabling their

contractor, Whitman Miller of Smithsonian Environmental Research Center, to download and
further explain the database.

### 1 1. EX ECUTIVE SUMMARY

Nonindigenous species (NIS) are organisms that enter an ecosystem beyond their native spatial range. The Sea Lamprey ( Petromyzon marinus ) was the first to enter the Great Lakes during the 1830s facilitated by the Erie and Welland canals. Since then, at least 185 other species have invaded the Great Lakes. Thirteen of these species have been labeled as invasive by causing ecological or economic harm. The zebra mussel ( Dreissena polymorpha ), for example, has impacted many Great Lakes native species and has imposed large expenses on the utility industry by clogging water intake pipes. The objective of this report is to develop data and tools that U.S. Great Lakes resource managers can use to more effectively prevent the establishment of aquatic NIS. This study maps the habitats of the Great Lakes most vulnerable to the entry of aquatic NIS and identifies particular NIS with the potential to enter U.S. Great Lakes ports (USGLP). Since the St. Lawrence Seaway opened in 1959, ballast water released from transoceanic vessels during commercial shipping operations has been identified as the predominant pathway for NIS to enter the Great Lakes. Transport of NIS occurs when a vessel takes-on ballast water containing NIS from outside the Seaway, the species survives in a ballast tank during transit, and is released when the ballast water is discharged into the Great Lakes. To become established in the new environment, the organisms must be able to survive, reproduce, and spread. To predict future invasions of NIS in the Great Lakes, the two most important determinants of successful

invasions were evaluated: whether there is suitable habitat in the Great Lakes for nonnative species and whether there are a sufficient number of these organisms and their larvae arriving in the Great Lakes. First, a species distribution model was used to identify the areas of the Great Lakes which could provide suitable habitat for NIS of concern. Second, commercial shipping and ballast water discharge data were used to evaluate if there are a sufficient number of these organisms entering the Great Lakes to become established. Based on a literature review of NIS life-history characteristics and invasion histories, 58 species that pose high or medium risk for becoming established in the Great Lakes and for causing ecological harm were identified. To predict the possible distributions of each of these species within the Great Lakes, spatial data sets that characterize aquatic conditions on a global scale were analyzed. These data sets were derived from remotely sensed space-based platforms, operated and made available by the National Aeronautics and Space Administration and the National Oceanic and Atmospheric Administration. Six of these data sets, each at a 4.6 k m (21 km 2 

### ) spatial resolution, were found to be useful for NIS modeling. Three of the environmental variables are direct measures of water temperature and the other three indirectly relate to primary productivity and water clarity, indicators of habitat suitability.

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The Genetic Algorithm for Rule-Set Production (GARP) model was used to determine habitat suitability. GARP predicts the potential distribution of species by comparing the environmental conditions of locations currently inhabited by the species (the reference area) with the environmental conditions in the region of concern. Adequate spatial distribution data were available to model only 9 of the 58 potential invasive species be cause the GARP model requires at least 30 spatially unique latitude and longitude points that describe the distribution of a particular species. In addition to these nine species, GARP was also used to predict regions within the Great Lakes that would provide suitable habitat for five species of concern that were selected by the U.S. Environmental Protection Agency ‘s Great Lakes National Program Office

. Since the existing location of these five species was already known, the model was validated by comparing the reported locations of three of these species with the predicted locations. Results from GARP modeling were used to produce 14 range maps, one for each of the modeled species, predicting their locations of suitable habitat within the Great Lakes. The overall results varied with each modeled species, but generally showed that all of Lake Erie and the shallower portions of the other Great Lakes appear to be most vulnerable for invasion by the 14 modeled species. Water depth appears to be the predominant factor limiting the potential spread of many of the modeled species. Yet, at least one species, the quagga mussel ( Dreissena bugensis ), is surviving at greater depths in the Great Lakes than in its native habitat. Releases of ballast water into USGLP were analyzed using 2006

– 2007 data obtained from the National Ballast Information Clearinghouse. The ports that received the most ballast water discharges from vessels entering the Seaway with ballast on board (BOB), after ballast water exchange outside the Seaway, are Duluth, MN; Toledo, OH ; Superior, Green Bay, and
Milwaukee, WI ; and Gary, IN. The most frequent original sources of ballast water came from Antwerpen, Belgium; Puerto Cabello, Venezuela; Haraholmen, Sweden; and Bremen, Germany. It is important to note that there were no clear relationships between foreign and USGLP relative to ballast water uptake and releases. For instance, 13 vessels that discharged ballast water in Toledo obtained ballast water from 12 different foreign ports. Some vessels enter the St. Lawrence Seaway without ballast water, but may still contain residual water or sediment containing NIS in their ballast tanks, and are referred to as no ballast on board vessels containing residual material (NOBOB- RM ). After entering the Seaway, these vessels can off-load cargo and pick up ballast water which would mix with the residual material and be subsequently released into Great Lakes ports. There were considerably more discharges

into USGLP from NOBOB-RM vessels than from those vessels with ballast on-board. Those ports receiving the most ballast water from NOBOB- RM vessels are Toledo, Ashtabula, and Sandusky, OH; Superior, WI; and Duluth, MN. Assuming the observed results for 2006 and 2007 are representative of discharge and shipping patterns over the past several years, the port of

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greatest concern for receiving sufficient propagules and providing the most suitable habitat is Toledo, OH. Toledo is located on Lake Erie, a region that the GARP model predicted would have a high chance of providing suitable habitat for the modeled species. Other ports of concern for receiving sufficient propagules and offering suitable habitat are Gary, IN; Ashtabula and Sandusky, OH; Milwaukee, WI; and Chicago, IL. Duluth, MN and Superior, WI, with high transport potential but low habitat suitability, could be a source of interlake transport of NIS. This study involved numerous assumptions resulting in uncertain findings. A major source of uncertainty for the GARP modeling is the lack of complete occurrence data for many of the modeled species, for many parts of the globe. Another source of uncertainty is due to the lack of an ideal suite of data for characterizing aquatic environments. Data on abiotic factors such as salinity, bathymetry, substrate, pH, and nutrient levels were not available globally at the

21 km 2 

### scale. The lack of species-specific data on significant biotic factors, such as competition and predation, also lead to uncertainty in the modeling results. Despite these limitations, a model validation exercise confirmed that GARP and the environmental variables used could produce useful predictions of potential NIS distributions. These predictions were validated using occurrence data from other regions to develop models that predicted known occurrences of three NIS already widespread in the Great Lakes. There were also limitations with the vessel traffic and ballast water discharge analys es , used as a surrogate for measuring propagule pressure. First, the analysis was only based on 2 years of data, 2006

– 2007. A second source of uncertainty is due to the self-reporting nature of data entered into the National Ballast Information Clearinghouse. Self-reporting by vessels is not guaranteed to be accurate or complete records of actual vessel practices and should be used with caution. The analysis of discharges from NOBOB-RM vessels is also uncertain because the source of the residual material cannot be known for certain and could even be from ports within the Great Lakes. Further, this data set only includes information on the last five ports of call and species could remain in ballast tanks from visits to previous ports. Both Canada and the United States implemented ballast water exchange procedures in 1989 and 1993, respectively. Although new NIS continue to be detected in the Great Lakes, it is possible the NIS were transported prior to 1993 and took several years to detect. Despite these procedures and subsequent regulations, it is likely that nonindigenous species will continue to

arrive in the Great Lakes. These findings support the need for detection and monitoring efforts at those ports believed to be at greatest risk. This study also demonstrates the importance of understanding invasion biology by evaluating the two most important predictors of invasion: propagule pressure and suitable habitat. Further, this may be the first time that remote sensing data were used in conjunction with GARP to predict the spread of aquatic invasive species.

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2. INTRODUCTION

— NONINDIGENOUS SPECIES POSE A THREAT TO LAKE ECOSYSTEMS

The U.S. Great Lakes have suffered ecological damage and economic costs from a number of aquatic nonindigenous species (NIS) that have successfully invaded this region (Mills et al., 1994; NOAA, 2007a). NIS that enter an ecosystem beyond their native spatial range are expected to continue to enter the Great Lakes (Ricciardi, 2006). Preventing the transport of NIS to the region is the best way to avoid their potential adverse impacts, but if this is not possible, the next best alternative is to monitor for their arrival and control their spread. Resource managers are most concerned with NIS that may become invasive. Invasive species are nonindigenous species that are likely to cause economic or environmental harm or harm to animal or human health losses, ecological impacts, or adversely affect human health (National Invasive Species Council, 2007). Our primary goal is to help scientists and managers to better focus aquatic NIS monitoring activities and resources by identifying new invasive species, their potential to spread, and the U.S. Great Lakes ports (USGLP) most susceptible to invasion. Another goal is to demonstrate the use of a habitat suitability model and ballast water discharge data to predict invasion potential. Clients for this report include the U.S. Environmental Protection Agency‘s (EPA‘s) Great Lakes National Program Office, Great Lakes port officials, the U.S. Coast Guard (USCG), environmental organizations, agencies in the United States and Canada concerned about invasive species, and invasion biologists. Our findings are intended to improve detection and monitoring programs by providing managers with an approach for (1) identifying newly established populations of invasive species, (2) tracking or detecting spatial-range expansions, and (3) estimating potential impacts of introductions or spread by gathering baseline data on pre-existing populations and habitat. Nonindigenous species a re one of the greatest threats to the world‘s ecosystems (Elton

, 1958), and represent the greatest threat to biodiversity in lakes worldwide (Sala et al., 2000). Nonindigenous invasive species are the second most important threat to threatened and endangered species in the United States, after habitat loss or alteration (Wilcove et al., 1998). To date, about 50,000 species have been introduced into the United States (Pimentel et al., 2000). While many beneficial food crops, such as corn, wheat, and rice are included in this number, about 4,500 introduced species are free-ranging and up to one-fifth of these are invasive (U.S. Congress, 1993) and cause economic losses, ecological impacts, or adversely affect human health. The economic cost of invasive species to the United States has been estimated at $97 billion (U.S. Congress, 1993) and $137 billion (Pimentel et al., 2000) annually. Crop weeds and crop plant pathogens are the most costly ($26 and $21 billion, respectively) followed by rats and

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cats ($19 and $17 billion, respectively). Pimentel (2005) estimated the total environmental and economic impact (damage and control costs) of biological invaders to the Great Lakes Basin at $5.7 billion per year. Biological invasion occurs when an organisms arrives somewhere beyond its previous range. Currently, most invasions are a result of human actions, deliberate or accidental. Fortunately, most invaders do not become pests, or reach invasive levels, but predicting those that do is difficult, at best. Invasions and introductions have long fascinated biologists from a theoretical perspective. As the economic consequences of invaders has increased, however, this fascination must now be used to develop tools that will allow one to predict future invaders, especially those that may affect whole ecosystems, such as the Great Lakes.

2.1. NONINDIGENOUS SPECIES AND THE GREAT LAKES
The Great Lakes have been subjected to biological invasions since the 1830s, when the sea lamprey ( Petromyzon marinus) became the first recorded species to enter the Great Lakes from the Atlantic Ocean (Mills et al. 1993). Ricciardi (2006) reports that 182 NIS are now established in the Great Lakes and the National Oceanic and Atmospheric Administration (NOAA) reports a similar number of 185 (NOAA, 2007a; Appendix A). While any NIS may cause alterations to ecosystem structure or function, 13 of the reported NIS have become invasive (Mills et al., 1994). The zebra mussel ( Dreissena polymorpha) , illustrates the impact of an invasive NIS. The zebra mussel is out-competing Diporeia , a deep-water macroinvertebrate, for food (IAGLR, 2002). Diporeia is a key source of food for many Great Lakes fish and has been a dominant benthic organism since the Great Lakes were formed (IAGRLR, 2002). The

loss of Diporeia from the Great Lakes system affects the structure and function of the food web and commercially important fish such as the lake whitefish (IAGLR, 2002). Zebra mussels also appear to be responsible for more frequent occurrences of toxic algal blooms ( Microcystis ) by selectively rejecting blue-green algae as food and removing competing algae (Vanderploeg et al., 2001). From an economic standpoint, dense populations of zebra mussels have clogged water intake pipes, imposing large costs on utilities. The St. Lawrence Seaway, which opened in 1959, is a system of canals and locks that permit ocean-going vessels (as large as 225.6 m long, 23.8 m wide, and 7.9 m deep), to travel from the Atlantic Ocean to the Great Lakes. While shipping pathways to the Great Lakes existed prior to 1959, the opening of the Seaway and technological changes in commercial shipping drastically increased international trade. The opening of the Seaway resulted in an increase in the number of ships entering the Great Lakes (Sala et al., 2000; MacIsaac et al., 2001; Duggan et

al ., 2003), larger ships conveying larger volumes of ballast water, and ships that have plied the

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waters in many geographic locations distant from the Great Lakes (Grigorovich et al ., 2003a; Holeck et al., 2004; Drake et al., 2005; Duggan et al., 2005). While the transportation of goods has been economically beneficial, the unintended side effect of increased international trade has resulted in the long-range transport of NIS to the Great Lakes.

2.1.1. Origin and Patterns of Species Invasions

Most NIS that have become established in the Great Lakes since 1985 are native to the Ponto-Caspian region or the Black, Azov, and Caspian Seas (Ricciardi and MacIsaac, 2000; Appendix A). The Baltic Sea has also served as the source of many invaders in part because it has a climate very similar to the Great Lakes (Leppakoski et al ., 2002) The NIS from these seas include a diverse array of taxa including fish: the round goby ( Neogobius melanostomus ), the tubenose goby ( Proterorhinus marmoratus ), the rudd ( Scardinius erythrophthalmus ), the zebra mussel, the quagga mussel ( Dreissena bugensis ), and several cladocerans (e.g., the fishhook
water flea, Cercopagis pengoi ), amphipods (e.g., Echinogammarus ischnus ), and harpacticoid copepods (e.g., Nitocra incerta ; MacIssaac et al., 2001; NOAA, 2007a). The success of Ponto-Caspian species may be related to their ability to survive ballast water exchange due to a broader salinity tolerance developed through a geological history that includes fluctuating water levels and salinities (Dumont, 1998). There is no direct (i.e., nonstop) shipping traffic between the Great Lakes and the Ponto-Caspian Sea (Colautti et al., 2003), implying that the dominance of the Ponto-Caspian region as a source of invaders might be due to indirect linkages. To account for invasions where no direct shipping connections exist between the occupied spatial range and the range that may be invaded, the potential natural and human transport patterns need to be considered. NIS can be transported from the Ponto-Caspian region to the Great Lakes via an intermediate step in Western Europe. In addition to direct invasion pathways from the Ponto-Caspian region to Western Europe, MacIsaac et al. (2001) proposed four indirect pathways along the major rivers: the Danube and Rhine River pathway; (2) the Dnieper, Pripiat, Nemuna, and Vistula River pathway; (3) the Volga River system pathway; and (4) the Don and Volga River pathway. Many of these connections are completed through man-made canals and waterways which have allowed considerable exchange of species between water bodies (Reid and Orlova, 2002). To fully understand past indirect linkages (which, in turn, might help predict future indirect linkages) it would be necessary to have complete shipping data both from the Great Lakes to the intermediate port and from the intermediate port to the Ponto-Caspian region. The natural construction of the Great Lakes, whereby water flows and boat traffic moves from one lake into another, facilitates natural and human-induced dispersal within and between

### 7

the lakes (Duggan et al., 2003). These dispersal patterns are likely to hasten the spread of a NIS once it has entered the Great Lakes but are unlikely to add new species.

2.1.2. Ballast Water and NIS
Cargo vessels frequently take on ballast water to maintain stability when traveling from port to port and especially when crossing an open sea. Some or all of the ballast water is later released when cargo is loaded at various ports and, with regards to this study, those Great Lakes ports shown in Figure 1. Ballast water is the largest source of NIS to the Great Lakes as shown in Figure 2. Additional sources of NIS to the Great Lakes include fish stocking programs, private aquaculture, the bait industry, the aquarium and ornamental pond industry, live fish food markets, recreational boating, and canals and diversions (Kerr et al., 2005). While ballast water discharge (BWD) is the most prevalent pathway, an increase in BWD does not directly translate to more species invasions. Most discharges of ballast water in the Great Lakes occur in Lake Superior (Colautti et al., 2003), yet Lake Superior has less invasive species than any of the other Great Lakes (Grigorovich et al., 2003b). The low NIS colonization rate in Lake Superior may be due to any of several factors including cooler temperatures, a high ratio of deeper waters, low food availability due to low productivity, and low calcium concentrations (Grigorovich et al., 2003b).

2.1.3. Measures to Control the Release of Ballast Water Containing NIS
In response to NIS invasions stemming from ballast water releases in the Great Lakes, voluntary ballast water exchange (BWE) guidelines were implemented by Canada in 1989 and made mandatory in 2006. Mandatory BWE regulations were instituted by the USCG in 1993. These regulations require vessels carrying ballast water and entering the U.S. Great Lakes from outside the U.S. Exclusive Economic Zone (usually 200 miles away from the United States) to comply with one of the following three options:

1) Vessels may exchange ballast water in open-ocean waters more than 200 nautical miles from any shore, and in waters more than 2,000 m deep, before entering the Snell Lock, at Massena, New York, provided that salinity of the ballast water is at least 30 parts per thousand (ppt).
2) Vessels may retain their ballast water on board (vessels in this status are referred to as Ballast-on-Board, or BOB vessels).

### 8

Figure 1. The five Great Lakes, some of the Great Lakes ports, and surrounding region.

Figure 2. Sources of Great Lakes species invasions from 1960

– 2006 (based on data provided in Appendix A, which are derived from NOAA, 2007a).

### 9

3) Vessels may use an alternative environmentally sound method of ballast water management that has been submitted to, and approved by, the Commandant of the USCG or an authorized representative before the vessel‘s voyage (33 CFR 151.1510)

.

Compliance with these ballast regulations has been high. From July 1999 to June 2001, 93% of regulated ships reported performing the necessary level of active BWE before arriving in Massena. The remaining 7% of ships were forced by the USCG to perform some sort of alternative action, such as decontamination, prior to being allowed to enter the Great Lakes (USCG, 2001). USCG reported high rates of BWE compliance (89 +/- 10%) for the period

1992

– 2004 (Ruiz and Reid, 2007). Ballast water exchange at sea works first by the dilution effect. Assuming a homogenous
distribution of flora and fauna in the ballast tank, 95

– 99% of the fresh water (and organisms) would be replaced by seawater (NRC, 2008). Second, BWE can be effective since most remaining freshwater organisms in the ballast tank are killed by the resulting high salinity levels. Despite these ballast water regulations, at least 13 new NIS are believed to have entered the Great Lakes from ballast water since 1993 (Appendix A; IAGLR, 2002; Holeck et al., 2004;

NOAA , 2007a). It is possible that BWE has been effective and that all the species found after 1993 were introduced before 1993; it just took many years to detect and report them (Costello et al ., 2007a). Others have noted that organisms can survive BWE, and that BWE practices have not been completely effective in terminating the flow of NIS into the Great Lakes (e.g., Grigorovich et al., 2003a, b; Holeck et al., 2004; Drake et al., 2005; Ricciardi, 2006). Recently, more stringent regulations have been implemented (e.g., 73 FR 37, p.9950), which should reduce the flow of NIS into the Great Lakes from commercial shipping.

2.1.4. NOBOB Vessels and Species Invasions
Vessels fully loaded with cargo generally carry no ballast water on board. Vessels with no ballast-on board, commonly called NOBOB vessels, entering USGLP were not required to flush their ballast tanks or use an alternative treatment method until 2006. It is possible that invasions may have occurred from NOBOB vessels arriving in the Great Lakes (MacIsaac et al., 2002; Johengen et al., 2005). The almost completely empty ballast water tanks in NOBO B vessels often still contain residual sediment and water from previous ballasting operations. These residuals cannot be pumped from the ballast water tanks since the pump-out ports cannot

be closer than several inches from the bottom of the tank. Residual material in ballast water tanks of NOBOB vessels can contain thousands of live organisms, their resting eggs and cysts, and microorganisms, including human pathogens, all of which may be discharged into Great Lakes waters (Johengen et al., 2005). When a NOBOB vessel off-loads cargo at a Great Lakes

### 10

port of call it often takes on Great Lakes water into its ballast tanks to reestablish ballast. This pumped in freshwater mixes with the residual material in the ballast tanks, thereby increasing the viability of organisms. When such a NOBOB vessel then moves to another port to take on cargo, it may then discharge some or all of the recently acquired ballast water, along with the

NIS from the earlier residual material. NOBOB vessels currently account for about 90% of all inbound traffic to the Great Lakes (MacIsaac et al., 2002). Due to the number of potential invasive species in the residual material in NOBOB vessels and due to the large relative proportion of NOBOB vessels entering the Great Lakes, NOBOB vessels could pose a significant invasion risk. MacIsaac et al. (2002) found that for bacteria, copepods, cladocerans, and rotifers, NOBOB vessels may be exerting 10 to 100 times as much propagule pressure as vessels with ballast on-board complying with the regulations.

2.1.5. Other Options for Controlling Species Invasions From Ballast Water
As a result of the threat from NOBOB vessels, Canada developed mandatory regulations in 2006, requiring that transoceanic NOBOB vessels arriving in Canada undergo ballast flushing to eliminate fresh or brackish water residuals in their ballast tanks. Coastal vessels entering Canadian ports must comply with fairly similar requirements, only the BWE or ballast flushing must occur in an area only 50 nautical miles from shore (GLBWWG, 2008). Since August 2005, NOBOB vessels entering U.S. waters have been strongly encouraged, but not required, to conduct saltwater flushing before entering the Great Lakes (71 FR 18, pages 4,605

– 4,606). The St. Lawrence Seaway Development Corporation published regulations, which became effective at the start of the 2008 navigation season, requiring all NOBOB vessels that have operated outside the exclusive economic zone (usually 200 miles from the United States) to conduct saltwater flushing of their ballast tanks before transiting the St. Lawrence Seaway, regardless of whether their destination is a U.S. or Canadian port (73 FR 37, p. 9,950). It is not yet possible to measure the effectiveness of recent regulations or guidelines because there is a time lag between when a species is transported, colonizes, and reproduces to a large enough population, to be detected and reported. However, the National Research Council recommends that a binational science-based surveillance program be established to monitor for aquatic invasive NIS (NRC, 2008). The recommended program should involve dedicated lake teams, as well as academic researchers, resource managers, and local citizens groups, and it should leverage existing monitoring activities whenever possible. NOAA is testing the effectiveness of BWE along with other various methods to treat ballast water using mechanical (e.g., filtration and separation), physical (e.g., sterilization by

### 11

ultraviolet light, ozone, heat, electric current, or ultrasound), and chemical (e.g., chlorine dioxide) methods (NOAA, 2007b). The State of Michigan has established its own ballast water legislation, and other Great Lakes states are considering similar regulations (NRC, 2008). In 2007, the Michigan Department of Environmental Quality started prohibiting ballast water releases from oceangoing vessels into Michigan waters until a permit was issued by the state. Permits require one of four approved treatments, either sodium hypochlorite, chlorine dioxide, ultraviolet light radiation treatment preceded by suspended solids removal, or de-oxygenation (MDEQ, 2008). Because Michigan is currently an import state, there have been no permit applications to discharge ballast water into Michigan ports since Michigan's law was

im plemented in 2007 (telephone conversation on August 8, 2008 between Barry Burns, Michigan Department of Environmental Quality, and Vic Serveiss, U.S. EPA, NCEA). Therefore, oceangoing vessels visiting Michigan ports have not needed to install the Michigan approved ballast water treatment methods.

2.2. CONCEPTUAL FRAMEWORK
Owing to the fact that invasive species are a major threat to ecosystems, there is a need to develop predictive tools and to demonstrate their use to natural resource managers as they consider ways to manage the problem. The approach used in this assessment, as shown in Figure 3, is based on Williamson‘s (1996) conceptual framework regarding biological invasions. Specifically, Williamson‘s thesis states that successful invasions are best predicted by knowing the propagule pressure (i.e., the number of larvae/individuals entering a new area) and matching the invaded habitat with the habitat in the invader‘s historical range.

