National Park Service

U.S. Department of the Interior

Natural Resource Program Center

Spatial Patterns of Persistent Contaminants in Bald Eagle 

Nestlings at Three National Parks in the Upper Midwest 

2006-2009 

Natural Resource Technical Report NPS/GLKN/NRTR—2011/431 
ON THE COVER

Eight- to nine-week old bald eagle nestling. Photograph by Bill Route.
Spatial Patterns of Persistent Contaminants in Bald 

Eagle Nestlings at Three National Parks in the Upper 

Midwest 

2006-2009 

Natural Resource Technical Report NPS/GLKN/NRTR—2011/431 

Bill Route

National Park Service

Great Lakes Inventory and Monitoring Network 

Ashland, Wisconsin 54806 

Paul Rasmussen

Wisconsin Department of Natural Resources

Monona, Wisconsin 53716 

Rebecca Key

National Park Service

Great Lakes Inventory and Monitoring Network 

Ashland, Wisconsin 54806 

Michael Meyer

Wisconsin Department of Natural Resources

Rhinelander, Wisconsin 54501 

Mark Martell

Audubon Minnesota 

St. Paul, Minnesota 55125 

February 2011

U.S. Department of the Interior 

National Park Service

Natural Resource Program Center

Fort Collins, Colorado 
ii

The National Park Service, Natural Resource Program Center publishes a range of reports that 

address natural resource topics of interest and applicability to a broad audience in the National 

Park Service and others in natural resource management, including scientists, conservation and 

environmental constituencies, and the public. 

The Natural Resource Technical Report Series is used to disseminate results of scientific studies 

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All manuscripts in the series receive the appropriate level of peer review to ensure that the 

information is scientifically credible, technically accurate, appropriately written for the intended 

audience, and designed and published in a professional manner. 

Data in this report were collected, analyzed, and interpreted using established, peer-reviewed 

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directly involved in the collection, analysis, or reporting of the data, and whose background and 

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Views, statements, findings, conclusions, recommendations, and data in this report do not 

necessarily reflect views and policies of the National Park Service, U.S. Department of the 

Interior. Mention of trade names or commercial products does not constitute endorsement or 

recommendation for use by the U.S. Government. 

This report is available from the Great Lakes Inventory and Monitoring Network website at 

(http://science.nature.nps.gov/im/units/GLKN/monitorreportpubs.cfm) and the Natural Resource 

Publications Management website (http://www.nature.nps.gov/publications/NRPM). 

Please cite this publication as:

Route, B., P. Rasmussen, R. Key, M. Meyer, and M. Martell. 2011. Spatial patterns of persistent 

contaminants in bald eagle nestlings at three national parks in the upper Midwest: 2006-2009. 

Natural Resource Technical Report NPS/GLKN/NRTR—2011/431. National Park Service, Fort 

Collins, Colorado. 

NPS 920/106819, February 2011
iii

Contents

Page

List of Figures ................................................................................................................................. v

List of Tables ................................................................................................................................ vii

List of Appendices ......................................................................................................................... ix

Executive Summary ....................................................................................................................... xi

Acknowledgments........................................................................................................................ xiii

Introduction ..................................................................................................................................... 1

Methods........................................................................................................................................... 5

Study Areas .............................................................................................................................. 5

Apostle Islands National Lakeshore ................................................................................... 5

Lake Superior’s South Shore .............................................................................................. 5

Mississippi National River and Recreation Area ................................................................ 6

St. Croix National Scenic Riverway ................................................................................... 7

Mississippi River Pools 3 & 4 ............................................................................................ 8

Occupancy and Productivity Estimates ................................................................................... 8

Sample Collections and Handling ........................................................................................... 9

Laboratory Analyses and Quality Assurance ........................................................................ 10

Statistical Analysis ................................................................................................................. 11

Results ........................................................................................................................................... 13

Productivity and Mortality ..................................................................................................... 13

Eaglet Sampling ..................................................................................................................... 15

Contaminant Levels and Spatial Patterns .............................................................................. 17

Heavy Metals .................................................................................................................... 17

Mercury ...................................................................................................................... 17

Lead ........................................................................................................................... 20

Legacy Contaminants........................................................................................................ 22

DDT and Metabolites DDE and DDD ....................................................................... 22

PCBs .......................................................................................................................... 26
iv

Emerging Contaminants of Concern ................................................................................. 32

PBDEs........................................................................................................................ 32

PFCs ........................................................................................................................... 35

Discussion ..................................................................................................................................... 45

Productivity and Mortality ..................................................................................................... 45

Contaminant Levels and Spatial Patterns .............................................................................. 45

Heavy Metals .................................................................................................................... 45

Mercury ...................................................................................................................... 45

Lead ........................................................................................................................... 48

Legacy contaminants ........................................................................................................ 49

DDT/DDD/DDE ........................................................................................................ 49

PCBs .......................................................................................................................... 51

Emerging contaminants of concern .................................................................................. 52

PBDEs........................................................................................................................ 52

PFCs ........................................................................................................................... 54

Conclusions and Management Implications ................................................................................. 59

Literature Cited ............................................................................................................................. 63
v

List of Figures

Page

Figure 1. Map of six study areas where bald eagle nestlings were sampled for 

environmental contaminants, 2006 through 2009. .......................................................................... 6

Figure 2. Number of young per occupied bald eagle nest for six study areas. ............................ 13

Figure 3. Estimated geometric mean concentrations and 95% confidence intervals for 

mercury in bald eagle nestling feathers from six study areas, 2006-2009. Geometric 

means and confidence intervals are calculated from mixed effects models. ................................ 17

Figure 4. Maximum concentrations of mercury found in breast feathers of bald eagle 

nestlings from all nests sampled across six study areas, 2006-2009. There were no 

non-detections. .............................................................................................................................. 18

Figure 5. Maximum concentrations of lead found in breast feathers of bald eagle 

nestlings from all nests sampled across six study areas, 2006-2009. ........................................... 21

Figure 6. Maximum concentrations of DDT found in plasma of bald eagle nestlings 

from all nests sampled across six study areas, 2006-2009. ........................................................... 22

Figure 7. Maximum concentrations of DDD found in plasma of bald eagle nestlings 

from all nests sampled across six study areas, 2006-2009. ........................................................... 23

Figure 8. Maximum concentrations of DDE found in plasma of bald eagle nestlings 

from all nests sampled across six study areas, 2006-2009. ........................................................... 25

Figure 9. Estimated geometric mean concentrations and 95% confidence intervals for 

DDE in bald eagle nestlings sampled at six study areas, 2006-2009. Geometric means 

and confidence intervals calculated from mixed effects models. ................................................. 26

Figure 10. Maximum concentrations of total PCBs found in plasma of bald eagle 

nestlings from all nests sampled across six study areas, 2006-2009. ........................................... 27

Figure 11. Estimated geometric mean concentrations and 95% confidence intervals 

for total PCBs in bald eagle nestlings sampled in six study areas in the upper 

Midwest, 2006-2009. The left panel shows total PCBs as calculated by the KaplanMeier nonparametric procedure; the right panel shows total PCBs as calculated by 

using zeroes to replace congener concentrations that were below the laboratory limit 

of detection.................................................................................................................................... 30

Figure 12. Estimated geometric mean concentrations and 95% confidence intervals 

for 15 PCB congeners in bald eagle nestlings sampled in six study areas, 2006-2009. 

Geometric means and confidence intervals were estimated from mixed effects

models. .......................................................................................................................................... 31
vi

Figure 13. Maximum concentrations of total PBDEs found in plasma of bald eagle 

nestlings from all nests sampled across six study areas, 2006-2009. ........................................... 32

Figure 14. Estimated geometric means and 95% confidence intervals of five PBDE 

congeners in plasma from bald eagle nestlings sampled in six study areas, 2006-2009. ............. 34

Figure 15. Maximum concentrations of total PFCs found in plasma of bald eagle 

nestlings from all nests sampled across six study areas, 2008 and 2009. ..................................... 38

Figure 16. Pie charts showing percent by volume of different PFC congeners in bald 

eagle plasma; top chart is for the Mississippi River study areas (MISS and Polls 3&4) 

and the bottom chart is for the Lake Superior study areas (APIS and LSSS), 2008 and 

2009............................................................................................................................................... 39

Figure 17. Estimated geometric mean concentrations and 95% confidence intervals 

of 10 PFC congeners in bald eagle nestlings sampled in six study areas in the upper 

Midwest, 2006-2009. .................................................................................................................... 40

Figure 18. Maximum concentrations of PFOS found in plasma of bald eagle nestlings 

from all nests sampled across six study areas, 2006-2009. ........................................................... 42

Figure 19. Maximum concentrations of PFOA found in plasma of bald eagle 

nestlings from all nests sampled across six study areas, 2006-2009. ........................................... 43

Figure 20. Maximum concentrations of PFDS found in plasma of bald eagle nestlings 

from all nests sampled across six study areas, 2008-2009. The U-SACN study area 

was not sampled in 2008 and 2009. .............................................................................................. 44
vii

List of Tables 

Page

Table 1. Number of nests occupied, percent nest success, and number of young 

produced per occupied nest for bald eagles in six study areas, 2006-2009. Data are 

from the Wisconsin Department of Natural Resources and this study. ND = no data 

available. ....................................................................................................................................... 14

Table 2. Causes of mortality for bald eagle nestlings found, proportion of young 

found dead, and number of addled eggs found during routine banding and sampling 

activities. ....................................................................................................................................... 15

Table 3. Number of bald eagle nestlings sampled, tissue samples analyzed, and 

tissues archived for environmental contaminants. Grey highlighting indicates years 

and study areas which are part of the routine sampling scheme for long-term 

monitoring at three national parks; those not highlighted were added with funding and 

in-kind support from partners. ...................................................................................................... 16

Table 4. Age, weight, and sex ratios for bald eagle nestlings sampled for 

environmental contaminants at six study areas, 2006-2009. ........................................................ 16

Table 5. Sample size, measures of central tendency, and variability of total mercury 

(ug/g wet wt.) in breast feathers of bald eagle nestlings, 2006-2009. To calculate 

summary statistics we used 1⁄2 the LOD for samples below laboratory detection limits. 

See text for study area names........................................................................................................ 19

Table 6. Sample size, measures of central tendency, and variability of lead (ug/g wet 

wt.) in breast feathers of bald eagle nestlings, 2006-2009. To calculate summary 

statistics we used 1⁄2 the LOD for samples below the labs detection limits. See text for 

study area names. .......................................................................................................................... 20

Table 7. Sample size, measures of central tendency, and variability of DDD and DDE 

(ug/L) in plasma from bald eagle nestlings, 2006-2009. To calculate summary 

statistics we used 1⁄2 the LOD for samples below the labs detection limits. See text for 

study area names. .......................................................................................................................... 24

Table 8. Sample size, measures of central tendency, and variability of total PCBs 

(ug/L) in plasma from bald eagle nestlings, 2006-2009. To calculate summary 

statistics we used 1⁄2 the LOD for samples below the labs detection limits. See text for 

study area names. .......................................................................................................................... 28

Table 9. Geometric mean concentrations (ug/L) of 15 PCB congeners found in 

plasma of bald eagle nestlings from six study areas, 2006-2009. Geometric means are 

estimates from mixed effects models. ........................................................................................... 28

Table 10. Results of mixed effects models comparing levels of 15 PCB congeners in 

bald eagle plasma among six study areas listed from the highest geometric mean 
viii

levels on the left to the lowest on the right. Those separated by commas do not differ 

significantly; those separated by “>” do. A study area listed in parentheses is 

intermediate (i.e., between groups that otherwise differ significantly). ....................................... 29

Table 11. Sample size, measures of central tendency, and variability of total PBDEs 

(ug/L) in plasma from bald eagle nestlings, 2006-2009. To calculate summary 

statistics we used 1⁄2 the LOD for samples below the labs detection limits. See text for 

study area names. .......................................................................................................................... 33

Table 12. Results of mixed effects models comparing concentrations of five PBDE 

congeners found in bald eagle plasma in six study areas. Study areas are listed from 

the highest geometric mean concentration on the left to the lowest on the right. Those 

separated by commas do not differ significantly; those separated by a greater than 

sign “>” do. Study areas in parentheses are intermediate from the next lowest region 

(i.e., is intermediate between two groups that otherwise differ significantly). ............................. 35

Table 13. Geometric mean concentrations (ug/L) of five PBDE congeners in bald 

eagle plasma from six study areas. Geometric means are estimates from mixed effects 

models. One-half the LOD was substituted for the concentration when observations 

were below the detection limit. ..................................................................................................... 35

Table 14. Sample size, measures of central tendency, and variability of total PFOA 

(upper table) and PFOS (lower table) (ug/L) in plasma from bald eagle nestlings, 

2006-2009. To calculate summary statistics we used 1⁄2 the LOD for samples below 

the labs detection limits. See text for study area names. .............................................................. 37

Table 15. Relationship of age and weight on concentration of six PFC analytes. 

These covariates were included in mixed effects models when examining differences 

in PFC concentrations between study areas. Four other PFC analytes showed no 

relationship to age and/or weight (P >0.05). ................................................................................. 40

Table 16. Results of mixed effects models comparing concentrations of six PFC 

congeners found in bald eagle plasma at six study areas. Areas are listed from highest 

geometric mean concentration on the left to the lowest on the right. Those separated 

by commas do not differ significantly; those separated by a greater than sign “>” do. 

A study area in parentheses is intermediate from the next lowest study area (i.e., is 

intermediate between two groups that otherwise differ significantly). ......................................... 41

Table 17. Geometric mean concentrations (ug/L) of 10 PFC congeners in bald eagle 

plasma from six study areas. Geometric means are estimates from mixed effects 

models. One-half the LOD was substituted for the concentration when observations 

were below the detection limit. ..................................................................................................... 41
ix

List of Appendices

Page

Appendix A. PBDE, PFC, and PCB congeners analyzed in bald eagle plasma by the 

Wisconsin State Laboratory of Hygiene, 2006-2009. ................................................................... 75

Appendix B. Quality assurance spikes conducted by the Wisconsin State Laboratory 

of Hygiene for contaminants in bald eagle plasma. ...................................................................... 77

Appendix C. Limits of detection (LOD) or report limit (RL) and the percent of 

samples that were above them as reported by the Wisconsin State Laboratory of 

Hygiene for bald eagle plasma and feather samples tested for environmental 

contaminants, 2006-2009. ............................................................................................................. 81

Appendix D. Spatial distribution of environmental contaminants monitored by the 

National Park Service, Great Lakes Inventory and Monitoring Network, at six study 

areas in the upper Midwest, 2006-2009. ....................................................................................... 83

xi

Executive Summary 

During 2006 through 2009 the National Park Service Great Lakes Inventory and Monitoring 

Network and its partners assessed levels of targeted environmental contaminants in bald eagle 

(Haliaeetus leucocephalus) nestlings at sites in and adjacent to the Apostle Islands National 

Lakeshore, Mississippi National River and Recreation Area, and St. Croix National Scenic 

Riverway. Adjacent study areas were added when funds were available for comparisons and 

these included the entire Wisconsin south shore of Lake Superior and a section of the Mississippi 

River below Minneapolis-St. Paul, Minnesota, to the southern end of Lake Pepin. A total of 288 

bald eagle nestlings were handled, and 154 plasma and 142 breast-feather samples were analyzed 

for six contaminants: total mercury, lead, DDT (and metabolites DDE and DDD), total PCBs (75 

congeners), total PBDEs (9 congeners), and total PFCs (16 analytes). We also archived 123 

plasma and 121 feather samples for possible future analysis. 

All study areas averaged >1.0 young per occupied bald eagle nest, considered the threshold for a 

healthy bald eagle population. The Apostle Islands had the lowest productivity (1.0 in 2008) and 

the Mississippi National River and Recreation Area had the highest (2.2 in 2006 and 2009). The 

percent of territories that were successful ranged from 63% at Apostle Islands in 2008 to 100% at 

the lower St. Croix River (2007 and 2009) and the Mississippi National River and Recreation 

Area in 2008. Lower productivity on Lake Superior was likely due to lower prey availability 

compared to other study areas, though some nestlings were subjected to levels of DDE that were

above published thresholds and may have suffered sublethal effects. 

Contaminant levels varied between study areas and years. The geometric mean (geo) mercury 

levels in feathers ranged from 2.69 ug/g in nestlings on Pools 3&4 of the Mississippi River in 

2008 to 7.96 ug/g on the upper St. Croix Riverway in 2006. Mean (geo) lead levels in feathers 

were lowest on the Apostle Islands in 2006 (0.0700 ug/g) and highest in the lower St. Croix 

River in 2006 (1.03 ug/g). Generally, lead was highest in the lower St. Croix River and along the 

Mississippi River and lowest on the Apostle Islands and the upper St. Croix Riverway. 

Trace levels of DDT in blood were found in seven nestlings from Lake Superior in 2006 and 

2007 and in two nestlings from the Mississippi National River and Recreation Area - one in 2006 

and one in 2009; DDE was found in all nestlings sampled and at high levels in nestlings from the 

Apostle Islands in 2006 (geo = 29.7 ug/L). Nestlings on islands furthest from the mainland had 

among the highest DDE levels all four years. Levels of DDE were lowest on the upper St. Croix 

River in 2006 (geo = 2.70 ug/L). 

Levels of PCBs were lowest on the upper St. Croix River in 2006 (geo = 10.0 ug/L) and highest 

on the lower St. Croix in 2006 (geo = 139 ug/L). 

Polybrominated diphenyl ethers (PBDEs), a group of chemicals of increasing concern, ranged 

from geo of 1.39 ug/L in the upper St. Croix River in 2006 to geo of 16.8 ug/L in the 

Mississippi National River and Recreation Area in 2007. Generally, spatial patterns of PBDEs 

were similar to those of PCBs across the study areas. 
xii

Perfluorinated compounds (PFCs), another group of chemicals of increasing concern, were found 

in all samples, and the primary analyte was PFOS (68% in Mississippi River sample sites and 

53% in Lake Superior sample sites by volume). We found PFOS at very high levels in nestlings 

on the lower St Croix River (geo = 1580 ug/L) and in the Mississippi National River and 

Recreation Area (geo = 1250 ug/L) in 2006. PFOS was generally low in eaglets from the upper 

St. Croix Riverway (geo = 13.4 ug/L in 2007) and at moderate levels on the Apostle Islands 

(146-174 ug/L). A second PFC analyte, PFDS, made up 26% of the PFC volume in eaglet 

plasma from the Mississippi River study areas but <1% of volume in Lake Superior eaglets. 

PFOA, another PFC analyte of high interest due to its toxicity and pervasiveness around the 

world, was found at very low levels in plasma from nestlings in our study areas (<1% of the 16 

PFCs by volume); yet it was consistently at its highest levels in nestlings from the Apostle 

Islands (4.97 ug/L in 2008). 

Differences in contaminant burdens between study areas provide insights into the sources, 

mechanisms of transport, and magnification through the food web. Generally, contaminants 

associated with industry and municipal waste (PCBs, PBDEs, and PFCs) were highest in bald 

eagle nestlings near large population centers. However, Lake Superior receives a wide array of 

pollutants from global sources through air deposition and retains them for longer periods of time; 

hence, nestlings in remote islands had high levels of some industrial contaminants. The patterns 

of distribution were further complicated by apparent magnification in the food web at individual 

nest territories. We theorize that several hot spots of contamination were the result of eagles 

feeding on prey that were positioned high on the aquatic food web. 
xiii

Acknowledgments

Major funding for this work was provided by the National Park Service, Great Lakes Inventory 

and Monitoring Network. In 2008 and 2009 substantial funding was provided by the Minnesota 

Pollution Control Agency and additional funds were granted by the Donald Weesner Foundation. 

The Wisconsin Department of Natural Resources conducted occupancy surveys in Wisconsin 

and the Ramsey County Parks Department contributed staff time for occupancy surveys in 

Minnesota. Nest tree climbing expertise was provided by the Institute for Wildlife Studies and by 

Eco-Ascension Research and Consulting. All laboratory analyses on bald eagle samples were 

conducted by the Wisconsin State Laboratory of Hygiene. The 3M Corporation verified that 

supplies and equipment were free of PFCs. Numerous employees of the National Park Service 

and Audubon Minnesota were instrumental in collecting field data and providing logistics. We 

thank J. Elias, T. Gostomski, S. Hennes, B. Mader, G. Miller, K. Rolfhus, S. Streets, D. 

VanderMeulen, and J. Wiener for constructive comments on earlier drafts of this report. 

1 

Introduction

In the upper Midwestern states of Minnesota, Wisconsin, and Michigan more than 120 million

pounds of toxic waste were released into the air, discharged into the water, or disposed on land in 

2006 (USEPA 2008). Similar amounts are released annually, and much of this toxic waste ends 

up in aquatic systems through direct discharge, air deposition, or through ground water inflow. 

Many of these chemicals, and the metabolites that result from their decomposition, are highly 

persistent and remain available for up-take by aquatic organisms. Many chemicals that are 

persistent tend to bioaccumulate and some bio-magnify as they are transferred to organisms 

higher in the food web. Some of these persistent and bioaccumulative chemicals are also highly 

toxic to fish, wildlife, and humans (Hudson et al. 1984, Hoffman et al. 2003, Beyer 2011).

The National Park Service (NPS) has a responsibility to understand the levels and potential 

effects of contaminants from human sources on the natural resources they manage and to inform 

the visiting public about the potential human health hazards. The NPS Great Lakes Inventory and 

Monitoring Network (GLKN or Network) began monitoring levels of selected contaminants in 

2006. The monitoring involves sampling nestling bald eagles (Haliaeetus leucocephalus) to track 

the bioavailability of targeted contaminants. Bald eagles are high on the aquatic food web and 

accumulate persistent chemicals, thus acting as effective indicators of contamination. 

From the hundreds of chemical contaminants known to occur in the environment, we narrowed 

our scope to those that are persistent, bioaccumulative, and toxic to fish, wildlife, and humans. 

We further reduced this list by considering only those chemicals that are of management concern 

to the NPS units being monitored. Specifically, we selected chemicals that are either (1) the 

cause of waters within a park being listed in section 303(d) of the Water Quality Act (including 

fish consumption advisories); (2) listed as a Level I Substance under the Great Lakes Binational 

Toxics Strategy; or (3) recently identified by state or federal authorities as a new and emerging 

chemical of concern. We then excluded chemicals that are being adequately monitored by other 

agencies or that are prohibitively expensive to monitor. The resulting target list is as follows. 

*Congeners and analytes: Some chemicals have several derivatives or molecular variants. For example, 

PCBs and PBDEs occur as 209 different congeners. Each differs by the number of chlorine or bromine 

atoms located at specific sites on the molecule. PFC chemistry is more complex and terms vary depending 

on how the chemical is produced. We use the more general term “analyte” to describe different PFCs. A list 

of all congeners and analytes measured in this study is provided in Appendix A.

