U.S. Department of the Interior

U.S. Geological Survey

Open-File Report 2018–1040

Trends and Habitat Associations of Waterbirds 

Using the South Bay Salt Pond Restoration Project, 

San Francisco Bay, California
Cover: Dunlin (Calidris alpina) in a South San Francisco Bay managed pond. Photograph by 

Kathleen Henderson and used with permission.
Trends and Habitat Associations of Waterbirds 

Using the South Bay Salt Pond Restoration Project, 

San Francisco Bay, California

By Susan E.W. De La Cruz, Lacy M. Smith, Stacy M. Moskal, Cheryl Strong, John Krause, Yiwei Wang, and 

John Y. Takekawa

Open-File Report 2018–1040

U.S. Department of the Interior

U.S. Geological Survey
U.S. Department of the Interior

RYAN K. ZINKE, Secretary

U.S. Geological Survey

William H. Werkheiser, Deputy Director

 exercising the authority of the Director

U.S. Geological Survey, Reston, Virginia: 2018 

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Suggested citation:

De La Cruz, S.E.W., Smith, L.M., Moskal, S.M., Strong, C., Krause, J., Wang, Y., and Takekawa, J.Y., 2018, 

Trends and habitat associations of waterbirds using the South Bay Salt Pond Restoration Project, San Francisco 

Bay, California: U.S. Geological Survey Open-File Report 2018–1040, 136 p., https://doi.org/10.3133/ofr20181040.

ISSN 2331-1258 (online)
iii

Contents

Executive Summary....................................................................................................................................................1

Introduction................................................................................................................................................................. 5

Research Questions................................................................................................................................................6

Study Area..................................................................................................................................................................6

Methods......................................................................................................................................................................7

Waterbird Surveys...................................................................................................................................................7

Environmental Data.................................................................................................................................................7

Statistical Analysis ..................................................................................................................................................8

Results......................................................................................................................................................................11

Abundance Trends................................................................................................................................................11

Pond-Scale Models...............................................................................................................................................12

Grid-Scale Models.................................................................................................................................................14

Discussion ................................................................................................................................................................15

Abundance Trends................................................................................................................................................15

Habitat Modeling ...................................................................................................................................................16

Management Implications .....................................................................................................................................19

Acknowledgments.....................................................................................................................................................20

References Cited......................................................................................................................................................21

Figures

Figure 1. Former and current salt ponds grouped in several complexes in South San Francisco Bay, California . 25

Figure 2. Dabbling duck population trends during fall, winter, and spring in South Bay Salt Pond Restoration 

Project ponds and active salt production ponds, South San Francisco Bay, California ........................................... 26

Figure 3. Diving duck population trend during fall, winter, and spring in South Bay Salt Pond Restoration Project 

ponds and active salt production ponds, South San Francisco Bay, California ....................................................... 27

Figure 4. Small shorebird population trend during fall, winter, and spring in South Bay Salt Pond Restoration 

Project ponds and active salt production ponds, South San Francisco Bay, California ........................................... 28

Figure 5. Medium shorebird population trend during fall, winter, and spring in South Bay Salt Pond Restoration 

Project ponds and active salt production ponds, South San Francisco Bay, California. .......................................... 29

Figure 6. Wader population trend during fall, winter, and spring in South Bay Salt Pond Restoration Project 

ponds and active salt production ponds, South San Francisco Bay, California ....................................................... 30

Figure 7. Piscivore population trend during fall, winter, and spring in South Bay Salt Pond Restoration Project 

ponds and active salt production ponds, South San Francisco Bay, California ....................................................... 31

Figure 8. Tern population trend during fall, winter, and spring in South Bay Salt Pond Restoration Project ponds 

and active salt production ponds, South San Francisco Bay, California. ................................................................. 32

Figure 9. Gull population trend during fall, winter, and spring in South Bay Salt Pond Restoration Project ponds 

and active salt production ponds, South San Francisco Bay, California. ................................................................. 33

Figure 10. Eared grebe population trend during fall, winter, and spring in South Bay Salt Pond Restoration 

Project ponds and active salt production ponds, South San Francisco Bay, California. .......................................... 34

Figure 11. Northern shoveler population trend during fall, winter, and spring in South Bay Salt Pond Restoration 

Project ponds and active salt production ponds, South San Francisco Bay, California ........................................... 35
iv

Figure 12. Ruddy duck population trend during fall, winter, and springin South Bay Salt Pond Restoration 

Project ponds and active salt production ponds, South San Francisco Bay, California ........................................... 36

Figure 13. American avocet population trend during fall, winter, and spring in South Bay Salt Pond Restoration 

Project ponds and active salt production ponds, South San Francisco Bay, California ........................................... 37

Figure 14. Model-averaged coefficients of pond water depth for foraging and roosting waterbirds at the pond 

scale in restoration ponds, South San Francisco Bay, California............................................................................. 38

Figure 15. Diving duck, medium and small shorebird, gull, piscivore and wader abundance response to water 

depth from generalized additive mixed models at the pond scale in restoration ponds, South San Francisco Bay, 

California ................................................................................................................................................................. 39

Figure 16. Model-averaged coefficients of pond salinity for foraging and roosting waterbirds at the pond scale 

in restoration ponds, South San Francisco Bay, California. Coefficients are considered significant only if the 

95-percent CI does not overlap zero........................................................................................................................ 41

Figure 17. Dabbling duck, diving duck, gull, piscivore, tern, and wader abundance response to salinity from 

generalized additive mixed models at the pond scale in restoration ponds, South San Francisco Bay, California .. 42

Figure 18. Model-averaged coefficients of pond area for foraging and roosting waterbirds at the pond scale in 

restoration ponds, South San Francisco Bay, California.......................................................................................... 44

Figure 19. Diving duck, gull, piscivore, tern, and wader abundance response to pond area from generalized 

additive mixed models at the pond scale in restoration ponds, South San Francisco Bay, California ..................... 45

Figure 20. Model-averaged coefficients of pond topography for foraging and roosting waterbirds at the pond 

scale in restoration ponds, South San Francisco Bay, California............................................................................. 47

Figure 21. Roosting dabbling duck and roosting medium shorebird abundance response to pond topography 

from generalized additive mixed models at the pond scale in restoration ponds, South San Francisco Bay, 

California ................................................................................................................................................................. 48

Figure 22. Model-averaged coefficients of islands for foraging and roosting waterbirds at the pond scale in 

restoration ponds, South San Francisco Bay, California. Coefficients are considered significant only if the 

95-percent CI does not overlap zero. ....................................................................................................................... 49

Figure 23. Model-averaged coefficients of distance to San Francisco Bay for foraging and roosting waterbirds at 

the pond scale in restoration ponds, South San Francisco Bay, California. ............................................................. 50

Figure 24. Diving duck and gull abundance response to pond distance to San Francisco Bay from generalized 

additive mixed models at the pond scale in restoration ponds, South San Francisco Bay, California ..................... 51

Figure 25. Gull abundance response to distance to landfill from generalized additive mixed models at the pond 

scale in restoration ponds, South San Francisco Bay, California............................................................................. 52

Figure 26. Model-averaged coefficients of percent of pond levees open to hunting for foraging and roosting 

waterbirds at the pond scale in restoration ponds, South San Francisco Bay, California ........................................ 53

Figure 27. Dabbling duck, gull, piscivore, tern, and wader abundance response to percentage of levees open 

to hunting from generalized additive mixed models at the pond scale in restoration ponds, South San Francisco 

Bay, California ......................................................................................................................................................... 54

Figure 28. Model-averaged coefficients of percentage of pond levees open to the public for foraging and 

roosting waterbirds at the pond scale in restoration ponds, South San Francisco Bay, California........................... 56

Figure 29. Small shorebird and gull abundance response to percentage of levees open to the public from 

generalized additive mixed models at the pond scale in restoration ponds, South San Francisco Bay, California .. 57

Figure 30. Model-averaged coefficients of distance to the urban areas for foraging and roosting waterbirds at 

the pond scale in restoration ponds, South San Francisco Bay, California.............................................................. 58

Figure 31. Model-averaged coefficients of pond management compared to not breached for foraging and 

roosting waterbirds at the pond scale in restoration ponds, South San Francisco Bay, California........................... 59

Figure 32. American avocet abundance response to depth and topography from generalized additive mixed 

models at the pond scale in restoration ponds, South San Francisco Bay, California ............................................. 60
v 

Figure 33. Northern shoveler abundance response to salinity, percentage of the levee open to hunting, and 

pond topography from generalized additive mixed models at the pond scale in restoration ponds, South San 

Francisco Bay, California......................................................................................................................................... 61

Figure 34. Ruddy duck abundance response to water depth, salinity, pond area, and distance to San Francisco 

Bay from generalized additive mixed models at the pond scale in restoration ponds, South San Francisco Bay, 

California ................................................................................................................................................................. 62

Figure 35. Eared grebe abundance response to depth, salinity, and pond area from generalized additive mixed 

models at the pond scale in restoration ponds, South San Francisco Bay, California ............................................. 63

Figure 36. Model-averaged coefficients for foraging and roosting eared grebes in salt production ponds at the 

pond scale in restoration ponds, South San Francisco Bay, California.................................................................... 64

Figure 37. Model-averaged coefficients of grid water depth for foraging and roosting waterbirds at the grid 

scale in restoration ponds, South San Francisco Bay, California............................................................................. 65

Figure 38. Dabbling duck, diving duck, medium and small shorebird, gull, piscivore, tern, and wader abundance 

response to water depth from generalized additive models at the grid scale in restoration ponds, South San 

Francisco Bay, California......................................................................................................................................... 66

Figure 39. Model-averaged coefficients of islands for foraging and roosting waterbirds at the grid scale in 

restoration ponds, South San Francisco Bay, California.......................................................................................... 68

Figure 40. Model-averaged coefficients of grid distance to levee for foraging and roosting waterbirds at the grid 

scale in restoration ponds, South San Francisco Bay, California............................................................................. 69

Figure 41. Medium and small shorebird, gull, piscivore, tern, and wader abundance response to grid distance 

to levees from generalized additive models at the grid scale in restoration ponds, South San Francisco Bay, 

California ................................................................................................................................................................. 70

Figure 42. Model-averaged coefficients of grid distance to creek for foraging and roosting waterbirds at the grid 

scale in restoration ponds, South San Francisco Bay, California............................................................................. 72

Figure 43. Medium shorebird, small shorebird and piscivore abundance response to grid distance to adjacent 

creek from generalized additive models at the grid scale in restoration ponds, South San Francisco Bay, 

California ................................................................................................................................................................. 73

Figure 44. Model-averaged coefficients of grid topography for foraging and roosting waterbirds at the grid scale 

in restoration ponds, South San Francisco Bay, California...................................................................................... 74

Figure 45. Dabbling duck, diving duck, medium and small shorebird, gull, wader abundance response to pond 

topography from generalized additive models at the grid scale in restoration ponds, South San Francisco Bay, 

California ................................................................................................................................................................. 75

Figure 46. American avocet abundance response to depth, distance to nearest creek, distance to levees, and

pond topography from generalized additive mixed models at the grid scale in restoration ponds, South San 

Francisco Bay, California......................................................................................................................................... 77

Figure 47. Northern shoveler abundance response to depth, distance to levees, and pond topography from

generalized additive mixed models at the grid scale in restoration ponds, South San Francisco Bay, California .... 78

Figure 48. Ruddy duck abundance response to depth and distance to levees from generalized additive mixed 

models at the grid scale in restoration ponds, South San Francisco Bay, California ............................................... 79

Figure 49. Eared grebe abundance response to depth and distance to levees from generalized additive mixed 

models at the grid scale in restoration ponds, South San Francisco Bay, California ............................................... 80
vi 

Tables

Table 1. Attributes of the South Bay Salt Pond Restoration Project ponds used in waterbird habitat association 

models, South San Francisco Bay, California.......................................................................................................... 81

Table 2. Common name, scientific name, and associated foraging guild of waterbird species observed on salt 

ponds, South San Francisco Bay, California, October–April 2002–13..................................................................... 83

Table 3. The ranking of candidate models at the pond scale for foraging dabbling ducks in the former salt 

production ponds of South San Francisco Bay, California....................................................................................... 85

Table 4. The ranking of candidate models at the pond scale for roosting dabbling ducks in the former salt 

production ponds of South San Francisco Bay, California....................................................................................... 87

Table 5. The ranking of candidate models at the pond scale for foraging and roosting diving ducks in the former 

salt production ponds of South San Francisco Bay, California ................................................................................ 88

Table 6. The ranking of candidate models at the pond scale for foraging small shorebirds in the former salt 

production ponds of South San Francisco Bay, California....................................................................................... 89

Table 7. The ranking of candidate models at the pond scale for roosting small shorebirds in the former salt 

production ponds of South San Francisco Bay, California....................................................................................... 90

Table 8. The ranking of candidate models at the pond scale for foraging medium shorebirds in the former salt 

production ponds of South San Francisco Bay, California....................................................................................... 93

Table 9. The ranking of candidate models at the pond scale for roosting medium shorebirds in the former salt 

production ponds of South San Francisco Bay, California....................................................................................... 95

Table 10. The ranking of candidate models at the pond scale for foraging wadersa in the former salt production 

ponds of South San Francisco Bay, California......................................................................................................... 96

Table 11. The ranking of candidate models at the pond scale for roosting waders in the former salt production 

ponds of South San Francisco Bay, California......................................................................................................... 98

Table 12. The ranking of candidate models at the pond scale for foraging and roosting piscivores in the former 

salt production ponds of South San Francisco Bay, California .............................................................................. 100

Table 13. The ranking of candidate models at the pond scale for foraging terns in the former salt production 

ponds of South San Francisco Bay, California....................................................................................................... 102

Table 14. The ranking of candidate models at the pond scale for roosting terns in the former salt production 

ponds of South San Francisco Bay, California....................................................................................................... 105

Table 15. The ranking of candidate models at the pond scale for foraging gulls in the former salt production 

ponds of South San Francisco Bay, California....................................................................................................... 107

Table 16. The ranking of candidate models at the pond scale for roosting gulls in the former salt production 

ponds of South San Francisco Bay, California....................................................................................................... 108

Table 17. The ranking of candidate models at the pond scale for foraging eared grebes in the former salt 

production ponds of South San Francisco Bay, California..................................................................................... 110

Table 18. The ranking of candidate models at the pond scale for roosting eared grebes in the former salt 

production ponds of South San Francisco Bay, California..................................................................................... 112

Table 19. The ranking of candidate models at the pond scale for foraging northern shovelers in the former salt 

production ponds of South San Francisco Bay, California..................................................................................... 113

Table 20. The ranking of candidate models at the grid scale for roosting northern shovelers in the former salt 

production ponds of South San Francisco Bay, California..................................................................................... 116

Table 21. The ranking of candidate models at the pond scale for foraging and roosting ruddy ducks in the 

former salt production ponds of South San Francisco Bay, California ................................................................... 118

Table 22. The ranking of candidate models at the pond scale for foraging American avocets in the former salt 

production ponds of South San Francisco Bay, California..................................................................................... 119
vii 

Table 23. The ranking of candidate models at the pond scale for roosting American avocets in the former salt 

production ponds of South San Francisco Bay, California..................................................................................... 121

Table 24. The ranking of candidate models at the pond scale for foraging and roosting eared grebes in the 

active salt production ponds of South San Francisco Bay, California .................................................................... 123

Table 25. Variable importance values for significant model-averaged coefficients describing foraging and 

roosting waterbird guild abundance at the pond scale, in restoration ponds in South San Francisco Bay, 

California ............................................................................................................................................................... 124

Table 26. Optimal habitat characteristic values for waterbird guilds at the pond scale, as determined from 

General Additive Models, in restoration ponds, South San Francisco Bay, California ........................................... 125

Table 27. Variable importance values for significant model-averaged coefficients describing foraging and 

roosting waterbird abundance at the pond scale in restoration ponds, South San Francisco Bay, California........ 126

Table 28. Optimal habitat characteristic values for waterbird species at the pond scale, as determined from 

General Additive Models, in restoration ponds, South San Francisco Bay, California. .......................................... 127

Table 29. The ranking of candidate models at the grid scale for foraging and roosting dabbling ducks in the 

former salt production ponds of South San Francisco Bay, California ................................................................... 128

Table 30. The ranking of candidate models at the grid scale for foraging and roosting diving ducks in the former 

salt production ponds of South San Francisco Bay, California .............................................................................. 129

Table 31. The ranking of candidate models at the grid scale for foraging and roosting small shorebirds in the 

former salt production ponds of South San Francisco Bay, California ................................................................... 130

Table 32. The ranking of candidate models at the grid scale for foraging and roosting medium shorebirds in the 

former salt production ponds of South San Francisco Bay, California ................................................................... 130

Table 33. The ranking of candidate models at the grid scale for foraging and roosting waders in the former salt 

production ponds of South San Francisco Bay, California..................................................................................... 131

Table 34. The ranking of candidate models at the grid scale for foraging and roosting piscivores in the former 

salt production ponds of South San Francisco Bay, California .............................................................................. 131

Table 35. The ranking of candidate models at the grid scale for foraging and roosting terns in the former salt 

production ponds of South San Francisco Bay, California..................................................................................... 132

Table 36. The ranking of candidate models at the grid scale for foraging and roosting gulls in the former salt 

production ponds of South San Francisco Bay, California..................................................................................... 132

Table 37. The ranking of candidate models at the grid scale for foraging and roosting eared grebes in the 

former salt production ponds of South San Francisco Bay, California ................................................................... 133

Table 38. The ranking of candidate models at the grid scale for foraging and roosting northern shovelers in the 

former salt production ponds of South San Francisco Bay, California ................................................................... 133

Table 39. The ranking of candidate models at the grid scale for foraging and roosting ruddy ducks in the former 

salt production ponds of South San Francisco Bay, California .............................................................................. 134

Table 40. The ranking of candidate models at the grid scale for foraging and roosting American avocets in the 

former salt production ponds of South San Francisco Bay, California ................................................................... 134

Table 41. Variable importance values for significant model-averaged coefficients describing foraging and 

roosting waterbird guild abundance at the grid scale in restoration ponds, South San Francisco Bay, 

California ............................................................................................................................................................... 135

Table 42. Optimal habitat characteristic values for waterbird guilds at the grid scale, as determined from 

General Additive Models (GAMs), in restoration ponds, South San Francisco Bay, California.............................. 135

Table 43. Variable importance values for significant model-averaged coefficients describing foraging and 

roosting waterbird abundance at the grid scale in restoration ponds, South San Francisco Bay, California.......... 136

Table 44. Optimal habitat characteristic values for waterbird species at the grid scale, as determined from 

General Additive Models (GAMs), in restoration ponds, South San Francisco Bay, California.............................. 136
viii

Conversion Factors

U.S. customary units to International System of Units

Multiply By To obtain

Area

acre 4,047 square meter (m2)

acre 0.4047 hectare (ha)

acre 0.4047 square hectometer (hm2)

acre 0.004047 square kilometer (km2)

International System of Units to U.S. customary units

Multiply By To obtain

Length

centimeter (cm) 0.3937 inch (in.)

meter (m) 3.281 foot (ft) 

kilometer (km) 0.6214 mile (mi)

meter (m) 1.094 yard (yd) 

Area

hectare (ha) 2.471 acre

hectare (ha) 0.003861 square mile (mi2)

square kilometer (km2) 0.3861 square mile (mi2)

square kilometer (km2) 247.1 acre

Volume

liter (L) 0.264172 gallon (gal)

milliter (mL) 0.033814 ounce, fluid (fl. oz)

Abbreviations

AIC Akaike’s Information Criterion

ΔAIC difference in AIC models

CDFW California Department of Fish and Wildlife

CI confidence interval

DEM digital elevation model

FWS U.S. Fish and Wildlife Service

GAM Generalized Additive Model

GLMM generalized linear mixed model

ISP Initial Stewardship Plan

Project South Bay Salt Pond Restoration Project

SFB San Francisco Bay

South Bay South San Francisco Bay

AMAV American avocet

EAGR eared grebe

NSHO northern shoveler

ppt parts per thousand

RUDU ruddy duck

USGS U.S. Geological Survey

UTM Universal Transverse Mercator

VIF Variance Inflation Factors
1 

Trends and Habitat Associations of Waterbirds 

Using the South Bay Salt Pond Restoration Project, 

San Francisco Bay, California

By Susan E. W. De La Cruz1, Lacy M. Smith1, Stacy M. Moskal1, Cheryl Strong2, John Krause3, Yiwei Wang4 and 

John Y. Takekawa1

Executive Summary

The aim of the South Bay Salt Pond Restoration Project (hereinafter “Project”) is to restore 50–

90 percent of former salt evaporation ponds to tidal marsh in San Francisco Bay (SFB). However, 

hundreds of thousands of waterbirds use these ponds over winter and during fall and spring migration. 

To ensure that existing waterbird populations are supported while tidal marsh is restored in the Project

area, managers plan to enhance the habitat suitability of ponds by adding islands and berms to change 

pond topography, manipulating water salinity and depth, and selecting appropriate ponds to maintain for 

birds. To help inform these actions, we used 13 years of monthly (October–April) bird abundance data 

from Project ponds to (1) assess trends in waterbird abundance since the inception of the Project, and (2) 

evaluate which pond habitat characteristics were associated with highest abundances of different avian 

guilds and species. For comparison, we also evaluated waterbird abundance trends in active salt 

production ponds using 10 years of monthly survey data. 

We assessed bird guild and species abundance trends through time, and created separate trend 

curves for Project and salt production ponds using data from every pond that was counted in a year. We 

divided abundance data into three seasons—fall (October–November), winter (December–February), 

and spring (March–April). We used the resulting curves to assess which periods had the highest bird 

abundance and to identify increasing or decreasing trends for each guild and species. 

 

1

U.S. Geological Survey. 

2

U.S. Fish and Wildlife Service. 

3

California Department of Fish and Wildlife.

4

San Francisco Bay Bird Observatory.
2 

We evaluated habitat characteristics associated with Project waterbird abundance at two scales—

(1) across the landscape at the pond scale, and (2) within ponds at the survey grid scale. At the pond 

scale, we analyzed bird abundance in relation to the following habitat characteristics: 

• Water depth,

• Water salinity,

• Pond area,

• Presence of one or more islands,

• Topographic relief, 

• Distance to SFB and urban areas,

• Percentage of levees open to public access including hunting, and

• Whether or not the pond was breached for tidal marsh restoration. 

For gulls, we also included distance to the Tri Cities Landfill. At the grid scale, we analyzed bird 

abundance in relation to the following habitat characteristics: 

• Water depth,

• Presence of one or more islands,

• Topographic relief, and 

• Distance to nearest levee and creek or slough. 

We separately analyzed data on eight foraging guilds, including dabbling ducks, diving ducks, small 

shorebirds, medium shorebirds, waders, piscivores, terns, and gulls. We also examined specific species 

of management interest owing to their abundance (northern shoveler [Anas clypeata], ruddy duck

[Oxyura jamaicensis], and American avocet [Recurvirostra americana]), or unique habitat requirements 

(eared grebe [Podiceps nigricollis]—a hypersaline specialist). Additionally, we analyzed eared grebe 

abundance in relation to habitat characteristics in a subset of active salt production ponds. We separated 

the abundance of each guild or species by foraging and roosting behavior to assess any potential 

differences in habitat use. We identified pond habitat characteristics related to peak abundances of 

different guilds or species at pond and grid scales. Our results support the following conclusions on 

waterbird trends over time and the influence of habitat characteristics on waterbird abundance:

1. Waterbird abundance peaked in the Project ponds during winter (December–February). Total 

winter waterbird abundance increased non-linearly over the study period, more than doubling 

from 98,151 ±38,826 (mean ±95-percent confidence interval) in 2002, prior to any Project 

management actions, to 235,936 ±16,564 in 2014, near the completion of Phase I restoration. 

2. Dabbling and diving ducks and small shorebirds constituted the largest proportion (73 percent) 

of all waterbirds in the Project ponds from October through April. Gulls (11 percent) and 

medium shorebirds (10 percent) were the next most abundant guilds, whereas all other guilds 

were less than 3 percent each.

3. Winter dabbling duck abundance in Project ponds increased nearly two-fold from 2002 to 2006, 

before plateauing to current values. In fall and spring, the number of dabbling duck also 

increased through 2006, but has since fluctuated.

4. Diving ducks and small and medium shorebirds had similar abundance trends over the study 

time period, increasing in fall and winter during two periods—2002–2005 and 2008–2014. 

Small shorebird abundance in Project ponds typically was highest in spring, although there was 

no pronounced trend and spring abundance was highly variable. Medium shorebird spring 

abundance in Project ponds increased from 2002 to 2007 and has since remained stable. 
3 

5. Abundances of individual guilds in salt production ponds were from 0.8 to 8.6 times lower than 

in Project ponds. Abundances of individual species in production ponds were from 2 to more 

than 900 times lower than in Project ponds. However, abundance trends were similar between 

the two pond types for most taxa except for small and medium shorebirds, which generally 

declined in salt production ponds during all seasons throughout the study period. 

6. Water depth was a key habitat variable that influenced the abundance of most guilds and species

at both the pond and grid scale. At the pond scale, bird abundances were highest at a range of 

depths, starting at 0 m for foraging and roosting small shorebirds to 1.49 m for roosting diving 

ducks, including ruddy ducks. At the grid scale, highest abundances of both roosting and 

foraging small and medium shorebirds were associated with 0-m depths, whereas diving duck, 

piscivore, and gull abundances were highest in grids that ranged from 0.33 to 2.51 m in depth. In 

addition to water-level manipulation, increasing topography and islands within ponds may be 

one approach to meeting the depth needs of multiple species. 

7. Foraging and roosting dabbling and diving ducks (including northern shoveler and ruddy duck), 

piscivores, terns, and waders were most abundant in ponds with relatively low salinity (≤33 parts 

per thousand [ppt]), and maintaining low salinities may further benefit these species. Only 

foraging gulls and eared grebes had a positive relation with salinity and were most abundant in 

ponds with salinities of 109–124 ppt. Conversely, roosting gulls and eared grebes, as well as 

foraging and roosting small and medium shorebirds, showed no relation with salinity.

8. Foraging and roosting diving ducks (including ruddy duck), piscivores, terns, gulls, waders, and 

eared grebes were most abundant in the largest ponds (1.0–2.5 km2

 ) included in the study. 

Maintaining ponds with large areas of open water (>1.0 km2) may benefit these guilds and 

species. Dabbling ducks (including northern shoveler) and small and medium shorebirds 

(including American avocets) were not influenced by pond size. As Project restorations progress 

and mature, additional work on the effect of pond area and proximity to other suitable habitat in 

the mosaic of tidal wetlands and ponds may be critical to determine habitat requirements for 

species that are not sensitive to pond area under current (2017) conditions. 

9. Variation in pond bottom elevation (topography) had a greater influence on bird abundance at 

the grid scale than at the pond scale. At the pond scale, topography influenced roosting dabbling 

ducks (including northern shoveler) and medium shorebirds (including American avocet), all of 

which were most abundant when topography ranged from 0 to -0.61 m. At the grid scale, 

foraging dabbling ducks (including northern shoveler), small and medium shorebirds (including 

roosting American avocet), waders, and roosting gulls were most abundant in grids where 

topography varied widely from 0.12 to 1.27 m. Foraging diving duck abundance was associated 

with grids that had minimal topography (0.12 m). Our grid-scale results suggest that increasing 

small-scale topography in shallow water Project ponds may increase foraging use by many of the 

guilds we studied. Experimental studies to evaluate the response of waterbirds to topographic 

manipulation may further inform management of this habitat feature.

10. At the pond scale, the presence of one or more islands supported higher abundances of all 

roosting birds except eared grebes, and most foraging birds except gulls, medium shorebirds 

(including American avocet), and eared grebes. At the grid scale, the presence of one or more 

islands supported a higher abundance of foraging medium shorebirds (including American 

avocet), waders, northern shovelers, ruddy ducks and all roosting birds except diving ducks. Our 

analyses suggest that islands may be as critical as other key pond characteristics such as water 

depth and salinity for maintaining foraging and roosting abundances of birds from multiple 

guilds. 
4 

11. Pond distance to SFB had little influence on the abundance of most guilds and species. However, 

roosting and foraging diving ducks (including foraging ruddy ducks) were most abundant in 

ponds that were about 1–2 km from SFB. Roosting and foraging gull abundance increased with 

pond distance from SFB. 

12. Ponds closer to landfills supported a higher abundance of roosting gulls; however, this relation 

leveled off at about 2.8 km away from landfills. 

13. The percentage of pond levees open to hunters during the hunting season (November–January) 

influenced both foraging and roosting guilds and species. Foraging piscivores, roosting dabbling 

ducks, and roosting wader abundances were linearly related to this variable and were least 

abundant in ponds with 100 percent of levees open to hunters. In contrast, the abundance of 

foraging gulls and roosting terns increased as the percentage of levees open to hunting increased. 

Additional analyses that include the presence of hunting blinds may be warranted. 

14. Public access (based on the percentage of pond levees open to public access other than hunting 

and not on actual use by the public) had no influence on the abundance of most guilds and 

species. However, foraging small shorebirds and roosting gulls were most abundant in ponds 

where 70 and 46 percent, respectively, of levees were open to the public. Additional studies that 

examine effects of frequency of public use may be warranted.

15. Pond distance to urban areas did not influence the abundance of any guild or species.

16. Breached ponds supported fewer diving ducks (including roosting ruddy ducks), piscivores, and 

foraging eared grebes compared to unbreached ponds. Abundance was positively related to

breached ponds only for foraging small shorebirds. As Project restorations progress, future work 

to investigate temporal and spatial use of breached habitat by all guilds may inform management 

of post-breach transitional habitat to maximize waterbird abundances. 

17. Grid distance to pond levee influenced the abundance of most guilds and species. Small and 

medium shorebirds, American avocets, piscivores, gulls, foraging terns, eared grebes, and 

foraging northern shovelers and ruddy ducks were most abundant in grids that were closest to 

levees (0.0–0.25 km away), whereas roosting terns were most abundant in grids that were 0.3 km 

from levees. Research to determine which levee features attract each guild may be informative.

18. Grid distance to nearest creek or slough did not influence most guilds and species. Foraging 

small shorebirds and roosting medium shorebirds (including American avocets) were most 

abundant in grids closest to creek and slough features (0.04 km). In contrast, roosting piscivores 

were most abundant in grids far from creeks or sloughs (0.95 km). 

19. Abundance of foraging eared grebes in salt production ponds was not related to any variable we 

evaluated; however, as with our findings in Project ponds, roosting eared grebes were most 

abundant in large ponds with increasing staff gauge values (a proxy for depth). 

20. Eared grebes are hypersaline specialists with unique depth and salinity requirements that often 

do not overlap those of other guilds and species. Additional targeted studies on movements and 

seasonal habitat use of eared grebes may inform management opportunities for this species. 

21. We summarize significant habitat characteristics identified in our analyses and compare their 

importance across guilds and species. Our results suggest that key habitat characteristics can be 

manipulated by managers to benefit one or more taxa. In many cases, the optimal habitat values 

overlap among guilds and species, providing the opportunity to manage individual or groups of 

ponds to benefit several species. 
5 

Introduction

San Francisco Bay (SFB) is the largest estuary on the west coast of North America, supporting 

large populations of humans and wildlife. This estuary supports more than one-half million wintering 

and migratory shorebirds (Page, Stenzel and Kjelmyr, 1999; Morrison, 2001), and has been designated a 

Western Hemisphere Shorebird Reserve Network site because of its status as one of the most important 

staging and wintering areas on the Pacific Flyway. San Francisco Bay also supports as much as 44 

percent of wintering diving duck populations observed in the Lower Pacific Flyway during U.S. Fish 

and Wildlife Service (FWS) mid-winter surveys (Richmond and others,. 2014; Trost, 2009). However, 

the SFB estuary has lost more than 80 percent of its historical tidal marsh habitat to urbanization, 

agriculture, and salt production (Goals Project, 1999). This includes about 14,000 ha (35,000 acres) that 

were converted to artificial salt evaporation ponds beginning in the 1850s (Goals Project, 1999). 

Although this conversion represented a major loss of tidal wetlands, the salt ponds are now used 

extensively as foraging and roosting habitat by migratory and resident waterbirds (Warnock and others, 

2002). For example, during one spring migration, shorebird abundance within a single salt pond

exceeded 200,000 birds (Stenzel and Page, 1988). 

In a 2003 purchase agreement, 53 former salt evaporation ponds comprising 6,110 ha in the 

Alviso, Eden Landing, and Ravenswood salt production complexes of South San Francisco Bay (South 

Bay) were purchased from the salt production company Cargill, Inc. and transferred to the FWS and 

California Department of Fish and Wildlife (CDFW) for management. The South Bay Salt Pond 

Restoration Project (hereinafter “Project”) is being conducted by a broad coalition of partners, including 

FWS, CDFW, the California Coastal Conservancy, and several other agencies (see 

http://www.southbayrestoration.org). These groups are working together to implement a large-scale, 

multi-phased plan to convert 50–90 percent of Project ponds to tidal marsh within the next 50 years 

(Goals Project, 1999; Steere and Schaefer, 2001; Siegel and Bachand, 2002; Stallings, 2004). One of the 

goals of the Project is to restore and enhance tidal marsh habitat for endemic species while maintaining 

and managing ponds to sustain migratory and resident waterbird populations. 

Under the Initial Stewardship Plan (ISP 2006–2009; Stallings, 2004), water control structures 

were installed within the ponds to promote circulation of SFB water and to prevent salt accumulation. 

Phase I (2010–2015), the first component of the Project, included breaching three ponds to restore tidal 

flow for marsh restoration, augmenting two ponds with islands for roosting and nesting shorebirds, and 

adjusting water levels in other ponds to improve habitat for waterfowl and shorebirds. In Phase II, which 

began in 2016, the proposed restoration actions include breaching additional ponds and improvements to

other pond habitat by adding features that support waterbird populations. Phase II and future restoration 

action alternatives are dependent on following adaptive management principles incorporating scientific 

findings to ensure that migratory waterbird populations are conserved as tidal marsh restoration moves 

forward.
6 

To assess the effects of management actions under the Project Final Environmental Impact 

Statement (EDAW and others, 2007), the U.S. Geological Survey (USGS) began monitoring waterbird 

abundance and distribution as well as water-quality parameters in the Project ponds in 2002. This early 

monitoring work was initiated with support of the USGS, FWS, California Coastal Conservancy, 

CDFW, and non-profit foundations to provide the Project with data to inform future restoration 

decisions. Data collected during the ISP period and Phase I provide an unparalleled opportunity to 

assess the effects of restoration actions and the potential for adaptive management to sustain waterbird 

populations. As the Project proceeds, there are several key uncertainties about how to enhance and 

manage existing ponds to maximize waterbird populations as the overall pond area is reduced. Whereas

previous research has highlighted the importance of island habitat within ponds for wintering and 

breeding waterbirds (Ackerman and others, 2014), manipulation of additional pond characteristics also 

may improve habitat and increase waterbird abundance. Herein, we evaluate relations between 

waterbird abundance and multiple pond characteristics to help inform future management actions 

designed to maximize waterbird habitat in managed ponds. 

Research Questions

The overarching objectives of our study were to evaluate waterbird abundance trends and to 

assess habitat characteristics that support high abundances of waterbirds within the former salt ponds of 

the South Bay. Specifically, we addressed the following research questions: 

1. How have waterbird abundances changed within the Project and active salt production ponds 

since the inception of the phased restoration process? 

2. How do pond spatial location and habitat characteristics influence waterbird abundance across 

Project ponds? 

3. How do pond habitat characteristics influence waterbird abundance within Project ponds? 

4. How do pond characteristics influence eared grebe abundance across active salt production 

ponds in the South Bay? 

Study Area

Our study area includes former and current salt production ponds in South Bay, California 

(37.42–37.62°N; 121.93–122.22°W; fig. 1), which are divided into five complexes: (1) Eden Landing 

(N=28), (2) Alviso (N=25), (3) Ravenswood (N=10), (4) Mowry (N=6), and (5) Newark (N=16). Ponds 

in the Eden Landing, Alviso, and Ravenswood complexes are part of the restoration Project. Eden 

Landing is managed by the CDFW as part of the Eden Landing Ecological Reserve, and Alviso and 

Ravenswood are both managed by the FWS as part of the Don Edwards San Francisco Bay National 

Wildlife Refuge. Cargill, Inc. manages the Mowry and Newark pond complexes for salt production. 

