OCS Study

BOEM 2016-039

Modeling At-Sea Occurrence and 

Abundance of Marine Birds to Support 

Atlantic Marine Renewable Energy 

Planning

Phase I Report

US Department of the Interior

Bureau of Ocean Energy Management

Office of Renewable Energy Programs

OCS Study 

BOEM 2016-039

Modeling At-Sea Occurrence and 

Abundance of Marine Birds to Support 

Atlantic Marine Renewable Energy 

Planning

Phase I Report

Authors

Brian P. Kinlan

Arliss J. Winship

Timothy P. White

John Christensen

Prepared under NCCOS IAA MOA-2013-046-8696, BOEM OCS Study 2016-039, and NCCOS 

BOEM IAA M13PG00005

by

U.S. Department of Commerce

National Oceanic and Atmospheric Administration

National Ocean Service

National Centers for Coastal Ocean Science

Center for Coastal Monitoring and Assessment

Biogeography Branch

1305 East-West Hwy, SSMC-4, N/SCI-1

Silver Spring, MD 20910

Published by

U.S. Department of the Interior

Bureau of Ocean Energy Management

Office of Renewable Energy Programs

May 2016
ii

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 Inter-Agency 

Agreement Number M13PG00005 with the US Department of Commerce, National Oceanic and 

Atmospheric Administration, National Ocean Service, National Centers for Coastal Ocean 

Science, Silver Spring, MD. 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 the US 

Government, nor does mention of trade names or commercial products constitute endorsement 

or recommendation for use.

REPORT AVAILABILITY

To download a PDF file of this Environmental Studies Program report, go to the US Department 

of the Interior, Bureau of Ocean Energy Management, Environmental Studies Program 

Information System website and search on OCS Study BOEM 2016-039. You may request the 

report from the BOEM Office of Renewable Energy Programs. The contact information is:

U.S. Department of the Interior

Bureau of Ocean Energy Management

Office of Renewable Energy Programs

45600 Woodland Road, VAM-OREP

Sterling, VA 20166

CITATION

Kinlan, B.P., A.J. Winship, T.P. White, and J. Christensen. 2016. Modeling At-Sea Occurrence 

and Abundance of Marine Birds to Support Atlantic Marine Renewable Energy Planning: Phase 

I Report. U.S. Department of the Interior, Bureau of Ocean Energy Management, Office of 

Renewable Energy Programs, Sterling, VA. OCS Study BOEM 2016-039. xvii+113 pp.

ACKNOWLEDGEMENTS

We thank the many scientists who collected and contributed the survey data analyzed in this 

study. We are grateful to Mark Wimer (USGS) and Allison Sussman (USGS) for processing the 

data in the Avian Compendium database and providing those data to us. We thank Peter 

Cornillon, Peter Miller, Matt Poti, and Tim Wynne for providing and helping with the processing 

of some of the environmental predictor data. This project was funded by the Bureau of Ocean 

Energy Management (BOEM) through Intra-agency Agreement M11PG00059 with the United 

States Geological Survey (USGS) and Inter-agency Agreement M13PG00005 with the U.S. 

Department of Commerce, National Oceanic and Atmospheric Administration, National Ocean 

Service, National Centers for Coastal Ocean Science (NCCOS), and by the United States 

Geological Survey through Inter-agency Agreement G13PG00008 with NCCOS. Brian Kinlan, 

Arliss Winship, and Tim White were supported by NOAA Contracts No. DG133C07NC0616 and 

EA-133C-14-NC-1384 with CSS-Dynamac.

ABOUT THE COVER

Cover photo (Great Shearwater) courtesy of David Pereksta (BOEM). Used with permission.
iii

Executive Summary

Marine birds have the potential to be affected by human activities in the marine environment such as 

offshore wind energy development. This report describes the first phase of a project aimed at producing 

maps of the spatial distributions of marine bird species in U.S. Atlantic Outer Continental Shelf (OCS)

waters that can be used to inform marine spatial planning in the region.

Visual sighting survey data from over three decades, contained in the ‘Compendium of Avian Occurrence 

Information for the Continental Shelf waters along the Atlantic Coast of the U.S.’ database, were 

analyzed to derive seasonal and annual maps of the spatial distributions of 40 marine bird species in U.S. 

Atlantic OCS waters from Florida to Maine.

Spatial predictive modeling was applied to the survey data to account for spatial and temporal 

heterogeneity in survey effort, platform, and protocol. An ensemble machine-learning technique, 

component-wise boosting of hierarchical zero-inflated count models, was used to relate the relative 

occurrence and abundance of each species to multiple spatial and temporal environmental predictor 

variables while accounting for survey heterogeneity and the aggregated nature of sightings. Dynamic 

spatial environmental predictor variables were formulated as long-term climatologies. The modeling 

technique allowed for complex non-linear relationships between response and predictor variables and 

interacting effects among predictors. Bootstrapping was used to derive estimates of the uncertainty in 

model predictions.

Model predictions are presented as seasonal and annual maps of the relative probability of occurrence and 

relative abundance of study species throughout the U.S. Atlantic OCS. These maps indicate where species 

are more or less likely to occur and where species are likely to be more or less abundant. The analysis was 

not designed to estimate the absolute probability of occurrence or the absolute number/density of 

individuals of a given species that would be expected in any location, so the maps should not be 

interpreted that way. Also, the maps represent the spatial distributions of birds averaged over time (e.g.,

across days within a season and across years for a given season). The analysis was not designed to 

provide predictions of the number of birds that would be expected in a specific location at a specific date 

or time, so the maps should also not be interpreted that way. Furthermore, large variations in predicted 

long-term relative occurrence and abundance at the 2-km spatial resolution of the study grid are not 

necessarily realistic. Interpretation of the maps is probably more reliable at the regional scale (i.e., 10-100 

km). The maps presented here provide preliminary broad-scale spatial information that can be used to 

guide future data collection efforts and aid marine spatial planning in the region.

Four types of supplementary information are provided along with the maps of predicted relative 

occurrence and abundance to indicate the quality of those predictions. First, the distribution of survey 

effort is presented as a series of isopleths to indicate where the majority of the survey data were collected. 

Model predictions in areas with few or no data should be interpreted with caution. Second, for each 

species-season combination a ‘badge’ is included on the maps, representing the statistical fit of the model 

to the data in terms of several performance metrics. The badge indicates the quality of model predictions

in areas with survey data but not in areas without survey data. Third, estimates of the precision of model 

predictions are presented as maps of the variability, quantiles, and confidence interval width of the 
iv

bootstrapped distributions of model predictions. Less precise predictions should be interpreted with more 

caution. Fourth, we present an expert assessment of how well the predictions for each species match what 

is known about the species’ distribution. These four supplementary sources of information should be 

considered in conjunction with the maps of predicted relative occurrence and abundance.

The relative importance of different predictor variables is presented, indicating which variables most 

influenced the predicted distributions for each species in each season. While the primary objective of this 

study was not to determine the ecological drivers and mechanisms behind the spatial distributions of 

marine bird species in the study area, our model results may provide useful hypotheses for future studies 

aimed more at ecological inference.

A second phase of this project is currently underway that will expand, refine, and improve the modeling 

and results presented here. The second phase is projected to be completed by the fall of 2017.
v

Modeling At-Sea Occurrence and Abundance of Marine Birds to 

Support Atlantic Marine Renewable Energy Planning: Phase I Report

Contents

Executive Summary ........................................................................................... iii

List of Figures.................................................................................................... vii

List of Tables .................................................................................................... xiv

List of Appendices (digital) ............................................................................. xvi

Abbreviations and Acronyms ........................................................................ xvii

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

2. Methods............................................................................................................ 2

2.1 Overview................................................................................................................ 2

2.2 Survey data............................................................................................................ 2

2.3 Predictor variables ................................................................................................. 3

2.4 Statistical modelling framework ............................................................................. 5

2.4.1 Likelihoods and model components ........................................................................... 5

2.4.2 Base-learners............................................................................................................. 5

2.4.3 Stochastic gradient boosting ...................................................................................... 6

2.4.4 Boosting offsets ......................................................................................................... 6

2.4.5 Tuning of shrinkage rate and number of boosting iterations ....................................... 6

2.4.6 Model selection and performance .............................................................................. 6

2.4.7 Spatial prediction........................................................................................................ 7

2.4.8 Variable importance ................................................................................................... 8

2.4.9 Uncertainty................................................................................................................. 8

2.4.10 Implementation......................................................................................................... 8

2.5 Map display............................................................................................................ 8

2.6 Warning regarding potentially spurious spatial predictions .................................... 8

3. Results ............................................................................................................. 9

3.1 Model selection and performance.......................................................................... 9

3.1.1 Example species-season combinations...................................................................... 9

3.1.2 All species.................................................................................................................10

3.2 Predictor variable relative importance.................................................................. 10
vi

3.2.1 Example species-season combinations.....................................................................10

3.2.2 All species and seasons............................................................................................11

3.2.3 Seasonal...................................................................................................................11

3.3 Predictor variable effects (example species-season combinations)..................... 11

3.3.1 Marginal effects.........................................................................................................11

3.3.2 Bivariate effect interactions .......................................................................................11

3.4 Predicted spatial distributions .............................................................................. 11

3.4.1 Example species-season combinations.....................................................................11

3.4.2 Species groups .........................................................................................................15

4. Discussion ..................................................................................................... 15

4.1 Interpretation of maps.......................................................................................... 16

4.2 Model performance.............................................................................................. 17

4.3 Species identification ........................................................................................... 17

4.4 Project Phase II ................................................................................................... 18

5. Literature Cited.............................................................................................. 18
vii

List of Figures

Figure 1. Study area with Wind Energy Planning and Lease Areas overlaid (approximate 

boundaries current as of 05 Feb 2015)..............................................................................47

Figure 2A. Map of survey effort. Midpoints of standardized transect segments within the study 

area are plotted in blue (boat surveys) and red (aerial surveys) for each season analyzed. 

For complete list of datasets, see Appendix A. ..................................................................48

Figure 2B. Map of survey effort. Number of survey midpoints of standardized transect segments 

summed across 10 x 10 km cells within the study area for each season analyzed: a) winter 

b) spring c) summer d) fall. Colored contours indicate survey intensity. For complete list of 

datasets, see Appendix A..................................................................................................49

Figure 3. Examples of temporal and spatial predictors used in models; a) Climate index time 

series (AMO); b) static spatial predictor (depth); c) seasonal spatial predictor (front 

probability climatology for spring). For complete set of predictor plots, see Appendix B. ...50

Figure 4. Schematic overview of statistical modeling process. See Section 2 Methods for 

details................................................................................................................................51

Figure 5.1. Cross-validation model performance metrics during stochastic gradient boosting for 

example model 7 (COTE/summer). a) Brier score, b) thresholded continuous rank 

probability score (CRPS), c) Median Absolute Error for non-zero data, d) Median Bias for 

non-zero data, e) root mean square error (RMSE), f) negative log-likelihood (risk). The 

optimized metrics were CRPS (panel b) and risk (panel f). For complete set of gradient 

descent plots, see Appendix C. .........................................................................................52

Figure 5.2. Cross-validation model performance metrics during stochastic gradient boosting for 

example model 7 (NOGA/fall). a) Brier score, b) thresholded continuous rank probability 

score (CRPS), c) Median Absolute Error for non-zero data, d) Median Bias for non-zero 

data, e) root mean square error (RMSE), f) negative log-likelihood (risk). The optimized 

metrics were CRPS (panel b) and risk (panel f). For complete set of gradient descent plots, 

see Appendix C.................................................................................................................53

Figure 5.3. Cross-validation model performance metrics during stochastic gradient boosting for 

example model 7 (RAZO/winter). a) Brier score, b) thresholded continuous rank probability 

score (CRPS), c) Median Absolute Error for non-zero data, d) Median Bias for non-zero 

data, e) root mean square error (RMSE), f) negative log-likelihood (risk). The optimized 

metrics were CRPS (panel b) and risk (panel f). For complete set of gradient descent plots, 

see Appendix C.................................................................................................................54

Figure 5.4. Cross-validation model performance metrics during stochastic gradient boosting for 

example model 8 (WWSC/winter). a) Brier score, b) thresholded continuous rank 

probability score (CRPS), c) Median Absolute Error for non-zero data, d) Median Bias for 

non-zero data, e) root mean square error (RMSE), f) negative log-likelihood (risk). The 

optimized metrics were CRPS (panel b) and risk (panel f). For complete set of gradient 

descent plots, see Appendix C. .........................................................................................55

Figure 6AB. Comparison of model performance metrics for the best models (the final selected 

model out of the two compared) for each species/season combination. a) bestm, number of 
viii

iterations to model convergence; b) rankR, Spearman rank correlation of observed nonzero data vs. predicted values. See also Tables 10 and 11 and Appendix E. ...................56

Figure 6CD. Comparison of model performance metrics for the best models (the final selected 

model out of the two compared) for each species/season combination. c) AUC, area under 

the receiver operating characteristic curve calculated for all data categorized as 0 or >0; d) 

AUC_nz, AUC calculated for non-zero data categorized as < the median or > the median. 

See also Tables 10 and 11 and Appendix E......................................................................57

Figure 6EF. Comparison of model performance metrics for the best models (the final selected 

model out of the two compared) for each species/season combination. e) 

MedianAE_nz_rel, median absolute error for non-zero data relative to their mean; f) 

MedianAE_nz_CV_rel, median absolute error for non-zero data relative to their mean 

during cross-validation tuning. See also Tables 10 and 11 and Appendix E. .....................58

Figure 6GH. Comparison of model performance metrics for the best models (the final selected 

model out of the two compared) for each species/season combination. g) 

MedianBias_nz_rel, median error for non-zero data relative to their mean; h) 

MedianBias_nz_rel, median error for non-zero data relative to their mean during crossvalidation tuning. See also Tables 10 and 11 and Appendix E...........................................59

Figure 6IJ. Comparison of model performance metrics for the best models (the final selected 

model out of the two compared) for each species/season combination. i) CRPS_0, Brier 

score (occupancy); j) CRPS_0_CV, Brier score during cross-validation tuning. See also 

Tables 10 and 11 and Appendix E.....................................................................................60

Figure 6KL. Comparison of model performance metrics for the best models (the final selected 

model out of the two compared) for each species/season combination. k) CRPS_Zinf, 

thresholded continuous rank probability score (CRPS); l) CRPS_Zinf_CV, CRPS during 

cross-validation tuning. See also Tables 10 and 11 and Appendix E.................................61

Figure 6MN. Comparison of model performance metrics for the best models (the final selected 

model out of the two compared) for each species/season combination. m) rankRG_nz, 

Gaussian rank correlation of observed non-zero data vs. predicted values; n) pde, percent 

deviance explained. See also Tables 10 and 11 and Appendix E......................................62

Figure 7.1. Receiver Operating Characteristic (ROC) curves for assessment of predictive 

accuracy of example model 7 (COTE/summer). a) ROC curve and area under the curve 

(AUC) statistic for prediction of median-thresholded non-zero data (i.e., non-zero data 

above/below median observed value), b) ROC curve and AUC statistic for prediction of 

presence/absence. For complete set of ROC curves for selected models, see Appendix F. 

For complete set of AUC statistics for selected models, see Table 11...............................63

Figure 7.2. Receiver Operating Characteristic (ROC) curves for assessment of predictive 

accuracy of example model 7 (NOGA/fall). a) ROC curve and area under the curve (AUC) 

statistic for prediction of median-thresholded non-zero data (i.e., non-zero data 

above/below median observed value), b) ROC curve and AUC statistic for prediction of 

presence/absence. For complete set of ROC curves for selected models, see Appendix F. 

For complete set of AUC statistics for selected models, see Table 11...............................64
ix

Figure 7.3. Receiver Operating Characteristic (ROC) curves for assessment of predictive 

accuracy of example model 7 (RAZO/winter). a) ROC curve and area under the curve 

(AUC) statistic for prediction of median-thresholded non-zero data (i.e., non-zero data 

above/below median observed value), b) ROC curve and AUC statistic for prediction of 

presence/absence. For complete set of ROC curves for selected models, see Appendix F. 

For complete set of AUC statistics for selected models, see Table 11...............................65

Figure 7.4. Receiver Operating Characteristic (ROC) curves for assessment of predictive 

accuracy of example model 8 (WWSC/winter). a) ROC curve and area under the curve 

(AUC) statistic for prediction of median-thresholded non-zero data (i.e., non-zero data 

above/below median observed value), b) ROC curve and AUC statistic for prediction of 

presence/absence. For complete set of ROC curves for selected models, see Appendix F. 

For complete set of AUC statistics for selected models, see Table 11...............................66

Figure 8.1. Brier score calculated at quantile thresholds of non-zero data for example model 7 

(COTE/summer). For complete set of Brier score plots for selected models, see Appendix 

G. For complete set of Brier score statistics and related thresholded continuous rank 

probability scores (CPRS) for selected models, see Table 11. ..........................................67

Figure 8.2. Brier score calculated at quantile thresholds of non-zero data for example model 7 

(NOGA/fall). For complete set of Brier score plots for selected models, see Appendix G. 

For complete set of Brier score statistics and related thresholded continuous rank 

probability scores (CPRS) for selected models, see Table 11. ..........................................67

Figure 8.3. Brier score calculated at quantile thresholds of non-zero data for example model 7 

(RAZO/winter). For complete set of Brier score plots for selected models, see Appendix G. 

For complete set of Brier score statistics and related thresholded continuous rank 

probability scores (CPRS) for selected models, see Table 11. ..........................................68

Figure 8.4. Brier score calculated at quantile thresholds of non-zero data for example model 8 

(WWSC/winter). For complete set of Brier score plots for selected models, see Appendix 

G. For complete set of Brier score statistics and related thresholded continuous rank 

probability scores (CPRS) for selected models, see Table 11. ..........................................68

Figure 9.1. Variable importance bar plots for top variables in example model 7 (COTE/summer). 

a) np component; b) mu component. For complete set of variable importance bar plots, see 

Appendix H........................................................................................................................69

Figure 9.2. Variable importance bar plots for top variables in example model 7 (NOGA/fall). a) 

np component; b) mu component. For complete set of variable importance bar plots, see 

Appendix H........................................................................................................................70

Figure 9.3. Variable importance bar plots for top variables in example model 7 (RAZO/winter). 

a) np component; b) mu component. For complete set of variable importance bar plots, see 

Appendix H........................................................................................................................71

Figure 9.4. Variable importance bar plots for top variables in example model 8 (WWSC/winter). 

a) np component; b) mu component; c) theta component. For complete set of variable 

importance bar plots, see Appendix H. ..............................................................................72
x

Figure 10. Summary of variable importance across all variables, species, and seasons modeled. 

a) np component; b) mu component; c) theta component. For complete set of variable 

importance plots, see Appendix H. ....................................................................................75

Figure 11. Variable importance for np model component: average importance of each variable 

calculated over all final selected models in each season. Note that average importance 

values have been sorted in descending order for each season, so that the order of 

variables along the x-axis varies from season to season. Also note that the species 

modeled in each season, and therefore the species included in the average for each 

seasonal panel of this plot, vary as shown in Table 2. For complete set of variable 

importance plots, see Appendix H. ....................................................................................76

Figure 12. Variable importance for mu model component: average importance of each variable 

calculated over all final selected models in each season. Note that average importance 

values have been sorted in descending order for each season, so that the order of 

variables along the x-axis varies from season to season. Also note that the species 

modeled in each season, and therefore the species included in the average for each 

seasonal panel of this plot, vary as shown in Table 2. For complete set of variable 

importance plots, see Appendix H. ....................................................................................77

Figure 13. Variable importance for theta (th) model component: average importance of each 

variable calculated over all final selected models in each season. Note that average 

importance values have been sorted in descending order for each season, so that the 

order of variables along the x-axis varies from season to season. Also note that the species 

modeled in each season, and therefore the species included in the average for each 

seasonal panel of this plot, vary as shown in Table 2. Only species for which the ZINB 

model (Model 8) was selected are included in the average for the theta component (see 

Table 10 for the selected model for each species/season combination). Predictors used in 

the theta ensemble were limited to survey variables (sid, boatplane) and an intercept. For 

complete set of variable importance plots, see Appendix H...............................................78

Figure 14.1. Selected bootstrap marginal plots for example model 7 (COTE/summer). Solid line 

is bootstrap mean and grey shading indicates +/- 1 bootstrap standard deviation. a) np 

component of model; b) mu component of model. Full sets of marginal plots in Appendix I.

..........................................................................................................................................79

Figure 14.2. Selected bootstrap marginal plots for example model 7 (NOGA/fall). Solid line is 

bootstrap mean and grey shading indicates +/- 1 bootstrap standard deviation. a) np 

component of model; b) mu component of model. Full sets of marginal plots in Appendix I.

..........................................................................................................................................80

Figure 14.3. Selected bootstrap marginal plots for example model 7 (RAZO/winter). Solid line is 

bootstrap mean and grey shading indicates +/- 1 bootstrap standard deviation. a) np 

component of model b) mu component of model. Full sets of marginal plots in Appendix I.

..........................................................................................................................................81

Figure 14.4. Selected bootstrap marginal plots for example model 8 (WWSC/winter). Solid line 

is bootstrap mean and grey shading indicates +/- 1 bootstrap standard deviation. a) np 

component of model; b) mu component of model. Full sets of marginal plots in Appendix I.