These chemicals have been shown to impact wildlife and humans or have been linked to adverse 

effects on laboratory animals including reproductive impairment, developmental deformities, 

neurological disorders, and effects on the endocrine system (Friend and Trainer 1970, Hudson et 

• Dichlorodiphenyl-trichloroethane (DDT) and metabolites

o Dichlorodiphenyl-dichloroethylene (DDE) 

o Dichlorodiphenyl-dichloroethane (DDD) 

• Polychlorinated biphenyls (PCBs; including 75 congeners*)

• Total mercury (THg)

• Lead (Pb) 

• Perfluorinated compounds (PFCs; including 16 different analytes*)

• Polybrominated diphenyl ethers (PBDEs; including 9 congeners*)
2 

al. 1984, Nisbet 1989, Noble and Elliot 1990, Bowerman et al. 1994, Gilbertson et al. 1998, 

Hoffman et al. 2003, Beyer and Meador 2011). 

Reproductive failure and population declines of many species of predatory birds were 

documented in the decades following the introduction of the organochlorine pesticide DDT in 

North America in 1946 (Gerrard and Bortolotti 1988). When DDT breaks down the metabolites 

DDE and DDD are formed. DDE is linked to egg-shell thinning, which caused eggs to break 

when adult birds attempted to incubate them (Nisbet 1989). With the ban of DDT in 1972 many 

bird populations began to recover (Newton 1979). Yet, through at least the early 1980s the 

productivity of bald eagles in the Great Lakes region was inversely correlated with levels of 

DDE and PCBs found in their eggs (Wiemeyer et al. 1984) and in blood plasma of nestlings 

(Bowerman et al. 2003). These chemicals may no longer limit bald eagle populations in Lake 

Superior (Dykstra et al. 2005, Dykstra et al. 2010) or the Upper Mississippi River valley (Wiener 

and Sandheinrich 2010), yet recovery has not been uniform. For example, bald eagles nesting 

along the shoreline of the Great Lakes have had lower reproductive success than inland 

populations in Minnesota, Wisconsin, and Michigan (Kozie and Anderson 1991, Dykstra et al. 

1998, Bowerman et al. 2003, Wiener and Sandheinrich 2010). 

A portion of the contaminants in the upper Midwest are from past use and emissions. Indeed 

DDT can take more than 30 years to degrade in the cold, clay soils found in some areas of the 

upper Midwest (ATSDR 2002). However, long-range transport can account for a significant 

fraction (Engstrom and Swain 1997). Although DDT was banned for sale in North America, it 

continues to be used in other parts of the world and is found in remote regions of the globe (Nash 

et al. 2008). Long range atmospheric transport, ingestion and subsequent infusion in the food 

web by transcontinental-migrant birds, and occasional illegal releases, continue to inoculate 

aquatic systems with this highly toxic chemical (Stickel et al. 1984, Kannon et al. 2006). 

PCBs have a similar story: following high use and disposal in the 1950s and 1960s they were 

banned in 1979 (Rapaport and Eisenreich 1988). By the 1980s concentrations of PCBs had 

dropped significantly, though they continued to fluctuate in biota of the Great Lakes basin 

(Knobeloch et al. 2008). Yet the mixture of PCB congeners found in the environment today 

differs from the commercial products used in the past (Bush et al. 1984, Hooper et al. 1990). 

Through time the natural decomposition of PCB congeners continuously changes the mixture 

that is present in the environment, conserving the more highly chlorinated, enzyme-inducing 

congeners, and increasing potential toxicity five to 10-fold as they move up the food chain 

(Hornbuckle et al. 2005). 

 

Mercury (Hg) and lead (Pb) are heavy metals with long histories of human use resulting in 

widespread contamination. Mercury has been released into the environment for more than a 

century as a byproduct of cinnabar mining (historically used as a red pigment) and during the 

mercury-amalgamation process used to extract gold and silver from ore (Wiener et al. 2003). 

More recently, the Minnesota Pollution Control Agency (MPCA 2004) estimated that in 2000, 

50% of the mercury emissions in Minnesota were from energy production (mostly coal burning 

power plants); 29% was from incineration and disposal of commercial products; and 21% was 

from taconite processing, pulp and paper processing, and soil roasting. Mercury emissions from 

Minnesota-based sources declined 68% from 1995 to 2000 (MPCA 2004). Yet mercury 
3 

emissions from international sources may increase with coal-fired power plants coming on-line 

in China, India, and other countries (U. S. Energy Information Administration 2010). 

Methylmercury (MeHg) is formed when inorganic mercury is deposited on land and water and 

then methylated by anaerobic bacteria in wetlands and sediments. Methylmercury biomagnifies 

in aquatic food webs and is highly toxic (Wiener et al. 2003). Laws regulating mercury 

emissions have led to reductions, yet it remains one of the most pervasive environmental 

contaminants in the upper Midwest and across the North American continent. All states in the 

region have issued advisories for fish-consumption in some or all waters (USEPA 2005). 

Lead is a neurotoxin that was used as an additive for gasoline and paints beginning in the 1920s. 

The U. S. Environmental Protection Agency (USEPA) began regulating use of lead in 1973 

leading to a total ban on its use in gasoline for on-road vehicles in 1996 under the Clean Air Act. 

Yet lead poisoning continues to be a concern in wildlife. The National Wildlife Health 

Laboratory in Madison, Wisconsin, analyzed records of 1,429 bald eagle mortalities across the 

United States during 1963 – 1984 and found lead poisoning to be the third greatest cause of 

mortality (11%), exceed only by gunshot and other traumatic injuries. Lead poisoning in bald 

eagles was believed almost entirely from ingestion of lead pellets when preying or scavenging on 

crippled or unretrieved waterfowl (Pattee and Hennes 1983). In a retrospective analysis of 138 

lead poisoned eagles spanning the years before and after federal regulations on the use of lead 

shot for waterfowl hunting, Kramer and Redig (1997) found that the prevalence of lead 

poisoning did not change although the mean levels of lead in poisoned eagles did. In a follow up 

analysis, Redig et al. (2009) added 12 years of data to the previous 16 years and found a 

temporal-spatial association with eagle poisonings and the onset of deer hunting season, lead 

isotope ratios in poisoned eagles were similar to ammunition, and levels of copper in kidneys of 

poisoned eagles that implied ingestion of copper-jacketed rifle bullets. Although lead shot was 

banned for waterfowl hunting in 1991, it is still widely used in ammunition for hunting terrestrial 

species and lead is still widely used in fishing tackle. Its continued use has prompted recent 

concern for wildlife poisonings including the endangered California condor (Pain et al. 2008). 

Perfluorinated compounds (PFCs) and polybrominated diphenyl ethers (PBDEs) are large 

chemical groups that are of increasing concern in the upper Midwestern United States and 

around the world (Renner 2000). PFCs have been widely used in a variety of industrial and 

commercial products such as water, oil, and grease repellents for fabrics (e.g., ScotchgardTM), 

fire-fighting foams, cookware (e.g., TeflonTM), polyurethane production, and surfactants. Two 

PFCs are of particular concern: perfluorooctane sulfonate (PFOS) and perfluorooctanoic acid 

(PFOA), because they are highly persistent in the environment and bioaccumulate in biota (Giesy 

and Kannan 2001). The toxicity of PFC compounds is still being defined but PFOS and PFOA 

have been linked to alterations in thyroid hormone production, liver damage, immunesuppression, and carcinogenesis (summarized by Minnesota Department of Health [MDH]

2010). Because of the global distribution of some perfluorinated compounds, the 3M Company 

based in St. Paul, Minnesota, phased out production of 8-carbon PFCs, including PFOS and 

PFOA, in 2000 and ceased production entirely in 2002. However, they continue to produce the 

shorter-chain C4 and C6 compounds and these and other PFCs can degrade in the environment 

with PFOS as one of the primary degradation products (Newman and Unger 2003). 
4 

PBDEs are similar in structure to PCBs and are widely used as flame retardants in plastic 

components of computers and televisions, in circuit boards, vehicle upholstery, and many 

textiles. These chemicals are released during production, product use, and disposal (Kim et al. 

2006). PBDEs are now ubiquitous in the environment and are found in human breast milk, 

marine mammals, birds, fish, sediments, and house dust (Ikonomou et al. 2002, Stapleton et al. 

2005, Ismail et al. 2009, Chen and Hale 2010, Fernie and Letcher 2010). The toxicity of PBDEs 

to humans is unknown, but based on animal studies, the USEPA lists decaBDE as a possible 

carcinogen. According to the Agency for Toxic Substances and Disease Registry (ATSDR), 

laboratory animals fed moderate amounts of lower brominated PBDEs have shown effects on the 

thyroid and liver and animals fed PBDEs as infants have shown subtle behavioral changes. It is 

not known whether similar effects occur in humans. 

The above persistent, bioaccumulative, toxicants (PBTs) are the focus of this monitoring 

program. Specifically, the monitoring program is designed to answer the following questions: 

1) What is the direction and magnitude of change in concentrations of DDT (including 

metabolites DDE and DDD), total PCBs, total PFCs, and total PBDEs in blood plasma, 

and of total mercury and lead in feathers, from nestling bald eagles within the monitored 

study areas? 

2) What is the incidence of addled eggs, dead nestlings, and developmental deformities in

nestling bald eagles within the study areas? 

3) What is the direction and magnitude of change in the number of young produced per 

occupied bald eagle nest within the study areas? 

4) Are the production of bald eagles and the incidences of addled eggs, dead nestlings, and 

developmental deformities correlated with exposure to the monitored contaminants in the 

study areas? 

 
5 

Methods

Study Areas

Apostle Islands National Lakeshore

The Apostle Islands National Lakeshore (APIS or Lakeshore) is located on Lake Superior in 

extreme northern Wisconsin (Figure 1). This island archipelago is administered by the National 

Park Service to preserve a 19 km (12 mi) mainland segment of the Lake Superior shoreline and 

21 islands with a combined Lake Superior shoreline of 257 km (160 mi) and combined land area 

of 28,086 ha (69,372 acres). The islands extend 24 km (16 mi) from the mainland into the lake 

and range in size from 1.2 ha (three acres) to 4,000 ha (10,000 acres). The Lakeshore is at the 

northwestern limits of the northern hardwood forest and contains elements of the southern boreal 

forest. There are about 20 km (12 mi) of perennial streams and 11 lagoons covering 43 ha (106 

acres) in the Lakeshore. 

The area surrounding APIS has a low human population density. According to the 2005 U.S. 

census there were 26 people / mi2 (10 / km2

) in the 710 mi2 (1,839 km2) area that includes four 

zip codes immediately adjacent to APIS. The NPS estimates that approximately 190,000 persons 

visit APIS each year.

There is very little heavy industry in the area though a coal-burning power plant is located in 

Ashland, 17.6 km (10.9 mi) from the APIS boundary. Moraska-Lafrancois and Glase (2005) 

reported only three industrial dischargers within three miles (4.8 km) of APIS. In addition to 

direct discharge, however, the total surface area of Lake Superior (82,100 km2) and its large 

drainage basin (127,700 km2) acts as a large receptor of chemicals that are aerially transported 

from regional and global sources. 

Lake Superior’s South Shore

The Lake Superior south shore (LSSS) study area is a 238 km long, 8 km wide strip of the Lake 

Superior shoreline in extreme northern Wisconsin extending from the Superior/Duluth Harbor to 

the Michigan border (an additional 21 km of Wisconsin shoreline within the APIS study area is 

not included in this estimate; Figure 1). Use of an 8 km strip along the shoreline is consistent 

with past studies on contaminants in eagles (e.g., Dykstra et al. 1995) and is generally believed to 

hold eagle territories that are primarily associated with feeding on Lake Superior. The vegetation 

is mixed conifer and hardwood. 

The twin port cities of Duluth, Minnesota, and Superior, Wisconsin, have a combined population 

of just over 110,000 (2006 estimate; U.S. Census Bureau). Ashland, Wisconsin, is the next 

largest community with 8,600 people and five smaller communities each have <1000 people. 

Ownership is a mixture of state, county, Tribal (Red Cliff and Bad River Bands of Chippewa), 

and private. 

Major point sources for contaminants are industry in Superior and Duluth and a large municipal 

landfill near shore about 1 km east of Superior. Ashland has a coal-fired power plant and a 

Superfund site where soil, ground water, and Lakefront sediments are contaminated with 

polynuclear aromatic hydrocarbons and volatile organic compounds. Additionally, as with the 
6 

APIS study area, the size of Lake Superior and its basin area receive chemicals that are aerially

transported from the region and globally. 

Figure 1. Map of six study areas where bald eagle nestlings were sampled for environmental 

contaminants, 2006 through 2009. 

Mississippi National River and Recreation Area

The Mississippi National River and Recreation Area (MISS or Recreation Area) includes 123 km 

(77 mi) of the Mississippi River (including navigation pools 1, 2, and the upper 1/3 of pool 3) 

and 6.4 km (4 mi) of the Minnesota River flowing through the Twin Cities of Minneapolis and 

St. Paul, Minnesota (Figure 1). The area also includes a short section of the Vermillion River and 

smaller perennial tributary streams and backwater sloughs. The St. Croix confluence occurs 7.7 

km upstream of the MISS outlet boundary. The Recreation Area encompasses 21,853 ha (54,000 

acres) of public and private land and water, though <25 ha (60 acres) are owned by the NPS. 

The MISS corridor is used primarily by residents of the metropolitan Twin Cities. In 2006, the 

seven-county Twin Cities Metropolitan area had a population of 2.8 million and U.S. census data 
7 

from 2005 showed 1,302 people / mi2 (500 / km2) in the 701 mi2 (1,816 km2) area that includes 

42 zip codes immediately adjacent to MISS.

This section of the Mississippi River is a major transportation corridor with use ranging from 

small pleasure craft to large barges. The area immediately surrounding the river is residential in 

the north, becoming more urban and industrialized centrally, and then turning rural after leaving 

the Twin Cities. The metropolitan Twin Cities contributes airborne pollutants, direct waste

streams from industrial and public disposal systems, and urban runoff. There are 114 hazardous 

waste sites within or near MISS; 19 are on Minnesota’s Superfund list and six are on the national 

Superfund list. Moraska-Lafrancois and Glase (2005) reported 98 industrial dischargers within 

three miles (4.8 km) of MISS. Moreover, 60% of the nearly 485,623 ha (1,875 mi2) in the Upper 

Mississippi River floodplain are now used for crop and pastureland (USGS 2010). Erosion in the 

basin exceeds the rate of soil formation, resulting in a net increase of sediments entering the 

Mississippi River. The sediments increase turbidity, carry excessive nutrients into the aquatic 

ecosystem, and are associated with pesticides and other toxic chemicals. 

St. Croix National Scenic Riverway

The St. Croix National Scenic Riverway (SACN or Riverway) encompasses 37,528 ha (145 mi2) 

including 405 km (252 miles) of the St. Croix and Namekagon Rivers in eastern Minnesota and 

northwest Wisconsin (Figure 1). The entire St. Croix River watershed covers approximately 2 

million ha (7,760 mi2), which transitions from forest and wetland-dominated lands in the north to 

more urban and agriculture areas in the south. Wetlands are common throughout the region, but 

encompass a much greater percent of the contributing drainage area in the upper St. Croix and 

Namekagon Rivers than in the lower sections of the St. Croix. 

The Riverway is divided into two distinct management zones: the lower St. Croix River (LSACN), and the upper St. Croix and Namekagon Rivers (U-SACN). We used these management 

zones to subdivide the Riverway into two study areas with the hydroelectric dam at St. Croix 

Falls, Wisconsin, as the boundary between upper and lower SACN (Figure 1). These study areas

differ ecologically and in patterns of human land-use (Magdalene et al.2008). Anthropogenic 

land use ranges from 2-32% in the upper SACN to >60% along the lower SACN (Wan et al. 

2007). U.S. census data from 2005 shows 25 people / mi2 in the 3,315 mi2 area including 18 zip 

codes adjacent to the upper SACN and 181 people / mi2

 in 868 mi2 in 17 zip codes adjacent to 

the lower SACN. The lower river and upper portions of the Riverway also differ in their 

groundwater source (Wan et al. 2007). Moreover, water levels are naturally flowing in the upper 

section of the Riverway whereas the lower St. Croix is regulated by a hydroelectric dam at St. 

Croix Falls. 

The NPS estimates about 480,000 visitors come to the Riverway annually and much of that use 

occurs in the lower sections of the river. Urban sprawl from Minneapolis and St. Paul greatly 

affects river use and pollution of the lower river. Moraska-Lafrancois and Glase (2005) report 41 

industrial dischargers within three miles (4.8 km) of SACN, many which occur in the lower 

river. Numerous private, state, county, and other federal landowners along the corridor 

complicate the management of resource use. As a result of these pressures, American Rivers, an 

organization dedicated to preserving free-flowing rivers, declared the lower St. Croix as one of 

America’s most endangered rivers in 2009. Nonetheless, water quality is currently considered 

excellent and the St. Croix and Namekagon Rivers are listed as ”Outstanding" and "Exceptional 
8 

Water Resources" by the Minnesota and Wisconsin Departments of Natural Resources 

respectively. 

Mississippi River Pools 3 & 4 

This section of the Mississippi River is 78 km (48 mi) long, immediately downstream from the 

MISS study area, and 7.7 km (4.8 mi) downstream from the confluence with the St. Croix River 

(Figure 1). The study area is named Pools 3 & 4, but it contains the lower two-thirds of Pool 3 

and the upper two-thirds of Pool 4 as they are delineated for river navigation. The administrative 

boundary of the Mississippi National River and Recreation Area (MISS study area) encompasses 

the upriver one-third of Pool 3, and we ended the downstream portion of the Pools 3 & 4 study 

area at the southern end of Lake Pepin. The study area is dominated by Lake Pepin, a 103 km2

(40 mi2

) lake formed by a natural alluvial dam where the Chippewa River flows into the 

Mississippi. Lake Pepin begins about 80 km (50 mi) downstream from Minneapolis-St. Paul. 

Two other large lakes above Lake Pepin, Sturgeon Lake and North Lake, are shallow backwaters 

of the Mississippi River and are surrounded by swamps and lowland forest. Ownership of the 

land around the Pools 3 & 4 area is a mixture of Tribal (Prairie Island Tribal Community), 

federal, state, and private. Large cities along the river include Red Wing (population 16,000), 

Lake City, Minnesota (4,900), and Pepin, Wisconsin (580 in Pepin Township). A half dozen 

other small communities are scattered along both sides of the river. Potential industrial discharge 

occurs at Prairie Island Nuclear Facility north of Red Wing and at the city of Red Wing.

Occupancy and Productivity Estimates

Bald eagle nest occupancy was determined during April and May each year by observers in a 

fixed-wing aircraft, helicopter, or boat. The Wisconsin Department of Natural Resources 

(WDNR) conducted all fixed-wing occupancy surveys for APIS, SACN, and LSSS. The WDNR 

has conducted such occupancy surveys since the early 1980s. The Minnesota Department of 

Natural Resources (MDNR) also has done occupancy surveys for over two decades but 

discontinued them after 2005. In 2006 we conducted the occupancy survey at MISS by boat; 

thereafter, the occupancy survey for nests at MISS and Pools 3& 4 were conducted from a 

helicopter by the Ramsey County Parks Department. 

A nest was considered occupied if one of the following criteria was met: an adult was observed 

on the nest in incubation posture, eggs or young were observed, two adults were observed near 

an empty nest, or the nest was recently repaired or contained new nest material (e.g., egg cup 

with fresh lining) even if adult(s) were not observed (Postupalsky 1983). GPS coordinates for all 

potentially active bald eagle nests were obtained during these occupancy surveys. 

Traditionally, aerial occupancy surveys are followed by aerial productivity surveys to document 

nest success and the number of young produced. Since neither Wisconsin nor Minnesota is now 

conducting productivity surveys on a regular basis, we based productivity on the number of 

young present during our ground visit. Occasionally nests were missed during the aerial 

occupancy survey. These nests were reported by others or were located during our field work. 

Data from these nests were excluded from productivity calculations as inclusion of data from 

nests found after hatching may cause over-estimation of reproductive rates. 

The occupancy and productivity surveys provided two critical measures of bald eagle 

productivity: 1) the percent of occupied nests (we use nest and territory synonymously because a 
9 

pair of bald eagles will have only one active nest in their territory each year) that were successful 

(number of nests with at least one young / the number of occupied nests), and 2) the number of 

young per occupied nest (number of young / number of occupied nests).

Sample Collections and Handling

From mid May through early June each year, when eaglets were between five and nine weeks 

old, we attempted to climb to each nest and bring the eaglets to the ground for sampling. Nests 

were excluded if they were extremely difficult to access, if they were unsafe to climb (e.g., in a 

dead tree or with unstable nest structure), or if the landowner denied access. Nests in only two 

areas were excluded due to difficulty with access: one in the upper SACN and one in the lower 

MISS). Both nest trees were in large expanses of bog/marsh that would have required more time 

and effort than we could allocate to single nests. Occasionally, nests were left un-sampled when 

young were too old to safely handle or if inclement weather precluded sampling. 

All safe, accessible nests were climbed using either traditional spur climbing methods (~80% of 

2006 nests) or soft-climbing techniques (~20% of 2006 nests and all nests thereafter). Softclimbing involved use of a cross bow to shoot a fly-fishing line over a branch near the nest. The 

fly-line was used to pull a larger cord and then a climbing rope over the branch. The climbing 

rope was then anchored to the base of a tree and its free-end climbed with mechanical ascenders. 

Climbers hand-captured young eaglets in the nest and brought eaglets to the ground in individual 

handling bags. Handling bags were made of rip-stop nylon with double bottoms and draw-cord 

openings for safe handling of the nestlings. Once on the ground, nestlings were examined for 

general health and abnormalities before being weighed, measured, banded, and sampled for 

environmental contaminants. Weight was determined to the nearest 0.5 kg with a spring scale; 

measurements (nearest 1 mm) were done with a metal ruler or calipers and included footpad 

length (between points of talon insertion of the hallux and the front middle toe), length of the 6th

and/or 8th primary, and length and depth of the beak. In 2006 we measured the 6th primary, in 

2007 both 6th and 8th were measured [R2

 = .99], and thereafter only the 8th was measured. Length 

of the 8th primary was used in combination with the eaglets sex to determine age in days 

following methods of Bortolotti (1984). All measurements followed methods described by 

Bortolotti (1984), and previously used by Bowerman (1993), Dykstra et al. (2005), and others. 

