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Potential Impacts of Non-Native Spartina Spread on Shorebird Populations in South San Francisco Bay

Final Report to Coastal Conservancy Invasive Spartina Project Contract # 02-212 February 29, 2004

Diana Stralberg*, Viola Toniolo, Gary W. Page and Lynne E. Stenzel PRBO Conservation Science, 4990 Shoreline Highway, Stinson Beach, CA 94970

(http://www.prbo.org) * corresponding author ([email protected])

http://www.prbo.org/

Potential Impacts of Non-Native Spartina Spread on Shorebird Populations in South San

Francisco Bay This project was made possible by funding from the California Coastal Conservancy, the State

Resources Agency, and the CALFED Program, through Coastal Conservancy contract #02-212.

The analyses presented herein were requested by the Coastal Conservancy’s Invasive Spartina

Project (ISP)—a coordinated regional effort among local, state and federal organizations

dedicated to preserving California's extraordinary coastal biological resources through the

elimination of introduced species of Spartina (cordgrass) (http://www.spartina.org/).

Executive Summary

San Francisco Bay holds 70% of California’s mudflats and provides habitat to more wintering

and migratory shorebirds than any other wetland along the Pacific coast of the contiguous U.S.

The bay’s mudflats are currently threatened by the spread of a non-native cordgrass, Spartina

alterniflora, and associated hybrids, which grow at lower elevations than the native S. foliosa

and can render large mudflat areas effectively unavailable to shorebirds for foraging. Using

shorebird and benthic invertebrate survey data, tidal benchmark data, and GIS-based habitat data,

we analyzed the potential effect of S. alterniflora on shorebird habitat in the South Bay by

creating grid-based spatial models of shorebird habitat value and potential S. alterniflora spread.

We developed 12 potential scenarios of habitat value loss for shorebirds based on assumptions

about invertebrate density, inundation tolerance of S. alterniflora, and temporal availability of

mudflat resources. Predictions of habitat value loss ranged from 9% to 80%. We identified the

upper mudflats, due to their greater exposure time, and the east and south shore mudflats, due to

the high numbers of birds detected there, as the areas of highest value to shorebirds in the South

Bay. These areas also coincide with the areas of greatest Spartina invasion potential.

Suggested citation:

Stralberg, D., V. Toniolo, G.W. Page, and L.E. Stenzel. 2004. Potential Impacts of Non-Native

Spartina Spread on Shorebird Populations in South San Francisco Bay. PRBO Report to

California Coastal Conservancy (contract #02-212). PRBO Conservation Science, Stinson

Beach, CA.

i

http://www.spartina.org/

Introduction

The San Francisco Bay estuary holds 70% of the mudflats in California (Ayres et al.

1999), providing habitat annually to over 350,000 migrating shorebirds (Charadrii) in the fall and

over 900,000 in the spring (based on single-day counts, Stenzel et al. 2002). Along the Pacific

coast of the contiguous United States alone (excluding Alaska), the bay holds more shorebirds

than any other wetland in all seasons (Page et al. 1999).

Although the current extent of S. alterniflora and associated hybrids is mostly limited to

tidal marsh plains and channels, further spread poses a great threat to the mudflats upon which

shorebirds depend. Shorebirds have difficulty landing in and utilizing areas of dense growth

(Josselyn 1983, Evans 1986, White 1995), and studies have shown that Spartina growth

effectively reduces the foraging area available to them (Goss-Custard and Moser 1988).

In light of this, PRBO Conservation Science (PRBO) has completed a preliminary GIS-

based analysis of the potential effects of non-native Spartina on shorebird habitat in South San

Francisco Bay (the South Bay), creating grid-based spatial models of (a) shorebird habitat value

and (b) potential S. alterniflora spread. This analysis was accompanied by a review of the

scientific literature pertaining to shorebird use of mudflats and potential effects of non-native

Spartina on shorebird numbers (see Appendix 1).

Methods

Many studies have demonstrated that shorebird use of mudflat habitats is spatially and

temporally variable, and that this variation is closely tied to cycles of tidal inundation and the

uneven distribution of sediments, prey densities, and prey availability across the intertidal zone

(Burger et al. 1977, Goss-Custard et al. 1977, Puttick 1977, Goss-Custard 1979, Page et al. 1979,

Quammen 1982, Evans 1986, Colwell and Landrum 1993, Yates et al. 1993, White 1995, Arcas

et al. 2003). Our quantification of shorebird habitat value incorporated this variation within

mudflats, which is based on tidal inundation cycles and presumed invertebrate distributions, as

well as variation among mudflats, which is based on shorebird use data from PRBO’s Pacific

Flyway surveys (1988-1993, Page et al. 1999). For the purpose of this exercise, we assumed that

South Bay mudflats were at carrying capacity (i.e., the maximum number of birds that can be

supported by a finite food supply) at the time of the surveys. By extension, we assumed that loss

PRBO Spartina-Shorebird Final Report, Feb 2004 Page 1 of 61

of habitat in one area would not be compensated for by increased use of other areas. (See

Appendix 1 for a discussion of carrying capacity issues.)

The spread potential of S. alterniflora and associated hybrids was based on percentiles of

cumulative monthly tidal inundation across the mudflats. The cumulative monthly duration of

inundation at a particular site is a function of mudflat elevation and tidal range, with a greater

tidal range resulting in a longer duration of inundation. According to Collins’ (2002) analyses of

non-native Spartina locations in San Francisco Bay, the lower limit of Spartina growth appears

to correspond with cumulative monthly inundation, and existing S. alterniflora locations suggest

that the maximum cumulative duration of inundation tolerated during the month of June is

approximately 70%, regardless of mean tidal range1. This means that the smaller the tidal range,

the lower the elevation at which non-native Spartina would be predicted to grow. Due to

uncertainty about the behavior of S. alterniflora hybrids, and because these plants are known to

change their environment over time (Ranwell 1964, Daehler and Strong 1996), accreting

sediment at rates of 1-2 cm/year in Willapa Bay (Sayce 1988) and up to 4 cm/year in Australia

(Bascand 1970), we evaluated a range of inundation tolerances between 60% and 80%. Thus, the

model based on a 60% inundation tolerance was intended to reflect what early stages of spread

may look like, while that based on a 80% inundation tolerance would represent a hypothetical

example of how much farther non-native Spartina could spread beyond its assumed maximum if

it caused substantial sediment accretion to occur, or if hybrid individuals were able to tolerate

greater inundation rates. We assumed that mudflat areas covered by S. alterniflora and

associated hybrids would be effectively lost to shorebirds.

Our GIS-based analysis was restricted to mudflats mapped by the San Francisco Estuary

Institute’s EcoAtlas (v. 1.50b, SFEI 1998) south of the San Francisco Bay Bridge. Using

EcoAtlas map layers, PRBO shorebird surveys (Stenzel et al. 2002), PRBO invertebrate data

from Bolinas Lagoon, and tide level data from the National Oceanic and Atmospheric

Administration’s (NOAA) tidal benchmarks, we developed a set of grid-based data layers

(ArcInfo format) that were combined to generate predictions about the potential loss of mudflat

habitat and shorebird numbers.

1 Initial estimates of 40% presented in Collins (2002) have since been revised.


To generate the GIS grid layers for this analysis, we completed the following steps using

Spatial Analyst for ArcView 3.2 (ESRI 1999) and the ArcInfo 8.3 GRID module (ESRI 2002).

