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. 
iii 

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/m3) 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 radiance 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. GARP-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. GARP-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 Agency

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 Imaging Spectroradiometer

NBIC National Ballast Information Clearinghouse

NIS nonindigenous species

NOAA National Oceanic and Atmospheric Administration

NOBOB no ballast on board

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

NVMC National Vessel Movement Center

nLW normalized water-leaving radiance

ppt parts per thousand

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

USCG 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. EXECUTIVE 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 km 

(21 km2) 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. 
2 

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 because 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 
3 

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 km2

 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 analyses, 

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. 
4 

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 are 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 
5 

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 
6 

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. 

 

Shipping/Ballast Water 65%

Unintentional Release

11%

Other 7%

(includes Canal, Bait, 

Intentional Release)

Unknown

11%

Aquarium Release

6%

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 NOBOB 

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 

implemented 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 range

Sufficient number 

of propagules Suitable habitat

Successful invasion

Propagule pressure

determined by ballast 

water discharges 

Habitat suitability 

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 of 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 keywords. 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 fish 229 GBIFa USGSb

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 GISc

Scardinius erythrophthalmus, rudd—1989 fish 57 GBIF, CIMSd

Proterorhinus marmoratus, tubenose goby—1990 fish 171 CIMS, BSRDBe

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

Pomatoschistus minutus, sand goby fish 102 GBIF, BSRDB

Rutilus rutilus, roach fish 117 GBIF, CIMS

Tinca tinca, tench fish 50 CIMS

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

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

cRegional Biological Invasions Center. INVADER, 2007 

(http://www.zin.ru/projects/invasions/gaas/invader/invader.htm).

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

eBlack 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 would 

provide suitable habitat for the 14 modeled species in the Great Lakes. The 

spatial resolution of each of these six data layers is ~21 km2.

Variable Units Source

Collection 

period

Mean monthly temperature °C AVHRRa1985–2002

Maximum mean monthly temperature °C AVHRR 1985–2002

Minimum mean monthly temperature °C AVHRR 1985–2002

Chlorophyll a concentration mg/m

3 MODISb

2001–2005

Diffuse attenuation coefficient (K490) m

-1 MODIS 2001–2005

Normalized water-leaving radiance 

(nLW551)

mW/(cm

2 

μm sr) MODIS 2001–2005

aAdvanced Very High Resolution Radiometer. 

bModerate 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 the 

investigator-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 (these 

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 km2

 spatial resolution available for the environmental data layers. These discarded 

occurrence points are more than likely in lakes and rivers smaller than 21 km2 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 

that 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 WATER

DISCHARGE DATA AND VESSEL TRAFFIC PATTERNS

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 

ballast-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 PONTO-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/m3

. The Great Lakes, shown mostly in blue in Figure 5, show a mean spatial 

productivity level of 1.7 mg/m3, ranging from 0.19–62.3 mg/m3. The Caspian and Black Seas 

are similar to the Great Lakes with a spatial mean of 2.2 mg/m3 and range from 0.2–56.2 mg/m3. 
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/m3) 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/(cm2 

μm sr) and a range of water clarity from 0.0–5.1 mW/(cm2 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/( cm2μm sr) indicating that Lake Superior is less productive. The 

Caspian and Black Seas have a spatial mean of 2.4 mW/(cm2 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 (Gymnocephalus 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 radiance (mW/cm2μm 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 these 

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 O2 regulators, 

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

Mellina and Rasmussen (1994) noted that calcium (Ca2+) 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 Ca2+ 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; therefore, 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 (an 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 the 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 the 

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 vessels. 

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 

each 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 each 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 each 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. 

0

20

40

60

80

Not from POC

Port Cartier,Canada

Antwerpen,Belgium

Baie Comeau,Canada

Port Alfred,Canada

Puerto Cabello,Venezuela

Sept Iles,Canada

Haraholmen,Sweden

Bremen,Germany

Manfredonia,Italy

St John,Canada

Ghent,Belgium

Amsterdam,Netherlands

New Haven,United States

Safi,Morocco

Casablanca,Morocco

Hafnarfjordur,Iceland

Santander,Spain

Albany,United States

Rotterdam,Netherlands

Hamburg,Germany

Houston,United States

Thisvi,Greece

Workington,UK

Yokkaichi,Japan Ballast Tanks Discharged

0

10,000

20,000

30,000

40,000

50,000

60,000

Ballast Water Discharged Volume (metric tons)

Ballast Tanks Discharged

Volume Discharged

80,000

90,000

St. Lawrence Estuary / Gulf of St. Lawrence

*

*

*

*

*

*
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 

0

50

100

150

Duluth

Toledo

Superior

Green Bay

Gary

Milwaukee

Oswego

Chicago

Ludington

Erie

Lorain

Menominee

Ashtabula Ballast Tanks Discharged

0

40,000

80,000

120,000

Volume of Ballast Water Discharged (metric tons)

Ballast Tanks Discharged

Volume of Ballast Water Discharged

400

450 200,000

180,000
52 

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

any 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.

0

40

80

120

160

Sept Iles, Canada

Port Cartier, Canada

Ijmuiden, Netherlands

Baie Comeau, Canada

Pointe Noire, Canada

Halifax, Canada

Europa Point, Gibraltar

Belledune, Canada

Antwerpen, Belgium

Casablanca, Morocco 

Riga, Latvia

Santos, Brazil

Rotterdam, Netherlands

Stephenville, Canada

Amsterdam, Netherlands

Ashdod, Israel

Police, Poland

Praia Mole, Brazil

Tynemouth, UK

Dunkerque, France

Tunis, Tunisia

Ghent, Belgium

Gdansk, Poland

Swinoujscie, Poland

Oran, Algeria

Syros Island, Greece Vessel-trips from specified port within last five ports of call 

St. Lawrence Estuary /

Gulf of St. Lawrence
53 

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

vessels. NOBOB-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. 

0

100

200

300

400

Duluth

Toledo

Superior

Green Bay

Gary

Milwaukee

Oswego

Chicago

Ludington

Erie

Lorain

Menominee

Ashtabula

Sandusky

Conneaut

Buffalo

Calumet

Two Harbors

Brevort Ballast Tanks Discharged

0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

Volume Ballast Dishcarged (metric tons)

Ballast Tanks Discharged

Volume of Ballast Water Discharged

* * * * *

* NOBOB-RM was 0
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 the 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 invasion 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 (<50 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 species 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 water 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 less 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 minutus—

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 shoreline 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—tench Found currently in St. Lawrence River. Potential to spread to shallower 

waters of most of Lake Erie, and to isolated portions 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. 

Corophium 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 all 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. 
59 

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A-1 

APPENDIX A. LIST OF NONINDIGENOUS SPECIES 

THAT HAVE BEEN REPORTED AS OCCURRING IN THE GREAT LAKES 
A-2

Year of 

Invasion Species Common Name Type Endemic Region

Location of 

First Sighting Vector

1840 Rumex obtusifolius bitter dock Plant 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 Eurasia 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 EurasiaWidespread 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 Canal 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 Plant 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 NA 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 auratusgoldfish 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 angustifolia 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 trutta 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

Salix 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 (unintentional

)

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, Solid 

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 Release (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 MichiganAquarium 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

Lower Great 

Lakes, Ontario Release

(deliberate)

1915 Mentha gentilis creeping whorled mint 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 (unintentional

)

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 Unknown

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 clover 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., Montreal

Release (unintentional

)

1928 Noturus insignis margined madtom Fish Atlantic NA Lake Ontario 

drainage Canals

1928 Lepomis microlophus redear sunfish FishSouthern U.S. Lake Michigan 

drainage Release

(deliberate)

1929 Lepomis humilis orange spotted 

sunfish 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 River Release (a

quarium)

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 Erie 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 (aquarium)

