Download methods - San Francisco State University

Predicting tidal marsh bird and plant community response to climate change: A Pacific
coast perspective using field experiments and spatial models
PI: V. Thomas Parker, San Francisco State University
Co-PI: Nadav Nur, PRBO Conservation Science
John C. Callaway, University of San Francisco
Mark Herzog, PRBO Conservation Science
Diana Stralberg, PRBO Conservation Science
ABSTRACT
We propose to use new and existing data to examine the influence of salinity and inundation on
the distribution, diversity, and establishment of tidal marsh plant and avian species. We will generate
spatially explicit models that predict patterns of response of plant and avian distribution and abundance
using a range of future climate change scenarios in a Mediterranean climate system.
We will address the following questions: (1) How do salinity and tidal inundation influence the
distribution, diversity, and establishment of tidal marsh plant species? (2) How do biotic and abiotic
factors influence the distribution and abundance of tidal marsh bird species? (3) How will these plant and
avian species respond to predicted climate change? Which species and what areas of their distributions
are most likely to be affected by climate change?
Research will be conducted within the tidal wetlands of the San Francisco Bay-Delta and adjacent
uplands. Survey data of plant and bird occurrences will be collected for modeling purposes from sites
throughout the Bay-Delta, while more intensive data collection will be conducted at six locations across
the Bay-Delta to quantify factors affecting these distributions.
At intensive study sites and in the greenhouse, we will complete a series of experiments to
evaluate plant establishment from the seedbank across a range of salinity and inundation conditions. We
also will measure seedling survival in the field across these same treatments and seed traps will be
deployed to assess dispersal potential, abundance, and distance, especially in the upriver direction where
recruitment is most likely to occur with changing climate. Using field-based abundance and GIS-based
environmental data, we will develop distribution models for dominant, rare, and invasive plant species, as
well as spatial models for plant species diversity. Model linkages will be based on path analysis among
physical factors (salinity, inundation, channel density), plant characterisitcs, and avian population metrics.
Based on relationships elucidated among physical factors, habitat distributions and bird occurrences, we
will model bird species distribution and abundance and validate models using data not included in model
development. Once models have been validated for existing conditions, Bay-Delta-specific predictions of
sea-level rise and salinity will be used to predict changes in species distribution and abundance, and
community composition.
Models will be used to predict shifts in tidal marsh species distribution patterns for the Bay-Delta
and identify species and geographic areas of conservation concern, as well as potential issues for rare or
invasive species. Experimental data will identify underlying mechanisms for shifts in plant distributions
and community composition. Predictive models and empirical results will be synthesized and testable
predictions will be developed to further refine these models.
-1-
INTRODUCTION
Mediterranean-climate tidal wetlands are particularly susceptible to the effects of climate change.
As with other tidal wetlands, they share the threat of submersion if accretion rates are not in equilibrium
with sea-level rise (SLR) (Morris et al. 2002, Turner et al. 2004) and differential impacts of CO2
fertilization on C3 and C4 plants (Rasse et al. 2005). However, Mediterranean-climate tidal systems are
additionally threatened by salt accumulation during the lengthy dry summers that will accelerate with
warmer temperatures. Changes in precipitation patterns and water management will exacerbate this
impact, especially given the increased societal demands for water in a semi-arid climate. The
composition, structure and dynamics of tidal wetland plant and bird communities will be significantly
changed by these influences, but current predictions are merely speculative. Current understanding of
how these tidal systems will respond and the resulting management or policy actions relies on a relatively
limited history of basic research. In this study, we propose a focused research plan comprised of
complementary observational studies, experimental and statistical analysis, and spatial modeling that will
provide critical insight needed for management.
Effective ecosystem management and species conservation require a thorough understanding of
direct and indirect responses to environmental change (Burkett et al. 2005). Climate change combined
with other anthropogenic influences is causing rapid, often non-linear, shifts in species’ distributions and
life history characteristics (Parmesan 1996; Inouye et al. 2000; Ostander et al. 2000; Scheffer et al. 2001;
Scheffer and Carpenter 2003; Folke et al. 2004; Hughes et al. 2005), and modifications at lower trophic
levels can rapidly affect entire ecosystems (Porter et al. 2000; Dunne et al. 2002a, 2002b; Root et al.
2002; Lawrence and Soame 2004). One approach to assessing ecosystem-wide changes over large areas is
the use of species distribution models (SDM), which use spatially-explicit empirical data to derive linear
and non-linear relationships between species’ occurrence and environmental conditions. Many studies
have modeled changes in species distribution due to climate change (Iverson and Prasad 1998; Bakkenes
et al. 2002; Pearson et al. 2002; Thuiller 2004; Rehfeldt et al. 2006), but most have been based on global
circulation model (GCM) predictions of temperature and precipitation at broad continental scales. Very
few have explicitly modeled distribution shifts within tidal wetland ecosystems (but see Rehfisch et al.
2004), which are narrowly distributed and highly sensitive to fine-scale changes in elevation and salinity.
Thus, we propose to apply the most recent developments in species distribution modeling to tidal
marsh plants and vertebrates using fine-scale, California-specific spatial inputs that incorporate future
tidal inundation and salinity patterns across the San Francisco Bay-Delta. We will investigate and model
the distribution, diversity, and productivity of selected plant species, as well as the distribution and
abundance of key tidal marsh endemic birds in relation to physical and biotic factors that may be altered
as a result of current and future climate change. To improve our understanding of community-level
species interactions and dispersal abilities, we propose to complement our modeling work with
experimental field and greenhouse studies of plant recruitment and a field-based study of up-river plant
dispersal. This will provide further insight into the mechanisms for shifts in plant distributions, and the
consequences for bird species that depend on the plant community.
Size and importance of San Francisco Bay-Delta
The San Francisco Bay-Delta (hereafter referred to as the Bay-Delta) is the third largest estuary in
the United States, covering approximately 4096 km2 of the central California coastal region and includes
a broad mix of salt, brackish, and freshwater marsh ecosystems (Atwater et al. 1976, 1979; Josselyn
1983). The Bay-Delta is characterized by a Mediterranean climate, with precipitation limited to the winter
and early spring seasons, and prolonged summer droughts. The wetland landscape is a complex mosaic of
remaining historic wetlands, recently developed wetlands, restored wetlands, and potentially restorable
diked bayland sites (farmland, former salt ponds, and seasonal and perennial wetlands), all situated within
one of the country’s largest urban areas.