A. Elevation/Bathymetry

We were not aware of any available elevation or bathymetry data layers for the South Bay of

a fine enough resolution to capture mudflat topography adequately. To enable the creation of

a spread model for S. alterniflora and associated hybrids, we elected to model mudflat

elevation at a 3x3 m2 (3-m) pixel resolution, creating a digital elevation model (DEM) based

on mapped mudflat boundaries, tide level data, and an assumed linear mudflat slope.

i. Mean tide level (MTL) and mean lower low water (MLLW) contours were estimated

from EcoAtlas (SFEI 1998) and were defined based on the boundaries between mudflat

and tidal marsh and between open water and mudflat, respectively. Actual elevations

along the MTL contour were not assumed to be constant, but were assigned based on

MTL elevation at the closest tidal benchmark location. MTL elevations were obtained

from NOAA’s National Oceanic Service (NOS) published benchmark sheets

(http://www.co-ops.nos.noaa.gov/bench_mark.shtml?region=ca) for seven South Bay

locations that have been referenced to the new National Tidal Datum Epoch (NTDE;

1983-2001) (Table 1).

ii. For each mudflat area we assumed that local MTL was the same as that of the nearest

NTDE-referenced benchmark and created a 3-m MTL grid covering the South Bay

mudflats.

iii. We used MTL and MLLW contours to determine the width of the mudflat for each 3-m

pixel. We calculated the distance from each pixel to the MTL line and to the MLLW line,

to obtain two separate distance grids, which were then added to obtain a single grid

representing mudflat width.

iv. For each mudflat section we estimated the slope (assumed linear) by dividing the total

change in elevation across the mudflat (MTL grid) by the mudflat width grid (slope =

rise/run). We removed values that exceeded a slope of 0.1 (10%), assuming that the low

gradient of mudflats would be well below this value.

v. Next we created two 3-m elevation grids based on the following equations, where each 3-

m pixel value was equal to the elevation at that point:

elevation 1 = slope * distance to MLLW


http://www.co-ops.nos.noaa.gov/bench_mark.shtml?region=ca

elevation 2 = MTL – (slope * distance to MTL)

We averaged these two grids to obtain the final 3-m mudflat elevation grid (DEM).

Values greater than MTL were redefined to be equal to local MTL.

B. Tidal Inundation

i. Published verified six-minute water level data was available from NOS (http://www.co-

ops.nos.noaa.gov/data_retrieve.shtml?input_code=100111111vwl) for only three of the

seven benchmark locations: Alameda (station ID 9414750, year 2001), Dumbarton

Bridge (station ID 9414509, year 1996), and Redwood City (station ID 9414523, year

2002). Assuming that the tidal inundation regime for each mudflat area was most similar

to the nearest benchmark with available 6-minute water level data, we created a

benchmark grid layer by allocating each 3m pixel to a benchmark (Fig. 1).

ii. Because our shorebird data collection was centered around April and September, we

generated monthly inundation curves for these months using cumulative water level data

from the Alameda, Dumbarton Bridge, and Redwood City benchmarks using Visual

FoxPro 3.0b (1995) and Stata 8.0 (2003) (Figs. 2, 3). Water level values were in meters

above MLLW.

iii. To predict the monthly tidal inundation percent of each 3-m mudflat pixel, we performed

separate polynomial regression analyses (Stata 8.0, 2003) for each benchmark and each

month (April and September) using the appropriate NOS 6-minute water level data.

Resulting regression equations (Table 2) were used to calculate grids representing April

and September inundation. Separate equations were developed for each benchmark so

grids for each of the three benchmark areas (Fig. 1) could be calculated separately and

then merged to create one seamless 3-m inundation grid for each season.

iv. Finally, we generated grids representing April and September mudflat exposure (100 -

monthly inundation percent).

C. Invertebrate Densities

Based on preliminary analysis of Bolinas Lagoon invertebrate data from the fall of 1973

(PRBO, unpublished data) we estimated a 5:1 ratio between the lower mudflats (higher

invertebrate densities) and upper mudflats (lower invertebrate densities). This ratio was used

to develop 3m grids of relative invertebrate density for South Bay mudflats. Assuming that

the distribution of invertebrates was strongly tied to tidal inundation regimes, we used a


http://www.co-ops.nos.noaa.gov/data_retrieve.shtml?input_code=100111111vwl

http://www.co-ops.nos.noaa.gov/data_retrieve.shtml?input_code=100111111vwl

reclassified version of our cumulative monthly inundation grids to develop April and

September invertebrate density index grids.

D. Shorebird Habitat Value: Variation Within Mudflats

Assuming that the value of mudflats for shorebirds is determined by a combination of

temporal availability (i.e., mudflat exposure time) and food quality (i.e., invertebrate

density), we combined the monthly exposure and invertebrate density grid layers to create an

overall index of habitat value. Standardizing exposure time (both April and September) and

invertebrate density on a scale of 1 to 100, we averaged the exposure time and invertebrate

density grids to obtain 3m grids representing fall and spring indices of overall shorebird

habitat value (Fig. 4)

E. Shorebird Habitat Value: Variation Among Mudflats

We used PRBO’s shorebird survey data to estimate fall and spring shorebird numbers and

overall biomass (kg) for each of six South Bay mudflat census tracts (Stenzel et al. 2002).

This resulted in fall and spring shorebird density grids, with densities uniformly distributed

over each census tract.

F. Potential Spartina Spread

i. Cumulative water level data from the Alameda, Dumbarton Bridge and Redwood City

benchmarks were used to generate monthly June inundation curves using Visual FoxPro

3.0b (1995) and Stata 8.0 (2003). Water level values were in meters above MLLW.

ii. For each tidal benchmark, June duration of inundation curves were used to identify the

elevations at which 60%, 70%, and 80% cumulative monthly inundation were achieved

(Fig. 5). Each elevation was considered a potential threshold below which S. alterniflora

would not grow (i.e., its inundation tolerance). We then calculated potential S.

alterniflora spread for each inundation tolerance, selecting all mudflat pixels with

modeled elevations above that particular elevation threshold.

G. Potential Effects of Spartina Spread on Shorebird Numbers

Using the GIS grid layers described above, we estimate the potential effects of non-native

Spartina spread on shorebird numbers, incorporating within- and among-marsh variation

(items D and E above).

i. For each of the six South Bay census tracts we calculated the proportion of habitat value


that would be lost under each Spartina spread scenario (60%, 70%, and 80% inundation

tolerance). As a quasi-sensitivity analysis we also compared four different approaches to

calculating the shorebird habitat value of mudflats:

a) All mudflat areas of equal value to shorebirds.

b) 5:1 ratio between lower and upper mudflat value, based on invertebrate densities.

c) Mudflat value inversely related to the percent of mudflat inundation time (i.e.,

upper mudflats have higher value).

d) Mudflat value based on invertebrate density and inundation time (b and c

combined, partially offsetting one another).

ii. For the three Spartina spread scenarios and four mudflat value scenarios (12

combinations total) we calculated a predicted loss of spring and fall shorebird biomass by

multiplying the proportion of mudflat value lost in each census tract with the total

estimated shorebird biomass supported by that census tract.

iii. To predict the potential loss of shorebird numbers (by species) we simply used the middle

Spartina spread scenario (70% inundation tolerance) and the middle mudflat value

scenario (index based on inundation and invertebrate density).

Results

PRBO’s shorebird surveys (1988-1993) found that shorebird density and biomass were

highest along the southern and eastern shores of the South Bay during fall (2.32-6.62 kg/ha) and

along the eastern shore during spring (4.33-6.37 kg/ha). Species that concentrated in the South

Bay were Black-bellied Plover, Willet, Marbled Godwit, small sandpipers, and dowitchers

(Stenzel et al. 2002). Overall shorebird numbers were higher in spring than in fall, driven

primarily by the large number of Western Sandpipers that use San Francisco Bay as a staging

area during spring migration (Table 4). Because Western Sandpipers are small-bodied

shorebirds, the difference between fall and spring biomass was much smaller than the difference

between fall and spring numbers (see Appendix 2 for shorebird use maps).