2.2.1. Propagule Pressure
Propagule pressure is a composite measure of the number of individuals of a species released into a region to which they are not native. It incorporates estimates of the absolute number of individuals involved in any one release event (propagule size) and the number of discrete release events (propagule number). The probability of establishment of an introduced species increases as propagule pressure increases (Menges, 1998, 2000; Simberlof and von Holle 1999; Kolar and Lodge, 2001). In considering the sources of propagules to the Great Lakes, ballast water becomes the primary concern as shown in Figure 3.

### 12

Figure 3. Conceptual framework for predicting future introductions of nonindigenous species into the Great Lakes of the United States.

The condition and life stage (resilient resting stages compared with sensitive juvenile stages) of propagules will also strongly affect the probability of establishment (Smith et al ., 1999; Hayes and Hewitt, 2000; Wonham et al., 2001). Thus, management actions
that reduce the number of released individuals, the number of introduction events, and the health of individuals released are likely to reduce the risk of invasion. Unfortunately, detailed quantification of these factors is limited and thus surrogate measures become necessary to estimate propagule pressure. As shown in Figure 3, the conceptual approach for this study uses ballast water discharge data as a surrogate measure for propagule pressure. Current scientific understanding of invasion biology suggests strongly that consideration of propagule pressure should be a major component of an assessment.

2.2.2. Habitat Suitability

2.2.2.1. Species Distribution Modeling
Assessing the degree to which a new environment is similar to the donor environment is a reasonable starting point to try to answer the question ― Is a species likely to survive in this Translocation of species from native r ange

Sufficient number of p ropagules Suitable h abitat

Successful i nvasion Propagule p ressure determined by ballast water discharges Habitat s uitability determined by GARP

### 13

environment if it were introduced here?‘‘ Good computer -based tools are available that provide a first-cut broad geographical answer to the question. Standard methods for modeling suitable habitat include traditional multivariate statistical methods (e.g., discriminate analysis, multiple regression, logistic regression), often coupled with geographic information systems (GIS) (e.g., Ramcharan et al., 1992; Buchan and Padilla, 2000). More recent methods that are tailor-made for identifying potential ranges include CLIMEX (Sutherst et al., 1999), Genetic Algorithm for Rule-Set Production (GARP) (Peterson and Vieglais, 2001; Drake and Bossenbroek, 2004) all of which are embodied in user-friendly and readily available software. Predicting suitable habitat is also possible for aquatic environments (Drake and Bossenbroek, 2004; Marchetti et al., 2004a), but currently less tractable than for terrestrial habitats because: (1) fewer aquatic physico-chemical data are available in appropriate electronic formats, and fewer distribution data have been collected for aquatic species; (2) terrestrial climatic data are often poor predictors of the aquatic environment; and (3) strongly predictive environmental variables for establishment are unknown for many aquatic species (Carlton et al., 1995). All of these predictive models have at least two intrinsic limitations. First, environment matching assumes that no evolution will occur in the nonindigenous species with respect to habitat requirements (Cox, 2004 ;
Sakai et al ., 2001). Second, biotic interactions in a new environment may limit or facilitate establishment independent of any climatic match (Torchin and Mitchell, 2004).

2.2.2.2. Genetic Algorithms for Rule-Set Production (GARP)
GARP develops predictions of the potential geographic extent of an invasion by first modeling relationships between known occurrences of a species and the corresponding abiotic environmental variables, and then projecting the modeled species-environment relationships to a region of interest. GARP modeling requires two types of inputs: (1) spatial data describing the location of species based on occurrence data and (2) digital data layers describing environmental conditions at locations coinciding with the species occurrence data. GARP develops outcomes consisting of a set of conditional rules in the form of  ̳if

- then‘ statements that describe the ecological conditions of the species in its studied habitat ( Stockwell and Peters, 1999 ). Habitats

are matched by searching iteratively for nonrandom correlations between a species‘ known location and a variety of environmental parameters. The GARP method is considered to be based on models of genetic evolution (Holland, 1975) because GARP models are built by an iterative process of rule selection, evaluation, testing, and incorporation or rejection of the rules produced ( Peterson et al., 1999 ). With each iteration, rules are modified by selection, crossover, and mutation

— resembling the genetic process. In the first phase, GARP selects a random population, based on a combination of initial

### 14

prediction rules, which might represent suitable solutions for the problem. The fitness to the characteristics of the population is then evaluated for each pixel in the search space. If the performance of the rule is adequate as determined by the rule's significance measure, the rule is retained for further runs of the algorithm, until an end condition

— consisting of a convergence

limit and maximum number iterations

— is satisfied ( Stockwell and Peters, 1999 ). One of the main advantages of GARP is its ability to apply different types of rules at once to explain complex nonlinear relationships between the species occurrence and predictive variables. This implies that the algorithm can  ̳learn‘ through each iteration and apply the type of rule that describes best the relationship among the variables for any particular portion of the search space (i.e., all possible combinations of variables) ( Stockwell and Peters, 1999 ).

2.2.2.3. Modification o

f GARP for Aquatic Systems
GARP has been used to predict a variety of species distributions including birds in Mexico ( Feria and Peterson, 2002 ; Stockwell and Peterson, 2002 ; Anderson et al., 2003 ) and North America ( Peterson and Cohoon, 1999 ); rodents in South America ( Anderson et al., 2002

and Anderson et al., 2003 ); and invasive vector disease insects in South America ( Peterson et al.,

2002 ). This may be the first time that remote sensing data were used in conjunction with GARP to predict invasive freshwater aquatic species.

### 15 3. METHODS

For a nonindigenous species (NIS) to become established in the Great Lakes, the species must (1) move or be transported from its existing spatial range to the Great Lakes and (2) be able to colonize, become established, and spread in the new environment (Williamson, 1996; Theoharides and Dukes, 2007). Others have also combined these two analyses to predict NIS spread, though different names were used to characterize their respective efforts. Leung and Mandrak (2007) combined invasability and propagule pressure, to make predictions about zebra mussel spread. Herborg et al. (2007) combined introduction effort and environmental niche models to predict the potential spread of the Chinese mitten crab ( Eriocheir sinensis ) in North

America. To address both requirements for successful invasion, we used information on ballast water discharges as a surrogate for propagule pressure and the Genetic Algorithm for Rule-Set Production (GARP) model to determine the suitability of habitat by matching the invaded habitat in the Great Lakes with the species native habitat.

3.1. HABITAT SUITABILITY USING THE GARP MODEL
Habitat suitability was modeled using a species distribution model to compare the environmental conditions associated with the distribution of invasive species in their home range

with the conditions found in the Great Lakes. GARP was selected because it is a well established model, is one of the few models that accepts presence-only distribution data (e.g., locations where the species has been observed without corresponding information on where the species has not

been observed), and incorporates multiple statistical approaches into a single

framework.

3.1.1. Selection of Modeled Species
The first step to using GARP is to select the species to be evaluated. Species of interest (i.e., those thought to be potential invaders of the Great Lakes system) were identified based upon a review of the literature and best professional judgment. We searched for species‘ scientific names and the keywords ―invasive‖ and ―Great Lakes‖ in publications after 1990 using Web of Science and international databases, such as Fishbase (Froese and Pauly, 2007) and Global Invasive Species (IUCN, 2006). We augmented this general search strategy to include authors who have studied Great Lakes invasive species to find potentially relevant papers that did not specifically include the terms ―invasive‖ or ―Great Lakes‖ in the article‘s title, abstract, or keyword s. Other sources include the U.S. Geological Survey, the States‘ Department of Natural Resources (for states adjacent to the Great Lakes), the Canadian Wildlife Federation, and

### 16

the Great Lakes Panel on Aquatic Species. We initially identified 156 species of concern based on a review of the literature (see Appendix B). Of the 156 species identified, using best professional judgment it was determined that 58 of these species pose the most risk for their potential to invade the Great Lakes and reach population levels that could cause ecological impact (see shaded entries in Appendix B). Twenty-eight of the 58 species identified are already in the Great Lakes. The remaining 30 species, not yet reported in the Great Lakes, were evaluated to see if sufficient data was available to run the GARP model. GARP requires at least 30 spatially unique occurrence points (i.e., latitude-longitude coordinates of locations where the species has been reported) to develop robust predictions (Stockwell and Peterson, 2002). For a variety of reasons, only 9 of the 30 species had sufficient data to be modeled. Of these nine, five species have not yet been detected in the Great Lakes, and the other four have been reported only infrequently. At the request of EPA‘s Great Lakes National Program Office, we modeled five additional species already found in the Great Lakes. Two of the five species, the zebra mussel and round goby, are currently widespread throughout the Great Lakes. The three other species, ruffe
( Gymnocephalus cernuus ), quagga mussel, and New Zealand mud snail ( Potamopyrgus antipodarum ), have been reported as established in the Great Lakes but are not yet widespread (USGS, 2007). Thus, a total of 14 NIS species were evaluated using the GARP model for the availability of suitable habitat in the Great Lakes (Table 1).

3.1.2. Model Inputs and Environmental Data Layers

3.1.2.1. Environmental Data Layers
Six specific parameters were used to define environmental variables suitable to develop data layers for GARP: mean, maximum, and minimum monthly surface water temperature; chlorophyll a concentration; the diffuse attenuation coefficient; and normalized water-leaving radiance (Table 2). These six parameters were chosen because they represent important environmental variables that tend to control the distribution of species. Three of the parameters are measures of temperature that affects species distribution worldwide. The other three are related to the productivity of aquatic systems. Chlorophyll a is an indicator of biological productivity. Water clarity, as measured by diffuse attenuation coefficient and the water-leaving radiance, is an indicator of the trophic state of the system. Water clarity also influences the depth of the photic zone and the ability of primary producers to acquire sunlight and flourish. Although some of these six data layers may be covariant, GARP is considered to be relatively robust to collinearity (Kluza et al., 2007). For the species modeled in this report, no literature

### 17

Table 1. Fourteen species modeled using GARP and the source of occurrence data

Species common name and year reported Description Useful

occurrence data records Data source

Species already widespread in the Great Lakes

Gymnocephalus cernuus, ruffe

— 1986 f ish 229 GBIF a 

### USGS b



### Dreissena polymorpha, zebra mussel

— 1988 mollusk 268 GBIF, USGS

Dreissena bugensis, quagga mussel

— 1989 mollusk 83 USGS

Neogobius melanostromus, round goby

— 1990 fish 145 GBIF, USGS

Potamopyrgus antipodarum, New Zealand mud snail

— 1991 mollusk 867 GBIF, USGS

Species reported in the Great Lakes but either not extensive or lacking spatial data

Cercopagis pengoi, fishhook waterflea

— 1998 crustacean 152 GIS c



### Scardinius erythrophthalmus, rudd

— 1989 fish 57 GBIF, CIMS d



### Proterorhinus marmorat us, tubenose goby

— 1990 fish 171 CIMS, BSRDB e



### Alosa aestivalis, blueback herring

— 1995 fish 408 GBIF

Species not yet reported in the Great Lakes

Corophium curvispinum, N/A amphipod 65 GBIF, CIMS

Neogobius fluviatilis, monkey goby fish 50 CIMS

Pomatosc histus minutus, sand goby fish 102 GBIF, BSRDB

Rutilus rutilus, roach fish 117 GBIF, CIMS

Tinca tinca , tench fish 50 CIMS

a Global Biodiversity Information Facility, 2007 ( http://www.gbif.org/ ).

b USGS Nonindigenous Aquatic Species Database, 2007 ( http://www.usgs.gov/pubprod/maps.html ).

c Regional Biological Invasions Center. INVADER, 2007 ( http://www.zin.ru/projects/invasions/gaas/invader/invader.htm ).

d Caspian Interactive Map Service, 2007 ( http://ipieca.unep-wcmc.org/imaps/ipieca/caspian/viewer.htm ).

e Black Sea Environment Programme Red Data Book, 2007 ( http://www.grid.unep.ch/bsein/redbook/index.htm ).

### 18

Table 2. Environmental variables used to predict locations that woul d provide suitable habitat for the 14 modeled species in the Great Lakes. The spatial resolution of each of these six data layers is ~21 km 2.



### Variable Units Source Collection period

Mean monthly temperature ° C AVHRR a 

### 1985

– 2002

Maximum mean monthly temp erature ° C AVHRR 1985

– 2002

Minimum mean monthly temperature ° C AVHRR 1985

– 2002

Chlorophyll a concentration m g / m 3 

### MODIS b 

### 2001

– 2005

Diffuse attenuation coefficient (K490) m - 1 

### MODIS 2001

– 2005

Normalized water

- leaving radiance (nLW551) mW /( cm 2 

### μm sr ) MOD IS 2001

– 2005

a Advanced Very High Resolution Radiometer.

b Moderate Resolution Imaging Spectroradiometer.



### reliably supports the a priori weighting of any of the selected environmental variables as more important than any other variable.

3.1.2.2. Environmental Data Sources
Water temperature was derived from the satellite-based Advanced Very High Resolution Radiometer (AVHRR) Oceans Pathfinder Sea Surface Temperature Data set and is accurate to within 0.5oC ( http://podaac-www.jpl.nasa.gov/sst/ ). We used temperature data from 1985 through 2001. We used the MMT data to calculate three data layers for use by the GARP models: Maximum MMT, Mean MMT, and Minimum MMT. Maximum MMT represents the highest value of each of the 12 sets of monthly averages of data. To calculate the mean MMT, we assigned each pixel the average of the 12 monthly averages. The minimum MMT represents the lowest value of each of the 12 monthly averages. Chlorophyll a concentrations were obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. The Diffuse Attenuation Coefficient (K490) relates to the presence of light-scattering organic and inorganic particles in the water column and is inversely related to water clarity
( http://oceancolor.gsfc.nasa.gov/PRODUCTS/k490.html ). The normalized water-leaving radiance is the radiance of reflected light at 551 nm. Since water absorbs very little light at 551

### 19

nm, increases are due to light reflection out of the water, which are usually caused by nonabsorbing particles such as suspended sediments.

3.1.3. Use of GARP model
A stand-alone version of Desktop GARP (version 1.1.6) (Scachetti-Pereira, 2002) was used to model the distributions of the first nine NIS; Desktop GARP (version 1.1.6) within Open Modeler (version 1.0.5) was used for five of the species. There is no functional difference between the two desktop versions used. GARP relies on species occurrence or presence data and synthetic species absence data, termed pseudo-absence data. The use of pseudo-absence data is an intrinsic and accepted part of GARP modeling. To develop pseudo-absence data, investigators must identify a region surrounding the occupied range of the target species to which the species could easily spread. It is assumed that the reason the species is absent from the surrounding region is because environmental conditions are different and outside the species

tolerance limits. The established range includes the species‘ occupied range and a surrounding unoccupied range. The pseudo-absence data are selected from a subset of locations within theinvestigator-defined study area that are not currently occupied by the species. The pseudoabsence points represent the locations presumed to be unsuitable for the target species and provide a contrast against which occurrence models can be developed. A new, random selection of pseudo-absence data was made for each model iteration. GARP divides the occurrence data into training and test data sets. Test data sets are reserved to test predictive performance of models that are developed using the training data sets. GARP then uses the training data and one of the individual algorithms to develop a model, and the model is tested and improved until the best solution is found. For each of the 14 modeled species, GARP randomly assigned the data into 50/50 splits of training and test data sets. We produced 1,000 model runs from the training data sets (i.e., 14,000 total GARP runs) that are all slightly different and vary in predictive ability. Each individual GARP model run produces a map of  ̳0s and 1s

, ‘ representing predicted absence and presence , respectively. The area of predicted presence for each run is simply the proportion of pixels that have a predicted value of 1. We used a procedure described by Anderson et al. (2003) to select the best subset of runs generated for each species to develop a final composite range map. The model is run for each species and compared to see which runs best predicted the known occurrence locations and omitted the fewest of the known occurrence locations of the modeled species. For each species, we retained the models with the lowest omission error rates when compared to the results of the

test data sets.

### 20

Next, the median area of predicted presence for each species (simply the number of pixels that have a predicted value of 1) was determined from the models with the lowest error

rates. The subset of models within 50% of this median value was then selected to form the final prediction of habitat suitability. Every pixel on the model output represents either a predicted presence or a predicted absence. The final composite prediction maps reflect the sum of the results from the models; that is, each pixel was assigned a value ranging from zero (i.e., no models predict presence) to 100 (i.e., all models predict presence). This value represents the relative environmental suitability of each location. The final habitat suitability maps reveal the frequency with which a pixel is predicted to provide suitable habitable and depict the repeatability of that prediction with different models developed from different, randomly divided, training data sets. The higher the value of a pixel, the more likely the modeled species is expected to find suitable habitat at that location. Another interpretation is that pixels that have higher values represent higher quality habitat for the modeled species because these locations are predicted to be suitable by more models. Those pixels that are predicted to provide highly suitable habitat within the Great Lakes are characteristic of environments similar to those known to be occupied by the modeled species in their natural range.

3.1.4. Assumptions and Limitations
The use of species distribution models to predict the spread of NIS required three assumptions: (1) the available distribution data describe the full range of environmental conditions that the modeled species can tolerate; (2) the environmental variables selected to model potential spread govern the current and future geographic ranges of the NIS under study; and (3) biotic factors do not influence species distributions, unless such biotic factors can be included explicitly as environmental data layers. Failure to meet these three assumptions can limit the ability of GARP to predict invasive spread and can result in two types of errors, which arise from two broad sources: (1) limitations inherent in the data or the model themselves (thes e
are often termed data errors ), and (2) ecological processes relevant to the distribution of the species that are not included in the model (often called biological errors ).

3.1.4.1. Data Errors
A major source of error in GARP modeling is the lack of complete occurrence data for the modeled species. GARP requires occurrence points that are both representative of the full range of environmental conditions associated with presence of the species and of the area inhabited by the species. In reality, the actual occurrence points reflect bias in both sampling and

### 21

reporting efforts, which is influenced by resources, accessibility, transportation corridors, and visitation frequency. Further, the occurrence data extracted from online databases were collected

ad hoc , and not for the purposes of constructing distribution models. If occurrence data do not describe the full environmental tolerances of the species, predictions will underestimate areas where a NIS could survive and establish itself in a particular region of the Great Lakes. For example, occurrence data for the monkey goby and the tench were available only for their distributions within the Iranian portion of the Caspian Sea. This may be part of the reason these two species have the smallest predicted distributions of any modeled species within the Great Lakes, perhaps suggesting under-prediction. Therefore, the predicted habitat suitability might

not include all environments which the NIS could invade. Some occurrence data were discarded because they did not fall in waters defined by the

21 km 2 

### spatial resolution available for the environmental data layers. These discarded occurrence points are more than likely in lakes and rivers smaller than 21 km 2 

### that could not be resolved by the satellite sensors used in this study. GARP requires that species absence data be developed by accurately selecting the region for which the species is absent because environmental conditions are different and outside the species tolerance limits. Determining the extent of the GARP prediction region assumes that these pseudo-absence points really are uninhabitable, and not, for example, simply suitable environments to which the target species has not yet dispersed. Model errors can also result from modeling habitat suitability with a limited set of environmental variables. While we know that each of the six selected variables has a strong influence on species distributions, other abiotic factors known to influence species distribution are not captured by the variables that we used. For example, salinity impacts the survival of many aquatic organisms (Bailey et al., 2005), but salinity is not included in the GARP analysis. We were unable to locate a global database with spatial salinity data at the same scale of resolution as the six variables included in this study. Including salinity at a coarser resolution would have introduced coarse range boundaries where salinity was the limiting factor. Bathymetry data were available, but not used, because species occurrence data did not include the depth at which the species was found. As many aquatic species may only survive in waters to a certain depth, the model would show some deeper waters as suitable habitat and may contradict what is known about the depth limitations of a particular species. Nutrient levels may also be biologically important to some NIS. Calcium concentrations, for example, are likely a limiting factor for zebra and quagga mussel distributions (Cohen, 2007), but global spatial databases of calcium concentrations are not available. Failure to include such key factors can lead to over-predictions (predicting that the species can survive in an area where the habitat is actually unsuitable due to the environmental variable not included in the model). Detailed

### 22

knowledge of the target species is required to reliably determine if one of more key factors have been excluded from the models. Because the consequences of under-prediction (failing to identify a place where NIS can establish) are much greater than those resulting from over-prediction, our approach is conservative and errs on the side of over-prediction.

3.1.4.2. Biological Errors
Even if the environmental variables could accurately reflect the abiotic factors controlling species distributions, the predictions are developed without considering biotic factors such as competition, predation, and parasitism. Biotic factors also are important determinants of the distributions of species, but it is not ordinarily possible to obtain data on biotic factors for incorporation into GARP, and failure to consider such factors can lead to poor predictions (e.g., Fitzpatrick et al., 2007). In a new environment, a species may be freed of restrictions (e.g., a predator may not exist), encounter new challenges in a new environment (e.g., competition from a species with a similar niche), or evolve and adapt. Thus, it is difficult to predict whether the impacts of excluding biotic factors would inhibit establishment or expand the colonization range of an introduced species. The fire ant in the southeastern United States is an example of an introduced NIS that established itself beyond the predicted range of a species distribution model, perhaps due to biotic factors that encouraged the species successfulness (Fitzpatrick et al., 2007).

3.1.5. Testing the GARP Model Performance
Despite the limitations described above, species distribution models are currently one of the few techniques readily available to predict the potential for an invasive NIS to become established in an area of interest (Peterson, 2003). Therefore, species distribution models should be considered a key component of a multi-faceted NIS prevention and management plans (Mack, 1996; Peterson and Vieglais, 2001). The GARP model outputs were validated by testing the ability to correctly predict independent data that were not used to develop the model. Specifically, we evaluated how well GARP performed by assessing how well the model predicted the known distributions of three NIS that are already widespread throughout the Great Lakes using distribution data collected
outside of the Great Lakes. Thus, occurrence data for the zebra mussel, ruffe, and New Zealand

mud snail within the Great Lakes were withheld from the GARP model runs and the model tested for its ability to correctly predict suitable habitat. The performance of the GARP model was assessed using area under the curve of the Receiver Operating Characteristic curve (Sing et

al ., 2005). Area under the curve is a threshold-independent evaluation of model performance

th at measures the ability of the model to differentiate between sites where a species is present

### 23

from sites where it is considered absent. Area under the curve represents the probability that,when a predicted-present site and a predicted-absent site are drawn at random, the predictedpresent site will have a higher predicted value. The effectiveness of the GARP modeling is based on the scale for determining model performance devised by Swets (1988). More details on the model validation approach we used are provided in Appendix C.

3.1.6. Determining GARP’s Power to Predict
GARP and other species distribution models make predictions about the suitability of habitat for a particular species within a region of concern. These models are developed by comparing the environmental conditions in the region containing the species to those found in the region of concern, in this case the Great Lakes. As noted previously, predictions from GARP and other species distribution models are valid only for the range of environmental conditions on which the model was developed. Reliable predictions cannot be made for any environment within the Great Lakes that are not similar to those found within the region containing the distribution of the study species. GARP does not have a method for determining when a reliable prediction cannot be made, and, instead, may report such environments as a predicted absence when they may indeed be habitable by the NIS. Reporting such areas as unsuitable habitat may be erroneous and could misdirect management attention away from these potentially susceptible

areas. We used a technique called ― power-of-prediction analysis, ‖ devised expressly for this project, to distinguish between areas of predicted absence from areas for which a reliable prediction cannot be made (null prediction). Like GARP models for individual species, power of prediction analysis uses GARP to develop predictions. However, instead of developing a model of environments represented by the distribution of the study species, power of prediction analysis attempts to (1) model all environments within the region containing the distribution of the species and (2) compare these environments with those characterizing the Great Lakes. To perform a power of prediction analysis, we identified a region encompassing the full range of environmental conditions to which the species is known to occur. For example, consider a hypothetical species in the Caspian Sea reported in regions with water temperatures between 15 and 20°C but not reported in regions with a water temperature from 10 to 15°C. GARP would then predict that places in the Great Lakes with water temperatures between 15 and 20°C provide suitable habitat and that all regions with temperatures less than 15°C are unsuitable. However, temperatures in the Great Lakes range from 6 to 20°C. This presents a problem regarding areas within the Great Lakes that range from 6 to 10°C, which is below any temperature found in the Caspian Sea. When considering temperature, in an isolated, univariate

### 24

way, it is likely the species would not tolerate temperatures from 6 to 10°C if it could not tolerate 10 to 15°C. Yet, it is not correct to assume that all locations beyond a particular extreme, in this

case less than 10° C, are unsuitable. The species experiences the environment in a multivariate manner and that could produce surprising and counterintuitive results. For example, a terrestrial species might be able to survive and reproduce in locations that were hotter, if they were also

wetter. Even though GARP cannot make a reliable prediction for such areas, GARP and many other species distribution models will report areas with temperatures outside of the range from 10 to 20°C as unsuitable for the species, when in reality the GARP model has no information to draw such a conclusion (Heikkinen et al., 2006). We used power of prediction analysis to denote the geographic extent of predictive power. We performed power of prediction analyses for 11 of the 14 modeled species. Power of prediction analysis was not performed for two of the invasive species already established in the Great Lakes

— quagga mussel and round goby

— due to the lack of occurrence data outside the Great Lakes. Also, no power of prediction analysis was needed for the blueback herring because GARP model runs predicted that the blueback herring can encompass essentially the entire area of the Great Lakes. Appendix D provides more details on how we applied the power of prediction analysis to this study.

