OCS Study

BOEM 2020-036

US Department of the Interior

Bureau of Ocean Energy Management

New Orleans Office

Ecological Drivers of Brown Pelican

Movement Patterns, Health, and

Reproductive Success in the

Gulf of Mexico
OCS Study

BOEM 2020-036

Published by

US Department of the Interior

Bureau of Ocean Energy Management

New Orleans Office

New Orleans, LA

June 2020

Ecological Drivers of Brown Pelican

Movement Patterns, Health, and

Reproductive Success in the

Gulf of Mexico

Authors

Juliet S. Lamb

Yvan G. Satgé

Rochelle A. Streker

Patrick G.R. Jodice

Prepared under BOEM Contract

M12PG00014

by

US Geological Survey

South Carolina Cooperative Fish and Wildlife Unit

260 Lehotsky Hall

Clemson University

Clemson, SC 29634
DISCLAIMER

This study was funded, in part, by the US Department of the Interior, Bureau of Ocean Energy 

Management (BOEM), Environmental Studies Program, Washington, DC, through Interagency 

Agreement Number M12PG00014 with the US Geological Survey South Carolina Cooperative Fish and 

Wildlife Research Unit. This report has been technically reviewed by BOEM, and it has been approved 

for publication. The views and conclusions contained in this document are those of the authors and should 

not be interpreted as representing the opinions or policies of BOEM. This report has been peer reviewed 

and approved for publication consistent with USGS Fundamental Science Practices 

(https://pubs.usgs.gov/circ/1367/).

REPORT AVAILABILITY

To download a PDF file of this report, go to the US Department of the Interior, Bureau of Ocean Energy 

Management Data and Information Systems webpage (http://www.boem.gov/Environmental-StudiesEnvData/), click on the link for the Environmental Studies Program Information System (ESPIS), and 

search on 2020-036. The report is also available at the National Technical Reports Library at 

https://ntrl.ntis.gov/NTRL/.

CITATION

Lamb JS, Satgé YG, Streker RA, Jodice PGR. 2020. Ecological drivers of brown pelican movement 

patterns, health, and reproductive success in the Gulf of Mexico. New Orleans (LA): US Department 

of the Interior, Bureau of Ocean Energy Management. 234 p. Report No.: BOEM 2020-036. Contract 

No.: M12PG00014. 

ABOUT THE COVER

An Eastern brown pelican (Pelecanus occidentalis carolinensis) returns to its nest on Raccoon Island, 

Louisiana. Photo credit: S. Desaivre 2014.
i

Contents

List of Figures .............................................................................................................................................................iv

List of Tables.............................................................................................................................................................viii

List of Abbreviations and Acronyms..........................................................................................................................x

1 Introduction .......................................................................................................................................................13

1.1 Baseline Habitat Use ..................................................................................................................................14

1.2 Risk Exposure ............................................................................................................................................15

1.3 Ecology and Physiology of Reproduction ...................................................................................................16

1.4 Foraging Ecology........................................................................................................................................18

2 Methods .............................................................................................................................................................20

2.1 Study Area..................................................................................................................................................20

2.1.1 Colony Selection ....................................................................................................................................20

2.1.2 Data Collection Schedule.......................................................................................................................21

2.2 Individual Tracking......................................................................................................................................21

2.2.1 Capture Technique.................................................................................................................................21

2.2.2 Measurements and Sampling.................................................................................................................21

2.2.3 Tracking Devices....................................................................................................................................22

2.3 Annual Habitat Use by Adults .....................................................................................................................23

2.3.1 Data Screening and Interpolation...........................................................................................................23

2.3.2 Habitat Variables....................................................................................................................................24

2.3.3 Habitat Selection ....................................................................................................................................24

2.3.4 Movement States ...................................................................................................................................25

2.3.5 Statistical Analyses ................................................................................................................................25

2.4 Risk Exposure ............................................................................................................................................25

2.4.1 Surface Pollutant Data Layer .................................................................................................................25

2.4.2 Individual Risk Exposure........................................................................................................................26

2.5 Ecology and Physiology of Reproduction ...................................................................................................26

2.5.1 Nest Monitoring ......................................................................................................................................26

2.5.2 Nestling Health.......................................................................................................................................27

2.5.3 Nest and Fledging Success....................................................................................................................28

2.5.4 Post-fledging Survival.............................................................................................................................29

2.5.5 Statistical Analysis..................................................................................................................................29

2.6 Nestling Diet ...............................................................................................................................................30

2.6.1 Diet Sampling.........................................................................................................................................30

2.6.2 Diet Composition....................................................................................................................................30

2.6.3 Provisioning Rates .................................................................................................................................31

2.6.4 Proximate Composition and Energy Density ..........................................................................................31

2.6.5 Energy Provisioning Rates .....................................................................................................................32

2.7 Health and Physiological Parameters.........................................................................................................33

2.7.1 Blood Analytes .......................................................................................................................................33

2.7.2 Polycyclic Aromatic Hydrocarbons .........................................................................................................35

3 Individual Tracking............................................................................................................................................46

3.1 Tag Effects .................................................................................................................................................46

3.1.1 Behavioral Effects ..................................................................................................................................46
ii

3.1.2 Effects on Nesting Success....................................................................................................................46

3.2 Foraging Movements and Home Range during the Breeding Period .........................................................49

3.3 Non-breeding Movements and Migratory Strategy .....................................................................................51

3.3.1 Influence of Individual Characteristics and Colony Size on Migratory Movements.................................51

3.3.2 Overlap of Populations During Staging ..................................................................................................53

3.3.3 Migration through the Tehuantepec Isthmus ..........................................................................................53

4 Habitat Use and Risk Exposure .......................................................................................................................78

4.1 Annual Habitat Use of Breeding Adults.......................................................................................................78

4.2 Risk Exposure ............................................................................................................................................79

4.2.1 Spatial and Temporal Aspects of Risk Exposure....................................................................................79

4.2.2 Individual Behavior and Model Structure................................................................................................80

5 Ecology and Physiology of Reproduction ......................................................................................................90

5.1 Individual Nestling Survival.........................................................................................................................90

5.1.1 Colony-specific Nest Productivity and Chick Survival.............................................................................90

5.1.2 Nutritional Stress....................................................................................................................................92

5.2 Daily Survival Rates–Gaillard and Cat Islands ...........................................................................................93

5.3 Implications for Long-term Monitoring.........................................................................................................95

6 Nestling Diet ....................................................................................................................................................106

6.1 Taxonomic Composition of Diet................................................................................................................106

6.2 Meal Attributes..........................................................................................................................................106

6.3 Nutritional Stress ......................................................................................................................................107

7 Hematology, Plasma Chemistry, and PAHs..................................................................................................123

7.1 Hematology and Plasma Chemistry .........................................................................................................123

7.1.1 Individual Attributes, Hematology, and Plasma Chemistry of Adults ....................................................123

7.1.2 Individual Attributes, Hematology, and Plasma Chemistry of Chicks ...................................................125

7.1.3 Ecological Relationships and Intraspecific Comparisons .....................................................................127

7.2 Polycyclic Aromatic Hydrocarbons ...........................................................................................................131

7.2.1 PAHs in Adult Blood.............................................................................................................................131

7.2.2 PAHs in Adult Feathers ........................................................................................................................133

7.2.3 PAHs in Chick Feathers .......................................................................................................................135

7.2.4 Ecological relationships........................................................................................................................137

8 Conclusions and Future Directions...............................................................................................................194

8.1 Spatial Ecology: Home Range, Migration, and Movement........................................................................195

8.2 Diet and Forage Fish ................................................................................................................................196

8.3 Risk Exposure ..........................................................................................................................................197

8.4 Methodological and Monitoring Assessments...........................................................................................199

8.5 Additional Information Needs....................................................................................................................200

8.5.1 Colony Atlas and Nonbreeding Surveys...............................................................................................200

8.5.2 Colony-based Monitoring .....................................................................................................................200

8.5.3 Prey Monitoring ....................................................................................................................................201

8.5.4 Expanded Scope: Taxonomic and Spatial............................................................................................201

Works Cited..............................................................................................................................................................203

Appendix A: Supplementary Methods for Analysis of Composition of Diet of Brown Pelican Chicks, 2013–

2015...........................................................................................................................................................................223

Appendix B: Technical Summary ...........................................................................................................................225

Appendix C: Publications, Data, and Other Materials Resulting from Data Collected Under This Study ........227
iii

C.1 Published peer-reviewed publications.............................................................................................................227

C.2 Peer-reviewed publications in review ..............................................................................................................227

C.3 Peer-reviewed publications in preparation ......................................................................................................227

C.4 Theses ............................................................................................................................................................227

C.5 Conference presentations ...............................................................................................................................228

C.6 Invited seminars ..............................................................................................................................................230

C.7 Data releases ..................................................................................................................................................231

C.8 Data releases in preparation ...........................................................................................................................231
iv

List of Figures

Figure 2.1. Map of colony locations of brown pelicans fitted with GPS transmitters, Gulf of Mexico, 2013–2015........43

Figure 2.2. Location of brown pelican colonies sampled, Gulf of Mexico, 2013–2015. ................................................44

Figure 2.3. Positioning of GPS transmitter and harness. .............................................................................................45

Figure 3.1. Deployment duration and behavioral states of GPS-tagged brown pelicans, GOM, 2013–2016. ..............60

Figure 3.2.a. GPS locations of brown pelican originally captured at breeding colonies in the eastern, central, and 

western planning areas of the northern GOM, 2013–2016, eastern planning area......................................................61

Figure 3.2.b. GPS locations of brown pelican originally captured at breeding colonies in the eastern, central, and 

western planning areas of the northern GOM, 2013–2016, central planning area. ......................................................62

Figure 3.2.c. GPS locations of brown pelican originally captured at breeding colonies in the eastern, central, and 

western planning areas of the northern GOM, 2013–2016, western planning area. ....................................................63

Figure 3.3.a. Change in activity pre- and post-tagging for brown pelicans in a captive holding facility, San Pedro, 

California, 2015, state behaviors (percent time)...........................................................................................................64

Figure 3.3.b. Change in activity pre- and post-tagging for brown pelicans in a captive holding facility, San Pedro, 

California, 2015, instant events (frequency..................................................................................................................64

Figure 3.4. Percentage time spent by brown pelicans in different behavioral states 1–3 days after capture in field 

trials, GOM, 2013–2014...............................................................................................................................................65

Figure 3.5.a. Influence of capture location, handling time, and sex on probability of successful breeding in GPStagged adult pelicans, GOM, 2013–2014, capture location..........................................................................................66

Figure 3.5.b. Influence of capture location, handling time, and sex on probability of successful breeding in GPStagged adult pelicans, GOM, 2013–2014, handling time..............................................................................................66

Figure 3.5.c. Influence of capture location, handling time, and sex on probability of successful breeding in GPStagged adult pelicans, GOM, 2013–2014, sex. ............................................................................................................67

Figure 3.6.a. Seasonal GPS locations of brown pelican originally captured at breeding colonies in the northern GOM, 

2013–2016, during the breeding season......................................................................................................................68

Figure 3.6.b. Seasonal GPS locations of brown pelican originally captured at breeding colonies in the northern GOM, 

2013–2016, during the non-breeding season...............................................................................................................69

Figure 3.7.a. Brown pelican breeding home ranges by colony for the eastern, central, and western breeding planning 

areas of the northern GOM, 2013–2016, captured in the eastern planning area. ........................................................70

Figure 3.7.b. Brown pelican breeding home ranges by colony for the eastern, central, and western breeding planning 

areas of the northern GOM, 2013–2016, captured in the central planning area. .........................................................71

Figure 3.7.c. Brown pelican breeding home ranges by colony for the eastern, central, and western breeding planning 

areas of the northern GOM, 2013–2016, captured in the western planning area.........................................................72

Figure 3.8.a. Mean home range areas of breeding adult brown pelicans at each study colony, Northern GOM, 2013–

2016, 50% kernel. ........................................................................................................................................................73

Figure 3.8.b. Mean home range areas of breeding adult brown pelicans at each study colony, Northern GOM, 2013–

2016, 95% kernel. ........................................................................................................................................................73

Figure 3.9.a. Relationships of brown pelican migratory characteristics between summer and winter home ranges to 

colony size, Northern GOM 2013–2016, proportion of migrants. .................................................................................74

Figure 3.9.b. Relationships of brown pelican migratory characteristics between summer and winter home ranges to 

colony size, Northern GOM 2013–2016, average distance from colony during winter.................................................74

Figure 3.10.a. Differences in body size between migratory behavior of brown pelicans birds, and within sexes, 

Northern GOM 2013–2016, all tracked birds................................................................................................................75

Figure 3.10.b. Differences in body size between migratory behavior of brown pelicans birds, and within sexes, 

Northern GOM 2013–2016, within sexes. ....................................................................................................................75

Figure 3.11. Annual 95% kernel density estimates for locations of brown pelicans originally captured at breeding 

colonies in the Northern GOM, 2013–2016..................................................................................................................76
v

Figure 3.12. Migration routes of brown pelicans in the Tehuantepec Isthmus, Mexico, 2013–2015. ...........................77

Figure 4.1. Niche center and breadth of resident and transient behavioral states of brown pelicans on measured 

habitat variables in the GOM, 2013–2016....................................................................................................................82

Figure 4.2. Annual patterns of strength and direction of selection by brown pelicans on measured habitat variables, 

GOM, 2013–2016.........................................................................................................................................................83

Figure 4.3. Annual patterns of strength and direction of selection by brown pelicans on seasonally varying habitat 

variables by planning area, GOM, 2013–2016.............................................................................................................84

Figure 4.4. Suitability scores of available brown pelican habitats in the GOM, based on Mahalanobis distances. ......85

Figure 4.5. Levels of monthly overlap between preferred brown pelican habitat and surface pollution concentrations, 

GOM. ...........................................................................................................................................................................86

Figure 4.6.a. Overlap between year-round brown pelican locations (weighted by movement state) and surface 

pollutant concentrations, GOM, 2013–2016: all individuals and all weights.................................................................87

Figure 4.6.b. Overlap between year-round brown pelican locations (weighted by movement state) and surface 

pollutant concentrations, GOM, 2013–2016: individuals separated by planning area and weighted by 1 and 0.5.......88

Figure 4.6.c. Overlap between year-round brown pelican locations (weighted by movement state) and surface 

pollutant concentrations, GOM, 2013–2016: individuals separated by sex and weighted by 1 and 0.5. ......................89

Figure 5.1.a. Distribution of individual measurements of BCI and feather CORT at 3–4 w post-hatch for brown pelican 

nestlings in the Northern GOM, 2013–2015: BCI.........................................................................................................98

Figure 5.1.b. Distribution of individual measurements of BCI and feather CORT at 3–4 w post-hatch for brown pelican 

nestlings in the Northern GOM, 2013–2015: feather CORT.........................................................................................98

Figure 5.2.a. Relationship between brown pelican nest productivity, body condition index, and CORT of nestling, and 

nest elevation in the Northern GOM, 2014–2015: mean nest productivity. ................................................................100

Figure 5.2.b. Relationship between brown pelican nest productivity, body condition index, and CORT of nestling, and 

nest elevation in the Northern GOM, 2014–2015: mean body condition index. .........................................................100

Figure 5.2.c. Relationship between brown pelican nest productivity, body condition index, and CORT of nestling, and 

nest elevation in the Northern GOM, 2014–2015: mean CORT concentration of nestlings. ......................................101

Figure 5.3.a. Correlation of mean brown pelican nest productivity to chick body condition index and feather CORT for 

colonies in the Northern GOM, 2014–2015: chick body condition index. ...................................................................101

Figure 5.3.b. Correlation of mean brown pelican nest productivity to chick body condition index and feather CORT for 

colonies in the Northern GOM, 2014–2015: feather CORT........................................................................................102

Figure 5.4.a. Relationship of colony-wide mean brown pelican nestling feather CORT to probability of survival to 

fledge and post-dispersal survival in the Northern GOM, 2014–2015: probability of survival to fledge......................103

Figure 5.4.b. Relationship of colony-wide mean brown pelican nestling feather CORT to probability of survival to 

fledge and post-dispersal survival in the Northern GOM, 2014–2015: probability of post-dispersal survival. ............103

Figure 5.5.a. Brown pelican nestling CORT levels, and body condition index, Northern GOM, 2013–2015: CORT 

levels..........................................................................................................................................................................104

Figure 5.5.b. Brown pelican nestling CORT levels, and body condition index, Northern GOM, 2013–2015: BCI. .....104

Figure 5.6.a. Relationship of brown pelican energy provisioning rate to chick health parameters BCI and feather 

CORT by colony in the Northern GOM, 2014–2015: BCI...........................................................................................105

Figure 5.6.b. Relationship of brown pelican energy provisioning rate to chick health parameters BCI and feather 

CORT by colony in the Northern GOM, 2014–2015: feather CORT...........................................................................105

Figure 6.1. Percentage of Gulf menhaden in the diets of brown pelican nestlings, Northern GOM, 2013–2015........112

Figure 6.2. Proportion of biomass of brown pelican nestling diets represented by all prey species other than Gulf 

menhaden, Northern GOM, 2014–2015.....................................................................................................................113

Figure 6.3. Mean energy densities of common prey items in the diet of brown pelican nestlings, Northern GOM, 

2014–2015. ................................................................................................................................................................114

Figure 6.4.a. Comparison of average meal mass, provisioning rate, and energy density of meals between brown 

pelican colony sites, Northern GOM, 2014–2015: meal mass. ..................................................................................115

Figure 6.4.b. Comparison of average meal mass, provisioning rate, and energy density of meals between brown 

pelican colony sites, Northern GOM, 2014–2015: provisioning rate. .........................................................................115
vi

Figure 6.4.c. Comparison of average meal mass, provisioning rate, and energy density of meals between brown 

pelican colony sites, Northern GOM, 2014–2015: energy density of meals...............................................................116

Figure 6.5.a. Drivers of between-colony variation in mean values of provisioning metrics and coefficients of variation 

for brown pelican colonies, Northern GOM, 2014–2015: mean values of provisioning metrics..................................116

Figure 6.5.b. Drivers of between-colony variation in mean values of provisioning metrics and coefficients of variation 

for brown pelican colonies, Northern GOM, 2014–2015: coefficients of variation. .....................................................117

Figure 6.6.a. Non-metric multidimensional scaling plots showing the distribution of species composition of individual 

meals collected from brown pelican nestlings, Northern GOM, 2014–2015: meal delivery rate.................................118

Figure 6.6.b. Non-metric multidimensional scaling plots showing the distribution of species composition of individual 

meals collected from brown pelican nestlings, Northern GOM, 2014–2015: mass. ...................................................119

Figure 6.6.c. Non-metric multidimensional scaling plots showing the distribution of species composition of individual 

meals collected from brown pelican nestlings, Northern GOM, 2014–2015: energy density......................................120

Figure 6.7. Linear relationship between energy provisioning rate and biomass provisioning rate, and nest productivity 

at brown pelican nesting colonies, Northern GOM, 2014–2015. ................................................................................121

Figure 6.8. Comparison of lipid content and energy density of forage fish in this study with published values from 

other regions. .............................................................................................................................................................122

Figure 7.1.a Relationships among independent variables used in modeling blood analytes of adult brown pelicans 

breeding in the Northern GOM, 2013–2015, BCI for female and male pelicans. .......................................................163

Figure 7.1.b. Relationships among independent variables used in modeling blood analytes of adult brown pelicans 

breeding in the Northern GOM, 2013–2015, home range size...................................................................................163

Figure 7.2.a. Box plots of blood analytes of adult brown pelicans breeding in the Northern GOM, in relation to 

planning area, 2013–2015, electrolytes, calcium, potassium, and sodium.................................................................164

Figure 7.2.b. Box plots of blood analytes of adult brown pelicans breeding in the Northern GOM, in relation to 

planning area, 2013–2015: enzymes: alanine aminotransferase, aspartate aminotransferase, creatine 

phosphokinase, lipase................................................................................................................................................165

Figure 7.2c. Box plots of blood analytes of adult brown pelicans breeding in the Northern GOM, in relation to planning 

area, 2013–2015, metabolites: creatinine, total protein..............................................................................................166

Figure 7.2.d. Box plots of blood analytes of adult brown pelicans breeding in the Northern GOM, in relation to 

planning area, 2013–2015: plasma proteins: albumin, alpha 1 globulin, beta globulin, ratio albumin:globulin...........167

Figure 7.3. Body condition index of adult brown pelicans breeding in the Northern GOM, in relation to four blood 

analytes, 2013–2015..................................................................................................................................................168

Figure 7.4.a. Sex of adult brown pelicans breeding in the Northern GOM, in relation to seven blood analytes, 2013–

2015: metabolites.......................................................................................................................................................169

Figure 7.4.b. Sex of adult brown pelicans breeding in the Northern GOM, in relation to seven blood analytes, 2013–

2015: Electrolyte (n = 1), Enzyme (n = 1), Lipid (n = 1)..............................................................................................170

Figure 7.5.a. Home range size (50% core area) of adult brown pelicans breeding in the Northern GOM, in relation to 

seven blood analytes: 2013–2015, enzymes. ............................................................................................................171

Figure 7.5.b. Home range size (50% core area) of adult brown pelicans breeding in the Northern GOM, in relation to 

seven blood analytes, 2013–2015: metabolites and lipids. ........................................................................................172

Figure 7.6.a. Concentrations of white blood cells of adult brown pelicans breeding in the Northern GOM, 2013–2015: 

count of monocytes for males and females................................................................................................................173

Figure 7.6.b. Concentrations of white blood cells of adult brown pelicans breeding in the Northern GOM, 2013–2015: 

count of all white blood cells by planning area...........................................................................................................173

Figure 7.6.c. Concentrations of white blood cells of adult brown pelicans breeding in the Northern GOM, 2013–2015: 

count of heterophils by planning area. .......................................................................................................................174

Figure 7.6.d. Concentrations of white blood cells of adult brown pelicans breeding in the Northern GOM, 2013–2015: 

count of monocytes in relation to BCI for females and males. ...................................................................................174

Figure 7.6.e. Concentrations of white blood cells of adult brown pelicans breeding in the Northern GOM, 2013–2015: 

count of lymphocytes in relation to size of home range (50% core area; km2)...........................................................175

Figure 7.6.f. Concentrations of white blood cells of adult brown pelicans breeding in the Northern GOM, 2013–2015: 

packed cell volume (%) for females and males..........................................................................................................175
vii

Figure 7.7. Box plots of body condition index of brown pelican chicks in the Northern GOM, in relation to planning 

area of study, 2014–2015. .........................................................................................................................................176

Figure 7.8.a. Box plots of blood analytes of adult brown pelican chicks in the Northern GOM, in relation to planning 

area, 2014–2015: blood gas (CO2) and electrolytes and minerals (calcium, sodium). ..............................................177

Figure 7.8.b. Box plots of blood analytes of adult brown pelican chicks in the Northern GOM, in relation to planning 

area, 2014–2015: enzymes: alanine aminotransferase, aspartate aminotransferase, lactate dehydrogenase, amylase.

...................................................................................................................................................................................178

Figure 7.8.c. Box plots of blood analytes of adult brown pelican chicks in the Northern GOM, in relation to planning 

area, 2014–2015: lipids (triglycerides) and metabolites (blood urea nitrogen, creatinine, glucose). ..........................179

Figure 7.8.d. Box plots of blood analytes of adult brown pelican chicks in the Northern GOM, in relation to planning 

area, 2014–2015: plasma proteins: albumin, alpha 1 globulin, gamma globulin........................................................180

Figure 7.9.a. Body condition index of brown pelican chicks in the Northern GOM, in relation to eight blood analytes, 

2014–2015: potassium, amylase, aspartate aminotransferase, and creatinine phosphokinase.................................181

Figure 7.9.b. Body condition index of brown pelican chicks in the Northern GOM, in relation to eight blood analytes, 

2014–2015: triglycerides, low density lipoprotein cholesterol, alpha 1 globulin, and CORT.......................................182

Figure 7.10. Count of PAHs by molecular weight from blood samples of adult brown pelicans breeding in the Northern 

GOM, 2013–2015.......................................................................................................................................................183

Figure 7.11.a. Summation of PAHs by individual from blood samples of adult brown pelicans breeding in the Northern 

GOM, 2013–2015: total PAH concentration (ng g-1; wet weight) of parent and alkylated PAHs in blood samples....184

Figure 7.11.b. Summation of PAHs by individual from blood samples of adult brown pelicans breeding in the Northern 

GOM, 2013–2015: Box plots of PAH concentration (ng g-1; wet weight) of parent and alkylated PAHs in blood 

samples......................................................................................................................................................................185

Figure 7.12. Concentrations of PAHs by planning area from blood samples of adult brown pelicans breeding in the 

Northern GOM, 2013–2015........................................................................................................................................186

Figure 7.13. Count of PAHs by molecular weight from samples of feathers of adult brown pelicans breeding in the 

Northern GOM, 2013–2015........................................................................................................................................187

Figure 7.14.a. Summation of PAHs by individual from feather samples of adult brown pelicans breeding in the 

Northern GOM, 2013–2015: total PAH concentration (ng g-1; dry weight) of parent and alkylated PAHs from adult 

feathers......................................................................................................................................................................188

Figure 7.14.b. Summation of PAHs by individual from feather samples of adult brown pelicans breeding in the 

Northern GOM, 2013–2015: box plots of PAH concentration (ng g-1; dry weight) of parent and alkylated PAHs from 

adult feathers. ............................................................................................................................................................189

Figure 7.15. Concentrations of Parent PAHs by migration class from feather samples of adult brown pelicans 

breeding in the Northern GOM, 2013–2015...............................................................................................................190

Figure 7.16. Count of PAHs by molecular weight from samples of feathers of brown pelican chicks in the Northern 

GOM, 2014–2015.......................................................................................................................................................191

Figure 7.17.a. Summation of PAHs by individual from feather samples of brown pelican chicks in the Northern GOM, 

2014–2015: total PAH concentration (ng g-1; dry weight) of parent and alkylated PAHs in feathers of chicks. ..........192

Figure 7.17.b. Summation of PAHs by individual from feather samples of brown pelican chicks in the Northern GOM, 

2014–2015: box plots of PAH concentration (ng g-1; dry weight) of parent and alkylated PAHs in feathers of chicks.

...................................................................................................................................................................................193
viii

List of Tables

Table 2.1. Environmental Data Layers and Data Sources Used for Analysis of Annual Habitat Use by Adult Brown 

Pelicans .......................................................................................................................................................................37

Table 2.2. Surface Pollutant Data Layers and Data Sources Used for Risk Analysis ..................................................37

Table 2.3. Environmental andNest-based Variables Collected at Brown Pelican Nests on Gaillard and Cat islands, 

Alabama, 2017 and 2018.............................................................................................................................................38

Table 2.4. Models Assessed in Relation to Daily Survival Rates of Eggs and Broods of Brown Pelicans Breeding on 

Gaillard and Cat islands, Alabama, 2017 and 2018 .....................................................................................................39

Table 2.5. Blood Analytes from Samples Collected from Adult and Chick Brown Pelicans from Colonies throughout 

the Northern Gulf of Mexico, 2013–2015 .....................................................................................................................40

Table 2.6. Models Used in an Information Theoretic Approach to Assess Relationships among Blood Analytes and 

Independent Variables for Brown Pelican Adults and Chicks Sampled from Breeding Colonies in the Northern Gulf of 

Mexico, 2013–2015......................................................................................................................................................41

Table 2.7. Models Used in an Information Theoretic Approach to Assess Relationships among PAHs and 

Independent Variables for Brown Pelican Adults and Chicks Sampled from Breeding Colonies in the Northern Gulf of 

Mexico, 2013–2015......................................................................................................................................................42

Table 3.1. Colony Characteristics and Measurements of Tracked Adults Captured at Six Brown Pelican Breeding 

Colonies, GOM, 2013–2014.........................................................................................................................................55

Table 3.2. Nest Persistence and Apparent Breeding Success of GPS-tagged and Untagged Brown Pelicans, GOM, 

2013–2014 ...................................................................................................................................................................56

Table 3.3. Candidate Models for Breeding Success of Brown Pelicans, GOM, 2013–2014 ........................................57

Table 3.4. Generalized Linear Models, Model Weights and Top Model Evidence Ratios for Breeding Movements of 

Adult Brown Pelicans in the Northern GOM, 2013–2016 .............................................................................................58

Table 3.5. Model-averaged Coefficients and Importance Values for Individual Covariates across the 95% Confidence 

Set of Models for Each Breeding Movement Parameter ..............................................................................................59

Table 3.6. Generalized Linear Models, Model Weights and Top Model Evidence Ratios for Non-breeding Movements 

of Adult Brown Pelicans in the GOM, 2013–2016 ........................................................................................................59

Table 3.7. Model-averaged Coefficients and Importance Values for Individual Covariates across the 95% Confidence 

Set of Models for Each Non-breeding Movement Parameter.......................................................................................60

Table 4.1. Mean Pollutants Overlap for Observed Brown Pelican Locations in the Northern GOM, 2013–2016 .........82

Table 5.1 Candidate Models for Brown Pelican Nest Productivity and Nestling Survival in the Northern GOM, 2014–

2015.............................................................................................................................................................................96

Table 5.2. Top Performing Models (△ AIC ≤ 2.0) of Daily Survival Rates of Nests and Broods of Brown Pelicans 

Breeding in Mobile Bay, Alabama, 2017 and 2018 ......................................................................................................96

Table 5.3. Coefficient Estimates and 95% Confidence Intervals from Models of Daily Survival Rates of Nests and 

Broods of Brown Pelicans, Gaillard Island and Cat Island, Alabama, 2017–2018 .......................................................97

Table 6.1. Fish Species Occurring in the Diets of Brown Pelican Chicks in the Northern GOM, 2013–2015.............109

Table 6.2. Mean Values for Brown Pelican Nest Productivity, Chick Health Metrics, and Energy Provisioning Metrics 

by Colony in the Northern GOM, 2014–2015.............................................................................................................110

Table 6.3. ANOVA comparisons of nested models for colony-specific mean brown pelican nestling energy 

provisioning rates and nest productivity based on feeding rate, meal mass, and energy density of meals, Northern 

GOM, 2014–2015.......................................................................................................................................................111

Table 7.1. Statistical Values for Mass and Serum Chemistry for Adult Brown Pelicans Sampled from Breeding 

Colonies in the Northern GOM, 2013–2014...............................................................................................................141

Table 7.2. Number of Blood Samples and Blood Smears Collected from Adult and Chick Brown Pelican Chicks in the 

Northern GOM, 2013–2015........................................................................................................................................143

Table 7.3. Summary of Model Selection Results Assessing Relationships between Blood Analytes of Adult Brown 

Pelicans Breeding in the Northern GOM and Four Independent Variables, 2013–2014 ............................................144
ix

Table 7.4. Summary of Coefficient Estimates from Model Selection Process Assessing Relationships between Blood 

Analytes of Adult Brown Pelicans Breeding in the Northern GOM and Four Independent Variables, 2013–2014 .....146

Table 7.5. Statistical Values for Packed Cell Volume (PCV, Averaged over 3 Readings) and Leukocyte Profiles for 

Adult Brown Pelicans Sampled from Breeding Colonies in the Northern GOM, 2013–2014......................................147

Table 7.6. Statistical Values for Mass and Serum Chemistry for Chicks of Brown Pelicans Sampled from Breeding 

Colonies in the Northern GOM, 2013–2015...............................................................................................................148

Table 7.7. Summary of Model Selection Results Assessing Relationships between Blood Analytes of Brown Pelican 

Chicks in the Northern GOM and Two Independent Variables, 2013–2015...............................................................150

Table 7.8. Summary of Coefficient Estimates from Model Selection Process Assessing Relationships between Blood 

Analytes of Brown Pelican Chicks in the Northern GOM and Two Independent Variables, 2013–2014 ....................151

Table 7.9. Statistical Values for Packed Cell Volume (PCV, Averaged over 3 Readings) and Leukocyte Profiles for 

Chicks of Brown Pelicans Sampled from Breeding Colonies in the Northern GOM, 2013–2015 ...............................152

Table 7.10. Frequency of Detection of Individual PAHs from Blood of Adult Brown Pelicans Breeding in the Northern 

GOM, 2013–2014.......................................................................................................................................................153

Table 7.11. Frequency of Cccurrence of Parent and Alkylated PAHs by Planning Area and Sex from Blood of Adult 

Brown Pelicans Breeding in the Northern GOM, 2013–2014.....................................................................................154

Table 7.12. Frequency of Occurrence of Individual PAHs by Planning Area and Sex from Blood of Adult Brown 

Pelicans Breeding in the Northern GOM, 2013–2014 ................................................................................................155

Table 7.13. Model Results from Logistic and Gamma Regressions Assessing the Relationships between PAHs 

Detected in Blood of Adult Brown Pelicans Breeding in the Northern GOM and a Suite of Independent Variables, 

2013–2014 .................................................................................................................................................................156

Table 7.14. Frequency of Detection of Individual PAHs from Feathers of Adult Brown Pelicans Breeding in the 

Northern GOM, 2013–2014........................................................................................................................................157

Table 7.15. Frequency of Occurrence of Parent and Alkylated PAHs by State and Sex from Feathers of Adult Brown 

Pelicans Breeding in the Northern GOM, 2013–2014 ................................................................................................158

Table 7.16. Frequency of Occurrence of Individual PAHs by Planning Area and Sex from Feathers of Adult Brown 

Pelicans Breeding in the Northern GOM, 2013–2014 ................................................................................................158

Table 7.17. Model Results from Logistic and Gamma Regressions Assessing the Relationships between PAHs 

Detected in Feathers of Adult Brown Pelicans Breeding in the Northern GOM and a Suite of Independent Variables, 

2013–2014 .................................................................................................................................................................159

Table 7.18. Frequency of detection of individual PAHs from feathers of brown pelican chicks in the northern GOM, 

2014–2015. ................................................................................................................................................................160

Table 7.19. Frequency of Cccurrence of Parent and Alkylated PAHs by Planning Area from Feathers of Brown 

Pelican Chicks in the Northern GOM, 2014–2015......................................................................................................161

Table 7.20. Frequency of Occurrence of Individual PAHs by Planning Area from Feathers of Brown Pelican Chicks in 

the Northern GOM, 2014–2015..................................................................................................................................161

Table 7.21. Model Results from Logistic and Gamma Regressions Assessing the Relationships between PAHs 

Detected in Feathers of Brown Pelican Chicks in the Northern GOM and a Suite of Independent Variables, 2014–

2015...........................................................................................................................................................................162
x

List of Abbreviations and Acronyms

Short Form Long Form

A:G albumin-to-globulin ratio 

A1G Alpha 1 globulin

A2G Alpha 2 globulin

AIC Akaike's information criterion

ALT alanine aminotransferase

ANOVA analysis of variance

ASE accelerated solvent extraction

AST aspartate aminotransferase

BCI body condition index

BOEM Bureau of Ocean Energy Management

BPR biomass provisioning rate

BUN blood urea nitrogen

CBC complete blood count

CI confidence interval

CORT corticosterone

CPK creatine phosphokinase

CV coefficient of variation

DDT Dichlorodiphenyltrichloroethane

DOI US Department of the Interior

DSR daily survival rate

EPR energy provisioning rate

GGT Gamma glutamyl transferase

GLM generalized linear model

GMT Greenwich Mean Time

GOM Gulf of Mexico

GoMAMN Gulf of Mexico Avian Monitoring Network

GoMMAPPS Gulf of Mexico Marine Assessment Program for Protected Species

HDLc high density lipoprotein cholesterol

HMM hidden Markov model

HSD honestly significant difference 

JFH junk-food hypothesis

MRPP multi-response permutation procedure

NOAA National Oceanic and Atmospheric Administration

OMI outlying mean index

PAH polycyclic aromatic hydrocarbons

PC1 first principal component axis

PCA principal component analysis
xi

Short Form Long Form

PCR polymerase chain reaction

PCV packed cell volume

PTT platform transmitter terminal

SD standard deviation

SE standard error

sumALK sum of alkylated PAHs 

sumPAH sum of all PAHs 

sumPAR sum of parent PAHs 

UD utilization distribution

UPLC ultra-performance liquid chromatograph 

VLDLc very low density lipoprotein cholesterol

WBC white blood cells
xii

ACKNOWLEDGMENTS

This work was supported by the US Department of the Interior, Bureau of Ocean Energy Management

(BOEM), Environmental Studies Program, Washington, DC, through Inter-Agency Agreement Number 

M12PG00014 with the US Geological Survey and the South Carolina Cooperative Fish and Wildlife 

Research Unit. Special thanks to Jeff Gleason for his efforts conceptualizing and initiating this research. 

Dave Moran (BOEM) provided administrative and contract support. Additional funding and/or in-kind 

support was provided by a Sigma Xi Grant-in-Aid of Research awarded to Juliet Lamb, by the Oiled 

Wildlife Care Network for GPS tracking used in the assessment of effects of satellite tags on bird 

behavior, and by Clemson University. Field research was conducted with permission from the Clemson 

University Animal Care and Use Committee (2013-026). Permitting for field data collection was provided 

by the US Geological Survey Bird Banding Laboratory (22408), Texas Parks and Wildlife (SPR-0113-

005), Audubon Texas, The Nature Conservancy in Texas, Louisiana Department of Wildlife and Fisheries 

(LNHP-13-058 and LNHP-14-045), and the Florida Fish and Wildlife Conservation Commission (LSSC13-00005). All laboratory work for analysis of corticosterone took place at University of Portland, in 

collaboration with Kathleen O’Reilly, and Clemson University facilities. Genetic sexing of pelicans was 

conducted in collaboration with Amy MacMillan (Buffalo State University) and Ludovic Besaury 

(Clemson University). Plasma chemistry and hematology samples were analyzed at the Avian & Wildlife 

Laboratory, University of Miami, Miami, Florida. Samples for polycyclic aromatic hydrocarbons were 

analyzed at the University of Connecticut Center for Environmental Sciences and Engineering by 

Christopher Perkins and Anthony Provatas. Michael Hooper and John Rodgers provided advice and 

insight on collection and interpretation of polycyclic aromatic hydrocarbon (PAH) data. Christine Fiorello 

advised on interpretation of data from blood panels. Videos of pelican behaviour were recorded by Justin 

Cox of the One Health Institute, University of California, Davis. 

For assistance and logistical support with field research and laboratory activities, we thank Elizabeth 

Ford, Steve Desaivre, Tim and Peggy Wilkinson, Mark Dumesnil, Beau Hardegree, Amanda Hackney, 

Scott Walter, John Dindo, Caroline Poli, Ludovic Besaury, Tommy King, and technicians from Texas 

Audubon Society and The Nature Conservancy. The Dauphin Island Sea Lab and the Buccaneer Yacht 

Club provided assistance and support for research in Mobile Bay. Christine Fiorello, Michael Ziccardi, 

Kyra Mills-Parker, Robert McMorran, Richard Golightly, Laird Henkel, and the International Bird 

Rescue staff at the Los Angeles Oiled Bird Care and Education facilities assisted with research conducted 

in California associated with the assessment of tags on pelican behavior. Carolyn Wakefield and Brenna 

Byler provided administrative support, and Brenna Byler assisted with editing and layout of the final 

report. Katherine McFadden (now deceased) was originally a Co-PI on the project and developed the 

initial research plan for health and contaminants. We also acknowledge Lisa Ferguson, Robert Baldwin, 

Robert Suryan, Yoichiro Kanno, Pablo Yorio, Emily Weiser, Beth Ross, Anthony Diamond, John Dindo, 

Troy Farmer, Robert Ronconi, Don Lyons, Pete Tuttle, Brian Spears, Mary Ann Ottinger, Dave Moran, 

Maureen DeZeeuw, and Jeri Wisman for input at various stages of the project. 

The South Carolina Cooperative Fish and Wildlife Research Unit is jointly supported by the US 

Geological Survey, South Carolina DNR, and Clemson University. Any use of trade, firm, or product 

names is for descriptive purposes only and does not imply endorsement by the US Government.
13

1 Introduction

The number of marine wind and tidal energy (Pelc and Fujita 2002) developments and proposals (Pelc 

and Fujita 2002), as well as petroleum extractions (Freudenburg and Gramling 1994), is increasing to 

meet the growing global demand for energy. However, the rapid progress of energy extraction and 

development often outpaces the understanding of these actions’ effects on marine systems and their

organisms (Ward et al. 1979, Burke et al. 2012). Assessments of post-installation offshore energy projects 

document that effects on marine species, whether positive or negative, can be more significant than 

anticipated (Boesch and Rabalais 1987, Daan and Mulder 1996, Sammarco et al. 2004). Energy extraction 

can impact marine species directly (e.g., adult mortality) and indirectly through various pathways,

including: compromised condition from contaminants exposure, altered availability or distribution of 

prey, altered behavior, or reduced reproductive output (Alonso-Alvarez et al. 2007, Dean et al. 2017, 

Haney et al. 2014).

Marine birds are valuable and commonly used models for studying the impacts of threats on their 

environment, such as offshore development influences on the broader marine ecosystem (Furness and 

Greenwood 1993). Seabirds are relatively accessible compared to other marine vertebrates, are wideranging migrators, and their foraging behaviors increase the likelihood for interactions with energy 

installation (Wiese and Jones 2001). Seabirds also rely on a variety of above- and below-water habitats,

including both terrestrial breeding colonies and pelagic foraging grounds (Hunt 1990, Pinaud and 

Weimerskirch 2005). As top-level marine predators they are particularly vulnerable to bioaccumulation of 

contaminants (Walker 1990, Pérez et al. 2008) and may provide indications of perturbations at lower 

trophic levels (Thompson et al. 1998, Wiese and Jones 2001). Understanding the effects of existing 

development and predicting the impacts of future development on seabirds requires a thorough 

understanding of seabird population dynamics, behavior, physiology, and habitat use under baseline 

conditions (Ballance 2007, Soanes et al. 2013, Jodice et al. 2019). However, such information is often not 

collected until after development or contamination has altered baseline processes. Also, the direct 

influence of anthropogenic stressors on demographic parameters in the marine environment varies widely 

and can be difficult to estimate (Burger 1993, Uhlmann et al. 2005).

The Gulf of Mexico (GOM) contains a high density of oil and gas infrastructure and coastal development. 

It also has a rich assemblage of nearshore seabirds, pelagic seabirds, wading birds, migratory waterfowl, 

and shorebirds (Duncan and Havard 1980). The region is of year-round importance to seabirds, including 

local breeding populations and breeders from distant locations that winter along the Gulf Coast (Mikuska

et al. 1998, Montevecchi et al. 2012, Haney et al. 2014, Jodice et al. 2019). Many terrestrial areas of 

known importance to breeding, migrating, and wintering waterbirds have been designated for protection 

at state and federal levels. However, few marine protected areas have been designated in the GOM, and 

much of the marine environment there, including offshore foraging and migratory habitat of seabirds, is 

open to oil development, ship traffic, fishing, and contaminants release (Coleman et al. 2004, Davis et al.

2000).

Because of its distribution patterns, behavior, and known sensitivity to chemical and oil contaminants

exposure (Blus 1982, King et al. 1985, Shields 2014), the brown pelican (Pelecanus occidentalis) is 

potentially a good indicator of species-level impacts from interactions with coastal and marine 

development (Wilkinson et al. 1994, Jodice et al. 2019). The species is widespread throughout the 

Northern GOM and common during all phases of the annual cycle. Brown pelicans nest along the Gulf 

coast from Corpus Christi Bay, Texas through southwestern Florida. During the non-breeding season, the 

species can be found throughout the Northern GOM as well as along the Yucatan Peninsula, Cuba, and as 

far south as Guatemala. Because of the spatial extent of this annual range, the species is exposed to a 
14

substantial array of environmental and anthropogenic stressors that may influence its health, habitat use, 

and survival. The species is generally regarded by managers and stakeholders in the region as a good 

indicator of ecosystem health for estuarine and nearshore habitats. For example, brown pelicans were 

recently included as a priority species for monitoring in the Seabird Monitoring Plan for the Gulf of 

Mexico Avian Monitoring Network (GoMAMN) (Jodice et al. 2019). Despite the species’ long history as 

a focus for conservation and restoration efforts, much of the information required to understand pelican 

population dynamics and habitat requirements (i.e., adult and fledgling mortality, dispersal, site fidelity, 

diet composition, foraging behavior, migration patterns, and nonbreeding habitat use) remains unknown 

or is poorly understood (Briggs et al. 1981 for habitat use, Schreiber and Mock 1988 for survival rates, 

Wood et al. 1995 for Florida colony site fidelity, and Shields 2014, Jodice et al. 2019 for addressing 

multiple data gaps).

In this study, we used several unique research avenues to address questions regarding movement, habitat 

use, physiology, and reproductive ecology of brown pelicans. Our research encompassed six principal 

objectives: (1) assessing reproductive ecology; (2) assessing baseline habitat use by the species in this 

region, particularly individual and regional variability; (3) assessing baseline health and exposure to 

contaminants in this region, particularly individual and regional variability; (4) predicting overlap of 

pelicans with anthropogenic risk factors; (5) understanding pathways by which changes to adult 

movement patterns might influence reproductive ecology and how to best measure such effects in wild 

populations; and (6) assessing movement ecology in the context of interactions with key prey resources. 

The remainder of the report is organized with a common introduction (Chapter 1), common methods 

(Chapter 2), and overall summary (Chapter 8). Chapter 3 through Chapter 7 are focused on specific data 

streams and objectives. Within chapters 3 through 7, the structure includes a brief introduction to the 

topic, followed directly by a combination of individual results and interpretation. Figures and tables 

appear at the end of each chapter. 

1.1 Baseline Habitat Use

Animals use various habitats for different needs, including: foraging, sheltering from predators, 

thermoregulating, raising young, moving among patches, and migrating stopovers (Börger et al. 2008, 

Morrison et al. 2012). Each need requires specific habitat characteristics and features; thus an animal’s 

interaction with its environment varies depending on its location on the landscape and its fine-scale 

movement and behavioral patterns (Garthe and Hüppop 2004). Seabirds are unique among many avian 

species in that they regularly require terrestrial and aquatic habitats, although the extent to which each is 

used varies between nearshore and pelagic seabirds. For example, in wide-ranging pelagic and semipelagic seabirds, habitat use typically changes between the breeding season, when birds are central-place 

foragers based in terrestrial colonies, and the nonbreeding season, when birds rely primarily on marine 

habitats (Weimerskirch and Wilson 2000). Within each stage of the breeding cycle, habitat use also 

depends on individual characteristics (Bearhop et al. 2006), phenology (Catry et al. 2009), colony size 

and location (Lewis et al. 2001), and environmental features (Tew Kai et al. 2009). These factors all 

contribute to variation in individual energy requirements, resulting in differences in foraging strategies 

and habitat preferences (Daunt et al. 2006, Phillips et al. 2009).

Compared to pelagic species, nearshore seabirds, such as brown pelicans, generally occupy smaller 

foraging ranges that extensively overlap human-dominated marine and coastal areas year-round (Thaxter 

et al. 2012). These smaller areas contain a higher diversity of habitat features and prey species 

assemblages (Becker and Bessinger 2003) and respond to different oceanographic processes than do large 

marine ecosystems (Gray 1997). Despite these habitat differences, many of the same individual, colonial, 

and environmental factors that influence habitat choice in pelagic species also operate within nearshore 

seabird populations (e.g., Erwin 1977, Suryan et al. 2000). However, the role of density-dependent prey 

depletion in determining movement patterns has been well-established in pelagic seabirds (e.g., Ainley et 
15

al. 2004, Ford et al. 2007, Ballance et al. 2009), but has received little study in nearshore seabirds. 

Additionally, partial migration (Lack 1944) influences individual differences in year-round seabird 

movements but has received little study in nearshore systems. 

A principal goal of our work was to establish a framework for understanding pelican movement patterns 

under baseline conditions (i.e., not immediately associated with a recent disturbance event), including 

preferred habitat characteristics, sources of individual variation in movement, and dispersal and habitat 

selection throughout the year (breeding and the lesser studied non-breeding periods). This work provides 

an important comparison point for studying the effects of any future changes to the GOM marine 

environment on brown pelican movements and energetic expenditure, as well as addressing key 

ecological questions in relation to the spatial ecology of nearshore seabirds.

1.2 Risk Exposure

Evaluating the effects of environmentally heterogeneous stressors on mobile wildlife requires 

understanding of the spatial and temporal overlap between individuals and threats as well as the extent of 

risk individuals encounter in relation to adverse effects based upon their habitat use and behavior 

(Desholm and Kahlert 2005, Jaeger et al. 2005, Beaudry et al. 2010). Increases in the spatial and temporal 

resolution of individual tracking technologies have resulted in a shift toward individual-based analysis of 

habitat requirements (Hebblewhite and Haydon 2010); however, habitat assessments derived from 

individual tracking data often incorporate only presence or absence across landscapes and do not account 

for behavior (Tremblay et al. 2009). Nearshore seabirds experience higher levels of human disturbance 

and habitat modification of breeding, resting, and foraging grounds than pelagic species (Croxall et al.

2012). Habitat features that concentrate nearshore seabirds and their prey may also concentrate risk 

factors such as pollutants, bycatch, and anthropogenic disturbance. Temporal variation in habitat needs 

and movement patterns can significantly contribute to the likelihood of risk exposure and the degree to 

which risk factors impact individuals and populations (Beaudry et al. 2010). The effects of environmental 

perturbations on seabirds depend on temporal factors (e.g., breeding stage) that influence their behavior 

and use of affected areas (Eppley and Rubega 1990, Montevecchi et al. 2012).

Due to its large size and persistence along human-dominated coastlines, the brown pelican represents one 

of the most high-profile nearshore seabirds for much of the GOM and southeastern US. The species was 

reduced to near-extinction by exposure to dichlorodiphenyltrichloroethane (DDT) during the midtwentieth century (McNease et al. 1992) and continues to experience high mortality rates during oil spills 

(USFWS 2011, DWH NRDAT 2016). Despite these factors, baseline assessments of health and exposure 

to petroleum-based contaminants are minimal for the species in the region (Jodice et al. 2019). 

Furthermore, within the GOM, the source of data on brown pelican movements are from observations of a 

small number of marked and banded birds across limited geographic areas (Schreiber and Mock 1988, 

Stefan 2008, King et al. 2013, Walter et al. 2014), despite their prevalence throughout the region.

The discrete nature of existing data makes it difficult to reliably predict how, or at what spatial and 

temporal scales, individuals may interact with current or future acute and chronic contamination from oil 

spills or other pollution sources. For example, until recently, home range size for the species was based on 

limited data from VHF telemetry or inferred from observations of individuals foraging in relation to the 

nearest colony, neither of which provide the spatial or temporal resolution needed to assess risk exposure. 

Therefore, if significant winter and migratory ranges of pelicans from different breeding colonies overlap 

with each other, and if these areas also overlap with contaminants (i.e., spilled oil) then relatively 

localized oiling events in certain GOM areas during the non-breeding season could affect birds from 

multiple colonies and result in population-level impacts. Moreover, though efforts to restore injured 

populations following stressor events, such as oil spills, generally target colony sites, most threats 

associated with marine energy development (e.g., acute or chronic spills) also affect offshore foraging 
16

grounds and therefore the risk to individuals and populations extends across multiple ecosystems

(Campagna et al. 2011). Developing reference ranges for various health metrics (e.g., hematology and 

serum chemistry) will improve management, conservation, and response activities (e.g., responses to oil 

spoils, hurricanes) for the species. Understanding the year-round overlap of brown pelicans with risk 

factors and contaminants (e.g., polycyclic aromatic hydrocarbons, PAHs) throughout the region could 

improve targeted mitigation efforts. Affected at-sea habitats could be linked to individual breeding 

colonies and improvements can be made in predicting which portions of the GOM-wide metapopulation 

are likely to be affected by contamination events.