Prior to 1850, tidal marshes in the Bay-Delta occupied 2200 km2, of which a substantial
majority– 1400 km2 –consisted of freshwater tidal marshes in the Delta region (Nichols et al. 1986; SFEP
-1-
Parker et al.
1991). These extensive tidal marshes have now been reduced by more than 80% (95% in the Delta).
Despite impacts from surrounding development, these remaining ecosystems are of critical regional
importance for biodiversity, harboring a number of rare plant and animal species, including almost 50
special status species (Goals Project 1999; Olofson 2000). In addition to the ecological value of the BayDelta, the Delta’s freshwater storage and transport system is vital to California’s economy, providing
water to meet agricultural, municipal, industrial, and environmental demands.
The Bay-Delta is one of the most invaded aquatic ecosystems in the world (Cohen and Carlton
1998). Over 234 exotic species, including algae, plants, 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. Within tidal marshes, non-native cordgrass (Spartina alterniflora and
recombinants with the native Spartina foliosa) (Callaway and Josselyn 1992; Ayres et al. 2004), as well
as pepperweed (Lepidium latifolium) (Young et al. 1995), have been particularly effective at changing
plant community composition and structure. Spartina alterniflora has invaded only the more saline
portions of the San Francisco Bay, where native S. foliosa is also found, suggesting that an increase in
salinity could increase invasibility in other areas of the Bay-Delta.
Climate change impacts on San Francisco Bay-Delta
Many studies have shown that the effects of a warmer global climate in this system would include
reduced snowpack storage in the mountains, higher flood peaks during the winter rainy season, and
reduced warm-season river flows after April (Gleick 1987a, 1987b; Roos 1989; Lettenmaier and Gan
1990; Gleick and Chalecki 1999; Knowles and Cayan 2002, 2004; Dettinger et al. 2004; Knowles et al.
2006). Even with some contention about which model might be the best and which direction certain
parameters may shift, most models are in coarse agreement for California (Dettinger 2005). Dettinger
(2005) compared multiple models and contingencies and determined that the most likely result of climate
shift is a total precipitation regime similar to present, combined with warmer springs, reduced snowpack,
and higher winter floods and lower summer flows. These hydrologic changes would propagate
downstream to the estuary, resulting in an altered salinity regime (i.e., increased in spring/summer,
decreased in winter) (Knowles and Cayan 2002). During the late spring and summer, the lower stream
flows and increased salinities would affect many species that depend on the estuary and rivers. While
several studies have examined current ecological conditions along the salinity gradient (Atwater et al.
1979, Pearcy and Ustin 1984), few have investigated how ecological systems in the estuary would
respond to these changing conditions (Josselyn and Callaway 1988, Williams 1989).
Another critical influence on estuarine conditions is SLR, which by conservative calculations is
projected to occur at a global rate of up to 59 cm over the next 100 years (IPCC 2007). Predictions for the
California coast range from 10-90 cm by 2100, an acceleration of the recent rate of approximately 20 cm
during the last century (Cayan et al. 2005). Some recent predictions posit that future rates could be much
greater due to more rapid melting of terrestrial ice sheets, primarily in Greenland and the Antarctic
(Overpeck et al. 2006; Rignot and Kanagaratnam 2006). Winter storm surges may also increase sea levels
by up to an additional 0.30 m (Flick 1998), 0.20 m within the San Francisco Bay (Cayan et al. 2005). In
response to increased rates of SLR, tidal marshes must either accumulate more sediment to keep pace
with SLR, migrate inland to adjacent terrestrial areas, or face increased inundation (Donnelly and
Bertness 2001, Morris et al. 2002). Most tidal marshes accumulate 2-8 mm of sediment per year
(Stevenson et al. 1986; Reed 1995; Callaway et al. 1996), and this compensates for SLR and other
processes. However, substantial data from Louisiana, Chesapeake Bay and modeling studies have shown
that as increases in relative sea level get close to 10 to 12 mm/yr, most marshes cannot keep pace and
vegetation eventually may be inundated and converted to open water/mudflats (Baumann et al. 1984;
Kearney and Stevenson 1991; Boesch et al. 1994; Morris et al. 2002, Rasse et al. 2005). Historic data
from other systems has shown that slower increases in relative sea level (or loss in elevation) can lead to
shifts in vegetation communities over time (Warren and Niering 1993). Although it may be possible for
marsh accretion in the San Francisco Bay to keep up with SLR (Orr et al. 2003), bathymetric mapping
2
Parker et al.
studies have shown a decline in bay sediments over time (Foxgrover et al. 2004), and future large-scale
tidal marsh restoration projects will further deplete existing bay sediments. Furthermore, in the heavily
impacted Bay-Delta system, filled, diked, and developed baylands tidal systems are severely restricted in
terms of adjacent terrestrial habitats for upslope migration in response to SLR, creating a high level of
uncertainty about tidal marsh responses to SLR. Most of the delta region is leveed and under agriculture,
and SLR further increases the pressure on these levees, adding to the probability of their failure
(Ingebritsen et al. 2000; Mount and Twiss 2005). The increased possibility of levee failure that would
result from higher wet-season flows, and SLR could have additional impacts on the region’s ecosystems,
particularly by drawing more saline water farther into the estuary.
Tidal marsh vegetation responses to salinity and SLR
Within the Bay-Delta, Atwater et al. (1979) first reported that freshwater wetlands of the Delta
are characterized by greater plant species diversity than the salt marshes of the lower estuary. There is a
dramatic, non-linear increase in plant species diversity and productivity in the fresh region of the BayDelta (Figure 1). Sites that are most saline have relatively low species diversity (Hopkins and Parker
1984, Sanderson et al. 2000) but contain threatened and federally listed species, such as soft bird’s beak
(Cordylanthus mollis ssp. mollis). Brackish sites are not markedly more diverse; however, wetlands
located further up the estuary are substantially more diverse and have greater numbers of locally
uncommon and rare species than lower estuary sites (Vasey, Parker, Callaway, and Schile, unpublished
data). The greater diversity at freshwater sites underscores the potential ecological importance of
freshwater tidal wetlands and their potential vulnerability to salt water intrusion. Within California, a high
proportion of imperiled and endemic species can be found within coastal ecosystems, including tidal
marshes (Seabloom et al. 2006). Given the large number of locally uncommon and rare species in the
brackish and freshwater tidal wetland ecosystem, as suggested by Lyons et al. (2005), the loss of these
wetlands could have severe consequences for ecosystem functions in this region.