We took <11 mL of blood to determine concentrations of the organic compounds DDT, DDE, 

DDD, PCBs, PBDEs, and PFCs. Blood was drawn from the brachial vein with a sterile, 10-cc 

polypropylene syringe, and 20-gauge needle. The blood sample was immediately transferred to a 

10-cc, green-top vacutainer containing sodium heparin to prevent clotting. The small amount of 

blood remaining in the syringe was forced on to a small square (5-10 cm) of cotton gauze. This 

blot was later sent to a genetics laboratory to determine the nestling’s gender. Three to four 

breast feathers were plucked from each nestling for determining concentrations of total mercury 

(THg) and lead (Pb). All samples were individually marked, dated, and placed in a cooler on 

blue ice until they were processed at the end of each field day. 

Within 12 hours of collection, blood samples were spun at 1200 rpm in a centrifuge for 10-12 

minutes to separate plasma from the red blood cells. A sterile glass pipette was used to draw-off 

the plasma into separate vials. One-half (0.5) or 1.0 mL of plasma was placed in a polypropylene 
10 

vial for PFC determinations and the remaining plasma (4-6 mL) placed in a glass vial for the 

other organic chemicals. Glass pipettes and vials had been previously baked at 650oF (343oC) to

remove chemical residue; polypropylene vials were verified by a second laboratory (see below) 

as being free of PFC contamination. All vials with plasma and red blood cells were immediately 

frozen and kept frozen until delivered to an analytical laboratory. 

Blood and feather samples were taken from all siblings in a nest; however, samples from only 

one nestling was submitted to the Wisconsin State Laboratory of Hygiene (WSLH) in Madison, 

Wisconsin, to quantify target contaminants. Samples from the remaining siblings were archived 

at WSLH (serum in freezers at -20 oC; feathers dried in envelopes) for future analyses. 

For PFCs we split samples for blind comparisons with two other laboratories. A small amount of 

plasma (~0.5 ml) remaining from the WSLH analysis in 2006 and 2007 were provided to the 

USEPA analytical laboratory at Research Triangle Park, NC for analysis. In 2008 we split all 

plasma samples for PFCs each day and submitted frozen samples to both WSLH and USEPA. In 

2009 we broadened this laboratory comparison and split PFC samples three ways and sent 0.5 

mL to WSLH, USEPA, and the 3M laboratory in St. Paul, Minnesota. All split samples were

blind, in identical vials, and were frozen. Analyses of three sets of all materials used to collect 

and store PFC sample--including syringes, needles, vacutainers, and vials--by the 3M laboratory 

verified that these materials were free of PFC contamination. Analyses from all three laboratories 

were not available at the time of this writing and will be reported in a future issue of this 

technical report series. 

Laboratory Analyses and Quality Assurance

All contaminant concentrations reported here were analyzed by the Wisconsin State Laboratory 

of Hygiene (WSLH) in Madison, Wisconsin. Concentrations of total mercury in feather samples 

are reported as wet weight (wet wt.) and were determined by cold-vapor atomic absorption 

spectrophotometry. Lead in feathers are reported as wet weight and determined by flameless 

atomic absorption spectrophotometry. Plasma concentrations of p,p’-DDT, p,p’-DDE, p,p’-DDD

(hereafter DDT, DDE, and DDD), nine PBDE congeners, and 75 PCB congeners were analyzed 

by gas chromatography/electron capture detection (GC-ECD). PFC analytes in plasma (from 2 to 

6 analytes) were determined by high performance liquid chromatography/tandem mass 

spectrometry (HPLC-MS/MS). For lists of chemical names and congeners, see Appendix A. 

The WSLH limits of detection (LOD) varied by analyte, ranging from 0.004 ug/g for mercury to 

6.0 ug/L for PCB congener #003 (Appendix C). For quality assurance WSLH added matrix 

spikes for each batch of 10 plasma samples. Matrix spikes consisted of a known quantity of all 

target compounds added to chicken serum as a check on the laboratory's ability to efficiently 

recover the target compounds. In addition, surrogate spikes were added to each sample to assess 

the extraction process. PCB congeners #14, #65, and #166 were used for surrogate spikes 

because of their similarity to the targeted compounds and their general absence in our eaglet 

samples. Surrogate spikes were used to verify that the extraction, clean-up, and concentration 

steps were performed correctly for each specific sample. Acceptable recoveries were determined 

by statistical analysis of multiple spikes and varied among compounds. 
11 

Lab measurements were reported as concentrations and coded as to quality. Codes included the 

following: good value with no problems, value below the laboratory’s limit of detection (LOD)

or report limit (RL) hereafter combined as LOD, interference with another chemical, calibration 

curve exceeded, above LOD but below limit of quantification (LOQ), problems with laboratory 

blank, and concentration estimated (results outside of the LOQ but an estimate was possible). We 

summarized data results for each compound or congener as the percentage of observations that

were below the LOD (censored), or for which a measured concentration was considered 

quantifiable (uncensored). Compounds tended to fall into three groups: 1) those that resulted in 

measured concentrations all or most of the time (mercury, DDE, some analytes of PFCs, PBDEs, 

and PCBs), 2) those that were below the LOD most of the time (DDT, some analytes of PFCs, 

PBDEs, and PCBs), and 3) those that had measurable concentrations in intermediate numbers of 

samples (lead and DDD). 

For comparisons among study areas with mixed effects models (see below) we used only results 

for analytes that were above the LOD in >80% of the samples, except for PCB congeners, which 

we used only if above the LOD in >90% of the samples. We also excluded PFC analytes when

the percent recovery of matrix spikes differed from the known concentration by more than 20%. 

We did not, however, exclude PCB and PBDE congeners that had recovery rates beyond this 

20% cutoff. This is because past investigators in our study area, using the same laboratory for 

both PCBs and PBDEs, have not excluded such analytical data and we wanted to keep our 

information comparable. Only one PBDE congener (#138) failed this criterion and in only one 

year (2009). Several PCB congeners failed this criterion, but usually they were within 30%, 

which many QA/QC programs (e.g., Minnesota Department of Health) feel is acceptable. 

Analytes that met our criteria were DDE, mercury, five congeners of PBDEs (47, 99, 100, 153, 

and 154), 10 PFC analytes (PFDA, PFDS, PFHpS, PFHxS, PFNA, PFOA, PFOS, PFTeDA, 

PFTrDA, and PFuDA), and 15 congeners of PCBs (178, 180, 183, 185, 187/182, 193, 194, 198, 

199, 201, 202/171, 203/196, 206, 207, and 208/195). Of these, only two were below the LOD in 

more than 10% of our samples (PBDE congeners 153 and 154). For analytes that met our 

criteria, but where samples were below the LOD, we used 1⁄2 the LOD as the estimated 

concentration in calculations except for PCBs where we used zero (see PCB result section for a 

comparison of both non-parametric and parametric techniques). 

Many of the contaminants not used in the mixed effects models were detected only rarely, yet 

when detected, the location and level of contamination is important. It is also important to know 

where contaminants were tested but not found. For these reasons, we included all measured 

values and non-detections on distribution maps. 

Statistical Analysis

Data sets that include observations below the LOD present special problems for quantitative 

analysis. Three methods of analysis of such data are possible: one is substitution, which involves 

replacing all censored observations with one value, such as 0, 1⁄2 the LOD, or the LOD itself; the 

second is maximum likelihood analysis in which the data are assumed to follow a specified 

distribution; and the third is nonparametric analysis in which ranking methods are used to 

estimate values and compare groups (Helsel 2005). For analytes that consisted mostly of 

censored observations we did no statistical analyses. Because many of these were below the 
12 

LOD in more than 50% of the samples, it was not possible to estimate the median concentration. 

Most of the remaining compounds and congeners had either no censored observations or very 

few (usually less than 10%). For these analytes we used the simple substitution procedure, with 

1⁄2 the LOD as the value. The one situation for which we did not use the substitution method was 

summing up total PCBs across the 75 measured congeners. In this case we used a nonparametric 

method suggested by Helsel (2009) to handle observations below the detection limit when 

summing across congeners to compute total PCBs. We also compared this method with the 

simpler method of replacing observations below the detection limit by zero when computing 

total PCBs, as a comparison with previous work on PCBs in bald eagles (Dykstra et al. 2005) 

The nonparametric method is most effective when congeners have different LODs, which is the 

case for PCBs (eight different LODs; Helsel 2009). 

All concentrations were log transformed before analysis. Most of the analytes exhibited skewed 

distributions with a long upper tail (a few high concentrations), suggesting better symmetry on

the log scale. Environmental contaminants are often lognormally distributed because they arise 

from random dilution processes (Ott 1995). We examined standard plots of residuals from all 

analyses to assess whether model assumptions were satisfied. 

 

For comparisons of contaminant levels among regions, we analyzed the data with mixed effects 

models that included territory as a random effect and region as a fixed effect. These models can 

account for the potential lack of independence among observations from the same territories. 

Samples collected from the same territory in different years are likely not independent because 

the territories are often occupied by the same adults for several years and the environmental 

characteristics of the territories, including the forage base, remain relatively constant over time. 

The Tukey-Kramer method was used for pair-wise comparisons among regions. 

We also examined the effect of eaglet age (days) and weight (kg) on concentrations of 

contaminants. Age and weight did not have a significant effect on contaminant concentrations for 

mercury, DDE, or for any congeners of PCBs or PBDEs, but they did have a significant effect 

for some PFC analytes. To account for this, we included age or weight as covariates in mixed 

effects models with study area as a fixed effect and territory as a random effect.

 

For statistical comparisons between study areas, the means of log-transformed values were 

estimated from the mixed effects models, which account for the random effects in the model and 

thus for differing numbers of observations for each territory (Searle 1987). These estimated 

regional means are very similar to means computed by averaging first over all observations 

within a territory, and then averaging over the territory means in each region. Geometric means

were computed by back-transforming the means of the log-transformed observations. A Type I 

error (α) of 0.05 was used to judge the significance of statistical tests.

For simplicity in reporting annual tabular summaries we calculated geometric means directly 

from the data, not from the random effects models. These values were back transformed to the 

original scale and the resulting geometric means are very close to those estimated from the 

mixed effects models.
13 

Results 

Productivity and Mortality

Productivity across the four years ranged from a low of 1.0 young/occupied nest at APIS to 2.2 

young/occupied nest at MISS (Figure 2, Table 1). Similarly, the percent of occupied nests that 

were successful ranged from 63% at APIS to 100% at MISS. Annual productivity and nest 

success estimates varied by year and study area, but in all years where data were available nest 

success and productivity met or exceeded the criteria of >50% nest success and >1.0 

young/occupied nest, which is considered necessary for a healthy bald eagle population 

(Wiemeyer et al. 1984, Bowerman 2003). 

Sample size in some years was small due to few active territories or no data collection. Pools 

3&4 and LSSS study areas were not part of the NPS’s normal monitoring activities, and data 

were obtained only when outside funding was available. Further, due to budget constraints 

WDNR did not conduct productivity flights in 2009 (occupancy only) and in accordance with 

our protocol we did not sample at U-SACN in 2008, or at APIS and U-SACN in 2009. We 

believe aerial surveys to locate occupied nests for the L-SACN were inadequate in three of the 

four years (2006, 2007, and 2009); in 2008 we augmented flight data with ground observations 

and the estimated number of occupied nests increased because of that effort. 

Figure 2. Number of young per occupied bald eagle nest for six study areas.

0.5

1

1.5

2

2.5

2006 2007 2008 2009

Young per occupied nest

Year

APIS

LSSS

U-SACN

L-SACN

MISS

Pools 

3&4
14 

Table 1. Number of nests occupied, percent nest success, and number of young produced per occupied 

nest for bald eagles in six study areas, 2006-2009. Data are from the Wisconsin Department of Natural 

Resources and this study. ND = no data available. 

Study area / 

parameter

Year

2006 2007 2008 2009 Avg

APIS

# occupied nests a 12(11) 9 8 12 10

% successful 91% 67% 63% ND 74%

young / occupied nest 1.5 1.1 1.0 ND 1.2

LSSS

# occupied nests a 16 14 24(4) 15 17

% successful 63% 79% 100%* ND 81%

young / occupied nest 1.1 1.3 1.3* ND 1.2

U-SACN

# occupied nests a 20 20(19) 17 18 19

% successful 90% 79% ND ND 85%

young / occupied nest 1.6 1.5 ND ND 1.6

L-SACN

# occupied nests a 5 5(4) 13(10) 5(4) 7*

% successful 80%* 100%* 70% 100%* 88%*

young / occupied nest 1.8* 1.5* 1.6 2.0* 1.7*

MISS

# occupied nests a 13(11) 20(15) 20(14) 28(20) 20

% successful 91% 80% 100% 95% 92%

young / occupied nest 2.2 1.5 2.1 2.2 2.0

Pools 3&4

# occupied nests a ND ND 30(24) 15(12) 23

% successful ND ND 88% 83% 86%

young / occupied nest ND ND 1.5 1.7 1.6

a = Number of occupied nests in the study area as determined by aerial flights and ground observations; number in 

parentheses was used for % occupancy and productivity calculations in cases where one or more nests were not 

examined for productivity.

* = Based on a small sample, estimate should be used with caution. 

We occasionally found addled eggs or dead eaglets in the nest and dead eaglets on the ground 

under the nest. Addled eggs were collected and frozen for later analysis. Cause of death was 

determined for five eaglets; others could not be determined due to advanced stages of decay 

(Table 2). Two eaglets were found dead in the water in 2006 when their nest blew down along 

the Mississippi River. These two specimens were donated to the Bell Museum of Minnesota. In 

2007 we collected one dead eaglet from its nest on Pig’s Eye Lake on the Mississippi River. The 

University of Minnesota, Veterinary Diagnostics Laboratory established cause of death as

Pasteurella multocida septicemia – a bacterium associated with waterfowl and sometimes 

transmitted to predators. In 2008 an injured eaglet was found in a nest on the south shore of Lake 

Superior with treble hooks from a fishing lure deeply embedded in its leg and monofilament line 
15 

wrapped tightly around the leg and anchored to the nest. The eaglet was transported to the Raptor 

Center at the University of Minnesota where it was euthanized after finding the leg was broken 

and unrepairable. One eaglet died in 2009 when it stepped off the nest as we approached it for 

handling; this was our only study-related mortality (0.3% of birds handled). 

We did not search for addled eggs in 2006. Bald eagles tend to add nest material on top of addled 

eggs and beginning in 2007 all nests were examined. A total of nine addled eggs were collected

in a three year period; causes of addling were unknown and laboratory analysis for egg-shell 

thinning and contaminant levels in these eggs is pending (Table 2). 

Table 2. Causes of mortality for bald eagle nestlings found, proportion of young found dead, and number 

of addled eggs found during routine banding and sampling activities; ND = no data.

Cause of death

Year

2006 2007 2008 2009

Unknown 2 2

Nest fell 2

Fishing line 1

Disease 1

Study-related 1

Total found dead 4 3 1 1

Total found alive 80 91 98 95

% found dead 4.8 3.2 1.0 1.1

Addled eggs found ND 2 4 3

Eaglet Sampling

We obtained feather and plasma samples from at least one nestling from each active nest to 

determine levels of target contaminants. We visited from 34 to 46 nests each year and measured, 

banded, and sampled a total of 288 nestling bald eagles (Table 3). Female nestlings were slightly 

larger than males, and age and sex ratios varied across study areas (Table 4). The overall sex 

ratio was near parity (94:100) though it differed in some years due to small sample sizes. 

Occasionally the feather or blood sample was not obtained due to the young age of the nestling 

or other circumstances (e.g., very young nestlings lack breast feathers and blood samples could 

not be obtained from some nestlings). Of the 288 nestlings handled, we sent 142 feather and 154 

blood samples to the WSLH for contaminant analysis. Annual sample size for each study area 

depended on the number of active nests and ranged from as few as five at L-SACN to as many as 

40 at MISS. We also sent 166 plasma samples for inter-laboratory comparisons of PFCs (33 in 

2006, 35 in 2007, 61 in 2008, and 37 in 2009). Data from the inter-laboratory comparisons are 

not yet available. 

Unanalyzed samples (121 feather and 123 plasma) from siblings were archived at WSLH (Table

3). 
16 

Table 3. Number of bald eagle nestlings sampled, tissue samples analyzed, and tissues archived for 

environmental contaminants. Grey highlighting indicates years and study areas that are part of the routine 

sampling scheme for long-term monitoring at three national parks; those not highlighted were added with 

funding and in-kind support from institutional partners. 

Study 

Area Year

Territories

sampled

Young

banded

Young

sampled

Feather

samples

analyzed

Feather

samples

archived

Plasma

samples

analyzed

Plasma

samples

archived

APIS 2006 9 10 11 9 2 8 2

2007 6 10 9 6 3 6 3

2008 5 8 8 4 3 5 3

LSSS 2007 6 10 10 6 4 6 4

2008 4 6 5 4 1 4 1

U-SACN 2006 11 17 17 11 6 11 6

2007 8 14 13 8 5 8 5

L-SACN 2006 3 5 5 3 2 3 2

2007 4 6 7 4 3 4 2

2008 7 16 16 7 9 7 9

2009 9 19 27 9 10 9 14

MISS 2006 11 24 21 11 9 11 7

2007 11 18 18 11 6 12 5

2008 15 33 31 14 14 15 15

2009 18 40 40 18 22 18 22

Pools 3&4 2008 15 29 28 14 12 15 13

2009 12 23 22 12 10 12 10

Totals 154 288 288 142 121 154 123

Table 4. Age, weight, and sex ratios for bald eagle nestlings sampled for environmental contaminants at 

six study areas, 2006-2009.

Study 

area

Mean age in days

(range; sample)

Average weight in kg (range; sample) Sex ratio (male:

Males Females female)

APIS 39 (26-52; n=28) 3.30 (2.5-4.0; n=14) 3.85 (2.2-4.7; n=13) 108:100

LSSS 52 (36-69; n=15) 3.47 (3.1-4.1; n=6) 4.55 (3.6-5.2; n=9) 67:100

U-SACN 45 (29-65; n=30) 3.42 (2.5-4.9; n=18) 4.41 (3.5-5.6; n=12) 150:100

L-SACN 41 (26-72; n=44) 3.15 (2.1-3.9; n=21) 4.00 (3.1-4.9; n=21) 100:100

MISS 37 (24-70; n=108) 2.99 (2.1-4.1; n=49) 3.68 (2.1-5.1; n=56) 88:100

Pools 3+4 36 (22-48; n=49) 2.82 (2.0-3.6; n=22) 3.69 (2.2-4.8; n=28) 79:100

Totals 42 (22-72; n=274) 3.19 (2.0-4.9; n=130) 4.03 (2.1-5.6; n=139) 94:100
17 

Contaminant Levels and Spatial Patterns

Heavy Metals

Mercury

Mercury concentrations in breast feathers differed among study areas with significantly higher 

means at U-SACN, LSSS, and L-SACN than at APIS, MISS, and Pools 3&4 (mixed effects 

model P<0.001; Figure 3). These spatial patterns are striking when maximum levels for each nest 

are mapped across the four years (Figure 4). Higher levels of mercury were often found along the 

upper St Croix River and portions of the Lake Superior shoreline where extensive wetlands 

likely contribute to the microbial production of methyl mercury and its entry into aquatic food 

webs (Hurley et al. 1995). The lower St. Croix has fewer contributing wetlands but a few nests 

had high levels, which when combined with a small sample of nests, put L-SACN in the higher

category of mercury contamination. Annual levels of mercury varied most at U-SACN and LSSS

(Table 5; Appendix D). Concentrations in feather samples from the U-SACN and LSSS study 

areas declined by 29% and 35% respectively from 2006 to 2007, whereas MISS decreased by 

16% and APIS increased by 11% in that same period. We did not explore annual trends with 

only four years of data; however, our data for APIS and LSSS were combined with data from 

previous studies by Dykstra et al. (2005) and together they show mercury has declined in eaglets 

from Lake Superior at a rate of 2.4% per year from 1991 through 2008 (Dykstra et al. 2010).

Figure 3. Estimated geometric mean concentrations and 95% confidence intervals (CI) for mercury (wet 

weight) in bald eagle nestling feathers from six study areas, 2006-2009. Means and CIs are estimated 

from mixed effects models. 

Study area

[Hg], g/g

3

4

5

6

7

8

APISLSSSU-SACNL-SACNMISSPools 3&4
18 

Figure 4. Maximum wet weight concentrations of mercury in breast feathers of bald eagle nestlings from 

all nests sampled across six study areas, 2006-2009. There were no non-detections. 
19 

Table 5. Sample size, measures of central tendency, and variability of total mercury (ug/g wet wt.) in 

breast feathers of bald eagle nestlings, 2006-2009. To calculate summary statistics we used 1⁄2 the LOD 

for samples below the laboratory’s limit of detection. See text for study area names.

Arithmetic Geometric

Study Area Year N Mean Median SD SE Mean

Upper 

95% CI

Lower 

95% CI

APIS 2006 9 3.26 3.70 1.04 0.35 3.02 4.09 2.24

2007 6 3.78 3.20 2.14 0.87 3.40 4.99 2.32

2008 4 3.40 3.15 0.88 0.44 3.32 4.23 2.61

LSSS 2007 6 7.48 5.85 4.45 1.82 6.66 9.93 4.47

2008 4 4.38 4.40 0.70 0.35 4.33 5.09 3.69

U-SACN 2006 11 8.25 8.30 2.44 0.74 7.96 9.38 6.76

2007 8 5.89 6.35 1.63 0.58 5.65 7.06 4.53

L-SACN 2006 3 6.20 5.70 1.51 0.87 6.08 7.94 4.66

2007 4 4.05 4.25 0.76 0.38 3.99 4.87 3.27

2008 7 4.40 3.80 1.17 0.44 4.28 5.12 3.58

2009 9 4.86 4.90 0.93 0.31 4.77 5.43 4.19

MISS 2006 11 3.76 3.70 0.73 0.22 3.70 4.16 3.28

2007 11 3.31 2.80 1.34 0.40 3.12 3.81 2.56

2008 14 3.46 3.55 0.97 0.26 3.33 3.89 2.85

2009 18 3.24 3.30 0.52 0.12 3.20 3.45 2.96

Pools 3+4 2008 14 2.76 2.65 0.67 0.18 2.69 3.05 2.38

2009 12 3.83 3.60 0.89 0.26 3.74 4.22 3.32
20 

Lead

Concentrations of lead in breast feathers exceeded the laboratory’s detection limits in 75.5% of 

the samples, which was fewer than our criteria of >80% for assessing statistical differences 

among study areas. Of those above LOD, the measured levels were mostly <1.0 ug/g wet weight 

across the study areas; though, a few nests around Pig’s Eye Lake in MISS, and one nest on the 

lower SACN had levels exceeding 2.0 ug/g (Table 6, Figure 5). Lead levels were particularly 

high in 2006 compared to other years for the same study areas. For example, levels at APIS and 

L-SACN were about five times higher and levels at U-SACN were twice as high in 2006 

compared to 2007. This finding is confounded, however, by many samples being non-detections, 

particularly on Lake Superior (Figure 5).