..........................................................................................................................................82
xi

Figure 15.1. Selected two-way interaction plots for example model 7 (COTE/summer). a) np 

component of model; b) mu component of model. Full sets of two-way interaction plots in 

Appendix J. .......................................................................................................................83

Figure 15.2. Selected two-way interaction plots for example model 7 (NOGA/fall). a) np 

component of model; b) mu component of model. Full sets of two-way interaction plots in 

Appendix J. .......................................................................................................................84

Figure 15.3. Selected two-way interaction plots for example model 7 (RAZO/winter) . a) np 

component of model; b) mu component of model. Full sets of two-way interaction plots in 

Appendix J. .......................................................................................................................85

Figure 15.4. Selected two-way interaction plots for example model 8 (WWSC/winter). a) np 

component of model; b) mu component of model. Full sets of two-way interaction plots in 

Appendix J. .......................................................................................................................86

Figure 16.1 ABCD. Relative occupancy prediction maps for example model 7 (COTE/summer) 

from full model (a) and bootstrap (b,c) with bootstrap uncertainty map (d). For complete set 

of prediction and uncertainty maps, see Appendix K. ........................................................87

Figure 16.1 EFGH. Relative abundance prediction maps for example model 7 (COTE/summer) 

from full model (e) and bootstrap (f,g) with bootstrap uncertainty map (h). For complete set 

of prediction and uncertainty maps, see Appendix K. ........................................................88

Figure 16.1 I. Average count per 10 x 10 km grid cell (COTE/summer)....................................89

Figure 16.2 ABCD. Relative occupancy prediction maps for example model 7 (NOGA/fall) from 

full model (a) and bootstrap (b,c) with bootstrap uncertainty map (d). For complete set of 

prediction and uncertainty maps, see Appendix K. ............................................................90

Figure 16.2 EFGH. Relative abundance prediction maps for example model 7 (NOGA/fall) from 

full model (e) and bootstrap (f,g) with bootstrap uncertainty map (h). For complete set of 

prediction and uncertainty maps, see Appendix K. ............................................................91

Figure 16.2 I. Average count per 10 x 10 km grid cell (NOGA/fall). ...........................................92

Figure 16.3 ABCD. Relative occupancy prediction maps for example model 7 (RAZO/winter) 

from full model (a) and bootstrap (b,c) with bootstrap uncertainty map (d). For complete set 

of prediction and uncertainty maps, see Appendix K. ........................................................93

Figure 16.3 EFGH. Relative abundance prediction maps for example model 7 (RAZO/winter) 

from full model (e) and bootstrap (f,g) with bootstrap uncertainty map (h). For complete set 

of prediction and uncertainty maps, see Appendix K. ........................................................94

Figure 16.3 I. Average count per 10 x 10 km grid cell (RAZO/winter). .......................................95

Figure 16.4 ABCD. Relative occupancy prediction maps for example model 8 (WWSC/winter) 

from full model (a) and bootstrap (b,c) with bootstrap uncertainty map (d). For complete set 

of prediction and uncertainty maps, see Appendix K. ........................................................96
xii

Figure 16.4 EFGH. Relative abundance prediction maps for example model 8 (WWSC/winter) 

from full model (e) and bootstrap (f,g) with bootstrap uncertainty map (h). For complete set 

of prediction and uncertainty maps, see Appendix K. ........................................................97

Figure 16.4 I. Average count per 10 x 10 km grid cell (WWSC/winter). .....................................98

Figure 17.1A. Annual average relative occupancy prediction for example species 1 (COTE). For 

complete set of annual prediction maps, see Appendix L. .................................................99

Figure 17.1B. Annual average relative abundance prediction for example species 1 (COTE). For 

complete set of annual prediction maps, see Appendix L. ...............................................100

Figure 17.2A. Annual average relative occupancy prediction for example species 2 (NOGA). For 

complete set of annual prediction maps, see Appendix L. ...............................................101

Figure 17.2B. Annual average relative abundance prediction for example species 2 (NOGA). 

For complete set of annual prediction maps, see Appendix L. .........................................102

Figure 17.3A. Annual average relative occupancy prediction for example species 3 (RAZO). For 

complete set of annual prediction maps, see Appendix L. ..............................................103

Figure 17.3B. Annual average relative abundance prediction for example species 3 (RAZO). For 

complete set of annual prediction maps, see Appendix L. ...............................................104

Figure 17.4A. Annual average relative occupancy prediction for example species 4 (WWSC). 

For complete set of annual prediction maps, see Appendix L..........................................105

Figure 17.4B. Annual average relative abundance prediction for example species 4 

(WWSC/winter). For complete set of annual prediction maps, see Appendix L................106

Figure 18A. Annual average relative occupancy prediction for group 1 (nearshore species). For 

complete set of group prediction maps, see Appendix M. For group definitions, see Table 

12....................................................................................................................................107

Figure 18B. Annual average relative abundance prediction for group 1 (nearshore species). For 

complete set of group prediction maps, see Appendix M. For group definitions, see Table 

12....................................................................................................................................108

Figure 19A. Annual average relative occupancy prediction for group 2 (pelagic species). For 

complete set of group prediction maps, see Appendix M. For group definitions, see Table 

12....................................................................................................................................109

Figure 19B. Annual average relative abundance prediction for group 2 (pelagic species). For 

complete set of group prediction maps, see Appendix M. For group definitions, see Table 

12....................................................................................................................................110

Figure 20A. Annual average relative occupancy prediction for group 3 (gulls and gannets). For 

complete set of group prediction maps, see Appendix M. For group definitions, see Table 

12....................................................................................................................................111
xiii

Figure 20B. Annual average relative abundance prediction for group 3 (gulls and gannets). For 

complete set of group prediction maps, see Appendix M. For group definitions, see Table 

12....................................................................................................................................112

Figure 21. Intensity of winter survey effort and predicted bootstrap median relative abundance of 

RAZO in winter................................................................................................................113
xiv

List of Tables

Table 1. Datasets analyzed. Data from these surveys were extracted from the USGS Avian 

Compendium Database (O’Connell et al. 2009) and standardized to 15-minute ship survey 

equivalent transect segments as described in the text. Transect counts reflect all transect 

segments within the study area, including some records that were later excluded from 

analysis due to incomplete records (missing predictor data)..............................................21

Table 2. List of species analyzed. Four-letter species codes in the first column are generally 

used in place of the full common or scientific name throughout this report. For each 

season, only species with >=50 sightings were modeled. Number of sightings includes 

some records that were later excluded from analysis due to incomplete records (missing 

predictor data). Shaded cells indicate model runs that were successfully completed.........23

Table 3. Predictor variables used in model with codenames, resolutions, and sources. ............25

Table 4a. Predictor correlation table (spring). Pairwise Spearman rank correlation coefficients 

for spatial predictor variables. High correlations are highlighted (|tau|>0.7 in yellow, 

|tau|>0.8 in orange, |tau|>0.9 in red)..................................................................................28

Table 4b. Predictor correlation table (summer). Pairwise Spearman rank correlation coefficients 

for spatial predictor variables. High correlations are highlighted (|tau|>0.7 in yellow, 

|tau|>0.8 in orange, |tau|>0.9 in red)..................................................................................29

Table 4c. Predictor correlation table (fall). Pairwise Spearman rank correlation coefficients for 

spatial predictor variables. High correlations are highlighted (|tau|>0.7 in yellow, |tau|>0.8 in 

orange, |tau|>0.9 in red). ...................................................................................................30

Table 4d. Predictor correlation table (winter). Predictor correlation table (fall). Pairwise 

Spearman rank correlation coefficients for spatial predictor variables. High correlations are 

highlighted (|tau|>0.7 in yellow, |tau|>0.8 in orange, |tau|>0.9 in red). ...............................31

Table 5. Base-learners employed in the boosted generalized additive modelling framework. 

Base-learner names are from the ‘mboost’ package for R (Hothorn et al. 2014; R Core 

Team. 2014), and predictor variable names are defined in Table 3. ..................................32

Table 6. Model performance metrics. In this report performance metrics are presented for crossvalidation tuning of the number of boosting iterations and for the final fitted models. The 

former set of performance metrics are indicated with the suffix ‘_CV’ elsewhere in the 

report. Sometimes the suffix ‘_rel’ is used to indicate that the performance metric is 

expressed relative to the mean of the data........................................................................33

Table 7. Models evaluated for each species/season. ................................................................34

Table 8. Performance metric thresholds used to define model performance categories. 

Performance metrics are defined in Table 6. .....................................................................35

Table 9. Model selection table. The model selection process is illustrated for 4 example 

species/season combinations. Two models were evaluated for each species/season 

combination (Table 7). Models are sorted in descending order of performance in terms of 

the thresholded continuous rank probability score (CRPS) from cross-validation tuning of 
xv

mstop (column marked with an * below). The selected model is in bold font, and all 

subsequent analyses in this report use this selected model. Other model performance 

metrics are also provided (see Table 6 for definitions). Note that risk is not directly 

comparable across model families (ZIP vs. ZINB). For similar model selection information 

for all species/season combinations evaluated, please see the Digital Data Package 

(contents listed in Appendix N). .........................................................................................36

Table 10. Final selected models for all species/season combination evaluated, with number of 

transect segments with sightings, number of individuals sighted, proportion of transect 

segments with sightings (prevalence), and mean number of individuals per transect 

segment with sightings. Numbers of sightings and individuals exclude incomplete records 

(missing predictor data) that were excluded from the analysis, and therefore may differ 

from Tables 1 and 2. .........................................................................................................37

Table 11. Best models with model performance metrics. All model performance metrics were 

calculated on the full dataset, except for columns divided into ‘Fit’ and ‘CV’, which denote 

metrics calculated separately for the full dataset and for out-of-bag data during crossvalidation tuning of mstop, respectively. The overall model performance category is the 

rounded average of performance categories across four performance metrics (PDE, AUC, 

Rank RG_nz, and MedianAE_nz_CV_rel; see Section 2.4.6). Particularly poor performance 

in terms of individual performance metrics is indicated in red. ...........................................41

Table 12. Groups of species with similar spatial distributions chosen for averaginga.................46
xvi

List of Appendices (digital)

Appendix A. Descriptions of individual survey datasets analyzed.

Appendix B. Maps of spatial environmental predictor variables used in spatial predictive 

modeling.

Appendix C. Diagnostic boosting performance plots for the best model for each species and 

season. Plots show the values of 6 performance metrics (CRPS_0_CV, CRPS_Zinf_CV, 

MedianAE_nz_CV, MedianBias_nz_CV, risk, and RMSE_CV) by boosting iteration, for each of 

20 cross-validation replicates during tuning of the number of boosting iterations. Vertical red 

lines indicate the optimal number of boosting iterations determined through cross-validation.

Appendix D. Supplemental model fit tables. Supplementary information presented includes 

shrinkage (learning) rates, boosting offsets, log-likelihoods, and risk (calculated during crossvalidation) for the best model for each species and season.

Appendix E. Best model comparison plots. Performance metrics for the best model for each 

species and season.

Appendix F. Receiver operating characteristic (ROC) curve plots for the best model for each 

species and season. Two plots are presented for each model: 1) ROC curve for prediction of 

presence/absence (‘occupancy’), and 2) ROC curve for prediction of non-zero data above/below 

median observed non-zero value (‘abundance_nz’).

Appendix G. Brier score plots for the best model for each species and season. Plots show Brier 

score calculated at quantile thresholds of non-zero data.

Appendix H. Variable importance plots for each component of the best model for each species 

and season.

Appendix I. Plots of the bootstrapped marginal effects of each predictor variable in each 

component of the best model for each species and season. The solid line represents the mean 

effect and grey shading indicates +/- 1 bootstrapped standard deviation.

Appendix J. Plots of two-way interactions between the effects of predictor variables in each 

component of the best model for each species and season.

Appendix K. Maps of seasonal predicted relative occupancy and abundance (and their 

uncertainty) for each species and season modeled.

Appendix L. Maps of average annual predicted relative occupancy and abundance (and their 

CVs) for each species modeled.

Appendix M. Maps of average annual predicted relative occupancy and abundance for three 

species groups (Table 12).

Appendix N. Description (manifest) of the digital data deliverable accompanying this report.
xvii

Abbreviations and Acronyms

AE absolute error

AUC area under the ROC curve

BOEM Bureau of Ocean Energy Management

COTE Common Tern

CRPS continuous rank probability score

CV cross-validation

GLM generalized linear model

NOGA Northern Gannet

OCS Outer Continental Shelf

PDE percent deviance explained

RAZO Razorbill

RMSE root mean square error

ROC receiver operating characteristic

USGS United States Geological Survey

USFWS United States Fish and Wildlife Service

WWSC White-winged Scoter

ZINB zero-inflated negative binomial

ZIP zero-inflated Poisson

* Please see Table 2 for full list of species codes and Table 6 for full list of model performance metric 

codes 
1

1. Introduction

Marine birds spend much of their time in coastal waters and on the open ocean. As a result, these species 

have the potential to be affected by human activities in the marine environment such as offshore wind 

energy development. A prerequisite for quantifying that potential is knowledge of the spatial distributions 

of marine birds at sea. This report describes the first phase of a project aimed at producing maps of the 

spatial distributions of marine bird species in U.S. Atlantic Outer Continental Shelf waters (Fig. 1) that 

can be used to inform marine spatial planning in the region.

Some of the best available information about the at-sea distributions of marine birds comes from visual 

sighting surveys conducted aboard boats and aircraft. For U.S. Atlantic Outer Continental Shelf waters 

many data from past sighting surveys have been compiled in the ‘Compendium of Avian Occurrence 

Information for the Continental Shelf waters along the Atlantic Coast of the U.S.’, hereinafter referred to 

as the ‘Avian Compendium’ (O’Connell et al. 2009). The Avian Compendium database was developed by 

the U.S. Geological Survey (USGS) Patuxent Wildlife Research Center and is currently maintained by the 

U.S. Fish and Wildlife Service (USFWS). The project described here analyzed sighting data from the 

Avian Compendium to derive maps of the spatial distributions of 40 marine bird species in U.S. Atlantic 

Outer Continental Shelf waters from Florida to Maine.

The data in the Avian Compendium represent numerous surveys over more than three decades. Survey 

coverage and intensity is highly variable geographically (Fig. 2) and temporally, especially between 

individual datasets. Furthermore, a wide range of survey platforms, observers, and protocols were used. 

This heterogeneity complicates the quantification of the at-sea distribution of marine birds from these 

data, and biases simple data summary approaches. To deal with this heterogeneity, the project described 

here employed spatial predictive modeling. An ensemble machine-learning technique was used to model 

counts of each species as a function of multiple predictor variables while accounting for heterogeneous 

survey effort. The fitted models were then used to predict the spatial distribution of relative occurrence 

and abundance of each species throughout the study area.

The distributions of marine birds at sea are a result of interactions between their behavior (e.g., foraging) 

and the environment. Atmospheric and oceanographic features and processes across a range of spatial and 

temporal scales influence the environmental conditions and prey availability experienced by marine birds, 

and thus ultimately determine their at-sea distributions. The spatial predictive modeling framework 

employed here relied on a wide suite of spatial and temporal environmental predictor variables to explain 

and predict the distributions of marine birds. In particular, static environmental variables (e.g., 

bathymetry) and long-term climatologies of dynamic environmental variables (e.g., sea surface 

temperature) were considered to explain spatial patterns of relative occurrence and abundance.

The project described here is designed to provide broad-scale spatial information that can be used to guide 

future data collection efforts and aid marine spatial planning in the region. This report describes 

preliminary results from the first phase of the project. A second phase of the project is currently underway 

that will expand, refine, and improve the modeling and results presented here. It is important to note that 

the results presented in this report represent the spatial distributions of birds averaged over time. The 

project was not designed to provide precise predictions of the absolute number of individuals of a given 

species that would be expected in a specific location at a specific time. The project was also not designed 

to determine the ecological drivers of marine bird distributions, although the results provide related

hypotheses for future research.
2

2. Methods

2.1 Overview

A statistical modeling framework was used to relate bird sighting data from historical surveys to a range 

of temporal and spatial environmental predictor variables. The estimated relationships between the 

relative occurrence and abundance of birds and the predictor variables were then used to predict the 

spatial distributions of birds across the entire study area. Separate models were developed for each 

combination of species and season for which there were sufficient data. Seasons reflected major 

transitions in environmental conditions in the study region: spring (March-May), summer (June-August), 

fall (September-November), and winter (December-February).

2.2 Survey data

Bird sighting data were derived from a large relational database (hereinafter referred to as the ‘Avian 

Compendium’) containing observational datasets on marine birds along the Atlantic Coast of the United 

States (see O’Connell et al. 2009 for more details). These datasets were collected by a range of entities 

including government agencies, non-governmental organizations, academic researchers, and other 

individuals. Developed and previously maintained by USGS Patuxent Wildlife Research Center, the 

Avian Compendium is now maintained by USFWS through an intra-agency agreement with BOEM 

(M14PG00014).

We analyzed science-quality, geographically-referenced, visual sighting data from the Avian 

Compendium. The original data took the form of species-specific counts of birds along boat-based or 

aerial strip transects. Counts were sometimes recorded continuously, and other times were aggregated into 

recording bins of mostly 10 or 15 minute duration, with the majority (79%) being 15 minutes. Binned 

data were only from boat surveys. To standardize effort across datasets, continuously recorded data were 

discretized into transect segments 4.63 km long corresponding to the distance that would be covered in 15 

minutes at a speed of 10 knots, which is a typical survey speed. Counts for each species were summed 

within each transect segment. We excluded data from discrete recording bins with duration <10 minutes, 

and we excluded transect segments <3.086 km that arose from the discretization of continuous data. Thus, 

our unit of analysis was a transect segment, the majority of which represented 15 minutes or 4.63 km of 

survey effort, and no less than 10 minutes or 3.086 km of effort. The response data were the numbers of 

individual birds of each species counted on each transect segment.

We analyzed 75 datasets from the Avian Compendium representing 111,776 transect segments that had 

survey effort within our study area (Table 1, Fig. 2, Appendix A). The datasets spanned 1978-2014 with 

most survey effort occurring from 1978-1988 and from 2002 onward. The datasets with the largest 

combined sample size and widest geographic coverage were collected by Manomet Bird Observatory in 

coordination with the National Marine Fisheries Service and other cruises between 1978 and 1988 

(datasets CSAP and NOAAMBO7880). More recent surveys by USFWS as part of the Atlantic Marine 

Assessment Program for Protected Species also covered the entire U.S. Atlantic Coast (datasets 

AMAPPS_FWS). Other multi-year survey efforts covered large sections of the coast including NOAA 

ecosystem monitoring cruises from North Carolina to the Gulf of Maine (datasets EcoMon) and pelagic 

surveys off Georgia, South Carolina, and Florida (dataset GeorgiaPelagic). The remaining datasets were 

more localized, often from New England and the Gulf of Maine, but sometimes had large sample sizes 

over multiple years (e.g., datasets CapeWind, HerringAcoustic, and MassAudNanAerial).
3

For analysis we considered species-season combinations that had ≥50 transect segments with sightings of 

≥1 individual. By this criterion 53 species and 144 species-season combinations were amenable to 

analysis representing sightings of 2,622,023 individual birds. The five species with the greatest numbers 

of transect segments with sightings in individual seasons were Wilson’s Storm-Petrel in summer, Herring 

Gull in fall, Northern Gannet in winter, Great Shearwater in fall, and Great Black-backed Gull in fall. For 

this first phase of the project, not all 144 species-season combinations could be modeled in the allotted 

time, so a prioritized subset of 40 species (118 species-season combinations) were analyzed (Table 2).

Prioritization considered Atlantic Marine Bird Conservation Cooperative Species of Concern, US 

Endangered Species Act listings, USFWS Birds of Conservation Concern (Florida, Southeast, MidAtlantic/New England, and Gulf of Maine regions), state endangered species legislation listings, species 

included in state Ocean Plans (NY, MA, or RI), Northeast Regional Ocean Planning Expert Working 

Group input (NROC 2016), and relative vulnerability to offshore wind energy projects (Robinson 

Willmott et al. 2013). For 5 of the modeled lower-priority species (Bonaparte’s Gull, Royal Tern, Manx 

Shearwater, Common Murre, and Band-rumped Storm-Petrel) there was insufficient time to conduct 

bootstrapping (see Section 2.4.9), so uncertainty estimates are not presented for those models. Also, some 

seasonal models failed for 3 species (Common Eider – spring, summer, fall; Red-necked Phalarope –

spring; and Red Phalarope – fall). These errors will be investigated further in Phase II of this project. 

Nevertheless, the models for the seasons with the greatest numbers of sightings of these 3 species were 

successful.

2.3 Predictor variables

A wide range of predictor variables were used to model variation in the number of birds sighted per 

transect segment and to predict the spatial distributions of birds throughout the study area (Table 3, Fig. 3, 

Appendix B). Predictor variables fell into one of six categories: survey, temporal, geographic, terrain, 

physical oceanographic and atmospheric, and biological.

Survey predictor variables were designed to account for variation in counts arising from heterogeneity in 

the type of survey platform, characteristics of the survey platform (e.g., observation height), observer 

identity and expertise, species focus, and sighting conditions. These factors influence the probability that 

individual birds will be detected and correctly identified to the species level. Of these factors, only the 

type of survey platform (boat or plane) was consistently recorded in all datasets, and thus was directly 

usable as a predictor variable. We attempted to account for the effects of the remaining factors through 

two random-effect predictor variables representing survey identity (ID) and transect ID, respectively. The 

exact definition of transect ID differed somewhat between datasets, but unique transect identities 

generally represented pre-defined survey transects or individual days of effort. The transect ID predictor 

variable also allowed for an accounting of some of the variation in counts arising from variation in survey 

effort (distance and strip width) among transect segments.

Temporal predictor variables were designed to account for variation in counts over time. Day of the year 

was used to account for changes in the numbers of birds in the study area over time within a season, for 

example arising from migratory movements in and out of the study area. Year was used to account for 

changes in the number of birds in the study area across years, for example arising from changes in 

population abundance or distributional shifts. Effects of day of the year and year were modelled as 

smooth continuous changes over time. Four climate indices (Table 3) were also included as temporal 

predictor variables to account for variation in counts across years arising from linkages between the 

environment and population abundance and distribution. For each climate index two values were included 
4

as predictor variables: the value for the month and year of a given transect segment and the value for the 

same month one year previous.

Geographic predictor variables were designed to account for variation in counts arising from spatial 

location per se. Projected longitude and latitude were included as predictor variables and their effects 

were modeled two ways. The first longitude-latitude predictor term allowed for smooth changes in 

numbers across the study area arising from spatial factors not captured by the other predictor variables 

(e.g., colonization history). The second longitude-latitude predictor term was formulated using radial 

basis functions with the intent of capturing some of the spatial autocorrelation in the data after accounting 

for the effects of other predictor variables. Distance to land and absolute distance to shelf break (200 m 

isobath) were also included as geographic predictor variables.

Terrain variables were designed to account for variation in counts arising from the direct and indirect 

effects of bathymetry on bird distributions. A depth predictor variable was developed by combining 

information from four different bathymetric datasets (Table 3). Other terrain variables were derived from 

depth including slope, slope of slope, and planform and profile curvature.

Physical oceanographic and atmospheric predictor variables were designed to account for variation in 

counts arising from the direct and indirect effects of the physical state and dynamics of the ocean and air 

above the ocean. Sixteen physical oceanographic and atmospheric predictor variables were developed 

from a range of data sources (Table 3). Remote sensing data were used to characterize sea surface height, 

temperature, turbidity, and wind stress. Other variables were derived from the remotely sensed variables 

including sea surface height and temperature variability, probabilities of cyclonic and anticyclonic eddy 

rings, probability of sea surface temperature fronts, wind divergence, and an index of upwelling. 

Estimates from a data-assimilating ocean dynamics model were used to characterize water currents, and 

divergence and vorticity were derived from current velocities.

One biological predictor variable was included to account for variation in counts arising from the direct 

and indirect effects of ocean productivity. Remote sensing data were used to characterize sea surface 

chlorophyll-a concentration.

All of the physical oceanographic and atmospheric and biological variables that we considered are 

dynamic. We formulated these predictor variables to characterize long-term spatial patterns in average 

values and variability. Long data time series ranging from 11-22 years were used (Table 3). To 

characterize average values, monthly mean or median climatologies across years were developed and then 

integrated to create seasonal climatologies. To characterize variability, standard deviations or 

probabilities (frequencies) were calculated from the native temporal resolution of the corresponding 

predictor variables.

Geographic, terrain, physical oceanographic and atmospheric, and biological predictor variables were 

spatially explicit. Each variable was calculated on a standard study grid with a spatial resolution of 2 km 

and an oblique Mercator projected coordinate system. When the native spatial resolution of a predictor 

variable was finer than that of the study grid, predictor values were averaged within study grid cells. 

When the native spatial resolution of a predictor variable was similar to or coarser than that of the study 

grid, bilinear interpolation was used to derive predictor values at the center of study grid cells. Each 

survey transect segment was matched to the predictor variable values from the study grid cell that 

contained the midpoint of that segment.
5

Some of the spatially explicit predictor variables were highly correlated with each other (Table 4). 