Spartina spread models predicted that between 14% and 54% of the total South Bay

mudflat area could be encroached upon by S. alterniflora and associated hybrids (Fig. 6, Table

3). Overall, the predicted loss of mudflat habitat value for shorebirds, based on all 12 potential

scenario combinations, ranged from 9% (assuming 60% Spartina inundation tolerance and


mudflat habitat value driven only by lower invertebrate densities in the upper mudflats) to 80%

(assuming 80% Spartina inundation tolerance and mudflat habitat value driven only by greater

exposure of the upper mudflats) (Fig. 7, Table 3). Using the middle scenarios (70% Spartina

inundation tolerance and mudflat habitat value driven by a combination of invertebrate density

and inundation), we obtained estimates of 34% and 33% habitat loss in fall and spring,

respectively. These estimates were remarkably close to the values obtained by assuming equal

value across mudflats (33%), due to the nearly counteracting effects of invertebrate density and

inundation, using our assumptions about invertebrate densities from Bolinas Lagoon. This

highlights the need to study the spatial distribution of invertebrates over San Francisco Bay

mudflats, especially given the dynamic nature of this largely non-native community (Nichols et

al. 1986, Cohen and Carlton 1998).

Our models did not incorporate any species-specific differences in shorebird foraging

habits; thus the predicted proportional change in numbers was the same for all groups. Due to

migration timing and overall numbers detected on shorebird surveys, Spartina spread would

have the biggest numerical impact on small shorebirds, dowitchers, and Marbled Godwits (Fig.

8, Table 4). Willets and Black-bellied Plovers would be most affected during the fall, when their

numbers are highest. In terms of total bird biomass (see Stenzel et al. 2002), the largest predicted

losses were in the spring, due to higher overall biomass densities (Fig. 7).

Combining our estimates of variation within and among mudflats, we identified the upper

mudflats, due to their greater exposure time, and the east and south shore mudflats, due to the

high numbers of birds detected there, as the areas of highest value to shorebirds in the South Bay

(Figs. 8, 9). These areas also coincide with the areas of greatest Spartina invasion potential,

based on elevation (upper mudflats), initial S. alterniflora introduction locations (mostly east

shore), and planned tidal marsh restoration activities (mostly east and south shore salt ponds).

Future Directions

The results presented herein are based on several basic assumptions, all of which should

be examined in further detail in order to restrict the wide range of predicted shorebird losses. The

following represents a summary of assumptions that we made, follow-up research questions, and

suggestions for testing or improving those assumptions.


1. Carrying Capacity

Our most basic assumption was that the mudflat habitats were at carrying capacity during the

fall and spring survey periods. However, it is possible that only preferred areas are

functioning at carrying capacity (Goss-Custard 1979). If individuals could switch to lower-

quality mudflat areas without significantly affecting their overall fitness, then the potential

loss of birds may have been overestimated (Goss-Custard 2003). Anecdotal evidence

suggests that San Francisco Bay mudflats may reach carrying capacity during the winter,

when storm-related flooding may prompt some species to move inland to the Central Valley

(Warnock et al. 1995, Takekawa et al. 2002) but we do not know if mudflats and neighboring

tidal and salt pond habitats are at carrying capacity during migration. Currently, we lack the

data on mudflat food resources, shorebird energetics, and individual foraging behavior

(especially prey preference) that would be necessary to obtain an estimate of carrying

capacity.

Research questions:

• When and where are mudflats at carrying capacity, if at all? What potential, if any, exists

for shorebirds to switch to other nearby habitats (e.g., salt ponds)?

• How does shorebird use of mudflats in winter compare with fall and spring use?

• What mudflat invertebrate species are preferred by different shorebird species?

• Is prey availability a limiting factor for shorebirds during migration?

• Is prey availability more limiting for wintering birds?

• Do winter storms exacerbate the effective mudflat habitat loss caused by Spartina

invasion? Would San Francisco Bay birds be more likely to go inland to the Central

Valley during the winter under a high Spartina invasion scenario?

Suggested next steps:

• Compare mudflat shorebird numbers and densities with South Bay salt pond and tidal

marsh numbers and densities, based on existing PRBO survey data. Incorporate mudflat

habitats and change predictions into future iterations of PRBO’s habitat conversion

model (see http://www.prbo.org/cms/index.php?mid=131&module=browse).

• Conduct new mudflat surveys across seasons (fall, winter, spring; at least two per season)

at a subset of South Bay mudflats. Identify periods of peak shorebird use and compare

with previous surveys.


http://www.prbo.org/wetlands

• Conduct shorebird use surveys concurrently with benthic invertebrate samples across a

subset of South Bay mudflats. Analyze relationships between densities of specific

shorebird and invertebrate species.

• Obtain South Bay water level data for winter storm periods and model available mudflat

areas during these periods.

• Collect and analyze diet data for shorebirds feeding on mudflats across tides and seasons.

2. Mudflat topography and tidal inundation

In order to estimate mudflat elevations, we interpolated between MTL and MLLW contours

by assuming a linear mudflat slope. In reality, South Bay mudflat slopes may be significantly

different from linear (B. Jaffe, USGS, pers. comm., Dec 2003). While high-resolution

topographic/bathymetric data are not currently available for the entire South Bay mudflat

region, we hope that future Light Detection and Ranging (LiDAR) flights planned by USGS

will result in a fine-scale DEM that may be used to improve our models. With respect to tidal

inundation regimes, we used water level data from three benchmarks to represent the entire

South Bay. In reality, the tidal range varies greatly in the South Bay, and small local

differences could have a large influence on our model predictions. In order to interpret our

predictions at the local scale, a refinement of tidal inundation maps, based on local water

level data, would be necessary.

Research questions:

• What are actual mudflat slopes and microtopographic characteristics?

• Could detailed bathymetry data (e.g., LiDAR) improve our ability to predict Spartina

spread?

• How do predictions of shorebird habitat loss vary with local tidal inundation regimes?

• How important is tidal range in determining potential Spartina spread?


• Work with Janie Civille of UC Davis to analyze existing LiDAR-derived Willapa Bay

mudflat topography, its relationship to Spartina spread characteristics, and potential

relevance to San Francisco Bay.


• Work with Bruce Jaffe of USGS and/or other coastal geomorphologists to develop more

detailed digital elevation models more accurately representing mudflat slopes. Rerun

model predictions with refined mudflat slopes.

• Obtain continuous water level data from additional (non-NOS) tide gauges and create

more locally appropriate tidal inundation graphs for each major mudflat section. Rerun

model predictions with refined inundation data.

3. Mudflat heterogeneity

To refine our predictions about potential shorebird habitat loss under various Spartina spread

scenarios, we attempted to incorporate mudflat heterogeneity into our models by considering

tidal inundation cycles and potential variation in invertebrate densities. In doing this, we

made some additional simplifying assumptions related to the temporal and spatial use of

mudflat habitats by shorebirds. For example, we assumed that exposed mudflat areas are

used evenly by shorebirds, although we know that individuals of many species tend to forage

along the rising or receding tide line (Colwell and Landrum 1993, Durell et al. 1997).

Furthermore, we used a crude linear estimate of the invertebrate density gradient that was

based on Bolinas Lagoon data from the 1970s. Given that San Francisco Bay has a different

invertebrate community than Bolinas Lagoon, estimates should eventually be recalculated

using San Francisco Bay data (T. Grosholz, UC Davis, pers. comm., Nov 2003).

Research questions:

• Is the value of the upper mudflats derived from their longer exposure time offset by

higher invertebrate densities in the lower mudflats?

• How do benthic invertebrate densities vary over the elevation/inundation gradient of

mudflats? How do they vary over mudflats of different substrates and salinities?

• How does prey availability and energetic value relate to prey density, both spatially and

temporally?

• How do shorebird species differences in prey selection affect their exploitation of mudflat

prey resources?

• How does the species composition and abundance of foraging shorebirds vary across the

mudflat elevation/inundation gradient by season?