1.3 Ecology and Physiology of Reproduction

Brown pelicans have been a species of high conservation concern in the GOM for decades (Schreiber 

1980, Nelson 2005). Following the 2010 Deepwater Horizon (DWH) oil spill, which caused widespread 

mortality of pelicans and other coastal birds (Haney et al. 2014), restoration activities for seabirds 

increased across the Northern GOM. However, to be successful, continued restoration efforts will require 

data beyond levels currently available. For example, detailed data are required on the relationship 

between environmental conditions at the nest site and reproductive success, which can be affected by 

numerous characteristics (e.g., density of breeders, exposure to inclement weather, vegetation 

characteristics, landscape features, and weather) (Robinson and Dindo 2011, Walter et al. 2013, Lamb 

2016). Reproductive output may also be limited by environmental variables beyond the nest site or even 

the nesting island. For example, weather and stochastic events, such as storms and flooding, can decrease 

egg and chick survival either directly (e.g., through overwash) or indirectly (e.g., through exposure) 

(Ramos et al. 2002, Frederiksen et al. 2006, Sherley et al. 2012, Bonter et al. 2014). Understanding which 

site-specific factors contribute to the success of nests will inform restoration efforts and better allow 

projects to maximize population-level impacts for the focal species. 

Impacts of acute or chronic environmental stressors on wildlife are typically quantified directly using 

mortality rates derived from carcass counts (Piatt et al. 1990, Burger 1993) or multi-year census data 

(Wiens et al. 1996, Yaukey 2012). Data are subsequently incorporated into demographic models to 

estimate the population-level effects of stressors (Haney et al. 2014). In addition to causing immediate 

mortality, stressors can impact seabirds sublethally through secondary pathways, including: reduced 

habitat quality (Cheng et al. 2009, Williams et al. 2010), compromised physical condition (Romero and 

Wikelski 2001), physiological and genetic modifications (Møller and Mousseau 2011), and/or increased 

susceptibility to existing threats (e.g., disease or environmental fluctuation) (Balseiro et al. 2005, 

Whitehead 2013). Many indirect and sublethal stressors subsequently impact demographic processes by 

reducing reproductive fitness in surviving individuals (Krebs and Burns 1977, Peterson 2001), but are

often not explicitly or adequately addressed in demographic calculations and projections. Moreover, the 

breeding process itself is likely to compound impacts of environmental stress as reductions in the adult 

condition and habitat suitability make it less likely for breeders to meet the energetic demands of territory 

defense, gestation, and provisioning young (Butler et al. 1988, Gannon and Willig 1994). Demographic 

models that do not accurately incorporate secondary effects of environmental stressors on breeding 

success and recruitment cannot accurately predict or quantify the complex population-level impacts of 

environmental perturbations (Peterson et al. 2003, Haney et al. 2014).
17

Despite widespread understanding of the capacity of sublethal environmental stress to negatively affect 

reproduction and recruitment, it can be difficult to determine the most appropriate endpoints for 

measuring these effects (Smits and Fernie 2013). There must be a pre-existing understanding of the level 

of variation in reproductive parameters expected under baseline conditions for post-disturbance 

measurements to be informative (Teal and Howarth 1984, Velando et al. 2005). Such data are not always 

available for species of interest before catastrophic events (Eppley 1992). Moreover, the collection of 

reproductive data can be time- and labor-intensive and can involve researcher disturbance, which may 

make it difficult to implement rapidly in the wake of an unexpected external change (Wiens et al. 1984). 

Snapshot measures of reproductive health (Jakob et al. 1996, Benson et al. 2003), which can be collected 

during a single visit with minimal disturbance, allow for rapid data collection across large areas after 

disturbance events; however, the relationship of such snapshot measurements to demographic parameters 

of interest (e.g., reproductive success) must be evaluated to select appropriate metrics.

Stress hormone production offers a broadly applicable metric for assessing the impacts of environmental 

stressors on free-living wildlife populations (Romero and Wikelski 2001). Corticosterone (CORT) is the 

principal glucocorticosteroid stress hormone in birds, rodents, reptiles, and amphibians, and is frequently 

used as a measure of individual stress responses to environmental conditions and disturbance (Marra and 

Holberton 1998, Kitaysky et al. 2001, Blas et al. 2005, Bonier et al. 2006, Almasi et al. 2009). Stress 

hormones are upregulated in response to perceived stressors, prompting short-term behavioral and 

physiological modifications (McEwen et al. 1997). Over time, however, chronic elevation in CORT levels 

in response to chronic stress may negatively affect organism health by compromising 

immunosuppression, growth rates, body condition, and behavior (Sapolsky et al. 2000). CORT levels can 

be complicated by individual physiology (Angelier et al. 2007) and may change over life stages (Williams 

et al. 2008, Bonier et al. 2009). Within avian taxa, measuring CORT in altricial young controls for some 

of these influences because their exposure to stress is localized and their range of behavioral responses is 

restricted (Kitaysky et al. 2003, Eggert et al. 2010). Elevated stress in early life can result in severe 

developmental consequences (Kitaysky et al. 2003, Müller et al. 2009, Spencer et al. 2008, Butler et al. 

2009). Therefore, the CORT stress response can be used to test whether chick development, condition, 

growth, and/or survival are affected by acute and/or chronic environmental stress during nestling 

development. CORT stress response can also explore mechanisms underlying survival, reproductive 

performance, and population dynamics (Kitaysky et al. 2010).

Though CORT levels in blood plasma can be elevated by short-term factors (e.g., stress resulting from 

capture; Love et al. 2003, Romero and Reed 2005), CORT in avian feathers provides a more sustained 

record of stress levels over days or weeks (Bortolotti et al. 2008, Harms et al. 2010). Feather CORT

measurements allow for a direct comparison of nestling condition between different breeding habitats, 

where variations in nutrition, contamination, predation, and parental attendance may affect chronic chick 

stress even if no physiological differences are apparent (Bortolotti et al. 2008, Harms et al. 2010). Recent 

laboratory and field studies have demonstrated that chronic nutritional stress elevates feather CORT 

levels in both captive and free-living seabirds (Will et al. 2015). We undertook a direct comparison of 

body condition index (BCI) with feather CORT as a predictor of fledging success and post-fledging 

survival. This information will help to create rapid evaluation metrics for brown pelicans and other 

seabirds following environmental perturbations and is already being considered as a monitoring tool by 

the Gulf of Mexico Avian Monitoring Network (Jodice et al. 2019).
18

1.4 Foraging Ecology

The ability of apex marine predators to survive and reproduce depends primarily on the production and 

availability of sufficient food resources at lower trophic levels to meet the energetic requirements of both 

adults and young (Frederiksen et al. 2006). Both the quantity and quality of available prey can influence 

survival, reproduction, and population dynamics in apex predators, and reductions in either prey 

availability or quality can affect demographic parameters (Trites and Donnelly 2003, Jodice et al. 2006, 

Hjernquist and Hjernquist 2010). A switch to nutrient-poor prey may cause reduced fitness even if 

abundant prey is available (Rosen and Trites 2000, Österblom et al. 2008). Both experimental (Rosen and 

Trites 2004, Romano et al. 2006) and field (Golet et al. 2000, Kadin et al. 2012, Cohen et al. 2014) 

studies have found that switching high-lipid prey for lower-energy alternatives can result in measurable 

reductions in breeding parameters, even when the amount and rate of delivery are unchanged. Most of 

these data come from cold-water systems, where prey species are likely to have higher lipid reserves on 

average than warm-water species (Stickney and Torres 1989). Few data are available from tropical 

systems (waters ≥ 23°C average temperature: Ballance and Pitman 1999), in which the relatively low 

variation in lipid levels among fish species may reduce the range of energetic values in prey species 

available to top predators.

Even in a prey community with limited interspecific variation in energy density, differences in prey 

quality may still exist. For example, the junk-food hypothesis posits that energy density, particularly as 

represented by lipid density, is positively related to productivity. Optimal foraging theory (MacArthur and 

Pianka 1966) takes into account the energy a predator obtains from prey and the energy it expends in 

finding, capturing, handling, and digesting prey. An optimal forager is expected to maximize the net 

energy gain, calculated as the difference between energy obtained from prey and energy expended in 

foraging. Thus, differences in both predator foraging strategies and prey behavior could result in variation 

in the amount of energy predators obtain from different prey types, even among prey species with similar 

energy content. Marine predators employ a wide variety of foraging strategies, which allow them to 

exploit different prey types and forage in different sections of the water column (Ashmole 1971, Spear 

and Ainley 1998). Tropical seabirds, most of which forage near the water’s surface, compete for limited 

prey resources using a variety of capture techniques including skimming, surface-plunging, surfaceseizing, plunge-diving, and, occasionally, pursuit-diving (Ballance and Pitman 1999). Though the various 

modifications of surface-feeding techniques allow some partitioning of prey, species at tropical latitudes 

do not partition prey species as extensively as high-latitude species that forage at a wider variety of depths 

and often specialize on different prey items. Thus, the definition of junk food should include not only the 

energy density of prey but also how readily prey can be captured given the foraging techniques used by 

the species of interest. Differences in availability between prey species reflect both abundance, which is 

an absolute measure, and accessibility, which can differ from predator to predator both within and among 

species.
19

Studies of brown pelicans in the tropical waters of the GOM have suggested reliance on a single prey 

species, Gulf menhaden (Brevoortia patronus), which can constitute over 95% of biomass in diet samples 

in the central Northern GOM (Arthur 1919). The Gulf menhaden is one of the most abundant forage fish 

species in the region and supports the second-largest fishery in the United States (Vaughan et al. 2007). 

Samples collected from eastern portions of the species’ GOM range, where menhaden are naturally less 

abundant than in the central and western portions of the GOM, show a decreasing trend in the 

predominance of menhaden in pelican diets (Fogarty 1981). Although this suggests that relative 

availability plays a role in the frequency of menhaden in pelican diets, it is unclear how or whether this 

underlying variation in diet composition affects demographic parameters, or how menhaden compares 

energetically to other available alternatives. Furthermore, pelicans may benefit from land- and ship-based 

supplemental feeding at fishing piers or trawlers (Wickliffe and Jodice 2010), although the extent of this 

behavior and its effect on nutritional status remains unclear. Because of the role of brown pelicans as an 

indicator species for assessing the effects of contamination and oil pollution in the region (Shields 2014), 

understanding underlying dietary and demographic variation provides a crucial reference point for 

quantifying the effects of environmental stressors. 
20

2 Methods

2.1 Study Area

2.1.1 Colony Selection

Research was conducted at pelican colonies within each of the BOEM planning areas (east, central, and 

west) within the Gulf of Mexico (GOM) (Figures 2.1 and 2.2). Study sites extended from the Florida 

panhandle in the east to the central coast of Texas in the west. We selected colonies within each of the 

three BOEM planning areas to sample among different levels of oil and gas development (central = most 

developed, east = least developed, and western = intermediate development between east and central).

We did not extend our research onto colonies in peninsular Florida due primarily to logistical constraints 

and to avoid adding additional ecological variability into the data (e.g., the addition of mangrove 

ecosystems). 

Individual tracking was conducted from seven colonies in the GOM. We selected 2–3 colonies each in the 

western (Texas), central (Louisiana, Mississippi, and Alabama), and eastern (Florida) planning areas of 

the GOM and selected 2–3 colonies each in the western (Texas), central (Louisiana, Mississippi, and 

Alabama), and eastern (Florida) planning areas of the GOM between 83° and 98° W and 27° and 31° N 

(Figure 2.1; hereafter, with respect to colonies and the coastal zone in general, the term “Northern GOM”

refers to this area). Within planning areas, colonies were 50–150 km (31–93 miles) apart, and colony 

groups in separate planning areas were 500–600 km (311–373 miles) apart. The number of breeding pairs 

at each study site was obtained from the most recent (i.e., 2013) colonial waterbird censuses for each 

planning area (Colibri Ecological Consulting and R. G. Ford Consulting, 2015, Texas Colonial Waterbird 

Survey1

). We also sampled and observed nestlings between 2013 and 2015. Colony locations varied 

between years but included nine colonies throughout the study area (Figure 2.2). 

We extracted environmental variables, including two fixed parameters (bathymetry and bottom substrate) 

and three seasonally-averaged parameters (salinity, sea surface temperature, and chlorophyll a) to 

compare underlying environmental conditions between colonies. Salinity, sea surface temperature, and 

chlorophyll a were measured at distances of 10, 20, 50, and 150 km from the colony, bounded by the 

coastline and up to 50 km offshore, sea surface temperature, and chlorophyll a were measured at distances 

of 10, 20, 50, and 150 km from the colony, bounded by the coastline and up to 50 km offshore. These 

distances were chosen post-hoc to GPS tracking to represent the range of movements we observed among 

individuals. We used a multivariate hierarchical clustering approach (K-means clustering; MacQueen 

1967) to compare environmental characteristics between sites, and tested the resultant clusters using 

multi-response permutation procedure (MRPP) on a Euclidean distance matrix (McCune and Grace 

2002). All statistical analyses were conducted in R (R Core Team 2014).

1 See the survey here: https://tpwd.texas.gov/huntwild/wild/wildlife_diversity/tcws/data.phtml
21

2.1.2 Data Collection Schedule

Between 26 April and 3 July 2013, we captured 60 adult pelicans that were breeding on colonies 

throughout the study area and equipped them with GPS transmitters. During the same period, we 

collected physical measurements and feather samples from 3–4 week-old chicks at the six colonies used 

for adult tracking. 

Between 26 April and 29 May 2014, we captured 25 additional adult pelicans that were breeding on 

colonies throughout the study area and equipped them with GPS transmitters. We also conducted chick 

sampling and monitored nest productivity at four colonies along the central and northern Texas coast 

from 8 May to 31 July 2014. 

Between 5 May and 31 July 2015, we conducted chick sampling and monitored nest productivity at three 

colonies in the Florida panhandle and one in Alabama. We did not capture or GPS tag any additional 

adults in 2015.

On 26 June 2016, we captured and attached GPS transmitters to five additional adult pelicans on Gaillard 

Island, Alabama.

Between 20 April and 15 August 2017, we monitored nests and chicks on Gaillard Island and Cat Island, 

Alabama.

Between 20 April and 15 August 2018, we monitored nests and chicks on Gaillard Island, Alabama. Cat 

Island did not support nesting pelicans in 2018.

2.2 Individual Tracking

2.2.1 Capture Technique

We captured and attached GPS transmitters to 90 breeding adult Eastern brown pelicans, with a maximum 

of one adult captured per nest. Capture and handling techniques were approved by the Clemson 

University Institutional Animal Care and Use Committee, in consultation with a veterinarian. All adults 

were captured on nests using leg nooses during the late incubation and early chick-rearing stages. If eggs 

were present in the nest, they were replaced with porcelain eggs during capture to prevent damage. If 

chicks were present, they were moved to the nest edge to avoid injury. Following successful captures, a 

plastic laundry basket was placed over the nest to protect nest contents from weather and predation during 

the adult’s absence. The basket was used to eliminate the possibility of predation and we did not observe 

chicks behaving abnormally during or following this procedure. Median handling time was 17.5 minutes 

(range = 11–35 mins) from capture to release. After release, we observed individuals for several minutes 

to ensure that they displayed normal flight, swimming, and balance capabilities. Observation methods and 

results are described below.

2.2.2 Measurements and Sampling

We collected physiological measurements from all individuals while captured. Immediately following 

capture, we measured body mass using a 5000 g Pesola spring scale (Pesola, Switzerland) to ensure that 

the transmitter weight represented less than 3% of total body mass. The minimum body mass necessary to 

attach a 65 g GPS unit was 2167 g, therefore pelicans falling below this threshold were released without a 

transmitter. Immediately after weighing, we collected 5 mL of metatarsal blood in a heparinized 

vacutainer for later analysis of contaminants and blood chemsitry. We also collected 0.1μL of metatarsal 

blood on filter paper, which was later used to determine the sex of all captured adults through PCR (Itoh 

et al. 2001). Then, we obtained three contour feathers for contaminant analysis. Finally, we measured 
22

three indices of skeletal size: wing chord length (last wing joint to tip of longest primary feather) and 

culmen length (forehead to bill tip) using a 600 mm wing rule, and tarsus length (intertarsal to metatarsal 

joint) using a 150 mm caliper.

2.2.3 Tracking Devices

2.2.3.1 Transmitter Specifications and Duty Cycle

We tracked adults using GPS-PTTs (65 g, GeoTrak, Inc., Apex, North Carolina: 65 units) and GPS-GSM 

(65 g, NorthStar Science and Technology, Oakton, Virginia: 20 units), which records GPS locations and 

uploads data to Argos satellites and cellular towers, respectively, for remote download. Transmitters were

programmed to collect 12 locations day -1 during breeding (April–August; every 90 minutes from 1030 to 

0130 GMT), 10 locations day-1 during pre- and post-breeding (September–October and February–March; 

every 90 minutes from 0700–0100 GMT), and 8 locations day-1 during winter (November–January; every 

120 minutes from 0700–0100 GMT). We obtained an average error estimate for GPS points from 

transmitters at known locations (N = 220) of 4.03 ± 2.79 m (13.22 ± 9.15 ft).

2.2.3.2 Transmitter Attachment

Transmitters were attached dorsally between the wings using a backpack-style Teflon ribbon harness 

(Dunstan 1972; Figure 2.3). Transmitters were constructed with sloped fronts, to minimize resistance 

while diving. Transmitters ranged from 1.5–2.9% of individual body mass (μ = 1.9%), below the 

generally accepted 3% threshold for seabirds (Phillips et al. 2003). To elevate the transmitters and prevent 

feathers from covering the solar panels and antenna, we mounted each device on a 6 mm thick neoprene 

pad that also extended 6 mm beyond the perimeter of the transmitter in all directions. 

2.2.3.3 Transmitter Effects

2.2.3.3.1 Captive Trial

Because captured birds often leave the colony area after release to forage or loaf off-site (i.e., short-term 

absence), we chose to assess the immediate behavioral responses of pelicans to transmitter attachment in a 

captive setting. Five adult California brown pelicans were fitted with transmitters at the Los Angeles 

Oiled Bird Care and Education Center rehabilitation facility in San Pedro, California on 11 June 2015. All 

GPS-tagged pelicans were released into a 6 × 13 × 5 m outdoor net enclosure containing a large pool and 

several perches 4 m in elevation, and filmed for 142 minutes pre- and 167 minutes post-transmitter 

attachment, for a total of approximately five hours (309 minutes) per individual and 25 total observation 

hours. Four additional adult pelicans that did not receive transmitters were housed in the same enclosure 

and filmed during the same time period to serve as behavioral controls. We used EthoLog 2.2 software 

(Ottoni 2000) to record behaviors of all pelicans during the pre- and post-attachment phases. Behaviors 

included six mutually exclusive state events: resting (standing or crouching with neck folded and head 

down), loafing (standing or crouching with head up), perching (standing or crouching in a location 

accessible only by flight), preening (using beak or feet to rearrange feathers), swimming (floating or 

paddling on water), and flying and nine instant events: walking, flapping (extension and rapid movement 

of wings while standing), stretching (brief extension of neck, leg or wing), scratching, eating, shaking 

(brief, rapid movement while stationary), bathing (splashing in water), diving (completely underwater) 

and interacting (behaviors directed at or responding to other individuals). To minimize observer bias, all 

coding was done by the same observer (JSL). 
23

2.2.3.3.2 Field Trial

1–3 days after capture, we conducted 3 h behavioral observations on all adults present at their respective 

nests during return visits to the colony (N = 35 individuals; 105 observation hours). The remaining 

individuals were not present during return visits, either due to nest abandonment or because their mates 

were attending the nest at the time. Before beginning the observation, we selected a nearby (≤ 2 m; ≤ 6.6 

ft distance) nest at the same phenological stage as each focal nest (i.e., incubation, small chick-rearing, or 

large chick-rearing) to act as a control for comparison of behaviors.

During the observation, we recorded the behavior of the tagged and control adults at 5 min intervals, 

classifying behaviors as resting, preening, alert (moving nest material, interacting with chicks or 

neighboring birds; comparable to loafing behavior in the captive trials), or agitated (alert and exhibiting 

signs of stress). For each individual observed, we calculated the percent of time spent in each behavior. 

We then separated the data by behavior and used paired t-tests to compare frequency of each individual 

behavior between GPS-tagged and untagged individuals.

Using transmitter data, we recorded the duration in days of subsequent nest attendance by all GPS-tagged 

individuals. Nests were considered active for as long as adults continued to visit the nesting colony at 

least once a day. We inferred approximate hatching dates from nest stage at date of capture, and 

considered breeding successful if adult attendance continued for at least 60 days after hatch. This 

represents the minimum age at which nestlings are likely to fledge (Shields 2014). For pelicans that renested following capture, we interpreted the start of attendance at the new site as the beginning of 

incubation and used a 90-day cutoff for successful breeding, incorporating 30 days of incubation time 

(Shields 2014) in addition to the 60-day fledging period. 

2.2.3.3.3 Statistical Analyses

To assess post-capture nest survival and breeding success, we used a generalized linear modeling (GLM) 

framework to model the probability that parents would attend the nest for at least 60 days after hatch, 

which we interpreted as likely brood success (binomial function, Bernoulli with logit link). To test which 

factors most influenced post-capture nest persistence and reproductive success, we included handling 

time, nest stage, sex, body condition index (BCI; residual of the linear relationship between mass and 

culmen length), capture date, and capture location (i.e., breeding colony) as predictor variables. We used 

a Hosmer-Lemeshow Goodness of Fit test to assess the fit of the global model and compared models 

using Akaike’s Information Criterion (AIC) values. Models were preferred if they resulted in a decrease 

in AIC of ≤ 2 relative to the best-fitting model, while models with Δ AIC of 4–7 were considered weakly 

supported (Burnham and Anderson 2004). We estimated means-parameterized model-averaged 

coefficients over the suite of preferred models, weighted by AIC weights.

2.3 Annual Habitat Use by Adults

Unless otherwise specified, all statistical manipulation of spatial data was conducted using the adehabitat 

family of packages (Calenge 2006) in R 3.2.3 (R Core Team 2014).

2.3.1 Data Screening and Interpolation

Of the 90 transmitters deployed in 2013 and 2014, 74 recorded at least one full breeding season of GPS 

data (87% of tagged individuals). Only these were included in subsequent analyses of reproductive 

success. We manually identified and removed outlying data points using a speed cutoff of 65 km hour-1

between successive points, which is the maximum travel speed recorded for brown pelicans (Schnell and 

Hellack 1978). Cleaned locations for each individual were then interpolated to regular 90-minute 

intervals. Because location data were not collected overnight, we chose not to interpolate tracks between 
24

successive days, and we differentiated each day as a separate trajectory by cutting tracks between each set 

of two successive points separated by a gap of greater than 6 h. Subsequent analyses focused on offcolony locations (i.e., habitat use at the nest site was not analyzed here).

2.3.2 Habitat Variables

Because the scale of movement that we observed was relatively small (on the order of tens of kilometers 

per day, rather than hundreds of kilometers as is commonly observed in pelagic seabirds), we selected 

environmental variables likely to relate to the distribution of prey rather than those that might facilitate 

long-distance movement (e.g., prevailing winds) or visual identification of foraging areas (e.g., ocean 

color). We measured environmental characteristics of brown pelican habitat using seven habitat variables. 

Four habitat variables were constant year-round for any given point (distance to coastline, distance to 

river outflow, bathymetry, and bottom substrate) and three habitat variables varied by month (net primary 

production, sea surface salinity, and sea surface temperature) (Table 2.1). We selected these variables to 

represent a suite of likely drivers of nearshore habitat variation, particularly the distribution of pelican 

prey populations (e.g., Deegan 1990). Because limited data are available on fine-scale variations in 

oceanographic features (e.g., currents and eddies), and because these features have a high degree of shortterm variability in coastal areas (Kaltenberg et al. 2010), we used the distance to physical features that 

influence the movement of water (coastline, river outflow) as proxies for these processes. Depth and 

bottom substrate can influence both prey distributions and oceanographic characteristics. Net primary 

production, which integrates chlorophyll concentrations over a range of depths (Behrenfeld and 

Falkowski 1997), provides an index of oceanographic productivity that influences the distribution of 

consumers at higher trophic levels. Salinity and temperature also influence the distribution of aquatic prey 

species, depending on their osmotic and thermal tolerances. Because some data were reported at finer 

spatial resolutions than others (Table 2.1), we standardized all variables to a resolution of 0.1 degree 

(approximately 10 km) grid squares. Distance values were calculated as the distance from the grid square 

centroid to the feature of interest. For all other variables, we resampled the data using the mean value for 

each 0.1 degree grid square.

2.3.3 Habitat Selection

We mapped preferred habitat characteristics in ecological space using a multivariate ordination of all 

habitat variables using a Hill-Smith principal components analysis (PCA; Hill and Smith 1976), which 

allows the inclusion of both categorical and continuous variables. For each grid square, we calculated 

habitat suitability as the squared Mahalanobis distance of that point from optimal location of the species 

in the multivariate ordination (i.e., higher distances indicate less suitable habitat) (Clark et al. 1993, 

Calenge et al. 2008). We projected habitat suitability as the probability of obtaining a higher squared 

Mahalanobis distance for that cell than the calculated value. Thus, in the final suitability scores, values 

closer to one indicate lower distance from the multivariate optimum location and higher habitat 

suitability.

To characterize individual responses to the measured habitat variables, we used an Outlying Mean Index 

(OMI) analysis (Dolédec et al. 2000). OMI is an ordination technique that characterizes available sites 

based on a set of environmental variables. It sets the mean of all conditions at zero in n-dimensional space 

and determines the axis that describes the maximum amount of marginality (difference from the mean) of 

individual animals or species in ecological space. Thus, the first axis of the OMI is the combination of 

environmental characteristics that best explains the position of animals across available resources. 

Similarly, the position of each habitat characteristic on the first axis of the OMI represents that variable’s 

contribution to animal distributions; that is, the strength of selection on that characteristic. OMI does not 

assume specific resource selection functions, and allows differences in individual niche selection to be 

taken into account when describing the distribution of a group of animals. We conducted OMIs for each 

month on all individuals and habitat variables for each behavioral state. We then averaged the scores of 
25

individuals on the first OMI axis to calculate niche location and breadth for groups within the population. 

We also examined the spatial distribution of breeders from different planning areas. We determined 50% 

and 95% kernel density estimates (utilization distributions, UD) for all individuals from each planning 

area using the “ks” package (Duong 2015) with a plugin bandwidth estimator (Wand and Jones 1994, 

Gitzen et al. 2006). We then used an Albers Conic Equal-area Projection to calculate the areas included 

within each planning area’s 95% kernel contour, and to estimate the intersection areas between kernels 

from different planning areas. 

2.3.4 Movement States

We fit a two-state Hidden Markov Model (HMM; Patterson et al. 2009) to the regularized movement 

trajectories using the moveHMM R package (Michelot et al. 2015) to distinguish resident behavior from 

commuting behavior. HMMs are a particularly flexible and efficient way of characterizing behavioral 

states from precise and regularized tracking data (Langrock et al. 2012), and thus are a good fit for GPS 

tracked locations. Briefly, the model assumes a priori that observed movement data are driven by 

underlying movement “states,” characterized by a distribution of step lengths (distance between 

successive points) and turning angles. A Markov chain is used to describe the state parameters and 

classify data according to its most probable state membership. 

Because we intended to characterize patterns of movement between days rather than within, we fit the 

model to a reduced data set of one location per day (i.e., the centroid of all locations for that day). We 

assumed that local (i.e., resident) movement would be characterized by short step lengths and sharp 

turning angles, and commuting movement by long step lengths and wide turning angles. Therefore, initial 

step lengths were set at 5 ± 5 km (3.1 ± 3.1 miles) for State 1 and 10 ±10 km (6.2 ± 6.2 miles) for State 2. 

Initial turn angles were set at π radians for State 1 and 0 radians for State 2. Angle concentration for each 

state was initially set at one. In subsequent analyses, all points along the trajectory for a given day were 

assigned to the movement state associated with that day.

2.3.5 Statistical Analyses

We modeled individual adult home ranges (50% UD, 95% UD) and migratory parameters (migration 

strategy, migration distance) using full-factorial generalized linear models as a function of colony size, 

environmental characteristics (principal component 1 and/or 2), and individual characteristics (body size 

(culmen length), sex, and BCI). In all cases, the global model including all five predictor variables fit the 

data well (Hosmer–Lemeshow goodness-of- fit tests, p > .1 for all). We selected the best candidate 

models using Akaike’s information criterion (AICc) values. Models that increased AICc by ≤2 relative to 

the top model were substantially supported, while models with ΔAICc of 4–7 received weak support 

(Burnham and Anderson, 2004). We calculated means-parameterized model-averaged Coefficients and 

importance values for each predictor based on the full 95% confidence set of tested models. We 

conducted model selection using the “AICcmodavg” package in R (Mazerolle 2016). To assess 

relationships between individual predictor and response variables, we used univariate linear models.

2.4 Risk Exposure

2.4.1 Surface Pollutant Data Layer

We created a combined index of potential pollutant sources to calculate surface pollutant concentrations 

for each grid square (Table 2.2). These potential sources included: an ocean pollution data layer generated 

from shipping traffic and port locations (Halpern et al. 2008), locations of oil drilling rigs and platforms, 

and locations of oil and gas pipelines (Bureau of Ocean Energy Management; State of Alabama Oil and 

Gas Board; Louisiana: Strategic Online Natural Resources Information System; Texas General Land 

Office). Together, these potential pollutant sources (i.e., non-plastic pollutants) account for the majority 
26

of acute and chronic pollution in this region (NOAA Incident news c2016). After restricting the dataset to 

active platforms and pipelines, we calculated oil infrastructure concentrations using values of platform 

counts and total lengths of pipeline per grid square. We assumed each layer contributed equally to 

pollution risk; therefore, we summed evenly across the three pollutant layers and normalized the resulting 

values to create a combined surface pollutant and oil infrastructure data layer.

2.4.2 Individual Risk Exposure

We calculated overall surface pollution overlap with potential brown pelican habitat by multiplying 

monthly habitat suitability values (Mahalanobis distance probabilities) by surface pollution scores for 

each grid square. For each interpolated individual location, we extracted the value of the surface pollution 

score at the corresponding grid cell. We then averaged the values of all points obtained from each 

individual by month to obtain a mean monthly pollution overlap index for that individual. To compare 

risk exposure between groups of individuals, we calculated the mean and standard deviations of 

individual overlap scores and tested for between-group differences using one-way analyses of variance 

(ANOVA). To assess the influence of behavioral states on exposure risk, we assigned resident points a 

weight of one and transient points weights of either one (equal exposure probability between states), 0.5 

(exposure during rapid linear movement is half as likely as during slow movement), 0.1 (exposure 

probability is proportional to travel speed), or zero (no exposure during rapid linear movement). We then 

multiplied the scores of transient squares by the range of potential weights and averaged across all 

locations for each individual.

2.5 Ecology and Physiology of Reproduction

2.5.1 Nest Monitoring

During 2013–2015 we visited nesting colonies close to the end of the incubation period and selected 3–4 

groups of focal nests per colony, each group containing 20–30 nests. In colonies containing both elevated 

(shrub) and ground nests, we selected closely spaced groups (i.e., with nests <2 m from each other within 

the group) such that nests of both types were represented to allow for comparison. During our initial visit, 

we photographed the nest group from marked observation points that could be accessed without 

disturbance to focal nests, assigned an identifying number to each nest, and recorded nest contents. 

During return visits, we identified nests using the numbered photograph and checked the contents of each 

nest from the observation point. Once nestlings reached 3–4 weeks of age, based on either hatch dates 

(when known) or plumage development (fully developed scapular contour feathers, remiges and rectrices 

in pin), we captured all monitored nestlings for sampling. Nestlings were readily captured by hand at or 

near nest sites. We collected physical measurements (culmen length, tarsus length, wing chord, and 

mass), checked for the presence of ectoparasites, and counted all ticks found on the underside of the left 

wing. We banded nestlings on the left tarsus with a permanent plastic band (Haggie Engraving, 

Crumpton, Maryland: 2014–Green; 2015–Blue) engraved with a unique three-digit white alphanumeric 

code. We also banded nestlings on the right tarsus with a metal engraved U.S. Geological Survey Bird 

Banding Lab band, with a unique nine-digit identifying code for later recovery outside the study area. 

Once nestlings began to disperse away from nest locations, we searched the surrounding areas of the 

colony with binoculars for banded chicks and recorded all bands observed. We continued observations 

until chicks reached at least 60 d of age.

During 2017 and 2018 we established productivity plots within brown pelican colonies on Cat Island 

(2017: n = 2 plots) and Gaillard Island (2017: n = 4 plots; 2018: n = 7 plots), Alabama, during early 

incubation. Each plot contained 10–30 nests, depending on nest configuration and proximity. All plots 

were spaced based on natural contours and aspects of the islands, resulting in distance between plots 

ranging from 60–260 m (197–840 ft). Plots were visited as often as possible given weather conditions and 
27

logistics (range: 2–11 d) although cameras were also used to record activity and status daily. During each 

visit, we enumerated and recorded nest contents. We subsequently banded all nestlings of approximately 

21 d of age with a permanent plastic band on the left tarsus (Haggie Engraving, Crumpton, Maryland; 

2017: n = 145; 2018: n = 156) engraved with a unique three-digit white alphanumeric code. We also 

banded nestlings on the right tarsus with a metal engraved US Geological Survey Bird Banding Lab band, 

with a unique nine-digit identifying code for later recovery outside the study area.

2.5.2 Nestling Health

During 2013–2015, we compared two different assessments of nestling health: BCI (a measurement of the 

ratio of mass to skeletal size) and feather CORT (a measurement of stress hormone levels in nestling 

feathers). 

2.5.2.1 Body Condition

We ran a PCA on the three measures of skeletal size (tarsus length, culmen length, and wing chord) to 

calculate BCI (Benson et al. 2003). Using each individual’s score on the first principal components axis 

(PC1) as an index of overall skeletal size, we calculated the best-fitting regression equation for the 

relationship between mass and PC1 score. We chose a second-order polynomial to accurately represent 

the asymptotic nestling growth process, which is initially linear but reaches a peak and descends slightly 

before fledging. Finally, we calculated BCI as the standardized residual of actual body mass from the 

value predicted by the regression equation.

2.5.2.2 Stress Hormone Levels

At capture, we collected 3–4 scapular contour feathers from each nestling. Feathers were bagged and 

stored at room temperature until processing. We used a random number generator to select 150 samples 

per year for CORT analysis, divided equally among study colonies. Following the recommendations of 

Lattin et al. (2011), we restricted the range of sample sizes analyzed by excluding samples that were 

extremely small (< 20 mg), and dividing samples larger than 160 mg into separate units for analysis.

We closely followed the methods for feather CORT extraction and analysis originally described by 

Bortolotti et al. (2008). Briefly, we removed the calamus from each feather, weighed and measured

feathers individually, and prepared the sample for analysis by snipping feathers into small (< 0.5 mm) 

pieces with scissors and transferring the entire sample into a 16 mL test tube. Each sample received 7 mL 

of methanol and was placed in a sonicating water bath overnight at 30 °C. Then we pipetted the methanol 

into a separate 13 mL tube and conducted two additional washes, each with 2.5 mL methanol. The 

cumulative methanol sample, totaling 12 mL, was dried down under N2, reconstituted in 200 μL buffer, 

and centrifuged to ensure that all accumulated CORT was dissolved in buffer. We conducted a 

radioimmunoassay (MP Biomedicals, LLC, Solon, Ohio: ImmuniChemTM Double Antibody CORT 125I 

RIA Kit) on diluted samples. Simultaneous parallelism tests indicated that the assay accurately detected 

CORT, and we used a standard sample with known CORT to measure intra-assay variation (1.7–1.9%) 

and subsampled a single feather sample to measure inter-assay variation (11%). We assessed feather 

CORT in a total of 365 chicks (2013: N = 126; 2014: N = 144; 2015: N = 95).

Because CORT concentrations may reflect feather quality as well as quantity (Patterson et al. 2014), we 

divided the total amount of CORT detected in each sample by the total mass of all feathers in the sample 

(pg mg-1

), log-transformed values to meet assumptions of normality, and calculated feather mass per unit 

length (mg mm-1

) as an index of feather quality. Because feather mass and feather length were 

significantly negatively correlated (p < 0.001, slope = -1.14 ± 0.15), we calculated the residual of the 

best-fitting regression line between log-transformed CORT mg-1 and feather mass per unit length, detrended the data by subtracting the regression line, and used the adjusted values in all analyses.
28

2.5.3 Nest and Fledging Success

During 2013–2015, beginning approximately 8 w after hatching, we conducted regular searches of the 

colony for dead banded chicks and recovered all bands found. Nestlings that were observed alive at least 

60 d after hatching and disappeared from the colony, but were not found dead, were presumed to have 

successfully fledged (Shields 2014). We used this information to determine apparent fledging success 

(fledglings nest-1

). We calculated plot- and colony-wide fledge success as the number of chicks fledged 

from observation nests, divided by the total number of nests observed. Because detectability of fledglings 

is high in this species and habitat, we considered this method to accurately represent overall fledging 

success.

During 2017–2018 (i.e., Gaillard and Cat islands, Alabama), we enumerated and recorded nest contents 

during each visit. During subsequent visits, we searched for banded chicks at the colony site and by 

observations from a small power boat within 70 m of the shore until all banded chicks were located and 

identified. We continued re-sighting efforts until ≥ 80% of the banded chicks were > 70 (2017) or 65 

(2018) days post hatch, which we defined as “fledged” (Schreiber 1980). All monitored nests were 

assigned a final fate of either successful (≥ 1 egg hatched) or failed (0 eggs hatched) and all broods were 

assigned a final fate of either successful (≥ 1 chick fledged) or failed (0 chicks fledged). We refer to these 

fates as nest success and brood success, respectively. 

At Gaillard and Cat islands, we measured ten environmental variables to assess in relation to daily 

survival rate (DSR) of nests and broods (Table 2.3). Nest-based variables that remained fixed throughout 

the breeding season (n = 3) included substrate beneath nest (rock from rip rap or bare ground), nest 

elevation above sea level (low = 0 - 0.59 m / 1.94 ft, medium = 0.60 - 0.75 m / 1.97–2.46 ft, high = 0.76–

1.0 m / 2.49–3.28 ft, and berm >1.0 m / 3.28 ft; except brood stage of 2017, when low = 0–0.75 m / 0–

2.46 ft, high > 0.75 m / 2.45 ft due to restricted sample sizes within categories), and distance from nest to 

Mobile Bay waters (range = 1.5–127.7 m). These are hereafter referred to as fixed variables and we 

recorded these once at the start of the nesting season. Nest-based variables that could change during the 

breeding season (n= 2) included nest height above ground and vegetation cover directly above the nest. 

These are hereafter referred to as dynamic variables and we measured these approximately every 3 weeks 

(range 2–4 weeks) beginning with the establishment of the plots. We used the average value of the 

dynamic variables in subsequent analyses. Nest height above ground level (range = 0–156 cm) was 

measured by placing a level across the nest, then measuring the distance from the ground to the edge of 

the level (i.e. the rim of the nest). Vegetation cover (range = 0–100%) was measured using photographs

taken from the center of the nest, with the lens facing the sky. These photographs were analyzed in 

Adobe® Photoshop® by overlaying a grid of 100 squares on each photo and enumerating the grids that 

contained vegetation to establish percent cover. 

We also measured nest-specific temperature using HOBO temperature dataloggers (Onset Computer 

Corporation, Bourne, Massachusetts). Not all nests received loggers as we stratified placement of loggers 

(2017: n = 28 nests; 2018: n = 31 nests) by nest height (approximate even sample of nest heights within 

10 cm/ 4 in intervals available from 0 cm to 140 cm/ 55 in). Dataloggers recorded the temperature of nests 

hourly throughout each 24 h period during incubation and chick-rearing, or until failure, and we 

subsequently calculated the average and maximum temperatures between each nest visit. We also 

measured regional weather by collecting hourly measures of barometric pressure and humidity from local 

climatological data and from the Mobile Downtown Airport weather station2

, which is located on the 

western side of Mobile Bay, 14 km to the northeast of Gaillard Island. We then calculated average and 

2 See https://www.ncdc.noaa.gov/cdo-web/datasets/GHCND/stations/GHCND:USW00013838
29

maximum values for each of these parameters for the time interval between each nest check from nest 

initiation until failure or the day the last chicks were classified as fledged. 

2.5.4 Post-fledging Survival

We relied on opportunistic re-sighting of banded chicks by colony monitors and birders along the coast of 

the GOM to determine survival post-fledging of birds banded in 2013–2015. We received band resightings and recoveries reported to the US Geological Survey Bird Banding Lab, as well as directly 

through a dedicated web portal. Sightings and recoveries were obtained throughout the US Gulf Coast and 

from Mexico through January 2016. To calculate colony-wide survival rates, we used a joint live 

recapture–dead recovery model (Burnham 1993). We assessed survival rates at two time steps: survival to 

fledge (3 months after hatch) and post-dispersal survival (6 months after hatch). Dead individuals were 

recovered in the intervals between time steps, and individuals were considered to have survived to a new 

time step if they were re-sighted alive after that period ended. Because re-sightings and recoveries 

occurred across the entire range of the population, we fixed dispersal parameters (F) at a value of one

(i.e., 100% probability that banded individuals remained in the sampling area). We derived parameter 

estimates for survival (S), recovery (r), and re-sighting (p) during each time interval using Markov chain 

Monte Carlo estimators with a burn-in of 1000 samples, followed by 4000 tuning samples and 10000 

runs.

2.5.5 Statistical Analysis

For data collected in 2013–2015, we conducted a logistic regression with a binary outcome (fledged/

died) on each metric and assessed the fit of the resulting models to evaluate health metrics as predictors of 

individual survival to fledge. We ran independent GLMs, each with a binary outcome (fledged/ died; resighted alive/ recovered dead) and logit link, with health parameters (CORT, BCI) and individual 

covariates (nest elevation, nesting colony, date, hatch order, and number of siblings) as fixed factors to 

assess the utility of measured covariates as predictors of individual nestling survival. We used a GLM

framework (Gamma, log link) with fledge success as the response variable and health metrics as predictor 

variables to compare the relative value of different metrics for predicting aggregate nest productivity and 

survival rates at the colony level. We computed AICc values to account for the small sample sizes that 

resulted from using colony as the sampling unit and used these values for model comparison. Models 

were considered to receive strong support if they resulted in a Δ AICc ≤ 2 (Burnham and Anderson 2004).

For data collected from 2017–2018 in Alabama, we assessed reproductive success by calculating the DSR 

of nests (incubation stage, laying to hatch) and broods (chick-rearing stage, hatch to fledge) using 

package RMark (Laake and Rexstad 2014). The nest survival module models the survival probability (i.e., 

DSR) over the course of each breeding stage as a function of user-specified covariates using generalized 

linear models with a logit-link function and binomial errors. Before analyses, we compared the DSR of 

nests and broods between Gaillard and Cat islands and, finding no difference (P > 0.10 for each), pooled 

data from both islands in subsequent analyses. We report DSR and apparent survival to allow for 

comparisons to previous studies.

We modeled the relationships of the independent variables with DSR separately for incubation and brood 

rearing. We also included the following independent variables: Julian date, nest age (nest success 

models), and age of first chick hatched (brood success models) (calculated in RMark using AgeFound and 

AgeDay1). We tested both linear and quadratic terms for the age and time covariates and used the best 

performing term for each variable (quadratic for age covariates in all breeding stages except for 2017 

brood rearing; linear for all time covariates in all models) in subsequent models (Streker 2019). We 

developed a suite of hypotheses to assess the relationship between the independent variables and daily 

survival rates (Table 2.4). Variables that were highly correlated (|r| ≥ 0.5) were not included in the same 

model and therefore multiple global models were developed to separate correlated values. For each year 
30

for incubation data we re-ran the top performing models (see below on identification of top-performing 

models) on the subset of nests within which temperature was recorded to assess whether the addition of 

nest-specific temperature variables substantially improved model fit. Temperature variables were not 

tested during chick-rearing due to the small sample size of broods that failed that also had temperature 

loggers (2017: n = 1 nest with temperature logger + brood failure; 2018: n = 7 nests with temperature 

logger + brood failures). 

We used Akaike’s information criterion (AIC) to rank the models and evaluated the strength of the 

models using normalized weights (Burnham and Anderson 2002). We ran models separately by year 

(2017, 2018) and breeding stage (incubation, brood-rearing). We report models that were within ΔAIC ≤ 

2 of the lowest-scoring model. We report coefficient estimates and confidence intervals from topperforming models. Daily survival rates were calculated from top performing models for each year and 

breeding stage. We reported apparent success as the total number of observed nests and broods divided by 

the number of nests and brood successful at the end of their respective breeding stage.

2.6 Nestling Diet

2.6.1 Diet Sampling

In Year 1 (2013), we collected meals opportunistically from chicks captured for banding and sampling. In 

years 2–3 (2014–2015), we visited each study colony regularly (every 5–7 days). We selected recentlyfed nestlings, based on either having seen a feeding occur or observing that the nestling had a visible 

bolus or engorged throat, to obtain meals from nestlings. We approached the nest from the colony edge 

and waited for the nestling to voluntarily regurgitate the meal. If the target nestling did not regurgitate, we 

selected a different nestling and repeated the process until we had obtained approximately ten complete 

meals. We targeted different areas of the colony on subsequent visits to limit disturbance to individual 

nests; we also varied the time of day at which samples were collected. We collected meals throughout the 

chick-rearing period, from hatch (late April) through fledging (early August). We targeted nestlings at the 

same stage of feather development to limit chick age variation within each sample, indicating similar 

hatch dates, and recorded overall nestling age for the sample as estimated from feather growth (sensu

Walter et al. 2013). We did not collect samples from recently hatched nestlings (one week old or less), 

both to limit disturbance and because pelican nestlings do not consume whole fish until several days after

hatch (Sachs and Jodice 2009). Additionally, because nestlings regurgitated food less readily as they 

reached adult size, we were not able to sample chicks older than approximately ten weeks of age. Samples 

were stored on ice in the field and then moved to a freezer within ~6 hours of collection.

2.6.2 Diet Composition

During processing, we thawed each sample in a warm-water bath, removed it from plastic, dried off 

surface water using paper towels, then weighed, measured, and identified the species of each individual 

fish. We based species identifications on descriptions in McEachran and Fechhelm (2010), relying on soft 

tissue and external characterists. We also classified each fish as whole (no visible damage), partial-whole 

(total or standard length obtained, but some soft tissues missing), and partial (length could not be 

obtained). For samples containing large numbers (50–1000) of small fish of the same species (26% of 

samples), we counted the total number of individuals of the species, weighed and measured a subsample 

of ten individual fish, and obtained a total weight and overall classification (whole, partial-whole, partial) 

for each species group. For samples containing extremely large numbers (> 1000) of small fish (<1% of 

samples), we weighed and measured a subsample of ten fish per species, weighed the overall sample, and 

used the average weight per fish to approximate the total number of fish in the sample. For samples in 

which individual fish were no longer intact, we counted the number of heads and tails present in the 
31

sample and used the larger of the two numbers as an approximate count. We did not analyze samples for 

which the digestive process was too advanced to identify fish to species (< 1% of all samples collected).

Where needed, we corrected standard lengths of fish to total lengths using the best-fitting regression 

equation between standard and total length for that species calculated from whole samples. We calculated 

the length-weight relationship as the best-fitting regression equation between log total length and log 

mass of whole fish for each species by year to estimate the mass of partial-whole and partial fish. For 

partial-whole fish (i.e., degraded fish for which we were able to measure total length), we used the 

regression line to estimate the corrected mass of the whole fish from its length. For partial fish (i.e., 

degraded fish for which total length was not measurable), we used the mean total length of whole and 

partial-whole individuals collected from the same breeding colony on the same day to estimate a 

corrected mass from the regression equation. Finally, we totaled the corrected masses of individual fish 

within each meal to obtain a total corrected meal mass.

2.6.3 Provisioning Rates

To assess meal delivery rates, we conducted 3 h nest observations during each colony visit throughout the 

chick-rearing period (i.e., every 5–7 d from hatch through fledging, late April to early August). We 

selected groups of 15–20 nests, varying both the location within the colony and the time of day of

observations. During each 3 h period, we recorded all direct feedings in which a nestling’s head entered

an adult’s throat and the nestling’s throat was subsequently engorged. Indirect feedings in which parents 

regurgitate prey onto the floor of the nest as opposed to the chick directly (Sachs and Jodice 2009) 

appeared to take place only within the first few days after hatch. Because chicks are brooded by adults 

during this time and are hidden from view, the frequency of such feedings was difficult to quantify; thus,

we excluded recently hatched nests from observation.

We calculated meal delivery rates on a per-nest basis. This measure reflects the rate of provisioning by 

adults, but not necessarily the rate at which each individual nestling consumes food. Pelicans can raise up 

to three young, hence meals delivered to a nest may be shared among as many as three nestlings. 

However, each nestling may not receive an equal share, because nestlings that hatch earlier can often 

consume a larger share of feedings based on superior competitive ability (larger body size, more advanced 

muscle development and mobility) or preferential feeding by adults. Because we were not able to 

consistently distinguish first, second, and third-hatched chicks in the field throughout the extended chickrearing period and subsequently allocate feedings to individual chicks, we chose to assess delivery rates 

by nest with number of chicks as a covariate. We standardized delivery rates to a 15 h day, representing 

the average day length (civil twilight) during the study period. Pelicans are visual foragers and are 

considered not to forage at night (Shields 2014), and our observations suggest that adult activity 

diminishes shortly after sunset.

2.6.4 Proximate Composition and Energy Density

We measured proximate composition and energy density of common prey species using methods 

described by Anthony et al. (2000). Briefly, we dried fish to a stable mass in a 60 °C oven and 

homogenized samples using a mortar and pestle. We then extracted lipids from the sample using a 

Soxhlet apparatus with a 7:2 (v:v) hexane: isopropyl alcohol solvent. Following the 10 h extraction, the 

sample was left to dry for 24 h and re-weighed to determine lean mass. We then extracted proteins from 

the sample by ashing at 600 °C for 12 h. The mass of the remaining skeletal ash was subtracted from the 

pre-ashing mass to determine the ash-free lean dry mass, which is composed primarily of proteins (94%: 

Anthony et al. 2000). We then multiplied the lipid and protein contents by standard energetic values based 

on their relative assimilation efficiencies (lipids: 39.5 kJ g-1

; proteins: 17.8 kJ g-1: Schmidt-Nielsen 1997)

to obtain the overall energy density of the sample.
32

We measured energy densities in both regurgitated fish that we identified as whole during processing and 

bait fish purchased live or freshly caught from fishing suppliers close to study colonies. For the three 

most common prey species (Gulf menhaden; Atlantic croaker, Micropogonius undulatus; and pinfish,

Lagodon rhomboides), we ran ANOVAs to determine whether energy content differed among planning 

areas or sample types (bait fish compared to regurgitated fish). Because energy values for one of the three 

species, Atlantic croaker, differed significantly between the eastern and western planning areas, we 

calculated energy densities separately for the two planning areas where possible. However, we did not 

find differences in energy content between bait and regurgitated samples, and therefore combined all 

samples within each planning area during further analysis. Gulf menhaden had an apparent difference in 

energy content between bait samples and regurgitated fish (p = 0.056). In this case, regurgitated fish were 

higher in energy than bait samples, so we chose to use only regurgitated samples to determine energy 

content for this species. We also tested for differences in energy density between locations within 

planning areas, and found over time that the energetic content in Atlantic croaker and Gulf menhaden did 

not differ within planning areas and did not change as the season progressed. Therefore, we considered 

energy density of prey to be consistent throughout the breeding season and within each planning area. 

Because Gulf menhaden were the only prey species to show a bimodal size distribution, we measured 

energy content of juveniles (< 110 mm total length: Ahrenholz 1991) and adults (> 110 mm) separately.

We multiplied the total mass of each prey species in the meal by the mean energetic value of that species

to determine meal-specific energy density. For species without directly measured energy density, we 

obtained energetic values for the same or closely related species from published literature. Species with

directly measured energy content accounted for 93% of the total biomass, while species with inferred 

values from closely related species measured directly (4%) and those with values obtained from scientific 

literature (3%) constituted the remaining 7%. We then summed the energy derived from each individual 

species and divided the sum by the total meal mass to obtain an energetic value (kJ g-1

) for the full meal. 

We calculated meal-specific lipid content using the same process. 