Table 6. Sample size, measures of central tendency, and variability of lead (ug/g wet wt.) in breast 

feathers of bald eagle nestlings, 2006-2009. To calculate summary statistics we used 1⁄2 the LOD for 

samples below the laboratories limits of detection. See text for study area names. 

Arithmetic Geometric

Study Area Year N Mean Median SD SE Mean

Upper 

95% CI

Lower 

95% CI

APIS 2006 9 0.393 0.330 0.205 0.068 0.351 0.487 0.252

2007 6 0.0700** 0.0700** 0.0000** 0.0000** 0.0700** 0.0700** 0.0700**

2008 4 0.150 0.110 0.113 0.057 0.123 0.247 0.061

LSSS 2007 6 0.153 0.130 0.095 0.039 0.128 0.219 0.075

2008 4 0.220 0.155 0.203 0.102 0.156 0.403 0.060

U-SACN 2006 11 0.297 0.340 0.105 0.032 0.277 0.354 0.217

2007 8 0.159 0.0700 0.1436 0.0508 0.118 0.202 0.069

L-SACN 2006 3 1.28 0.870 1.028 0.594 1.03 2.53 0.42

2007 4 0.233 0.160 0.210 0.105 0.176 0.403 0.077

2008 7 0.373 0.280 0.283 0.107 0.279 0.533 0.146

2009 9 0.172 0.190 0.077 0.026 0.155 0.216 0.111

MISS 2006 11 1.06 0.750 1.008 0.304 0.753 1.244 0.456

2007 11 0.740 0.400 0.622 0.188 0.567 0.874 0.368

2008 14 0.341 0.330 0.253 0.068 0.244 0.396 0.150 

2009 18 0.371 0.245 0.526 0.124 0.231 0.351 0.152

Pools 3+4 2008 14 0.201 0.185 0.124 0.033 0.167 0.235 0.119

2009 12 0.218 0.195 0.135 0.039 0.180 0.263 0.124

**All samples were non-detects. 
21 

Figure 5. Maximum wet weight concentrations of lead in breast feathers of bald eagle nestlings from all 

nests sampled across six study areas, 2006-2009. 
22 

Legacy Contaminants

DDT and Metabolites DDE and DDD

DDT, DDD, and DDE were detected in 5.9%, 74.3%, and 100% of the plasma samples 

respectively; therefore statistical comparisons among study areas were done only for DDE. We 

expected few detections of DDT since it was banned more than 30 years prior to our sampling, 

yet we found quantifiable levels of DDT in nine samples - seven from Lake Superior eaglets and 

two from the Mississippi River (Figure 6). Eight of the nine had <2.0 ug/L of DDT, but one 

nestling at Durnam Island at MISS in 2009 had 7.30 ug/L of DDT, about four times greater than

the next highest sample. 

Figure 6. Maximum concentrations of DDT in plasma of bald eagle nestlings from all nests sampled 

across six study areas, 2006-2009. 
23 

DDD and DDE are the breakdown products of DDT and we found these metabolites in plasma 

from a majority of the nestlings sampled. DDD was detected consistently and found at higher 

levels in the lower SACN and the Mississippi River study areas compared to the upper SACN, 

LSSS, and APIS, though this was not tested statistically (Figure 7, Table 7). DDE was found at 

higher levels than DDD and in all nestlings sampled. This highly persistent metabolite, in 

contrast to DDD, was found at higher levels at APIS (Figure 8). Differences in the geometric 

mean concentrations of DDE among study areas were significant (P<0.001) with highest levels 

found in eaglets from APIS; high levels at LSSS (though not significantly different from APIS 

nor the next lower group); intermediate levels at MISS, L-SACN, and Pools 3&4; and 

significantly lower at U-SACN (mixed effects model P<0.001; Figure 9). 

Figure 7. Maximum concentrations of DDD in plasma of bald eagle nestlings from all nests sampled 

across six study areas, 2006-2009. 
24 

Table 7. Sample size, measures of central tendency, and variability of DDD and DDE (ug/L) in plasma 

from bald eagle nestlings, 2006-2009. To calculate summary statistics we used 1⁄2 the LOD for samples 

below the laboratories limits of detection. See text for study area names. 

Arithmetic Geometric

Study Area Year N Mean Median SD SE Mean

Upper 

95% CI

Lower 

95% CI

DDD:

APIS 2006 8 0.221 0.150 0.133 0.047 0.196 0.275 0.139

2007 6 0.150** 0.150** 0.000 0.000 0.150** 0.150** 0.150**

2008 5 0.150** 0.150** 0.000 0.000 0.150** 0.150** 0.150**

LSSS 2007 6 0.563 0.595 0.239 0.098 0.498 0.819 0.303

2008 4 0.210 0.150 0.120 0.060 0.190 0.304 0.119 

U-SACN 2006 11 0.165 0.150 0.048 0.015 0.160 0.182 0.141

2007 8 0.253 0.150 0.147 0.052 0.220 0.320 0.152

L-SACN 2006 3 1.49 1.10 0.79 0.46 1.37 2.38 0.79

2007 4 1.16 0.875 0.704 0.352 1.04 1.73 0.62

2008 7 0.734 0.630 0.310 0.117 0.677 0.939 0.489

2009 9 0.760 0.660 0.528 0.176 0.571 1.016 0.321

MISS 2006 10 2.28 1.55 1.28 0.40 1.98 2.80 1.40

2007 11 1.90 1.90 0.90 0.27 1.71 2.28 1.28

2008 15 2.42 1.70 2.02 0.52 2.03 2.66 1.55

2009 18 1.37 1.25 0.79 0.19 1.12 1.57 0.80

Pools 3+4 2008 15 1.09 0.990 0.293 0.076 1.05 1.20 0.91

2009 12 1.08 1.10 0.23 0.07 1.05 1.22 0.91

DDE:

APIS 2006 8 33.9 28.0 21.6 7.6 29.7 42.4 20.8

2007 6 29.2 21.0 29.6 12.1 17.7 44.0 7.1

2008 5 29.3 13.0 29.3 13.1 17.5 49.5 6.2

LSSS 2007 6 17.8 16.5 12.2 5.0 14.7 25.3 8.5

2008 4 7.70 7.70 3.52 1.76 7.01 11.75 4.18

U-SACN 2006 11 3.78 2.10 4.53 1.37 2.70 4.17 1.75

2007 8 2.80 2.75 1.12 0.40 2.62 3.43 1.99

L-SACN 2006 3 14.7 11.0 6.4 3.7 13.9 21.8 8.8

2007 4 7.10 6.00 3.49 1.75 6.54 10.24 4.17

2008 7 5.81 6.10 2.46 0.93 5.30 7.65 3.67

2009 9 8.58 7.30 3.42 1.14 8.02 10.32 6.24

MISS 2006 10 12.7 11.5 6.4 2.0 11.5 15.2 8.6

2007 11 10.9 10.0 3.1 0.9 10.5 12.4 8.9

2008 15 13.3 11.0 8.9 2.3 11.8 14.8 9.3

2009 18 7.92 7.50 2.63 0.62 7.52 8.77 6.45

Pools 3+4 2008 15 7.45 6.90 2.71 0.70 6.99 8.45 5.79

2009 12 8.77 8.15 3.42 0.99 8.34 9.91 7.02

** All samples were non-detects
25 

Figure 8. Maximum concentrations of DDE in plasma of bald eagle nestlings from all nests sampled 

across six study areas, 2006-2009. 

Seven nests had nestlings in one or more years with DDE levels at or above 28.0 ug/L in plasma, 

a threshold concentration for adverse reproductive effects on bald eagle populations (Elliot and 

Harris 2001/2002). Five of these nests were from APIS and both LSSS and MISS each had one 

sample above this threshold (Figure 8). However, when combined with data from earlier studies 

(Dykstra et al. 2005) our data show that DDE has declined in Lake Superior nestlings at a rate of 

3% annually from 1989 to 2008 (Dykstra et al. 2010).
26 

Figure 9. Estimated geometric mean concentrations and 95% confidence intervals for DDE in bald eagle 

nestlings sampled at six study areas, 2006-2009. Geometric means and confidence intervals calculated 

from mixed effects models.

PCBs

PCBs were detected in plasma from all nestlings sampled. Total PCB concentrations (sum of all 

75 congeners evaluated) were consistently higher in eaglets from L-SACN, MISS and Pools 3 & 

4 with some nests on the outer islands of APIS also showing high levels (Figure 10, Table 8). Of 

the 75 congeners we measured, 15 were detected in >80% of the samples and had geometric 

mean concentrations ranging from 0.10 to 12.7 ug/L (Table 9). There were significant regional 

differences among these 15 PCB congeners (Table 10). For all congeners, and using either the 

Kaplan-Meier nonparametric method or with non-detections set at 0, eaglets from U-SACN had 

the smallest concentrations, usually an order of magnitude lower than other regions (Figure 11, 

Table 10). Eight of the 15 congeners had their highest concentrations in eaglets from Pools 3 & 4 

and these congeners were usually high in L-SACN eaglets also. The remaining seven congeners 

had their highest concentrations in eaglets from APIS and these congeners were usually higher in 

LSSS eaglets as well. Total PCBs followed the pattern of highest concentrations in eaglets from 

Mississippi River and L-SACN study areas, intermediate concentrations in eaglets from APIS 

and LSSS, and lowest concentrations from the U-SACN (mixed effects model P<0.001; Figure 

12, Table 10). 

Study area

[DDE], g/L

5

10

15

20

25

APISLSSSU-SACNL-SACNMISSPools 3&4
27 

 

Figure 10. Maximum concentrations of total PCB in plasma of bald eagle nestlings from all nests sampled 

across six study areas, 2006-2009. 

Three nests, one at each of APIS, L-SACN, and MISS, had total PCB levels above 190 ug/L, 

which is considered the threshold for adverse effects on bald eagle populations (Elliot and Harris 

2001/2002). However, our data, combined with data from earlier studies (Dykstra et al. 2005), 

show that the total PCB load in Lake Superior nestlings has declined at about 4.3% annually 

from 1989 to 2008 (Dykstra et al. 2010).
28 

Table 8. Sample size, measures of central tendency, and variability of total PCB (ug/L) in plasma from 

bald eagle nestlings, 2006-2009. To calculate summary statistics we used 1⁄2 the LOD for samples below 

the laboratories limits of detection. See text for study area names. 

Arithmetic Geometric

Study Area Year N Mean Median SD SE Mean

Upper 

95% CI

Lower 

95% CI

APIS 2006 8 125 112 95 33 98.8 165.1 59.1

2007 6 52.8 50.0 28.5 11.6 46.1 73.4 29.0

2008 5 58.0 65.5 29.3 13.1 51.4 84.7 31.2

LSSS 2007 6 88.9 89.4 34.7 14.2 81.9 119.8 56.0

2008 4 36.6 40.1 11.3 5.6 35.0 49.7 24.6

U-SACN 2006 11 10.2 9.55 1.99 0.60 10.0 11.1 9.1

2007 8 10.8 9.79 2.95 1.04 10.5 12.4 9.0

L-SACN 2006 3 156 118 95 55 139 268 72

2007 4 68.8 67.0 25.1 12.6 65.2 94.7 44.9

2008 7 85.0 92.9 30.4 11.5 79.4 108.2 58.3

2009 9 90.8 73.4 35.2 11.7 85.5 108.0 67.6

MISS 2006 10 100 85.4 77.0 24.3 84.5 120.3 59.3

2007 11 92.2 99.5 42.3 12.8 84.0 109.8 64.3

2008 15 91.7 94.2 32.3 8.3 86.4 104.1 71.8

2009 18 68.8 73.0 25.3 6.0 63.1 78.2 50.9

Pools 3+4 2008 15 112 109 27 7 109 124 96

2009 12 104 103 18 5 103 113 93

Table 9. Geometric mean concentrations (ug/L) of 15 PCB congeners in plasma of bald eagle nestlings 

from six study areas, 2006-2009. Geometric means are estimates from mixed effects models. 

Congener APIS LSSS Pools 3&4 MISS L-SACN U-SACN

077/110 0.6 1.2 5.4 3.2 4.5 0.1

099 0.9 0.8 2.9 1.7 2.4 0.1

118 2.8 2.4 5.1 3.2 4.8 0.2

123/149 0.5 1.0 3.0 1.8 2.3 0.1

128 1.3 1.1 2.0 1.1 1.7 0.1

132/153/105 8.2 7.1 10.9 6.8 9.0 0.5

146 1.7 1.5 1.8 1.2 1.5 0.1

163/138 9.2 8.3 12.7 7.9 10.8 0.6

170/190 2.3 1.9 1.8 1.4 1.5 0.1

180 6.2 5.3 4.7 3.6 3.9 0.4

183 1.4 1.2 1.3 0.9 1.0 0.1

187/182 2.5 2.3 1.7 1.4 1.4 0.2

194 1.4 1.1 1.0 0.8 0.7 0.1

201 2.5 2.2 1.6 1.3 1.2 0.1

203/196 3.0 2.6 2.0 1.6 1.5 0.1
29 

Table 10. Results of mixed effects models comparing levels of 15 PCB congeners in bald eagle plasma 

among six study areas listed from the highest geometric mean levels on the left to the lowest on the right. 

Those separated by “~” do not differ significantly; those separated by “>” do. A study area listed in 

parentheses is intermediate (i.e., among groups that otherwise differ significantly).

Congener(s) P-value Study area differences

077/110 <0.001 Pools 3&4 ~ (L-SACN) > MISS > LSSS ~ APIS > U-SACN

099 <0.001 Pools 3&4 ~ (L-SACN) > MISS > APIS ~ LSSS > U-SACN

118 <0.001 Pools 3&4 ~ (L-SACN ~ MISS ~ APIS) > LSSS > U-SACN

123/149 <0.001 Pools 3&4 ~ (L-SACN) > MISS > LSSS ~ APIS > U-SACN

128 <0.001 Pools 3&4 ~ (L-SACN ~ APIS ~ LSSS) > MISS > U-SACN

132/153/105 <0.001 Pools 3&4 ~ (L-SACN ~ APIS ~ LSSS) > MISS > U-SACN 

146 <0.001 Pools 3&4 ~ (APIS ~ L-SACN ~ LSSS) > MISS > U-SACN

163/138 <0.001 Pools 3&4 ~ (L-SACN ~ APIS ~ LSSS) > MISS > U-SACN

170/190 <0.001 APIS ~ LSSS ~ Pools 3&4 ~ L-SACN ~ MISS > U-SACN

180 <0.001 APIS ~ (LSSS ~ Pools 3&4 ~ L-SACN) > MISS > U-SACN

183 <0.001 APIS ~ Pools 3&4 ~ LSSS ~ L-SACN ~ MISS > U-SACN

187/182 <0.001 APIS ~ LSSS ~ (Pools 3&4) > MISS ~ L-SACN > U-SACN

194 <0.001 APIS ~ (LSSS ~ Pools 3&4) > MISS ~ L-SACN > U-SACN

201 <0.001 APIS ~ (LSSS ~ Pools 3&4 ~ MISS) > L-SACN > U-SACN

203/196 <0.001 APIS ~ (LSSS ~ Pools 3&4) > MISS ~ L-SACN > U-SACN

Total PCBs <0.001 Pools 3&4 ~ (L-SACN ~ MISS) > APIS ~ LSSS > U-SACN
30 

Figure 11. Estimated geometric mean concentrations and 95% confidence intervals for total PCB in bald 

eagle nestlings sampled in six study areas in the upper Midwest, 2006-2009. The left panel shows total 

PCB as calculated by the Kaplan-Meier nonparametric procedure; the right panel shows total PCB as 

calculated by replacing congener concentrations below the limit of detection by zeroes.

Study area

Total PCBs, 

g/L

0

50

100

APISLSSSU-SACNL-SACNMISSPools 3&4

Kaplan-Meier

APISLSSSU-SACNL-SACNMISSPools 3&4

Nondetects 0
31 

Figure 12. Estimated geometric mean concentrations and 95% confidence intervals for 15 PCB 

congeners in bald eagle nestlings sampled in six study areas, 2006-2009. Geometric means and 

confidence intervals were estimated from mixed effects models.

Study area

PCBs, 

g/L

0

2

4

6

APISLSSS U-SACNL-SACNMISSPools 3&4

077/110

0

1

2

3

APISLSSS U-SACNL-SACN MISSPools 3&4

099

0

2

4

6

APISLSSS U-SACNL-SACNMISSPools 3&4

118

0

1

2

3

APISLSSS U-SACNL-SACN MISSPools 3

123/149

0.0

0.5

1.0

1.5

2.0

2.5

128

0

5

10

132/153/105

0.0

0.5

1.0

1.5

2.0

146

0

5

10

15

163/138

0.0

0.5

1.0

1.5

2.0

2.5

3.0

170/190

0

2

4

6

8

180

0.0

0.5

1.0

1.5

183

0

1

2

3

187/182

0.0

0.5

1.0

1.5

194

0

1

2

3

201

0

1

2

3

4

203/196
32 

Emerging Contaminants of Concern

PBDEs

We detected PBDEs in all bald eagle nestlings sampled, with geometric mean levels ranging 

from 1.39 ug/L at U-SACN to 16.8 ug/L at MISS (Table 11). Levels were particularly high in

nestlings from Pig’s Eye Lake in South St. Paul, Minnesota, (MISS) and from one nestling 

sampled in 2007 in the LSSS study area (Figure 13). Otherwise, total PBDE concentrations were 

uniformly <15 ug/L on U-SACN and <30 ug/L elsewhere.

Figure 13. Maximum concentrations of total PBDE in plasma of bald eagle nestlings from all nests 

sampled across six study areas, 2006-2009. 
33 

Table 11. Sample size, measures of central tendency, and variability of total PBDE (ug/L) in plasma from 

bald eagle nestlings, 2006-2009. To calculate summary statistics we used 1⁄2 the LOD for samples below 

the laboratories detection limits. See text for study area names. 

Arithmetic Geometric

Study Area Year N Mean Median SD SE Mean

Upper 

95% CI

Lower 

95% CI

APIS 2006 8 16.5 12.5 9.2 3.3 14.1 22.0 9.0

2007 6 8.94 8.44 3.52 1.44 8.39 11.47 6.14

2008 5 6.91 7.54 1.45 0.65 6.78 8.24 5.58

LSSS 2007 6 18.0 14.2 13.6 5.6 15.1 24.5 9.4

2008 4 6.47 6.18 2.32 1.16 6.17 8.78 4.33

U-SACN 2006 11 1.42 1.29 0.29 0.09 1.39 1.57 1.23

2007 8 1.69 1.22 1.09 0.39 1.49 2.08 1.07

L-SACN 2006 3 14.1 12.8 7.7 4.4 12.6 24.2 6.6

2007 4 6.53 6.29 2.76 1.38 6.09 9.40 3.94

2008 7 6.13 6.81 2.16 0.82 5.76 7.72 4.30

2009 9 12.4 9.68 5.17 1.72 11.6 15.0 8.9

MISS 2006 10 16.0 13.5 7.2 2.3 14.7 19.3 11.1

2007 11 18.7 13.3 10.1 3.0 16.8 22.1 12.7

2008 15 14.4 14.7 4.1 1.1 13.8 16.3 11.8

2009 18 13.6 12.1 6.1 1.4 12.5 15.1 10.3

Pools 3+4 2008 15 9.74 9.57 3.24 0.84 9.27 10.92 7.86

2009 12 10.4 10.3 2.7 0.8 10.1 11.6 8.9

Five of the nine PBDE congeners we measured were detected in >80% of samples and could be 

tested for significance among areas. All five of these congeners had significantly lower

concentrations in eaglets from the upper St. Croix River, slightly higher concentrations in eaglets 

from the lower St. Croix, and often the highest concentrations in eaglets from the Mississippi 

River and Lake Superior regions (Figure 14, Tables 12 and 13). 
34 

Figure 14. Estimated geometric means and 95% confidence intervals of five PBDE congeners in plasma 

from bald eagle nestlings sampled in six study areas, 2006-2009. 

Study area

PBDEs, 

g/L

2

4

6

8

APISLSSSU-SACNL-SACNMISSPools 3&4

47

0.0

0.5

1.0

1.5

2.0

2.5

3.0

APISLSSSU-SACNL-SACNMISSPools 3&4

99

0

1

2

3

APISLSSSU-SACNL-SACNMISSPools 3&

100

0.2

0.4

0.6

0.8

1.0

153

0.2

0.4

0.6

0.8

1.0

154
35 

Table 12. Results of mixed effects models comparing concentrations of five PBDE congeners found in 

bald eagle plasma in six study areas. Study areas are listed from the highest geometric mean 

concentration on the left to the lowest on the right. Those separated by “~” do not differ significantly; those 

separated by a greater than sign “>” do. Study areas in parentheses are intermediate from the next lowest 

region (i.e., intermediate between two groups that otherwise differ significantly).

Congener P-value Study area differences

47 <0.001 MISS > Pools 3&4 ~ L-SACN ~ LSSS ~ APIS > U-SACN 

99 <0.001 APIS ~ LSSS ~ MISS > Pools 3&4 ~ L-SACN > U-SACN

100 <0.001 LSSS ~ APIS ~ MISS > Pools 3&4 ~ L-SACN > U-SACN

153 <0.001 APIS ~ MISS ~ (LSSS ~ Pools 3&4) > L-SACN > U-SACN

154 <0.001 APIS ~ LSSS ~ (MISS) > Pools 3&4 ~ L-SACN > U-SACN

Table 13. Geometric mean concentrations (ug/L) of five PBDE congeners in bald eagle plasma from six 

study areas. Geometric means are estimates from mixed effects models. One-half the LOD was 

substituted for the concentration when observations were below the limits of detection.

Congener APIS LSSS U-SACN L-SACN MISS Pools 3&4

47 3.6 4.3 0.6 4.9 7.8 5.4

99 2.3 2.1 0.1 0.9 1.9 1.2

100 1.9 2.0 0.2 0.9 1.8 0.9

153 0.7 0.6 0.1 0.3 0.6 0.4

154 0.8 0.7 0.1 0.4 0.6 0.4

PFCs

In 2006 and 2007 we measured only two PFC analytes, PFOA and PFOS, whereas in 2008 and 

2009 we measured 14 additional analytes for a total of 16 (Appendix A). Considering just PFOA 

and PFOS for all four years, PFOS accounted for the majority of the PFC volume with geometric 

means ranging from 13.4 ug/L at U-SACN in 2007 to 1580 ug/L at L-SACN in 2006 (upper 

portion of Table 14). Concentrations of PFOA were much lower, with geometric means ranging 

from 0.122 ug/L at L-SACN in 2009 to 4.97 ug/L at APIS in 2008 (lower portion of Table 14). 