1936 Sparganium glomeratum bur reed Plant Eurasia 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 binderanus 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 Erie 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 Widespread 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 white 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 Annelid 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 acutiformis swamp sedge Plant Eurasia Lake Michigan 

drainage Unknown

1952 Myriophyllum spicatum Eurasian watermilfoil Plant Eurasia Lake Erie Release (aquarium)

1952 Potamothrix moldaviensis oligochaete Annelid Ponto-Caspian Lake Ontario Unknown
A-6

Year of 

Invasion Species Common Name Type Endemic Region

Location of 

First Sighting Vector

1956 Cordylophora 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) Widespread Lake Ontario Shipping, Ballast Water

1959 Trapa natans water chestnut Plant Eurasia Lake Ontario 

drainage Release (aquarium

)

1959 Lasmigona subviridis mussel Mollusk Atlantic NA Lake Ontario 

drainage Canals

1959 Pisidium supinum humpback pea clam 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 River Shipping, Ballast 

Water

1963 Skeletonema potamos diatom Phytoplankton Widespread Lake 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 Phytoplankton 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 herb 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 Enneacanthus gloriosus bluespotted sunfish Fish Atlantic NA Lake Ontario 

drainage Release (aquarium)

1972 Cyclops strenuus copepod Zooplankton 

(crustacean) Hudson Bay Lake Superior Canals (water diversion)

1972 Hydrocharis morsus-ranae European frogbit Plant 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 Thalassiosira 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 (aquarium) 

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

1975 Renibacterium (Corynebacterium) 

salmoninarum 

bacterial kidney 

disease Bacteria Unknown Lake Superior Release(unintentional)

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 Phytoplankton 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 buchanani ghost shiner Fish Mississippi R. Lake St. Clair 

drainage Release (bait)

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

drainage Unknown

1980 Corbicula fluminea Asiatic clam Mollusk East Asia Lake Erie Release (aquarium)
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 Superior

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 (Phallodrilus) aquaedulcis oligochaete Annelid Europe Niagara River Shipping, Ballast 

Water

1985 Salmincola lotae copepod Zooplankton 

(crustacean) Eurasia Lake Superior Unknown

1986 Gymnocephalus cernuus Eurasian ruffe Fish Ponto-Caspian Lake Superior Shipping, 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 (aquarium)

1988 Thalassiosira baltica diatom 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, Ballast 

Water

1989 Scardinius erythrophthalmus rudd Fish Eurasia Lake Ontario Release (Bait)

1989 Dreissena bugensis quagga mussel Mollusk Ponto-Caspian Lake Ontario Shipping, Ballast 

Water

1990 Neogobius melanostomus round goby Fish Ponto-Caspian St. Clair River Shipping, Ballast 

Water

1990 Proterorhinus marmoratus tubenose goby Fish Ponto-Caspian St. Clair River Shipping, Ballast 

Water

1991 Potamopyrgus antipodarum New Zealand mud 

snail Mollusk Australasia Lake Ontario Shipping, Ballast Water

1992 Acanthostomum sp. digenean fluke Other 

Invertebrate Eurasia Lake Superior Shipping, Ballast Water

1992 Ichthyocotylurus pileatus digenean fluke Other 

Invertebrate Ponto-Caspian Lake Superior Shipping, Ballast Water
A-9

Year of 

Invasion Species Common Name Type Endemic Region

Location of 

First Sighting Vector

1992 Neascus brevicaudatus digenean fluke Other 

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 Superior 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 japonicus 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 Zooplankton 

(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)

Africa, 

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 (aquarium)

2001 Gammarus tigrinus Amphipod Benthic 

Crustacean Atlantic NA

Lake Superior 

(Lake Huron in 

2002)

Shipping, Ballast 

Water

2001 Psammonobiotus communis testate amoeba Other 

Invertebrate Ponto-Caspian Lake Ontario Shipping, Ballast Water
A-10

Year of 

Invasion Species Common Name Type Endemic Region

Location of 

First Sighting Vector

2002 Ranavirus sp. largemouth bass virus Virus Unknown Lake Michigan 

drainage

Release

(unintentional)

2002 Psammonobiotus linearis testate amoeba Other 

Invertebrate Ponto-Caspian Lake Ontario Shipping, Ballast Water

2002 Psammonobiotus dziwnowi testate amoeba Other 

Invertebrate

PontoCaspian? Lake Ontario Shipping, Ballast 

Water

2002 Cylindrospermopsis raciborskii cyanobacterium Phytoplankton South 

America?

Lake Michigan 

drainage Unknown

2002 Piscirickettsia cf. salmonis muskie pox Other 

Invertebrate Unknown 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-1 
Table B-1. Summary of Literature Review for potentially invasive species. The 58 species shown as shaded re

greatest risk. 

Type of Organism 

Common Name 

(Scientific Name) 

In 

GL?a 

Invasion 

Historyb Spread Potential in 

GLc 

Ecol. 

Impact in 

GLd 

Species 

Origine 

Possible 

Sourcef Concern, Consequence, 

cnidarian Hydroid (Cordylophora caspia) 

Yes Yes High Med PontoCaspian 

Sea 

aquarium; ballast water 

established in L. Erie, Baltic Sea history 

crustacean Amphipod (Pontogammarus crassus) 

No Yes NEK NEK Caspian 

Sea 

ballast water 

established in Baltic Sea; not estamay be introduced 

crustacean Amphipod (Corophium curvispinum) 

No Yes High Med Caspian 

Sea 

ballast water 

found in L. St. Clair; invasion histfilter-feeding caddisflies widely diEurope; high densities in Baltic pitself in British Isles 

crustacean Amphipod (Dikerogammarus villosus) 

No Yes High High PontoCaspian 

Sea 

ballast water; canals (Europe) 

not established yet; established ibasin; eat and replace native ampinvading and decreasing natives rapid impact on macroinvertebratleading to population declines; inWestern Europe’s hydrosystems 

crustacean Amphipod (Pontogammarus robustoides) 

No Yes High NEK PontoCaspian 

Sea 

ballast water; canals (Europe) 

not established yet; established ibasin 

crustacean Amphipod (Echinogammarus ischnus) 

Yes Yes High Med PontoCaspian 

Sea 

ballast water 

established in L. Erie, Huron, Onthistory; limited dispersal capabilitnative amphipod Gmelinoides fas

crustacean Amphipod (Dikerogammarus haemobaphes) 

No Yes High Med PontoCaspian 

Sea 

ballast water; canals (Europe) 

not established yet; established ibasin 

crustacean Amphipod (Corophium sowinskyi) 

No Yes Med Med PontoCaspian 

Sea 

canals spreading across Europe; not est

could alter littoral communities an

crustacean Amphipod (Echinogammarus warpachowskyi) 

No Yes High Med PontoCaspian 

Sea 

ballast water 

not established yet; established ibasin 

crustacean Amphipod (Pontogammarus aralensis) 

No Yes High Med PontoCaspian 

Sea 

ballast water 

not established yet; established ibasin 
 B-3 

crustacean Amphipod (Echinogammarus berilloni) 

No NEK NEK NEK PontoCaspian 

Sea 

ballast water 

not established yet, may be introd

crustacean Amphipod (Echinogammarus trichiatus) 

No NEK NEK NEK PontoCaspian 

Sea 

ballast water 

not established yet, may be introd

crustacean Amphipod (Gammarus tigrinus) 

No NEK NEK NEK North 

America 

ballast water 

not established yet, may be introd

crustacean Amphipod (Gmelinoides fasciatus) 

No NEK NEK NEK Asia ballast 

water 

not established yet, may be introd

crustacean Amphipod (Iphigenella shablensis) 

No NEK NEK NEK PontoCaspian 

Sea 

ballast water 

not established yet, may be introd

crustacean Amphipod (Pontogammarus maeoticus) 

No NEK NEK NEK PontoCaspian 

Sea 

ballast water 

not established yet, may be introd

crustacean Amphipod (Pontogammarus obesus) 

No NEK NEK NEK PontoCaspian 

Sea 

ballast water 

not established yet, may be introd

crustacean Amphipod (Pontogammarus subnudas) 

No NEK NEK NEK PontoCaspian 

Sea 

ballast water 

not established yet, may be introd

crustacean Isopod (Jaera istri) 

No NEK NEK NEK PontoCaspian 

Sea 

ballast water 

not established yet, may be introd

crustacean Isopod (Jaera sarsi) 