3.2. DETERMINING PROPAGULE PRESSURE USING BALLAST WAT ER DISCHARGE DATA AND VESSEL TRAFFIC PATTER NS
The probability that a NIS can become established increases with increased propagule pressure (Simberloff and Von Holle, 1999; Kolar and Lodge, 2001; Lockwood et al., 2005). Propagule pressure, as explained in the introduction, is the number of individuals (including larvae, seeds, and spores) released in a nonnative region over a specified period of time (Simberloff and Von Holle, 1999). We used two sources of data as a surrogate for propagule pressure: Data from the U.S. Coast Guard‘s (USCG‘s) National Vessel Movement Center (NVMC) and the National Ballast Information Clearinghouse (NBIC). Ultimately, the NBIC data proved to be the most useful in predicting propagule pressure. The NBIC collects, analyzes, and interprets data on ballast water management practices of commercial ships that operate in the United States. NBIC was created by the USCG and the Smithsonian Environmental Research Center (NBIC, 2008) pursuant to the National Invasive Species Act of 1996 (16 USC 67 § 4712). NBIC‘s data are electronic and are accessible on the Internet ( http://invasions.si.edu/nbic/ ).

3.2.1. Analysis of Ballast Water Discharge Data
The principal aim of the NBIC database is to quantify the amounts and origins of ballast water discharges in U.S. coastal systems and to determine the degree to which such water has

### 25

undergone open-ocean exchange or alternative treatments designed to reduce the likelihood of

balla st -mediated invasions by exotic species (NBIC, 2008). NBIC data come from national ballast water management reporting forms submitted to the USCG by vessels arriving to ports and places in the United States. The data includes port of arrival, date of arrival, and last port of call, along with the source of ballast water (either a specific port or a latitude/longitude coordinate at sea), date of ballast water intake, type of ballast water management, date discharged, and the volume discharged.

This database allowed us to locate the source of ballast water and to determine those Great Lakes ports receiving the most ballast water discharges with the most potential to transport

NIS. NBIC data from for 2004

– 2007 for Illinois, Indiana, Michigan, Minnesota, New York, Ohio, Pennsylvania, and Wisconsin were downloaded and analyzed using a relational database (Microsoft ® 

### Office Access). The original data set contained records of 44,461 vessel arrivals and 121,031 ballast water discharges. By excluding records of vessels arriving in ports outside the Great Lakes system, the NBIC data set was reduced to 63,574 ballast water discharges. Since NIS that were in the ballast tank before ballast water exchange at sea may survive the exchange and can later be released in the Great Lakes, we needed to determine the original source of ballast water. Discharges of ballast water that originated within the Great Lakes (which we defined as west of Quebec City, see Figure 1) was excluded along with discharges of ballast water that was derived from outside 200 nautical miles of any shore and deeper than 2,000 m. We analyzed the remaining 618 ballast water discharges because these waters have the most risk of transporting NIS. We identified the most common original source of ballast water and the U.S. Great Lakes ports (USGLP) receiving the most discharges. As discussed previously, some vessels enter the St. Lawrence Seaway declaring to have no ballast on board (NOBOB) but as they traverse the Great Lakes, they take on ballast water

which can mix with residual water or sediment in the ballast tanks. We combined NBIC with NVMC data since the later includes information on the last five ports of call. Starting with NBIC records, we matched the NBIC arrival port and arrival date to the corresponding data in the NVMC data set. For each of the vessel records with matching sets of data, we obtained the last five ports of call records from the NVMC database. If one of more of the last five ports of
call were not in the Great Lakes, we considered the vessel to have entered the Great Lakes with no ballast on board but containing residual material (NOBOB-RM). We then calculated the number of ballast water discharges and the volume of ballast water discharged from each of

these vessels.

### 26

3.2.2. Assumptions and Uncertainty
Although NBIC employs a rigorous quality assurance and quality control protocol, the accuracy and completeness of the self-reported data cannot be guaranteed (NBIC, 2008)

. 

### The NVMC data set also has limitations. Although a vessel may have stopped at a foreign port during one of its last five ports of call it does not necessarily mean that ballast water was taken on at that port. It is possible that any residual material in the ballast tank may be from within the Great Lakes and not the foreign port of call included in the last five ports of call records. This would lead to over-predicting the potential for NIS release. Similarly, it is also possible that we missed a source of residual material because it may have been picked-up sometime earlier than the last five ports of call. This would lead to an under-estimation of NIS releases. Finally, we

assumed that the data from 2006

– 2007 used in this study are representative of discharge and shipping patterns over the past several years.

### 27 4. RESULTS

We first present results comparing the Great Lakes with the rest of the world, especially the Ponto-Caspian region with respect to six environmental parameters used to model species distribution. We next identify those locations within the Great Lakes that would provide suitable habitat for each of the 14 modeled nonindigenous species (NIS). And third, we identify the ports within the U.S. Great Lakes that received the most ballast water discharges from the vessel traffic we analyzed, including the identification of ports around the globe that provided the original source of ballast water discharged at a U.S. Great Lakes port.

4.1. COMPARISON OF THE GREAT LAKES TO THE PON TO -CASPIAN SEA
The Ponto-Caspian region has been identified as a significant source of nonindigenous species entering the Great Lakes. The comparison of the Great Lakes to the Ponto-Caspian region, based on the six environmental parameters used in the Genetic Algorithm for Rule-Set Production (GARP) modeling, reveals that the regions are indeed quite similar. Figures 4

– 7 illustrate the environmental conditions for those parameters used in the habitat suitability modeling as shown in Table 2. Latitudinal differences and discernable patterns in deeper open ocean waters are clearly evident.

4.1.1. Temperature
Overall, the maximum monthly temperature (MMT) shows a strong latitudinal gradient (Figure 4). The Great Lakes, shown mostly in yellow, have a spatial mean of 9.9oC and range

from 5.9o

– 13.7oC. Lake Superior is colder than the other four lakes and has a spatial mean of 7.9°C with a range of 5.9°

– 11.1°C. The Caspian and Black Seas are somewhat warmer and less variable (in terms of maximum MMT) than the Great Lakes, with a spatial mean of 13.7°C and a range of 12.1°

– 16.1°C. The impact of climate change will likely cause the Great Lakes to reach MMT levels even more similar to the Ponto-Caspian Sea region (IPCC, 2007).

4.1.2. Chlorophyll a Concentrations
Due to upwelling, the western edges of continents display high concentrations of chlorophyll a (dark blue color in Figure 5), a surrogate measure of productivity. Colder arctic waters are also more productive. The global spatial mean concentration of chlorophyll a is 0.24 mg/m 3 

### . The Great Lakes, shown mostly in blue in Figure 5, show a mean spatial productivity level of 1.7 mg/m 3 

### , ranging from 0.19

– 62.3 mg/m 3 

### . The Caspian and Black Seas are similar to the Great Lakes with a spatial mean of 2.2 mg/m 3 

### and range from 0.2

– 56.2 mg/m 3



### .

### 28

Figure 4. Maximum monthly mean temperature (MMT) (°C) as determined by AVHRR sensor (1985

– 2001). Warmest temperatures are indicated by red; cooler temperatures are indicated by shades of blue. Global view (top), Ponto-Caspian region (left), and Great Lakes (right).

### 29

Figure 5. Average chlorophyll a concentration (mg/m 3 

### ) as determined by MODIS (2001

– 2005). High chlorophyll a concentrations are represented with blue and dark green. Brown and yellow indicate low concentrations of chlorophyll a. Global view (top), Ponto-Caspian region (left), and Great Lakes (right).

### 30

4.1.3. Diffuse Attenuation Coefficient (K490)
Overall, open ocean waters are generally clearer than waters with higher biological productivity. The more light that is scattered from the surface, the Diffuse Attenuation Coefficient (K490), the greater the amount of suspended solids, a measure of productivity. Greater K490 values imply more light attenuation and lower water clarity. The global mean K490 is 0.032/m, which translates to a photic zone depth of ~144 m. The K490 of the Great Lakes, shown mostly in blue in Figure 6, is much higher than the global average and has a spatial mean of 0.099/m (equivalent to a photic zone depth of ~47 m) and ranges from 0.037

– 0.741/m. The Caspian and Black Seas also are fairly turbid and similar to the Great Lakes with a spatial mean of 0.11/m (photic zone depths of ~42 m) and range from 0.05

– 0.72/m.

4.1.4. Normalized Water-Leaving Radiance
As with the Diffuse Attenuation Coefficient, this is a measure of the productivity of waters. As shown in Figure 7, and consistent with the previous figures, waters near the continents are generally more productive. The Great Lakes have a spatial mean of 1.5 mW/(cm 2


### μm sr) and a range of water clarity from 0.0

– 5.1 mW/(cm 2 

### μm sr). Lake Superior, being much deeper (averaging 147 m and with a maximum depth of 406 m) than the other Great Lakes, has a spatial mean of 0.5 mW/( cm 2 

### μm sr) indicating that Lake Superior is less productive. The Caspian and Black Seas have a spatial mean of 2.4 mW/(cm 2 

### μm sr), similar to the lower Great

Lakes.

4.2. HABITAT SUITABILITY FOR MODELED SPECIES
The results of using the GARP species distribution model reveals that the Great Lakes offers suitable habitat for all of the 14 modeled species in this study, with Lakes Erie and Ontario the most likely to be invaded. Five of the species modeled are already established in the Great Lakes and the remaining nine, selected from an original list of 156 species, are most likely to invade and become established in the Great Lakes. Figures 8 through 10 illustrate the suitability of habitat within the Great Lakes for the blueback herring ( Alosa aestivalis ), quagga mussel
( Dreissena bugensis ) and round goby ( Neogobius melanostromus ). Unfortunately, due to limits with occurrence data, a power of prediction analysis could not be performed for the quagga mussel and round goby. There was no reason to perform a power of prediction analysis for the blueback herring because it is predicted to find suitable habitat throughout the Great lakes. Figures 11 through 21 show the suitable habitat for the fishhook waterflea ( Cercopagis pengoi ), zebra mussel ( Dreissena polymorpha ), ruffe (G ymnocephalus cernus ), monkey goby ( Neogobius fluviatilis ), New Zealand mud snail ( Potamopyrgus antipodarum ), tubenose goby ( Proterorhinus

### 31

Figure 6. Average diffuse attenuation coefficient (m -1 

### ) at 490 nm as determined by MODIS (2001

– 2005). Yellow and green colors indicate less light absorption, blues indicate greater attenuation of light. Global view (top), Ponto-Caspian region (left), and Great Lakes (right).

### 32

Figure 7. Average normalized water leaving radian ce ( mW / cm 2 

### μ m sr) as determined by MODIS (2001

– 2005). Blues are higher values (i.e., higher concentrations of particles in the water which reflect more light), and reds and yellows indicate lower values (i.e., less light is emitted). Global view (top), Ponto-Caspian region (left), and Great Lakes (right).

### 33

marmoratus ), rudd ( Scardinius erythrophthalmus ), an amphipod ( Corophorum curvispinum ), sand goby ( Potamoschistus minutus) , roach ( Rutilus rutilus ), and tench ( Tinca tinca ). These figures also show areas where no reliable prediction could be made based on a power of prediction analysis. The habitat suitability maps are based on the best 100 of 1,000 model runs for each species. The color scale (from blue to red) in the figures reflects the number of GARP model runs, from 0 to 100, that predict the modeled species would find suitable environmental conditions in the location being considered. Specifically, the red colored regions indicate where nearly all GARP model runs predicted that NIS would find suitable habitat. The blue-colored areas indicate where few or no GARP models runs predicted that NIS would find suitable habitat. The power of prediction analysis helps to distinguish between areas with a low likelihood of providing suitable habitat from areas where a prediction could not reliably be made. The gray regions denote areas where no reliable prediction can be made about the potential distribution of an invader. The color scale does not imply any measure of credibility or precision, but rather expresses commonality among predictions developed via a stochastic process of model generation. To further validate the GARP model, results for the three species already reported in the Great Lakes (zebra mussel, ruffe, and the New Zealand mud snail) were compared with their current spatial distribution. This analysis indicates that GARP modeling is a good predictor of habitat suitability according to the scale for evaluating the performance of species distribution models devised by Swets (1988). The model performance scores are 0.79 for the zebra mussel and ruffe and 0.74 for the New Zealand mud snail. These scores, representing the area under the curve of predicted accuracy (Sing et al., 2005), suggest that the six environmental data layers that were selected as inputs for the GARP modeling are appropriate for predicting the locations that would provide suitable habitat. Appendix C provides more information on model validation.

4.2.1. Blueback Herring
If the blueback herring, a medium-sized fish, enters the Great Lakes it is very likely to find suitable habitat throughout the Great Lakes system, according to GARP (Figure 8). Only the deeper portions of Lake Superior and other isolated spots in other Lakes may not provide suitable habitat for this species. Without a power of prediction analysis, it is not possible to know if the blue colored areas reflect unsuitable habitat or areas where no prediction is possible. The blueback herring and alewife are of similar shape and general appearance, and distinguishing between them is difficult. Bluebacks tend to have a smaller eye than alewives, with the eye diameter usually smaller than the snout length. As their name implies, these fish

### 34

often have dark blue backs. An anadromous fish, the blueback herring spends the greater part of its life in salt water and returns to fresh water to spawn. It usually spawns later in the spring than the alewife, when water temperatures are a bit warmer. During spawning, many eggs are deposited over the stream bottom where they stick to gravel, stones, logs, or other objects. A few surviving, spent fish move back to the sea after spawning. Young fish usually move to sea when about l month old and 1 1/2 to 2 inches long. Bluebacks feed on plankton, various small floating animals, small fish fry, and fish eggs. Although the Great Lakes are distant from marine waters, the blueback herring can spend its whole life and develop reproducing populations entirely in freshwater (VA Inland Fisheries, 2008). If blueback herring became established in the Great Lakes, they could impede recovery of depressed populations of indigenous fishes such as
cisco and lake trout (Owens et al., 1998).

Figure 8. GARP-predicted habitat suitability of blueback herring ( Alosa aestivalis ) in the Great Lakes.

### 35

4.2.2. Quagga Mussel
The quagga mussel, a mollusk, already occupies most shoreline areas in Lakes Erie and Ontario and southern Michigan (Figure 9). According to GARP modeling, the rest of Lake Erie and the southern shoreline zones of Lakes Michigan and Huron also are likely to provide suitable habitat for this species. Without a power of prediction analysis, predictions cannot reliably be made for the other regions. However, the species has already been reported in several of thes e locations, including the shorelines of Lake Michigan, Lake Superior, and Lake Huron. Quaggas are prodigious water filterers, removing substantial amounts of phytoplankton and suspended particulate from the water. As such, their impacts are similar to those of the zebra mussel. By removing the phytoplankton, quaggas in turn decrease the food source for zooplankton, therefore altering the food web. Impacts associated with the filtration of water include increases in water transparency, decreases in mean chlorophyll a concentrations, and accumulation of pseudofeces (Claxton et al., 1998). Quagga mussels prefer silty or sandy lake bottoms and can live in warm or cold water. MacIsaac (1994) correctly speculated that the quagga mussel was still expanding its nonindigenous range in the Great Lakes. It has spread to depths greater than it occupies in its native range (Mills et al., 1996) and is abundant to a depth of 150 m (Wisconsin DNR, 2008) and 174 m in Lake Ontario (Watkins et al., 2007). By 1999, the quagga mussel dominated southern Lake Ontario, where the zebra mussel was once dominant (Mills et al., 1999), and it continues to spread into regions previously occupied by the zebra mussel (Watkins et al., 2007). The ability to spread to areas that can be potentially occupied by the zebra mussel further supports the notion that spread and colonization may occur until the species reaches its depth limitation.

4.2.3. Round Goby
The GARP model predicts the round goby, a medium-sized, bottom-dwelling fish, would find suitable habitat throughout Lakes Erie and Ontario and along the shorelines of the other Lakes (Figure 10). In fact, this species became established in all five Great Lakes by 1998 (Rasmussen, 2002). Round gobies perch on rocks and other substrates in shallow areas, yet they have also been reported to flourish in a variety of habitat types (USGS, 2008a). Gobies also have a well developed sensory system that enhances their ability to detect water movement. This allows them to feed in complete darkness, giving them an advantage over other fish in the same habitat (Wisconsin Sea Grant, 2008). Zebra mussels may have facilitated the invasion of the round goby and other Eurasian species by providing an abundant food source (Ricciardi and
MacIsaac, 2000). The distribution of the round goby around the inshore areas of the Black and Caspian seas indicates their potential for widespread occupation of inshore habitats with cover, especially plants, in the lower Great Lake, yet they can migrate to deeper water (50

– 60 m) in

### 36

Figure 9. GARP-predicted habitat suitability of quagga mussel ( Dreissena bugensis ) in the Great Lakes. Inset map shows the locations where the species has been reported.

Figure 10. GARP-predicted habitat suitability of round goby ( Neogobius melanstromus ) in the Great Lakes. Inset map shows the locations where the species has been reported.

### 37

winter (Jude et al., 1992). The numbers of native fish species have declined in areas where the round goby has become abundant (Crossman et al

. , 1992). This species has been found to prey on darters, other small fish, and lake trout eggs and fry in laboratory experiments. They also may feed on eggs and fry of sculpins, darters, and logperch (Marsden and Jude, 1995) and have also been found to have a significant overlap in diet preference with many native fish species. They compete with rainbow darters ( Etheostoma caeruleum ), logperch ( Percina caprodes ), and northern madtoms ( Noturus stigmosus ) for small macroinvertebrates (French and Jude, 2001).

4.2.4. Fishhook Waterflea
According to the GARP model, if transported to the Great Lakes, the fishhook waterflea, a free-swimming macroinvertebrate, would likely find suitable habitat throughout the region, except for the deeper waters of Lake Superior (Figure 11). The fishhook waterflea has been reported in Lakes Ontario, Michigan (USGS, 2008b), and Erie (University of Minnesota, 2006). The species is predicted to spread to the other Great Lakes, and, once established, it becomes difficult to eradicate (University of Minnesota, 2006). Unlike several of the other modeled species, population densities of the fishhook waterflea increase with distance from shore (IUCN, 2006), suggesting that this species may be able to occupy, given sufficient time, the entire region including the deeper waters of Lake Superior.

4.2.5. Zebra Mussel
The zebra mussel, a mollusk, has already invaded the shoreline areas of all five Great Lakes (Figure 12). The GARP model predicts the zebra model could potentially find suitable habitats throughout most of the Great Lakes region. Zebra mussels were first discovered in North America in 1988 in the Great Lakes. The first account of an established population came from Canadian waters of Lake St. Clair, a water body connecting Lake Huron and Lake Erie. By 1990, zebra mussels had been found in all the Great Lakes. The following year, zebra mussels escaped the Great Lakes basin and found their way into the Illinois and Hudson rivers. Zebra mussels are notorious for their biofouling capabilities by colonizing water supply pipes of hydroelectric and nuclear power plants, public water supply plants, and industrial facilities. Zebra mussels can have profound effects on the ecosystems they invade. They primarily consume phytoplankton, but other suspended material is filtered from the water column including bacteria, protozoans, zebra mussel veligers, other microzooplankton, and silt. Large populations of zebra mussels in the Great Lakes and Hudson River reduced the biomass of phytoplankton significantly following invasion. Diatom abundance declined over 80% and

### 38

Figure 11. GARP-predicted habitat suitability of fishhook waterflea ( Cercopagis pengoi ) in the Great Lakes.

Figure 12. GARP-predicted habitat suitability of zebra mussel ( Dreissena polymorpha ) in the Great Lakes. Inset map shows the locations where the species has been reported.

### 39

transparency, as measured by Secchi depth, increased by 100% during the first years of the invasion in Lake Erie (Holland, 1992). Zebra mussels represent one of the most important biological invasions into North America, having profoundly affected the science of Invasion Biology, public perception, and policy. Zebra mussels are described as poor O 2 

### regulators, which may explain their low success rate in colonizing eutrophic lakes and the hypolimnion.

Me llina and Rasmussen (1994) noted that calcium (Ca 2+ 

### ) levels and water temperatures in the open waters of Lake Superior are too low for the zebra mussel. Zebra mussels require 10 mg/L

of Ca 2+ 

### to initiate shell growth and 25 mg/L to maintain shell growth (USGS, 2008c). Zebra mussels are generally within 2 to 7 m of the water surface (O‘Neill, 2004) but, on rare occasions, have been found at depths exceeding 90 m ( Watkins et al., 2007) . The depth limitation of the species should further restrict the maximum potential spread of the species to Lake Erie and to the shallower waters of the other four Great Lakes (NOAA, 2008). Competition with the quagga mussel also appears to limit zebra mussel spread. Zebra mussels were outcompeted and almost completely replaced by quagga mussels in Lake Ontario between 1995 and 2003 and this trend could occur in other Lakes (Watkins et al., 2007).

4.2.6. Ruffe
The ruffe, a small to medium-sized fish, has already invaded Lake Superior and GARP modeling predicts it will find suitable habitat almost everywhere in all five Great Lakes (Figure 13). GARP models are not able to make a prediction about some of the deeper waters of Lake Superior. Established in the western portion of Lake Superior since about 1988 it has expanded in an easterly direction. It has now become the dominant species in the St. Louis River estuary (Leigh, 1998). Based on bottom trawl samples, ruffe make up an estimated 80% of fish abundances in the southwestern regions of Lake Superior (Leigh, 1998). Ruffe exhibit rapid growth and high reproductive output, and adapt to a wide range of habitat types; therefo re , the species may pose a threat to native North American fish. There is much concern that ruffe may have a detrimental effect on the more desirable species in Lake Superior, such as yellow perch and walleye, by feeding on the young of these species or by competing for food (Fuller and Jacobs, 2008). Ruffe are often associated with bottom waters and can tolerate lacustrine and lotic systems and depths to 85 m (Sandlund et al.

, 1985). The species intolerance to deeper waters may limit its range of potential suitable habitat to Lake Erie, southern Lake Michigan, and

the shallower waters of the other Great Lakes.

### 40

Figure 13. GARP-predicted habitat suitability of ruffe ( Gymnocephalus

cernuus ) in the Great Lakes. Inset map shows the locations where the species has been reported.

4.2.7. Monkey Goby
The GARP model predicts that the monkey goby, a member of the goby fish family, could find suitable habitat in most of Lake Erie and in some portions of Lake Ontario and Lake Huron (Figure 14). Predictions cannot be made for most of the Great Lakes because of data limitations. The monkey goby is closely related to the round goby. Currently, the monkey goby

is confined to Eurasia but it has traveled up the Danube, Dnieper, and Volga Rivers from its native waterways and is becoming an invasive nuisance in these areas. Similar to other

Gobiidaes , the monkey goby prefers shallow water and would likely not survive in deeper

waters.