In 2008 and 2009, when all 16 analytes were measured, we found total PFC concentrations to be 

highest in a stretch of the Mississippi River below St. Paul, Minnesota, and at a few nests on LSACN (Figure 15). The lowest levels were found in Lake Superior study areas (U-SACN was

not sampled in 2008 and 2009). This pattern was also evident for PFOS, the congener that 

contributes more than 50% of the total PFC load by volume. PFOS made up a greater proportion 

of total PFCs in eaglets from the Mississippi River study areas (68%) compared to those from the 

Lake Superior study areas (53%; Figure 16). 

Differences in the proportions of PFDS, PFuDA, and PFNA in the Mississippi River versus the 

Lake Superior nestlings were also clearly evident (Figure 16). PFDS composed 26% of the 

Mississippi River PFC volume but only 1% at Lake Superior. By contrast PFuDA and PFNA 

both made up a greater proportion of PFCs in the Lake Superior nestlings (16% and 15% at Lake 
36 

Superior versus 1% each on the Mississippi River respectively). Overall, PFC analytes were 

more evenly proportioned in Lake Superior eaglets than those on the Mississippi River. 

Ten of the 16 PFC analytes passed our quality control criteria and could be tested for differences 

among study areas with mixed effects models. Six of these analytes were affected by either 

weight or age of the eaglet sampled (Table 15). The age and weight of eaglets sampled were 

highly correlated (r = 0.79; P <0.001; Pearson correlation coefficient). Because of this strong 

correlation, if the concentration of an analyte increases with one of these factors, it is likely to 

increase with the other. We used the Akaike Information Criterion (AIC; Burnham and Anderson 

1998) to select the best fitting model for each analyte among three models, including (1) study 

area alone, (2) study area and age, and (3) study area and weight. Five PFC analytes (PFHxS, 

PFNA, PFTeDA, PFTrDA, and PFuDA) increased significantly with the weight of the eaglet, 

while one analyte, PFOS, increased significantly with the age of the eaglet. Four analytes, 

PFDA, PFDS, PFHpS, and PFOA, were not significantly related to either weight or age. For 

analytes that increased most significantly with weight, the rate of increase on the log scale 

ranged from 0.09 to 0.13 per kg (Table 15). These correspond to increases of 23% to 35% in 

analyte concentration per kilogram of body weight. The log of PFOS concentration increased at a 

rate of 0.013 per day, corresponding to an increase in concentration of 3% per day. To account 

for these relationships, we included age and weight as covariates in the mixed effects models for 

the following comparisons among study areas.

When comparing study areas three distinct patterns emerged: (1) U-SACN consistently had the 

lowest concentrations of all PFC analytes; (2) APIS and LSSS had the highest concentrations of 

PFNA, PFTrDA, PFOA, and PFuDA; and (3) MISS and Pools 3&4 had the highest 

concentrations of PFDS, PFOS, PFHpS, and PFHxS (Figure 17, Tables 16 and 17). The lower 

SACN study area alternated between the latter two patterns, but more often followed the 

Mississippi River study areas. The striking difference in spatial distribution in the levels of some 

analytes in Lake Superior eaglets compared to those in the Mississippi River is illustrated by 

PFOS, PFOA, and PFDS (Figures 18, 19, and 20, respectively). 
37 

Table 14. Sample size, measures of central tendency, and variability of total PFOS (upper table) and 

PFOA (lower) (ug/L) in plasma from bald eagle nestlings, 2006-2009. To calculate summary statistics we 

used 1⁄2 the LOD for samples below the laboratories limits of detection. See text for study area names. 

Arithmetic Geometric

Study Area Year N Mean Median SD SE Mean

Upper 

95% CI

Lower 

95% CI

PFOS: 

APIS 2006 8 187 195 75 26 174 230 131

2007 6 164 130 103 42 146 215 99

2008 5 172 150 49 22 167 211 132

LSSS 2007 6 191 175 77 31 177 252 124

2008 4 153 155 28 14 151 181 126

U-SACN 2006 11 30.5 29.0 19.4 5.9 26.3 36.7 18.8

2007 8 14.5 13.5 6.2 2.2 13.4 17.8 10.1

L-SACN 2006 3 1810 2300 942 544 1580 3432 731

2007 4 345 350 270 135 169 1100 26

2008 7 539 460 248 94 496 682 361

2009 9 466 530 156 52 439 561 344

MISS 2006 10 1360 1250 640 202 1250 1630 954

2007 11 828 830 250 75 791 958 653

2008 15 846 860 339 88 749 1021 550

2009 18 665 645 368 87 541 762 385

Pools 3+4 2008 15 942 900 318 82 890 1065 744

2009 12 746 800 217 63 717 848 606

PFOA:

APIS 2006 8 2.53 2.40 1.46 0.52 2.13 3.34 1.35

2007 6 5.80 2.90 5.66 2.31 3.76 8.61 1.64

2008 5 6.28 4.30 4.37 1.95 4.97 10.09 2.45 

LSSS 2007 6 1.32 1.05 0.68 0.28 1.19 1.75 0.81

2008 4 2.59 2.10 1.95 0.97 2.07 4.45 0.97

U-SACN 2006 11 0.285 0.330 0.154 0.047 0.222 0.369 0.134

2007 8 0.726 0.720 0.045 0.016 0.725 0.756 0.695

L-SACN 2006 3 0.430 0.330 0.255 0.147 0.385 0.730 0.203

2007 4 3.10 0.835 4.602 2.301 1.50 5.19 0.43

2008 7 0.304 0.270 0.117 0.044 0.285 0.383 0.212

2009 9 0.159 0.140 0.127 0.042 0.122 0.201 0.075

MISS 2006 10 1.13 0.625 1.138 0.360 0.818 1.312 0.509

2007 11 1.26 1.20 0.35 0.11 1.22 1.43 1.05

2008 15 0.695 0.580 0.316 0.082 0.639 0.789 0.518

2009 18 0.564 0.425 0.423 0.100 0.416 0.620 0.279

Pools 3+4 2008 15 0.676 0.760 0.297 0.077 0.600 0.791 0.455

2009 12 0.383 0.265 0.281 0.081 0.309 0.450 0.212
38 

Figure 15. Maximum concentrations of total PFC in plasma of bald eagle nestlings from all nests sampled 

across six study areas, 2008 and 2009. 
39 

Figure 16. Percent by volume of different PFC congeners in bald eagle plasma, 2008 and 2009; top chart 

shows the Mississippi River study areas (MISS and Pools 3&4) and the bottom chart shows the Lake 

Superior study areas (APIS and LSSS). 

PFBA 0%

PFBS 0%

PFDA 2%

PFDoA 1%

PFDS 26%

PFHpA 

0%

PFHpS 

PFHxA 0% 0%

PFHxS 1%

PFNA 0%

PFOA 0%

PFOS 68%

PFTrDA PFPA 0% PFTeDA 0%

0%

PFuDA 1%

PFBA 0% PFBS 0%

PFDA 6% PFDoA 3%

PFDS 1%

PFHpA 0%

PFHpS 0%

PFHxA 0%

PFHxS 1%

PFNA 15%

PFOA 1%

PFOS 53%

PFPA 0%

PFTeDA 0%

PFTrDA 4% PFuDA 16%

Mississippi River study areas: MISS and Pools 3 & 4

Lake Superior study areas: APIS and LSSS
40 

Table 15. Relation of age and weight of nestling bald eagles on the levels of six PFC analytes (ug/L). 

These covariates were included in mixed effects models when examining differences in PFC 

concentrations among study areas. Four other PFC analytes showed no relation to age or weight (P 

>0.05).

Congener Covariate Estimate (SE) P-value

PFHxS Weight 0.13 (0.056) 0.03

PFNA weight 0.11 (0.028) <0.001

PFOS Age 0.013 (0.0024) <0.001

PFTeDA Weight 0.13 (0.045) 0.01

PFTrDA Weight 0.09 (0.034) 0.01

PFuDA Weight 0.11 (0.044) 0.01

 

 

 

Figure 17. Estimated geometric mean concentrations and 95% confidence intervals of 10 PFC congeners 

in bald eagle nestlings sampled in six study areas in the upper Midwest, 2006-2009. 

Study area

Concentration, 

g/L

10

20

30

APISLSSS U-SACNL-SACNMISS Pools 3&4

PFDA

0

100

200

300

400

500

APISLSSSU-SACNL-SACN MISS Pools 3&4

PFDS

1

2

3

4

5

6

APISLSSSU-SACNL-SACN MISS Pools 3&4

PFHpS

2

4

6

8

APISLSSSU-SACNL-SACN MISSPools 3

PFHxS

0

20

40

60

80

PFNA

0

1

2

3

4

PFOA

0

200

400

600

800

1000

PFOS

0.5

1.0

1.5

PFTeDA

5

10

15

20

PFTrDA

20

40

60

80

PFuDA
41 

Table 16. Results of mixed effects models comparing concentrations of six PFC congeners found in bald 

eagle plasma at six study areas. Areas are listed from highest geometric mean concentration on the left 

to the lowest on the right. Those separated by “~” do not differ significantly; those separated by a greater 

than sign “>” do. A study area in parentheses is intermediate from the next lowest study area (i.e.,

intermediate between two groups that otherwise differ significantly). 

Congener P-value Study area differences

PFDA 0.74 none

PFDS < 0.001 Pools 3&4 ~ L-SACN ~ MISS > LSSS ~ APIS 

PFHpS < 0.001 Pools 3&4 ~ MISS > L-SACN ~ APIS ~ LSSS 

PFHxS <0.001 MISS ~ (Pools 3&4 ~ APIS) > L-SACN ~ LSSS 

PFNA <0.001 APIS > LSSS > MISS > Pools 3&4 ~ L-SACN 

PFOA < 0.001 APIS ~ LSSS > MISS ~ Pools 3&4 ~ U-SACN ~ L-SACN 

PFOS <0.001 Pools 3&4 ~ MISS > L-SACN > APIS ~ LSSS > U-SACN 

PFTeDA 0.39 none

PFTrDA <0.001 APIS ~ LSSS > MISS ~ L-SACN ~ Pools 3&4

PFuDA <0.001 APIS ~ (LSSS ~ MISS) > Pools 3&4 ~ L-SACN 

Table 17. Geometric mean concentrations (ug/L) of 10 PFC congeners in bald eagle plasma from six 

study areas. Geometric means are estimates from mixed effects models. One-half the LOD was 

substituted for the concentration when observations were below the laboratories limits of detection. 

Congener APIS LSSS Pools 3&4 MISS L-SACN U-SACN

PFDA 7.9 10.6 11.3 15.5 11.9 . 

PFDS 2.9 3.5 373 122 198 . 

PFHpS 1.6 1.0 4.8 4.4 2.1 . 

PFHxS 1.5 1.0 4.2 5.6 1.4 . 

PFNA 55.8 18.2 2.8 3.8 2.7 . 

PFOA 2.9 1.5 0.4 0.6 0.3 0.4

PFOS 164 121 905 776 456 17.2

PFTeDA 1.1 0.7 0.8 1.1 0.8 . 

PFTrDA 13.7 7.6 1.9 2.2 1.9 . 

PFuDA 55.4 26.0 8.2 10.2 7.0 . 
42 

 

Figure 18. Maximum concentrations of PFOS found in plasma of bald eagle nestlings from all nests 

sampled across six study areas, 2006-2009. 
43 

Figure 19. Maximum concentrations of PFOA found in plasma of bald eagle nestlings from all nests 

sampled across six study areas, 2006-2009. 
44 

Figure 20. Maximum concentrations of PFDS found in plasma of bald eagle nestlings from all nests 

sampled across six study areas, 2008-2009. The U-SACN study area was not sampled in 2008 and 2009.
45 

Discussion 

Productivity and Mortality

On average the bald eagle population across the study region exceeded the thresholds of >50% 

nest success and >1.0 young per active territory considered necessary for sustaining a healthy 

bald eagle population (Wiemeyer et al. 1984). In particular, bald eagles at MISS and L-SACN

were highly productive. One nest at MISS (Pig’s Eye Island number one) had four young in both 

2006 and 2009 and three in 2007 and 2008. Nests with three young were common in the MISS 

study area, and we found eagles nesting within a kilometer of each other. Nest success and 

productivity at APIS and LSSS were lower, and in 2008 productivity measures for the APIS 

population were at the threshold for a healthy population. Nonetheless, the number and range of 

nesting bald eagles has been expanding across the region and we believe that the population is 

fundamentally healthy.

Bald eagles sampled in our study areas are part of a larger population, and their reproductive 

success and mortality rates are influenced by multiple factors. Dykstra et al. (2005) argued that 

productivity of bald eagles on Lake Superior, including both the APIS and LSSS study areas, 

was low at least partly because of low food availability - although other factors such as PCBs 

could not be ruled out. They measured prey delivery rates by adult bald eagles to nestlings and 

concluded that Lake Superior bald eagles made 56% fewer deliveries to the nest than breeding 

pairs in a comparable inland population (Dykstra 2005). Moreover, the authors calculated that 

the amount of energy received by Lake Superior nestlings in broods with two eaglets was less 

than typically expended for growth and development. Lake Superior is classified as an 

oligotrophic lake (i.e. unproductive) so it is reasonable to assume that foraging bald eagles need 

to work harder to attain food there than at more productive aquatic environments. The Lake 

Superior bald eagle population has recovered from lows of the 1960s, but at a much slower rate 

than inland populations. It is likely that nest success and productivity of eagles on Lake Superior 

will continue at lower levels relative to inland populations, and hence they will be more 

vulnerable to natural and human-caused perturbations like weather and contaminants. 

We observed little direct mortality of young and could not determine the cause of four of the 

nine deaths that we documented. Of the non-study related mortalities, three were from natural 

causes and one died of disease. One nestling was euthanized after being found entangled in 

fishing line with a broken leg. We assume these estimates of mortality are low because we likely 

missed remains of nestlings removed by predators and scavengers. 

Contaminant Levels and Spatial Patterns

Heavy Metals

Mercury

Nearly all of the mercury we measured in bald eagle feathers is assumed to be methylmercury, 

the most toxic form. Eagle nestlings get their mercury primarily from the fish they eat and nearly

all (e.g., 99%; Grieb et al. 2009) of the mercury in fish is present as methylmercury. Eagle 

nestlings incorporate this mercury into their feathers while they grow (Lewis and Furness 1991) 

and the resulting levels serve as an indicator of methylmercury in their diet. 
46 

The geometric mean concentrations of mercury in feathers (2.69 to 6.08 ug/g wet wt.) were 

comparable or slightly lower than those measured elsewhere. Eagle nestlings in central Florida

had feather concentrations of 3.23 ug/g wet wt., (1991-1993; Wood et al. 1996) and in South 

Carolina, concentrations of 3.06 ug/g dry weight (dry wt.; 1998-1999; Jagoe et al. 2002). Slightly 

higher concentrations were found in Maine, averaging 6.6 and 7.8 ug/g wet wt. (1991 and 1992) 

and these levels were not correlated with productivity rates, although any such relationship may 

have been masked by high concentrations of DDE (Welch 1994). Nearer to our study areas, 

Bowerman (1993) reported nestling feather mercury concentrations during 1985 to1989 of 8.8 

ug/g wet wt. in the interior lower peninsula of Michigan, 8.1 ug/g in the interior upper peninsula, 

8.0 ug/g at Lakes Michigan and Huron, 3.7 ug/g at Lake Erie, 8.7 ug/g at Lake Superior, and 

20.2 ug/g at Voyageurs National Park in northern Minnesota. This study also reported a lack of 

relationship between mercury concentrations and productivity, but again, the analysis may have 

been confounded by high concentrations of organochlorines (Bowerman 1993). 

Although mercury has been measured in adult and nestling feathers by several investigators, a 

threshold has not been determined for feathers of bald eagles. Eisler (1987) proposed that 5 ug/g

in feathers might be cause for concern while others have suggested that >20 ug/g mercury in 

adult feathers should trigger an evaluation (Welsh 1994, Evers et al. 2004, DeSorbo and Evers 

2007). We set a provisional threshold of 7.5 ug/g wet wt. in eaglet breast feathers, as proposed 

by Jagoe et al. (2002). During the four years of our study, this threshold was exceeded in two 

nestlings along the LSSS and in five nestlings from the U-SACN. In 2006 the geometric mean 

for the entire upper St. Croix study area exceeded this provisional level. 

These attempts to set threshold values are based on effects at the population scale, but the levels 

at which sublethal effects occur are also important. Scheuhammer et al. (2008) showed that bald 

eagles could demethylate mercury in the brain but that significant neurochemical changes

occurred with increasing levels of mercury in the brain. Effect levels were not reported by 

Scheuhammer et al. (2008), but we are currently collaborating with these investigators to 

correlate mercury levels in feathers to changes in brain chemistry in eagles. 

We found mercury levels to be highest in eaglets from the upper St. Croix River and Lake 

Superior shoreline, and lowest in eaglets from the Apostle Islands and Mississippi River study 

areas. These patterns likely reflect water chemistry and drainage basin characteristics that 

influence the conversion of inorganic mercury (the dominant forms in atmospheric deposition) to 

methylmercury, the organic compound that bioaccumulates in organisms and biomagnifies in 

food webs. Many of the nestlings with high levels were from eagle territories immediately down 

river from extensive wetlands. For example, individual drainage basins for tributaries to the 

upper St. Croix River have >12% of their area classified as wetlands compared to <7% for the 

lower St. Croix, Mississippi River, and Apostle Islands. One nestling on the Lake Superior 

shoreline that was particularly high in mercury was from a nest at the mouth of the Amnicon 

River where wetlands comprise approximately 22% of the watershed (NLCD 2009). 

The association between wetlands and methylmercury is well documented (Hurley et al. 1995, 

Wiener et al. 2003). Levels of both total mercury and methylmercury in water from eight streams 

in Wisconsin, Florida, and Oregon were highly correlated with the percent of wetlands within the 
47 

drainages (Brigham et al. 2009). These same streams had top predatory fish with mercury levels 

that were positively correlated with the percent of wetlands in the drainage (Chasar et al. 2009). 

The streams studied by both Brigham et al. (2009) and Chasar et al. (2009) spanned large ranges 

in climate, landscape characteristics, atmospheric mercury deposition, and stream chemistry, but 

wetlands were a common factor in the availability and accumulation of mercury. 

Similar findings were presented by Christensen et al. (2006) who examined the spatial variation 

of mercury in fish at 14 sites along the St. Croix River. They found that sites with mixed 

forest/wetland drainages had significantly higher median fish-tissue concentrations than sites

with mixed forest/agricultural lands (i.e., few wetlands). Our findings at individual eagle nest 

sites correspond well with site-specific levels in fish found by Christensen et al. (2006). 

In addition to wetlands there are other ecosystem parameters that contribute to mercury 

availability. Low alkalinity or low pH lakes, waters adjacent to uplands subjected to periodic 

flooding, and lakes and streams with high dissolved organic carbon (DOC) are all associated 

with high levels of mercury (numerous studies summarized in Wiener et al. 2003). All of these 

conditions can occur in the study areas we measured and they likely contribute to the overall 

mercury load in biota. 

Recent data suggest a decline in the accumulation of mercury in sediments deposited in the 

region. Balogh et al. (2010) showed that mercury in upper Lake St. Croix, which serves as an 

indicator of deposits from the upper river, increased rapidly after 1900, leveled off in the 1960s, 

and declined through 2000. Using data from nearby Square Lake, these authors concluded that 

atmospheric inputs controlled mercury accumulation in upper Lake St. Croix sediments and that 

the recent (1990s) rates of mercury accumulation were 40% lower than the peak of the 1960s. 

Similarly, Evers (2005) showed that accumulation rates of mercury in lake sediments from 

coring studies in the northeastern U.S. peaked around 1980 with a subsequent decline. Both 

Evers (2005) and Balogh et al. (2010) attributed the declines in mercury accumulation rates to 

regulatory controls on emissions. Such controls began in the 1950s with local and state laws (e.g.

California’s Bay Area Air Pollution Control Law of 1955) and more broadly in 1965 with the 

passage of the National Emissions Standards Act in the United States. Nonetheless, the rate of 

mercury accumulation still greatly exceeds pre-industrial conditions. 

In combination with a longer data set from the WDNR our data show that between 1991 and 

2008 mercury levels declined at 3% per year in eaglet feathers from Lake Superior (Dykstra et 

al. 2010). Similar declines have been documented in other biota in the region. In inland northern 

Wisconsin lakes, mercury concentrations in blood of nestling common loons (Gavia immer) 

declined 4.9% annually from 1992 to 2000 (Fevold et al. 2003) and about 0.5% per year in 

walleye (Sander vitreus; 1982-2005; Rasmussen et al. 2007). By contrast, during this same time 

frame other studies showed no declines and even increases in mercury in some biota.

Mercury concentrations in eaglet plasma from inland lakes in Maine did not decrease between

1991 and 2006, nor did Hg concentrations in eagle eggs decline since the 1970s (DeSorbo and 

Evers 2007). In Minnesota lakes, mercury concentrations in predatory fish declined from 1982 

through 1992 but then increased at 0.8% per year through 2006 (Monson 2009). In walleye from 

Wisconsin lakes there were mixed results in mercury trends depending on latitude: levels 
48 

decreased 0.5% in northern lakes, increased 0.8% in southern lakes, and remained constant in 

middle latitudes over the period 1982-2005 (Rasmussen et al. 2007). For the Canadian portion of 

the Great Lakes, Bhavsar et al. (2010) reported that concentrations of mercury in walleye (fillet 

and whole fish) generally declined over three decades (mid 1970s-2007), but recent trends show 

a leveling off in Lake Ontario and possible increases in Lake Erie. In the Canadian Arctic, 

Braune et al. (2005) showed steady increases in total mercury in eggs of three species of sea 

birds from 1970 to 2005. 

The methylation and accumulation of mercury in the food web is influenced by seasonal weather

and climate patterns. Park et al. (2008) found that water temperature and the intensity of UV 

light were positively correlated with dissolved gaseous mercury in water. And Balogh et al. 