No NEK NEK NEK PontoCaspian 

Sea 

ballast water 

not established yet, may be introd

crustacean Isopod (Proasellus coxalis) 

No NEK NEK NEK PontoCaspian 

Sea 

ballast water 

not established yet, may be introd

crustacean Isopod (Proasellus meridianus) 

No NEK NEK NEK PontoCaspian 

Sea 

ballast water 

not established yet, may be introd
 B-4 

crustacean Mysid shrimp (Hemimysis anomala) 

No Yes High Med PontoCaspian 

Sea 

ballast water 

not established yet; established ibasin; reduction of zooplankton; b

of contaminants at higher trophic to shallow, warm waters 

crustacean Mysid shrimp (Paramysis ullskyi) 

No Yes Med NEK PontoCaspian 

Sea 

ballast water 

not established yet; established ibasin; reduction of zooplankton; b

of contaminants at higher trophic to shallow, warm waters 

crustacean Mysid shrimp (Limnomysis benedeni) 

No Yes NEK NEK PontoCaspian 

Sea 

ballast water 

not established yet; established ibasin; reduction of zooplankton; b

of contaminants at higher trophic to shallow, warm waters 

crustacean Mysid shrimp (Paramysis intermedia) 

No Yes NEK NEK PontoCaspian 

Sea 

ballast water 

not established yet; established ibasin; reduction of zooplankton; b

of contaminants at higher trophic to shallow, warm waters 

crustacean Mysid shrimp (Paramysis lacustris) 

No Yes NEK NEK PontoCaspian 

Sea 

ballast water 

not established yet; established ibasin; reduction of zooplankton; b

of contaminants at higher trophic to shallow, warm waters 

crustacean Pseudocumid (Pseudocuma cercaroides) 

No NEK NEK NEK PontoCaspian 

Sea 

ballast water 

not established yet, may be introd

crustacean Pseudocumid (Pterocuma pectinata) 

No NEK NEK NEK PontoCaspian 

Sea 

ballast water 

not established yet, may be introd

crustacean Rusty crayfish (Orconectes rusticus) 

No Yes High High Southeas 

t United States 

bait found in inland waters of Michiga

Lakes); invasion history; spreadinOntario, northern Midwest; reducvegetation important to native fish cover; habitat destruction; affects industry (Ontario) 

fish Alewife (Alosa pseudoharengus) 

Yes Yes Present High Atlantic 

coast of North America 

bait fish; canals 

found in L. Ontario, Erie, Michigarestructure a lake’s food web, leafor native fish; contributed to extinnative species 

fish Arowana (Osteoglossum bicirrhosum) 

No NEK NEK NEK South 

America 

ornamental potential there but have slow grow

to survive temperatures below 58 Fahrenheit 
 B-5 

fish Bighead carp (Hypophthalmichthys nobilis / Aristichthys nobilis) 

Yes Yes High Med China aquaculture; 

fish market; canals 

few isolated cases in L. Erie; toleestablished in Mississippi River bhistory; vast mobility; voracious chabits; clog fishing nets and scarcommercial fish; competes with n

fish Black carp (Mylopharyngodon piceus) 

No No NEK NEK China aquaculture not established yet; reports found 

Ohio, Missouri, and Mississippi Rmobility; voracious consumption fishing nets and scare away comto commercial shellfish stocks 

fish Black sea silverside (Aphanius boyeri) 

No NEK High NEK PontoCaspian 

Sea 

bait; aquaculture 

fish Bleak (Alburnus alburnus) 

No Yes High Med PontoCaspian 

Sea 

aquaculture; bait 

invasive in Baltic; feed on crustac

fish Blue catfish (Ictalurus furcatus) 

No Yes Med Med North 

America 

aquaculture; sport fish 

spreading outside natural range; species of fish they can catch, alocrayfish, freshwater mussels, frogreadily available aquatic food sou

fish Blue tilapia (Oreochromis aureus) 

No Yes NEK NEK Africa/ 

Eurasia 

aquaculture; sport fish 

spreading in U.S.; invasive in FL; abundance and high densities in have resulted in marked changes community structure; private cultu

fish Blueback herring (Alosa aestivalis) 

Yes No Present High Atlantic 

coast of North America 

canals invasive in L. Ontario; impede rec

depressed populations of indigen

as cisco and lake trout; cold wateestablishment 

fish Bullhead (Cottus gobio) 

No NEK High NEK Europe bait often behave aggressively toward

and competition for shelter and focan be intense 

fish Caspian shad (Caspialosa caspia) 

No NEK Med NEK PontoCaspian 

Sea 

fish market 

fish Caucasian goby (Knipowitschia caucasica) 

No Yes High Med PontoCaspian 

Sea 

ballast water 

invasion in Baltic; predators whicbenthic animals 
 B-6 

fish Cherry salmon (Oncorhynchus masou) 

No NEK Med NEK North 

Pacific; Japan 

sport fish widespread 

fish Chub (Leucaspius cephalus) 

No NEK NEK NEK PontoCaspian 

Sea 

ornamental; live bait; sport fish 

fish Chum salmon (Oncorhynchus keta) 

No NEK High NEK Asia/Nort 

h Pacific 

sport fish widespread 

fish Clown loach (Botia macracanthus) 

No NEK NEK NEK Indonesia ornamental potential there but have slow grow

fish Common dace (Leuciscus leuciscus) 

No Yes High NEK PontoCaspian 

Sea 

bait widespread in Europe, showed in

country 

fish Common tilapia (Oreochromis mossambica) 

No NEK NEK NEK Africa fish market; 

aquaculture 

private culture in Ontario 

fish Eurasian minnow (Phoxinus phoxinus) 

No Yes High NEK PontoCaspian 

Sea 

bait invasive in Baltic 

fish European perch (Perca fluviatilis) 

No Yes High Med PontoCaspian 

Sea 

sport fish widespread prized for angling (int

many countries); because widespdesignated as invasive due to imspecies; cannibalism is common 

fish European ruffe (Gymnocephalus cernuus) 

Yes Yes Present High PontoCaspian 

Sea 

ballast water; canals; bait 

invaded L. Superior, Huron; now invasive history in Europe; displaspecies; predation on native fish competition with native fish; L. Sufish declined since introduction of 

fish European whitefish (Vendace) (Coregonus albula) 

No Yes High NEK Europe sport fish some invasion history 

fish Fourspine stickleback (Apeltes quadracus) 

Yes NEK NEK NEK Europe/ 

Atlantic coast of North America 

ballast water; ornamental 

confined to coasts; rapid increase

Thunder Bay (L. Superior) suggeis displacing native sticklebacks a
 B-7 

fish Ghost (or glass) catfish (Kryptopterus bicirrhis) 

No NEK NEK NEK Asia ornamental potential there but have slow grow

fish Giant or red snakehead (Channa micropeltes) 

No NEK NEK NEK Asia ornamental potential there but have slow grow

fish Goldfish (Carassius auratus) 

Yes Yes High High China/ 

Japan 

ornamental of the large established populatio

from the vicinity of western L. Erieextensive invasion history; concecommunity, increasing turbidity, pnative fish, help facilitate algal blolow temperatures 

fish Grass carp (Ctenopharyngodon idella) 

No Yes High High Eastern 

Asia 

aquaculture; fish market 

found isolated in L. Erie, Ontario, on aquatic vegetation reducing plremoving all aquatic vegetation frwater; competes with native fish; temperatures; invasion history 

fish Inland silverside (Menidia beryllina) 

No Yes NEK NEK Eastern 

U.S. coast 

aquaculture; sport fish 

could replace native fish; replaceCA, OK; found in Mississippi conand Ohio Rivers 

fish Koi (common) carp (Cyprinus carpio) 

Yes Yes High High Eurasia ornamental; 

aquaculture 

found in Great Lakes; extensive iglobally widespread; uproot and dsubmerged aquatic vegetation anbe detrimental to duck and native populations; tolerate low tempera

fish Longtail goby (Ctenogobius sagittula) 

No NEK NEK NEK Eastern 

Pacific 

fish Monkey goby (Neogobius fluviatilis) 