• Enter and analyze additional PRBO invertebrate data from Bolinas Lagoon collected in

the 1970s, as well as data from upcoming 2004 surveys, in order to describe more

comprehensively the spatial distribution patterns of benthic invertebrates.

• Work with Ted Grosholz at UC Davis and researchers at Bodega Bay Marine Lab to

analyze existing benthic invertebrate distribution data from San Francisco and Bodega

Bays, respectively. Compare distribution patterns with Bolinas Lagoon and rerun model

predictions under different assumptions about invertebrate distribution patterns.

• Work with Ted Grosholz and other aquatic entomologists to collect new benthic

invertebrate data, concurrent with shorebird use data, across seasons, tides, substrates,

and salinities in the South Bay. Refine model predictions based on new results pertaining

to foraging shorebird and benthic invertebrate distributions (temporal and spatial).

4. Upper vs. lower limits of non-native Spartina spread

Our models examined only the lower limits of Spartina spread and the subsequent impacts on

shorebird habitat value. In reality, the upward spread of S. alterniflora and hybrids may pose

an equally serious threat to shorebirds as mudflats along tidal channels and open areas within

the marsh plain are colonized by invasive Spartina and become unavailable to foraging

shorebirds. Tidal marsh breeding birds such as the Alameda Song Sparrow (Melospiza

melodia pusillula) and California Clapper Rail (Rallus longirostris obsoletus) are also likely

to be affected by invasive Spartina spread, as nesting substrates and foraging opportunities

change.

Research questions:

• What is the magnitude and relative importance for shorebirds of invasive Spartina spread

along channel mudflats, compared to its predicted spread across open mudflats?

• How will tidal marsh birds be affected by invasive Spartina spread? Which species will

be adversely affected and which will benefit?


• Using shorebird data collected by PRBO, combined with South Bay tidal marsh area

surveys used to develop PRBO’s habitat conversion model, calculate relative shorebird

densities along major and minor tidal channels, and compare these densities with open

mudflat densities.


• Identify the potential upper limits of invasive Spartina spread, in terms of elevation and

monthly tidal inundation, using Spartina location data and site-specific water level data.

• Establish a new study to monitor nest site selection and reproductive success in South

Bay tidal marsh breeding birds, comparing Spartina-invaded and non-invaded marsh

areas.

5. Future change

Finally, our model predictions were based on a static picture of mudflat spatial extent and

elevation. In reality, there are several factors in addition to S. alterniflora spread that may

affect mudflat extent and quality, including sea level rise (Galbraith et al. 2002), tidal marsh

restoration, and natural geomorphologic processes. Thus it would be useful to apply our

models to future mudflat predictions developed by coastal geomorphologists.

Research questions:

• What will San Francisco Bay mudflats look like in the future? Will a baywide sediment

deficit combined with sea level rise cause existing mudflats to shrink? How will tidal

marsh restoration affect mudflats?

• Will Spartina spread cause enough sediment accretion to raise mudflat elevations?

• Will Spartina spread affect invertebrate populations on outboard mudflats?

• How will shorebirds respond to invasive Spartina removal/treatment areas? Will

treatment areas support similar bird densities as non-invaded areas?


• As predictions for mudflat change are developed by coastal geomorphologists, generate

new predictions for Spartina spread and shorebird habitat value.

• Survey multiple treatment and control areas for birds prior to, during, and after the

treatment of invasive Spartina. Analyze the effects of Spartina removal on bird

communities, controlling for local site variation.


Acknowledgments

This project was requested by the Invasive Spartina Project (ISP), a project of the

California Coastal Conservancy, and funded by the California Coastal Conservancy, the State

Resources Agency, and the CALFED Program, under contract #02-212. We would like to thank

Katy Zaremba and Peggy Olofson of the ISP, Don Strong, Debra Ayres, Janie Civille and Ted

Grosholz of UC Davis, and Bruce Jaffe of USGS for their valuable input and support. We are

also grateful to Josh Collins and Stuart Siegel for advice on methods, and to Hildie Spautz for

assistance with the literature review. Finally, we are very grateful for the assistance of the 100+

people who volunteered their time and expertise to make the shorebird surveys possible. This is

PRBO contribution number 1078.


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Goss-Custard, J. D. 1979. Effect of habitat loss on the numbers of overwintering shorebirds.

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Goss-Custard, J. D. 2003. Fitness, demographic rates and managing the coast for wader

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Goss-Custard, J. D., R. E. Jones, and P. E. Newbery. 1977. The ecology of the Wash. I.

Distribution and diet of wading birds (Charadrii). Journal of Applied Ecology. 14:681-

700.

Goss-Custard, J. D. and M. E. Moser. 1988. Rates of change in the numbers of Dunlin, Calidris

alpina, wintering in British estuaries in relation to the spread of Spartina Anglica. Journal

of Applied Ecology. 25:95-109.

Josselyn, M. 1983. The ecology of San Francisco Bay tidal marshes: a community profile. U.S.

Fish and Wildlife Service, Division of Biological Services, FWS/OBS-83/23.

Washington D.C.

Microsoft. 1995. Visual FoxPro 3.0b. Microsoft Corporation, Seattle, WA.

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Page, G. W., L. E. Stenzel, and J. E. Kjelmyr. 1999. Overview of shorebird abundance and

distribution in wetlands of the Pacific Coast of the contiguous United States. Condor

101:461-471.

Page, G. W., L. E. Stenzel, and C. M. Wolfe. 1979. Aspects of the occurrence of shorebirds on a

central California estuary. Studies in Avian Biology 2:15-32.

Puttick, G. M. 1977. Spatial and temporal variations in intertidal animal distribution at

Langebaan Lagoon, South Africa. Royal Society of South Africa. Transactions 42:403-

440.

Quammen, M. L. 1982. Influence of subtle substrate differences on feeding by shorebirds on

intertidal mudflats. Marine Biology 71:339-343.

Ranwell, D. S. 1964. Spartina salt marshes in southern England: II. rate and seasonal pattern of

sediment accretion. Journal of Ecology 52:79-94.

Sayce, K. 1988. Introduced cordgrass, Spartina alterniflora Loisel in saltmarshes and tidelands

of Willapa Bay, Washington. Report to US Fish and Wildlife Service, Willapa National

Wildlife Refuge.


SFEI. 1998. EcoAtlas beta release, version 1.5b4. San Francisco Estuary Institute, Oakland, CA.

Stata. 2003. Intercooled Stata 8.0 for Windows. Stata Corporation, College Station, TX.

Stenzel, L. E., C. M. Hickey, J. E. Kjelmyr, and G. W. Page. 2002. Abundance and distribution

of shorebirds in the San Francisco Bay area. Western Birds 33:69-98.

Takekawa, J. Y., N. Warnock, G. M. Martinelli, A. K. Miles, and D. C. Tsao. 2002. Waterbird

use of bayland wetlands in the San Francisco Bay Estuary: movements of Long-billed

Dowitchers during winter. Waterbirds 25 (Special Publication 2):93-105.

Warnock, N., G. W. Page, and L. E. Stenzel. 1995. Non-migratory movements of Dunlin on their

California wintering grounds. Wilson Bulletin 107:131-139.

White, B. C. 1995. The shorebird foraging response to the eradication of the introduced

cordgrass, Spartina alterniflora. M.A. Thesis. San Francisco State University, San

Francisco, CA.

Yates, M. G., J. D. Goss-Custard, S. McGrorty, K. H. Lakhani, S. Durell, R. T. Clarke, W. E.

Rispin, I. Moy, T. Yates, R. A. Plant, and J. Frost. 1993. Sediment characteristics,

invertebrate densities and shorebird densities on the inner banks of the Wash. Journal of

Applied Ecology 30:599-614.


FIGURES

FIGURE 1. Benchmark locations, station IDs, and allocation of benchmarks with continuous water level data to mudflat areas. Benchmark information was obtained from NOAA/NOS (http://www.co-ops.nos.noaa.gov/bench_mark.shtml?region=ca).