On a regional scale, vegetation community structure of estuarine tidal wetlands is affected by
salinity and inundation regimes, with clear differences in plant communities across fresh, brackish, and
salt marshes (Atwater et al. 1979; Mitsch and Gosselink 2000; Cronk and Fennessy 2001, Pennings et al.
2005). Interspecific interactions are critical for within marsh vegetation patterns (Bertness 1991, Pennings
et al. 2003), and large-scale distribution patterns of estuarine plants along a salinity gradients are driven
by competition at low salinities, but freshwater plants are limited by physical factors at higher salinities
(Crain et al. 2004). Mahall and Park (change to 1976a, 1976b?1976b, 1976c) showed that both salinity
and soil aeration changed with elevation and that both were critical in determining the relative abundance
of S. foliosa and Sarcocornia pacifica (formerly Salicornia virginica) in San Francisco Bay. Detailed
surveys at San Quintín Bay, Baja California found that salt marsh plants respond to elevation differences
as small as 8 cm (Zedler et al. 1999). Sanderson et al. (2000) found similar sensitivity of salt marsh plant
distributions to elevation in San Francisco Bay and also identified the importance of tidal channels in
influencing plant distributions.
Increased inundation rates associated with increases in global SLR will stress marsh plants,
reduce productivity, and potentially shift plant distributions (Scavia et al. 2002; Schile, Callaway, Parker,
and Vasey, unpublished data). Lower estuarine salinities in the winter and spring could increase seed
germination rates, but higher salinities during the summer differentially will stress plants during the
growing season, potentially shifting competitive ranking. Increases in SLR will further affect vegetation,
particularly at the low end of the marsh where plants typically are stressed by excessive inundation and
anaerobiosis (Chapman 1974; Mendelssohn and Morris 2000). Wetland plants have many specific
adaptations that allow them to tolerate anaerobic conditions, including well developed aerenchyma
(Armstrong 1979), pressurized ventilation and convective gas flow (Grosse et al. 1991), and physiological
adaptations (Mendelssohn et al. 1981); however, increased inundation rates will shift tolerances of species
across the marsh as areas are flooded to a greater extent. A recent model by Morris (2006) predicts that
3
Parker et al.
shifts in SLR will have significant effects on competitive interactions among species, as well as the
geomorphological development of intertidal marshes.
Beyond impacts to existing, well-developed vegetation, climate change impacts will also affect
recruitment patterns within estuarine wetlands. Plant recruitment is a multi-staged, temporally and
spatially structured process central to the dynamics of plant communities. Current community or
population theories (like plant metapopulation, source-sink, and metacommunity dynamics) require
dispersal, seed bank dynamics, and seedling establishment to structure populations and communities
(Hubbell 2001, Leibold et al. 2004). Our understanding of plant dispersal (Howe and Smallwood 1982,
Nathan and Muller-Landau 2000, Levine and Murrell 2003), seed germination ecology (Baskin and
Baskin 1998, Fenner and Thompson 2005), and seed bank dynamics (Leck et al. 1989), as well as the
structure and dynamics of adult plants and communities, emphasize the need to focus on the seedling
stage. This is particularly important to assess recruitment limitation (Hurtt and Pacala 1995).
Given SF Bay-Delta climate change predictions, earlier climatic fluxes suggest the rapidity of
potential recruitment changes. For example, Atwater et al. (1979) documented large-scale changes in
brackish to near freshwater wetland plant communities during a severe drought year, indicating the
potential importance of dispersal effects on future distribution changes. That result parallels more recent
studies from other wetland systems that have suffered temporary shifts toward more saline conditions, for
example, along the Gulf Coast (Wang 1988; Flynn et al. 1995; Howard and Mendelssohn 1999, 2000;
Thomson et al. 2001; Visser et al. 2002). Freshwater and oligohaline plant species will be the most
sensitive to any increases in salinity (e.g., Baldwin et al. 1996). Knowledge of the relationships among
seed dispersal, seed banks, plant recruitment and physical processes is crucial to predicting potential
effects of climate change on tidal wetland (Baldwin et al. 1996); both salinity and inundation regimes are
significant drivers of wetland plant germination and establishment. Prolonged inundation reduces species
diversity and biomass (Casanova and Brock 2000) and can have differential effects along an inundation
gradient (Keddy and Ellis 1985). Research conducted in coastal marshes of Louisiana suggests that higher
salinity and prolonged inundation reduces germination (Baldwin et al. 1996), and these effects are
amplified with disturbance (Baldwin and Mendelssohn 1998); however, comparable research in the
western coast of North America has not been conducted to adequately address concerns of SLR and
increased salinity on marsh plant recruitment.
In addition to shifts in plant distributions, there are likely to be shifts in productivity due to
gradual changes in salinity, with lower productivity in saline marshes (Pearcy and Ustin 1984, Rasse et al.
2005). Productivity studies from the Bay-Delta are limited (Mahall and Park 1976c?1976a); however,
data from across the Bay-Delta demonstrate a trend of decreased productivity with increasing salinity
(Figure 1). Atwater et al. (1979) measured high annual biomass of fresh and brackish marsh dominant
Schoenoplectus californicus (formerly Scirpus californicus; approximately 2500 g/m2) in comparison to
salt marsh biomass for Spartina foliosa (300 to1700 g/m2, with only one site near the high end of this
range) or Sarcocornia pacifica (500-1200 g/m2). Similarly, in other estuarine ecosystems, production
rates are consistently lower in salt marshes (Odum 1988), likely due to the added stress of high salinities
in salt marsh soils. While the proposed research will not evaluate plant productivity directly, two recently
funded companion studies supported by CALFED will research productivity rates along the estuarine
gradient, including an evaluation of plant interspecific interactions from fresh, brackish and salt marshes,
with both greenhouse and transplant studies of dominant plant species (see section below on synergistic
research)
Tidal marsh avian community responses to salinity and SLR
Due to the harsh environment created by high salinity and tidal inundation regimes, as well as the
low structural diversity of these systems, tidal marshes are generally characterized by low vertebrate
species diversity (Greenberg et al. 2006). However, they are also characterized by a high proportion of
endemic vertebrate subspecies, specially adapted to tolerate those harsh environments (Basham and
Mewaldt 1987; Greenberg and Droege 1990). Brackish and fresh marshes support more vertebrate species
4
Parker et al.
than salt marshes (PRBO unpublished data), but the additional species are generally more common and
generalist in their habitat preferences. In the Bay-Delta, salt marshes support six avian subspecies of
conservation concern—California Clapper Rail (Rallus longirostris obsoletus), California Black Rail
(Laterallus jamaicensis coturniculus), Tidal Marsh Song Sparrow (Melospiza melodia samuelis, M.m.
pusillula, M.m. maxillaris), and Salt Marsh Yellowthroat (Geothlypis trichas sinuosa). These same
species are also found in brackish, but usually not in fresh marshes.