4.2.8. New Zealand Mud Snail
The New Zealand mud snail, another mollusk, is predicted by GARP modeling to find suitable habitat in most if not all of Lakes, Erie, Ontario, and Michigan (Figure 15) and shorelines of Lakes Huron and Superior. It was first established in Lake Ontario in 1991 (Zaranko et al., 1997) and in Lake Erie in 2005 (Levri et al., 2007). It may also be established in Lake Superior, where some individuals were found in 2001 (Grigorovich et al., 2003b). Mud

### 41

Figure 14. GARP-predicted habitat suitability of monkey goby ( Neogobius fluviatilis ) in the Great Lakes.

Figure 15. GARP predicted habitat suitability of New Zealand mud snail ( Potamopyrgus antipodarum ) in the Great Lakes. Inset map shows the locations where the species has been reported.

### 42

snail populations consist mostly of asexually reproducing females that are born with developing embryos in their reproductive system. This species can be found in all types of aquatic habitats from eutrophic mud bottom ponds to clear rocky streams. It can tolerate a wide range of water temperatures (except freezing), salinity, and turbidity in clean as well as degraded waters. They feed on dead and dying plant and animal material, algae, and bacteria. It can tolerate a broad range of ecological factors thus facilitating its further spread. In moist conditions, this snail can withstand short periods of desiccation. Since this snail is found at depths from 5 to 45 m (Levri et al., 2007) it is unlikely the species will survive in deeper waters.

4.2.9. Tubenose Goby
The tubenose goby, another member of the goby fish family, is predicted by the GARP

model to become established in Lake Erie and the shoreline areas of the other Great Lakes (Figure 16). Predictions could not be made for most of the rest of the region. Their distribution around the inshore areas of the Black and Caspian Seas indicates their potential for widespread occupation of inshore habitats where cover, especially plants, occurs in the lower Great Lakes (Jude et al., 1992). Tubenose gobies have been shown to have a significant overlap in diet preference with rainbow darters, Etheostoma caeruleum , and may compete with these native fish for food (French and Jude, 2001). The usual habitat for this species is shallow bays, offshore banks, or flowing water of streams. However, the tubenose goby also can be found in ponds and canals overgrown with vegetation. Where current is strong, it hides under boulders. It is often found under stones or among weeds, to which it retreats rapidly if disturbed. Some individuals can be found at depths greater than 3 m in the sea. The preferred conditions probably restrict its probable range of suitable habitat to shallower waters.

4.2.10. Rudd
Already occurring in the Great Lakes with an unknown frequency at this time, significant portions of Lakes Erie and Ontario as well as portions of Superior and Michigan are prone to invasion by the rudd, a medium-sized, thick-bodied fish (Figure 17). The rudd‘s tolerance of a variety of habitats has likely contributed to its widespread distribution. In streams and rivers, this fish usually prefers long, slow pools and backwaters. The rudd can be expected to compete for invertebrate food sources with native fishes. In addition, being omnivorous, the rudd can shift its diet to plants, unlike most native fishes. Because rudd are fairly hardy, Nico et al. (2008) indicate that the fish will fare better than many native fishes in waters that are eutrophic or

### 43

Figure 16. GARP-predicted habitat suitability of tubenose goby ( Proterorhinus marmoratus ) in the Great Lakes.

Figure 17. GARP-predicted habitat suitability of rudd ( Scardinius erythrophthalmus ) in the Great Lakes.

### 44

polluted. Predictions cannot be made about the habitat suitability for rudd in parts of Lake Superior, but, given the species preference for littoral waters, it is unlikely the rudd would find suitable habitat in the deeper regions of all the lakes.

4.2.11. Corophium Curvispinum (a n Amphipod)
According to the GARP model, almost all of Lake Erie and the southern shores of Lakes Ontario, Huron, and Michigan are prone to invasion by the amphipod Corophium curvispinum. Predictions for the other locations in the Great Lakes were not possible due to limited data (Figure 18). This amphipod builds tubes on firm surfaces such as rocks, wood, submerged vegetation, or bivalve shells on otherwise sandy or muddy substrata in shallow waters (Frammandearter, 2008). C. curvispinum prefers rivers, estuaries, and other areas with brackish
water, but it can also tolerate freshwater environments.

Figure 18. GARP-predicted habitat suitability of Corophium curvispinum (no common name reported) in the Great Lakes.

### 45

4.2.12. Sand Goby
The sand goby is predicted by GARP modeling to find suitable habitat almost everywhere in all five Great Lakes (Figure 19). This occasionally schooling species occurs primarily in inshore sandy and muddy areas (Froese and Pauly, 2008). The sand goby is a coastal goby of European waters from th e Baltic to the Mediterranean Sea and can grow up to 10 cm in length. Some variation from the GARP modeling prediction is expected because similar to other gobys, the sand goby is unlikely to find suitable habitat in deeper waters.

Figure 19. GARP-predicted habitat suitability of sand goby ( Potamoschistus minutus ) in the Great Lakes.

### 46

4.2.13. Roach
GARP predicts that Lakes Erie and Ontario would provide suitable habitat for the roach, a medium-sized fish in the carp family. Most of the other regions would be unsuitable (Figure 20) although predictions cannot be made about the suitability of habitat of parts of Lake Superior. Adults inhabit slow-flowing or still muddy waters and are abundant in their native rivers, lakes, canals, and reservoirs. Brackish water populations in the Baltic and the Black Sea are anadromous and they are known to thrive in poor quality, even polluted water (Nico and Fuller, 2008). As omnivores, they feed on insects, crustaceans, mollusks, and plants.

Figure 20. GARP-predicted habitat suitability of roach ( Rutilus rutilus ) in the Great Lakes.

4.2.14. Tench
The tench, a medium-sized fish already established in many rivers within the United States, is likely to find suitable habitat in most of Lake Erie and small portions of Lake Ontario and Lake Huron (Figure 21) according to GARP models. The diet consists mainly of aquatic insect larvae and mollusks. Nico and Fuller (2008) considered it a potential competitor for food with sport fishes and native cyprinids and noted that the species is known to stir up bottom sediments, possibly affecting water quality, similar to the common carp.

### 47

Figure 21. GARP-predicted habitat suitability of tench ( Tinca tinca ) in th e Great Lakes.

4.3. VESSEL TRAFFIC AND GREAT LAKES PORTS

The U.S. Great Lakes receive substantial vessel traffic from around the world because of the commodities shipped in and out of the area. The second phase of this study was to better understand whether there is sufficient numbers of propagules (e.g., larvae, seeds, spores, adults) entering the Great Lakes for species to become established. As described in the methods section, we used vessel traffic and ballast water discharges as a surrogate for propagule pressure since no data exists that actually measures the number of propagules released from discharges. By analyzing ballast water discharges, we were able to identify those ports that are at greatest risk.

4.3.1. Analysis of Vessels With Ballast on Board (BOB)
In accordance with U.S. Coast Guard (USCG) regulations, ballast water from transoceanic vessels with ballast on board is exchanged at sea prior to entering the St. Lawrence Seaway. Despite the ballast water exchange (BWE), some ballast water and residue may remain and NIS may survive in the ballast tank and then potentially be released when the ballast water is discharged. We evaluated ballast water discharges (BWD) into U.S. Great Lakes ports from vessels entering the Seaway whose original source of ballast water (i.e., the ballast in the tank

### 48

prior to open ocean exchange) was taken-on from areas outside of the Great Lakes. To interpret these results one must consider that transoceanic vessels carry multiple ballast tanks and each tank may have a different history of ballast water source, exchange, and discharge. Therefore, each ballast tank discharge was counted as a BWD. A transoceanic vessel may carry over 20,000 metric tons of ballast water and have as many as 20 ballast tanks, implying one vessel-trip could have up to 20 ballast discharges at any one Great Lakes port. Our study found that in the period of 2006

– 2007, 618 ballast tanks and 382 thousand metric tons of ballast water were discharged at Great Lakes ports from 107 different vesse ls

. From a global perspective, the BWDs that were evaluated and could be linked to a particular port usually came from the eastern and western areas of the northern Atlantic Ocean (Figure 22). The Gulf of St. Lawrence region, near the St. Lawrence Seaway was the original source of ballast water for over 1/3 of the 618 discharges (Figure 23). Western Europe was the second-most common source, with most of the ballast originating from the southeastern portion of the North Sea (Figure 24). Fifty-eight different foreign ports provided the original source of ballast water ultimately discharged at Great Lakes ports. Figure 25 identifies the most important ports based on the number of vessels leaving these ports and entering the Great Lakes. However, the most common source of ballast water was obtained while in transit and not at any particular port of call. The ports of Antwerpen, Belgium; Puerto Cabello, Venezuela; Haraholmen, Sweden; and Bremen, Germany are responsible for the greatest number of discharges, not including those ports near the entrance to the St. Lawrence Seaway (Figure 25). These ports, however, are not necessarily the source of the greatest volume of BWD. For instance, Haraholmen was ranked fourth among nonNorth American ports in terms of number of tanks discharged, but was ranked eighth in terms of metric tons of BWD (Figure 25 and Appendix E, Table E-1). In this study it was rare to find more than one vessel originating from the 58 foreign ports (Appendix E, Table E-1). Only six ports from outside North America were the source of two or more vessels included in this analysis. Duluth received more than twice the BWDs and twice the volume of ballast water as any other Great Lake port in 2006

– 2007 (Figure 26). The ports of Toledo, Superior, Green Bay, Gary, and Milwaukee, also received over 10,000 metric tons of ballast water (Figure 26 and Appendix E, Table E-2). Appendix E, Table E-3 provides detailed information for those vessels with ballast on board discharging to Great Lakes ports in 2006

– 2007.

### 49

Figure 22. Location of the original source of ballast water taken-on prior to ballast water exchange and discharge in the U.S. Great Lakes. The area of

e ach green circle is proportional to the number of ballast tank discharges.

Figure 23. Location of the source of ballast water taken-on from Canadian ports in or near the Gulf of St. Lawrence prior to ballast water exchange and discharges in the U.S. Great Lakes. The area of e ach green circle is proportional to the number of ballast tank discharges.

### 50

Figure 24. Location of the source of ballast water taken-on from European ports prior to ballast water exchange and discharges in the U.S. Great Lakes. The area of e ach green circle is proportional to the number of ballast tank discharges.

Figure 25. Frequency, volume, and original source of ballast water (prior to ballast water exchange) discharged into U.S. Great Lakes ports, from sources outside the Great Lakes.

### 51

Figure 26. Frequency and volume of ballast water discharges (after ballast water exchange at sea) from ballast on board vessels, when the original source of ballast water came from outside the Great Lakes.

4.3.2. Analysis of Vessels with No Ballast on Board (NOBOB)
Some vessels enter the St. Lawrence Seaway with no ballast on board but may have organisms that remain and survive in the residual material left in the ballast tanks, and are

referred to as NOBOB-RM vessels. These NOBOB-RM vessels can then take-on ballast water in the Seaway (most likely when cargo is off-loaded) and later discharge the ballast water along with residual materials at a Great Lakes port. We combined the 2006 NVMC data with the 2006 NBIC data to identify these types of vessels. There were 1,730 discharges of ballast water at Great Lakes ports from NOBOB-RM vessels in 2006. This is substantially more than the discharges from vessels with ballast on board, supporting the notion that NOBOB vessels may pose a much greater risk. The distribution of the potential sources of ballast water from

NOBOB-RM vessels is somewhat similar to the vessels with ballast on board. Over half of the last five ports of call by these vessels were in southeastern Canada with Western Europe the second most common source of ballast water (Figure 27 and Appendix E, Table E-4). Some of the foreign ports of origin are different between BOB and NOBOB vessels. For example, ten NOBOB-RM vessels included a stop at Europa Point, Gibraltar as one of the last five ports of call before discharging ballast water into the Great Lakes. Other vessels stopped at

### 52

Riga, Latvia; and Santos, Brazil (Figure 27 and Appendix E, Table E-4). Yet, we did not find

an y vessels with ballast on board stopping at these ports. Some ports are visited by both types of vessels, especially Sept Iles, Canada; and Ijmuiden, Netherlands (Figure 27 and Appendix E, Table E-4). Several of the Great Lakes ports, including Duluth, Toledo, and Superior, receive ballast discharges from both NOBOB-RM vessels and vessels with ballast on board. Most Great Lakes ports received far more ballast discharges from NOBOB-RM vessels than BOB vessels in 2006. Several ports receive most of their ballast water from NOBOB-RM vessels, including Sandusky, Conneaut, Buffalo, and Calumet as shown in Figure 28 and Appendix E, Table E-5. Ashtabula was the extreme case, receiving 297 discharges from NOBOB-RM vessels (Figure 28) and only one discharge from a BOB vessel (Figure 26). When both vessel types are considered, the Great Lakes ports at greatest risk of receiving sufficient propagule pressure to facilitate invasion are Duluth, MN; Superior and Milwaukee, WI; Toledo, Ashtabula, and Sandusky, OH; Gary, IN; and Chicago, IL.

Figure 27. Possible source locations of residual materials discharged from vessels that entered the St. Lawrence Seaway with no ballast on board, based on last five ports of call in 2006. After entering the Seaway these vessels picked up ballast water and discharged the ballast water (along with residual materials) at a U.S. Great Lakes port.

### 53

Figure 28. Frequency and volume of discharges in 2006 from NOBOB-RM vessels. NO BOB-RM vessels are vessels that entered the Seaway without ballast on board, picked up ballast water in the Great Lakes, and then discharged the ballast water along with residual material at a Great Lake port. NOBOB-RM vessels must also have visited a port outside the Great Lakes during one of the last five ports of call. Ports listed from left to right according to the results shown in 26 to facilitate comparison.

### 54 5. DISCUSSION

The Great Lakes system has been adversely affected by invasive species. Preventing the transport of these species to the Great Lakes from outside the system is the best way to avert potential ecological and economic impacts. Our analysis of ballast water discharges using vessel traffic data, evaluating similar habitats using th e Genetic Algorithm for Rule-Set Production (GARP) niche model, and a literature review indicate that invasions are likely to occur over the next decade or so. If it is not possible to eliminate the transport of nonindigenous species (NIS) to the Great Lakes, the next best alternative is to monitor for the arrival of potentially invasive species and to control their spread as soon as they arrive. Since we began our investigation, additional ballast-water control measures have been implemented. Beginning with the 2008 navigation season, all vessels must either undergo ballast water exchange (BWE) or flushing before entering the St. Lawrence Seaway (73 FR 37, p. 9,950), even those vessels that heretofore were declared as having no ballast on board. However, even with the more extensive requirements, additional NIS may still reach the Great Lakes. Some saltwater tolerant species may survive the BWE or flushing, and other vectors (e.g., hull fouling organisms) continue to pose a threat. This report provides information that may help resource managers prioritize monitoring efforts by identifying potential invaders and ports at risk. The National Research Council recommends that a binational (United States and Canada) science-based surveillance program be established to monitor for aquatic NIS and that the program involve dedicated lake teams, as well as academic researchers, resource managers, and local citizens groups (NRC, 2008). Since early detection and rapid response is a priority of the National Invasive Species Council (NISC, 2007), the ports and species we identified could be used to structure an early warning and detection system to help evaluate the effectiveness of ballast exchange regulations and practices.

5.1. PREDICTING THE SPREAD OF SPECIES
This study identified 30 potentially invasive species with medium or high risk for spreading to the Great Lakes and causing ecological impacts and another 28 potentially invasive species that have already become established in one or more of the Great Lakes (see Appendix B). Habitat suitability maps are provided along with a summary of invasion potential for 14 modeled species. All of the modeled species are predicted to have the capability to colonize Lake Erie and the shallower waters of the other lakes. Several species may be able to colonize the entire Great Lakes region. Literature regarding the species environmental

### 55

tolerances reveals that the predominant limitation to the spread of several modeled species is their tolerance to water depth. However, managers need to recognize that when NIS are transported to a new environment, species- tolerance ―surprises‖ can occur. For instance, the quagga mussel has been found at deeper depths in the Great Lakes than in its native range (Watkins et al., 2007). Table 3 summarizes the habitat suitability and current status for the 14 modeled species. The modeled species are categorized into two groups: (1) NIS already established in the Great Lakes and having the potential to spread to at least parts of all five lakes; and (2) NIS, not yet established but with the potential to invade the Great Lakes.

5.2. POTENTIAL MONITORING SITES BASED ON VESSEL TRAFFIC
The source of most of the ballast water discharged into the Great Lakes came from 58 different ports located predominantly in Canada and Western Europe, thereby complicating surveillance programs. If just a few foreign ports were the original source of ballast water (prior to exchange) then programs could focus on species found at those foreign ports. The six ports

which received the most ballast water from vessels with ballast on board in 2006

– 2007, in rank order, were Duluth, MN; Toledo, OH; Superior, WI; Green Bay, WI; Gary, IN; and Milwaukee, WI (see Figure 26 and Appendix E, Table E-2). The first three ports, Duluth, Toledo, and Superior, account for 86% of the total volume of ballast water discharged into the Great Lakes. There was no evidence of a frequent, repeated connection from any specific foreign port to a specific port within the Great Lakes. For instance, 11 different vessels discharged ballast water

in Toledo in 2006

– 2007 (see Appendix E, Table E-3). If all 11 vessels obtained ballast water from a single foreign port than monitoring could be targeted for those species occurring at that particular port. Unfortunately, the 11 vessels discharging ballast water in Toledo took-on ballast from 10 different foreign ports. Invasive species can also be transported to the Great Lakes via vessels with no ballast on board but with residue left in the tanks (NOBOB-RM vessels). A different set of foreign ports were found to be the source of ballast water from these vessels (see Figure 27), although ports in Canada and Western Europe predominated. Consistent with the vessels with ballast on board, it was rare to find a frequent connection between particular Great Lakes ports and foreign ports. The ports receiving the greatest volume of ballast water from NOBOB-RM vessels are Duluth, MN, Toledo, OH, and Superior, WI accounting for 54% of the total volume discharged from

these vessels in 2006

– 2007. Ashtabula and Sandusky, OH receive 32% of the ballast water

released from NOBOB-RM vessels.

### 56

Table 3. Composite results for 14 species modeled using GARP

Species/common name Summary of i nvasion potential

Species already established in the Great Lakes and potential for spread to all five Great Lakes
Neogobius melanostromus

— round goby Already spread to all five Great Lakes, with large populations in Lakes Erie and Ontario. Likely to find suitable habitat throughout Lake Erie and in all Great Lakes waters at depths less than 60 m.

Potamopyrgus antipodarum

— New Zealand mud snail Already occurs in isolated areas of Lakes Erie, Ontario, and Superior. Likely to find all shallower waters (<5 0 m depth) as suitable habitat. High spread potential.
Dreissena bugensis

— quagga mussel Already found in all five Great Lakes, with large populations established in Lakes Erie and Ontario. The only possible identified limitation for spread is a specie s depth limitation which is questionable and currently appears to be as deep as 200 m.
Dreissena polymorpha

— zebra mussel Already occurs in all five Great Lakes. Likely to find suitable habitat in most of Lake Erie and portions of other lakes where wate r depth is less than 60 m. May be outcompeted by the quagga mussel.
Gymnocephalus cernuus

— ruffe Already found in Lakes Superior, Michigan, and Huron. The species is probably capable of colonizing most areas within the Great Lakes where water depth is le ss than 85 m.
Species that may invade at least parts of all five Great Lakes

Alosa aestivalis

—

blueback herring Models predict it could find the entire region as suitable habitat, except possibly the deeper waters of Lake Superior.

Pomatoschistus minu tus

— sand goby It is likely this species would find all shallower waters as suitable habitat.

Rutilus rutilus

—

roach Already reported in Lakes Erie and Ontario. Predicted to find suitable

habitat throughout these lakes, and probably into other shoreli ne areas.

Scardinius erythrophthalmus

—

rudd Predicted to find suitable habitat throughout Lake Erie and into the

shallower waters of the other four Great Lakes.
Cercopagis pengoi

— fishhook waterflea Established in Lake Ontario and reported in Lakes Erie and Michigan. Predicted to find suitable habitat throughout the region. Densities increase in deeper waters.

Tinca tinca

—

t ench Found currently in St. Lawrence River. Potential to spread to shallower

waters of most of Lake Erie, and to isolated p ortions of the other Lakes. Tench can spread rapidly once established.

Proterorhinus

marmoratus

—

tubenose goby Already reported as present in Lake St. Clair and western Lake Erie. May

be able to occupy all shallow waters of all five Great Lakes.

Coro phium curvispinum

— (an amphipod) Capable of invading Lake Erie and shallower waters. Not enough data is available to predict if it can find suitable habitat elsewhere.
Neogobius fluviatilis

— monkey goby May be capable of inhabiting shallower waters of al l five Great Lakes.

### 57

Since it is unknown which type of vessel (ballast on board [BOB] or NOBOB-RM) is more likely to transport NIS, ports receiving ballast water from either type of vessel are presumed to be at risk. In order to recommend ports for monitoring we cannot just consider the transport potential, we also need to consider the potential to find suitable habitat. The results of GARP modeling and the literature review reveal that Lake Erie and shallower portions of the other Lakes provide the most favorable habitat for the modeled species, and that the deeper portions of Lake Superior are less hospitable to species invasions (Grigorovich et al., 2003b). However, the shallower portions of Lake Superior, especially the Duluth-Superior harbor, are at greater risk for invasion. Assuming the observed vessel traffic and ballast-water discharge information for 2006 and 2007 is representative, the port of greatest concern for receiving sufficient propagules and providing the most suitable habitat is Toledo, OH. Toledo is located on Lake Erie, a region that the GARP model predicted would have a high chance of providing suitable habitat for the modeled species. Other ports of elevated concern for receiving sufficient propagules and offering suitable habitat are Gary, IN; Milwaukee, WI; Chicago, IL; and Ashtabula and Sandusky, OH. Ports with high transport potential but generally low habitat suitability are Duluth, MN and Superior, WI. The spread of invasive species from beyond the Duluth-Superior harbor may be limited by the colder and deeper waters in the main portion of Lake Superior. Yet, since inter-lake transport can occur to other ports, Duluth and Superior also warrant a monitoring program. Managers may wish to emphasize detection programs at the ports of concern that were identified and may wish to focus on the list of 58 potentially invasive species with a moderate or strong chance to invade and cause ecological or economic impacts. For the 14 modeled species, the focus can be narrowed based on the summary shown in Table 3. Given the new regulations which require all vessels entering the Seaway to undergo either ballast water exchange or flushing at sea, additional research on the tolerance of invasive species to saltwater would enable managers and scientists to better focus monitoring activities on those species that are likely to survive salt water flushing. Subsequent analyses of the NBIC

database is recommended to determine if the 2006

– 2007 data are indeed representative. In summary, we have provided a list of NIS of concern, predicted locations that would provide suitable habitat for 14 modeled species, identified those U.S. Great Lakes ports receiving the most ballast water from sources originating from outside the Great Lakes, and predicted the ports most at risk of invasion. Our findings support the need for detection and monitoring efforts at those ports believed to be at greatest risk. This study also demonstrates the importance of understanding invasion biology by evaluating the two most important predictors of invasion, as suggested by Williamson (1996): propagule pressure and suitable habitat. Further, this may be

### 58

the first time that remote sensing data were used in conjunction with GARP to predict the spread of aquatic invasive species.