020

4060

8010

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erce

nt In

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tion

-1 0 1 2 3Elevation (m)

Alam eda Dumbarton Bridge Redwood City

September Inundation

FIGURE 2. Cumulative duration of inundation curves for September based on water level data from Alameda, Redwood City, and Dumbarton Bridge benchmarks. Elevations corresponding to 60% inundation were 0.898 m, 1.172 m, and 1.162 m, respectively. Elevations corresponding to 70% inundation were 0.705 m, 0.932 m, and 0.929 m, respectively. Elevations corresponding to 80% inundation were 0.479 m, 0.671 m, and 0.659 m, respectively.

020

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April Inundation

FIGURE 3. Cumulative duration of tidal inundation curves for April based on water level data from Alameda, Redwood City, and Dumbarton Bridge benchmarks. Elevations corresponding to 60% inundation were 0.762 m, 1.018 m, and 1.083 m, respectively. Elevations corresponding to 70% inundation were 0.540 m, 0.763 m, and 0.836 m, respectively. Elevations corresponding to 80% inundation were 0.286 m, 0.457 m, and 0.553 m, respectively.


FIGURE 4. Fall (September) and spring (April) indices of mudflat habitat value used to model shorebird distributions. The invertebrate index assumes a 5:1 ratio between lower and upper mudflat value, based on invertebrate densities (Scenario B). The exposure index assumes that mudflat value is positively related to the percent of time that the mudflat is exposed during that month (Scenario C). The combined index is an average of the exposure index and the invertebrate index (Scenario D). Color shadings represent mudflat habitat quantiles, where 25% of the total area is contained in each shading category and darker colors have higher value.


020

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June Inundation

FIGURE 5. Cumulative duration of inundation curves for June based on water level data from Alameda, Redwood City, and Dumbarton Bridge benchmarks. Elevations corresponding to 60% inundation were 0.864 m, 1.101 m, and 1.113 m, respectively. Elevations corresponding to 70% inundation were 0.674 m, 0.882 m, and 0.874 m, respectively. Elevations corresponding to 80% inundation were 0.413 m, 0.575 m, and 0.646 m, respectively.


FIGURE 6. Predicted extent of Spartina alterniflora spread based on monthly inundation tolerances ranging from 60% to 80% (from Collins 2002). Sharp breaks in predictions are due to breaks in nearest benchmark locations.


FIGURE 7. Predicted fall and spring shorebird biomass (kg) lost, based on a range of shorebird habitat value and Spartina spread scenarios for South San Francisco Bay, compared with baseline biomass values calculated from 1988-1993 PRBO surveys. Scenario A assumes that all mudflat areas are of equal value to shorebirds. Scenario B assumes a 5:1 ratio between lower and upper mudflat value, based on invertebrate densities. Scenario C assumes that mudflat value is positively related to the percent of time that the mudflat is exposed during that month. Scenario D is based on an average of scenarios B and C. Scenario 1 assumes that Spartina alterniflora and its hybrids can tolerate being inundated 60% of the time during month of June; scenario 2 assumes 70% inundation; and scenario 3 assumes 80% inundation. All predictions assume that mudflats are currently at carrying capacity during fall and spring migration.


Fall Fall

Spring Spring

FIGURE 8. Current fall (orange) and spring (green) numbers of individual shorebird species from PRBO survey data (1988-1993) and predicted shorebird numbers under three different Spartina spread scenarios, assuming mudflats are currently at carrying capacity. Scenario 1 assumes that Spartina alterniflora and its hybrids can tolerate being inundated 60% of the time during month of June; scenario 2 assumes 70% inundation; and scenario 3 assumes 80% inundation. AMAV = American Avocet, REKN = Red Knot, SEPL = Semipalmated Plover, LBCU = Long-billed Curlew, Small Shorebirds = Least and Western Sandpipers and Dunlin, BBPL = Black-bellied Plover, DOWI = Long- and Short-billed Dowitchers, MAGO = Marbled Godwit, WILL = Willet.


FIGURE 9. Predicted extent of Spartina alterniflora spread based on a 70% inundation tolerance and overlap with tidal mudflats, classified according to their potential fall season value for shorebirds. Shorebird habitat value was based on: (a) PRBO Pacific Flyway shorebird survey data (1988-1993); (b) length of mudflat inundation during September; and (c) presumed invertebrate densities (Scenario D as described in text, adjusted for actual shorebird survey numbers).


FIGURE 10. Predicted extent of Spartina alterniflora spread based on a 70% inundation tolerance and overlap with tidal mudflats, classified according to their potential spring season value for shorebirds. Shorebird habitat value was based on: (a) PRBO Pacific Flyway shorebird survey data (1988-1993); (b) length of mudflat inundation during April; and (c) presumed invertebrate densities (Scenario D as described in text, adjusted for actual shorebird survey numbers).


TABLES TABLE 1. Tidal benchmark locations used to obtain mean tide level elevations, based on the National Tidal Datum Epoch (NTDE; 1983-2001). From NOAA’s NOS website (http://www.co-ops.nos.noaa.gov/bench_mark.shtml?region=ca).

Station ID Name Period Lat Lon

9414750 ALAMEDA, SAN FRANCISCO BAY January 1983 - December 2001 37ø 46.3' N 122ø 17.9' W

9414575 COYOTE CREEK, ALVISO SLOUGH April 1984 - March 1985 37ø 27.9' N 122ø 1.4' W

9414509 DUMBARTON BRIDGE, SF BAY April 1996 - March 1997 37ø 30.4' N 122ø 6.9' W

9414746 OAKLAND/ALAMEDA PARK ST. BRIDGE April 1980 - March 1981 37ø 46.3' N 122ø 14.1' W

9414525 PALO ALTO YACHT HARBOR, S. F. BAY June 1984 - December 1984 37ø 27.5' N 122ø 6.3' W

9414523 REDWOOD CITY, WHARF 5, S. F. BAY November 1997 - October 2002 37ø 30.4' N 122ø 12.6' W

9414458 SAN MATEO BRIDGE, WEST SIDE January 1981 - December 1987 37ø 34.8' N 122ø 15.2' W

TABLE 2. Coefficients for regression equations used to predict inundation from elevation. Equations were based on inundation curves obtained from 6-minute water level data from NOS benchmarks and took the form y = ax + bx2 + cx3 + d, where Y = inundation percent and X = elevation / water level above MLLW.

Benchmark Month a b c d R2

Alameda April -27.9 -24.9 7.85 91.3 0.9974

Alameda Sept -21.6 -28.4 7.92 96.4 0.9997

Dumbarton Bridge April -19.7 -13.7 2.93 94.6 0.9990

Dumbarton Bridge Sept -20.4 -12.1 2.36 98.2 0.9984

Redwood City April -20.4 -16.8 4.01 93.9 0.9974

Redwood City Sept -15.7 -18.2 3.79 97.9 0.9994




TABLE 3. Predicted percent of effective mudflat habitat lost based on a range of shorebird habitat value and Spartina spread scenarios. Scenario A assumes that all mudflat areas are of equal value to shorebirds. Scenario B assumes a 5:1 ratio between lower and upper mudflat value based on invertebrate densities. Scenario C assumes that mudflat value is inversely related to the percent of time that the mudflat is inundated during that month. Scenario D is based on an average of scenarios B and C. Scenarios 1, 2, and 3 assume that Spartina can tolerate being inundated 60%, 70%, and 80% of the time during June, respectively. All predictions assume that mudflats are currently at carrying capacity during fall and spring migration.