Thus, while an increase in salinity may lead to declines in tidal marsh plant diversity, and perhaps
the loss of several rare and endemic plant species, we do not expect the same pattern in avian
communities, in which many species may benefit from an increase in high salinity tidal marshes. Rather,
SLR may pose a larger threat to tidal marsh vertebrates. Several taxa, including California Black Rail, are
known to depend on the presence of refugia from predators at high tide, which may be reduced or
eliminated with SLR (Evens 1986). Others, including Tidal Marsh Song Sparrow and Salt Marsh
Yellowthroat have been observed to have lower densities in smaller, more fragmented marshes (Spautz et
al. 2006). For the Song Sparrow, which generally nests in low-lying Sarcocornia pacifica or Grindelia
stricta, high tide and storm-related flooding has been demonstrated to be a major source (up to 25%) of
nestling mortality (Johnston 1956, N. Nur, unpubl. data). Furthermore, not all tidal marsh-associated
vertebrate species are likely to respond in the same manner to the effects of climate change, given the
variation in salinity tolerance, vegetation associations, vulnerability to edge-associated predation, impacts
of tidal inundation and flooding, and response to tidal channels. The disparate shifts in ranges of plant, as
well as avian species, may therefore result in a “tearing apart” of ecological communities (Parmesan
1996), which could cascade up and down the food chain, creating other disruptions in ecosystem
functions. In conjunction with habitat fragmentation, the disruption could provide new opportunities for
introduced exotic species to invade. Furthermore, the spread of exotic invasive plant species such as S.
alterniflora has great potential to change tidal marsh plant community structure, and exclude some
species, such as Song Sparrow, that have low densities and low reproductive success in this vegetation
type (Gutenspergen and Nordby 2006). In addition to improving or reducing nesting habitat opportunities
for certain avian species, changes in plant community composition may also facilitate new or different
species interactions by altering their spatial distributions. For example, Marsh Wrens may exclude Song
Sparrows via aggressive territorial behavior, as well as nest depredation, in Spartina-invaded marshes
(J.C. Nordby unpublished data).
For avian species, high diversity has been associated with high structural vegetation diversity,
more than plant species diversity (Rotenberry and Wiens 1980; James and Warner 1982). However, San
Francisco Bay studies have demonstrated that individual marsh plant species are important predictors of
individual avian species’ abundance (Spautz et al. 2006), as has been found in other systems (Wiens and
Rotenberry 1981). Statistically controlling for landscape context, geomorphic characteristics, and
vegetation structure, Song Sparrow density has been shown to increase with percent cover of Grindelia
stricta (saline-brackish) and Baccharis pilularis (upland), while Common Yellowthroat density has been
shown to increase with percent cover of Schoenoplectus acutus (brackish-fresh), Bolboschoenus
maritimus (saline-brackish), and Lepidium latifolium (invasive), as well as overall vegetation diversity
(Spautz et al. 2006). Thus, to a certain extent, we would also expect avian species’ distributions to shift in
response to shifts in dominant or subdominant tidal marsh plant species that may occur with climate
change.
Thus for avian communities, the potential pathways of climate change effects are numerous and
complex. Changes in salinity, elevation, and inundation may effect some bird species directly, while the
indirect effects of changes in marsh composition and structure (including plant species and channel
characteristics), as well as resulting changes in avian species interactions (e.g., competition) may be
equally or more important to consider.
Modeling species distributions
Species distribution models (SDM), also known as niche models or bioclimatic models, have seen
increasing popularity in recent years as tools for predicting potential shifts in species’ distributions as a
5
Parker et al.
result of climate change (Pearson and Dawson 2003; Thuiller 2004; Araujo et al. 2005). This empirical
approach has distinct practical advantages in that it tends to provide more realistic (data-driven)
predictions than theoretical models and can also provide a high level of generality given proper inputs and
informed ecological assumptions (Guisan and Zimmerman 2000). However, most SDM work has been
done at a broad, continental or regional scale, often at spatial resolutions of grid cells 1 km2 or greater.
Furthermore, the great majority of such modeling has been done for upland terrestrial habitats. Very little
species distribution modeling has been conducted explicitly for coastal systems, which necessitate a
relatively fine-scale approach, due to their limited narrow extent. Although several researchers have
conducted spatial evaluations of SLR on the availability and quality of shorebird habitat (Galbraith et al.
2002; Austin and Rehfisch 2003), we know of only one example of an SDM used to predict climate
change-induced shifts in coastal or estuarine species (Rehfisch et al. 2004).
There are several common approaches to SDMs, which can be categorized as simple bioclimatic
envelope models such as BIOCLIM (Busby 1991) and DOMAIN (Carpenter et al. 1993); statistical
models such as generalized linear models (GLM; McCullagh and Neder 1989), generalized additive
models (GAM; Hastie and Tibshirani 1990), and classification and regression trees (CART; Breiman et
al. 1984); or machine learning approaches, such as genetic algorithms for rule-set prediction (GARP;
Peterson 2001), artificial neural networks (ANN; Ripley 1996), and maximum entropy (MaxEnt; Phillips
et al. 2006). In general, statistical approaches are considered the most rigorous and are usually used with
species occurrence datasets that contain both presence and absence data, while envelope models and some
machine learning approaches are most suitable for presence-only occurrence data, such as museum
specimens or natural heritage databases. However, there is wide variation in the performance of these
models, and this depends on a large number of factors that are difficult to control. Recent comparative
studies have suggested that novel methods such as MaxEnt (Elith et al. 2006) and “boosted” or modelaveraged CARTs (Lawler et al. 2006, Leathwick et al. 2006) have the highest rates of prediction success
in some contexts. However, standard GLMs and GAMs are widely used, have strong statistical
foundations, identify functional relationships, are relatively easy to interpret, and perform well in
comparison tests (Wintle et al. 2005).