No Yes High High Eurasia ballast 

water 

invasion history in Europe; mass monkey goby is connected with thconsumption of plankton crustace

with other small fish for food and 

fish Mummichog (Fundulus heteroclitus) 

No NEK NEK NEK Western 

Atlantic/ Eastern U.S. coast 

bait; aquarium 

fish Nile tilapia (Oreochromis niloticus) 

No NEK NEK NEK Africa fish market; 

aquaculture 

private culture in Ontario; Mississ
 B-8 

fish Oriental weatherfish (Misgurnus anguillicaudatus 

Yes Yes High Med Asia ornamental; 

aquaculture 

established in Shiawassee River Michigan; reduce populations of aimportant as food to native fisheshistory; tolerate low temperatures 

fish Pacu (Colossoma macropomum) 

No NEK NEK NEK South 

America 

aquaculture; ornamental 

may compete with larvae of nativfor plankton 

fish Pike killifish (Belonesox belizanus) 

No NEK NEK NEK Central 

America 

aquarium voracious predator and has been 

reduce populations of eastern mo(Gambusia holbrooki) and other nand cyprinodontid populations 

fish Pontic shad (Alosa pontica) 

No NEK Med NEK PontoCaspian 

Sea 

fish market; aquarium 

fish Racer goby (Neogobius gymnotrachelus 

No Yes High Med PontoCaspian 

Sea 

canals; ballast water 

spreading in Europe 

fish Rainbow smelt (Osmerus mordax) 

Yes Yes Med High Atlantic 

coast of Central America 

sport fish Erie Canal; Lake Ontario, Michiga

Minnesota; dominant prey form focontributed to extinction of blue pimperiled species 

fish Red tail botia (Botia modesta) 

No NEK NEK NEK Asia ornamental potential there but have slow grow

fish Redear sunfish (Lepomis microlophus) 

Yes NEK High NEK Atlantic 

coast of North America 

aquaculture; sport fish 

found in L. Michigan; introduced associated with ecological changpopulations of pumpkinseed L. gimolluscivore; preference for moll

fish Roach (Rutilus rutilus) 

No Yes High Med PontoCaspian 

Sea 

sport fish invasive in Baltic; nuisance once 

fish Round goby (Neogobius melanostromus) 

Yes Yes Present High PontoCaspian 

Sea 

ballast water 

found in Great Lakes; invasion hirapidly; zebra mussels dominate benthic communities; aggressivesturgeon egg predators 

fish Rudd (Scardinius erythrophthalmus) 

Yes Yes Present High Eurasia bait fish; 

ballast water 

introduced into L. Ontario; expanhabitat degradation for native fishintroduced to open waters will hygolden shiner 
 B-9 

fish Sand goby (pomatoschistus minutus) 

No Yes High Med Eastern 

Atlantic/ PontoCaspian Sea 

aquarium; aquaculture 

abundant; invasion in Baltic; predon small benthic animals 

fish Silver carp (Hypophthalmichthys molitrix) 

No NEK High NEK China aquaculture documented in Cal-Sag Channel 

spread along Mississippi River; ccommercially significant native fishabitat; vast mobility; voracious chabits; clog fishing nets; widesprestates 

fish Snakehead (Channa argus argus) 

No Yes High Med Asia fish market sporadic places; devastation to fr

ecosystems of the U.S. because nature, lack of natural predators, and adaptability to a wide range oconditions 

fish Starry goby (Benthophilus stellatus) 

No Yes High NEK PontoCaspian 

Sea 

ballast water? 

spreading in Europe 

fish Striped bass (Morone saxatilis) 

No Yes NEK NEK Atlantic 

coast of North America 

fish market invasion history 

fish Sunbleak (Leucaspius delineatus) 

No Yes High High PontoCaspian 

Sea 

canals widespread in Europe; affecting c

populations in England (limited raphytoplankton and zooplankton 

fish Tench (Tinca tinca) 

No Yes High High PontoCaspian 

Sea/ Europe/ Asia 

ornamental; canals; sport fish 

been captured in St. Lawrence Rto L. Ontario; competition with benative fish; potential competitor fosport fishes and native cyprinids; 

fish Toothed carp (Aphanius fasciatus) 

No NEK High NEK PontoCaspian 

Sea 

canal competes with native fish; declini

Mediterranean 

fish Tubenose goby (Proterorhinus marmoratus) 

Yes Yes Med High PontoCaspian 

Sea 

ballast water 

found/restricted to L. St. Clair, weInvasion history but not spread raaggressive 

fish Tyulka / Caspian kilka (Clupeonella cultriventris/caspia) 

No Yes High NEK PontoCaspian 

Sea 

ballast water 

expanded in Volga River; suppre
 B-10 

fish Ukranian or nine-spined stickleback (Pungitius platygaster) 

Yes NEK Present NEK PontoCaspian 

Sea 

canal?; bait? 

found in L. Huron, Michigan 

fish Weather fish (Misgurnus fossilis) 

No Yes NEK NEK Europe ornamental tolerate low temperature; establis

Italy, Spain, Croatia 

fish White cloud mountain minnow (Tanichthys albonubes) 

No Yes High Med China ornamental tolerate low temperature; extensi

history; high occurrence frequenc

fish White perch (Morone americana) 

Yes Yes Present High Atlantic coast of 

North America 

fish market; canals 

already invaded Great Lakes; comnative fish; potential to cause decpopulations; prey on eggs of wall

fish Zander (Sander lucioperca) 

No Yes High High PontoCaspian 

Sea 

sport fish; canals; aquaculture 

found everywhere in Europe; depnative fish; hunts in packs 

flatworm Flatworm (Dendrocoelum romanodanubiale) 

No NEK NEK NEK North 

Sea basin 

ballast water 

not established yet, may be introd

flatworm Trematode (Apophallus muehlingi) 

No NEK NEK NEK Black 

Sea 

ballast water 

not established yet, may be introd

flatworm Trematode (Nicolla skrjabini) 

No NEK NEK NEK Black 

Sea 

ballast water 

not established yet, may be introd

flatworm Trematode (Rossicotrema donicum) 

No NEK NEK NEK Black 

Sea 

ballast water; fish host 

not established yet, may be introd

insect Apterogote (wingless) (Campodea staphylinus) 

No Yes High NEK Eurasia ballast 

water 

widespread; been recorded in Nofresh waters; tolerate range of sa

microcrustacean Baltic water flea (Bosmina coregoni) 

Yes Yes Present High Eurasia/ 

PontoCaspian 

ballast water 

established in L. Ontario, Superiohistory 

microcrustacean Calanoid copepod (Eurytemora affinis) 

Yes Yes Present NEK PontoCaspian 

Sea 

ballast water 

widespread; invasion history; estaGreat Lakes; high populations 
 B-11 

microcrustacean Calanoid copepod (Heterocope appendiculata) 

No Yes High NEK PontoCaspian 

Sea 

ballast water 

established in Baltic Sea basin 

microcrustacean Calanoid copepod (Calanipeda aquaedulcis) 

No Yes High NEK PontoCaspian 

Sea 

ballast water 

established in Baltic Sea basin 

microcrustacean Calanoid copepod (Heterocope caspia) 

No Yes High NEK PontoCaspian 

Sea 

ballast water 

established in Baltic Sea basin 

microcrustacean Cladoceran (Daphnia cristata) 

No Yes High NEK PontoCaspian 

Sea 

ballast water 

established in Baltic Sea basin 

microcrustacean Cladoceran (Bosmina obtusirostris) 

No Yes High NEK PontoCaspian 

Sea 

ballast water 

established in Baltic Sea basin 

microcrustacean Cladoceran (Podonevadne trigona ovum) 

No Yes High NEK PontoCaspian 

Sea 

ballast water 

established in Baltic Sea basin 

microcrustacean Cladoceran (water flea) (Daphnia lumholtzi) 

No Yes Med NEK Australia; 

SE Asia 

ballast water; boating; waterfowl 

expanding in U.S.; widespread; pwater; competes with native daphof its ability to avoid predation 

microcrustacean Cladoceran (Cornigerius maeoticus maeoticus) 