2.6.5 Energy Provisioning Rates

We compared values of meal mass (g meal-1

), nest-specific provisioning rate (meals nest-1 hour-1), and 

energy density of meals (kJ g-1

) for each colony using ANOVAs with post-hoc Tukey’s Honestly 

Significant Difference (HSD) tests to assess nutritional stress by colony. The product of these three 

components is the energy-provisioning rate (EPR: g nest-1 hour-1

, Jodice et al. 2006). We modeled energydays for each colony, similarly to Jodice et al. (2006), by randomly selecting (with replacement) 100 

values for provisioning rate (meals day-1

) from the set of measured values to obtain a combined measure 

of EPR by colony. The model then randomly selected (with replacement) a mass and an energetic value 

for each meal, multiplied the meal mass by energy density to obtain the total energy content per meal, and 

summed the total energy across all meals for each modeled day to obtain a set of EPRs (kJ day-1

). We 

calculated the mean and standard deviation of EPR for each colony by averaging values obtained from 

1000 runs of the model. We calculated EPR on a per-nest basis rather than a per-chick basis, to avoid the 

confounding relationship between higher provisioning rates and increased longevity of second- and thirdhatched chicks (Jodice et al. 2006). Then we evaluated the relationships of individual provisioning 

metrics to EPR using ANOVAs on nested sequential linear models. Finally, we conducted non-metric 

multidimensional scaling on proportional composition of meals by species to assess the relationship 

between species composition and rate of energy delivery to nestlings, and overlaid provisioning metrics 

on the resulting ordination.
33

2.7 Health and Physiological Parameters

2.7.1 Blood Analytes

2.7.1.1 Sample Collection

Adult brown pelicans were sampled from active nests during the breeding seasons of 2013–2015 from six 

different colonies in the Northern GOM. We collected samples from Audubon and Smith islands, Florida; 

Gaillard Island, Alabama; Felicity and Raccoon islands, Louisiana; and Chester and Shamrock islands, 

Texas. We collected blood smears from 90 adults and blood samples from 81 of the 90 adults for 

complete blood counts (CBCs). Not all samples, however, were suitable for complete analyses and so 

sample size varies among analytes and blood smears. We measured body mass (± 50 g/ 1.76 oz), culmen 

length (± 1mm/ 0.04 in), tarsus length (± 1mm/ 0.04 in), and wing length (± 5mm/ 0.20 in). These 

variables were not assessed individually; instead they were used to create a new variable, BCI (n = 64), 

which provides an index for the mass of the bird in relation to its size (see 2.5.2.1). The more positive the 

BCI, the better the condition of the individual. In brown pelicans, sex cannot be easily determined in situ.

Therefore, the distribution of samples between sexes is opportunistic. Sex was later determined from

collected blood samples through PCR (Itoh et al. 2001).

Brown pelican chicks were sampled from active nests during the breeding seasons of 2014–2015 from 

seven colonies in the Northern GOM. We sampled from Audubon and Ten Palms islands, Florida; 

Gaillard Island, Alabama; Marker 52 and North Deer (regrouped as Galveston Bay colonies), Chester, 

and Shamrock islands, Texas. We collected blood smears and blood samples for CBCs from 35 

individuals. As in adults, we measured body mass, culmen length, tarsus length, and wing length, and 

used these variables to assess BCI (n = 35). Sex was not determined for chicks.

For both adults and chicks, blood samples were collected within two minutes of capture from the 

tarsometatarsal vein. After sterilizing the collection site, we collected a 5 mL blood sample using a 23-

gauge needle and VacuTainer tube (Becton Dickinson, Franklin Lakes, New Jersey) with lithium heparin 

anticoagulant. Samples were stored over cold packs until returning from the field (~5–10 h). 

2.7.1.2 Sample Processing

In the lab, we created blood smears from stored samples, filled three capillary tubes for hematocrit 

analysis, and spun down both samples and capillary tubes using a centrifuge (Becton Dickinson, Franklin 

Lakes, New Jersey). We recorded hematocrit percent volume from each of the three capillary tubes. We 

separated plasma from red blood cells in centrifuged samples by pipetting. All plasma and red blood cell 

samples were then stored frozen until analysis. 

Biochemical, protein electrophoresis, and serological tests were conducted at the University of Miami 

(Department of Pathology, Miami, Florida). A full biochemical analysis was conducted on plasma 

samples on a dry-slide chemistry analyzer (Ortho Vitros 250 XR, Ortho Clinical Diagnostics, Rochester, 

New York) controlled daily for quality and ran per manufacturer’s instructions. Evaluated analytes (Table 

2.5) included alanine aminotransferase (ALT), aspartate aminotransferase (AST), creatinine 

phosphokinase (CPK), gamma glutamyl transferase (GGT), lactate dehydrogenase, amylase, bile acids, 

blood urea nitrogen (BUN), calcium, cholesterol, CO2, creatinine, glucose, lipase, phosphorus, potassium, 

sodium, total protein, triglycerides, and uric acid. Lipoprotein analysis included high-density (HDLc) and 

very low-density (VLDLc) lipoprotein cholesterol. Plasma samples were analyzed following procedures 

provided in the Helena SPIFE 3000 system with the use of Split Beta gels (Helena Laboratories, Inc. 

Beaumont, Texas). Protein electrophoresis were scanned and analyzed by Helena software for prealbumin, albumin, and Alpha 1 (A1G), Alpha 2 (A2G), Beta and Gamma globulins. Percentages for each 

fraction were determined by this software, and absolute concentrations (g dL-1

) for each fraction were 
34

obtained by multiplying the percentage by the total protein concentration. The albumin to globulins ratio 

(A:G) was calculated by dividing albumin by the sum of the globulin fractions. Concentrations of CORT 

were measured by radioimmunoassay (MP Biomedicals Double Antibody Corticosterone 

radioimmunoassay, Santa Ana, California). We classified each analyte as a blood gas, electrolyte/mineral, 

enzyme, lipid, metabolite, plasma protein, or stress hormone (Table 2.5). We also noted typical 

indications from each analyte (e.g., nutrition, hepatic damage). 

Blood smears were stained with Diff-Quik (Siemens Healthcare Ltd., Ontario, Canada) and reviewed at 

1000x to determine differential counts of white blood cells (WBC). We also measured the concentration 

(*10^3 mu L-1

) of all WBC and the concentration of heterophils, lymphocytes, monocytes, eosinophils, 

and basophils (Table 2.5).

2.7.1.3 Statistical Analysis

Among the 30 blood analytes, we identified any with a binomial distribution and separated the data into 

“high” and “low” categories, then treated those as two separate analytes (i.e., analysed each category 

separately). We tested for differences in the independent variables between the low and high categories 

using t-tests, χ2 tests, and Wilcoxon rank tests. Sodium as measured in samples from adults was the only 

analyte for which this bimodal treatment was necessary. Measures of sodium classified as “low” ranged 

from 111–156 mEq L-1 and measures of sodium classified as “high” included only the maximum 

measured value for sodium of 250 mEq L-1

. 

We assessed outliers for all data that did not have a binomial distribution (i.e., all analytes other than adult 

sodium) using the Dixon outlier range statistic. Following guidance in Geffré et al. (2011), we retained 

rather than deleted an outlier unless it seemed likely to be an aberrant observation. We examined the 

distance between points identified as potential outliers and non-outlying points to make this decision. We 

also compared points identified as potential outliers to published values for species within the same order. 

Once aberrant outliers were removed, we checked normality of the data using the Anderson-Darling test. 

We then calculated reference values using Reference Value Advisor (Geffré et al. 2011). When sample 

sizes were sufficient, we computed a nonparametric reference interval, calculated the lower and upper 

confidence intervals on the reference intervals using a bootstrap approach, and provided descriptions of 

alternate approaches when appropriate (Geffré et al. 2011). We reported reference values from raw data 

for all analytes. For analytes that did not meet assumptions of normality, we followed guidance from 

Geffré et al. (2011) and conducted a Box-Cox transformation for subsequent statistical analyses. The 

Box-Cox transformation is commonly used for data such as concentrations of blood analytes that are 

often heteroscedastic (i.e., non-constant variance). 

We assessed the relationship between each blood analyte and a suite of independent variables using 

GLMs. Independent variables for analyses of blood analytes and smears included: sex (adults; 

categorical), BCI (adults and chicks; continuous), planning area (adults and chicks; categorical), and 

home range size (adults; continuous). Planning area was classified using the planning areas for BOEM in 

the GOM. Oil and gas development are highest in the central planning area (coasts of Louisiana, 

Mississippi, and Alabama), least in the eastern planning area (Florida Panhandle), and moderate in the 

western planning area (Texas coast). Home range was reported as the 50% core area for any individual 

that was equipped with a satellite transmitter (n = 64). We selected the 50% core area as opposed to the 

95% use area for analysis with blood analytes to assess the overall individual condition. We suggest the 

50% core area better represents the conditions encountered regularly by an individual compare to the 95% 

use area and therefore the core area is most likely to affect an individual’s overall condition. Deployment 

methods for satellite tags and calculation of home range size are detailed in Chapter 2 and Chapter 3. 

Note, the sample of animals with home range data is a subsample of those reported in Chapter 3 and 

therefore reported measures of home range size are not identical between Chapter 3 and Chapter 8. 

Continuous variables were scaled before running logistic models. Therefore, coefficient estimates were on 
35

a standardized scale with respect to independent variables and on either a raw or Box-Cox scale with 

respect to dependent variables. However, all figures used a raw scale for both dependent and independent 

variables. Correction procedures for repeated tests were not conducted (Moran 2003, García 2004, 

Nakagawa 2004).

We built eight models (Table 2.6) to assess the relationships among blood analytes and independent 

variables and compared them using AIC. We reported coefficient estimates from the top-ranked model

when coefficient estimates ± SE did not overlap 0. Transformed data were used for analyses when 

appropriate, but raw data were presented for ease of interpretation. 

When box plots were used to display data, the median and quartiles defined the boxes, the whiskers 

defined the 10th and 90th percentiles, and data beyond the 10th and 90th percentiles were shown as circles. 

2.7.2 Polycyclic Aromatic Hydrocarbons

2.7.2.1 Sample Collection

Adult brown pelicans were sampled from active nests during the breeding seasons of 2013–2015 from 

seven different colonies in the Northern GOM. We sampled from Audubon and Smith islands, Florida; 

Gaillard Island, Alabama; Felicity and Raccoon islands, Louisiana; and Chester and Shamrock islands, 

Texas. We collected feathers from 92 individuals and randomly selected blood from a subset of 33 

individuals (from the pool of sample available described in 2.8.1.1) for PAH analysis. We measured body 

mass (± 50 g/ 1.76 oz), culmen length (± 1 mm/ 0.04 in), tarsus length (± 1 mm/ 0.04 in), and wing length 

(± 5mm/ 0.2 in) of every individual. These variables were not assessed individually; instead they were 

used to create a new variable, BCI (n = 79), which provides an index for the mass of the bird in relation to 

its size. The more positive the BCI, the better the condition of the individual. In brown pelicans, sex 

cannot be easily determined in situ. Therefore, the distribution of samples between sexes is opportunistic. 

Sex was determined from collected blood PCR (Itoh et al. 2001). 

Brown pelican chicks were sampled from active nests during the breeding seasons of 2014–2015 from 

seven colonies in the Northern GOM. We sampled from Audubon and Ten Palms islands, Florida; 

Gaillard Island, Alabama; Marker 52 and North Deer (regrouped as Galveston Bay colonies), Chester, 

and Shamrock islands, Texas. We collected feathers from 606 individuals and randomly selected 35 of 

these samples for PAH analysis. We did not collect blood from chicks for PAH analysis. As with adults,

we used measurements of body mass, culmen length, tarsus length, and wing length to calculate body 

condition index. Sex was not determined for chicks.

We collected blood samples within two minutes of capture from the tarsometatarsal vein. After sterilizing 

the collection site, we collected a 5 mL blood sample using a 23-gauge needle and VacuTainer tube 

(Becton Dickinson, Franklin Lakes, New Jersey) with lithium heparin anticoagulant. We then stored 

samples over cold packs until returning from the field (~5–10 hours). We collected 3–4 scapular feathers 

from each adult and chick. Feathers were stored at room temperature until processing. 

2.7.2.2 Sample Processing

PAH analyses were conducted at the University of Connecticut Center for Environmental Sciences and 

Engineering (Storrs, Connecticut). In the lab, we weighed 0.2 g (0.007 oz) of blood sample into a 1.5 mL 

plastic centrifuge tube. Samples were spiked with quality control standard solutions and vortexed for 1 

min at 2500 rounds per minute. Methanol or acetonitrile (500 μL) were added to each tube along with 

MgSO4. Samples were then vortexed for 5 min at 2500 rounds per minute, then centrifuged for 10 min at 

14,000 rounds per minute. Next, 190 μL of the supernatant were transferred to a 300 μL liquidchromatography vial. These samples were then spiked with an internal standard and vortexed.
36

Following extraction, the samples were analyzed for alkylated PAHs using an Agilent 6890 gas 

chromatograph (Agilent Technologies, Inc., Santa Clara, California) equipped with a Restek Rxi-5Sil MS 

column (Restek, Bellefonte, Pennsylvania; 30 m) using splitless injection coupled to a Waters 

QuattroMicro triple quadrupole mass spectrometer (Waters Corporation, Milford, Massachusetts). Parent 

PAHs were quantified using a Waters Acquity ultra-performance liquid chromatograph (UPLC; Waters 

Corporation, Milford, Massachusetts) with fluorescence and photo diode array detection, which was 

equipped with an Acquity UPLC BEH C18 column (Waters Corporation, Milford, Massachusetts; 1.7 

μm, 2.1 × 100 mm). All peaks were quantified against the internal standard, and the extraction efficiency 

was evaluated using a surrogate standard of naphthalene-d8. Standard quality assurance procedures were 

employed, including analysis of duplicate samples, method blanks, post-digestion spiked samples, and 

laboratory control samples.

Feathers were washed three times in acetone, three times in high performance liquid chromatography 

water, and one additional time in acetone before allowing them to dry overnight (ca. 10 h). Feathers were 

weighed (± 0.2 g/ 0.007 oz) on folded weighing paper and transferred directly into the accelerated solvent 

extraction (ASE) cell using forceps when needed. Hydromatrix powder was added to pack the 11 mL 

ASE cells. Using gelatin as the matrix for the blank and laboratory control samples, a 0.2 g (0.007 oz)

sample was weighed out and transferred to ASE cells. Samples were then spiked with quality control

standards. ASE extracts were subsequently run and collected utilizing acetonitrile solvent, and the 

solution was transferred into the pre-marked conical evaporation vials and evaporated to just below 0.5 

mL under a gentle nitrogen stream (set flowrate on N-Evap unit to 180 mL/min). Samples were spiked 

again with internal standard. The volume was then brought up to 500uL with acetonitrile and vortexed for 

a few seconds to mix. Filtered samples were injected into liquid-chromatography vials using 1 mL plastic 

syringes and 4 mm, 0.2 μm syringe filter. 

The detection limit was 5 ng g-1 (i.e., part per billion) and values for PAHs were reported as wet weight 

for blood and dry weight for feathers. 

2.7.2.3 Statistical Analysis

For subsequent PAHs analyses, we considered three dependent variables: the sum of all PAHs detected 

(sumPAH), the sum of Parent PAHs detected (sumPAR), and the sum of Alkylated PAHs detected 

(sumALK). We assessed the relationship between each and a suite of independent variables, including:

BCI (adults and chicks; continuous), planning area (adults and chicks; categorical), sex (adults; 

categorical), migration class (adults; categorical) and home range size (adults; continuous). Planning area

was classified using the planning areas for the BOEM as defined above. Home range was reported as the 

95% use area for any individual that was equipped with a satellite transmitter. We selected the 95% use 

area for PAHs analysis rather than the core (50%) use area because we were interested in assessing the 

overall exposure of the individuals and suggest that this is best represented by the full extent of the area 

used. Methods for deployment of satellite tags and calculation of home range size are detailed in Chapters 

2 and 3. Note, the sample of animals with home range data is a subsample of those reported in Chapter 3; 

therefore, reported measures of home range size are not identical between Chapters 3 and 8. Migration 

distance was calculated as the distance between the center of breeding home range and the center of 

winter home range, and classified as short (i.e., resident: < 200 km/ 124 mi), medium (200–800 km/ 124–

497 mi), and long (> 800 km/ 497 mi).

We used a hurdle modeling approach to assess relationships between PAHs and independent variables. 

Step one of the hurdle model used a binomial logistic regression with a log link function, using the 

presence or absence of each of the three PAH variables as the response variable. Step two of the hurdle 

model used a GLM with a gamma distribution and a log link function, using the sum of the concentration 

of each of the three PAH variables as the response variable. The gamma model included individuals 

where sumPAH, sumPAR, or sumALK were superior to detectable limit of PAHs (i.e., sum ≠ 0). 
37

We built 12 models for adult blood samples, 16 models for adult feather samples, and 3 models for chick 

feather samples and compared them using AIC (Table 2.7) to assess the relationships among PAHs and 

independent variables. We reported coefficient estimates from all models within delta AIC ≤ 2.0 and 

average coefficient estimates if they appeared in > 1 of the top models. We reported coefficient estimates 

when estimate ± SE did not overlap 0. We provided odds ratios for coefficient estimates from binomial 

logistic models (the odds of a PAH being detected for a change in categorical levels, or for a one unit 

increase in a continuous variable) and from gamma models (the odds of the concentration of a PAH 

increasing by 1 ng g-1 for a change in categorical levels, or for a one unit increase in a continuous 

variable).

Table 2.1. Environmental Data Layers and Data Sources Used for Analysis of Annual Habitat Use by Adult 

Brown Pelicans

Variable name Layer name Data source Resolution

Distance to coast World Vector Shoreline, 

Intermediate Resolution

Global Self-consistent 

Hierarchical High-Resolution 

Geography Database, NOAA

(Wessel et al. 1996)

1:25000

Distance to river 

outflow

North American Rivers 

and Lakes 

North American Data Atlas, US 

Geological Survey

1:100000

Bathymetry 2-minute Gridded Global 

Relief Data, (ETOPO2) 

v2

NOAA (National Geophysical 

Data Center 2006)

0.033

Bottom substrate Dominant Bottom Types 

and Habitats

Gulf of Mexico Data Atlas, 

NOAA

Net primary 

production

Vertically Generated 

Production Model 

Ocean Productivity, Oregon 

State University (O’Malley 

2012)

0.083

Sea surface 

temperature

Sea Surface 

Temperature, 

Climatological Mean, 10 

m depth

NOAA National Centers for 

Environmental Information 

(Boyer et al. 2011)

0.1

Sea surface 

salinity

Sea Surface Salinity, 

Climatological Mean, 10 

m depth

NOAA National Centers for 

Environmental Information 

(Boyer et al. 2011) 

0.1

Table 2.2. Surface Pollutant Data Layers and Data Sources Used for Risk Analysis

Variable name Layer name Data source Resolution

Surface pollution Ocean Pollution (Ship 

Traffic and Ports)

Global Map of Human Impact 

Project, National Center for 

Ecological Analysis and 

Synthesis (Halpern et al. 2008)

0.01

Platforms Drilling Platforms–

federal waters

Bureau of Safety and 

Environmental Enforcement 

Gulf of Mexico OCS Region 

NA

Drilling Platforms–state 

waters

Texas General Land Office; 

Louisiana Strategic Online 

Natural Resources Information 

System; Alabama Oil and Gas 

Board

NA

Pipelines Oil and Gas Pipelines–

Gulf of Mexico

Bureau of Safety and 

Environmental Enforcement 

Gulf of Mexico OCS Region 

NA
38

Table 2.3. Environmental andNest-based Variables Collected at Brown Pelican Nests on Gaillard and Cat 

islands, Alabama, 2017 and 2018

Variable Description Data Range and/or Category

Nest height Height of nest above ground Continuous 0–156 cm (61 in)

Location Location of the nest, either by 

island (2017) or by location on 

Gaillard (2018)

Discrete 2017: Gaillard or Cat

2018: Interior or Exterior

Substrate Material under and supporting the 

nest

Discrete Rock or Ground

Elevation Elevation of nest location (m 

above sea level) stratified using 

elevational range of nests 

Discrete Low, Medium, High, or 

Berm

Distance to water Distance from the nest to the 

closest water’s edge

Continuous 1.5–127.7 m (4.9–419.0 

ft)

Vegetation Cover Amount of vegetation covering 

the nest

Continuous 0–100%

Humidity Average Average humidity between nest 

visits (% water vapor) from the 

nearest NOAA weather station

Continuous 70.31–90.22%

Barometric pressure 

average

Average barometric pressure 

between nest visits from the 

nearest NOAA weather station

Continuous 100.772–102.225 kPa

Average temperature Average temperature within 

selected nests between nest 

visits

Continuous 24.09–32.29 °C

Maximum 

temperature

Maximum temperature within 

selected nests between nest 

visits

Continuous 31.03–41.07 °C
39

Table 2.4. Models Assessed in Relation to Daily Survival Rates of Eggs and Broods of Brown Pelicans 

Breeding on Gaillard and Cat islands, Alabama, 2017 and 2018

Model name Hypothesis Variables included 

Time Survival has a linear relationship 

with time

Julian date

Age Survival has a nonlinear 

relationship with age

Nest age*

Weather^ Survival has a positive 

relationship with mild weather

Average humidity + Average barometric 

pressure

Storm Survival has a negative 

relationship with increasing storm 

activity

Average humidity + Average barometric 

pressure + Distance to water

Location Survival has a nonlinear 

relationship with location

Distance to water + Elevation + Location+ Julian 

date + Nest age* 

Habitat^ Survival as a positive relationship 

with habitat variables

Nest height + Vegetation cover +Substrate + 

Julian date + Nest age* 

Null Survival is constant ~1

Global Survival has a linear relationship 

with all variables

All variables

Temperature^ Survival has a linear relationship 

with temperature within the nest

Average temperature + Maximum temperature + 

Julian date + Nest age*

*Quadratic age term for all years and breeding stages except for 2017 chick-rearing, when a linear 

term was used

^ Ran both the models listed and additional models that included average daily temperature and 

maximum daily temperature as recorded by loggers placed in a subset of nests. 
40

Table 2.5. Blood Analytes from Samples Collected from Adult and Chick Brown Pelicans from Colonies 

throughout the Northern Gulf of Mexico, 2013–2015

Analyte Abbrev Units Primary indicator of: Category

Carbon dioxide CO2 mEq L-1 Respiratory function Blood gas

Calcium Ca mg dL-1 Nutrition, hepatic damage Electrolytes & minerals

Potassium K mEq L-1 Nutrition Electrolytes & minerals

Phosphorous P mg dL-1 Nutrition, hepatic damage Electrolytes & minerals

Sodium Na mEq L-1 Nutrition Electrolytes & minerals

Alanine aminotransferase ALT Units L-1 Hepatic damage Enzyme

Amylase AML Units L-1 Pancreatic function Enzyme

Aspartate aminotransferase AST Units L-1 Hepatic damage Enzyme

Creatine phosphokinase CPK Units L-1 Tissue damage Enzyme

Gamma-glutamyl transferase GGT Units L-1 Hepatic damage Enzyme

Lactate dehydrogenase LDH Units L-1 Tissue damage Enzyme

Lipase LIP Units L-1 Pancreatic function Enzyme

High density lipoprotein 

cholesterol HDLc mg dL-1

Cardiovascular function Lipids

Triglycerides TRI mg dL-1 Nutrition Lipids

Very low density lipoprotein 

cholesterol VLDLc mg dL-1

Cardiovascular function Lipids

Blood urea nitrogen BUN mg dL-1 Hepatic damage Metabolite

Cholesterol CHL mg dL-1 Nutrition Metabolite

Creatinine CRE mg dL-1 Nutrition, hepatic damage Metabolite

Glucose GLU mg dL-1 Nutrition Metabolite

Ratio BUN/CRE BUN:CRE Unitless Nutrition Metabolite-Ratio

Total protein TPR g dL-1 Nutrition, hepatic damage Metabolite

Uric acid UA mg dL-1 Nutrition, hepatic damage Metabolite

Albumin ALB mg dL-1 Nutrition, hepatic damage, 

immune response Plasma protein

Alpha 1 globulin A1G mg dL-1 Hepatic damage, respiratory 

function Plasma protein

Alpha 2 globulin A2G mg dL-1 Immune response Plasma protein

Beta globulin BEG mg dL-1 Immune response Plasma protein

Gamma globulin GAG mg dL-1 Immune response Plasma protein

Pre-albumin PRA mg dL-1 Nutrition, immune response Plasma protein

Ratio ALB/Globulin A:G Unitless Nutrition, immune response Plasma protein

Corticosterone CORT mg dL-1 Stress response Stress hormone

White blood cell count WBC *10^3 μL Immune response, condition White Blood Cell

Heterophils HET *10^3 μL Immune response, stress White Blood Cell

Lymphocytes LYM *10^3 μL Bacterial infection, hepatic disease White Blood Cell

Monocytes MON *10^3 μL Immune response, parasite 

infection White Blood Cell
41

Analyte Abbrev Units Primary indicator of: Category

Eosinophils EOS *10^3 μL Allergies, vascular disease White Blood Cell

Basophils BAS *10^3 μL Allergies, vascular disease White Blood Cell

Hematocrit (average of 3 

readings) HEM % blood volume Hydration, O2 storage Red Blood Cell

Table 2.6. Models Used in an Information Theoretic Approach to Assess Relationships among Blood 

Analytes and Independent Variables for Brown Pelican Adults and Chicks Sampled from Breeding Colonies 

in the Northern Gulf of Mexico, 2013–2015 

Variables included Adults Chicks Comments

Model 1 sex Yes No Sex not available for chicks

Model 2 BCI Yes Yes

Model 3 planning area Yes Yes

Model 4 home range size Yes No Home range irrelevant for chicks

Model 5 home range size + sex Yes No Neither variable available for chicks

Model 6 BCI + planning area Yes No Two terms are related so not modeled 

together

Model 7 BCI + home range size Yes No Home range irrelevant for chicks

Model 8 null model Yes Yes

Models were compared using Akaike’s Information Criteria. BCI = body condition index (see 

methods for definition).
42

Table 2.7. Models Used in an Information Theoretic Approach to Assess Relationships among PAHs and 

Independent Variables for Brown Pelican Adults and Chicks Sampled from Breeding Colonies in the Northern 

Gulf of Mexico, 2013–2015 

Variables included Adult 

blood

Adult 

feathers

Chick 

feathers Comments

Model 1 sex Yes Yes No Sex not available for chicks

Model 2 BCI Yes Yes Yes

Model 3 planning area Yes Yes Yes

Model 4 home range size Yes Yes No Home range irrelevant for 

chicks

Model 5 sex + BCI Yes Yes No Sex not available for chicks

Model 6 sex + planning area Yes Yes No Two terms are related so not 

modeled together

Model 7 sex + home range size Yes Yes No Home range irrelevant for 

chicks

Model 8 BCI + planning area Yes Yes No Two terms are related so not 

modeled together

Model 9 BCI + home range size Yes Yes No Home range irrelevant for 

chicks

Model 10 planning area + home 

range size Yes Yes No Home range irrelevant for chicks

Model 11

sex + BCI + planning 

area + home range 

size

Yes Yes No See above

Model 12 migration class No Yes No Temporal mismatch w/ adult 

blood; not relevant for chicks 

Model 13 migration class + BCI No Yes No Temporal mismatch w/ adult 

blood; not relevant for chicks 

Model 14 migration class + sex No Yes No Temporal mismatch w/ adult 

blood; not relevant for chicks 

Model 15 migration class + 

planning area No Yes No Temporal mismatch w/ adult blood; not relevant for chicks 

Model 16 null model Yes Yes Yes Temporal mismatch w/ adult 

blood; not relevant for chicks 

Models were compared using Akaike’s Information Criteria. BCI = body condition index (see methods for 

definition).
43

Figure 2.1. Map of colony locations of brown pelicans fitted with GPS transmitters, Gulf of Mexico, 2013–

2015.

Number of adult pelicans tracked through the end of the breeding season from each colony is indicated in 

parentheses. Marker sizes represent relative colony size (75–5000 nesting pairs). Planning areas are 

delineated by dashed lines (Base layer: Esri, DeLorme, GEBCO, NOAA, NGDC, and other contributors).
44

Figure 2.2. Location of brown pelican colonies sampled, Gulf of Mexico, 2013–2015.

Marker sizes represent relative colony size (75–5000 nesting pairs). Nestling health samples were collected 

from all colonies, and nutrition and productivity data were also collected from colonies outlined in red. 

Locations of other brown pelican nesting colonies in the Northern GOM are indicated in yellow. (Base layer: 

Esri, DeLorme, GEBCO, NOAA, NGDC, and other contributors).
45

Figure 2.3. Positioning of GPS transmitter and harness.

(Left) Dorsal location of GPS transmitter and (right) ventral location of harness. Los Angeles Oiled 

Bird Care and Education Center, San Pedro, California, 11 June 2015 (J. Lamb). Green arrows 

point to the locations of the dorsal and ventral attachments. 
46

3 Individual Tracking

Between 2013 and 2015 we deployed 86 transmitters on breeding pelicans throughout the Northern Gulf 

of Mexico (GOM) (Table 3.1; Figure 2.1). Transmitters were evenly distributed between the eastern 

(Smith, Audubon, and Gaillard Islands, Florida-Alabama), central (Felicity and Raccoon Islands; 

Louisiana), and western (Shamrock and Chester Islands, Texas) planning areas, with colony sizes ranging 

from 40 to 4500 breeding pairs. Transmitters typically collected data for between six months and three 

years, with most transmitting for 1–2 years before tag failure or mortality occurred (Figure 3.1). 

Transmitters collected data for breeding and non-breeding movements, including staging and migration. 

Brown pelicans used local habitat during the breeding season but showed substantial movement during 

the non-breeding season (Figure 3.2). Individuals breeding in the eastern GOM wintered as far west as 

southern Louisiana and as far south as central Cuba. Individuals breeding in the central GOM wintered 

along the entire GOM coastline, travelling as far south as Chiapas, Mexico and Belize. Individuals 

breeding in the western GOM staged as far east as southeastern Louisiana and wintered as far south as 

Chiapas, Mexico.

3.1 Tag Effects

3.1.1 Behavioral Effects

Before treatment, captive pelicans spent the majority of time loafing (18–47%), preening (11–32%), or 

resting (20–49%). Swimming, perching, and flying each occupied less than 10% of individual time 

budgets. In the first 1–2 h after receiving transmitters, GPS-tagged individuals spent an increased 

percentage of time preening (mean = + 16.4%, F(1,7) = 6.41, p = 0.038) and decreased time resting (mean 

= -29.1%, p = 0.047, F(1,7) = 5.62) relative to individuals that had not been tagged or handled (Figure

3.3a). Changes in time spent swimming, flying, loafing, and perching did not differ from zero (Figure 

3.3a). We did not find significant differences in frequency (events hour-1

) after tagging for any of the 

instant events we quantified (Figure 3.3b; also see Lamb et al. 2017). In free-ranging pelicans 1–3 days 

post-capture, we did not observe differences between tagged individuals and untagged neighbors in the 

proportion of observation time spent in preening (t31 = -0.59, p = 0.56), resting (t31 = -0.88, p = 0.38), 

alert/loafing (t31 = 1.60, p = 0.12), or agitated (t31 = -1.42, p = 0.17) behavioral states (Figure 3.4).

3.1.2 Effects on Nesting Success

Overall, GPS-tagged pelicans (N = 74) continued attending nests for an average of 50 d (SD ± 34; Range 

0–113) after capture, with a 51% apparent success rate for breeding (N = 38 successful nests). Apparent 

success rates of tagged breeders were slightly lower than but not significantly different from success rates 

of untagged adults measured in the same colonies in 2014–2015 (62%; N = 482; Χ2

1 = 3.46; p = 0.06). 

The majority (88%; N = 65) continued breeding at their original nest sites following capture. The 

remaining adults either abandoned the breeding colony within one day of capture and did not re-nest that 

season (N = 3), re-nested at a different nest site in the same breeding colony (N = 3), or re-nested at 

different breeding colonies between 30 and 65 km (18.64–40.40 mi) from the original nesting colony (N 

= 3) (Table 3.2). Successful breeders attended colony sites for an average of 83 d after hatch (SD ± 13 d),

while unsuccessful breeders attended on average 18 d (SD ± 14.7 d). We observed successful breeding in 

pelicans that re-nested elsewhere as well as pelicans that remained at their original nest sites (Table 3.2). 

Breeding success was similar in the eastern (76%) and central (67%) planning areas and lower in the 

western (15%) planning area (Figure 3.5a). In the eastern planning area, breeding success of tagged 

pelicans in 2013–2014 was similar to that of untagged pelicans at the same study colonies in 2015 (72%: 

Χ2

1 = 0.23; p = 0.63). In the western planning area, breeding success was lower in tagged pelicans in 
47

2013–2014 than in untagged pelicans in 2014 (45%; Χ2

1 = 9.91; p = 0.002). We did not measure breeding 

success of untagged pelicans at the central colonies during any of the three study years.

The global model predicting breeding success of tagged birds was a good fit for the observed data, 

indicating that the full suite of parameters effectively explained variation in breeding success (Χ2

8 = 1.85, 

p = 0.99). The four best-performing models for breeding success included capture location (Table 3.3), an 

index of underlying variability among planning areas. The model-averaged coefficient estimates (± SE) 

for location, with the eastern planning area set as the reference location, were -0.43 ± 0.66 for the central 

planning area and -2.83 ± 0.75 for the western planning area. Two of the top models also included 

handling time (-0.64 ± 0.54), and two included sex (0.67 ± 0.56). Phenological variables (capture date and 

nest stage), year of capture, physical condition (BCI), and percent body mass of transmitters were not 

included in the best-performing models for breeding success. 

Handling time at capture was significantly longer in unsuccessful than successful breeders (t55 = 1.7, onetailed p = 0.047), with a significant decrease in breeding success among birds that were handled for more 

than 20 min (Figure 3.5b: Fisher’s Exact Test, one-tailed p = 0.045). Sex did not differ significantly 

between successful and unsuccessful breeders (Figure 3.5c: Fisher’s Exact Test, one-tailed p = 0.33); 

however, females were more likely than males to abandon or re-nest within one day of capture (Fisher’s 

Exact Test, one-tailed p = 0.045).

We observed short-term behavioral effects of handling and transmitter attachment in a captive setting 1–2 

h post-release, but not in a field setting 1–3 d post-release. Captive and free-ranging groups were 

observed under different conditions and had different histories. Because of these differences, the 

behavioral patterns we observed in captive birds may differ from those of free-ranging individuals. 

However, both captive and free-ranging pelicans were observed relative to control individuals under the 

same conditions that were not captured or GPS-tagged. Because we observed behavioral changes 

immediately after transmitter attachment but not within several days of capture, we suggest that behaviors 

indicative of stress or discomfort in our study (due to either the attached device, the harness, the capture 

process, or any combination of the above) diminished rapidly. Although we did not separate handling 

from device effects (i.e., include procedural controls), the process of fitting an individual with a 

transmitter inevitably involved both handling and device effects. A meta-analysis by Barron et al. (2010) 

found that behavioral effects of transmitter attachment are generally indistinguishable between studies 

with and without procedural controls, indicating that most effects can be attributed to the device alone.

Immediately after transmitter attachment, we observed differences in tagged captive birds’ time spent 

preening and resting relative to the controls. Because both handling and harness attachment may disrupt 

plumage and reduce waterproofing, increased preening behavior suggests an attempt to restore feather 

structure and represents a potential short-term increase in energy expenditure following handling and 

transmitter attachment. Other behaviors (e.g., swimming, perching, flying, loafing, and instantaneous 

events) did not increase or decrease following transmitter attachment. As swimming and flight are 

particularly critical to foraging, migrating, provisioning chicks, and escaping predators, these behaviors 

are often tested for adverse effects of transmitter attachment (Pennycuick et al. 2012; Matyjasiak et al.

2016). Our results suggest that individuals fitted with external transmitters continued to engage in 

swimming and flight at similar rates to control individuals immediately post-capture. However, our 

observations are limited to captive birds in a small enclosure, and we did not measure foraging 

movements or flight and swimming behavior in the field. Further, we did not assess the speed or 

efficiency of either swimming or flight, which can be altered by the presence of an external transmitter 

(Barron et al. 2010; Vandenabeele et al. 2011).
48

All supported models for breeding success included capture location as a predictor variable, which may 

result from underlying differences in breeding success among planning areas rather than from capture and 

tracking. Currently, there are limited data on factors affecting productivity in brown pelicans throughout 

their range. However, Walter et al. (2014) also reported regional differences within the state of Louisiana 

in failure rates of brown pelican nests following capture and GPS-tagging, thus suggesting that nesting 

success may vary spatially depending on underlying conditions (e.g., prey distribution, habitat 

availability, and environmental conditions). In 2015 in the eastern planning area, we recorded similar 

rates of apparent brood success of untagged brown pelicans compared to those of tagged pelicans in 

2013–2014 (i.e., suggesting no effect of tagging on reproductive success). In comparison we did observe 

apparently higher rates of brood success among untagged (45%) compared to tagged (15%) pelicans at

the two Texas colonies included in this study, although this comparison was confounded among years. 

Our data are not, therefore, conclusive as to whether lower rates or brood success in the Texas colonies 

were due to tagging or other underlying factors (e.g., prey availability, see 5.1). It is important to note that 

measured rates of nest success for brown pelicans in previous studies have ranged widely between years 

and locations (Shields 2014). Direct comparisons are limited by likely inter-annual variation and the small 

sample size of tagged pelicans relative to untagged individuals. Further assessment of the environmental 

factors underlying regional variation in nest productivity could help to elucidate the conditions under 

which tagging may depress nesting success.

Handling time appeared in two of the top models for breeding success. Longer handling periods resulted 

in a decrease in breeding success, with sharply reduced breeding success among birds that were handled 

for more than 20 minutes. Adults handled for longer time periods spent more time away from the nest 

during handling, which resulted in longer exposure of eggs to ambient temperature and may have affected 

egg viability. Longer periods of high stress resulting from handling may also have affected adult 

condition and likelihood of returning to the nest site. Effects of increased handling time on behavior have 

also been observed by Jodice et al. (2003) for black-legged kittiwakes (Rissa tridactyla). Handling time 

decreased during the course of our study, suggesting that researcher experience is an important factor in 

minimizing the effects of capture and tagging. 

Sex also appeared as a predictor in two of the four top models. Although we did not observe a significant 

difference in breeding success between tagged male and female pelicans, our results indicate that females 

may be more likely than males to abandon immediately after being captured and fitted with GPS 

transmitters. As pelicans are sexually dimorphic, the percentage of body weight represented by a 

transmitter is higher for females (μ = 2.2 ± 0.2%) than for males (μ = 1.7 ± 0.1%). However, sex alone 

remained the strongest individual-level predictor of breeding success and transmitter mass, and neither 

body condition nor transmitter payload improved model fit. Transmitter weight represented < 3% of body 

mass for all individuals included in this study, which is generally considered an acceptable payload for 

seabirds (Phillips et al. 2003, although see Vandenabeele et al. 2012 for discussion of the limitations of 

this rule). There is limited evidence that females of some seabird species may take longer than males to 

recover from disturbance (Weimerskirch et al. 2002) and may be more sensitive to environmental 

conditions (Jodice et al. 2003). Female seabirds can also exhibit higher baseline CORT levels than males

following the physiologically intensive egg-laying process (Lormée et al. 2003; Goutte et al. 2010), which 

may exacerbate the effects of stressors (e.g., capture and handling).

We did not observe the high rates of nest failure previously reported in GPS-tagged brown pelicans in the 

Northern GOM following transmitter attachment (Walter et al. 2014). Our study included pelicans from a 

much broader geographic range, but among birds breeding in the central planning area (comparable to the 

Louisiana study area in Walter et al. 2014) we observed a lower rate of relocation and nest failure (48% in 

our study, vs. 94% in Walter et al. 2014), a lower rate of abandonment within 48 h of tagging (19% 

compared to 44%), and a longer duration of nesting among breeders that remained on their original nest 

sites but eventually failed (40 ± 9 d vs. 7 ± 10 d). Both studies used the same capture method, transmitter 
49

weight and profile, attachment location, and harness shape. However, average handling times were 

significantly shorter in our study (18.8 ± 6.5 min) compared to the aforementioned study (approximately

45 minutes). This was likely due to differences in harness attachment methods. Though the previous study 

used metal clamps and sutures to fasten harnesses, our study used knots covered in cyanoacrylate, which 

could be secured more rapidly. Additionally, following observations by the authors of the previous study 

that neighboring pelicans would often destroy unattended nests, we used a plastic mesh basket to protect 

nest contents while captured adults were absent from the nest. These differences may have contributed to 

lower rates of abandonment and egg loss in our study. Future tracking studies of nesting brown pelicans 

might include such precautions to ensure that nest contents are protected during the tagging process and to 

improve the likelihood of successful breeding by tracked adults.

Our study suggests that capture and GPS-tagging in brown pelicans results in short-term behavioral 

effects, but that these effects are minimal and do not persist into the days following transmitter 

attachment. According to our data, behavioral changes resulting from the transmitter attachment process 

could be accounted for by excluding locations obtained during the first 24 h after transmitter attachment 

to avoid biased inference in the GPS data analysis. Our study also indicates that tagged individuals can 

continue breeding and successfully raise young following capture, and that efforts to minimize handling 

time and protect nest contents during capture may contribute to improved nesting success. However, 

female breeders and individuals in relatively poor breeding locations may be more likely to abandon after 

capture and transmitter attachment. Because our study included only the breeding season following 

capture, we did not assess long-term effects of transmitter attachment on adult overwinter or inter-annual 

survival, or on lifetime fitness. Though reproductive and survival values are key to understanding the 

demographic effects of such perturbations, baseline data on these parameters are lacking in brown 

pelicans and many other seabird species. Future studies are needed on long-term impacts of carrying a 

GPS transmitter on site fidelity, survival, and reproductive success in the years following transmitter 

attachment in pelicans and other seabirds.

3.2 Foraging Movements and Home Range during the Breeding Period

During the breeding period, brown pelicans utilized coastal areas in the vicinity of their nesting site 

(Figure 3.6.a). We found some degree of overlap between neighbouring colonies in both 50% UD and 

95% UD home ranges (Figure 3.7). This suggests that individuals from neighbouring colony sites were 

not partitioning foraging habitat. During the breeding season, colony size alone was the top predictor of 

individual 50% UD and 95% UD areas (Table 3.4). Overall, the linear relationship between colony size 

and breeding season home range size was significantly positive for both 50% UD (t65 = 3.65, p = 0.005) 

and 95% UD home ranges (t65 = 3.56, p = 0.007) (Table 3.5). For each increase of 100 breeding pairs at a 

colony, mean core home range size of individual breeders increased by approximately 3 km2 (1.86 mi2

; 

Figure 3.8a) and mean full home range size increased by approximately 19 km2 (11.81 mi2

; Figure 3.8b). 

A model including both colony size and body condition also received substantial support as a predictor of 

95% UD areas (Table 3.5). The relationship between body condition and 95% UD area was positive, 

indicating an increase in 95% UD area with increasing body condition. However, condition was not a 

significant predictor of 95% UD area (t65 = 1.20, p > 0.2).

Density-dependent competition for prey resources is one of several factors potentially influencing 

foraging distances, and migratory movements of colonial seabirds. We tested for effects of densitydependent resource competition on several parameters related to movement patterns in brown pelicans 

and related these effects between-individual variation. We found a strong positive linear relationship 

between the size of both core (50% UD) and full (95% UD) home ranges of individual breeders, and the 

size of the breeding colony. Individuals at larger colonies consistently foraged over larger areas than 

individuals at smaller colonies. Body condition was included as a predictor in one of our top models of 

full home range area (95% UD), but only in combination with colony size, and breeders that foraged over 
50

greater distances were generally in better physical condition. Other individual characteristics (e.g., sex, 

body size) and regional environmental conditions were not included as predictors in any highly supported 

models of either core or full home range areas during the breeding season. This adds to a growing body of 

evidence that colonial birds consistently increase their foraging radius in response to localized densitydependent prey depletion (e.g., Brown and Brown 1996; Lewis et al. 2001; Ainley et al. 2003; Ford et al.

2007; Bonal and Aparicio 2008). The fact that we did not observe a decline in adult body condition with 

increased foraging area further suggests that pelicans in this system were able to increase their foraging 

effort without experiencing compromised physical condition.

Most research to date has concentrated on pelagic seabirds breeding at temperate latitudes. Our study adds 

a new perspective to the understanding of the relationship between colony size and foraging distance in 

seabirds. For instance, in contrast to previous studies (e.g., Grémillet et al. 2004; Wakefield et al. 2013), 

we did not observe strong spatial segregation in foraging ranges between closely neighbouring colonies. 

For example, adults from two Florida colonies (Audubon and Smith Islands) frequently travelled over 100 

km (62.14 mi) to a common foraging area at the mouth of the Apalachicola River. Prey concentrations in 

nearshore environments may occur predictably in and around stationary coastal features, including:

headlands, river mouths, and upwelling zones (Becker and Beissinger 2003). Thus, the overlap we 

observed between neighbouring colonies may represent common exploitation of prey-concentrating 

features that are spatially predictable. 

We did not find a significant relationship between colony size and either of the nestling condition metrics 

we tested (body condition or feather CORT). We previously determined that both feather CORT and body 

condition are effective predictors of chick survival in this system (Lamb et al. 2016), so we can 

extrapolate from our results that the reproductive rates of pelicans do not decline with colony size. This 

result contradicts several previous studies suggesting a relationship between chick condition and colony 

size (e.g., Gaston et al. 1983, Hunt et al. 1986, Cairns 1992); however, several other studies have failed to 

find a correlation (Brown and Brown 1996; Ainley et al. 2004; Gaston et al. 2007). The identified

relationship between colony size and adult foraging ranges, but not chick condition, indicates that, within 

the range of colony sizes included in this study, adults can adjust their foraging ranges in response to 

density-dependent prey depletion without sacrificing reproductive output. Brown pelicans in this system 

may be operating well below metabolic limitations on their energetic expenditure (Drent and Daan 1980), 

allowing for plasticity in foraging effort.

This study also differs from previous studies on the effects of colony size on seabird breeding success by 

focusing on nearshore seabirds in subtropical waters. Previous studies have suggested a negative effect of 

colony size on chick condition, but have been conducted in high-latitude, pelagic systems. Both the lifehistory strategies of nearshore compared to pelagic seabirds and the relative complexity of nearshore 

compared to pelagic habitats may affect the relationship between colony size and chick condition (Suryan 

et al. 2006). For example, nearshore seabirds tend to have a more variable clutch and brood size compared 

to pelagic seabirds, allowing for adjustments in reproductive output in response to changes in local prey 

availability. Similarly, higher concentrations of resources within nearshore environments compared to 

pelagic habitats may allow nearshore seabirds to remain well below their energetic thresholds during 

chick-rearing (Ballance et al. 2009); thus, increases in foraging effort due to density-dependent 

competition might be less likely to result in measurable declines in chick condition than in pelagic 

environments. 
51

3.3 Non-breeding Movements and Migratory Strategy

3.3.1 Influence of Individual Characteristics and Colony Size on Migratory Movements

We obtained data on migratory movements of 63 individuals. Proportion of migrants per colony was 

similar among colony sites.

Colony size was included as a predictor in 11 of 13 substantially supported models of migratory 

movements (Table 3.6), and had the highest importance value among all parameters for predicting 

migration distance. Colony size had a significant positive correlation with both migratory strategy (t62 = 

2.16, p = 0.03) and migration distance (t62 = 2.85, p = 0.006). For each increase of 100 pairs at the 

breeding colony, individuals were 1% more likely to migrate (Figure 3.9a), and wintered approximately 

16 km (10 mi) further from their breeding sites (Figure 3.9b). 

Twelve of 13 top models for migratory movements included individual covariates. Body size was 

included in ten supported models, including all models of migratory strategy, and had the highest 

importance value among all parameters for predicting migratory strategy (Table 3.7). Body size had a 

negative correlation with migratory strategy (i.e., smaller individuals were more likely to migrate) (t62

= -3.15, p = 0.001; Figure 3.10a) but not with migration distance (t62 = -1.19, p > 0.2). 

Sex was included in four of the top models. Males (47% migrants; N = 36) were less likely to migrate 

than females (78% migrants; N = 32) t62 = -2.50, p = 0.01), although migration distances did not differ 

significantly between sexes (t62 = -1.03, p > 0.2). Since males were larger-bodied than females, we also 

tested for within-sex differences in body size. Resident males were significantly larger than migrant males 

(F1,34 = 4.65, p = 0.04), but resident and migrant females did not differ significantly in body size (F1,30 = 

2.18, p = 0.14) (Figure 3.10b). 

Body condition was included in three of the top models, and environmental parameters appeared in one 

model, but neither was a significant predictor of either migratory strategy or migration distance (p > 0.2 in 

all cases).

Interaction terms were included in three of the 13 supported models (Table 3.6). A model including 

colony size, body size, and their interaction was the best-supported model of migration distance and was 

among the top models of migration strategy. A model including colony size, body size, sex, and their 

interactions was included among the top models of migration distance.

We chose study colonies within a single ecoregion to limit environmental variation, but we were unable 

to control all factors that could contribute to local variation in foraging conditions. Underlying resource 

availability, which is difficult to measure directly in marine systems, may also vary between colonies and 

can confound an assessment of the influence of colony size on seabird behaviour. Gulf menhaden, which 

comprise a large portion of pelican diets in the Northern GOM (Shields 2014), are concentrated in the 

central portion of the Gulf, meaning that colonies at the margins of our study area may have experienced 

lower availability of this prey item. We incorporated remotely-sensed environmental variables associated 

with menhaden availability (Ahrenholz 1991) into our models of adult movement patterns and chick 

condition to help account for this underlying prey variation. However, such variables are only a proxy for 

underlying prey variation, and the most effective way to account for prey availability would be to 

measure prey concentrations directly.
52

We found a positive correlation between breeding colony size and the proportion of individuals that 

migrated away from the colony during nonbreeding, as well as the distance travelled by migrants. Partial 

migration in seabirds has been little-studied and, to the best of our knowledge, a relationship between 

migratory strategies of individual breeders and breeding colony size has not previously been observed in 

either nearshore or pelagic seabirds. Density dependent competition for resources may present a 

significant obstacle to remaining residents in the Northern GOM. During winter months, prey populations 

in the region migrate offshore, , further reducing prey availability. By reducing predation pressure during 

periods of resource scarcity, partial migration provides a potential mechanism for increasing overwinter 

survival in the face of density-dependent competition. 

Previous research on density-dependent population regulation in seabirds has focused almost exclusively 

on foraging movements and nesting health during the breeding season. The study of migratory behaviour 

in relation to conspecific prey depletion due to density-dependence has been less common, and has 

primarily been limited to species-level patterns (Diamond 1978). In contrast, investigations of 

relationships between colony size and migratory behaviour within a single species have been rare. 

Previous evidence has indicated a complex migration strategy in brown pelicans (King et al. 2013), but 

has not explored how migratory behaviour varies throughout the population or what drives individual 

migration patterns.

In addition to suggesting a relationship between colony size and migration propensity, our results also 

highlight the importance of individual physical characteristics in driving migration patterns. Whether 

individuals were migratory or resident was highly dependent on body size, as well as the interaction 

between body size and colony size. Five of the seven top models of migratory strategy included both body 

size and colony size, including one model with an interaction between the two covariates. The bestsupported model of migration distance included a body size–colony size interaction. Partial migration 

patterns have previously been associated with individual differences in social status (e.g., Terril 1987; 

Cristol et al. 1999), variation in thermal tolerance with body size (e.g., Belthoff and Gathreaux 1991; 

Chapman et al. 2011, Macdonald et al. 2016), and/or differential fitness benefits to males of early arrival 

at the breeding site (e.g., Myers 1981; Pérez et al. 2013). The majority of our top models for migratory 

behaviour contained colony size in combination or interaction with one or more individual characteristics 

(e.g., sex, body size, and/or condition), indicating that the influence of individual characteristics on 

migration propensity and distance is mediated by density-dependent competition. Smaller individuals and 

females were more likely to migrate overall and were increasingly likely to migrate as colony size 

increased, lending support to the importance of social status as a driver of migration decisions. Local 

intraspecific competition may place subdominant individuals at a competitive disadvantage during 

periods of reduced prey availability, thus forcing them to move further from colony sites during the 

winter. 