Ponds range in size from 0.1 to 2.8 km2 and vary in depth, salinity, and spatial proximity to SFB 

(table 1). 
7 

Methods 

Waterbird Surveys

From October 2002 through April 2013, USGS conducted monthly waterbird counts in Eden 

Landing, Alviso, and Ravenswood. San Francisco Bay Bird Observatory counted waterbirds every 6 

weeks from January 2014 to April 2015 in Alviso, Eden Landing, and Ravenswood, and monthly from 

October 2005 to April 2015 in Mowry and Newark. We counted waterbirds at high tide when adjacent 

mudflats were inundated and shorebird abundances within ponds were likely at their peak (Warnock and 

others, 2002). We overlaid pond maps with 250 × 250-m (6.25-ha) Universal Transverse Mercator

(UTM) grids and recorded species and abundance by grid to document the spatial distribution of birds 

within ponds. We counted all birds using spotting scopes and binoculars either on foot or from a vehicle 

along pond levees. We recorded each bird as foraging or roosting based on behavior at the time of 

observation. We assigned species observed (table 2) to foraging guilds (listed as follows with example 

species): 

1. Dabbling ducks (dabblers)—northern shoveler (Anas clypeata) and American wigeon 

(A. americana), 

2. Diving ducks—ruddy duck (Oxyura jamaicensis), 

3. Piscivores–double-crested cormorant (Phalacrocorax auritus) and American white pelican 

(Pelecanus erythrorhynchos); 

4. Gulls—ring-billed gull (Larus delawarensis); 

5. Terns—Forster’s tern (Sterna forsteri); 

6. Waders—great egret (Ardea alba); 

7. Medium shorebirds–marbled godwit (Limosa fedoa) and willet (Tringa semipalmata), and 

8. Small shorebirds—western sandpiper (Calidris mauri), dunlin (C. alpina), and long-billed 

dowitcher (Limnodromus scolopaceus).

Based on Project goals (ISP 2006–2009; Stallings, 2004), we also identified four species of 

interest (American avocet, Recurvirostra americana; eared grebe, Podiceps nigricollis; northern 

shoveler; and ruddy duck) to evaluate individually. Except for eared grebes, individual species also 

were included in their respective guild for analysis. 

Environmental Data

We collected monthly salinity readings from each surveyed pond using a Hydrolab Mini 

Sonde® (Hydrolab-Hach Company, Loveland, Colorado) or, if salinity exceeded the maximum value 

measured by the instrument, we measured specific gravity with a hydrometer (Ertco, West Paterson, 

New Jersey) and converted to salinity with the 1978 Practical Salinty Scale (Lewis, 1980). Each month, 

we sampled the same 1–5 locations in each pond and averaged the result across all locations within the 

pond. 

We obtained pond elevation data for 35 Project ponds from bathymetric surveys conducted 

during 2003 – 04 (Athearn and others, 2010). For 19 Project ponds lacking bathymetric data, we used 

lidar data obtained when ponds were exposed (dewatered), and for 2 Project ponds with partial 

bathymetry, we used a combination of lidar and bathymetry (Athearn and others, 2010). We interpolated 
8 

bathymetric and lidar elevation data using Inverse Distance Weighting in ArcMapTM 9.1 (Spatial 

Analyst, ArcGISTM 9.1, ESRI, Redlands, California) to create digital elevation models (DEMs) with 25-

m resolution for each pond. Within the ponds, there is a 0–2 cm difference between the lidar and 

bathymetric DEMs (Athearn and others, 2010). During each survey, we recorded water height within a 

pond by reading staff gauges of known elevation installed within each pond. We calculated water depth 

by taking the difference between staff gauge readings and pond elevation data (NAVD 88 vertical 

datum). We defined pond and grid topography as the standard deviation of the mean elevation (for 

example, large deviations meant greater topographic relief on the pond bottom). We did not include 

elevations of islands or levees in our calculation of topography. 

We used ArcGISTM 10.1 (ESRI, Redlands, California) to derive spatial pond attributes. We used 

2009 National Agricultural Imagery Program (1-m resolution) aerial imagery to digitize islands within 

ponds in ArcMapTM 10.1. We modified island outlines using ground-truthed, real-time kinematic Global 

Positioning System (Leica Geosystems Inc., Norcross, Georgia) survey points. We defined an island as 

land that was surrounded by water and exposed year-round. Our analysis included 20 ponds with 

islands, and the number of islands per pond ranged from 1 to 22. We calculated the number of islands 

that intersected each bird survey grid (minimum=0, maximum=9). We used the center point of each 

pond to calculate the distance between ponds and SFB, urban areas, and landfills. We used the center 

point of each grid to calculate the distance between grids and levees, adjacent creeks, and sloughs. We 

obtained information on the timing and percentage of levees open to public access, including hunting, 

from State and Federal maps and discussions with area managers. The percentage of levees open to 

hunting was considered zero during non-hunting months (February–October). We considered the four 

units of Ravenswood Pond RSF2 (fig. 1) as separate ponds because each unit has water control 

structures that allow for individual management. 

Statistical Analysis

We assessed bird guild and species abundance trends from October 2002 through April 2015 

using non-parametric locally weighted scatterplot smoothing (LOESS) regression in R (package

ggplot2). We created separate trend curves for Project and salt production ponds using data from every 

pond counted in a year. We divided abundance data into three seasons—fall (October–November), 

winter (December–February), and spring (March–April). We excluded September and May count data 

from our analyses because differences in the timing of migration among species introduced variability in 

the abundance data that obscured seasonal abundance patterns which were the focus of this study. While 

understanding arrival and departure times of migratory waterbirds is an important aspect of their 

ecology, addressing migration phenology was not within the scope of this study. We used the resulting 

LOESS curves to assess which periods had the highest bird abundance and to identify increasing or 

decreasing trends for each guild and species. 

We separately modeled foraging and roosting abundances of waterbird guilds and species using 

generalized linear mixed models (GLMMs) with a negative binomial distribution to account for the 

large number of zero observations in our dataset (Warton, 2005). We divided our analyses into two

scales—pond and grid,—to evaluate the influence of predictor variables on abundance at landscape and 

within-pond levels. 
9 

In the first stage of our analysis, we examined variables that may influence bird abundance at the 

pond scale. We tested all variables for collinearity using Variance Inflation Factors (VIF; R package car; 

Zuur and others, 2009). We excluded pond perimeter because it was highly correlated with pond area; 

however, the VIF for the remaining variable pairs ranged from 1.07 to 2.28 and were below the 

threshold of 3.0 recommended for exclusion (Zuur and others, 2009). We built a candidate set of models 

that included all possible combinations of the following variables: 

• Water depth,

• Water salinity,

• Pond area,

• Pond distance to SFB and urban areas, 

• Pond management type, 

• Presence of one or more islands,

• Variation in pond topography, and

• Percentage of the pond levees open to the public or to waterfowl hunting (table 1). 

We did not have complete information on the location or number of hunting blinds in all ponds; thus, 

we only accounted for hunting from levees and did not account for hunting access using blinds. The 

total number of models in each candidate set was 1,024. We built a separate set of 2,048 candidate 

models for gulls, which included distance to the Tri-Cities Landfill (Fremont, California; UTM 10S 

589251.34, E 4149803.26) in addition to the listed variables. We considered two pond management 

types—ponds with breached levees for tidal marsh restoration and un-breached ponds. Nine ponds were 

breached during our study period and were classified as breached only during the post-breach period. 

We included pond area in the model to assess the influence of area on abundance. We might expect to 

find higher bird abundances in larger ponds because the ponds can accommodate more birds; however, 

we wanted to test this assumption, as contrary results could have important implications for 

management of birds. We excluded data collected during identified construction periods in a given 

pond. 

In the second stage of our analysis, we evaluated the influence of habitat variables on bird

abundance within ponds at the grid scale. We assessed all variable pairs for collinearity and determined

that VIF ranged from 1.00 to 1.34 for all combinations. We developed a candidate set of models that 

included all possible combinations of the following variables: 

• Grid water depth,

• Grid distance to the pond levee and to nearest creek or slough, 

• Variation in grid topography (calculated as the standard deviation of grid elevation), and 

• Presence of island(s) in grid. 

Grid area was an artifact of our survey design method and we were not interested in assessing its effect; 

however, we included this variable in every model to account for potential minor differences in grid size 

at the edges of ponds. 

An assumption of our GLMM analyses is that bird abundance has a log-linear relation with the 

independent habitat variables. We included pond, grid (grid-scale analysis), month, and year as random 

effects in each model to account for potential sources of additional dispersion and because we were not 

interested in assessing temporal differences. We used the glmmADMB package in R statistical software 

to fit all models (Fournier and others, 2012; Skaug and others, 2012; R Core Team, 2014). As a 

reference for comparison, we included null models in each of our pond-scale (intercept only) and gridscale (intercept + grid area) analyses. We ranked models with Akaike’s Information Criterion (AIC) 

using a customized package (ModelInference package in R, M. Herzog, unpublished package) based on 

the information-theoretic model selection paradigm (Burnham and Anderson, 2002). 
10 

We calculated model-averaged parameter estimates across all models and assessed variable 

importance (sum of the weights of all models containing that variable; Burnham and Anderson, 2002) to 

determine the effect of each parameter on bird abundance. We graphed and evaluated the 95-percent

confidence interval (CI) of each model-averaged parameter estimate and considered variables 

significant only if the CI did not overlap zero. 

To examine relations between model-averaged predictor variables and response variables, we 

used Generalized Additive Models (GAMs, gamlss package in R; Tavares and others, 2015). These 

models have the flexibility to model discrete statistical distributions (for example, negative binomial) 

commonly associated with abundance data as well as nonlinear effects of covariates on the predicted 

mean (Hastie and Tibshirani, 1990). For each guild and species, we built GAMs using only the variables 

determined to be significant in the GLMM analysis. This included variables that had variable 

importance values greater than or equal to (≥) 0.92 and model-averaged parameter estimates with 95-

percent CIs that did not overlap zero. We graphed abundance as a function of each habitat variable and 

held all other variables in the model at a mean value. Month and year were included as random effects, 

and we used a cubic spline smoother in all models. We assessed GAM plots to determine optimal values 

of significant habitat variables for a given guild or species at the pond and grid scale. For a few guilds

and species (roosting and foraging piscivores, roosting terns, roosting eared grebe, and foraging ruddy 

duck), grid-scale data were too patchily distributed to find a solution using GAMs that contained all 

significant variables. For these taxa only, we ran GAMs on each variable individually; thus, the 

resulting plots represent bird abundance as a function of a single habitat variable and do not account for 

the effect of other variables determined to be significant in GLMM models. Binomial variables, such as 

island presence or absence, could only be included in models as linear variables and, therefore, are not 

shown in GAM plots. 

In addition to modeling eared grebe abundance in Project ponds, we developed a separate 

analysis for eared grebes using the active salt production ponds in Mowry and Newark, where eared

grebe abundance historically has been highest (Athearn and others, 2012). Pond elevation data were not 

available for the salt production ponds, so we used staff gauge value as a relative approximation for 

depth. We assumed that staff gauges were installed to the same elevation datum across all ponds and 

were comparable to each other. Island data were not available for analyses. Thus, our analyses of eared

grebes in salt production ponds included the following variables: 

• Salinity,

• Staff gauge value,

• Pond area, and

• Pond distance to SFB and to urban areas. 

We restricted our analyses to the pond scale because of limited grid-scale data in salt production ponds. 

Following the same methods used for Project ponds, we modeled all possible combinations of variables 

using a GLMM with a negative binomial distribution. The total number of models in our candidate set 

was 32. 
11 

Results

Abundance Trends

We conducted 5,055 Project pond surveys during 13 field seasons from fall 2002 through spring 

2015 and observed 101 species of waterbirds (table 2). In general, the waterfowl (dabbling and diving 

ducks, 17 percent each), shorebird (small shorebirds, 39 percent; medium shorebirds, 10 percent), and 

gull (11 percent) guilds represented the most abundant bird taxa in Project ponds across all years of the 

study, whereas the other guilds (waders, terns, piscivores) represented less than (<) 3 percent each. 

Overall, peak waterbird abundances occurred in the Project area during winter (December–February). 

Total winter waterbird abundance increased non-linearly over the study period, more than doubling 

from 98,340 ±38,761 (mean ± 95-percent confidence interval) in 2002, prior to the ISP period, to 

235,936 ±16,564 in 2014, near the completion of Phase I.

We conducted 1,399 surveys of active salt production ponds (Mowry and Newark complexes) 

during nine field seasons from fall 2005 through spring 2015, and observed 79 species of waterbirds 

(table 2). Waterfowl (dabbling ducks, 11 percent; diving ducks, 12 percent), shorebirds (small 

shorebirds, 28 percent; medium shorebirds, 12 percent), gulls (23 percent), and eared grebes (12

percent) were the most abundant bird taxa in salt production ponds across all years of the study, whereas

the other guilds (piscivores, waders, terns) represented <3 percent each. Overall, yearly peak waterbird 

abundances occurred during winter (December–February), with little change in mean abundance 

between winters of 2005 (47,730 ±16,867) and 2014 (43,033 ±5570). 

Across guilds and species, abundance was variable by season and year. For example, dabbling 

duck abundance was highest during winter in Project ponds and increased more than three-fold between 

2002 and 2006, before plateauing to current mean values of just less than 50,000 birds (fig. 2). In fall 

and spring, dabbling duck numbers also increased through 2006, but have since fluctuated (fig. 2). 

Diving duck abundance was highest during winter and increased within the Project ponds during two 

periods—2002–2005 and 2008–2014 (fig. 3). Overall, abundances of diving ducks in fall and winter 

have doubled on Project ponds since surveys began in 2002, whereas spring abundances have remained 

similar, albeit with large confidence intervals (fig. 3). Diving duck abundance trends within active salt 

production ponds also are increasing during winter and have remained stable during fall and spring (fig. 

3).

Small and medium shorebird abundance trends in the Project ponds were similar to trends of 

diving ducks, showing increases in fall and winter during two periods, 2002–2005 and 2008–2014 (figs. 

4 and 5). Small shorebird abundances in Project ponds typically were highest in spring; however, spring 

numbers were variable with large 95-percent CIs and a flat trendline since 2002 (fig 4). Medium 

shorebird spring abundances in Project ponds increased from 2002 to 2007 and have since remained 

fairly stable (fig. 5). In contrast, small and medium shorebird abundances in the salt production ponds 

generally have decreased across all seasons (figs. 4 and 5). 

The highest abundance of waders occurred during fall in Project ponds; their population 

increased from 2002 to 2005, decreased from 2006 to 2008, and then remained stable (fig. 6). Wader 

abundance in Project Ponds during winter and spring increased from 2002 to 2006 and from 2011 to 

2014 (fig. 6).Wader population trends in the salt production ponds generally followed the trends in the 

Project ponds (fig. 6).

The highest abundance of piscivores occurred during fall in Project and salt production ponds 

(fig. 7). Within Project ponds, their fall abundance increased from 2002 to 2005 and from 2008 to 2012, 

whereas in salt production ponds their abundance has continued to increase (fig. 7). There was a gradual 

increase in piscivore abundance during winter and spring in Project and salt production ponds (fig. 7). 
12 

Tern abundance trends were similar to piscivore abundance trends, except that their abundance in salt 

production ponds during fall decreased from 2010 to 2014 and there was a sharper increase in 

abundance in Project ponds during spring from 2011 to 2014 (fig. 8).

Gull abundance reached its peak in Project ponds during winter 2004–06 and in salt production 

ponds during fall and winter 2007–08, after which the population decreased (fig. 9). During winter and 

spring 2013 and 2014, the winter and spring gull abundance in Project ponds began increasing, but 

remained lower than the peak abundances (fig. 9).

The highest abundance of eared grebes occurred in salt production ponds during spring (fig. 10). 

Eared grebe abundance decreased in the Project ponds from 2002 to 2006, and in the salt production 

ponds from 2005 to 2008 (fig. 10). The population began increasing in the Project ponds in 2011 and in 

the salt production ponds after 2008 (fig. 10). 

The highest abundance of northern shoveler and ruddy ducks occurred in Project ponds during 

fall and winter (figs. 11 and 12). Northern shoveler abundance in these ponds increased consistently 

during winter and fluctuated during fall and spring (fig. 11). Ruddy duck abundance in Project ponds 

increased consistently during fall, increased during two periods in winter (2002–2005 and 2008–2014), 

and remained stable in spring from 2002 to 2008 before slightly increasing from 2008 to 2014 (fig. 12).

The highest abundance of American avocets occurred in Project ponds during fall and winter, 

increasing from 2002 to 2006 and from 2010 to 2014 (fig. 13). Their abundance in salt production ponds 

decreased in winter and spring and followed the same trend as in the Project ponds during fall (fig. 13). 

Pond-Scale Models

In any single year, only a subset of the ponds had a complete set of associated environmental 

data; thus, the average number of ponds included in the pond-scale dataset each year was 43 (range 37–

48), excluding fall 2002, in which we only had data for seven ponds (table 1). We modeled foraging and 

roosting abundance of seven guilds and four species of interest, for a total of 22 separate analyses 

(tables 3–24). Variable importance values for all significant pond-scale, model-averaged coefficients for 

guilds were ≥0.93 (table 25). 

Overall, our model results indicated that the influence of pond characteristics varied by guild and 

species, and to a lesser degree by foraging and roosting behavior. Model-averaged GLMM coefficients 

and GAM-derived relations between bird abundance and pond depth showed that ponds with deeper 

water (0.4–1.5 m in depth; figs. 14 and 15, tables 25 and 26) supported the highest abundances of 

foraging and roosting diving ducks, piscivores, and foraging gulls. Ponds with shallower water (0–0.4 

m; figs. 14 and 15, tables 25 and 26) supported the highest abundances of foraging and roosting small 

and medium shorebirds, foraging waders, and roosting gulls. 

Pond salinity was a significant parameter for several guilds (fig. 16). Foraging and roosting 

dabbling and diving ducks, piscivores, terns, and waders were present in highest abundances in low 

salinity (<17 ppt) ponds, whereas foraging gull abundances peaked at high salinities (124 ppt; fig. 17; 

tables 25 and 26). For many guilds (foraging and roosting waders, as well as foraging piscivores, terns 

and gulls), abundance increased with pond area, suggesting that there was not an optimal pond size 

within the range of ponds we evaluated (figs. 18 and 19). However, foraging and roosting diving duck 

abundance leveled off in ponds that were about 1.25 km2 (125 ha), roosting terns in ponds that were 

>1.5 km2 (150 ha), and roosting gulls in ponds > 1.75 km2 (175 ha; fig. 19A–B, H, and J; table 26).

Responses to landscape features, such as topography and distance to SFB, varied among guilds. 

Increased pond topography (variation in pond bottom elevation) was important to some roosting guilds,

but did not have a significant effect on foraging abundances of any guild (fig. 20). Roosting dabbling 

duck abundance was highest at the maximum pond topography value, 0.61 m (fig. 21A; table 26), 

whereas roosting medium shorebird abundances peaked in ponds with topography values ≥0.15 m (fig. 
13 

21B; table 26). Ponds with at least one island (compared to ponds with no islands) supported higher 

abundances of all roosting guilds as well as of foraging dabbling and diving ducks, piscivores, terns, and 

waders (fig. 22). Ponds within 1.1 km of SFB were optimal for roosting and foraging diving ducks, 

whereas gull abundances increased linearly in ponds that were >0.9 km from SFB (figs. 23 and 24; table 

26). Roosting gull abundance was highest in ponds adjacent (2.8 km) to landfill, decreased linearly as 

distance from landfill increased, but leveled off in ponds ≥14 km from the landfill (fig. 25; table 26). 

The proportion of levees open to the public for hunting or other access also influenced the 

abundance of several guilds. Foraging gull and roosting tern abundances increased linearly with 

increasing percentages of levees open to hunting, whereas roosting dabbling ducks and waders and 

foraging piscivores abundances had negative linear relations with percentage of levees hunted (figs. 26 

and 27A–E; table 26). Ponds with a higher proportion of levees open to public access (other than 

hunting), supported higher abundances of foraging small shorebirds (fig. 28). Foraging small shorebird 

abundance had a sinusoidal relation with percentage of levees open to the public, with highest 

abundances in ponds that had 0 and 70 percent of their levees open, whereas their lowest abundances 

occurred in ponds with 20–40 percent of their levees open (fig. 29A; table 26). In contrast, roosting 

gulls reached their peak abundance in ponds with about 46 percent of their levees open to the public 

(fig. 29B; table 26).

There was no relation between pond distance to urban areas and the abundance of any guild (fig. 

30). Pond management type did influence abundance; breached ponds supported a higher abundance of 

foraging and roosting small shorebirds and a lower abundance of foraging and roosting diving ducks 

and piscivores relative to unbreached ponds (fig. 31).

Specific species generally followed the trend of their associated guilds. For example, like the 

medium shorebird guild, foraging and roosting American avocet abundance was highest in shallow 

ponds (0.30-0.32 m; figs. 14 and 32, tables 27 and 28) with one or more islands (figs. 20 and 22, table 

27). Roosting avocet abundance also increased with pond topography, peaking at 0.25 m (fig. 32C; 

tables 27 and 28). 

Ponds with low salinity (8.0 ppt; figs. 17, 33A–B; tables 27 and 28) and one or more islands (fig. 

22, tables 27 and 28) supported higher abundances of foraging and roosting northern shoveler. 

Abundances of roosting northern shovelers increased non-linearly as pond topography increased, and 

peaked at the maximum value of 0.61 m (fig. 20, fig. 33D; tables 27 and 28). Northern shoveler roosting 

abundances were negatively associated with the percentage of levees open to hunting, peaking at 0%

(fig. 26, fig. 33C; tables 27 and 28).

Ponds with lower salinity, deeper water, one or more islands, larger area, and closer to SFB

relative to other ponds supported higher abundances of foraging and roosting ruddy ducks (figs. 14, 16, 

18, 22–23; table 27). Unbreached ponds supported a higher abundance of roosting ruddy ducks, but did 

not have a significant influence on foraging ruddy ducks (fig. 31, table 27). Foraging and roosting ruddy

ducks reached peak abundances at about 11 and 7 ppt salinity, respectively (fig. 34C–D; table 28). 

Optimal depth for foraging ruddy ducks was about 0.74 m, whereas optimal depth for roosting ruddy 

ducks was 1.73 m (fig. 34A–B; table 28). Foraging ruddy duck abundance peaked when pond area was 

about 2.30 km2

 (230 ha), ponds were about 1 km from the edge of SFB, and roosting abundance peaked 

in ponds ≥1.25 km2 (125 ha) that were less than 1.62 km from SFB (fig. 34E–G, table 28).

We assessed eared grebes within Project ponds, as well as separately within active salt 

production ponds at the pond scale. Within the Project ponds, those with deeper water or greater area 

supported higher abundances of foraging and roosting eared grebes (figs. 14 and 18; table 27). 

Unbreached ponds and those with higher salinity supported higher abundances of foraging eared grebes 

(figs. 16 and 31; table 27). Optimal depths for foraging and roosting eared grebes in Project ponds were 

> 0 m and 1.29 m, respectively (fig. 35A–B; table 28). Foraging eared grebe abundances peaked in 
14 

ponds with salinity around 109 ppt, and foraging and roosting grebe abundances increased with a pond 

area as large as 2.30 km2 (230 ha; fig. 35C–E; table 28). Within salt production ponds, the 95-percent

confidence intervals of all model-averaged variables overlapped zero for foraging eared grebes, 

suggesting none of these variables influenced them (fig. 36). However, roosting eared grebes were more 

abundant in larger, deeper production ponds (fig. 36).

Grid-Scale Models 

We modeled foraging and roosting abundance of nine guilds and three species of interest, for a 

total of 12 separate analyses at the grid-scale (tables 29–40). Model-averaged results from the GLMM 

analyses indicate that grid water depth was an important predictor variable for all guilds regardless of 

foraging or roosting behavior (fig. 37), whereas GAM results show great variability in depths used 

among guilds (fig. 38A–P). Grids with deeper water supported higher abundances of foraging and 

roosting diving ducks and roosting piscivores (fig. 37, table 41), with optimal depths for these groups 

ranging from 0.8 to 1.0 m (fig. 38C–D, K–L; table 42). Grids with deeper water also supported higher 

abundances of foraging terns (0.57 m) and gulls (2.51 m); however, roosting gulls and terns were 

associated with shallower grids (0 m and 0.48 m, respectively; fig. 38 I–J, M–N; table 42). All other 

guilds had a negative relation with depth (fig. 37), indicating a higher abundance of birds in grids with 

shallower water (≤0.65 m; fig. 38A–P, table 42). 

Other grid characteristics also predicted waterbird abundance. Grids with one or more islands 

supported higher abundances of foraging waders and medium shorebirds, as well as all roosting guilds

except diving ducks (fig. 39; table 41). Grids located closer to levees supported highest abundances of 

foraging and roosting small and medium shorebirds (0.05 km), foraging (0.05 km) and roosting gulls 

(<0.45km), foraging (>0.05km) and roosting piscivores (0.05km), waders (0.08 km foraging and 

roosting), and foraging terns (0.06 km), and a lower abundance of roosting terns (peak abundance at 

0.35 km; figs. 40, 41 A–L; tables 41 and 42). Grids located closer to sloughs and creeks supported 

highest abundances of foraging small shorebirds (0.04 km) and roosting medium shorebirds (0.04 km), 

whereas grids farther from creeks and sloughs had highest abundances of roosting piscivores (0.95 km; 

figs. 42, 43A–B; tables 41 and 42). Grids with increased topography supported a higher abundance of 

foraging and roosting small shorebirds (0.25m), foraging waders (1.25 m), roosting medium shorebirds 

(1.10 m) and gulls (0.84 m), and foraging dabbling ducks (1.07 m; figs. 44, 45A, D–I; tables 41 and 42). 

However, grids with minimal topography supported highest abundances of foraging diving ducks (0.12 

m; fig. 45B; tables 41 and 42).Species responses to grid characteristics were similar to their respective 

guild-level responses. Abundances of foraging and roosting American avocets were highest in shallow 

grids (0.01–0.03 m), closer to creeks (0.4 km) and levees (0.06–0.07 km), with one or more islands 

(figs. 37, 39, 40, 42, 46A–F; tables 43 and 44). Grids with increased topography (>0.5m) also supported 

a higher abundance of roosting American avocets (figs. 44B, 46G, tables 43 and 44). Shallow grids

(0.19–0.42 m) with one or more islands supported the highest abundance of foraging and roosting 

northern shovelers (figs. 37, 39, 47A–B, tables 43 and 44), and grids closer to levees (0.06 km; figs. 40, 

47C, tables 43 and 44) and with greater topography (1.03 m; figs. 44A, 47D; tables 43 and 44) 

supported highest abundances of foraging northern shovelers. Highest abundances of foraging and 

roosting ruddy ducks were in deep grids (0.78–0.86 m; fig. 48A–B, 43 and 44) that were situated close 

to levees (0.05 km; fig. 48C, tables 43 and 44) and contained one or more islands (table 43). Grids 

closer to levees (0.05 km; figs. 40, 49C–D, tables 43 and 44), with deeper water (1.5 m; figs. 37, 49A–

B, tables 43 and 44) supported the highest abundances of foraging and roosting eared grebes.
15 

Discussion

The goal of our study was to provide science support to resource managers tasked with 

maintaining migratory bird populations in the Project. To understand waterbird response to active pond 

management and preliminary wetland restoration efforts in the Project area, we investigated trends in 

abundance over a 13-year period that encompassed the pre-restoration period (2002–06), the ISP (2006–

10) and Phase I (2010–15), and we evaluated the effects of habitat characteristics on waterbird 

abundance. We assessed bird-habitat relations at two spatial scales to determine how pond location in 

the landscape, as well as conditions across and within ponds, influenced bird abundance. 

Abundance Trends 

We noted a net increase in overall waterbird abundance during winter in Project ponds, driven 

primarily by the waterfowl and shorebird guilds. For waterfowl, this pattern mirrors long-term (1989–

2014) and short-term (2005–14) baywide trends showing significant increases in SFB estuary dabbling 

duck and weak increases in South Bay diving duck abundances, although elsewhere in SFB diving 

ducks are decreasing (San Francisco Estuary Partnership, 2015). Diving duck trends in the Project area 

can be attributed to ruddy ducks, which represented an average of 64 percent (±6 percent standard 

deviation) of all diving ducks in the ponds across the study period. Analyses of SFB mid-winter survey 

data show that ruddy ducks have increased significantly in the South Bay in general, but that other 

diving ducks, such as Scaup (Aythya spp.), have decreased substantially (San Francisco Estuary 

Partnership, 2015). After a period of rapid increase, winter abundance of dabbling ducks in Project 

ponds leveled off in 2006 and the trend has remained flat. Given that dabbling duck numbers have been 

increasing in SFB (San Francisco Estuary Partnership 2015) overall, this flat trend may indicate Project 

ponds are reaching carrying capacity for this guild.

A comparison of shorebird counts conducted in November 1990–92 and 2006–08 across SFB 

showed that 14 of 22 shorebird species had stable or increasing populations between these time periods 

(Wood and others, 2010). The South Bay subregion, including all the Project ponds, held more than

one-half of all shorebirds in SFB and had the least amount of change in shorebird abundances between 

the two time periods (Wood and others, 2010). Fall and winter shorebird trends observed in our study 

had similar stable to increasing patterns, and shorebird abundance has continued to increase in Project 

ponds since the conclusion of the Wood and others (2010) study in 2008. 

Although we observed a net increase for most waterbirds over the study period, this trend was 

non-linear because of a conspicuous decrease for shorebirds and diving ducks starting in 2005 to 2006 

and reversing in 2008. The cause of this decrease is not clear. The ISP period, during which pond water 

depth and salinity management was initiated, began in 2006 and could have contributed to this change. 

However, during the same period, we observed similar trends in managed ponds of the Napa-Sonoma 

Marsh in North San Francisco Bay (author’s unpub. data, U.S. Geological Survey, accessed 15 

September 2016), suggesting that Project pond management may not have been the cause. Other 

possible explanations for this temporary decrease include range-wide or regional shifts in distribution of 

these species owing to climatic conditions or management practices elsewhere. Based on 2006 

waterfowl breeding population data, most North American species estimates were greater than their 

long-term average during the period of decrease in Project ponds, suggesting that continental population 

changes did not influence abundance (U.S. Fish and Wildlife Service, 2006). However, population 

estimates for Scaup were at record lows during this same period, and Bufflehead estimates were 51

percent lower than 2005 estimates (U.S. Fish and Wildlife Service, 2006), potentially having some 

effect on the diving duck numbers we observed in SFB overall. 
16 

Habitat Modeling

Management of Project pond characteristics may have played a role in the increased abundance 

we observed for some waterbird guilds. For example, the primary changes to Project ponds under ISP 

and Phase I management actions during our study period included manipulating water depths (both 

increasing and decreasing), adding islands, and decreasing salinities, in part to maintain or enhance 

abundance of waterbirds representing multiple guilds (Stallings, 2004). We determined that water depth 

was a significant driver of abundance for nearly all guilds and species we studied, particularly at the grid 

scale. The influence of water depth on species occurrence, richness, and abundance has been shown in 

other systems, and many of our results were within range of those reported in other studies. For 

example, wading bird abundance and habitat selection in the Everglades is largely influenced by water 

depth (Bancroft, Gawlik, and Rutchey, 2002; Beerens and others, 2011). In agricultural fields, highest 

species richness was observed at depths of 10–15 cm and 35–40 cm (Elphick and Oring, 2003) and 

highest densities of foraging dabbling ducks occurred in large areas of shallow water (Tajiri and

Ohkawara, 2013). Elsewhere, small shorebirds used water depths of <6 cm, medium shorebirds used 

water depths of <12 cm (Twedt, 2013), and dabbling ducks used water depths of 22–25 cm (Isola and 

others, 2000). 

Water availability and management, as well as underlying pond topography, influence water 

depth and habitat availability in Project ponds. Although increased topography only supported more 

roosting dabbling ducks and medium shorebirds at the pond scale, we determined that it was beneficial 

for most guilds at the grid scale. Some ponds created for salt production may have a relatively uniform 

pond bottom, with the largest changes in topography being related to borrow ditches adjacent to the 

levee and the shoreline that gradually transitions from the borrow ditch to the top of the levee. 

Increasing managed wetland topography to provide a range of foraging depths for different species has 

been recommended elsewhere (Weber and Haig, 1996; Taft and others, 2002), and our results suggest 

that increasing small-scale topography in shallow water Project ponds may improve habitat for a 

number of guilds and species. 

In deeper ponds, Project managers have added islands with gradually sloping edges to modify 

topography and increase shallow foraging and roosting area availability for shorebirds and other guilds. 

Islands and their associated features were previously determined to influence the abundance of 

wintering waterbirds within Project ponds (Ackerman and others, 2014). Here we evaluated the 

importance of islands in comparison to other habitat variables and determined, based on variable 

importance rankings, that islands may be as critical as water depth, salinity, and other pond 

characteristics for foraging and roosting birds from multiple guilds. Other results from our study 

indirectly point to the importance of islands as well. For example, we observed a parabolic relation 

between shorebird abundance and water depth at the pond scale, where abundances were highest in very 

shallow ponds (0–0.25 m) and in the deepest ponds (2.5 m). This relation likely is owing to shorebird 

use of islands and their adjacent shallow waters in deeper ponds. 

Artificial islands can provide important functions for foraging and roosting waterbirds, 

particularly in areas where natural habitats have been compromised (Burton, Evans, and Robinson, 

1996). One advantage of island habitat is that it can afford protection from terrestrial predators and other 

types of disturbance (Giroux, 1981). In addition to increasing shallow foraging habitat, island creation 

also may increase deep water foraging areas for benthic-foraging diving ducks and fish-eating species

within Project ponds because it often is accomplished by excavating adjacent pond sediments. 

Availability of island and levee edge habitat is essential for shorebirds not only for foraging, but also to 

meet their roosting needs. Maintenance of multiple roost site options is important for some species that 

use several sites, depending on factors such as distance to foraging area (Van Gils and others, 2006), 

windspeed, disturbance (Peters and Otis, 2007), daily tidal cycles, and seasonal habitat needs (Conklin, 
17 

Colwell, and Fox-Fernandez, 2008). However, roost site fidelity can vary by region; Dunlin have high 

roost site fidelity in Tagus Estuary, Portugal, and low roost site fidelity in Humboldt Bay, California 

(Dias and others, 2006; Conklin and Colwell, 2008). Several habitat characteristics may be important 

for roosting shorebirds. For example, salt pans managed with shallow water can serve as important 

high-tide roosting sites for shorebirds (Catry and others, 2011).

Next to depth, distance to levee was the variable that significantly influenced the most foraging 

and roosting guilds at the grid scale. Abundances of all non-waterfowl guilds except for roosting terns 

were greater in grids closer to levees, indicating the broad importance of levee habitat for both roosting 

and foraging birds. Internal levees in particular may be cut off from the pond exterior and function as 

high-elevation, disturbance-free islands. Water control structures installed within levees provide a 

connection point between pond borrow ditches and SFB or adjacent sloughs. Thus, within the pond, 

deep areas near levees have high water circulation and may contain important prey resources such as 

fish that attract foraging piscivores to these sites. Shorebirds rely on levees as high-tide roosting sites, 

but also use shallow water near levee edges for foraging, as shown by radio-marked Western Sandpipers 

in the South Bay that selected salt pond levees at low and high tides (Warnock and Takekawa, 1995). 

Many of the guilds and species we evaluated were most abundant in lower salinity (mixohaline, 

about 0.5–30 ppt) ponds, although small and medium shorebirds showed no relation to salinity, and 

foraging eared grebes and gulls were associated with high-salinity ponds. Some waterbird species may 

avoid high-salinity ponds because of their own physiological constraints (Gordus, Shivaprasad, and

Swift, 2002) or limitations in prey resources (Sánchez, Green, and Castellanos, 2006). Common prey 

taxa of waterfowl and piscivores (including amphipods, polychaetes, and copepods, and several small 

fish species) often are most abundant in mixohaline environments (Thompson, Lowe, and Kellogg, 

2000; Thompson and others, 2013). In addition to mixohaline invertebrates, many shorebird species also

can take advantage of invertebrates such as Artemia and Ephydra spp., commonly present in highsalinity environments (Masero, 2003; Takekawa and others, 2009), which may explain why these guilds 

showed no relation to pond salinity. Eared grebes are hypersaline specialists that forage on Artemia and 

other high-salinity-associated prey during winter and migratory periods (Cullen, Jehl, and Neuchterlein, 

1999). Our results suggest that, although it is clearly important to maintain high-salinity pond habitat to 

meet eared grebe foraging needs in the Project area, roosting birds may be able to take advantage of 

ponds across a much broader array of salinities.