Predictor variables were chosen to avoid correlations >0.9, although a few pairs still had correlations 

>0.9. All but one of the correlations >0.9 involved spatial coordinate variables that were a key structural 

component of our model. The other correlation >0.9 was between the mean and standard deviation of sea 

surface height during the summer. Because of the high correlations between some predictor variables, 

inferences regarding relative variable importance should be made with caution. The accuracy of 

predictions should be less affected by collinearity among predictor variables.

2.4 Statistical modelling framework

A boosted generalized additive modelling framework (Bühlmann and Hothorn 2007; Hofner et al. 2012) 

was used to estimate relationships between the numbers of birds counted per transect segment and the 

predictor variables (Fig. 4). Those relationships were then used to predict the relative occurrence and 

abundance of each species throughout the study area in each season. Our main objective was to provide 

accurate predictions, so we chose a modelling framework that allowed for flexible relationships and 

multi-way interactions between predictor variables while accounting for sampling heterogeneity between 

and within datasets.

2.4.1 Likelihoods and model components

The number of individuals of a given species counted per transect segment was modelled using zeroinflated Poisson (Eq. 1) and zero-inflated negative binomial likelihoods (Eq. 2) to account for the 

overdispersed nature of the count data. Each component/parameter of the likelihood was modelled as a 

separate function of the predictor variables (Schmid et al. 2008; Mayr et al. 2012). For the zero-inflated 

Poisson likelihood, the two model components were the probability of an ‘extra’ zero (p) and the mean of 

the Poisson distribution (μ):

[1] L(p, μ; y) = ∏[p + (1 − p)e

−μ]

Iyi=0

[(1 − p)

μ

yie−μ

yi!

]

Iyi>0

n

i=1

The same components were modelled for the zero-inflated negative binomial likelihood in addition to the 

dispersion parameter of the negative binomial distribution (θ):

[2] L(p, μ, θ; y) = ∏[p + (1 − p) (

θ

θ + μ

)

θ

]

Iy n i=0

i=1

[(1 − p)

Γ(yi + θ)

yi! Γ(θ)

(

θ

θ + μ

)

θ

(

μ

θ + μ

)

yi

]

Iyi>0

The probability of an extra zero was modelled on the logit scale (symbolized by np) while the mean of the 

Poisson/negative binomial distribution and the dispersion parameter of the negative binomial distribution 

were modelled on the log scale (mu and th, respectively).

In Eqs 1 and 2, yi represents the total count for transect segment i, n represents the total number of 

segments, and Iyi=0 and Iyi>0 are indicators of whether yi is equal to or greater than zero, respectively 

(I=1 when the condition is true and I=0 when the condition is false).

2.4.2 Base-learners

Within the boosting framework, each model component was essentially modelled as a function of an 

ensemble of ‘base-learners’. Each base-learner represented a specific functional relationship between a 

model component and one or more predictor variables. We utilized a suite of base-learners each 
6

representing different predictor variables, and different sets of base-learners were employed for different 

model components (Table 5).

All spatially explicit predictor variables except geographic coordinates were included together in a single 

tree base-learner. The trees for that learner had a maximum depth of 5, which allowed for interacting 

effects among the spatially explicit predictor variables. Projected longitude and latitude appeared in two 

base learners, and those variables always entered the model as a pair. The remaining survey and temporal 

predictor variables entered the model individually, either through their own base-learners or in the case of 

climate indexes one at a time through a tree base-learner with a maximum depth of 1. Thus, our model 

structure did not allow for interactions between temporal and spatial predictor variables.

2.4.3 Stochastic gradient boosting

Stochastic gradient boosting was used to fit models whereby a sub-sample of the data was fitted in each 

boosting iteration (Friedman 2002). Rather than resampling the data for each boosting iteration, a set of 

25 or 50 random samples was created before boosting, and one sample was randomly drawn from this set 

for each boosting iteration. Root mean square error (RMSE) was used to select the base-learner that gave 

the best fit to the gradient (all data) in each boosting iteration.

2.4.4 Boosting offsets

Model component estimates were initialized (‘offset’ in boosting terminology; Hofner et al. 2012) by 

conducting a preliminary generalized linear model (GLM) analysis. For that analysis, predictor variables 

were first reduced through principal component and cluster analyses to a smaller set of derived predictors. 

Those new predictors were then discretized into different numbers of classes. For each number of classes 

a GLM with a zero-inflated negative binomial likelihood was fit, and the mean estimates for each model 

component were calculated. Model component estimates were then averaged across the fitted models with 

the different numbers of predictor classes, weighted by the Akaike Information Criterion for those 

models.

2.4.5 Tuning of shrinkage rate and number of boosting iterations

A stratified (by transect ID) k-fold cross-validation approach was used to determine values for the 

shrinkage rate (nu) and number of boosting iterations (mstop) that resulted in the best predictive 

performance. The shrinkage rate was tuned first by fixing the number of boosting iterations and 

evaluating out-of-bag model performance in terms of the thresholded continuous rank probability score 

(CRPS_Zinf; Table 6) for different shrinkage rates. The number of boosting iterations was tuned second 

by fixing the shrinkage rate and evaluating out-of-bag model performance in terms of the negative loglikelihood. The number of boosting iterations at which performance was maximized was averaged across 

cross-validation samples (excluding the top and bottom 5%) and used as the number of boosting iterations 

for the final model fitting. If the number of boosting iterations was less than or greater than specified 

values, the shrinkage rate was decreased or increased, respectively, and the number of boosting iterations 

was tuned again. We allowed for a maximum of 8000 boosting iterations, so models with that number of 

boosting iterations should be interpreted with caution as their performance may have improved with 

additional boosting iterations. A suite of cross-validation performance metrics were calculated during the 

tuning of mstop (Table 6).

2.4.6 Model selection and performance

The performance of each of the two fitted models for each species-season combination (Table 7) was 

evaluated from a suite of performance metrics (Table 6). Cross-validation performance during the tuning 
7

of mstop in terms of the thresholded continuous rank probability score (CRPS_Zinf_CV) was used to 

select either the zero-inflated Poisson or the zero-inflated negative binomial model as the best final model 

for each species and season.

The final model for each species and season was assigned an overall model performance category on the 

basis of four of the performance metrics (Table 8). Performance categories were defined for each of these 

performance metrics and assigned a numeric code (5=highest to 1=lowest; Table 8). The performance of 

each final model was assigned an overall performance category equal to the average performance across 

these four performance metrics. Model performance is displayed on each map figure using a ‘badge’.

It is important to recognize that the model performance metrics and badge mainly reflect the statistical fit 

of the models to the data. They reflect only the data that were analyzed, and they do not reflect the quality 

of model predictions away from the data. For example, the survey data did not cover everywhere within 

the study area, so some model predictions are essentially interpolations/extrapolations from data in other 

parts of the study area. The accuracy of those predictions is not necessarily reflected by the model 

performance metrics. Nevertheless, the performance metrics and badge give an indication of how 

accurately a model was able to predict the observed data, and good performance provides a measure of 

confidence in the modelled distributions, especially within the temporal and spatial coverage of the 

observed survey data.

As a second assessment of overall model quality the maps were reviewed by one of us (TPW), a marine 

bird ecologist with substantial knowledge of and firsthand experience with the study area and species. For 

each species and season the correspondence between the modeled distributions of relative occurrence and 

abundance and independent expectations was assigned a quality class: ‘good’, ‘fair’, or ‘poor’. This 

expert evaluation focused on the bootstrap median model predictions.

2.4.7 Spatial prediction

The final fitted model for each species and season was used to predict relative occurrence and abundance 

throughout the study area. Relative occurrence was defined as the probability of observing ≥1 individuals 

on a transect segment, and relative abundance was defined as the mean number of individuals per transect 

segment. Both relative occurrence and abundance integrated the zero-inflated and Poisson/negative 

binomial components of the likelihood.

Spatially explicit predicted values were calculated for each cell of the study grid from the values of the 

spatially explicit predictor variables for that cell. Thus, the predicted relative occurrence and abundance in 

a given grid cell correspond to predictions for a transect segment whose mid-point falls within that grid 

cell. All other predictor variables except year were set to their mean values. Three different spatial 

predictions were made with respect to the year predictor: 1) prediction for the mean year, 2) the average 

of the predictions for each year, and 3) the average of the predictions for each of the last 10 years.

We excluded predictions outside of the observed geographic range of sightings for each species by 

masking the spatial predictions. First, a ‘traveling salesperson’ algorithm was used to connect the 

midpoints of all of the transect segments on which a given species was sighted in a given season (Hahsler 

and Hornik 2015). The connecting lines were then buffered with a radius of 100 km and predictions 

outside of the buffer area were omitted. An annual mask was derived for each species from the union of 

the seasonal buffer areas.
8

2.4.8 Variable importance

While our primary objective was not to determine the ecological drivers and mechanisms behind the 

spatial distributions of marine bird species in the study area, our model results do provide some indication 

of which variables were most useful for predicting those distributions. Those variables may provide 

useful starting points for future studies aimed more at ecological inference.

We calculated the relative importance of a given predictor variable in the final fitted models by summing 

the decrease in the negative log-likelihood in each boosting iteration attributable to that predictor variable. 

Thus, variable importance reflects the frequency with which a given predictor variable occurred in the 

selected base-learners across boosting iterations and that variable’s ability to explain variation in the data 

when it was selected. When multiple predictor variables occurred in the selected base-learner for a given 

boosting iteration, the decrease in the negative log-likelihood was divided evenly among those predictor 

variables. Relative variable importance was re-scaled so that it summed to 1 across predictor variables.

2.4.9 Uncertainty

Uncertainty in model predictions was estimated using a non-parametric bootstrapping framework. For 

each bootstrap iteration, the set of unique transect IDs was resampled with replacement, and the data for 

each transect ID were assigned weights proportional to the frequency of that ID in the sample. These data 

weights were then applied when fitting the model during that bootstrap iteration. Predictor variables that 

were not included in the final model were excluded from the bootstrap analysis. Two hundred bootstrap 

iterations were conducted producing a sample of predictions from which we calculated quantiles and 

confidence intervals to characterize uncertainty in the predictions.

2.4.10 Implementation

The analysis was coded in R (R Core Team 2014) and relied on multiple existing contributed packages 

(e.g., mboost; Hothorn et al. 2014).

2.5 Map display

Model spatial predictions are displayed as maps (Figs 16-20 and Appendices K-M). A color spectrum is 

employed to visualize relative occupancy and abundance ranging from blue (lower values) to red (higher 

values). The number ranges corresponding to each color are indicated in the map legends. The break 

points between the number ranges were evenly distributed on one of three scales: arithmetic, natural log, 

or cumulative distribution. The scale chosen for each map depended on the distribution of model 

predictions across the study area.

2.6 Warning regarding potentially spurious spatial predictions

A bug in the computer code used for the analysis presented here resulted in spurious spatial patterns in 

some of the predictions. Essentially, one of the spatial coordinate predictors was scaled incorrectly when 

making spatial predictions, which sometimes distorted spatial patterns. When present, this distortion is 

evident as a dominant east-west trend in predicted occupancy and abundance (i.e., vertical banding in the 

maps). It is difficult to quantify the amount of distortion in the predictions for any given model, but model 

test results suggest that the potential for distortion was greatest in areas with few survey data or sightings 

and when the relative importance of the spatial coordinate predictor variables in a model was high.

Predictions in areas with a lot of survey data and sightings were less impacted by the bug. The calculated 

model performance metrics (see Section 2.4.6) reflect any distortion in predictions, so good performance 

indicates that the model predictions more closely matched the observed data in areas with survey effort.
9

The maps of predicted relative occurrence and abundance for Common Tern (Fig 16.1A-H) illustrate the 

effect of this bug. Predictions nearshore where most Common Tern were sighted (Fig. 16.1I) were a good 

representation of the distribution of Common Tern (see Section 3.4.1.1). The model performance metrics 

reflected this correspondence as did the expert assessment of model quality (Table 11; overall 

performance category = 4, expert assessment = fair). However, in offshore areas where there were fewer 

survey data and sightings the predictions of relative occurrence and abundance exhibit a vertical banding 

pattern with a strong east-west gradient that is unrealistic.

All predictions in areas with few survey data (Fig. 2B) or sightings should be interpreted with caution, 

and predictions in these areas that exhibit a vertical banding pattern are likely an artifact of the bug. The 

bug has been corrected for the second phase of the project.

3. Results

3.1 Model selection and performance

3.1.1 Example species-season combinations

Detailed model selection and performance results are presented here for four example species-season 

combinations: Common Tern (summer), Northern Gannet (fall), Razorbill (winter), and White-winged 

Scoter (winter). We chose these examples to illustrate: 1) offshore distribution of a seasonal breeder 

(Common Tern); concentrated migratory patterns (Northern Gannet); and highly aggregated wintertime

distributions of diving birds with disparate feeding ecologies (Razorbill – pelagic feeder; White-winged 

Scoter – benthivore).

3.1.1.1 Model selection---The performances of the two models for each species and season (Table 7) in 

terms of multiple performance metrics (Table 6) were compared, and the ‘best’ model was selected on the 

basis of the lowest thresholded continuous rank probability score from cross validation tuning of the 

number of boosting iterations (CRPS_Zinf_CV).

Table 9 presents these model comparisons for the four example species-season combinations. The value 

of CRPS_Zinf_CV was very similar between models for a given species and season, but this was not 

always the case with other performance metrics. The zero-inflated Poisson model was selected for three 

of the example species-season combinations, while the zero-inflated negative binomial model was 

selected for White-winged scoter (winter). It is possible that the negative binomial distribution provided a 

better description of the distribution of counts for the latter, highly aggregated species, although the zeroinflated Poisson model performed better in terms of some performance metrics.

3.1.1.2 Cross validation performance during tuning of the number of boosting iterations---Figs 

5.1-5.4 illustrate cross validation performance during the tuning of the number of boosting iterations

(mstop) for the final selected models for the four example species-season combinations (Table 9). Six 

performance metrics (Table 6) were calculated with respect to out-of-bag data for each of 20 cross 

validation replicates. The negative log-likelihood (risk) was minimized to determine the optimal number 

of boosting iterations (indicated by the vertical red line in the figures).

The Brier score (CRPS_0), thresholded continuous rank probability score (CRPS_Zinf), and the negative 

log-likelihood (risk) all decreased with the number of boosting iterations indicating improving 

performance. Performance in terms of the other metrics as a function of the number of boosting iterations 

was more variable.
10

3.1.1.3 Receiver Operating Characteristic (ROC) curves---Receiver operating characteristic (ROC) 

curves and the area under them (AUC) were calculated to assess the ability of a model to classify transect 

segments with at least one sighting versus segments with no sightings (i.e., occurrence), and to classify

numbers of individuals below versus above the median count on segments with sightings.

For the four example species-season combinations (Figs 7.1-7.4), AUC statistics indicated better 

classification in terms of occurrence (0.91-0.95) than in terms of the median count (0.62-0.72).

3.1.1.4 Brier scores---Brier scores were calculated for zero and different quantiles of the observed nonzero count distribution for each species-season combination. The Brier score for a given quantile indicates 

the accuracy of the model when predicting the occurrence of a count above or below that value.

For the four example species-season combinations the Brier score decreased with increasing count values 

indicating increasing prediction accuracy (Figs 8.1-8.4). For example, the predicted probability of a count 

greater than a very high value is low, and the frequency of occurrence of a count greater than a very high 

value is also consistently low, which means accurate prediction in the sense of the Brier score as 

calculated here. Predictive accuracy would be lower near the mean or median where there is substantial 

probability of counts above and below those values.

3.1.2 All species

Table 10 presents the best models for all species-combinations. Zero-inflated negative binomial models 

were selected more frequently as the best model than zero-inflated Poisson models, but there were a 

substantial number of the latter.

Table 11 and Fig. 6 present the performance of the final selected models across all species and seasons in 

terms of a range of performance metrics (Table 6). In general, performance was highly variable among 

species and seasons. About half of the best models did not converge within 8000 boosting iterations (the 

maximum tried), and a large proportion of the remaining models may not have converged (m values close 

or equal to 6000 or 7000 possibly indicate lack of convergence) (Fig. 6A). Performance metrics 

calculated for both final fitted models and during cross-validation tuning of mstop were highly correlated 

between the two versions (‘Fit’ and ‘CV’ columns in Table 11). Also, the Brier score calculated for 

occupancy (CRPS_0) was highly correlated with the thresholded continuous rank probability score 

(CRPS_Zinf or ‘CRPS’).

3.2 Predictor variable relative importance

Our modelling framework was designed to provide the most accurate predictions. It was not designed to 

determine which environmental predictors were most ecologically relevant in determining the distribution 

of birds. Ecological inferences from the variable importance results should be cautious. Nevertheless, 

these results may suggest interesting hypotheses for future research.

3.2.1 Example species-season combinations

For the four example species-season combinations, a large number of predictor variables were ranked as 

relatively important (Figs 9.1 - 9.4). For three of these combinations the same predictor variable was 

ranked most important for both the np and mu model components, although which variable that was 

varied among the combinations. The model for Northern Gannet (fall) had the most skewed distribution 

of variable importance with day of the year accounting for about 40-60% of the total variable importance 

for both the np and mu components.
11

3.2.2 All species and seasons

The most important predictor variables varied across species (Fig. 10). Day of the year was relatively 

important in the np and mu components of the best models for many species. Mean sea surface

temperature appeared as relatively important for some alcid species, particularly in the summer. Turbidity 

appeared as relatively important for some nearshore species (e.g., ducks, Horned Grebe, Double-crested 

Cormorant, Brown Pelican), at least during some seasons. Year was relatively important in the models for 

many species and seasons. The Atlantic Multidecadal Oscillation (AMO) climate index appeared as 

relatively important in the models for a few species-season combinations. 

3.2.3 Seasonal

Averaged across species, day of the year was the most important predictor variable in spring and fall

models for both the np and mu model components (Figs 11-13). This result may reflect the movement of 

migratory species in and out of the study area during the transition between summer and winter. Mean sea 

surface temperature was the most important variable in summer, while turbidity ranked as first (np 

component) or second (mu component) most important in winter.

3.3 Predictor variable effects (example species-season combinations)

As with predictor variable importance, our modelling framework was not designed to determine the 

functional relationships between environmental predictors and the distribution of birds, so ecological 

inferences from the predictor effect results should be cautious. However, again, these results may suggest 

interesting hypotheses for future research.

3.3.1 Marginal effects

Estimated marginal univariate effects of predictor variables on the np (zero inflation) and mu (count 

distribution mean) components of predictions exhibited a wide variety of patterns. Figs 14.1-14.4

illustrate some example effects for the four example species-season combinations. In some cases the 

effect of a given predictor was consistent between the np and mu components. For example, in the best 

Common Tern (summer) model the probability of an extra zero (np) decreased and the mean of the count 

distribution (mu) increased at higher values of the upwelling index, both indicating an increase in the 

expected number of birds sighted per transect segment. As another example, the probability of an extra 

zero generally decreased and the mean of the count distribution generally increased throughout the fall in 

the best Northern Gannet model suggesting an average increase in bird numbers in the study area, which 

is consistent with birds migrating south from their more northern summer breeding areas.

3.3.2 Bivariate effect interactions

The tree base-learners in the model allowed for interactions among the effects of different spatially 

explicit predictor variables. As a result, the marginal univariate effect of a given predictor variable was 

sometimes different depending on the value of other predictor variables. Figs 15.1-15.4 illustrate some 

example marginal bivariate effects of predictor variables on the np and mu components of predictions for 

the four example species-season combinations.

3.4 Predicted spatial distributions

3.4.1 Example species-season combinations

In this section we present a very brief review of the ecology and what is known of the distribution of the 

four example species: Common Tern (Sterna hirundo), White-winged Scoter (Melanitta fusca), Razorbill 
12

(Alca torda), and Northern Gannet (Morus bassanus). For each species’ example season we then present 

eight prediction maps organized into two groups: relative occupancy (4 maps; Figs 16.1-16.4 ABCD), and 

relative abundance (4 maps; Figs 16.1-16.4 EFGH). We also present two annual maps (relative occupancy 

and abundance) for each of these species (Figs 17.1-17.4 AB), representing the predictions averaged 

across seasons that were modeled. For species that were modeled in more than one season, these annual 

maps represent their average spatial distribution during the year or part of the year during which they are 

present in substantial numbers in the study area. Seasons in which a species was more abundant 

contributed more to the annual pattern. Finally, we discuss the correspondence between these predicted 

maps and what is known of the distribution of these species.

3.4.1.1 Common Tern--- Increasing population trend. Common Terns nest on islands, beaches, and 

saltmarshes on the coast, where it is a common breeder from the Bay of Fundy to northern South Carolina

(Nisbet et al. 2013). During spring and fall migration, they generally occur on the entire Atlantic coast 

and winter mainly in Central and South America. Small numbers winter on the Gulf Coast from Texas to 

Florida and fewer to North Carolina; these are likely first winter birds from the Great Lakes (Nisbet 2002; 

Nisbet et al. 2013).

Common Terns take live prey on the surface and usually forage within 20 km from shore during the 

breeding season when they consume and provision chicks with small forage fish, of which American sand 

lance (Ammodytes americanus) is a major food item (Nisbet 1983; Veit and Petersen 1993). In Long 

Island Sound, large foraging groups of Common Terns had greater success than smaller groups when 

exploiting dense patchy prey (Duffy 1986). Feeding associations may form with sub-surface predatory 

fish, such as schools of blue fish that drive prey to the surface during feeding frenzies; also over tidal rips 

(e.g., sand rips of Cape Cod, Nantucket, Tuckernuck, and Muskeget Islands) and shoals, where prey is 

concentrated and brought to the surface by means of rapid tidal flux and convergent flow (Duffy 1986; 

Safina et al. 1988; Schneider 1990; Veit and Petersen 1993 ).

Modeled relative occurrence and abundance of Common Tern in summer reflect this species’ breeding 

distribution, a period when adults are nesting and provisioning chicks (Fig. 16.1). Bootstrapped median 

occupancy and abundance models reveal similar patterns (Fig. 16.1 C,G). Highest predictions of 

occupancy are closest to shore near documented nesting sites and local foraging areas; e.g., coastal 

Maine; Grand Manan Archipelago; Petit Manan; Monomoy National Wildlife Refuge, Cape Cod, 

Massachusetts; Nantucket Sound, Massachusetts; shoals surrounding Muskeget and Tuckernuck Islands, 

Massachusetts; and Muskeget Channel, Massachusetts. East of Cape Cod, a local area of predicted high 

relative occupancy and abundance is predicted in Franklin Basin over Franklin Swell (Fig. 16.1). This 

shoal is within range of major breeding colonies on Cape Cod and may produce favorable foraging 

conditions for nesting terns. Other areas of predicted high relative occupancy and abundance include east 

of Long Island, New York; Sandy Hook, New Jersey; and barrier beaches of New Jersey, Delaware, and 

Maryland. These coastal zones support local breeding colonies in spring and summer. 