Although community-based multivariate approaches to SDMs have been developed (Ferrier et al.
2002, Hirzel et al. 2002, Leathwick et al. 2006), SDMs are still based on current species distributions in
relation to environmental conditions, and do not explicitly incorporate interactions among species or
dispersal abilities. Thus SDMs alone may not be sufficient to accurately predict future shifts in species
occurrence. In this study we will statistically estimate the effects of one species on other species through
path analysis (Wooton 1994) and thus characterize important direct and indirect pathways linking
physical factors, plant species, and bird species. In addition, competitive interactions will be addressed
through field experiments. In this way we will be able to evaluate the assumptions of standard SDMs
that outcomes for one species can be predicted independently of its competitors.
OBJECTIVES AND HYPOTHESES
Using a combination of field sampling and data analysis, experimental manipulations, and species
distribution modeling we propose to address the following overall question: How will tidal marsh extent
and community processes respond to a range of future SLR and salinity scenarios? In our study we will
focus on the following specific questions:
1. How do salinity and tidal inundation influence the distribution, diversity, and establishment of tidal
marsh plant species in the San Francisco Bay-Delta?
We will address this question through (a) intensive vegetation sampling at six mature marsh sites
across the salinity gradient, and analysis of within-marsh patterns of distribution and diversity; (b)
extensive sampling of vegetation distributions across the Bay-Delta, and spatial modeling of estuarywide patterns of distribution, diversity, and establishment; (c) experimental evaluation of the
differential impact of salinity and inundation on the establishment of tidal marsh plant species to
6
Parker et al.
determine threshold sensitivities for establishment; (d) sampling study of upstream seed dispersal to
evaluate species-specific dispersal limitations and order of colonization, and (e) coordination with an
on-going CALFED-funded project to evaluate plant productivity and community interactions,
including competition, across these same gradients. Our combined efforts will identify the influence
of biotic and abiotic factors on overall plant distributions under current conditions and with projected
climate change.
2. How do biotic (vegetation-based) and abiotic factors (channel density, inundation patterns) influence
the distribution and abundance of tidal marsh bird species?
We will address this question by using an extensive dataset of avian occurrence and abundance across
the Bay-Delta, in conjunction with plant distribution, diversity, and productivity data, as well spatial
environmental data layers, to develop and compare various empirical models of avian distribution and
abundance. A complementary path analysis will provide a potent means to identify linkages among
physical (salinity, inundation, geomorphology) and biotic (plants and other bird species) on tidal
marsh bird species.
3. How will the distributions of key freshwater, brackish and salt marsh plant species, including rare
and invasive species, respond to various climate change scenarios, as indicated by SLR and estuarine
salinity patterns? Which species will migrate together or separately?
We will address this question by applying future predictions of SLR and salinity shifts to spatial
models developed using current plant distribution data. A range of climate change scenarios will be
used to assess conservative to more extreme predictions of future conditions. Model-predicted future
distributions will be compared with current distributions, and will consider results of field-based
salinity and inundation experiments, as well as seed dispersal observations, to describe an envelope of
future change potential. The importance of interactions among plant species will be assessed based on
statistical and experiment studies described above.
4. How will the distributions of tidal marsh bird species shift under various climate change scenarios?
Which species and what parts of their distributions are most likely to be threatened by climate
change?
We will address this question by applying future predictions of SLR and salinity shifts, as well as
predicted changes in plant species distributions, primary productivity, and plant species diversity, to
models of avian distribution and abundance.
METHODS
This proposed research will incorporate a broad suite of plant and bird data collected throughout
the Bay-Delta. Much of these data have been collected as part of two regional, multi-disciplinary research
efforts, partly overlapping in research objectives and spatial extent. The Integrated Regional Wetlands
Monitoring Pilot Project (IRWM; www.irwm.org) is an interdisciplinary research effort examining
wetland restoration in the North Bay and Delta, with primary goals of (1) understanding how ecosystem
restoration efforts affect ecosystem processes at different scales and (2) identifying useful monitoring
indicators and protocols. IRWM activities have involved the intensive collection of nutrient, elevation,
salinity, vegetation, invertebrate, fish, and avian data at six sites over two years throughout the northern
bay and into the Delta. The PIs and senior personnel involved in this proposal have collaborated together
under the IRWM project, which will be completed in 2007, with several publications in progress.
BREACH is another interdisciplinary research effort, seeking to gain a conceptual and empirical
understanding of the important mechanisms and thresholds of restoration processes in Bay-Delta tidal
marshes. Two BREACH phases focusing on the Delta (fresh) and North Bay (saline to brackish) marshes
have been completed (Simenstad et al. 2000), and a third phase, focusing on intensive monitoring and
7
Parker et al.
modeling of a single Delta restoration site, will commence in late 2007. One of the PIs (Nur) has been
involved in all three phases, and two senior personnel (Stralberg, Herzog) will play a role in the avian and
landscape ecology components of the upcoming third phase. In addition, Parker and Callaway recently
received a grant from CALFED to build on IRWM results and evaluate the effects of climate change on
marsh productivity, decomposition, and fish food webs (see below).
Data collected through BREACH and IRWM, supplemented by long-term avian monitoring data
collected by PRBO Conservation Science (PRBO; see Spautz et al. 2006), provide the basis for a
comprehensive investigation into the effects of climate change on tidal wetland vegetation and vertebrate
distribution and diversity in the Bay-Delta. Additional field sampling will be conducted to fill in any gaps
along the salinity gradient, and public databases will be used to improve sample sizes for rare and special
status species.
Sampling sites
Intensive sampling sites
We have selected six natural marsh systems as the focus of intensive research in this investigation
(Figure 2). These six sites span the full salinity gradient of the estuary and represent some of best
representatives of historic tidal wetland landscapes in the region. They also have a rich legacy of
scientific investigation and baseline data. We chose relatively undisturbed remnants of the Bay-Delta’s
historic wetland ecosystem, rather than restoration sites, because the former should provide greater insight
into how different salinity regimes affect existing wetland conditions.
The first two sites represent the saline end of the spectrum (25-45 ppt summer salinity). China
Camp State Park is part of the San Francisco Bay National Estuarine Research Reserve and consists of
about 125 ha with an uncharacteristically intact upland transition and large expanse of tidal mudflats. It
has been studied by PRBO since 1996. Petaluma Marsh represents the largest intact salt marsh in
California, covering over 800 ha and was part of the BREACH 2 project.