Invertebrate prey is an important driver of waterbird abundance and species richness. In 

Portugal, medium shorebirds foraged in man-made salt pans during low tide rather than adjacent 

mudflats, suggesting the importance of these alternative habitats for providing shorebird prey resources 

(Dias and others, 2014). Invertebrate densities also can drive waterbird community composition within 

salt pans (Horvath and others, 2013). Prior research suggests that the invertebrate composition of Project 

ponds could support 79–102 percent of the energy needs of diving ducks, and 29–52 percent of the 

energy needs of small shorebirds (Brand and others, 2014); however, we lacked invertebrate data at the 

appropriate temporal and spatial scales to directly investigate relations between prey densities and 

waterbird abundance in this modeling effort. Ongoing (for example, Eden Landing Ecological Reserve 

ponds E12/13) and future studies of relations among salinity, depth, invertebrate communities, and 

waterbird density in experimental ponds may help identify important invertebrate taxa and management 

methods for maintaining them. 

Pond area had a positive effect on abundance for diving ducks, piscivores, terns, and waders, 

which was similar to findings at a restoration site in Southern Spain that included man-made ponds 

(Sebastián-González and Green, 2013). Here, larger ponds supported higher bird abundances and 

species richness, which is consistent with the idea that, as wetland area increases, additional 

microhabitats become available and more species can use them (Sebastián-González and Green, 2013). 
18 

Interestingly, we determined that the pond area did not have an influence on shorebird and dabbing duck 

abundances. One explanation might be that tidal marsh vegetation has only been established within 

three restored Project ponds thus far, leaving most of the Project landscape as interconnected, open 

water habitat that may function for some species as a larger area for foraging, roosting, and predator 

evasion. As vegetation begins to grow in additional restored ponds, remaining open water ponds will 

become increasingly isolated from each other and surrounded by vegetation, which could have effects 

on predator access and waterbird use (Bennett, Radford, and Haslem, 2006; Sebastián-González and

Green, 2013). As Project restorations progress and mature, additional work on the effect of pond area 

and proximity to other suitable habitat in the mosaic of tidal wetlands and ponds may be critical to 

determine habitat requirements for species that are not sensitive to pond area under current conditions. 

During our study period, about 13 percent of the surveyed Project area was restored to tidal flow 

under Phase I restorations. As ponds transition from open water habitat to fully vegetated tidal marsh, 

they first develop into tidal mudflat habitat that can provide foraging and roosting areas for shorebirds 

and other guilds. Passive tidal marsh restoration results in sediment accretion over time as tidewater 

brings substrate into the pond, and eventually allows the pond to reach elevations suitable for marsh 

plant colonization (Brand and others, 2012). Much like initial findings from work on waterbird use of 

breached habitat in the former salt ponds in North San Francisco Bay (Athearn, Takekawa and Shinn, 

2009), our study suggests that ponds breached for wetland restoration may provide important foraging 

habitat for shorebirds during this transitional phase. Breached pond mudflat habitat typically is available 

at a different point in the tidal cycle than open Bay mudflat, and thus may increase the time period over 

which shorebirds have foraging opportunities each day. However, our results also indicated that diving 

duck and piscivore abundances were not associated with breached ponds, perhaps because water levels 

were not deep enough to support foraging and roosting by these guilds for extended periods each day. 

We noted no influence of proximity to urban areas on bird abundance, and that the influence of 

pond proximity to the edge of SFB was limited to diving ducks and gulls. Adair and others (1996) also 

reported high abundances of diving ducks in coastal ponds that were close to estuary foraging sites. In 

the Tagus Estuary, Portugal, dunlin foraged within 5 km of their roosting site (Dias and others, 2006). 

Only four of our ponds were greater than 5 km from SFB, suggesting that most ponds likely were within 

a reasonable flight distance for small shorebirds to move to mudflats at low tide. Radio-marked Western 

sandpipers used mudflats immediately adjacent to the salt pond complexes in SFB and regularly moved 

between the two habitats (Warnock and Takekawa, 1996). 

Human activity may alter habitat selection by sensitive waterbird species (Peters and Otis, 2007; 

Meager, Schlacher, and Nielsen, 2012); therefore, we evaluated the influence of public and hunting 

access on abundance. We noted positive relations between small shorebird and gull abundances and the 

percentage of pond levees open to the public. However, the proportion of levees open to hunting 

negatively influenced the abundance of piscivores, waders, and dabbling ducks. Brochet and others

(2009) also reported a negative influence of hunting on dabbling ducks in the Camargue marshes of 

France. Murray and others (2013) reported that human access had a negative influence on diving ducks 

in urban ponds, although we did not note any effect on diving duck abundance. Our results differ 

somewhat from the findings of Trulio and Sokale (2008), who noted that trail use did not positively or 

negatively influence the shorebird abundance in the Project ponds. However, as with our findings on 

levees open to hunting, Trulio and others (2012) reported that waterfowl responded to new trail use in 

the Project area by swimming or flying away and that dabbling duck abundance was lower near levees 

with trails compared to those without trails. Our results on gulls were similar to those noted in another 

urbanized estuary in Portugal, where gull abundance increased with increasing human presence (Rosa, 

Palmeirim, and Moreira, 2003). 
19 

Our analyses did not comprehensively test the effects of hunting on abundance because we did 

not have reliable information on hunting blind placement in all Project ponds for the entire length of the 

study and, therefore, only considered effects from levee-based hunting. Likewise, for other types of 

public access, we only evaluated waterbird response to levees that were open to the public or not, but we 

did not have appropriate data to distinguish among levels of actual use of trails adjacent to ponds. 

Although some experimental work has been conducted in the Project areas to evaluate these parameters 

(Trulio and Sokale, 2008; Trulio and others, 2012), additional studies may be warranted given the 

variation in responses of species to different types and levels of disturbance, and the potential for local 

population consequences to some species (Gill, Norris, and Sutherland, 2001). 

Management Implications

Given that much of the habitat conversion planned for Project ponds has not yet occurred and 

that current management is geared towards optimizing conditions for waterbirds, present bird numbers 

may be approaching peak values for this former salt pond system. With the planned conversion of 50–

90 percent of Project ponds to tidal marsh, optimizing the quality of remaining open water habitat to 

maintain waterbird abundance and diversity within a smaller footprint may be needed. We identified 

several significant landscape and micro-habitat level characteristics for key guilds and species of 

waterbirds, and our results support evidence from other studies suggesting that a mosaic of habitat 

features is important for avian diversity and abundance (Burger, Niles, and Clark, 1997; Bennett, 

Radford, and Haslem, 2006; Mohd-Azlan, Noske, and Lawes, 2015). Many of the habitat characteristics 

that we identified as important can be manipulated by managers to benefit one or more taxa. In many 

cases, the ranges of optimal habitat values overlap among guilds and species, providing the opportunity 

to manage individual or groups of ponds to benefit several species. For example: 

• Depth was a significant driver of abundance; however, guilds had variable relations to depth, 

with abundances peaking across a wide range (0–2.5 m) at the pond and grid scales. In addition 

to water level manipulation, increasing topography and islands within ponds may be one 

approach to meeting the depth needs of multiple species.

• Our grid-scale results suggest that increasing small-scale topography from 0.12 to 1.27 m in 

shallow water Project ponds may increase foraging use by most of the guilds and species we 

studied. Experimental studies to evaluate the response of waterbirds to various topographic 

manipulations may further inform management of this habitat feature. 

• Presence of islands was an important pond feature that influenced the abundance of several

guilds as much as depth and salinity. Our analyses suggest that maintaining islands may help

sustain foraging and roosting waterbirds from multiple guilds.

• Close proximity to levees was important for roosting and foraging birds from all non-waterfowl 

guilds, suggesting that maintaining levees and the habitat heterogeneity immediately adjacent to 

them may increase abundances of several guilds. Additional research to determine which levee 

features attract each guild may be informative.

• Maintaining water salinity of ≤33 ppt may benefit foraging and roosting dabbling and diving 

ducks (including northern shoveler and ruddy duck), piscivores, terns, and waders. However, 

gulls and eared grebes may require ponds maintained at much higher salinities (≥109 ppt) for 

foraging. Foraging and roosting small and medium shorebirds may be able to take advantage of 

conditions across multiple salinities. 
20 

• Invertebrate prey availability is known to be a main driver of foraging waterbird abundance, yet 

we lacked information on invertebrate density to inform our models. Ongoing and future studies 

of relations among salinity, depth, invertebrate communities, and waterbird density in 

experimental ponds may help identify important invertebrate taxa and management methods for 

maintaining them. 

• Maintaining ponds with large areas of open water (>1.0 km2) may benefit foraging and roosting 

diving ducks, piscivores, terns, gulls, waders, and eared grebes. As Project restorations progress 

and mature, additional work on the effects of pond area and proximity to other suitable habitat in 

the mosaic of tidal wetlands and ponds may help determine area requirements for all guilds and 

species.

• Foraging small shorebirds were the only guild for which abundance was positively related to 

breached ponds. As Project restorations progress, future work to investigate temporal and spatial 

use of breached habitat by all guilds may inform management of post-breach transitional habitat 

to maximize waterbird abundances. 

• We noted variable responses to hunting and other public access among guilds. The information 

available for these variables limited our analyses; thus, additional studies may be warranted 

given the variation in responses of species to different types and levels of disturbance, and the 

potential for local population consequences to some species. 

• Eared grebes are hypersaline specialists with unique depth and salinity requirements that often 

do not overlap those of other guilds and species. Additional targeted studies on movements and 

seasonal habitat use of eared grebes may inform management opportunities for this species. 

• Our model results can be used in future analyses to evaluate the effects of different management 

scenarios on waterbird abundances, including weighing costs and benefits of maintaining

individual or groups of ponds or converting them to tidal marsh.

This modeling exercise used a detailed 13-year dataset to identify trends and key drivers of 

waterbird abundance, underscoring the value of restoration monitoring and research to inform adaptive 

management. The results presented here are powerful tools for advancing adaptive management actions 

that can help reach the long-term goals of this 50-year Project.

Acknowledgments

We thank the Don Edwards San Francisco Bay National Wildlife Refuge Complex (Anne 

Morkill, Eric Mruz, Joy Albertson, Rachel Tertes), Eden Landing Ecological Reserve, South Bay Salt 

Pond Restoration Project (Laura Valoppi and John Bourgeois), and U.S. Fish and Wildlife Service 

Inventory and Monitoring Program (Giselle Block, Kaylene Keller) for logistical and project support. J. 

Falgout and B. Williams at U.S. Geological Survey (USGS) Core Science Analytics, Synthesis, and 

Libraries in Denver provided integral assistance in using the Simple Linux Utility for Resource 

Management (SLURM). We thank N. Athearn and L. Brand (USGS), and J. Bluso-Demers, C. Burns, 

and N. Washburn (San Francisco Bay Bird Observatory [SFBBO]) for waterbird project coordination; J. 

Shinn (USGS) and J. Scullen (SFBBO) for data management; K. Spragens (USGS) for helpful 

discussion; J. Yee (USGS) for statistical advice; and T. Graham (USGS) for review. We thank the many 

field technicians that conducted surveys and entered data throughout the years of this project. 
21 

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25 

Figure 1. Former and current salt ponds grouped in several complexes in South San Francisco Bay, 

California. Ponds in Alviso and Ravenswood (both managed by Don Edwards San Francisco Bay National 

Wildlife Refuge), and the Eden Landing Ecological Reserve (California Department of Fish and Wildlife) are 

part of the South San Francisco Bay Restoration Project. Cargill, Inc. manages the Mowry and Newark 

pond complexes for salt production
26 

Figure 2. Dabbling duck population trends during fall (A), winter (B), and spring (C) in South Bay Salt Pond Restoration Project ponds (dark gray) and active 

salt production ponds (light gray), South San Francisco Bay, California. Points represent abundance of each monthly count, where fall includes October and 

November; winter includes December, January, and February; and spring includes March and April. Trend lines were created using locally weighted 

scatterplot smoothing in R. Counts were not conducted during fall 2013. Year corresponds to the year of fall, winter 2013 includes January and February 

2014, and spring 2013 includes March and April 2014.
27 

Figure 3. Diving duck population trend during fall (A), winter (B), and spring (C) in South Bay Salt Pond Restoration Project ponds (dark gray) and active 

salt production ponds (light gray), South San Francisco Bay, California. Points represent abundance of each monthly count, where fall includes October and 

November; winter includes December, January, and February; and spring includes March and April. Trend lines were created using locally weighted 

scatterplot smoothing in R. Counts were not conducted during fall 2013. Year corresponds to the year of fall, winter 2013 includes January and February 

2014, and spring 2013 includes March and April 2014.
28 

Figure 4. Small shorebird population trend during fall (A), winter (B), and spring (C) in South Bay Salt Pond Restoration Project ponds (dark gray) and 

active salt production ponds (light gray), South San Francisco Bay, California. Points represent abundance of each monthly count, where fall includes 

October and November; winter includes December, January, and February; and spring includes March and April. Trend lines were created using locally 

weighted scatterplot smoothing in R. Counts were not conducted during fall 2013. Year corresponds to the year of fall, winter 2013 includes January and 

February 2014, and spring 2013 includes March and April 2014.
29 

Figure 5. Medium shorebird population trend during fall (A), winter (B), and spring (C) in South Bay Salt Pond Restoration Project ponds (dark gray) and 

active salt production ponds (light gray), South San Francisco Bay, California. Points represent abundance of each monthly count, where fall includes 

October and November; winter includes December, January, and February; and spring includes March and April. Trend lines were created using locally 

weighted scatterplot smoothing in R. Counts were not conducted during fall 2013. Year corresponds to the year of fall, winter 2013 includes January and 

February 2014, and spring 2013 includes March and April 2014.
30 

Figure 6. Wader population trend during fall (A), winter (B), and spring (C) in South Bay Salt Pond Restoration Project ponds (dark gray) and active salt 

production ponds (light gray), South San Francisco Bay, California. Points represent abundance of each monthly count, where fall includes October and 

November; winter includes December, January, and February; and spring includes March and April. Trend lines were created using locally weighted 

scatterplot smoothing in R. Counts were not conducted during fall 2013. Year corresponds to the year of fall, winter 2013 includes January and February 

2014, and spring 2013 includes March and April 2014.
31 

Figure 7. Piscivore population trend during fall (A), winter (B), and spring (C) in South Bay Salt Pond Restoration Project ponds (blue) and active salt 

production ponds (red), South San Francisco Bay, California. Points represent abundance of each monthly count, where fall includes October and 

November; winter includes December, January, and February; and spring includes March and April. Trend lines were created using locally weighted 

scatterplot smoothing in R. Counts were not conducted during fall 2013. Year corresponds to the year of fall, winter 2013 includes January and February 

2014, and spring 2013 includes March and April 2014.
32 

Figure 8. Tern population trend during fall (A), winter (B), and spring (C) in South Bay Salt Pond Restoration Project ponds (dark gray) and active salt 

production ponds (light gray), South San Francisco Bay, California. Points represent abundance of each monthly count, where fall includes October and 

November; winter includes December, January, and February; and spring includes March and April. Trend lines were created using locally weighted 

scatterplot smoothing in R. Counts were not conducted during fall 2013. Year corresponds to the year of fall, winter 2013 includes January and February 

2014, and spring 2013 includes March and April 2014.
33 

Figure 9. Gull population trend during fall (A), winter (B), and spring (C) in South Bay Salt Pond Restoration Project ponds (dark gray) and active salt 

production ponds (light gray), South San Francisco Bay, California. Points represent abundance of each monthly count, where fall includes October and 

November; winter includes December, January, and February; and spring includes March and April. Trend lines were created using locally weighted 

scatterplot smoothing in R. Counts were not conducted during fall 2013. Year corresponds to the year of fall, winter 2013 includes January and February 

2014, and spring 2013 includes March and April 2014.
34 

Figure 10. Eared grebe population trend during fall (A), winter (B), and spring (C) in South Bay Salt Pond Restoration Project ponds (dark gray) and active 

salt production ponds (light gray), South San Francisco Bay, California. Points represent abundance of each monthly count, where fall includes October and 

November; winter includes December, January, and February; and spring includes March and April. Trend lines were created using locally weighted 

scatterplot smoothing in R. Counts were not conducted during fall 2013. Year corresponds to the year of fall, winter 2013 includes January and February 

2014, and spring 2013 includes March and April 2014.
35 

Figure 11. Northern shoveler population trend during fall (A), winter (B), and spring (C) in South Bay Salt Pond Restoration Project ponds (dark gray) and 

active salt production ponds (light gray), South San Francisco Bay, California. Points represent abundance of each monthly count, where fall includes 

October and November; winter includes December, January, and February; and spring includes March and April. Trend lines were created using locally 

weighted scatterplot smoothing in R. Counts were not conducted during fall 2013. Year corresponds to the year of fall, winter 2013 includes January and 

February 2014, and spring 2013 includes March and April 2014. 
36 

Figure 12. Ruddy duck population trend during fall (A), winter (B), and spring (C) in South Bay Salt Pond Restoration Project ponds (dark gray) and active 

salt production ponds (light gray), South San Francisco Bay, California. Points represent abundance of each monthly count, where fall includes October and 

November; winter includes December, January, and February; and spring includes March and April. Trend lines were created using locally weighted 

scatterplot smoothing in R. Counts were not conducted during fall 2013. Year corresponds to the year of fall, winter 2013 includes January and February 

2014, and spring 2013 includes March and April 2014.
37 

Figure 13. American avocet population trend during fall (A), winter (B), and spring (C) in South Bay Salt Pond Restoration Project ponds (dark gray) and 

active salt production ponds (light gray), South San Francisco Bay, California. Points represent abundance of each monthly count, where fall includes 

October and November; winter includes December, January, and February; and spring includes March and April. Trend lines were created using locally 

weighted scatterplot smoothing in R. Counts were not conducted during fall 2013. Year corresponds to the year of fall, winter 2013 includes January and 

February 2014, and spring 2013 includes March and April 2014.
38 

Figure 14. Model-averaged coefficients (±95-percent confidence interval [CI]) of pond water depth for (A) 

foraging and (B) roosting waterbirds at the pond scale in restoration ponds, South San Francisco Bay, California. 

Coefficients were considered significant only if the 95-percent CI does not overlap zero. Bar length indicates the 

coefficient effect size and is comparable across species and guilds. AMAV, American avocet; EAGR, eared

grebe; NSHO, northern shoveler; RUDU, ruddy duck. 
39 

Figure 15. Diving duck (A–B), medium (C–D) and small shorebird (E–F), gull (G–H), piscivore (I–J) and wader 

(K) abundance response to water depth (in meters) from generalized additive mixed models at the pond scale in 

restoration ponds, South San Francisco Bay, California. Dashed lines indicate the 95-percent confidence interval. 

Note: Y-axis differs among figures.
40 

Figure 15.—Continued
41 

Figure 16. Model-averaged coefficients (±95-percent confidence interval [CI]) of pond salinity for (A) foraging 

and (B) roosting waterbirds at the pond scale in restoration ponds, South San Francisco Bay, California. 

Coefficients are considered significant only if the 95-percent CI does not overlap zero. Bar length indicates the 

coefficient effect size and is comparable across species and guilds. AMAV, American avocet; EAGR, eared

grebe; NSHO, northern shoveler; RUDU, ruddy duck. 
42 

Figure 17. Dabbling duck (A–B), diving duck (C–D), gull (E), piscivore (F–G), tern (H–I),and wader (J–K) 

abundance response to salinity (in log[parts per thousand]) from generalized additive mixed models at the pond 

scale in restoration ponds, South San Francisco Bay, California. Dashed lines indicate the 95-percent confidence 

interval. Note: Y-axis differs among figures.
43 

Figure 17.—Continued. 
44 

Figure 18. Model-averaged coefficients (±95-percent confidence interval [CI]) of pond area for (A) foraging and 

(B) roosting waterbirds at the pond scale in restoration ponds, South San Francisco Bay, California. Coefficients 

are considered significant only if the 95-percent CI does not overlap zero. Bar length indicates the coefficient 

effect size and is comparable across species and guilds. AMAV, American avocet; EAGR, eared grebe; NSHO, 

northern shoveler; RUDU, ruddy duck. 
45 

Figure 19. Diving duck (A–B), gull (C–D), piscivore (E–F), tern (G–H), and wader (I–J) abundance response to 

pond area (in square kilometers) from generalized additive mixed models at the pond scale in restoration ponds, 

South San Francisco Bay, California. Dashed lines indicate the 95-percent confidence interval. Note: Y-axis 

differs among figures.
46 

Figure 19.—Continued.
47 

Figure 20. Model-averaged coefficients (±95-percent confidence interval [CI]) of pond topography for (A) 

foraging and (B) roosting waterbirds at the pond scale in restoration ponds, South San Francisco Bay, California. 

Coefficients are considered significant only if the 95-percent CI does not overlap zero. Bar length indicates the 

coefficient effect size and is comparable across species and guilds. AMAV, American avocet; EAGR, eared

grebe; NSHO, northern shoveler; RUDU, ruddy duck. 
48 

Figure 21. Roosting dabbling duck (A) and roosting medium shorebird (B) abundance response to pond 

topography (in meters) from generalized additive mixed models at the pond scale in restoration ponds, South San 

Francisco Bay, California. Dashed lines indicate the 95-percent confidence interval. Note: Y-axis differs between 

figures.
49 

Figure 22. Model-averaged coefficients (±95-percent confidence interval [CI]) of islands for (A) foraging and (B) 

roosting waterbirds at the pond scale in restoration ponds, South San Francisco Bay, California. Coefficients are 

considered significant only if the 95-percent CI does not overlap zero. Bar length indicates the coefficient effect 

size and is comparable across species and guilds. AMAV, American avocet; EAGR, eared grebe; NSHO, 

northern shoveler; RUDU, ruddy duck. 
50 

Figure 23. Model-averaged coefficients (±95-percent confidence interval [CI]) of distance to San Francisco Bay 

for (A) foraging and (B) roosting waterbirds at the pond scale in restoration ponds, South San Francisco Bay, 

California. Coefficients are considered significant only if the 95-percent CI does not overlap zero. Bar length 

indicates the coefficient effect size and is comparable across species and guilds. AMAV, American avocet; 

EAGR, eared grebe; NSHO, northern shoveler; RUDU, ruddy duck. 
51 

Figure 24. Diving duck (A–B) and gull (C–D) abundance response to pond distance (in kilometers) to San 

Francisco Bay from generalized additive mixed models at the pond scale in restoration ponds, South San 

Francisco Bay, California. Dashed lines indicate the 95-percent confidence interval. Note: Y-axis differs among 

figures.
52 

Figure 25. Gull abundance response to distance to landfill (in kilometers) from generalized additive mixed 

models at the pond scale in restoration ponds, South San Francisco Bay, California. Dashed lines indicate the 95-

percent confidence interval.
53 

Figure 26. Model-averaged coefficients (±95-percent confidence interval [CI]) of percent of pond levees open to 

hunting for (A) foraging and (B) roosting waterbirds at the pond scale in restoration ponds, South San Francisco 

Bay, California. Coefficients are considered significant only if the 95-percent CI does not overlap zero. Bar length 

indicates the coefficient effect size and is comparable across species and guilds. AMAV, American avocet; 

EAGR, eared grebe; NSHO, northern shoveler; RUDU, ruddy duck. 
54 

Figure 27. Dabbling duck (A), gull (B), piscivore (C), tern (D), and wader (E) abundance response to percentage

of levees open to hunting from generalized additive mixed models at the pond scale in restoration ponds, South 

San Francisco Bay, California. Dashed lines indicate the 95-percent confidence interval. Note: Y-axis differs 

between figures.
55 

Figure 27.—Continued.
56 

Figure 28. Model-averaged coefficients (±95-percent confidence interval [CI]) of percentage of pond levees open 

to the public for (A) foraging and (B) roosting waterbirds at the pond scale in restoration ponds, South San 

Francisco Bay, California. Coefficients are considered significant only if the 95-percent CI does not overlap zero. 

Bar length indicates the coefficient effect size and is comparable across species and guilds. AMAV, American 

avocet; EAGR, eared grebe; NSHO, northern shoveler; RUDU, ruddy duck.
57 

Figure 29. Small shorebird (A) and gull (B) abundance response to percentage of levees open to the public from 

generalized additive mixed models at the pond scale in restoration ponds, South San Francisco Bay, California. 

Dashed lines indicate the 95-percent confidence interval. Note: Y-axis differs between figures.
58 

Figure 30. Model-averaged coefficients (±95-percent confidence interval [CI]) of distance to the urban areas for 

(A) foraging and (B) roosting waterbirds at the pond scale in restoration ponds, South San Francisco Bay, 

California. Coefficients are considered significant only if the 95-percent CI does not overlap zero. Bar length 

indicates the coefficient effect size and is comparable across species and guilds. AMAV, American avocet; 

EAGR, eared grebe; NSHO, northern shoveler; RUDU, ruddy duck.
59 

Figure 31. Model-averaged coefficients (±95-percent confidence interval [CI]) of pond management (breached 

[positive values on x-axis] compared to not breached [negative values on x-axis]) for (A) foraging and (B) roosting 

waterbirds at the pond scale in restoration ponds, South San Francisco Bay, California. Coefficients are 

considered significant only if the 95-percent CI does not overlap zero. Bar length indicates the coefficient effect 

size and is comparable across species and guilds. AMAV, American avocet; EAGR, eared grebe; NSHO, 

northern shoveler; RUDU, ruddy duck. The model-averaged parameter estimate (-26.998) for roosting eared

grebe is not shown here as it exceeds the scale of this graph, but it is not considered significant (95-percent

confidence interval overlaps zero). 
60 

Figure 32. American avocet (AMAV) abundance response to depth (in meters relative to NAVD88 vertical datum; 

A–B) and topography (in meters; C) from generalized additive mixed models at the pond scale in restoration 

ponds, South San Francisco Bay, California. Dashed lines indicate the 95-percent confidence interval.
61 

Figure 33. Northern shoveler (NSHO) abundance response to salinity (in log[parts per thousand]; A–B), 

percentage of the levee open to hunting (C), and pond topography (in meters; D) from generalized additive mixed 

models at the pond scale in restoration ponds, South San Francisco Bay, California. Dashed lines indicate the 95-

percent confidence interval.
62 

Figure 34. Ruddy duck (RUDU) abundance response to water depth (in meters; A–B), salinity (in log[parts per 

thousand]; C–D), pond area (in square kilometers; E–F), and distance to San Francisco Bay (in kilometers; G)

from generalized additive mixed models at the pond scale in restoration ponds, South San Francisco Bay, 

California. Dashed lines indicate the 95-percent confidence interval.
63 

-

Figure 35. Eared grebe (EAGR) abundance response to depth (in meters relative to NAVD 88 vertical datum; A–

B), salinity (in log[parts per thousand]; C), and pond area (in square kilometers; D–E) from generalized additive 

mixed models at the pond scale in restoration ponds, South San Francisco Bay, California. Dashed lines indicate 

the 95-percent confidence interval. Note: Y-axis may differ among figures.
64 

Figure 36. Model-averaged coefficients (±95-percent confidence interval [CI]) for (A) foraging and (B) roosting 

eared grebes in salt production ponds at the pond scale in restoration ponds, South San Francisco Bay, 

California. Coefficients are considered significant only if the 95-percent CI does not overlap zero. Bar length 

indicates the coefficient effect size and is comparable across species and guilds.
65 

Figure 37. Model-averaged coefficients (±95-percent confidence interval [CI]) of grid water depth for (A) foraging 

and (B) roosting waterbirds at the grid scale in restoration ponds, South San Francisco Bay, California. 

Coefficients are considered significant only if the 95-percent CI does not overlap zero. Bar length indicates the 

coefficient effect size and is comparable across species and guilds. AMAV, American avocet; EAGR, eared

grebe; NSHO, northern shoveler; RUDU, ruddy duck. 
66 

Figure 38. Dabbling duck (A–B), diving duck (C–D), medium (E–F) and small shorebird (G–H), gull (I–J), 

piscivore (K–L), tern (M–N), and wader (O–P) abundance response to water depth (in meters relative to NAVD 88 

vertical datum) from generalized additive models at the grid scale in restoration ponds, South San Francisco Bay, 

California. Dashed lines indicate 95-percent confidence interval. 
67 

Figure 38.—Continued.
68 

Figure 39. Model-averaged coefficients (±95-percent confidence interval [CI]) of islands for (A) foraging and (B) 

roosting waterbirds at the grid scale in restoration ponds, South San Francisco Bay, California. Coefficients are 

considered significant only if the 95-percent CI does not overlap zero. Bar length indicates the coefficient effect 

size and is comparable across species and guilds. AMAV, American avocet; EAGR, eared grebe; NSHO, 

northern shoveler; RUDU, ruddy duck. 
69 

Figure 40. Model-averaged coefficients (±95-percent confidence interval [CI]) of grid distance to levee for (A) 

foraging and (B) roosting waterbirds at the grid scale in restoration ponds, South San Francisco Bay, California. 

Coefficients are considered significant only if the 95-percent CI does not overlap zero. Bar length indicates the 

coefficient effect size and is comparable across species and guilds. AMAV, American avocet; EAGR, eared

grebe; NSHO, northern shoveler; RUDU, ruddy duck. 
70 

Figure 41. Medium (A–B) and small shorebird (C–D), gull (E–F), piscivore (G–H), tern (I–J), and wader (K–L) 

abundance response to grid distance to levees (in kilometers) from generalized additive models at the grid scale

in restoration ponds, South San Francisco Bay, California. Dashed lines indicate 95-percent confidence interval.
71 

Figure 41.—Continued. 
72 

Figure 42. Model-averaged coefficients (±95-percent confidence interval [CI]) of grid distance to creek for (A) 

foraging and (B) roosting waterbirds at the grid scale in restoration ponds, South San Francisco Bay, California. 

Coefficients are considered significant only if the 95-percent CI does not overlap zero. Bar length indicates the 

coefficient effect size and is comparable across species and guilds. AMAV, American avocet; EAGR, eared

grebe; NSHO, northern shoveler; RUDU, ruddy duck. 
73 

Figure 43. Medium shorebird (A), small shorebird (B) and piscivore (C) abundance response to grid distance to 

adjacent creek (in kilometers) from generalized additive models at the grid scale in restoration ponds, South San 

Francisco Bay, California. Dashed lines indicate 95-percent confidence interval.
74 

Figure 44. Model-averaged coefficients (±95-percent confidence interval [CI]) of grid topography for (A) foraging 

and (B) roosting waterbirds at the grid scale in restoration ponds, South San Francisco Bay, California. 

Coefficients are considered significant only if the 95-percent CI does not overlap zero. Bar length indicates the 

coefficient effect size and is comparable across species and guilds. AMAV, American avocet; EAGR, eared

grebe; NSHO, northern shoveler; RUDU, ruddy duck. 
75 

Figure 45. Dabbling duck (A), diving duck (B), medium (C–D) and small shorebird (E–F), gull (G), wader (H–I) 

abundance response to pond topography (in meters) from generalized additive models at the grid scale in 

restoration ponds, South San Francisco Bay, California. Dashed lines indicate 95-percent confidence interval.
76 

Figure 45.—Continued.
77 

Figure 46. American avocet (AMAV) abundance response to depth (in meters relative to NAVD 88 vertical 

datum; A–B), distance to nearest creek (in meters; C–-D), distance to levees (in meters; E–-F), and pond 

topography (in meters; G) from generalized additive mixed models at the grid scale in restoration ponds, South 

San Francisco Bay, California. Dashed lines indicate the 95-percent confidence interval.
78 

Figure 47. Northern shoveler (NSHO) abundance response to depth (in meters; A–B), distance to levees (in 

meters relative to NAVD 88 vertical datum; C), and pond topography (in meters; D) from generalized additive 

mixed models at the grid scale in restoration ponds, South San Francisco Bay, California. Dashed lines indicate 

the 95-percent confidence interval.
79 

Figure 48. Ruddy duck abundance response to depth (in meters relative to NAVD 88 vertical datum; A–B) and 

distance to levees (in meters; C) from generalized additive mixed models at the grid scale in restoration ponds,

South San Francisco Bay, California. Dashed lines indicate the 95-percent confidence interval.
80 

Figure 49. Eared grebe abundance response to depth (in meters relative to NAVD 88 vertical datum; A–B) and 

distance to levees (in meters; C–D) from generalized additive mixed models at the grid scale in restoration ponds,

South San Francisco Bay, California. Dashed lines indicate the 95-percent confidence interval.
81 

Table 1. Attributes of the South Bay Salt Pond Restoration Project ponds used in waterbird habitat association 

models, South San Francisco Bay, California.

[Data were collected during 2002–15. Maximum % levee open to public: We accounted for change in public access over 

time. For some ponds in some months and years this value may be lower. Maximum % of levee open to hunting: We 

accounted for changes in hunter access over time and we only included levees open to hunters during the hunting season 

(November–January). Abbreviations and symbol: km, kilometer; km2

; square kilometer; %, percent] 

Pond Area 

(km2)

Bay 

distance 

(km)

Urban 

distance 

(km)

Landfill 

distance 

(km)

Maximum % 

levee open to 

public

Maximum % 

of levee open 

to hunting

Number 

of

islands

A1 1.1 0.9 1.4 10.0 0.0 0.0 2

A101 1.0 1.9 3.4 4.9 0.5 0.0 0

A111 1.1 3.1 2.2 5.1 0.3 0.0 0

A121 1.3 4.5 0.8 6.1 0.7 0.0 0

A131 1.1 4.0 1.5 5.1 0.5 0.0 2

A141 1.4 2.7 2.9 4.1 0.4 0.0 0

A15 1.0 3.6 2.4 4.1 1.0 0.0 0

A161 1.0 5.1 1.0 5.5 1.0 0.0 5

A17 0.5 4.8 2.2 4.3 1.0 0.0 1

A19 1.1 5.9 1.3 4.0 0.0 1.0 0

A20 0.3 4.7 2.5 3.4 0.0 0.0 0

A21 0.6 3.9 3.1 3.1 0.0 0.0 0

A22 1.1 5.7 0.6 2.8 0.2 0.0 0

A23 1.9 5.5 1.2 3.1 0.0 0.0 0

A2E 1.3 0.8 1.0 8.3 0.4 0.4 0

A2W 1.8 0.7 1.5 9.2 0.0 0.0 3

A3N 0.7 0.3 2.3 6.4 0.0 0.8 0

A3W 2.3 1.6 1.2 7.6 0.2 0.6 0

A5 2.6 2.4 1.9 6.3 0.0 0.7 1

A7 1.1 2.8 2.4 5.8 0.0 0.4 7

A8 1.7 4.7 1.0 6.9 0.0 0.5 0

A9* 1.5 1.4 4.0 4.0 0.5 0.0 0

AB1 0.6 0.2 1.6 8.1 0.2 0.5 1

AB2 0.7 0.9 1.1 7.6 0.1 0.2 17

B1 1.2 1.5 3.1 16.2 0.0 1.0 1

B10 0.9 0.4 0.6 19.2 0.0 0.6 3

B11 0.5 0.9 0.6 19.0 0.0 1.0 0

B12 0.4 2.2 0.7 18.1 0.0 1.0 0

B13 0.6 1.8 1.1 18.0 0.0 1.0 0

B14 0.7 1.9 1.5 17.5 0.0 1.0 0

B1C 0.3 2.3 2.0 13.5 0.0 0.0 0

B2 2.8 0.8 3.7 15.7 0.0 0.8 6

B2C 0.1 2.5 1.7 12.9 0.0 0.0 1

B4 0.8 2.1 2.3 14.5 0.0 0.6 4

B5 0.7 3.4 1.0 14.3 0.0 1.0 0

B5C 0.4 2.9 1.4 13.1 0.0 0.0 0

B6 0.8 3.8 0.7 14.5 0.0 1.0 1

B6A 1.3 4.1 0.5 15.3 0.0 0.6 2

B6B 1.1 3.2 1.2 16.0 0.0 1.0 0

B6C 0.3 3.1 1.3 13.8 0.0 0.2 0

B7 0.9 2.4 2.1 15.1 0.0 1.0 2

B8 0.8 2.7 1.9 15.9 0.0 1.0 0

B8AE 0.5 1.8 2.8 17.1 0.0 1.0 0

B8X 0.1 2.4 1.5 16.9 0.0 1.0 0

B9 1.5 1.1 2.2 17.6 0.0 1.0 2

R2 0.6 0.8 0.9 13.3 0.4 0.4 0

R3 1.1 1.3 0.4 14.5 0.0 0.0 0
82 

Pond Area 

(km2)

Bay 

distance 

(km)

Urban 

distance 

(km)

Landfill 

distance 

(km)

Maximum % 

levee open to 

public

Maximum % 

of levee open 

to hunting

Number 

of

islands

R4 1.2 0.5 1.1 15.1 0.2 0.0 0

R5 0.1 1.2 0.4 15.8 0.3 0.0 0

RSF2U1 0.2 0.2 1.3 12.2 0.6 0.0 8

RSF2U2 0.3 0.5 0.9 12.5 0.3 0.0 22

RSF2U3 0.4 1.0 0.5 13.0 0.5 0.0 0

1

Indicates ponds surveyed in fall 2002. 
83 

Table 2. Common name, scientific name, and associated foraging guild of waterbird species observed on salt 

ponds, South San Francisco Bay, California, October–April 2002–13. 