(2008) documented increases in methylmercury during major spring runoff events to streams in 

the Midwest. We found the highest levels of mercury in 2006 on the upper St Croix River, a year 

with an unusually warm and wet spring. For instance gridded 4 km isopleths of weather station 

data for the upper St. Croix area in 2006 shows 16-11 o

C (2-12 oF ) above 30-year normal 

temperatures in January, March, April, May and June with 20-80 mm above 30-year normal 

precipitation in March and April (PRISM 2010). We hypothesize that the high Hg levels in 

eaglets sampled in 2006 were the result of a warm, wet spring. The Wisconsin Initiative on 

Climate Change Impacts (WICCI), using global climate models for 1980-2055, projects a rise of 

15-14 oC (4.5 to 6 oF ) in spring temperatures and 2 to 2.5 cm above normal precipitation for the 

region immediately surrounding our study areas (WICCI 2010). If these climate forecasts are

accurate, and mercury emissions are not reduced, then mercury levels may begin to increase in 

our study areas. 

Lead 

We found very little published information on levels of lead in breast feathers of bald eagle 

nestlings for comparison to our results. Lead is usually measured in blood and much research has 

been done to determine the lead-toxicity thresholds in blood of bald eagles. Lead levels are 

routinely measured in blood by veterinarians and raptor rehabilitators to determine if poisoning 

has occurred. However, blood levels may not be the most useful measure of lead exposure for 

long-term monitoring. Lead in blood will dissipate slowly over 30 to 40 days after exposure, 

whereas in feathers it will be integrated throughout the growth period (until molt). Feathers can

also be sampled and analyzed in segments to establish exposure history (Finkelstein et al. 2010) 

and further compared with museum specimens for retrospective analyses (but see Pain et al.

2008 for cautions on this approach). 

In our study the levels of lead in eaglet breast feathers ranged from .070 ug/g wet wt. (half the 

LOD) to 3.71 ug/g wet wt. Burger and Gochfeld (2009) reported a geometric mean of 0.769 ug/g 

dry wt. (n = two eaglet breast feathers) and 3.57 ug/g dry wt. (n = 17 adult breast feathers) from 

the Aleutian Islands, Alaska. Pain et al. (2008) found 1.05 to 14.3 ug/g dry wt. of lead in feathers 

of adult Spanish imperial eagles (Aquila adalberti), which feed primarily on fish and waterfowl, 

much like bald eagles in North America. The authors believed that the higher levels resulted 

from atmospheric deposition on the museum specimens following death of the birds. MartinezLopez et al. (2004) found 0.35 to 1.66 μg/g dry wt. of lead in feathers of three adult raptor 

species, including the booted eagle (Hieraaetus pennatus), that were from regions of Spain that 

were far from sources of metal contamination. Custer et al. (2008) reported <0.9 ug/g dry wt. of 

lead in feathers of nestling black-crowned night heron (Nycticorax nycticorax) in rural 
49 

Minnesota and 0.11-0.41 dry wt. at industrial sites in Delaware and Maryland. Lead poisoning 

has been of concern for the endangered California condor (Gymnogyps californianus). Two 

events of acute lead poisoning were recently documented in which concentrations of lead in 

feathers increased from pre-exposure levels of <0.3 ug/g dry wt. to just over 3.0 and 4.0 ug/g dry 

wt. with peak levels occurring within 20 days of the bird ingesting lead fragments from a hunterkilled pig (Finkelstein et al. 2010). Both of these birds were treated and survived, but a third 

condor died of lead poisoning with a peak of 55 ug/g of lead in feathers (Finkelstein et al. 2010). 

The lead levels in our eaglets were considerably less that the pre-exposure levels of these 

condors; however, differences between the species and their relative ages regarding uptake and 

shedding of lead into feathers is not well understood. 

Lead poisoning in adult bald eagles has long been associated with their feeding on dead or 

crippled waterfowl contaminated with lead shot (Hennes 1985), however, retrospective and 

current studies suggest that many poisonings result from ingestion of hunter-crippled or killed 

terrestrial game (Kramer and Redig 1997, Martin et al. 2007, Pain et al. 2008, Redig et al. 2009, 

Lambertucci et al. 2010). Evidence from studies on common loons (Scheuhammer and Norris 

1996) suggests that lead sinkers or fishing lures in fish eaten by eagles could also cause 

poisoning. In our case, hunter-killed game is not available to eaglets because most hunting 

occurs in fall. Fishing is common across the study areas, but if fishing lures and sinkers were the 

cause of the levels we observed, we would expect exposure to be distributed somewhat evenly 

across the six study areas. More likely, the hotspot at Pig’s Eye Lake is related to the historical 

and current use of this area for waste disposal and wastewater treatment, the latter including 

urban run-off . The City of St. Paul used a 300-acre area up-gradient from Pig’s Eye Lake as a 

dump from the mid-1950s to 1972 and the area is listed on the Minnesota Pollution Control 

Agency's (MPCA) Superfund list. From 1977 to 1985 the Metropolitan Waste Control 

Commission was permitted to dispose of ash from burned sewage sludge at this site. Currently, 

the city of St. Paul operates a wastewater treatment facility near the lake and effluent is 

discharged into the Mississippi River nearby. The relative contribution of these industrial/public 

sources to the lead we found in bald eagle nestlings is not known. The concentrations of lead in 

virtually all environmental media have declined significantly since the phase out of alkyl lead as 

an additive in gasoline (e.g., Wiener and Sandheinrich 2010). In the Upper Mississippi River, the 

annual accumulation of lead in sediments of Lake Pepin (Pool 4) in the 1990s was half of that in 

the 1960s (17 versus 35 tons, respectively)—the decade of peak lead pollution—but still much 

greater than in sediments deposited before the 1830s (0.7 ton) (Balogh et al. 2009). 

To better understand the relationship between levels of lead in blood and feathers, we collected 

samples from both of these tissues from each eaglet in 2010. These data were not available for 

this report, but future analyses may provide correlations that will allow us to link levels of lead in 

feathers to thresholds known for blood. 

Legacy contaminants

DDT/DDD/DDE

We expected few detections and very low levels of DDT because this pesticide was banned in 

North America more than 30 years before this study. However, DDT continues to enter the Great 

Lakes by atmospheric transport and deposition from regional sources, such as windblown 

sediments from abandoned manufacturing sites (Kannon et al. 2006), and global sources where it 
50 

is still used to control the spread of malaria (Roberts et al. 2000). We found quantifiable levels of 

DDT in plasma from nine eaglets - seven from Lake Superior and two from the Mississippi 

River. One nestling in 2009 on the upper portion of the MISS study area (Durnam Island) had 

7.30 ug/L of DDT. This was four times greater than the next highest sample, and levels this high 

in bald eagles have not been reported in recent literature. The source is unknown, but an illegal 

release of DDT in the area cannot be ruled out. 

According to USEPA the levels of total DDT (sum of DDT and its metabolites DDE and DDD) 

in Lake Superior lake trout (Salvelinus namaycush) have declined since 1977 and have remained 

below the Great Lakes Water Quality Agreement criteria of 1.0 μg/g since 1989 (USEPA 2010). 

We found DDT in bald eagles from Lake Superior to be <2.0 ug/L and this was probably the 

result of dietary intake of contaminated fish and/or birds. Much of this contamination may come 

from outside of the region. For example, Giesy et al. (1994) used the ratio of DDE to DDT in 

fish above and below dams on rivers flowing into the Great Lakes to show that DDT in fish is 

primarily from long-range atmospheric transport and deposition. 

We found that levels of DDD were often higher in eaglets from the Mississippi River study areas 

than in eaglets from Lake Superior, whereas the opposite was true for DDE. Both chemicals are 

metabolites of DDT, but they differ in that DDE exists entirely from the breakdown of DDT 

whereas DDD itself was used as an insecticide in the past (ATSDR 2002). Differences in source 

inputs, such as past use of DDD in agricultural areas of central Minnesota, and the chemical and 

physical properties of the two areas (e.g., water temperature, biological activity, very short 

versus long retention time) probably account for the differences we observed in these two 

metabolites. Very little comparative literature exists on DDD levels alone.

Levels of DDE were significantly higher in eaglets from the Apostle Islands and Lake Superior 

shoreline than in eaglets from the upper St. Croix River. Moreover, DDE was consistently 

highest in nestlings from the outer-most islands of the Apostles (all three years they were 

measured; Appendix D). The reason for this pattern on the Apostles is unknown but is likely 

related to food availability and magnification in the food web. 

Kozie and Anderson (1991) reported that the relative percent of prey remains found under bald 

eagle nests on the Apostle Islands was 21% herring gull (Larus argentatus), 15.4% longnose 

sucker (Catostomus catostomus), and 13.5% burbot (Lota lota), with other fish and birds 

generally found in less than 6% of the collections. Burbot are fatty and long-lived fish (up to 22 

years; McPhail 1997), factors associated with bioaccumulation of organochlorine compounds. 

Kozie and Anderson (1991) proposed that herring gulls are a major dietary source of DDE in 

bald eagles at APIS, and this relation was further supported by Dykstra et al. (2010). Herring 

gulls are top-level predators/scavengers in the Lake Superior food web, and blood concentrations 

of DDE have been positively correlated with trophic position (Elliott et al. 2009). Herring gull 

predation may also have contributed to the findings of Bowerman (2003) who found levels of 

DDE to be significantly higher in nestlings using the Great Lakes and Voyageurs National Park 

than those using interior lakes in Michigan and Minnesota. Predation on birds, including herring 

gulls, tends to be less frequent in inland eagle populations. At 80 bald eagle nests in inland 

Wisconsin, only 6% of the remains were of birds (P. Keasling and C. Sindelar, unpublished data 

cited in Kozie and Anderson 1991). To examine this relationship at APIS would require 
51 

continuous monitoring of nests to document prey delivery, because collections of prey remains 

from under nests tend to be biased towards species that are not fully digested. A better approach 

would be to analyze ratios of stable isotopes (C and N) in eaglets and prey to examine how 

trophic position affects levels of contaminants in bald eagle nestlings at different study sites. 

A recent analysis of our data combined with past data collected by the WDNR shows that DDE

levels declined in nestling bald eagles on Lake Superior at a rate of about 3% per year from 1989 

to 2009 (Dykstra et al. 2010), but the rate of decline has slowed (estimated at 5.2% between 1989 

and 2001; Dykstra et al. 2005). Average productivity of bald eagles on the Great Lakes has 

increased since the lows in the 1960s and 1970s. We estimated average productivity well above 

the 0.7 young per occupied nest considered necessary for population stability (Sprunt et al. 1973) 

and above the 1.0 young per occupied nest considered representative of a healthy bald eagle 

population (Wiemeyer et al. 1984, Bowerman 2003). Yet in 2008 we found productivity in the 

APIS population to be just at the healthy population threshold and the mean productivity on the 

two Lake Superior study areas to be 1.2 young per occupied nest, still far below that observed for

inland bald eagle populations - including those we monitored (1.6 - 2.0 young per occupied nest). 

This low productivity on Lake Superior is likely due to low food availability as a result of the 

lake being highly oligotrophic (unproductive). Dykstra et al. (1998, 2005) reported that prey 

delivery rates to eaglets in Lake Superior nests were significantly lower than at inland nests and 

may have been inadequate for growth of two young at some nests. By contrast, riverine nesting 

eagles have higher productivity when compared to eagles nesting on inland lakes in Wisconsin 

(M. Meyer, unpublished data). 

 

Exposure to contaminants can be an added stressor to an eagle population stressed by limited 

food resources. Though DDE is slowly declining on the Great Lakes we found that about 50% of 

the nestlings we sampled at APIS (n = 19) still had more than 28 ug/L DDE in plasma, the 

threshold associated with a healthy population (Elliot and Harris 2001/2002). Moreover, APIS 

nestlings had DDE concentrations that were 1.5 times higher than adjacent Lake Superior south 

shore nestlings (Dykstra et al. 2010). We have no evidence of direct population-level effects 

from DDE, but sublethal effects on individuals with high concentrations cannot be ruled out. 

PCBs

Elliott and Harris (2001/2002) estimated the threshold for significant impairment of bald eagle 

productivity (<0.7 young per occupied territory) to be 190 μg/kg of total PCBs in plasma. We 

found geometric mean levels of total PCBs in eaglet plasma were below this threshold at all 

study areas (highest geometric mean was 139 ug/L at L-SACN in 2006), suggesting that PCBs 

were not depressing reproductive rates from 2006-2009. 

However, as with DDE, levels of PCBs in some individual nestlings exceeded this threshold 

level, and reproduction at individual nests near contaminant “hotspots” could have been affected. 

Concentrations at these nests are likely linked to local prey availability and/or selection by the 

adult eagles. Eagles preying on piscivorous birds or older aged predatory fish consume greater 

amounts of contaminants because of biomagnification through the food chain. 

When combined with data from the WDNR our data support an estimated decline of 4.3% per 

year of total PCBs in plasma of bald eagle nestlings from the Great Lakes (Dykstra et al. 2010). 
52 

Others have reported similar declines of PCBs in Great Lakes water, sediments, and other biota 

(Bowerman et al. 2003, Carlson et al. 2010). Similar declines have also been reported for fish in 

the upper Mississippi River (Lee and Anderson 1998, Wiener and Sandheinrich 2010). 

Differences in contaminant concentrations in the water, sediment, and biota among the four 

study areas may be attributed to differences in hydrology and potential source inputs. Lake 

Superior is cold, deep, oligotrophic, has a large watershed with slow turnover time (estimated at 

191 years; Hites 2006), and receives much of its contaminant load from atmospheric deposition 

(Strachan and Eisenreich 1988). Contaminant concentrations in Lake Superior and its biota likely 

represent the slow recovery from the high levels of contamination of decades past. Elevated 

concentrations of total PCBs in the Mississippi River and some lower St. Croix River eaglets 

(compared to the Lake Superior eaglets) are likely due to industrial development. Wiener and 

Sandheinrich (2010), who summarized published information on the upper Mississippi River,

showed that PCB levels in emergent female mayflies (Hexagenia bilineata) were greatest near 

industrialized areas, such as the Twin Cities. Such contamination low on the aquatic food web 

will be magnified to fish and higher trophic-level species, such as eagles.

Emerging contaminants of concern

PBDEs 

PBDEs are a large group of chemicals (209 congeners) that are widely used to retard fire in a 

number of commercial products from plastics to textiles and foams. There are different 

formulations of these chemicals with the most common ones referred to as penta-, octa-, and 

deca-brominated flame retardants, relative to the number of bromine-carbon bonds. The PBDEs 

are chemically very similar to the PCBs.

The nine congeners we monitored are primarily in the penta-, and some in the octa-, formulations

(Appendix A). These formulations have been under intense scrutiny because of their global 

occurrence in biological systems (Chen and Hale 2010), their persistence in the environment, the 

fact that they bioaccumulate in biota including humans, and because there is increasing evidence 

of toxicity (ATSDR 2004, Talsness 2008, Vonderheide et al. 2008). Moreover, past market 

demand for products with penta-PBDEs show that North America is the primary source/sink of 

these chemicals with global sales in 2003 estimated at 70,000 metric tons and North America

consuming 98% in 1999 and 95% in 2001 (Hites 2004). 

As a consequence of scientific and regulatory scrutiny, the European Union, Canada, and some 

States (California, Oregon, and Washington) have banned or have laws pending to ban the pentaand octa-PBDEs. Industry has, therefore, started phasing out these formulations. Some of these 

laws took effect just before or during this study and our results should be considered in this 

context. Though some industries may have decreased use prior to these bans, others may have 

increased production to use available stocks. 

We found the highest levels of total PBDEs (sum of the nine congeners) in nestlings around 

Pig’s Eye Lake in South St. Paul on the MISS study area and at a nest east of the city of 

Superior, Wisconsin, on Lake Superior. To the best of our knowledge these nests are the only 

ones immediately adjacent to waste disposal sites. As previously described above for lead 

contamination, Pig’s Eye Lake is the site of decades of waste disposal and current wastewater 
53 

treatment for the city of St. Paul. Similarly, the Lake Superior nest location is on the abandoned 

open-dump and within 1.5 km of the new landfill for the city of Superior. Both sites have readily 

available eagle prey that is high on the trophic food web, including a great blue heron (Ardea

herodias) rookery at Pig’s Eye Lake and a gull colony near the Superior, Wisconsin, site. This 

connection is speculative because we did not study the food habits of sampled eaglets. However, 

PBDEs are known to leach from plastics and textiles from a number of commercial and 

household products found in landfills (Kim et al. 2006). And elevated levels of PBDEs are well 

documented in gulls (Norstrom et al. 2002) and great blue herons (Custer et al. 2009, 2010). 

We found significant differences in the spatial patterns of PBDE congeners among the six study 

areas. Geometric mean levels of all five congeners, compared with mixed effects models, were 

significantly lower in eaglets from the more remote upper St. Croix Riverway, slightly higher in 

the lower St. Croix, and usually highest in eaglets from the Mississippi River and Lake Superior 

study areas, though the latter two varied depending on the congener. 

Others have also found high levels near urban areas. Strandberg et al. (2001) found the sum of 

eight PBDEs in the air around Chicago to be 3-fold higher than those at Sleeping Bear Dunes in 

northeastern Lake Michigan and 10-fold higher than those on the Keweenaw Peninsula in 

southcentral Lake Superior. This pattern is also evident in Great Lakes bottom sediments where 

studies have found that levels of PBDEs are highest near human population centers, and that 

elevated levels in more rural areas can be linked to prevailing wind patterns away from these

urban areas (Song et al. 2005). 

We found that PBDE congeners #47, #99, and #100 accounted for the bulk of the total PBDE 

volume (84%) in eaglet plasma with 53%, 16%, and 15% respectively. The high proportion of 

#47 was also found in bald eagle nestlings sampled on the Pacific Northwest coast of North 

America, where investigators estimated it accounted for half of the total PBDE volume 

(McKinney et al. 2006). In the air around the Great Lakes, Strandberg et al. (2001) found that 

PBDE #47 accounted for 50-65% and PBDE #99 about 35-40%, whereas the remaining five 

congeners together totaled less than 10%. Moreover, they concluded that this composition 

remained relatively uniform across the Great Lakes. Similar ratios were found in sediments from 

Lakes Michigan and Huron (Song et al. 2005) and lake trout in Lake Ontario (Ismail et al. 2009). 

Interestingly, the commercial penta-flame retardants used in textiles as an additive to

polyurethane foams has a congener composition similar to what we found in bald eagle plasma. 

There is variation in commercial mixtures, but the major PBDE congeners in commercial 

mixtures are #47, # 99, and #100 (the so called penta-BDEs) with #47 and #99 accounting for 

approximately 75% of the total mass (WHO 1994).

In a summary of information on PBDEs in the Great Lakes, Hites (2006b) concluded that PBDEs 

(most studies measured the penta- and octa- formulations) have increased greatly in all media on 

the Great Lakes (from sediments to fish and birds) since 1980, with a doubling time of three to 

five years, but that there are signs that concentrations are leveling off. Specific to our study areas 

on Lake Superior, Dykstra et al. (2005) sampled five eaglets on Lake Superior in 2001 for the 

same nine PBDE congeners that we measured. They found the geometric mean of total PBDEs in 

eaglet plasma to be 8.40 ug/kg. In 2006 we sampled eight nestlings at APIS with a geometric 

mean of 14.1 ug/L, which is 1.6 times higher than the 2001 estimate. Sample sizes are admittedly 

small and we did not sample from the same nests, however, this increase in five years fits well 
54 

with other studies across the Great Lakes and globally (see Chen and Hale 2010 for a global 

review of PBDEs in birds). To further investigate this apparent increase, in 2007 we sampled 

both APIS and the remaining Lake Superior south shore (LSSS study area) including all nesting 

territories sampled in 2001 by Dykstra et al. (2005). We estimated a combined geometric mean 

of 11.3 ug/L (n = 12). In 2008 we sampled five nests at APIS and four nests near the APIS 

boundary in the LSSS study area. The geometric mean level of the combined sample was 6.50 

ug/L. These data suggest that the penta- and octa- formulations of PBDEs increased in bald eagle 

nestlings from 2001 to 2006 and have begun to decline, in general agreement with the findings of 

others in a variety of media on the Great Lakes (Hites 2006). 

PFCs

Perfluorinated compounds (PFCs) have the unique chemical properties of repelling both water 

and oil, which makes them useful for many products. The number and variety of products that 

incorporate PFCs in their manufacture is enormous, including drugs, anesthetics, 

chemotherapeutic agents, pesticides, refrigerants, and polymers such as TeflonTM and GortexTM. 

The primary PFCs of interest during this study were PFOS and PFOA. These two analytes are 

ubiquitous in the environment and have come under intense scientific and regulatory scrutiny 

(Giesy and Kannan 2001, Kannan et al. 2006). Most other investigators have also focused on 

PFOS and PFOA; hence there is very little published literature on the chemical properties, 

bioaccumulation factors, sources, and fates of the other PFC analytes. This, combined with our 

limited data on 14 of the analytes we monitored (four years of data on PFOS and PFOA; only 

two years on the other 14), limits the following discussion primarily to PFOS and PFOA. 

The bond between carbon and fluorine is one of the strongest known among organic compounds 

and as a result many PFCs are highly persistent in the environment (Moody et al. 2002, MDH 

2010). However, the adsorption, solubility, and atmospheric transport of the various PFCs differ 

greatly. Higgins and Luthy (2006) found that the carbon chain length in PFCs had a major effect 

on sorption and that PFOS tended to bind more readily to sediment than PFOA. Organic carbon 

content, pH, dissolved calcium, and other environmental conditions of soil and water can also 

affect adsorption. In a review of the bioaccumulative properties of PFCs, Conder et al. (2008) 

found that the longer chained PFCs, which include PFOS, had greater potential to accumulate in 

living organisms. Some of the more volatile PFCs, such as PFOA, are known to reach remote 

locations around the world where they can metabolize to form PFOS (Martin et al. 2002). 

We found PFOS to be the most prevalent PFC in bald eagle nestling plasma, making up over 

50% of the total PFC load by volume. Similar findings have been noted by others in water, algae, 

amphipods, freshwater mussels, crayfish, and several fish species in the Great Lakes food web 

(Kannan et al. 2005), in fish from lakes and streams of the upper Midwest (Ye et al. 2008a, Ye et 

al. 2008b, Delinsky et al. 2010, Nakayama et al. 2010), and in human blood (Kubwabo et al. 

2004, Olsen et al. 2008). Kannan et al. (2005) showed that PFOS has a high capacity to 

biomagnify in food webs with an approximate overall bioaccumulation factor (BAF) of 1,000 in 

lower trophic-level organisms (water to: algae, amphipods, mussels, and crayfish), increasing to 

2,400 in the round goby (Neogobius melanostomus; water to whole body), and increasing 

another 10- to 20-fold from round goby to Chinook salmon (Oncorhynchus tshawytscha; whole 

goby to salmon liver). Direct relations between PFOS contamination in water and the levels 

found in higher organisms may be confounded, however, because some PFCs are metabolized by 
55 

organisms with PFOS as the end product (Tomy et al. 2004). Interestingly, the Minnesota 

Department of Health (MDH) has sampled soils, ground water, surface water, private wells, and 

aquifers around 3M disposal sites and has often found that the PFC with the highest 

concentration is PFBA (MDH 2010), an analyte that was negligible in bald eagle plasma in our 

study (<1% by volume). Yet, the MDH studies did not quantify PFDS, an analyte we found in 

bald eagles at concentrations second only to PFOS on Mississippi River sites. The reasons for the 

relatively high ratio of PFDS in eaglets from the Mississippi River are unknown. 