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### A

- 1 APPENDIX A. LIST OF NONINDIGENOUS SPECIES THAT HAVE BEEN REPORTED AS OCCURRING IN THE GREAT LAKESA-2 Year of

Invasion Species Common Name Type Endemic Region Location of

First Sighting Vector

1840 Rumex obtusifolius bitter dock Plan t Eurasia Widespread Unknown
1843 Echinochloa crusgalli barnyard grass Plant Eurasia Widespread Release ( deliberate )
1843 Solanum dulcamara bittersweet nightshade Plant Eurasia Widespread Release ( deliberate )

1843 Mentha piperita peppermint Plant Eurasi a Widespread Release ( deliberate )

1843 Conium maculatum poison hemlock Plant Eurasia Widespread Release ( deliberate )

1843 Poa trivalis rough

- stalked meadow grass Plant Eurasia Widespread Release ( deliberate )

1843 Mentha spicata spearmint Plant Eurasia W idespread Release ( deliberate )

1843 Polygonum persicaria lady's thumb Plant Eurasia Widespread Unknown
1847 Rorippa nasturtium

- aquaticum water cress Plant Eurasia Lake Ontario Release ( deliberate )
1860 Elimia virginica snail Mollusk Atlantic NA Erie Can al Canals

1862 Juncus gerardii black

- grass rush Plant Atlantic NA Chicago Shipping, Solid Ballast

1864 Najas marina spiny naiad Plant Eurasia Lake Ontario drainage Shipping, Solid

Ballast

1865 Sonchus arvensis field sow thistle Plant Eurasia Central NY Release ( unintentional )

1866 Carex disticha sedge Plant Eurasia Lake Ontario Shipping, Solid Ballast

1867 Chenopodium glaucum oak leaved goose foot Plant Eurasia Lake Ontario drainage Railroads and

Highways
1869 Lythrum salicaria purple loosestrife Plan t Eurasia Lake Ontario Canals

1871 Bithynia tentaculata faucet snail Mollusk Eurasia Lake Michigan Shipping, Solid Ballast
1873 Alosa pseudoharengus alewife Fish Atlantic NA Lake Ontario Canals

1873 Oncorhynchus tshawytscha chinook salmon Fish Pacific N A Widespread Release ( deliberate )

1874 Epilobium hirsutum great hairy willow herb Plant Eurasia Lake Ontario Release ( unintentional )

1876 Oncorhynchus mykiss rainbow trout Fish Pacific NA Lake Huron drainage Release ( deliberate )

1878 Carassius auratus g oldfish Fish Asia Widespread Release ( unintentional )
1879 Cyprinus carpio common carp Fish Ponto

- Caspian Widespread Release ( deliberate )

1879 Potamogeton crispus curlyleaf pondweed Plant Eurasia Lake Ontario drainage Release ( deliberate )

1880 Typha angu stifolia narrow leaved cattail Plant Eurasia Lake Ontario Canals
1882 Lysimachia nummularia moneywort Plant Eurasia Lake Ontario Release ( deliberate )A-3 Year of

Invasion Species Common Name Type Endemic Region Location of

First Sighting Vector

1882 Alopecurus geniculatus water foxtail Plant Eurasia Lake Erie Release ( deliberate )

1883 Salmo trut ta brown trout Fish Eurasia Lake Michigan drainage Release ( deliberate )

1884 Agrostis gigantea redtop Plant Eurasia Lake Superior Release ( deliberate )

1884 Rorippa sylvestris creeping yellow cress Plant Eurasia Lake Ontario Shipping, Solid Ballast
1886 Salix fragilis crack willow Plant Eurasia Widespread Release ( deliberate )

1886 Salix purpurea purple willow Plant Eurasia Widespread Release ( deliberate )

1886 Myosotis scorpioides true forgot

- me

- not Plant Eurasia Lake Ontario Release ( deliberate )
1886 S alix alba white willow Plant Eurasia Widespread Release ( deliberate )

1886 Iris pseudacorus yellow flag Plant Eurasia Lake Ontario Release ( deliberate )

1892 Lycopus asper western water horehound Plant Mississippi R. Lake Erie Release, ( unintention al )

1893 Puccinellia distans weeping alkali grass Plant Eurasia Lake Ontario Shipping, Solid Ballast
1894 Stellaria aquatica giant chickweed Plant Eurasia Lake St. Clair Unknown

1895 Juncus compressus flattened rush Plant Eurasia Lake Ontario drainage Release

( u nintentional )

1895 Pisidium moitessierianum pea clam Mollusk Eurasia Lake Superior Shipping, Solid Ballast
1896 Carex flacca sedge Plant Eurasia Detroit River Unknown

1897 Valvata piscinalis European valve snail Mollusk Eurasia Lake Ontario Shipping, So lid Ballast

1897 Pisidium amnicum pea clam Mollusk Eurasia Lake Ontario Shipping, Solid Ballast
1901 Rumex longifolius yard dock Plant Eurasia Lake Superior Release ( deliberate )

1901 Radix auricularia European ear snail Mollusk Eurasia Lake Michigan Rel ease ( unintentional )

1902 Aeromonas salmonicida furunculosis Other Invertebrate Unknown Unknown Release ( unintentional )

1902 Sonchus arvensis var.

glabrescens smooth field sow thistle Plant Eurasia Lake Erie Release ( unintentional )

1903 Lycopus europaeu s European water horehound Plant Eurasia Lake Ontario Shipping, Solid Ballast

1905 Butomus umbellatus flowering rush Plant Eurasia Detroit River Shipping, Solid Ballast
1906 Viviparus georgianus banded mystery snail Mollusk Mississippi R. Lake Michigan A quarium release
1912 Impatiens glandulifera Indian balsam Plant Asia Lake Huron Release ( deliberate )A-4 Year of

Invasion Species Common Name Type Endemic Region Location of

First Sighting Vector

1912 Osmerus mordax rainbow smelt Fish Atlantic NA Lake Michigan drainage Release ( deliberate )

1913 Alnus glutinosa black alder Plant Eurasia Widespread Release ( deliberate )

1913 Lysimachia vulgaris garden loosetrife Plant Eurasia Lake Ontario Release ( deliberate )

1913 Rhamnus frangula glossy buckthorn Plant Eurasia L ower Great Lakes, Ontario Release ( deliberate )

1915 Mentha gentilis creeping whorled m int Plant Eurasia Lake Ontario Release ( deliberate )

1915 Veronica beccabunga European brookline Plant Eurasia Lake Ontario Shipping, Solid Ballast

1916 Pluchea odorata var.

purpurescens salt

- marsh fleabane Plant Atlantic NA Lake Erie drainage Release

( un intentional )

1916 Pisidium henslowanum henslow's pea clam Mollusk Eurasia Lake Ontario Shipping, Solid Ballast

1918 Gillia altilis snail Mollusk Atlantic NA Lake Ontario drainage Canals

1922 Juncas inflexus rush Plant Eurasia Lake Ontario drainage Unkno wn

1923 Gambusia affinis western mosquitofish Fish Mississippi R. Lake Michigan drainage Release ( deliberate )

1924 Sphaerium corneum fingernail clam Mollusk Eurasia Lake Ontario drainage Shipping, Ballast

Water

1925 Marsilea quadrifolia European water c lover Plant Eurasia Lake Ontario drainage Release ( deliberate )

1926 Enteromorpha intestinalis green alga Benthic Alga Atlantic NA Lake Ontario Release ( unintentional )

1927 Acentropus niveus aquatic moth Other Invertebrate Eurasia St

. Lawrence R

. , Montrea l Release

( unintentional )

1928 Noturus insignis margined madtom Fish Atlantic NA Lake Ontario drainage Canals

1928 Lepomis microlophus redear sunfish Fish S outhern U.S. Lake Michigan drainage Release ( deliberate )

1929 Lepomis humilis orange spotted sunf ish Fish Mississippi R. Lake Erie drainage Canals

1930 Nymphoides peltata yellow floating heart Plant Eurasia Lake Erie drainage Release

( unintentional )

1931 Cipangopaludina chinensis

malleata Oriental mystery snail Mollusk Asia Niagara R iver Release (a q uarium )

1932 Najas minor minor naiad Plant Eurasia Lake Erie drainage Release ( deliberate )

1933 Oncorhynchus kisutch coho salmon Fish Pacific Lake Erie Release ( deliberate )A-5 Year of

Invasion Species Common Name Type Endemic Region Location of

First Sighting Vector

1933 Craspedacusta sowerbyi freshwater jellyfish Other Invertebrate Asia Lake E rie drainage Release

( unintentional )

1934 Lophopodella carteri bryozoan Other Invertebrate Asia Lake Erie Canals

1935 Cabomba caroliniana fanwort Plant Southern U.S. Lake Michigan drainage Release (a quarium )

1936 Sparganium glomeratum bur reed Plant Eu rasia Lake Superior Unknown

1938 Actinocyclus normanii fo. subsalsa diatom Phytoplankton Eurasia Lake Ontario Shipping, Ballast Water

1938 Diatoma ehrenbergii diatom Phytoplankton Widespread Lake Michigan Shipping, Ballast Water

1938 Stephanodiscus bind eranus diatom Phytoplankton Eurasia Lake Michigan Shipping, Ballast Water

1939 Misgurnus anguillicaudatus Oriental weatherfish Fish Asia Lake Huron drainage Release

( unintentional )
1940 Cipangopaludina japonica Oriental mystery snail Mollusk Asia Lake Er ie Release ( deliberate )
1940 Glyceria maxima reed sweet

- grass Plant Eurasia Lake Ontario Release ( deliberate )

1943 Tanysphyrus lemnae aquatic weevil Other Invertebrate Eurasia Unknown Unknown

1946 Cyclotella pseudostelligera diatom Phytoplankton Widespr ead Lake Michigan Shipping, Ballast Water

1946 Stephanodiscus subtilis diatom Phytoplankton Eurasia Lake Michigan Shipping, Ballast Water

1950 Phenacobius mirabilis suckermouth minnow Fish Mississippi R. Lake Erie drainage Canals

1950 Morone americana w hite perch Fish Atlantic NA Lake Ontario Canals

1950 Oncorhynchus nerka kokanee Fish Pacific NA Lake Ontario drainage Release ( deliberate )

1950 Cirsium palustre marsh thistle Plant Eurasia Lake Superior Unknown
1950 Potamothrix bedoti oligochaete Anneli d Ponto

- Caspian Lake Ontario Unknown

1950 Pluchea odorata var. succulenta salt

- marsh fleabane Plant Atlantic NA Lake Ontario drainage Unknown

1951 Branchiura sowerbyi oligochaete Annelid Asia Lake Michigan drainage Release

( unintentional )

1951 Carex acu tiformis swamp sedge Plant Eurasia Lake Michigan drainage Unknown

1952 Myriophyllum spicatum Eurasian watermilfoil Plant Eurasia Lake Erie Release (a quarium )
1952 Potamothrix moldaviensis oligochaete Annelid Ponto

- Caspian Lake Ontario UnknownA-6 Year of

Invasion Species Common Name Type Endemic Region Location of

First Sighting Vector

1956 Cor

dylophora caspia hydroid Other Invertebrate Ponto

- Caspian Lake Erie Release ( unintentional )

1956 Oncorhynchus gorbuscha pink salmon Fish Pacific NA Lake Superior Release ( unintentional )

1958 Eurytemora affinis calanoid copepod Zooplankton (crustacean) Wi despread Lake Ontario Shipping, Ballast Water

1959 Trapa natans water chestnut Plant Eurasia Lake Ontario drainage Release (a quarium )

1959 Lasmigona subviridis mussel Mollusk Atlantic NA Lake Ontario drainage Canals

1959 Pisidium supinum humpback pea c lam Mollusk Europe Lake Ontario Shipping, Ballast Water

1960 Glugea hertwigi protozoan Other Invertebrate Eurasia Lake Erie Release ( unintentional )

1960 Polygonum caespitosum var.

longisetum Bristly Lady's Thumb Plant E. Asia Lake Erie drainage Unknown

1962 Lepisosteus platostomus shortnose gar Fish Mississippi R. Lake Michigan drainage Canals

1962 Thalassiosira weissflogii diatom Phytoplankton Widespread Detroit R iver Shipping, Ballast Water

1963 Skeletonema potamos diatom Phytoplankton Widespread Lak e Erie drainage Shipping, Ballast

Water
1964 Bangia atropurpurea red alga Benthic Alga Atlantic NA Lake Erie Shipping

1964 Cyclotella atomus diatom Phytoplankton Widespread Lake Michigan Shipping, Ballast Water

1964 Cyclotella cryptica diatom Phytoplan kton Widespread Lake Michigan Shipping, Ballast Water

1964 Cyclotella woltereki diatom Phytoplankton Widespread Lake Michigan Shipping, Ballast Water

1964 Chroodactylon ramosum red alga Benthic Alga Atlantic Ocean Lake Erie Shipping, Ballast Water

1965 Potamothrix vejdovskyi oligochaete Annelid Ponto

- Caspian Lake Erie Shipping? Ballast Water?

1966 Eubosmina coregoni waterflea Zooplankton (crustacean) Eurasia Lake Michigan Shipping, Ballast Water

1966 Epilobium parviflorum small flowered hairy willow he rb Plant Eurasia Lake Michigan drainage Unknown

1967 Skistodiaptomus pallidus calanoid copepod Zooplankton (crustacean) Mississippi R. Lake Ontario Release ( unintentional )A-7 Year of

Invasion Species Common Name Type Endemic Region Location of

First Sighting Vector

1968 Myxobolus (Myxosoma) cerebralis salmonid whirling disease Other Invertebrate Unknown Lake Erie drainage Release

( unintentional )

1968 Dugesia polychroa flatworm Other Invertebrate Europe Lake Ontario Shipping, Ballast Water

1969 Solidago sempervirens seaside goldenrod Plant Atlantic NA Lake Michigan Release ( unintentional )

1971 E

nneacanthus gloriosus bluespotted sunfish Fish Atlantic NA Lake Ontario drainage Release (a quarium )

1972 Cyclops strenuus copepod Zooplankton (crustacean) Hudson Bay Lake Superior Canals (water diversion)

1972 Hydrocharis morsus

- ranae European frogbit P lant Eurasia Lake Ontario Release ( unintentional )

1973 Skeletonema subsalsum diatom Phytoplankton Eurasia Lake Erie Shipping, Ballast Water

1973 Thalassiosira guillardii diatom Phytoplankton Widespread Lake Erie Shipping, Ballast Water

1973 Thalassiosir a pseudonana diatom Phytoplankton Widespread Lake Erie drainage Shipping, Ballast

Water

1973 Nitocra hibernica harpacticoid copepod Benthic Crustacean Eurasia Lake Ontario Shipping, Ballast Water

1975 Sphacelaria fluviatilis brown alga Benthic Alga Asia Lake Michigan drainage Release (a quarium )

1975 Lotus corniculatus birdsfoot trefoil Plant Eurasia Lake Superior Release ( deliberate )

1975 Renibacterium (Corynebacterium)

salmoninarum b acterial k idney d isease Bacteria Unknown L ake Superior Release ( unin tentional )

1975 Sphacelaria lacustris brown alga Benthic Alga Unknown Lake Michigan Shipping, Ballast Water

1975 Hymenomonas roseola cocco

- lithophorid alga Phytoplankton Eurasia Lake Huron Shipping, Ballast Water

1978 Biddulphia laevis diatom Phytoplank ton Widespread Lake Michigan Shipping, Ballast Water

1978 Chaetoceros hohnii diatom Phytoplankton Unknown Lake Huron Shipping, Ballast Water

1978 Thalassiosira lacustris diatom Phytoplankton Eurasia Lake Erie Shipping, Ballast Water

1979 Notropis bucha nani ghost shiner Fish Mississippi R. Lake St. Clair drainage R elease (bait)

1979 Enteromorpha prolifera green alga Benthic Alga Atlantic NA Lake St. Clair drainage Unknown

1980 Corbicula fluminea Asiatic clam Mollusk E ast Asia Lake Erie Release (a quari um )A-8 Year of

Invasion Species Common Name Type Endemic Region Location of

First Sighting Vector

1980 Ripistes parasita oligochaete Annelid Eurasia Lake Huron Shipping, Ballast Water

1980 Daphnia galeata galeata waterflea Zooplankton (crustacean) Eurasia Lake Erie Shipping, Ballast Water

1982 Lupinus polyphyllus lupine Plant Eurasia Lake Superi or Release ( unintentional )

1982 Bythotrephes longimanus spiny waterflea Zooplankton (crustacean) Eurasia Lake Ontario Shipping, Ballast Water

1983 Nitellopsis obtusa green alga Phytoplankton Eurasia Lake St. Clair Shipping, Ballast Water

1983 Gianius (P hallodrilus) aquaedulcis oligochaete Annelid Europe Niagara R iver Shipping, Ballast Water

1985 Salmincola lotae copepod Zooplankton (crustacean) Eurasia Lake Superior Unknown

1986 Gymnocephalus cernuus Eurasian ruffe Fish Ponto

- Caspian Lake Superior Ship ping, Ballast Water

1986 Apeltes quadracus fourspine stickleback Fish Atlantic NA Lake Superior Shipping, Ballast Water

1988 Argulus japonicus parasitic copepod Zooplankton (crustacean) Asia Lake Michigan Release (a quarium )

1988 Thalassiosira baltica di atom Phytoplankton Europe Lake Ontario Shipping, Ballast Water

1988 Bosmina maritima waterflea Zooplankton (crustacean) Eurasia Lake Erie Shipping, Ballast Water

1988 Dreissena polymorpha zebra mussel Mollusk Ponto

- Caspian Lake St. Clair Shipping, Ballas t Water
1989 Scardinius erythrophthalmus rudd Fish Eurasia Lake Ontario R elease (Bait)

1989 Dreissena bugensis quagga mussel Mollusk Ponto

- Caspian Lake Ontario Shipping, Ballast Water

1990 Neogobius melanostomus round goby Fish Ponto

- Caspian St. Clair R iver Shipping, Ballast Water

1990 Proterorhinus marmoratus tubenose goby Fish Ponto

- Caspian St. Clair R iver Shipping, Ballast Water

1991 Potamopyrgus antipodarum New Zealand mud snail Mollusk Australasia Lake Ontario Shipping, Ballast Water

1992 Acantho stomum sp. digenean fluke Other Invertebrate Eurasia Lake Superior Shipping, Ballast Water

1992 Ichthyocotylurus pileatus digenean fluke Other Invertebrate Ponto

- Caspian Lake Superior Shipping, Ballast WaterA-9 Year of

Invasion Species Common Name Type Endemic Region Location of

First Sighting Vector

1992 Neascus brevicaudatus digenean fluke Othe r Invertebrate Eurasia Lake Superior Shipping, Ballast Water

1992 Trypanosoma acerinae flagellate Other Invertebrate Ponto

- Caspian Lake Superior Shipping, Ballast Water

1992 Dactylogyrus amphibothrium monogenetic fluke Other Invertebrate Eurasia Lake Sup erior Shipping, Ballast Water

1992 Dactylogyrus hemiamphibothrium monogenetic fluke Other Invertebrate Eurasia Lake Superior Shipping, Ballast Water

1994 Echinogammarus ischnus amphipod Benthic Crustacean Ponto

- Caspian Detroit River /Lake Erie Shipping, Ballast

Water

1994 Scolex pleuronectis cestode Other Invertebrate Ponto

- Caspian Lake St. Clair Shipping, Ballast Water

1994 Sphaeromyxa sevastopoli mixosporidian Other Invertebrate Ponto

- Caspian Lake St. Clair Shipping, Ballast Water

1994 Neoergasilus j aponicus copepod Zooplankton (crustacean) Eastern Asia St. Clair River Unknown

1994 Megacyclops viridis cyclopoid copepod Zooplankton (crustacean) Europe Lake Huron Unknown

1995 Alosa aestivalis blueback herring Fish Atlantic NA Lake Ontario Canals

1996 Heteropsyllus nr. nunni harpacticoid copepod Benthic Crustacean Atlantic NA? Lake Michigan Shipping, Ballast Water

1997 Acineta nitocrae suctorian Other Invertebrate Eurasia Lake Erie Shipping, Ballast Water

1998 Cercopagis pengoi fish

- hook waterflea Zo oplankton (crustacean) Ponto

- Caspian Lake Ontario Shipping, Ballast Water

1998 Schizopera borutzkyi harpacticoid copepod Benthic Crustacean Ponto

- Caspian Lake Michigan Shipping, Ballast Water

1999 Daphnia lumholtzi waterflea Zooplankton (crustacean) Afri ca,

Australasia Lake Erie Release ( unintentional )

1999 Nitocra incerta harpacticoid copepod Benthic Crustacean Ponto

- Caspian Detroit River Shipping, Ballast Water

2000 Heterosporis sp. microsporidian Other Invertebrate Unknown Lake Ontario Unknown

2001 Rhabdovirus carpio spring viraemia of
carp (SVC) Virus Eurasia Lake Michigan drainage Release (a quarium )

2001 Gammarus tigrinus A mphipod Benthic Crustacean Atlantic NA L ake Superior (L ake Huron in 2002) Shipping, Ballast

Water

2001 Psammonobiotus communi s testate amoeba Other Invertebrate Ponto

- Caspian Lake Ontario Shipping, Ballast WaterA-10 Year of

Invasion Species Common Name Type Endemic Region Location of

First Sighting Vector

2002 Ranavirus sp. l argemouth b ass v irus Virus U nknown Lake Michigan drainage Release

( unintentional )

2002 Psammonobiotus linearis testate amoeba Other Invertebrate Po nto

- Caspian Lake Ontario Shipping, Ballast Water

2002 Psammonobiotus dziwnowi testate amoeba Other Invertebrate Ponto

Caspian? Lake Ontario Shipping, Ballast Water

2002 Cylindrospermopsis raciborskii cyanobacterium Phytoplankton South America? Lake Michi gan

drainage Unknown

2002 Piscirickettsia cf. salmonis muskie pox Other Invertebrate U nknown Lake St. Clair Unknown

2003 Enteromorpha flexuosa green alga Benthic Alga Widespread Lake Michigan drainage Shipping

2005 Novirhabdovirus sp. VHS Virus Atlantic NA? Lake Ontario Shipping?

, Ballast Water?

2006 Hemimysis anomala bloody

- red mysid Benthic Crustacean Ponto

- Caspian Lake Michigan drainage Shipping, Ballast

Water



### Source: http://www.glerl.noaa.gov/res/Programs/ncrais/docs/great-lakes-list.xls (accessed March 5, 2008).

### APPENDIX B. NONINDIGENOUS SPECIES THAT MAY SPREAD TO THE GREAT LAKES

B-1Table B-1. Summary of Literature Review for potentially invasive species. The 58 species shown as shaded represent those withthegreatest risk.