[Common name: Species that were evaluated individually in modeling efforts are identified by four-letter species coding. 

(J) indicates species present only on Project ponds; (D) indicates species present only on Production ponds. Species for 

which we observed 10 or fewer individuals across the study period were excluded from this list (Project ponds = 20 

species; Production Ponds = 8 species).] 

Common name Scientific name Foraging guild

American avocet (AMAV) Recurvirostra americana Medium shorebird

American coot Fulica americana Dabbling duck

American green-winged teal Anas crecca Dabbling duck

American white pelican Pelecanus erythrorhynchos Piscivore

American wigeon Anas americana Dabbling duck

Belted kingfisher Megaceryle alcyon Piscivore

Black oystercatcher Haematopus bachmani Medium shorebird

Black skimmer Rynchops niger Tern

Black turnstone Arenaria melanocephala Medium shorebird

Black-bellied plover Pluvialis squatarola Medium shorebird

Black-crowned night-heron Nycticorax nycticorax Wader

Black-necked stilt (BNST) Himantopus mexicanus Medium shorebird

Blue-winged teal Anas discors Dabbling duck

Bonaparte's gull Chroicocephalus philadelphia Gull

Brown pelican Pelecanus occidentalis Piscivore

Bufflehead Bucephala albeola Diving duck

Cackling goose (J) Branta hutchinsii Goose

California gull Larus californicus Gull

Canada goose Branta canadensis Goose

Canvasback Aythya valisineria Diving duck

Caspian tern Hydroprogne caspia Tern

Cinnamon teal Anas cyanoptera Dabbling duck

Clark's grebe Aechmophorus clarkii Piscivore

Common goldeneye Bucephala clangula Diving duck

Common merganser Mergus merganser Piscivore

Double-crested cormorant Phalacrocorax auritus Piscivore

Dunlin Calidris alpina Small shorebird

Eared grebe (EAGR) Podiceps nigricollis Eared grebe

Elegant tern Thalasseus elegans Tern

Eurasian wigeon Anas penelope Dabbling duck

Forster's tern (FOTE) Sterna forsteri Tern

Gadwall Anas strepera Dabbling duck

Glaucous gull Larus hyperboreus Gull

Glaucous-winged gull Larus glaucescens Gull

Great blue heron Ardea herodias Wader

Great egret Ardea alba Wader

Greater scaup Aythya marila Diving duck

Greater white-fronted goose Anser albifrons Goose
84 

Common name Scientific name Foraging guild

Greater yellowlegs Tringa melanoleuca Medium shorebird

Herring gull Larus argentatus Gull

Hooded merganser Lophodytes cucullatus Piscivore

Horned grebe Podiceps auritus Eared grebe

Killdeer Charadrius vociferus Medium shorebird

Least sandpiper Calidris minutilla Small shorebird

Least tern Sternula antillarum Tern

Lesser scaup Aythya affinis Diving duck

Lesser yellowlegs Tringa flavipes Medium shorebird

Long-billed curlew Numenius americanus Medium shorebird

Long-billed dowitcher Limnodromus scolopaecus Small shorebird

Mallard Anas platyrhynchos Dabbling duck

Marbled godwit Limosa fedoa Medium shorebird

Mew gull Larus canus Gull

Northern pintail Anas acuta Dabbling duck

Northern shoveler Anas clypeata Dabbling duck

Pectoral sandpiper (D) Calidris melanotos Small shorebird

Pied-billed grebe Podilymbus podiceps Piscivore

Red knot Calidris canutus Medium shorebird

Red phalarope Phalaropus fulicaria Phalarope

Red-breasted merganser Mergus serrator Piscivore

Redhead Aythya americana Diving duck

Red-necked phalarope Phalaropus lobatus Phalarope

Red-throated loon Gavia stellata Piscivore

Ring-billed gull Larus delawarensis Gull

Ruddy duck Oxyura jamaicensis Diving duck

Ruddy turnstone Arenaria interpres Medium shorebird

Sabine's gull (J) Xema sabini Gull

Sanderling Calidris alba Small shorebird

Semipalmated plover Charadrius semipalmatus Small shorebird

Short-billed dowitcher Limnodromus griseus Small shorebird

Snow goose Chen caerulescens Goose

Snowy egret Egretta thula Wader

Snowy plover Charadrius alexandrinus Small shorebird

Spotted sandpiper Actitis macularius Small shorebird

Surf scoter Melanitta perspicillata Diving duck

Thayer's gull Larus thayeri Gull

Western grebe Aechmophorus occidentalis Piscivore

Western gull Larus occidentalis Gull

Western sandpiper (WESA) Calidris mauri Small shorebird

Whimbrel Numenius phaeopus Medium shorebird

White-winged scoter Melanitta fusca Diving duck

Willet Tringa semipalmata Medium shorebird

Wilson's phalarope (J) Phalaropus tricolor Phalarope
85 

Table 3. The ranking of candidate models at the pond scale for foraging dabbling ducks in the former salt 

production ponds of South San Francisco Bay, California. 

[Models are ranked by differences in Akaike’s information criterion and only models of ∆AIC <4. Birds were surveyed across the former 

salt ponds during October through April 2002–2015. Variables considered included the mean pond depth (Depth), the presence of islands 

(Island), variation in topography (DepthSD), % of pond levee open to hunting (HuntLevee), % of pond levee open to the public 

(OpenLevee), pond area (Area), water salinity, pond distance to urban areas (UrbanDist), and pond distance to the bay (BayDist). k: 

Number of estimated parameters in the model. AICc: Second-order Akaike’s information criterion. ∆AICc: The difference between the 

AICc of the current model and the AICc of the top model. wi: Akaike weight–likelihood of the model relative to candidate models. 

Evidence ratio: The weight of evidence that the current model is inferior to the top model]

Model name k -2LogL AICc ∆AICc wi

Evidence 

ratio

Salinity + Island + DepthSD 8 25006.2 25022.3 0.00 0.04 1.00

Salinity + Area + OpenLevee + Island + DepthSD 10 25002.6 25022.7 0.43 0.03 1.24

Salinity + UrbanDist + Island + DepthSD 9 25005.0 25023.1 0.81 0.03 1.50

Salinity + Island + Breached + DepthSD 9 25005.0 25023.1 0.81 0.03 1.50

Depth + Salinity + Island + DepthSD 9 25005.2 25023.3 1.01 0.02 1.66

Salinity + OpenLevee + Island 8 25007.6 25023.7 1.40 0.02 2.01

Salinity + Area + Island 8 25007.8 25023.9 1.60 0.02 2.23

Depth + Salinity + UrbanDist + Island + DepthSD 10 25003.8 25023.9 1.63 0.02 2.26

Depth + Salinity + Area + OpenLevee + Island 10 25003.8 25023.9 1.63 0.02 2.26

Salinity + UrbanDist + Island + Breached + DepthSD 10 25003.8 25023.9 1.63 0.02 2.26

Salinity + HuntLevee + Island + DepthSD 9 25006.0 25024.1 1.81 0.02 2.48

Depth + Salinity + Area + Island + Breached + DepthSD 11 25002.0 25024.1 1.84 0.02 2.51

Salinity + BayDist + Island + DepthSD 9 25006.2 25024.3 2.01 0.01 2.74

Salinity + UrbanDist + OpenLevee + Island + DepthSD 10 25004.2 25024.3 2.03 0.01 2.75

Depth + Salinity + OpenLevee + Island + DepthSD 10 25004.4 25024.5 2.23 0.01 3.04

Depth + Salinity + Island + Breached + DepthSD 10 25004.4 25024.5 2.23 0.01 3.04

Salinity + OpenLevee + Island + Breached + DepthSD 10 25004.4 25024.5 2.23 0.01 3.04

Salinity + Area + OpenLevee + HuntLevee + Island + DepthSD 11 25002.4 25024.5 2.24 0.01 3.07

Salinity + Island 7 25010.6 25024.6 2.39 0.01 3.30

Salinity + UrbanDist + OpenLevee + Island 9 25006.6 25024.7 2.41 0.01 3.34

Salinity + BayDist + Area + OpenLevee + Island + DepthSD 11 25002.6 25024.7 2.44 0.01 3.39

Salinity + UrbanDist + HuntLevee + Island + DepthSD 10 25004.8 25024.9 2.63 0.01 3.72

Salinity + BayDist + Area + Island + Breached + DepthSD 11 25002.8 25024.9 2.64 0.01 3.75

Salinity + OpenLevee + Island + Breached 9 25007.0 25025.1 2.81 0.01 4.08

Salinity + BayDist + UrbanDist + Island + DepthSD 10 25005.0 25025.1 2.83 0.01 4.11

Salinity + BayDist + Island + Breached + DepthSD 10 25005.0 25025.1 2.83 0.01 4.11

Salinity + HuntLevee + Island + Breached + DepthSD 10 25005.0 25025.1 2.83 0.01 4.11

Depth + Salinity + UrbanDist + Island + Breached + DepthSD 11 25003.0 25025.1 2.84 0.01 4.14

Depth + Salinity + UrbanDist + Area + OpenLevee + Island + DepthSD 12 25001.0 25025.1 2.86 0.01 4.18

Depth + Salinity + OpenLevee + Island 9 25007.2 25025.3 3.01 0.01 4.51

Depth + Salinity + BayDist + Island + DepthSD 10 25005.2 25025.3 3.03 0.01 4.54

Depth + Salinity + HuntLevee + Island + DepthSD 10 25005.2 25025.3 3.03 0.01 4.54

Salinity + BayDist + OpenLevee + Island + DepthSD 10 25005.2 25025.3 3.03 0.01 4.54

Salinity + OpenLevee + HuntLevee + Island + DepthSD 10 25005.2 25025.3 3.03 0.01 4.54

Depth + Salinity + UrbanDist + OpenLevee + Island + DepthSD 11 25003.2 25025.3 3.04 0.01 4.58

Salinity + BayDist + UrbanDist + Area + Island + DepthSD 11 25003.2 25025.3 3.04 0.01 4.58

Depth + Salinity + Area + OpenLevee + Island + Breached + DepthSD 12 25001.2 25025.3 3.06 0.01 4.62

Salinity + BayDist + OpenLevee + Island 9 25007.4 25025.5 3.21 0.01 4.98
86 

Model name k -2LogL AICc ∆AICc wi

Evidence 

ratio

Salinity + OpenLevee + HuntLevee + Island 9 25007.4 25025.5 3.21 0.01 4.98

Salinity + UrbanDist + OpenLevee + Island + Breached + DepthSD 11 25003.4 25025.5 3.24 0.01 5.06

Depth + Salinity + Area + OpenLevee + HuntLevee + Island + DepthSD 12 25001.4 25025.5 3.26 0.01 5.10

Salinity + UrbanDist + Area + OpenLevee + Island + Breached + DepthSD 12 25001.4 25025.5 3.26 0.01 5.10

Salinity + UrbanDist + Island 8 25009.6 25025.7 3.40 0.01 5.47

Salinity + Island + Breached 8 25009.6 25025.7 3.40 0.01 5.47

Depth + Salinity + BayDist + Area + OpenLevee + Island 11 25003.6 25025.7 3.44 0.01 5.59

Depth + Salinity + Area + OpenLevee + Island + Breached 11 25003.6 25025.7 3.44 0.01 5.59

Salinity + BayDist + Area + HuntLevee + Island + DepthSD 11 25003.6 25025.7 3.44 0.01 5.59

Salinity + UrbanDist + HuntLevee + Island + Breached + DepthSD 11 25003.6 25025.7 3.44 0.01 5.59

Depth + Salinity + BayDist + Area + OpenLevee + Island + DepthSD 12 25001.6 25025.7 3.46 0.01 5.64

Depth + Salinity + BayDist + UrbanDist + Island + DepthSD 11 25003.8 25025.9 3.64 0.01 6.18

Depth + Salinity + UrbanDist + HuntLevee + Island + DepthSD 11 25003.8 25025.9 3.64 0.01 6.18

Depth + Salinity + Area + OpenLevee + HuntLevee + Island 11 25003.8 25025.9 3.64 0.01 6.18

Depth + Salinity + OpenLevee + Island + Breached + DepthSD 11 25003.8 25025.9 3.64 0.01 6.18

Salinity + BayDist + UrbanDist + Island + Breached + DepthSD 11 25003.8 25025.9 3.64 0.01 6.18

Salinity + BayDist + Area + OpenLevee + Island + Breached 11 25003.8 25025.9 3.64 0.01 6.18

Depth + Salinity + UrbanDist + Area + HuntLevee + Island + DepthSD 12 25001.8 25025.9 3.66 0.01 6.23

Depth + Salinity + Area + HuntLevee + Island + Breached + DepthSD 12 25001.8 25025.9 3.66 0.01 6.23

Salinity + BayDist + Area + OpenLevee + Island + Breached + DepthSD 12 25001.8 25025.9 3.66 0.01 6.23

Salinity + BayDist + Island 8 25010.0 25026.1 3.80 0.01 6.69

Salinity + BayDist + HuntLevee + Island + DepthSD 10 25006.0 25026.1 3.83 0.01 6.78

Salinity + UrbanDist + OpenLevee + Island + Breached 10 25006.0 25026.1 3.83 0.01 6.78

Salinity + UrbanDist + OpenLevee + HuntLevee + Island + DepthSD 11 25004.0 25026.1 3.84 0.01 6.83
87 

Table 4. The ranking of candidate models at the pond scale for roosting dabbling ducks in the former salt 

production ponds of South San Francisco Bay, California. 

[Models are ranked by differences in Akaike’s information criterion and only models of ∆AIC <4 and the null model are presented. Birds 

were surveyed across the former salt ponds during October through April 2002–2015. Variables considered included the mean pond 

depth (Depth), the presence of islands (Island), variation in topography (DepthSD), % of pond levee open to hunting (HuntLevee), % of 

pond levee open to the public (OpenLevee), pond area (Area), water salinity, pond distance to urban areas (UrbanDist), and pond 

distance to the bay (BayDist). k: Number of estimated parameters in the model. AICc: Second-order Akaike’s information criterion. 

∆AICc: The difference between the AICc of the current model and the AICc of the top model. wi: Akaike weight–likelihood of the 

model relative to candidate models. Evidence ratio: The weight of evidence that the current model is inferior to the top model]

Model name k -2LogL AICc ∆AICc wi

Evidence 

ratio

Salinity + Area + HuntLevee + Island + DepthSD 10 27679.0 27699.1 0.00 0.08 1.00

Salinity + UrbanDist + Area + HuntLevee + Island + DepthSD 11 27677.4 27699.5 0.42 0.07 1.23

Salinity + BayDist + Area + HuntLevee + Island + DepthSD 11 27678.0 27700.1 1.02 0.05 1.66

Depth + Salinity + Area + HuntLevee + Island + DepthSD 11 27678.2 27700.3 1.22 0.04 1.84

Salinity + UrbanDist + HuntLevee + Island + DepthSD 10 27680.4 27700.5 1.40 0.04 2.01

Salinity + Area + HuntLevee + Island + Breached + DepthSD 11 27678.6 27700.7 1.62 0.04 2.24

Salinity + BayDist + UrbanDist + Area + HuntLevee + Island + DepthSD 12 27676.8 27700.9 1.83 0.03 2.50

Salinity + HuntLevee + Island + DepthSD 9 27683.0 27701.1 1.99 0.03 2.70

Salinity + Area + OpenLevee + HuntLevee + Island + DepthSD 11 27679.0 27701.1 2.02 0.03 2.74

Salinity + UrbanDist + Area + HuntLevee + Island + Breached + DepthSD 12 27677.2 27701.3 2.23 0.03 3.05

Depth + Salinity + BayDist + Area + HuntLevee + Island + DepthSD 12 27677.4 27701.5 2.43 0.02 3.37

Salinity + UrbanDist + Area + OpenLevee + HuntLevee + Island + 

DepthSD 12 27677.4 27701.5 2.43 0.02 3.37

Depth + Salinity + UrbanDist + HuntLevee + Island + DepthSD 11 27679.6 27701.7 2.62 0.02 3.70

Salinity + BayDist + Area + HuntLevee + Island + Breached + DepthSD 12 27677.6 27701.7 2.63 0.02 3.73

Depth + Salinity + Area + HuntLevee + Island + Breached + DepthSD 12 27677.8 27701.9 2.83 0.02 4.12

Salinity + BayDist + UrbanDist + HuntLevee + Island + DepthSD 11 27680.0 27702.1 3.02 0.02 4.52

Salinity + BayDist + Area + OpenLevee + HuntLevee + Island + DepthSD 12 27678.0 27702.1 3.03 0.02 4.55

Depth + Salinity + BayDist + UrbanDist + Area + HuntLevee + Island + 

DepthSD 13 27676.0 27702.1 3.05 0.02 4.60

Depth + Salinity + UrbanDist + Area + HuntLevee + Island + Breached + 

DepthSD 13 27676.0 27702.1 3.05 0.02 4.60

Salinity + UrbanDist + HuntLevee + Island + Breached + DepthSD 11 27680.2 27702.3 3.22 0.02 4.99

Depth + Salinity + Area + OpenLevee + HuntLevee + Island + DepthSD 12 27678.2 27702.3 3.23 0.02 5.03

Depth + Salinity + HuntLevee + Island + DepthSD 10 27682.4 27702.5 3.40 0.01 5.47

Salinity + BayDist + HuntLevee + Island + DepthSD 10 27682.4 27702.5 3.40 0.01 5.47

Salinity + UrbanDist + OpenLevee + HuntLevee + Island + DepthSD 11 27680.4 27702.5 3.42 0.01 5.52

Salinity + BayDist + UrbanDist + Area + HuntLevee + Island + Breached + 

DepthSD 13 27676.4 27702.5 3.45 0.01 5.61

Salinity + Area + OpenLevee + HuntLevee + Island + Breached + DepthSD 12 27678.6 27702.7 3.63 0.01 6.15

Depth + Salinity + UrbanDist + Area + OpenLevee + HuntLevee + Island + 

DepthSD 13 27676.6 27702.7 3.65 0.01 6.20

Salinity + HuntLevee + Island + Breached + DepthSD 10 27682.8 27702.9 3.80 0.01 6.69

Depth + Salinity + BayDist + Area + HuntLevee + Island + Breached + 

DepthSD 13 27676.8 27702.9 3.85 0.01 6.86

Salinity + BayDist + UrbanDist + Area + OpenLevee + HuntLevee + Island 

+ DepthSD 13 27676.8 27702.9 3.85 0.01 6.86

Null Model 5 27976.6 27986.6 287.54 2.97E-64 2.75E+62
88 

Table 5. The ranking of candidate models at the pond scale for foraging and roosting diving ducks in the former 

salt production ponds of South San Francisco Bay, California. 

[Models are ranked by differences in Akaike’s information criterion and only models of ∆AIC <4 and the null model are presented. Birds 

were surveyed across the former salt ponds during October through April 2002–2015. Variables considered included the mean pond 

depth (Depth), the presence of islands (Island), variation in topography (DepthSD), % of pond levee open to hunting (HuntLevee), % of 

pond levee open to the public (OpenLevee), pond area (Area), water salinity, pond distance to urban areas (UrbanDist), and pond 

distance to the bay (BayDist). k: Number of estimated parameters in the model. AICc: Second-order Akaike’s information criterion. 

∆AICc: The difference between the AICc of the current model and the AICc of the top model. wi: Akaike weight–likelihood of the 

model relative to candidate models. Evidence ratio: The weight of evidence that the current model is inferior to the top model]

Model name k -2LogL AICc ∆AICc wi

Evidence 

ratio

 Foraging

Depth + Salinity + BayDist + Area + Island + Breached 11 22517.6 22539.7 0.00 0.24 1.00

Depth + Salinity + BayDist + Area + Island + Breached + DepthSD 12 22517.0 22541.1 1.42 0.12 2.03

Depth + Salinity + BayDist + UrbanDist + Area + Island + Breached 12 22517.6 22541.7 2.02 0.09 2.74

Depth + Salinity + BayDist + Area + OpenLevee + Island + Breached 12 22517.6 22541.7 2.02 0.09 2.74

Depth + Salinity + BayDist + Area + HuntLevee + Island + Breached 12 22517.6 22541.7 2.02 0.09 2.74

Depth + Salinity + BayDist + UrbanDist + Area + Island + Breached + 

DepthSD 13 22516.8 22542.9 3.23 0.05 5.04

Depth + Salinity + BayDist + Area + OpenLevee + Island + Breached + 

DepthSD 13 22517.0 22543.1 3.43 0.04 5.57

Depth + Salinity + BayDist + Area + HuntLevee + Island + Breached + 

DepthSD 13 22517.0 22543.1 3.43 0.04 5.57

Depth + Salinity + BayDist + UrbanDist + Area + OpenLevee + Island + 

Breached 13 22517.4 22543.5 3.83 0.04 6.80

Null Model 5 22718.0 22728.0 188.33 3.04E-42 7.85E+40

 Roosting 

Depth + Salinity + BayDist + Area + HuntLevee + Island + Breached 12 29025.0 29049.1 0.00 0.18 1.00

Depth + Salinity + BayDist + Area + Island + Breached 11 29027.6 29049.7 0.58 0.13 1.34

Depth + Salinity + BayDist + UrbanDist + Area + Island + Breached 12 29026.4 29050.5 1.40 0.09 2.01

Depth + Salinity + BayDist + Area + HuntLevee + Island + Breached + 

DepthSD 13 29024.4 29050.5 1.42 0.09 2.03

Depth + Salinity + BayDist + Area + OpenLevee + HuntLevee + Island + 

Breached 13 29024.6 29050.7 1.62 0.08 2.25

Depth + Salinity + BayDist + Area + Island + Breached + DepthSD 12 29026.8 29050.9 1.80 0.07 2.46

Depth + Salinity + BayDist + Area + OpenLevee + Island + Breached 12 29027.2 29051.3 2.20 0.06 3.00

Depth + Salinity + BayDist + UrbanDist + Area + HuntLevee + Island + 

Breached + DepthSD 14 29023.2 29051.4 2.24 0.06 3.06

Depth + Salinity + BayDist + UrbanDist + Area + Island + Breached + 

DepthSD 13 29025.6 29051.7 2.62 0.05 3.70

Depth + Salinity + BayDist + UrbanDist + Area + OpenLevee + 

HuntLevee + Island + Breached 14 29023.6 29051.8 2.64 0.05 3.74

Depth + Salinity + BayDist + UrbanDist + Area + OpenLevee + Island + 

Breached 13 29026.0 29052.1 3.02 0.04 4.52

Depth + Salinity + BayDist + Area + OpenLevee + HuntLevee + Island + 

Breached + DepthSD 14 29024.4 29052.6 3.44 0.03 5.58

Depth + Salinity + BayDist + Area + OpenLevee + Island + Breached + 

DepthSD 13 29026.8 29052.9 3.82 0.03 6.75

Null Model 5 29462.6 29472.6 423.51 1.95E-93 9.21E+91
89 

Table 6. The ranking of candidate models at the pond scale for foraging small shorebirds in the former salt 

production ponds of South San Francisco Bay, California. 

[Models are ranked by differences in Akaike’s information criterion and only models of ∆AIC <4 and the null model are presented. Birds 

were surveyed across the former salt ponds during October through April 2002–2015. Variables considered included the mean pond 

depth (Depth), the presence of islands (Island), variation in topography (DepthSD), % of pond levee open to hunting (HuntLevee), % of 

pond levee open to the public (OpenLevee), pond area (Area), water salinity, pond distance to urban areas (UrbanDist), and pond 

distance to the bay (BayDist). k: Number of estimated parameters in the model. AICc: Second-order Akaike’s information criterion. 

∆AICc: The difference between the AICc of the current model and the AICc of the top model. wi: Akaike weight–likelihood of the 

model relative to candidate models. Evidence ratio: The weight of evidence that the current model is inferior to the top model]

Model name k -2LogL AICc ∆AICc wi Evidence 

ratio

Depth + Salinity + OpenLevee + Island + Breached 10 27025.8 27045.9 0.00 0.10 1.00

Depth + Salinity + BayDist + OpenLevee + Island + Breached 11 27024.8 27046.9 1.02 0.06 1.66

Depth + Salinity + OpenLevee + Island + Breached + DepthSD 11 27025.0 27047.1 1.22 0.05 1.84

Depth + Salinity + Area + OpenLevee + Island + Breached 11 27025.2 27047.3 1.42 0.05 2.03

Depth + OpenLevee + Island + Breached 9 27029.4 27047.5 1.59 0.04 2.21

Depth + Salinity + UrbanDist + OpenLevee + Island + Breached 11 27025.6 27047.7 1.82 0.04 2.48

Depth + Salinity + OpenLevee + HuntLevee + Island + Breached 11 27025.8 27047.9 2.02 0.04 2.74

Depth + BayDist + OpenLevee + Island + Breached 10 27028.2 27048.3 2.40 0.03 3.32

Depth + Salinity + BayDist + Area + OpenLevee + Island + Breached 12 27024.2 27048.3 2.43 0.03 3.37

Depth + Salinity + BayDist + OpenLevee + Island + Breached + DepthSD 12 27024.4 27048.5 2.63 0.03 3.73

Depth + Salinity + BayDist + UrbanDist + OpenLevee + Island + Breached 12 27024.6 27048.7 2.83 0.02 4.12

Depth + Salinity + Area + OpenLevee + Island + Breached + DepthSD 12 27024.6 27048.7 2.83 0.02 4.12

Depth + Salinity + BayDist + OpenLevee + HuntLevee + Island + 

Breached 12 27024.8 27048.9 3.03 0.02 4.55

Depth + Salinity + UrbanDist + Area + OpenLevee + Island + Breached 12 27024.8 27048.9 3.03 0.02 4.55

Depth + Salinity + UrbanDist + OpenLevee + Island + Breached + 

DepthSD 12 27024.8 27048.9 3.03 0.02 4.55

Depth + Area + OpenLevee + Island + Breached 10 27029.0 27049.1 3.20 0.02 4.95

Depth + Salinity + OpenLevee + HuntLevee + Island + Breached + 

DepthSD 12 27025.0 27049.1 3.23 0.02 5.03

Depth + UrbanDist + OpenLevee + Island + Breached 10 27029.2 27049.3 3.40 0.02 5.47

Depth + Salinity + Area + OpenLevee + HuntLevee + Island + Breached 12 27025.2 27049.3 3.43 0.02 5.56

Depth + OpenLevee + HuntLevee + Island + Breached 10 27029.4 27049.5 3.60 0.02 6.05

Depth + Salinity + UrbanDist + OpenLevee + HuntLevee + Island + 

Breached 12 27025.6 27049.7 3.83 0.01 6.79

Null Model 5 27319.6 27329.6 283.74 2.37E-63 4.11E+61
90 

Table 7. The ranking of candidate models at the pond scale for roosting small shorebirds in the former salt 

production ponds of South San Francisco Bay, California. 

[Models are ranked by differences in Akaike’s information criterion and only models of ∆AIC <4 and the null model are presented. Birds 

were surveyed across the former salt ponds during October through April 2002–2015. Variables considered included the mean pond 

depth (Depth), the presence of islands (Island), variation in topography (DepthSD), % of pond levee open to hunting (HuntLevee), % of 

pond levee open to the public (OpenLevee), pond area (Area), water salinity, pond distance to urban areas (UrbanDist), and pond 

distance to the bay (BayDist). k: Number of estimated parameters in the model. AICc: Second-order Akaike’s information criterion. 

∆AICc: The difference between the AICc of the current model and the AICc of the top model. wi: Akaike weight–likelihood of the 

model relative to candidate models. Evidence ratio: The weight of evidence that the current model is inferior to the top model]

Model name k -2LogL AICc ∆AICc wi

Evidence 

ratio

Depth + Area + OpenLevee + Island + Breached 10 26771.8 26791.9 0.00 0.03 1.00

Depth + Area + OpenLevee + HuntLevee + Island + Breached 11 26769.8 26791.9 0.02 0.03 1.01

Depth + OpenLevee + Island + Breached 9 26774.2 26792.3 0.39 0.02 1.21

Depth + OpenLevee + HuntLevee + Island + Breached + DepthSD 11 26770.2 26792.3 0.42 0.02 1.23

Depth + Area + OpenLevee + HuntLevee + Island + Breached + DepthSD 12 26768.2 26792.3 0.43 0.02 1.24

Depth + Island + Breached + DepthSD 9 26774.4 26792.5 0.59 0.02 1.34

Depth + OpenLevee + Island + Breached + DepthSD 10 26772.4 26792.5 0.60 0.02 1.35

Depth + HuntLevee + Island + Breached + DepthSD 10 26772.4 26792.5 0.60 0.02 1.35

Depth + BayDist + OpenLevee + HuntLevee + Island + Breached 11 26770.4 26792.5 0.62 0.02 1.36

Depth + Area + OpenLevee + Island + Breached + DepthSD 11 26770.4 26792.5 0.62 0.02 1.36

Depth + BayDist + Area + OpenLevee + HuntLevee + Island + Breached 12 26768.4 26792.5 0.63 0.02 1.37

Depth + BayDist + OpenLevee + Island + Breached 10 26772.6 26792.7 0.80 0.02 1.49

Depth + BayDist + Area + OpenLevee + Island + Breached 11 26770.6 26792.7 0.82 0.02 1.50

Depth + Area + HuntLevee + Island + Breached + DepthSD 11 26770.8 26792.9 1.02 0.02 1.66

Depth + Salinity + Area + OpenLevee + HuntLevee + Island + Breached 12 26769.0 26793.1 1.23 0.01 1.85

Depth + Salinity + Area + OpenLevee + HuntLevee + Island + Breached + 

DepthSD 13 26767.2 26793.3 1.45 0.01 2.07

Depth + Salinity + OpenLevee + HuntLevee + Island + Breached 11 26771.4 26793.5 1.62 0.01 2.24

Depth + Salinity + HuntLevee + Island + Breached + DepthSD 11 26771.4 26793.5 1.62 0.01 2.24

Depth + Salinity + Area + OpenLevee + Island + Breached + DepthSD 12 26769.4 26793.5 1.63 0.01 2.26

Depth + Salinity + OpenLevee + HuntLevee + Island + Breached + DepthSD 12 26769.4 26793.5 1.63 0.01 2.26

Depth + BayDist + OpenLevee + HuntLevee + Island + Breached + DepthSD 12 26769.4 26793.5 1.63 0.01 2.26

Depth + BayDist + Area + OpenLevee + HuntLevee + Island + Breached + 

DepthSD 13 26767.4 26793.5 1.65 0.01 2.28

Depth + Salinity + OpenLevee + Island + Breached 10 26773.6 26793.7 1.80 0.01 2.46

Depth + BayDist + Island + Breached + DepthSD 10 26773.6 26793.7 1.80 0.01 2.46

Depth + Salinity + OpenLevee + Island + Breached + DepthSD 11 26771.6 26793.7 1.82 0.01 2.48

Depth + BayDist + OpenLevee + Island + Breached + DepthSD 11 26771.6 26793.7 1.82 0.01 2.48

Depth + BayDist + HuntLevee + Island + Breached + DepthSD 11 26771.6 26793.7 1.82 0.01 2.48

Depth + BayDist + Area + OpenLevee + Island + Breached + DepthSD 12 26769.6 26793.7 1.83 0.01 2.50

Depth + Salinity + BayDist + Area + OpenLevee + HuntLevee + Island + Breached 13 26767.6 26793.7 1.85 0.01 2.52

Depth + Salinity + Area + Island + Breached + DepthSD 11 26771.8 26793.9 2.02 0.01 2.74

Depth + UrbanDist + Area + OpenLevee + Island + Breached 11 26771.8 26793.9 2.02 0.01 2.74

Depth + UrbanDist + OpenLevee + HuntLevee + Island + Breached 11 26771.8 26793.9 2.02 0.01 2.74

Depth + Salinity + Area + HuntLevee + Island + Breached + DepthSD 12 26769.8 26793.9 2.03 0.01 2.76

Depth + BayDist + UrbanDist + OpenLevee + HuntLevee + Island + Breached 12 26769.8 26793.9 2.03 0.01 2.76

Depth + UrbanDist + Area + OpenLevee + HuntLevee + Island + Breached 12 26769.8 26793.9 2.03 0.01 2.76

Depth + UrbanDist + OpenLevee + Island + Breached 10 26774.0 26794.1 2.20 0.01 3.00
91 

Model name k -2LogL AICc ∆AICc wi

Evidence 

ratio

Depth + BayDist + UrbanDist + OpenLevee + Island + Breached 11 26772.0 26794.1 2.22 0.01 3.03

Depth + BayDist + Area + Island + Breached + DepthSD 11 26772.0 26794.1 2.22 0.01 3.03

Depth + UrbanDist + HuntLevee + Island + Breached + DepthSD 11 26772.0 26794.1 2.22 0.01 3.03

Depth + UrbanDist + OpenLevee + HuntLevee + Island + Breached + DepthSD 12 26770.0 26794.1 2.23 0.01 3.05

Depth + UrbanDist + Area + OpenLevee + HuntLevee + Island + Breached + 

DepthSD 13 26768.0 26794.1 2.25 0.01 3.08

Depth + BayDist + Island + Breached 9 26776.2 26794.3 2.39 0.01 3.30

Depth + UrbanDist + OpenLevee + Island + Breached + DepthSD 11 26772.2 26794.3 2.42 0.01 3.35

Depth + BayDist + Area + HuntLevee + Island + Breached + DepthSD 12 26770.2 26794.3 2.43 0.01 3.37

Depth + UrbanDist + Area + OpenLevee + Island + Breached + DepthSD 12 26770.2 26794.3 2.43 0.01 3.37

Depth + BayDist + UrbanDist + Area + OpenLevee + HuntLevee + Island + 

Breached 13 26768.2 26794.3 2.45 0.01 3.40

Depth + BayDist + UrbanDist + Area + OpenLevee + Island + Breached 12 26770.4 26794.5 2.63 0.01 3.73

Depth + UrbanDist + Area + Island + Breached + DepthSD 11 26772.6 26794.7 2.82 0.01 4.09

Depth + Salinity + BayDist + OpenLevee + HuntLevee + Island + Breached + 

DepthSD 13 26768.6 26794.7 2.85 0.01 4.16

Depth + Salinity + BayDist + Area + OpenLevee + HuntLevee + Island + Breached 

+ DepthSD 14 26766.6 26794.8 2.87 0.01 4.20

Depth + BayDist + Area + Island + Breached 10 26774.8 26794.9 3.00 0.01 4.48

Depth + Salinity + BayDist + OpenLevee + Island + Breached + DepthSD 12 26770.8 26794.9 3.03 0.01 4.55

Depth + Salinity + BayDist + HuntLevee + Island + Breached + DepthSD 12 26770.8 26794.9 3.03 0.01 4.55

Depth + UrbanDist + Area + HuntLevee + Island + Breached + DepthSD 12 26770.8 26794.9 3.03 0.01 4.55

Depth + Salinity + BayDist + Area + OpenLevee + Island + Breached + DepthSD 13 26768.8 26794.9 3.05 0.01 4.60

Depth + Salinity + UrbanDist + OpenLevee + HuntLevee + Island + Breached + 

DepthSD 13 26768.8 26794.9 3.05 0.01 4.60

Depth + BayDist + UrbanDist + OpenLevee + HuntLevee + Island + Breached + 

DepthSD 13 26768.8 26794.9 3.05 0.01 4.60

Depth + Area + Island + Breached 9 26777.0 26795.1 3.19 0.01 4.92

Depth + HuntLevee + Island + Breached 9 26777.0 26795.1 3.19 0.01 4.92

Depth + Salinity + UrbanDist + Island + Breached + DepthSD 11 26773.0 26795.1 3.22 0.01 4.99

Depth + BayDist + UrbanDist + Island + Breached + DepthSD 11 26773.0 26795.1 3.22 0.01 4.99

Depth + BayDist + Area + HuntLevee + Island + Breached 11 26773.0 26795.1 3.22 0.01 4.99

Depth + Salinity + UrbanDist + OpenLevee + HuntLevee + Island + Breached 12 26771.0 26795.1 3.23 0.01 5.03

Depth + Salinity + UrbanDist + HuntLevee + Island + Breached + DepthSD 12 26771.0 26795.1 3.23 0.01 5.03

Depth + BayDist + UrbanDist + HuntLevee + Island + Breached + DepthSD 12 26771.0 26795.1 3.23 0.01 5.03

Depth + Salinity + UrbanDist + Area + OpenLevee + HuntLevee + Island + 

Breached 13 26769.0 26795.1 3.25 0.01 5.08

Depth + Salinity + UrbanDist + Area + OpenLevee + HuntLevee + Island + 

Breached + DepthSD 14 26767.0 26795.2 3.27 0.01 5.13

Depth + Salinity + UrbanDist + OpenLevee + Island + Breached 11 26773.2 26795.3 3.42 0.00 5.52

Depth + Salinity + BayDist + Area + Island + Breached + DepthSD 12 26771.2 26795.3 3.43 0.00 5.56

Depth + Salinity + UrbanDist + Area + OpenLevee + Island + Breached 12 26771.2 26795.3 3.43 0.00 5.56

Depth + Salinity + UrbanDist + OpenLevee + Island + Breached + DepthSD 12 26771.2 26795.3 3.43 0.00 5.56

Depth + BayDist + UrbanDist + OpenLevee + Island + Breached + DepthSD 12 26771.2 26795.3 3.43 0.00 5.56

Depth + Salinity + BayDist + UrbanDist + OpenLevee + HuntLevee + Island + 

Breached 13 26769.2 26795.3 3.45 0.00 5.61

Depth + Salinity + BayDist + Area + HuntLevee + Island + Breached + DepthSD 13 26769.2 26795.3 3.45 0.00 5.61
92 

Model name k -2LogL AICc ∆AICc wi

Evidence 

ratio

Depth + BayDist + UrbanDist + Area + OpenLevee + HuntLevee + Island + 

Breached + DepthSD 14 26767.2 26795.4 3.47 0.00 5.67

Depth + Area + HuntLevee + Island + Breached 10 26775.4 26795.5 3.60 0.00 6.05

Depth + Salinity + BayDist + UrbanDist + OpenLevee + Island + Breached 12 26771.4 26795.5 3.63 0.00 6.15

Depth + Salinity + UrbanDist + Area + OpenLevee + Island + Breached + 

DepthSD 13 26769.4 26795.5 3.65 0.00 6.20

Depth + Area + OpenLevee + HuntLevee + Breached + DepthSD 11 26773.6 26795.7 3.82 0.00 6.74

Depth + Salinity + UrbanDist + Area + HuntLevee + Island + Breached + DepthSD 13 26769.6 26795.7 3.85 0.00 6.86

Depth + BayDist + UrbanDist + Area + OpenLevee + Island + Breached + 

DepthSD 13 26769.6 26795.7 3.85 0.00 6.86

Depth + Salinity + BayDist + UrbanDist + Area + OpenLevee + HuntLevee + 

Island + Breached 14 26767.6 26795.8 3.87 0.00 6.92
93 

Table 8. The ranking of candidate models at the pond scale for foraging medium shorebirds in the former salt 

production ponds of South San Francisco Bay, California. 