Our results offer insight into the ecological underpinnings of migratory decisions, suggesting that local 

intraspecific competition may be a driver of partial migration, and that changes to brown pelican breeding 

densities could result in corresponding migratory behaviour and nonbreeding location shifts that 

differentially affect individuals within the population. The relationship between colony size and migration 

includes a complex combination of factors, including competition, survival, and site selection. By 

establishing a link between intraspecific competition and migration, our results may elucidate a 

demographic mechanism underlying the differences observed in migration strategies among individuals.
53

3.3.2 Overlap of Populations During Staging

We examined specific habitat use by brown pelicans captured while breeding in colonies in three sections 

of the Northern Gulf: the eastern (Florida panhandle), central (Alabama, Mississippi, and Louisiana 

coasts), and western planning areas. We observed a distinct separation between birds from eastern Gulf 

colonies and those in the central and western planning areas. Year-round habitat overlap between breeders 

from colonies in the central and western planning areas totaled 30–40%; eastern breeders shared only 

15% of their total habitat area. Moreover, while central and western Gulf breeders extensively used the 

same set of nonbreeding areas in the southern Gulf along the east coast of Mexico and throughout the 

Yucatan Peninsula, eastern Gulf breeders typically migrated southward to the Florida Keys and Cuba. We 

did not observe overlap between the eastern breeding population and the central or western groups in 

southern Gulf wintering habitat. The only area in which breeders from all three planning areas overlapped 

was in the Mississippi Delta, located in the central Gulf (Figure 3.11). The apparent separation between 

the eastern breeding colonies and the rest of the Northern Gulf population is unexpected as translocations

from eastern colonies were used to re-establish the central Gulf breeding population following DDTrelated extirpation (McNease et al. 1992).

To date, studies of brown pelican nonbreeding movements have been limited to information on band 

recoveries, typically from birds banded as juveniles (Schreiber and Mock 1988, Stefan 2008) and tracking 

data from individuals captured during non-breeding (King et al. 2013). This has limited the possibility of 

linking non-breeding birds to breeding colonies outside the breeding season. Ours is the first study to 

incorporate individual data on year-round movements of brown pelicans from known breeding locations. 

Understanding the likelihood of overlap between different breeding populations in different planning 

areas of the Gulf helps to refine current understanding of the distribution of environmental risk among 

breeding populations, and to better identify which segments of the overall breeding population are 

affected by spatially explicit threats in the marine environment.

3.3.3 Migration through the Tehuantepec Isthmus

Two of the 34 (6%) brown pelicans tracked from the western and central GOM through a full migratory 

cycle wintered along the Pacific coast of the Tehuantepec Isthmus (Figure 3.12), including one breeder 

from Louisiana tracked through a single annual cycle, and another from Texas tracked through two annual 

cycles.

Migratory routes for both pelicans crossed the isthmus within a longitudinal span of 250 km (155 mi)

from its narrowest point, and each individual crossed wind energy installations on at least one route. 

Distance to the nearest wind energy installation from the estimated migratory path ranged from 0 to 59 

km (x̅= 27 ± 30 km) (0 to 37 km, x̅= 17 ± 19 mi). Fall migrations occurred between late October and late 

November; spring migrations occurred from March through May, with average dates falling between late 

March and early April. Brown pelicans traveled only during daylight hours. Fall migrations occurred 

within a single day, while spring migrations spanned 2–3 days of daylight travel and overnight stops. 

Brown pelicans that passed through wind energy installations did so either at the end (fall) or beginning 

(spring) of their migration (Figure 3.12). Brown pelicans conducted fall migrations through the isthmus 

under lower-than average wind speeds (-58%), while spring migrations occurred within 10% of average 

wind speeds.
54

Although the Tehuantepec Isthmus has previously been identified as a potential migration route through 

which marine bird species breeding on the East coast of North America might cross to the Pacific 

(Binford 1989), regular inter-oceanic migrations of marine birds across Central America have not 

previously been described, with the exception of a single red-necked phalarope (Phalaropus lobatus) 

tracked from Scotland (Smith et al. 2014). Molecular data suggest that Central America is a significant 

barrier to gene flow for marine birds (Steeves et al. 2005, Friesen et al. 2007); however, the use of this 

migratory route by brown pelicans suggests that Atlantic and Pacific populations may overlap in nonbreeding areas.

Our data suggest that the proportion of brown pelicans involved in inter-oceanic migration is low relative 

to overall population size. However, anomalous migratory routes can serve important roles in population 

health and persistence by facilitating genetic mixing (Liedvogel et al. 2011). Dispersive migration may 

also help to distribute risk across the population (Johnson and Gaines 1990), and individuals in remote 

wintering areas may serve as a source of re-colonization following environmental catastrophe (King et al.

1985). A full understanding of complex migration pathways is also critical to estimating how risks (e.g., 

disease transmission and spatially heterogeneous anthropogenic stressors) are distributed across 

populations (Martin et al. 2007), as well as to develop conservation strategies for preserving species with 

complex migratory movements in the face of global change (Martin et al. 2007).

Our study also establishes that brown pelican migratory paths overlap spatially with terrestrial wind 

energy installations in the Tehuantepec Isthmus. Our analysis is focused on coastal marine bird species 

that breed or stage in the GOM; however, recent data from Arctic-breeding semipalmated sandpipers

(Calidris pusilla) suggest that other species typically associated with coastal migration routes may be 

using the Tehuantepec Isthmus to move between Atlantic and Pacific coastal flyways (Brown et al. 2017). 

It is important to note that, though tracking data can suggest the potential for individuals to interact with 

terrestrial features along their movement paths, it does not prove that interaction is taking place (Drewitt 

and Langston 2006, Furness et al. 2013). Because our data do not include flight altitudes, we are able to 

establish only macro-scale overlap between migrating birds and wind turbines. Establishing macro-scale 

interaction represents only an initial step in identifying locations and extent of potential conflict, with 

further targeted research needed to determine whether micro-scale interaction is likely. Furthermore, 

migratory birds in the Tehuantepec region may adjust their routes to avoid turbines (Villegas-Patraca et 

al. 2014, Cabrera-Cruz et al. 2017), incurring energetic costs even in the absence of direct interaction 

(Masden et al. 2010). 

Mitigation strategies to reduce impacts of wind turbines on birds, including changing operations 

schedules, reducing rotor speeds, and improving turbine visibility (Drewitt and Langston 2006), require 

knowledge of both distribution and biology of at-risk species in the region of the installation. Wind 

turbine mitigation efforts in the Tehuantepec Isthmus have previously targeted Swainson’s hawks (Buteo 

swainsoni) (Kochert et al. 2011) and Franklin’s gulls (Larus pipixcan) (Villegas-Patraca and HerreraAlsina 2015). However, brown pelicans differ from these target species in their migration patterns, flight 

behavior, and residence times. Moreover, risk factors of wind turbines are highly variable among avian 

taxa and depend on flight behavior, body size, and wing loading (Herrera-Alsina et al. 2013). Further 

information is needed on micro-scale flight altitude and behavior throughout the residence period to 

accurately evaluate collision risk for the species included in this study. 
55

Table 3.1. Colony Characteristics and Measurements of Tracked Adults Captured at Six Brown Pelican Breeding Colonies, GOM, 2013–2014 

Smith Audubon Gaillard Felicity Raccoon Shamrock Chester

Colony size (breeding pairs) 40a 100a 4500b 1800c 4300c 1400d 3200d

# of adults tracked 9 11 5 12 14 11 10

% male 78 64 40 50 57 55 30

Mass (g) 3414 ± 432 3414 ± 558 3190 ± 329 3448 ± 36 3546 ± 353 3459 ± 562 3070 ± 508

Culmen length (mm) 322 ± 22 315 ± 21 312 ± 20 313 ± 23 316 ± 23 321 ± 25 309 ± 19

BCIe -141 ± 273 -241 ± 205 -131 ± 343 77 ± 195 121 ± 263 -19 ± 306 -147 ± 281

Data Sources:

a Florida Fish and Wildlife Conservation Commission (https://public.myfwc.com/crossdoi/shorebirds/) b Dauphin Island Sea Labs

c Walter et al. (2014) d Texas Colonial Waterbird Census (https://tpwd.texas.gov/huntwild/wild/wildlife_diversity/tcws/data.phtml) e Body Condition Index (BCI) is a derived parameter representing the relationship between mass and skeletal size. Positive values indicate higher mass than 

predicted by the regression between mass and skeletal size, while negative values indicate lower mass than predicted.
56

Table 3.2. Nest Persistence and Apparent Breeding Success of GPS-tagged and Untagged Brown Pelicans, 

GOM, 2013–2014

N

Mean days 

attending nest 

after hatch (SD)

%

successful

GPS-Tagged 74 50 (34) 51

 Remained at original site 65 49 (33) 52

 Re-nested (same colony) 3 57 (22) 67

 Re-nested (different colony) 3 47 (24) 67

 Abandoned 3 0 0

Breeding success is defined as adults attending nests for at least 60 days posthatching for the purposes of this study.
57

Table 3.3. Candidate Models for Breeding Success of Brown Pelicans, GOM, 2013–2014

Model ID Terms AIC Δi (AIC) wi (AIC) Σw Li (AIC)

10 Location 85.75 0 0.30 0.30 1

13 Sex + Location (2 + 10) 86.3 0.55 0.23 0.53 0.76

16 Handling + Location (9 + 10) 86.97 1.22 0.16 0.69 0.54

19 Sex + Handling + Location (2 + 9 + 10) 87.56 1.81 0.12 0.81 0.40

15 Phenology + Location (8 + 10) 88.81 3.06 0.07 0.88 0.22

18 Sex + Phenology + Location (2 + 8 + 10) 89.46 3.71 0.05 0.93 0.16

20 Phenology + Handling + Location (8 + 9 + 10) 90.15 4.4 0.03 0.96 0.11

21 Global (2 + 8 + 9 + 10) 90.91 5.16 0.02 0.98 0.08

11 Sex + Phenology (2 + 8) 95.29 9.54 < 0.01 < 0.01

8 Phenology (5 + 6) 95.45 9.7 < 0.01 < 0.01

15 Sex + Phenology + Handling (2 + 8 + 9) 96.72 10.97 < 0.01 < 0.01

14 Phenology + Handling (8 + 9) 96.75 11.0 < 0.01 < 0.01

5 Nest stage 97.8 12.05 < 0.01 < 0.01

2 Sex 103.9 18.15 < 0.01 < 0.01

12 Sex + Handling (2 + 9) 104.3 18.55 < 0.01 < 0.01

6 Capture date (Julian) 104.5 18.75 < 0.01 < 0.01

22 Null model 104.5 18.75 < 0.01 < 0.01

9 Handling time 104.6 18.85 < 0.01 < 0.01

1 BCI 105.1 19.35 < 0.01 < 0.01

3 Payload (% body mass of transmitter) 106.5 20.75 < 0.01 < 0.01

7 Capture year 106.5 20.75 < 0.01 < 0.01

4 Individual (BCI + sex + payload) 107.5 21.75 < 0.01 < 0.01

Models are ranked in order of increasing AIC values with model weights (wi), cumulative weights (Σw) 

and relative likelihoods (Li). Models above the dashed line were considered strongly preferred (Δ AIC < 

2). Terms used in models are defined in Methods. Numbers in parentheses represent model IDs.
58

Table 3.4. Generalized Linear Models, Model Weights and Top Model Evidence Ratios for Breeding 

Movements of Adult Brown Pelicans in the Northern GOM, 2013–2016

K AICc Δi (AICc) wi (AICc) Σw E

Breeding (N = 73)

Core home range (gamma)

 Colony size 3 835.44 0 0.31 0.31 3.0

Full home range (gamma)

 Colony size 3 1094.34 0 0.27 0.27 2.12

 Colony size + Condition 4 1095.84 1.50 0.13 0.40

Substantially supported (Δi AICc ≤ 2) generalized linear models are shown. wi= 

model weights; E= evidence ratios. Link functions are given in parentheses.
59

Table 3.5. Model-averaged Coefficients and Importance Values for Individual Covariates across the 95% 

Confidence Set of Models for Each Breeding Movement Parameter

Variable 50% UD 95% UD

Coefficient Importance Coefficient Importance

Colony size 0.03 ± 0.01 0.96 0.19 ± 0.07 0.92

Body size (culmen) 0.7 ± 1.0 0.25 3.6 ± 6.5 0.25

Condition 9.3 ± 79.7 0.24 548 ± 544 0.31

SexMale -10.8 ± 46.3 0.25 -43.4 ± 307 0.24

Environment 2.3 ± 17.7 0.27 75.5 ± 120 0.29

SE are shown. Bold values indicate the highest importance value for each outcome.

Table 3.6. Generalized Linear Models, Model Weights and Top Model Evidence Ratios for Non-breeding 

Movements of Adult Brown Pelicans in the GOM, 2013–2016

K AICc Δi (AICc) wi (AICc) Σw E

Migratory strategy (binomial)

 Colony size + Body size 4 80.82 0 0 .18 0.18 1.63

 Colony size + Body size + Sex 5 81.80 0.97 0.11 0.29

 Colony size + Body size + Condition 5 81.87 1.05 0.11 0.40

 Body size + Environment 4 82.51 1.68 0.08 0.48

 Colony size * Body size 5 82.54 1.71 0.07 0.55

 Body size 3 82.72 1.90 0.07 0.62

 Colony size + Body size + Sex + Condition 6 82.74 1.91 0.07 0.69

Migration distance (gamma)

 Colony size * Body size 5 1075.11 0 0.15 0.15 1.03

 Colony size 3 1075.17 0.06 0.14 0.29

 Colony size * Body size * Sex 9 1075.59 0.47 0.12 0.41

 Colony size + Body size 4 1076.51 1.34 0.10 0.51

 Colony size + Condition 4 1077.04 1.87 0.08 0.59

 Colony size + Sex 4 1077.06 1.89 0.08 0.67

Substantially supported (Δi AICc ≤ 2) generalized linear models are shown. wi= 

model weights; E= evidence ratios. Link functions are given in parentheses.
60

Table 3.7. Model-averaged Coefficients and Importance Values for Individual Covariates across the 95% 

Confidence Set of Models for Each Non-breeding Movement Parameter

Variable Migratory strategy Distance

Coefficient Importance Coefficient Importance

Colony size 0.01 ± 0.005 0.63 0.12 ± 0.05 0.78

Body size (culmen) -0.06 ± 0.03 0.95 -4.5 ± 5.5 0.34

Condition -1.6 ± 2.0 0.37 -283 ± 448 0.28

Sex–Male 1.3 ± 1.5 0.39 -3.3 ± 278 0.28

Environment 0.2 ± 0.3 0.36 76.9 ± 86.2 0.38

SE are shown. Bold values indicate the highest importance value for each outcome.

Figure 3.1. Deployment duration and behavioral states of GPS-tagged brown pelicans, GOM, 2013–2016.

Total numbers of GPS locations after cleaning and interpolation are listed to the right of each bar. Bar colors indicate 

behavioral states derived from HMM (red: resident; green: transient).
61

Figure 3.2.a. GPS locations of brown pelican originally captured at breeding colonies in the eastern, 

central, and western planning areas of the Northern GOM, 2013–2016, eastern planning area.

Black triangles indicate capture locations. Base layer: Esri, DeLorme, GEBCO, NOAA, NGDC, and other 

contributors.
62

Figure 3.3.b. GPS locations of brown pelican originally captured at breeding colonies in the eastern, 

central, and western planning areas of the Northern GOM, 2013–2016, central planning area.

Black triangles indicate capture locations. Base layer: Esri, DeLorme, GEBCO, NOAA, NGDC, and other 

contributors.
63

Figure 3.4.c. GPS locations of brown pelican originally captured at breeding colonies in the eastern, 

central, and western planning areas of the Northern GOM, 2013–2016, western planning area.

Black triangles indicate capture locations. Base layer: Esri, DeLorme, GEBCO, NOAA, NGDC, and other 

contributors.
64

Figure 3.5.a. Change in activity pre- and post-tagging for brown pelicans in a captive holding facility, San 

Pedro, California, 2015, state behaviors (percent time).

Error bars represent 95% confidence intervals.

Figure 3.6.b. Change in activity pre- and post-tagging for brown pelicans in a captive holding facility, San 

Pedro, California, 2015, instant events (frequency).

Error bars represent 95% confidence intervals.
65

Figure 3.7. Percentage time spent by brown pelicans in different behavioral states 1–3 days after capture 

in field trials, GOM, 2013–2014.

Tagged individuals are shown in dark grey, and untagged neighbors in light grey. Error bars represent 95% 

confidence intervals. All differences between tagged and untagged individuals were non-significant (p > 

0.05).
66

Figure 3.8.a. Influence of capture location, handling time, and sex on probability of successful breeding 

in GPS-tagged adult pelicans, GOM, 2013–2014, capture location.

Shaded bars represent successful breeders. N = number of tagged individuals. ** = p < 0.001; * = p 

< 0.05 

Figure 3.9.b. Influence of capture location, handling time, and sex on probability of successful breeding 

in GPS-tagged adult pelicans, GOM, 2013–2014, handling time.

Shaded bars represent successful breeders. N = number of tagged individuals. ** = p < 0.001; * = p < 0.05.
67

Figure 3.10.c. Influence of capture location, handling time, and sex on probability of successful breeding 

in GPS-tagged adult pelicans, GOM, 2013–2014, sex.

Shaded bars represent successful breeders. N = number of tagged individuals. ** = p < 0.001; * = p < 0.05.
68

Figure 3.11.a. Seasonal GPS locations of brown pelican originally captured at breeding colonies in the 

Northern GOM, 2013–2016, during the breeding season.

White triangles indicate capture locations. Base layer: Esri, DeLorme, GEBCO, NOAA, NGDC, and other 

contributors.
69

Figure 3.12.b. Seasonal GPS locations of brown pelican originally captured at breeding colonies in the 

Northern GOM, 2013–2016, during the non-breeding season.

White triangles indicate capture locations. Base layer: Esri, DeLorme, GEBCO, NOAA, NGDC, and other 

contributors.
70

Figure 3.13.a. Brown pelican breeding home ranges by colony for the eastern, central, and western 

breeding planning areas of the Northern GOM, 2013–2016, captured in the eastern planning area.

Breeding home ranges of brown pelicans captured in the eastern planning area. Darker contours represent 

50% UD estimates, and lighter contours represent 95% UD estimates. Base layer: ESRI, DeLorme, GEBCO, 

NOAA, NGDC, and other contributors.
71

Figure 3.14.b. Brown pelican breeding home ranges by colony for the eastern, central, and western 

breeding planning areas of the Northern GOM, 2013–2016, captured in the central planning area.

Breeding home ranges of brown pelicans captured in the central planning area. Darker contours represent 

50% UD estimates, and lighter contours represent 95% UD estimates. Base layer: Esri, DeLorme, GEBCO, 

NOAA, NGDC, and other contributors.
72

Figure 3.15.c. Brown pelican breeding home ranges by colony for the eastern, central, and western 

breeding planning areas of the Northern GOM, 2013–2016, captured in the western planning area.

Breeding home ranges of brown pelicans captured in the western planning area. Darker contours represent 50% UD

estimates, and lighter contours represent 95% UD estimates. Base layer: Esri, DeLorme, GEBCO, NOAA, NGDC, 

and other contributors.
73

Figure 3.16.a. Mean home range areas of breeding adult brown pelicans at each study colony, Northern 

GOM, 2013–2016, 50% kernel.

Regression lines are shown with 95% confidence estimates (shaded).

Figure 3.17.b. Mean home range areas of breeding adult brown pelicans at each study colony, Northern 

GOM, 2013–2016, 95% kernel.

Regression lines are shown with 95% confidence estimates (shaded).
74

Figure 3.18.a. Relationships of brown pelican migratory characteristics between summer and winter 

home ranges to colony size, Northern GOM 2013–2016, proportion of migrants. 

95% confidence estimates of regression lines are shaded.

Figure 3.19.b. Relationships of brown pelican migratory characteristics between summer and winter 

home ranges to colony size, Northern GOM 2013–2016, average distance from colony during winter. 

95% confidence estimates of regression lines are shaded.
75

Figure 3.20.a. Differences in body size between migratory behavior of brown pelicans birds, and within 

sexes, Northern GOM 2013–2016, all tracked birds.

Residents are shown in red and migrants in blue. Asterisks denote significance levels of between-group 

comparisons (***: p < 0.001; *: 0.1 < p < 0.05; NS: p > 0.05).

Figure 3.21.b. Differences in body size between migratory behavior of brown pelicans birds, and within 

sexes, Northern GOM 2013–2016, within sexes.

Residents are shown in red and migrants in blue. Asterisks denote significance levels of between-group 

comparisons (***: p < 0.001; *: 0.1 < p < 0.05; NS: p > 0.05).
76

Figure 3.22. Annual 95% kernel density estimates for locations of brown pelicans originally captured at 

breeding colonies in the Northern GOM, 2013–2016.

Polygons show utilization distributions of brown pelicans nesting in the eastern (blue), central (orange), and 

western (green) planning areas. Areas shared by two or more planning areas are shaded in purple, and 

areas shared by all planning areas are shaded in red (detail in inset). Base layer: ESRI, DeLorme, GEBCO, 

NOAA, NGDC, and other contributors.
77

Figure 3.23. Migration routes of brown pelicans in the Tehuantepec Isthmus, Mexico, 2013–2015.

N = 2. Arrows indicate direction of fall and spring migration; hatched area represents wind energy 

installations. Base layer: ESRI, DeLorme, GEBCO, NOAA, NGDC, and other contributors. Revised from 

Lamb et al. 2018.
78

4 Habitat Use and Risk Exposure

4.1 Annual Habitat Use of Breeding Adults

Overall, 61.5% of bird-days were classified as resident and 38.5% as transient (Figure 3.1). The 

proportion of time individuals spent in each state did not differ significantly by sex (ANOVA, F1,76 = 

2.12, p = 0.15). Between planning areas, individuals tagged in the eastern planning area spent relatively 

more time in the resident state (μ = 0.73 ± 0.04) than did individuals tagged in the central (μ = 0.53 ± 

0.03) or western (μ = 0.65 ± 0.05) planning areas (ANOVA, F2,74 = 6.61, p = 0.002). Both states were 

observed year-round; however, resident behavior was relatively more common between December and 

March and between May and August. Transient behavior was more frequently observed during the 

remaining months. Niche position and breadth on measured habitat variables did not change depending on 

behavioral state (Figure 4.1).

The habitat variables most strongly associated with brown pelican residency year-round were net primary 

production (positive) and sea surface salinity (negative) (Figure 4.2). Sea surface temperature was 

negatively associated with residency during non-breeding, but the association diminished to near zero 

during the breeding season. Compared to seasonally dependent variables, fixed factors were less 

associated and less variable in their relationship to pelican habitat use, and did not vary during the year. 

Bathymetry had a positive relationship with residency (i.e., pelicans were more likely to occupy shallower 

waters), while distance to coastline and distance to river outflow were both negatively associated with use 

by brown pelicans.

Patterns of association with seasonally dependent habitat variables varied between planning areas (Figure

4.3). Pelicans breeding in the central planning area of the GOM exhibited the highest degree of variation 

in environmental characteristics of selected habitat. Pelicans were more associated with waters 

characterized by high productivity and low salinity during summer (breeding) than during winter (nonbreeding). Pelicans from the central and eastern planning areas selected habitat with a lower degree of 

seasonal variation in environmental characteristics, although pelicans from all planning areas associated 

more with sea surface temperature during breeding than during non-breeding.

Overall, areas of highest habitat suitability year-round were located in the Northern GOM, particularly the 

central and western planning areas (Figure 4.4). The total area of preferred habitat was narrowly restricted 

to coastal areas of the Northern Gulf during the summer; however, suitable habitat characteristics also 

occurred from the nearshore region out to approximately 600 km (372.8 mi) offshore during the fall and 

winter. Breeders from central GOM colonies shared 41% of their total habitat with breeders from other 

locations, western GOM breeders shared 36%, and eastern GOM breeders shared 15%. Habitats shared by 

central and western breeders accounted for 94% of total shared habitat. 

Although extensive work has described the environmental factors driving seabird at-sea habitat usage in 

pelagic waters (e.g., Haney 1985, Pinaud and Weimerskirch 2005, Tew-Kai et al. 2009), relatively little is 

known about the factors driving marine habitat use in nearshore seabirds, particularly in the North 

Atlantic. For the most part, previous studies of habitat preferences in nearshore-foraging species have 

been conducted in northern temperate waters (e.g., Becker and Beissinger 2003, Yen et al. 2006, McLeay 

et al. 2010). We found that, similar to results from these systems, marine productivity was the most 

significant driver of brown pelican habitat selection in nearshore GOM environments. Also, in 

concordance with previous results (Day et al. 2000, Becker and Beissinger 2003), we found that the 

influence of sea surface temperature on at-sea distribution was significant but highly variable over time. 

In a departure from previous assessments of habitat use of nearshore seabirds, which generally found little 
79

effect of salinity on habitat use (Zamon et al. 2014), we found that salinity strongly influenced brown 

pelicans’ habitat use. Although the effects of salinity have not been extensively documented for this 

species or other coastal seabirds, recent studies (e.g., Zamon et al. 2014) have suggested that river plumes 

can be important nearshore foraging habitat for seabirds as prey are concentrated in a manner analogous 

to pelagic system oceanic fronts. Though distance to river outflow only weakly related to pelican habitat 

suitability in this study, pelicans were often located in relatively large estuarine complexes. Therefore, 

brown pelicans may ultimately be responding to salinity gradients that exist even at a greater distance 

from river mouths.

Because the scale of movement that we observed was relatively small (on the order of tens of kilometers 

per day, rather than hundreds of kilometers as is commonly observed in pelagic seabirds), we selected 

environmental variables likely to relate to the distribution of prey rather than those that might facilitate 

long-distance movement (e.g., prevailing winds) or visual identification of foraging areas (e.g., ocean 

color). The influence of salinity, in particular, is correlated to the abundance and distribution of prey 

items. Brown pelicans in the GOM forage primarily on Gulf menhaden, which concentrate during the 

spring and summer in low-salinity estuarine environments (Deegan 1990). Both summer and winter 

distribution of preferred brown pelican habitat corresponded closely with Gulf menhaden distributions, 

indicating that pelicans select habitat principally as a function of prey concentrations. We did not find that 

the tested spatially fixed metrics had a strong influence on habitat suitability (e.g., distance to coastline, 

distance to river outflow, bathymetry, or bottom substrate). Previous studies (e.g., Suryan et al. 2012) 

have suggested that such metrics tend to provide a more consistent predictor of seabird distributions than 

seasonally varying environmental characteristics. The lack of a strong relationship of pelican distributions 

to static marine features may result from the short timescale of this study. They may also be a feature of 

the GOM, which is dominated by silt and sand and has a highly dynamic coastal geography and 

bathymetry relative to rocky shores in more northern regions where most other studies have occurred 

(Britton and Norton 2014).

Another possible explanation for the lack of a strong relationship of pelican habitat suitability to static 

features may relate to the scale of our analysis. The spatial scale of the environmental data available (10 

km2

/ 6.21 mi2) and the temporal resolution of the GPS data we collected (90 min intervals) did not allow 

us to distinguish fine-scale foraging areas from commuting or resting habitat. We confined observations 

to mesoscale movement patterns and habitat selection on a monthly timescale. The fact that seasonally 

varying parameters were more related to habitat selection than physical oceanographic features is 

consistent with previous observations that mesoscale habitat use is likely to be driven by primary 

productivity, and physical features become more important at the micro (<10 km/ 6.21 mi) scale (Becker 

and Bessinger 2003). Habitat selection likely also occurs at finer scales than those described by this study 

(Kristan 2006), and may vary with daily or weekly changes in estuarine dynamics that alter distribution 

and concentrations of prey.

4.2 Risk Exposure

4.2.1 Spatial and Temporal Aspects of Risk Exposure 

Hot spots of overlap between preferred brown pelican habitat and surface pollutants (i.e., areas of high 

overlap) were consistent throughout the year and included most of the central and western planning areas 

of the Northern Gulf, particularly the Mississippi Delta and Galveston Bay (Texas) areas (Figure 4.5). 

Other hot spots varied seasonally in intensity and included Corpus Christi Bay (Texas), Tampa Bay 

(Florida), the Florida Keys, the mouth of the Apalachicola River (Florida), and locations along the 

Yucatan Peninsula (Mexico) and in the Caribbean.
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Pollutant exposure through the annual cycle varied by breeding location and sex among individuals 

(Table 4.1). Average overlap between individuals and pollution sources was lowest during non-breeding, 

increased at the start of the breeding season, and reached a maximum during post-breeding (Figure 4.6a). 

Overlap rates differed significantly by planning areas (ANOVA: F2,74 = 11.97, p < 0.001). Breeders from 

the eastern planning area experienced lower year-round exposure to potential surface pollutants, while 

central and western breeders had similar year-round exposure rates (Table 4.1). Exposure varied 

seasonally in both central and western breeders, but individuals breeding in the eastern planning area 

experienced lower overall exposure and seasonal variation (Figure 4.6b). Between sexes, males averaged 

higher exposure than females (ANOVA, F1,75 = 4.48, p = 0.037), which was driven by higher levels of 

overlap with surface pollutants during the non-breeding season (Figure 4.6c).

Down-weighting transient locations that were classified as transient generally reduced or removed the 

localized peak in pollutant exposure that occurred during the late fall (October–November) in most 

groups. This also emphasized the downward trend in exposure risk from a peak at early breeding to a low 

during winter (Figure 4.6 a-c). Between planning areas differences in individual exposure probability 

were still significant after down-weighting transient points by 0.5 (ANOVA: F2,74 = 5.93, p = 0.004); 

however, between sex differences were not (ANOVA: F1,75 = 2.53, p = 0.11).

We found the highest levels of overlap between preferred brown pelican habitat and potential surface 

pollution in the Northern GOM. Other overlap hotspots were concentrated around large river outflows, 

which experienced high pollution pressure from ports and shipping as well as favorable pelican habitat 

characteristics (e.g., low salinity, high productivity). Overall exposure risk increased sharply at the start of 

the breeding season, when pelicans returned to the higher pollution levels of the Northern GOM to breed 

and environmental factors restricted suitable habitat to a very narrow range in the nearshore environment. 

Risk levels either remained constant or declined during the breeding season, then peaked again during 

autumn (September–November), which coincides with the annual molt in brown pelicans. The postbreeding, molt phase of the annual cycle represents a period of constrained resident behavior, since 

molting limits flight capabilities. Our model indicated that breeders from the western GOM, which 

supports less oil infrastructure than the central planning area, experienced statistically similar levels of 

risk to pollutant exposure year-round compared to those from the highly developed central planning area. 

The similarity in risk despite the difference in exposure (i.e., infrastructure and development) may be due 

to the high level of spatial overlap between breeders from these two planning areas, or that major pelican 

breeding colonies in the western planning area are located near major shipping lanes (another significant 

source of pollutants). Our model suggested that female brown pelicans experienced a lower year-round 

probability of pollution exposure. Female pelicans were more likely to migrate to the less-developed 

southern GOM, which had generally lower concentrations of surface pollutants during the non-breeding 

season, and usually departed the breeding colony immediately following breeding completion or failure.

4.2.2 Individual Behavior and Model Structure 

Spatial distribution and habitat use of seabirds are often used in combination with threat distributions to 

assess exposure to risk (e.g., Le Corre et al. 2012, Tranquilla et al. 2013, Renner and Kuletz 2015);

however, overlap models have generally accounted for exposure only in terms of co-occurrence of birds 

and threats. The likelihood of threat exposure also varies depending on how birds interact with their 

environments, which can differ from species to species (Garthe and Hüppop 2004) or between 

phenological states within a species (Eppley and Rubega 1990). We used a HMM to distinguish resident 

behavior (individuals were restricted to limited habitat areas) from transient behavior (more frequent and 

longer-distance movements). This technique can improve predictive risk models by incorporating a priori

biological understanding of expected behavioral states (Patterson et al. 2009) to better predict the 

likelihood that co-occurrence of individual locations with threats will result in exposure. 
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The parameters used to model risk could easily be modified to reflect future improvements in our 

understanding of pelican behavior or the spatiotemporal aspects of marine pollution risk. We assessed the 

effects of down-weighting transient points by 50%, by 90%, and 100% (complete removal from analysis), 

reflecting different levels of inferred interaction with surface pollutants during long-distance movement. 

Although the same general patterns in temporal pollution risk were not altered by lowering the assumed 

risk of pollutant exposure during long-distance movement, down-weighting transient points had the effect 

of reducing estimated peaks in exposure risk during the late-autumn migration and dispersal period. 

Further direct observations of pelicans outside the breeding season, especially during staging and molting, 

would help refine understanding of how behavior affects surface pollution exposure risk during periods of 

frequent long-distance movement. We chose to equally weight contributions of oil platforms and drilling 

rigs, oil pipelines, and ship- and port-based pollution to overall pollution risk; however, there are 

important differences between these factors. Pollution at ports and along shipping lanes is likely chronic 

and low-level, while pollution from oil infrastructure is more likely short-term and acute. Both sources 

can be acute or chronic pollution. This approach could be refined by monitoring the frequency, size, and 

location of pollutant spills and incorporating frequency and intensity of spills into analysis of pollution 

probability. Evaluating pollutant concentrations in brown pelican tissue from different planning areas

would also provide a useful test of the model’s exposure risk predictions.
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Table 4.1. Mean Pollutants Overlap for Observed Brown Pelican Locations in the Northern GOM, 2013–2016

Mean Standard deviation Number of individuals

Planning area

Eastern 0.082 0.023 23

Central 0.133 0.034 26

Western 0.122 0.049 28

Sex

Female 0.102 0.043 33

Male 0.123 0.041 44

Month

January 0.050 0.059 44

February 0.041 0.056 31

March 0.057 0.054 28

April 0.119 0.068 27

May 0.136 0.051 56

June 0.127 0.048 63

July 0.125 0.058 69

August 0.115 0.053 64

September 0.109 0.060 63

October 0.119 0.063 60

November 0.103 0.075 63

December 0.074 0.076 51

Figure 4.1. Niche center and breadth of resident and transient behavioral states of brown pelicans on 

measured habitat variables in the GOM, 2013–2016.

Resident behavioral state is shown in red, and transient behavioral state is shown in green.
83

Figure 4.2. Annual patterns of strength and direction of selection by brown pelicans on measured habitat 

variables, GOM, 2013–2016. 

Strength of selection (positive or negative) is generated from OMI and increases with distance from zero. 

Lines represent generalized additive model regressions (smoothing parameter = 1.3) of monthly averages 

for each variable, and grey bars are 95% confidence intervals of regression lines.
84

Figure 4.3. Annual patterns of strength and direction of selection by brown pelicans on seasonally varying habitat variables by planning area, 

GOM, 2013–2016.

Strength of selection (positive or negative) is generated from OMI and increases with distance from zero. Lines represent generalized additive 

model regressions (smoothing parameter = 1.3) of monthly averages for each variable, and grey bars are 95% confidence intervals of regression 

lines.
85

Figure 4.4. Suitability scores of available brown pelican habitats in the GOM, based on Mahalanobis distances.

Darker colors indicate higher suitability.
86

Figure 4.5. Levels of monthly overlap between preferred brown pelican habitat and surface pollution concentrations, GOM. 

Darker colors indicate higher surface pollution concentration.
87

Figure 4.6.a. Overlap between year-round brown pelican locations (weighted by movement state) and 

surface pollutant concentrations, GOM, 2013–2016: all individuals and all weights.

Dotted lines represent 95% confidence intervals of monthly mean values. 
88

Figure 4.7.b. Overlap between year-round brown pelican locations (weighted by movement state) and 

surface pollutant concentrations, GOM, 2013–2016: individuals separated by planning area and weighted 

by 1 and 0.5.

Dotted lines represent 95% confidence intervals of monthly mean values. 
89

Figure 4.8.c. Overlap between year-round brown pelican locations (weighted by movement state) and 

surface pollutant concentrations, GOM, 2013–2016: individuals separated by sex and weighted by 1 and 

0.5.

Dotted lines represent 95% confidence intervals of monthly mean values. 

 
90

5 Ecology and Physiology of Reproduction

5.1 Individual Nestling Survival

CORT concentrations from feathers of nestlings were significantly negatively correlated to BCI (linear 

model: coefficient = -194 ± 31.6, F1,364 = 37.7, p < 0.001, R2 = 0.09). Chicks that died before fledging had 

lower BCI (F1, 239 = 6.1, p = 0.01) and higher CORT deposited in feathers (F1, 239 = 24.7, p < 0.001) at 3–4 

w of age than chicks that were presumed fledged (i.e., survived until at least 60 d after hatching) (Figure

5.1). Of the other covariates we tested, only nest height (linear model, ground relative to elevated: 

coefficient = -2.79 ± 0.80, z109 = -3.76, p < 0.001) and body size (linear model: coefficient = 1.25 ± 0.43, 

z109 = 2.88, p = 0.004) were significantly correlated with individual fledging success. Nestlings from 

ground nests had significantly lower BCI (ground: μ74 = -97.2 ± 479; elevated: μ117 = 72.0 ± 363; F1,191 = 

7.74, p = 0.006) and higher feather CORT (ground: μ74 = 2.08 ± 0.71; elevated: μ117 = 1.72 ± 0.64, F1,191 = 

17.8, p < 0.001) than nestlings from elevated nests. We did not find a significant effect of colony, 

planning area, year, sampling date, hatch order, number of siblings, or their interactions on fledging 

probability (linear models: p > 0.10 for each).

Survival probabilities of individual nestlings > 60 d post-hatch were positively related to BCI and 

negatively related to CORT (Figure 5.1). Chicks found dead at the colony post-fledging had significantly 

lower BCI (ANOVA: F1,40 = 11.4, p = 0.002) and significantly higher CORT (ANOVA: F1,40 = 18.4, p < 

0.001) at 3–4 w after hatching than did chicks that were re-sighted alive after fledge.

5.1.1 Colony-specific Nest Productivity and Chick Survival

CORT levels were correlated with nest productivity at individual observation plots within breeding 

colonies. Nest productivity (Figure 5.2a) and nestling feather CORT (Figure 5.2c), but not nestling BCI 

(Figure 5.2b), differed significantly between ground and elevated subplots at two of the four colonies with 

both ground and elevated nests. Two of the three remaining colonies contained only shrub nests; the third 

contained too few ground nests to assess differences in productivity relative to shrub nests.

Overall, colony-wide productivity rates were positively correlated with average BCI (Figure 5.3a) and 

negatively correlated with average CORT (Figure 5.3b) of sampled chicks. 

The strongest model predicting colony-specific nest productivity as a function of chick health parameters

contained CORT alone (Table 5.1). This was also the only model supported by comparison to AICc

values. The top model explained 84% of the observed deviance (null = 1.91; residual = 0.31). 

Modeled chick survival to fledge (3 m after hatch) at individual colony sites was negatively correlated 

with average CORT (Figure 5.4a). The strongest model predicting chick survival to fledge as a function 

of chick health parameters, which was also the only model supported by comparison of AICc values, 

contained CORT alone (Table 5.1). The top model explained 91% of the observed deviance (null = 0.144; 

residual = 0.013). The relationship between BCI and survival to fledge showed a non-significant positive 

trend, and BCI was not supported as a predictor of average colony-wide survival rates (Table 5.1).

Average CORT values at individual colony sites were negatively correlated with modeled chick survival 

post-dispersal (to 6 m after hatch) (Figure 5.4b). Both the CORT-only model and the null model were 

supported as predictors of post-dispersal survival, although the former was 1.7 times as likely as the latter 

to be the best model (Table 5.1). The top model explained 48% of the observed deviance (null = 0.026; 

residual = 0.012). BCI was not included in any supported models of post-dispersal survival.
91

We found a weak negative correlation between colony size and nestling CORT levels (t253 = -2.00, p = 

0.05) (Figure 5.5a). Colony size and nestling BCI were not significantly correlated (t253 = -1.04, p > 0.20) 

(Figure 5.5b). We did not find a significant relationship between environmental conditions or 

environment-colony size interactions and either of the chick health parameters (p > 0.20 for all variables).

We found that CORT in nestling feathers, which represents an integrated measure of developmental stress 

during feather growth, was highly correlated with traditional measures of reproductive success (i.e., 

fledglings per nest) and nestling health (BCI) at individual, sub-colony, and colony-wide scales. 

Moreover, our results indicate that information gathered from measuring feather CORT in young, altricial 

chicks can explain differences in chick health, fledging success, and post-fledging survival that are not 

captured by body condition alone. 

Our first objective was to assess the relationship between feather CORT and a more traditional measure of 

nestling health, BCI (Benson et al. 2003), as predictors of nestling survival. In accordance with recent 

work on other avian taxa, we found that nestling feather CORT was negatively correlated to both body 

condition (Fairhurst et al. 2013; López-Jiménez et al. 2016) and fledging probability (Fairhurst et al.

2013; Lodjack et al. 2015) at the individual level. Although both feather CORT and BCI were 

significantly correlated with chick survival to fledge, feather CORT predicted the fate of individual 

nestlings slightly better than BCI. At the colony level, models containing only feather CORT were 

favored over models containing BCI with and without feather CORT as predictors of nest productivity, 

survival to fledge, and post-dispersal survival. Additionally, feather CORT predicted within-colony 

differences in fledging success by habitat type that were not apparent in comparisons of BCI. The 

enhanced explanatory power of CORT compared to BCI may be due to both the longer time frame over 

which CORT integrates physiological condition and the sensitivity that BCI has to short-term variation in 

nutritional stress. For example, at the Shamrock Island colony the average mass of chicks was 2,660 g 

(93.8 oz) and average meal mass was 181 g (6.4 oz), or about 7% of body weight. This relatively high 

ratio of meal mass to body mass, combined with the daily variation we observed in mass of meals (range 

= 5.6–1039.8 g (0.2–36.7 oz), CV = 0.76), makes BCI highly sensitive to feeding frequency and time 

since feeding. Meal delivery rates and the size of meals in relation to chick mass can vary by more than 

one order of magnitude both among and within avian species (Ricklefs et al. 1985; Anderson and 

Ricklefs, 1992). Therefore, the use of BCI as a measure of nestling condition requires consideration of 

how these short-term factors may influence its usefulness in describing long-term patterns of chick 

condition. Feather CORT integrates a longer time series of conditions (Bortolotti et al. 2008) and thus 

may be less susceptible than BCI to short-term variation. The fact that we measured feather CORT early 

in development (about 20–30 d into a 60–90 d nesting period) and found a strong relationship to fledging 

probability further indicates that feather CORT levels during early development can accurately predict 

survival through the breeding season.

We also assessed the relationship between feather CORT and variation in local (site-and nest-specific) 

conditions. Although nestling feather CORT is strongly correlated to environmental conditions during 

development (e.g., Harms et al. 2010; Will et al. 2015; Lodjack et al. 2015), site and nest-specific factors 

can still confound the environment-stress relationship (Fairhurst et al. 2012; Lodjack et al. 2015). We did 

not find a significant influence of either hatch order or number of siblings on feather CORT. A previous 

study of plasma CORT in brown pelican nestlings (Eggert et al. 2010) also found no effect of brood size 

or hatch order on stress levels; however, sibling dynamics have been found to affect feather CORT levels 

in nestling raptors (Yosef et al. 2013; López-Jiménez et al. 2016). We did find an influence of 

microhabitat characteristics (elevated compared to ground nest location) on feather CORT. Nestlings at 

elevated nests may benefit from improved passive thermoregulation, reduced energy expended in 

movement, and reduced aggressive interactions with neighboring adults and nestlings that subsequently 

act to maintain lower levels of feather CORT. Our study concurs with data on brown pelican nest 

productivity in Louisiana (Walter et al. 2013), suggesting that nestlings from elevated nests tend to 
92

survive longer than nestlings from ground nests, contributing to increased nest productivity at elevated 

sites. If elevated nest sites offer improved fledging success, positive reinforcement may occur at these 

sites if experienced or dominant breeders preferentially select and defend elevated nesting sites.

5.1.2 Nutritional Stress

Energy provisioning rate showed a non-significant positive trend in relation to BCI (linear model, 

coefficient = 1.04 ± 0.52, t5 = 2.02, p = 0.10; Figure 5.6a) and a significant negative relationship to 

feather CORT (linear model, coefficient = -613 ± 155, t5 = 3.97, p = 0.01; Figure 5.6b). The two biomass 

components of EPR, feeding frequency (meals chick-1 day-1

, μ = 2.51, N = 142) and meal mass (g meal-1, 

μ = 157.6, N = 583), had similarly high levels of overall variation (CV frequency = 0.64; CV mass = 

0.76), while energy density of meals (kJ g-1

, μ = 4.34, N = 583) was less variable (CV = 0.10). EPR 

explained 76% of observed variance in colony-wide average feather CORT and 45% of observed variance 

in colony-wide average BCI (Figure 5.6). Of the separate components of EPR, meal delivery rate 

explained the largest portion of variance in each of the two chick health metrics (CORT: 30.5%; BCI: 

33.0%), followed by meal mass (CORT: 22.1%; BCI: 3.7%) and energy density (CORT: 3.2%; BCI: 

0.1%). EPR was positively correlated to nest productivity (coefficient = 739 ± 258, t5 = 2.85, p < 0.04, R2

= 0.62) and nestling survival to fledge (coefficient = 3365 ± 580, t4 = 5.80, p = 0.002, R2 = 0.87), and the 

relationship between EPR and post-fledging survival rates showed a positive but non-significant trend 

(coefficient = 6482 ± 3042, t4 = 2.13, p = 0.09).

Finally, we tested the relationship between nestling health metrics, nutritional stress (energy provisioning 

rate), and breeding success. Brown pelicans in our study area rarely experience nest predation, human 

disturbance, or extreme weather events during breeding, and hence few factors are likely to confound the 

relationship between developmental stress and chick mortality. Our results indicated that both nestling 

feather CORT and nestling BCI were highly correlated to EPR, and that EPR explained 87% of the 

variation in chick survival between the colonies we studied. Of the components of EPR, meal delivery 

rate explained a larger portion of the variance in survival metrics and nestling health than did meal mass 

or energy density of prey. Meal mass also explained a high proportion of variance in nestling feather 

CORT, although not BCI or survival, while energy density had no significant linear relationships with 

nestling health or survival metrics. The low correlation between nestling health and energy density in this 

system is in contrast to previous studies of seabirds (reviewed in Österblom et al. 2008) that have 

suggested prey quality as a key driver of nestling survival. We posit that the weaker relationship we 

observed may be due, in part, to a narrower range of energy content of prey in the GOM, particularly a 

lack of prey with the high levels of energy density and lipid content that occur in high latitude systems 

(Stickney and Torres 1989, Anthony et al. 2000). Once nestlings fledged, EPR at the natal colony was no 

longer a strong predictor of survival probability, indicating that differences in the quantity of food during 

development are not a dominant driver of survival after dispersal. However, both feather CORT and BCI 

were correlated to post-fledging survival, which suggests that nutritional stress during development may 

continue to influence the probability that individuals will survive to recruit back into the breeding 

population once they have fledged. The demographic effects of negative feedbacks between 

developmental stress and recruitment have been documented in other seabird species (e.g., Kitaysky et al.

2010). Linking these parameters is a necessary step toward understanding the long-term demographic 

consequences of perturbations in the developmental environment.
93

5.2 Daily Survival Rates: Gaillard and Cat Islands

During 2017–2018, we monitored 245 nests during incubation (2017: n = 97; 2018: n = 148) and 185 

broods containing 279 chicks (2017: n = 85 broods, n = 128 chicks; 2018: n = 100 broods, n = 151 

chicks). The DSR (± SE) of nests during incubation in 2017 and 2018 was 0.9940 ± 0.002 and 0.9138 ± 

0.002, respectively, and the apparent survival of nests to hatching was 0.86 and 0.67, respectively. The 

DSR (± SE) of broods during chick-rearing in 2017 and 2018 was 0.9998 ± 0.0003 and 0.9952 ± 0.006, 

respectively, and the apparent survival of broods from hatch to fledge was 0.94 and 0.78, respectively.

In 2017, three highly supported models best predicted DSR during incubation (Table 5.2). Average 

barometric pressure appeared in all top models, average humidity appeared in two of the top models, and 

distance from nest to water appeared in one top model. Barometric pressure and humidity were negatively 

related to DSR during incubation in 2017 (Table 5.3), and distance to water was not related to DSR 

during incubation (i.e., 95% confidence interval overlapped zero). The odds of a nest surviving an 

additional day decreased by 0.5 times for each 1% decrease in average humidity and by 0.4 times for each 

1 kPa decrease in average barometric pressure. 

In 2018, the global model best predicted DSR during incubation (Table 5.2). DSR was significantly 

related to Julian date, distance from nest to water, nest elevation, barometric pressure, humidity, and 

maximum nest temperature (Table 5.3). The three weather variables had stronger negative effects on DSR 

of nests during 2018 compared to date or nest-based variables. The odds of a nest surviving an additional 

day decreased by 0.02 times for each one degree increase in maximum temperature, by 0.3 times for each 

1 kPa decrease in average barometric pressure and each 1% decrease in average humidity. The odds of a 

nest surviving an additional day decreased by 0.3 times for each 1 m decrease in elevation of the nest site, 

decreased by 0.5 times for each 1 m in distance that a nest was closer to the shore, and decreased by 0.9 

times for each day later the nest was initiated. 

In 2017, two models best predicted DSR during brood-rearing (Table 5.2). Average barometric pressure 

and average humidity appeared in both top models, and distance from nest to water appeared in one top 

model. The 95% confidence intervals strongly overlapped zero for distance to water and did not overlap 

zero for barometric pressure or humidity. Barometric pressure was negatively related to DSR, humidity 

positively related to DSR, and distance to water not related to DSR during brood-rearing in 2017 (Table 

5.3). The odds of a brood surviving an additional day increased by 11.8 times for each 1% increase in 

average humidity and decreased by 0.5 times for each 1 kPa decrease in average barometric pressure. 

In 2018 a single model with 9 of the 10 variables available (average barometric pressure not included) 

best predicted DSR during brood-rearing and carried 99% cumulative weight (Table 5.2). Humidity and 

chick age2 were positively related to DSR and date negatively related to DSR during brood-rearing in 

2018 (Table 5.3). The odds of a brood surviving an additional day increased by 4.6 times for each 1% 

increase in average humidity and decreased by 0.8 times for each 1 day increase in date of hatching. 

During both the 2017 and 2018 breeding seasons, DSR of brown pelican nests and broods was high, but 

the apparent survival from hatch or fledge appeared to vary by year and reproductive stage (incubation or 

brood) during the two years of our study. Several variables consistently appeared in top performing 

models in both years of the study during both incubation and chick-rearing. Weather variables (e.g., 

average barometric pressure and humidity) occurred more often and with more significance compared to 

habitat variables (e.g., nest height and vegetation cover) in the top performing models for both nest and 

brood success in 2017 and 2018. Previous studies on brown pelican nest selection in the GOM, including 

previous studies on the Gaillard Island colony, found that the reproductive (hatching, nest, and chick) 

success of brown pelicans was related to habitat variables including vegetation cover, nest height, and 

substrate beneath the nest (Ranglack et al. 1991, Robinson and Dindo 2011, Walter et al. 2013). Lamb 

(2016) found that among seven colonies in the Northern GOM, including Gaillard Island, chicks in 
94

elevated nests were in better condition and had higher apparent fledging success than chicks in ground 

nests. Our results differed from these previous studies in that we did not find significant relationships 

between most habitat variables and survival of nests or broods. These differences could result from 

differences in the response variables being measured: our study examined nest success using DSR, but

other studies focused on nest site selection, chick condition, or individual fledging success. The 

differences could also be due to the addition of weather variables in our modeling, which were not 

included in the previous studies. Our results suggest that the effects of habitat on reproductive success 

may be overwhelmed by the importance of weather variables, at least in some years.

Barometric pressure negatively affected daily survival rates of both eggs and broods, despite different 

requirements during these breeding stages. The negative relationship between success and barometric 

pressure we observed could be a result of cloudy, but not stormy, days having a positive effect on 

reproductive success. This may have occurred because barometric pressure was not indicative of storms 

and severe inclement weather in our study area, but instead was a signal of cloudy days with occasional 

rain. The barometric pressure range for storms is commonly considered to be 98.21–98.88 kPa (Breuner 

et al. 2013). The minimum average barometric pressure we recorded from weather station data was 

100.77 kPa, much higher than the storm range, thus demonstrating that the barometric pressure we 

observed was not associated with storm conditions. The shading effect of clouds could reduce 

temperature and sun exposure of eggs and chicks during the summer breeding season and so increase their 

daily survival. 