Two sites have been chosen that represent brackish tidal wetlands (15 ppt average summer
salinity). Coon Island is one of the last undiked, large tidal wetland landscapes in the upper San Pablo
Bay area, covers about 175 ha, and has received intensive investigation as part of the IRWM project.
Rush Ranch Open Space Preserve is also part of the SF Bay NERR and contains the largest remnant
brackish tidal wetland in the Bay-Delta, covering over 400 ha. It has been studied by PRBO since 1996.
The last two sites represent freshwater or near freshwater tidal marshes. Browns Island is in the
western end of the freshwater delta created by the confluence of the Sacramento and San Joaquin rivers. It
covers about 200 ha and has also received intensive investigation as part of the IRWM project. Sand
Mound Slough is farther up the estuary and is comprised of a number of small, intra-channel remnants of
historic Delta wetlands, covering a total of approximately 25 ha.
Extensive sampling sites
PRBO (Nur, Stralberg, Herzog) has been conducting breeding season point count surveys
according to standardized protocols (Ralph et al. 1993) in Bay-Delta marshes since 1996, and has
accumulated an extensive long-term database of avian occurrence and abundance, as well as data on plant
species composition, structure, and cover proportions collected at each point location using a modified
relevé protocol (Figure 2; Spautz et al. 2006). Currently, we have bird and vegetation data from over 450
survey points at over 55 marshes, including BREACH and IRWM sites (Figure 2). We will also select
additional freshwater sites throughout the Delta (i.e., Lindsey Slough marsh and Upper Mandeville Tip)
to conduct plant and avian surveys for modeling purposes. These sites will be used to fill in the gaps in
our existing dataset, which may not adequately represent the fresh end of the salinity spectrum for some
organisms (Figure 2).
Vegetation surveys will be conducted on many remnant freshwater marshes throughout the Delta
in order to supplement species distribution and abundance data collected at the intensive sampling sites.
8
Parker et al.
At extensive sampling sites, we will do field surveys of each location to develop presence/absence data
for plant occurrences. These data will be used in conjunction with the existing and new bird data to
further evaluate bird habitat relationships and to expand data for modeling on plant species distributions.
Field and greenhouse studies
Plant distribution and diversity
To accurately determine different dimensions of plant diversity, we plan to utilize randomly
placed 0.1 ha plots to survey for plant composition (Stohlgren et al. 1995, Peet et al. 1998, Stohlgren
2007). Smaller subplots within these larger plots will be randomly placed to assess relative cover and
frequency. We will use species-area/species-accumulation curves to assess sampling intensity (EstimateS
version 8; Colwell 2006). During IRWM vegetation surveys, we collected vegetation presence and cover
data in over 300 randomly located plots at Coon and Browns Islands. We plan to compare the sampling
techniques to analyze within-marsh patterns of distribution, diversity, and productivity. For distribution
modeling purposes, we will use all available distribution and abundance data, including PRBO relevé data
on plants and intensive plot samples, and also conduct new surveys at a variety of remnant freshwater
wetlands in the Delta to increase data coverage (approximately 15 sites). These data will be used to
develop spatial models of individual species’ distribution and abundance (percent cover), as well as plant
species diversity. We will also make use of extensive existing datasets on invasive and special status plant
species distributions within the Bay-Delta. We intentionally have chosen additional sites farther upstream
in the Delta so that we will be able to evaluate the migration of more salinity tolerant species up the
estuary.
Seed dispersal and plant establishment
To estimate seed dispersal abilities of salt marsh plant species into the tidal freshwater zones of
the Bay-Delta, we will establish 0.25m2 seed traps at two sites representing the transition from oligohaline
to freshwater tidal, Browns Island and Sand Mound Slough, and two sites farther up the Sacramento
River (Lindsey Slough) and San Joaquin River (Upper Mandeville Tip). Seed traps made from multiple
layers of burlap will be anchored to the marsh surface near channel edges (Hicks and Hartman 2004`,
Neff and Baldwin 2005). All existing vegetation will be clipped from around the traps, and a 3-m
diameter vegetation survey will be conducted to determine presence and abundance of local species.
Depending on the size of the site, 10-20 traps will be deployed at each site in September 2008 and will be
replaced every three months for an entire year. Shallow soil cores will be removed near seed trap
locations to document the local seed bank. In a lab, traps and soil cores will be placed in cold storage for
2 weeks and then germinated in a greenhouse with freshwater in flats filled with sand. All seedlings will
be identified to species and counted. This method has been used successfully to document seed dispersal
and seed bank characteristics at tidal marshes along the Napa River, San Pablo Bay, CA (Diggory and
Parker, manuscript in prep.). These results will be used to modify model predictions for species found to
be dispersal-limited.
In order to evaluate potential rates of plant establishment under changing salinity and inundation
regimes, we will conduct companion field and greenhouse experiments. First we will collect seed bank
material during midwinter (prior to germination) from each of the four freshwater marshes above (Browns
Island, Sand Mound Slough, Lindsey Slough, and Upper Mandeville Tip), as well as the targeted brackish
and salt marsh sites (Rush Ranch, Coon Island, Petaluma Marsh, and China Camp). Sampling locations
will be randomly selected, but stratified into two groups at each marsh, in low marsh and in high marsh,
and samples taken in clusters. Individual seed bank samples will be collected from 25 x 25 cm surface
area, taken to a 5 cm depth. Locations of samples will be marked and GPS coordinates taken. Samples
will be divided into 3 groups. One group will assess natural seed bank composition and relative
dominance for each site using emergence technique (Hopkins and Parker 1984). For the other two groups,
samples from all sites will be bulked together and mixed thoroughly. Subsamples of half of the bulked
material will be germinated in the greenhouse under several treatment conditions. Treatments will include
9
Parker et al.
watering with a range of salinity concentrations (0 ppt, 4 ppt, 8 ppt, 16 ppt) and two flooding treatments
(well-drained, flooded to 2 cm) in a random block design. The other half of the bulked seed banks also
will be split into smaller subsamples, and placed into the field at 6 sites (2 freshwater tidal, 2 brackish
tidal and 2 salt marshes) under both high and low marsh conditions. We will use 25 x 25 cm quarter flats,
with bottoms replaced by fine mesh weed cloth, buried to the surface, to place the seed banks in the field.