Shorebird Habitat Value Scenario

Spartina Spread Scenario A (All Areas Equal) B (Invertebrates) C (Inundation

Time) D (Invertebrates &

Inundation)

Fall

1 (60% Inundation Tolerance) 0.14 0.09 0.29 0.15



Spring





TABLE 4. Predicted loss of shorebird numbers by species, based on a range of Spartina spread scenarios. Scenario B assumes a 5:1 inundation-based ratio between lower and upper mudflat value, based on invertebrate densities. Scenario 1 assumes that invasive Spartina can tolerate being inundated 60% of the time during month of June; scenario 2 assumes 70% inundation; and scenario 3 assumes 80% inundation. All predictions assume that mudflats are currently at carrying capacity during fall and spring migration.

FALL SPRING

Current 1 (60%) 2 (70%) 3 (80%) Current 1 (60%) 2 (70%) 3 (80%)

Hectares 6,062 -904 -1,988 -3,269 6,062 -904 -1,988 -3,269

Species

American Avocet 5,023 -628 -1,561 -2,617 844 -113 -268 -445

Black-bellied Plover 8,138 -1,258 -2,781 -4,496 4,595 -642 -1,485 -2,456

Short/Long-billed Dowitcher 13,377 -2,099 -4,577 -7,376 33,008 -5,206 -11,277 -18,244

Long-billed Curlew 371 -61 -130 -209 218 -34 -75 -121

Marbled Godwit 14,251 -2,210 -4,884 -7,884 13,437 -2,088 -4,569 -7,406

Red Knot 1,678 -353 -691 -1,037 503 -97 -193 -299

Semipalmated Plover 1,501 -244 -521 -839 725 -125 -256 -411

Willet 15,612 -2,444 -5,370 -8,671 2,112 -366 -759 -1,206

Western/Least Sandpipers and Dunlin 160,374 -24,224 -54,299 -88,119 450,817 -66,817 -149,731 -245,213

TOTAL 220,325 -48,615 -70,055 -94,175 506,259 -104,793 -156,097 -212,813


APPENDIX 1, Literature Review

By Viola Toniolo ([email protected])

Importance And Status Of San Francisco Bay

San Francisco Bay is one of the largest and most important estuaries in the Western

Hemisphere. The bay comprises 1500 km2 of aquatic habitat and carries runoff from a 163,000

km2 watershed, which equals approximately 40% of California’s surface area (Nichols et al.

1986, Cohen and Carlton 1998). It also includes 70% of the mudflats in California (see Ayres et

al. 1999), which provide habitat to over 350,000 migrating shorebirds (Charadrii) at a time in the

fall and over 900,000 in the spring (based on single-day counts, Stenzel et al. 2002). Its

importance to shorebirds has earned the San Francisco Bay estuary status among the top five

nationally and internationally important sites for shorebirds (Galbraith et al. 2002), and as a

Western Hemisphere Shorebird Reserve Network site of hemispheric importance (Page et al.

1999, Stenzel et al. 2002). Along the Pacific coast of the contiguous United States alone

(excluding Alaska), the bay holds more shorebirds than any other wetland in all seasons (Page et

al. 1999).

Nonetheless the bay is also one of the most altered estuaries in the United States (Nichols

et al. 1986, Stenzel et al. 2002). Following the 1850s gold rush and the resulting influx of

hydraulic mining sediments, years of agricultural, urban, and industrial development throughout

bay watersheds have led to the widespread loss and alteration of historic intertidal habitats

throughout the bay (Nichols et al. 1986, Cohen and Carlton 1998). According to the Goals

Project (1999), more than 80% of pre-settlement tidal marsh and 40% of mudflat areas have been

lost to dredging, filling, diking and development. Furthermore, San Francisco Bay may be one of

the most invaded aquatic ecosystems in the world (Cohen and Carlton 1998). Over 234 exotic

species, including algae, plants, protozoans, invertebrates, and vertebrates were introduced via a

number of anthropogenic activities between 1850 and 1990, with most introductions having

taken place in the latter part of the 20th century. Many of these species now dominate much of

the bay’s ecosystem both in terms of numbers and biomass, and the bay’s benthic community

consists of a constantly changing mosaic of non-native invertebrate and plant species along with

what remains of the native communities. Ballast water from ships has been identified as one of

the most important factors contributing to these invasions (see Ruiz et al. 1997).


Spartina Invasion

Smooth cordgrass, Spartina alterniflora (Loisel) was originally introduced in the mid-

1970s as part of a marsh restoration and erosion control project in the Coyote Hills Slough in the

South Bay. By 1992 it had spread to many mudflats and channels throughout the bay (Callaway

and Josselyn 1992, Daehler and Strong 1994). S. alterniflora readily hybridizes where it coexists

with the native S. foliosa (Daehler and Strong 1997, Ayres et al. 1999). Both pure S. alterniflora

and hybrids are now found primarily in the South Bay from Alameda Island to Fremont on the

eastern side and from San Bruno to Hunter’s Point on the western side (Ayres et al. 1999).

As compared with its native congener, S. alterniflora and associated hybrids exhibit

higher tolerance to tidal submersion and salinity (Callaway and Josselyn 1992, Donnelly and

Bertness 2001, Collins 2002), earlier growth initiation, higher vegetative and lateral growth rates,

copious pollen (“pollen swamping”) and seed production, high germination rates, and greater

seed viability (Callaway and Josselyn 1992, Daehler and Strong 1996, Anttila et al. 1998, Ayres

et al. 1999). Its tendency to grow at lower elevations and establish new patches at relatively high

rates makes this non-native species an “ideal invader” (Callaway and Josselyn 1992) that

severely affects the relatively common native S. foliosa (Anttila et al. 1998). Hybrids, which can

be difficult to tell apart from the parental species, have greater reproductive and ecological vigor

than the parent plants and are thus thought to pose an additional and perhaps more serious threat

to S. foliosa (Daehler and Strong 1997, Anttila et al. 1998, Ayres et al 1999). The native

cordgrass is now absent from areas where S. alterniflora was purposefully introduced, whereas

areas colonized by dispersal of seed contain a full range of levels of hybridization (Ayres et al.

1999).

Although the current extent of non-native Spartina is mostly limited to tidal marsh plains

and channels, further spread of S. alterniflora and associated hybrids poses a great threat to the

continued existence of bordering mudflats. Due to the gentle gradient of mudflat surfaces, a

small change in the inundation tolerance of non-native Spartina would translate into a fairly

extensive horizontal expansion across the mudflat (Callaway and Josselyn 1992, Daehler and

Strong 1996) and the formation of the typical monospecific, circular patches in formerly open

habitat (Daehler and Strong 1994). These patches can then slow tidal flow and cause the

precipitation and accretion of fine sediments that are suspended in the water column, thus raising


the overall elevation of the mudflat over time (Ranwell 1964, Daehler and Strong 1996) and

effectively acting as an ecosystem engineer (see definition in Jones et al. 1994, 1997), potentially

resulting in the eventual conversion of tidal flats to non-native cordgrass meadows.

Spartina effects on invertebrates

Studies on the effects of non-native cordgrass on invertebrate fauna are inconclusive and

difficult to compare because they range geographically and by species. Studies in France (Triplet

et al. 2002), England (Evans 1986), and Tasmania (Hedge and Kriwoken 2000) looked at the

effects of removal of S. anglica, a recent hybrid of S. alterniflora and the European native S.

maritima, and suggested that cleared areas held higher densities of invertebrates than nearby

vegetated areas, especially if the removal had taken place within three years (Evans 1986).

Similarly, Capehart and Hackney (1989) found that the density of a burrowing clam, Polymesoda

caroliniana, was lower in S. alterniflora stands due to the higher densities of roots and rhizomes.

In contrast, both White (1995) and Rader (1984) reported that invertebrate densities were higher

in S. alterniflora swards than in mudflats or areas where Spartina has been removed. This might

be due to the greater structural complexity present in vegetative stands of Spartina (Daehler and

Strong 1996, Josselyn et al. 1993). Whatever the relationship between Spartina and invertebrate

populations, it is unlikely that shorebirds would be able to utilize dense or even patchy Spartina

habitats to nearly the same degree as mudflats, if at all (White 1995), because shorebirds are

unable to move through and forage in areas with high stem density.