### B-2Type of OrganismCommon Name(Scientific Name)InGL?aInvasionHistorybSpreadPotential inGLcEcol.Impact inGLdSpeciesOriginePossibleSourcefConcern, Consequence, InvasiongLit. SourcehcnidarianHydroid(Cordylophora caspia)YesYesHighMedPontoCaspianSeaaquarium;ballastwaterestablished in L. Erie, Baltic Sea basin; invasionhistory13, 19, 52crustaceanAmphipod(Pontogammarus crassus)NoYesNEKNEKCaspianSeaballastwaterestablished in Baltic Sea; not established yet,may be introduced13, 19, 30crustaceanAmphipod(Corophium curvispinum)NoYesHighMedCaspianSeaballastwaterfound in L. St. Clair; invasion history; displacedfilter-feeding caddisflies widely distributed inEurope; high densities in Baltic ports; establisheditself in British Isles14, 19, 23, 29,30crustaceanAmphipod(Dikerogammarus villosus)NoYesHighHighPontoCaspianSeaballastwater;canals(Europe)not established yet; established in Baltic Seabasin; eat and replace native amphipods;invading and decreasing natives in Europe; haverapid impact on macroinvertebrate survival,leading to population declines; invading most ofWestern Europe’s hydrosystems6, 7, 8, 13, 19,30crustaceanAmphipod(Pontogammarusrobustoides)NoYesHighNEKPontoCaspianSeaballastwater;canals(Europe)not established yet; established in Baltic Seabasin13, 19, 30crustaceanAmphipod(Echinogammarus ischnus)YesYesHighMedPontoCaspianSeaballastwaterestablished in L. Erie, Huron, Ontario; invasionhistory; limited dispersal capability; displacednative amphipodGmelinoides fasciatus4, 9, 13, 19, 25,26, 29, 32, 52,53crustaceanAmphipod(Dikerogammarushaemobaphes)NoYesHighMedPontoCaspianSeaballastwater;canals(Europe)not established yet; established in Baltic Seabasin13, 19, 30crustaceanAmphipod(Corophium sowinskyi)NoYesMedMedPontoCaspianSeacanalsspreading across Europe; not established yet;could alter littoral communities and food webs30crustaceanAmphipod(Echinogammaruswarpachowskyi)NoYesHighMedPontoCaspianSeaballastwaternot established yet; established in Baltic Seabasin13, 19crustaceanAmphipod(Pontogammarus aralensis)NoYesHighMedPontoCaspianSeaballastwaternot established yet; established in Baltic Seabasin13

### B-3crustaceanAmphipod(Echinogammarus berilloni)NoNEKNEKNEKPontoCaspianSeaballastwaternot established yet, may be introduced13crustaceanAmphipod(Echinogammarustrichiatus)NoNEKNEKNEKPontoCaspianSeaballastwaternot established yet, may be introduced13crustaceanAmphipod(Gammarus tigrinus)NoNEKNEKNEKNorthAmericaballastwaternot established yet, may be introduced13crustaceanAmphipod(Gmelinoides fasciatus)NoNEKNEKNEKAsiaballastwaternot established yet, may be introduced13crustaceanAmphipod(Iphigenella shablensis)NoNEKNEKNEKPontoCaspianSeaballastwaternot established yet, may be introduced13crustaceanAmphipod(Pontogammarusmaeoticus)NoNEKNEKNEKPontoCaspianSeaballastwaternot established yet, may be introduced13crustaceanAmphipod(Pontogammarus obesus)NoNEKNEKNEKPontoCaspianSeaballastwaternot established yet, may be introduced13, 30crustaceanAmphipod(Pontogammarussubnudas)NoNEKNEKNEKPontoCaspianSeaballastwaternot established yet, may be introduced13crustaceanIsopod(Jaera istri)NoNEKNEKNEKPontoCaspianSeaballastwaternot established yet, may be introduced13crustaceanIsopod(Jaera sarsi)NoNEKNEKNEKPontoCaspianSeaballastwaternot established yet, may be introduced13crustaceanIsopod(Proasellus coxalis)NoNEKNEKNEKPontoCaspianSeaballastwaternot established yet, may be introduced13crustaceanIsopod(Proasellus meridianus)NoNEKNEKNEKPontoCaspianSeaballastwaternot established yet, may be introduced13

### B-4crustaceanMysid shrimp(Hemimysis anomala)NoYesHighMedPontoCaspianSeaballastwaternot established yet; established in Baltic Seabasin; reduction of zooplankton; biomagnificationof contaminants at higher trophic levels; adaptionto shallow, warm waters13, 19, 30crustaceanMysid shrimp(Paramysis ullskyi)NoYesMedNEKPontoCaspianSeaballastwaternot established yet; established in Baltic Seabasin; reduction of zooplankton; biomagnificationof contaminants at higher trophic levels; adaptionto shallow, warm waters30crustaceanMysid shrimp(Limnomysis benedeni)NoYesNEKNEKPontoCaspianSeaballastwaternot established yet; established in Baltic Seabasin; reduction of zooplankton; biomagnificationof contaminants at higher trophic levels; adaptionto shallow, warm waters13, 19, 30crustaceanMysid shrimp(Paramysis intermedia)NoYesNEKNEKPontoCaspianSeaballastwaternot established yet; established in Baltic Seabasin; reduction of zooplankton; biomagnificationof contaminants at higher trophic levels; adaptionto shallow, warm waters13, 30crustaceanMysid shrimp(Paramysis lacustris)NoYesNEKNEKPontoCaspianSeaballastwaternot established yet; established in Baltic Seabasin; reduction of zooplankton; biomagnificationof contaminants at higher trophic levels; adaptionto shallow, warm waters13, 19, 30crustaceanPseudocumid(Pseudocumacercaroides)NoNEKNEKNEKPontoCaspianSeaballastwaternot established yet, may be introduced13crustaceanPseudocumid(Pterocuma pectinata)NoNEKNEKNEKPontoCaspianSeaballastwaternot established yet, may be introduced13crustaceanRusty crayfish(Orconectes rusticus)NoYesHighHighSoutheast UnitedStatesbaitfound in inland waters of Michigan (not GreatLakes); invasion history; spreading north intoOntario, northern Midwest; reduction ofvegetation important to native fish for food andcover; habitat destruction; affects native crayfishindustry (Ontario)4, 17, 46, 48,49, 56fishAlewife(Alosa pseudoharengus)YesYesPresentHighAtlanticcoast ofNorthAmericabait fish;canalsfound in L. Ontario, Erie, Michigan, Superior;restructure a lake’s food web, leaving less foodfor native fish; contributed to extinction of somenative species5, 9, 11, 29, 30,35, 44, 49, 52,55, 56fishArowana(Osteoglossum bicirrhosum)NoNEKNEKNEKSouthAmericaornamentalpotential there but have slow growth rate; inabilityto survive temperatures below 58 degreesFahrenheit11, 31

### B-5fishBighead carp(Hypophthalmichthys nobilis /Aristichthys nobilis)YesYesHighMedChinaaquaculture;fish market;canalsfew isolated cases in L. Erie; tolerate low temp;established in Mississippi River basin; invasionhistory; vast mobility;voracious consumptionhabits; clog fishing nets and scare awaycommercial fish; competes with native fish11, 12, 14, 17,31, 34, 39, 43,44, 46, 49, 56fishBlack carp(Mylopharyngodon piceus)NoNoNEKNEKChinaaquaculturenot established yet; reports found in conclave ofOhio, Missouri, and Mississippi Rivers; vastmobility; voracious consumption habits; clogfishing nets and scare away commercial fish; riskto commercial shellfish stocks11, 18, 34, 39,43, 46fishBlack sea silverside(Aphanius boyeri)NoNEKHighNEKPontoCaspianSeabait;aquaculture18fishBleak(Alburnus alburnus)NoYesHighMedPontoCaspianSeaaquaculture;baitinvasive in Baltic; feed on crustaceans11,18fishBlue catfish(Ictalurus furcatus)NoYesMedMedNorthAmericaaquaculture;sport fishspreading outside natural range; will eat anyspecies of fish they can catch, along withcrayfish, freshwater mussels, frogs, and otherreadily available aquatic food sources11, 18fishBlue tilapia(Oreochromis aureus)NoYesNEKNEKAfrica/Eurasiaaquaculture;sport fishspreading in U.S.; invasive in FL; localabundance and high densities in certain areashave resulted in marked changes in fishcommunity structure; private culture in Ontario11,17, 18, 42,52fishBlueback herring(Alosa aestivalis)YesNoPresentHighAtlanticcoast ofNorthAmericacanalsinvasive in L. Ontario; impede recovery ofdepressed populations ofindigenous fishes suchas cisco and lake trout; cold water may preventestablishment9, 11, 28, 29,44, 52, 53fishBullhead(Cottus gobio)NoNEKHighNEKEuropebaitoften behave aggressively towards one another,and competition for shelter and foraging spacecan be intense11, 18fishCaspian shad(Caspialosa caspia)NoNEKMedNEKPontoCaspianSeafish market11, 18fishCaucasian goby(Knipowitschiacaucasica)NoYesHighMedPontoCaspianSeaballastwaterinvasion in Baltic; predators which feed on smallbenthic animals11, 18

### B-6fishCherry salmon(Oncorhynchus masou)NoNEKMedNEKNorthPacific;Japansport fishwidespread11, 18fishChub(Leucaspius cephalus)NoNEKNEKNEKPontoCaspianSeaornamental;live bait;sport fish11, 18fishChum salmon(Oncorhynchus keta)NoNEKHighNEKAsia/North Pacificsport fishwidespread11, 18fishClown loach(Botia macracanthus)NoNEKNEKNEKIndonesiaornamentalpotential there but have slow growth rate11, 31fishCommon dace(Leuciscus leuciscus)NoYesHighNEKPontoCaspianSeabaitwidespread in Europe, showed invasion in onecountry11, 18fishCommon tilapia(Oreochromismossambica)NoNEKNEKNEKAfricafish market;aquacultureprivate culture in Ontario11, 17, 18, 42fishEurasian minnow(Phoxinus phoxinus)NoYesHighNEKPontoCaspianSeabaitinvasive in Baltic11, 18fishEuropean perch(Perca fluviatilis)NoYesHighMedPontoCaspianSeasport fishwidespread prized for angling (introduced inmany countries); because widespread,designated as invasive due to impacts on nativespecies; cannibalism is common11, 18, 49fishEuropean ruffe(Gymnocephaluscernuus)YesYesPresentHighPontoCaspianSeaballastwater;canals; baitinvaded L. Superior, Huron; now in L. Michigan;invasive history in Europe; displace nativespecies; predation on native fish eggs;competition with native fish; L. Superior nativefish declined since introduction of ruffe11, 16, 23, 29,30, 37, 43, 16,48, 49, 52, 56fishEuropean whitefish (Vendace)(Coregonus albula)NoYesHighNEKEuropesport fishsome invasion history11, 18fishFourspine stickleback(Apeltesquadracus)YesNEKNEKNEKEurope/Atlanticcoast ofNorthAmericaballastwater;ornamentalconfined to coasts;rapid increases ofApeltesinThunder Bay (L. Superior) suggests the speciesis displacing native sticklebacks at a rapid rate11, 52

### B-7fishGhost (or glass) catfish(Kryptopterus bicirrhis)NoNEKNEKNEKAsiaornamentalpotential there but have slow growth rate11, 31fishGiant or red snakehead(Channa micropeltes)NoNEKNEKNEKAsiaornamentalpotential there but have slow growth rate11, 31fishGoldfish(Carassius auratus)YesYesHighHighChina/Japanornamentalof the large established populations are recordedfrom the vicinity of western L. Erie, widespread;extensive invasion history; concern of impacts oncommunity, increasing turbidity, predation ofnative fish, help facilitate algal blooms; toleratelow temperatures9, 11, 31, 49fishGrass carp(Ctenopharyngodon idella)NoYesHighHighEasternAsiaaquaculture;fish marketfound isolated in L. Erie, Ontario, Huron; grazeson aquatic vegetation reducing plant density orremoving all aquatic vegetation from a body ofwater; competes with native fish; tolerate lowtemperatures; invasion history11, 14, 17, 31,43, 44, 46, 52fishInland silverside(Menidia beryllina)NoYesNEKNEKEasternU.S.coastaquaculture;sport fishcould replace native fish; replaced native fish inCA, OK; found in Mississippi conclave of Illinoisand Ohio Rivers11, 18, 52fishKoi (common) carp(Cyprinus carpio)YesYesHighHighEurasiaornamental;aquaculturefound in Great Lakes; extensive invasion history;globally widespread; uproot and destroysubmerged aquatic vegetation and therefore maybe detrimental to duck and native fishpopulations; tolerate low temperatures11, 31, 49fishLongtail goby(Ctenogobius sagittula)NoNEKNEKNEKEasternPacific11, 18fishMonkey goby(Neogobius fluviatilis)NoYesHighHighEurasiaballastwaterinvasion history in Europe; mass invasion ofmonkey goby is connected with the intensiveconsumption of planktoncrustaceans; competeswith other small fish for food and space11, 14, 18, 30,51fishMummichog(Fundulus heteroclitus)NoNEKNEKNEKWesternAtlantic/EasternU.S.coastbait;aquarium11, 18fishNile tilapia(Oreochromis niloticus)NoNEKNEKNEKAfricafish market;aquacultureprivate culture in Ontario; Mississippi11, 17,18, 42

### B-8fishOriental weatherfish(MisgurnusanguillicaudatusYesYesHighMedAsiaornamental;aquacultureestablished in Shiawassee River and L.Michigan; reduce populations of aquatic insectsimportant as food to native fishes; invasionhistory; tolerate low temperatures11, 14, 31, 44fishPacu(Colossoma macropomum)NoNEKNEKNEKSouthAmericaaquaculture;ornamentalmay compete with larvae of native fish speciesfor plankton11,18fishPike killifish(Belonesox belizanus)NoNEKNEKNEKCentralAmericaaquariumvoracious predator and has been known toreduce populations of eastern mosquitofish(Gambusia holbrooki) and other native poeciliidand cyprinodontid populations11,18fishPontic shad(Alosa pontica)NoNEKMedNEKPontoCaspianSeafish market;aquarium11,18fishRacer goby(Neogobius gymnotrachelusNoYesHighMedPontoCaspianSeacanals;ballastwaterspreading in Europe11,18, 51fishRainbow smelt(Osmerus mordax)YesYesMedHighAtlanticcoast ofCentralAmericasport fishErie Canal; Lake Ontario, Michigan, Superior;Minnesota; dominant prey form for salmonids;contributed to extinction of blue pike; affectimperiled species11, 44, 52, 53,55, 56fishRed tail botia(Botia modesta)NoNEKNEKNEKAsiaornamentalpotential there but have slow growth rate11, 31fishRedear sunfish(Lepomis microlophus)YesNEKHighNEKAtlanticcoast ofNorthAmericaaquaculture;sport fishfound in L. Michigan; introduced redear areassociated with ecological changes inpopulations of pumpkinseedL. gibbosus, a nativemolluscivore; preference for mollusks11, 18, 52fishRoach(Rutilus rutilus)NoYesHighMedPontoCaspianSeasport fishinvasive in Baltic; nuisance once established11, 18fishRound goby(Neogobiusmelanostromus)YesYesPresentHighPontoCaspianSeaballastwaterfound in Great Lakes; invasion history; spreadsrapidly; zebra mussels dominate diet; alteringbenthic communities; aggressive; lake trout egg,sturgeon egg predators3, 9, 11, 29, 30,32, 43, 46, 48,49, 51, 52, 53,56fishRudd(Scardinius erythrophthalmus)YesYesPresentHighEurasiabait fish;ballastwaterintroduced into L. Ontario; expanded in L. Erie;habitat degradation for native fish; ruddintroduced to open waters will hybridize withgolden shiner11, 17, 29, 41,46, 49

### B-9fishSand goby(pomatoschistus minutus)NoYesHighMedEasternAtlantic/PontoCaspianSeaaquarium;aquacultureabundant; invasion in Baltic; predators that feedon small benthic animals11, 18fishSilver carp(Hypophthalmichthysmolitrix)NoNEKHighNEKChinaaquaculturedocumented in Cal-Sag Channel of IL Waterway;spread along Mississippi River; competition withcommercially significant native fish for food andhabitat; vast mobility; voracious consumptionhabits; clog fishing nets; widespread in U.S. Gulfstates11, 12, 18, 34,39, 43, 44, 46,52, 56fishSnakehead(Channa argus argus)NoYesHighMedAsiafish marketsporadic places; devastation to freshwaterecosystems of the U.S.because of its predaciousnature, lack of natural predators, high fertility,and adaptability to a wide range of environmentalconditions11, 12, 46, 49fishStarry goby(Benthophilus stellatus)NoYesHighNEKPontoCaspianSeaballastwater?spreading in Europe11, 18, 30fishStriped bass(Morone saxatilis)NoYesNEKNEKAtlanticcoast ofNorthAmericafish marketinvasion history11, 31fishSunbleak(Leucaspius delineatus)NoYesHighHighPontoCaspianSeacanalswidespread in Europe; affecting coarse fishpopulations in England (limited range); feeds onphytoplankton and zooplankton11, 18fishTench(Tinca tinca)NoYesHighHighPontoCaspianSea/Europe/Asiaornamental;canals;sport fishbeen captured in St. Lawrence River; may headto L. Ontario; competition with benthic feeders,native fish; potential competitor for food withsport fishes and native cyprinids; widespread11, 17, 18, 37,49fishToothed carp(Aphanius fasciatus)NoNEKHighNEKPontoCaspianSeacanalcompetes with native fish; declining inMediterranean11, 18fishTubenose goby(Proterorhinusmarmoratus)YesYesMedHighPontoCaspianSeaballastwaterfound/restricted to L. St. Clair, western L. Erie;Invasion history but not spread rapidly;aggressive11, 29, 32, 43,51, 52fishTyulka / Caspian kilka(Clupeonella cultriventris/caspia)NoYesHighNEKPontoCaspianSeaballastwaterexpanded in Volga River; suppressed native fish11, 14, 18, 30

### B-10fishUkranian or nine-spined stickleback(Pungitius platygaster)YesNEKPresentNEKPontoCaspianSeacanal?;bait?found in L. Huron, Michigan11, 18, 52fishWeather fish(Misgurnus fossilis)NoYesNEKNEKEuropeornamentaltolerate low temperature; established invasion inItaly, Spain, Croatia11, 31fishWhite cloud mountain minnow(Tanichthys albonubes)NoYesHighMedChinaornamentaltolerate low temperature; extensive invasionhistory; high occurrence frequency11, 14, 31fishWhite perch(Morone americana)YesYesPresentHighAtlanticcoast ofNorthAmericafish market;canalsalready invaded Great Lakes; competition withnative fish; potential to cause declines of walleyepopulations; prey on eggs of walleye9, 11, 17, 31,44, 46, 48, 52,53, 56fishZander(Sander lucioperca)NoYesHighHighPontoCaspianSeasport fish;canals;aquaculturefound everywhere in Europe; depleted stocks ofnative fish; hunts in packs11, 18flatwormFlatworm(Dendrocoelumromanodanubiale)NoNEKNEKNEKNorthSeabasinballastwaternot established yet, may be introduced13flatwormTrematode(Apophallus muehlingi)NoNEKNEKNEKBlackSeaballastwaternot established yet, may be introduced13flatwormTrematode(Nicolla skrjabini)NoNEKNEKNEKBlackSeaballastwaternot established yet, may be introduced13flatwormTrematode(Rossicotrema donicum)NoNEKNEKNEKBlackSeaballastwater; fishhostnot established yet, may be introduced13insectApterogote (wingless)(Campodea staphylinus)NoYesHighNEKEurasiaballastwaterwidespread; been recorded in North Americanfresh waters; tolerate range of salinity10microcrustaceanBaltic water flea(Bosmina coregoni)YesYesPresentHighEurasia/PontoCaspianballastwaterestablished in L. Ontario, Superior; invasionhistory13microcrustaceanCalanoid copepod(Eurytemora affinis)YesYesPresentNEKPontoCaspianSeaballastwaterwidespread; invasion history; established inGreat Lakes; high populations13

### B-11microcrustaceanCalanoid copepod(Heterocopeappendiculata)NoYesHighNEKPontoCaspianSeaballastwaterestablished in Baltic Sea basin13microcrustaceanCalanoid copepod(Calanipeda aquaedulcis)NoYesHighNEKPontoCaspianSeaballastwaterestablished in Baltic Sea basin13microcrustaceanCalanoid copepod(Heterocopecaspia)NoYesHighNEKPontoCaspianSeaballastwaterestablished in Baltic Sea basin13microcrustaceanCladoceran(Daphnia cristata)NoYesHighNEKPontoCaspianSeaballastwaterestablished in Baltic Sea basin13microcrustaceanCladoceran(Bosmina obtusirostris)NoYesHighNEKPontoCaspianSeaballastwaterestablished in Baltic Sea basin13microcrustaceanCladoceran(Podonevadne trigonaovum)NoYesHighNEKPontoCaspianSeaballastwaterestablished in Baltic Sea basin13microcrustaceanCladoceran (water flea)(Daphnialumholtzi)NoYesMedNEKAustralia;SE Asiaballastwater;boating;waterfowlexpanding in U.S.; widespread; prefers warmwater; competes with native daphnia for food andof its ability to avoid predation14, 34, 52, 53microcrustaceanCladoceran(Cornigerius maeoticusmaeoticus)NoYesHighNEKPontoCaspianSeaballastwaterestablished in Baltic Sea basin13microcrustaceanCyclopoid copepod(Cyclops strenuus)YesYesPresentNEKEurasiaballastwaterestablished in Great Lakes10microcrustaceanCyclopoid copepod(Cyclops kolensis)NoYesHighNEKPontoCaspianSeaballastwaterestablished in Baltic Sea basin13microcrustaceanFishhook waterflea(Cercopagispengoi)YesYesPresentHighPontoCaspianSeaballastwater;boatingestablished in Great Lakes except L. Huron,Superior; Baltic Sea basin; compete with smallfish for zooplankton; invasive in Europe; clogsnets for fisheries; damage cost data available9, 13, 19, 20,29, 32, 38, 49,52, 53, 54, 56microcrustaceanHarpactacoid copepod(Ectinosomaabrau)NoNEKHighNEKPontoCaspianSeaballastwaterpotential to invade Great Lakes13

### B-12microcrustaceanHarpactacoid copepod(Onychocamptus mohammed)YesNEKPresentNEKPontoCaspianSeaballastwaterestablished in L. Ontario13, 14microcrustaceanHarpactacoid copepod(Paraleptastacus spinicaudata triseta)NoYesHighNEKPontoCaspianSeaballastwaterinvasion history13microcrustaceanHarpactacoid copepod(Schizoperaborutzkyi)YesYesPresentHighDanubeRiverdelta ofBlackSeaballastwaterestablished in L. Michigan; altering speciescomposition of nearshore communities1, 10, 13, 14,15, 28microcrustaceanHarpactacoid copepod(Nitocraincerta)YesYesPresentNEKBlackSeaballastwaterestablished in L. Michigan13microcrustaceanSpiny waterflea(Bythotrepheslongimanus)YesYesHighHighGreatBritain;Europeballastwater;boatingestablished in Great Lakes; Great Lakes - hascaused major changes in the zooplanktoncommunity structure; invasion history; reproducerapidly; competes directly with small fish and canhave impact on zooplankton community; damagecost data available13, 21, 33, 38,48, 49, 52, 53,54microcrustaceanSpiny waterflea(Bythotrephescederstroemi)YesYesPresentHighEurasiaballastwaterestablished in Great Lakes; the invasion into theLaurentian Great Lakes has resulted insubstantial and sustained decreases in thepopulations of a numberof (mostly cladoceran)native zooplankton species; coincided withdramatic declines in abundance of Daphnia32, 49, 50, 56microsporidianFish parasite(Heterosporis sp.)YesYesNEKNEKEurope/Asia ?fish release;baitfound in L. Ontario, attacks muscle cells in yellowperch; found in Wisconsin9, 46molluscAsian clam(Corbicula fluminea)YesYesNEKNEKAsiaornamental;fishmarket/baitestablished in L. Michigan, Superior, Erie; doesnot tolerate low temperatures; fouled waterplants; damage cost data available13, 46, 49, 52,54molluscBasket (European) shell(Corbulagibba)NoNEKNEKNEKAtlanticcoast ofEuropeballastwatercapacity to achieve very high populationdensities, giving it the potential to affect thegrowth and recruitment of a wide range of softbottom organisms13

### B-13molluscClam(Hypanis colorata)NoNEKNEKNEKCaspianSeaballastwaterpotential to invade Great Lakes13, 30molluscEuropean fingernail clam(Sphaeriumcorneum)YesYesPresentNEKEurasiaunknown -canals?found in L. Huron, Ontario; effect unknown52molluscGolden mussel(Limnoperna fortunei)NoYesMedMedChina/Asiaballastwaterproduces a rapid change in benthic communitiesand threatens native biodiversity; producesmacrofouling in the water systems of facilities;spreading in South America27, 49molluscChinese mystery snail [(prosobranch](Cipangopaludina chinensis malleata)YesYesHighMedAsiafish market;ornamentalisolated pop. in L. Erie and upper St. LawrenceRiver; established L. Michigan; clog screens ofwater intake pipes; vectors for the transmissionof parasites and diseases31, 42, 46, 52molluscNew Zealand mudsnail [hydrobid](Potamopyrgus antipodarum)YesYesPresentHighNewZealandballastwaterestablished in L. Ontario, Superior; invasionhistory; reduce native species, harm troutpopulations; suspected that can alter primaryproduction of streams; spread rapidly13, 23, 28, 44,46, 49, 52molluscQuagga mussel(Dreissena bugensis)YesYesPresentMedUkraine/PontoCaspianSeaballastwaterfound in L. Erie, Ontario; invasion history;historically dominated the biomass oftransition(30-50m) and profundal regions(>50m);negative impact on raw water-using industries,potable water treatment plants; damage costdata available9, 26, 29, 30,32, 44, 49, 52,53, 54, 56molluscSnail (hydrobid)(Lithoglyphusnaticoides)NoNEKNEKNEKPontoCaspianballastwaterpotential to invade Great Lakes13molluscSnail (neritid)(Theodoxus fluviatilis)NoNEKNEKNEKBaltic/BlackSeaballastwaterpotential to invade Great Lakes13molluscSnail (neritid)(Theodoxus pallasi)NoNEKNEKNEKCaspianSeaballastwaterpotential to invade Great Lakes13molluscZebra mussel(Dreissena polymorpha)YesYesPresentHighPontoCaspianSeaballastwateralready established in Great Lakes; invasionhistory; interferes with native molluscs’ ecologicalfunctions; damage cost data available2, 9, 29, 32, 44,48, 49, 52, 53,54, 56