[Models are ranked by differences in Akaike’s information criterion and only models of ∆AIC <4 and the null model are 

presented. Variables considered included the mean pond depth (Depth), the presence of islands (Island), variation in 

topography (DepthSD), % of pond levee open to hunting (HuntLevee), % of pond levee open to the public (OpenLevee), 

pond area (Area), water salinity, pond distance to urban areas (UrbanDist), and pond distance to the bay (BayDist). k: 

Number of estimated parameters in the model. AICc: Second-order Akaike’s information criterion. ∆AICc: The difference 

between the AICc of the current model and the AICc of the top model. wi: Akaike weight–likelihood of the model relative 

to candidate models. Evidence ratio: The weight of evidence that the current model is inferior to the top model]

Model name k -2LogL AICc ∆AICc wi

Evidence 

ratio

Depth + Salinity + BayDist + HuntLevee + Island + Breached + 

DepthSD 12 19571.9 19596.1 0.00 0.05 1.00

Depth + BayDist + HuntLevee + Island + Breached + DepthSD 11 19574.1 19596.2 0.12 0.05 1.06

Depth + Salinity + BayDist + OpenLevee + HuntLevee + Island + 

Breached + DepthSD 13 19571.1 19597.2 1.14 0.03 1.77

Depth + BayDist + OpenLevee + HuntLevee + Island + Breached + 

DepthSD 12 19573.2 19597.3 1.26 0.03 1.88

Depth + Salinity + BayDist + UrbanDist + HuntLevee + Island + 

Breached + DepthSD 13 19571.4 19597.6 1.50 0.02 2.12

Depth + BayDist + UrbanDist + HuntLevee + Island + Breached + 

DepthSD 12 19573.4 19597.6 1.50 0.02 2.12

Depth + BayDist + OpenLevee + HuntLevee + Island + Breached 11 19575.5 19597.6 1.52 0.02 2.14

Depth + Salinity + BayDist + OpenLevee + HuntLevee + Island + 

Breached 12 19573.5 19597.6 1.56 0.02 2.18

Depth + Salinity + HuntLevee + Island + Breached + DepthSD 11 19575.5 19597.6 1.58 0.02 2.21

Depth + Salinity + BayDist + Area + HuntLevee + Island + Breached + 

DepthSD 13 19571.9 19598.0 1.94 0.02 2.64

Depth + BayDist + Area + HuntLevee + Island + Breached + DepthSD 12 19574.0 19598.1 2.04 0.02 2.77

Depth + HuntLevee + Island + Breached + DepthSD 10 19578.0 19598.1 2.07 0.02 2.81

Depth + Salinity + BayDist + Island + Breached + DepthSD 11 19576.3 19598.4 2.30 0.02 3.16

Depth + BayDist + HuntLevee + Island + Breached 10 19578.3 19598.4 2.37 0.02 3.27

Depth + Salinity + OpenLevee + HuntLevee + Island + Breached + 

DepthSD 12 19574.4 19598.6 2.50 0.01 3.49

Depth + Salinity + BayDist + HuntLevee + Island + Breached 11 19576.5 19598.6 2.52 0.01 3.53

Depth + Salinity + BayDist + UrbanDist + OpenLevee + HuntLevee + 

Island + Breached + DepthSD 14 19570.5 19598.6 2.58 0.01 3.63

Depth + BayDist + UrbanDist + OpenLevee + HuntLevee + Island + 

Breached + DepthSD 13 19572.5 19598.6 2.58 0.01 3.63

Depth + BayDist + Island + Breached + DepthSD 10 19578.6 19598.7 2.61 0.01 3.68

Depth + Salinity + UrbanDist + HuntLevee + Island + Breached + 

DepthSD 12 19574.7 19598.9 2.80 0.01 4.06

Depth + BayDist + UrbanDist + OpenLevee + HuntLevee + Island + 

Breached 12 19574.8 19598.9 2.84 0.01 4.14

Depth + Salinity + BayDist + UrbanDist + OpenLevee + HuntLevee + 

Island + Breached 13 19572.9 19599.0 2.98 0.01 4.43

Depth + OpenLevee + HuntLevee + Island + Breached + DepthSD 11 19577.0 19599.1 3.00 0.01 4.49
94 

Model name k -2LogL AICc ∆AICc wi

Evidence 

ratio

Depth + Salinity + BayDist + Area + OpenLevee + HuntLevee + Island + 

Breached + DepthSD 14 19571.0 19599.2 3.12 0.01 4.75

Depth + BayDist + Area + OpenLevee + HuntLevee + Island + Breached 

+ DepthSD 13 19573.2 19599.3 3.24 0.01 5.05

Depth + BayDist + HuntLevee + Breached + DepthSD 10 19579.4 19599.5 3.43 0.01 5.55

Depth + BayDist + UrbanDist + Area + HuntLevee + Island + Breached 

+ DepthSD 13 19573.4 19599.6 3.50 0.01 5.75

Depth + Salinity + BayDist + UrbanDist + Area + HuntLevee + Island + 

Breached + DepthSD 14 19571.4 19599.6 3.52 0.01 5.81

Depth + BayDist + Area + OpenLevee + HuntLevee + Island + Breached 12 19575.5 19599.6 3.54 0.01 5.87

Depth + Salinity + BayDist + Area + OpenLevee + HuntLevee + Island + 

Breached 13 19573.5 19599.6 3.58 0.01 5.98

Depth + Salinity + Area + HuntLevee + Island + Breached + DepthSD 12 19575.5 19599.6 3.58 0.01 5.99

Depth + Salinity + BayDist + OpenLevee + Island + Breached + 

DepthSD 12 19575.6 19599.8 3.70 0.01 6.36

Depth + Salinity + BayDist + UrbanDist + Island + Breached + DepthSD 12 19575.7 19599.8 3.72 0.01 6.42

Depth + BayDist + UrbanDist + HuntLevee + Island + Breached 11 19577.8 19599.9 3.80 0.01 6.70

Depth + BayDist + UrbanDist + Island + Breached + DepthSD 11 19577.8 19599.9 3.88 0.01 6.97

Depth + Salinity + Island + Breached + DepthSD 10 19579.9 19599.9 3.89 0.01 6.99

Depth + BayDist + HuntLevee + Island + DepthSD 10 19579.9 19600.0 3.95 0.01 7.20

Depth + UrbanDist + OpenLevee + HuntLevee + Island + Breached + 

DepthSD 12 19575.9 19600.0 3.98 0.01 7.32

Null Model 5 19720.5 19730.5 134.45 3.16E-31 1.57E+29
95 

Table 9. The ranking of candidate models at the pond scale for roosting medium shorebirds in the former salt 

production ponds of South San Francisco Bay, California

[Models are ranked by differences in Akaike’s information criterion and only models of ∆AIC <4 and the null model are presented. Birds 

were surveyed across the former salt ponds during October through April 2002–2015. Variables considered included the mean pond 

depth (Depth), the presence of islands (Island), variation in topography (DepthSD), % of pond levee open to hunting (HuntLevee), % of 

pond levee open to the public (OpenLevee), pond area (Area), water salinity, pond distance to urban areas (UrbanDist), and pond 

distance to the bay (BayDist). k: Number of estimated parameters in the model. AICc: Second-order Akaike’s information criterion. 

∆AICc: The difference between the AICc of the current model and the AICc of the top model. wi: Akaike weight–likelihood of the 

model relative to candidate models. Evidence ratio: The weight of evidence that the current model is inferior to the top model]

Model name k -2LogL AICc ∆AICc wi

Evidence 

ratio

Depth + Salinity + HuntLevee + Island + Breached + DepthSD 11 26039.2 26061.3 0.00 0.10 1.00

Depth + HuntLevee + Island + Breached + DepthSD 10 26042.0 26062.1 0.78 0.07 1.48

Depth + Salinity + UrbanDist + HuntLevee + Island + Breached + DepthSD 12 26038.4 26062.5 1.22 0.05 1.84

Depth + Salinity + OpenLevee + HuntLevee + Island + Breached + DepthSD 12 26038.8 26063.0 1.62 0.04 2.24

Depth + UrbanDist + HuntLevee + Island + Breached + DepthSD 11 26041.0 26063.1 1.80 0.04 2.46

Depth + Salinity + BayDist + HuntLevee + Island + Breached + DepthSD 12 26039.0 26063.1 1.82 0.04 2.48

Depth + Salinity + PondArea + HuntLevee + Island + Breached + DepthSD 12 26039.0 26063.1 1.82 0.04 2.48

Depth + OpenLevee + HuntLevee + Island + Breached + DepthSD 11 26041.6 26063.7 2.40 0.03 3.32

Depth + BayDist + HuntLevee + Island + Breached + DepthSD 11 26041.8 26063.9 2.60 0.03 3.67

Depth + PondArea + HuntLevee + Island + Breached + DepthSD 11 26041.8 26063.9 2.60 0.03 3.67

Depth + Salinity + HuntLevee + Island + DepthSD 10 26044.2 26064.3 2.98 0.02 4.45

Depth + Salinity + UrbanDist + OpenLevee + HuntLevee + Island + Breached 

+ DepthSD 13 26038.2 26064.3 3.03 0.02 4.56

Depth + Salinity + UrbanDist + PondArea + HuntLevee + Island + Breached + 

DepthSD 13 26038.4 26064.5 3.23 0.02 5.04

Depth + Salinity + PondArea + OpenLevee + HuntLevee + Island + Breached + 

DepthSD 13 26038.4 26064.5 3.23 0.02 5.04

Depth + UrbanDist + OpenLevee + HuntLevee + Island + Breached + DepthSD 12 26040.6 26064.7 3.42 0.02 5.52

Depth + Salinity + BayDist + OpenLevee + HuntLevee + Island + Breached + 

DepthSD 13 26038.6 26064.7 3.43 0.02 5.57

Depth + Salinity + BayDist + PondArea + HuntLevee + Island + Breached + 

DepthSD 13 26038.8 26064.9 3.63 0.02 6.16

Depth + UrbanDist + PondArea + HuntLevee + Island + Breached + DepthSD 12 26041.0 26065.1 3.82 0.01 6.74

Depth + Island + Breached + DepthSD 9 26047.2 26065.3 3.97 0.01 7.28

Null Model 5 26158.6 26168.6 107.33 4.84E-25 2.02E+23
96 

Table 10. The ranking of candidate models at the pond scale for foraging wadersa in the former salt production 

ponds of South San Francisco Bay, California. 

[Models are ranked by differences in Akaike’s information criterion and only models of ∆AIC <4 and the null model are presented. Birds 

were surveyed across the former salt ponds during October through April 2002–2015. Variables considered included the mean pond 

depth (Depth), the presence of islands (Island), variation in topography (DepthSD), % of pond levee open to hunting (HuntLevee), % of 

pond levee open to the public (OpenLevee), pond area (Area), water salinity, pond distance to urban areas (UrbanDist), and pond 

distance to the bay (BayDist). k: Number of estimated parameters in the model. AICc: Second-order Akaike’s information criterion. 

∆AICc: The difference between the AICc of the current model and the AICc of the top model. wi: Akaike weight–likelihood of the 

model relative to candidate models. Evidence ratio: The weight of evidence that the current model is inferior to the top model]

Model name k -2LogL AICc ∆AICc wi

Evidence 

ratio

Depth + Salinity + UrbanDist + PondArea + OpenLevee + HuntLevee + 

Island + Breached 13 11109.9 11134.0 0.00 0.05 1.00

Depth + Salinity + UrbanDist + PondArea + OpenLevee + Island + 

Breached 12 11111.9 11136.0 0.02 0.05 1.01

Depth + Salinity + UrbanDist + PondArea + OpenLevee + Island 11 11114.0 11136.1 0.09 0.05 1.04

Depth + Salinity + UrbanDist + PondArea + OpenLevee + HuntLevee + 

Island 12 11112.1 11136.3 0.26 0.04 1.14

Depth + Salinity + UrbanDist + PondArea + OpenLevee + Island + 

Breached + DepthSD 13 11110.5 11136.7 0.68 0.03 1.40

Depth + Salinity + UrbanDist + PondArea + OpenLevee + HuntLevee + 

Island + Breached + DepthSD 14 11108.5 11136.7 0.70 0.03 1.42

Depth + Salinity + UrbanDist + PondArea + HuntLevee + Island + 

Breached + DepthSD 13 11110.6 11136.8 0.76 0.03 1.46

Depth + Salinity + BayDist + UrbanDist + PondArea + OpenLevee + 

Island + DepthSD 13 11110.8 11136.9 0.90 0.03 1.57

Depth + Salinity + BayDist + UrbanDist + PondArea + OpenLevee + 

Island + Breached + DepthSD 14 11108.8 11136.9 0.94 0.03 1.60

Depth + Salinity + UrbanDist + PondArea + OpenLevee + Island + 

DepthSD 12 11112.8 11137.0 0.96 0.03 1.62

Depth + Salinity + BayDist + UrbanDist + PondArea + OpenLevee + 

HuntLevee + Island + Breached + DepthSD 15 11106.8 11137.0 1.00 0.03 1.65

Depth + Salinity + UrbanDist + PondArea + Island + Breached + 

DepthSD 12 11112.9 11137.0 1.00 0.03 1.65

Depth + Salinity + BayDist + UrbanDist + PondArea + OpenLevee + 

HuntLevee + Island + DepthSD 14 11109.0 11137.1 1.14 0.03 1.77

Depth + Salinity + BayDist + UrbanDist + PondArea + OpenLevee + 

Island 12 11113.0 11137.2 1.16 0.03 1.79

Depth + Salinity + UrbanDist + PondArea + OpenLevee + HuntLevee + 

Island + DepthSD 13 11111.0 11137.2 1.18 0.03 1.80

Depth + Salinity + BayDist + UrbanDist + PondArea + HuntLevee + 

Island + Breached + DepthSD 14 11109.1 11137.2 1.24 0.03 1.86

Depth + Salinity + BayDist + UrbanDist + PondArea + OpenLevee + 

HuntLevee + Island 13 11111.2 11137.3 1.34 0.02 1.95

Depth + Salinity + BayDist + UrbanDist + PondArea + OpenLevee + 

HuntLevee + Island + Breached 14 11109.2 11137.4 1.36 0.02 1.97

Depth + Salinity + BayDist + UrbanDist + PondArea + OpenLevee + 

Island + Breached 13 11111.2 11137.4 1.36 0.02 1.97
97 

Model name k -2LogL AICc ∆AICc wi

Evidence 

ratio

Depth + Salinity + BayDist + UrbanDist + PondArea + Island + Breached 

+ DepthSD 13 11111.3 11137.5 1.48 0.02 2.10

Depth + Salinity + PondArea + OpenLevee + HuntLevee + Island 11 11116.2 11138.3 2.29 0.02 3.13

Depth + Salinity + PondArea + OpenLevee + Island 10 11118.2 11138.3 2.29 0.02 3.14

Depth + Salinity + BayDist + PondArea + OpenLevee + Island + 

DepthSD 12 11114.2 11138.4 2.36 0.01 3.26

Depth + Salinity + UrbanDist + PondArea + HuntLevee + Island + 

Breached 12 11114.3 11138.4 2.38 0.01 3.29

Depth + Salinity + BayDist + PondArea + OpenLevee + HuntLevee + 

Island + DepthSD 13 11112.3 11138.4 2.42 0.01 3.35

Depth + Salinity + PondArea + OpenLevee + HuntLevee + Island + 

Breached 12 11114.3 11138.4 2.42 0.01 3.36

Depth + Salinity + BayDist + PondArea + OpenLevee + Island 11 11116.4 11138.5 2.53 0.01 3.53

Depth + Salinity + BayDist + PondArea + OpenLevee + HuntLevee + 

Island 12 11114.4 11138.5 2.54 0.01 3.56

Depth + Salinity + PondArea + OpenLevee + Island + Breached 11 11116.5 11138.6 2.63 0.01 3.72

Depth + Salinity + BayDist + PondArea + OpenLevee + HuntLevee + 

Island + Breached + DepthSD 14 11110.6 11138.7 2.74 0.01 3.93

Depth + Salinity + UrbanDist + PondArea + HuntLevee + Island + 

DepthSD 12 11114.6 11138.7 2.74 0.01 3.94

Depth + Salinity + UrbanDist + PondArea + Island + DepthSD 11 11116.7 11138.8 2.77 0.01 3.98

Depth + Salinity + BayDist + UrbanDist + PondArea + HuntLevee + 

Island + DepthSD 13 11112.7 11138.9 2.86 0.01 4.18

Depth + Salinity + BayDist + PondArea + OpenLevee + Island + 

Breached + DepthSD 13 11112.7 11138.9 2.86 0.01 4.18

Depth + Salinity + BayDist + UrbanDist + PondArea + Island + DepthSD 12 11114.7 11138.9 2.86 0.01 4.18

Depth + Salinity + UrbanDist + PondArea + Island + Breached 11 11116.8 11138.9 2.91 0.01 4.27

Depth + Salinity + BayDist + PondArea + OpenLevee + HuntLevee + 

Island + Breached 13 11112.8 11139.0 2.98 0.01 4.44

Depth + Salinity + BayDist + PondArea + OpenLevee + Island + 

Breached 12 11115.0 11139.2 3.16 0.01 4.86

Depth + Salinity + PondArea + OpenLevee + HuntLevee + Island + 

Breached + DepthSD 13 11113.3 11139.5 3.48 0.01 5.70

Depth + Salinity + PondArea + OpenLevee + Island + DepthSD 11 11117.4 11139.5 3.49 0.01 5.71

Depth + Salinity + PondArea + OpenLevee + HuntLevee + Island + 

DepthSD 12 11115.4 11139.5 3.50 0.01 5.76

Depth + Salinity + PondArea + OpenLevee + Island + Breached + 

DepthSD 12 11115.6 11139.7 3.68 0.01 6.30

Depth + Salinity + BayDist + PondArea + HuntLevee + Island + 

Breached + DepthSD 13 11113.7 11139.8 3.80 0.01 6.69

Null Model 5 11424.0 11434.1 298.07 8.96E-67 5.32E+64
98 

Table 11. The ranking of candidate models at the pond scale for roosting waders in the former salt production 

ponds of South San Francisco Bay, California. 

[Models are ranked by differences in Akaike’s information criterion and only models of ∆AIC <4 and the null model are presented. Birds 

were surveyed across the former salt ponds during October through April 2002–2015. Variables considered included the mean pond 

depth (Depth), the presence of islands (Island), variation in topography (DepthSD), % of pond levee open to hunting (HuntLevee), % of 

pond levee open to the public (OpenLevee), pond area (Area), water salinity, pond distance to urban areas (UrbanDist), and pond 

distance to the bay (BayDist). k: Number of estimated parameters in the model. AICc: Second-order Akaike’s information criterion. 

∆AICc: The difference between the AICc of the current model and the AICc of the top model. wi: Akaike weight–likelihood of the 

model relative to candidate models. Evidence ratio: The weight of evidence that the current model is inferior to the top model]

Model name k -2LogL AICc ∆AICc wi

Evidence 

ratio

Salinity + UrbanDist + PondArea + HuntLevee + Island 10 11390.3 11410.4 0.00 0.09 1.00

Salinity + BayDist + UrbanDist + PondArea + HuntLevee + Island 11 11388.7 11410.8 0.36 0.08 1.19

Salinity + BayDist + UrbanDist + PondArea + HuntLevee + Island + 

DepthSD 12 11387.3 11411.4 1.01 0.06 1.66

Salinity + UrbanDist + PondArea + OpenLevee + HuntLevee + Island 11 11389.8 11411.9 1.48 0.04 2.09

Salinity + UrbanDist + PondArea + HuntLevee + Island + DepthSD 11 11390.0 11412.1 1.70 0.04 2.33

Depth + Salinity + UrbanDist + PondArea + HuntLevee + Island 11 11390.3 11412.4 1.94 0.03 2.63

Salinity + UrbanDist + PondArea + HuntLevee + Island + Breached 11 11390.3 11412.4 1.94 0.03 2.63

Depth + Salinity + BayDist + UrbanDist + PondArea + HuntLevee + 

Island 12 11388.3 11412.4 2.01 0.03 2.73

Salinity + BayDist + UrbanDist + PondArea + OpenLevee + HuntLevee + 

Island + DepthSD 13 11386.3 11412.5 2.05 0.03 2.79

Salinity + BayDist + UrbanDist + PondArea + OpenLevee + HuntLevee + 

Island 12 11388.4 11412.5 2.09 0.03 2.85

Salinity + BayDist + UrbanDist + PondArea + HuntLevee + Island + 

Breached 12 11388.5 11412.6 2.19 0.03 2.99

Salinity + BayDist + PondArea + HuntLevee + Island 10 11392.9 11413.0 2.60 0.03 3.67

Salinity + UrbanDist + PondArea + OpenLevee + HuntLevee + Island + 

DepthSD 12 11389.0 11413.1 2.69 0.02 3.84

Salinity + BayDist + UrbanDist + PondArea + HuntLevee + Island + 

Breached + DepthSD 13 11387.1 11413.3 2.83 0.02 4.12

Depth + Salinity + BayDist + UrbanDist + PondArea + HuntLevee + 

Island + DepthSD 13 11387.2 11413.3 2.87 0.02 4.20

Salinity + PondArea + HuntLevee + Island 9 11395.4 11413.4 3.01 0.02 4.50

Salinity + BayDist + PondArea + HuntLevee + Island + DepthSD 11 11391.4 11413.5 3.04 0.02 4.56

Depth + Salinity + UrbanDist + PondArea + OpenLevee + HuntLevee + 

Island 12 11389.7 11413.8 3.41 0.02 5.51

Salinity + UrbanDist + PondArea + OpenLevee + HuntLevee + Island + 

Breached 12 11389.8 11413.9 3.49 0.02 5.73

Salinity + UrbanDist + PondArea + HuntLevee + Island + Breached + 

DepthSD 12 11390.0 11414.1 3.65 0.01 6.21

Depth + Salinity + UrbanDist + PondArea + HuntLevee + Island + 

DepthSD 12 11390.0 11414.1 3.69 0.01 6.33

Depth + Salinity + BayDist + UrbanDist + PondArea + OpenLevee + 

HuntLevee + Island 13 11388.0 11414.2 3.77 0.01 6.59

Depth + Salinity + UrbanDist + PondArea + HuntLevee + Island + 

Breached 12 11390.2 11414.3 3.91 0.01 7.07
99 

Model name k -2LogL AICc ∆AICc wi

Evidence 

ratio

Depth + Salinity + BayDist + UrbanDist + PondArea + HuntLevee + 

Island + Breached 13 11388.3 11414.4 3.97 0.01 7.28

Depth + Salinity + BayDist + UrbanDist + PondArea + OpenLevee + 

HuntLevee + Island + DepthSD 14 11386.3 11414.4 3.99 0.01 7.35

Null Model 5 11601.0 11611.1 200.64 2.48E-45 3.71E+43
100 

Table 12. The ranking of candidate models at the pond scale for foraging and roosting piscivores in the former 

salt production ponds of South San Francisco Bay, California

[Models are ranked by differences in Akaike’s information criterion and only models of ∆AIC <4 and the null model are presented. Birds 

were surveyed across the former salt ponds during October through April 2002–2015. Variables considered included the mean pond 

depth (Depth), the presence of islands (Island), variation in topography (DepthSD), % of pond levee open to hunting (HuntLevee), % of 

pond levee open to the public (OpenLevee), pond area (Area), water salinity, pond distance to urban areas (UrbanDist), and pond 

distance to the bay (BayDist). k: Number of estimated parameters in the model. AICc: Second-order Akaike’s information criterion. 

∆AICc: The difference between the AICc of the current model and the AICc of the top model. wi: Akaike weight–likelihood of the 

model relative to candidate models. Evidence ratio: The weight of evidence that the current model is inferior to the top model]

Model name k -2LogL AICc ∆AICc wi

Evidence 

ratio

 Foraging

Depth + Salinity + BayDist + UrbanDist + Area + OpenLevee + 

HuntLevee + Island + Breached 14 12685.2 12713.4 0.00 0.24 1.00

Depth + Salinity + UrbanDist + Area + OpenLevee + HuntLevee + Island 

+ Breached 13 12688.3 12714.5 1.08 0.14 1.72

Depth + Salinity + BayDist + Area + OpenLevee + HuntLevee + Island + 

Breached 13 12689.2 12715.4 2.00 0.09 2.72

Depth + Salinity + BayDist + UrbanDist + Area + OpenLevee + 

HuntLevee + Island + Breached + DepthSD 15 12685.2 12715.4 2.00 0.09 2.72

Depth + Salinity + UrbanDist + Area + HuntLevee + Island + Breached 12 12691.4 12715.6 2.18 0.08 2.98

Depth + Salinity + BayDist + UrbanDist + Area + HuntLevee + Island + 

Breached 13 12689.6 12715.7 2.32 0.08 3.19

Depth + Salinity + UrbanDist + Area + OpenLevee + HuntLevee + Island 

+ Breached + DepthSD 14 12688.1 12716.2 2.86 0.06 4.18

Depth + Salinity + BayDist + UrbanDist + Area + HuntLevee + Island + 

Breached + DepthSD 14 12688.9 12717.0 3.64 0.04 6.17

Null Model 5 13062.0 13072.0 358.61 3.25E-79 7.45E+77

 Roosting

Depth + Salinity + BayDist + UrbanDist + Area + OpenLevee + Island + 

Breached 13 15499.4 15525.6 0.00 0.11 1.00

Depth + Salinity + BayDist + UrbanDist + Area + OpenLevee + 

HuntLevee + Island + Breached 14 15497.9 15526.0 0.46 0.09 1.26

Depth + Salinity + BayDist + UrbanDist + Area + OpenLevee + Island + 

Breached + DepthSD 14 15498.3 15526.4 0.88 0.07 1.55

Depth + Salinity + UrbanDist + Area + OpenLevee + Island + Breached 12 15502.3 15526.4 0.88 0.07 1.55

Depth + Salinity + UrbanDist + Area + OpenLevee + HuntLevee + Island 

+ Breached 13 15500.8 15526.9 1.34 0.06 1.95

Depth + Salinity + BayDist + UrbanDist + Area + Island + Breached + 

DepthSD 13 15500.8 15526.9 1.36 0.06 1.97

Depth + Salinity + BayDist + UrbanDist + Area + OpenLevee + 

HuntLevee + Island + Breached + DepthSD 15 15496.8 15526.9 1.38 0.06 1.99

Depth + Salinity + BayDist + UrbanDist + Area + HuntLevee + Island + 

Breached + DepthSD 14 15499.0 15527.2 1.64 0.05 2.27

Depth + Salinity + UrbanDist + Area + Island + Breached 11 15505.3 15527.4 1.83 0.04 2.49

Depth + Salinity + UrbanDist + Area + HuntLevee + Island + Breached 12 15503.4 15527.6 2.00 0.04 2.72

Depth + Salinity + BayDist + UrbanDist + Area + Island + Breached 12 15503.5 15527.6 2.06 0.04 2.80

Depth + Salinity + BayDist + UrbanDist + Area + HuntLevee + Island + 

Breached 13 15501.6 15527.6 2.20 0.04 3.00
101 

Model name k -2LogL AICc ∆AICc wi

Evidence 

ratio

Depth + Salinity + UrbanDist + Area + OpenLevee + Island + Breached 

+ DepthSD 13 15502.1 15528.3 2.70 0.03 3.86

Depth + Salinity + UrbanDist + Area + Island + Breached + DepthSD 12 15504.2 15528.4 2.80 0.03 4.06

Depth + Salinity + UrbanDist + Area + HuntLevee + Island + Breached + 

DepthSD 13 15502.5 15528.6 3.08 0.02 4.66

Depth + Salinity + UrbanDist + Area + OpenLevee + HuntLevee + Island 

+ Breached + DepthSD 14 15500.6 15528.7 3.18 0.02 4.90

Depth + Salinity + BayDist + Area + OpenLevee + Island + Breached 12 15504.9 15529.0 3.48 0.02 5.70

Depth + Salinity + BayDist + Area + OpenLevee + HuntLevee + Island + 

Breached 13 15503.3 15529.5 3.92 0.02 7.10

Null Model 5 15846.3 15856.4 330.81 1.61E-73 6.84E+71
102 

Table 13. The ranking of candidate models at the pond scale for foraging terns in the former salt production 

ponds of South San Francisco Bay, California. 

[Models are ranked by differences in Akaike’s information criterion and only models of ∆AIC <4 and the null model are presented. Birds 

were surveyed across the former salt ponds during October through April 2002–2015. Variables considered included the mean pond 

depth (Depth), the presence of islands (Island), variation in topography (DepthSD), % of pond levee open to hunting (HuntLevee), % of 

pond levee open to the public (OpenLevee), pond area (Area), water salinity, pond distance to urban areas (UrbanDist), and pond 

distance to the bay (BayDist). k: Number of estimated parameters in the model. AICc: Second-order Akaike’s information criterion. 