Shorebirds

Pacific Flyway Project

Between April 1988 and April 1993 PRBO Conservation Science, with the help of

hundreds of volunteers, conducted three fall and six spring shorebird censuses in the intertidal

portion of San Francisco and San Pablo Bay and associated wetlands (Stenzel et al. 2002). This

was part of a larger project whose primary goal was to obtain an overview of shorebird

abundance and distribution in wetlands of the Pacific Coast of the contiguous United States

(Page et al. 1999). The surveys were designed to minimize the double counting of flocks and

keep track of all flock movements between plots, and involved hundreds of observers

simultaneously conducting surveys at multiple locations during moderately high rising tides


(Stenzel et al. 2002). This is the most comprehensive survey of shorebirds in the San Francisco

Bay to date.

Shorebird use patterns in San Francisco Bay

Thirty-eight species of shorebirds comprising 18 species groups were detected on Pacific

Flyway Project surveys. On a seasonal basis the bay held 340,000-360,000 individuals during

fall and 589,000-932,000 individuals during spring. The most abundant species on each survey

was Western Sandpiper, followed by Dunlin, Marbled Godwit, Least Sandpiper, and dowitchers

(Stenzel et al. 2002). Areas with the highest overall density and biomass were along the east side

of central San Francisco Bay, the east and south shores of South San Francisco Bay, and the

Napa River flats in the north bay (Stenzel et al. 2002). Most areas held high densities of at least

some species groups. High overall density seemed to be related to the presence of nearby active

salt ponds, which provide roosting and feeding habitat during high tides and may cause mudflats

to hold more birds than they would otherwise (Stenzel et al. 2002). Peak abundance of temperate

breeders occurred in fall and late winter, and peak abundance for arctic breeders occurred in fall

or spring (Page et al. 1999).

In the South Bay (south of the Richmond Bridge), density and biomass were highest

along the southern and eastern shores during fall (2.32-6.62 kg/ha) and eastern shore during

spring (4.33-6.37 kg/ha). Species that concentrated in the South Bay were Black-bellied Plover,

Willet, Marbled Godwit, small sandpipers and dowitchers (Stenzel et al. 2002).

Shorebird use of mudflats

Although the Pacific Flyway Project accomplished its goal of estimating the overall

abundance and distribution of shorebirds throughout San Francisco Bay, the scope and

geographic scale of the surveys did not allow for the fine spatial resolution necessary to capture

the variation of shorebird distributions across individual mudflats, though much can be gleaned

from the literature.

Many studies have demonstrated that shorebird utilization of mudflat habitats is spatially

and temporally variable on both the inter- and intra-specific level (Burger et al. 1977, Goss-

Custard 1979, Quammen 1982, Colwell and Landrum 1993). This variation is closely tied to

cycles of tidal inundation and the uneven distribution of sediments, prey densities, and prey


availability across the intertidal zone (Burger et al. 1977, Goss-Custard et al. 1977, Puttick 1977,

Page et al. 1979, Evans 1986, Colwell and Landrum 1993, Yates et al. 1993, White 1995, Arcas

2003). Burger et al. (1977), for example, compared shorebird use of three intertidal habitats in

coastal New Jersey and found that dowitchers, knots, Black-bellied Plovers, and oystercatchers

preferred the algal zones of the mudflat, while Semipalmated and Western Sandpipers utilized

more sandy areas. The authors noted that spatial segregation was less marked when the mudflat

was more exposed, and suggest that when foraging habitat is limited, species exhibit spatial and

temporal segregation in order to reduce competition. They also observed temporal patterns and

found that the number of species present on the mudflat increased faster than the mudflat area

during first 1 ½ hrs after high tide, then remained constant, with the maximum number of birds

occurring 1 hr after low tide. Colwell and Landrum (1993) examined shorebird distribution and

abundance in the Mad River estuary in northern California and found that Least and Western

Sandpipers foraged closer to the tide edge, whereas Semipalmated Plovers foraged at an average

of 10 m from the tide edge. In San Francisco Bay Semipalmated Plover, Least Sandpiper, and

Black-bellied Plover tend to forage higher along the tidal gradient, whereas American Avocet,

dowitchers, Marbled Godwit, and other species forage closer to the tide line (G. Page, pers. obs.).

Spatial and seasonal patterns in prey density are also strongly correlated with bird

density, provided of course that the habitat is accessible to birds (Puttick 1977, Evans 1986,

Yates et al. 1993). Colwell and Landrum (1993) found that the spatial distribution of

invertebrates was substrate-dependent, with a significant relationship between the density of an

amphipod, Corophium spp., and bird abundance, especially for Least and Western Sandpipers.

In addition to invertebrate density and distribution, the availability of invertebrates plays

an equal if not more important role in predicting shorebird density (White 1995). Invertebrates

are more abundant and more accessible in wet substrates and tend to burrow deeper as the tide

recedes and the mud dries out (Goss-Custard 1984, White 1995; reviewed in Durell 2000). Drier

mud is also more difficult to penetrate by bird bills (Quammen 1982). Prey also become less

available in cold weather and are harder to obtain during winter (Goss-Custard 1979).

Effects Of Habitat Loss On Shorebirds

The further spread of S. alterniflora and associated hybrids has the potential to greatly

reduce the size of mudflats throughout San Francisco Bay. While there is little doubt that such


habitat loss would have a negative impact on shorebird fitness, possibly reducing fat reserves

needed for migration (Stillman 2003), it is difficult to estimate the magnitude of decline in

shorebird populations in the Bay.

The relationship between habitat availability and bird populations is generally thought to

be non-linear, such that a 50% reduction in habitat would not necessarily translate into a 50%

loss in the numbers of birds. This is due to patchiness in habitat quality, the propensity of certain

species or individuals to move to other areas, and the degree of responses and general trends

among other aspects of a given ecosystem (e.g., invertebrate prey, rates of predation) that are

likely to influence shorebirds. Habitat loss, for example, can lead to emigration to alternative

wintering sites (Stillman 2003, Goss-Custard 1979), which is very difficult to measure, and/or an

increase in density in the present habitat, which in turn can result in birds moving to less

preferred areas (e.g., areas with lower foraging quality), greater impacts on the prey populations,

an increase in interactions among birds, and a reduction in fitness (Goss-Custard 1979). Even if

one could figure out the degree of emigration and changes in density, it is still very difficult to

predict what effects these will have on fitness and survival at a larger scale, for birds may

respond to habitat loss with compensatory density-dependent reproduction on their breeding

grounds that offset greater mortality on their wintering grounds (Stillman 2003, Goss-Custard

2003). At the same time, even a small increase in mortality can significantly reduce the

population over the long term, especially in species with low annual mortality rates. A 2%

increase in mortality in the Eurasian Oystercatcher, Haematopus ostralegus, can reduce the

population by as much as 30-60% (see Goss-Custard 2003). The importance of adult survival

was also found for the Pacific Coast population of the Western Snowy Plover, Charadrius

alexandrinus nivosus. Based on a population viability analysis, the population was shown to be

sensitive to small changes in adult survival (Nur et al. 2001). In general, adult survival has been

shown to be the most important limiting factor across shorebird taxa (Sandercock 2003).

Survival and fitness can also vary depending on whether the habitat is functioning at

carrying capacity, which is defined as either the maximum bird-days a given habitat can support

or the maximum number of birds that can survive the non-breeding season (Goss-Custard et al.

2002). Because of the disproportionate responses of individuals (e.g., juveniles v. adults) to

depleted resources, many birds may emigrate or starve before carrying capacity has been reached

(Durell 2000, Goss-Custard et al 2002, Goss-Custard 2003, Stillman 2003). Once the system is


functioning at or beyond carrying capacity, density-dependent mortality can then cause a

population to decrease at an increasing rate. Changes in invertebrate productivity can either

offset this loss if positive or exacerbate it if negative. The latter is especially true during winter

when food is less abundant and less available (West et al. 2002, Goss-Custard 1979, 2003).