No Yes High NEK PontoCaspian 

Sea 

ballast water 

established in Baltic Sea basin 

microcrustacean Cyclopoid copepod (Cyclops strenuus) 

Yes Yes Present NEK Eurasia ballast 

water 

established in Great Lakes 

microcrustacean Cyclopoid copepod (Cyclops kolensis) 

No Yes High NEK PontoCaspian 

Sea 

ballast water 

established in Baltic Sea basin 

microcrustacean Fishhook waterflea (Cercopagis pengoi) 

Yes Yes Present High PontoCaspian 

Sea 

ballast water; boating 

established in Great Lakes excepSuperior; Baltic Sea basin; compfish for zooplankton; invasive in Enets for fisheries; damage cost da

microcrustacean Harpactacoid copepod (Ectinosoma abrau) 

No NEK High NEK PontoCaspian 

Sea 

ballast water 

potential to invade Great Lakes 
 B-12 

microcrustacean Harpactacoid copepod (Onychocamptus mohammed) 

Yes NEK Present NEK PontoCaspian 

Sea 

ballast water 

established in L. Ontario 

microcrustacean Harpactacoid copepod (Paraleptastacus spinicaudata triseta) 

No Yes High NEK PontoCaspian 

Sea 

ballast water 

invasion history 

microcrustacean Harpactacoid copepod (Schizopera borutzkyi) 

Yes Yes Present High Danube 

River delta of Black Sea 

ballast water 

established in L. Michigan; alterincomposition of nearshore commu

microcrustacean Harpactacoid copepod (Nitocra incerta) 

Yes Yes Present NEK Black 

Sea 

ballast water 

established in L. Michigan 

microcrustacean Spiny waterflea (Bythotrephes longimanus) 

Yes Yes High High Great 

Britain; Europe 

ballast water; boating 

established in Great Lakes; Greacaused major changes in the zoocommunity structure; invasion hisrapidly; competes directly with smhave impact on zooplankton comcost data available 

microcrustacean Spiny waterflea (Bythotrephes cederstroemi) 

Yes Yes Present High Eurasia ballast 

water 

established in Great Lakes; the inLaurentian Great Lakes has resusubstantial and sustained decreapopulations of a number of (mostnative zooplankton species; coincdramatic declines in abundance o

microsporidian Fish parasite (Heterosporis sp.) 

Yes Yes NEK NEK Europe/ 

Asia ? 

fish release; bait 

found in L. Ontario, attacks muscperch; found in Wisconsin 

mollusc Asian clam (Corbicula fluminea) 

Yes Yes NEK NEK Asia ornamental; 

fish market/bait 

established in L. Michigan, Supernot tolerate low temperatures; fouplants; damage cost data availab

mollusc Basket (European) shell (Corbula gibba) 

No NEK NEK NEK Atlantic 

coast of Europe 

ballast water 

capacity to achieve very high popdensities, giving it the potential to growth and recruitment of a wide bottom organisms 
 B-13 

mollusc Clam (Hypanis colorata) 

No NEK NEK NEK Caspian 

Sea 

ballast water 

potential to invade Great Lakes 

mollusc European fingernail clam (Sphaerium corneum) 

Yes Yes Present NEK Eurasia unknown - 

canals? 

found in L. Huron, Ontario; effect 

mollusc Golden mussel (Limnoperna fortunei) 

No Yes Med Med China/ 

Asia 

ballast water 

produces a rapid change in benthand threatens native biodiversity; macrofouling in the water systemspreading in South America 

mollusc Chinese mystery snail [(prosobranch] (Cipangopaludina chinensis malleata) 

Yes Yes High Med Asia fish market; 

ornamental 

isolated pop. in L. Erie and upper River; established L. Michigan; clwater intake pipes; vectors for the of parasites and diseases 

mollusc New Zealand mudsnail [hydrobid] (Potamopyrgus antipodarum) 

Yes Yes Present High New 

Zealand 

ballast water 

established in L. Ontario, Superiohistory; reduce native species, hapopulations; suspected that can aproduction of streams; spread rap

mollusc Quagga mussel (Dreissena bugensis) 

Yes Yes Present Med Ukraine/ 

PontoCaspian Sea 

ballast water 

found in L. Erie, Ontario; invasion historically dominated the biomastransition(30-50m) and profundal negative impact on raw water-usipotable water treatment plants; ddata available 

mollusc Snail (hydrobid) (Lithoglyphus naticoides) 

No NEK NEK NEK PontoCaspian ballast water potential to invade Great Lakes 

mollusc Snail (neritid) (Theodoxus fluviatilis) 

No NEK NEK NEK Baltic/ 

Black Sea 

ballast water 

potential to invade Great Lakes 

mollusc Snail (neritid) (Theodoxus pallasi) 

No NEK NEK NEK Caspian 

Sea 

ballast water 

potential to invade Great Lakes 

mollusc Zebra mussel (Dreissena polymorpha) 

Yes Yes Present High PontoCaspian 

Sea 

ballast water 

already established in Great Lakehistory; interferes with native molfunctions; damage cost data avai
 B-14 

plant Curly-leaf pondweed (Potamogeton crispus) 

Yes Yes Present Med Eurasia ornamental; 

horticulture 

established in U.S.; invasion histoproblems due to excessive growt

plant Dwarf hygrophila (Hygrophila polysperma) 

No Yes High Med India ornamental tolerate low temp; established in 

invasion history; clogs irrigation acanals 

plant Brazilian elodea or waterweed (Egeria densa) 

No Yes Med High Central/ 

South America 

ornamental established in OR, NY, MD, CT; i

slow dispersal; create dense matimpede water recreation; water mrestricted causing fish population 

cause fluctuations in water quality 

plant Eurasian watermilfoil (Myriophyllum spicatum) 

Yes Yes High High Eurasia ornamental; 

boaters 

invaded L Ontario; interferes with recreation; canopy can crowd out native plants; decrease oxygen ledecays; rapid colonization; damaavailable; threat factor to fish 

plant European frogbit (Hydrocharis morusranae) 

Yes Yes Med High Eurasia ornamental; 

horticulture; boaters 

invaded L. Ontario (only lake foundense mats of vegetation and thuand nutrients from reaching submvegetation; plants die in the fall sdissolved oxygen is possible 

plant European water clover (Marsilea quadrifolia) 

Yes Yes Med Med Eurasia ornamental found in L. Ontario; poses a realis

threat to ecosystems; affect local communities 

plant Fanwort (Cabomba caroliniana) 

Yes Yes Present High Southeas 

t United States 

ornamental Already invaded L. Michigan; clog

canals and freshwater streams; fostands crowding out previously wplants 

plant Flowering rush (Butomus umbellatus) 

Yes Yes Med NEK Eurasia ornamental; 

horticulture 

spread in limited areas in Great LOntario, Michigan); actively expawith native shoreline vegetation 

plant Giant salvinia (Salvinia molesta) 

No Yes Med Med South 

America 

ornamental; horticulture 

established in lower U.S.; invasioimpede the flow of water to irrigatother water intake pipes; rapidly epopulations can overgrow and replants,resulting dense surface colight and atmospheric oxygen from water; decomposing material drobottom, greatly consuming dissolneeded by fish and other aquatic 
 B-15 

plant Hydrilla (Hydrilla verticillata) 

No Yes High High Central 

Africa 

ornamental; horticulture 

established in southern U.S.; high invasion history; adaptable; denshydrilla will alter the waters chempH, cause wide oxygen fluctuatioincrease water temperature; elimplants 

plant Minor (slender) naiad (Najas minor) 

Yes Yes NEK NEK Europe ornamental limited in L. Erie; can form dense

stands in shallow water and hindefishing, boating, and other forms recreation 

plant Parrot’s feather (Myriophyllum aquaticum) 

No Yes High High South 

America 

ornamental invasion history; very adaptive to 

environments 

plant Purple loosestrife (Lythrum salicaria) 

Yes Yes Present High Eurasia ornamental; 

horticulture 

established in Great Lakes; invasplant can form dense, impenetrabare unsuitable as cover, food, or a wide range of native wetland ancost data available 

plant Sessile joyweed (Alternanthera sessilis) 