∆AICc: The difference between the AICc of the current model and the AICc of the top model. wi: Akaike weight–likelihood of the 

model relative to candidate models. Evidence ratio: The weight of evidence that the current model is inferior to the top model]

Model name k -2LogL AICc ∆AICc wi

Evidence 

ratio

Depth + Salinity + BayDist + Area + Island + Breached 11 5488.4 5510.5 0.00 0.03 1.00

Depth + Salinity + BayDist + Area + HuntLevee + Island + Breached 12 5486.5 5510.6 0.16 0.02 1.08

Depth + Salinity + BayDist + UrbanDist + Area + HuntLevee + Island + 

Breached 13 5484.6 5510.7 0.21 0.02 1.11

Depth + Salinity + BayDist + UrbanDist + Area + Island + Breached 12 5486.6 5510.7 0.22 0.02 1.11

Salinity + BayDist + UrbanDist + Area + HuntLevee + Island + Breached 12 5486.6 5510.7 0.22 0.02 1.11

Salinity + BayDist + UrbanDist + Area + Island + Breached 11 5488.7 5510.8 0.32 0.02 1.17

Salinity + BayDist + Area + Island + Breached 10 5491.0 5511.1 0.60 0.02 1.35

Salinity + BayDist + Area + HuntLevee + Island + Breached 11 5489.0 5511.1 0.66 0.02 1.39

Salinity + BayDist + Area + OpenLevee + HuntLevee + Island 11 5489.1 5511.2 0.70 0.02 1.42

Salinity + UrbanDist + Area + HuntLevee + Island + Breached 11 5489.1 5511.2 0.72 0.02 1.43

Salinity + UrbanDist + Area + Island + Breached 10 5491.4 5511.5 0.98 0.02 1.64

Salinity + BayDist + UrbanDist + Area + OpenLevee + HuntLevee + Island 12 5487.4 5511.5 1.02 0.02 1.66

Salinity + BayDist + UrbanDist + Area + OpenLevee + HuntLevee + Island 

+ Breached 13 5485.4 5511.5 1.05 0.02 1.69

Salinity + BayDist + Area + OpenLevee + HuntLevee + Island + Breached 12 5487.5 5511.6 1.10 0.02 1.73

Salinity + BayDist + UrbanDist + Area + HuntLevee + Island 11 5489.5 5511.6 1.16 0.01 1.79

Salinity + BayDist + UrbanDist + Area + Island 10 5491.6 5511.7 1.18 0.01 1.81

Salinity + BayDist + Area + Island 9 5493.6 5511.7 1.19 0.01 1.81

Salinity + BayDist + Area + HuntLevee + Island 10 5491.7 5511.8 1.30 0.01 1.92

Depth + Salinity + BayDist + Area + OpenLevee + HuntLevee + Island + 

Breached 13 5485.7 5511.8 1.33 0.01 1.95

Depth + Salinity + BayDist + Area + Island 10 5491.7 5511.8 1.34 0.01 1.96

Salinity + BayDist + Area + OpenLevee + Island 10 5491.8 5511.9 1.38 0.01 2.00

Depth + Salinity + BayDist + Area + HuntLevee + Island 11 5489.9 5512.0 1.52 0.01 2.14

Depth + Salinity + BayDist + UrbanDist + Area + OpenLevee + HuntLevee 

+ Island + Breached 14 5483.9 5512.1 1.61 0.01 2.24

Depth + Salinity + BayDist + Area + OpenLevee + Island + Breached 12 5488.0 5512.1 1.62 0.01 2.24

Depth + Salinity + BayDist + Area + OpenLevee + HuntLevee + Island 12 5488.0 5512.1 1.66 0.01 2.29

Salinity + BayDist + Area + OpenLevee + Island + Breached 11 5490.0 5512.1 1.66 0.01 2.29

Salinity + BayDist + UrbanDist + Area + OpenLevee + Island + Breached 12 5488.1 5512.2 1.70 0.01 2.34

Depth + Salinity + BayDist + UrbanDist + Area + Island 11 5490.1 5512.2 1.76 0.01 2.41

Salinity + BayDist + UrbanDist + Area + OpenLevee + Island 11 5490.1 5512.2 1.76 0.01 2.41

Depth + Salinity + BayDist + UrbanDist + Area + HuntLevee + Island 12 5488.2 5512.3 1.80 0.01 2.46

Depth + Salinity + BayDist + Area + Island + Breached + DepthSD 12 5488.2 5512.3 1.82 0.01 2.48

Depth + Salinity + BayDist + UrbanDist + Area + OpenLevee + Island + 

Breached 13 5486.3 5512.5 1.99 0.01 2.71
103 

Model name k -2LogL AICc ∆AICc wi

Evidence 

ratio

Depth + Salinity + BayDist + Area + HuntLevee + Island + Breached + 

DepthSD 13 5486.4 5512.5 2.03 0.01 2.77

Depth + Salinity + BayDist + UrbanDist + Area + Island + Breached + 

DepthSD 13 5486.4 5512.6 2.07 0.01 2.82

Depth + Salinity + BayDist + UrbanDist + Area + HuntLevee + Island + 

Breached + DepthSD 14 5484.5 5512.6 2.13 0.01 2.91

Depth + Salinity + UrbanDist + Area + HuntLevee + Island + Breached 12 5488.5 5512.6 2.14 0.01 2.91

Depth + Salinity + BayDist + Area + OpenLevee + Island 11 5490.5 5512.6 2.14 0.01 2.92

Salinity + Area + HuntLevee + Island + Breached 10 5492.6 5512.6 2.16 0.01 2.95

Depth + Salinity + BayDist + UrbanDist + Area + OpenLevee + HuntLevee 

+ Island 13 5486.6 5512.7 2.23 0.01 3.06

Salinity + BayDist + UrbanDist + Area + HuntLevee + Island + Breached + 

DepthSD 13 5486.6 5512.7 2.23 0.01 3.06

Salinity + UrbanDist + Area + HuntLevee + Island + Breached + DepthSD 12 5488.6 5512.7 2.24 0.01 3.06

Salinity + Area + Island + Breached 9 5494.7 5512.8 2.29 0.01 3.14

Salinity + UrbanDist + Area + HuntLevee + Island 10 5492.7 5512.8 2.30 0.01 3.17

Salinity + BayDist + UrbanDist + Area + Island + Breached + DepthSD 12 5488.7 5512.8 2.32 0.01 3.18

Salinity + BayDist + Area + OpenLevee + HuntLevee + Island + DepthSD 12 5488.7 5512.8 2.32 0.01 3.18

Salinity + UrbanDist + Area + OpenLevee + HuntLevee + Island + 

Breached 12 5488.7 5512.9 2.38 0.01 3.28

Depth + Salinity + UrbanDist + Area + Island + Breached 11 5490.8 5512.9 2.38 0.01 3.29

Salinity + UrbanDist + Area + Island + Breached + DepthSD 11 5490.8 5512.9 2.38 0.01 3.29

Salinity + UrbanDist + Area + Island 9 5495.0 5513.0 2.55 0.01 3.58

Salinity + BayDist + Area + Island + Breached + DepthSD 11 5491.0 5513.1 2.62 0.01 3.71

Salinity + BayDist + Area + HuntLevee + Island + Breached + DepthSD 12 5489.0 5513.2 2.68 0.01 3.81

Salinity + BayDist + UrbanDist + Area + OpenLevee + HuntLevee + Island 

+ DepthSD 13 5487.1 5513.2 2.71 0.01 3.89

Depth + Salinity + BayDist + UrbanDist + Area + OpenLevee + Island 12 5489.2 5513.3 2.80 0.01 4.05

Depth + Salinity + BayDist + Area + OpenLevee + HuntLevee + Island + 

Breached + DepthSD 14 5485.1 5513.3 2.81 0.01 4.08

Salinity + BayDist + Area + OpenLevee + HuntLevee + Island + Breached + 

DepthSD 13 5487.2 5513.3 2.81 0.01 4.09

Salinity + BayDist + UrbanDist + Area + OpenLevee + HuntLevee + Island 

+ Breached + DepthSD 14 5485.2 5513.4 2.87 0.01 4.21

Salinity + UrbanDist + Area + OpenLevee + Island + Breached 11 5491.3 5513.4 2.92 0.01 4.31

Salinity + BayDist + Area + OpenLevee + Island + DepthSD 11 5491.4 5513.5 3.02 0.01 4.53

Depth + Salinity + BayDist + Area + OpenLevee + HuntLevee + Island + 

DepthSD 13 5487.4 5513.5 3.03 0.01 4.56

Depth + Salinity + BayDist + Area + OpenLevee + Island + Breached + 

DepthSD 13 5487.5 5513.6 3.11 0.01 4.75

Depth + Salinity + UrbanDist + Area + HuntLevee + Island + Breached + 

DepthSD 13 5487.5 5513.6 3.11 0.01 4.75

Depth + Salinity + UrbanDist + Area + Island + Breached + DepthSD 12 5489.5 5513.6 3.12 0.01 4.75

Salinity + BayDist + UrbanDist + Area + HuntLevee + Island + DepthSD 12 5489.5 5513.7 3.18 0.01 4.90

Salinity + BayDist + UrbanDist + Area + Island + DepthSD 11 5491.6 5513.7 3.20 0.01 4.95

Salinity + BayDist + Area + Island + DepthSD 10 5493.6 5513.7 3.20 0.01 4.96
104 

Model name k -2LogL AICc ∆AICc wi

Evidence 

ratio

Salinity + UrbanDist + Area + OpenLevee + HuntLevee + Island + 

Breached + DepthSD 13 5487.6 5513.7 3.23 0.01 5.04

Depth + Salinity + BayDist + UrbanDist + Area + OpenLevee + HuntLevee 

+ Island + Breached + DepthSD 15 5483.6 5513.7 3.26 0.01 5.09

Depth + Salinity + BayDist + Area + Island + DepthSD 11 5491.6 5513.7 3.26 0.01 5.10

Salinity + BayDist + Area + HuntLevee + Island + DepthSD 11 5491.7 5513.8 3.32 0.01 5.26

Salinity + UrbanDist + Area + OpenLevee + HuntLevee + Island 11 5491.8 5513.9 3.40 0.00 5.47

Depth + Salinity + Area + HuntLevee + Island + Breached 11 5491.8 5513.9 3.42 0.00 5.53

Salinity + BayDist + Area + OpenLevee + Island + Breached + DepthSD 12 5489.8 5513.9 3.44 0.00 5.58

Salinity + Area + Island + Breached + DepthSD 10 5493.8 5513.9 3.44 0.00 5.60

Salinity + Area + HuntLevee + Island 9 5495.9 5513.9 3.47 0.00 5.67

Depth + Salinity + BayDist + Area + HuntLevee + Island + DepthSD 12 5489.8 5514.0 3.48 0.00 5.69

Salinity + BayDist + UrbanDist + Area + OpenLevee + Island + DepthSD 12 5489.8 5514.0 3.48 0.00 5.69

Depth + Salinity + Area + Island + Breached 10 5493.9 5514.0 3.48 0.00 5.71

Salinity + Area + HuntLevee + Island + Breached + DepthSD 11 5491.9 5514.0 3.50 0.00 5.75

Depth + Salinity + BayDist + Area + OpenLevee + Island + DepthSD 12 5489.9 5514.0 3.52 0.00 5.80

Salinity + BayDist + UrbanDist + Area + OpenLevee + Island + Breached + 

DepthSD 13 5487.9 5514.0 3.53 0.00 5.86

Salinity + Area + Island 8 5498.0 5514.0 3.56 0.00 5.92

Depth + Salinity + BayDist + UrbanDist + Area + OpenLevee + Island + 

Breached + DepthSD 14 5486.0 5514.1 3.63 0.00 6.15

Salinity + Area + OpenLevee + HuntLevee + Island + Breached 11 5492.1 5514.2 3.68 0.00 6.30

Depth + Salinity + BayDist + UrbanDist + Area + Island + DepthSD 12 5490.1 5514.2 3.70 0.00 6.35

Salinity + UrbanDist + Area + OpenLevee + HuntLevee + Island + 

DepthSD 12 5490.1 5514.2 3.70 0.00 6.35

Depth + Salinity + BayDist + UrbanDist + Area + OpenLevee + HuntLevee 

+ Island + DepthSD 14 5486.1 5514.2 3.75 0.00 6.54

Salinity + UrbanDist + Area + HuntLevee + Island + DepthSD 11 5492.1 5514.2 3.76 0.00 6.55

Depth + Salinity + BayDist + UrbanDist + Area + HuntLevee + Island + 

DepthSD 13 5488.1 5514.3 3.77 0.00 6.60

Salinity + UrbanDist + Area + OpenLevee + Island + Breached + DepthSD 12 5490.2 5514.3 3.84 0.00 6.81

Depth + Salinity + Area + Island + Breached + DepthSD 11 5492.2 5514.3 3.84 0.00 6.82

Salinity + UrbanDist + Area + Island + DepthSD 10 5494.2 5514.3 3.84 0.00 6.84

Null Model 5 5585.2 5595.3 84.79 1.03E-20 2.58E+18
105 

Table 14. The ranking of candidate models at the pond scale for roosting terns in the former salt production 

ponds of South San Francisco Bay, California. 

[Models are ranked by differences in Akaike’s information criterion and only models of ∆AIC <4 and the null model are presented. Birds 

were surveyed across the former salt ponds during October through April 2002–2015. Variables considered included the mean pond 

depth (Depth), the presence of islands (Island), variation in topography (DepthSD), % of pond levee open to hunting (HuntLevee), % of 

pond levee open to the public (OpenLevee), pond area (Area), water salinity, pond distance to urban areas (UrbanDist), and pond 

distance to the bay (BayDist). k: Number of estimated parameters in the model. AICc: Second-order Akaike’s information criterion. 

∆AICc: The difference between the AICc of the current model and the AICc of the top model. wi: Akaike weight–likelihood of the 

model relative to candidate models. Evidence ratio: The weight of evidence that the current model is inferior to the top model]

Model name k -2LogL AICc ∆AICc wi

Evidence 

ratio

Salinity + BayDist + Area + OpenLevee + HuntLevee + Island 11 7217.5 7239.6 0.00 0.07 1.00

Salinity + Area + OpenLevee + HuntLevee + Island 10 7220.0 7240.1 0.48 0.06 1.27

Depth + Salinity + Area + OpenLevee + HuntLevee + Island 11 7218.3 7240.3 0.70 0.05 1.42

Salinity + Area + OpenLevee + HuntLevee + Island + Breached 11 7218.3 7240.3 0.70 0.05 1.42

Salinity + BayDist + Area + OpenLevee + HuntLevee + Island + Breached 12 7216.3 7240.5 0.82 0.05 1.50

Depth + Salinity + BayDist + Area + OpenLevee + HuntLevee + Island 12 7216.6 7240.7 1.08 0.04 1.71

Salinity + BayDist + UrbanDist + Area + OpenLevee + HuntLevee + Island 12 7217.0 7241.1 1.50 0.03 2.11

Salinity + Area + HuntLevee + Island + Breached 10 7221.1 7241.2 1.56 0.03 2.19

Depth + Salinity + Area + OpenLevee + HuntLevee + Island + Breached 12 7217.3 7241.4 1.78 0.03 2.43

Salinity + BayDist + Area + OpenLevee + HuntLevee + Island + DepthSD 12 7217.5 7241.6 1.94 0.03 2.63

Salinity + UrbanDist + Area + OpenLevee + HuntLevee + Island 11 7219.9 7242.0 2.36 0.02 3.25

Salinity + Area + OpenLevee + HuntLevee + Island + DepthSD 11 7219.9 7242.0 2.38 0.02 3.29

Depth + Salinity + BayDist + Area + OpenLevee + HuntLevee + Island + 

Breached 13 7215.9 7242.0 2.39 0.02 3.31

Salinity + BayDist + UrbanDist + Area + OpenLevee + HuntLevee + Island 

+ Breached 13 7215.9 7242.0 2.39 0.02 3.31

Salinity + UrbanDist + Area + OpenLevee + HuntLevee + Island + Breached 12 7218.1 7242.2 2.60 0.02 3.66

Depth + Salinity + UrbanDist + Area + OpenLevee + HuntLevee + Island 12 7218.2 7242.3 2.66 0.02 3.77

Salinity + BayDist + Area + OpenLevee + HuntLevee + Island + Breached + 

DepthSD 13 7216.2 7242.3 2.67 0.02 3.81

Salinity + Area + OpenLevee + HuntLevee + Island + Breached + DepthSD 12 7218.2 7242.3 2.70 0.02 3.85

Depth + Salinity + Area + OpenLevee + HuntLevee + Island + DepthSD 12 7218.2 7242.4 2.72 0.02 3.89

Depth + Salinity + BayDist + UrbanDist + Area + OpenLevee + HuntLevee 

+ Island 13 7216.3 7242.4 2.77 0.02 4.00

Salinity + BayDist + Area + HuntLevee + Island + Breached 11 7220.4 7242.5 2.82 0.02 4.10

Depth + Salinity + BayDist + Area + OpenLevee + HuntLevee + Island + 

DepthSD 13 7216.3 7242.5 2.83 0.02 4.13

Salinity + Area + HuntLevee + Island 9 7224.4 7242.5 2.85 0.02 4.16

Salinity + Area + HuntLevee + Island + Breached + DepthSD 11 7220.7 7242.8 3.14 0.01 4.81

Depth + Salinity + Area + HuntLevee + Island + Breached 11 7220.7 7242.8 3.18 0.01 4.90

Salinity + BayDist + UrbanDist + Area + OpenLevee + HuntLevee + Island 

+ DepthSD 13 7217.0 7243.1 3.45 0.01 5.63

Salinity + BayDist + Area + HuntLevee + Island + Breached + DepthSD 12 7219.0 7243.1 3.48 0.01 5.69

Salinity + UrbanDist + Area + HuntLevee + Island + Breached 11 7221.1 7243.2 3.54 0.01 5.87

Depth + Salinity + UrbanDist + Area + OpenLevee + HuntLevee + Island + 

Breached 13 7217.2 7243.4 3.73 0.01 6.47

Depth + Salinity + Area + OpenLevee + HuntLevee + Island + Breached + 

DepthSD 13 7217.3 7243.4 3.77 0.01 6.60
106 

Model name k -2LogL AICc ∆AICc wi

Evidence 

ratio

Salinity + BayDist + Area + HuntLevee + Island 10 7223.4 7243.4 3.80 0.01 6.70

Depth + Salinity + Area + HuntLevee + Island 10 7223.4 7243.5 3.82 0.01 6.77

Null Model 5 7302.1 7312.1 72.45 1.32E-17 5.4E+15
107 

Table 15. The ranking of candidate models at the pond scale for foraging gulls in the former salt production 

ponds of South San Francisco Bay, California. 

[Models are ranked by differences in Akaike’s information criterion and only models of ∆AIC <4 and the null model are presented. Birds 

were surveyed across the former salt ponds during October through April 2002–2015. Variables considered included the mean pond 

depth (Depth), the presence of islands (Island), variation in topography (DepthSD), % of pond levee open to hunting (HuntLevee), % of 

pond levee open to the public (OpenLevee), pond area (Area), water salinity, pond distance to urban areas (UrbanDist), and pond 

distance to the bay (BayDist). k: Number of estimated parameters in the model. AICc: Second-order Akaike’s information criterion. 

∆AICc: The difference between the AICc of the current model and the AICc of the top model. wi: Akaike weight–likelihood of the 

model relative to candidate models. Evidence ratio: The weight of evidence that the current model is inferior to the top model]

Model name k -2LogL AICc ∆AICc wi

Evidence 

ratio

Depth + Salinity + BayDist + UrbanDist + Area + OpenLevee + HuntLevee + Island + 

DistLandfill + Breached + DepthSD 16 11006.0 11038.2 0.00 0.09 1.00

Depth + Salinity + BayDist + Area + OpenLevee + HuntLevee + Island + DistLandfill 

+ Breached 14 11010.4 11038.6 0.38 0.07 1.21

Depth + Salinity + BayDist + UrbanDist + Area + OpenLevee + HuntLevee + Island + 

DistLandfill + Breached 15 11008.5 11038.6 0.44 0.07 1.24

Depth + Salinity + BayDist + Area + HuntLevee + Island + DistLandfill + Breached 13 11012.6 11038.8 0.56 0.07 1.32

Depth + Salinity + BayDist + UrbanDist + Area + HuntLevee + Island + DistLandfill 

+ Breached 14 11010.8 11038.9 0.72 0.06 1.43

Depth + Salinity + BayDist + UrbanDist + Area + OpenLevee + HuntLevee + Island + 

DistLandfill + DepthSD 15 11008.8 11039.0 0.76 0.06 1.46

Depth + Salinity + BayDist + Area + OpenLevee + HuntLevee + Island + DistLandfill 

+ Breached + DepthSD 15 11008.8 11039.0 0.80 0.06 1.49

Depth + Salinity + BayDist + Area + OpenLevee + HuntLevee + Island + DistLandfill 

+ DepthSD 14 11011.9 11040.0 1.82 0.04 2.48

Depth + Salinity + BayDist + UrbanDist + Area + OpenLevee + HuntLevee + Island + 

DistLandfill 14 11011.9 11040.1 1.88 0.03 2.56

Depth + Salinity + BayDist + Area + OpenLevee + HuntLevee + Island + DistLandfill 13 11014.0 11040.1 1.92 0.03 2.61

Depth + Salinity + BayDist + UrbanDist + Area + HuntLevee + Island + DistLandfill 

+ Breached + DepthSD 15 11010.0 11040.2 1.98 0.03 2.69

Depth + Salinity + BayDist + Area + HuntLevee + Island + DistLandfill + Breached + 

DepthSD 14 11012.3 11040.4 2.20 0.03 3.00

Depth + Salinity + BayDist + Area + OpenLevee + HuntLevee + Island + Breached 13 11014.3 11040.4 2.20 0.03 3.00

Depth + Salinity + BayDist + Area + OpenLevee + HuntLevee + Island 12 11017.5 11041.6 3.38 0.02 5.42

Depth + Salinity + BayDist + Area + HuntLevee + Island + DistLandfill 12 11017.6 11041.7 3.54 0.02 5.87

Depth + Salinity + BayDist + Area + OpenLevee + HuntLevee + Island + Breached + 

DepthSD 14 11013.6 11041.8 3.56 0.02 5.92

Depth + Salinity + BayDist + UrbanDist + Area + OpenLevee + HuntLevee + Island + 

Breached 14 11013.6 11041.8 3.58 0.01 5.98

Depth + Salinity + BayDist + UrbanDist + Area + HuntLevee + Island + DistLandfill 13 11015.7 11041.8 3.60 0.01 6.04
108 

Table 16. The ranking of candidate models at the pond scale for roosting gulls in the former salt production 

ponds of South San Francisco Bay, California. 

[Models are ranked by differences in Akaike’s information criterion and only models of ∆AIC <4 and the null model are presented. Birds 

were surveyed across the former salt ponds during October through April 2002–2015. Variables considered included the mean pond 

depth (Depth), the presence of islands (Island), variation in topography (DepthSD), % of pond levee open to hunting (HuntLevee), % of 

pond levee open to the public (OpenLevee), pond area (Area), water salinity, pond distance to urban areas (UrbanDist), and pond 

distance to the bay (BayDist). k: Number of estimated parameters in the model. AICc: Second-order Akaike’s information criterion. 

∆AICc: The difference between the AICc of the current model and the AICc of the top model. wi: Akaike weight–likelihood of the 

model relative to candidate models. Evidence ratio: The weight of evidence that the current model is inferior to the top model]

Model name k -2LogL AICc ∆AICc wi

Evidence 

ratio

Depth + Salinity + BayDist + Area + OpenLevee + HuntLevee + Island + 

DistLandfill + Breached + DepthSD 15 24567.8 24598.0 0.00 0.10 1.00

Depth + Salinity + BayDist + Area + OpenLevee + Island + DistLandfill + 

Breached + DepthSD 14 24570.0 24598.1 0.18 0.09 1.09

Depth + BayDist + Area + OpenLevee + Island + DistLandfill + Breached + 

DepthSD 13 24572.8 24598.9 0.96 0.06 1.62

Depth + BayDist + Area + OpenLevee + HuntLevee + Island + DistLandfill 

+ Breached + DepthSD 14 24570.8 24598.9 0.98 0.06 1.63

Depth + Salinity + BayDist + UrbanDist + Area + OpenLevee + HuntLevee 

+ Island + DistLandfill + Breached + DepthSD 16 24567.4 24599.6 1.62 0.04 2.25

Depth + Salinity + BayDist + Area + OpenLevee + HuntLevee + Island + 

DistLandfill + Breached 14 24571.6 24599.7 1.78 0.04 2.43

Depth + Salinity + BayDist + UrbanDist + Area + OpenLevee + Island + 

DistLandfill + Breached + DepthSD 15 24569.6 24599.8 1.80 0.04 2.46

Depth + BayDist + Area + OpenLevee + Island + DistLandfill + DepthSD 12 24575.8 24599.9 1.94 0.04 2.64

Depth + BayDist + Area + OpenLevee + HuntLevee + Island + DistLandfill 

+ DepthSD 13 24573.8 24599.9 1.96 0.04 2.66

Depth + Salinity + BayDist + Area + OpenLevee + Island + DistLandfill + 

Breached 13 24574.0 24600.1 2.16 0.03 2.94

Depth + Salinity + BayDist + Area + OpenLevee + HuntLevee + Island + 

DistLandfill + DepthSD 14 24572.0 24600.1 2.18 0.03 2.97

Depth + Salinity + BayDist + Area + OpenLevee + Island + DistLandfill + 

DepthSD 13 24574.2 24600.3 2.36 0.03 3.25

Depth + BayDist + UrbanDist + Area + OpenLevee + Island + DistLandfill +

Breached + DepthSD 14 24572.4 24600.5 2.58 0.03 3.63

Depth + BayDist + UrbanDist + Area + OpenLevee + HuntLevee + Island + 

DistLandfill + Breached + DepthSD 15 24570.4 24600.6 2.60 0.03 3.67

Depth + BayDist + Area + OpenLevee + HuntLevee + Island + DistLandfill 

+ Breached 13 24575.0 24601.1 3.16 0.02 4.85

Depth + BayDist + Area + OpenLevee + Island + DistLandfill + Breached 12 24577.2 24601.3 3.34 0.02 5.32

Depth + BayDist + UrbanDist + Area + OpenLevee + Island + DistLandfill +

DepthSD 13 24575.4 24601.5 3.56 0.02 5.93

Depth + BayDist + UrbanDist + Area + OpenLevee + HuntLevee + Island + 

DistLandfill + DepthSD 14 24573.4 24601.5 3.58 0.02 5.99

Depth + Salinity + BayDist + UrbanDist + Area + OpenLevee + HuntLevee 

+ Island + DistLandfill + Breached 15 24571.6 24601.8 3.80 0.01 6.69

Depth + BayDist + Area + OpenLevee + HuntLevee + Island + DistLandfill 12 24577.8 24601.9 3.94 0.01 7.18
109 

Model name k -2LogL AICc ∆AICc wi

Evidence 

ratio

Depth + Salinity + BayDist + Area + OpenLevee + HuntLevee + Island + 

DistLandfill 13 24575.8 24601.9 3.96 0.01 7.24

Depth + Salinity + BayDist + UrbanDist + Area + OpenLevee + Island + 

DistLandfill + Breached 14 24573.8 24601.9 3.98 0.01 7.31

Depth + Salinity + BayDist + UrbanDist + Area + OpenLevee + Island + 

DistLandfill + DepthSD 14 24573.8 24601.9 3.98 0.01 7.31

Null Model 5 24683.0 24693.0 95.05 2.19E-22 4.37E+20
110 

Table 17. The ranking of candidate models at the pond scale for foraging eared grebes in the former salt 

production ponds of South San Francisco Bay, California. 

[Models are ranked by differences in Akaike’s information criterion and only models of ∆AIC <4 and the null model are presented. Birds 

were surveyed across the former salt ponds during October through April 2002–2015. Variables considered included the mean pond 

depth (Depth), the presence of islands (Island), variation in topography (DepthSD), % of pond levee open to hunting (HuntLevee), % of 

pond levee open to the public (OpenLevee), pond area (Area), water salinity, pond distance to urban areas (UrbanDist), and pond 

distance to the bay (BayDist). k: Number of estimated parameters in the model. AICc: Second-order Akaike’s information criterion. 

∆AICc: The difference between the AICc of the current model and the AICc of the top model. wi: Akaike weight–likelihood of the 

model relative to candidate models. Evidence ratio: The weight of evidence that the current model is inferior to the top model]

Model name k -2LogL AICc ∆AICc wi

Evidence 

ratio

Depth + Salinity + Area + OpenLevee + Breached 10 13733.1 13753.2 0.00 0.04 1.00

Depth + Salinity + BayDist + Area + Island + Breached 11 13731.4 13753.5 0.34 0.04 1.18

Depth + Salinity + Area + OpenLevee + Island + Breached 11 13731.5 13753.6 0.38 0.04 1.21

Depth + Salinity + BayDist + Area + OpenLevee + Island + Breached 12 13729.5 13753.6 0.41 0.04 1.23

Depth + Salinity + BayDist + Area + OpenLevee + Breached 11 13731.6 13753.7 0.50 0.03 1.28

Depth + Salinity + BayDist + Area + Breached 10 13733.7 13753.7 0.56 0.03 1.32

Depth + Salinity + Area + Breached 9 13736.0 13754.0 0.87 0.03 1.54

Depth + Salinity + BayDist + UrbanDist + Area + Island + Breached 12 13730.0 13754.2 0.97 0.03 1.63

Depth + Salinity + Area + Island + Breached 10 13734.3 13754.3 1.16 0.02 1.79

Depth + Salinity + BayDist + UrbanDist + Area + OpenLevee + Island + 

Breached 13 13728.4 13754.5 1.31 0.02 1.93

Depth + Salinity + Area + OpenLevee + HuntLevee + Breached 11 13732.4 13754.5 1.34 0.02 1.95

Depth + Salinity + BayDist + UrbanDist + Area + Breached 11 13732.4 13754.5 1.36 0.02 1.97

Depth + Salinity + Area + Breached + DepthSD 10 13734.5 13754.6 1.38 0.02 1.99

Depth + Salinity + UrbanDist + Area + OpenLevee + Breached 11 13732.5 13754.6 1.40 0.02 2.01

Depth + Salinity + BayDist + Area + HuntLevee + Island + Breached 12 13730.5 13754.6 1.45 0.02 2.07

Depth + Salinity + BayDist + UrbanDist + Area + OpenLevee + Breached 12 13730.6 13754.7 1.53 0.02 2.15

Depth + Salinity + BayDist + Area + HuntLevee + Breached 11 13732.7 13754.6 1.58 0.02 2.20

Depth + Salinity + Area + OpenLevee + Breached + DepthSD 11 13732.8 13754.9 1.68 0.02 2.31

Depth + Salinity + UrbanDist + Area + OpenLevee + Island + Breached 12 13730.8 13754.9 1.71 0.02 2.35

Depth + Salinity + Area + OpenLevee + HuntLevee + Island + Breached 12 13730.8 13755.0 1.77 0.02 2.43

Depth + Salinity + BayDist + Area + OpenLevee + HuntLevee + Island + 

Breached 13 13728.8 13755.0 1.79 0.02 2.45

Depth + Salinity + BayDist + Area + OpenLevee + HuntLevee + Breached 12 13730.9 13755.0 1.81 0.02 2.47

Depth + Salinity + Area + Island + Breached + DepthSD 11 13732.9 13755.0 1.82 0.02 2.48

Depth + Salinity + Area + HuntLevee + Breached 10 13734.9 13755.0 1.84 0.02 2.51

Depth + Salinity + BayDist + Area + Island + Breached + DepthSD 12 13731.2 13755.3 2.09 0.02 2.85

Depth + Salinity + UrbanDist + Area + Breached 10 13735.2 13755.3 2.10 0.02 2.86

Depth + Salinity + Area + OpenLevee + Island + Breached + DepthSD 12 13731.2 13755.3 2.11 0.02 2.88

Depth + Salinity + BayDist + UrbanDist + Area + HuntLevee + Island + 

Breached 13 13729.2 13755.3 2.13 0.02 2.90

Depth + Salinity + BayDist + Area + Breached + DepthSD 11 13733.2 13755.3 2.14 0.02 2.91

Depth + Salinity + Area + HuntLevee + Island + Breached 11 13733.3 13755.4 2.24 0.01 3.06

Depth + Salinity + UrbanDist + Area + Island + Breached 11 13733.4 13755.51 2.34 0.01 3.21

Depth + Salinity + BayDist + UrbanDist + Area + HuntLevee + Breached 12 13731.5 13755.6 2.41 0.01 3.34

Depth + Salinity + BayDist + Area + OpenLevee + Island + Breached + 

DepthSD 13 13729.5 13755.6 2.43 0.01 3.37

Depth + Salinity + BayDist + Area + OpenLevee + Breached + DepthSD 12 13731.6 13755.7 2.49 0.01 3.48
111 

Model name k -2LogL AICc ∆AICc wi

Evidence 

ratio

Depth + Salinity + UrbanDist + Area + Breached + DepthSD 11 13733.6 13755.7 2.52 0.01 3.52

Depth + Salinity + Area + HuntLevee + Breached + DepthSD 11 13733.6 13755.7 2.54 0.01 3.55

Depth + Salinity + BayDist + UrbanDist + Area + OpenLevee + HuntLevee 

+ Island + Breached 14 13727.7 13755.9 2.71 0.01 3.88

Depth + Salinity + BayDist + UrbanDist + Area + Island + Breached + 

DepthSD 13 13729.8 13755.9 2.73 0.01 3.92

Depth + Salinity + UrbanDist + Area + OpenLevee + HuntLevee + 

Breached 12 13731.8 13755.9 2.73 0.01 3.92

Depth + Salinity + BayDist + UrbanDist + Area + OpenLevee + HuntLevee 

+ Breached 13 13729.9 13756.0 2.85 0.01 4.16

Depth + Salinity + UrbanDist + Area + Island + Breached + DepthSD 12 13732.0 13756.1 2.89 0.01 4.25

Depth + Salinity + BayDist + UrbanDist + Area + Breached + DepthSD 12 13732.0 13756.1 2.95 0.01 4.38

Depth + Salinity + UrbanDist + Area + OpenLevee + Breached + DepthSD 12 13732.1 13756.2 2.99 0.01 4.46

Depth + Salinity + Area + OpenLevee + HuntLevee + Breached + DepthSD 12 13732.1 13756.2 3.03 0.01 4.55

Depth + Salinity + Area + HuntLevee + Island + Breached + DepthSD 12 13732.1 13756.2 3.05 0.01 4.60

Depth + Salinity + UrbanDist + Area + OpenLevee + HuntLevee + Island + 

Breached 13 13730.2 13756.3 3.11 0.01 4.74

Depth + Salinity + UrbanDist + Area + HuntLevee + Breached 11 13734.2 13756.3 3.12 0.01 4.75

Depth + Salinity + BayDist + Area + HuntLevee + Breached + DepthSD 12 13732.3 13756.4 3.25 0.01 5.08

Depth + Salinity + BayDist + Area + HuntLevee + Island + Breached + 

DepthSD 13 13730.3 13756.5 3.27 0.01 5.13

Depth + Salinity + BayDist + UrbanDist + Area + OpenLevee + Island + 

Breached + DepthSD 14 13728.4 13756.5 3.33 0.01 5.29

Depth + Salinity + UrbanDist + Area + OpenLevee + Island + Breached + 

DepthSD 13 13730.4 13756.6 3.37 0.01 5.39

Depth + Salinity + UrbanDist + Area + HuntLevee + Island + Breached 12 13732.5 13756.6 3.45 0.01 5.62

Depth + Salinity + BayDist + UrbanDist + Area + OpenLevee + Breached + 

DepthSD 13 13730.6 13756.7 3.53 0.01 5.84

Depth + Salinity + Area + OpenLevee + HuntLevee + Island + Breached + 

DepthSD 13 13730.6 13756.7 3.53 0.01 5.84

Depth + Salinity + UrbanDist + Area + HuntLevee + Breached + DepthSD 12 13732.8 13756.9 3.71 0.01 6.40

Depth + Salinity + BayDist + Area + OpenLevee + HuntLevee + Island + 

Breached + DepthSD 14 13728.8 13757.0 3.81 0.01 6.72

Depth + Salinity + BayDist + Area + OpenLevee + HuntLevee + Breached + 

DepthSD 13 13730.7 13757.0 3.81 0.01 6.72

Depth + Salinity + BayDist + UrbanDist + Area + HuntLevee + Island + 

Breached + DepthSD 14 13729.0 13757.2 3.97 0.01 7.28

Null Model 5 14023.5 14033.5 280.30 6.01E-63 7.37E+60
112 

Table 18. The ranking of candidate models at the pond scale for roosting eared grebes in the former salt 

production ponds of South San Francisco Bay, California. 

[Models are ranked by differences in Akaike’s information criterion and only models of ∆AIC <4 and the null model are presented. Birds 

were surveyed across the former salt ponds during October through April 2002–2015. Variables considered included the mean pond 

depth (Depth), the presence of islands (Island), variation in topography (DepthSD), % of pond levee open to hunting (HuntLevee), % of 

pond levee open to the public (OpenLevee), pond area (Area), water salinity, pond distance to urban areas (UrbanDist), and pond 

distance to the bay (BayDist). k: Number of estimated parameters in the model. AICc: Second-order Akaike’s information criterion. 

∆AICc: The difference between the AICc of the current model and the AICc of the top model. wi: Akaike weight–likelihood of the 

model relative to candidate models. Evidence ratio: The weight of evidence that the current model is inferior to the top model]

Model name k -2LogL AICc ∆AICc wi

Evidence 

ratio

Depth + BayDist + PondArea + Breached 9 11686.2 11704.2 0.00 0.07 1.00

Depth + Salinity + BayDist + PondArea + Breached 10 11685.3 11705.4 1.17 0.04 1.80

Depth + BayDist + PondArea + OpenLevee + Breached 10 11685.3 11705.4 1.17 0.04 1.80

Depth + BayDist + PondArea + Island + Breached 10 11685.8 11705.9 1.67 0.03 2.31

Depth + BayDist + PondArea + HuntLevee + Breached 10 11686.0 11706.1 1.85 0.03 2.53

Depth + PondArea + Breached 8 11690.0 11706.1 1.87 0.03 2.54

Depth + BayDist + UrbanDist + PondArea + Breached 10 11686.1 11706.1 1.91 0.03 2.60

Depth + BayDist + PondArea + Breached + DepthSD 10 11686.1 11706.2 1.95 0.03 2.66

Depth + Salinity + BayDist + PondArea + OpenLevee + Breached 11 11684.4 11706.5 2.29 0.02 3.14

Depth + PondArea + OpenLevee + Breached 9 11688.5 11706.6 2.38 0.02 3.29

Depth + Salinity + PondArea + Breached 9 11688.6 11706.6 2.40 0.02 3.32

Depth + Salinity + BayDist + PondArea + Island + Breached 11 11684.8 11706.9 2.69 0.02 3.84

Depth + BayDist + PondArea + OpenLevee + Breached + DepthSD 11 11684.9 11707.0 2.77 0.02 3.99

Depth + BayDist + PondArea + OpenLevee + Island + Breached 11 11685.0 11707.1 2.87 0.02 4.20

Depth + Salinity + PondArea + OpenLevee + Breached 10 11687.0 11707.1 2.87 0.02 4.21

Depth + BayDist + PondArea + OpenLevee + HuntLevee + Breached 11 11685.1 11707.2 2.95 0.02 4.37

Depth + Salinity + BayDist + UrbanDist + PondArea + Breached 11 11685.2 11707.3 3.05 0.02 4.59

Depth + Salinity + BayDist + PondArea + HuntLevee + Breached 11 11685.2 11707.3 3.05 0.02 4.59

Depth + BayDist + UrbanDist + PondArea + OpenLevee + Breached 11 11685.3 11707.4 3.13 0.01 4.78

Depth + Salinity + BayDist + PondArea + Breached + DepthSD 11 11685.3 11707.4 3.15 0.01 4.83

Depth + BayDist + PondArea + HuntLevee + Island + Breached 11 11685.7 11707.8 3.53 0.01 5.84

Depth + PondArea + Breached + DepthSD 9 11689.7 11707.8 3.56 0.01 5.93

Depth + BayDist + UrbanDist + PondArea + Island + Breached 11 11685.7 11707.8 3.57 0.01 5.96

Depth + BayDist + PondArea + Island + Breached + DepthSD 11 11685.7 11707.8 3.59 0.01 6.02

Depth + PondArea + Island + Breached 9 11689.9 11708.0 3.76 0.01 6.55

Depth + BayDist + UrbanDist + PondArea + HuntLevee + Breached 11 11685.9 11708.0 3.77 0.01 6.58

Depth + BayDist + PondArea + HuntLevee + Breached + DepthSD 11 11685.9 11708.0 3.81 0.01 6.72

Depth + Salinity + BayDist + PondArea + OpenLevee + Island + Breached 12 11684.0 11708.1 3.85 0.01 6.84

Depth + BayDist + UrbanDist + PondArea + Breached + DepthSD 11 11686.0 11708.1 3.85 0.01 6.85

Depth + UrbanDist + PondArea + Breached 9 11690.0 11708.1 3.86 0.01 6.89

Depth + Salinity + BayDist + PondArea + OpenLevee + Breached + 

DepthSD 12 11684.0 11708.1 3.91 0.01 7.05

Null Model 5 12015.5 12025.5 321.26 1.24E-71 5.76E+69
113 

Table 19. The ranking of candidate models at the pond scale for foraging northern shovelers in the former salt 

production ponds of South San Francisco Bay, California. 