Average humidity also consistently appeared in top performing models and significantly affected nest 

survival in 2018 and brood survival in both years; however, the relationship differed among stages (a 

negative relationship with egg success but a positive relationship with brood success). During incubation, 

a negative relationship between nest survival and humidity could be caused by higher humidity and 

higher temperatures creating conditions where eggs can overheat, resulting in decreased survival (Sherley 

et al. 2011, Oswald and Arnold 2012).We observed a significant positive relationship between brood 

success and humidity in both years, in contrast to the negative relationship between brood success and 

humidity in both years. This could be due to an increased resilience of chicks to heat compared to eggs. 

The positive relationship we observed with humidity also may be due to higher air temperature (positively 

correlated with humidity) reducing any chilling effects from frequent rain (Konarzewski and Taylor 1989,

Schreiber and Burger 2001).

Most habitat variables did not appear in top performing models for either nest or brood success in 2017 

and 2018. However, the “distance to water” variable did appear in all the top models for both nest and 

brood survival during the study; it was significant only during incubation in 2018. We posit that the 

topography and structure of Gaillard Island, particularly the berm and the large rocks that surround the 

island, provided elevation and a physical barrier that limit the effects of flooding and storms on 

reproductive success. For example, following Tropical Storm Cindy and hurricanes Harvey, Irma, and 

Nate in 2018, vegetation and nesting material and/or substrate was reduced on Cat Island and brown 

pelicans did not nest there. Our data suggest that, regardless of storm activity, Gaillard Island may 

provide quality nesting habitat due to the elevated nesting, armored shoreline, and abundant shrub and 

nesting material. This unique set of habitat attributes may reduce the effect and strength of micro-scale 

habitat variables on breeding success.
95

5.3 Implications for Long-term Monitoring

Because of its ease of implementation and strong relationship to nestling survival, feather CORT is 

uniquely suited to detecting the sub-lethal stress effects on reproductive success and can be collected 

rapidly in response to unexpected environmental perturbations. Although measuring feather CORT 

requires more post-collection laboratory analysis than traditional reproductive success and chick health 

metrics, its advantages include minimal disturbance at breeding colonies, ease of collection and storage, 

and the ability to sample multiple colonies in a short time. However, interspecific differences in stress 

response may make this technique more suitable for some seabird species than others (e.g., Kitaysky et al.

2005). The existence of a detectable relationship between environmental covariates and nestling stress is a 

crucial prerequisite for using feather CORT as an indicator of environmental conditions. Moreover, to 

draw inferences at broad spatial scales (e.g., between colonies or planning areas), sampling regimes 

would need to account for the influence of varying habitat characteristics. Several recent studies of feather 

CORT, particularly Fairhurst et al. (2014), Lodjack et al. (2015), and López-Jiménez et al. (2016), have 

described the context-dependence of the stress-environment relationship and its sensitivity to local-scale 

habitat quality and climatic variation. Our results indicate that, though sibling dynamics do not confound 

variation in feather CORT in this species, nest height can affect both physiology and survival. These 

differences highlight the importance of understanding how different site- and individual-specific factors 

contribute to underlying variation in measured parameters, and how these factors could interact 

cumulatively or multiplicatively with environmental conditions to mask or exaggerate the effects of 

perturbations on reproduction. 

We found variation within and among planning areas in colony-specific nestling health and reproductive 

success under baseline conditions across the Northern GOM. The foraging environment experienced by 

breeding seabirds depends on a variety of biotic and abiotic factors that can change across a species’ 

range as well as between and within breeding seasons. Distinguishing the effects of environmental 

perturbations requires that the effects of short-term changes to foraging conditions be distinguished from 

the background noise of pre-existing variation. Endpoints that can be measured consistently across space 

and time provide a unifying approach for long-term monitoring efforts that can compare baseline 

measures to post-disturbance conditions. 

Our study provides evidence that feather CORT can be used to detect differences in underlying nutritional 

quality and predict reproductive parameters in a free-living seabird population in which nestlings elevate 

stress hormone levels in response to nutritional constraints, making it an appropriate basis for long-term 

monitoring of population-wide reproductive health and, ultimately, detection of the indirect demographic 

effects of environmental change. 
96

Table 5.1 Candidate Models for Brown Pelican Nest Productivity and Nestling Survival in the Northern GOM, 

2014–2015

Terms AICc Δi (AICc) wi (AICc) Σw Li (AICc)

Productivity

CORT 4.17 0 0.94 0.94 1.00

BCI 11.14 6.97 0.03 0.97 0.03

BCI + CORT 11.40 7.22 0.02 0.99 0.02

Null model 13.06 8.88 0.01 1.00 0.01

Post-banding survival

CORT -17.66 0 0.96 0.96 1.00

BCI + CORT -10.70 6.87 0.03 0.99 0.03

BCI -6.34 11.32 < 0.01 1.00 < 0.01

Null model -5.40 12.27 < 0.01 1.00 < 0.01

Post-dispersal survival

CORT -19.80 0 0.55 0.55 1.00

Null model -18.74 1.06 0.32 0.87 0.59

BCI -16.53 3.27 0.11 0.98 0.19

BCI + CORT -13.00 6.81 0.02 1.00 0.03

Candidate models as a function of colony-average body condition (BCI) and feather 

CORT of 3- to 4-week-old chicks, ranked in order of increasing AIC values with 

model weights (wi), cumulative weights (Σw) and relative likelihoods (Li). Models in 

bold were considered strongly supported.

Table 5.2. Top Performing Models (△ AIC ≤ 2.0) of Daily Survival Rates of Nests and Broods of Brown 

Pelicans Breeding in Mobile Bay, Alabama, 2017 and 2018

Models △ AIC AIC Weight

2017 Incubation

Average humidity + Average barometric pressure 0.00 0.36

Average barometric pressure 0.93 0.22

Average humidity + Average barometric pressure + Distance to water 1.90 0.14

2018 Incubation

Nest height + Vegetation cover + Average humidity + Average barometric 

pressure + Julian date + Distance to water + Elevation of nest + Substrate + 

Location

0.00 0.99

2017 Brood-rearing

Average humidity + Average barometric pressure 0.00 0.63

Average humidity + Average barometric pressure + Distance to water 1.14 0.36

2018 Brood-rearing

Chick age2 + Nest height + Vegetation cover + Average humidity + Julian date 

+ Distance to water + Nest elevation + Substrate + Location

0.00 0.99
97

Table 5.3. Coefficient Estimates and 95% Confidence Intervals from Models of Daily Survival Rates of Nests 

and Broods of Brown Pelicans, Gaillard Island and Cat Island, Alabama, 2017–2018

Coefficient 

Estimate

95% Confidence 

Interval

2017 Incubation

Average barometric pressure -0.98 -1.53, -0.43

Average humidity -0.73 -1.47, 0.00

2017 Brood-rearing

Average barometric pressure -0.69 -1.12, - 0.28

Average humidity 2.47 1.47, 3.46

2018 Incubation

Julian date -0.08 -0.11, -0.05

Low elevation -1.05 -1.97, -0.13

Distance to water -0.73 -1.42, -0.03

Average barometric pressure -1.11 -1.65, -0.58

Average humidity -1.23 -1.69, -0.76

Maximum temperature -3.69 -6.43, -0.95

2018 Brood-rearing

Chick age2 0.09 0.05, 0.12

Julian date -0.22 -0.45, 0.00

Average humidity 1.53 0.95, 2.11

Coefficient estimates displayed are from highest-ranked model with that 

variable. Only coefficient estimates for which confidence intervals do not overlap 

zero (i.e., significant variables) are shown.
98

Figure 5.1.a. Distribution of individual measurements of BCI and feather CORT at 3–4 w post-hatch for 

brown pelican nestlings in the Northern GOM, 2013–2015: BCI. 

Nestlings were later found dead after banding, presumed fledged, or re-sighted alive after leaving the 

breeding colony.

Figure 5.2.b. Distribution of individual measurements of BCI and feather CORT at 3–4 w post-hatch for 

brown pelican nestlings in the Northern GOM, 2013–2015: feather CORT. 

Nestlings were later found dead after banding, presumed fledged, or re-sighted alive after leaving the 

breeding colony.
99
100

Figure 5.3.a. Relationship between brown pelican nest productivity, body condition index, and CORT of 

nestling, and nest elevation in the Northern GOM, 2014–2015: mean nest productivity.

In elevated (green) and ground (brown) nests.

Figure 5.4.b. Relationship between brown pelican nest productivity, body condition index, and CORT of 

nestling, and nest elevation in the Northern GOM, 2014–2015: mean body condition index.

In elevated (green) and ground (brown) nests.
101

Figure 5.5.c. Relationship between brown pelican nest productivity, body condition index, and CORT of 

nestling, and nest elevation in the Northern GOM, 2014–2015: mean CORT concentration of nestlings.

In elevated (green) and ground (brown).

Figure 5.6.a. Correlation of mean brown pelican nest productivity to chick body condition index and 

feather CORT for colonies in the Northern GOM, 2014–2015: chick body condition index.
102

Figure 5.7.b. Correlation of mean brown pelican nest productivity to chick body condition index and 

feather CORT for colonies in the Northern GOM, 2014–2015: feather CORT.
103

Figure 5.8.a. Relationship of colony-wide mean brown pelican nestling feather CORT to probability of 

survival to fledge and post-dispersal survival in the Northern GOM, 2014–2015: probability of survival to 

fledge. 

Figure 5.9.b. Relationship of colony-wide mean brown pelican nestling feather CORT to probability of 

survival to fledge and post-dispersal survival in the Northern GOM, 2014–2015: probability of postdispersal survival. 
104

Figure 5.10.a. Brown pelican nestling CORT levels, and body condition index, Northern GOM, 2013–2015:

CORT levels.

Colony mean values are shown ± standard deviations. 

Figure 5.11.b. Brown pelican nestling CORT levels, and body condition index, Northern GOM, 2013–2015:

BCI.

Colony mean values are shown ± standard deviations.
105

Figure 5.12.a. Relationship of brown pelican energy provisioning rate to chick health parameters BCI and 

feather CORT by colony in the Northern GOM, 2014–2015: BCI.

Figure 5.13.b. Relationship of brown pelican energy provisioning rate to chick health parameters BCI and 

feather CORT by colony in the Northern GOM, 2014–2015: feather CORT.
106

6 Nestling Diet

6.1 Taxonomic Composition of Diet

Over three years, we collected 641 chick meals (Year 1: N = 27; Year 2: N = 423; Year 3: N = 191), 

totaling 98 kg (212 lbs) of prey. We identified 46 prey species representing 25 families (Table 6.1). 

Thirty-six of the prey species represented less than 1% each of biomass collected; of these, 16 species 

represented less than 0.05% each of biomass collected (Table 6.1). Gulf menhaden was the most common 

prey species by weight overall, as well as at each study site. The proportion of menhaden in total biomass

varied by colony, with higher proportions of menhaden within the central GOM (Figure 6.1). Other 

common prey species did not show a pattern of abundance in meals across sites, except for anchovy 

(Anchoa spp.), which increased from the western to the eastern GOM, and spot (Leiostomus xanthurus),

which declined from the western to eastern GOM (Figure 6.2). The majority of meals (76%) contained a 

single fish species, and the maximum number of species in a meal was seven. 

The overall proportion of menhaden in chick diets declined through the breeding season, (coefficient = -

0.34 ± 0.10, F1,596 = 12.3, p < 0.001), driven by a decrease in juvenile menhaden < 110 mm (4.33 in) total 

length (coefficient = -0.75 ± 0.09, F1,596 = 66.0, p < 0.001). The proportions of adult menhaden, 

anchovies, and pinfish increased over the same period (p < 0.01 for all). The remaining prey species 

showed no seasonal trends in proportional occurrence.

Energetic content ranged from 3.3 to 5.5 kJ g-1 among all species with a mean (± SD) of 4.38 ± 0.98 kJ g-1

wet mass (Figure 6.3). Protein content had low variation across measured samples (CV = 8%) and 

correlated weakly with energy density per wet gram of fish (F1,217 = 22.3, p < 0.001, r2 = 0.09); lipid 

content was variable both between and within species (CV = 75%) and was highly correlated with energy 

density (F1,217 = 1929, p < 0.001, r2 = 0.90). First-year menhaden had significantly lower energy densities 

and lower lipid content than adult menhaden in the northeastern and northwestern Gulf of Mexico (Figure

6.3; p < 0.1 for all).

6.2 Meal Attributes 

Average meal mass, meal delivery rate, and energy density of meals each differed significantly among 

colony sites (Figure 6.4). The two biomass components of EPR, feeding frequency and meal mass had 

similarly high levels of overall variation (CV frequency = 0.67; CV mass = 0.76), while energy density of 

meals was less variable (CV = 0.10). Relative to averages within planning areas, individual colony sites

showed a generally opposing pattern between meal mass and meal delivery rates (Figure 6.5a). Colonies 

with below-average meal delivery rates tended to have above-average meal masses, and conversely. 

Energy densities followed a similar pattern to meal masses, but did not deviate more than 10% from the 

overall mean. Site-specific variation in all three provisioning metrics tended to covary (Figure 6.5b), with 

below-average variability toward the central and eastern Gulf and higher variability in the west.

Provisioning metrics also varied seasonally within the chick rearing period. Both meal mass and energy 

density increased over the course of the breeding season (meal mass: coefficient = 2.48 ± 0.24, F1,596 = 

104, p < 0.001; energy density: coefficient = 0.007 ± 0.001, F1,596 = 29.5, p < 0.001), while meal delivery 

rate decreased during the same period (coefficient = -0.036 ± 0.005, F1,135 = 46.1, p < 0.001). However, 

rates of energy delivery, calculated as the product of daily average meal mass, energy density, and meal 

delivery rates, neither increased nor decreased during the breeding season (F1,40 = 0.60, p > 0.20).
107

Mean biomass provisioning rate (BPR) varied by colony from 454 ± 294 to 1106 ± 587 g day-1. Mean 

EPR varied by colony from 1977 to 4876 kJ day-1 (Table 6.2). BPR and EPR were highly correlated 

(coefficient = 4.48 ± 0.34, F1,5 = 168.0, p < 0.001). Of the individual provisioning covariates measured at 

each colony, meal delivery rate explained 38% of variance in energy provisioning rate, followed by meal 

mass (24%) and energy density of meals (1%). Both feeding frequency and meal mass improved model fit 

when added sequentially to the intercept-only model, but adding energy density did not significantly 

improve the fit of the model (Table 6.3).

Meal delivery rates increased with increasing proportions of menhaden and anchovy, which were also 

associated with decreasing energy density of meals (Figure 6.6a–c). By comparison, meals containing 

higher proportions of spot, croaker, and pinfish were associated with lower delivery rates and higher 

energy densities (Figure 6.6a–c). Meal masses were highest for meals containing striped mullet (Mugil 

cephalus) or Atlantic cutlassfish (Trichiurus lepturus) and lowest for meals containing anchovies (Figure

6.6b). The proportion of biomass represented by small size-class fish (< 110 mm (4.33 in) total length) at 

individual colonies correlated to feeding frequency (F1,5 = 7.18, p = 0.04, r2 = 0.59, coefficient = 0.108 ± 

0.04), but not to meal mass or energy density (F1,5 = 1.82, p > 0.20 for both).

Average fledging success (chicks nest-1

) was strongly correlated to both mean EPR and BPR at the colony 

level (Figure 6.7). Of the individual components of EPR, feeding frequency explained the largest portion 

of variance in nest productivity (49%), followed by meal mass (15%) and energy density of meals (0.1%). 

Both feeding frequency and meal mass significantly improved the fit of a null model for average fledging 

success by colony, while energy density did not improve model fit (Table 6.3). Diet composition (% 

menhaden) did not correlate with fledging success (F1,5 = 0.89; p > 0.20).

6.3 Nutritional Stress 

We found that, in comparison to seabirds at high latitudes, brown pelicans in the GOM experience a 

narrow range of variation in energy content between prey species. Furthermore, our results suggest that 

nest productivity of brown pelicans is more closely associated with feeding frequency, followed by meal 

mass, and that species composition and energy content of meals have little effect on productivity. 

Combined, these results suggest that brown pelicans provisioning nestlings in this system use a feeding 

strategy that prioritizes frequent deliveries of highly available prey regardless of energy density. Though

our results indicate that the junk-food hypothesis may not be useful for explaining the relationship 

between nestling provisioning and nest productivity in this system, our study also highlights the key 

importance of small, highly abundant schooling fish for breeding brown pelicans in the GOM.

Although brown pelicans delivered a wide variety of prey species to nestlings, both lipid content and 

overall energetic value of prey items in nestling diets varied within a narrow range. Compared to results 

from previous work in temperate and subpolar systems, average energetic content of prey species in our 

study was 15–30% lower, with 55–78% less variation between species (Figure 6.8). Our observations 

accord with previous work on mesopelagic fish species in the GOM (Stickney and Torres 1989) and the 

South Atlantic Bight (Jodice et al. 2011), which suggest that fish species in the tropical northwest Atlantic 

have relatively higher protein levels, lower lipid reserves, and lower overall energetic values than species 

at northern and southern latitudes characterized by cooler oceanic temperatures and higher inter-seasonal 

variability. Despite the wide longitudinal variation of our sampling area and the variation in prey species 

composition relative to prey distribution, energetic content of meals fed to pelican chicks varied little 

between colonies. As a result, colony-specific EPRs closely reflected a combination of meal mass and 

frequency of meal deliveries (i.e., BPR), but did not relate to energy content of meals. Our results suggest 

that prey energy content is not a significant driver of energy delivery rates to nestlings for brown pelicans 

in this system, given the lack of variation in energy density between prey species. 
108

Our results support previous observations of the predominance of Gulf menhaden in brown pelican diets 

(e.g., Arthur 1919, Fogarty 1981); however, the proportions of menhaden consumed by pelicans in our 

study varied both spatially and temporally depending on underlying distribution and inferred availability. 

The proportion of juvenile menhaden in nestling diets declined over the course of the chick-rearing 

period, during which young-of-the year menhaden move gradually from shallow estuarine waters to 

offshore habitats, decreasing their availability to foraging pelicans (Ahrenholz 1991). Other prey, 

including pinfish, anchovy, and adult menhaden, increased proportionally during the same period, and 

overall rates of energy delivery to nestlings remained consistent throughout the breeding season. Gulf 

menhaden constituted 60–84% of pelican nestling diets in colonies at the core of its range (i.e., the central 

Northern GOM), but less than 40% of diets in colonies at the eastern and western margins of its range. 

Notably, first-year menhaden (individuals hatched during the previous winter) represented 56% of 

nestling pelican diets at the colony closest to the core of their range and 3% or less outside the range 

margins. As the proportion of menhaden in nestling diets declined, other prey species, principally 

anchovy in the eastern GOM and spot, croaker, and pinfish in the western GOM, contributed more 

significantly to nestling diets. The comparatively larger size of pelican breeding colonies at the core of the 

gulf menhaden range than at its margins may indicate that areas with high menhaden availability can 

support larger aggregations of breeding pelicans. However, further study is required to distinguish the 

effects of variation in prey availability among planning areas on population size from those of 

conservation history (e.g., King et al. 1985, Wilkinson et al. 1994) and breeding habitat availability (e.g., 

Walter et al. 2013).

We tested the effects of prey energy content on demographic rates by relating inter-colony variation in 

reproductive success to nutritional parameters. We found that the rate of biomass delivery alone explained 

over 90% of variation in nest productivity between sites, with a feeding rate of approximately 800 g d-1

(1.76 lbs d-1

) (approximately 56,000 g (123.46 lbs) total from hatch through fledging) required to 

successfully fledge one nestling. Despite variation in prey composition, energy density of meals varied 

little between colony sites, thus did not contribute to variation in fledging rates. The lack of relationship 

between energy density of meals and nestling survival indicates that the junk food hypothesis (JFH) may 

not be relevant in this context. Though support for the JFH to date has come from cold-water systems at 

high latitudes (Österblom et al. 2008), our results suggest that prey communities in the subtropical 

Northern GOM present top predators with a narrow range of energetic options, which may contribute to a 

lack of transferability of the JFH to warm-water systems. However, several previous studies in highlatitude systems have also found biomass provisioning metrics to be considerably better predictors of 

fledging success than energetic content of food items (e.g., Jodice et al. 2006, Hjernquist and Hjernquist 

2010). Österblom et al. (2008) suggest that the negative influence of lower-energy food items is 

particularly pronounced in certain species of seabirds, especially species specialized to carry single prey 

items or small masses of prey, species with energetically expensive foraging strategies, and species with 

low digestive efficiency. Although plunge-diving is energetically demanding (Green et al. 2009), brown

pelicans are able to capture and carry large volumes of prey, which may allow them to buffer the effects 

of reduced prey quality by increasing prey quantity with minimal increases in foraging effort. 

In general, as meal delivery rates increased, meal masses decreased on a colony-wide basis. The relative 

magnitude of variation in these two metrics provides a useful basis for assessing how foraging conditions 

and strategies differ from site to site, indicating that there may be a trade-off between prey load 

maximization and time spent foraging. We found that differences in prey species composition helped to 

explain the negative relationship between meal mass and provisioning rates. Across species comprising > 

1% of nestling diets, higher percentages of schooling fish of the order Clupeiformes (menhaden, Atlantic 

threadfin herring Opisthonema oglinum, and anchovies) were related to higher provisioning rates and 

generally lower meal masses, while spot, croaker, and pinfish (order Perciformes), as well as striped 

mullet (Mugiliformes), corresponded to lower feeding rates and higher meal masses. Clupeiformes are 
109

typically schooling fish that occur in large aggregations in clear and relatively shallow water; the 

remaining species are bottom-dwellers that do not school and avoid waters where visibility is high 

(Nelson 1994). For a multiple-prey loader that can capture several prey items at once, targeting highly 

concentrated prey resources regardless of energetic content could be a means of maximizing biomass 

delivery. 

Overall, we found that higher meal delivery rates were driven by the proportion of diet biomass composed 

of fish less than 110 mm (4.33 in) total length, regardless of species. Despite being among the least 

energy-rich prey items observed, juvenile menhaden constituted over 50% of pelican diets at the core of 

their range, suggesting that pelicans target accessible and highly aggregated prey without regard for 

energetic content. The importance of small, abundant schooling fish to brown pelican reproductive output 

is of potential conservation interest. Recruitment rates in gulf menhaden are highly sensitive to 

temperature and precipitation, with warmer and wetter winters producing comparatively fewer recruits in

the next year class (Deegan 1990). Given that winter temperatures and precipitation are expected to rise 

under current climate change projections (Biasutti et al. 2012), the biomass of larval fish available to 

upper-level predators (e.g., Muhling et al. 2011) could become more limited or more variable in future 

climactic conditions. Additionally, pollution events can significantly depress survival of larval fish 

(Incardona et al. 2014) and could have indirect effects on prey dynamics that compound the direct effects 

of pollutants exposure to predators.

Table 6.1. Fish Species Occurring in the Diets of Brown Pelican Chicks in the Northern GOM, 2013–2015

Order Family Species Common Year % 

biomass

Atheriniformes Atherinidae Menidia beryllina Inland silverside 1,2,3 0.1

Aulopiformes Synodontinae Sybodus foetens Inshore lizardfish 1,2 *

Beloniformes Belonidae Tylosurus crocodilus Houndfish 3 *

Hemiramphidae Hemiramphus brasiliensis Ballyhoo halfbeak 1,2 0.1

Clupeiformes Clupeidae Brevoortia patronus Gulf menhaden 1,2,3 61.0

Harengula jaguana Scaled sardine 1 0.3

Opisthonema oglinum Atlantic threadfin 

Herring

2,3 1.7

Engraulidae Anchoa hepsetus Striped anchovy 2,3 1.5

Anchoa lyolepis Dusky anchovy 3 2.2

Anchoa mitchilli Bay anchovy 1,2,3 7.5

Cyprinodontiformes Cyprinodontidae Cyprinodon variegatus Sheepshead 

minnow

2 0.2

Fundulus majalis Striped killifish 2 *

Decapoda Penaeidae Farfantepenaeus duorarum Pink shrimp 2,3 *

Mugiliformes Mugilidae Mugil cephalus Striped mullet 2,3 4.8

Perciformes Carangidae Caranx crysos Blue runner 1 0.1

Chloroscombrus chrysurus Atlantic bumper 1,2,3 0.6

Decapterus punctatus Round scad 3 0.1

Hemicaranx 

amblyrhynchus

Bluntnose jack 2 *

Selene setapinnis Atlantic moonfish 2 *

Gobiidae Gobioides broussonetii Violet goby 2 *

Haemulidae Orthopristis chrysoptera Pigfish 1,2 *
110

Order Family Species Common Year % 

biomass

Lutjanidae Lutjanus campechanus Red snapper 15 0.3

Sciaenidae Bairdiella chrysoura Silver perch 1,2 0.4

Cynoscion arenarius Sand seatrout 2,3 1.2

Cynoscion 

nebulosus

Spotted seatrout 2,3 1.1

Larimus fasciatus Banded drum 2 *

Leiostomus xanthurus Spot 1,2,3 2.9

Menticirrhus americanus Southern kingfish 2 0.7

Micropogonias undulatus Atlantic croaker 1,2,3 3.8

Sciaenops ocellata Red drum 2,3 0.5

Scombridae Auxis thazard Frigate mackerel 3 0.2

Scomberomorus cavalla King mackerel 2 0.1

Scomberomorus 

maculatus

Spanish mackerel 2 0.3

Serranicae Diplectrum formosun Sand perch 3 0.2

Sparidae Calamus proridens Littlehead porgy 1 *

Lagodon 

rhomboides

Pinfish 1,2,3 2.4

Stenotomus 

caprinus

Longspine porgy 1 *

Stromateidae Peprilus burti Gulf butterfish 2,3 0.1

Peprilus paru American 

harvestfish

2 0.1

Trichiuridae Trichiurus lepturus Atlantic cutlassfish 1,2,3 3.6

Pleuronectiformes Cynoglossidae Symphurus 

urospilus

Spottail tonguefish 3 0.1

Paralichthyidae Citharichthys spilopterus Bay whiff 2,3 0.1

Scorpaeniformes Triglidae Prionotus tribulus Bighead searobin 2,3 *

Siluriformes Ariidea Bagre marinus Gafftopsail catfish 1,2,3 0.3

Tetraodontiformes Diodontidae Diodon holocanthus Longspine 

porcupinefish

2 *

Teuthida Loliginidae Lolligunculla brevis Atlantic brief squid 1,3 *

Other Isopod 3 *

Bait (chicken) 3 *

Unknown 1.2

An asterisk (*) in the biomass column denotes less than 0.05 % of total biomass. Year 1: 2013; Year 2: 

2014; Year 3: 2015.

Table 6.2. Mean Values for Brown Pelican Nest Productivity, Chick Health Metrics, and Energy Provisioning 

Metrics by Colony in the Northern GOM, 2014–2015

Colony Productivity Meals day-1 g meal-1 Energy g-1 BPR EPR

2014 Shamrock 0.51 ± 0.66 2.23 ± 1.28 205 ± 138 4.66 ± 0.50 552 ± 345 2574 ± 1618

2014 Chester 0.68 ± 0.79 3.10 ± 2.80 174 ± 136 4.53 ± 0.61 644 ± 559 2902 ± 2548

2014 Galveston 0.94 ± 0.86 5.68 ± 3.08 124 ± 91 3.99 ± 0.63 749 ± 446 2995 ± 1804

2015 Gaillard 1.06 ± 0.85 3.84 ± 1.89 175 ± 102 4.69 ± 0.36 758 ± 408 3451 ± 1879
111

± standard deviations are shown. 

Table 6.3. ANOVA Comparisons of Nested Models for Colony-specific Mean Brown Pelican Nestling Energy 

Provisioning Rates and Nest Productivity Based on Feeding Rate, Meal Mass, and Energy Density of Meals, 

Northern GOM, 2014–2015 

Terms Residual 

df

Residual 

deviance df deviance F p

Energy provisioning rate

Intercept only 6 7236805

+ feeding rate 5 4498564 1,5 2738240 24.79 0.016

 + meal mass 4 379699 1,5 4118866 37.3 0.009

 + energy 

density 3 331316 1,5 48383 0.44 0.56

Nest productivity

Intercept only 6 1.397 1,5 

+ feeding rate 5 0.714 1,5 0.683 47.83 0.006

 + meal mass 4 0.056 1,5 0.658 46.12 0.007

 + energy 

density 3 0.043 1,5 0.896 0.90 0.41

Terms are added sequentially, and a p-value of < 0.05 indicates a significant improvement in fit compared to the 

previous model.

2015 Audubon 1.42 ± 0.85 5.32 ± 2.33 191 ± 170 4.33 ± 0.38 1106 ± 587 4793 ± 2554

2015 Ten 

Palms 1.64 ± 0.95 5.84 ± 3.14 168 ± 105 4.59 ± 0.35 1042 ± 586 4876 ± 2722

2015 Smith 0.30 ± 0.64 4.21 ± 3.08 106 ± 78 4.35 ± 0.39 454 ± 294 1977 ± 1286 
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Figure 6.1. Percentage of Gulf menhaden in the diets of brown pelican nestlings, Northern GOM, 2013–

2015.

Pie charts represent the portion by biomass of adult menhaden (dark grey), first year menhaden (light grey) 

and other prey species (white) in chick diets. Yellow shaded areas: summer distributions of adult (solid: 

major; hatched: minor) and first-year (red dashed outline) menhaden (Love et al. 2013) (Base layer: ESRI, 

DeLorme, GEBCO, NOAA, NGDC, and other contributors).
113

Figure 6.2. Proportion of biomass of brown pelican nestling diets represented by all prey species other 

than Gulf menhaden, Northern GOM, 2014–2015.

Species comprising <1% overall prey biomass are grouped. Parentheses: sample sizes (mass of 

recovered meals, kg) for each colony.
114

Figure 6.3. Mean energy densities of common prey items in the diet of brown pelican nestlings, Northern 

GOM, 2014–2015.

Only prey items each > 1% of total biomass are shown. Prey items collected from the northwestern (Corpus 

Christi Bay to Galveston Bay, TX: solid bars) and northeastern (Mobile Bay, AL to Apalachee Bay, FL: 

patterned bars). Error bars represent 95% confidence intervals. Sample sizes are listed in parentheses. Note 

error bars are specific to each segment of the stacked bar. 
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Figure 6.4.a. Comparison of average meal mass, provisioning rate, and energy density of meals between 

brown pelican colony sites, Northern GOM, 2014–2015: meal mass. 

Letters denote Tukey post-hoc groups, error bars are 95% confidence intervals of means, and dashed 

lines are global mean values.

Figure 6.5.b. Comparison of average meal mass, provisioning rate, and energy density of meals between 

brown pelican colony sites, Northern GOM, 2014–2015: provisioning rate. 

Letters denote Tukey post-hoc groups, error bars are 95% confidence intervals of means, and dashed lines 

are global mean values.
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Figure 6.6.c. Comparison of average meal mass, provisioning rate, and energy density of meals between 

brown pelican colony sites, Northern GOM, 2014–2015: energy density of meals. 

Letters denote Tukey post-hoc groups, error bars are 95% confidence intervals of means, and dashed lines 

are global mean values.

Figure 6.7.a. Drivers of between-colony variation in mean values of provisioning metrics and coefficients 

of variation for brown pelican colonies, Northern GOM, 2014–2015: mean values of provisioning metrics.

The mean value for each metric across all samples is set at zero, and individual points represent deviation from 

the global mean (as a percentage of global mean) at that colony site.
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Figure 6.8.b. Drivers of between-colony variation in mean values of provisioning metrics and coefficients 

of variation for brown pelican colonies, Northern GOM, 2014–2015: coefficients of variation.

The mean value for each metric across all samples is set at zero, and individual points represent deviation from 

the global mean (as a percentage of global mean) at that colony site.
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Figure 6.9.a. Non-metric multidimensional scaling plots showing the distribution of species composition 

of individual meals collected from brown pelican nestlings, Northern GOM, 2014–2015: meal delivery 

rate.

Grey dots represent individual meals. Surface plots of the three components of energy provisioning rate are 

overlaid to show isoclines; warmer colors represent direction of increase in ordination space.
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Figure 6.10.b. Non-metric multidimensional scaling plots showing the distribution of species 

composition of individual meals collected from brown pelican nestlings, Northern GOM, 2014–2015:

mass.

Grey dots represent individual meals. Surface plots of the three components of energy provisioning rate are 

overlaid to show isoclines; warmer colors represent direction of increase in ordination space.
120

Figure 6.11.c. Non-metric multidimensional scaling plots showing the distribution of species 

composition of individual meals collected from brown pelican nestlings, Northern GOM, 2014–2015:

energy density.

Grey dots represent individual meals. Surface plots of the 3 components of energy provisioning rate are 

overlaid to show isoclines; warmer colors represent direction of increase in ordination space.
121

Figure 6.12. Linear relationship between energy provisioning rate and biomass provisioning rate, and 

nest productivity at brown pelican nesting colonies, Northern GOM, 2014–2015.

(a) Linear relationship between EPR and nest productivity (y = 0.0004 x − 0.508; r2 = 0.952); (b) linear 

relationship between BPR and nest productivity (y = 0.0019 x − 0.535; r2 = 0.943). Error bars represent 95% 

confidence interval of means.
122

1 This study

2 Spitz et al. 2010 3 Meynier et al. 2008 4 Anthony et al. 2000 5 Van Pelt et al. 1997 6 Payne et al. 1999

Figure 6.13. Comparison of lipid content and energy density of forage fish in this study with published 

values from other regions.

(a) Lipid content, (b) energy density of forage fish. Colors indicate marine regions (orange: tropical; green: 

temperate; blue: subpolar). Data sources are listed as footnotes.
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7 Hematology, Plasma Chemistry, and PAHs

7.1 Hematology and Plasma Chemistry

CBCs provide a wealth of data within an individual and across populations. The half-life for red blood 

cells in birds is ~13 d (Maceda-Veiga et al. 2015), therefore blood samples provide insight into the recent 

condition of individuals (e.g., on the order of 2–4 w). For data in this study, analytes are indicative of 

condition during the incubation and early chick-rearing stage for adults, and for the early development 

stage for chicks. Furthermore, analytes of chicks may reflect nutritional conditions, and can be responsive 

to overall food quality as well as short-term changes in provisioning. It is not uncommon, therefore, to 

observe a substantial level of variability within an analyte among individuals (e.g., Ferguson et al. 2014, 

Fiorello 2019). Caution should be applied when interpreting such data, since attempting to provide 

detailed ecological explanations for the values of each analyte can be misleading due to the high levels of 

variability. We concentrated our interpretation on groups of analytes (e.g., plasma metabolites, enzymes) 

rather than individual analytes for each of the independent variables we assessed: BCI, planning area, sex, 

and home range size. This approach allows for a more ecologically-focused assessment of the data, in 

contrast to an analyte-specific clinical review. We also compare the reference levels from this study to 

other data available from brown pelicans. 

7.1.1 Individual Attributes, Hematology, and Plasma Chemistry of Adults 

7.1.1.1 Individual Attributes of Adults

BCI of adults ranged from -515.0–491.2 (mean = -6.3 ± 253.7). BCI of adults differed by sex (F 1,63 = 4.1, 

P = 0.04) but not by planning area or sex * planning area (P > 0.10). Males had higher BCI compared to 

females (Figure 7.1a). The 50% core use area (i.e., home range) ranged from ~ 1km2

–909 km2 (mean = 

102.9 ± 157.9 km2

) (0.62–564.8 mi2, mean = 63.9 ± 98.11 mi2). Home range size differed by planning 

area (P < 0.05; Figure 7.1b). Home range did not differ with BCI, sex, or sex * planning area (P > 0.10). 

Correlated independent variables were not used within the same models, although all variables of interest 

were included in the overall suite of models.

7.1.1.2 Blood Analytes of Adults

Table 7.1 includes reference values for adults for all blood analytes. Sodium, which was the only analyte 

with a bimodal distribution, was treated as two separate analytes (see 2.8.1.3 Statistical Analysis). Of the 

30 analytes examined, four had outliers removed (high values were removed for CORT, potassium, and 

alanine aminotransferase; a low value was removed for low-density lipoprotein cholesterol), 12 required 

transformation, and all had sufficient sample sizes to use a nonparametric reference interval (Table 7.1). 

Maximum sample sizes (i.e., the number of samples collected from each colony; not all samples provided 

data for all analytes) from each colony for blood analytes are summarized in Table 7.2. 

Reference intervals were calculated for 30 blood analytes. Among the 30 analytes there were nine cases 

of moderate to strong correlation. Beta globulin was most commonly correlated with other analytes (n = 3 

pair), followed by A:G, blood urea nitrogen, uric acid, and creatinine (n = 2 pair each). Pooled among all 

30 blood analytes, the models that were most often highly supported or for which AICc ≤ 2.0 of the topranked model were (1) home range + sex (n = 13 analytes), (2) home range (n = 10 analytes), and (3) BCI 

+ sex (n = 10 analytes; Table 7.3). Single-variable models for sex, BCI, and planning area were 
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infrequently ranked among top models (n ≤ 5 for each), although the null model was more frequently 

ranked as a top model (n = 7 analytes) compared to single-variable models. 

We calculated coefficient estimates using the value provided by the top-ranked model for an analyte

(Table 7.4); coefficient estimates included only when estimate ± SE did not overlap zero). 

7.1.1.3 Relationship of Planning Area with Blood Analytes of Adults

Among the independent variables we tested, coefficient estimates for the categorical variable “planning 

area” were most often strongly associated with the concentration of a blood analyte (Table 7.4, Figure

7.2). The eastern planning area was set as the reference level because it had the lowest level of oil and gas 

activity in coastal and marine waters. Concentrations of analytes were lower in both the central and 

western planning area compared to the eastern planning area for CPK, lipase, total protein, beta globulin, 

and gamma globulin. Concentrations of analytes were lower in the central or western planning area when 

compared to the eastern planning area for calcium (west), ALT (west), AST (central), albumin (west), and

A1G (central). Concentrations of analytes were higher in both the central and western planning area

compared to the eastern planning area for potassium and creatinine. Concentrations of analytes were 

higher in either the central or western planning area compared to the eastern planning area for calcium 

(central). Pelicans in the western planning area were likely to have lower levels of sodium than birds in 

either the eastern or central planning area (χ2

2 = 29.0, P < 0.0001).

7.1.1.4 Relationship of Body Condition Index, Sex, and Home Range with Blood 

Analytes of Adults

BCI was positively related to potassium and negatively related to creatinine, uric acid, and BUN:CRE

(Figure 7.3). Differences in analytes by sex occurred for seven analytes. Levels of BUN, uric acid, and 

BUN:CRE were higher in females compared to males, but levels of calcium, lipase, cholesterol, and 

HDLc were higher in males compared to females (Figure 7.4). We found a negative relationship between 

home range size and CPK, lactate dehydrogenase, lipase, BUN, creatinine, and uric acid; and a positive 

relationship between home range size and HDLc (Figure 7.5). 

7.1.1.5 White Blood Cells from Adults

Reference values were calculated for the counts (103ml-1) of WBC for adults (Table 7.5). Outliers (n = 2) 

were removed for the count of WBC, heterophils, and lymphocytes. Each of the WBC types including the 

total count were sufficient in sample size to use a nonparametric reference interval (Table 7.2). 

Heterophils were the most common WBC type and were observed in all samples. Samples frequently 

failed to include monocytes (n = 24), eosinophils (n = 38), and basophils (n = 66); the modal value for 

each = 0. 

Because WBC data did not fit a normal distribution (either as raw data or when transformed), we 

performed nonparametric analyses (Kruskal-Wallis or Kendall correlation) to assess the relationships 

between WBC data and sex, state, BCI, and home range size. Monocytes were slightly less common 

(Kruskal Wallis H 1 = 2.8, P = 0.09) in males (median = 0.18, quartiles = 0, 0.36) compared to females 

(median = 0.3, quartiles = 0.07, 0.8; Figure 7.6a). No other significant relationships occurred between sex 

and WBC counts. WBC counts were higher in the western planning area (median = 13, quartiles = 9–15) 

than in the central planning area (median = 7, quartiles = 5.7–12) (Kruskal Wallis H 2 = 5.3, P = 0.07; 

(Figure 7.6b). Heterophils were higher in the eastern planning area (median = 8.3, quartiles = 6.7–10.0) 

than in the central planning area (median = 5.7, quartiles = 3.8–7.6) (Kruskal Wallis H 2 = 6.8, P = 0.03;

Figure 7.6c). No other significant relationships occurred among between planning area and WBC counts.

Monocyte counts were negatively correlated to BCI (Kendall tau = -0.17, P = 0.04; Figure 7.6d). When 
125

analyzed separately by sex (i.e., because monocyte count differed by sex), there was a moderate negative 

correlation between monocyte counts and BCI in females (Kendall tau =-0.23) but no correlation between 

monocyte counts and BCI in males (Kendall tau = -0.01). Lymphocyte counts declined moderately with 

home range size (Kendall tau = -0.15, P = 0.07; (Figure 7.6e). No other significant correlations were 

found for BCI or home range size with WBC counts. Hematocrit (i.e., packed cell volume, PCV) was 

higher in males compared to females (F 1,62= 8.1, P = 0.006; Figure 7.6f). Hematocrit did not vary with 

planning area, sex, home range size, or BCI (P > 0.3 for each).

7.1.2 Individual Attributes, Hematology, and Plasma Chemistry of Chicks

7.1.2.1 Individual Attributes of Chicks

The two independent variables (BCI and planning area) were interrelated and therefore not used within 

the same models. BCI ranged from -976.9–1148.2 (mean = -13.9 ± 584.5). BCI of chicks differed by 

planning area (F 2,32 = 11.4, P = < 0.0002). BCI of chicks differed between the western and central 

planning areas (Tukey HSD P = 0.003) and between the western and eastern planning areas (Tukey HSD 

P = 0.001; Figure 7.7). 

7.1.2.2 Blood Analytes from Chicks

Reference values for chicks appear in Table 7.6. Of the 30 analytes examined, four had outliers removed 

(high values were removed for BUN, BUN:CRE, CPK, and GGT, and seven required transformation. 

Sample sizes were insufficient for calculating nonparametric reference intervals and therefore we instead 

calculated reference intervals using the robust method with a Box-Cox transformation. For cases where a 

better fit was provided by an untransformed robust estimator (CO2, amylase, and CORT) or a Box-Cox 

standard estimator (pre-albumin, A1G, and gamma globulin) (Table 7.6), these estimators were used 

instead. Maximum sample sizes from each colony for blood analytes and for WBC counts (i.e., the 

number of samples collected from each colony; not all samples provided data for all analytes) are shown 

in Table 7.2.

Reference intervals were calculated for 30 blood analytes. Among the 30 analytes there were 19 cases of 

moderate to strong correlation. Gamma globulin and total protein were most commonly correlated with 

other analytes (n = 5 pair each), followed by AST and A:G (n = 4 pair), A2G and beta globulin (n = 3 pair 

each).

Pooled among all 30 blood analytes, the models that were most often highly supported included the null 

model (n = 21 analytes), planning area (n = 17 analytes), and BCI (n = 12 analytes; Table 7.7). Both the 

null model and the planning area model ranked as the top model for 13 analytes, while BCI ranked as the 

top model in four analytes. Planning area appeared as the only ranked model for six analytes, BCI 

appeared as the only ranked model for one analyte, and the null appeared as the only ranked model for 

four analytes. 
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7.1.2.3 Relationship of Planning area with Blood Analytes of Chicks

Coefficient estimates for the categorical variable “planning area” were more frequently found to be 

associated with the concentration of a blood analyte than BCI (Table 7.8). The eastern planning area was 

set as the reference level because it had the lowest level of oil and gas activity in coastal and marine 

waters. Relationships among blood analytes and planning area are displayed in Figure 7.8. Concentrations 

of analytes were lower in both the central and western planning areas compared to the eastern planning 

area for amylase and A1G. Concentrations of analytes were lower in either the central or western 

planning area compared to the eastern planning area for CO2 (west), calcium (west), ALT (central), AST 

(central), glucose (west), BUN:CRE (central), albumin (west), gamma globulin (central), and A:G (west).

Concentrations of analytes were never found to be higher in both the central and western planning area

compared to the eastern planning area. Concentrations of analytes were higher in either the central and 

western planning areas compared to the eastern planning area for AST (west), lactate dehydrogenase 

(west), triglycerides (west), BUN (west), creatinine (central), BUN:CRE (west), A2G (west), prealbumin 

(west), and A:G (central). 

7.1.2.4 Relationship of Body Condition Index with Blood Analytes of Chicks

BCI was positively related to amylase, VLDLc, albumin, A1G and negatively related to potassium, AST, 

CPK, triglycerides, and CORT (Figure 7.9). 

7.1.2.5 White Blood Cells from Chicks

Reference values were calculated for the counts (103 ml-1) of WBC for chicks (Table 7.9). No outliers 

were identified for the count of total WBC or any WBC type. Sample sizes (n = 35 for all WBC types) 

were insufficient for calculating nonparametric reference intervals and therefore we instead calculated 

reference intervals using the robust method with a Box-Cox transformation (WBC count, heterophils, 

lymphocytes) or with an untransformed standard estimator (monocytes, eosinophils, and basophils) 

(Table 7.9). Heterophils were the most common WBC type and were observed in all samples. Samples 

frequently failed to include monocytes (n = 14), eosinophils (n = 20), and basophils (n = 23); the modal 

value for each = 0. 

Because WBC data did not fit a normal distribution (either as raw data or when transformed), we 

performed nonparametric analyses (Kruskal-Wallis or Kendall correlation) to assess the relationships 

between WBC data and planning area and BCI. No significant relationships occurred for any WBC counts 

with either planning area (Kruskal Wallis H P > 0.21 for each) or BCI (|r| < 0.18 for each). Hematocrit 

also did not vary with planning area (F 2,31 = 1.1, P = 0.3) or BCI (F 1,32= 0.06, P = 0.8).
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7.1.3 Ecological Relationships and Intraspecific Comparisons

7.1.3.1 Hematology of Adults and Chicks

Reference values from our study for PCV, counts of WBCs, and counts of each type of WBC were all 

within the reference intervals of previously published data for the species for adults and chicks (Ferguson 

et al. 2014, Fiorello 2019). In both adults and chicks, heterophils were the most common type of WBC, 

although in chicks the difference between heterophil and lymphocyte counts was less substantial. Fiorello 

(2019) notes that pelicans may be heterophilic and our data appear to support that observation. Within 

chicks we found no difference in any WBC count or PCV by planning area or BCI. A negative 

relationship between monocyte counts in adults and BCI is consistent with an increase in monocyte 

activity with infection or stress, both of which are likely to increase with lower BCI. Caution should be 

applied when comparing WBC data among planning areas for adults as all values were well within 

normal ranges. 

7.1.3.2 Ecological Relationships in Adults

Two previously published data sets present measures of blood analytes in adult brown pelicans (Wolf et 

al. 1985, Zaias et al. 2000). Wolf et al. (1985) included captive birds from sub-adult to adult. Zaias et al.

(2000) included captive, healthy, but flightless pelicans in a rehabilitation center and wild caught birds 

during the non-breeding season (presumably from the Atlantic coast of South Florida). Zaias et al. (2000)

found no differences between captive and wild individuals, or between sub-adults and adults, for any 

analytes. Neither data set provided reference levels, presenting values as means ± SE. Compared to these 

data, we identify five analytes for which our mean values appeared to differ from those reported in Wolf 

et al. (1985) and Zaias et al. (2000). AST appeared lower by ~ 50% in our sample, but the means reported 

in the other two studies were within the upper end of our reference intervals. Our measures of ALT, 

albumin, A1G, and A2G all appeared lower by ~ 50% and mean values from the aforementioned studies 

did not occur within our reference intervals, suggesting a substantial difference. Lower levels of ALT and 

albumin in our sample may be indicative of nutritional or physiological stress relative to captive birds or 

those during the nonbreeding season when foraging effort is less demanding (Dean et al. 2017, Fiorello 

2019). Higher values of A1G and A2G in the aforementioned studies may be indicative of infection or 

parasitism, particularly in captive birds (Ferguson et al. 2014).

7.1.3.2.1 Effects of Sex in Adults

Differences in blood analytes are not uncommon between sexes (e.g., Maness et al. 2017). During this 

study, sex appeared as a variable in a top-performing model for 17 analytes but in only seven cases did 

the relationship between sex and the analyte concentration appear to be ecologically relevant. The 

differences we observed in analytes between males and females appear to be consistent with requirements 

of egg-laying and with body condition. For example, lower levels of calcium and cholesterols in females 

compared to males in our study may indicate sex-specific deficits from egg formation and egg laying 

(Nisbet 1997, Bauch et al. 2010). Lower levels of lipase and cholesterols, and higher levels of BUN, uric 

acid, and BUN:CRE can all be indicative of dehydration and nutritional stress (Alonso-Alvarez et al.

2007, Fiorello 2019). In our study, males had higher BCI compared to females, and we also found lower 

levels of lipase and cholesterols, and higher levels of BUN, uric acid, and BUN:CRE in females than in 

males. Uric acid and BUN:CRE were also negatively related to BCI across both sexes in our study, 

further supporting the contention that these differences may be indicative of overall condition at the time 

of sampling. 
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7.1.3.2.2 Effects of Body Condition Index in Adults

BCI is used as a relative and coarse descriptor of the condition of an individual based on its body mass 

and body size. Because body mass can fluctuate substantially within an individual over short periods of 

time (e.g., due to recent feeding), caution should be applied when applying and interpreting BCIs. Within 

our data set BCI did not differ by planning area, suggesting that the condition of the adults we sampled, 

although variable, was not related to any region-wide conditions in the Northern GOM. Males did have 

higher BCI compared to females. A sex-based difference in BCI is not uncommon during early breeding 

due to the physiological stress experienced by females from egg-laying (Kalmbach et al. 2004). During 

this study, BCI appeared as a variable in a top-performing model for 15 analytes. For adult pelicans in the 

Northern GOM, 20% of the analytes measured were substantially influenced by BCI. The positive 

relationship that we observed between BCI and potassium indicates that pelicans in better condition likely 

had higher levels of electrolytes and hence were less likely to be nutritionally stressed. We also observed 

negative relationships between BCI and BUN:CRE, creatinine, and uric acid. These three analytes often 

indicate dehydration and kidney function (Fiorello 2019). A negative relationship of each with BCI would 

therefore be consistent with individuals presenting with lower body mass (i.e., lower total body water). 

7.1.3.2.3 Effects of Planning Area in Adults

We collected our data from colonies across three planning areas of BOEM that represented different 

levels of oil and gas activity (eastern = least activity, central = most activity, western = intermediate 

activity). These areas can be considered as surrogates for exposure to oil and associated chemicals based 

on the level of infrastructure within each planning area. Other sources of petrochemicals may, however, 

exist. Such sources include but are not limited to pipelines, shipping lanes, and industrial ports and 

refineries (see 2.4 and 4.2). Therefore, a strong signature of certain analytes to planning area may indicate 

exposure to oil and gas activity and a weaker signature may suggest that exposure to toxins is more 

regionally widespread or not only linked to oil and gas activity. Planning area can also serve as a 

surrogate for many other factors that may be undefined or highly variable within the study set. For 

example, in our sample we found that home range size differed among planning areas, and data from 

other aspects of this study demonstrate that diet can differ among planning areas as well (Chapters 4 and 

6). Planning area effects can be further confounded because samples were collected from different 

planning areas in different years. Therefore, it would be prudent to use caution when seeking to interpret 

differences or a lack of differences in analytes among planning areas.

Within each grouping of blood analytes (electrolytes, enzymes, metabolites, and plasma proteins), greater 

than 50% of the individual analytes showed some relationship with planning area. The most consistent 

pattern we observed was for plasma metabolites to be higher in the eastern planning area compared to the 

central and/or western planning areas. Lower levels of plasma metabolites can indicate nutritional stress 

and/or oiling (Alonso-Alvarez et al. 2007, Ibañez et al. 2015, Dean et al. 2017). In our study area, 

exposure to oil from oil and gas activity would be expected to be higher in the central and western 

planning areas, and as such would be consistent with these results. While it is difficult to assess the levels 

of nutritional stress among planning areas, the fact that we did not observe a difference in BCI by 

planning area suggests consistency in nutrition across the study area. We did, however, find that in this 

data set the size of home ranges was smaller in the eastern planning area compared to the central and 

western planning areas. Home range size is often inversely related to food availability and, in seabirds, to 

colony size (see Chapter 3). Therefore, lower levels of plasma metabolites might be expected in areas 

with larger home range sizes, which is consistent with our results. Lower levels of total proteins and 

creatinine (metabolites) are also indicative of nutritional or physiological stress (Alonso-Alvarez et al.