Locations within marshes will use the previous seed bank sample sites. Soil samples lacking seeds
(blanks) will be placed at every site to account for new dispersal. Sites will be monitored throughout the
season for changes in soil salinity using refractometers. Data from these experiments will be used to
assess the potential influence of SLR and increases in salinity on future differential recruitment patterns,
including the influences of seedlings from other species. Because establishment rates from seed could be
quite low, we also will evaluate establishment rates from small seedlings (approximately 4 to 6 weeks
old) in the field. Seedlings of each species will be raised in the greenhouse and transplanted into the field
using the same sampling locations as the transplanted seedbank experiment (stratified high and low marsh
locations at all eight sites).
Combinations of experiments such as those proposed have provided direction into possible shifts
in seed bank and recruitment dynamics (Hopkins and Parker 1984, Parker and Leck 1985, Leck and
Simpson 1995, Baldwin et al. 1996, 2001). The seed trap data will allow assessment of potential upriver
dispersal limitation (Neff and Baldwin 2005, Diggory and Parker, manuscript in prep.). The seed bank
and seedling out-planting data will permit assessment of recruitment limitations, either by environmental
constraint (flooding or salinity) (Noe and Zedler 2001, Seabloom et al. 2001, Peterson and Baldwin 2004)
or by differential exclusion (competitive displacement or lack of facilitation) (Bertness and Ellison 1987,
Pennings and Callaway 1992, Callaway and Pennings 2000).
Avian distribution and abundance
At least 20 study sites have been annually surveyed in at least 10 breeding seasons during the
period 1996 to 2007. The other 35+ sites vary in the number of years in which avian surveys were
conducted, but points at all sites have at least one associated vegetation survey. We will revisit a subset of
our core sites and also identify and survey approximately 10 new freshwater sites in the delta region.
Standardized avian point count surveys (Ralph et al. 1993) accompanied by modified relevé surveys
(Spautz et al. 2006) will be used to collect additional bird and plant species distribution and abundance
data.
Statistical analysis and spatial modeling
Before developing predictive spatial models, we will carry out statistical analyses of plant and
avian survey data, so as to identify linkages between drivers (especially SLR and salinity) and proximate
factors (geomorphic features such as channel density; plant characteristics) as well as influences of both
physical and biotic factors on birds. Thus plant characteristics will be used as both outcome variables and
as independent variables. We will use insights gathered to develop and validate spatial models suitable for
predicting current distribution and abundance of plants and birds (SDMs) and then use these models to
make predictions of plant and bird response to future changes in salinity and SLR.
Statistical Analysis
Using path analysis (Wooton 1994), or more generally, structural equation modeling (Grace and Pugesek
1997), we will characterize causal and correlational relationships among the physical and biotic factors
and the outcomes of interest. This process will guide the development of spatially predictive models
described below. Structural equation modeling (SEM) is a very general, yet powerful multivariate
analysis technique that will allow us to consider multiple outcome variables and how they are linked,
directly and indirectly. The models we will develop will have salinity and inundation as driver variables,
which then have both direct and indirect pathways of influence. Thus tidal inundation could influence
channel density which influences plant productivity or structure which then influences bird species
10
Parker et al.
abundance, but there may also be direct influences of tidal inundation on bird abundance. These
pathways will be elucidated and evaluated using SEM. In addition, we will use this approach to evaluate
influences of one plant species on other species, as well as influences of one bird species on others (all
ecologically relevant species to be considered). These analyses will not only assist in predictive model
development, but results from these statistical analyses can be used to examine (confirm or question)
assumptions of the single species predictive modeling described below.
Modeling current species distribution and abundance
SDMs will be constructed for tidal marsh plant and avian species, using three primary data
sources for species occurrence data: (1) vegetation sampling plots and bird surveys conducted by the
principal investigators and their organizations (see Figure 2 for locations); (2) statewide public database
records for special status species occurrence, including the California Natural Diversity Database
(CNDDB; http://www.dfg.ca.gov/whdab/html/cnddb.html) and Jepson Herbarium on-line database
(http://ucjeps.berkeley.edu/db/smasch/); and (3) other data on the distribution of Bay-Delta plant and
animal species collected by research colleagues and public agencies, including the Invasive Spartina
Project (http://www.spartina.org). Species to be modeled include dominant plant species, special status
tidal marsh plant species, invasive plant species, and tidal marsh specialist avian species. In addition to
species distributions, we will develop models of abundance for common avian species, and models for
plant abundance (percent cover), productivity, and species diversity.
Environmental inputs will include spatial data layers representing salinity, elevation, tidal
inundation, and land use. Predicted plant and bird distributions will also be used as inputs to other
species’ models. The spatial resolution of our models will be tied to the resolution of available digital
elevation models (DEMs), which will also be used as a basis for future SLR and tidal inundation
scenarios: 10-m x 10-m pixels from the national elevation dataset (NED, http://ned.usgs.gov/). From
elevation, we will model tidal inundation, using continuous water level data from NOAA, various
municipalities, and restoration projects, including IRWM sites. We will develop tidal inundation graphs
for each tide gauge location and calculate total monthly and maximum daily tidal inundation during the
growing season (June/July), as well as tidal range. Inundation metrics will be interpolated across the
subtidal and intertidal portions of the Bay-Delta.
Coarser salinity data layers (approximately 3-km resolution) will be obtained from the CALFEDfunded CASCaDE project (http://sfbay.wr.usgs.gov/cascade/, see next section), and sampled down to the
10-m resolution. Current land use from NOAA’s 2000 C-CAP dataset, will be included in models for
vertebrate species, whose distributions and abundances are known to be limited by the composition and
configuration of surrounding uplands (Shriver et al. 2004; Spautz et al. 2006). From this and other land
use data, we will identify barriers to shoreward migration and use them as a mask for future distributions.
Depending on the type of data that are available for each species/metric, we will use and compare
variations on several different distribution modeling approaches:
 Presence-only data: Maximum entropy (MaxEnt) models
 Presence/absence data: Generalized linear models (GLM) or Generalized additive models
(GAM) with a binary distribution; and/or boosted regression trees
 Abundance data: GLMs or GAMs with a negative binomial distribution; and/or boosted
regression trees
 Species diversity / productivity data: GLMs with a Gaussian distribution; and/or boosted
regression trees.