When all four years of data are considered, we found the U-SACN and LSSS study areas had the 

lowest concentrations of PFOS (<600 ug/L). Concentrations of PFOS were substantially higher

on the L-SACN study area, especially below St. Croix Falls, and on the MISS study area below 

the confluence of the Minnesota and Mississippi Rivers. The highest level we found exceeded 

2400 ug/L PFOS in plasma of a nestling on the Vermillion River bottoms southeast of Hastings, 

Minnesota. These results are comparable to those of Kannan and Giesy (2001) who reported 

PFOS levels to range from <1 ug/L to 2570 ug/L (mean 360 ug/L) in plasma from 26 bald eagle 

nestlings sampled between 1991-1993 across the upper Midwestern United States. We know of 

no other data on PFOS levels in plasma of bald eagle nestlings for comparison. Giesy et al. 

(2006) summarized data from a variety of organisms across the Great Lakes region and 

consistently found PFOS at much higher levels than PFOA in higher taxa: reptiles, amphibians, 

mammals, birds, and fish. This relationship was less prevalent for water, invertebrates and 

benthic algae. 

The “hotspots” we found on the Mississippi and St. Croix rivers were bracketed upstream and 

downstream by nestlings with much lower levels of PFOS, suggesting a source with limited 

downstream transport (see Figure 19). High levels of PFOS were not surprising in the reach of 

river below South St. Paul where 3M has manufactured and disposed of PFCs from the 1950s 

through 2000. Less easily explained were the high levels at two nests on the lower St. Croix 

River. We hypothesize that the high concentrations in some eaglets are due in part to an 

accumulation of PFOS in sediments and the addition of a trophic-step in the eagle’s diet. Both 

the Pig’s Eye Lake area on the Mississippi River and the hot spots on the lower St. Croix River 

have backwater sloughs where sediments settle. Both areas also have great blue heron rookeries. 

PFOS readily binds to sediments (Giesy et al. 2006), and herons often feed in backwater sloughs 

where they can accumulate these contaminants from their prey. Custer et al. (2009, 2010) has 

shown that great blue herons accumulate PFOS to high levels, and bald eagles are known to prey 

on young heron (Norman et al. 1989). 

While the bulk of the Mississippi River PFOS is probably from past disposal practices by 3M, 

the source of PFOS on the lower St. Croix River is less evident. A number of industries located 

in the vicinity of St. Croix Falls, Wisconsin, and Taylors Falls, Minnesota, may have used PFOS 

or precursor analytes. For example, the MDH and the Minnesota Pollution Control Agency 

(MPCA) found high levels of PFOS in wastewater effluent below some cities with chrome 

plating industries (MDH and MPCA, St. Paul, Minnesota, unpublished data). 

The PFC analytes PFDS, PFHpS, and PFHxS showed patterns similar to PFOS in eaglet plasma, 

albeit at substantially lower concentrations. Of these, PFDS was second only to PFOS and 

contributed 26% of the PFC load in nestlings on the Mississippi River study areas, but only 1% 

on the Lake Superior study areas (2008 and 2009 estimates). We found little information on the 
56 

chemistry, sources, transport, and environmental fate of PFDS and the other analytes in this 

group. 

By contrast we found that PFOA, PFuDA, PFTrDA and PFNA were at their highest levels in 

nestlings from Lake Superior. PFOA, the analyte of high interest in this group, made up <1% on 

average of total PFCs by volume. Though proportionally small, geometric mean levels of PFOA 

were highest in eaglets from Lake Superior compared to the other study areas. This was most 

notable in nestlings from the outer islands of APIS, though one nestling on the lower St. Croix 

River (at High Bridge) reached the highest level we measured at 10 ug/L. Giesy and Kannan 

(2006) showed that PFOA has relatively low biomagnification properties, citing others who 

estimated BAFs of 4.0 for PFOA in juvenile rainbow trout (Oncorhynchus mykiss) compared to 

1,100 for PFOS. PFOA is also more water soluble than PFOS and has a longer half-life in the 

atmosphere (92 years; Giesy et al. 2006). Major manufactures of PFOA include 3M in our study 

area and DuPont Corporation’s TeflonTM production facility in Parkersburg, West Virginia 

(Karstadt 2007), but others are scattered globally. Simcik and Dorweiler (2005) used ratios of 

polluted urban waters to unpolluted remote waters to argue that proximate source of PFCs in

Lake Michigan was wastewater treatment plants (WWTPs). This finding was further refined by 

Scott et al. (2010) who examined surface waters, tributary inputs, and effluent from WWTPs. 

They found PFOA to be the predominant PFC (0.07 to 1.2 ng/L) in surface waters of Lake 

Superior and concluded that WWTPs increased concentrations of PFOA up to 20-fold more than

intake water and that precipitation accounted for 35% of the annual input (Scott et al. 2010). 

A general conclusion from these studies may be that about 35% of the PFOA in Lake Superior is 

from atmospheric deposition and 65% is from water-borne inflow, a large portion of which is 

from WWTPs on the lake or on a tributary to the lake. We expect both PFOA and PFOS levels to 

begin declining across our study areas, given that 3M, DuPont and seven other major producers 

of this chemical have agreed to phase-out production by 2015, and both 3M and DuPont have 

met or exceeded their 2010 goals (MDH 2010, personal communication with DuPont public 

information officer, respectively). 

The other PFC analytes that were significantly higher in Lake Superior versus the Mississippi 

River were PFuDA, PFTrDA and PFNA, and we have no information on the sources or chemical 

properties of these analytes. PFTDA and PFDoA were also measured at higher concentrations on 

Lake Superior, but these measurements were not statistically significant. Overall, Lake Superior 

had a distribution of PFCs that was less skewed towards PFOS compared to the Mississippi 

River. This probably reflects the greater capacity of Lake Superior to receive and retain globally

generated airborne contaminants while the Mississippi River tends to carry contaminants from 

more regional sources. 

We found that levels of PFOS and five other PFCs increased significantly with the age and 

weight of the sampled eaglets. Eaglet age influenced PFOS levels more than weight, but the 

reverse was true for PFHxS, PFNA, PFTeDA, PFTrDA, and PFuDA. However, both age and 

weight of eaglets were highly correlated (r = 0.79), so both factors were contributory. On average 

PFOS levels increased in eaglets at a rate of 3% per day while the other five analytes increased

by 23% to 35% for each kilogram of body weight. These findings are supportive of the high 

bioaccumulation factor found by others for PFOS and suggest that the other five PFCs share in 

this property. The age of our eaglets varied by as much as 50 days and the weights varied by 3.5 
57 

kg; hence, a two- to four-fold difference in levels of these six PFCs might be expected during 

routine monitoring of eaglets. We took these factors into account by including them in mixed 

effects models in our analyses and we caution that others should consider them when designing

programs and comparing among study areas and through time. 

Four analytes, PFOA, PFDA, PFDS, and PFHpS did not vary significantly with either weight or 

age. Again, this is supportive of the low bioaccumulation factors found by others for PFOA and 

suggests the other three in this group have low potential for bioaccumulation as well. 

A number of studies have documented toxicity of PFOS and PFOA in laboratory animals (Betts 

2007, MDH 2010). At high concentrations, they can alter the production of thyroid hormones, 

cause liver damage, suppress the immune system, and are carcinogenic (MDH 2010). But the 

toxicity of PFOS and PFOA has not been directly measured for top level predatory birds. 

Newsted et al. (2005) calculated a toxicity reference value (TRV) of 1700 ug/L PFOS in serum, 

which is a conservative number to be protective of avian species. The authors used USEPA Great 

Lakes Initiative methodology (USEPA 1995) incorporating uncertainty factors for observed 

effects, duration of exposure, and intertaxa relationships. The resulting TRV was based on acute 

and chronic dietary exposures to PFOS on bobwhite quail (Colinus virginianus) reproduction and 

extrapolated to the characteristics of a trophic level IV fish-eating bird such as the bald eagle. 

Newsted et al. (2005) reasoned that the resulting TRV incorporates contributions of PFOS from 

both aquatic and terrestrial pathways. We are not aware of any TRVs for PFOA or other PFCs in 

serum. 

Giesy et al. (2006) further explored the potential toxicity of PFOS in wildlife and calculated a no 

observable adverse effects level (NOAEL) of 30,500 ug/L for bird serum. The authors further 

concluded that current concentrations of PFOS in birds - and our data fit within these levels - are 

within a factor of 10 of the threshold likely to cause ecologically relevant effects. Indeed our 

highest measured concentration was less than this NOAEL by a factor of 12. Moreover, none of 

our study areas had geometric mean levels of PFOS exceeding the TRV of 1700 ug/L. The LSACN and MISS study areas had the highest levels with 1580 ug/L and 1250 ug/L respectively. 

We found two nestlings at L-SACN (8.7%) and three at MISS (5.5%) that exceeded 1700 ug 

PFOS/L, but there were no exceedences in the other study areas. We found no measurable effects

on either the individual or population level, though our measurements were limited to gross 

external features and overall productivity. 

PFC concentrations in eaglets appear to have declined from 2006 through 2009 in at least the 

Mississippi River and lower St. Croix study areas. We will continue monitoring PFC 

concentrations through 2011, adding two years of observations to this data set. Temporal trends 

will be analyzed at the conclusion of the 2011 field season. 

59 

Conclusions and Management Implications

The NPS Great Lakes Inventory and Monitoring Network and its partners have implemented this 

monitoring program to assist national parks and other resource managers in the upper Midwest to 

better understand the spatial patterns and trends of targeted environmental contaminants. This 

report covers the beginning of this program, from 2006 through 2009. Data will continue to be 

collected in 2010 and 2011 and thereafter at two-year intervals. Conclusions and 

recommendations made here are subject to change as we learn more about the patterns and trends 

of these contaminants and the complex systems they affect. 

At this juncture we make the following observations and recommendations: 

1. Bald eagle productivity in the region has increased dramatically from lows in the 1960s 

and 1970s and is at or above levels considered necessary for a healthy population. 

Productivity is highest on the Mississippi River and lowest on Lake Superior, with the St. 

Croix River nearly spanning this gradient. The lower productivity observed on Lake 

Superior is likely due to lower food availability compared to the other study areas.

2. We have no evidence that the contaminants being monitored are adversely affecting bald 

eagle productivity at the population level. However, some contaminants may exert 

sublethal and occasionally lethal effects on individuals at “hot spots” where 

concentrations in eaglets were high. Current research on the effects of some contaminants 

on brain chemistry of eagles may provide answers in the coming years. 

3. Mercury levels in biota including bald eagles have been steadily declining in the region, 

but these trends could reverse. Increasing concentrations in some biota, together with the 

relatively high levels we measured in eaglets from the upper St. Croix River, indicates 

that continued monitoring of this pervasive contaminant is warranted.

4. The National Park Service and its partners should determine threshold levels for mercury 

in eagle nestling feathers. Cooperative work with the University of Michigan on the 

effects of mercury on neurochemistry of eagles holds promise for this need and should be 

continued. 

5. There were five instances of elevated lead exposure in bald eagle nestlings, four of which

occurred in eaglets from Pig’s Eye Lake on Pool 2 of the Upper Mississippi River. We 

recommend further research into the levels of lead in sediments, soils, and other media in 

this area for potential remediation. Continued monitoring of lead levels at one nest on the 

lower St. Croix is warranted (High Bridge Island). 

6. The relationship between lead levels in feathers and known toxicity thresholds in blood 

are unclear. We should measure lead in both blood and feathers in future years to explore 

this relationship with the hope of developing a threshold for lead in feathers. 

7. DDT and its metabolites DDD and DDE continue to linger more than 30 years after DDT 

was banned in North America. This is particularly evident in Lake Superior, where 

eaglets continue to bioaccumulate DDE, and occasionally DDT, from the food web. This 

was especially evident for DDE on the outer islands of the Apostle Islands. We propose, 

as have others, that this is due to an additional trophic step in the food chain in this area 

(such as older fish with higher lipid content and/or a higher percentage of piscivorous 
60 

birds in the diet), but this should be researched more thoroughly with sampling of 

sediments and prey in areas with consistently high DDE levels. One extremely high 

concentration of DDT in a nestling on Durnam Island in the Mississippi National River 

and Recreation Area warrants further investigation from local authorities for potential 

illegal use. 

8. The Great Lakes Network and its partners should seek funding to analyze stable isotopes 

(C and N) in eaglets and/or prey to assess how trophic position affects levels of

contaminants in bald eagle nestlings. 

9. Though levels are still elevated in some areas, our data in combination with a longer data 

set from the Wisconsin Department of Natural Resources, shows that DDE declined in 

eaglets on Lake Superior at a rate of 3% annually between 1989 and 2008. There is 

evidence this trend is slowing, however, probably due to the capacity of Lake Superior to 

retain contaminants in its cold, deep, oligotrophic waters. Similar trend data were not 

available for our other study areas, but the literature suggests this decline is regional.

10. In combination with a longer data set from the Wisconsin Department of Natural 

Resources, our data show that PCBs declined in bald eagle nestlings on Lake Superior at 

an annual rate of 4.3% between 1989 and 2008. Similar data were not available for the 

other study areas, but as with DDE, the literature suggests that this trend is regional. We 

found average levels to be below known thresholds in all study areas, though some 

nestlings, particularly those near urban centers or that feed on higher trophic-level prey, 

may suffer sublethal effects of PCB exposure. 

11. The penta- and octa- formulations of PBDEs appear to have increased from the 2001 to 

our first sampling in 2006, but average levels have since dropped on Lake Superior. This 

apparent increase and decline coincides with industry use and subsequent international 

bans and phasing-out of these flame retardant formulations. We will begin monitoring the 

deca-formulations in 2010 to determine if they are increasing as a result industry 

switching to them. There are no thresholds set for PBDEs in bald eagles, but a 

cooperative effort with the University of Michigan holds some promise for determining 

sublethal effects of PBDEs on eagle brain chemistry, and this effort should be continued. 

12. We found high levels of PFCs, especially PFOS, in bald eagle nestlings on the lower 

Mississippi and lower St. Croix Rivers. Due to changing numbers of PFCs measured 

during this program and too few years of data we did not evaluate trends in these 

chemicals. Preliminary evaluations of PFOA and PFOS, however, suggest that these 

PFCs are declining on the Mississippi River - coinciding with the phase-out of production 

by 3M. It is likely that the high levels we observed are associated with accumulations in 

sediments and subsequent biomagnification in the local food web. We expect these 

chemicals to decline post phase-out, however, some are highly persistent and could linger 

for decades. Hence continued monitoring is highly recommended. 

13. Statistical differences in contaminant burdens among study areas provide insights into the 

sources, mechanisms of transport, and magnification through the food web. Generally, 

contaminants associated with industry and municipal waste (PCBs, PBDEs, PFCs, and to 

a lesser extent, lead) were highest in bald eagle nestlings near large population centers. 

Lake Superior, however, receives a wide array of pollutants from global sources through 

air deposition and retains them for longer periods of time; hence nestlings in remote 
61 

islands had high levels of some industrial contaminants. These patterns of distribution 

were further complicated by apparent magnification in the food web at individual nest 

territories where high trophic-level prey was available. 

14. Much of the background levels of lead in eaglets are probably from legacy use of alkyl 

lead in gasoline, but we found elevated levels in some nestlings near sites with

contaminated soils and sediments from industrial or municipal waste. 

15. There is great potential for public education with the results from this program. Bald 

eagles are a species of high interest to the public, and they can serve as a biosentinel to 

foster greater awareness of the hazards of toxic substances in the environment. Resource 

managers and educators should adopt programs, such as “Get the Lead Out,” and provide 

information on hazardous waste disposal and proper use of burn barrels. 

16. The relationships among environmental contaminants and climate, water quality, and 

land use in and around parks should be examined more thoroughly in future analyses. 

17. Following data analysis of samples collected in 2011 a thorough analysis of trends for 

each contaminant should be completed. 

63 

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75 

Appendix A. PBDE, PFC, and PCB congeners analyzed in 

bald eagle plasma by the Wisconsin State Laboratory of 

Hygiene, 2006-2009. 

Nine polybrominated diphenyl ethers (PBDEs): Sixteen perfluorinated chemicals (PFCs):

PBDE #28 *Perflouro-N-tetradecanoic acid (PFTeDA)

PBDE #47 *Perflouro-N-tridecanoic acid (PFTrDA)

PBDE #66 *Perfluoro-1-butanesulfonate (PFBS)

PBDE #85 *Perfluoro-1-decanesulfonate (PFDS)

PBDE #99 *Perfluoro-1-heptanesulfonate (PFHpS)

PBDE #100 *Perfluoro-1-hexanesulfonate (PFHxS)

PBDE #138 Perfluoro-1-octanesulfonate (PFOS)

PBDE #153 *Perfluoro-N-butanoic acid (PFBA)

PBDE #154 *Perfluoro-N-decanoic acid (PFDA)

*Perfluoro-N-dodecanoic acid (PFDoA)

*Perfluoro-N-heptanoic acid (PFHpA)

*Perfluoro-N-hexanoic acid (PFHxA)

*Perfluoro-N-nonanoic acid (PFNA)

Perfluoro-N-octanoic acid (PFOA)

*Perfluoro-N-pentanoic acid (PFPA)

*Perfluoro-N-undodecanoic acid (PFuDA)

* = PFCs done in 2008 and 2009 only; PFOS and PFOA 

done all four years.

Seventy-five polychlorinated biphenyls (PCBs). Those separated by “/” could not be differentiated:

3 66 151 004/010 137/176

6 74 158 007/009 163/138 

18 82 167 008/005 170/190

19 83 172 015/017 187/182

22 85 174 016/032 202/171

25 87 177 024/027 203/196

26 89 178 028/031 208/195

33 91 180 037/042

40 95 183 041/071/064

44 97 185 047/048

45 99 193 056/060

46 101 194 070/076

49 118 198 077/110

51 128 199 092/084

52 136 201 123/149 

53 141 206 132/153/105 

63 146 207 135/144

77

Appendix B. Quality assurance spikes conducted by the Wisconsin State Laboratory 

of Hygiene for contaminants in bald eagle plasma.

Quality Assurance Spikes – 2009 (From Wisconsin State Lab of Hygiene, 2009 eagle serum analytical laboratory report.) 

Analyte N

Average % 

Recovery

Standard 

Deviation Analyte N

Average % 

Recovery

Standard 

Deviation Analyte N

Average % 

Recovery

Standard 

Deviation

PCB #14 (3,5) - SURROGATE SPIKE 39 76.6 10.5 PCB #99 5 90.8 3.9 PBDE #28 5 100.2 11.4

PCB #65 (2,3,5,6) - SURROGATE SPIKE 39 87.9 11.9 PCB #83 5 97.3 4.4 PBDE #47 5 91.4 4.9

PCB #166 (2,3,4,4',5,6) - SURROGATE SPIKE 39 85.9 6.1 PCB #97 5 94.6 4 PBDE #66 5 98.6 6.5

PCB #3 5 65.2 13.3 PCB #87 5 94.2 4.6 PBDE #100 5 112 4.5

PCB #4/10 5 76 8.9 PCB #85 5 98.7 3.6 PBDE #99 5 96.8 8.4

PCB #7/9 5 78.1 6.5 PCB #136 5 94.3 6.4 PBDE #85 5 114 5.5

PCB #6 5 67.7 7.8 PCB #77/110 5 94.3 6 PBDE #154 5 118 8.4

PCB #8/5 5 76.3 5.9 PCB #82 5 88 2.8 PBDE #153 5 116 5.5

PCB #19 5 81.1 6.3 PCB #151 5 90.8 3.4 PBDE #138 5 126 5.5

PCB #18 5 78.5 4.4 PCB #135/144 5 88.1 1.9

PCB #15/17 5 79.2 3.4 PCB #123/149 5 89.6 2.4 P,P'-DDD 5 85 13.9

PCB #24/27 5 84.7 15.3 PCB #118 5 88.8 1.2 P,P'-DDT 5 96 15.2

PCB #16/32 5 79.3 4.7 PCB #146 5 87.2 1.5 p,p'-DDE 5 100.2 6.7

PCB #26 5 76.4 9.7 PCB #132/153/105 5 86.7 1.2

PCB #25 5 82 5.7 PCB #141 5 92.3 0 PFOS 8 92.4 13.9

PCB #28/31 5 82.9 4.1 PCB #137/176 5 87.3 2 PFOA 4 100 4.3

PCB #33 5 86.7 5.4 PCB #163/138 5 88.8 1.8 PFBS 4 88.8 3.8

PCB #53 5 88.9 7 PCB #158 5 100.9 2 PFHxS 4 93.5 4.4

PCB #51 5 90.8 8.4 PCB #178 5 87.7 1.2 PFHpS 4 90.3 5.1

PCB #22 5 86.4 5.6 PCB #187/182 5 90 3 PFDS 8 84.5 13.8

PCB #45 5 85.7 7.2 PCB #183 5 92.9 0 PFBA 4 155 70

PCB #46 5 84.8 1.9 PCB #128 5 90.6 1.7 PFPA 4 93 3.4

PCB #52 5 86.5 4.8 PCB #167 5 90.2 1.7 PFHxA 4 94.3 4.8

PCB #49 5 87.1 4.9 PCB #185 5 90.8 2.4 PFHpA 4 92.8 2.5

PCB #47/48 5 85.9 5.3 PCB #174 5 89.2 1.7 PFNA 4 102.5 7.5

PCB #44 5 89.3 4.9 PCB #177 5 88.6 3.9 PFDA 4 112.8 12.8

PCB #37/42 5 86 4.2 PCB #202/171 5 85.5 5.8 PFuDA 4 90.8 15.3

PCB #41/71/64 5 88.4 6.3 PCB #172 5 85.4 2.2 PFDoA 4 274.5 202.7

PCB #40 5 87.4 3.6 PCB #180 5 91.9 1.6 PFTrDA 4 113 24

PCB #63 5 84.8 20.5 PCB #193 5 92.2 2.3 PFTeDA 4 84 18

PCB #74 5 95 5.2 PCB #199 5 94.1 2.1

PCB #70/76 5 92.8 5.2 PCB #170/190 5 90 3.4

PCB #66 5 93.5 7.1 PCB #198 5 96.3 2.6

PCB #95 5 87.1 6 PCB #201 5 93.1 3.3

PCB #91 5 91.3 3.8 PCB #203/196 5 93.5 3.1

PCB #56/60 5 97 4.8 PCB #208/195 5 93.4 3

PCB #92/84 5 97.2 2.6 PCB #207 5 94.1 2.9

PCB #89 5 93.9 3.1 PCB #194 5 91.8 3.2

PCB #101 5 93.9 3.4 PCB #206 5 93.8 3.5
78

Appendix B. Quality assurance spikes conducted by the Wisconsin State Laboratory of Hygiene for contaminants 

in bald eagle plasma (continued). 