The average annual modeled distribution of Common Tern (average of spring, summer, and fall model 

predictions) indicates high relative occupancy and abundance near the coast reflecting migratory 

distributions and associations with breeding colonies, e.g. Maine’s coastal islands, Cape Cod, Long Island 

and New Jersey (Fig. 17.1).

It is possible that the number of Common Tern sightings in the South Atlantic Bight (Cape Hatteras to 

Southern Florida) in the survey dataset was biased low due to the challenges of identifying terns from 

fixed-winged aircraft. Sightings of terns from aerial and ship surveys in spring, summer, and fall suggest 
13

that observers on ships positively identify Common Terns more often than observers on aerial surveys. A 

total of 2883 terns were sighted from ship surveys of which 883 (31%) were identified as Common Tern, 

765 (27%) were identified as other tern species, and 1235 (43%) were not identified to species. From 

aerial surveys a total of 2361 terns were sighted of which none were identified as Common Tern, 673 

(29%) were identified as other tern species, and 1688 (71%) were not identified to species. Because the 

ratio of aerial survey effort to ship survey effort was higher in the South Atlantic Bight (Fig. 2A), the 

information in the survey data about Common Tern in this region was lower, so model predictions for this 

species in this area should be interpreted cautiously. In particular, predictions of high relative occupancy 

and abundance offshore in this area are questionable (Fig. 17.1).

3.4.1.2 Northern Gannet--- Increasing population trend. In North America, breeding occurs on colonies 

in the Gulf of St. Lawrence and off the coast of Newfoundland, Canada. Northern Gannets can form 

dense feeding aggregations composed of 1000s individuals that frenetically plunge-dive into ephemeral 

sub-surface schooling fish, such as herring and menhaden. In spring and fall, gannets form large 

migratory groups that stream by headlands and are easily observed from shore-based vantage points. 

Standardized counts and radar studies of gannets and other seabirds are organized along the coast; e.g. 

Avalon Seawatch, where a high count of approximately 20,000 gannets occurred on 12 November 2008 

(New Jersey Audubon 2006); however, migratory movements also occur over a broad swath of the 

continental shelf (Powers 1984; Stenhouse et al. 2015). On Georges Bank, gannets were most abundant 

during spring and fall (Powers and Brown 1987), with reported high concentrations in New York and 

New Jersey waters during winter. Stenhouse et al. (2015) used satellite transmitters to track gannets 

across three winter periods and observed core areas south of Gloucester, Massachusetts, an area known 

for its commercial fishing fleet; Chesapeake and Delaware Bay; the Outer Banks, North Carolina; and in 

the vicinity of a Frying Pan Shoals in southern North Carolina. Also in winter, Veit and Petersen (1993) 

reported high concentrations at the shelf break in association with fishing trawlers. Data from Christmas 

Bird Counts (CBCs) report 93% of observations between MA-NC (data available from National Audubon 

Society 2016; Nisbet et al. 2013). Juveniles tend to travel farther south to offshore areas from North 

Carolina to Florida (Palmer 1976, Nelson 1978; Nisbet et al 2013).

Model predictions of median relative occupancy and abundance generally support what has been reported 

about the distribution and abundance of Northern Gannets in fall in US waters (Fig. 16.2). Relative 

occupancy is predicted to be broad across the continental shelf, with highest probabilities in close 

proximity to the coast, specifically east of New York, New Jersey, Delaware, and Virginia (Fig. 16.2 AD). Predicted median relative abundance reveals similar patterns close to the coast and offshore; e.g., 

Georges Bank (Fig. 16.2 E-H). Relative abundance predictions generally agree with reports of low 

abundance off Cape Hatteras in autumn (Sep – Nov) and off east Florida (Nisbet et al. 2013). Predicted 

higher relative abundance off southern NJ is generally supported by shore-based estimates during late fall 

(New Jersey Audubon 2006; Nisbet et al. 2013). Predicted high relative occupancy and abundance east of 

the shelf break and far offshore in the South Atlantic Bight are questionable (Nisbet et al. 2013).

The average annual modeled distribution of Northern Gannet (average of spring, summer, fall, and winter 

model predictions) generally reflects patterns of spring and fall migration and the species’ winter 

distribution (Fig. 17.2). Predicted high relative occupancy and abundance offshore of the shelf break and 

slope likely do not reflect persistent areas of aggregation as they fall outside regions of concentrated 

survey effort (Fig. 2).
14

3.4.1.3 Razorbill---Increasing population trend. There are approximately 1000 breeding pairs on islands 

in the Bay of Fundy and Gulf of Maine. The bulk of the North American breeding distribution is in lowarctic waters of southern Labrador and the lower North Shore of the Gulf of St. Lawrence (Lavers et al. 

2009; Nisbet et al. 2013). In winter, 94% of total birds reported on CBC surveys stretch between Nova 

Scotia and Massachusetts (data available from National Audubon Society 2016; Nisbet et al. 2013). Birds 

winter mainly in waters south to 40°, and are highly concentrated in the Bay of Fundy and in productive 

waters of Georges Bank, Nantucket Shoals, Nantucket Sound, and east of Cape Cod (Powers and Brown 

1987; Veit and Petersen 1993; Nisbet et al. 2013; White 2011; White et al. 2013); however, numbers have 

increased off Cape Hatteras in recent years (Veit and Guris 2009). Razorbills often form highly 

aggregative foraging flocks in association with tidal fronts that manifest in the vicinity of sloped 

topography on shallow banks and ledges (Durazo et al. 1998; White 2011). Their diet is primarily 

composed of small forage fish (e.g., sand lance, young herring) and crustaceans (e.g., krill), that also form 

dense aggregations in association with hydrographic fronts (Huettmann et al. 2005; Gaston and Woo 

2008; White 2011). Abundant sand lance populations occur in the Gulf of Maine and southern New 

England where they burrow in sandy bottom, particularly in the waters of Cape Cod, Nantucket Shoals, 

and Georges Bank. Strong topographically rectified currents, tidal fronts, and vertical mixing occur 

within these regions and spatially correspond with high model predictions of Razorbills. In winter 2012-

2013, the Atlantic coast witnessed an incursion of Razorbills in unprecedented numbers that spanned 

from the Gulf of Maine to the Gulf of Mexico. Many emaciated carcasses were found onshore indicating 

a lack of suitable prey, which was likely due to anomalous warm water conditions in the Atlantic 

(Brinkley 2013).

Model predictions of median relative occupancy and abundance of Razorbills in winter reveal similar 

patterns (Fig. 16.3). Patterns of relative occupancy and abundance broadly agree with recent at-sea 

surveys and published reports, especially in the vicinity Cape Cod (Nantucket Sound, east of outer Cape 

Cod, Stellwagen Bank), Nantucket Shoals, Georges Bank, and the Bay of Fundy. Modeled median 

relative occupancy appears to reflect increasing presence of Razorbills in the Mid-Atlantic Bight (Fig. 

16.3).

The average annual modeled distribution of Razorbill (average of spring, summer, fall, and winter model 

predictions) generally reflects the summer distribution associated with breeding islands in the Gulf of 

Maine (Fig. 17.3). Winter, spring, and fall distributions coalesce around shallow banks and ledges and 

Georges Bank in the Gulf of Maine; Cape Cod, Nantucket Sound and Nantucket Shoals in southern New 

England; and to a lesser extent, off eastern Long Island, NY and southern New Jersey (Fig 17.3).

3.4.1.4 White-winged Scoter---Decreasing population trend. This largest scoter species nests in the 

northwestern interior of North America and winters on both coasts. White-winged Scoters arrive to the 

US east coast in September, increase in numbers throughout fall and winter, and depart for breeding areas 

in mid-May. Also known as diving ducks, they prey mainly upon molluscs attached to substrate; e.g. blue 

mussels, and infaunal clams embedded in the seafloor, and sometimes select soft-bodied crustaceans. 

White-winged Scoters are generally found in close proximity to the coast between depths of 5 and 40 m. 

They are highly gregarious and form patchy feeding flocks, which can comprise 1000s of individuals and 

stretch across miles of ocean; e.g., Nantucket Shoals (White 2013; White et al. 2013). The Nantucket 

Shoals feeding hotspot is associated with high concentrations of clams and pelagic crustaceans and 

overlaps with a hotspot of wintering Long-tailed Ducks (White 2013). The majority or 86% of the total 

number of White-winged Scoters reported on CBCs concentrate between Maine and New York (data 

available from National Audubon Society 2016; Nisbet et al. 2013).
15

Model predictions of median relative occupancy and abundance in winter are similar for White-winged 

Scoters (Fig. 16.4). Both relative occupancy and abundance is predicted to be higher in and around Cape 

Cod Bay, Nantucket Sound, and Nantucket Shoals and agree with reported areas of high abundance. 

Reasonably high predicted relative occupancy and abundance also occur east of Long Island, NY and the 

New Jersey coast. Use of these areas in winter is supported by satellite telemetry (birds fixed with GPS 

transmitters; Environment Canada 2013), systematic surveys, and local reports (Nisbet et al. 2013). 

Predictions of high relative occupancy and abundance of this species in offshore waters are questionable. 

The model predicts White-winged Scoters in deep waters (> 90 m) and approximately 100 nm south of 

Nova Scotia. These zones appear unlikely to be favored by White-winged Scoters given what is known of 

their wintering ecology. Predictions of high relative occupancy and abundance south of Nantucket Shoals 

and offshore of the shelf break are also questionable.

The average annual modeled distribution of White-winged Scoter (average of fall, winter, and spring) 

reflects core winter foraging areas in the vicinity of Nantucket Island (Nantucket Sound and Nantucket 

Shoals) and Cape Cod Bay (Fig. 7.4). Lower predicted coastal relative occupancy and abundance extend 

south into the mid-Atlantic region, and reveal a peak along eastern Long Island, NY and in the vicinity of 

Montauk Point. Predicted relative occupancy and abundance attenuates south until the barrier islands of 

New Jersey, and mouths of Delaware and Chesapeake Bay. Higher predicted relative occupancy and 

abundance greater than 20 nm from the New England coast suggest migratory groups travelling to 

southern foraging destinations in fall, and should not be interpreted as persistent foraging areas. 

Predictions of moderate relative occupancy and abundance far offshore from the mid-Atlantic coast are 

almost certainly spurious (Fig. 17.4).

3.4.2 Species groups

Figs 18-20 show modeled annual distributions of relative occupancy and abundance averaged across 

species within three species groups: nearshore, pelagic, and gulls and gannets (Table 12). Each species’ 

annual predictions were normalized (divided by their sum) prior to averaging so that each species 

contributed equally to the multi-species average. Because each species’ predictions were divided by their 

sum, the actual numeric values of these normalized predictions are very small. As with all predictions of 

occupancy and abundance presented in this report, it is the relative differences in predictions across space 

that are relevant, not the actual numbers themselves.

Figs 18-20 should be regarded as an exploratory exercise to generate multi-species products. These 

species groupings were based on loosely similar spatial domains and should not be interpreted as 

ecological groups. Some member species of the ‘pelagic’ group are more of a ‘nearshore’ species during 

the breeding season (e.g. Atlantic Puffin, Razorbill) as revealed in the modeled distribution for this group 

which exhibits predictions of high relative occupancy and abundance around breeding colonies in the 

Gulf of Maine (Fig. 19).

4. Discussion

This report describes the first phase of a project aimed at producing maps of the spatial distributions of 

marine bird species in U.S. Atlantic Outer Continental Shelf waters (Fig. 1) that can be used to inform 

marine spatial planning in the region. Maps were developed for 40 species and 118 species-season 

combinations.
16

4.1 Interpretation of maps

The maps presented in this report represent spatial predictions of relative occurrence and relative 

abundance averaged over time. The maps do not provide predictions of the absolute number of 

individuals of a given species that would be expected in a specific location at a specific time. The maps 

only provide information about where a given species may be more or less likely to occur and where a 

species may be more or less abundant.

The maps presented here represent model predictions that ultimately rely on the underlying survey data 

that the models were fit to. The distribution of survey effort was highly uneven across the study area (Fig. 

2), so some areas were much better sampled than others. For example, there were many fewer data from 

offshore areas than nearshore areas. Model predictions in areas with low survey effort should be 

interpreted cautiously. The survey effort contours provided can be used to identify such areas. For 

example, Fig. 21 shows the map of predicted relative abundance for Razorbill in winter with the survey 

effort contours overlaid. The area of predicted high relative abundance off southern Nova Scotia at the 

northeast end of the study area is outside of the 95% survey effort contour indicating that these model 

predictions are based on few, if any local data.

Predictions very close to shore (i.e., within 1-2 km) should be interpreted with caution for several reasons. 

First, survey effort was limited very close to shore (i.e., within 1-2 km). Second, the boundaries of the 

square study grid cells did not perfectly align with the shoreline, so there may be gaps between the 

shoreline and the nearest study grid cell. Third, data for some remotely-sensed predictor variables were 

less reliable or missing (precluding prediction) very close to shore. As a result, predictions for species that 

tend to be very close to shore (e.g., Brown Pelican, Laughing Gull, and Least Tern) should be interpreted 

with caution.

Large variations in model predictions at the 2-km resolution of the prediction grid may not be realistic. 

For example, the modeled annual average distribution of Common Tern exhibits patchy predictions in 

offshore areas of the South Atlantic Bight and Gulf of Maine (Fig. 17.1). Such large variation in average 

long-term relative occurrence and abundance at such fine spatial scales is likely unrealistic in many cases. 

Management applications should not assume that fine-scale variation in model predictions from one grid 

cell to the next is realistic. Interpretation of the maps presented here to inform spatial planning is probably 

more reliable at the regional scale (i.e., 10-100 km).

Some model predictions of high relative occurrence and abundance may partially reflect large temporal 

and spatial aggregations of birds coinciding with survey effort rather than average long-term spatial 

patterns per se. For example, pockets of very high predicted relative occurrence and abundance in 

offshore areas often reflect high counts on a limited number of survey transects (e.g., Audubon 

Shearwater fall model). In many cases, it is unlikely that these are persistent areas of much higher relative 

occurrence and abundance than adjacent areas that had no survey effort. While our spatial predictive 

modeling framework theoretically accounts for effort and attempts to account for the aggregated nature of 

animal distributions and sightings, limited sample size combined with extreme aggregations can unduly 

influence model predictions.

The maps of predicted relative occurrence and abundance presented here are accompanied by 

corresponding maps of the estimated uncertainty in those predictions including bootstrapped quantiles, 

confidence intervals, and coefficients of variation. It is important that the uncertainty products are 

considered alongside the prediction maps. In many cases the confidence intervals and coefficients of 
17

variation are very large indicating substantial statistical uncertainty and variability associated with the 

corresponding predictions, and those predictions should be interpreted cautiously.

4.2 Model performance

The performance of the models varied among species and seasons (Table 11). We provide two 

assessments of the quality of each model.

First, a suite of performance metrics were calculated to determine the statistical fit of each model to the 

data. Four of these metrics were combined to provide an overall model performance class, and a badge 

indicating this performance is included on the map products. It is important to recognize that the model 

performance metrics and badge reflect only the statistical fit of the model to the data that were analyzed, 

and they do not reflect the quality of model predictions away from the data. For example, the accuracy of 

predictions in areas with little survey effort is not necessarily reflected by the model performance metrics. 

Nevertheless, the performance metrics and overall performance class give a relative indication of how 

accurately a model was able to predict the observed data, and better performance provides a measure of 

confidence in the model predictions, especially within the temporal and spatial coverage of the observed 

survey data. On the other hand, when metrics indicate poor performance (e.g., zero percent deviance 

explained, negative Gaussian rank correlation coefficient, or extremely high median absolute error) those 

models should be interpreted cautiously.

Second, we provide a judgement about the quality of each model based on an expert review of the maps 

by one of the authors of this report (TPW). This assessment is more subjective than the performance 

assessment, and it is the opinion of one expert, but it provides a broader evaluation of how the modeled 

distributions of relative occurrence and abundance match what is known about the distributions of these 

marine bird species in the study area.

4.3 Species identification

The survey data analyzed in this project were from a large number of surveys representing numerous 

survey platforms and protocols. A fundamental assumption of the analysis presented here is that all 

species, when present, were recorded. This assumption might have been violated on one or more surveys 

if observers were focused on particular groups of species and failed to record occurrences of other 

species. A related issue is the identification of observed birds to the species level. From some survey 

platforms, or again if observers were more focused on some species than others, some birds might have 

been less likely to be identified to species. For example, it can be difficult to identify the species of some 

small birds from typical aerial surveys. Birds that were not identified to species were not included in the 

analysis here. The result of failure to record some species or failure to identify the species of some types 

of birds is the same: the expected count of those species will vary among survey datasets, independent of 

other factors.

Our statistical modeling framework allowed for differences in the expected count of a given species 

among survey platforms and transects, so theoretically the models could account for differences arising 

from failure to record or identify species. However, if a geographic area is covered by a limited number of 

surveys or platforms, then it would be difficult or impossible for the model to determine whether 

differences in counts in that area were because of fewer birds in that area or because of differences in 

species recording and identification in that area. Model predictions in such instances should be interpreted 
18

cautiously. For example, model predictions for species that are difficult to identify from aircraft should be 

interpreted cautiously in areas where most of the survey effort was aerial.

4.4 Project Phase II

A second phase of this project is currently underway that will expand, refine, and improve the modeling 

and results presented in this report. The results presented here should be considered preliminary. In 

particular, an error in the computer code used for Phase I of the analysis resulted in spurious spatial 

patterns in some of the predictions (Section 2.6). The error has been corrected for the second phase of the 

project.

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21

Table 1. Datasets analyzed. Data from these surveys were extracted from the USGS Avian Compendium 

Database (O’Connell et al. 2009) and standardized to 15-minute ship survey equivalent transect 

segments as described in the text. Transect counts reflect all transect segments within the study area, 

including some records that were later excluded from analysis due to incomplete records (missing 

predictor data).

Year Number of transect segments surveyedc

(15-minute-ship-survey-equivalentsd)

Source Dataset IDa Platform Methodb Start End Total Spring Summer Fall Winter

AMAPPS_FWS_Aerial_Fal

l2012 Aerial cts 2012 2012 5157 0 0 5157 0

AMAPPS_FWS_Aerial_Pr

eliminary_Summer2010 Aerial cts 2010 2010 1863 0 1863 0 0

AMAPPS_FWS_Aerial_Sp

ring2012 Aerial cts 2012 2012 5270 5270 0 0 0

AMAPPS_FWS_Aerial_Su

mmer2011 Aerial cts 2011 2011 5177 0 5177 0 0

AMAPPS_FWS_Aerial_Wi

nter2010-2011 Aerial cts 2010 2011 914 0 0 0 914

AMAPPS_NOAA/NMFS_N

EFSCBoat2011 Boat cts 2011 2011 1274 0 1274 0 0

AMAPPS_NOAA/NMFS_N

EFSCBoat2013 Boat cts 2013 2013 1318 0 1318 0 0

AMAPPS_NOAA/NMFS_N

EFSCBoat2014 Boat cts 2014 2014 859 859 0 0 0

AMAPPS_NOAA/NMFS_S

EFSCBoat2011 Boat cts 2011 2011 822 0 822 0 0

AMAPPS_NOAA/NMFS_S

EFSCBoat2013 Boat cts 2013 2013 813 0 582 231 0

BarHarborWW05 Boat cts 2005 2005 911 0 755 156 0

BarHarborWW06 Boat cts 2006 2006 1022 0 730 292 0

CapeHatteras0405 Boat cts 2004 2005 276 0 154 0 122

CapeWindAerial Aerial cts 2002 2004 4035 963 959 1014 1099

CapeWindBoat Boat cts 2002 2003 252 100 123 29 0

CDASMidAtlantic Aerial cts 2001 2003 1402 201 0 0 1201

CSAP Boat dts 1980 1988 26271 7640 7028 7368 4235

DOEBRIBoatApr2014 Boat cts 2014 2014 140 140 0 0 0

DOEBRIBoatApril2012 Boat cts 2012 2012 142 142 0 0 0

DOEBRIBoatAug2012 Boat cts 2012 2012 142 0 142 0 0

DOEBRIBoatAug2013 Boat cts 2013 2013 145 0 145 0 0

DOEBRIBoatDec2012 Boat cts 2012 2013 139 0 0 0 139

DOEBRIBoatDec2013 Boat cts 2013 2013 147 0 0 0 147

DOEBRIBoatJan2013 Boat cts 2013 2013 143 0 0 0 143

DOEBRIBoatJan2014 Boat cts 2014 2014 143 0 0 0 143

DOEBRIBoatJune2012 Boat cts 2012 2012 143 0 143 0 0

DOEBRIBoatJune2013 Boat cts 2013 2013 146 0 146 0 0

DOEBRIBoatMar2013 Boat cts 2013 2013 145 145 0 0 0

DOEBRIBoatMay2013 Boat cts 2013 2013 147 147 0 0 0

DOEBRIBoatNov2012 Boat cts 2012 2012 142 0 0 142 0

DOEBRIBoatOct2013 Boat cts 2013 2013 147 0 0 147 0

DOEBRIBoatSep2012 Boat cts 2012 2012 144 0 0 144 0

DOEBRIBoatSep2013 Boat cts 2013 2013 148 0 0 148 0

EcoMonAug08 Boat cts 2008 2008 411 0 411 0 0

EcoMonAug09 Boat cts 2009 2009 395 0 395 0 0

EcoMonAug10 Boat cts 2010 2010 427 0 415 12 0

EcoMonAug2012 Boat cts 2012 2012 560 0 560 0 0

EcoMonFeb10 Boat cts 2010 2010 292 0 0 0 292

EcoMonFeb2012 Boat cts 2012 2012 472 0 0 0 472

EcoMonJan09 Boat cts 2009 2009 341 0 0 0 341

EcoMonJun2012 Boat cts 2012 2012 389 27 362 0 0

EcoMonMay07 Boat cts 2007 2007 435 374 61 0 0

EcoMonMay09 Boat cts 2009 2009 543 173 370 0 0

EcoMonMay10 Boat cts 2010 2010 550 235 315 0 0

EcoMonNov09 Boat cts 2009 2009 379 0 0 379 0

EcoMonNov10 Boat cts 2010 2010 356 0 0 356 0

EcoMonNov2011 Boat cts 2011 2011 391 0 0 391 0

EcoMonOct2012 Boat cts 2012 2012 428 0 0 428 0

FWS_MidAtlanticDetection Aerial cts 2012 2012 456 456 0 0 0
22

_Spring2012

FWS_SouthernBLSC_Wint

er2012 Aerial cts 2012 2012 1582 0 0 0 1582

FWSAtlanticWinterSeaduc

k2008 Aerial cts 2008 2011 14377 128 0 0 14249

GeorgiaPelagic Boat dts 1982 1985 2187 681 699 576 231

HatterasEddyCruise2004 Boat cts 2004 2004 93 0 93 0 0

HerringAcoustic06 Boat cts 2006 2006 243 0 0 243 0

HerringAcoustic07 Boat cts 2007 2007 283 0 0 283 0

HerringAcoustic08 Boat cts 2008 2008 710 0 0 710 0

HerringAcoustic09Leg1 Boat cts 2009 2009 109 0 0 109 0

HerringAcoustic09Leg2 Boat cts 2009 2009 245 0 0 245 0

HerringAcoustic09Leg3 Boat cts 2009 2009 227 0 0 227 0

HerringAcoustic2010 Boat cts 2010 2010 482 0 0 482 0

HerringAcoustic2011 Boat cts 2011 2011 690 0 0 690 0

MassAudNanAerial Aerial cts 2002 2006 4131 753 604 1034 1740

NewEnglandSeamount06 Boat dts 2006 2007 66 61 4 1 0

NJDEP2009 Boat cts 2008 2009 4446 1094 1191 1272 889

NOAA/NMFS_NEFSCBoat

2004 Boat cts 2004 2004 1017 0 1017 0 0

NOAA/NMFS_NEFSCBoat

2007 Boat cts 2007 2007 516 0 516 0 0

NOAAMBO7880 Boat dts 1978 1979 6979 1682 2462 2044 791

PlattsBankAerial Aerial cts 2005 2005 732 0 732 0 0

RISAMPAerial Aerial cts 2009 2010 2158 849 663 0 646

RISAMPBoat Boat cts 2009 2010 716 185 239 142 150

SEFSC1992 Boat cts 1992 1992 674 0 0 0 674

SEFSC1998 Boat cts 1998 1998 1146 0 1146 0 0

SEFSC1999 Boat cts 1999 1999 1058 0 611 447 0

WHOIJuly2010 Boat cts 2010 2010 71 0 71 0 0

WHOISept2010 Boat cts 2010 2010 74 0 0 74 0

TOTALS ALL ALL 1978 2014 111776 22305 34298 24973 30200

a

The Source Dataset ID can be used to look up datasets in Appendix A, Table A1, which gives detailed additional background 

information about each survey. Table A1 lists several additional datasets; these additional datasets are available but did not contain 

any segments that fell within the study area.

bSurvey method: cts, continuous-time strip transects; dts, discrete-time strip transects

cCounts exclude segments whose midpoint falls outside the study area (as shown in Figure 1), and any partial segments that were 

less than 60% of standard transect segment length (i.e., only segments >2.778km in length were included). 

dA15-minute-ship-survey-equivalent is defined as the distance a ship would travel in 15 minutes at 10 knots.
23

Table 2. List of species analyzed. Four-letter species codes in the first column are generally used in place 

of the full common or scientific name throughout this report. For each season, only species with >=50 

sightings were modeled. Number of sightings includes some records that were later excluded from 

analysis due to incomplete records (missing predictor data). Shaded cells indicate model runs that were 

successfully completed.