2007, Fiorello 2019). During our study, each were lower in the central and western planning areas

compared to the eastern planning area, consistent with the patterns we observed in plasma proteins.
129

Potential mechanisms underlying the higher levels of enzyme activity in birds from the eastern planning 

area are not clear. Higher levels of AST, ALT, and CPK are often noted as being indicative of muscle 

damage, hepatic damage, and/or oil exposure (Fiorello 2019, Alonso-Alvarez et al. 2007). The smaller 

home ranges of birds in the eastern planning area are inconsistent with high levels of exercise (flight) and 

possibly subsequent muscle damage. Inherently lower levels of exposure to oil and gas activity would 

also appear inconsistent with high enzyme levels. We did, however, find that blood samples of birds in 

the eastern planning area were more likely to increase in the concentration of parent PAHs and alkylated 

PAHs compared to birds in the central planning area (see section 8.3). Such elevations in PAHs may 

therefore explain the higher levels of enzyme activity in the eastern planning area. Nonetheless, Harr 

(2002) notes that while these enzymes are sensitive to the stressors, they are not specific and therefore 

caution should be applied when interpreting patterns in these enzymes.

7.1.3.2.4 Effects of Home Range in Adults

Data comparing home range sizes to blood profiles in birds appear to be lacking in the published 

literature. In our samples, home ranges are measured following the collection of the blood sample over a 

period of days to months. In contrast, the blood samples reference the time-period before blood sampling 

over a period of days to weeks, Therefore, attempts to relate home range size to blood analyte levels 

assume that the two can be linked despite the difference in the temporal frame of reference. 

In our samples, three of five metabolites appeared to be related to the size of the home range (BUN, 

creatinine, and uric acid). For each analyte, the relationship with home range size was negative. Higher 

levels of creatinine are common with increased energetic reserves and nutritional condition, and therefore 

would be consistent with smaller home ranges if smaller home ranges are indicative of higher food 

quantity/quality near the breeding site. High levels of uric acid and BUN are typically associated with 

nutritional stress or dehydration and are therefore inconsistent with other patterns we observed (e.g., 

increased creatinine with smaller home ranges).

We also observed higher levels of enzyme activity with smaller home range sizes. Higher enzyme values 

are indicative of muscle damage among other issues, so they might signal increased effort, but this would 

appear to be inconsistent with a smaller home range size. The higher levels of enzyme activity we 

observed with smaller home ranges may also be confounded with other factors. For example, hemolysis 

can result in enzyme activity appearing higher in a sample. Coincidentally, we found that the home range 

size of pelicans from which blood samples were characterized as hemolyzed (48.4 ± 57.1 km2

) appeared 

lower compared to those that were not characterized as hemolyzed (116.7 ± 172.3 km2

). It is possible, 

therefore, that the pattern we observed in enzyme activity was not ecologically based but instead may 

have been an artifact of sample condition. Such a finding also would be consistent with higher enzyme 

levels in birds from the eastern planning area, where home ranges were smaller than those in the central 

or western planning areas s.

One possible explanation for the observed increase in enzyme activity and metabolites with smaller home 

range sizes may be that active flight styles (e.g., flapping flight) comprise a higher proportion of flight 

within smaller home ranges compared to larger home ranges. For example, larger home ranges may 

require more commuting flight which can include less active flight styles such as soaring. A second 

explanation for the observed increase in enzyme activity and metabolites with smaller home range sizes 

may be that the driver of the relationship has been misidentified. For example, individuals experiencing 

muscle damage or other physiological stress would be expected to present with high enzyme activity and 

high levels of certain metabolites such as BUN and uric acid. Individuals in poor condition might also 

have smaller foraging ranges due to their reduced condition. Therefore, it may be that home range does 
130

not drive the pattern in the blood analytes but rather that the blood analytes drive the reduced size of the 

home range. Because of the confounding nature of the data herein with respect to home range size, the 

lack of studies explicitly designed to examine the relationship between home range size and blood 

analytes, and the potential temporal mismatch between blood samples and home range size, a study 

designed to specifically assess this relationship appears warranted.

7.1.3.3 Ecological Relationships in Chicks

The most relevant data set for a comparison to ours appears in Ferguson et al. (2014) and includes similar 

data from brown pelican chicks sampled at colonies in South Carolina. Most of the analytes common to 

both studies were similar in magnitude and range, with means from each study occurring well within the 

reference intervals from the other study. Three exceptions occurred. HDLc values from the Gulf were 

above the reference interval for the South Carolina sample. CO2 and VLDLc values from the Gulf were 

each below the reference interval provided for the South Carolina samples. Lipids and cholesterol (e.g., 

VLDLc and HDLc) are highly sensitive to current nutritional state and therefore may reflect time since 

feeding or food type, and therefore the opposing patterns in VLDLc and HDLc should not be overinterpreted. For example, we found no correlation in the measures of HDLc and VLDLc in either chicks 

or adults during our study. Furthermore, the level of amylase from the Gulf samples was at the extreme 

upper end of the reference interval of the sample from South Carolina. A lack of detailed studies on the 

role of amylase in birds (Fiorello 2019) makes an interpretation of this comparison challenging, but the 

disparate data from these two samples suggests that the analyte should be measured in future studies to 

establish a more robust reference interval for the species. The difference in CO2 between the two studies 

is also not clear. CO2 is an indicator of respiratory function. One hypothesis to consider is that chicks in 

the Gulf are exposed to higher levels of airborne toxins from oil and gas activity compared to the South 

Carolina coast, and this may impair CO2 levels. Our data also showed reduced CO2 levels in chicks from 

the western GOM compared to the eastern GOM (BCI also was higher in the eastern gulf compared to the 

western GOM), a pattern that would be consistent with this hypothesis. We did not, however, observe 

differences in CO2 levels in adults among planning areas.

Only two variables were considered for the assessment of blood analytes for chicks, planning area and 

BCI. BCI differed by planning area, with BCI being least in the western planning area. This result is 

consistent with a similar analysis of a larger sample from our study (see Chapter 5) and is attributed to 

higher levels of nutritional stress in the western planning area. Caution should be applied, however, when 

comparing the results of analyses suggesting that both planning area and BCI are strong predictors of a 

blood analyte because the two explanatory variables are related to each other. Although planning area

appeared as a top model for 17 analytes and BCI as a top model for 12 analytes, the null model appeared 

most frequently as a top model (n = 21 analytes) suggesting that in many cases there were no strong 

patterns of influence of BCI or planning area, or that measurable levels of influence were slight.

7.1.3.3.1 Effects of Body Condition Index in Chicks

Only one electrolyte, potassium, was related to BCI. The negative relationship we observed is consistent 

with electrolyte levels decreasing with nutritional status and hydration. Two enzymes, AST and CPK, 

showed a negative relationship with BCI while one (amylase) showed a positive relationship. CPK 

increases with stress (Maceda-Veiga et al. 2015) and therefore higher levels of CPK would be consistent 

with poorer BCI. AST increases with hepatic and muscular damage as well as with inflammation, and 

therefore higher levels of AST would be consistent with poorer BCI. A lack of detailed studies on the role 

of amylase in birds (Fiorello 2019) makes an interpretation of its negative relationship with BCI 

challenging, although the pattern appears to be consistent with the difference observed in amylase by 

planning area (i.e., amylase highest in the eastern planning area where BCI was highest).
131

The negative relationship we observed between triglycerides and BCI is challenging to interpret. 

Triglycerides typically decline with poor nutrition; however, levels of triglycerides can also increase with 

hepatic damage or soon after nutritional stress ensues (Alonso-Alvarez and Ferrer 2001). Therefore, the 

negative relationship we observed between BCI and triglycerides may suggest the onset of nutritional 

stress for the chicks in this sample. The positive but relatively weak relationship we observed between 

BCI and VLDLc suggests that energy stores increase as BCI increases. Similarly, lower levels of A1G (a 

plasma protein) in relation to BCI are also consistent with nutritional stress (Alanso-Alvarez et al. 2007). 

Last, higher levels of CORT are indicative of stress, and stress is negatively correlated with BCI in 

pelican chicks (see Chapter 5).

7.1.3.3.2 Effects of Planning Area in Chicks

We observed some patterns that suggest chicks in the west and/or central planning area were in poorer 

condition compared to those in the east. Two of three electrolytes, calcium and sodium, were highest in 

the eastern planning area and each tends to decline with nutritional stress or dehydration (Maceda-Veiga 

et al. 2015). Lower levels of glucose (metabolite) in the western planning area suggest poorer nutrition 

there compared to the east and central planning areas, and the highest levels of BUN (metabolite) in the 

west also suggest dehydration which is consistent with lower BCIs (Fiorello 2019). We also observed 

patterns in plasma proteins that support a regional pattern in BCIs. Albumin was lowest in the western 

planning area, gamma globulin and A1G were both least in the central planning area, and A1G was lower 

in the western compared to the eastern planning area. These patterns are also consistent with a gradient of 

BCIs across the planning areas. The patterns in enzymes are more difficult to interpret. Four enzymes 

appeared to differ among planning areas, but the pattern was inconsistent. ALT, AST, and amylase were 

all lowest in the central planning area, although the coefficient estimate for AST was weak and for ALT 

the null model held the most weight. Enzymes also can represent reactions to capture and therefore could 

also be depressed in the central planning area if capture efforts there were for some reason less stressful to 

chicks. Ultimately, enzyme levels represent short-term reactions and therefore differences may be 

difficult to relate to larger scale variables such as planning area.

7.2 Polycyclic Aromatic Hydrocarbons

7.2.1 PAHs in Adult Blood

There was no correlation between home range size (either the 50% core area or the 95% use area) and 

BCI (r < -0.18 for each) within adults sampled for PAH in blood. We also failed to detect any 

relationships among home range size (either the 50% core area or the 95% use area) and sex or planning 

area, and between BCI and sex or planning area (P > 0.11 for all) within adults sampled for PAH in 

blood. 

7.2.1.1 PAH Profiles in Adult Blood

We analyzed blood for PAHs from 33 adult pelicans. The occurrence and concentrations of each PAH are 

summarized in Table 7.10 and reported as wet weight (ng g-1

). The most frequently occurring PAHs were 

of intermediate molecular weight (Figure 7.10). The PAH with the highest concentration detected was 

Fluorene (165.4 and 161.1 ng g-1

; both birds from Audubon Island, Florida). We failed to detect two 

alkylated compounds and eight parent compounds out of the full test set. The two most frequently 

detected PAHs, 1,3-dimethylnaphthalene and 2,3,5-trimethylnaphthalene, also had the widest range in 

measured concentrations (~ 80 ng g-1 - ~ 128 ng g-1

; Table 7.10).
132

Of the 24 PAHs assessed, 14 were detected in at least one individual (Table 7.10). Alkylated PAHs were 

detected in 48% of individuals and parent PAHs were detected in 30% of individuals. Three alkylated 

compounds were each detected in > 10% of individuals; 2,6-dimethyl naphthalene (n = 4),

1,3-dimethylnaphthalene (n = 5), and 2,3,5-trimethylnaphthalene (n = 6).

Of 33 birds sampled, 21 (63.6%) had at least 1 PAH detected (Table 7.11; 1 PAH: n = 10; 2 PAHs: n = 

10; 4 PAHs: n = 1). Parent compounds were detected in 10 of 33 birds, alkylated compounds were 

detected in 16 of 33 birds, and both parent and alkylated PAHs were detected in 5 of 33 birds. The 

proportion of birds sampled with detectable limits of PAHs in blood was 50% in the central planning 

area, 63% in the western planning area, and 80% in the eastern planning area (Table 7.11). PAHs were 

detected in 75% of females sampled and in 57% of males sampled (Table 7.11). The occurrence of each 

PAH detected in blood is summarized by planning area and sex in Table 7.12. 

Among birds with detectable limits of PAHs, the sum of all PAHs ranged from 42.44 - 463.75 ng g-1

, the 

sum of alkylated PAHs ranged from 58.46 - 220.51 ng g-1

, and the sum of parent PAHs ranged from 

42.44 - 245.47 ng g-1 (Figure 7.11a). There was no significant difference in the concentration of parent 

compared to alkylated PAHs using either the full data set or using only data from birds above detectable 

limits (Wilcoxon rank sum test P > 0.13 for each; Figure 7.11b). When all individuals were considered, 

there was no correlation between summed concentrations of alkylated and parent PAHs (Kendall tau r 

= -0.05, Pearson r = -0.09). Similarly, when only individuals with detectable limits of PAHs were 

considered, there was no correlation between summed concentrations of alkylated and parent PAHs 

(Pearson r = 0.25). The individual with the highest concentration of summed parent PAHs (245.47 ng g-1

) 

did, however, present with the 2nd highest level of alkylated PAHs (218.28 ng g-1

; female, Audubon 

Island, Florida).

7.2.1.2 Total PAHs (Parent PAHs + Alkylated PAHs) in Adult Blood

Five models for the presence of sumPAH in pelican blood received support, and these included each of 

the main variables we assessed except planning area (Table 7.13a). There was little separation among 

these five models, with the most supported model only carrying an AICc weight = 0.21. The only variable 

that appeared to have a measurable relationship with sumPAH was sex (coefficient estimate = -1.12 ± 

0.95). Females were 3.1 times more likely to be detected with a PAH compared to males although there 

was substantial variability around the estimate (90% CI for odds ratio = 0.7, 16.7).

The concentration of sumPAH in pelican blood was best described by a single model that included sex 

and home range size (Table 7.13b). There was substantial separation among the first and second ranked 

model, with the most supported model carrying 90% of the AICc weight. There was a strong relationship 

between sumPAH concentrations and sex (coefficient estimate = -0.73 ± 0.23) and a weak negative 

relationship between sumPAH concentrations and home range size which was strongly leveraged by a 

single individual and therefore discounted (coefficient estimate = -0.00022 ± 0.000099). The odds of the 

sumPAH concentration increasing by 1 ng g-1 increased by 2.1 times for females compared to males (90% 

CI for odds ratio = 1.4, 3.0). 

7.2.1.3 Parent PAHs in Adult Blood

Three models for the presence of sumPAR in pelican blood received support, and these included home 

range, sex, and BCI (Table 7.13c). There was little separation among these three models, with the most 

supported model only carrying an AICc weight = 0.28. Sex was weakly associated with sumPAR 

(coefficient estimate = -0.94 ± 0.89). Females were approximately 2.5 times more likely to be detected 

with a PAR compared to males, although there was substantial variability around the estimate (90% CI 
133

for odds ratio = 0.6, 11.1). Coefficient estimates for home range size and BCI had standard errors that 

overlapped zero indicating no relationship. 

Four models for the concentration of sumPAR in pelican blood received support, and these included each 

of the main variables we assessed (Table 7.13d). There was little separation among these four models, 

with the most supported model only carrying an AICc weight = 0.38. The only variable with a 

measurable, yet weak, effect on sumPAR was planning area. Coefficient estimates indicated that the 

concentrations of sumPAR were lower in the central compared to the eastern planning area (coefficient 

estimate = -0.70 ± 0.41; Figure 7.12a). The odds of the sumPAR concentration increasing by 1 ng g-1

increase by 2.0 times for birds in the eastern compared to the central planning area, although there was 

substantial variability around the estimate (90% CI for odds ratio = 1.0, 4.0).

7.2.1.4 Alkylated PAHs in Adult Blood

Two models for the presence of sumALK in pelican blood received support, and these included the 

variables sex and home range (Table 7.13e). There was little separation among these two models, with the 

most supported model being 1.5x as likely to be the best model compared to the second-ranked model. 

Sex had a measurable relationship with sumALK (coefficient estimate = -1.45 ± 0.91). Females were 4.3 

times more likely to be detected with an ALK compared to males, although there was substantial 

variability around the estimate (90% CI for odds ratio = 1.1, 25.0).

Only the full model for the concentration of sumALK in pelican blood, which included each main 

variable and carried an AICc weight = 0.86, received support (Table 7.13f). Coefficient estimates 

indicated that the concentrations of ALK were likely to decrease in the central compared to the eastern 

planning area (coefficient estimate = -0.36 ± 0.24; Figure 7.12b), decrease in males compared to females 

(coefficient estimate = -0.67 ± 0.21), decrease with an increase in BCI (coefficient estimate = -0.0011 ± 

0.0004), and decrease with an increase home range size (coefficient estimate = -0.00019 ± 0.00011). The 

odds of the sumALK concentration increasing by 1 ng g-1 increase by 1.4 times for birds in the eastern 

compared to the central planning area (90% CI for odds ratio = 1.0, 2.2), and increase by 1.9 times for 

females compared to males (90% CI for odds ratio = 1.4, 2.8). The odds of a change in sumALK for BCI 

and home range do not differ from 1.0 even when each independent variable is scaled up one order of 

magnitude. 

7.2.2 PAHs in Adult Feathers

There was no correlation between home range size (either the 50% core area or the 95% use area) and 

BCI (r < 0.11 for each). We also failed to detect any relationships among home range size (either the 50% 

core area or the 95% use area) and sex or planning area (P > 0.39 for each). BCI was moderately related 

to sex (t77 = 1.68, P = 0.10; BCI males > BCI females). BCI was lower in the central planning area

compared to the western planning area (t76 = -2.2, P = 0.03). Home range size was related to migration 

class (t63 < -4.6, P < 0.0001 for each).

7.2.2.1 PAH Profiles in Adult Feathers

We analyzed feathers for PAHs from 92 adult pelicans. The occurrence and concentrations of each PAH 

are summarized in Table 7.14 and reported as dry weight (ng g-1

). The most frequently occurring PAHs 

were of intermediate molecular weight (Figure 7.13). The PAHs with the highest concentrations detected 

were 1,3-dimethylnaphthalene (256.2 and 221.8 ng g-1

; both birds from Felicity Island, Louisiana) and 2-

methyl naphthalene (229.0 ng g-1

; Shamrock Island, Texas). 
134

All of the alkylated compounds were detected in ≥ 2 birds while nine parent compounds were not 

detected. 1,3-dimethylnaphthalene, the most frequently detected PAH, also had the widest range in 

measured concentration (~ 72 ng g-1 - ~ 256 ng g-1

; Table 7.14). Of the 24 PAHs assessed, 15 were 

detected in at least one individual. Alkylated PAHs were detected in 46% of individuals and parent PAHs 

were detected in 26% of individuals. Three alkylated compounds were detected in > 10% of individuals 

(2,6-dimethyl naphthalene (n = 10), 1,3-dimethylnaphthalene (n = 11), and 2,3,5-trimethylnaphthalene (n 

= 10).

Of 92 birds sampled, 56 (60.8%) had at least 1 PAH detected (Table 7.15) (1 PAH: n = 39; 2 PAHs: n = 

13; 3 PAHs: n = 2; 4 and 6 PAHs: n = 1). Parent compounds were detected in 24 of 92 birds, alkylated 

compounds were detected in 42 of 92 birds, and both parent and alkylated PAHs were detected in 10 of

92 birds (Table PAH blood 2). The proportion of birds sampled with detectable levels of PAHs in feathers 

was 45% in the central planning area, 16% in the western planning area, and 17% in the eastern planning 

area (Table 7.15). PAHs were detected in 67% of females sampled and in 55% of males sampled. The 

occurrence of each PAH in feathers is summarized by planning area and sex in Table 7.16. 

7.2.2.2 Total PAHs (Parent PAHs + Alkylated PAHs) in Adult Feathers

Among birds with detectable limits of PAHs, the sum of all PAHs ranged from 60.20–623.80, the sum of 

alkylated PAHs ranged from 71.50–479.00, and the sum of parent PAHs ranged from 32.40–166.70 

(Figure 7.14a). The sum of alkylated PAHs was greater than the sum of parent PAHs for both the full data 

set (W = 3135.5, p-value = 0.0004) and data from birds above detectable limits (W = 235.5, p-value = 

0.0003; Figure 7.14b). When all individuals were considered, there was no correlation between summed 

concentrations of alkylated and parent PAHs (Kendall tau r = -0.06, Pearson r = -0.03). When only 

individuals with detectable limits of PAHs were considered, there was a moderate positive correlation 

between summed concentrations of alkylated and parent PAHs (Pearson r = 0.57). 

One model for the presence of sumPAH in pelican feathers received support, and it included BCI and 

migration class (Table 7.17a). The migration class “long distance” was set as the reference level. This 2-

variable model carried 63% of the AIC weight and was 3x as likely to be the best model compared to the 

second ranked model (which was a single variable model for migration class). Both BCI (coefficient 

estimate = -0.002 ± 0.001) and migration class (coefficient estimates: medium -1.58 ± 0.77, short -1.38 ± 

0.76) appeared to have a measurable relationship with sumPAH. The odds of a bird having a PAH 

increased 1.002 times (90% CI for odds ratio = 1.0006, 1.004) for every unit decrease in BCI (i.e., 1.2 

times for every 100 unit decrease in BCI). The odds of a bird having a PAH decreased 4.8 times for 

medium v. long distance migrants (90% CI for odds ratio = 1.4, 20.0) and decreased 4.0 times for short v. 

long distance migrants (90% CI for odds ratio = 1.2, 16.7). 

One model for the concentration of sumPAH in pelican feathers received support, and it included BCI and 

migration class (Table 7.17b). Although the best-performing model carried 94% of the AIC weight, 

neither BCI nor migration class had a measurable effect on the concentration of sumPAH (i.e., SE > 

coefficient estimate). 

7.2.2.3 Parent PAHs in Adult Feathers

Three models for the presence of sumPAR in pelican feathers received support (Table 7.17c). The top 

ranked model carried 31% of the AIC weight and was 1.2 times as likely to be the best model compared 

to the second ranked model. Home range size appeared to have a measurable relationship with sumPAR 

(coefficient estimate = 0.00081 ± 0.0003). The odds of a bird having a PAR increased 1.0008 times (90% 

CI for odds ratio = 1.0004, 1.001) for every 1 km2 (0.62 mi2

) increase in home range size (i.e., 1.08 times 

for every 100 km2 unit increase in home range). There was a marginal increase in the odds of a bird 
135

having a PAR for females compared to males (2.4 times, 90% CI for odds ratio = 0.8, 7.1). The odds of a 

bird having a PAR decreased 4.3 times for medium compared to long distance migrants (90% CI for odds 

ratio = 1.4, 20.0). The odds of a bird having a PAR increased 4.4 times for birds in the central compared 

to the eastern planning area (90% CI for odds ratio = 1.3, 15.9). 

One model for the concentration of sumPAR in pelican feathers received support, and it included 

migration class and BCI (Table 7.17d). This two-variable model carried 70% of the AIC weight and was 

4.4x as likely to be the best model compared to the second ranked model which was > 2 AIC points 

removed. Only migration class (coefficient estimates: medium -0.31 ± 0.21, short -0.39 ± 0.15) had a 

measurable relationship with sumPAR (Figure 7.15). The odds of the sumPAR concentration increasing 

by 1 ng g-1 increased by 1.4 times for birds in the long-distance compared to medium-distance migrant 

class (90% CI for odds ratio = 0.9, 2.0) while the odds of the sumPAR concentration increasing by 1 ng g1 increased by 1.5 times for birds in the long-distance compared to short-distance migrant class (90% CI 

for odds ratio = 1.1, 1.9).

7.2.2.4 Alkylated PAHs in Adult Feathers

Two models for the presence of sumALK received support, and both included migration class (Table 

7.17e). The first-ranked model carried 68% of the model weights. This top ranked model included 

migration class and BCI and was 2.6 times as likely to be the best model compared to the second ranked 

model which included only migration class. BCI was negatively related to the presence of sumALK 

(coefficient estimate = -0.0014 ± 0.0009). The odds of a bird having an ALK increased 1.001 times (90% 

CI for odds ratio = 1.0002, 1.003) for every 1 unit decrease in BCI (i.e., 1.15 times for every 100 unit 

decrease in BCI). Migration class did not have a measurable effect on sumALK.

The concentration of sumALK in pelican feathers was represented by one model that included variables 

for migration class and planning area (Table 7.17f). The best performing model carried 74% of the AIC 

weight and was 5.7 times as likely to be the best model compared to the second ranked model. Neither 

variable, however, had a measurable effect on the concentration of sumALK.

7.2.3 PAHs in Chick Feathers

BCI of chicks differed among planning area (F 2,32 = 10.3, P = 0.0003) and was highest in the central 

planning area (502.3 ± 614.9), intermediate in the eastern planning area (204.4 ± 434.2), and lowest in the 

western planning area (-530.8 ± 584.5). 

7.2.3.1 PAH Profiles in Chick Feathers

We analyzed feathers for PAHs from 35 pelican chicks. The occurrence and concentrations of each PAH 

are summarized in Table 7.18 and reported as wet weight (ng g-1

). The most frequently occurring PAHs 

were of intermediate molecular weight (Figure 7.16). The PAH with the highest concentration detected 

was 2,6-dimethyl naphthalene (250.0 ng g-1

; Galveston Bay). 

All of the alkylated compounds were detected in ≥ 1 bird while 13 parent compounds were not detected. 

2,6-dimethyl naphthalene, one of two most frequently detected PAH, also had the widest range in 

measured concentration (~ 50 ng g-1 - ~ 250 ng g-1

; Table 7.18).
136

Of the 24 PAHs assessed, 11 were detected in at least one individual (Table 7.18). Alkylated PAHs were 

detected in 34% of individuals and parent PAHs were detected in 9% of individuals. Two alkylated 

compounds were each detected in > 10% of individuals; 2,6-dimethyl naphthalene (n = 4) and 2,3,5-

trimethylnaphthalene (n = 4).

Of 35 birds sampled, 13 (37.1%) had at least 1 PAH detected (Table 7.19) (1 PAH n = 8; 2 PAHs n = 5).

Parent compounds were detected in 3 of 35 birds, alkylated compounds were detected in 12 of 35 birds, 

and both parent and alkylated PAHs were detected in 2 of 35 birds (Table PAH feathers 2). The 

proportion of birds sampled with detectable limits of PAHs in feathers was 80% in the central planning 

area, 25% in the western planning area, and 40% in the eastern planning area (Table 7.19). The 

occurrence of each PAH in blood is summarized by region in Table 7.20. 

7.2.3.2 Total PAHs (Parent PAHs + Alkylated PAHs) in Chick Feathers

Among birds with detectable levels of PAHs, the sum of all PAHs ranged from 100.5–309.6, the sum of 

alkylated PAHs ranged from 50.3–265.3, and the sum of parent PAHs ranged from 57.9–193.5 (Figure

7.17a). The sum of alkylated PAHs was not significantly different from the sum of parent PAHs when 

using the full data set (W = 458, P = 0.01) and did not differ when only birds above detectable limits were 

examined (W = 21.0, P = 0.7; Figure 7.17b). When all individuals are considered, there was no 

correlation between summed concentrations of alkylated and parent PAHs (Kendall tau r = 0.13, Pearson 

r = 0.05). Sample sizes were insufficient (n = 3 birds) to assess the correlation between alkylated and 

parent PAHs when only individuals with detectable limits of PAHs were considered.

Two models for the presence of sumPAH in chick feathers received support. The top ranked model 

included planning area and the null model also was supported (Table 7.21a) The planning area model 

carried 54% of the AIC weight and was 1.9 times as likely to be the best model compared to the second 

ranked model. Birds in the central planning area were 6 times as likely to have a PAH compared to birds 

in the eastern planning area (coefficient estimates = 1.79 ± 1.29) although the variability on the estimate 

was wide (90% CI for odds ratio = 0.9, 75.9).

All three models received support for the concentration of sumPAH in chick feathers (Table 7.21b). The 

model including planning area carried 41% of the AIC weight and was 1.1 times as likely to be the best 

model compared to the null model which ranked second. Planning area (coefficient estimates: central 

= -0.59 ± 0.29, western = -0.36 ± 0.27) had a measurable relationship with sumPAH. Birds in the eastern 

planning area were 1.8 times (90% CI for odds ratio = 1.1, 2.9) more likely to have elevated sumPAH 

concentrations with respect to birds in the central planning area and birds in the eastern planning area

were 1.4 times (90% CI for odds ratio = 0.9, 2.3) more likely to have elevated sumPAH concentrations 

with respect to birds in the western planning area.

7.2.3.3 Parent PAHs in Chick Feathers

Only three birds had detectable levels of sumPAR. Although the model including BCI received support 

for the presence of sumPAR, it ranked second to the null model which was 2.6 times as likely to be the 

best model (Table 7.21c). The SE on the estimate of BCI was superior to the coefficient estimate,

indicating no measurable relationship. A hurdle model was not conducted for sumPAR due to insufficient 

sample size.

7.2.3.4 Alkylated PAHs in Chick Feathers

Only one model supported the presence of sumALK in chick feathers. The top ranked model included 

planning area (Table 7.21d). The planning area model carried 70% of the AIC weight and was 3.5 times 
137

as likely to be the best model compared to the null model which ranked second. Birds in the central 

planning area were 6 times more likely (90% CI for odds ratio = 0.9, 75.9) to have a PAH compared to 

birds in the eastern planning area (coefficient estimate = 1.79 ± 1.29) while birds in the eastern planning 

area were 2.6 times more likely (90% CI for odds ratio = 0.7, 11.1) to have a PAH compared to birds in 

the western planning area (coefficient estimates = -0.98 ± 0.85). 

Although the model including BCI received support for the concentration of sumALK, it ranked second to 

the null model which was 1.9 times as likely to be the best model (Table 7.21e). 

7.2.4 Ecological relationships

7.2.4.1 PAHs in Adult Blood

7.2.4.1.1 PAH Profiles in Adult Blood

PAHs in blood likely derive from consumption of contaminated prey or ingestion of preen oil, although 

bioaccumulation through food webs is thought to be low (Paruk et al. 2014, Paruk et al. 2016, Acampora 

et al. 2018). Furthermore, blood samples provide a recent assessment of exposure and represent 

substances currently circulating within an individual (Paruk et al. 2014, Paruk et al. 2016). The most 

commonly occurring PAHs and the PAHs with the highest summed concentration included 2,3,5-

trimethylnaphthalene, 1,3-dimethylnaphthalene, and 2,6-dimethyl naphthalene. Analysis for 2,6-dimethyl 

naphthalene was recommended following the DWH oil spill due to developments in sourcing of PAHs 

from the spill (Seegar et al. 2015). The two highest within-individual concentrations and the highest 

average concentration were for fluorene. The two individuals from which these samples were taken were 

both nesting adults on Audubon Island, Florida, with home ranges centered in and proximate to bays 

surrounding Panama City, Florida. Within adult blood samples, alkylated PAHs were detected in 48% of 

individuals and parent PAHs were detected in 30% of individuals. Alkylated PAHs are more abundant in 

crude oil, more persistent, and less prone to metabolization compared to parent PAHs (Seegar et al. 2015). 

Alkylated PAHs also were more common in blood samples of common loons (Gavia immer) wintering in 

the Northern GOM compared to parent PAHs (Paruk et al. 2014, Paruk et al. 2016). Liu et al. (2012) 

found that alkylated PAHs had a stronger signature in DWH crude oil, weathered oil, and oil mousse 

compared to parent PAHs. The sum of both parent and alkylated PAHs in blood appears to be higher in 

the brown pelicans we sampled compared to blood samples collected from migratory common loons 

(2011–2013) and migratory tundra peregrine falcons (Falco peregrinus tundrius; 2011), the two data sets 

that currently appear to be the most comparable to ours. Similarly, the maximum individual values for 

blood samples from brown pelicans also appear to be an order of magnitude higher than for blood 

samples collected from migratory common loons and migratory tundra peregrine falcons.

7.2.4.1.2 Effects of Sex on PAHs in Adult Blood

The presence or absence of PAHs in adult blood samples was most frequently predicted by sex. On 

average, females were roughly three times more likely to have either type of PAH compared to males, 

although this relationship varied from strong (sumALK) to moderate (sumPAH and sumPAR) and the 

confidence interval on each estimate was wide. We also assessed which variables best predicted an 

increase in PAH concentrations among individuals with PAH greater than zero. For both sumPAH and 

sumALK, sex was likely to be related to an increase in concentration. Given that blood reflects a 

relatively recent signature, our results suggest that females are being exposed to PAHs more frequently or 

perhaps more recently than males during incubation and early chick-rearing. A dimorphic result based on 

sex could occur through differential resource selection by males and females, in terms of habitat occupied 

and/or prey consumption (Seegar et al. 2015). Sex is not a predictor for either the presence or 
138

concentration levels of PAHs in feathers of adults, further suggesting that the sex-effect observed in blood 

samples may be indicative of recent exposure.

7.2.4.1.3 Effects of Planning Area on PAHs in Adult Blood

Planning area was never a strong predictor of the presence of PAHs in pelican blood, and only moderately 

appeared to affect the level of PAHs in those individuals identified with PAHs. A slight increase in levels 

of sumPAR in the eastern planning area was driven by two individuals from Florida, each with a high 

level of fluorene. Similarly, the three highest levels of sumALK occurred in birds nesting in Florida. 

Caution needs to be applied to interpreting these results as sample sizes are small. Nonetheless, even a 

conservative interpretation suggests that the levels of PAHs in pelican blood are not driven by differences 

in oil and gas activity among planning areas.

7.2.4.2 PAH in Adult Feathers

7.2.4.2.1 PAH Profiles in Adult Feathers

The profile of PAHs in adult feathers was similar to the profile of PAHs in adult blood. For example, 

alkylated PAHs were detected in 46% of feather samples (48% in adult blood) and parent PAHs were 

detected in 26% of feather samples (30% in adult blood). The most commonly occurring PAHs and the 

PAHs with the highest summed concentrations included 2,3,5-trimethylnaphthalene, 1,3-

dimethylnaphthalene, and 2,6-dimethyl naphthalene. 1,3-dimethylnaphthalene and 2-methyl naphthalene 

were found in the highest individual concentrations from birds nesting in Louisiana and Texas, 

respectively. PAHs detected from feathers may provide a different spatial, temporal, or source signature 

compared to PAHs detected in blood or other fluids that turnover relatively quickly (Jaspers et al. 2004, 

Acampora et al. 2018). For example, sources of PAHs in feathers may include blood supply (during 

feather growth), contact with the external environment, or contact with contaminated preen oil (Jaspers et 

al. 2004, Acampora et al. 2018). Feather samples may thus provide an assessment of local and/or 

transboundary contamination in migratory species with a longer temporal signature compared to blood 

(Acampora et al. 2018). The higher frequency of alkylated PAHs in feathers suggests a petrogenic source. 

Alkylated PAHs are more abundant in crude oil, more persistent, and less prone to metabolization 

compared to parent PAHs (Seegar et al. 2015). Comparisons of PAH levels in feathers of adult pelicans to 

other studies of PAH levels in feathers of free-ranging birds from this region are, to the best of our 

knowledge, unavailable at this time.

7.2.4.2.2 Effects of Migration Class on PAHs in Adult Feathers

The presence or absence of PAHs in adult feathers was predicted most often by migration class. Three of 

the six model steps showed that longer distance migrants were more likely to be found with a PAH 

(sumPAH and sumPAR) and that the level of PAHs was likely to increase in that group (sumPAR). Given 

that feathers represent a longer-term signature of PAHs compared to blood, our results suggest that birds 

that migrate longer distances may experience increased exposure to PAHs away from the colony 

(Acampora et al. 2018). Most long-distance migrants in our study departed the Northern GOM and 

occupied wintering areas in the southern GulfGOM southwestern GOM, or Cuba. However, an 

assessment of molting locations may also be warranted to better understand PAH levels. Many 

individuals also attended distinct molting sites separate from both their breeding and wintering areas, and 

approximately 75% of birds in our sample occupied ranges in the Louisiana Delta during the postbreeding period. An investigation into potential sources of parent PAHs in molting and wintering areas 

may be warranted but likely would include combustion processes (which could be quite varied) as 

opposed to exposure to crude oil.
139

7.2.4.2.3 Effects of Body Condition Index on PAHs in Adult Feathers

The presence or absence of PAHs in adult feathers was also partially explained by BCI. Individuals in 

poorer body condition were more likely to be found with a PAH, and it was more likely that the PAH was 

from the alkylated group. BCI represents a current measure of condition and therefore caution should be 

applied when interpreting these results given their potential temporal mismatch with PAHs on feathers 

and the frequency with which BCI appeared as an influential variable among the model sets (i.e., only 2 

of 6 model sets). Nonetheless, our data suggest that individuals with high levels of PAHs over time may 

experience reduced physical condition.

7.2.4.2.4 Effect of Other Independent Variables on PAHs in Adult Feathers

Three of the independent variables we assessed (home range, planning area, sex) did not frequently or 

reliably predict PAH presence or concentrations in adult feathers. Because home range reflects activity of 

individuals post-sampling, and feathers represent the pre-sample time period, the lack of a pattern is not 

surprising. The lack of a relationship between feather PAHs and planning area also suggests that the 

levels of PAHs in pelican feathers is driven by a complex set of factors that cannot be distilled solely to 

the planning area within which the colony occurs. The lack of a sex effect suggests that males and 

females may not experience different levels of contact exposure to these specific PAHs. Other pollutants, 

however, may still present a differential risk to males and females (see Chapter 5).

7.2.4.3 PAH in Chick Feathers

7.2.4.3.1 PAH Profiles in Chick Feathers

Chick feathers likely represent contact transfer of PAHs from adults, deposition from aerial sediments, or 

contact with PAHs in ground/nesting material or prey. Alkylated PAHs were detected in 34% of samples 

and parent PAHs were detected in 9% of samples. Alkylated PAHs are more abundant in crude oil, more 

persistent, and less prone to metabolization compared to parent PAHs (Seegar et al. 2015). The most 

commonly occurring PAHs were 2,3,5-trimethylnaphthalene, 1,3-dimethylnaphthalene, and 2,6-dimethyl 

naphthalene, the same common PAHs found in adult feathers. 2,6-dimethyl naphthalene and pyrene were 

found in the highest individual concentrations from birds nesting in Louisiana and Texas, respectively. 

Two of the three highest concentrations of PAHs in chick feathers were recorded from Galveston Bay, 

Texas, and the third from a colony on the Florida Panhandle. The ratio of alkylated to parent PAHs also 

appeared to be higher in chick feathers compared to adult blood or adult feathers, suggesting that PAHs in 

chick feathers may reflect a petrogenic source of PAHs. A petrogenic source may be more likely if 

contamination occurs from contact with recently foraging parents. Further, we posit that the location from 

which feathers are sampled may reflect different sources of contact, e.g., scapular feathers contacting 

adults and chest feathers contacting nesting material. We are not aware, however, of any efforts to 

analyze data to assess this hypothesis. 
140

7.2.4.3.2 Effects of Body Condition Index on PAHs in Chick Feathers

BCI was not a predictor of PAH levels in chick feathers. Planning area irregularly and weakly predicted 

PAH levels in feathers of chicks. The presence of PAHs, and particularly sumALK, was more likely in 

the central planning area. Alkylated PAHs tend to be associated with petrogenic sources (Seegar et al.

2015, Paruk et al. 214) in the Northern GOM and the central planning area has a higher concentration of 

oil and gas activity compared to the western and eastern planning areas. Because of relative infrequency 

of PAHs in chick feathers, however, caution should be applied when interpreting these results. 

Comparable data sets assessing PAH loads in chicks are, to the best of our knowledge, not available for 

other nearshore seabirds within the region. Given that the route of contamination for PAHs on chick 

feathers is not entirely clear, we suggest that additional assessments be considered to determine if shortor long-term changes may occur in PAH loads dependent upon chick age or parental activity such as 

provisioning or brooding (i.e., activities that may enhance contact with chicks).
141

Table 7.1. Statistical Values for Mass and Serum Chemistry for Adult Brown Pelicans Sampled from Breeding Colonies in the Northern GOM, 2013–2014

Analyte (units) n Mean Median SD Min Max Reference 

Interval

Lower 90% CI Upper 90% CI

Sodium (mEq L-1) 72 205.9 250.0 54.5 111 250 116.8–250.0 111.0–130.0 250.0–250.0

Potassium (mEq L-1) *^ 72 3.95 3.65 1.79 1.2 10.9 1.37–9.66 1.20–1.65 6.42–10.90

CO2 (mEq L-1) 73 16.2 16.0 3.5 9 24 9.0–24.0 9.0–10.7 22.2–24.0

Calcium (mg dL-1) ^ 72 8.30 8.55 1.54 4.5 13.1 4.83–11.20 4.50–5.64 10.09–13.10

Phosphorus (mg dL-1) ^ 71 4.98 4.70 1.63 2.5 12.6 2.74–9.88 2.50–3.18 7.00–12.60

Glucose (mg dL-1) 73 204.8 210.0 43.4 25 307 98.1–293.4 25.0–145.8 264.9–307.0

Blood urea nitrogen (mg dL-1) 73 4.3 3.0 3.9 1 18 1.0–16.3 1.0–1.0 13.2–18.0

Creatinine (mg dL-1) ^ 71 0.7 0.7 0.3 0.2 1.9 0.28–1.42 0.20–0.38 1.20–1.90

Blood urea nitrogen:Creatinine ratio^ 68 6.0 5.0 4.32 1.7 23.3 1.70–19.46 1.70–1.92 13.38–23.30

Amylase (U L-1) 72 1166.2 1171.0 145.2 760 1637 801.3–1553.7 760.0–942.0 1377.4–1637.0

Lipase (U L-1) ^ 73 23.8 19.0 16.1 1 74 3.6–62.9 1.0–5.0 55.8–74.0

Cholesterol (mg dL-1) 73 151.9 153.0 31.7 80 252 86.8–223.1 80.0–103.4 199.4–252.0

Triglycerides (mg dL-1) ^ 72 57.4 49.0 29.2 27 142 28.7–140.4 27.0–30.0 134.4–142.0

High density lipoprotein cholesterol 

(mg dL-1) 72 83.5 84.5 15.1 50 111 52.5–110.2 50.0–58.0 104.0–111.0

Low density lipoprotein cholesterol 

(mg dL-1) *^ 72 11.4 10.0 5.8 5 28 5.8–28.0 5.0–6.0 27.0–28.0

Uric acid (mg dL-1) ^ 72 12.15 9.55 7.81 0.7 32.6 2.19–32.19 0.70–3.27 27.73–32.60

Total protein (g dL-1) 74 4.34 4.20 0.90 2.4 6.7 2.93–6.26 2.40–3.19 5.90–6.70

Aspartate aminotransferase (U L-1) ^ 72 175.9 163.5 57.1 71 380 93.3–371.8 71.0–109.0 265.9–380.0

Alanine aminotransferase (U L-1) * 70 28.5 28.5 7.3 12 44 12.0–43.2 12.0–15.9 39.1–44.0

Lactate dehydrogenase (U L-1) 72 4674.3 4592.5 1403.3 1764 7960 1953.8–7521.1 1764.0–2632.8 7218.1–7960.0

Creatine phosphokinase (U L-1) 72 964.4 942.0 442.5 48 1854 143.7–1737.7 48.0–256.3 1617.3–1854.0

Gamma-glutamyl transferase (U L-1) 72 10.2 7.0 6.9 5 34 5.0–32.4 5.0–5.0 24.9–34.0

Albumin:Globulin ratio 74 0.57 0.55 0.18 0.21 1.01 0.27–1.01 0.21–0.32 0.90–1.01
142

Analyte (units) n Mean Median SD Min Max Reference 

Interval

Lower 90% CI Upper 90% CI

Pre-albumin (mg dL-1) 74 0.25 0.25 0.07 0.09 0.42 0.12–0.40 0.09–0.15 0.36–0.42

Albumin (mg dL-1) 74 1.24 1.24 0.21 0.78 1.67 0.79–1.66 0.78–0.93 1.59–1.67

Alpha-1 globulin (mg dL-1) 74 0.14 0.13 0.03 0.09 0.27 0.09–0.23 0.09–0.09 0.18–0.27

Alpha-2 globulin (mg dL-1) 74 0.68 0.64 0.16 0.40 1.18 0.43–1.11 0.40–0.49 1.00–1.18

Beta globulin (mg dL-1) ^ 74 1.18 1.10 0.37 0.47 2.24 0.61–2.22 0.47–0.74 1.84–2.24

Gamma globulin (mg dL-1) ^ 74 0.85 0.70 0.50 0.24 2.13 0.24–2.02 0.24–0.32 1.79–2.13

CORT (mg dL-1) * 73 36.13 35.80 13.86 11.9 78.0 12.41–65.08 11.90–15.77 59.61–78.00

* outlier(s) removed

^required box-cox transformation

Sample size (n), mean, median, standard deviation (SD), minimum (Min), and maximum (Max) values with reference intervals and 90% confidence intervals (CI) 

of reference limits.
143

Table 7.2. Number of Blood Samples and Blood Smears Collected from Adult and Chick Brown Pelican Chicks in the Northern GOM, 2013–2015 

State and Colony Adult CBC Adult WBC counts Chick 

CBC

Chick 

WBC 

counts

Female Male Unknown Female Male Unknown Unknown Unknown

Florida

Audubon Island 4 7 0 4 7 3 5 5

Smith Island 3 8 3 3 8 3 - -

Ten Palms Island - - - - - - 5 5

Alabama

Gaillard Island - - - - - - 5 5

Louisiana

Felicity Island 4 4 1 5 5 1 - -

Raccoon Island 2 5 0 4 6 6 - -

Texas

Chester 4 5 3 7 5 3 5 5

Galveston Bay - - - - - - 10 10

Shamrock 8 11 2 9 11 3 5 5

Blood samples were used for complete blood counts (CBC) and blood smears for white blood cell counts (WBC). Actual number of samples for CBC and WBC 

varied by analyte because not all samples resulted in a successful or complete CBC or WBC. Sex for adults (Female, Male, or Unknown) was determined postcapture from collected blood and therefore sex could not be balanced throughout the sampling effort. 
144

Table 7.3. Summary of Model Selection Results Assessing Relationships between Blood Analytes of Adult Brown Pelicans Breeding in the Northern 

GOM and Four Independent Variables, 2013–2014

Analyte Category Sex BCI Planning 

area

HR50 HR50 

+ sex

BCI + 

planning 

area

HR50 

+ BCI

Null

Carbon dioxide Blood gas 1 2 3

Calcium Electrolytes & minerals 3 1 2

Potassium Electrolytes & minerals 1

Phosphorous Electrolytes & minerals 1 2

Sodium* Electrolytes & minerals

Alanine aminotransferase Enzyme 1 2 4 3 5

Amylase Enzyme 1

Aspartate aminotransferase Enzyme 1 2

Creatine phosphokinase Enzyme 1 2 3

Gamma-glutamyl transferase Enzyme 2 1

Lactate dehydrogenase Enzyme 1 2

Lipase Enzyme 1 2

High density lipoprotein cholesterol Lipids 1

Triglycerides Lipids 1 2

Low density lipoprotein cholesterol Lipids 2 3 1

Blood urea nitrogen Metabolite 1

Cholesterol Metabolite 1

Creatinine Metabolite 1 2

Glucose Metabolite 1 2

Ratio BUN:CRE Metabolite-Ratio 2 1

Total protein Metabolite 1

Uric acid Metabolite 1 2

Albumin Plasma protein 1 2

Alpha 1 globulin Plasma protein 3 2 1

Alpha 2 globulin Plasma protein 1

Beta globulin Plasma protein 1

Gamma globulin Plasma protein 1

Pre-albumin Plasma protein 1

Ratio Albumin:Globulin Plasma protein 1
145

Analyte Category Sex BCI Planning 

area

HR50 HR50 

+ sex

BCI + 

planning 

area

HR50 

+ BCI

Null

CORT Stress hormone 1 2

Count of analytes for which model was ranked highly 3 3 5 10 13 10 5 7

*see section 2.7 for details of analysis process for sodium

Body condition index and 50% kernel home range size (HR50) are continuous variables and are defined in the methods. Sex and planning 

area (East = Florida, Central = Alabama and Louisiana, West = Texas) are categorical variables with Female and East as the reference 

levels, respectively. Null = null model with only an intercept term. Cell values indicate the rank of that model for a given blood analyte. Only 

rankings for models with AIC values ≤ 2.0 are shown. 
146

Table 7.4. Summary of Coefficient Estimates from Model Selection Process Assessing Relationships between Blood Analytes of Adult Brown Pelicans 

Breeding in the Northern GOM and Four Independent Variables, 2013–2014

Analyte Group Body condition 

index

50% home 

range

Sex Planning area

Calcium* Electrolytes & 

minerals 1.50 ± 0.82

W = -2.30 ± 0.91

C = -1.50 ± 1.07

Potassium Electrolytes & 

minerals 0.16 ± 0.09 W = 0.59 ± 0.20

C = 0.25 ± 0.23

Sodium Electrolytes & 

minerals

W = -71.8 ± 13.2

C = -33.7 ± 15.6

Alanine aminotransferase Enzyme W = -3.87 ± 2.12

Aspartate aminotransferase* Enzyme C = -1.4x104 ± 6.3x105

Creatine phosphokinase Enzyme -115.9 ± 54.7 

W = -317.0 ± 123.4

C = -326.2 ± 145.6

Lactate dehydrogenase Enzyme -279.4 ± 165.6 

Lipase* Enzyme -0.38 ± 0.20 0.96 ± 0.41

W = -1.02 ± 0.44

C = -2.07 ± 0.53

High density lipoprotein 

cholesterol Lipids 3.33 ± 1.80 11.23 ± 3.67

Blood urea nitrogen Metabolite -1.25 ± 0.45 -2.51 ± 0.91

Cholesterol Metabolite 16.9 ± 8.2

Creatinine* Metabolite -0.09 ± 0.04 -0.12 ± 0.04

W = -0.30 ± 0.10

C = -0.34 ± 0.12

Total protein Metabolite W = -0.88 ± 0.22

C = -1.05 ± 0.26

Uric acid* Metabolite -0.34 ± 0.15 -0.62 ± 0.15 -0.79 ± 0.30

Ratio BUN:CRE* Metabolite-Ratio -0.27 ± 0.12 -0.64 ± 0.23

Albumin Plasma protein W = -0.11 ± 0.06

Alpha 1 globulin Plasma protein C = -0.02 ± 0.01

Beta globulin* Plasma protein W = -0.26 ± 0.07

C = -0.32 ± 0.08

Gamma globulin* Plasma protein W = -0.60 ± 0.16

C = -0.74 ± 0.19
147

Analyte Group Body condition 

index

50% home 

range

Sex Planning area

Ratio ALB:Globulin Plasma protein W = 0.10 ± 0.04

C = 0.20 ± 0.05

Body condition index and 50% kernel home range size are continuous variables and are defined in the methods. Sex and planning area 

(West = Texas, Central = Louisiana, East = Florida) are categorical variables with Female and East as the reference levels, respectively. 

Only coefficients where the SE < coefficient estimate (i.e., an ecological relationship between the analyte and the independent variable is 

more likely) are reported. Analytes marked with * were transformed before analysis and coefficient estimates are presented on the 

transformed scale. No interactions of the independent variables returned coefficients likely to be ecologically relevant (i.e., SE > mean in 

all cases).