[Models are ranked by differences in Akaike’s information criterion and only models of ∆AIC <4 and the null model are presented. Birds 

were surveyed across the former salt ponds during October through April 2002–2015. Variables considered included the mean pond 

depth (Depth), the presence of islands (Island), variation in topography (DepthSD), % of pond levee open to hunting (HuntLevee), % of 

pond levee open to the public (OpenLevee), pond area (Area), water salinity, pond distance to urban areas (UrbanDist), and pond 

distance to the bay (BayDist). k: Number of estimated parameters in the model. AICc: Second-order Akaike’s information criterion. 

∆AICc: The difference between the AICc of the current model and the AICc of the top model. wi: Akaike weight–likelihood of the 

model relative to candidate models. Evidence ratio: The weight of evidence that the current model is inferior to the top model]

Model name k -2LogL AICc ∆AICc wi

Evidence 

ratio

Salinity + Island + DepthSD 8 18522.6 18538.6 0.00 0.03 1.00

Salinity + OpenLevee + Island 8 18522.7 18538.7 0.10 0.03 1.05

Salinity + OpenLevee + Island + DepthSD 9 18521.3 18539.4 0.73 0.02 1.44

Salinity + Area + OpenLevee + Island 9 18521.4 18539.5 0.87 0.02 1.55

Depth + Salinity + Island + DepthSD 9 18521.6 18539.6 0.99 0.02 1.64

Salinity + HuntLevee + Island + DepthSD 9 18521.6 18539.6 0.99 0.02 1.64

Salinity + OpenLevee + HuntLevee + Island 9 18521.6 18539.6 1.01 0.02 1.66

Salinity + Area + Island + DepthSD 9 18521.7 18539.8 1.17 0.01 1.80

Salinity + Island 7 18526.0 18540.0 1.39 0.01 2.00

Salinity + UrbanDist + Island + DepthSD 9 18522.0 18540.0 1.41 0.01 2.03

Depth + Salinity + OpenLevee + Island 9 18522.1 18540.1 1.51 0.01 2.13

Salinity + OpenLevee + HuntLevee + Island + DepthSD 10 18520.1 18540.2 1.57 0.01 2.19

Depth + Salinity + OpenLevee + Island + DepthSD 10 18520.2 18540.2 1.61 0.01 2.23

Salinity + Island + Breached + DepthSD 9 18522.2 18540.3 1.63 0.01 2.26

Salinity + UrbanDist + OpenLevee + Island 9 18522.3 18540.3 1.71 0.01 2.35

Salinity + Area + OpenLevee + HuntLevee + Island 10 18520.3 18540.4 1.73 0.01 2.37

Salinity + Area + OpenLevee + Island + DepthSD 10 18520.3 18540.4 1.75 0.01 2.39

Salinity + BayDist + OpenLevee + Island 9 18522.3 18540.4 1.75 0.01 2.40

Salinity + BayDist + Island + DepthSD 9 18522.6 18540.6 1.97 0.01 2.68

Depth + Salinity + HuntLevee + Island + DepthSD 10 18520.6 18540.6 2.01 0.01 2.73

Salinity + OpenLevee + Island + Breached 9 18522.6 18540.6 2.01 0.01 2.74

Depth + Salinity + Area + Island + DepthSD 10 18520.7 18540.7 2.11 0.01 2.87

Salinity + Area + HuntLevee + Island + DepthSD 10 18520.7 18540.8 2.15 0.01 2.92

Depth + Salinity + Area + OpenLevee + Island 10 18520.7 18540.8 2.19 0.01 2.98

Depth + Salinity + UrbanDist + Island + DepthSD 10 18520.8 18540.9 2.27 0.01 3.11

Salinity + Area + Island 8 18524.9 18540.9 2.28 0.01 3.13

Salinity + UrbanDist + OpenLevee + Island + DepthSD 10 18520.8 18540.9 2.29 0.01 3.14

Salinity + UrbanDist + HuntLevee + Island + DepthSD 10 18520.9 18541.0 2.37 0.01 3.26

Depth + Salinity + OpenLevee + HuntLevee + Island 10 18521.0 18541.1 2.45 0.01 3.40

Depth + Salinity + OpenLevee + HuntLevee + Island + DepthSD 11 18519.0 18541.1 2.46 0.01 3.42

Salinity + BayDist + Island 8 18525.1 18541.2 2.52 0.01 3.53

Depth + Salinity + Area + OpenLevee + Island + DepthSD 11 18519.1 18541.2 2.54 0.01 3.56

Salinity + OpenLevee + Island + Breached + DepthSD 10 18521.1 18541.2 2.55 0.01 3.57

Salinity + Area + OpenLevee + HuntLevee + Island + DepthSD 11 18519.1 18541.2 2.56 0.01 3.60

Salinity + UrbanDist + OpenLevee + HuntLevee + Island 10 18521.1 18541.2 2.59 0.01 3.64

Salinity + HuntLevee + Island 8 18525.2 18541.2 2.60 0.01 3.67

Salinity + HuntLevee + Island + Breached + DepthSD 10 18521.2 18541.2 2.61 0.01 3.68

Salinity + BayDist + Area + OpenLevee + Island 10 18521.2 18541.3 2.63 0.01 3.72
114 

Model name k -2LogL AICc ∆AICc wi

Evidence 

ratio

Salinity + BayDist + OpenLevee + HuntLevee + Island 10 18521.2 18541.3 2.65 0.01 3.76

Salinity + BayDist + OpenLevee + Island + DepthSD 10 18521.3 18541.3 2.71 0.01 3.87

Salinity + UrbanDist + Area + OpenLevee + Island 10 18521.3 18541.4 2.75 0.01 3.95

Depth + Salinity + Island + Breached + DepthSD 10 18521.3 18541.4 2.79 0.01 4.03

Salinity + Area + OpenLevee + Island + Breached 10 18521.4 18541.4 2.81 0.01 4.07

Salinity + UrbanDist + Island 8 18525.4 18541.5 2.84 0.01 4.14

Salinity + UrbanDist + Area + Island + DepthSD 10 18521.4 18541.5 2.87 0.01 4.19

Salinity + Area + Island + Breached + DepthSD 10 18521.4 18541.5 2.87 0.01 4.19

Depth + Salinity + BayDist + Island + DepthSD 10 18521.5 18541.6 2.93 0.01 4.32

Salinity + OpenLevee + HuntLevee + Island + Breached 10 18521.5 18541.6 2.93 0.01 4.32

Depth + Salinity + BayDist + OpenLevee + Island 10 18521.5 18541.6 2.95 0.01 4.36

Salinity + BayDist + HuntLevee + Island + DepthSD 10 18521.5 18541.6 2.97 0.01 4.41

Depth + Salinity + UrbanDist + OpenLevee + Island + DepthSD 11 18519.6 18541.7 3.02 0.01 4.53

Salinity + UrbanDist + Island + Breached + DepthSD 10 18521.6 18541.7 3.05 0.01 4.59

Depth + Salinity + UrbanDist + OpenLevee + Island 10 18521.6 18541.7 3.07 0.01 4.63

Salinity + Island + Breached 8 18525.7 18541.7 3.08 0.01 4.66

Depth + Salinity + Area + OpenLevee + HuntLevee + Island 11 18519.6 18541.7 3.08 0.01 4.67

Salinity + UrbanDist + OpenLevee + HuntLevee + Island + DepthSD 11 18519.6 18541.7 3.08 0.01 4.67

Depth + Salinity + Area + HuntLevee + Island + DepthSD 11 18519.6 18541.7 3.10 0.01 4.72

Depth + Salinity + Island 8 18525.7 18541.8 3.14 0.01 4.81

Salinity + BayDist + Area + Island + DepthSD 10 18521.7 18541.8 3.17 0.01 4.87

Depth + Salinity + UrbanDist + HuntLevee + Island + DepthSD 11 18519.8 18541.9 3.24 0.01 5.06

Salinity + BayDist + UrbanDist + OpenLevee + Island 10 18521.8 18541.9 3.25 0.01 5.07

Salinity + BayDist + UrbanDist + Island + DepthSD 10 18521.9 18542.0 3.33 0.01 5.28

Depth + Salinity + Area + OpenLevee + HuntLevee + Island + DepthSD 12 18517.9 18542.0 3.38 0.00 5.42

Salinity + OpenLevee + HuntLevee + Island + Breached + DepthSD 11 18519.9 18542.1 3.38 0.00 5.42

Salinity + Area + HuntLevee + Island 9 18524.0 18542.1 3.47 0.00 5.68

Salinity + BayDist + Area + OpenLevee + HuntLevee + Island 11 18520.0 18542.1 3.48 0.00 5.70

Depth + Salinity + OpenLevee + Island + Breached 10 18522.1 18542.1 3.51 0.00 5.77

Depth + Salinity + BayDist + OpenLevee + Island + DepthSD 11 18520.1 18542.2 3.54 0.00 5.88

Salinity + BayDist + OpenLevee + HuntLevee + Island + DepthSD 11 18520.1 18542.2 3.54 0.00 5.88

Salinity + BayDist + Area + Island 9 18524.1 18542.2 3.55 0.00 5.91

Depth + Salinity + OpenLevee + Island + Breached + DepthSD 11 18520.1 18542.2 3.56 0.00 5.94

Salinity + UrbanDist + Area + OpenLevee + Island + DepthSD 11 18520.1 18542.2 3.56 0.00 5.94

Salinity + UrbanDist + Area + OpenLevee + HuntLevee + Island 11 18520.1 18542.2 3.58 0.00 6.00

Salinity + BayDist + Island + Breached + DepthSD 10 18522.1 18542.2 3.59 0.00 6.01

Salinity + BayDist + OpenLevee + Island + Breached 10 18522.2 18542.2 3.61 0.00 6.07

Salinity + Area + OpenLevee + Island + Breached + DepthSD 11 18520.2 18542.3 3.62 0.00 6.12

Salinity + UrbanDist + OpenLevee + Island + Breached 10 18522.2 18542.3 3.63 0.00 6.13

Depth + Salinity + UrbanDist + Area + Island + DepthSD 11 18520.2 18542.3 3.68 0.00 6.30

Salinity + Area + OpenLevee + HuntLevee + Island + Breached 11 18520.2 18542.3 3.68 0.00 6.30

Salinity + BayDist + HuntLevee + Island 9 18524.3 18542.3 3.69 0.00 6.34

Depth + Salinity + BayDist + Area + OpenLevee + Island 11 18520.3 18542.4 3.72 0.00 6.43

Salinity + BayDist + Area + OpenLevee + Island + DepthSD 11 18520.3 18542.4 3.74 0.00 6.49

Salinity + BayDist + UrbanDist + Island 9 18524.3 18542.4 3.77 0.00 6.59

Depth + Salinity + HuntLevee + Island + Breached + DepthSD 11 18520.3 18542.4 3.78 0.00 6.63

Salinity + UrbanDist + Area + HuntLevee + Island + DepthSD 11 18520.3 18542.4 3.80 0.00 6.69
115 

Model name k -2LogL AICc ∆AICc wi

Evidence 

ratio

Salinity + Area + HuntLevee + Island + Breached + DepthSD 11 18520.4 18542.4 3.84 0.00 6.83

Depth + Salinity + UrbanDist + OpenLevee + HuntLevee + Island + 

DepthSD 12 18518.4 18542.5 3.86 0.00 6.88

Depth + Salinity + BayDist + OpenLevee + HuntLevee + Island 11 18520.4 18542.5 3.88 0.00 6.97

Depth + Salinity + BayDist + HuntLevee + Island + DepthSD 11 18520.5 18542.6 3.92 0.00 7.11

Depth + Salinity + Area + Island + Breached + DepthSD 11 18520.5 18542.6 3.94 0.00 7.18

Depth + Salinity + UrbanDist + OpenLevee + HuntLevee + Island 11 18520.5 18542.6 3.96 0.00 7.25

Salinity + UrbanDist + HuntLevee + Island + Breached + DepthSD 11 18520.5 18542.6 3.98 0.00 7.32

Depth + Salinity + Area + Island 9 18524.6 18542.6 3.99 0.00 7.36

Null Model 5 18586.2 18596.2 57.61 8.21E-15 3.24E+12
116 

Table 20. The ranking of candidate models at the grid scale for roosting northern shovelers in the former salt 

production ponds of South San Francisco Bay, California. 

[Models are ranked by differences in Akaike’s information criterion and only models of ∆AIC <4 and the null model are presented. Birds 

were surveyed across the former salt ponds during October through April 2002–2015. Variables considered included the mean pond 

depth (Depth), the presence of islands (Island), variation in topography (DepthSD), % of pond levee open to hunting (HuntLevee), % of 

pond levee open to the public (OpenLevee), pond area (Area), water salinity, pond distance to urban areas (UrbanDist), and pond 

distance to the bay (BayDist). k: Number of estimated parameters in the model. AICc: Second-order Akaike’s information criterion. 

∆AICc: The difference between the AICc of the current model and the AICc of the top model. wi: Akaike weight–likelihood of the 

model relative to candidate models. Evidence ratio: The weight of evidence that the current model is inferior to the top model]

Model name k -2LogL AICc ∆AICc wi

Evidence 

ratio

Depth + Salinity + HuntLevee + Island + DepthSD 10 21675.8 21695.9 0.00 0.07 1.00

Depth + Salinity + UrbanDist + HuntLevee + Island + DepthSD 11 21673.8 21695.9 0.02 0.06 1.01

Depth + Salinity + PondArea + HuntLevee + Island + DepthSD 11 21674.0 21696.1 0.22 0.06 1.11

Salinity + HuntLevee + Island + DepthSD 9 21678.6 21696.7 0.79 0.04 1.48

Depth + Salinity + UrbanDist + PondArea + HuntLevee + Island + DepthSD 12 21672.8 21696.9 1.03 0.04 1.68

Salinity + UrbanDist + HuntLevee + Island + DepthSD 10 21677.0 21697.1 1.20 0.04 1.82

Salinity + PondArea + HuntLevee + Island + DepthSD 10 21677.0 21697.1 1.20 0.04 1.82

Depth + Salinity + UrbanDist + HuntLevee + Island + Breached + DepthSD 12 21673.4 21697.5 1.63 0.03 2.26

Depth + Salinity + PondArea + HuntLevee + Island + Breached + DepthSD 12 21673.6 21697.7 1.83 0.03 2.50

Depth + Salinity + BayDist + HuntLevee + Island + DepthSD 11 21675.8 21697.9 2.02 0.02 2.74

Depth + Salinity + OpenLevee + HuntLevee + Island + DepthSD 11 21675.8 21697.9 2.02 0.02 2.74

Depth + Salinity + BayDist + UrbanDist + HuntLevee + Island + DepthSD 12 21673.8 21697.9 2.03 0.02 2.76

Depth + Salinity + UrbanDist + OpenLevee + HuntLevee + Island + DepthSD 12 21673.8 21697.9 2.03 0.02 2.76

Salinity + UrbanDist + PondArea + HuntLevee + Island + DepthSD 11 21676.0 21698.1 2.22 0.02 3.03

Depth + Salinity + BayDist + PondArea + HuntLevee + Island + DepthSD 12 21674.0 21698.1 2.23 0.02 3.05

Salinity + BayDist + HuntLevee + Island + DepthSD 10 21678.4 21698.5 2.60 0.02 3.67

Salinity + OpenLevee + HuntLevee + Island + DepthSD 10 21678.4 21698.5 2.60 0.02 3.67

Depth + Salinity + UrbanDist + PondArea + HuntLevee + Island + Breached 

+ DepthSD 13 21672.4 21698.5 2.65 0.02 3.76

Salinity + HuntLevee + Island + Breached + DepthSD 10 21678.6 21698.7 2.80 0.02 4.06

Salinity + BayDist + PondArea + HuntLevee + Island + DepthSD 11 21676.8 21698.9 3.02 0.01 4.52

Salinity + UrbanDist + OpenLevee + HuntLevee + Island + DepthSD 11 21676.8 21698.9 3.02 0.01 4.52

Salinity + PondArea + OpenLevee + HuntLevee + Island + DepthSD 11 21676.8 21698.9 3.02 0.01 4.52

Depth + Salinity + UrbanDist + PondArea + OpenLevee + HuntLevee + 

Island + DepthSD 13 21672.8 21698.9 3.05 0.01 4.60

Salinity + BayDist + UrbanDist + HuntLevee + Island + DepthSD 11 21677.0 21699.1 3.22 0.01 4.99

Salinity + UrbanDist + HuntLevee + Island + Breached + DepthSD 11 21677.0 21699.1 3.22 0.01 4.99

Salinity + PondArea + HuntLevee + Island + Breached + DepthSD 11 21677.0 21699.1 3.22 0.01 4.99

Depth + Salinity + OpenLevee + HuntLevee + Island + Breached + DepthSD 12 21675.2 21699.3 3.43 0.01 5.56

Depth + Salinity + UrbanDist + OpenLevee + HuntLevee + Island + 

Breached + DepthSD 13 21673.2 21699.3 3.45 0.01 5.61

Depth + Salinity + PondArea + OpenLevee + HuntLevee + Island + Breached 

+ DepthSD 13 21673.2 21699.3 3.45 0.01 5.61

Depth + Salinity + BayDist + HuntLevee + Island + Breached + DepthSD 12 21675.4 21699.5 3.63 0.01 6.15

Depth + Salinity + BayDist + UrbanDist + HuntLevee + Island + Breached + 

DepthSD 13 21673.4 21699.5 3.65 0.01 6.20

Depth + Salinity + BayDist + PondArea + HuntLevee + Island + Breached + 

DepthSD 13 21673.4 21699.5 3.65 0.01 6.20
117 

Model name k -2LogL AICc ∆AICc wi

Evidence 

ratio

Depth + Salinity + BayDist + OpenLevee + HuntLevee + Island + DepthSD 12 21675.6 21699.7 3.83 0.01 6.79

Null Model 5 21841.2 21851.2 155.34 1.20E-35 5.40E+33
118 

Table 21. The ranking of candidate models at the pond scale for foraging and roosting ruddy ducks in the former 

salt production ponds of South San Francisco Bay, California. 

[Models are ranked by differences in Akaike’s information criterion and only models of ∆AIC <4 and the null model are presented. Birds 

were surveyed across the former salt ponds during October through April 2002–2015. Variables considered included the mean pond 

depth (Depth), the presence of islands (Island), variation in topography (DepthSD), % of pond levee open to hunting (HuntLevee), % of 

pond levee open to the public (OpenLevee), pond area (Area), water salinity, pond distance to urban areas (UrbanDist), and pond 

distance to the bay (BayDist). k: Number of estimated parameters in the model. AICc: Second-order Akaike’s information criterion. 

∆AICc: The difference between the AICc of the current model and the AICc of the top model. wi: Akaike weight–likelihood of the 

model relative to candidate models. Evidence ratio: The weight of evidence that the current model is inferior to the top model]

Model name k -2LogL AICc ∆AICc wi

Evidence 

ratio

 Foraging

Depth + Salinity + BayDist + Area + Island + Breached 11 14502.9 14525.0 0.00 0.15 1.00

Depth + Salinity + BayDist + Area + HuntLevee + Island + Breached 12 14502.0 14526.1 1.06 0.09 1.70

Depth + Salinity + BayDist + UrbanDist + Area + Island + Breached 12 14502.1 14526.2 1.18 0.08 1.80

Depth + Salinity + BayDist + Area + OpenLevee + Island + Breached 12 14502.8 14526.9 1.88 0.06 2.56

Depth + Salinity + BayDist + Area + Island + Breached + DepthSD 12 14502.8 14526.9 1.92 0.06 2.61

Depth + Salinity + BayDist + UrbanDist + Area + HuntLevee + Island + 

Breached 13 14501.2 14527.3 2.27 0.05 3.12

Depth + Salinity + BayDist + Area + Island 10 14507.5 14527.6 2.56 0.04 3.60

Depth + Salinity + BayDist + Area + HuntLevee + Island + Breached + 

DepthSD 13 14501.8 14528.0 2.95 0.03 4.38

Depth + Salinity + BayDist + Area + OpenLevee + HuntLevee + Island + 

Breached 13 14501.9 14528.0 2.99 0.03 4.47

Depth + Salinity + BayDist + UrbanDist + Area + Island + Breached + 

DepthSD 13 14502.0 14528.1 3.11 0.03 4.75

Depth + Salinity + BayDist + UrbanDist + Area + OpenLevee + Island + 

Breached 13 14502.0 14528.2 3.13 0.03 4.79

Depth + Salinity + BayDist + Area + HuntLevee + Island 11 14506.5 14528.6 3.58 0.03 5.99

Depth + Salinity + BayDist + Area + OpenLevee + Island + Breached + 

DepthSD 13 14502.6 14528.7 3.67 0.02 6.28

Depth + Salinity + BayDist + UrbanDist + Area + Island 11 14506.8 14528.9 3.92 0.02 7.10

Null Model 5 14760.6 14770.7 245.65 6.95E-55 2.20E+53

 Roosting

Depth + Salinity + BayDist + UrbanDist + Area + HuntLevee + Island + 

Breached 13 24693.4 24719.5 0.00 0.28 1.00

Depth + Salinity + BayDist + Area + HuntLevee + Island + Breached 12 24696.4 24720.5 0.98 0.17 1.63

Depth + Salinity + BayDist + UrbanDist + Area + OpenLevee + 

HuntLevee + Island + Breached 14 24693.2 24721.4 1.82 0.11 2.48

Depth + Salinity + BayDist + UrbanDist + Area + HuntLevee + Island + 

Breached + DepthSD 14 24693.4 24721.6 2.02 0.10 2.75

Depth + Salinity + BayDist + Area + OpenLevee + HuntLevee + Island + 

Breached 13 24696.2 24722.3 2.80 0.07 4.06

Depth + Salinity + BayDist + Area + HuntLevee + Island + Breached + 

DepthSD 13 24696.2 24722.3 2.80 0.07 4.06

Depth + Salinity + BayDist + UrbanDist + Area + Island + Breached 12 24699.0 24723.1 3.58 0.05 5.99

Depth + Salinity + BayDist + UrbanDist + Area + OpenLevee + 

HuntLevee + Island + Breached + DepthSD 15 24693.2 24723.4 3.84 0.04 6.82

Null Model 5 25173.8 25183.8 464.29 4.17E-102 6.61E+100
119 

Table 22. The ranking of candidate models at the pond scale for foraging American avocets in the former salt 

production ponds of South San Francisco Bay, California. 

[Models are ranked by differences in Akaike’s information criterion and only models of ∆AIC <4 and the null model are presented. Birds 

were surveyed across the former salt ponds during October through April 2002–2015. Variables considered included the mean pond 

depth (Depth), the presence of islands (Island), variation in topography (DepthSD), % of pond levee open to hunting (HuntLevee), % of 

pond levee open to the public (OpenLevee), pond area (Area), water salinity, pond distance to urban areas (UrbanDist), and pond 

distance to the bay (BayDist). k: Number of estimated parameters in the model. AICc: Second-order Akaike’s information criterion. 

∆AICc: The difference between the AICc of the current model and the AICc of the top model. wi: Akaike weight–likelihood of the 

model relative to candidate models. Evidence ratio: The weight of evidence that the current model is inferior to the top model]

Model name k -2LogL AICc ∆AICc wi

Evidence 

ratio

Depth + BayDist + HuntLevee + Island + DepthSD 10 12182.4 12202.5 0.00 0.04 1.00

Depth + BayDist + Island + DepthSD 9 12184.8 12202.9 0.37 0.04 1.20

Depth + BayDist + OpenLevee + HuntLevee + Island + DepthSD 11 12181.0 12203.1 0.58 0.03 1.33

Depth + BayDist + OpenLevee + Island + DepthSD 10 12183.0 12203.1 0.60 0.03 1.35

Depth + HuntLevee + Island + DepthSD 9 12185.1 12203.1 0.61 0.03 1.35

Depth + BayDist + Island 8 12187.2 12203.25 0.73 0.03 1.44

Depth + Island + DepthSD 8 12187.5 12203.6 1.05 0.03 1.69

Depth + OpenLevee + HuntLevee + Island + DepthSD 10 12183.7 12203.7 1.22 0.02 1.84

Depth + OpenLevee + Island + DepthSD 9 12185.8 12203.8 1.33 0.02 1.94

Depth + BayDist + UrbanDist + HuntLevee + Island + DepthSD 11 12182.1 12204.2 1.66 0.02 2.29

Depth + Salinity + BayDist + HuntLevee + Island + DepthSD 11 12182.2 12204.3 1.82 0.02 2.48

Depth + BayDist + UrbanDist + Island + DepthSD 10 12184.4 12204.5 1.98 0.02 2.69

Depth + BayDist + Area + HuntLevee + Island + DepthSD 11 12182.4 12204.5 2.02 0.02 2.74

Depth + UrbanDist + HuntLevee + Island + DepthSD 10 12184.5 12204.6 2.06 0.02 2.80

Depth + Salinity + BayDist + Island + DepthSD 10 12184.6 12204.7 2.20 0.01 3.00

Depth + BayDist + Area + Island + Breached 10 12184.6 12204.7 2.20 0.01 3.00

Depth + BayDist + UrbanDist + OpenLevee + Island + DepthSD 11 12182.7 12204.8 2.26 0.01 3.09

Depth + BayDist + UrbanDist + OpenLevee + HuntLevee + Island + 

DepthSD 12 12180.7 12204.8 2.27 0.01 3.11

Depth + BayDist + UrbanDist + Island 9 12186.7 12204.8 2.29 0.01 3.14

Depth + BayDist + OpenLevee + Island 9 12186.8 12204.8 2.33 0.01 3.20

Depth + BayDist + Area + Island + DepthSD 10 12184.8 12204.9 2.38 0.01 3.29

Depth + UrbanDist + Island + DepthSD 9 12186.9 12205.0 2.45 0.01 3.40

Depth + Salinity + BayDist + OpenLevee + HuntLevee + Island + DepthSD 12 12180.9 12205.0 2.45 0.01 3.41

Depth + Salinity + BayDist + Island 9 12186.9 12205.0 2.49 0.01 3.47

Depth + Salinity + BayDist + OpenLevee + Island + DepthSD 11 12182.9 12205.0 2.50 0.01 3.48

Depth + Salinity + HuntLevee + Island + DepthSD 10 12185.0 12205.0 2.52 0.01 3.53

Depth + BayDist + Area + OpenLevee + HuntLevee + Island + DepthSD 12 12181.0 12205.1 2.55 0.01 3.58

Depth + BayDist + Area + OpenLevee + Island + DepthSD 11 12183.0 12205.1 2.58 0.01 3.62

Depth + Area + HuntLevee + Island + DepthSD 10 12185.1 12205.1 2.62 0.01 3.71

Depth + UrbanDist + OpenLevee + HuntLevee + Island + DepthSD 11 12183.1 12205.2 2.72 0.01 3.89

Depth + BayDist + Area + Island 9 12187.2 12205.2 2.73 0.01 3.91

Depth + UrbanDist + OpenLevee + Island + DepthSD 10 12185.2 12205.3 2.78 0.01 4.01

Depth + Salinity + Island + DepthSD 9 12187.4 12205.5 2.97 0.01 4.41

Depth + Area + Island + DepthSD 9 12187.5 12205.6 3.05 0.01 4.59

Depth + Salinity + OpenLevee + HuntLevee + Island + DepthSD 11 12183.6 12205.7 3.16 0.01 4.84

Depth + Area + OpenLevee + HuntLevee + Island + DepthSD 11 12183.6 12205.7 3.22 0.01 4.99

Depth + Salinity + OpenLevee + Island + DepthSD 10 12185.7 12205.8 3.28 0.01 5.16
120 

Model name k -2LogL AICc ∆AICc wi

Evidence 

ratio

Depth + Area + OpenLevee + Island + DepthSD 10 12185.8 12205.8 3.32 0.01 5.26

Depth + Salinity + BayDist + UrbanDist + HuntLevee + Island + DepthSD 12 12181.8 12205.9 3.41 0.01 5.51

Depth + BayDist + UrbanDist + Area + HuntLevee + Island + DepthSD 12 12182.1 12206.2 3.65 0.01 6.21

Depth + Salinity + BayDist + UrbanDist + Island + DepthSD 11 12184.2 12206.3 3.76 0.01 6.54

Depth + Salinity + BayDist + Area + HuntLevee + Island + DepthSD 12 12182.2 12206.4 3.83 0.01 6.79

Depth + HuntLevee + DepthSD 8 12190.3 12206.4 3.85 0.01 6.87

Depth + Salinity + UrbanDist + HuntLevee + Island + DepthSD 11 12184.3 12206.4 3.90 0.01 7.01

Depth + BayDist + UrbanDist + OpenLevee + Island 10 12186.3 12206.4 3.90 0.01 7.03

Depth + Salinity + BayDist + UrbanDist + Island 10 12186.4 12206.5 3.96 0.01 7.24

Depth + BayDist + UrbanDist + Area + Island + DepthSD 11 12184.4 12206.5 3.98 0.01 7.30

Null Model 5 12224.2 12234.2 31.68 5.75E-09 7588217
121 

Table 23. The ranking of candidate models at the pond scale for roosting American avocets in the former salt 

production ponds of South San Francisco Bay, California. 

[Models are ranked by differences in Akaike’s information criterion and only models of ∆AIC <4 and the null model are presented. Birds 

were surveyed across the former salt ponds during October through April 2002–2015. Variables considered included the mean pond 

depth (Depth), the presence of islands (Island), variation in topography (DepthSD), % of pond levee open to hunting (HuntLevee), % of 

pond levee open to the public (OpenLevee), pond area (Area), water salinity, pond distance to urban areas (UrbanDist), and pond 

distance to the bay (BayDist). k: Number of estimated parameters in the model. AICc: Second-order Akaike’s information criterion. 

∆AICc: The difference between the AICc of the current model and the AICc of the top model. wi: Akaike weight–likelihood of the 

model relative to candidate models. Evidence ratio: The weight of evidence that the current model is inferior to the top model]

Model name k -2LogL AICc ∆AICc wi

Evidence 

ratio

Depth + Salinity + BayDist + HuntLevee + Island + DepthSD 11 15837.0 15859.1 0.00 0.05 1.00

Depth + Salinity + HuntLevee + Island + DepthSD 10 15839.2 15859.3 0.16 0.05 1.09

Depth + HuntLevee + Island + DepthSD 9 15841.6 15859.6 0.53 0.04 1.30

Depth + BayDist + HuntLevee + Island + DepthSD 10 15839.6 15859.6 0.54 0.04 1.31

Depth + Salinity + BayDist + Island + DepthSD 10 15840.4 15860.5 1.42 0.03 2.04

Depth + Salinity + Island + DepthSD 9 15842.6 15860.7 1.61 0.02 2.24

Depth + Island + DepthSD 8 15844.7 15860.8 1.68 0.02 2.31

Depth + BayDist + Island + DepthSD 9 15842.7 15860.8 1.69 0.02 2.33

Depth + Salinity + BayDist + UrbanDist + HuntLevee + Island + DepthSD 12 15837.0 15861.1 2.00 0.02 2.71

Depth + Salinity + BayDist + Area + HuntLevee + Island + DepthSD 12 15837.0 15861.1 2.00 0.02 2.71

Depth + Salinity + BayDist + HuntLevee + Island + Breached + DepthSD 12 15837.0 15861.1 2.00 0.02 2.71

Depth + Salinity + BayDist + OpenLevee + HuntLevee + Island + 

DepthSD 12 15837.0 15861.1 2.02 0.02 2.74

Depth + Salinity + HuntLevee + Island + Breached + DepthSD 11 15839.4 15861.2 2.14 0.02 2.92

Depth + Salinity + UrbanDist + HuntLevee + Island + DepthSD 11 15839.2 15861.3 2.18 0.02 2.97

Depth + Salinity + Area + HuntLevee + Island + DepthSD 11 15839.2 15861.3 2.18 0.02 2.97

Depth + Salinity + OpenLevee + HuntLevee + Island + DepthSD 11 15839.2 15861.3 2.18 0.02 2.97

Depth + HuntLevee + Island + Breached + DepthSD 10 15841.3 15861.4 2.32 0.02 3.20

Depth + BayDist + UrbanDist + HuntLevee + Island + DepthSD 11 15839.5 15861.6 2.50 0.02 3.49

Depth + BayDist + HuntLevee + Island + Breached + DepthSD 11 15839.5 15861.6 2.52 0.02 3.53

Depth + UrbanDist + HuntLevee + Island + DepthSD 10 15841.5 15861.6 2.52 0.02 3.53

Depth + BayDist + Area + HuntLevee + Island + DepthSD 11 15839.5 15861.6 2.54 0.02 3.56

Depth + Area + HuntLevee + Island + DepthSD 10 15841.5 15861.6 2.54 0.01 3.57

Depth + OpenLevee + HuntLevee + Island + DepthSD 10 15841.5 15861.6 2.54 0.01 3.57

Depth + BayDist + OpenLevee + HuntLevee + Island + DepthSD 11 15839.5 15861.7 2.56 0.01 3.60

Depth + Island + Breached + DepthSD 9 15844.4 15862.4 3.35 0.01 5.34

Depth + Salinity + BayDist + UrbanDist + Island + DepthSD 11 15840.4 15862.5 3.44 0.01 5.58

Depth + Salinity + BayDist + Area + Island + DepthSD 11 15840.4 15862.5 3.44 0.01 5.58

Depth + Salinity + BayDist + OpenLevee + Island + DepthSD 11 15840.4 15862.5 3.44 0.01 5.58

Depth + Salinity + BayDist + Island + Breached + DepthSD 11 15840.4 15862.5 3.44 0.01 5.58

Depth + BayDist + Island + Breached + DepthSD 10 15842.6 15862.7 3.58 0.01 6.00

Depth + Salinity + UrbanDist + Island + DepthSD 10 15842.6 15862.7 3.62 0.01 6.12

Depth + Salinity + Area + Island + DepthSD 10 15842.6 15862.7 3.62 0.01 6.12

Depth + Salinity + OpenLevee + Island + DepthSD 10 15842.6 15862.7 3.62 0.01 6.12

Depth + BayDist + UrbanDist + Island + DepthSD 10 15842.7 15862.8 3.66 0.01 6.25

Depth + OpenLevee + Island + DepthSD 9 15844.7 15862.8 3.67 0.01 6.27

Depth + BayDist + Area + Island + DepthSD 10 15842.7 15862.8 3.68 0.01 6.31

Depth + UrbanDist + Island + DepthSD 9 15844.7 15862.8 3.69 0.01 6.33
122 

Model name k -2LogL AICc ∆AICc wi

Evidence 

ratio

Depth + Area + Island + DepthSD 9 15844.7 15862.8 3.69 0.01 6.33

Depth + BayDist + OpenLevee + Island + DepthSD 10 15842.7 15862.8 3.70 0.01 6.37

Depth + Salinity + BayDist + UrbanDist + Area + HuntLevee + Island + 

DepthSD 13 15837.0 15863.1 3.99 0.01 7.37

Null Model 5 15919.0 15929.0 69.93 3.49E-17 1.53E+15
123 

Table 24. The ranking of candidate models at the pond scale for foraging and roosting eared grebes in the active 

salt production ponds of South San Francisco Bay, California. 

[Models are ranked by differences in Akaike’s information criterion and only models of ∆AIC <4 and the null model are presented. Birds 

were surveyed across the former salt ponds during October through April 2002–2015. Variables considered included the mean pond 

depth (Depth), the presence of islands (Island), variation in topography (DepthSD), % of pond levee open to hunting (HuntLevee), % of 

pond levee open to the public (OpenLevee), pond area (Area), water salinity, pond distance to urban areas (UrbanDist), and pond 

distance to the bay (BayDist). k: Number of estimated parameters in the model. AICc: Second-order Akaike’s information criterion. 