No Yes Med NEK Asia ornamental; 

horticulture 

invasion history; aggressive 

plant Spiny naiad (Najas marina) 

Yes Yes Med NEK Eurasia ornamental found in L. Ontario; widespread; i

recreational boating; pose a realithreat to ecosystems 

plant Variable-leaved watermilfoil (Myriophyllum heterophyllum) 

No Yes High Med Eastern 

North America 

ornamental; boaters 

spreading in New England statesnative aquatic vegetation, resultinmonotypic growth with less habitainterferes with recreational boatinrealistic nuisance threat to ecosy

plant Water primrose (Ludwigia uruguayensis) 

No NEK NEK NEK South 

America 

ornamental large colonies can prevent small 

and recreational use of shoreline 

plant Waterchestnut (Trapa natans) 

Yes Yes High Med Eurasia ornamental; 

horticulture 

released in L. Ontario; reproduce degradation through floating matsnavigation of waters and inhibitin

native aquatic plant species; damavailable 
 B-16 

plant Yellow floating heart (Nymphoides peltata) 

No Yes High Med Eurasia ornamental; 

horticulture 

spreading in New England; grows patches, excluding native species creating stagnant areas with low underneath the floating mats 

protozoans Amoebae (testate) (Psammonobiotus communis) 

Yes Yes NEK NEK Baltic 

Sea 

ballast water 

found in L. Ontario; newly discoveLakes 

protozoans Amoebae (testate) (Psammonobiotus dziwnowii) 

Yes Yes NEK NEK Baltic 

Sea 

ballast water 

found in L. Ontario; newly discoveLakes 

protozoans Amoebae (testate) (Psammonobiotus linearis) 

Yes Yes NEK NEK Baltic 

Sea (?) 

ballast water 

found in L. Ontario and other Gre

virus Largemouth bass virus (Iridoviridae family) 

Yes Yes High Med ? bait fish extended range in southeast bas

L. Michigan; kills fish (commerciainvasive) 

worm Oligochaete (Potamothrix bedoti) 

Yes Yes NEK NEK PontoCaspian 

Sea 

ballast water 

established in Great Lakes; invasBaltic 

worm Oligochaete (tubificid) (Potamothrix heuscheri) 

No Yes NEK NEK PontoCaspian 

Sea 

ballast water 

invasion history in Baltic; continuoto the west over Central Europe awest towards the Baltic Sea 

worm Oligochaete (Potamothrix moldaviensis) 

Yes Yes NEK NEK PontoCaspian 

Sea 

ballast water 

established in Great Lakes; invasBaltic 

worm Oligochaete (Potamothrix vejdovskyi) 

Yes Yes NEK NEK PontoCaspian 

Sea 

ballast water 

established in Great Lakes; invasBaltic 

worm Polychaete worm (Hypania invalida) 

No Yes NEK NEK PontoCaspian 

Sea 

ballast water 

invasion history in Baltic; slow sp
worm 

Polychaete worm (Hypaniola kowalevskyi) 

No Yes NEK NEK PontoCaspian 

Sea 

ballast water 

invasion history in Baltic; slow sp

aPresent in Great Lakes is scored “yes” if the organism has been reported for 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 

 species. 

cSpread potential within the Great Lakes after introduction scored “High” or “Med” based on comments in two or m

that species, or NEK (not enough known). dEcological impact if a species becomes established in Great Lakes scored as “High” or “Med” if so reported in at le

 for that species; else, it is scored as NEK (not enough known). 

eSpecies origin notes the probable native area or region of occurrence for the species in question, as cited in the papefThe known, suspected, or probable mechanism of introduction of the species prior invasion history is listed based o

invasion history. gComments related to investigators’ concerns about the consequences of invasion for the species in question are abs

the species in question. hArticles referenced here were identified on the basis of species names plus reference to “Great Lakes”. Thus, the li

 include many papers that consider the biology of the organism in its native range. 
Table B-2. References that identify 156 invasive species as described in Table B-1. 

Reference Number References 1 Horvath, TG; Oberdick, EJ; Last, LL; et al. (2001) Invading species dominate the Harpacticoid fauna in the n

Lake Michigan. NABS Annual Meeting, La Crosse WI (abstract) 

2 Buchan, LAJ; Padilla, DK. (1999) Estimating the probability of long-distance overland dispersal of invading a

9(1):254-265. 

3 Corkum, LD; Sapota, RM; Skora, KE. (2004) The Round Goby, Neogobius malanostomus, a fish invader on 

Ocean. Biol Invasions 6(2):173-181. 

4 Cristescu, MEA; Witt, JDS; Grigorovich, IA; et al. (2004) Dispersal of the Ponto-Caspian amphipod Echinoga

waves from the Pleistocene to the present. Heredity 92:197-203. 

5 Daniels, RA. (2001) Untested assumptions: the role of canals in the dispersal of Sea Lamprey, Alewife, and 

United States. Environ Biol Fishes 60(4):309-329. 

6 Devin, S; Piscart, C; Beisel, JN; et al. (2003) Ecological traits of the amphipod invader Dikerogammarus villo

scale. Arch Hydrobiol 158(1):43–56. 

7 Dick, JTA; Platvoet, D. (2000) Invading predatory crustacean Dikerogammarus villosus eliminates both nativ

Biol Sci 267(1447):977-983. 

8 Dick, JTA; Platvoet,D; Kelly, DW. (2002) Predatory impact of the freshwater invader Dikerogammarus villosu

Can J Fish Aquat Sci 59(6):1078-1084. 

9 Duggan, IC; Bailey, SA; Colautti, RI; et al. (2003) Biological invasions in Lake Ontario: past, present, and fut

State of Lake Ontario (SOLO) – Past, Present, and Future. 

10 Duggan, IC; van Overdijk, DA; Bailey, SA; et al. (2005) Invertebrates associated with residual ballast water a

carrying ships entering the Great Lakes. Can J Fish Aquat Sci 62:2463-2474. 

11 Froese, R; Pauly, D, Eds. (2005) FishBase.World Wide Web electronic publication. www.fishbase.org, versio12 Goehle, M. (2004) Keeping track of aquatic invasive species in the Great Lakes. ANS Update. 9(4):1-2. Ava

http://www.glc.org/ans/ansupdate/pdf/2004/Update-02-04.pdf 

13 Grigorovich, IA; Colautti, RI; Mills, EL; et al. (2003) Ballast-mediated animal introductions in the Laurentian G

and prospective analyses. Can J Fish Aquat Sci 60:740-756. 

14 Holeck, KT; Mills, EL; MacIsaac, HJ; et al. (2004). Bridging troubled waters: Biological invasions, transocean

Laurentian Great Lakes. Bioscience 54(10):919-929. 

15 Horvath, TG; Whitman, RL; Last, LL. (2001) Establishment of two invasive crustaceans (Copepoda: Harpact

sands of Lake Michigan. Can J Fish Aquat Sci 58:1261-1264. 

16 Keppner, SM; Busiahn, TR; McClain, J; et al. (1997) Surveillance for Ruffe in the Great Lakes – an overview

3(1):17-26. 

17 Kerr, SJ; Brousseau, CS; Muschett, M. (2005) Invasive aquatic species in Ontario: a review and analysis of 

introduction. Fisheries 30(7):21-30. 

 B-18 
Reference References Number 

 B-19 18 Kolar, CS; Lodge, DM. (2002) Ecological predictions and risk assessment for alien fishes in North America. 

19 MacIsaac, H; Grigorovich, IA; Ricciardi, A. (2001) Reassessment of species invasions concepts: the Great L

Biol Invasions 3:405-416. 20 MacIsaac, HJ; Robbins, TC; Lewis, MA. (2002) Modeling ships’ ballast water as invasion threats to the Grea

Sci 59:1245-1256. 21 MacIsaac, HJ; Borbely, JVM; Muirhead, JR; et al. (2004) Backcasting and forecasting biological invasions of 

14(3):773-783. 22 Maki, K; Galatowitsch, S. (2004) Movement of invasive aquatic plants into Minnesota (USA) through horticul

118:389-396. 23 Mills, EL; Leach, JH; Carlton, JT; et al. (1994) Exotic species and the integrity of the Great Lakes. BioScienc

24 Nicholls, KH. (2005) Psammonobiotus dziwnowi and Corythionella georgiana, two new freshwater sand-dwe

(Rhizopoda: filosea). Acta Protozool 44:271-278. 25 Palmer, ME; Ricciardi, A. (2004) Physical factors affecting the relative abundance of native and invasive amRiver. Can J Zool 82:1886-1893. 26 Palmer, ME; Ricciardi, A. (2005) Community interactions affecting the relative abundances of native and invaLawrence River. Can J Fish Aquat Sci 62:1111-1118. 