Table 7.5. Statistical Values for Packed Cell Volume (PCV, Averaged over 3 Readings) and Leukocyte Profiles for Adult Brown Pelicans Sampled from 

Breeding Colonies in the Northern GOM, 2013–2014

Analyte n Mean Median SD Min Max Reference 

Interval

Lower Upper

PCV average * 73 45.4 46.0 4.3 30 54.0 32.1–54.0 30.0–39.0 52.1–54.0

White blood cell count * 82 11.5 11.0 4.9 3 27.0 5.0–25.0 3.0–5.1 21.0–27.0

Heterophil (103/ml) * 82 8.1 7.7 3.2 1 15.0 1.9–14.7 1.0–3.6 13.3–15.0

Lymphocytes (103/ml) * 82 2.9 2.2 2.6 0.3 12.7 0.5–12.3 0.3–0.8 8.6–12.7

Monocyte(103/ml) 84 0.3 0.2 0.3 0 1.6 0.0–1.3 0.0–0.0 0.9–1.6

Eosinophil(103/ml) 84 0.16 0.09 0.2 0 1.1 0.0–1.0 0.0–0.0 0.7–1.1

Basophil(103/ml) 84 0.05 0.0 0.1 0 0.6 0.0–0.5 0.0–0.0 0.3–0.6

* outlier(s) removed

Sample size (n), mean, median, standard deviation (SD), minimum (Min), and maximum (Max) values with reference intervals and 

90% confidence intervals of reference limits.
148

Table 7.6. Statistical Values for Mass and Serum Chemistry for Chicks of Brown Pelicans Sampled from Breeding Colonies in the Northern GOM, 2013–

2015

Analyte (units) n Mean Median SD Min Max Reference 

Interval

Lower 90% CI Upper 90% CI

Sodium (mEq L-1) 34 146.5 147.0 4.7 136 157 136.3–155.7 133.6–139.5 15378–157.7

Potassium (mEq L-1) *^ 34 3.52 3.45 0.62 2.4 5.3 2.44–5.00 2.25–2.70 4.57–5.56

CO2 (mEq L-1) 34 15.4 16.0 3.0 9 19 6.0–20.2 7.7–10.1 19.3–21.0

Calcium (mg dL-1) ^ 34 10.75 10.75 0.79 8.9 12.1 8.95–12.44 8.50–9.48 11.92–12.54

Phosphorus (mg dL-1) ^ 34 7.21 7.05 1.22 5.4 10.4 5.19–10.14 4.93–5.57 9.19–11.27

Glucose (mg dL-1) 34 182.4 182.0 30.3 100 248 119.7–244.3 104.3–137.3 225.7–260.7

Blood urea nitrogen (mg dL-1) 33 3.2 3.0 1.0 1 6 1.00–5.33 0.6–1.6 4.7–5.9

Creatinine (mg dL-1) ^ 34 0.48 0.40 0.20 0.1 0.9 0.15–1.00 0.11–0.22 0.81–1.15

Blood urea nitrogen:Creatinine ratio ^ 31 7.05 6.70 2.54 2.5 13.3 2.45–12.82 1.88–3.40 11.26–14.29

Amylase (U L-1) 34 1293.0 1268.0 209.2 919 1756 826.0–1694.6 720.7–922.3 1555.9–1839.5

Lipase (U L-1) ^ 34 27.6 28.0 8.8 13 61 13.9–50.0 11.4–16.9 41.7–59.2

Cholesterol (mg dL-1) 34 203.1 201.5 35.2 140 284 139.3–283.6 129.3–154.1 260.9–309.3

Triglycerides (mg dL-1) ^ 34 99.3 95.5 37.3 42 177 27.2–180.9 14.7–42.0 159.3–203.2

High density lipoprotein cholesterol (mg dL-1) 34 80.7 77.5 13.8 57 110 54.8–112.1 50.9–60.5 103.4–120.4

Low density lipoprotein cholesterol (mg dL-1) *^ 34 19.8 19.0 7.5 8 35 5.8–36.7 3.4–8.6 32.1–41.5

Uric acid (mg dL-1) ^ 34 15.77 14.90 8.24 5.4 46 4.67–38.71 3.65–6.65 31.16–48.90
149

Analyte (units) n Mean Median SD Min Max Reference 

Interval

Lower 90% CI Upper 90% CI

Total protein (g dL-1) 34 4.23 4.20 0.56 3.3 5.6 3.26–5.57 3.13–3.47 5.15–6.01

Aspartate aminotransferase (U L-1) ^ 34 167.5 149.0 57.6 97 339 92.4–332.2 85.6–101.3 262.7–423.2

Alanine aminotransferase (U L-1) * 34 31.0 30.0 8.3 19.0 51.0 17.8–51.8 16.1–20.3 45.5–59.3

Lactate dehydrogenase (U L-1) 34 5629.3 5455.5 1623.7 801 9663 2076.9–8793.2 1079.7–3170.6 7747.4–9696.5

Creatine phosphokinase (U L-1) 33 1308.7 1357.0 261.0 466 1687 631.1–1727.2 219.7–885.3 1641.3–1793.8

Gamma-glutamyl transferase (U L-1) 32 7.3 7.0 1.6 5 10 4.27–11.12 3.9–4.9 10.2–11.8

Albumin:Globulin ratio 34 0.76 0.79 0.14 0.43 1.06 0.44–1.03 0.36–0.55 0.98–1.09

Pre-albumin (mg dL-1) 34 0.27 0.25 0.06 0.18 0.39 0.16–0.42 0.14–0.18 0.38–0.47

Albumin (mg dL-1) 34 1.53 1.54 0.21 0.98 1.92 1.04–1.90 0.87–1.20 1.82–1.97

Alpha-1 globulin (mg dL-1) 34 0.13 0.12 0.03 0.09 0.18 0.08–0.20 0.07–0.09 0.18–0.23

Alpha-2 globulin (mg dL-1) 34 0.73 0.73 0.16 0.47 1.27 0.48–1.15 0.44–0.54 1.01–1.31

Beta globulin (mg dL-1) ^ 34 0.98 0.96 0.15 0.76 1.26 0.70–1.33 0.66–0.75 1.24–1.43

Gamma globulin (mg dL-1) ^ 34 0.58 0.51 0.18 0.34 1.02 0.33–1.08 0.31–0.37 0.88–1.32

CORT (mg dL-1) * 34 57.31 57.45 22.80 16.6 128.2 7.99–101.9 0.01–21.14 87.37–115.8

* outlier(s) removed

^ required box-cox transformation

Sample size (n), mean, median, standard deviation (SD), minimum (Min), and maximum (Max) values with reference intervals and 90% confidence intervals 

(CI) of reference limits. Untransformed robust values reported when available, otherwise untransformed standard data reported.
150

Table 7.7. Summary of Model Selection Results Assessing Relationships between Blood Analytes of Brown Pelican Chicks in the Northern GOM and 

Two Independent Variables, 2013–2015

Analyte Category Planning 

area

BCI Null

Carbon dioxide Blood gas 1 2

Calcium Electrolytes & minerals 1

Potassium Electrolytes & minerals 2 1

Phosphorous Electrolytes & minerals 1

Sodium Electrolytes & minerals 1 2

Alanine aminotransferase Enzyme 1 1

Amylase Enzyme 3 1 2

Aspartate aminotransferase Enzyme 1 2

Creatine phosphokinase Enzyme 1 2

Gamma-glutamyl transferase Enzyme 2 1

Lactate dehydrogenase Enzyme 1

Lipase Enzyme 2 1

High density lipoprotein cholesterol Lipids 2 1 3

Triglycerides Lipids 2 1

Very low density lipoprotein cholesterol Lipids 2 1

Blood urea nitrogen (BUN) Metabolite 2 1

Cholesterol Metabolite 2 1

Creatinine Metabolite 1 2

Glucose Metabolite 1

Ratio BUN/Creatinine Metabolite-Ratio 1

Total protein Metabolite 1

Uric acid Metabolite 1

Albumin Plasma protein

Alpha 1 globulin Plasma protein 3 2 1

Alpha 2 globulin Plasma protein 1 2

Beta globulin Plasma protein 1

Gamma globulin Plasma protein 1 2

Pre-albumin Plasma protein 1

Ratio Albumin/Globulin Plasma protein 1
151

Analyte Category Planning 

area

BCI Null

CORT Stress hormone 1

Count of analytes for which model was ranked highly 17 12 21

BCI is a continuous variable and is defined in the methods. Planning area is a categorical variable (East = reference level and 

includes Florida, Central includes Alabama and Louisiana, and West includes Texas). Null = null model with only an intercept 

term. Cell values indicate the rank of that model for a given blood analyte. Only rankings for models with AIC values ≤ 2.0 are 

shown.

Table 7.8. Summary of Coefficient Estimates from Model Selection Process Assessing Relationships between Blood Analytes of Brown Pelican Chicks 

in the Northern GOM and Two Independent Variables, 2013–2014

Analyte Group Body condition 

index

Central 

planning area

Western 

planning area

Carbon dioxide* Blood gas 0.3 ± 1.7 -2.4 ± 1.1

Calcium Electrolytes & minerals -0.83 ± 0.27

Potassium Electrolytes & minerals -3.0x10-4 ± 2.0x10-4

Sodium Electrolytes & minerals -5.0 ± 2.6 -3.3 ± 1.7

Alanine aminotransferase Enzyme -9.5 ± 4.6

Amylase Enzyme 0.11 ± 0.06 -212.8 ± 119.3 -140.7 ± 78.1

Aspartate aminotransferase Enzyme -0.032 ± 0.017 -49.8 ± 31.6 25.5 ± 20.7

Creatine phosphokinase Enzyme -0.12 ± 0.07

Lactate dehydrogenase Enzyme 1672.7 ± 548.5

Triglycerides Lipids -8.0x10-3 ± 4.0x10-3

Low density lipoprotein cholesterol Lipids 3.0x10-3 ± 2.0x10-3

Blood urea nitrogen Metabolite 0.62 ± 0.40

Creatinine Metabolite 0.25 ± 0.11

Glucose Metabolite -36.9 ± 9.5

Ratio BUN:CRE Metabolite-Ratio -1.79 ± 0.85 0.60 ± 0.58

Albumin Plasma protein -0.14 ± 0.08
152

Analyte Group Body condition 

index

Central 

planning area

Western 

planning area

Alpha 1 globulin* Plasma protein 2.5x10-4 ± 1.8x10-4 -0.56 ± 0.35 -0.35 ± 0.23

Gamma globulin* Plasma protein -0.49 ± 0.25

Ratio Albumin:Globulin Plasma protein 0.08 ± 0.02

CORT Stress hormone -0.019 ± 0.006

BCI is a continuous variable and is defined in the methods. Planning area (West = Texas, Central = Louisiana and Alabama, East 

= Florida) is a categorical variable with east as the reference level. Only coefficients where the SE < coefficient estimate (i.e., an 

ecological relationship between the analyte and the independent variable is more likely) are reported. Analytes marked with * 

were transformed before analysis and coefficient estimates are presented on the transformed scale. No interactions of the 

independent variables returned coefficients likely to be ecologically relevant (i.e., SE > mean in all cases). 

Table 7.9. Statistical Values for Packed Cell Volume (PCV, Averaged over 3 Readings) and Leukocyte Profiles for Chicks of Brown Pelicans Sampled 

from Breeding Colonies in the Northern GOM, 2013–2015

Analyte n Mean Median SD Min Max Reference 

Interval

Lower Upper

PCV average * 34 40.5 40.0 4.8 30 48.7 30.3–50.2 28.1–32.7 47.7–52.6

White blood cell count * 35 22.1 17.0 16.1 5.0 71.0 6.5–84.1 5.6–7.9 48.7–139.9

Heterophil (103/ml) * 35 11.9 8.6 9.6 2.3 46.2 3.07–48.9 2.6–3.8 27.1–88.2

Lymphocytes (103/ml) * 35 9.5 6.3 7.3 2.7 34.1 2.7–47.7 2.3–3.2 23.3–115.9

Monocyte(103/ml) 35 0.3 0.1 0.7 0 3.7 0.0–1.7 0.0 0.7–2.7

Eosinophil(103/ml) 35 0.2 0.0 0.4 0 1.6 0.0–1.1 0.0 0.5–1.4

Basophil(103/ml) 35 0.1 0.0 0.3 0 1.3 0.0–0.7 0.0 0.4–1.0

* outlier(s) removed

Sample size (n), mean, median, standard deviation (SD), minimum (min), and maximum (Max) values with reference intervals and 

90% confidence intervals of reference limits.
153

Table 7.10. Frequency of Detection of Individual PAHs from Blood of Adult Brown Pelicans Breeding in the 

Northern GOM, 2013–2014

Number 

birds > 

detection 

limit

Number 

birds < 

detection 

limit 

Measured values [ng/g wet weight] 

(% total PAH burden)

Alkylated Compounds

2-methyl naphthalene 3 30 [80.7] [86.0] [86.3] Σ252.9 (8.2%)

2,6-dimethyl naphthalene 4 29 [79.3] [84.4] [92.9] [98.1] Σ354.7 (11.5%)

1,3-dimethylnaphthalene 5 28 [82.8] [86.8] [122.4] [125.3] [128.1] Σ545.2 

(17.7%)

1,5-dimethylnaphthalene 0 33 -

2,3,5-trimethylnaphthalene 6 27 [81.4] [84.4] [86.2] [90.2] [90.5] [127.6] 

Σ560.3 (18.2%)

1-methylfluorene 0 33 -

3-methylphenanthrene 1 32 [78.0] Σ78.0 (2.5%)

9-methylphenanthrene 2 30 [58.5] [61.9] Σ120.4 (3.9%)

Parent Compounds

Naphthalene 2 31 [83.9] [102.2] Σ186.1 (6.1%)

Acenaphthalene 0 33 -

Acenapthene 2 31 [102.2] [108.5] Σ210.7 (6.8%)

Fluorene 2 31 [161.1] [165.4] Σ326.6 (10.6%)

Phenanthrene 1 32 [48.9] Σ48.9 (1.6%)

Anthracene 3 29 [73.9] [80.5] [84.3] Σ238.7 (7.8%)

Fluoranthene 1 32 [42.4] Σ42.4 (1.4%)

Pyrene 1 32 [50.0] Σ50.0 (1.6%)

Benzo(a)anthracene 1 32 [57.8] Σ57.8 (1.9%)

Chrysene 0 33 -

benzo(b)fluoranthene 0 33 -

Benzo(k)fluoranthene 0 33 -

benzo(a)pyrene 0 33 -

Indeno(1,2,3-cd)pyrene 0 33 -

Dibenz(a,h)anthracene 0 33 -

Benzo(g,h,i)perylene 0 33 -

(n = 33)
154

Table 7.11. Frequency of Cccurrence of Parent and Alkylated PAHs by Planning Area and Sex from Blood of 

Adult Brown Pelicans Breeding in the Northern GOM, 2013–2014.

Category Number 

birds 

sampled

Number of 

birds ≥ 1 

parent or

alkylated PAH

Number of 

birds ≥ 1 

parent and

alkylated PAH

Number of 

birds with 

parent 

PAHs

Number of 

birds with 

alkylated 

PAHs

Planning area

East 10 8 1 4 5

Central 12 6 2 2 6

West 11 7 2 4 5

Total 33 21 5 10 16

Sex

Female 12 8 4 5 8

Male 21 12 1 5 8

Total 33 20 5 10 16
155

Table 7.12. Frequency of Occurrence of Individual PAHs by Planning Area and Sex from Blood of Adult 

Brown Pelicans Breeding in the Northern GOM, 2013–2014

Number 

birds ≥ 

detectable 

limit

East

(n = 10)

Central 

(n =12)

West

(n = 11) Female Male

Alkylated Compounds

2-methyl naphthalene 3 0 2 1 3

2,6-dimethyl naphthalene 4 2 2 1 2 2

1,3-dimethylnaphthalene 5 2 2 1 2 3

1,5-dimethylnaphthalene 0

2,3,5-trimethylnaphthalene 6 2 2 2 6

1-methylfluorene 0

3-methylphenanthrene 1 1 1

9-methylphenanthrene 2 1 1 1 1

Parent Compounds

Naphthalene 2 2 2

Acenaphthalene 0

Acenapthene 2 1 1 2

Fluorene 2 2 1 1

Phenanthrene 1 1 1

Anthracene 3 1 2 2 1

Fluoranthene 1 1 1

Pyrene 1 1 1

Benzo(a)anthracene 1 1 1

Chrysene 0

Benzo(b)fluoranthene 0

Benzo(k)fluoranthene 0

Benzo(a)pyrene 0

Indeno(1,2,3-cd)pyrene 0

Dibenz(a,h)anthracene 0

Benzo(g,h,i)perylene 0

(n = 33)
156

Table 7.13.a–f. Model Results from Logistic and Gamma Regressions Assessing the Relationships between 

PAHs Detected in Blood of Adult Brown Pelicans Breeding in the Northern GOM and a Suite of Independent 

Variables, 2013–2014

K = number of parameters, AICc = Akaike Information Criteria, Delta AICc = difference in AIC values between 

indicated model and top ranked model. AICc weight = probability of model being the best model given the models 

tested and data analyzed. sumPAH = sum of all PAHs, sumPAR = sum of Parent PAHs, sumALK = sum of alkylated 

PAH.

(a) Logistic regression models for sumPAH (response variable = presence or absence of sumPAH; n = 33). Only 

models ≤ delta AICc 2.0 are included. 

Variables K AICc Delta AICc AICc Weight

Home range 2 42.29 0.21

Sex + home range 3 42.97 0.49 0.17

Home range + BCI 3 43.62 1.13 0.12

BCI 2 43.78 1.29 0.11

Sex + BCI 3 44.11 1.62 0.09

(b) Gamma regression models for sumPAH (response variable = concentration of sumPAH; only individuals with 

sumPAH > 0 included in analyses, n = 21). Only models ≤ delta AICc 2.0 are included. 

Variables K AICc Delta AICc AICc Weight

Sex + home range 3 203.67 0.90

(c) Logistic regression models for sumPAR (response variable = presence or absence of sumPAR; n = 33). Only 

models ≤ delta AICc 2.0 are included. 

Variables K AICc Delta AICc AICc Weight

Home range 2 39.92 0.28

Sex + home range 3 40.80 0.89 0.45

Home range + BCI 3 41.89 1.97 0.10

(d) Gamma regression models for sumPAR (response variable = concentration of sumPAR; only individuals with 

sumPAR > 0 included in analyses, n = 10). Only models ≤ delta AICc 2.0 are included. 

Variables K AICc Delta AICc AICc Weight

Home range 3 103.18 0.38

Planning area + home range 5 104.28 1.11 0.22

Home range + BCI 4 104.86 1.68 0.16

Sex + home range 4 104.94 1.76 0.16

(e) Logistic regression models for sumALK (response variable = presence or absence of sumALK; n = 33). Only 

models ≤ delta AICc 2.0 are included. 

Variables K AICc Delta AICc AICc Weight

Sex + home range 3 43.15 0.35

Home range 2 43.89 0.74 0.24
157

(f) Gamma regression models for sumALK (response variable = concentration of sumALK; only individuals with 

sumALK > 0 included in analyses, n = 16). Only models ≤ delta AICc 2.0 are included. 

Variables K AICc Delta AICc AICc Weight

Sex + home range + BCI + 

planning area 7 137.7 0.86

Table 7.14. Frequency of Detection of Individual PAHs from Feathers of Adult Brown Pelicans Breeding in the 

Northern GOM, 2013–2014

Number 

birds > 

detection 

limit

Number 

birds < 

detection 

limit 

Measured values (ng/g dry weight) 

(% total PAH burden)

Alkylated Compounds

2-methyl naphthalene 6 86 [56.3] [108.9] [113.7] [118.6] [138.1] [229.0] Σ764.6 

(7.9%)

2,6-dimethyl naphthalene 10 82 [71.5] [84.9] [98.5] [98.5] [126.9] [130.3] [144.3] 

[151.2] [152.7] [208.9] Σ126.7 (13.1%)

1,3-dimethylnaphthalene 11 81 [71.7] [77.6] [94.6] [128.9] [135.5] [146.1] [168.3] 

[188.3] [205.6] [221.8] [256.2] Σ1694.6 (17.5%)

1,5-dimethylnaphthalene 6 86 [84.0] [131.0] [132.4] [142.0] [184.0] [190.0] Σ863.4 

(8.9%)

2,3,5-trimethylnaphthalene 10 82 [50.3] [52.7] [82.3] [82.8] [100.8] [103.2] [116.6] 

[129.1] [166.7] [183.1] Σ1067.6 (11.0%)

1-methylfluorene 7 85 [49.7] [74.5] [85.0] [92.5] [103.5] [105.3] [146.3] 

Σ656.8 (6.8%)

3-methylphenanthrene 2 90 [120.5] [181.5] Σ302.0 (3.1%)

9-methylphenanthrene 3 89 [124.4] [180.3] [261.8] Σ566.5 (5.8%)

Parent Compounds

Naphthalene 4 88 [90.7] [95.1] [130.9] [132.2] Σ448.9 (4.6%)

Acenaphthalene 2 90 [101.3] [137.2] Σ238.5 (2.5%)

Acenapthene 3 89 [89.3] [121.3] [166.7] Σ377.3 (3.9%)

Fluorene 0 92 -

Phenanthrene 4 88 [32.4] [73.8] [84.3] [100.0] Σ290.5 (3.0%)

Anthracene 2 90 [63.2] [70.5] Σ133.7 (1.4%)

Fluoranthene 6 86 [60.2] [71.6] [75.5] [78.3] [106.4] [121.6] Σ513.6 

(5.3%)

Pyrene 5 87 [55.8] [59.7] [98.8] [126.1] [160.5] Σ500.9 (5.2%)

Benzo(a)anthracene 0 92 -

Chrysene 0 92 -

Benzo(b)fluoranthene 0 92 -

Benzo(k)fluoranthene 0 92 -

Benzo(a)pyrene 0 92 -

Indeno(1,2,3-cd)pyrene 0 92 -

Dibenz(a,h)anthracene 0 92 -

Benzo(g,h,i)perylene 0 92 -

(n = 92)
158

Table 7.15. Frequency of Occurrence of Parent and Alkylated PAHs by State and Sex from Feathers of Adult 

Brown Pelicans Breeding in the Northern GOM, 2013–2014

Category Number 

of birds 

sampled

Number of 

birds ≥ 1 

parent or 

alkylated 

PAH

Number of 

birds ≥ 1 

parent and 

alkylated 

PAH

Number of 

birds with 

parent 

PAHs

Number of 

birds with 

alkylated 

PAHs

Planning area

East 25 16 2 5 16

Central 36 25 7 16 13

West 31 15 1 3 13

Total 92 56 10 24 42

Sex

Female 43 29 3 14 18

Male 49 27 7 10 24

Total 92 56 10 24 42

Table 7.16. Frequency of Occurrence of Individual PAHs by Planning Area and Sex from Feathers of Adult 

Brown Pelicans Breeding in the Northern GOM, 2013–2014

Number 

birds ≥ 

detectable 

limit

East

(n = 10)

Central 

(n =12)

West

(n = 11) Female Male

Alkylated Compounds

2-methyl naphthalene 6 1 4 1 2 4

2,6-dimethyl naphthalene 10 6 2 2 3 7

1,3-dimethylnaphthalene 11 1 4 6 7 4

1,5-dimethylnaphthalene 6 2 4 0 1 5

2,3,5-trimethylnaphthalene 10 4 3 3 4 6

1-methylfluorene 7 1 2 4 2 5

3-methylphenanthrene 2 1 1 0 2 0

9-methylphenanthrene 3 1 2 0 2 1

Parent Compounds

Naphthalene 4 4 0 0 3 1

Acenaphthalene 2 1 0 1 2 0

Acenapthene 3 3 0 0 1 2

Fluorene 0 0 0 0 0 0

Phenanthrene 4 2 2 0 2 2

Anthracene 2 2 0 0 1 1

Fluoranthene 6 3 2 1 2 4

Pyrene 5 4 0 1 3 2

Benzo(a)anthracene 0 0 0 0 0 0

Chrysene 0 0 0 0 0 0
159

Number 

birds ≥ 

detectable 

limit

East

(n = 10)

Central 

(n =12)

West

(n = 11) Female Male

Benzo(b)fluoranthene 0 0 0 0 0 0

Benzo(k)fluoranthene 0 0 0 0 0 0

Benzo(a)pyrene 0 0 0 0 0 0

Indeno(1,2,3-cd)pyrene 0 0 0 0 0 0

Dibenz(a,h)anthracene 0 0 0 0 0 0

Benzo(g,h,i)perylene 0 0 0 0 0 0

(n = 92)

Table 7.17.a–f. Model Results from Logistic and Gamma Regressions Assessing the Relationships between 

PAHs Detected in Feathers of Adult Brown Pelicans Breeding in the Northern GOM and a Suite of 

Independent Variables, 2013–2014

K = number of parameters, AICc = Akaike Information Criteria, Delta AICc = difference in AIC values between 

indicated model and top ranked model. AICc weight = probability of model being the best model given the models 

tested and data analyzed. sumPAH = sum of all PAHs, sumPAR = sum of Parent PAHs, sumALK = sum of alkylated 

PAHs.

(a) Logistic regression models for sumPAH (response variable = presence or absence of sumPAH; n = 92). Only 

models ≤ delta AICc 2.0 are included. 

Variables K AICc Delta AICc AICc Weight

BCI + migration class 4 86.41 0.63

(b) Gamma regression models for sumPAH (response variable = concentration of sumPAH; only individuals with 

sumPAH > 0 included in analyses, n = 56). Only models ≤ delta AICc 2.0 are included. 

Variables K AICc Delta AICc AICc Weight

BCI + migration class 5 522.89 0.94

(c) Logistic regression models for sumPAR (response variable = presence or absence of sumPAR; n = 92). Only 

models ≤ delta AICc 2.0 are included. 

Variables K AICc Delta AICc AICc Weight

Migration class + planning area 5 83.10 0.31

Migration class + planning area + sex 6 83.54 0.45 0.25

Home range 2 84.74 1.64 0.14

(d) Gamma regression models for sumPAR (response variable = concentration of sumPAR; only individuals with 

sumPAR > 0 included in analyses, n = 24). Only models ≤ delta AICc 2.0 are included. 

Variables K AICc Delta AICc AICc Weight

BCI + migration class 5 206.15 0.69
160

(e) Logistic regression models for sumALK (response variable = presence or absence of sumALK; n = 92). Only 

models ≤ delta AICc 2.0 are included. 

Variables K AICc Delta AICc AICc Weight

BCI + migration class 4 98.22 0.68

Migration class 3 100.12 1.90 0.26

(f) Gamma regression models for sumALK (response variable = concentration of sumALK; only individuals with 

sumALK > 0 included in analyses, n = 42). Only models ≤ delta AICc 2.0 are included. 

Variables K AICc Delta AICc AICc Weight

Planning area + migration class 6 391.17 0.74

Table 7.18. Frequency of Detection of Individual PAHs from Feathers of Brown Pelican Chicks in the Northern 

GOM, 2014–2015. 

Number 

birds > 

detection 

limit

Number 

birds < 

detection 

limit 

Measured values (ng/g dry weight)

(% total PAH burden)

Alkylated Compounds

2-methyl naphthalene 1 86 [69.0] Σ69.0 (3.2%)

2,6-dimethyl naphthalene 4 82 [50.3] [164.1] [178.4] [250.0] Σ642.8 (30.2%)

1,3-dimethylnaphthalene 2 81 [62.7] [116.1] Σ178.8 (8.4%)

1,5-dimethylnaphthalene 1 86 [140.7] Σ140.7 (6.6%)

2,3,5-trimethylnaphthalene 4 82 [69.0] [72.3] [101.6] [124.7] Σ367.6 (17.3%)

1-methylfluorene 1 85 [104.3] Σ104.3 (4.9%)

3-methylphenanthrene 1 90 [81.3] Σ81.3 (3.8%)

9-methylphenanthrene 1 89 [100.5] Σ100.5 (4.7%)

Parent Compounds

Naphthalene 1 88 [186.9] Σ186.9 (8.8%)

Acenaphthalene 0 90 -

Acenapthene 0 89 -

Fluorene 0 92 -

Phenanthrene 1 88 [57.9] Σ57.9 (2.7%)

Anthracene 0 90 -

Fluoranthene 0 86 -

Pyrene 1 87 [193.5] Σ193.5 (9.1%)

Benzo(a)anthracene 0 92 -

Chrysene 0 92 -

Benzo(b)fluoranthene 0 92 -

Benzo(k)fluoranthene 0 92 -

Benzo(a)pyrene 0 92 -

Indeno(1,2,3-cd)pyrene 0 92 -

Dibenz(a,h)anthracene 0 92 -

Benzo(g,h,i)perylene 0 92 -

(n = 35)
161

Table 7.19. Frequency of Occurrence of Parent and Alkylated PAHs by Planning Area from Feathers of Brown 

Pelican Chicks in the Northern GOM, 2014–2015

Planning 

area

Number 

birds 

sampled

Number of 

birds ≥ 1 

parent or

alkylated 

PAH

Number of 

birds ≥ 1 

parent and

alkylated 

PAH

Number of 

birds with 

parent 

PAHs

Number of 

birds with 

alkylated 

PAHs

East 10 4 1 1 4

Central 5 4 0 0 4

West 20 5 1 2 4

Total 35 13 2 3 12

(n = 35)

Table 7.20. Frequency of Occurrence of Individual PAHs by Planning Area from Feathers of Brown Pelican

Chicks in the Northern GOM, 2014–2015

Number birds ≥ 

detectable limit

East

(n = 10)

Central 

(n = 5)

West

(n = 20)

Alkylated Compounds

2-methyl naphthalene 1 1

2,6-dimethyl naphthalene 4 1 1 2

1,3-dimethylnaphthalene 2 1 0 1

1,5-dimethylnaphthalene 1 1 0 0

2,3,5-trimethylnaphthalene 4 2 1 1

1-methylfluorene 1 0 1 0

3-methylphenanthrene 1 0 0 1

9-methylphenanthrene 1 0 1 0

Parent Compounds

Naphthalene 1 0 0 1

Acenaphthalene 0 0 0 0

Acenapthene 0 0 0 0

Fluorene 0 0 0 0

Phenanthrene 1 0 0 1

Anthracene 0 0 0 0

Fluoranthene 0 0 0 0

Pyrene 1 1 0 0

Benzo(a)anthracene 0 0 0 0

Chrysene 0 0 0 0

Benzo(b)fluoranthene 0 0 0 0

Benzo(k)fluoranthene 0 0 0 0

Benzo(a)pyrene 0 0 0 0

Indeno(1,2,3-cd)pyrene 0 0 0 0

Dibenz(a,h)anthracene 0 0 0 0

Benzo(g,h,i)perylene 0 0 0 0

(n=35)
162

Table 7.21. a–e. Model Results from Logistic and Gamma Regressions Assessing the Relationships between 

PAHs Detected in Feathers of Brown Pelican Chicks in the Northern GOM and a Suite of Independent 

Variables, 2014–2015

K = number of parameters, AICc = Akaike Information Criteria, Delta AICc = difference in AIC values between 

indicated model and top ranked model. AICc weight = probability of model being the best model given the models 

tested and data analyzed. sumPAH = sum of all PAHs, sumPAR = sum of Parent PAHs, sumALK = sum of alkylated 

PAHs.

(a) Logistic regression models for sumPAH (response variable = presence or absence of sumPAH; n = 35). Only 

models ≤ delta AICc 2.0 are included. 

Variables K AICc Delta AICc AICc Weight

Planning area 3 46.96 0.54

Null 1 48.18 1.22 0.29

(b) Gamma regression models for sumPAH (response variable = concentration of sumPAH; only individuals with 

sumPAH > 0 included in analyses, n = 13). Only models ≤ delta AICc 2.0 are included. 

Variables K AICc Delta AICc AICc Weight

Planning area 4 150.21 0.41

Null 2 150.51 0.31 0.36

BCI 3 151.37 1.17 0.23

(c) Logistic regression models for sumPAR (response variable = presence or absence of sumPAR; n = 35). Only 

models ≤ delta AICc 2.0 are included. 

Variables K AICc Delta AICc AICc Weight

Null 1 22.48 0.63

BCI 2 24.41 1.94 0.24

(d) Logistic regression models for sumALK (response variable = presence or absence of sumALK; n = 35). Only 

models ≤ delta AICc 2.0 are included. 

Variables K AICc Delta AICc AICc Weight

Planning area 3 44.48 0.70

(e) Gamma regression models for sumALK (response variable = concentration of sumALK; only individuals with 

sumALK > 0 included in analyses, n = 12). Only models ≤ delta AICc 2.0 are included. 

Variables K AICc Delta AICc AICc Weight

Null 2 135.13 0.55

BCI 3 136.40 1.27 0.29

Home range + BCI 4 420.52 1.07 0.19

Home range + sex 4 421.28 1.82 0.13
163

Female Male

Body condition index

-600

-400

-200

0

200

400

600

Figure 7.1.a Relationships among independent variables used in modeling blood analytes of adult brown 

pelicans breeding in the Northern GOM, 2013–2015, BCI for female and male pelicans. 

East Central West

50% core use area (km 2

)

0

200

400

600

800

1000

Figure 7.2.b. Relationships among independent variables used in modeling blood analytes of adult 

brown pelicans breeding in the Northern GOM, 2013–2015, home range size. 

Home range size is the 50% core use area for adult brown pelicans in the eastern, central, and western 

planning areas of the GOM. 
164

Figure 7.3.a. Box plots of blood analytes of adult brown pelicans breeding in the Northern GOM, in 

relation to planning area, 2013–2015, electrolytes, calcium, potassium, and sodium.

Only analytes found to be related to planning area (coefficient estimate > SE) from highly ranked models are 

displayed. In all cases, the eastern planning area is set as the reference level. 
165

Figure 7.4.b. Box plots of blood analytes of adult brown pelicans breeding in the Northern GOM, in 

relation to planning area, 2013–2015: enzymes: alanine aminotransferase, aspartate aminotransferase, 

creatine phosphokinase, lipase.

Only analytes found to be related to planning area (coefficient estimate > SE) from highly ranked models 

are displayed. In all cases, the eastern planning area is set as the reference level. 
166

Creatinine

East Central West

mg/dL

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

Total Protein

East Central West

g/dL

2

3

4

5

6

7

Figure 7.5c. Box plots of blood analytes of adult brown pelicans breeding in the Northern GOM, in 

relation to planning area, 2013–2015, metabolites: creatinine, total protein.

Only analytes found to be related to planning area (coefficient estimate > SE) from highly ranked models are 

displayed. In all cases, the eastern planning area is set as the reference level. 
167

Albumin

East Central West

mg/dL

0.6

0.8

1.0

1.2

1.4

1.6

1.8

Alpha 1 Globulin

East Central West

mg/dL

0.08

0.10

0.12

0.14

0.16

0.18

0.20

0.22

0.24

0.26

0.28

Beta globulin

East Central West

mg/dL

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

2.2

2.4

Gamma globulin

East Central West

mg/dL

0.0

0.5

1.0

1.5

2.0

2.5

Albumin: Globulin Ratio

East Central West

mg/dL

0.0

0.2

0.4

0.6

0.8

1.0

1.2

Figure 7.6.d. Box plots of blood analytes of adult brown pelicans breeding in the Northern GOM, in 

relation to planning area, 2013–2015: plasma proteins: albumin, alpha 1 globulin, beta globulin, ratio 

albumin:globulin.

Only analytes found to be related to planning area (coefficient estimate > SE) from highly ranked models are 

displayed. In all cases, the eastern planning area is set as the reference level. 

(23) (16) (33)
168

Potassium

Body condition index

-600 -400 -200 0 200 400 600

mEq/L

0

2

4

6

8

10

12

Creatinine

Body condition index

-600 -400 -200 0 200 400 600

mg/dL

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

Uric acid

Body condition index

-600 -400 -200 0 200 400 600

mg/dL

0

5

10

15

20

25

30

35

Ratio Blood urea nitrogen:Creatinine

Body condition index

-600 -400 -200 0 200 400 600

Ratio BUN:Creatinine

0

2

4

6

8

10

12

14

16

18

20

Figure 7.7. Body condition index of adult brown pelicans breeding in the Northern GOM, in relation to 

four blood analytes, 2013–2015.

Only analytes found to be strongly related to BCI based on coefficient estimates from highly ranked models are 

displayed. 
169

Blood Urea Nitrogen

Female Male

mg/dL

0

2

4

6

8

10

12

14

16

18

20

Cholesterol

Female Male

mg/dL

60

80

100

120

140

160

180

200

220

240

260

280

Uric Acid

Female Male

mg/dL

0

5

10

15

20

25

30

35

Ratio BUN:Creatinine

Female Male

0

5

10

15

20

25

Figure 7.8.a. Sex of adult brown pelicans breeding in the Northern GOM, in relation to seven blood 

analytes, 2013–2015: metabolites.

Only analytes found to be strongly related to BCI based on coefficient estimates from highly ranked models are 

displayed.
170

Calcium

Female Male

mg/dL

4

6

8

10

12

14

HDLc

Female Male

mg/dL

40

50

60

70

80

90

100

110

120

Lipase

Female Male

mg/dL

40

50

60

70

80

90

100

110

120

Figure 7.9.b. Sex of adult brown pelicans breeding in the Northern GOM, in relation to seven blood 

analytes, 2013–2015: Electrolyte (n = 1), Enzyme (n = 1), Lipid (n = 1).

Only analytes found to be strongly related to BCI based on coefficient estimates from highly ranked models are 

displayed.
171

Creatine phosphokinase

50% core area

0 200 400 600 800 1000

Units/L

0

500

1000

1500

2000

Lactate dehydrogenase

50% core area

0 200 400 600 800 1000

Units/L

0

2000

4000

6000

8000

Lipase

50% core area

0 200 400 600 800 1000

Units/L

0

20

40

60

80

Figure 7.10.a. Home range size (50% core area) of adult brown pelicans breeding in the Northern GOM, in 

relation to seven blood analytes: 2013–2015, enzymes. 

Only analytes found to be strongly related to home range based on coefficient estimates from highly ranked models 

are displayed. 
172

HDLc

50% core area

0 200 400 600 800 1000

mg/dL

0

20

40

60

80

100

120

Blood urea nitrogen

50% core area

0 200 400 600 800 1000

mg/dL

0

2

4

6

8

10

12

14

16

18

20

Creatinine

50% core area

0 200 400 600 800 1000

mg/dL

0.0

0.5

1.0

1.5

2.0

Uric acid

50% core area

0 200 400 600 800 1000

mg/dL

0

5

10

15

20

25

30

35

Figure 7.11.b. Home range size (50% core area) of adult brown pelicans breeding in the Northern GOM, in 

relation to seven blood analytes, 2013–2015: metabolites and lipids. 

Only analytes found to be strongly related to home range based on coefficient estimates from highly ranked models 

are displayed.
173

Females Males

Monocyte count (10^3/ml)

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

Figure 7.12.a. Concentrations of white blood cells of adult brown pelicans breeding in the Northern GOM, 

2013–2015: count of monocytes for males and females.

East Central West

Count of white blood cells (10^3/ml)

0

5

10

15

20

25

30

Figure 7.13.b. Concentrations of white blood cells of adult brown pelicans breeding in the Northern GOM, 

2013–2015: count of all white blood cells by planning area.
174

East Central West

Count of heterophils (10^3/ml)

0

2

4

6

8

10

12

14

16

Figure 7.14.c. Concentrations of white blood cells of adult brown pelicans breeding in the Northern GOM, 

2013–2015: count of heterophils by planning area. 

Body condition index

-1000 -800 -600 -400 -200 0 200 400 600

Monocyte count (10^3/ml)

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

Females

Males

Figure 7.15.d. Concentrations of white blood cells of adult brown pelicans breeding in the Northern GOM, 

2013–2015: count of monocytes in relation to BCI for females and males.
175

50% core use area (km2)

0 200 400 600 800 1000

Lymphocyte count (10^3/ml)

0

2

4

6

8

10

12

14

Figure 7.16.e. Concentrations of white blood cells of adult brown pelicans breeding in the Northern GOM, 

2013–2015: count of lymphocytes in relation to size of home range (50% core area; km2).

Females Males

Packed cell volume (%)

38

40

42

44

46

48

50

52

54

56

Figure 7.17.f. Concentrations of white blood cells of adult brown pelicans breeding in the Northern GOM, 

2013–2015: packed cell volume (%) for females and males. 
176

Region

West Central East

Body condition index

-1500

-1000

-500

0

500

1000

1500

Figure 7.18. Box plots of body condition index of brown pelican chicks in the Northern GOM, in relation 

to planning area of study, 2014–2015.
177

Carbon Dioxide

East Central West

mEq/L

8

10

12

14

16

18

20

Calcium

East Central West

mg/dL

8.5

9.0

9.5

10.0

10.5

11.0

11.5

12.0

12.5

Sodium

East Central West

mEq/L

130

135

140

145

150

155

160

Figure 7.19.a. Box plots of blood analytes of adult brown pelican chicks in the Northern GOM, in relation 

to planning area, 2014–2015: blood gas (CO2) and electrolytes and minerals (calcium, sodium). 

Only analytes found to be strongly related to planning area based on coefficient estimates from highly ranked 

models are displayed. In all cases, the eastern region is set as the reference level.
178

Alanine Aminotransferase

East Central West

units/L

15

20

25

30

35

40

45

50

55

Aspartate Aminotransferase

East Central West

units/L

50

100

150

200

250

300

350

400

Lactate dehydrogenase

East Central West

units/L

0

2000

4000

6000

8000

10000

12000

Amylase

East Central West

units/L

800

1000

1200

1400

1600

1800

Figure 7.20.b. Box plots of blood analytes of adult brown pelican chicks in the Northern GOM, in relation 

to planning area, 2014–2015: enzymes: alanine aminotransferase, aspartate aminotransferase, lactate 

dehydrogenase, amylase. 

Only analytes found to be strongly related to planning area based on coefficient estimates from highly ranked 

models are displayed. In all cases, the eastern planning area is set as the reference level.
179

Triglycerides

East Central West

mg/dL

20

40

60

80

100

120

140

160

180

200

Blood Urea Nitrogen

East Central West

mg/dL

0

1

2

3

4

5

6

7

Glucose

East Central West

mg/dL

80

100

120

140

160

180

200

220

240

260

Blood Urea Nitrogen

East Central West

mg/dL

0.0

0.2

0.4

0.6

0.8

1.0

Figure 7.21.c. Box plots of blood analytes of adult brown pelican chicks in the Northern GOM, in relation 

to planning area, 2014–2015: lipids (triglycerides) and metabolites (blood urea nitrogen, creatinine, 

glucose). 

Only analytes found to be strongly related to planning area based on coefficient estimates from highly ranked 

models are displayed. In all cases, the eastern planning area is set as the reference level.
180

Albumin

East Central West

mg/dL

0.8

1.0

1.2

1.4

1.6

1.8

2.0

Alpha 1 Globulin

East Central West

mg/dL

0.08

0.10

0.12

0.14

0.16

0.18

0.20

Gamma Globulin

East Central West

mg/dL

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

1.1

Figure 7.22.d. Box plots of blood analytes of adult brown pelican chicks in the Northern GOM, in relation 

to planning area, 2014–2015: plasma proteins: albumin, alpha 1 globulin, gamma globulin. 

Only analytes found to be strongly related to planning area based on coefficient estimates from highly ranked 

models are displayed. In all cases, the eastern planning area is set as the reference level.
181

Potassium

BCI

-1500 -1000 -500 0 500 1000 1500

mEq/L

2.0

2.5

3.0

3.5

4.0

4.5

5.0

5.5

Amylase

BCI

-1500 -1000 -500 0 500 1000 1500

units/L

800

1000

1200

1400

1600

1800

Aspartate Aminotransferase

BCI

-1500 -1000 -500 0 500 1000 1500

units/L

50

100

150

200

250

300

350

400

Creatine Phosphokinase

BCI

-1500 -1000 -500 0 500 1000 1500

units/L

400

600

800

1000

1200

1400

1600

1800

Figure 7.23.a. Body condition index of brown pelican chicks in the Northern GOM, in relation to eight

blood analytes, 2014–2015: potassium, amylase, aspartate aminotransferase, and creatinine 

phosphokinase.

Only analytes found to be strongly related to BCI based on coefficient estimates from highly ranked 

models are displayed. 
182

Triglycerides

BCI

-1500 -1000 -500 0 500 1000 1500

mg/dL

20

40

60

80

100

120

140

160

180

200

Low-density Lipoprotein Cholesterol

BCI

-1500 -1000 -500 0 500 1000 1500

mg/dL

0

2000

4000

6000

8000

10000

12000

Alpha 1 Globulin

BCI

-1500 -1000 -500 0 500 1000 1500

mg/dL

0.08

0.10

0.12

0.14

0.16

0.18

0.20

Corticosterone

BCI

-1500 -1000 -500 0 500 1000 1500

mg/dL

0

20

40

60

80

100

120

140

Figure 7.24.b. Body condition index of brown pelican chicks in the Northern GOM, in relation to eight 

blood analytes, 2014–2015: triglycerides, low density lipoprotein cholesterol, alpha 1 globulin, and 

CORT.

Only analytes found to be strongly related to BCI based on coefficient estimates from highly ranked models are 

displayed. 
183

Molecular weight

120 130 140 150 160 170 180 190 200 210 220 230 240 250 260 270 280

Number of occurrences

0

1

2

3

4

5

6

7

8

9

Figure 7.25. Count of PAHs by molecular weight from blood samples of adult brown pelicans breeding in 

the Northern GOM, 2013–2015.
184

Bird ID

AU42-BAU43-BAU44-BAU45-BAU49-B

SM31-BSM32-BSM36-BSM64-BSM68-B

FE58-BFE59-BFE73-BFE74-BFE77-B

GA90-BGA91-BGA92-B

RA50-BRA51-BRA60-BRA62-B

CH18-BCH20-BCH22-BCH25-BCH26-B

RA69-B

SH14-BSH15-BSH16-BSH17-BSH27-B

Sum PAH (ng/g)

0

100

200

300

400

500

Parent PAH 

Alkylated PAH 

Figure 7.26.a. Summation of PAHs by individual from blood samples of adult brown pelicans breeding in 

the Northern GOM, 2013–2015: total PAH concentration (ng g-1; wet weight) of parent and alkylated 

PAHs in blood samples.

Bird ID = two letter abbreviation for the colony of origin of the sample (AU = Audubon Island, SM = Smith 

Island, FE = Felicity Island, GA = Gaillard Island, RA = Raccoon Island, CH = Chester Island, SH =

Shamrock Island; see Figs. 2.1 and 2.2), a unique identification number, and B = blood sample). 
185

Parent Alkylated Parent (>0) Alkylated (>0)

Sum PAH (ng/g)

0

50

100

150

200

250

300

Figure 7.27.b. Summation of PAHs by individual from blood samples of adult brown pelicans breeding in 

the Northern GOM, 2013–2015: box plots of PAH concentration (ng g-1; wet weight) of parent and 

alkylated PAHs in blood samples.

The two left-side boxes include data from all individuals. The two right-side boxed include only data where PAHs 

were > detectable limit (i.e., non-zero data). Bird ID = two letter abbreviation for the colony of origin of the sample 

(AU = Audubon Island, SM = Smith Island, FE = Felicity Island, GA = Gaillard Island, RA = Raccoon Island, CH = 

Chester Island, SH = Shamrock Island; see Figs. 2.1 and 2.2), a unique identification number, and B = blood 

sample). 
186

East Central West

sum Parent PAH (ng/g)

40

60

80

100

120

140

160

180

200

220

240

260

East Central West

sum Alkylated PAH (ng/g)

40

60

80

100

120

140

160

180

200

220

240

Figure 7.28. Concentrations of PAHs by planning area from blood samples of adult brown pelicans 

breeding in the Northern GOM, 2013–2015.

Only samples above detectable limits are included (i.e., zeros are excluded)

(a) Total concentration of parent PAHs (ng g-1; wet weight) for all samples above detectable limits. (b) Total 

concentration of alkylated PAHs (ng g-1; wet weight) for all sample above detectable limits.

(a)

(b)
187

Molecular weight

120 130 140 150 160 170 180 190 200 210 220 230 240 250 260 270 280

Number of occurrences

0

1

2

3

4

5

6

7

8

9

Figure 7.29. Count of PAHs by molecular weight from samples of feathers of adult brown pelicans 

breeding in the Northern GOM, 2013–2015.
188

Bird ID

AU39AU40AU41AU42AU43AU44AU45AU46AU47AU48AU49SM30SM31SM32SM33SM34SM35SM36SM37SM38SM64SM65SM66SM67SM68

FE06FE07FE08FE09FE10FE54FE55FE56FE57FE58FE59FE73FE74FE75FE76FE77

GA88GA89GA90GA91GA92

RA02RA03RA04RA05RA50RA51RA60RA61RA62RA63RA69RA70

RA71RA72RA23

CH18CH19CH20CH21CH22CH23CH24CH25CH26CH53CH78CH79

CH80CH81CH82SH11

SH12SH13SH14

SH15SH16SH17

SH27SH28SH29SH52

SH83SH84SH85

SH86SH87

Sum PAH (ng/g)

0

100

200

300

400

500

600

700

Parent PAH

Alkylated PAH

East Central West

Figure 7.30.a. Summation of PAHs by individual from feather samples of adult brown pelicans breeding 

in the Northern GOM, 2013–2015: total PAH concentration (ng g-1; dry weight) of parent and alkylated 

PAHs from adult feathers.

Bird ID = two letter abbreviation for the colony of origin of the sample (AU = Audubon Island, SM = Smith 

Island, FE = Felicity Island, GA = Gaillard Island, RA = Raccoon Island, CH = Chester Island, SH = 

Shamrock Island; see Figs. 2.1 and 2.2, and a unique identification number). 
189

Parent Alkylated Parent (>0) Alkylated (>0)

Sum PAH (ng/g)

0

100

200

300

400

500

600

All samples Only samples > detectable limit

Figure 7.31.b. Summation of PAHs by individual from feather samples of adult brown pelicans breeding 

in the Northern GOM, 2013–2015: box plots of PAH concentration (ng g-1; dry weight) of parent and 

alkylated PAHs from adult feathers.

The two left-side boxes include data from all individuals. The two right-side boxed include only data where PAHs 

were > detectable limit (i.e., non-zero data). Bird ID = two letter abbreviation for the colony of origin of the sample 

(AU = Audubon Island, SM = Smith Island, FE = Felicity Island, GA = Gaillard Island, RA = Raccoon Island, CH = 

Chester Island, SH = Shamrock Island; see Figs. 2.1 and 2.2, and a unique identification number). 
190

Short distance Medium distance Long distance

sum Parent PAH (ng/g)

20

40

60

80

100

120

140

160

180

Figure 7.32. Concentrations of Parent PAHs by migration class from feather samples of adult brown 

pelicans breeding in the Northern GOM, 2013–2015.

Only samples above detectable limits are included (i.e., zeros are excluded). Concentrations are in ng g-1

(wet weight).
191

Molecular weight

120 130 140 150 160 170 180 190 200 210 220 230 240 250 260 270 280

Number of occurrences

0

1

2

3

4

5

6

7

8

9

Figure 7.33. Count of PAHs by molecular weight from samples of feathers of brown pelican chicks in the 

Northern GOM, 2014–2015.
192

Bird ID

FMF-F

FMJ-F

FMR-F

FNC-F

FNE-F

FNF-F

FNH-F

FNI-F

FNP-F

FNS-F

FMX-F

FMY-F

FMZ-F

FNA-FFNB-F

THL-F

THU-F

TIB-FTIE-FTIE-F

TIL-F

TIW-F

TJB-F

TJD-F

TJH-F

TKR-F

TKZ-F

TLB-F

TLC-FTLD-F

TLE-F

TLG-F

TLL-F

TLM-F

TLN-F

Sum PAH (ng/g)

0

50

100

150

200

250

300

350

Parent PAH

Alkylated PAH

East Central West

Figure 7.34.a. Summation of PAHs by individual from feather samples of brown pelican chicks in the 

Northern GOM, 2014–2015: total PAH concentration (ng g-1; dry weight) of parent and alkylated PAHs in 

feathers of chicks.

Bird ID = the three-letter combination from the field-readable plastic leg band applied to the chick. F = 

feather sample.
193

Parent Alkylated Parent (>0) Alkylated (>0)

Sum PAH (ng/g)

0

50

100

150

200

250

300

All samples Only samples > detectable limit

Figure 7.35.b. Summation of PAHs by individual from feather samples of brown pelican chicks in the 

Northern GOM, 2014–2015: box plots of PAH concentration (ng g-1; dry weight) of parent and alkylated 

PAHs in feathers of chicks.

The two left-side boxes include data from all individuals. The two right-side boxed include only data 

where PAHs were > detectable limit (i.e., non-zero data). 
194

8 Conclusions and Future Directions

To date, the research described in this report is one of the most spatially and temporally extensive 

research efforts conducted on the Eastern brown pelican. Although the brown pelican has been a species 

of conservation concern in the GOM for decades, the species has been relatively understudied in the 

Northern GOM until research was initiated in approximately 2010 and after (e.g., Walter et al. 2013, 

2014; this report). For example, before 2010, there were substantial data gaps on productivity, chick 

survival, and chick condition, and on the variability within each of those parameters both within and 

among colonies and years. Our understanding of movement patterns during breeding and nonbreeding 

was limited to band return studies and anecdotal evidence of home range sizes, often from areas outside 

the GOM. These data gaps set the context for the research described herein. We focused on three primary 

areas of pelican ecology: (a) reproductive ecology, (b) spatial ecology and movement, and (c) health and 

exposure to contaminants. 