∆AICc: The difference between the AICc of the current model and the AICc of the top model. wi: Akaike weight–likelihood of the 

model relative to candidate models. Evidence ratio: The weight of evidence that the current model is inferior to the top model]

Model name k -2LogL AICc ∆AICc wi

Evidence 

ratio

 Foraging

Depth + Salinity + UrbanDist + Area 9 7555.9 7574.1 0.00 0.21 1.00

Depth + Salinity + BayDist + Area 9 7556.1 7574.3 0.16 0.19 1.08

Depth + Salinity + BayDist + UrbanDist + Area 10 7555.6 7575.8 1.68 0.09 2.31

Depth + UrbanDist + Area 8 7559.7 7575.8 1.71 0.09 2.35

Depth + BayDist + Area 8 7560.0 7576.1 2.05 0.07 2.78

Salinity + UrbanDist + Area 8 7560.5 7576.6 2.51 0.06 3.50

Depth + Salinity + Area 8 7561.5 7577.6 3.51 0.04 5.78

Depth + BayDist + UrbanDist + Area 9 7559.5 7577.7 3.60 0.03 6.05

Depth + Area 7 7563.7 7577.8 3.70 0.03 6.36

Depth + Salinity + BayDist 8 7561.9 7578.1 3.97 0.03 7.27

Null Model 5 7574.5 7584.6 10.49 0.00 189.94

 Roosting

Depth + Salinity + BayDist + Area 9 6838.2 6856.4 0.00 0.43 1.00

Depth + Salinity + UrbanDist + Area 9 6838.9 6857.1 0.72 0.30 1.43

Depth + Salinity + BayDist + UrbanDist + Area 10 6837.9 6858.1 1.70 0.18 2.33

Null Model 5 6900.0 6910.1 53.69 9.35E-13 4.56E+11
124 

Table 25. Variable importance values for significant model-averaged coefficients describing foraging and roosting 

waterbird guild abundance at the pond scale, in restoration ponds in South San Francisco Bay, California. 

[Numbers not highlighted indicate a significant positive relation, and numbers highlighted in gray indicate a significant 

negative relation. Higher values indicate increased importance of the variable for a given guild. Distance to landfill was 

included only in the models for gulls and is not applicable (N/A) to all other guilds. “--” indicates no significant relation

was observed] 

Characteristic Dabbling 

ducks

Diving 

ducks

Medium 

shorebirds

Small 

shorebirds Gulls Piscivores Terns Waders

Foraging

Depth -- 1 1 1 0.98 1 -- 1

Salinity 1 1 -- -- 0.99 1 1 1

Pond area -- 0.98 -- -- 0.98 1 0.96 0.96

Distance to San 

Francisco Bay -- 0.99 -- -- 0.94 -- -- --

Distance to urban

areas -- -- -- -- -- -- -- --

Islands 1 0.95 -- 1 -- 0.98 1 1

Percentage of levees

open to hunting -- -- -- -- 1 0.98 -- --

Topography -- -- -- -- -- -- -- --

Breached -- 1 -- 1 -- 1 -- --

Percentage of levees

open to public -- -- -- 0.93 -- -- -- --

Distance to landfill N/A N/A N/A N/A -- N/A N/A N/A

Roosting

Depth -- 1 1 1 0.99 1 -- --

Salinity 1 1 -- -- -- 1 1 1

Pond area -- 1 -- -- 0.99 1 0.98 0.99

Distance to San 

Francisco Bay -- 1 -- -- 0.95 -- -- --

Distance to urban

areas -- -- -- -- -- -- -- --

Islands 1 1 1 0.94 1 0.93 1 1

Percentage of levees

open to hunting 1 -- -- -- -- -- 1 0.99

Topography 0.97 -- 0.94 -- -- -- -- --

Breached -- 1 -- 0.93 -- 1 -- --

Percentage of levees

open to public -- -- -- -- 0.93 -- -- --

Distance to landfill N/A N/A N/A N/A 0.98 N/A N/A N/A
125 

Table 26. Optimal habitat characteristic values for waterbird guilds at the pond scale, as determined from 

General Additive Models (GAMs), in restoration ponds, South San Francisco Bay, California. 

[Variables with a linear trend (islands and breached), are not included here. “--” indicates no significant relation was 

observed. Abbreviations and symbols: km, kilometer; km2

, square kilometer; m, meter, ppt, parts per thousand; >, greater 

than; ≥, greater than or equal to; <, less than; %, percent] 

Dabbling 

ducks

Diving 

ducks

Medium 

shorebirds

Small

shorebirds Gulls Piscivores Terns Waders

Foraging

Depth (m) -- 0.75 0.13 0 1.48 ≥0.4 -- 0.4

Salinity (ppt) 6 <17 -- -- 124 4 15 1

Pond area (km2) -- 1.25 -- -- >0 >0 >0.75 >0

Distance to San Francisco 

Bay (km)

-- 1.1 -- -- >0.9 -- -- --

Distance to urban areas

(km)

-- -- -- -- -- -- -- --

Levees open to hunting

(%)

-- -- -- -- >78 25 -- --

Topography (m) -- -- -- -- -- -- -- --

Levees open to public (%) -- -- -- 0 and 70 -- -- -- --

Distance to landfill (km) -- -- -- -- -- -- -- --

Roosting

Depth (m) -- 1.5 0.25 0 0.3 >1.25 -- --

Salinity (ppt) 6 <5 -- -- -- 15 17

Pond area (km2) -- 1.25 -- -- >1.75 >0.5 >1.5 >0

Distance to San Francisco 

Bay (km)

-- 1.1 -- -- >0 -- -- --

Distance to urban 

areas(km)

-- -- -- -- -- -- -- --

Levees open to hunting

(%)

<77 -- -- -- -- -- 100 77

Topography (m) 0.61 -- >0.15 -- -- -- -- --

Levees open to public (%) -- -- -- -- 46 -- -- --

Distance to landfill (km) -- -- -- -- 2.8 -- -- --
126 

Table 27. Variable importance values for significant model-averaged coefficients describing foraging and roosting 

waterbird abundance at the pond scale in restoration ponds, South San Francisco Bay, California. 

[Numbers not highlighted indicate a significant positive relation, and numbers highlighted in gray indicate a significant 

negative relation. Higher values indicate increased importance of the variable for a given species. “--” indicates no 

significant relation was observed. Abbreviations: American avocet, AMAV; eared grebe, EAGR; northern shoveler, 

NSHO; ruddy duck, RUDU.

Characteristic AMAV EAGR NSHO RUDU

Foraging

Depth 1 1 -- 1

Salinity -- 1 1 1

Pond area -- 1 -- 0.97

Distance to San 

Francisco Bay -- -- -- 0.96

Distance to urban areas -- -- -- --

Islands -- -- 1 1

Percentage of levees

open to hunting -- -- -- --

Topography -- -- -- --

Breached -- 1 -- --

Percentage of levees

open to public -- -- -- --

Roosting

Depth 1 1 -- 1

Salinity -- -- 1 1

Pond area -- 1 -- 0.99

Distance to San 

Francisco Bay -- -- -- 0.96

Distance to urban areas -- -- -- --

Islands 1 -- 1 1

Percentage of levees

open to hunting -- -- 0.96 --

Topography 0.99 -- 0.95 --

Breached -- -- -- 1

Percentage of levees

open to public -- -- -- --
127 

Table 28. Optimal habitat characteristic values for waterbird species at the pond scale, as determined from 

General Additive Models (GAMs), in restoration ponds, South San Francisco Bay, California. 

[Variables with a linear trend (islands and breached) are not included here. “--” indicates no significant relation was 

observed. Abbreviations and symbols: American avocet, AMAV; eared grebe, EAGR; northern shoveler, NSHO; ruddy

duck, RUDU; km, kilometer; km2

, square kilometer; m, meter, ppt, parts per thousand; >, greater than; %, percent] 

AMAV EAGR NSHO RUDU

Foraging

Depth (m) 0.30 >0 -- 0.74

Salinity (ppt) -- 109 8 11

Pond area (km2) -- 2.30 -- 2.35

Distance to San Francisco 

Bay (km) -- -- -- 1.01

Distance to urban areas (km) - -- -- --

Levees open to hunting (%) -- -- -- --

Topography (m) 0.25 -- -- --

Levees open to public (%) -- -- -- --

Roosting

Depth (m) 0.32 1.29 -- 1.73

Salinity (ppt) -- -- 6 7

Pond area (km2) -- 2.30 -- ≥1.25

Distance to San Francisco 

Bay (km) -- -- -- 1.62

Distance to urban areas (km) -- -- -- --

Levees open to hunting (%) -- --

0 and

56 --

Topography (m) 0.25 -- 0.61 --

Levees open to public (%) -- -- -- --
128 

Table 29. The ranking of candidate models at the grid scale for foraging and roosting dabbling ducks in the former 

salt production ponds of South San Francisco Bay, California. 

[Models are ranked by differences in Akaike’s information criterion and only models of ∆AIC <4 and the null model are presented. Birds 

were surveyed across the former salt ponds during October through April 2002–2015. Variables considered included the mean grid depth 

(Depth), the presence of islands (Island), variation in topography (DepthSD), grid distance to the neared pond levee (LeveeDist), and grid 

distance to the nearest creak or slough (CreekDist). Grid area was included in every model. k: Number of estimated parameters in the 

model. AICc: Second-order Akaike’s information criterion. ∆AICc: The difference between the AICc of the current model and the AICc 

of the top model. wi: Akaike weight–likelihood of the model relative to candidate models. Evidence ratio: The weight of evidence that 

the current model is inferior to the top model]

Model name k -2LogL AICc ∆AICc wi

Evidence 

ratio

 Foraging

GridArea + LeveeDist + CreekDist + ElevSD + Island + Depth 12 194715.6 194739.6 0.00 0.40 1.00

GridArea + LeveeDist + CreekDist + ElevSD + Depth 11 194719.2 194741.2 1.60 0.18 2.22

GridArea + LeveeDist + ElevSD + Island + Depth 11 194720.2 194742.2 2.60 0.11 3.67

GridArea + CreekDist + ElevSD + Island + Depth 11 194720.6 194742.6 3.00 0.09 4.48

GridArea + LeveeDist + ElevSD + Depth 10 194723.4 194743.4 3.80 0.06 6.68

GridArea (null model) 7 194788.0 194802.0 62.40 1.14E-14 3.54E+13

 Roosting

GridArea + Island + Depth 9 235178.0 235196.0 0.00 0.24 1.00

GridArea + ElevSD + Island + Depth 10 235176.0 235196.0 0.00 0.24 1.00

GridArea + LeveeDist + Island + Depth 10 235178.0 235198.0 2.00 0.09 2.72

GridArea + CreekDist + Island + Depth 10 235178.0 235198.0 2.00 0.09 2.72

GridArea + LeveeDist + CreekDist + Island + Depth 11 235176.0 235198.0 2.00 0.09 2.72

GridArea + LeveeDist + ElevSD + Island + Depth 11 235176.0 235198.0 2.00 0.09 2.72

GridArea + CreekDist + ElevSD + Island + Depth 11 235176.0 235198.0 2.00 0.09 2.72

GridArea (null model) 7 235246.0 235260.0 64.00 3.04E-15 7.89E+13
129 

Table 30. The ranking of candidate models at the grid scale for foraging and roosting diving ducks in the former 

salt production ponds of South San Francisco Bay, California. 

[Models are ranked by differences in Akaike’s information criterion and only models of ∆AIC <4 and the null model are presented. Birds 

were surveyed across the former salt ponds during October through April 2002–2015. Variables considered included the mean grid depth 

(Depth), the presence of islands (Island), variation in topography (DepthSD), grid distance to the neared pond levee (LeveeDist), and grid 

distance to the nearest creak or slough (CreekDist). Grid area was included in every model. k: Number of estimated parameters in the 

model. AICc: Second-order Akaike’s information criterion. ∆AICc: The difference between the AICc of the current model and the AICc 

of the top model. wi: Akaike weight–likelihood of the model relative to candidate models. Evidence ratio: The weight of evidence that 

the current model is inferior to the top model]

Model name k -2LogL AICc ∆AICc wi

Evidence 

ratio

 Foraging

GridArea + LeveeDist + ElevSD + Depth 10 179753.8 179773.8 0.00 0.36 1.00

GridArea + LeveeDist + ElevSD + Island + Depth 11 179752.6 179774.6 0.80 0.24 1.49

GridArea + LeveeDist + CreekDist + ElevSD + Depth 11 179753.6 179775.6 1.80 0.15 2.46

GridArea + LeveeDist + CreekDist + ElevSD + Island + Depth 12 179752.4 179776.4 2.60 0.10 3.67

GridArea + ElevSD + Depth 9 179759.8 179777.8 4.00 0.05 7.39

GridArea (null model) 7 180175.2 180189.2 415.40 2.26E-91 1.59E+90

 Roosting

GridArea + ElevSD + Depth 9 318776.0 318794.0 0.00 0.22 1.00

GridArea + Depth 8 318780.0 318796.0 2.00 0.08 2.72

GridArea + LeveeDist + Depth 9 318778.0 318796.0 2.00 0.08 2.72

GridArea + CreekDist + Depth 9 318778.0 318796.0 2.00 0.08 2.72

GridArea + LeveeDist + ElevSD + Depth 10 318776.0 318796.0 2.00 0.08 2.72

GridArea + CreekDist + ElevSD + Depth 10 318776.0 318796.0 2.00 0.08 2.72

GridArea + ElevSD + Island + Depth 10 318776.0 318796.0 2.00 0.08 2.72

GridArea + LeveeDist + CreekDist + ElevSD + Depth 11 318774.0 318796.0 2.00 0.08 2.72

GridArea (null model) 7 319796.0 319810.0 1016.00 5.23E-222 4.18E+220
130 

Table 31. The ranking of candidate models at the grid scale for foraging and roosting small shorebirds in the 

former salt production ponds of South San Francisco Bay, California. 

[Models are ranked by differences in Akaike’s information criterion and only models of ∆AIC <4 and the null model are presented. Birds 

were surveyed across the former salt ponds during October through April 2002–2015. Variables considered included the mean grid depth 

(Depth), the presence of islands (Island), variation in topography (DepthSD), grid distance to the neared pond levee (LeveeDist), and grid 

distance to the nearest creak or slough (CreekDist). Grid area was included in every model. k: Number of estimated parameters in the 

model. AICc: Second-order Akaike’s information criterion. ∆AICc: The difference between the AICc of the current model and the AICc 

of the top model. wi: Akaike weight–likelihood of the model relative to candidate models. Evidence ratio: The weight of evidence that 

the current model is inferior to the top model]

Model name k -2LogL AICc ∆AICc wi

Evidence 

ratio

 Foraging

GridArea + LeveeDist + CreekDist + ElevSD + Island + Depth 12 168372.0 168396.0 0.00 0.66 1.00

GridArea + LeveeDist + CreekDist + ElevSD + Depth 11 168375.8 168397.8 1.80 0.27 2.46

GridArea (null model) 7 169836.8 169850.8 1454.80 8.17e-317 Inf

 Roostingg

GridArea + LeveeDist + ElevSD + Island + Depth 11 125281.0 125303.0 0.00 1.00 1.00

Table 32. The ranking of candidate models at the grid scale for foraging and roosting medium shorebirds in the 

former salt production ponds of South San Francisco Bay, California. 

[Models are ranked by differences in Akaike’s information criterion and only models of ∆AIC <4 and the null model are presented. Birds 

were surveyed across the former salt ponds during October through April 2002–2015. Variables considered included the mean grid depth 

(Depth), the presence of islands (Island), variation in topography (DepthSD), grid distance to the neared pond levee (LeveeDist), and grid 

distance to the nearest creak or slough (CreekDist). Grid area was included in every model. k: Number of estimated parameters in the 

model. AICc: Second-order Akaike’s information criterion. ∆AICc: The difference between the AICc of the current model and the AICc 

of the top model. wi: Akaike weight–likelihood of the model relative to candidate models. Evidence ratio: The weight of evidence that 

the current model is inferior to the top model]

Model name k -2LogL AICc ∆AICc wi

Evidence 

ratio

 Foraging

GridArea + LeveeDist + CreekDist + ElevSD + Island + Depth 12 105309.0 105333.0 0.00 0.71 1.00

GridArea + LeveeDist + ElevSD + Island + Depth 11 105312.8 105334.8 1.80 0.29 2.46

GridArea (null model) 7 106183.4 106197.4 864.40 1.41E-188 5.03E+187

 Roosting

GridArea + LeveeDist + CreekDist + ElevSD + Island + Depth 12 117043.4 117067.4 0.00 0.95 1.00

GridArea (null model) 7 117591.2 117605.2 537.80 1.58E-117 6.04E+116
131 

Table 33. The ranking of candidate models at the grid scale for foraging and roosting waders in the former salt 

production ponds of South San Francisco Bay, California. 

[Models are ranked by differences in Akaike’s information criterion and only models of ∆AIC <4 and the null model are presented. Birds 

were surveyed across the former salt ponds during October through April 2002–2015. Variables considered included the mean grid depth 

(Depth), the presence of islands (Island), variation in topography (DepthSD), grid distance to the neared pond levee (LeveeDist), and grid 

distance to the nearest creak or slough (CreekDist). Grid area was included in every model. k: Number of estimated parameters in the 

model. AICc: Second-order Akaike’s information criterion. ∆AICc: The difference between the AICc of the current model and the AICc 

of the top model. wi: Akaike weight–likelihood of the model relative to candidate models. Evidence ratio: The weight of evidence that 

the current model is inferior to the top model]

Model name k -2LogL AICc ∆AICc wi Evidence ratio

 Foraging

GridArea + LeveeDist + ElevSD + Island + Depth 11 54034.8 54056.8 0.00 0.50 1.00

GridArea + LeveeDist + CreekDist + ElevSD + Island + Depth 12 54032.8 54056.8 0.00 0.50 1.00

GridArea (null model) 7 54420.0 54434.0 377.20 6.16E-83 8.08E+81

 Roosting

GridArea + LeveeDist + ElevSD + Island + Depth 11 49898.0 49920.0 0.00 0.48 1.00

GridArea + LeveeDist + CreekDist + ElevSD + Island + Depth 12 49896.0 49920.0 0.00 0.48 1.00

GridArea (null model) 7 50192.4 50206.4 286.40 3.11E-63 1.55E+62

Table 34. The ranking of candidate models at the grid scale for foraging and roosting piscivores in the former salt 

production ponds of South San Francisco Bay, California. 

[Models are ranked by differences in Akaike’s information criterion and only models of ∆AIC <4 and the null model are presented. Birds 

were surveyed across the former salt ponds during October through April 2002–2015. Variables considered included the mean grid depth 

(Depth), the presence of islands (Island), variation in topography (DepthSD), grid distance to the neared pond levee (LeveeDist), and grid 

distance to the nearest creak or slough (CreekDist). Grid area was included in every model. k: Number of estimated parameters in the 

model. AICc: Second-order Akaike’s information criterion. ∆AICc: The difference between the AICc of the current model and the AICc 

of the top model. wi: Akaike weight–likelihood of the model relative to candidate models. Evidence ratio: The weight of evidence that 

the current model is inferior to the top model]

Model name k -2LogL AICc ∆AICc wi

Evidence 

ratio

 Foraging

GridArea + LeveeDist + ElevSD + Island + Depth 11 87810.4 87832.4 0.00 0.21 1.00

GridArea + LeveeDist + ElevSD + Depth 10 87812.6 87832.6 0.20 0.19 1.10

GridArea (null model) 7 87928.2 87942.2 109.80 2.99E-25 6.96E+23

 Roosting

GridArea + LeveeDist + CreekDist + Island + Depth 11 102824.6 102846.6 0.00 0.55 1.00

GridArea + LeveeDist + CreekDist + ElevSD + Island + Depth 12 102823.6 102847.6 1.00 0.33 1.65

GridArea (null model) 7 102973.6 102987.6 141.00 1.33E-31 4.14E+30
132 

Table 35. The ranking of candidate models at the grid scale for foraging and roosting terns in the former salt 

production ponds of South San Francisco Bay, California. 

[Models are ranked by differences in Akaike’s information criterion and only models of ∆AIC <4 and the null model are presented. Birds 

were surveyed across the former salt ponds during October through April 2002–2015. Variables considered included the mean grid depth 

(Depth), the presence of islands (Island), variation in topography (DepthSD), grid distance to the neared pond levee (LeveeDist), and grid 

distance to the nearest creak or slough (CreekDist). Grid area was included in every model. k: Number of estimated parameters in the 

model. AICc: Second-order Akaike’s information criterion. ∆AICc: The difference between the AICc of the current model and the AICc 

of the top model. wi: Akaike weight–likelihood of the model relative to candidate models. Evidence ratio: The weight of evidence that 

the current model is inferior to the top model]

Model name k -2LogL AICc ∆AICc wi

Evidence 

ratio

 Foraging

GridArea + LeveeDist + CreekDist + Depth 10 18129.7 18149.7 0.00 0.34 1.00

GridArea + LeveeDist + CreekDist + ElevSD + Depth 11 18128.9 18150.9 1.28 0.18 1.90

GridArea + LeveeDist + CreekDist + Island + Depth 11 18129.0 18151.0 1.34 0.18 1.95

GridArea + LeveeDist + CreekDist + ElevSD + Island + Depth 12 18128.3 18152.3 2.68 0.09 3.82

GridArea + LeveeDist + Depth 9 18135.1 18153.1 3.44 0.06 5.58

GridArea (Null Model) 7 18159.6 18173.6 23.92 2.20E-06 1.56E+05

 Roosting

GridArea + LeveeDist + ElevSD + Island + Depth 11 24905.4 24927.4 0.00 0.51 1.00

GridArea + LeveeDist + Island + Depth 10 24909.2 24929.2 1.80 0.21 2.46

GridArea + LeveeDist + CreekDist + ElevSD + Island + Depth 12 24905.2 24929.2 1.80 0.21 2.46

GridArea + LeveeDist + CreekDist + Island + Depth 11 24909.2 24931.2 3.80 0.08 6.69

GridArea (Null Model) 7 24960.4 24974.4 47.00 3.16E-11 1.61E+10

Table 36. The ranking of candidate models at the grid scale for foraging and roosting gulls in the former salt 

production ponds of South San Francisco Bay, California. 

[Models are ranked by differences in Akaike’s information criterion and only models of ∆AIC <4 and the null model are presented. Birds 

were surveyed across the former salt ponds during October through April 2002–2015. Variables considered included the mean grid depth 

(Depth), the presence of islands (Island), variation in topography (DepthSD), grid distance to the neared pond levee (LeveeDist), and grid 

distance to the nearest creak or slough (CreekDist). Grid area was included in every model. k: Number of estimated parameters in the 

model. AICc: Second-order Akaike’s information criterion. ∆AICc: The difference between the AICc of the current model and the AICc 

of the top model. wi: Akaike weight–likelihood of the model relative to candidate models. Evidence ratio: The weight of evidence that 

the current model is inferior to the top model]

Model name k -2LogL AICc ∆AICc wi

Evidence 

ratio

Foraging

GridArea + LeveeDist + ElevSD + Island + Depth 11 42409.4 42431.4 0.00 0.40 1.00

GridArea + LeveeDist + Island + Depth 10 42412.6 42432.6 1.20 0.22 1.82

GridArea + LeveeDist + CreekDist + ElevSD + Island + Depth 12 42409.4 42433.4 2.00 0.15 2.72

GridArea + LeveeDist + CreekDist + Island + Depth 11 42412.6 42434.6 3.20 0.08 4.95

GridArea + LeveeDist + ElevSD + Depth 10 42414.8 42434.8 3.40 0.07 5.47

GridArea (null model) 7 42456.6 42470.6 39.20 1.23E-09 3.25E+08

Roosting

GridArea + LeveeDist + CreekDist + ElevSD + Island + Depth 12 145624.4 145648.4 0.00 0.54 1.00

GridArea + LeveeDist + ElevSD + Island + Depth 11 145626.8 145648.8 0.40 0.44 1.22

GridArea (null model) 7 145738 145752.0 103.60 1.73E-23 3.13E+22
133 

Table 37. The ranking of candidate models at the grid scale for foraging and roosting eared grebes in the former 

salt production ponds of South San Francisco Bay, California. 

[Models are ranked by differences in Akaike’s information criterion and only models of ∆AIC <4 and the null model are presented. Birds 

were surveyed across the former salt ponds during October through April 2002–2015. Variables considered included the mean grid depth 

(Depth), the presence of islands (Island), variation in topography (DepthSD), grid distance to the neared pond levee (LeveeDist), and grid 

distance to the nearest creak or slough (CreekDist). Grid area was included in every model. k: Number of estimated parameters in the 

model. AICc: Second-order Akaike’s information criterion. ∆AICc: The difference between the AICc of the current model and the AICc 

of the top model. wi: Akaike weight–likelihood of the model relative to candidate models. Evidence ratio: The weight of evidence that 

the current model is inferior to the top model]

Model name k -2LogL AICc ∆AICc wi

Evidence 

ratio

 Foraging

GridArea + LeveeDist + ElevSD + Island + Depth 11 88864.2 88886.2 0.00 0.36 1.00

GridArea + LeveeDist + Island + Depth 10 88867.0 88887.0 0.80 0.24 1.49

GridArea + LeveeDist + CreekDist + ElevSD + Island + Depth 12 88863.8 88887.8 1.60 0.16 2.23

GridArea + LeveeDist + CreekDist + Island + Depth 11 88866.2 88888.2 2.00 0.13 2.72

GridArea (null model) 7 89207.8 89221.8 335.60 4.84E-74 7.49E+72

 Roosting

GridArea + LeveeDist + CreekDist + ElevSD + Island + Depth 12 60658.6 60682.6 0.00 0.38 1.00

GridArea + LeveeDist + CreekDist + Island + Depth 11 60661.2 60683.2 0.60 0.28 1.35

GridArea + LeveeDist + ElevSD + Island + Depth 11 60661.6 60683.6 1.00 0.23 1.65

GridArea + LeveeDist + Island + Depth 10 60665.4 60685.4 2.80 0.09 4.05

GridArea (null model) 7 61031.8 61045.8 363.20 5.20E-80 7.37E+78

Table 38. The ranking of candidate models at the grid scale for foraging and roosting northern shovelers in the 

former salt production ponds of South San Francisco Bay, California. 

[Models are ranked by differences in Akaike’s information criterion and only models of ∆AIC <4 and the null model are presented. Birds 

were surveyed across the former salt ponds during October through April 2002–2015. Variables considered included the mean grid depth 

(Depth), the presence of islands (Island), variation in topography (DepthSD), grid distance to the neared pond levee (LeveeDist), and grid 

distance to the nearest creak or slough (CreekDist). Grid area was included in every model. k: Number of estimated parameters in the 

model. AICc: Second-order Akaike’s information criterion. ∆AICc: The difference between the AICc of the current model and the AICc 

of the top model. wi: Akaike weight–likelihood of the model relative to candidate models. Evidence ratio: The weight of evidence that 

the current model is inferior to the top model]

Model name k -2LogL AICc ∆AICc wi

Evidence 

ratio

 Foraging

GridArea + LeveeDist + ElevSD + Island + Depth 11 117718.6 117740.6 0.00 0.57 1.00

GridArea + LeveeDist + CreekDist + ElevSD + Island + Depth 12 117717.2 117741.2 0.60 0.42 1.35

GridArea (null model) 7 117834.6 117848.6 108.00 2.00E-24 2.83E+23

 Roosting

GridArea + ElevSD + Island + Depth 10 145984.6 146004.6 0.00 0.31 1.00

GridArea + Island + Depth 9 145988.0 146006.0 1.40 0.15 2.01

GridArea + LeveeDist + ElevSD + Island + Depth 11 145984.2 146006.2 1.60 0.14 2.23

GridArea + CreekDist + ElevSD + Island + Depth 11 145984.2 146006.2 1.60 0.14 2.23

GridArea + LeveeDist + Island + Depth 10 145987.4 146007.4 2.80 0.08 4.06

GridArea + CreekDist + Island + Depth 10 145987.4 146007.4 2.80 0.08 4.06

GridArea + LeveeDist + CreekDist + ElevSD + Island + Depth 12 145983.8 146007.8 3.20 0.06 4.96

GridArea (null model) 7 146146.8 146160.8 156.20 3.76E-35 8.28E+33
134 

Table 39. The ranking of candidate models at the grid scale for foraging and roosting ruddy ducks in the former 

salt production ponds of South San Francisco Bay, California. 

[Models are ranked by differences in Akaike’s information criterion and only models of ∆AIC <4 and the null model are presented. Birds 

were surveyed across the former salt ponds during October through April 2002–2015. Variables considered included the mean grid depth 

(Depth), the presence of islands (Island), variation in topography (DepthSD), grid distance to the neared pond levee (LeveeDist), and grid 

distance to the nearest creak or slough (CreekDist). Grid area was included in every model. k: Number of estimated parameters in the 

model. AICc: Second-order Akaike’s information criterion. ∆AICc: The difference between the AICc of the current model and the AICc 

of the top model. wi: Akaike weight–likelihood of the model relative to candidate models. Evidence ratio: The weight of evidence that 

the current model is inferior to the top model]

Model name k -2LogL AICc ∆AICc wi

Evidence 

ratio

 Foraging

GridArea + LeveeDist + Island + Depth 10 96255.6 96275.6 0.00 0.50 1.00

GridArea + LeveeDist + ElevSD + Island + Depth 11 96255.2 96277.2 1.60 0.23 2.23

GridArea + LeveeDist + CreekDist + Island + Depth 11 96255.6 96277.6 2.00 0.19 2.72

GridArea + LeveeDist + CreekDist + ElevSD + Island + Depth 12 96255.2 96279.2 3.60 0.08 6.05

GridArea (null model) 7 96607.4 96621.4 345.80 4.10E-76 1.23E+75

 Roosting

GridArea + Island + Depth 9 265590.0 265608.0 0.00 0.39 1.00

GridArea + LeveeDist + Island + Depth 10 265590.0 265610.0 2.00 0.14 2.72

GridArea + CreekDist + Island + Depth 10 265590.0 265610.0 2.00 0.14 2.72

GridArea + ElevSD + Island + Depth 10 265590.0 265610.0 2.00 0.14 2.72

GridArea (null model) 7 266616.0 266630.0 1022.00 4.65E-223 8.40E+221

Table 40. The ranking of candidate models at the grid scale for foraging and roosting American avocets in the 

former salt production ponds of South San Francisco Bay, California. 

[Models are ranked by differences in Akaike’s information criterion and only models of ∆AIC <4 and the null model are presented. Birds 

were surveyed across the former salt ponds during October through April 2002–2015. Variables considered included the mean grid depth 

(Depth), the presence of islands (Island), variation in topography (DepthSD), grid distance to the neared pond levee (LeveeDist), and grid 

distance to the nearest creak or slough (CreekDist). Grid area was included in every model. k: Number of estimated parameters in the 

model. AICc: Second-order Akaike’s information criterion. ∆AICc: The difference between the AICc of the current model and the AICc 

of the top model. wi: Akaike weight–likelihood of the model relative to candidate models. Evidence ratio: The weight of evidence that 

the current model is inferior to the top model]

Model name k -2LogL AICc ∆AICc wi

Evidence 

ratio

 Foraging

GridArea + LeveeDist + CreekDist + ElevSD + Island + Depth 12 49780.0 49804.0 0.00 0.72 1.00

GridArea + LeveeDist + CreekDist + Island + Depth 11 49784.0 49806.0 2.00 0.27 2.72

GridArea (null model) 7 50107.8 50121.8 317.80 7.06E-70 1.02E+69

 Roosting

GridArea + LeveeDist + CreekDist + ElevSD + Island + Depth 12 54598.6 54622.6 0 0.99 1

GridArea (null model) 7 54954.8 54968.8 346.20 6.63E-76 1.50E+75
135 

Table 41. Variable importance values for significant model-averaged coefficients describing foraging and roosting 

waterbird guild abundance at the grid scale in restoration ponds, South San Francisco Bay, California. 

[Numbers not highlighted indicate a significant positive relation, and numbers highlighted in gray indicate a significant 

negative relation. Higher values indicate increased importance of the variable for a given guild. “--” indicates no significant 

relation was observed.] 

Characteristic Dabbling 

ducks

Diving 

ducks

Medium 

shorebirds

Small 

shorebirds Gulls Piscivores Terns Waders

Foraging

Depth 1 1 1 1 1 1 0.98 1

Islands -- -- 1 -- -- -- -- 1

Distance to creek -- -- -- 0.93 -- -- -- --

Distance to levees -- -- 1 1 1 1 0.98 1

Topography 0.92 0.95 1 1 -- -- -- 1

Roosting

Depth 1 1 1 1 1 1 1 1

Islands 0.95 -- 1 1 0.98 1 1 1

Distance to creek -- -- 1 -- -- 0.95 -- --

Distance to levees -- -- 1 1 1 0.93 1 1

Topography -- -- 0.95 1 1 -- -- 0.97

Table 42. Optimal habitat characteristic values for waterbird guilds at the grid scale, as determined from General 

Additive Models (GAMs), in restoration ponds, South San Francisco Bay, California. 

[Variables with a linear trend (islands and breached) are not included here. “--” indicates no significant relation was 

observed. Abbreviations and symbol: km, kilometer; m, meter; >, greater than] 

Dabbling 

ducks

Diving 

ducks

Medium 

shorebirds

Small 

shorebirds Gulls Piscivores Terns Waders

Foraging

Depth (m) 0.51 0.82 0 0 2.51 >0 0.57 0.33

Distance to creek (km) -- -- -- 0.04 -- -- -- --

Distance to levees (km) -- -- 0.05 0.05 0.05 >0.05 0.06 0.08

Topography (m) 1.07 0.12 1.10 0.25 -- -- -- 1.25

Roosting

Depth (m) 0.65 0.88 0 0 0 1.0 0.48 0.52

Distance to creek (km) -- -- 0.04 -- -- 0.95 --

Distance to levees (km) -- -- 0.05 0.05 <0.45 0.05 0.35 0.08

Topography (m) -- -- 1.10 0.25 0.84 -- -- 0
136 

Table 43. Variable importance values for significant model-averaged coefficients describing foraging and roosting 

waterbird abundance at the grid scale in restoration ponds, South San Francisco Bay, California. 

[Numbers not highlighted indicate a significant positive relation, and numbers highlighted in gray indicate a significant 

negative relation. Higher values indicate increased importance of the variable for a given species. “--” indicates no 

significant relation was observed. Abbreviations: American avocet, AMAV; eared grebe, EAGR; northern shoveler, 

NSHO; ruddy duck, RUDU] 

Characteristic AMAV EAGR NSHO RUDU

Foraging

Depth 1 1 1 1

Islands 1 -- 1 1

Distance to creek 0.99 -- -- --

Distance to levees 1 1 1 0.99

Topography -- -- 0.99 --

Roosting

Depth 1 1 1 1

Islands 1 0.99 1 1

Distance to creek 1 -- -- --

Distance to levees 1 1 -- --

Topography 1 -- -- --

Table 44. Optimal habitat characteristic values for waterbird species at the grid scale, as determined from 

General Additive Models (GAMs), in restoration ponds, South San Francisco Bay, California. 

[Variables with a linear trend (islands and breached), are not included here. “--” indicates no significant relation was 

observed. Abbreviations and symbol: American avocet, AMAV; eared grebe, EAGR; northern shoveler, NSHO; ruddy

duck, RUDU; km, kilometer; m, meter; >, greater than] 

AMAV EAGR NSHO RUDU

Foraging

Depth (m) 0.03 1.5 0.42 0.78

Distance to creek (km) 0.04 -- -- --

Distance to levees (km) 0.06 0.05 0.06 0.05

Topography (m) -- --

0 and

1.03 --

Roosting

Depth (m) 0.01 1.58 0.19 0.86

Distance to creek (km) 0.04 -- -- --

Distance to levees (km) 0.07 0.04 -- --

Topography (m) >0.5 -- -- --
Publishing support provided by the U.S. Geological Survey

Science Publishing Network, Tacoma Publishing Service Center

For more information concerning the research in this report, contact the

Director, Western Ecological Research Center

U.S. Geological Survey

3020 State University Drive East

Sacramento, California 95819

https://www.werc.usgs.gov/
De La Cruz and others—Trends and Habitat Associations of Waterbirds Using the South Bay Salt Pond Restoration Project, San Francisco Bay, California—Open-File Report 2018-1040

ISSN 2331-1258 (online)

https://doi.org/10.3133/ofr20181040