27 Ricciardi, A. (1998) Global range expansion of the Asian mussel Limnoperna fortunei (Mytilidae): Another fo

systems. Biofouling 13(2):97-106. 

28 Ricciardi, A. (2001) Facilitative interactions among aquatic invaders: is an “invasive meltdown” occurring in t

Fish Aquat Sci 58:2513-2525. 

29 Ricciardi, A; MacIsaac, HJ. (2000) Recent mass invasion of the North American Great Lakes by Ponto-Casp

15(2):62-65. 

30 Ricciardi, A; Rasmussen, JB. (1998) Predicting the identity and impact of future biological invaders: a priority 

management. Can J Fish Aquat Sci 55:1759-1765. 

31 Rixon, CAM; Duggan, IC; Bergeron, NMN; et al. (2005) Invasion risks posed by the aquarium trade and live 

Laurentian Great Lakes. Biodiversity Conserv 14:1365-1381. 

32 Vanderploeg, HA; Nalepa, TF; Jude, DJ; et al. (2002) Dispersal and emerging ecological impacts of Ponto-C

Laurentian Great Lakes. Can J Fish Aquat Sci 59(7):1209-1228. 

33 Yan, ND; Girard, R; Boudreau, S. (2002) An introduced invertebrate predator (Bythotrephes) reduces zoopla

Ecol Lett 5:481-485. 

34 International Association for Great Lakes Research. (2002) Research and management priorities for aquatic 

Great Lakes. Available online at http://www.iaglr.org/scipolicy/ais/ais_iaglr02.pdf 

35 CANOE News. (2005) Invasive fish species found in Lake Champlain, say experts from Quebec, Vermont. O

http://cnews.canoe.ca [11/21/05] 
Reference References Number 

 B-20 36 Madsen, J. (1999) A quantitative approach to predict potential nonindigenous aquatic plant species problem

1999. Available online at http://www.glc.org/ans/ansupdate/pdf/ansdec99.pdf [11/22/05] 37 Cherwaty, S; Shear, H. (2002) Great Lakes indicators target aquatic invasive species. ANS Update Fall/Winat http://www.glc.org/ans/ansupdate/pdf/ansv8n4.pdf [11/22/05] 38 Sturtevant, R. (2002) The Great Lakes waterflea report. ANS Update Spring/Summer 2002. Available online http://www.glc.org/ans/ansupdate/pdf/ansv8n2.pdf [11/22/05] 39 Froese, R; Pauly, D, Eds. (2005) FishBase.World Wide Web electronic publication. www.fishbase.org, versio

40 Rendall, J. (2003) Watercraft inspections: an opportunity to prevent the spread of aquatic nuisance species. 

2003. Available online at http://www.glc.org/ans/ansupdate/pdf/Update-05-03.pdf [11/22/05] 41 Maki, K; Galatowitsch, S. (2003) Mail-order sales of aquatic plants: a pathway for ANS introduction. ANS Up

Available online at http://www.glc.org/ans/ansupdate/pdf/Update08-03.pdf [11/22/05] 42 Higbee, E; Glassner-Shwayder, K. (2004) The live food fish industry: new challenges in preventing the introd

aquatic invasive species. ANS Update Fall/Winter 2004. Available online at http://www.glc.org/ans/ansupdate/pdf/2004/ANSUpdateFW.pdf [11/22/05] 43 Rasmussen, J. (2002) The Cal-Sag and Chicago sanitary and ship canal: a perspective on the spread and c

nuisance fish species. US Fish and Wildlife Service, East Lansing Ecological Services Field Office, Great LaOnline at http://www.fws.gov/midwest/LaCrosseFisheries/reports/Connecting_Channels_Paper_Final.pdf [11

44 Canadian Wildlife Federation. (2003) Invasive Species in Canada. Available online at http://www.cwf-fcf.org/

[12/05/05] 

45 Whelan, G. (2004) Largemouth bass virus continues to spread in Michigan waters. Michigan Department of 

Lansing, MI. Available online at http://www.michigan.gov/dnr/0,1607,7-153-10364_10950-93547--,00.html [1

46 Indiana Department of Natural Resources. (2005) Invasive species. Available online at http://www.in.gov/dn

[12/09/05] 

47 Klein, B. (2004) Making A List: Prevention Strategies for Invasive Plants in the Great Lakes States. Environm

Washington D.C. Available online at http://www.elistore.org [11/23/05] 

48 Minnesota Department of Natural Resources. (2005) Invasive species. Available online at http://www.dnr.state.mn.us/invasives/index.html [12/09/05] 49 Invasive Species Specialist Group Global Invasive Species Programme. (2005) ISSG Global Invasive Speci

online at http://www.issg.org/database [01/2006] 

50 Berg, D. (1992) The spiny water flea, Bythotrephes cederstroemi Another unwelcome newcomer to the Grea

College Program, Fact Sheet 49. Available online at http://sgnis.org/publicat/papers/bergdj92.pdf [01/18/06] 

51 Stepien, CA; Tumeo, MA. (2006) Invasion genetics of Ponto-Caspian gobies in the Great Lakes: a ‘cryptic’ s

effects, and comparative risk analysis. Biol Invasions 8:61-78. 

52 USGS (2005) NAS - Nonindigenous Aquatic Species. Available online at http://nas.er.usgs.gov/ [01/17/06] 
Reference References Number 

 B-21 53 Bailey, SA; Reid, DF; Colautti, RI; et al. (2005) Management of nonindigenous species in the Great Lakes. T

Science & Policy 5:101-112. 54 Pimentel, D. (2005) Aquatic nuisance species in the New York State Canal and Hudson River Systems and 

economic and environmental assessment. Environ Manage 35(5):692-701. 55 Dextrase, AJ; Mandrak, NE. (2006) Impacts of alien invasive species on freshwater fauna at risk in Canada. 

56 Wisconsin Department of Natural Resources. (2005) Aquatic Invasive Species Program. Available online at http://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 correctly predict 

independent data that have not been processed by the model. In this report, Genetic Algorithm 

for Rule-set Production (GARP) models were evaluated by comparing the known occurrences of 

three invasive species already within the Great Lakes with the predictive ability of GARP models 

developed for these species using occurrence data from other regions. 

 Model performance was assessed using area under the curve of the receiver operating 

characteristic curve (Sing et al., 2005) using R 2.4.0 (R Development Core Team, 2006). Area 

under the curve is a threshold-independent evaluation of model performance that, in this case, 

measures the ability of the model to differentiate between sites where a species is considered 

present versus 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 

predicted-present site will have a higher predicted 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 (Graham et al., 2004). 

 GARP produces predictions of habitat suitability 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 model validation, values above this threshold (i.e., 50) are considered present (assigned a 

value of 1) while values below this threshold are considered absent (assigned a value of 0). The 

threshold that is selected is typically the threshold 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 correctly 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 his study are that (1) it weights 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 help interpret 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 habitat suitability. Thus, by 

moving along the curve, one can stop at any color transition, say between yellow and orange (or 

a threshold value of 0.81 as determined by the right-hand y-axis or 81 from the legend in the 

map). By moving horizontally from this threshold value to the left-hand y-axis, one can 

determine the rate at which known presence correctly are predicted as present, also known as the 

true-positive rate (about 0.75 in this example). By moving vertically downward from this 

threshold to the x-axis, one can determine the rate 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 predicted, but it would also predict roughly 30% of 

presumed absences as present. In this manner, the receiver operating characteristic curve 

provides a means to assess the rates of false-positive predictions (predicting a species present 

where it is considered absent) and false-negative predictions (predicting a species absent where it 

is known to be present). 