Individual variation can play an even more important role in determining the fate of

populations undergoing losses in wintering and migratory habitat. This issue is reviewed

thoroughly in Durell (2000). Many species exhibit significant differences in morphology, social

status, and skill (e.g., experience) among individuals, sexes, and across different ages, such that

any changes that affect one sector of the population more than another are likely to have greater

effects on population size than if all individuals were affected equally (Durell 2000, Goss-

Custard 2003). Examples of individual variation in shorebirds include differential migration (i.e.,

males and adults overwinter closer to the breeding grounds, females and juveniles are found in

high proportions at the same sites) and dimorphism (e.g., differences in bill size and morphology

between ages and sexes). Young animals are also less efficient at foraging than older, more

experienced animals. What this means is that different portions of the population will have

differences in energetic and habitat requirements, foraging strategies (e.g., prey size), and

behavior, and individuals that specialize in less profitable strategies will have an increased

vulnerability to factors that decrease fitness such as parasite loading, bill damage, and predation;

if mortality varies between males and females then the sex ratio of a population can change and

the numbers of breeding pairs would be less than if males and females were affected equally; if

mortality is higher among juveniles then population size will decrease at a faster rate over the

long term than if all ages were affected equally (Durell 2000, Goss-Custard 1979, 2003).

Unfortunately, individual variation can be difficult to estimate because differences in plumage

between males and females and juveniles and adults are not easily discernible in wintering

populations (Durell 2000).

Stillman (2003) suggests that in order to increase the level of accuracy in estimating the

effects of habitat loss on shorebird populations, we need to develop behavior-based models that

take into account mortality rate, feeding rate, changes in distribution, and the tendency of

individual birds to maximize their own fitness in response to change. West et al. (2002), for

example, developed a behavior-based model in order to assess the impacts of disturbance on

foraging birds and found that many small disturbances cause greater damage than fewer, larger


disturbances and that disturbance can be more damaging (i.e., lead to increased mortality) than

permanent habitat loss.

Effect Of Spartina Spread On Shorebirds

Several studies have looked at the effects of habitat loss as a result of the spread of

Spartina. In Britain, where S. alterniflora hybridized with the native S. maritima to form S.

anglica, numbers of wintering Dunlins (Calidris alpina) have decreased by almost half since the

winter of 1973-1974, and these declines were greatest in estuaries where S. anglica has spread

over most of the available tidal flat (Goss-Custard and Moser 1988). Numbers did not increase in

estuaries where Spartina experienced a natural dieback, suggesting that Dunlins have not been

able to emigrate from areas where they were experiencing habitat loss to these newly available

habitats and perhaps to other areas in their wintering range. The authors suggest that this species

might have been more affected by invasive Spartina due to its small size, higher need for

constant foraging, and more extensive use of higher elevations during the tidal cycle.

Evans (1986) investigated the effects of herbicide removal and found that shorebirds feed

more in areas recently cleared of Spartina than areas cleared 3-4 years prior, and that bird

densities in the recently cleared plots were higher than in areas of nearby mud that were never

colonized by Spartina. This pattern is also true for amphipod (Corophium spp.) density and

overall invertebrate activity due to the presence of more standing water (less dried out) in the

recently cleared patches. Triplet et al. (2002) also found that cordgrass removal in northern

France had a positive effect on some species, especially the Ringed Plover, Charadrius hiaticula,

and Dunlin.

White (1995) compared bird use of plots with different Spartina removal treatments and

found that bird density decreased from open mudflats to herbicide-treated mudflats, plastic-

treated mudflats, and plots vegetated by cordgrass, in that order. The main difference between all

the plots was the degree of vegetative cover, and birds generally avoided areas with even

minimal amounts of vegetation. When birds did enter patches with some cordgrass they usually

did so by landing in nearby open mud and then wandering in on foot.

In Willapa Bay, Washington, where S. alterniflora increased from 800 ha in 1994 to over

2500 ha in 2002 (Buchanan 2003), aerial surveys conducted in 2000-2001 suggest a reduction in

shorebird numbers by as much as 67% and foraging time by as much as 50% in the southern


portions of the bay as compared with data from the 1991-1995 surveys (Jaques 2002).

Unfortunately, bird data are missing prior to 1991, making it difficult to estimate how much of

this reduction is attributable to the invasion of Spartina (Buchanan 2003).

Many of these studies indicate that birds have difficulty landing in and utilizing areas of

dense growth (Evans 1986, Josselyn et al. 1993, White 1995), and that Spartina effectively

reduces the foraging area available to shorebirds and diminishes their feeding time (Goss-

Custard and Moser 1988).

Future

Unchecked Spartina growth has the potential of converting open mudflats into dense

stands of S. alterniflora and alterniflora x foliosa hybrids, not just in south San Francisco Bay

but in other important shorebird migratory routes and wintering areas throughout the Pacific

Flyway. Daehler and Strong (1996) used physical characteristics to identify 31 estuaries along

the pacific coast as vulnerable to future Spartina invasions and found that all of them are

specifically vulnerable to S. alterniflora. Cordgrass poses additional threats to many habitats that

are already under extreme pressure from development and increased human use of coastal areas

(Zedler 1996). Sea level rise associated with climate change can further reduce the seaward

extent of mudflats and cause the shoreward migration of S. alterniflora, which, due to its higher

tolerance for inundation, can outcompete and eventually dominate the tidal marsh vegetation

(Donnelly and Bertness 2001). Under normal conditions sea level rise is gradual enough that

wetlands can adapt to these changes and shift inland, but the presence of coastal development

will hinder this process (Orr et al. 2003). Galbraith et al. (2002) examined current and projected

trends in sea level rise and, using conservative estimates, predict a conversion of 39% of the

intertidal habitat in San Francisco Bay to subtidal habitat. In South San Francisco Bay, that

conversion is expected to be as high as 70% by 2100. This is because the South Bay is subsiding

faster and is experiencing greater aquifer depletion and compaction than other areas in the

greater bay. Other geomorphologic (e.g., tectonic activity) and anthropogenic factors (e.g.,

construction of seawalls) can play unforeseen and unpredictable role in the future of wetlands

and intertidal areas.

Habitat loss and future sea level rise can have deleterious effects on the size of shorebird

populations, many of which are already in severe decline. As many as 19 of 35 shorebird species


that breed in Arctic and temperate regions throughout North America show negative population

trends (Morrison et al. 2001). In general, large population sizes, long migrations, high

concentrations of individuals in restricted areas during migration, and high demand for

development along coastal areas make shorebirds particularly vulnerable to environmental

degradation (Morrison et al. 2001). While it is unclear whether these populations are limited

most by changes in their breeding, migration, or wintering habitats (Page and Gill 1994,

Morrison et al. 2001), temperate breeders seem to be among the most vulnerable shorebird

species (Page and Gill 1994).

Restoration that involves the use of chemicals seems to be effective only as a short-term

measure (Evans 1986, White 1995), while other restoration methods appear to be only

marginally effective in restoring the same numbers of birds found in mudflats that have never

been colonized by cordgrass (White 1995). Furthermore, sea level rise, increased development,

and other anthropogenic factors need to be taken into consideration when managing coastal areas

for shorebirds. Finally, given the uncertainty about the future spread of invasive Spartina and

hybrids, as well as shorebird responses, it is only with the use of regular, systematic survey data

(Buchanan 2003) over large areas (Goss-Custard 2003) that we can fully assess the impact of

habitat loss to shorebirds by Spartina and devise restoration methods that will reclaim habitat

and prevent further losses.


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APPENDIX 2, Shorebird Use Maps


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