Species 

code Common name Scientific name Family

Number of sightings

Total

Spring

Summer

Fall

Winter

arte Arctic Tern Sterna paradisaea Sternidae 242 44 154 44 0

atpu Atlantic Puffin Fratercula arctica Alcidae 795 209 246 91 249

aush Audubon's 

Shearwater Puffinus lherminieri Procellariidae 1460 129 876 286 169

bcpe Black-capped Petrel Pterodroma hasitata Procellariidae 689 158 356 92 83

blgu Black Guillemot Cepphus grylle Alcidae 141 7 93 7 34

blki Black-legged 

Kittiwake Rissa tridactyla Laridae 6434 621 24 2083 3706

blsc Black Scoter Melanitta americana Anatidae 1958 423 16 356 1163

bogu Bonaparte's Gull Chroicocephalus 

philadelphia Laridae 1678 397 20 280 981

brpe Brown Pelican Pelecanus occidentalis Pelecanidae 356 66 127 87 76

brsp Band-rumped StormPetrel Oceanodroma castro Hydrobatidae 290 14 266 10 0

coei Common Eider Somateria mollissima Anatidae 3800 893 159 537 2211

colo Common Loon Gavia immer Gaviidae 6949 2367 182 1185 3215

comu Common Murre Uria aalge Alcidae 277 90 22 5 160

cosh Cory's Shearwater Calonectris diomedea Procellariidae 4579 106 2925 1547 1

cote Common Tern Sterna hirundo Sternidae 2713 488 1538 683 4

dcco Double-crested 

Cormorant Phalacrocorax auritus Phalacrocoracidae 700 145 187 206 162

dove Dovekie Alle alle Alcidae 1675 260 49 404 962

gbbg Great Black-backed 

Gull Larus marinus Laridae 15654 3423 3186 5390 3655

grsh Great Shearwater Puffinus gravis Procellariidae 12907 586 6011 6176 134

herg Herring Gull Larus argentatus Laridae 21087 5721 2941 7439 4986

hogr Horned Grebe Podiceps auritus Podicipedidae 128 21 0 13 94

lagu Laughing Gull Leucophaeus atricilla Laridae 3987 711 1602 1560 114

lesp Leach's Storm-Petrel Oceanodroma leucorhoa Hydrobatidae 2816 223 2140 452 1

lete Least Tern Sterna antillarum Sternidae 185 27 121 37 0
24

ltdu Long-tailed Duck Clangula hyemalis Anatidae 4852 1152 1 485 3214

mash Manx Shearwater Puffinus puffinus Procellariidae 689 100 309 264 16

nofu Northern Fulmar Fulmarus glacialis Procellariidae 6613 2244 737 1823 1809

noga Northern Gannet Morus bassanus Sulidae 17270 5667 1187 4002 6414

poja Pomarine Jaeger Stercorarius pomarinus Stercorariidae 984 110 144 709 21

razo Razorbill Alca torda Alcidae 2527 720 78 170 1559

rbgu Ring-billed Gull Larus delawarensis Laridae 1243 181 46 312 704

reph Red Phalarope Phalaropus fulicarius Scolopacidae 1005 461 214 286 44

rnph Red-necked 

Phalarope Phalaropus lobatus Scolopacidae 469 132 167 156 14

rost Roseate Tern Sterna dougallii Sternidae 328 56 195 74 3

royt Royal Tern Sterna maxima Sternidae 842 269 283 279 11

rtlo Red-throated Loon Gavia stellata Gaviidae 3999 1699 11 387 1902

sosh Sooty Shearwater Puffinus griseus Procellariidae 2439 790 1542 104 3

susc Surf Scoter Melanitta perspicillata Anatidae 3260 745 8 761 1746

wisp Wilson's Storm-Petrel Oceanites oceanicus Hydrobatidae 11400 1650 8392 1348 10

wwsc White-winged Scoter Melanitta fusca Anatidae 2302 415 5 550 1332
25

Table 3. Predictor variables used in model with codenames, resolutions, and sources.

Predictor variable Codename Native resolution Source

Survey variables

Survey platform boatplane Categorical variable USGS/USFWS Compendium of Avian 

Information Database

Survey ID sid Categorical variable USGS/USFWS Compendium of Avian 

Information Database

Transect ID tid Categorical variable USGS/USFWS Compendium of Avian 

Information Database

Temporal variables

Year yearscaled 1 year n/a

Day of year jday 1 day n/a

Atlantic Multidecadal 

Oscillation (AMO) index 

(current and 1 year lag)

index_amo, 

index_amo_lag1

Monthly NOAA ESRL 

http://www.esrl.noaa.gov/psd/data/correl

ation/amon.us.data

Multivariate El NiñoSouthern Oscillation Index 

(MEI) (current and 1 year 

lag)

index_mei, 

index_mei_lag1

Monthly NOAA ESRL 

http://www.esrl.noaa.gov/psd/data/correl

ation/mei.data

North Atlantic Oscillation 

(NAO) index (current and 1 

year lag)

index_nao, 

index_nao_lag1

Monthly NOAA ESRL 

http://www.esrl.noaa.gov/psd/data/correl

ation/nao.data

Trans-Niño Index (TNI) 

(current and 1 year lag)

index_tni, 

index_tni_lag1

Monthly NOAA ESRL 

http://www.esrl.noaa.gov/psd/data/correl

ation/tni.data

Geographic variables

Longitude projected 

(oblique Mercator)

prlon n/a n/a

Latitude projected (oblique 

Mercator)

prlat n/a n/a

Distance to land1dland 50 m Derived from Prototype Global 

Shoreline Data

(http://msi.nga.mil/NGAPortal/DNC.port

al?_nfpb=true&_pageLabel=dnc_portal_

page_72)

Distance to shelf break1dshelf Same as depth (approx. 25 

m to 927 m)

Derived from depth (200-m isobath)

Terrain variables

Depth2depth CRM: 3 arc-seconds 

(approx. 92 m); ACUMEN: 

approx. 25 m; CCOM: 

approx. 111 m; GEBCO: 30 

arc-seconds (approx. 927 m) 

NOAA National Geophysical Data 

Center Coastal Relief Model (CRM) 

(http://www.ngdc.noaa.gov/mgg/coastal/

crm.html); NOAA Atlantic Canyons 

Undersea Mapping Expeditions 

(ACUMEN) 

(http://oceanexplorer.noaa.gov/okeanos/

explorations/acumen12/summary/welco

me.html); University of New Hampshire 

Center for Coastal and Ocean Mapping 

(CCOM) / Joint Hydrographic Center 

(http://ccom.unh.edu/about-ccomjhc); 

General Bathymetric Chart of the 

Oceans (GEBCO) GEBCO_08 grid 

(http://www.gebco.net/data_and_produc

ts/gridded_bathymetry_data/) 
26

Slope (derived at 1500 m 

scale)1,4

slp1500m approx. 1500 m Derived from depth

Slope (derived at 10 km 

scale)1,4

slp10km approx. 10 km Derived from depth

Slope of slope (derived at 

10 km scale)1,4

slpslp10km approx. 10 km Derived from depth

Planform curvature (derived 

at 10 km scale)1,3,4

plcurv10km approx. 10 km Derived from depth

Profile curvature (derived at 

10 km scale)1,3,4

prcurv10km approx. 10 km Derived from depth

Physical variables 

(seasonal climatologies)

Sea surface height sshmean 1/3 degree (approx. 37km) AVISO Global DT-Upd Merged MADT 

weekly sea surface altimetry product

(1992-2013) 

(http://www.aviso.altimetry.fr/en/data/pro

ducts/sea-surface-heightproducts/global/madt.html; note that 

‘Upd’ is now referred to as ‘all sat 

merged’)

Sea surface height standard 

deviation

sshsd 1/3 degree (approx. 37km) Derived from sea surface height

Probability of anticyclonic 

eddy ring5

peddacyc 1/3 degree (approx. 37km) Derived from sea surface height

Probability of cyclonic eddy 

ring5

peddcyc 1/3 degree (approx. 37km) Derived from sea surface height

Sea surface temperature sstmean approx. 1 km AVHRR Pathfinder daily or sub-daily 

SST (1985-2010) from Peter Cornillon 

(University of Rhode Island) served at 

http://www.sstfronts.org/opendap/

Sea surface temperature 

standard deviation

sstsd approx. 1 km Derived from sea surface temperature

Probability of sea surface 

temperature front6

sstfront 0.011 degree (approx. 1.2 

km)

Monthly ‘climatologies’ from Peter Miller 

(Plymouth Marine Laboratory) derived 

from monthly front presence/absence in 

turn derived from daily NASA MUR 1-

km SST data (2002-2013)

Current velocity (u direction) wateru 1/6 degree (approx. 19 km) Hybrid vertical coordinate system ocean 

model (HYCOM) reanalysis (GLBu0.16 

grid, experiments 19.0 and 19.1) 3-

hourly data (1992-2005) 

(http://hycom.org/dataserver)

Current velocity (v direction) waterv 1/6 degree (approx. 19 km) Hybrid vertical coordinate system ocean 

model (HYCOM) reanalysis (GLBu0.16 

grid, experiments 19.0 and 19.1) 3-

hourly data (1992-2005) 

(http://hycom.org/dataserver)

Current divergence diverg 1/6 degree (approx. 19 km) Derived from current velocity

Current vorticity vort 1/6 degree (approx. 19 km) Derived from current velocity

Wind stress (x direction) windstrx 0.25 degrees (approx. 28 

km)

NOAA CoastWatch monthly grids 

(1999-2009) derived from NASA Quick 

Scatterometer (QuikSCAT) data 

(http://coastwatch.pfeg.noaa.gov/erddap

/griddap/erdQSstressmday.html)

Wind stress (y direction) windstry 0.25 degrees (approx. 28 

km)

NOAA CoastWatch monthly grids 

(1999-2009) derived from NASA Quick 

Scatterometer (QuikSCAT) data 

(http://coastwatch.pfeg.noaa.gov/erddap

/griddap/erdQSstressmday.html)
27

Wind divergence divw 0.25 degrees (approx. 28 

km)

NOAA CoastWatch monthly grids 

(1999-2009) derived from NASA Quick 

Scatterometer (QuikSCAT) data 

(http://coastwatch.pfeg.noaa.gov/erddap

/griddap/erdQSdivmodmday.html)

Upwelling index upwelling 0.25 degrees (approx. 28 

km)

NOAA CoastWatch monthly grids 

(1999-2009) derived from NASA Quick 

Scatterometer (QuikSCAT) data 

(http://coastwatch.pfeg.noaa.gov/erddap

/griddap/erdQSstressmday.html)

Turbidity (reflectance at 670 

nm)

turb approx. 1 km Monthly products (1997-2010) from Tim 

Wynne (NOAA NCCOS) derived from 

daily Sea-Viewing Wide Field of-View 

Sensor (SeaWiFS) data processed 

using the Naval Research Laboratories’ 

Automated Processing System software

Biological variables 

(seasonal climatologies)

Surface chlorophyll-a chla approx. 1 km Monthly products (1997-2010) from Tim 

Wynne (NOAA NCCOS) derived from 

daily Sea-Viewing Wide Field of-View 

Sensor (SeaWiFS) data processed 

using the Naval Research Laboratories’ 

Automated Processing System software

1 Derived using ArcGIS 10.2 Spatial Analyst Tools.

2 Datasets were converted to a common 100-m grid then mosaicked in order of decreasing priority: CRM, ACUMEN, 

CCOM, and GEBCO.

3 Derived using the ArcGIS 10.2 extension DEM Surface Tools (Jenness 2012).

4 Calculated from depth grids that were smoothed using a Gaussian low-pass filter for each spatial scale.

5 Derived using Duke University’s Marine Geospatial Ecology Lab's Marine Geospatial Ecology Tools (MGET) for 

ArcGIS 10.2 (Okubo-Weiss algorithm).

6

A low-pass filter was applied to the climatologies to remove banding artifacts.
28

Table 4a. Predictor correlation table (spring). Pairwise Spearman rank correlation coefficients for spatial predictor variables. High correlations are highlighted 

(|tau|>0.7 in yellow, |tau|>0.8 in orange, |tau|>0.9 in red). chla diverg divw peddacyc peddcyc sshmean sshsd sstfront sstmean

sstsdturbupwellingvortwateruwatervwindstrxwindstrydepthdlanddshelfplcurv10kmprcurv10kmprlatprlonslp10kmslp1500mslpslp10km

chla 0.2 0.28 -0.47 -0.59 -0.7 -0.48 0.06 -0.87 0.77 0.89 0.27 0.14 -0.22 -0.33 -0.04 -0.62 0.59 -0.53 -0.47 0.04 -0.08 0.8 0.47 -0.24 -0.22 -0.18

diverg 0.02 0 -0.11 -0.22 -0.04 0 -0.14 0.17 0.19 0.28 0.09 0.01 0.08 -0.04 -0.25 0.12 -0.12 -0.12 0.02 0.05 0.16 0.12 0 -0.01 0.04

divw -0.16 -0.17 -0.44 -0.37 0.16 -0.29 0.34 0.26 0.05 0.21 -0.23 -0.25 -0.23 -0.29 0.26 -0.19 -0.33 0.01 -0.06 0.12 0 -0.01 -0.06 0.05

peddacyc 0.24 0.21 0.53 0.09 0.29 -0.09 -0.38 0.03 -0.13 0.25 0.17 0.28 0.06 -0.61 0.55 0.45 -0.05 0.24 -0.21 0.1 0.27 0.27 0.11

peddcyc 0.59 0.61 -0.04 0.5 -0.39 -0.5 -0.36 0.14 0.12 0.04 0.2 0.46 -0.55 0.54 0.6 -0.01 0.02 -0.54 -0.26 0.07 0.08 0.02

sshmean 0.52 -0.29 0.66 -0.72 -0.64 -0.34 -0.32 0.18 0.22 0.23 0.77 -0.52 0.46 0.61 -0.03 0.02 -0.63 -0.35 -0.06 -0.04 -0.11

sshsd 0.15 0.43 -0.11 -0.35 -0.04 0.02 0.47 0.26 0.63 0.13 -0.72 0.69 0.68 -0.01 0.16 -0.27 0.07 0.2 0.24 0.01

sstfront 0.07 0.47 0.12 0.13 0.35 0.29 0.23 0.29 -0.47 0 0.01 -0.33 0.01 -0.01 0.1 0.03 0.53 0.44 0.35

sstmean -0.66 -0.76 -0.23 -0.07 0.38 0.54 -0.07 0.55 -0.29 0.24 0.22 -0.02 0.01 -0.9 -0.73 0.21 0.16 0.14

sstsd 0.74 0.32 0.27 0.02 -0.19 0.18 -0.78 0.29 -0.24 -0.33 0.01 0.05 0.66 0.44 0.08 0.07 -0.02

turb 0.26 0.12 -0.14 -0.28 -0.01 -0.61 0.49 -0.42 -0.36 0.05 -0.06 0.7 0.43 -0.18 -0.16 -0.14

upwelling -0.07 0.18 0.14 0.13 -0.43 0.17 -0.14 -0.24 0.05 0.06 0.35 0.26 -0.01 0 0

vort 0.04 0.05 -0.08 -0.35 0.06 -0.09 -0.23 0.01 -0.09 0.01 -0.02 0.2 0.18 0.21

wateru 0.58 0.37 -0.12 -0.22 0.16 0.09 0.01 0.12 -0.15 -0.09 0.19 0.19 0.02

waterv 0.06 0.01 0.01 -0.05 -0.1 0 0.04 -0.36 -0.36 0.16 0.15 0.07

windstrx -0.2 -0.63 0.61 0.39 0.02 0.15 0.3 0.53 0.22 0.26 0.01

windstry -0.21 0.2 0.45 -0.06 -0.06 -0.65 -0.5 -0.19 -0.17 -0.12

depth -0.9 -0.76 0.07 -0.29 0.21 -0.26 -0.24 -0.29 -0.08

dland

0.76 -0.02 0.15 -0.18 0.3 0.16 0.2 0.07

dshelf -0.06 0.16 -0.25 0.14 -0.14 -0.05 -0.21

plcurv10km -0.21 0.03 0.03 0.02 0.01 0

prcurv10km 0.05 0.12 0.16 0.18 0.09

prlat 0.84 -0.03 0.03 -0.04

prlon 0.03 0.11 -0.01

slp10km 0.88 0.74

slp1500m 0.63

slpslp10km
29

Table 4b. Predictor correlation table (summer). Pairwise Spearman rank correlation coefficients for spatial predictor variables. High correlations are highlighted 

(|tau|>0.7 in yellow, |tau|>0.8 in orange, |tau|>0.9 in red). chla diverg divw peddacyc peddcyc sshmean sshsd sstfront sstmean

sstsdturbupwellingvortwateruwatervwindstrxwindstrydepthdlanddshelfplcurv10kmprcurv10kmprlatprlonslp10kmslp1500mslpslp10km

chla 0.24 0.2 -0.52 -0.7 -0.78 -0.6 0.67 -0.66 0.63 0.48 0.39 0.14 -0.03 -0.14 -0.15 -0.72 0.76 -0.71 -0.66 0.05 -0.11 0.72 0.31 -0.2 -0.19 -0.13

diverg 0.07 -0.07 -0.14 -0.2 -0.07 0.1 -0.1 0.11 0.06 0.19 0.08 0.01 0.12 -0.02 -0.2 0.16 -0.18 -0.09 0 0.04 0.12 0.05 -0.1 -0.08 -0.06

divw -0.24 -0.11 -0.24 -0.3 -0.06 -0.07 0.11 0.04 -0.09 0.17 -0.23 -0.15 -0.25 -0.27 0.28 -0.23 -0.26 0.02 -0.14 -0.03 -0.13 -0.09 -0.15 0

peddacyc 0.37 0.29 0.6 -0.17 0.13 -0.11 -0.52 -0.04 -0.12 0.09 0.03 0.4 0.33 -0.65 0.59 0.48 -0.04 0.23 -0.14 0.16 0.39 0.4 0.2

peddcyc 0.57 0.65 -0.46 0.39 -0.39 -0.27 -0.37 0.14 -0.06 -0.05 0.22 0.6 -0.61 0.61 0.65 -0.01 0.06 -0.48 -0.18 0.1 0.11 0.05

sshmean 0.6 -0.6 0.63 -0.67 -0.13 -0.42 -0.25 0.09 0.22 0.25 0.87 -0.61 0.55 0.64 -0.03 0.05 -0.64 -0.32 0 0.02 -0.1

sshsd -0.16 0.32 -0.3 -0.42 -0.13 0.07 0.31 0.19 0.62 0.59 -0.77 0.76 0.68 -0.01 0.17 -0.27 0.1 0.25 0.28 0.05

sstfront -0.55 0.6 0.11 0.45 0.2 0.22 0.01 0.24 -0.58 0.32 -0.28 -0.48 0.03 -0.02 0.7 0.5 0.2 0.18 0.14

sstmean

-0.96 -0.09 -0.39 0.02 0.23 0.52 -0.23 0.49 -0.16 0.11 0.15 -0.03 -0.03 -0.94 -0.84 0.11 0.05 0.08

sstsd 0.01 0.41 0.03 -0.21 -0.5 0.24 -0.54 0.13 -0.07 -0.19 0.02 0.03 0.9 0.81 -0.05 -0.02 -0.04

turb 0.03 -0.04 -0.03 -0.03 -0.32 -0.12 0.56 -0.52 -0.31 0.04 -0.2 0.13 -0.21 -0.35 -0.35 -0.25

upwelling -0.13 0.21 0.09 0.16 -0.37 0.18 -0.16 -0.26 -0.01 0.11 0.51 0.38 0.07 0.09 0.05

vort

0.05 0.08 -0.09 -0.31 0.07 -0.09 -0.19 0.04 -0.1 -0.03 -0.04 0.19 0.15 0.19

wateru

0.59 0.3 0.03 -0.1 0.07 -0.02 0.02 0.13 -0.01 -0.01 0.2 0.2 0.02

waterv

0 0.06 0 -0.07 -0.1 -0.01 0.09 -0.35 -0.36 0.17 0.15 0.06

windstrx 0.39 -0.58 0.56 0.37 0 0.16 0.33 0.55 0.21 0.24 0

windstry -0.6 0.58 0.7 -0.06 0.06 -0.51 -0.22 -0.08 -0.04 -0.14

depth -0.9 -0.76 0.07 -0.29 0.21 -0.26 -0.24 -0.29 -0.08

dland 0.76 -0.02 0.15 -0.18 0.3 0.16 0.2 0.07

dshelf -0.06 0.16 -0.25 0.14 -0.14 -0.05 -0.21

plcurv10km -0.21 0.03 0.03 0.02 0.01 0

prcurv10km 0.05 0.12 0.16 0.18 0.09

prlat 0.84 -0.03 0.03 -0.04

prlon 0.03 0.11 -0.01

slp10km 0.88 0.74

slp1500m 0.63

slpslp10km
30

Table 4c. Predictor correlation table (fall). Pairwise Spearman rank correlation coefficients for spatial predictor variables. High correlations are highlighted 