To date (February 2020), our research effort has contributed, either in whole or in part, to the following 

theses, dissertations, and published manuscripts (listed chronologically):

Lamb JS. 2016 Ecological drivers of brown pelican movement patterns and reproductive success in the 

Gulf of Mexico [dissertation]. Clemson (SC): Clemson University. 

Lamb JS, O’Reilly KM, Jodice PGR. 2016. Physical condition and stress levels during early development 

reflect feeding rates and predict pre- and post-fledging survival in a nearshore seabird. Conserv 

Physiol. 4(1): cow060. doi:10.1093/conphys/cow060

Lamb JS, Satgé YG, Fiorello CV, Jodice PGR. 2016. Behavioral and reproductive effects of bird-borne 

data logger attachment on Brown Pelicans on three temporal scales. J Ornith. 158: 617-627. 

Haney JC, Jodice PGR, Montevecchi WA, Evers DC. 2017. Challenges to oil spill assessment for 

seabirds in the deep ocean. Arch Environ Contam Toxicol. 73: 33-39. 

Lamb JS, Satgé YG, Jodice PGR. 2017. Diet composition and provisioning rates of nestlings determine 

reproductive success in a subtropical seabird. Mar Ecol Prog Ser. 581:149-164. 

Lamb JS, Satgé YG, Jodice PGR. 2017. Influence of density-dependent competition on foraging and 

migratory behavior of a subtropical colonial seabird. Ecol Evol.7(16):1–13. DOI: 

10.1002/ece3.3216 

Lamb JS, Newstead DJ, Koczur LM, Ballard BM, Green CM, Jodice PGR. 2018. A bridge between 

oceans: Overland migration of marine birds in a wind energy corridor. J Avian Biol. 49(2): 

e01474. doi: 10.1111/jav.01474

Streker R. 2019. Reproductive ecology and diet of brown pelicans in the Gulf of Mexico [thesis].

Clemson (SC): Clemson University. 

Lamb JS, Satgé YG, Jodice PGR. 2019. Seasonal variation in environmental and behavioral drivers of 

annual-cycle habitat selection in a nearshore seabird. Divers Distrib. 00:1–13. 

doi.org/10.1111/ddi.13015
195

The remainder of this section provides a summary of key findings, data gaps, and potential directions for 

future research, particularly because each may relate to decision-making. Summaries are grouped into 

four topics and discussed in turn: spatial ecology, diet and forage fish, risk exposure, and long-term 

monitoring. 

8.1 Spatial Ecology: Home Range, Migration, and Movement

Tracking individual pelicans using GPS-equipped satellite tags allowed us to accumulate several locations 

per day per bird. Tag duration was typically sufficient to allow us to calculate home range sizes during the 

breeding season and migration patterns during the nonbreeding season. We assessed characteristics of 

movement patterns during both breeding and nonbreeding in relation to colony and individual 

characteristics, with a particular emphasis on the potential role that density of breeding birds might have 

on spatial ecology. 

Data from GPS tracking revealed that colony characteristics more so than individual characteristics 

determined the foraging ranges of breeding pelicans, and that foraging locations were not unique or 

specific to a given colony. For example, 50% core areas for individuals ranged from < 10 km2 (6.21 mi2

) 

to ca. 500 km2 (310.69 mi2

) and 95% use areas for individuals ranged from < 10 km2 (6.21 mi2) to ca.

3500 km2 (2175 mi2

). The home range size of pelicans during the breeding was not consistent among 

colonies and we found a positive relationship between both 50% and 95% use areas and the abundance of 

breeding birds at a colony, suggesting a density dependent effect on movement. We did not detect any 

effect of sex, body size, or body condition on home range size. We also found that individuals from 

different colonies overlapped in their space use during the breeding season in all three planning areas of 

the GOM. Therefore, in the event of an acute stress event that may occur off colony, such as an oil spill, 

the probability of an individual from a given colony interacting with that stressor will not be based strictly 

on the distance from the point-event to the nearest colony, but also will need to consider colony size and 

the distribution of colonies in the area. For example, our data demonstrated that individuals may forage > 

100 km (62.1 mi) from their breeding colony and in a locale also frequented by birds from neighboring 

colonies. 

Our data demonstrated that brown pelicans in the GOM are partially migratory. As a population, partial 

migration leads to migration probabilities and distances that are inconsistent even within a single colony. 

In our study we found that migration distance of individuals ranged from < 50 km (31 mi) to 

approximately 2500 km (1553 mi). At the colony level the proportion of migrants ranged from 

approximately 25% to approximately 75%. Both migration distance and migration probability were 

positively related to the abundance of breeding birds at a colony, suggesting a positive effect of density on 

migration. Migration strategies also varied with individual characteristics, including sex and body size. As 

with home ranges during the breeding season we also found that individuals from different colonies and 

even different planning areas of the GOM overlapped spatially and temporally during staging and

migration. For example, pelicans from all three planning areas overlapped in the Mississippi River Delta 

region of Louisiana during staging, suggesting that this area presents a hot spot for migrating pelicans at a 

specific time of year (migration) when birds may be physically stressed. Birds from the western and 

central GOM also overlapped on wintering grounds along the Yucatan Peninsula (an area not used by 

pelicans from the eastern GOM). As with the breeding season, therefore, our ability to predict the

probability of birds from a specific colony being affected by a localized stressor event is complicated by 

the intra- and inter-colony differences observed in migration patterns. Furthermore, our data showed that 

pelicans breeding in Texas and Louisiana also winter along the Pacific coast of Mexico. Movement 

between ocean basins are uncommon for seabirds and, for pelicans, such migratory routes may facilitate 

genetic mixing and help distribute risk across populations. 
196

We also documented some unique attributes of migration paths of pelicans during our study. Over-water 

migrations were not uncommon. These included north-south transits across the GOM from the Louisiana 

Delta region to the Yucatan Peninsula, and from the Florida Keys to Cuba. During these transits, 

individuals were using pelagic waters. Additional data on pelicans’ use of GOM pelagic waters is being 

collected by vessel-based surveys of the Gulf of Mexico Marine Assessment Program for Protected 

Species (GoMMAPPS). Combining tracking data from our study with observation-based data from those 

vessel surveys may provide a unique opportunity to explorepelicans’ use of GOM pelagic waters. The 

migration of GOM pelicans to Cuba also suggests that spatial overlap occurs with pelicans that breed 

along the Atlantic coast of the US and also migrate to Cuban waters (Poli 2015). Whether pelicans that 

breed in other areas of the Caribbean also migrate to Cuban waters is not well known, but it appears that 

Cuba could represent an overlap in migration among multiple populations of pelicans. 

The movement data we collected clearly demonstrates a wide range of patterns within and among 

colonies. Such variability presents numerous challenges with respect to our ability to link a specific 

colony to a specific location that may experience an acute stress event such as an oil spill. Our data 

demonstrate that proximity from a colony to a localized stress event cannot be used as the sole predictor 

of the probability of a bird encountering that stress event. The spatial and temporal overlap in use areas by 

birds from different colonies furthers that concept. Several factors related to colony and individual 

dynamics will also affect that probability. Our data suggest that the continued development of maps of 

use areas specific to colonies are an important step in our ability to assess risk or damage to specific 

colonies. In the context of linking environmental attributes or stress events to specific colonies, our data 

demonstrate that colonies and the individuals that occupy them are being affected by conditions that range 

spatially over three orders of magnitude during the breeding season (1s–100s km) and over four orders of 

magnitude during the nonbreeding season (1s–1000s km). 

8.2 Diet and Forage Fish

Although the diet of pelicans in the GOM has been broadly defined as being focused on menhaden, 

details pertaining to diet and its effect on productivity have been less clear. We collected diet data from 

chicks during the breeding season from pelican colonies throughout the Northern GOM. Our analysis 

sought to define the diet composition of pelican chicks and the relative quantity and quality of the diet. 

We assessed the contribution of provisioning rates, meal size, and energy density of meals to productivity, 

and did so within the context of the nutritional-stress and junk-food hypotheses. Our study marks one of 

the first to do so for subtropical seabirds, with most previous assessments occurring in temperate or coldwater systems. 

We found that menhaden made up 60–85% of the diet of pelican chicks at colonies within the summer 

range of menhaden, but < 40% at colonies outside of the core summer range of menhaden. Other primary 

prey included fish from the Perciformes, Mugiliformes, and Clupeiformes. Our data suggest that diet 

composition is not uniform throughout the Northern Gulf and therefore that any efforts to assess the 

availability of forage fish as it may relate to pelican foraging or breeding need to consider species beyond 

menhaden and colonies from throughout the region. 

We also examined the primary components of meals fed to chicks and related those to productivity. Meal 

delivery rate, meal size, and energy density of meals all were positively related to productivity. Of these, 

however, the rate at which meals were provisioned had the strongest effect on chick survival. We found 

that the lipid density of prey was not as important as the provisioning rate or the size of the meal. Our 

results indicate, therefore, that the biomass of food provisioned rather than the energy content of the food 

provisioned was a more important driver of reproductive success in pelicans. Most previous studies that 

have examined the relationship of the quantity and quality of diet to reproductive success have occurred 
197

in colder water systems and our data suggest that applying concepts from such studies to pelicans in 

subtropical waters could be misleading. In the Northern GOM, therefore, it appears that reproductive 

success of pelicans is strongly linked to their ability to locate and successfully forage on abundant, small, 

schooling fish and in so doing maintain a high rate of meal provisioning. Any stressors that would reduce 

the availability of such prey could subsequently have a detrimental effect on reproductive success of 

pelicans and hence population dynamics.

Despite the spatially and temporally extensive nature of our diet assessment, several important aspects of 

pelican diet remain unknown or unclear. Here we summarize three issues that may warrant attention. 

(1) The frequency, timing, and location of interactions between pelicans and commercial fisheries 

throughout the GOM are relatively unclear. Our diet data included several species of fish that were likely 

sourced from discarded bycatch at trawlers (e.g., Perciformes not typically found in the upper meters of 

the water column). The abundance of these prey in the diet appears to differ among planning areas in the 

GOM, being more predominant in the western colonies that are located on the edge or outside of the 

range of menhaden. Improving our understanding of the role these prey items have in the diet of pelicans 

during the breeding season may be particularly important in the western planning area of the GOM, and in 

other locations if menhaden availability declined or if changes in trawling activity (e.g., intensity or 

location) occurred. 

(2) The diet of pelicans during migration and wintering seasons remains understudied. Given the wide 

range in migration strategies and endpoints, and the potential stress that individuals encounter outside of 

the breeding season, an assessment of diet throughout their nonbreeding range would be an important 

contribution to better understanding their annual cycle. 

(3) It is unclear how natural and anthropogenic stressors, either acute or chronic, may affect the 

availability of prey or the quality of prey. For example, acute stress events (e.g., oil spill) may inhibit 

recruitment of year classes of prey, and chronic stress events (e.g., continued exposure to a pollutant) may 

increase contaminant loads in prey. Our diet data, as well as the features of the diet that are less clear, all 

suggest that a monitoring program for diet throughout the Northern GOM would provide useful baseline 

and longitudinal data for managers and decision-makers. The opportunity to collect diet samples directly, 

as well as to assess diet composition using fecal DNA signatures and analysis of stable isotopes, provides

a range of techniques with which to undertake such long-term monitoring. 

8.3 Risk Exposure

For brown pelicans in the Northern GOM, risk exposure is a complex response that considers spatial and 

temporal dynamics as well as characteristics of individual birds. We set our risk exposure models within 

the three BOEM planning areas: oil and gas development was highest in the central planning area and 

least in the eastern planning area. We expanded our assessment of risk exposure, however, beyond oil and 

gas infrastructure and also included exposure to surface pollutants and shipping lanes, two additional 

stressors of potential significance for pelicans that forage and move primarily in nearshore waters. 

Exposure was least in the eastern planning area and higher but similar between the central and western 

planning areas. The similarity in exposure between the central and western planning areas is due to the 

inclusion of multiple sources of risk. Had our assessment of risk been based solely on the abundance of 

oil and gas platforms within a planning area, pelicans in the central planning area would have been 

exposed to higher levels of risk compared to pelicans in the eastern or western planning areas. By adding 

risk associated with shipping lanes, however, the western and central planning areas became more similar 

with respect to exposure. The similarity in exposure between the western and central planning areas also 
198

was based in part on movement patterns of individual birds. Our tracking data revealed that pelicans that 

were breeding in the western planning area often migrated through the central planning area (e.g., 

Louisiana delta) and therefore they became exposed to each of the three sources of risk we modeled in 

that planning area. In general, males experienced higher levels of risk compared to females due in large 

part to the lower probability of migration in males.

Spatially, we found that each planning area had some overlap between preferred habitat of pelicans and 

surface pollutants at some time of the year. We also detected hot spots of overlap between preferred 

habitat and surface pollutants year-round within the areas of the Louisiana delta and Galveston Bay. 

Temporally, exposure tended to be highest when individuals were constrained spatially within a location 

that had higher levels of pollutants. For example, individuals constrained by central place foraging during 

the breeding season or by molt during the nonbreeding season tended to have higher exposure to the risks 

we modeled. In terms of the avian annual cycle, post breeding was the time of maximum exposure and 

this was due in part to the overlap of use of the central planning area (i.e., Louisiana delta) by birds from 

all three planning areas during the physiologically stressful process of molt. 

Many models of risk exposure for seabirds are limited to assessing spatial and temporal overlap of birds 

with sources of risk, particularly those based on data obtained by surveys (e.g., aerial or vessel-based 

surveys of specific locations). An advantage to the models we present is the inclusion of behavior to 

further refine the level of risk an individual is experiencing. We used detailed aspects of movement 

available from tracking data to determine the behavioral state (i.e., transient or resident) of birds and by 

doing so were able to weight risk more towards birds that were resident. By considering the spatial and 

temporal resolution at which tracking data are collected (e.g., by recording more locations in a shorter 

amount of time), researchers and decision-makers could enhance the short-term accuracy of the classified 

behaviors and therefore model the exposure risk at different scales. 

Our risk exposure model does not consider pelican use of the southern GOM or Cuba during the 

nonbreeding season. For example, females in our model were predicted to be exposed to lower levels of 

risk compared to males, and this was due in part to the greater probability of females migrating out of the 

Northern GOM, compared to males. Our models were not able to readily parameterize risk outside of the 

Northern GOM, however, creating a data gap that may alter the risk experienced by migrating birds. Risk 

models also can be enhanced by including exposure to potential development of wind power 

infrastructure, an aspect of risk not assessed during this study. Our research was also not focused on 

assessing risk to natural stressors. For example, pelicans appear to be susceptible to acute spells of cold 

weather and modeling the risk associated with such exposure both spatially and temporally could enhance 

the ability of managers to predict injury to birds during the nonbreeding season. Recent research in the 

Atlantic is also assessing the behavioral and spatial response of pelicans to tropical storms (Wilkinson et 

al. 2019). Developing such a model for the Northern GOM also could inform managers to potential 

seasonal risks. Our risk assessment models also do not explicitly consider the extended breeding season 

for pelicans in the GOM, and the asynchronous nature of their breeding within a season. For example, an 

acute stressor (e.g., flood event) could occur during a time when, even within a colony, breeding effort 

ranged from early incubation to mid chick-rearing. The reproductive cost of such an event could easily be

misinterpreted if it were assumed all pairs were at the same stage of breeding. 
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The PAH profile of brown pelicans in the Northern GOM was diverse and comprised predominantly of 

alkylated PAHs, suggesting petrogenic sources. Of the 24 PAHs assessed, 17 occurred at least once when 

data were pooled among all three sample sets (i.e., adult blood, adult feathers, chick feathers). Each of the 

eight alkylated PAHs was observed at least once among the three sample sets, and nine of the 16 parent 

PAHs were detected at least once. Six of the eight alkylated PAHs were found in all three sample sets and

only three of the 16 parent PAHs were detected in all three data sets. There were no PAHs found in chicks 

that were not also found in adults, suggesting that chicks represent a subset of the adult data.

Three alkylated PAHs were the most frequently occurring among all three data sets: 2,3,5-

trimethylnaphthalene, 1,3-dimethylnaphthalene, and 2,6-dimethyl naphthalene. In contrast, seven parent 

PAHs were never detected: chrysene, benzo(b)fluoranthene, benzo(k)fluoranthene, benzo(a)pyrene, 

indeno(1,2,3-cd)pyrene, dibenz(a,h)anthracene, benzo(g,h,i)perylene. Among parent PAHs that were 

detected, there was no clear pattern in the frequency of their occurrence among the three sample sets.

8.4 Methodological and Monitoring Assessments

Two aspects of our study design that are relevant for the development of future research and monitoring 

are (1) the effects of loggers on individual behavior and (2) the potential use of biomarkers for long-term 

monitoring. Each are briefly reviewed in turn. 

The use of GPS tracking loggers is common in all aspects of wildlife research yet direct examinations of 

the effects of capture and attachment are still lacking. We used both field data and captive trials to assess 

the short-term effects of loggers on pelican behavior, and season-long monitoring to assess impacts of 

tagging on attendance and abandonment of nests. We found no strong effects of tagging on individual 

behavior or nest abandonment, but did find that the duration of handling during the tagging event was an 

important factor in the short- and long-term response of individuals. Our data suggest that efforts to 

minimize handling time will reduce negative effects of tagging on breeding birds in both the short- and 

long-term. One aspect of tagging that we did not investigate was the use of harness materials other than 

Teflon ribbon. Such an assessment was beyond the scope of this study but other studies of plunge-diving 

birds have used or are experimenting with implanted transmitters and silicone harnesses. Results from 

such assessments should be considered in future study design. 

Biomarkers can be used to establish baselines of overall health for a population, or to investigate specific 

aspects of a stressor on a population. Here we used a specific biomarker, CORT, to assess levels of 

baseline stress in chicks. The use of CORT is widespread in wildlife conservation research although the 

use of feathers as a medium is more recent. Feathers have the advantage of incorporating stress over a 

longer period of time compared to blood, and are less invasive to collect. We found that the concentration 

of CORT in feathers served as a viable predictor for a suite of relevant ecological and biological 

parameters including nutritional stress and ultimately survival of chicks to fledging. Given the relative 

ease with which feathers can be collected, our data suggest that a long-term monitoring program could be 

established using this physiological marker. Doing so would allow for the long-term assessment of 

environmental conditions on pelican chicks, and may also serve as an index for productivity, typically a 

time-consuming parameter to measure in pelicans. If similar relationships were established between 

feather CORT and condition and survival in other coastal species (e.g., terns, skimmers), a region-wide, 

long-term, multi-species monitoring plan could be established. 

Reference levels for hematology and plasma chemistry also can provide a tool for long-term tracking of 

individual health as well as changing relationships between blood analytes and among environmental 

variables of interest (e.g., planning area, sex, BCI). Our data provide reference levels for a time period 

following a major spill event, and therefore a further assessment conducted additional years out from the 
200

event (e.g., 5 and 10-years post spill) may also provide context for the stability of reference levels. Longterm tracking of PAHs, as well as an assessment of sublethal effects of PAHs on pelicans, can also 

enhance our understanding of the persistence and effects of this contaminant in the Northern GOM. We 

assessed PAH loads in blood and feathers, but recent studies also suggest that preen oil can also be used 

to assess PAH levels (Acampora et al. 2018). Assessing PAH levels through multiple matrices (e.g., 

blood, feathers, preen oil) may provide a more complete picture of contamination (Acampora et al. 2018). 

Further, the expansion or establishment of time series monitoring of ecosystem components likely to be 

exposed to petroleum releases, including the incorporation of alkylated PAHs into analyses to reflect 

exposure to crude oil, has been recommended (National Research Council 2003). 

8.5 Additional Information Needs

8.5.1 Colony Atlas and Nonbreeding Surveys

A current registry or colony atlas for pelicans in the Northern GOM that is region-wide and accessible to 

the broader avian conservation community is an important but currently missing tool and/or data source 

(Jodice et al. 2019, Ferguson et al. 2018). Such an atlas could also include the entire suite of coastal 

seabirds that nest in the region. Although each state collects some level of data on abundance of breeding 

seabirds, the timing, frequency, type, and protocols associated with surveys are not consistent, inhibiting 

effective and efficient regional assessments. Furthermore, such a product would provide a useful tool for 

response to future anthropogenic or natural stressors such as oil spills, contamination events, hurricanes, 

or flooding events. Periodic assessments of variables beyond nest counts (e.g., productivity, provisioning 

rates, chick condition, nestling diets) also are lacking. Such colony-level attributes would enhance our 

understanding of mechanisms underlying colony dynamics and also would be a valuable resource when 

responding to acute or chronic stress events (Jodice et al. 2019). 

Similarly, data on the distribution and abundance of pelicans and their allies during the nonbreeding 

season are lacking. Although tracking data such as ours can inform managers and stakeholders regarding 

habitat use during the nonbreeding season, a coordinated survey effort of the coastal zone that emphasized 

nearshore species would provide longer-term data that could be used in a manner similar to that contained 

in a breeding atlas. Such data would allow for the assessment of distribution and abundance of migrants 

and an identification of roosting and foraging sites that may not be a focus during the breeding season. 

Given that pelicans range extensively throughout the GOM, declines observed at any single colony may 

not be due strictly to on-colony factors, but rather may be a function of environmental conditions or 

threats experienced at wintering sites (Szostek and Becker 2015). Therefore, assessment of environmental 

conditions and stressors in core areas of the wintering range appear warranted. 

8.5.2 Colony-based Monitoring

For brown pelicans, data are still lacking on various components of their reproductive ecology, 

particularly given the spatial extent of nesting in the region. For example, the measurement of growth 

rates of chicks can provide substantial levels of detail for long-term monitoring strategies or for 

assessment of stressors. Chick growth can be measured repeatedly on the same individuals to provide 

growth curves that can be assessed, for example, in relation to environmental stressors or diet (Eggert et 

al. 2008), or single measures of chick size when collected on many chicks at once can be used to make 

comparisons among colonies or across time and space (Benson et al. 2003). Results described herein also 

have demonstrated that biomarkers, such as the measure of CORT in chick feathers, can be used to 

compare reproductive success among colonies and can be correlated with body condition or fledging 

success. A monitoring program that included all or some of the above tools would enhance our 
201

understanding of baseline measures of reproductive parameters and provide useful methods for assessing 

responses to natural and anthropogenic stressors. 

For brown pelicans, and for other seabirds that nest in the region, data on adult and juvenile survival are 

lacking (Shields 2014). Seabirds are long lived (commonly > 20 yrs) and adult survival rates tend to be 

drivers of population dynamics and recovery in long-lived species (Champagnon et al. 2018). Though 

banding and extensive band-resight efforts can inform estimates of survival, these approaches can also be 

challenging. For example, the extensive spatial distribution of colonies throughout the region, the 

remoteness of some colonies, the ability of individuals to move among colonies within or between years, 

and delayed maturity that results in a multi-year state of non-residency and multiple transition 

probabilities among sub-adults makes the detailed estimation of survival using band-resighting 

challenging and requires a long-term commitment of resources. Therefore, measures of juvenile survival 

are also difficult to obtain. An alternative or additional approach can include the continuation of longterm tracking studies using various tracking devices. For example, the recent advance in nano-tag 

technology could allow for the marking of 1000s of individuals throughout the GOM and these could 

subsequently be “re-captured” by nano-tag arrays deployed throughout the region (Loring et al. 2017). 

Such a design would overcome the sample size issue associated with deploying expensive satellite tags 

and the logistical issues associated with manually resighting individuals throughout remote areas. 

8.5.3 Prey Monitoring

Our data demonstrated the importance of provisioning rates, and hence prey availability, to reproductive 

success. Long-term monitoring of prey resources in the Northern GOM would therefore benefit not only 

our understanding of population dynamics of brown pelicans but also trends in other avian taxa within the 

region. For example, developing diet databases for focal avian species would allow for long-term 

assessments in changes of diet composition, diet quality, and diet quantity (Sydeman et al. 2001). A 

variety of tools including direct observation of chick feeding, stable isotope analysis of blood and 

feathers, and examination of fecal material for DNA signatures of prey items could be used to develop 

and maintain a diet database. Further, pelicans and other nearshore and pelagic seabirds interact regularly 

with commercial fisheries (Jodice et al. 2011). It is likely that discarded bycatch from trawlers in the 

GOM provides some proportion of the diet but the extent of this has yet to be determined. Without such 

data, it is difficult to fully understand how changes in commercial fishing activity might affect local 

populations of prey and their predators. Assessing contaminant loads in prey, such as PAHs, also would 

allow for longitudinal assessments within food webs. 

8.5.4 Expanded Scope: Taxonomic and Spatial

Although our research provided a substantial increase in the quantity and quality of data available on the 

ecology of brown pelicans, it is important to note that other avian taxa within the guild of nearshore 

seabirds are understudied and that numerous and substantial data gaps exist. Though some are addressed 

indirectly above, here we highlight the potential value of expanding data collection focused on spatial 

ecology and tracking. 

The renewed interest in research on brown pelicans during the past decade has not been surprising given 

their former status as endangered, their focus during oil spill events, and the general interest the species 

has with stakeholders and the public. Nonetheless, the life history of pelicans and their ecology is 

somewhat unique among coastal seabirds in the region. Although their breeding sites overlap spatially 

with many other species, their foraging style and breeding strategy differ from other species in significant 

ways. For example, pelicans plunge dive and capture large quantities of fish at once, whereas most coastal 

seabirds that overlap spatially with pelicans in the Northern GOM are surface feeders that capture single 

prey items (e.g., terns, gulls, and skimmers). Pelicans also have altricial young and a protracted breeding 
202

season. In contrast, terns and skimmers have precocial young and a compact breeding season, relative to 

pelicans. Taken together, these two differences in life-history strategies suggest that foraging behavior, 

home range size, provisioning rates, and attendance patterns all may differ among this suite of coastal 

seabirds and that, as a result, spatial ecology data of pelicans may not be entirely representative of other 

coastal seabirds. Therefore, expanding tracking efforts to include other coastal seabirds (e.g., terns and 

skimmers) would enhance our ability to understand the effects of natural and anthropogenic stressors on 

the coastal seabird community of the Northern GOM. Beyond an expansion of tracking efforts to other 

coastal seabirds, stakeholders also would benefit from research efforts to simultaneously tag and track 

apex marine predators across taxonomic classes. For example, examining the spatial and temporal overlap 

of seabirds and coastal sharks or marine mammals may provide insight into coastal hot spots or forage 

bases that support multiple predators. Such multi-species assessments can provide a more broad-based 

view of the structure and function of coastal and marine ecosystems (Harrison et al. 2018). 

Two additional opportunities also exist to expand tracking efforts specifically for pelicans. To date, little 

is known about movement patterns, foraging behavior, and spatial ecology of sub-adult pelicans in the 

Northern GOM. Efforts to tag and track birds immediately post-fledging could provide important data 

streams about an individual’s initial exposure to natural and anthropogenic threats in the region. For 

example, it appears that young-of-year birds forage upon discarded bycatch from trawlers, but the extent 

to which this occurs is unclear and the impact it may have on first-winter survival also remains unknown. 

Furthermore, although our study area was extensive, there were still spatial gaps in the Northern Gulf that 

warrant coverage. Two of note include Gaillard Island, Alabama, and the Florida peninsula. Gaillard 

Island is the largest colony of pelicans in the Northern GOM and a tracking study specifically focused on 

that area would provide critical data on a core portion of the breeding range. The Florida peninsula also 

supports pelican colonies and that portion of the GOM, because it is not currently impacted by oil and gas 

activities, can provide important baseline data for colonies less likely to be affected by such

anthropogenic stressors. 
203

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Appendix A: Supplementary Methods for Analysis of Composition of 

Diet of Brown Pelican Chicks, 2013–2015

Processing: In the lab, we thawed samples in a hot water bath, separated and counted individual fish, and 

classified each fish according to its condition (Whole, W: completely intact; Partial-whole, PW: complete 

length, some skin and/or soft tissues and/or caudal fin missing; Partial, PA: incomplete length). We then 

identified each fish to species, individually weighed and measured (Total Length, TL and/or Standard 

Length, SL; in mm) all W and PW fish, and collected a full sample weight. For PA and PW samples that 

contained more than 10 fish of the same species, we randomly selected a sample of 10 to weigh and 

measure.

Estimation of Total Length of Partial samples: In 2015, based on Whole (W: completely intact sample) 

regurgitates with recorded values for both TL and SL, we used a linear regression TL ~ SL to populate the 

missing values in both parameters. Regression equations were as follows (Table A.1):

Table A.1. Regression Values for Total Length of Main Prey Species Encountered in Diet Samples of Juvenile 

Brown Pelicans, Northern Gulf of Mexico 2013–2015

Species Regression equation R2 P

Brevoortia patronus TL = 1.251 SL + 0.547 0.986 < 0.005

Micropogonias undulatus TL = 1.167 SL + 6.531 0.989 < 0.005

Leiostomus xanthurus TL = 1.259 SL - 1.118 0.997 < 0.005

Lagodon rhomboides TL = 1.118 SL + 12.029 0.956 < 0.005

Anchoa mitchilli TL = 1.117 SL + 4.387 0.945 < 0.005

Anchoa lyolepis TL = 1.192 SL + 0.203 0.958 < 0.005

Anchoa hepsetus TL = 1.114 SL + 4.060 0.970 < 0.005

Opisthonema oglinum TL = 1.284 SL - 2.198 0.997 < 0.005

Estimation of mass of Partial-whole samples: based on TL and mass (in g) of W regurgitates and fresh 

bait bought near the breeding colonies, we calculated linear regressions (log(mass) ~ log(TL)) for the 

main prey species of samples collected in 2014 and in 2015. We used the regression equations to correct 

the mass of PW samples. Regression equations were as follows (Table A.2).
224

Table A.2. Regression Values for Mass of Main Prey Species Encountered in Diet Samples of Juveniles 

Brown Pelicans, Northern Gulf of Mexico 2013–2015

Year Species Regression equation R2 P

2014 Brevoortia patronus mass = e-12.233 × TL3.138 0.988 < 0.005

Micropogonias undulatus mass = e-11.298 × TL2.926 0.826 < 0.005

Leiostomus xanthurus mass = e-11.324 × TL2.976 0.982 < 0.005

Opisthonema oglinum mass = e-16.051 × TL3.278 0.972 < 0.005

2015 Brevoortia patronus mass = e-12.060 × TL3.100 0.978 < 0.005

Micropogonias undulatus mass = e-9.862 × TL2.630 0.989 < 0.005

Lagodon rhomboides mass = e-9.980 × TL2.763 0.974 < 0.005

Anchoa mitchilli mass = e-10.470 × TL2.641 0.858 < 0.005

Anchoa hepsetus mass = e-8.603 × TL2.223 0.876 < 0.005

Opisthonema oglinum mass = e-16.051 × TL3.278 0.972 < 0.005

Estimation of mass of Partial samples: If there were W or PW samples of the same species in the 

regurgitate, we calculated TL of PA samples as the average of observed TL of W and PW samples for that 

species in the same regurgitate. We then used the linear regressions to estimate the initial mass (in g) of 

the sample. If there were no W or PW samples of the same species in the same regurgitate, or if the 

species was not one of the main species, the initial mass of the sample was kept as the corrected mass. For 

Leiostomus xanthurus and Anchoa lyolepis the sample sizes were too small and we used equations 

calculated from our 2014 samples.
225

Appendix B: Technical Summary

STUDY TITLE: Eastern brown pelicans: dispersal, seasonal movements, and monitoring of PAHs and other oil 

contaminants among breeding colonies in the Northern Gulf of Mexico

REPORT TITLE: Ecological drivers of brown pelican movement patterns, health, and reproductive success in the 

Gulf of Mexico

CONTRACT NUMBER: M12PG00014

SPONSORING OCS REGION: Gulf of Mexico

APPLICABLE PLANNING AREA(S): Western Gulf, Central Gulf, Eastern Gulf

FISCAL YEAR(S) OF PROJECT FUNDING: 2012–2019

COMPLETION DATE OF REPORT: May 2019

COSTS: $1.2M

PROJECT MANAGER(S): Dr. Patrick Jodice

AFFILIATION of Project manager: US Geological Survey South Carolina Cooperative Fish and Wildlife Research Unit

ADDRESS: 260 Lehotsky Hall, Clemson University, Clemson, SC, 29634

PRINCIPAL INVESTIGATOR(S)3: Dr. Patrick Jodice

KEY WORDS: brown pelican, diet, migration, nutritional stress, polycyclic aromatic hydrocarbons, risk exposure, 

spatial ecology, tracking

BRIEF ABSTRACT: We investigated spatial, reproductive, and physiological ecology of brown pelicans (Pelecanus 

occidentalis) throughout the Northern Gulf of Mexico (GOM). Home range size and the probability and distance of 

migration were positively correlated with colony size. Pelicans from all colonies overlapped in space use in the 

Mississippi River Delta region of Louisiana during the nonbreeding season. Diet was primarily of menhaden in the 

eastern and central planning areas but was more GOM in the western planning area. Risk of exposure to 

contaminants was often higher in the west and central Gulf compared to the eastern Gulf, although factors other than 

planning area also contributed to exposure risk. 

BACKGROUND: The GOM contains a high density of oil infrastructure. It also supports a rich assemblage of 

seabirds. Understanding the effects of oil and gas activity on seabirds in the region requires a thorough 

understanding of reproductive ecology, foraging ecology, physiology, and habitat use. Because of its distribution 

patterns, behavior, and known sensitivity to contaminants, the brown pelican is a good indicator of species-level 

effects of interaction with coastal and marine development. 

3P.I.’s affiliation may be different than that listed for Project Manager(s).
226

OBJECTIVES: Our objectives were to assess reproductive ecology, movement patterns, habitat use, diet, 

physiology, and exposure to polycyclic aromatic hydrocarbons.

DESCRIPTION: Research was conducted at seven pelican colonies throughout the Northern GOM between 83° and 

98° W and 27° and 31° N. We deployed 85 GPS satellite tags on breeding adults to measure and describe home 

ranges, migration strategies, and risk exposure to oil and gas development and other surface pollutants. We also 

collected data on colonies (e.g., reproductive success, diet, contaminants). 

SIGNIFICANT CONCLUSIONS: Most of the response variables we measured or modeled varied substantially among 

colonies and planning areas, although individual characteristics also were significant. Therefore, the effects of 

environmental or anthropogenic stressors on pelicans may differ based on a combination of environmental, individual, 

and colony-based attributes and may not be strictly “region-based”. The use of the Mississippi River Delta region of 

Louisiana during migration by individuals from all planning areas suggests this is a hot spot for pelicans and risk 

exposure. 

STUDY RESULTS: Colony size was positively related to home range size, and to distance and probability of 

migration. Individuals from different colonies and planning areas overlapped spatially and temporally during breeding, 

staging, and migration. These overlap areas (e.g., the Mississippi River Delta region of Louisiana) represent 'hot 

spots for pelicans at specific times of year such as staging and migration. Menhaden comprised 60–85% of the diet 

of pelican chicks at colonies within the summer range of menhaden, but < 40% at colonies outside of the core 

summer range of menhaden (i.e., Texas). Reproductive success was positively related to meal provisioning rates, 

which in turn were lowest at colonies in Texas. Fledging success was positively related to stress levels and body 

condition of chicks. Exposure to oil and gas activity, surface pollutants, and shipping lanes was least in the eastern

planning area and higher but similar between the central and western planning areas. The similarity in exposure 

between the central and western planning areas is due to inclusion of multiple sources of risk. Hot spots of risk were 

located in the Mississippi River Delta (Louisiana) and Galveston Bay (Texas). The PAH profile of brown pelicans in 

the Northern GOM was diverse and comprised predominantly of alkylated PAHs. The level of oil and gas 

development in a planning area was not consistently the best predictor of PAH levels or individual health, and other 

factors (e.g. body condition, sex) also were influential. 

STUDY PRODUCT(S): Primary products noted, complete list available in Final Report.

Lamb JS. 2016. Ecological drivers of brown pelican movement patterns and reproductive success in the Gulf of 

Mexico [dissertation]. Clemson (SC): Clemson University. 

Lamb JS, O’Reilly KM, Jodice PGR. 2016. Physical condition and stress levels during early development reflect 

feeding rates and predict pre- and post-fledging survival in a nearshore seabird. Conserv Physiol. 4 (1): cow060. 

Lamb JS, Satgé YG, Fiorello CV, Jodice PGR. 2016. Behavioral and reproductive effects of bird-borne data logger 

attachment on Brown Pelicans on three temporal scales. J Ornith. 158: 617-627. 

Lamb JS, Satgé YG, Jodice PGR. 2017. Diet composition and provisioning rates of nestlings determine reproductive 

success in a subtropical seabird. Mar Ecol Prog Ser. 581:149-164. 

Lamb JS, Satgé YG, Jodice PGR. 2017. Influence of density-dependent competition on foraging and migratory 

behavior of a subtropical colonial seabird. Ecol Evol. 7(16):6469-6481.

Streker R. 2019. Reproductive ecology and diet of brown pelicans in the Gulf of Mexico [thesis]. Clemson (SC):

Clemson University. 

Lamb JS, Satgé YG, Jodice PGR. 2020. Environmental and behavioral drivers of annual-cycle habitat selection in a 

nearshore seabird, the Brown Pelican. Divers Distrib. 26(2):254–266.
227

Appendix C: Publications, Data, and Other Materials Resulting from 

Data Collected Under This Study

C.1 Published peer-reviewed publications

Lamb JS, O’Reilly KM, Jodice PGR. 2016. Physical condition and stress levels during early development 

reflect feeding rates and predict pre- and post-fledging survival in a nearshore seabird. Conserv 

Physiol. 4 (1):cow060. https://doi.org/10.1093/conphys/cow060

Lamb JS, Satgé YG, Fiorello CV, Jodice PGR. 2016. Behavioral and reproductive effects of bird-borne 

data logger attachment on Brown Pelicans on three temporal scales. J Ornith. 158:617-627. 

Haney JC, Jodice PGR, Montevecchi WA, Evers DC. 2017. Challenges to oil spill assessment for 

seabirds in the deep ocean. Arch Environ Contam Toxicol. 73:33-39. 

Lamb JS, Satgé YG, Jodice PGR. 2017. Diet composition and provisioning rates of nestlings determine 

reproductive success in a subtropical seabird. Mar Ecol Prog Ser. 581:149-164. 

Lamb JS, Satgé YG, Jodice PGR. 2017. Influence of density-dependent competition on foraging and 

migratory behavior of a subtropical colonial seabird. Ecol Evol. 7(16):6469-6481. 

https://doi.org/10.1002/ece3.3216

Lamb JS, Newstead DJ, Koczur LM, Ballard BM, Green CM, et al. 2018. A bridge between oceans: 

Overland migration of marine birds in a wind energy corridor. J Avian Biol. (2018):e01474. 

https://doi.org/10.1111/jav.01474

Lamb JS, Satgé YG, Jodice PGR. 2019. Seasonal variation in environmental and behavioural drivers of 

annual‐cycle habitat selection in a nearshore seabird. Divers Distrib. 00:1-13. 

https://doi.org/10.1111/ddi.13015

C.2 Peer-reviewed publications in review

Streker RA, Lamb JS, Jodice PGR. Submitted. Local weather as an ecological driver of reproductive 

success in the Brown Pelican. Waterbirds.

C.3 Peer-reviewed publications in preparation

Lamb JS, Satgé YG, Jodice PGR. Individual movement patterns inform exposure to surface pollutants in 

a nearshore seabird, the Brown Pelican.

C.4 Theses

Lamb JS. 2016. Ecological drivers of brown pelican movement patterns and reproductive success in the 

Gulf of Mexico [dissertation]. Clemson (SC): Clemson University. 
228

Streker R. 2019. Reproductive ecology and diet of brown pelicans in the Gulf of Mexico [thesis]. 

Clemson (SC): Clemson University. 

C.5 Conference presentations

Jodice PGR, Lamb JS, Satgé YG, Poli CR. 2017. Spatial ecology of seabirds in the Gulf of Mexico. 

Bureau of Ocean Energy Management Information Transfer Meeting; 22–24 August 2017; New 

Orleans, LA.

Jodice PGR, Lamb JS, Satgé YG. 2019. Health assessments of brown pelicans from the Gulf of Mexico. 

Paper presented at: 5th World Seabird Twitter Conference; 9–11 April 2019.

Jodice PGR, Mackin W, Harrison A-L, Ronconi R, Lamb JS, et al. 2015. Tracking Atlantic and 

Caribbean seabirds. Paper presented at: 2nd World Seabird Conference; 26–30 October 2015; Cape 

Town, South Africa.

Jodice PGR, Woodrey MS, Fournier A. Paper presented at: 2018. Movement ecology of seabirds: multiscale and multi-taxa approaches to addressing conservation needs in the Gulf of Mexico. Paper 

presented at: 72nd Conference of the Southeastern Association of Fish and Wildlife Agencies; 21–24 

October 2018; Mobile, AL.

Jodice, PGR, Lamb JS. 2019. An overview of research and conservation on Pelicans in the 21st century: 

data gaps and data strengths. Paper presented at: 43rd Annual Conference of the Waterbird Society, 

Salisbury, MD.

Jodice, PGR, Lamb JS, Satgé YG. 2019. Exposure of Brown Pelicans to polycyclic aromatic 

hydrocarbons in the northern Gulf of Mexico. Paper presented at: 43rd Annual Conference of the 

Waterbird Society, Salisbury, MD.

Lamb JS Jodice PGR. 2014. Gradients in breeding Brown Pelican foraging radius, chick condition, and 

diet across the Northern Gulf of Mexico. Paper presented at: 41st Pacific Seabird Group Annual 

Meeting; 19–22 February Juneau, AK.

Lamb JS, Jodice PGR. 2014. Comparative performance of cellular and satellite transmitters deployed on 

Brown pelicans in the Northern Gulf of Mexico. Poster presented at: 41st Pacific Seabird Group 

Annual Meeting; 19–22 February Juneau, AK.

Lamb JS, Jodice PGR. 2014. Year-round spatial overlap between energy infrastructure and brown pelican 

movements in the Gulf of Mexico. Paper presented at: 3rd International Marine Conservation 

Congress; 14–18 August 2014; Glasgow, Scotland.

Lamb JS, Jodice PGR. 2015. Brown pelican fledging success and diet in the northwestern Gulf of 

Mexico: potential drivers of changing regional distribution. Poster presented at: 42nd Pacific Seabird 

Group Annual Meeting; 18–21 February 2015; San Jose, CA.

Lamb JS, Jodice PGR. 2015. Physiology and geography predict individual migratory strategies in the 

Brown Pelican. Paper presented at: Texas Bays and Estuaries Meeting; 8–9 April 2015; Port Aransas, 

TX.
229

Lamb JS, Jodice PGR. 2015. Sex, death, and oil: Conservation implications of individual variation in 

Brown Pelican movement patterns. Paper presented at: 2nd World Seabird Conference; 26–30 

October 2015; Cape Town, South Africa.

Lamb JS, Jodice PGR. 2015. Should I stay or should I go? Physiology and geography predict individual 

migratory strategies in the brown pelican. Paper presented at: 42nd Pacific Seabird Group Annual 

Meeting; 18–21 February 2015; San Jose, CA.

Lamb JS, Jodice PGR. 2016. Spatial distribution and behavior mediate surface pollutant exposure risk in 

a nearshore seabird, the Brown Pelican. Paper presented at: 4th International Marine Conservation 

Congress;30 July – 3 August 2016; St. John’s, Canada. 

Lamb JS, Jodice PGR. 2017. Movement patterns and habitat selection of Brown Pelicans in the Gulf of 

Mexico. Paper presented at: 41st Waterbird Society Annual Meeting; 8–11 August 2017; Reykjavik, 

Iceland. 

Lamb JS, Newstead D, Ballard B, Koczur L, Jodice PGR, et al. 2014. Paper presented at: A bridge 

between oceans: evidence for use of the Tehuantepec Isthmus by waterbirds during dispersal and 

migration. 38th Waterbird Society Annual Meeting; 4–7 November 2014; La Paz, Mexico.

Lamb JS, O’Reilly K, Jodice PGR. 2016. Feather corticosterone as possible index of developmental 

conditions and probability of post-fledging survival in Brown pelicans. Paper presented at: 43rd 

Pacific Seabird Group Annual Meeting; 10–11 February 2016; O’ahu, HA.

Lamb JS, O’Reilly K, Jodice PGR. 2016. Long-term physiological responses of nestling seabirds to 

variation in prey availability and nest conditions. Paper presented at: Society for Integrative and 

Comparative Biology; 3–7 January 2016; Portland OR.

Lamb JS, O’Reilly KM, Jodice PGR. 2016. Long-term physiological responses of nestling seabirds to 

variation in prey availability and nest site characteristics. Paper presented at: 40th Waterbird Society 

Annual Meeting; 21–23 September 2016; New Bern, NC.

Lamb JS, Satgé Y, Jodice N, Jodice PGR. 2017. Should I stay or should I go? Physiology and geography 

predict individual migratory strategies in the Brown pelican. Paper presented at: 3rd World Seabird 

Twitter Conference; 12–14 April 2017. (Special Achievement in Scientific Communication)

Lamb JS, Satgé YG, Jodice PGR. 2015. Brown pelican fledging success and diet in the Western Gulf of 

Mexico: potential drivers of changing regional distribution. Paper presented at: 1st World Seabird 

Twitter Conference; 19–21 March 2015.

Lamb JS, Satgé YG, Jodice PGR. 2015. Conservation implications of individual variation in Brown 

Pelican migration strategies. Poster presented at: 27th International Congress for Conservation 

Biology; 2–6 August 2015; Montpellier, France.

Lamb JS, Satgé YG, Jodice PGR. 2015. Variation in Brown Pelican energy provisioning rates across a 

range of juvenile forage fish availability. Paper presented at: 39th Waterbird Society Annual Meeting; 

11 – 15 August 2015; Bar Harbor, ME.
230

Lamb JS, Satgé YG, Jodice PGR. 2017. Movement patterns and habitat selection of Brown Pelicans in 

the Gulf of Mexico. Paper presented at: 44th Pacific Seabird Group Annual Meeting; 22–25 February 

2017; Tacoma, WA.

Lamb JS, Satgé YG, Jodice PGR. 2018. A bridge between oceans: overland migration of brown pelicans 

increases exposure to terrestrial wind energy. Paper presented at: 4th World Seabird Twitter 

Conference; 17–19 April 2018.

Satgé YG, Lamb JS, Jodice PGR. 2015. Brown pelican fledgling success and diet: potential drivers of 

changing regional distribution in the Texas coast. Paper presented at: Texas Bays and Estuaries 

Meeting 8–9 April 2015; Port Aransas, TX.

Satgé YG, Lamb JS, Jodice PGR. 2016. Diet composition and provisioning rates determine reproductive 

success in a nearshore seabird. Paper presented at: 2nd World Seabird Twitter Conference; 13–15 

April 2016.

Satgé YG, Lamb JS, Jodice PGR. 2017. Using a novel way to communicate seabird research: Physiology 

and geography predict individual migratory strategies in the Brown Pelican. Paper presented at: 1st 

British Ornithology Union Twitter Conference; 28–29 November 2017.

Streker RA, Jodice PGR, Lamb JS. 2018. Daily survival rates of Brown Pelican (Pelecanus occidentalis 

carolinensis) nests and chicks and their relationships with nest habitat. Poster presented at: 

Association of Field Ornithologists and Wilson Ornithological Society Joint Meeting; 6–9 June 2018; 

Chattanooga, TN.

Streker RA, Lamb JS, Dindo J., Jodice PGR. 2019. Diet of maturing Brown Pelican chicks in Coastal 

Alabama. Poster presented at: 43rd Annual Conference of the Waterbird Society, Salisbury, MD.

C.6 Invited seminars

Jodice PGR. 2014. Seabird ecology and conservation in the Northwest Atlantic and Gulf of Mexico: 

insights from individual tracking. Invited seminar at: Department of Biology, Wake Forest 

University; September 2014; Winston-Salem, NC.

Jodice PGR. 2015. Examining the movement ecology of seabirds across a range of spatial and temporal 

scales. Invited seminar at: Department of Wildlife Ecology and Conservation, University Florida; 

January 2015; Gainesville, FL.

Jodice PGR. 2019. Multi-scale movement and risk in seabirds. Invited seminar at: Duke Marine Lab, 

Duke University; March 2019; Beaufort, NC.

Lamb, J.S. 2018. Using individual movements to evaluate risk factors for brown pelicans in the Gulf of 

Mexico. Invited seminar at: University of Florida; February 2018; Gainesville, FL.

Lamb, J.S. 2017. Developing ecologically-motivated risk assessments for marine birds in the Gulf of 

Mexico. Invited seminar at: University of Georgia; March 2017; Athens, GA.

Lamb, J.S. 2017. Evaluating year-round seabird habitat needs in the Gulf of Mexico to improve oil 

pollution risk assessment and mitigation. Invited seminar at: Queen’s University; January 2017;

Kingston, Ontario.
231

C.7 Data releases

Lamb JS, Satgé YG, Jodice PGR. 2017. Foraging ecology of Brown Pelican in the northern Gulf of 

Mexico (2013–2015) [datasets]. US Geological Survey data release. Washington (DC): US 

Department of the Interior, US Geological Survey. https://doi.org/10.5066/F7R78D6J.

- Composition of diet of juvenile Brown Pelican in the northern Gulf of Mexico (2013-2015)

- Productivity of Brown Pelican in the northern Gulf of Mexico (2014-2015)

- Provisioning rate of Brown Pelican in the northern Gulf of Mexico (2014-2015)

- Proximate Composition and Energy Density of Brown Pelican Prey in the Northern Gulf of 

Mexico (2014-2015)

- Reference_Brown pelican foraging ecology in the northern Gulf of Mexico (2013-2015)_Colonies

- Reference_Taxonomical identification of Brown Pelican prey in the northern Gulf of Mexico 

(2013-2015)

Lamb JS, Satgé YG, Jodice PGR. 2017. Data from: Influence of density-dependent competition on 

foraging and migratory behavior of a subtropical colonial seabird [datasets]. Movebank Data 

Repository. https://doi.org/10.5441/001/1.7856r086. 

- GPS tracking of Brown Pelican in the northern Gulf of Mexico (2013-2016) - Location data

- GPS tracking of Brown Pelican in the northern Gulf of Mexico (2013-2016) - Reference data

Lamb JS, Satgé YG, Jodice PGR. 2019. Brown Pelican Utilization Distribution, Breeding Season, 

Northern Gulf of Mexico, 2013-2015 [datasets]. South Carolina Cooperative Fish and Wildlife 

Research Unit data release. Clemson (SC). https://doi.org/10.5066/P9K0HK27.

- BRPE_GoM_20132015_AL_RastUD_NAD83

- BRPE_GoM_20132015_AL_RastUD_WGS84

- BRPE_GoM_20132015_FL_RastUD_NAD83

- BRPE_GoM_20132015_FL_RastUD_WGS84

- BRPE_GoM_20132015_LA_RastUD_NAD83

- BRPE_GoM_20132015_LA_RastUD_WGS84

- BRPE_GoM_20132015_TX_RastUD_NAD83

- BRPE_GoM_20132015_TX_RastUD_WGS84

C.8 Data releases in preparation

Lamb JS, Satgé YG, Jodice PGR. 2019. Physiological ecology of Brown Pelican in the Gulf of Mexico, 

2013-2016 [datasets]. US Geological Survey data release. Washington (DC): US Department of the 

Interior, US Geological Survey. Reserved DOI: 10.5066/P94GBJ4G.

- Blood Analytes in Brown Pelican in the Northern Gulf of Mexico (2013-2015)

- Polycyclic Aromatic Hydrocarbons in Brown Pelican in the Northern Gulf of Mexico (2013-2015)

Streker R, Lamb JS, Satgé YG, Jodice PGR. Reproductive physiology of Brown Pelican along the coast 

of Alabama, 2017-2018 [datasets]. U.S. Geological Survey data release. Washington (DC): US 

Department of the Interior, US Geological Survey. Reserved DOI: 10.5066/P9AB53QZ .

- Physiology of Reproduction of Brown Pelican Along the Coast of Alabama, 2017-2018_Nest -

monitoring

- Physiology of Reproduction of Brown Pelican Along the Coast of Alabama, 2017-2018_Nest 

monitoring-temperature
232
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