### B-14plantCurly-leaf pondweed(Potamogetoncrispus)YesYesPresentMedEurasiaornamental;horticultureestablished in U.S.; invasion history; causesproblems due to excessive growth22, 41, 47, 48,49, 56plantDwarf hygrophila(Hygrophilapolysperma)NoYesHighMedIndiaornamentaltolerate low temp; established in FL, TX, VA;invasion history; clogsirrigation and flood-controlcanals14, 31plantBrazilian elodea or waterweed(Egeriadensa)NoYesMedHighCentral/SouthAmericaornamentalestablished in OR, NY, MD, CT; invasion history;slow dispersal; create dense mats that canimpede water recreation; water movement isrestricted causing fishpopulation imbalances;cause fluctuations in water quality14, 31, 36, 46,49plantEurasian watermilfoil(Myriophyllumspicatum)YesYesHighHighEurasiaornamental;boatersinvaded L Ontario; interferes with waterrecreation; canopy can crowd out importantnative plants; decrease oxygen levels when plantdecays; rapid colonization; damage cost dataavailable; threat factor to fish9, 17, 23, 47,48, 49, 54, 55,56plantEuropean frogbit(Hydrocharis morusranae)YesYesMedHighEurasiaornamental;horticulture;boatersinvaded L. Ontario (only lake found); createsdense mats of vegetation and thus prevents lightand nutrients from reaching submergedvegetation; plants die in the fall so depleteddissolved oxygen is possible9, 17, 22, 36,41, 46, 49, 56plantEuropean water clover(Marsileaquadrifolia)YesYesMedMedEurasiaornamentalfound in L. Ontario; poses a realistic nuisancethreat to ecosystems; affect local molluscancommunities36plantFanwort(Cabomba caroliniana)YesYesPresentHighSoutheast UnitedStatesornamentalAlready invaded L. Michigan; clogs drainagecanals and freshwater streams; form densestands crowding out previously well-establishedplants17, 31, 36, 49,plantFlowering rush(Butomus umbellatus)YesYesMedNEKEurasiaornamental;horticulturespread in limited areas in Great Lakes (Erie,Ontario, Michigan); actively expanding; competeswith native shoreline vegetation22, 46, 48, 49,56plantGiant salvinia(Salvinia molesta)NoYesMedMedSouthAmericaornamental;horticultureestablished in lower U.S.; invasion history;impede the flow of water to irrigation pipes andother water intake pipes; rapidly expandingpopulations can overgrow and replace nativeplants,resulting dense surface cover preventslight and atmospheric oxygen from entering thewater; decomposing material drops to thebottom, greatly consuming dissolved oxygenneeded by fish and other aquatic life22, 41, 46, 49

### B-15plantHydrilla(Hydrilla verticillata)NoYesHighHighCentralAfricaornamental;horticultureestablished in southern U.S.; high movement;invasion history; adaptable; dense mats ofhydrilla will alter the waters chemistry by raisingpH, cause wide oxygen fluctuations, andincrease water temperature; eliminate nativeplants22, 36, 41, 46,49, 56plantMinor (slender) naiad(Najas minor)YesYesNEKNEKEuropeornamentallimited in L. Erie; can form dense, monospecificstands in shallow water and hinder swimming,fishing, boating, and other forms of water contactrecreation36,plantParrot’s feather(Myriophyllumaquaticum)NoYesHighHighSouthAmericaornamentalinvasion history; very adaptive to variety ofenvironments14, 31, 49plantPurple loosestrife(Lythrum salicaria)YesYesPresentHighEurasiaornamental;horticultureestablished in Great Lakes; invasion history;plant can form dense, impenetrable stands whichare unsuitable as cover, food, or nesting sites fora wide range of native wetland animals; damagecost data available22, 23, 41, 47,48, 53, 54, 56plantSessile joyweed(Alternantherasessilis)NoYesMedNEKAsiaornamental;horticultureinvasion history; aggressive22plantSpiny naiad(Najas marina)YesYesMedNEKEurasiaornamentalfound in L. Ontario; widespread; interferes withrecreational boating; pose a realistic nuisancethreat to ecosystems36plantVariable-leaved watermilfoil(Myriophyllum heterophyllum)NoYesHighMedEasternNorthAmericaornamental;boatersspreading in New England states; out competenative aquatic vegetation, resulting in nearlymonotypic growth with less habitat value;interferes with recreational boating; pose arealistic nuisance threat to ecosystems31, 36, 40plantWater primrose(Ludwigiauruguayensis)NoNEKNEKNEKSouthAmericaornamentallarge colonies canprevent small boat navigationand recreational use of shoreline areas36plantWaterchestnut(Trapa natans)YesYesHighMedEurasiaornamental;horticulturereleased in L. Ontario;reproduce rapidly; habitatdegradation through floating mats, hinderingnavigation of waters and inhibiting the growth ofnative aquatic plant species; damage cost dataavailable9, 36, 44, 46,49, 54, 56

### B-16plantYellow floating heart(Nymphoidespeltata)NoYesHighMedEurasiaornamental;horticulturespreading in New England; grows in densepatches, excluding native species and evencreating stagnant areas with low oxygen levelsunderneath the floating mats36, 46, 49protozoansAmoebae (testate) (Psammonobiotuscommunis)YesYesNEKNEKBalticSeaballastwaterfound in L. Ontario; newly discovered in GreatLakes9, 24protozoansAmoebae (testate)(Psammonobiotusdziwnowii)YesYesNEKNEKBalticSeaballastwaterfound in L. Ontario; newly discovered in GreatLakes9, 24protozoansAmoebae (testate)(Psammonobiotuslinearis)YesYesNEKNEKBalticSea (?)ballastwaterfound in L. Ontario and other Great Lakes9virusLargemouth bass virus(Iridoviridaefamily)YesYesHighMed?bait fishextended range in southeast basin; spreading inL. Michigan; kills fish (commercial, native,invasive)34, 45, 46wormOligochaete(Potamothrix bedoti)YesYesNEKNEKPontoCaspianSeaballastwaterestablished in Great Lakes; invasion history inBaltic13wormOligochaete (tubificid)(Potamothrixheuscheri)NoYesNEKNEKPontoCaspianSeaballastwaterinvasion history in Baltic; continuously dispersingto the west over Central Europe and to the northwest towards the Baltic Sea13wormOligochaete(Potamothrixmoldaviensis)YesYesNEKNEKPontoCaspianSeaballastwaterestablished in Great Lakes; invasion history inBaltic13wormOligochaete(Potamothrix vejdovskyi)YesYesNEKNEKPontoCaspianSeaballastwaterestablished in Great Lakes; invasion history inBaltic13wormPolychaete worm(Hypania invalida)NoYesNEKNEKPontoCaspianSeaballastwaterinvasion history in Baltic; slow spread in Europe13, 30wormPolychaete worm(Hypaniolakowalevskyi)NoYesNEKNEKPontoCaspianSeaballastwaterinvasion history in Baltic; slow spread in Europe13aPresent in Great Lakes is scored “yes” if the organism has been reportedfor any of the Lakes.bInvasion history is scored “yes” if the species is reported to have an invasion history/exotic in any other country of the papers cited for thatspecies.cSpread potential within the Great Lakes after introduction scored “High” or “Med” basedon comments in two or more of the papers cited forthat species, or NEK (not enough known).dEcological impact if a species becomes established in Great Lakes scored as “High” or “Med” if soreported in at least two of the papers citedfor that species; else, it is scoredas NEK (not enough known).eSpecies origin notes the probable native areaor region of occurrence for the species inquestion, as cited in the papers reviewed for the species.fThe known, suspected, or probable mechanism ofintroduction of the species prior invasion history is listed based on the paperscited and priorinvasion history.gComments related to investigators’ concernsabout the consequences ofinvasion for the species in question are abstracted frompapers cited forthe species in question.hArticles referenced here were identified on the basis of species names plus reference to“Great Lakes”. Thus, the listed references do notinclude many papers that consider the biology of the organism in its native range.



### B-17Table B-2. References that identify 156 invasive species as described in Table B-1.ReferenceNumberReferences1Horvath, TG; Oberdick, EJ; Last, LL; et al. (2001) Invading species dominate theHarpacticoid fauna in the nearshore sands of southernLake Michigan. NABS Annual Meeting, La Crosse WI (abstract)2Buchan, LAJ; Padilla, DK. (1999) Estimating the probability of long-distance overland dispersal of invading aquatic species. Ecol Appl9(1):254-265.3Corkum, LD; Sapota, RM; Skora, KE. (2004) The Round Goby,Neogobius malanostomus, a fish invader on both sides of the AtlanticOcean. Biol Invasions 6(2):173-181.4Cristescu, MEA; Witt, JDS; Grigorovich,IA; et al. (2004) Dispersal of the Ponto-Caspian amphipodEchinogammarus ischnus: invasionwaves from the Pleistocene to the present. Heredity 92:197-203.5Daniels, RA. (2001) Untested assumptions: the role of canals inthedispersal of Sea Lamprey, Alewife, and other fishes in theeasternUnited States. Environ Biol Fishes 60(4):309-329.6Devin, S; Piscart, C; Beisel, JN; et al. (2003)Ecological traits of the amphipod invaderDikerogammarus villosuson a mesohabitatscale. Arch Hydrobiol 158(1):43–56.7Dick, JTA; Platvoet, D. (2000) Invading predatory crustaceanDikerogammarus villosuseliminates both native and exotic species. ProcBiol Sci 267(1447):977-983.8Dick, JTA; Platvoet,D; Kelly, DW. (2002)Predatory impact of the freshwater invaderDikerogammarus villosus(Crustacea: Amphipoda).Can J Fish Aquat Sci 59(6):1078-1084.9Duggan, IC; Bailey, SA; Colautti, RI; et al. (2003) Biological invasions in Lake Ontario: past, present, and future. In: Munawar, M, ed.State of Lake Ontario (SOLO) – Past, Present, and Future.10Duggan, IC; van Overdijk, DA; Bailey, SA; et al. (2005) Invertebrates associated with residual ballast water and sediments of cargo-carrying ships entering the Great Lakes. Can J Fish Aquat Sci 62:2463-2474.11Froese, R; Pauly, D, Eds. (2005) FishBase.World Wide Web electronic publication. www.fishbase.org, version (10/2005). [12/07/05]12Goehle, M. (2004) Keeping track of aquatic invasive species intheGreat Lakes. ANS Update. 9(4):1-2. Available online athttp://www.glc.org/ans/ansupdate/pdf/2004/Update-02-04.pdf13Grigorovich, IA; Colautti, RI; Mills, EL;et al. (2003) Ballast-mediated animal introductions in the Laurentian Great Lakes: retrospectiveand prospective analyses. Can JFish Aquat Sci 60:740-756.14Holeck, KT; Mills, EL; MacIsaac, HJ; et al. (2004). Bridging troubled waters: Biological invasions, transoceanic shipping, andtheLaurentian Great Lakes. Bioscience 54(10):919-929.15Horvath, TG; Whitman, RL; Last, LL. (2001) Establishment of twoinvasive crustaceans (Copepoda: Harpacticoida) in the nearshoresands of Lake Michigan. Can J Fish Aquat Sci 58:1261-1264.16Keppner, SM; Busiahn, TR; McClain, J; et al. (1997) Surveillancefor Ruffe in the Great Lakes – an overview. G Lakes Res Rev3(1):17-26.17Kerr, SJ; Brousseau, CS;Muschett, M. (2005) Invasive aquaticspecies in Ontario: a review andanalysis of potential pathways forintroduction. Fisheries 30(7):21-30.



### B-18ReferenceReferencesNumber



### B-1918Kolar, CS; Lodge, DM. (2002) Ecological predictions and risk assessment for alien fishes in North America. Science 298:1233-1236.19MacIsaac, H; Grigorovich, IA; Ricciardi, A.(2001)Reassessment of species invasions concepts: the Great Lakes basin as a model.Biol Invasions 3:405-416.20MacIsaac, HJ; Robbins, TC; Lewis, MA. (2002) Modeling ships’ ballastwater as invasion threats to the Great Lakes. Can J FishAquatSci 59:1245-1256.21MacIsaac, HJ; Borbely, JVM; Muirhead, JR; et al. (2004) Backcasting and forecasting biological invasions of inland lakes. EcolAppl14(3):773-783.22Maki, K; Galatowitsch, S. (2004) Movement of invasive aquatic plants into Minnesota (USA) through horticultural trade. Biol Conserv118:389-396.23Mills, EL; Leach, JH; Carlton, JT; et al.(1994) Exoticspecies and the integrity ofthe Great Lakes. BioScience 44(10):666-676.24Nicholls, KH. (2005)Psammonobiotus dziwnowiandCorythionella georgiana, two new freshwater sand-dwelling testate amoebae(Rhizopoda: filosea). Acta Protozool 44:271-278.25Palmer, ME; Ricciardi, A. (2004) Physical factors affecting the relative abundance of native and invasive amphipods in the St.LawrenceRiver. Can J Zool 82:1886-1893.26Palmer, ME; Ricciardi, A. (2005) Community interactions affectingthe relative abundances of native and invasive amphipods in the St.Lawrence River. Can J Fish Aquat Sci 62:1111-1118.27Ricciardi, A. (1998) Global range expansion of the Asian musselLimnoperna fortunei(Mytilidae): Another fouling threat to freshwatersystems. Biofouling 13(2):97-106.28Ricciardi, A. (2001) Facilitative interactions among aquatic invaders: is an “invasive meltdown” occurring in the Great Lakes?Can JFish Aquat Sci 58:2513-2525.29Ricciardi, A; MacIsaac, HJ. (2000) Recent mass invasion of the North American Great Lakes by Ponto-Caspian species. TREE15(2):62-65.30Ricciardi, A; Rasmussen, JB. (1998) Predicting the identity and impact of future biological invaders: a priority for aquatic resourcemanagement. Can J Fish Aquat Sci 55:1759-1765.31Rixon, CAM; Duggan, IC; Bergeron, NMN; et al. (2005) Invasion risks posed by the aquarium trade and live fish markets on theLaurentian Great Lakes. Biodiversity Conserv 14:1365-1381.32Vanderploeg, HA; Nalepa, TF; Jude, DJ; etal. (2002)Dispersal and emerging ecological impacts of Ponto-Caspian species in theLaurentian Great Lakes. Can J Fish Aquat Sci 59(7):1209-1228.33Yan, ND; Girard, R; Boudreau, S. (2002)An introduced invertebrate predator (Bythotrephes) reduces zooplankton species richness.Ecol Lett 5:481-485.34International Association for Great Lakes Research. (2002) Research and management priorities for aquatic invasive species in theGreat Lakes. Available online at http://www.iaglr.org/scipolicy/ais/ais_iaglr02.pdf35CANOE News. (2005) Invasive fish species found in Lake Champlain, say experts from Quebec, Vermont. Online athttp://cnews.canoe.ca [11/21/05]ReferenceReferencesNumber



### B-2036Madsen, J. (1999) A quantitative approach topredictpotential nonindigenous aquatic plant species problems. ANS Update Fall/Winter1999. Available online at http://www.glc.org/ans/ansupdate/pdf/ansdec99.pdf [11/22/05]37Cherwaty, S; Shear, H. (2002) Great Lakes indicators target aquatic invasive species. ANS Update Fall/Winter 2002. Available onlineat http://www.glc.org/ans/ansupdate/pdf/ansv8n4.pdf [11/22/05]38Sturtevant, R. (2002) The Great Lakes waterflea report. ANS Update Spring/Summer 2002. Available online athttp://www.glc.org/ans/ansupdate/pdf/ansv8n2.pdf [11/22/05]39Froese, R; Pauly, D, Eds. (2005) FishBase.World Wide Web electronic publication. www.fishbase.org, version (10/2005). [12/07/05]40Rendall, J. (2003) Watercraft inspections: an opportunity to prevent the spread of aquatic nuisance species. ANS Update Winter/Spring2003. Available online at http://www.glc.org/ans/ansupdate/pdf/Update-05-03.pdf [11/22/05]41Maki, K; Galatowitsch, S. (2003) Mail-order sales of aquatic plants: a pathway for ANS introduction. ANS Update Summer 2003.Available online at http://www.glc.org/ans/ansupdate/pdf/Update08-03.pdf [11/22/05]42Higbee, E; Glassner-Shwayder, K. (2004) The live food fish industry: new challenges in preventing the introduction and spread ofaquatic invasive species. ANS Update Fall/Winter 2004. Available online athttp://www.glc.org/ans/ansupdate/pdf/2004/ANSUpdateFW.pdf [11/22/05]43Rasmussen, J. (2002) The Cal-Sag and Chicago sanitary and shipcanal: a perspective on the spread and control of selected aquaticnuisance fish species. US Fish and Wildlife Service, East Lansing Ecological Services Field Office, Great Lakes Coastal Program.Online at http://www.fws.gov/midwest/LaCrosseFisheries/reports/Connecting_Channels_Paper_Final.pdf [11/30/05]44Canadian Wildlife Federation. (2003) Invasive Species in Canada. Available online at http://www.cwf-fcf.org/invasive/chooseSC.asp[12/05/05]45Whelan, G. (2004) Largemouth bass virus continues to spread in Michigan waters. Michigan Department of Natural Resources,Lansing, MI. Available online at http://www.michigan.gov/dnr/0,1607,7-153-10364_10950-93547--,00.html [12/07/05]46Indiana Department of Natural Resources. (2005) Invasive species. Available online at http://www.in.gov/dnr/invasivespecies/[12/09/05]47Klein, B. (2004) Making A List: Prevention Strategies for Invasive Plants in the Great Lakes States. Environmental Law Institute,Washington D.C. Available online athttp://www.elistore.org [11/23/05]48Minnesota Department of Natural Resources. (2005) Invasive species. Available online athttp://www.dnr.state.mn.us/invasives/index.html [12/09/05]49Invasive Species Specialist Group Global Invasive Species Programme. (2005) ISSG Global Invasive Species Database. Availableonline at http://www.issg.org/database [01/2006]50Berg, D. (1992) The spiny water flea, Bythotrephes cederstroemi Another unwelcome newcomer to the Great Lakes. Ohio Sea GrantCollege Program, Fact Sheet 49. Available online athttp://sgnis.org/publicat/papers/bergdj92.pdf [01/18/06]51Stepien, CA; Tumeo, MA. (2006) Invasion genetics of Ponto-Caspian gobies in the Great Lakes: a ‘cryptic’ species, absence of foundereffects, and comparative risk analysis. Biol Invasions 8:61-78.52USGS (2005) NAS - Nonindigenous Aquatic Species.Available online at http://nas.er.usgs.gov/ [01/17/06]ReferenceReferencesNumber



### B-2153Bailey, SA; Reid, DF; Colautti, RI; et al.(2005) Management of nonindigenous species in the Great Lakes. Toledo J Great Lakes' Law,Science & Policy 5:101-112.54Pimentel, D. (2005) Aquatic nuisance species in the New York State Canal and Hudson River Systems and the Great Lakes Basin: Aneconomic and environmental assessment. Environ Manage 35(5):692-701.55Dextrase, AJ; Mandrak, NE. (2006) Impacts of alien invasive species on freshwater fauna at risk in Canada. Biol Invasions 8:13-24.56Wisconsin Department of Natural Resources. (2005) Aquatic Invasive Species Program. Available online athttp://www.dnr.wi.gov/invasives/aquatic.htm [12/09/05]

### APPENDIX C. GARP MODEL VALIDATION

C-1

### GARP MODEL VALIDATION

The most rigorous evaluation of any model is a test of its ability to co rrectly predict
independent data that have not been processed by the model. In this report, Genetic Algorithm

for Rule-set Production (GARP) models were ev aluated by comparing th e known occurrences of
three invasive species already w ithin the Great Lakes with the pr edictive ability of GARP models
developed for these species using occu rrence data from other regions.
Model performance was assessed using area under the curve of th e receiver operating
characteristic curve (Sing et al., 2005) using R 2.4.0 (R Devel opm ent Core Team, 2006). Area

under the curve is a threshold-in dependent evaluation of model pe rformance that, in this case,
measures the ability of the model to differentia te between sites where a species is considered
present versus where it is considered absent. Area under the curve repr esents the probability
that, when a predicted-present site and a predicted-absent site are drawn at random, the
predicted-present site will have a higher predicte d value than the predicted-absent site. Because
true absence data were not available, randomly generated absence data, termed pseudo-absence
data (i.e., points selected randomly from sites where the species have not been recorded as
present within the Great Lakes), were used to validate the GARP models. This is a standard
approach when true absence data are not available (Graha m et al., 2004).
GARP produces predictions of habitat suit ability ranging from 0 to 100 that can be
converted to a binary prediction of presence or absence by selecting a threshold. For the purpose

of m odel validation, values above this threshold (i.e., 50) are considered present (assigned a

value of 1) while values below th is threshold are considered abse nt (assigned a value of 0). The
threshold that is selected is typically the th reshold that maximizes model performance which
may bias estimates of model performance. Area under the curve avoids the subjectivity in the
threshold selection process and, therefore, provides an unbiased evaluation of model
performance by plotting the false-positive rate (i.e ., over-prediction, the rate at which the model
predicts the species to be present at sites at which it is considered absent) versus the true-positive

rate (i.e., the rate at which the model correc tly predicts known presences as present) across all
possible thresholds. For these reasons, area under the curve is considered one of the best
approaches for model validation (Pearce and Ferrier, 2000). Nonetheless, area under the curve
poses three important limitations. Notable to hi s study are that (1) it we ights over-prediction and

C-2

### under-prediction errors equally, (2) it does not give information about the spatial distribution of
prediction errors, and (3) the size of the study area to which models are projected influences the
rate of correctly predicted absences and the area under the curve scores.

Explanation of figures
The first figure, Figure C-1, is explained to he lp inte rpret the set of three figures. Figure

C-1 shows the results from model validation for the zebra mussel, including a plot of the receiver
operating characteristic curve with the area under the curve statistic . The colors along this curve
correspond to the colors in the map of the zebra mussel predicted habita t suitability. Thus, by
moving along the curve, one can stop at any color transition, sa y between yellow and orange (or
a threshold value of 0.81 as determined by the ri ght-hand y-axis or 81 from the legend in the
map). By moving horizontally from this thre shold value to the left-hand y-axis, one can
determine the rate at which known presence correctly are predicted as presen t, also known as the
true-positive rate (about 0.75 in this example). By moving vert ically downward from this

threshold to the x-axis, one can determine the ra te at which pseudo-absences were predicted as
present (the true-false rate, which is about 0.3 in this case). In other words, if values greater than
81 in the map are considered as present and values less than 81 as absent , we would get roughly
75% of the known occurrences correctly predicte d, but it would also predict roughly 30% of
presumed absences as present. In this manne r, the receiver operating characteristic curve
provides a means to assess the rates of false-pos itive predictions (predi cting a species present

where it is considered absent) a nd false-negative predictions (predi cting a species absent where it
is known to be present).

Model Evaluation Results
Swets (1988) suggested the following scale for determ ining model performance using

area under the curve: 0.90–1.00 = excel lent; 0.80–0.90 = good; 0.70–0.80 = fair; 0.60–0.70 =

poor; ≤ 0.60 = fail. The area under the curve for all 3 species falls between 0.74 and 0.79, so the

models would fall into the 0.70 to 0.80 cat egory of “fair” (see Table C-1).