(|tau|>0.7 in yellow, |tau|>0.8 in orange, |tau|>0.9 in red). chla diverg divw peddacyc peddcyc sshmean sshsd sstfront sstmean

sstsdturbupwellingvortwateruwatervwindstrxwindstrydepthdlanddshelfplcurv10kmprcurv10kmprlatprlonslp10kmslp1500mslpslp10km

chla 0.11 0.24 -0.57 -0.68 -0.77 -0.57 0.62 -0.74 0.68 0.49 0.16 0.14 0.09 -0.29 0.53 -0.43 0.77 -0.72 -0.66 0.04 -0.1 0.71 0.3 -0.21 -0.21 -0.13

diverg 0.05 -0.06 -0.06 -0.13 -0.04 0.07 -0.09 0.09 0.07 0.06 0.05 0.03 0.06 0.07 -0.08 0.06 -0.06 -0.04 0.01 0.06 0.13 0.11 -0.04 -0.03 0.02

divw -0.11 -0.24 -0.29 -0.3 0.05 -0.37 0.39 0.14 0.04 -0.03 -0.21 -0.34 0.21 -0.04 0.18 -0.15 -0.14 0.01 -0.03 0.26 0.16 -0.07 -0.08 0

peddacyc 0.37 0.28 0.54 -0.2 0.32 -0.21 -0.49 0.02 -0.04 0.06 0.21 -0.09 0.19 -0.61 0.54 0.45 -0.03 0.24 -0.24 0.05 0.36 0.35 0.2

peddcyc 0.54 0.68 -0.4 0.5 -0.44 -0.31 -0.32 0.17 -0.07 0.09 -0.36 0.27 -0.58 0.6 0.59 0 0.01 -0.5 -0.18 0.1 0.11 0.05

sshmean 0.56 -0.64 0.66 -0.71 -0.13 -0.13 -0.34 -0.01 0.29 -0.51 0.47 -0.58 0.53 0.64 -0.03 0.04 -0.63 -0.32 -0.03 -0.02 -0.12

sshsd -0.2 0.38 -0.32 -0.41 -0.14 0.06 0.3 0.32 -0.07 0.17 -0.73 0.72 0.68 -0.01 0.16 -0.26 0.08 0.21 0.24 0.01

sstfront -0.4 0.48 -0.01 0.14 0.33 0.34 0.04 0.45 -0.59 0.4 -0.35 -0.61 0.04 -0.07 0.52 0.29 0.25 0.19 0.2

sstmean -0.81 -0.4 -0.18 -0.03 0.1 0.52 -0.85 0.01 -0.29 0.24 0.22 -0.02 -0.01 -0.93 -0.75 0.19 0.13 0.14

sstsd 0.3 0.06 0.2 -0.1 -0.51 0.6 -0.26 0.31 -0.25 -0.26 -0.01 0.01 0.67 0.47 -0.12 -0.12 -0.1

turb 0.03 -0.09 -0.21 -0.35 0.1 0.13 0.46 -0.41 -0.13 0.03 -0.18 0.24 0.07 -0.51 -0.48 -0.34

upwelling -0.33 0.15 0.07 0.28 -0.04 0.05 -0.04 -0.1 0.01 0.09 0.29 0.25 0.03 0.05 0

vort 0.04 0.03 -0.03 -0.34 0.08 -0.09 -0.25 0.01 -0.09 0 -0.02 0.22 0.19 0.24

wateru 0.57 0.14 -0.33 -0.09 0.03 -0.07 0.04 0.12 0.14 0.11 0.22 0.23 0.05

waterv -0.28 -0.22 -0.2 0.11 0.03 0 0.11 -0.31 -0.23 0.24 0.23 0.11

windstrx 0.01 -0.01 0.05 -0.1 0.02 0.09 0.94 0.91 0.04 0.09 -0.01

windstry -0.45 0.42 0.67 -0.05 0.04 -0.07 0.23 -0.2 -0.11 -0.13

depth -0.9 -0.76 0.07 -0.29 0.21 -0.26 -0.24 -0.29 -0.08

dland 0.76 -0.02 0.15 -0.18 0.3 0.16 0.2 0.07

dshelf -0.06 0.16 -0.25 0.14 -0.14 -0.05 -0.21

plcurv10km -0.21 0.03 0.03 0.02 0.01 0

prcurv10km 0.05 0.12 0.16 0.18 0.09

prlat 0.84 -0.03 0.03 -0.04

prlon 0.03 0.11 -0.01

slp10km 0.88 0.74

slp1500m 0.63

slpslp10km
31

Table 4d. Predictor correlation table (winter). Predictor correlation table (fall). Pairwise Spearman rank correlation coefficients for spatial predictor variables. High 

correlations are highlighted (|tau|>0.7 in yellow, |tau|>0.8 in orange, |tau|>0.9 in red). chla diverg divw peddacyc peddcyc sshmean sshsd sstfront sstmean sstsd turb upwelling vort wateru

watervwindstrxwindstrydepthdlanddshelfplcurv10kmprcurv10kmprlatprlonslp10kmslp1500mslpslp10km

chla 0.22 0.36 -0.5 -0.63 -0.74 -0.43 0.4 -0.79 0.74 0.71 0.38 0.09 -0.16 -0.35 0.22 -0.69 0.75 -0.68 -0.62 0.04 -0.12 0.7 0.3 -0.23 -0.24 -0.16

diverg 0.1 -0.02 -0.07 -0.21 0 0.07 -0.15 0.22 0.18 0.22 0.05 0.07 0.11 0.06 -0.19 0.14 -0.13 -0.11 0.02 0.03 0.17 0.12 -0.04 -0.06 0

divw -0.09 -0.13 -0.43 -0.19 0.33 -0.31 0.39 0.19 0.05 0.22 -0.16 -0.24 -0.06 -0.2 0.29 -0.22 -0.22 0 -0.05 0.12 -0.03 -0.06 -0.11 -0.04

peddacyc 0.29 0.2 0.5 -0.14 0.28 -0.07 -0.27 -0.05 -0.06 0.24 0.26 0.19 0.12 -0.62 0.58 0.48 -0.05 0.24 -0.18 0.13 0.24 0.25 0.08

peddcyc 0.59 0.64 -0.26 0.5 -0.39 -0.36 -0.41 0.16 0.11 0.19 -0.1 0.52 -0.57 0.55 0.59 0 0.04 -0.51 -0.21 0.07 0.09 0.01

sshmean 0.47 -0.51 0.66 -0.71 -0.39 -0.37 -0.32 0.19 0.31 -0.2 0.77 -0.57 0.5 0.64 -0.03 0.03 -0.61 -0.3 -0.06 -0.04 -0.12

sshsd -0.15 0.34 -0.04 -0.09 -0.17 0.05 0.46 0.3 0.37 0.15 -0.68 0.66 0.65 0 0.14 -0.18 0.11 0.16 0.2 -0.04

sstfront -0.04 0.48 0.07 0.21 0.35 0.14 0.15 -0.05 -0.44 0.43 -0.43 -0.58 0.02 -0.1 0.07 -0.2 0.32 0.21 0.22

sstmean -0.71 -0.7 -0.28 -0.03 0.34 0.63 -0.48 0.64 -0.35 0.3 0.25 -0.02 0.01 -0.89 -0.69 0.23 0.18 0.17

sstsd 0.58 0.4 0.22 0.02 -0.25 0.54 -0.77 0.29 -0.23 -0.37 0.03 0.05 0.73 0.53 0.09 0.08 0

turb 0.22 -0.06 -0.09 -0.38 0.45 -0.47 0.32 -0.21 -0.13 0.06 -0.08 0.64 0.5 -0.28 -0.25 -0.24

upwelling -0.11 0.1 0.07 0.27 -0.45 0.26 -0.22 -0.35 0.02 0.07 0.41 0.27 0.07 0.06 0.06

vort 0.02 0.05 -0.03 -0.25 0.06 -0.08 -0.25 0.01 -0.06 -0.02 -0.04 0.26 0.24 0.26

wateru 0.66 0.17 -0.08 -0.24 0.18 0.14 0.01 0.12 -0.1 -0.04 0.18 0.19 0.01

waterv -0.2 0.16 -0.14 0.06 0.04 -0.01 0.09 -0.44 -0.37 0.18 0.16 0.05

windstrx -0.58 -0.37 0.42 0.15 0.04 0.14 0.71 0.84 0.19 0.24 0.05

windstry -0.32 0.32 0.51 -0.05 -0.04 -0.76 -0.53 -0.17 -0.18 -0.12

depth -0.9 -0.76 0.07 -0.29 0.21 -0.26 -0.24 -0.29 -0.08

dland 0.76 -0.02 0.15 -0.18 0.3 0.16 0.2 0.07

dshelf -0.06 0.16 -0.25 0.14 -0.14 -0.05 -0.21

plcurv10km -0.21 0.03 0.03 0.02 0.01 0

prcurv10km 0.05 0.12 0.16 0.18 0.09

prlat 0.84 -0.03 0.03 -0.04

prlon 0.03 0.11 -0.01

slp10km 0.88 0.74

slp1500m 0.63

slpslp10km
32

Table 5. Base-learners employed in the boosted generalized additive modelling framework. Base-learner 

names are from the ‘mboost’ package for R (Hothorn et al. 2014; R Core Team. 2014), and predictor 

variable names are defined in Table 3.

Name Description Predictor variables Model 

component

bols linear intercept np, mu, th

bols linear boatplane np, mu, th

brandom random effect sid th

brandom random effect tid np, mu

bbs penalized regression spline1yearscaled np, mu

bbs penalized regression spline1jday np, mu

btree tree2all climate indexes (current and lagged) np, mu

bspatial penalized tensor product1prlon

prlat

np, mu

brad penalized radial basis3prlon

prlat

np, mu

btree tree4dland

dshelf

all terrain, physical oceanographic and 

atmospheric, and biological variables

np, mu

1

P-spline basis

2 Maximum depth = 1

3 Matern correlation function

4 Maximum depth = 5
33

Table 6. Model performance metrics. In this report performance metrics are presented for cross-validation 

tuning of the number of boosting iterations and for the final fitted models. The former set of performance 

metrics are indicated with the suffix ‘_CV’ elsewhere in the report. Sometimes the suffix ‘_rel’ is used to 

indicate that the performance metric is expressed relative to the mean of the data.

Name Description

loglikelihood log-likelihood

risk negative log-likelihood

RMSE root mean square error of residual errors

rankR_nz Spearman’s rank correlation coefficient1

rankRG_nz Gaussian rank correlation coefficient1,2

MeanAE_nz mean absolute residual error1

MedianAE_nz median absolute residual error1

MeanBias_nz mean residual error1

MedianBias_nz median residual error1

AUC area under the receiver operating characteristic (ROC) curve3

AUC_nz area under the receiver operating characteristic (ROC) curve1,4

Brier5 multinomial Brier score

CRPS_Zinf6thresholded continuous rank probability score7

CRPS_0 Brier score8

CRPS_nz_Q05 Brier score1,8

CRPS_ nz_Q25 Brier score1,8

CRPS_ nz_Q50 Brier score1,8

CRPS_ nz_Q75 Brier score1,8

CRPS_ nz_Q95 Brier score1,8

PDE9percent deviance explained

1 Non-zero observed values and corresponding fitted values

2

Boudt et al. (2012) and Bodenhofer et al. (2013)

3 Classification as 0/>0

4 Classification above/below median non-zero observed value

5

This performance metric should be interpreted with caution because the categories are ordinal, so the 

standard Brier score is not appropriate

6

Also simply referred to as ‘CRPS’

7 Classification thresholds at 150 equally spaced quantiles of the observed values

8 Classification as 0/>0 or indicated quantile of non-zero data (e.g., Q05 classified above/below 5th

quantile of non-zero observed values)

9

To calculate percent deviance explained, the saturated likelihood was assumed to be the maximum 

possible likelihood value, and the null likelihood was calculated from an intercepts-only zero-inflated 

model fit to the data (unpublished)
34

Table 7. Models evaluated for each species/season.

Model Number Family Maximum Tree Depth Start Values

7 ZIP 5 glm

8 ZINB 5 glm
35

Table 8. Performance metric thresholds used to define model performance categories. Performance 

metrics are defined in Table 6.

Performance metric

Performance category

1 2 3 4 5

AUC x < 0.6 0.6 ≤ x < 0.7 0.7 ≤ x < 0.8 0.8 ≤ x < 0.9 x ≥ 0.9

RankRG_nz x < 0.1 0.1 ≤ x < 0.2 0.2 ≤ x < 0.4 0.4 ≤ x < 0.6 x ≥ 0.6

MedianAE_nz_CV_rel x >= 2.0 2.0 > x ≥ 1.0 1.0 > x ≥ 0.5 0.5 > x ≥ 0.25 x < 0.25

PDE x < 0.1 0.1 ≤ x < 0.2 0.2 ≤ x < 0.4 0.4 ≤ x < 0.6 x ≥ 0.6
36

Table 9. Model selection table. The model selection process is illustrated for 4 example species/season combinations. Two models were 

evaluated for each species/season combination (Table 7). Models are sorted in descending order of performance in terms of the thresholded 

continuous rank probability score (CRPS) from cross-validation tuning of mstop (column marked with an * below). The selected model is in bold 

font, and all subsequent analyses in this report use this selected model. Other model performance metrics are also provided (see Table 6 for 

definitions). Note that risk is not directly comparable across model families (ZIP vs. ZINB). For similar model selection information for all 

species/season combinations evaluated, please see the Digital Data Package (contents listed in Appendix N).

Species 

code Season Mod #

a Model Family Max. 

depth

Start 

values

m

(iterations) PDE AUC(occupancy)

AUC 

(nz)

Rank 

R 

(nz)

Rank

RG

(nz)

Median AE 

(nz)

Median bias 

(nz) Brier (occup.) CRPS Loglikelihood

Risk 

(CV) Fit CV Fit CV Fit CV Fit CV*

cote

summer

7 ZIP 5 glm 6996 0.44 0.93 0.62 0.26 0.3 0.27 0.29 -0.2 -0.21 0.031 0.034 0.03 0.033 -9439 2757

8 ZINB 5 glm 6999 0.43 0.93 0.63 0.28 0.32 0.27 0.3 -0.2 -0.21 0.031 0.034 0.03 0.033 -6799 -973

noga fall 7 ZIP 5 glm 7901 0.52 0.91 0.72 0.48 0.52 0.21 0.22 -0.14 -0.14 0.082 0.088 0.075 0.08 -20417 5380

8 ZINB 5 glm 7999 0.53 0.91 0.74 0.53 0.55 0.2 0.23 -0.15 -0.16 0.084 0.089 0.076 0.081 -14239 -5690

razo winter 7 ZIP 5 glm 8000 0.44 0.93 0.72 0.45 0.47 0.29 0.31 -0.24 -0.24 0.038 0.038 0.037 0.037 -11365 3049

8 ZINB 5 glm 8000 0.38 0.93 0.71 0.44 0.46 0.26 0.25 -0.22 -0.23 0.038 0.039 0.037 0.038 -7816 -3389

wwsc winter 8 ZINB 5 glm 8000 0.5 0.95 0.63 0.29 0.29 0.2 0.18 -0.01 -0.03 0.027 0.028 0.027 0.028 -6766 -22354

7 ZIP 5 glm 5933 0.59 0.95 0.68 0.39 0.40 0.15 0.14 -0.06 -0.08 0.028 0.03 0.027 0.029 -17676 4957

a Model number as in Table 7.
37

Table 10. Final selected models for all species/season combination evaluated, with number of transect 

segments with sightings, number of individuals sighted, proportion of transect segments with sightings 

(prevalence), and mean number of individuals per transect segment with sightings. Numbers of sightings 

and individuals exclude incomplete records (missing predictor data) that were excluded from the analysis, 

and therefore may differ from Tables 1 and 2.

Species Season No. 

sightings

No. 

individuals Prevalence

Mean

non-zero 

count

Model 

Family

Max. 

depth Start values

m 

(boosting 

iterations)

arte summer 154 507 0.005 3.3 ZIP 5 glm 5623

atpu spring 209 472 0.009 2.3 ZIP 5 glm 8000

atpu summer 245 528 0.007 2.2 ZINB 5 glm 8000

atpu fall 91 129 0.004 1.4 ZIP 5 glm 8000

atpu winter 248 444 0.008 1.8 ZINB 5 glm 6999

aush spring 129 443 0.006 3.4 ZINB 5 glm 6994

aush summer 876 2664 0.026 3 ZINB 5 glm 6999

aush fall 286 965 0.012 3.4 ZINB 5 glm 5765

aush winter 163 330 0.006 2 ZINB 5 glm 5548

bcpe spring 158 314 0.007 2 ZINB 5 glm 5999

bcpe summer 356 987 0.01 2.8 ZINB 5 glm 5397

bcpe fall 92 243 0.004 2.6 ZINB 5 glm 5967

bcpe winter 83 212 0.003 2.6 ZINB 5 glm 5999

blgu summer 87 159 0.003 1.8 ZINB 5 glm 8000

blki spring 621 3149 0.028 5.1 ZIP 5 glm 5677

blki fall 2080 14453 0.084 6.9 ZIP 5 glm 4184

blki winter 3684 32431 0.126 8.8 ZINB 5 glm 6999

blsc spring 406 9549 0.018 23.5 ZINB 5 glm 8000

blsc fall 346 8605 0.014 24.9 ZINB 5 glm 8000

blsc winter 1091 55798 0.037 51.1 ZINB 5 glm 8000

bogu spring 394 5425 0.018 13.8 ZINB 5 glm 8000

bogu fall 269 1963 0.011 7.3 ZINB 5 glm 6998

bogu winter 959 9163 0.033 9.6 ZIP 5 glm 5998

brpe spring 62 290 0.003 4.7 ZINB 5 glm 7999

brpe summer 114 413 0.003 3.6 ZIP 5 glm 5959

brpe fall 80 268 0.003 3.4 ZIP 5 glm 5287

brpe winter 65 291 0.002 4.5 ZINB 5 glm 8000

brsp summer 266 552 0.008 2.1 ZINB 5 glm 7999

coei winter 2031 553969 0.069 272.8 ZINB 5 glm 8000

colo spring 2306 5943 0.105 2.6 ZINB 5 glm 8000

colo summer 178 239 0.005 1.3 ZINB 5 glm 6851

colo fall 1158 2715 0.047 2.3 ZIP 5 glm 8000

colo winter 3044 8213 0.104 2.7 ZINB 5 glm 7999

comu spring 90 252 0.004 2.8 ZIP 5 glm 6902

comu winter 158 499 0.005 3.2 ZIP 5 glm 8000
38

cosh spring 106 235 0.005 2.2 ZINB5 glm 6999

cosh summer 2924 13128 0.086 4.5 ZINB5 glm 8000

cosh fall 1546 7574 0.063 4.9 ZINB5 glm 8000

cote spring 467 2182 0.021 4.7 ZINB5 glm 8000

cote summer 1470 6958 0.043 4.7 ZIP5 glm 6996

cote fall 663 6509 0.027 9.8 ZINB5 glm 8000

dcco spring 114 1232 0.005 10.8 ZIP5 glm 5896

dcco summer 168 848 0.0055 ZIP5 glm 6999

dcco fall 196 4600 0.008 23.5 ZIP5 glm 4735

dcco winter 138 1933 0.005 14 ZINB5 glm 8000

dove spring 259 1919 0.012 7.4 ZINB5 glm 5902

dove fall 404 3927 0.016 9.7 ZINB5 glm 6999

dove winter 959 7630 0.0338 ZINB5 glm 6999

gbbg spring 3375 26602 0.153 7.9 ZIP5 glm 5893

gbbg summer 3138 11222 0.093 3.6 ZINB5 glm 6998

gbbg fall 5333 35763 0.216 6.7 ZINB5 glm 5992

gbbg winter 3578 31540 0.122 8.8 ZINB5 glm 6999

grsh spring 585 5121 0.027 8.8 ZINB5 glm 7998

grsh summer 6001 178900 0.177 29.8 ZINB5 glm 8000

grsh fall 6175 93255 0.25 15.1 ZINB5 glm 6998

grsh winter 133 580 0.005 4.4 ZINB5 glm 5861

herg spring 5644 53900 0.256 9.5 ZINB5 glm 5999

herg summer 2880 10669 0.085 3.7 ZINB5 glm 6998

herg fall 7359 59576 0.298 8.1 ZINB5 glm 5942

herg winter 4805 30569 0.164 6.4 ZINB5 glm 8000

hogr winter 82 142 0.003 1.7 ZIP5 glm 8000

lagu spring 673 1642 0.031 2.4 ZIP5 glm 8000

lagu summer 1562 5230 0.046 3.3 ZINB5 glm 7999

lagu fall 1512 8343 0.061 5.5 ZINB5 glm 6978

lagu winter 110 324 0.004 2.9 ZIP5 glm 7877

lesp spring 223 780 0.01 3.5 ZINB5 glm 8000

lesp summer 2138 9504 0.063 4.4 ZINB5 glm 8000

lesp fall 452 1317 0.018 2.9 ZINB5 glm 7999

lete summer 113 414 0.003 3.7 ZIP5 glm 5890

ltdu spring 1121 81577 0.051 72.8 ZINB5 glm 5656

ltdu fall 473 18625 0.019 39.4 ZINB5 glm 8000

ltdu winter 3046 139620 0.104 45.8 ZINB5 glm 8000

mash spring 100 152 0.005 1.5 ZIP5 glm 5987

mash summer 308 878 0.009 2.9 ZINB5 glm 8000

mash fall 264 452 0.011 1.7 ZINB5 glm 8000

nofu spring 2244 30593 0.102 13.6 ZINB5 glm 6964
39

nofu summer 737 12835 0.022 17.4 ZINB5 glm 5999

nofu fall 1823 10055 0.074 5.5 ZINB5 glm 5997

nofu winter 1809 24327 0.062 13.4 ZIP5 glm 5999

noga spring 5585 37629 0.253 6.7 ZINB5 glm 8000

noga summer 1162 2412 0.034 2.1 ZINB5 glm 8000

noga fall 3961 19213 0.16 4.9 ZIP5 glm 7901

noga winter 6298 45486 0.215 7.2 ZIP5 glm 7999

poja spring 110 142 0.005 1.3 ZIP5 glm 8000

poja summer 144 173 0.004 1.2 ZINB5 glm 8000

poja fall 709 999 0.029 1.4 ZINB5 glm 8000

razo spring 712 4373 0.032 6.1 ZINB5 glm 8000

razo summer 78 217 0.002 2.8 ZINB5 glm 8000

razo fall 169 1246 0.007 7.4 ZINB5 glm 6998

razo winter 1535 11661 0.052 7.6 ZIP5 glm 8000

rbgu spring 172 412 0.008 2.4 ZINB5 glm 8000

rbgu fall 285 998 0.012 3.5 ZINB5 glm 8000

rbgu winter 642 2647 0.022 4.1 ZINB5 glm 6998

reph spring 461 85772 0.021 186.1 ZINB5 glm 8000

reph summer 214 26267 0.006 122.7 ZINB5 glm 6999

rnph summer 167 2272 0.005 13.6 ZINB5 glm 8000

rnph fall 156 1105 0.006 7.1 ZIP5 glm 6999

rost spring 53 195 0.002 3.7 ZIP5 glm 8000

rost summer 176 738 0.005 4.2 ZINB5 glm 7990

rost fall 73 467 0.003 6.4 ZINB5 glm 8000

royt spring 262 732 0.012 2.8 ZINB5 glm 5513

royt summer 272 629 0.008 2.3 ZINB5 glm 8000

royt fall 259 624 0.01 2.4 ZINB5 glm 6993

rtlo spring 1644 4927 0.0753 ZINB5 glm 6992

rtlo fall 360 1363 0.015 3.8 ZINB5 glm 8000

rtlo winter 1802 5583 0.061 3.1 ZINB5 glm 8000

sosh spring 788 5925 0.036 7.5 ZIP5 glm 3901

sosh summer 1540 35148 0.045 22.8 ZINB5 glm 5883

sosh fall 104 284 0.004 2.7 ZINB5 glm 6632

susc spring 718 14302 0.033 19.9 ZINB5 glm 8000

susc fall 748 28091 0.03 37.6 ZINB5 glm 5999

susc winter 1664 48178 0.057 29 ZINB5 glm 7999

wisp spring 1650 14890 0.0759 ZINB5 glm 7999

wisp summer 8383 96536 0.247 11.5 ZINB5 glm 5998

wisp fall 1348 7214 0.055 5.4 ZINB5 glm 8000

wwsc spring 400 4889 0.018 12.2 ZINB5 glm 8000

wwsc fall 533 9954 0.022 18.7 ZINB5 glm 8000
40

wwsc winter 1270 20061 0.043 15.8 ZINB 5 glm 8000
41

Table 11. Best models with model performance metrics. All model performance metrics were calculated on the full dataset, except for columns 

divided into ‘Fit’ and ‘CV’, which denote metrics calculated separately for the full dataset and for out-of-bag data during cross-validation tuning of 

mstop, respectively. The overall model performance category is the rounded average of performance categories across four performance metrics 

(PDE, AUC, Rank RG_nz, and MedianAE_nz_CV_rel; see Section 2.4.6). Particularly poor performance in terms of individual performance metrics 

is indicated in red.