Quality Assurance Spikes – 2008 (From Wisconsin State Lab of Hygiene, 2008 eagle serum analytical laboratory report.)

Analyte N

Average % 

Recovery

Standard 

Deviation Analyte N

Average % 

Recovery

Standard 

Deviation Analyte N

Average % 

Recovery

Standard 

Deviation

PCB #14 (3,5) - SURROGATE SPIKE 47 75.8 12.7 PCB #99 5 94.8 7.2 PBDE #28 5 101.6 10.5

PCB #65 (2,3,5,6) - SURROGATE SPIKE 47 91.4 11.4 PCB #83 5 101.5 10.5 PBDE #47 5 87.8 6.6

PCB #166 (2,3,4,4',5,6) - SURROGATE SPIKE 47 95.1 6.8 PCB #97 5 95.8 12.8 PBDE #66 5 94 8.3

PCB #3 5 68.1 12.4 PCB #87 5 99.7 8.2 PBDE #100 5 97.8 4.3

PCB #4/10 5 88.8 10.6 PCB #85 5 100 11.4 PBDE #99 5 90 7.3

PCB #7/9 5 83.6 5 PCB #136 5 101 8.4 PBDE #85 5 87.8 19.5

PCB #6 5 66.5 5.1 PCB #77/110 5 91.9 15.4 PBDE #154 5 91.8 8.5

PCB #8/5 5 80.5 12 PCB #82 5 93.6 5.4 PBDE #153 5 95.2 4.4

PCB #19 5 81 7.5 PCB #151 5 95.7 4.7 PBDE #138 5 95.4 16.8

PCB #18 5 83.1 5.8 PCB #135/144 5 96.4 4.1

PCB #15/17 5 82.5 8.4 PCB #123/149 5 96.5 3.6 P,P'-DDD 5 99.5 5.8

PCB #24/27 5 85.6 17.8 PCB #118 5 96.7 5 P,P'-DDT 5 81.1 13

PCB #16/32 5 85 6.4 PCB #146 5 95.6 4.2 p,p'-DDE 5 100.8 10.9

PCB #26 5 74 10.3 PCB #132/153/105 5 94.5 7.2

PCB #25 5 80 9.4 PCB #141 5 99.1 6.8 PFOS 32 103.8 7.7

PCB #28/31 5 84 9.5 PCB #137/176 5 94.6 3.8 PFOA 13 105.5 10.2

PCB #33 5 90 7.6 PCB #163/138 5 96 4 PFBS 13 105.9 8

PCB #53 5 96.9 8 PCB #158 5 105.5 5.9 PFHxS 13 96.6 23.2

PCB #51 5 95.1 8.6 PCB #178 5 90 5.4 PFHpS 13 96 11.8

PCB #22 5 90 8.7 PCB #187/182 5 97.1 6.4 PFDS 13 97.4 25.1

PCB #45 5 91.5 8.5 PCB #183 5 98 5 PFBA 13 91.9 18.3

PCB #46 5 91.7 12.1 PCB #128 5 96.6 3.8 PFPA 13 93.1 29.6

PCB #52 5 90.3 9.1 PCB #167 5 95.6 4 PFHxA 13 95.7 15.6

PCB #49 5 93.6 9 PCB #185 5 97.9 4.5 PFHpA 13 100 15

PCB #47/48 5 89.6 7.9 PCB #174 5 96.2 5.4 PFNA 13 107.1 13.9

PCB #44 5 88 15.7 PCB #177 5 93.1 5.6 PFDA 13 83.6 18.6

PCB #37/42 5 84.8 16.8 PCB #202/171 5 93.5 2.8 PFuDA 13 98.2 18

PCB #41/71/64 5 87.8 9 PCB #172 5 100.5 12.9 PFDoA 13 126.5 32.7

PCB #40 5 90 8 PCB #180 5 98.1 3.6 PFTrDA 13 86.6 18.5

PCB #63 5 91.3 9.5 PCB #193 5 99.4 6 PFTeDA 13 88.9 17.5

PCB #74 5 92.8 9.3 PCB #199 5 95.9 1.6

PCB #70/76 5 96.8 10.4 PCB #170/190 5 87.8 17.1

PCB #66 5 97.7 11.5 PCB #198 5 99.2 3.5

PCB #95 5 92.6 6.8 PCB #201 5 94.3 8.1

PCB #91 5 97.3 9.3 PCB #203/196 5 94 13.7

PCB #56/60 5 96.3 10.1 PCB #208/195 5 93.4 11.1

PCB #92/84 5 98.2 7.1 PCB #207 5 86.4 28.1

PCB #89 5 103.6 17.8 PCB #194 5 91.1 12

PCB #101 5 97.9 7.7 PCB #206 5 95.9 6.5
79

Appendix B. Quality assurance spikes conducted by the Wisconsin State Laboratory of Hygiene for contaminants 

in bald eagle plasma (continued). 

Quality Assurance Spikes – 2007 (From Wisconsin State Lab of Hygiene, 2007 Eagle Serum Analytical Laboratory Report.)

Analyte N 

Average % 

Recovery

Standard 

Deviation Analyte N 

Average % 

Recovery

Standard 

Deviation Analyte N 

Average % 

Recovery

Standard 

Deviation

PCB #14 (3,5) - SURROGATE SPIKE 35 77.3 5.6 PCB #99 4 97.4 6.3 PBDE #28 4 96.8 9 

PCB #65 (2,3,5,6) - SURROGATE SPIKE 35 95.3 6.7 PCB #83 4 111 9.8 PBDE #47 4 97 3.9

PCB #166 (2,3,4,4',5,6) - SURROGATE SPIKE 35 103 15.4 PCB #97 4 103 6.6 PBDE #66 4 103 3.9

PCB #3 4 31 14.6 PCB #87 4 106 1.9 PBDE #100 4 111 4

PCB #4/10 4 65.3 16.2 PCB #85 4 104 17.1 PBDE #99 4 100 7.1

PCB #7/9 4 70.4 4.3 PCB #136 4 102 10.7 PBDE #85 4 112 5.5

PCB #6 4 73.2 3.5 PCB #77/110 4 107 3.7 PBDE #154 4 113 7.1

PCB #8/5 4 77.9 3.9 PCB #82 4 88.7 3.4 PBDE #153 4 115 10.9

PCB #19 4 80.5 3.7 PCB #151 4 90.1 3.1 PBDE #138 4 126 16.2

PCB #18 4 81.2 5 PCB #135/144 4 90.5 3.2

PCB #15/17 4 77.2 8.7 PCB #123/149 4 92.9 5.9 P,P'-DDD 4 114 19.5

PCB #24/27 4 85.5 6.5 PCB #118 4 95.7 7.6 P,P'-DDT 4 103 8.7

PCB #16/32 4 87.3 5.1 PCB #146 4 89.3 3.6 p,p'-DDE 4 102 2.6

PCB #26 4 67.7 6.1 PCB #132/153/105 4 88.7 5.9

PCB #25 4 74.6 2.1 PCB #141 4 89.9 6.5

PCB #28/31 4 84.7 5 PCB #137/176 4 92.4 6.3

PCB #33 4 85.7 6.2 PCB #163/138 4 93.6 5.1

PCB #53 4 99.8 5.7 PCB #158 4 110 15.8

PCB #51 4 94.3 7.8 PCB #178 4 87.7 9.5

PCB #22 4 96.2 10 PCB #187/182 4 92.5 5.7

PCB #45 4 90.4 4.8 PCB #183 4 95.3 2.8

PCB #46 4 92.2 6.3 PCB #128 4 94.9 1.2

PCB #52 4 95.9 4.9 PCB #167 4 102 10.1

PCB #49 4 94.9 4.6 PCB #185 4 96.5 4.4

PCB #47/48 4 94.1 4.7 PCB #174 4 95.3 3.2

PCB #44 4 94.3 0.7 PCB #177 4 94.1 2.3

PCB #37/42 4 92.4 6.9 PCB #202/171 4 92 8.7

PCB #41/71/64 4 93.4 5.3 PCB #172 4 99.9 6

PCB #40 4 96.4 6.1 PCB #180 4 94.6 1.9

PCB #63 4 90.6 15.1 PCB #193 4 108 14.6

PCB #74 4 98.5 2.2 PCB #199 4 96.3 2.5

PCB #70/76 4 103 5.9 PCB #170/190 4 91.9 2.8

PCB #66 4 108 11.6 PCB #198 4 96.5 5.3

PCB #95 4 95.2 7 PCB #201 4 94.3 5.1

PCB #91 4 103 16 PCB #203/196 4 95.6 3.3

PCB #56/60 4 103 3.5 PCB #208/195 4 96.5 2.9

PCB #92/84 4 104 7.3 PCB #207 4 95.7 4 

PCB #89 4 106 15 PCB #194 4 95.8 10.3

PCB #101 4 104 5.3 PCB #206 4 97.8 6.1
80

Appendix B. Quality assurance spikes conducted by the Wisconsin State Laboratory of Hygiene for contaminants 

in bald eagle plasma (continued). 

Quality Assurance Spikes – 2006 (From data files provided by the Wisconsin State Lab of Hygiene regarding the QA spikes for 2006.)

Analyte N 

Average % 

Recovery

Standard 

Deviation Analyte N 

Average % 

Recovery

Standard 

Deviation Analyte N 

Average % 

Recovery

Standard 

Deviation

PCB #14 (3,5) - SURROGATE SPIKE 32 83.5 10.5 PCB #99 4 100.0 2.6 PBDE #28 4 101.1 9.1

PCB #65 (2,3,5,6) - SURROGATE SPIKE 32 92.1 7.6 PCB #83 4 99.5 8.8 PBDE #47 4 92.2 4.9

PCB #166 (2,3,4,4',5,6) - SURROGATE SPIKE 25 103.9 5.5 PCB #97 4 100.0 5.5 PBDE #66 4 89.8 2.1

PCB #3 4 51.2 17.2 PCB #87 4 102.0 6.0 PBDE #100 4 98.1 2.6

PCB #4/10 4 77.5 5.0 PCB #85 4 106.0 7.6 PBDE #99 4 97.2 6.9

PCB #7/9 4 74.0 6.5 PCB #136 4 97.0 13.0 PBDE #85 4 99.2 2.1

PCB #6 4 77.4 4.0 PCB #77/110 4 101.7 4.9 PBDE #154 4 100.0 6.3

PCB #8/5 4 79.0 4.0 PCB #82 4 96.9 3.6 PBDE #153 4 97.9 7.3

PCB #19 4 86.1 5.5 PCB #151 4 95.3 0.9 PBDE #138 4 103.6 4.1

PCB #18 4 82.8 6.0 PCB #135/144 4 99.5 3.1

PCB #15/17 4 82.8 6.0 PCB #123/149 4 98.3 3.3 P,P'-DDD 4 94.4 7.4

PCB #24/27 4 92.4 8.1 PCB #118 4 106.9 7.4 P,P'-DDT 4 115.0 9.8

PCB #16/32 4 88.2 4.8 PCB #146 4 98.9 2.2 p,p'-DDE 4 90.2 3.0

PCB #26 4 81.5 5.5 PCB #132/153/105 4 96.1 0.9

PCB #25 4 91.7 6.8 PCB #141 4 96.8 1.1

PCB #28/31 4 87.0 5.2 PCB #137/176 4 105.8 3.8

PCB #33 4 91.7 3.3 PCB #163/138 4 100.8 3.9

PCB #53 4 82.8 4.9 PCB #158 4 103.5 14.6

PCB #51 4 95.2 8.8 PCB #178 4 99.5 2.3

PCB #22 4 96.2 4.4 PCB #187/182 4 98.6 2.8

PCB #45 4 90.8 3.4 PCB #183 4 99.7 2.2

PCB #46 4 95.6 2.9 PCB #128 4 98.2 1.4

PCB #52 4 94.7 4.3 PCB #167 4 105.8 6.7

PCB #49 4 96.3 2.5 PCB #185 4 100.0 3.4

PCB #47/48 4 97.0 2.9 PCB #174 4 95.6 2.9

PCB #44 4 90.8 2.6 PCB #177 4 97.8 1.8

PCB #37/42 4 97.9 4.2 PCB #202/171 4 100.0 1.9

PCB #41/71/64 4 94.7 4.3 PCB #172 4 108.1 8.5

PCB #40 4 99.4 3.1 PCB #180 4 100.0 0.0

PCB #63 4 100.8 1.5 PCB #193 4 103.3 4.2

PCB #74 4 97.7 4.5 PCB #199 4 97.8 2.5

PCB #70/76 4 96.9 3.6 PCB #170/190 4 93.2 4.5

PCB #66 4 102.7 5.4 PCB #198 4 97.8 5.8

PCB #95 4 95.7 6.5 PCB #201 4 101.1 2.2

PCB #91 4 101.4 2.8 PCB #203/196 4 100.0 0.0

PCB #56/60 4 98.6 2.8 PCB #208/195 4 99.5 1.1

PCB #92/84 4 103.8 4.1 PCB #207 4 92.7 11.8

PCB #89 4 124.3 17.8 PCB #194 4 100.0 0.0

PCB #101 4 101.2 5.4 PCB #206 4 101.6 1.1
81 

Appendix C. Limits of detection (LOD) or report limit (RL) and 

the percent of samples that were above them as reported by 

the Wisconsin State Laboratory of Hygiene for bald eagle 

plasma and feather samples tested for environmental 

contaminants, 2006-2009. 

Table C-1. Limits of detection (LOD) or report limit (RL) and the percent of samples that were above and 

below them for six targeted contaminants that did not have multiple congeners.

Contaminant LOD/RL % >LOD % <LOD

DDE 0.15 100.0 0.0

Mercury 0.004 100.0 0.0

Selenium 0.28 97.4 2.6

Lead 0.14 75.5 24.5

DDD 0.3 74.3 25.7

DDT 0.35 5.9 94.1

Table C-2. Percent of bald eagle plasma samples above the laboratories limits of detection (LOD) for 16 

PFC and 9 PBDE congeners.

PFC telomer % above LOD PBDE congener % above LOD

PFDS 100.0 47 100.0

PFDoA 100.0 99 94.7

PFHpS 100.0 100 94.1

PFHxS 100.0 153 88.8

PFNA 100.0 154 86.8

PFOS 100.0 28 43.4

PFTrDA 100.0 66 34.2

PFuDA 100.0 138 1.3

PFTeDA 98.8 85 0.0

PFDA 96.5

PFOA 94.7

PFBA 81.2

PFHpA 20.0

PFHxA 3.5

PFPA 2.4

PFBS 0.0
82 

Appendix C. Limits of detection (LOD) or report limit (RL) and the percent of 

samples that were above them as reported by the Wisconsin State Laboratory of 

Hygiene for bald eagle plasma and feather samples tested for environmental 

contaminants, 2006-2009 (continued). 

Table C-3. List of PCB congeners in bald eagle plasma that were above the laboratory limits of detection 

(LOD; shown in parentheses) grouped by percent-categories. For example, the 15 congeners listed in the 

first column were detected at levels above the LOD >90% of the time. In all, 75 PCB congeners or 

congener pairings were considered; congener pairings separated by a slash could not be differentiated.

90% to 100% 80% to 90% 60% to 80% 40% to 60% 0% to 40% Never

15 congeners: 22 congeners: 9 congeners: 4 congeners: 18 congeners: 7 congeners:

132/153/105 (0.15) 101 (0.1) 056/060 (0.1) 083 (0.1) 026 (0.15) 003 (6.0)

163/138 (0.15) 174 (0.07) 049 (0.1) 092/084 (0.15) 051 (0.1) 004/010 (0.7)

180 (0.1) 087 (0.1) 097 (0.1) 136 (0.2) 016/032 (0.2) 006 (0.2)

187/182 (0.07) 177 (0.07) 091 (0.15) 141 (0.1) 018 (0.15) 007/009 (0.15)

201 (0.1) 178 (0.1) 028/031 (0.3) 033 (0.15) 019 (0.15)

118 (0.15) 206 (0.07) 037/042 (0.1) 063 (0.1) 024/027 (0.15)

077/110 (0.1) 066 (0.15) 041/071/064 (0.15) 015/017 (0.2) 025 (0.15)

183 (0.07) 085 (0.1) 185 (0.07) 022 (0.2)

170/190 (0.1) 095 (0.1) 082 (0.1) 045 (0.1)

146 (0.1) 158 (0.15) 207 (0.07)

123/149 (0.1) 167 (0.1) 053 (0.2)

203/196 (0.15) 172 (0.1) 040 (0.1)

128 (0.07) 151 (0.1) 046 (0.1)

194 (0.07) 193 (0.1) 199 (0.07)

099 (0.1) 208/195 (0.1) 198 (0.1)

135/144 (0.1) 089 (0.1)

052 (0.1) 008/005 (0.4)

070/076 (0.1) 137/176 (0.1)

202/171 (0.07)

074 (0.1)

044 (0.1)

047/048 (0.1)
83 

Appendix D. Spatial distribution of environmental 

contaminants monitored by the National Park Service, Great 

Lakes Inventory and Monitoring Network, at six study areas 

in the upper Midwest, 2006-2009. 

The following spatial distribution maps summarize all data we collected on the contaminants we 

monitored. Samples that were below the laboratory limits of detection (LOD) were given zero 

and are shown as none-detects (white dots). Yearly summary maps show all data points for that 

year; no hidden values. Locations are generally within 100 m of the true location of each nest, 

depending on method of obtaining the location (aerial or from the ground) and on GPS accuracy. 

Summary maps with maximum values provide the greatest concentration measured for that 

contaminant at each nest over the four years, hence all nests sampled over the four years show up 

exactly once and small values are hidden. The maximum value provides an assessment of 

exceedences and maximum levels. Compare yearly maps to examine annual variability. 

Study areas were not sampled every year. The monitoring design called for sampling the three 

National Park Service areas in 2006 and 2007 and then on two-year rotations thereafter. 

Additional sample years and sampling in adjacent areas were added as partnerships and funding 

made it possible. Accordingly, Apostle Islands was not sampled in 2009; the Lake Superior 

South Shore was not sampled in 2006 or 2009; the upper St. Croix Riverway was not sampled in 

2008 and 2009; and the Pools 3 & 4 study area was not sampled in 2006 and 2007. 

Maps are shown in the order presented in the results section of the report. 
84 

 

 

Figure D-1. Spatial distribution of total 

mercury measured in breast feathers of bald 

eagle nestlings sampled at their nest from six 

study areas in the upper Midwest, 2006-2009. 

Most of this mercury (>95%) is assumed to be 

methylmercury. We selected 7.5 ug/g (wet 

weight) of mercury in breast feathers as a 

provisional threshold above which toxicity may 

be indicated for some wildlife and humans. Nest 

locations shown as bright and dark red dots 

(first category rounded up from 7.5 to 8.0 ug/g) 

are above this provisional threshold.
85 

 

 

Figure D-2. Spatial distribution of lead

measured in breast feathers of bald eagle 

nestlings sampled at their nest from six study 

areas in the upper Midwest, 2006-2009. We 

selected 2.0 ug/g (wet weight) of lead in breast 

feathers as a provisional threshold above which 

toxicity may be indicated for some wildlife and 

humans. Nest locations shown as bright and 

dark red dots are above this provisional 

threshold.
86 

 

 

Figure D-3. Spatial distribution of DDT

measured in plasma of bald eagle nestlings 

sampled at their nest from six study areas in the 

upper Midwest, 2006-2009. We selected 0.0 

ug/L (wet weight) of DDT in plasma as our 

threshold since this pesticide was banned in 

North America in 1972. Several detections of 

DDT indicate this chemical continues to enter 

the ecosystem due either to its persistence in 

soils and sediments, local illegal use, and/or 

through long range aerial or biological (e.g. 

migrant birds) transport from global sources 

where it is still being used. 
87 

 

 

Figure D-4. Spatial distribution of DDD

measured in plasma of bald eagle nestlings 

sampled at their nest from six study areas in the 

upper Midwest, 2006-2009. No threshold value 

has been determined. DDD is a metabolite of 

DDT.
88 

 

 

Figure D-5. Spatial distribution of DDE

measured in plasma of bald eagle nestlings 

sampled at their nest from six study areas in the 

upper Midwest, 2006-2009. We selected 28.0 

ug/L (wet weight) of DDE in nestling plasma as 

our threshold. Studies have linked lowered 

productivity in some bald eagle populations 

when concentrations reach this level. DDE is a 

metabolite of DDT. 
89 

 

 

Figure D-6. Spatial distribution of total PCBs

measured in plasma of bald eagle nestlings 

sampled at their nest from six study areas in the 

upper Midwest, 2006-2009. We selected 190.0 

ug/L (wet weight) of PCBs in plasma as a 

threshold. Studies have linked reduced bald 

eagle reproductive rates when concentrations 

are above this level. 
90 

 

 

Figure D-7. Spatial distribution of total PBDEs

(sum of nine congeners) measured in plasma of 

bald eagle nestlings sampled at their nest from 

six study areas in the upper Midwest, 2006-

2009. There is no known threshold value.
91 

 

Figure D-8. Spatial distribution of total PFCs

(sum of 16 congeners) measured in plasma of 

bald eagle nestlings sampled at their nest from 

six study areas in the upper Midwest, 2008 and 

2009. There is no known threshold value.
92 

 

 

Figure D-9. Spatial distribution of total PFOS

measured in plasma of bald eagle nestlings 

sampled at their nest from six study areas in the 

upper Midwest, 2006-2009. There is no known 

threshold value.
93 

 

 

Figure D-10. Spatial distribution of total PFOA

measured in plasma of bald eagle nestlings 

sampled at their nest from six study areas in the 

upper Midwest, 2006-2009. There is no known 

threshold value.
94 

 

Figure D-11. Spatial distribution of total PFDS

measured in plasma of bald eagle nestlings 

sampled at their nest from six study areas in the 

upper Midwest, 2008 and 2009. There is no 

known threshold value.
The Department of the Interior protects and manages the nation’s natural resources and cultural heritage; provides scientific and 

other information about those resources; and honors its special responsibilities to American Indians, Alaska Natives, and affiliated 

Island Communities.

NPS 920/106819, February 2011
National Park Service

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Natural Resource Program Center

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www.nature.nps.gov

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