C-3

### Table C-1. Summary of area under the curve (AUC) values and occurrence data sets used for model construction (tr aining points) and evaluation points. Evaluation points within the Great Lakes are shown as hollow points on Figures C-1 to C-3

Species and common name No. of Great Lakes evalua tion points No. and location of training points AUC Dreissena polymorpha
zebra mussel 238 24 (Europe) 0.79

Gymnocephalus cernuus
ruffe 46 183 (Europe) 0.79

Potamopyrgus antipodarum
New Zealand mud snail 10 844 (Europe, Australia) 0.74

Model Evaluation All three model validation Figures (C-1 to C- 3) show (1) the predicted habitat suitability
for each species within the Great Lakes when us ing only occurrence data from outside the Great
Lakes, (2) the corresponding receiv er operating characteristic curv e plot and area under the curve
value and bootstrap stat istics for each model,

and (3) the occurrence data within the Great La kes withheld from GARP and used for evaluation
of predictive performance (hollow points). No te that for reasons discussed under “Selecting
Species to Model and Development of Occurrence Data” in Section 3.1, th ese occurrence points
may not be inclusive of all know n occurrences in the Great La kes and represent only those

suitable for model evaluation.
Taken together, these area under the curve scor es and the predicted distributions suggest

three im portant conclusions. First, in our te sts, GARP models adequa tely predic t the known
distributions of potential invasive species within the Great Lakes and, therefore, may be capable
of accurately identifying areas of the Great Lake s susceptible to aquatic invasive species that
have yet to be introduced. T hus, distribution data from a sp ecies’ existing range can produce
useful predictions of invasion potential us ing these GARP methods. Second, the observed
patterns of invasion closely match those predicted for both known and potential invaders,
suggesting that Lakes Erie and Ontario, near-shore areas of all of the Great Lakes in general,
Saginaw Bay in Lake Huron, La ke St. Clair (located betwee n Lakes Erie and Huron), and
Thunder Bay in Lake Superior ar e particularly prone to futu re invasion when considering

C-4

### C-5 environmental tolerances alone. Finally, the universally high ar ea under the curve scores suggest

that the six environm ental data layers we selected as inputs fo r the GARP models provide useful
information for predicting the potenti al distributions of invasive species within the Great Lakes.
In sum, the model validation exercise sugge sts that GARP predictions provide a useful
assessment of invasion potential, given the availabi lity of adequate occu rrence data outside the

Great Lakes.

REFERENCES FOR APPENDIX C.

Graham, CH; Ferrier, S; Huettman, F; et al (2004) New developm ents in museum-based informatics and applications in biodiversity analysis. TRENDS Ecol Evol 19(9): 497-503.

Pearce, J; Ferrier, S. (2000) Ev aluating the predictive perform anc e of habitat models developed using logistic regression. Ecol Model 133(3): 225–245.

R Development Core Team (2006). R: A Language and Environm ent for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. Available on-line at http://www.Rproject.org

.
Sing, T; Sander, O; Beerenwinkel N; et al. (2005) ROCR: visualiz ing classifier perform ance in R. Bioinformatics 21:3940–3941.

Swets, JA. (1988) Measuring the accuracy of diagnostic systems. Science 240:1285–1293.

### C-6Figure C-1. GARP model validation for zebra mussel showing predicted suitability and areaunder the curve (inset).

### C-7Figure C-2. GARP model validation for ruffe showing predicted suitability and area under the curve (inset).

### C-8Figure C-3. GARP model validation for New Zealand mud snailshowing predicted suitability and area under the curve (inset).

### APPENDIX D. GARP POWER OF PREDICTION ANALYSIS

D-1

### DETAILS OF HOW GARP POWER OF PREDICTION ANALYSIS WAS USED

If environmental conditions within a partic ular region of the Gr eat Lakes differ from
those near a species’ currently oc cupied range, then a prediction about the suitability of habitat
cannot be made (null prediction). A prediction cannot be made b ecause no information exists to
predict whether a species under these novel conditi ons will or will not find suitable habitat.
Unfortunately, the Genetic Algorithm for Rule-set Production (GARP), along with most species
distribution models, simply pred icts environments that are no n-analogous to the environments
found near the species’ currently oc cupied range to be unsuitable. But, in reality, such “null”
areas may actually be habitable by the species . GARP power of prediction analysis was
conceived to distinguish areas that do not provide suitable habitat (predicted absence) from areas
for which a prediction cannot be made (null predic tion). More details about the theoretical basis
for using GARP power of prediction analysis ar e provided in Section 3.5. The remainder of
Appendix D presents the specific application of GARP power of prediction anal ysis to this study.
Power of prediction analyses were performed for 11 of the 14 species to help distinguish
the regions for which a prediction could be made from regions where a prediction could not be
made for each species. Power of prediction analysis was not performed for two of the invasive
species already established in the Great Lakes, quagga mussel ( Dreissena bugensis ) and round
goby ( Neogobius melanostromus ), because of a lack of occurr ence data outside the Great Lakes
(i.e., these species had no distri bution data describing their home range, the basis for a power of
prediction analysis). Also, because GARP mo dels predict that the blueback herring ( Alosa

aestivalis ) may potentially find the entire Great Lakes region as suitable habitat, no power of
prediction analysis is needed, because power of prediction analyses are only concerned with
areas of predicted absence.
Power of prediction analyses for species can be grouped if they share a sim ilar home
range region, eliminating the need to perform se parate power of prediction analysis for each
species studied. The 11 species ha d home-range occurrence data orig inating from one or more of
four large regions: the northwes t Pacific coast of North America, the northeast Atlantic coast of

North America, the southeastern coast of Aust ralia, and a large regi on comprising the northern
coast of Europe and Northern Africa. Thus, we performed four power of prediction analyses.

D-2

### As discussed, power of prediction analyses are only concerned with areas of predicted
absence. Therefore, no power of prediction analys es were required for those species originating

from southeast Australia and the Atlantic U.S. regions because the models developed for species
from these regions predicted the entire Great La kes region as suitable ha bitat. Similarly, no
power of prediction analysis was performed for th e U.S. Pacific coast region because (1) only the

New Zealand mud snail, ( Potamopyrgus antipodarum) had occurrence points within this region
and (2) this species was already pr edicted to be fairly widespread within the Great Lakes. Thus,
only Europe and North Africa remained as re gions on which to focus power of prediction
analyses. This complex region was subdivided into three sub-regions: the United Kingdom and
northern Europe (designated UK) shoreline, the Baltic Sea, and the Ponto-Caspian Sea. GARP
power of prediction analyses were performed on combinations of th ese regions. The gray shaded
regions in Figures 7–17 and 24–27 refl ect the results of the GARP power of prediction analysis.
Reverse power of prediction analysis, whic h processes in the opposite direction from
invaded range to home range, was also performed, but these results are not included here. When
we looked globally for places with conditions si milar to those in the Great Lakes, we found
largely salt-water environments at homologous latitu des. This result is not surprising because
the vast majority of the earth’s surface waters are oceanic. Assuming that salinity represents a
dispersal barrier, most species found in these saline, but otherwise simila r aquatic environments,

would not be able to survive in th e freshwater of the Great Lakes.

GROUPED RESULTS FOR 11 SPECIES Figures D1 through D3 show the results of the G ARP power of prediction analyses.
Multiple species are shown in most figures because the species are grouped according to the
presence of species occurrence points in each sub -region. Each of the 11 species collectively
shown in D1 through D3, can be associated with a corresponding GARP figure (Figures 8-17
and 31-34). Similar to the GARP predictions, the power of prediction analyses produced a
continuous measure of the ability to predict by adding the binary presence/absence outcomes

from the best 100 GARP models out of 1,000 to tal models constructed. For clarity, we
simplified the figures by converting these continu ous predictions into tw o areas: areas where
prediction is possible (shown in red) and areas where prediction is not possible (shown in black).
Red areas represent location where more than 50 percent of the models have predictive power

D-3

### and black areas depict locations in which less than 50 percent of the models could predict
presence or absence.
The power of prediction analyses for the fi shhook waterflea, the roach, the rudd, and the

ruffe (see Figure D1) indicate that GARP can make predictions for these species everywhere
throughout the Great Lakes except for a central deep-water portion of Lake Superior. GARP
originally predicted a rather limited group of areas as susceptible for invasion by roach and rudd.
Coupled with the power of prediction analysis for these two species, we can conclude that
indeed, most of the areas thought to be low-risk areas are in fact predic ted by the models as lowrisk areas. Similarly, Lakes Erie and Ontario we re predicted to be particularly suitable for
invasion by both species, and the power of pr ediction analysis showed that the modeling
approach had good predictive capabil ities within these two lakes. Susceptibility to invasion by
these two species in the upper Gr eat Lakes was predicted to be limited primarily to near-shore
areas, and the power of predicti on analysis shows that many of the areas predicted to be not
susceptible to invasion by these two species fa ll well within the red areas determined to be
predictable by GARP. Thus, these, too, are vali d predictions of low ri sk of invasion by roach

and rudd. Therefore, the GARP mo del results, coupled with the power of prediction analysis,
successfully identify large areas of the Great La kes as definitely unsuitable for invasion by roach
or rudd. These areas represent an opportunity for effectively focusing effort and resources for
monitoring these species.
The power of prediction analysis for th e zeb ra mussel and New Zealand mud snail
indicate a large area within the Great Lakes th at is predictable by GAR P (see Figure D2 top
panel). Much of the area that is indicated as predictable based on the power of prediction
analysis is predicted to be highly susceptible to invasion by these two already-established NIS.
The GARP models predict that the deep-water areas of Lake Supe rior should be unsuitable and,

therefore, not at risk of futu re invasion by these species. Ho wever, the power of prediction
analysis for these species indicates that this area is simply not predictable. Therefore, the risk to
central Lake Superior from i nvasion by these two species is, at this point, undeterminable by
GARP models alone. GARP predicti ons show parts of Lake Huron to be at low risk from the
New Zealand mud snail and the power of predicti on analysis shows that these areas can be well
predicted by GARP.

D-4

### The set of power of prediction an alyses that were performed for Corophium curvispinum

, monkey goby, tubenose goby, and tench i ndicate that large areas with in the Great Lakes are not
predictable by GARP (see Figure D2 lower panel). GARP predictability zones (red areas) are

restricted to Lake Erie, the s outhern half of Lake Ontario, an d the southernmost tip of Lake
Michigan. All four of thes e potential invasive NIS were predicted by GARP models to
encounter large areas of the Great Lakes that were unsuitable as habitat, which could be

classified as immune to invasi on. Many of these predicted absen ce areas actually fall within the
black zone of the power of prediction analysis , within which no GARP prediction can be made.
From the GARP predictions alone, these exte nsive areas would have been interpreted by
managers as no-invasion-risk z ones. However, the areas act ually are no-prediction possible
zones, meaning they might be susceptible to inva sion and they might not. Without the benefit of
the power of prediction analysis, this condi tion would not have been known from the GARP
predictions alone. The power of prediction analysis prevents th is critical misinterpretation.
When viewed in conjunction with the corre sponding GARP power of prediction analysis
figures, the results present a cl earer prediction of invasion-sus ceptible habitat areas. W ithout
power of prediction analysis, only 5 species (the blueback herring, the sand goby, the zebra

mussel, the ruffe, and the New Zealand mud snail) are predicted by GARP m odels to be able to
find suitable habitat throughout the Great Lakes. With the power of prediction analyses, it is
possible that the entire Great Lakes region could also be susceptible to three additional potential
invasive species: C

. curvispinum , the tubenose goby, and the round goby. Managers cannot rule

out much of the Great Lakes as unsuitable for th ese three species, as was suggested by the GARP
predictions alone.

D-5Figure D-1. Power of prediction analysis for fishhook waterflea, roach, and rudd (top) and ruffe (bottom)



### D-6Figure D-2. Power of prediction analysis for zebra mussel and New Zealand mud snail (top) and C. curvispinum , monkey goby, tubenose goby and tench (bottom).



### D-7Figure D-3. Power of prediction analysis for sand goby.



### D-8

### APPENDIX E. TABLES DEPICTING SOURCES AND DESTINATION OF BALLAS T WATER DISCHARGES IN U.S. GREAT LAKES

E-1

### Table E-1. Source of ballast water discharges (prior to ballast water exchange) into U.S. Great Lakes ports (2006-2007). Includes only discharges from ve ssels whose source of ballast water (prior to ballast water excha nge) came from outside the Great Lakes.

Source of Ballast Water Country Ballast Tank s Discharged Volume Discharged (metric ton s) Vessels Discharging

Not from POC -- 79 31,323 20

Port Cartier Canada 57 80,233 8
Antwerpen Belgium 40 14,134 4

Baie Comeau Canada 34 46,353 5

Port Alfred Canada 34 18,699 2

Puerto Cabello Venezuela 29 14,267 3
Sept Iles Canada 28 16,014 5

Haraholmen Sweden 22 3,586 1

Bremen Germany 20 5,858 1

Manfredonia Italy 18 4,364 1

St John Canada 15 3,680 1

Ghent Belgium 14 7,332 1

Amsterdam Netherlands 13 3,613 1

New Haven US 13 17,252 1

Safi Morocco 13 7,231 1

Casablanca Morocco 12 10,552 2
Hafnarfjordur Iceland 12 9,718 1

Santander Spain 12 10,633 2
Albany US 11 1,427 1

Rotterdam Netherlands 8 1,389 2
Hamburg Germany 7 4,374 1

Houston US 7 1,593 2

Thisvi Greece 7 1,998 1
Workington UK 7 1,477 1

Yokkaichi Japan 7 1,512 1
Agadir Morocco 6 3,383 1

E-2

### Source of Ballast Water Country Ballast Tank s Discharged Volume Discharged (metric ton s) Vessels Discharging

Rouen France 6 2,006 1

South Shields UK 6 1,926 1
Bahia Quintero Chile 5 16,839 1
Mostaganem Algeria 5 1,196 1
Philadelphia US 5 5,299 1
Porsgrunn Norway 5 877 1
Algeciras Spain 4 490 1

Baltimore US 4 8 1
Barranquilla Colombia 4 5,098 2

Ciwandan Indonesia 4 491 1
Dunkerque (east) France 4 2,194 1

Hancock US 4 9,632 1
Troy US 4 57 4

Brake Germany 3 970 1

Shuwaikh Kuwait 3 739 1
Al Burayqah Libya 2 350 1

Dammam Saudi Arabia 2 360 1

Gros Cacouna Canada 2 2,426 1
Holyhead UK 2 1,374 1

Mina Jabal Ali UAE 2 577 1

Mobile US 2 612 1

Oulu Finland 2 182 1
Searsport US 2 395 1
Southampton UK 2 548 1

Chiba Ko Japan 1 379 1

Halifax Canada 1 910 1

Montevideo Uruguay 1 237 1
Port Of Pasajes Spain 1 126 1

Santa Marta Colombia 1 160 1

E-3

### Source of Ballast Water Country Ballast Tanks Discharged Volume Discharged (metric tons) Vessels Discharging

Sundsvall Sweden 1 254 1

Thessaloniki Greece 1 1,000 1

Veracruz Mexico 1 719 1
Wilmington US 1 1,500 1

TOTAL 618 381,927 107

E-4

### Table E-2. Ballast water discharges at U. S. Great Lakes during 2006-2007. Includes only vessels whose original source of ballast water (prior to ballast water exchange) came from outside the Great Lakes.

US Great Lak e Port Tanks Discharged Volume Discharged (metric ton s) Vessels Discharging

Duluth 407 184,844 58

Toledo 85 65,335 13
Superior 50 78,085 10
Green Bay 18 5,984 4
Gary 17 11,154 4

Milwaukee 11 10,768 2
Oswego 8 1,239 5
Chicago 7 17,916 3
Ludington 5 1,913 1

Erie 4 490 1

Lorain 3 2,320 3

Menominee 2 380 2

Ashtabula 1 1,500 1

TOTAL 618 381,927 107

E-5

### Table E-3. Source of 2006-2007 ballast water disc harges from vessels whose original source of ballast water (prior to ballast water exch ange) came from outside the Great Lakes. Sorted by U.S. Great Lake port of call.

US Great Lak e Port Source of Ballast Water Country Tanks Discharged Volume Discharged (metric

tons) Vessels Discharging

Ashtabula Wilmington US 1 1,500 1
Chicago Bahia Quintero Chile 5 16,839 1
Chicago Not from POC -- 1 77 1
Chicago Thessaloniki Greece 1 1,000 1

Duluth Agadir Morocco 6 3,383 1

Duluth Al Burayqah Libya 2 350 1

Duluth Albany US 11 1,427 1

Duluth Amsterdam Netherlands 13 3,613 1

Duluth Antwerpen Belgium 33 11,668 2

Duluth Baie Comeau Canada 3 1,922 1

Duluth Casablanca Morocco 12 10,552 2

Duluth Chiba Ko Japan 1 379 1

Duluth Ciwandan Indonesia 4 491 1

Duluth Dammam Saudi Arabia 2 360 1

Duluth Dunkerque (east) France 4 2,194 1

Duluth Ghent Belgium 14 7,332 1

Duluth Hamburg Germany 7 4,374 1

Duluth Haraholmen Sweden 22 3,586 1

Duluth Houston US 7 1,593 2

Duluth Manfredonia Italy 18 4,364 1

Duluth Mina Jabal Ali UAE 2 577 1

Duluth Mobile US 2 612 1

Duluth Montevideo Uruguay 1 237 1

Duluth Mostaganem Algeria 5 1,196 1

Duluth New Haven US 13 17,252 1

Duluth Not from POC -- 66 27,054 13

Duluth Oulu Finland 2 182 1

E-6

### U.S. Great Lak e Port Source of Ballast Water Country Tanks Discharged Volume Discharged (metric

tons) Vessels Discharging

Duluth Philadelphia US 5 5,299 1

Duluth Port Alfred Canada 27 11,679 1

Duluth Port Cartier Canada 13 15,803 2

Duluth Puerto Cabello Venezuela 26 11,719 2

Duluth Rotterdam Netherlands 6 1,249 1

Duluth Safi Morocco 13 7,231 1

Duluth Santa Marta Colombia 1 160 1

Duluth Santander Spain 12 10,633 2

Duluth Searsport US 2 395 1

Duluth Sept Iles Canada 11 6,024 2

Duluth Shuwaikh Kuwait 3 739 1

Duluth Southampton UK 2 548 1

Duluth St John Canada 15 3,680 1

Duluth Thisvi Greece 7 1,998 1

Duluth Workington UK 7 1,477 1

Duluth Yokkaichi Japan 7 1,512 1

Erie Algeciras Spain 4 490 1
Gary Baie Comeau Canada 9 10,077 1
Gary Not from POC -- 1 60 1
Gary Porsgrunn Norway 5 877 1
Gary Rotterdam Netherlands 2 140 1
Green Bay Antwerpen Belgium 3 1,082 1
Green Bay Brake Germany 3 970 1
Green Bay Rouen France 6 2,006 1
Green Bay South Shields UK 6 1,926 1

Lorain Barranquilla Colombia 1 780 1

Lorain Not from POC -- 1 821 1

Lorain Veracruz Mexico 1 719 1

E-7

### U.S. Great Lake Port Source of Ballast Water Country Tanks Discharged Volume Discharged (metric

tons) Vessels Discharging
Ludington Sept Iles Canada 5 1,913 1

Menominee Port Of Pasajes Spain 1 126 1

Menominee Sundsvall Sweden 1 254 1

Milwaukee Baie Comeau Canada 9 10,077 1

Milwaukee Not from POC -- 2 691 1
Oswego Not from POC -- 4 1,182 1
Oswego Troy US 4 57 4
Superior Baie Comeau Canada 8 16,988 1
Superior Barranquilla Colombia 3 4,318 1
Superior Hancock US 4 9,632 1
Superior Not from POC -- 1 95 1
Superior Port Cartier Canada 28 42,855 4
Superior Puerto Cabello Venezuela 3 2,548 1
Superior Sept Iles Canada 3 1,649 1

Toledo Antwerpen Belgium 4 1,384 1

Toledo Baie Comeau Canada 5 7,290 1

Toledo Baltimore US 4 8 1

Toledo Bremen Germany 20 5,858 1

Toledo Gros Cacouna Canada 2 2,426 1

Toledo Hafnarfjordur Iceland 12 9,718 1

Toledo Halifax Canada 1 910 1

Toledo Holyhead UK 2 1,374 1

Toledo Not from POC -- 3 1,343 1

Toledo Port Alfred Canada 7 7,020 1

Toledo Port Cartier Canada 16 21,576 2

Toledo Sept Iles Canada 9 6,428 1

TOTAL 618 381,927 107

E-8

### Table E-4. Number of vessel-trips includi ng specified port as a last five ports of call, for NOBOB-RM vessels (2006). NOBOB-RM vessels are vessels that entered the Seaway wit hout ballast on board, picked up ballast water in the Great Lakes, and then di scharged the ballast water along with residual material at a Great Lake port . NOBOB-RM vessels must also have visited a port outside the Great Lakes during one of the la st five ports of call.

Port outside St. Law rence Seaway Country NOBOB L5POC
Sept Iles Canada 145

Port Cartier Canada 77
Ijmuiden Netherlands 23

Baie Comeau Canada 23

Pointe Noire Canada 22

Halifax Canada 12
Europa Point Gibraltar 10

Belledune Canada 10
Antwerpen Belgium 9

Casablanca Morocco 9
Riga Latvia 8

Santos Brazil 8

Rotterdam Netherlands 7
Stephenville Canada 7

Amsterdam Netherlands 7

Ashdod Israel 7

Police Poland 6

Praia Mole Brazil 6
Tynemouth UK 5
Dunkerque (east) France 5

Tunis Tunisia 5

Ghent Belgium 5

Gdansk Poland 5
Swinoujscie Poland 5

Oran Algeria 5

E-9

### Port outside St. Lawrence Seaway Country NOBOB L5POC
Syros Island Greece 5
Puerto Quetzal Guatemala 4
Magdalen Canada 4
Sankt-peterburg Russia 4
Kao-hsiung Taiwan 3

Vitoria Brazil 3

Charlottetown Canada 3
Hamburg Germany 3

La Goulette Tunisia 3
Richards Bay South Africa 3

Coatzacoalcos Mexico 2

Balboa Panama 2

Corinto Nicaragua 2

Piombino Italy 2
Barranquilla Colombia 2

Bahia San Nicolas Peru 2
Arkhangelsk Russia 2

Puerto Cabello Venezuela 2
Rocky Point Jamaica 2

Rouen France 2
Tarragona Spain 2

Puntarenas Costa Rica 2

Summerside Canada 2

Porto De Maceio Brazil 2
Paranagua Brazil 2

Santander Spain 2
Shanghai China 1
Chi-lung Taiwan 1

Setubal Portugal 1
Bunbury Australia 1

E-10

### Port outside St. Lawrence Seaway Country NOBOB L5POC
Singapore Singapore 1
Cartagena Spain 1
Campana Argentina 1

Ceuta Spain 1

Brunsbuttel Germany 1

Sao Francisco Brazil 1

Sluiskil Netherlands 1

Fos France 1

Szczecin Poland 1

Terneuzen Netherlands 1

Avonmouth UK 1

Trieste Italy 1

Venezia Italy 1
Argentia Canada 1

Aratu Brazil 1
Xingang China 1

Annaba Algeria 1

Bremen Germany 1

Recife Brazil 1

Port Alfred Canada 1

Port St Joe USA 1
Point Tupper Canada 1

Yosu-bando Republic of Korea 1
Porto Empedocle Italy 1
Map Ta Phut Thailand 1
Liverpool UK 1

Las Palmas Spain 1

La Plata Argentina 1

Durban South Africa 1
Kingston Upon Hull UK 1

E-11

### Port outside St. Lawrence Seaway Country NOBOB L5POC
Djen-djen Algeria 1

Itea Greece 1
Reka Luga Russia 1

Huelva Spain 1
Hong Kong Hong Kong 1
Guayaquil Ecuador 1

Safi Morocco 1

Geraldton Australia 1

San Lorenzo Argentina 1
Port Of Spain Trinidad and Tobago 1

Santa Marta Colombia 1

Pusan Republic of Korea 1

TOTAL 544

E-12

### Table E-5. Frequency and volume of ba llast tank discharges into U.S. Great Lakes ports of call from NOBOB-RM vessels (2006). NOBOB-RM vessels are vessels that entered the Seaw ay without ballast on board, picked up ballast water in the Great Lakes, and th en discharged the ballast water along with residual material at a Great Lake port. NOBOB-RM vessels must also have visited a port outside the Great Lakes during one of the last five ports of call.

U.S. Great Lak e port Ballast Tanks Discharged Volume Discharged (metric tons)

Toledo 353 511,181
Superior 340 524,446

Ashtabula 297 579,785

Duluth 289 379,999
Sandusky 116 243,137

Milwaukee 89 68,522
Gary 60 52,987
Chicago 48 76,607

Conneaut 43 76,335

Buffalo 37 73,197

Calumet 30 33,974

Lorain 13 25,729

Two Harbors 8 13,444

Brevort 7 20,914

TOTAL 1730 2,680,255

E-13