Species 

code Season Model Family

Max. 

depth

Start 

values PDE AUC AUC_nz

RankR

_nz

RankRG

_nz

MedianAE_nz_rel MedianBias_nz_rel CRPS_0 CRPS_Zinf Overall 

model 

performance 

category

Model 

quality 

(expert 

opinion)

Fit CV Fit CV Fit CV Fit CV

arte summer ZIP 5 glm 0.11 0.94 0.74 0.44 0.41 0.36 0.41 -0.32 -0.37 0.000 0.000 0.000 0.000 4 FAIR

atpu spring ZIP 5 glm 0.34 0.93 0.71 0.41 0.4 0.44 0.44 -0.44 -0.44 0.010 0.010 0.010 0.010 4 GOOD

atpu summer ZINB 5 glm 0.53 0.98 0.7 0.41 0.47 0.44 0.46 -0.44 -0.46 0.010 0.010 0.010 0.010 4 GOOD

atpu fall ZIP 5 glm 0.4 0.96 0.7 0.32 0.37 0.69 0.7 -0.69 -0.7 0.000 0.000 0.000 0.000 4 FAIR

atpu winter ZINB 5 glm 0.4 0.95 0.58 0.17 0.22 0.55 0.55 -0.55 -0.55 0.010 0.010 0.010 0.010 4 FAIR

aush spring ZINB 5 glm 0.41 0.99 0.7 0.44 0.49 0.43 0.38 -0.16 -0.26 0.000 0.000 0.000 0.000 4 FAIR

aush summer ZINB 5 glm 0.51 0.95 0.73 0.44 0.46 0.32 0.32 -0.3 -0.31 0.020 0.020 0.020 0.020 4 GOOD

aush fall ZINB 5 glm 0.53 0.95 0.76 0.52 0.54 0.29 0.3 -0.29 -0.29 0.010 0.010 0.010 0.010 4 FAIR

aush winter ZINB 5 glm 0.76 1 0.84 0.61 0.63 0.38 0.48 -0.32 -0.45 0.000 0.000 0.000 0.000 5 FAIR

bcpe spring ZINB 5 glm 0.54 0.99 0.79 0.51 0.49 0.5 0.5 -0.32 -0.48 0.000 0.000 0.000 0.000 4 GOOD

bcpe summer ZINB 5 glm 0.63 0.98 0.78 0.53 0.54 0.34 0.35 -0.32 -0.35 0.010 0.010 0.010 0.010 5 GOOD

bcpe fall ZINB 5 glm 0.33 1 0.82 0.59 0.59 0.36 0.38 -0.17 -0.38 0.000 0.000 0.000 0.000 4 GOOD

bcpe winter ZINB 5 glm 0.28 1 0.95 0.84 0.81 0.39 0.66 -0.28 -0.39 0.000 0.000 0.000 0.000 4 GOOD

blgu summer ZINB 5 glm 0.33 0.99 0.58 -0.17 -0.17 1.7E+9 9.9E+8 1.7E+9 9.9E+8 0.000 0.000 0.000 0.000 3 FAIR

blki spring ZIP 5 glm 0.45 0.93 0.59 0.18 0.22 0.19 0.2 -0.19 -0.19 0.020 0.020 0.020 0.020 4 FAIR

blki fall ZIP 5 glm 0.58 0.94 0.69 0.38 0.4 0.18 0.19 -0.09 -0.1 0.050 0.050 0.040 0.050 5 GOOD

blki winter ZINB 5 glm 0.57 0.95 0.75 0.51 0.53 0.18 0.18 -0.06 -0.07 0.060 0.060 0.050 0.050 5 GOOD

blsc spring ZINB 5 glm 0.43 0.94 0.61 0.3 0.33 0.18 0.17 -0.11 -0.12 0.010 0.020 0.010 0.020 4 FAIR

blsc fall ZINB 5 glm 0.47 0.96 0.55 0.18 0.21 0.23 0.26 -0.09 -0.13 0.010 0.010 0.010 0.010 4 FAIR

blsc winter ZINB 5 glm 0.38 0.91 0.64 0.26 0.28 0.11 0.1 -0.05 -0.06 0.030 0.030 0.030 0.030 4 FAIR
42

bogu spring ZINB 5 glm 0.27 0.9 0.54 0.08 0.11 0.12 0.13 -0.11 -0.13 0.020 0.020 0.020 0.020 4 POOR

bogu fall ZINB 5 glm 0.4 0.92 0.67 0.34 0.39 0.25 0.26 -0.22 -0.24 0.010 0.010 0.010 0.010 4 POOR

bogu winter ZIP 5 glm 0.44 0.87 0.68 0.39 0.43 0.18 0.18 -0.13 -0.14 0.030 0.030 0.030 0.030 4 FAIR

brpe spring ZINB 5 glm 0 0.98 0.66 0.13 0.15 0.24 0.43 -0.21 -0.37 0.000 0.000 0.000 0.000 3 FAIR

brpe summer ZIP 5 glm 0 0.92 0.58 0.23 0.31 0.28 0.28 -0.28 -0.28 0.000 0.000 0.000 0.000 3 POOR

brpe fall ZIP 5 glm 0.48 0.99 0.65 0.29 0.3 0.3 0.33 -0.29 -0.3 0.000 0.000 0.000 0.000 4 GOOD

brpe winter ZINB 5 glm 0 0.93 0.65 0.35 0.35 0.45 0.34 -0.45 -0.34 0.000 0.000 0.000 0.000 3 POOR

brsp summer ZINB 5 glm 0.52 0.96 0.7 0.39 0.46 0.46 0.47 -0.46 -0.47 0.010 0.010 0.010 0.010 4 GOOD

coei winter ZINB 5 glm 0.55 0.97 0.55 0.1 0.1 0.33 0.35 0.24 0.27 0.030 0.040 0.030 0.030 4 FAIR

colo spring ZINB 5 glm 0.42 0.9 0.7 0.4 0.41 0.35 0.36 -0.32 -0.34 0.060 0.070 0.060 0.070 4 FAIR

colo summer ZINB 5 glm 0.36 0.95 0.63 0.21 0.25 0.73 0.73 -0.73 -0.73 0.000 0.000 0.000 0.000 4 FAIR

colo fall ZIP 5 glm 0.41 0.94 0.68 0.33 0.33 0.4 0.41 -0.37 -0.39 0.030 0.030 0.030 0.030 4 FAIR

colo winter ZINB 5 glm 0.36 0.83 0.65 0.29 0.31 0.32 0.33 -0.32 -0.32 0.080 0.080 0.070 0.070 4 FAIR

comu spring ZIP 5 glm 0.24 0.95 0.69 0.4 0.46 0.48 0.36 -0.48 -0.36 0.000 0.010 0.000 0.010 4 FAIR

comu winter ZIP 5 glm 0.34 0.96 0.74 0.52 0.53 0.5 0.61 -0.45 -0.61 0.000 0.010 0.000 0.010 4 FAIR

cosh spring ZINB 5 glm 0.48 0.98 0.66 0.27 0.27 0.43 0.44 -0.43 -0.44 0.000 0.000 0.000 0.000 4 GOOD

cosh summer ZINB 5 glm 0.33 0.87 0.66 0.33 0.34 0.2 0.21 -0.18 -0.19 0.060 0.070 0.060 0.070 4 GOOD

cosh fall ZINB 5 glm 0.46 0.91 0.72 0.46 0.48 0.22 0.23 -0.19 -0.19 0.040 0.050 0.040 0.040 5 GOOD

cote spring ZINB 5 glm 0.53 0.97 0.6 0.23 0.24 0.34 0.38 -0.29 -0.35 0.010 0.010 0.010 0.010 4 GOOD

cote summer ZIP 5 glm 0.44 0.93 0.62 0.26 0.3 0.27 0.29 -0.2 -0.21 0.030 0.030 0.030 0.030 4 FAIR

cote fall ZINB 5 glm 0.44 0.93 0.66 0.38 0.39 0.24 0.21 -0.15 -0.18 0.020 0.020 0.020 0.020 4 FAIR

dcco spring ZIP 5 glm 0.26 0.93 0.55 0.08 0.11 0.17 0.24 -0.15 -0.22 0.000 0.010 0.000 0.010 4 POOR

dcco summer ZIP 5 glm 0.06 0.92 0.56 0.18 0.24 0.2 0.2 -0.2 -0.2 0.000 0.010 0.000 0.010 4 FAIR

dcco fall ZIP 5 glm 0.42 0.88 0.63 0.27 0.3 0.08 0.08 -0.04 -0.06 0.010 0.010 0.010 0.010 4 POOR

dcco winter ZINB 5 glm 0.34 0.92 0.72 0.47 0.52 0.13 0.09 -0.07 -0.07 0.000 0.000 0.000 0.000 4 FAIR

dove spring ZINB 5 glm 0.41 0.93 0.75 0.56 0.58 0.26 0.27 -0.26 -0.26 0.010 0.010 0.010 0.010 4 GOOD

dove fall ZINB 5 glm 0.62 0.99 0.71 0.46 0.47 0.28 0.28 -0.1 -0.13 0.010 0.010 0.010 0.010 5 GOOD

dove winter ZINB 5 glm 0.49 0.93 0.68 0.45 0.49 0.22 0.23 -0.14 -0.17 0.020 0.020 0.020 0.020 5 GOOD
43

gbbg spring ZIP 5 glm 0.6 0.87 0.69 0.41 0.44 0.17 0.17 -0.09 -0.09 0.090 0.100 0.090 0.090 5 GOOD

gbbg summer ZINB 5 glm 0.47 0.91 0.67 0.34 0.35 0.26 0.26 -0.21 -0.21 0.060 0.070 0.060 0.060 4 GOOD

gbbg fall ZINB 5 glm 0.37 0.84 0.67 0.36 0.38 0.2 0.2 -0.07 -0.08 0.130 0.130 0.110 0.120 4 GOOD

gbbg winter ZINB 5 glm 0.53 0.9 0.73 0.45 0.48 0.13 0.13 -0.07 -0.07 0.070 0.070 0.070 0.070 5 FAIR

grsh spring ZINB 5 glm 0.72 0.98 0.78 0.59 0.62 0.19 0.21 -0.08 -0.09 0.010 0.010 0.010 0.010 5 FAIR

grsh summer ZINB 5 glm 0.56 0.92 0.72 0.44 0.45 0.1 0.09 0.01 0 0.080 0.090 0.080 0.080 5 GOOD

grsh fall ZINB 5 glm 0.59 0.95 0.72 0.47 0.48 0.26 0.26 0.05 0.04 0.080 0.080 0.080 0.080 4 GOOD

grsh winter ZINB 5 glm 0.71 0.98 0.85 0.65 0.66 0.23 0.23 -0.23 -0.23 0.000 0.000 0.000 0.000 5 FAIR

herg spring ZINB 5 glm 0.41 0.84 0.71 0.47 0.49 0.18 0.18 -0.05 -0.06 0.130 0.140 0.120 0.120 4 FAIR

herg summer ZINB 5 glm 0.48 0.91 0.68 0.37 0.39 0.25 0.25 -0.21 -0.2 0.060 0.060 0.050 0.060 4 FAIR

herg fall ZINB 5 glm 0.38 0.84 0.67 0.39 0.41 0.21 0.21 -0.03 -0.03 0.150 0.150 0.130 0.130 4 GOOD

herg winter ZINB 5 glm 0.43 0.87 0.69 0.41 0.44 0.17 0.16 -0.1 -0.1 0.100 0.100 0.090 0.090 4 FAIR

hogr winter ZIP 5 glm 0.24 0.95 0.71 0.32 0.33 0.57 0.58 -0.57 -0.58 0.000 0.000 0.000 0.000 4 POOR

lagu spring ZIP 5 glm 0.47 0.94 0.67 0.32 0.36 0.38 0.39 -0.38 -0.39 0.020 0.020 0.020 0.020 4 FAIR

lagu summer ZINB 5 glm 0.53 0.95 0.72 0.44 0.47 0.29 0.29 -0.27 -0.28 0.030 0.030 0.030 0.030 4 FAIR

lagu fall ZINB 5 glm 0.52 0.94 0.68 0.42 0.45 0.25 0.27 -0.16 -0.17 0.040 0.040 0.040 0.040 4 GOOD

lagu winter ZIP 5 glm 0.42 0.98 0.74 0.51 0.54 0.34 0.37 -0.34 -0.34 0.000 0.000 0.000 0.000 4 FAIR

lesp spring ZINB 5 glm 0.53 0.97 0.69 0.34 0.37 0.28 0.28 -0.27 -0.28 0.010 0.010 0.010 0.010 4 GOOD

lesp summer ZINB 5 glm 0.54 0.94 0.7 0.43 0.47 0.26 0.28 -0.2 -0.21 0.040 0.040 0.040 0.040 4 GOOD

lesp fall ZINB 5 glm 0.59 0.97 0.72 0.45 0.47 0.33 0.34 -0.31 -0.33 0.010 0.010 0.010 0.010 4 GOOD

lete summer ZIP 5 glm 0.03 0.91 0.62 0.29 0.36 0.27 0.27 -0.27 -0.27 0.000 0.000 0.000 0.000 3 FAIR

ltdu spring ZINB 5 glm 0.64 0.98 0.75 0.55 0.55 0.13 0.14 0.03 0.03 0.020 0.020 0.020 0.020 5 GOOD

ltdu fall ZINB 5 glm 0.72 0.99 0.81 0.62 0.62 0.16 0.15 0.01 0 0.010 0.010 0.010 0.010 5 GOOD

ltdu winter ZINB 5 glm 0.6 0.97 0.73 0.47 0.48 0.23 0.23 0.1 0.08 0.040 0.040 0.040 0.040 5 GOOD
44

mash spring ZIP 5 glm 0.2 0.89 0.6 0.16 0.25 0.65 0.65 -0.65 -0.65 0.000 0.000 0.000 0.000 3 GOOD

mash summer ZINB 5 glm 0.25 0.83 0.68 0.29 0.33 0.34 0.34 -0.34 -0.34 0.010 0.010 0.010 0.010 4 FAIR

mash fall ZINB 5 glm 0.31 0.9 0.74 0.38 0.41 0.57 0.57 -0.57 -0.57 0.010 0.010 0.010 0.010 4 GOOD

nofu spring ZINB 5 glm 0.62 0.96 0.76 0.57 0.58 0.14 0.14 -0.03 -0.04 0.050 0.050 0.040 0.050 5 GOOD

nofu summer ZINB 5 glm 0.7 0.98 0.72 0.48 0.52 0.07 0.07 -0.04 -0.05 0.010 0.010 0.010 0.010 5 GOOD

nofu fall ZINB 5 glm 0.61 0.95 0.77 0.58 0.59 0.19 0.19 -0.15 -0.15 0.040 0.040 0.040 0.040 5 GOOD

nofu winter ZIP 5 glm 0.62 0.98 0.73 0.49 0.52 0.18 0.17 -0.03 -0.05 0.020 0.020 0.020 0.020 5 GOOD

noga spring ZINB 5 glm 0.39 0.85 0.7 0.44 0.46 0.2 0.19 -0.07 -0.08 0.140 0.150 0.120 0.130 4 GOOD

noga summer ZINB 5 glm 0.47 0.93 0.72 0.41 0.45 0.43 0.45 -0.42 -0.44 0.030 0.030 0.020 0.030 4 POOR

noga fall ZIP 5 glm 0.52 0.91 0.72 0.48 0.52 0.21 0.22 -0.14 -0.14 0.080 0.090 0.070 0.080 5 GOOD

noga winter ZIP 5 glm 0.55 0.85 0.72 0.45 0.48 0.16 0.18 -0.07 -0.07 0.120 0.130 0.110 0.110 4 GOOD

poja spring ZIP 5 glm 0.31 0.93 0.69 0.27 0.31 0.76 0.76 -0.76 -0.76 0.000 0.000 0.000 0.000 4 FAIR

poja summer ZINB 5 glm 0.12 0.83 0.63 0.17 0.2 0.83 0.83 -0.83 -0.83 0.000 0.000 0.000 0.000 3 FAIR

poja fall ZINB 5 glm 0.29 0.89 0.66 0.26 0.28 0.66 0.66 -0.66 -0.66 0.030 0.030 0.020 0.020 3 GOOD

razo spring ZINB 5 glm 0.4 0.94 0.7 0.4 0.44 0.35 0.37 -0.33 -0.32 0.020 0.030 0.020 0.030 4 FAIR

razo summer ZINB 5 glm 0.44 0.98 0.8 0.6 0.63 0.71 0.71 -0.6 -0.6 0.000 0.000 0.000 0.000 4 GOOD

razo fall ZINB 5 glm 0.51 0.97 0.74 0.54 0.57 0.26 0.24 -0.2 -0.15 0.010 0.010 0.010 0.010 5 FAIR

razo winter ZIP 5 glm 0.44 0.93 0.72 0.45 0.47 0.29 0.31 -0.24 -0.24 0.040 0.040 0.040 0.040 4 GOOD

rbgu spring ZINB 5 glm 0.31 0.91 0.67 0.3 0.32 0.41 0.41 -0.41 -0.41 0.010 0.010 0.010 0.010 4 POOR

rbgu fall ZINB 5 glm 0.36 0.9 0.75 0.46 0.5 0.28 0.28 -0.28 -0.28 0.010 0.010 0.010 0.010 4 POOR

rbgu winter ZINB 5 glm 0.28 0.86 0.61 0.2 0.23 0.24 0.24 -0.24 -0.24 0.020 0.020 0.020 0.020 4 FAIR

reph spring ZINB 5 glm 0.48 0.96 0.66 0.35 0.39 0.1 0.09 0 -0.01 0.010 0.020 0.010 0.010 4 GOOD

reph summer ZINB 5 glm 0.51 0.96 0.73 0.45 0.45 0.04 0.05 -0.01 -0.01 0.000 0.000 0.000 0.000 5 GOOD

rnph summer ZINB 5 glm 0.33 0.93 0.65 0.33 0.38 0.31 0.33 -0.26 -0.3 0.000 0.000 0.000 0.000 4 GOOD
45

rnph fall ZIP 5 glm 0 0.87 0.6 0.2 0.23 0.29 0.31 -0.28 -0.31 0.010 0.010 0.010 0.010 3 FAIR

rost spring ZIP 5 glm 0 0.97 0.56 0.08 0.13 0.31 0.52 -0.31 -0.52 0.000 0.000 0.000 0.000 3 POOR

rost summer ZINB 5 glm 0.45 0.96 0.58 0.25 0.31 0.48 0.48 -0.22 -0.23 0.000 0.010 0.000 0.010 4 FAIR

rost fall ZINB 5 glm 0.54 0.97 0.61 0.27 0.31 0.29 0.29 -0.16 -0.27 0.000 0.000 0.000 0.000 4 FAIR

royt spring ZINB 5 glm 0.49 0.96 0.57 0.15 0.2 0.35 0.36 -0.35 -0.35 0.010 0.010 0.010 0.010 4 FAIR

royt summer ZINB 5 glm 0.52 0.97 0.74 0.47 0.51 0.42 0.43 -0.42 -0.43 0.010 0.010 0.010 0.010 4 GOOD

royt fall ZINB 5 glm 0.44 0.96 0.68 0.39 0.4 0.41 0.41 -0.41 -0.41 0.010 0.010 0.010 0.010 4 GOOD

rtlo spring ZINB 5 glm 0.41 0.9 0.69 0.39 0.42 0.3 0.31 -0.29 -0.3 0.050 0.060 0.050 0.060 4 FAIR

rtlo fall ZINB 5 glm 0.51 0.96 0.72 0.48 0.51 0.26 0.26 -0.25 -0.26 0.010 0.010 0.010 0.010 4 FAIR

rtlo winter ZINB 5 glm 0.34 0.87 0.69 0.39 0.43 0.3 0.31 -0.3 -0.31 0.050 0.050 0.050 0.050 4 FAIR

sosh spring ZIP 5 glm 0.45 0.95 0.67 0.38 0.41 0.17 0.17 -0.11 -0.12 0.020 0.020 0.020 0.020 5 GOOD

sosh summer ZINB 5 glm 0.56 0.93 0.72 0.45 0.48 0.05 0.06 -0.04 -0.04 0.030 0.030 0.030 0.030 5 GOOD

sosh fall ZINB 5 glm 0.22 0.9 0.65 0.29 0.34 0.36 0.36 -0.36 -0.36 0.000 0.000 0.000 0.000 4 GOOD

susc spring ZINB 5 glm 0.54 0.97 0.62 0.24 0.27 0.22 0.23 -0.06 -0.08 0.020 0.020 0.020 0.020 4 FAIR

susc fall ZINB 5 glm 0.62 0.97 0.7 0.42 0.38 0.34 0.32 0.07 0.07 0.010 0.010 0.010 0.010 4 FAIR

susc winter ZINB 5 glm 0.6 0.97 0.73 0.44 0.45 0.28 0.3 0.02 -0.01 0.020 0.030 0.020 0.020 5 FAIR

wisp spring ZINB 5 glm 0.61 0.97 0.69 0.39 0.4 0.21 0.2 -0.01 -0.03 0.040 0.040 0.030 0.040 5 GOOD

wisp summer ZINB 5 glm 0.46 0.86 0.68 0.4 0.42 0.2 0.2 0 -0.01 0.130 0.130 0.110 0.120 4 FAIR

wisp fall ZINB 5 glm 0.5 0.96 0.63 0.3 0.31 0.21 0.21 -0.11 -0.13 0.030 0.040 0.030 0.030 4 GOOD

wwsc spring ZINB 5 glm 0.44 0.95 0.59 0.22 0.24 0.11 0.14 -0.08 -0.12 0.010 0.020 0.010 0.020 4 FAIR

wwsc fall ZINB 5 glm 0.54 0.97 0.74 0.51 0.54 0.21 0.2 -0.07 -0.1 0.010 0.010 0.010 0.010 5 FAIR

wwsc winter ZINB 5 glm 0.5 0.95 0.63 0.29 0.29 0.2 0.18 -0.01 -0.03 0.030 0.030 0.030 0.030 4 FAIR
46

Table 12. Groups of species with similar spatial distributions chosen for averaginga.