Model Evaluation Results 

 Swets (1988) suggested the following scale for determining model performance using 

area under the curve: 0.90–1.00 = excellent; 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 category 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 (training 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 

evaluation 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 using only occurrence data from outside the Great 

Lakes, (2) the corresponding receiver operating characteristic curve plot and area under the curve 

value and bootstrap statistics for each model, 

and (3) the occurrence data within the Great Lakes withheld from GARP and used for evaluation 

of predictive performance (hollow points). Note that for reasons discussed under “Selecting 

Species to Model and Development of Occurrence Data” in Section 3.1, these occurrence points 

may not be inclusive of all known occurrences in the Great Lakes and represent only those 

suitable for model evaluation. 

Taken together, these area under the curve scores and the predicted distributions suggest 

three important conclusions. First, in our tests, GARP models adequately predict the known 

distributions of potential invasive species within the Great Lakes and, therefore, may be capable 

of accurately identifying areas of the Great Lakes susceptible to aquatic invasive species that 

have yet to be introduced. Thus, distribution data from a species’ existing range can produce 

useful predictions of invasion potential using 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, Lake St. Clair (located between Lakes Erie and Huron), and 

Thunder Bay in Lake Superior are particularly prone to future invasion when considering 

 C-4 
 C-5 

environmental tolerances alone. Finally, the universally high area under the curve scores suggest 

that the six environmental data layers we selected as inputs for the GARP models provide useful 

information for predicting the potential distributions of invasive species within the Great Lakes. 

In sum, the model validation exercise suggests that GARP predictions provide a useful 

assessment of invasion potential, given the availability of adequate occurrence data outside the 

Great Lakes. 

REFERENCES FOR APPENDIX C. 

Graham, CH; Ferrier, S; Huettman, F; et al (2004) New developments in museum-based 

informatics and applications in biodiversity analysis. TRENDS Ecol Evol 19(9): 497-503. 

Pearce, J; Ferrier, S. (2000) Evaluating the predictive performance of habitat models developed 

using logistic regression. Ecol Model 133(3): 225–245. 

R Development Core Team (2006). R: A Language and Environment 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: visualizing classifier performance in 

R. Bioinformatics 21:3940–3941. 

Swets, JA. (1988) Measuring the accuracy of diagnostic systems. Science 240:1285–1293. 
 C-6 

Figure C-1. GARP model validation for zebra mussel showing predicted suitability and area under the curve (inset). 
 C-7 

 Figure C-2. GARP model validation for ruffe showing predicted suitability and area under the curve (inset). 
 C-8 

 Figure C-3. GARP model validation for New Zealand mud snail showing predicted suitability and area under the cur
APPENDIX D. GARP POWER OF PREDICTION ANALYSIS 

 D-1 
DETAILS OF HOW GARP POWER OF PREDICTION ANALYSIS WAS USED 

If environmental conditions within a particular region of the Great Lakes differ from

those near a species’ currently occupied range, then a prediction about the suitability of habitat 

cannot be made (null prediction). A prediction cannot be made because no information exists to 

predict whether a species under these novel conditions will or will not find suitable habitat. 

Unfortunately, the Genetic Algorithm for Rule-set Production (GARP), along with most species 

distribution models, simply predicts environments that are non-analogous to the environments 

found near the species’ currently occupied 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 prediction). More details about the theoretical basis 

for using GARP power of prediction analysis are provided in Section 3.5. The remainder of 

Appendix D presents the specific application of GARP power of prediction analysis 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 occurrence data outside the Great Lakes 

(i.e., these species had no distribution data describing their home range, the basis for a power of 

prediction analysis). Also, because GARP models 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 similar home 

range region, eliminating the need to perform separate power of prediction analysis for each 

species studied. The 11 species had home-range occurrence data originating from one or more of 

four large regions: the northwest Pacific coast of North America, the northeast Atlantic coast of 

North America, the southeastern coast of Australia, and a large region 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 analyses 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 Lakes region as suitable habitat. Similarly, no 

power of prediction analysis was performed for the 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 predicted to be fairly widespread within the Great Lakes. Thus, 

only Europe and North Africa remained as regions 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 these regions. The gray shaded 

regions in Figures 7–17 and 24–27 reflect the results of the GARP power of prediction analysis. 

 Reverse power of prediction analysis, which 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 similar to those in the Great Lakes, we found 

largely salt-water environments at homologous latitudes. 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 similar aquatic environments, 

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

 

GROUPED RESULTS FOR 11 SPECIES 

Figures D1 through D3 show the results of the GARP 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 total models constructed. For clarity, we 

simplified the figures by converting these continuous predictions into two 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 fishhook 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 predicted by the models as lowrisk areas. Similarly, Lakes Erie and Ontario were predicted to be particularly suitable for 

invasion by both species, and the power of prediction analysis showed that the modeling 

approach had good predictive capabilities within these two lakes. Susceptibility to invasion by 

these two species in the upper Great Lakes was predicted to be limited primarily to near-shore 

areas, and the power of prediction analysis shows that many of the areas predicted to be not 

susceptible to invasion by these two species fall well within the red areas determined to be 

predictable by GARP. Thus, these, too, are valid predictions of low risk of invasion by roach 

and rudd. Therefore, the GARP model results, coupled with the power of prediction analysis, 

successfully identify large areas of the Great Lakes 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 the zebra mussel and New Zealand mud snail 

indicate a large area within the Great Lakes that is predictable by GARP (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 Superior should be unsuitable and, 

therefore, not at risk of future invasion by these species. However, the power of prediction 

analysis for these species indicates that this area is simply not predictable. Therefore, the risk to 

central Lake Superior from invasion by these two species is, at this point, undeterminable by 

GARP models alone. GARP predictions show parts of Lake Huron to be at low risk from the 

New Zealand mud snail and the power of prediction analysis shows that these areas can be well 

predicted by GARP. 

 D-4 
 The set of power of prediction analyses that were performed for Corophium curvispinum, 

monkey goby, tubenose goby, and tench indicate that large areas within the Great Lakes are not 

predictable by GARP (see Figure D2 lower panel). GARP predictability zones (red areas) are 

restricted to Lake Erie, the southern half of Lake Ontario, and the southernmost tip of Lake 

Michigan. All four of these 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 invasion. Many of these predicted absence 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 extensive areas would have been interpreted by 

managers as no-invasion-risk zones. However, the areas actually are no-prediction possible 

zones, meaning they might be susceptible to invasion and they might not. Without the benefit of 

the power of prediction analysis, this condition would not have been known from the GARP 

predictions alone. The power of prediction analysis prevents this critical misinterpretation. 

 When viewed in conjunction with the corresponding GARP power of prediction analysis 

figures, the results present a clearer prediction of invasion-susceptible habitat areas. Without 

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 models 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 these three species, as was suggested by the GARP 

predictions alone. 

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

ruffe (bottom) 

 D-6 
Figure 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-7 
Figure D-3. Power of prediction analysis for sand goby. 

 D-8 
APPENDIX E. TABLES DEPICTING SOURCES AND DESTINATION OF 

BALLAST 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 vessels whose source of ballast 

water (prior to ballast water exchange) came from outside the Great Lakes.

Source of Ballast Water Country 

Ballast 

Tanks 

Discharged 

Volume 

Discharged 

(metric tons) 

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 

Tanks 

Discharged 

Volume Discharged 

(metric tons) 

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 

Lake Port 

Tanks 

Discharged 

Volume 

Discharged 

(metric tons) 

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 discharges from vessels whose original source 

of ballast water (prior to ballast water exchange) came from outside the Great Lakes. 

Sorted by U.S. Great Lake port of call. 

US Great 

Lake 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 

Lake 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 including specified port as a last five 

ports of call, for NOBOB-RM vessels (2006). NOBOB-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. 

Port outside St. 

Lawrence 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 ballast tank discharges into U.S. 

Great Lakes ports of call from NOBOB-RM vessels (2006). NOBOB-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. 

U.S. Great 

Lake 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