Group 

Number

Family Number 

of 

Species

Species in group Types of species

1 Nearshore 15 arte, blsc, brpe, coei, colo, 

cote, dcco, hogr, lete, ltdu, 

rost, royt, rtlo, susc, wwsc

Seaducks, Loons, Terns, 

2 Pelagic 18 atpu, aush, bcpe, blgu, 

brsp, comu, cosh, dove, 

grsh, lesp, mash, nofu, 

poja, razo, reph, rnph, 

sosh, wisp

Petrels, Shearwaters, 

Jaegers, Phalaropes, Alcids

3 Gulls and 

Gannets

7 blki, bogu, gbbg, herg, lagu, 

noga, rbgu

Small gulls, Large gulls, 

Gannets

a Note that the groups described in this table are not based on similar taxonomy or ecological traits, but 

rather on broad similarities in patterns of spatial distribution as reflected in species model results.
47

Figure 1. Study area with Wind Energy Planning and Lease Areas overlaid 

(approximate boundaries current as of 05 Feb 2015).
48

Figure 2A. Map of survey effort. Midpoints of standardized transect segments within 

the study area are plotted in blue (boat surveys) and red (aerial surveys) for each 

season analyzed. For complete list of datasets, see Appendix A.
49

Figure 2B. Map of survey effort. Number of survey midpoints of standardized 

transect segments summed across 10 x 10 km cells within the study area for each 

season analyzed: a) winter b) spring c) summer d) fall. Colored contours indicate 

survey intensity. For complete list of datasets, see Appendix A.

a. b.

c. d.
50

Figure 3. Examples of temporal and spatial predictors used in models; a) Climate 

index time series (AMO); b) static spatial predictor (depth); c) seasonal spatial 

predictor (front probability climatology for spring). For complete set of predictor plots,

see Appendix B.

c.

a.

b.
51

Figure 4. Schematic overview of statistical modeling process. See Section 2 

Methods for details.
52

Figure 5.1. Cross-validation model performance metrics during stochastic gradient 

boosting for example model 7 (COTE/summer). a) Brier score, b) thresholded 

continuous rank probability score (CRPS), c) Median Absolute Error for non-zero 

data, d) Median Bias for non-zero data, e) root mean square error (RMSE), f) 

negative log-likelihood (risk). The optimized metrics were CRPS (panel b) and risk 

(panel f). For complete set of gradient descent plots, see Appendix C.

a. b.

c. d.

e. f.
53

Figure 5.2. Cross-validation model performance metrics during stochastic gradient 

boosting for example model 7 (NOGA/fall). a) Brier score, b) thresholded continuous 

rank probability score (CRPS), c) Median Absolute Error for non-zero data, d) 

Median Bias for non-zero data, e) root mean square error (RMSE), f) negative loglikelihood (risk). The optimized metrics were CRPS (panel b) and risk (panel f). For 

complete set of gradient descent plots, see Appendix C.

a. b.

c. d.

e. f.
54

Figure 5.3. Cross-validation model performance metrics during stochastic gradient 

boosting for example model 7 (RAZO/winter). a) Brier score, b) thresholded 

continuous rank probability score (CRPS), c) Median Absolute Error for non-zero 

data, d) Median Bias for non-zero data, e) root mean square error (RMSE), f) 

negative log-likelihood (risk). The optimized metrics were CRPS (panel b) and risk 

(panel f). For complete set of gradient descent plots, see Appendix C.

a. b.

c. d.

e. f.
55

Figure 5.4. Cross-validation model performance metrics during stochastic gradient 

boosting for example model 8 (WWSC/winter). a) Brier score, b) thresholded 

continuous rank probability score (CRPS), c) Median Absolute Error for non-zero 

data, d) Median Bias for non-zero data, e) root mean square error (RMSE), f) 

negative log-likelihood (risk). The optimized metrics were CRPS (panel b) and risk 

(panel f). For complete set of gradient descent plots, see Appendix C.

a. b.

c. d.

e. f.
56

Figure 6AB. Comparison of model performance metrics for the best models (the final 

selected model out of the two compared) for each species/season combination. a) 

bestm, number of iterations to model convergence; b) rankR, Spearman rank 

correlation of observed non-zero data vs. predicted values. See also Tables 10 and 

11 and Appendix E.

a.

b.
57

Figure 6CD. Comparison of model performance metrics for the best models (the final selected 

model out of the two compared) for each species/season combination. c) AUC, area under the 

receiver operating characteristic curve calculated for all data categorized as 0 or >0; d) 

AUC_nz, AUC calculated for non-zero data categorized as < the median or > the median. See 

also Tables 10 and 11 and Appendix E.

c.

d.
58

Figure 6EF. Comparison of model performance metrics for the best models (the final selected 

model out of the two compared) for each species/season combination. e) MedianAE_nz_rel, 

median absolute error for non-zero data relative to their mean; f) MedianAE_nz_CV_rel, median 

absolute error for non-zero data relative to their mean during cross-validation tuning. See also 

Tables 10 and 11 and Appendix E.

e.

f.
59

Figure 6GH. Comparison of model performance metrics for the best models (the final 

selected model out of the two compared) for each species/season combination. g) 

MedianBias_nz_rel, median error for non-zero data relative to their mean; h) 

MedianBias_nz_rel, median error for non-zero data relative to their mean during 

cross-validation tuning. See also Tables 10 and 11 and Appendix E.

g.

h.
60

Figure 6IJ. Comparison of model performance metrics for the best models (the final 

selected model out of the two compared) for each species/season combination. i) 

CRPS_0, Brier score (occupancy); j) CRPS_0_CV, Brier score during crossvalidation tuning. See also Tables 10 and 11 and Appendix E.

i.

j.
61

Figure 6KL. Comparison of model performance metrics for the best models (the final 

selected model out of the two compared) for each species/season combination. k) 

CRPS_Zinf, thresholded continuous rank probability score (CRPS); l) 

CRPS_Zinf_CV, CRPS during cross-validation tuning. See also Tables 10 and 11 

and Appendix E.

k.

l.
62

Figure 6MN. Comparison of model performance metrics for the best models (the final selected 

model out of the two compared) for each species/season combination. m) rankRG_nz, 

Gaussian rank correlation of observed non-zero data vs. predicted values; n) pde, percent 

deviance explained. See also Tables 10 and 11 and Appendix E.

m.

n.
63

Figure 7.1. Receiver Operating Characteristic (ROC) curves for assessment of predictive 

accuracy of example model 7 (COTE/summer). a) ROC curve and area under the curve 

(AUC) statistic for prediction of median-thresholded non-zero data (i.e., non-zero data 

above/below median observed value), b) ROC curve and AUC statistic for prediction of 

presence/absence. For complete set of ROC curves for selected models, see Appendix F. 

For complete set of AUC statistics for selected models, see Table 11.

a.

b.
64

Figure 7.2. Receiver Operating Characteristic (ROC) curves for assessment of 

predictive accuracy of example model 7 (NOGA/fall). a) ROC curve and area under 

the curve (AUC) statistic for prediction of median-thresholded non-zero data (i.e., 

non-zero data above/below median observed value), b) ROC curve and AUC 

statistic for prediction of presence/absence. For complete set of ROC curves for 

selected models, see Appendix F. For complete set of AUC statistics for selected 

models, see Table 11.

a.

b.
65

Figure 7.3. Receiver Operating Characteristic (ROC) curves for assessment of predictive 

accuracy of example model 7 (RAZO/winter). a) ROC curve and area under the curve 

(AUC) statistic for prediction of median-thresholded non-zero data (i.e., non-zero data 

above/below median observed value), b) ROC curve and AUC statistic for prediction of 

presence/absence. For complete set of ROC curves for selected models, see Appendix F. 

For complete set of AUC statistics for selected models, see Table 11.

a.

b.
66

Figure 7.4. Receiver Operating Characteristic (ROC) curves for assessment of 

predictive accuracy of example model 8 (WWSC/winter). a) ROC curve and area 

under the curve (AUC) statistic for prediction of median-thresholded non-zero data 

(i.e., non-zero data above/below median observed value), b) ROC curve and AUC 

statistic for prediction of presence/absence. For complete set of ROC curves for 

selected models, see Appendix F. For complete set of AUC statistics for selected 

models, see Table 11.

a.

b.
67

Figure 8.1. Brier score calculated at quantile thresholds of non-zero data for example 

model 7 (COTE/summer). For complete set of Brier score plots for selected models, 

see Appendix G. For complete set of Brier score statistics and related thresholded 

continuous rank probability scores (CPRS) for selected models, see Table 11.

Figure 8.2. Brier score calculated at quantile thresholds of non-zero data for example 

model 7 (NOGA/fall). For complete set of Brier score plots for selected models, see 

Appendix G. For complete set of Brier score statistics and related thresholded 

continuous rank probability scores (CPRS) for selected models, see Table 11.
68

Figure 8.3. Brier score calculated at quantile thresholds of non-zero data for example 

model 7 (RAZO/winter). For complete set of Brier score plots for selected models, 

see Appendix G. For complete set of Brier score statistics and related thresholded 

continuous rank probability scores (CPRS) for selected models, see Table 11.

Figure 8.4. Brier score calculated at quantile thresholds of non-zero data for example 

model 8 (WWSC/winter). For complete set of Brier score plots for selected models, 

see Appendix G. For complete set of Brier score statistics and related thresholded 

continuous rank probability scores (CPRS) for selected models, see Table 11.
69

Figure 9.1. Variable importance bar plots for top variables in example model 7 (COTE/summer). 

a) np component; b) mu component. For complete set of variable importance bar plots, see 

Appendix H.

a. b.
70

Figure 9.2. Variable importance bar plots for top variables in example model 7 

(NOGA/fall). a) np component; b) mu component. For complete set of variable importance 

bar plots, see Appendix H.

a. b.
71

Figure 9.3. Variable importance bar plots for top variables in example model 7 

(RAZO/winter). a) np component; b) mu component. For complete set of variable 

importance bar plots, see Appendix H.

a. b.
72

Figure 9.4. Variable importance bar plots for top variables in example model 8 (WWSC/winter). 

a) np component; b) mu component; c) theta component. For complete set of variable 

importance bar plots, see Appendix H.

a.
73

Figure 9.4. Continued.

b.
74

Figure 9.4. Continued.

c.
75

Figure 10. Summary of variable importance across all variables, species, and 

seasons modeled. a) np component; b) mu component; c) theta component. For 

complete set of variable importance plots, see Appendix H.

a. np

b. mu

c. th
76

Figure 11. Variable importance for np model component: average importance of each variable 

calculated over all final selected models in each season. Note that average importance values 

have been sorted in descending order for each season, so that the order of variables along the 

x-axis varies from season to season. Also note that the species modeled in each season, and 

therefore the species included in the average for each seasonal panel of this plot, vary as 

shown in Table 2. For complete set of variable importance plots, see Appendix H.
77

Figure 12. Variable importance for mu model component: average importance of each variable 

calculated over all final selected models in each season. Note that average importance values 

have been sorted in descending order for each season, so that the order of variables along the 

x-axis varies from season to season. Also note that the species modeled in each season, and 

therefore the species included in the average for each seasonal panel of this plot, vary as 

shown in Table 2. For complete set of variable importance plots, see Appendix H.
78

Figure 13. Variable importance for theta (th) model component: average importance of each 

variable calculated over all final selected models in each season. Note that average importance 

values have been sorted in descending order for each season, so that the order of variables 

along the x-axis varies from season to season. Also note that the species modeled in each 

season, and therefore the species included in the average for each seasonal panel of this plot, 

vary as shown in Table 2. Only species for which the ZINB model (Model 8) was selected are 

included in the average for the theta component (see Table 10 for the selected model for each 

species/season combination). Predictors used in the theta ensemble were limited to survey 

variables (sid, boatplane) and an intercept. For complete set of variable importance plots, see 

Appendix H.
79

Figure 14.1. Selected bootstrap marginal plots for example model 7 

(COTE/summer). Solid line is bootstrap mean and grey shading indicates +/- 1 

bootstrap standard deviation. a) np component of model; b) mu component of model. 

Full sets of marginal plots in Appendix I.

a. selected bootstrap marginal plots from np model component 

b. selected bootstrap marginal plots from mu model component 
80

Figure 14.2. Selected bootstrap marginal plots for example model 7 (NOGA/fall). 

Solid line is bootstrap mean and grey shading indicates +/- 1 bootstrap standard 

deviation. a) np component of model; b) mu component of model. Full sets of 

marginal plots in Appendix I.

a. selected bootstrap marginal plots from np model component 

b. selected bootstrap marginal plots from mu model component 
81

Figure 14.3. Selected bootstrap marginal plots for example model 7 (RAZO/winter). 

Solid line is bootstrap mean and grey shading indicates +/- 1 bootstrap standard 

deviation. a) np component of model b) mu component of model. Full sets of 

marginal plots in Appendix I.

a. selected bootstrap marginal plots from np model component 

b. selected bootstrap marginal plots from mu model component 
82

Figure 14.4. Selected bootstrap marginal plots for example model 8 (WWSC/winter). 

Solid line is bootstrap mean and grey shading indicates +/- 1 bootstrap standard 

deviation. a) np component of model; b) mu component of model. Full sets of 

marginal plots in Appendix I.

a. selected bootstrap marginal plots from np model component 

b. selected bootstrap marginal plots from mu model component 
83

Figure 15.1. Selected two-way interaction plots for example model 7 

(COTE/summer). a) np component of model; b) mu component of model. Full sets of 

two-way interaction plots in Appendix J.

a. selected two-way interaction plots from np model component 

b. selected two-way interaction plots from mu model component 
84

Figure 15.2. Selected two-way interaction plots for example model 7 (NOGA/fall). a) 

np component of model; b) mu component of model. Full sets of two-way interaction 

plots in Appendix J.

a. selected bootstrap marginal plots from np model component 

b. selected bootstrap marginal plots from mu model component 
85

Figure 15.3. Selected two-way interaction plots for example model 7 (RAZO/winter) . 

a) np component of model; b) mu component of model. Full sets of two-way 

interaction plots in Appendix J.

a. selected bootstrap marginal plots from np model 

component 

b. selected bootstrap marginal plots from mu model 

component 
86

Figure 15.4. Selected two-way interaction plots for example model 8 (WWSC/winter). a) np

component of model; b) mu component of model. Full sets of two-way interaction plots in 

Appendix J.

a. selected bootstrap marginal plots from np model component 

b. selected bootstrap marginal plots from mu model component 
87

Figure 16.1 ABCD. Relative occupancy prediction maps for example model 7 

(COTE/summer) from full model (a) and bootstrap (b,c) with bootstrap uncertainty map (d). 

For complete set of prediction and uncertainty maps, see Appendix K.

a. occupancy-full model mean 

year

b. occupancy-bootstrap mean

c. occupancy-bootstrap median d. occupancy-bootstrap uncertainty (CI90)
88

Figure 16.1 EFGH. Relative abundance prediction maps for example model 7 

(COTE/summer) from full model (e) and bootstrap (f,g) with bootstrap uncertainty map (h). 

For complete set of prediction and uncertainty maps, see Appendix K.

e. abundance-full model mean 

year

f. abundance-bootstrap mean

g. abundance-bootstrap median h. abundance-bootstrap uncertainty (CI90)
89

Figure 16.1 I. Average count per 10 x 10 km grid cell (COTE/summer).

i. average count per 10 x 10 km grid cell
90

Figure 16.2 ABCD. Relative occupancy prediction maps for example model 7 (NOGA/fall) from 

full model (a) and bootstrap (b,c) with bootstrap uncertainty map (d). For complete set of 

prediction and uncertainty maps, see Appendix K.

a. occupancy-full model mean 

year

b. occupancy-bootstrap mean

c. occupancy-bootstrap median d. occupancy-bootstrap uncertainty (CI90)
91

Figure 16.2 EFGH. Relative abundance prediction maps for example model 7 (NOGA/fall) 

from full model (e) and bootstrap (f,g) with bootstrap uncertainty map (h). For complete set 

of prediction and uncertainty maps, see Appendix K.

e. abundance-full model mean 

year

f. abundance-bootstrap mean

g. abundance-bootstrap median h. abundance-bootstrap uncertainty (CI90)
92

Figure 16.2 I. Average count per 10 x 10 km grid cell (NOGA/fall).

i. average count per 10 x 10 km grid cell
93

Figure 16.3 ABCD. Relative occupancy prediction maps for example model 7 

(RAZO/winter) from full model (a) and bootstrap (b,c) with bootstrap uncertainty map (d). 

For complete set of prediction and uncertainty maps, see Appendix K.

a. occupancy-full model mean 

year

b. occupancy-bootstrap mean

c. occupancy-bootstrap median d. occupancy-bootstrap uncertainty (CI90)
94

Figure 16.3 EFGH. Relative abundance prediction maps for example model 7 

(RAZO/winter) from full model (e) and bootstrap (f,g) with bootstrap uncertainty map (h). 

For complete set of prediction and uncertainty maps, see Appendix K.

e. abundance-full model mean 

year

f. abundance-bootstrap mean

g. abundance-bootstrap median h. abundance-bootstrap uncertainty (CI90)
95

Figure 16.3 I. Average count per 10 x 10 km grid cell (RAZO/winter).

i. average count per 10 x 10 km grid cell
96

Figure 16.4 ABCD. Relative occupancy prediction maps for example model 8 

(WWSC/winter) from full model (a) and bootstrap (b,c) with bootstrap uncertainty map (d). 

For complete set of prediction and uncertainty maps, see Appendix K.

a. occupancy-full model mean 

year

b. occupancy-bootstrap mean

c. occupancy-bootstrap median d. occupancy-bootstrap uncertainty (CI90)
97

Figure 16.4 EFGH. Relative abundance prediction maps for example model 8 (WWSC/winter) 

from full model (e) and bootstrap (f,g) with bootstrap uncertainty map (h). For complete set of 

prediction and uncertainty maps, see Appendix K.

e. abundance-full model mean 

year

f. abundance-bootstrap mean

g. abundance-bootstrap median h. abundance-bootstrap uncertainty (CI90)
98

Figure 16.4 I. Average count per 10 x 10 km grid cell (WWSC/winter).

i. average count per 10 x 10 km grid cell
99

Figure 17.1A. Annual average relative occupancy prediction for example species 1 

(COTE). For complete set of annual prediction maps, see Appendix L.

a. Annual average occupancy
100

Figure 17.1B. Annual average relative abundance prediction for example species 1 

(COTE). For complete set of annual prediction maps, see Appendix L.

b. Annual average abundance
101

Figure 17.2A. Annual average relative occupancy prediction for example species 2 (NOGA). For 

complete set of annual prediction maps, see Appendix L.

a. Annual average occupancy
102

Figure 17.2B. Annual average relative abundance prediction for example species 2 

(NOGA). For complete set of annual prediction maps, see Appendix L.

b. Annual average abundance
103

Figure 17.3A. Annual average relative occupancy prediction for example species 3 (RAZO). For 

complete set of annual prediction maps, see Appendix L.

a. Annual average occupancy
104

Figure 17.3B. Annual average relative abundance prediction for example species 3 

(RAZO). For complete set of annual prediction maps, see Appendix L.

b. Annual average abundance
105

Figure 17.4A. Annual average relative occupancy prediction for example species 4 (WWSC). 

For complete set of annual prediction maps, see Appendix L.

a. Annual average occupancy
106

Figure 17.4B. Annual average relative abundance prediction for example species 4 

(WWSC/winter). For complete set of annual prediction maps, see Appendix L.

b. Annual average abundance
107

Figure 18A. Annual average relative occupancy prediction for group 1 (nearshore species). For 

complete set of group prediction maps, see Appendix M. For group definitions, see Table 12.

a. Annual average occupancy - Nearshore Group
108

Figure 18B. Annual average relative abundance prediction for group 1 (nearshore 

species). For complete set of group prediction maps, see Appendix M. For group 

definitions, see Table 12.

b. Annual average abundance – Nearshore Group
109

Figure 19A. Annual average relative occupancy prediction for group 2 (pelagic 

species). For complete set of group prediction maps, see Appendix M. For group 

definitions, see Table 12.

a. Annual average occupancy - Pelagic Group
110

Figure 19B. Annual average relative abundance prediction for group 2 (pelagic 

species). For complete set of group prediction maps, see Appendix M. For group 

definitions, see Table 12.

b. Annual average abundance – Pelagic Group
111

Figure 20A. Annual average relative occupancy prediction for group 3 (gulls and gannets). For 

complete set of group prediction maps, see Appendix M. For group definitions, see Table 12.

a. Annual average occupancy – Gulls and Gannets Group
112

Figure 20B. Annual average relative abundance prediction for group 3 (gulls and gannets). 

For complete set of group prediction maps, see Appendix M. For group definitions, see 

Table 12.

b. Annual average abundance – Gulls and Gannets Group
113

Figure 21. Intensity of winter survey effort and predicted bootstrap median relative 

abundance of RAZO in winter.

The Department of the Interior Mission

As the Nation's principal conservation agency, the Department of the Interior has 

responsibility for most of our nationally owned public lands and natural 

resources. This includes fostering sound use of our land and water resources; 

protecting our fish, wildlife, and biological diversity; preserving the 

environmental and cultural values of our national parks and historical places; and 

providing for the enjoyment of life through outdoor recreation. The Department 

assesses our energy and mineral resources and works to ensure that their 

development is in the best interests of all our people by encouraging stewardship 

and citizen participation in their care. The Department also has a major 

responsibility for American Indian reservation communities and for people who 

live in island territories under US administration.

The Bureau of Ocean Energy Management

As a bureau of the Department of the Interior, the Bureau of Ocean Energy 

(BOEM) primary responsibilities are to manage the mineral resources located on 

the Nation's Outer Continental Shelf (OCS) in an environmentally sound and safe 

manner.

The BOEM Environmental Studies Program

The mission of the Environmental Studies Program (ESP) is to provide the 

information needed to predict, assess, and manage impacts from offshore energy 

and marine mineral exploration, development, and production activities on 

human, marine, and coastal environments.