Relationships between species metrics and environmental inputs (including the presence of other
similar species) will have been determined through the path analysis/SEM described above. Spatially
predictive models (“spatial models”) will be developed that predict current distributions (and abundance,
etc.), as well as potential future distributions under various climate change scenarios (Table 1). The use of
interaction terms in our models will allow better incorporation of interspecific competition effects, which
may limit or enhance the effects of physical drivers on plant communities. Predicted distributions of
11
Parker et al.
dominant and invasive (but not special-status) tidal marsh plant species, as well as predicted primary
productivity and species diversity, will also be used as inputs to the bird models (with non-tidal areas
masked out for model development). Tidal marsh channel density will also be modeled based on
environmental inputs and used as an input to bird models.
Model predictions will consist of spatial data layers covering potentially tidal habitats and
immediately adjacent uplands within the Bay-Delta system (see Figure 3). Each model will be built using
75% of the dataset, and evaluated using the other 25%, to obtain indicators of model predictive ability;
this will be repeated three more times so that all data are included in one test dataset. Functional
relationships will be evaluated, during the initial statistical analysis phase and as part of the field
experiments and in the model building phase, and used to further evaluate the performance of each model.
Examples of preliminary current and future predictions for a special status tidal marsh plant species,
based on coarse environmental inputs, are shown in Figure 4.
Modeling future distribution and abundance
Once models have been developed that adequately account for present distribution and abundance
of plants and birds, consistent with the SEM analysis and results of field experiments, we will turn to
applying these models to future changes in salinity and SLR. Our SLR scenarios will encompass a range
of predictions based on several emissions scenarios from the recent Intergovernmental Panel on Climate
Change (IPCC) Assessment 4 (AR4) simulations (IPCC 2007), incorporating thermal expansion as well
as melting of glaciers and ice caps, and adjusted for California (Cayan et al. 2005) (Table 1).
Future marsh elevation and tidal inundation predictions will be based on current topography and
will not include geomorphic change, unless such predictions become available for San Francisco Bay.
Future values for marsh relative elevation will be based on current elevation values, predicted SLR, and
rates of marsh accretion (Figure 4). Because there is uncertainty as to how much future marsh accretion
may occur, we will use two different estimates of marsh accretion: one which is indicative of current
conditions (based on sampling by the BREACH team in north San Francisco Bay and Callaway in other
Bay locations, as well as published values in Patrick and DeLaune 1990 and other sources) and the
potential for maximum marsh accretion based on a survey of other marsh systems (Stevenson et al. 1986;
Reed 1995; Callaway et al. 1996) (Table 1).
For estimates of future salinity, we will rely on predictions being generated by the CALFEDfunded CASCaDE project (http://sfbay.wr.usgs.gov/cascade/), an extension of previous California climate
modeling work conducted by the principal investigators (Knowles and Cayan 2002; Dettinger et al. 2004;
Knowles et al. 2006). Using GCMs scaled-down for California, temperature and precipitation predictions
were converted to monthly estimates of snowmelt runoff and stream flow, which were used to generate
salinity predictions (approximately 3-km resolution across bay salinity gradient) under various scenarios.
These predictions will be available from the CASCaDE team and will be used in conjunction with tidal
marsh salinity measurements collected by the IRWM project and the South Bay Salt Pond Restoration
Project, to extend salinity predictions into the tidal marsh zone.
Shifts in distribution, abundance, productivity, and species diversity will be assessed and
compared under each scenario, and the key environmental drivers will be identified in each model. For
plant species, we will assess the relative importance of physical factors compared with species
competition, for those species where competitive interactions were implicated (see Statistical Analysis
section, above). For avian species, we will evaluate the direct and indirect contributions of physical
(salinity, elevation, inundation, channel density) factors compared with biotic factors (plant species
composition and diversity) to better understand the mechanisms influencing their distribution and
abundance. Species currently co-occurring will be evaluated in terms of the similarity of future
distributions, providing an indication of maintenance or disruption of future community integrity.
Finally, for each species/metric, we will evaluate areas of highest potential loss and gain, across
emissions scenarios and accretion rate assumptions, providing insight for conservation and restoration
priorities. Results of field experiments will be used to modify or augment the predictions from the spatial
12
Parker et al.
models developed. For example, plant establishment studies may indicate broader ranges of
environmental tolerance (i.e., inundation and salinity ranges) than indicated by models for some plant
species, due to competitive exclusion. Conversely, dispersal experiments may indicate limits on modelpredicted distribution shifts. Incorporating these factors explicitly into models is beyond the scope of the
current proposed work, but this study will lay the foundation for future modeling by identifying and
quantifying the relevant constraints and interactions.
Research Schedule
We propose a three-year study incorporating 2 years of data collection that will supplement
previously-collected data throughout the Bay-Delta. In Year 1, personnel from San Francisco State
University (SFSU) and University of San Francisco (USF) will begin field sampling for plant diversity,
seed dispersal ,seed banks, and establishment. PRBO, SFSU, and USF will also summarize and prepare
existing species occurrence data, and prepare elevation and inundation inputs to SDMs. In Year 2, SFSU
and USF will conduct experiments investigating effects of increased salinity and inundation on plant
establishment. PRBO will conduct statistical analyses (SEM) as well as obtain spatial salinity projections,
develop, and validate SDMs, and generate predictions for SLR and salinity scenarios. In Year 3, USF and
SFSU will complete all analysis of field-collected observational and experimental data, and together with
PRBO, will finalize models, synthesize results, and write findings for publication.
Significance and Synergism of Collaborative Research Team
The collaborators in this project have worked together during the IRWM project, which was
previously mentioned. We combine extensive field experience with plants and birds, as well as modeling
and spatial analysis expertise. Through the IRWM project, we began developing a variety of metrics as a
predictive tool for plant and animal distributions and abundances in marshes, and in this proposed
research we will build on those findings. In addition, the plant PI and senior scientist (Parker, Callaway)
recently received funding from CALFED. The CALFED research would be synergistic with this one
(http://science.calwater.ca.gov/pdf/psp/PSP_TSP_results_summary_112206.pdf), focusing on
overlapping research sites along the same salinity gradient but targeting questions related to plant
productivity, decomposition rates, sedimentation dynamics, plant elevational distributions, and the
linkage to estuarine fish food webs. The CALFED research includes a different modeling approach,
focusing on potential climate change impacts for fish food webs in Bay-Delta marshes. In addition a
CALFED Ph.D. fellowship recently awarded to Lisa Schile (former Research Technician with Parker and
Callaway) will provide detailed data on plant interspecific interactions through field transplant
experiments and greenhouse experiments under a range of different salinity and inundation regimes. This
companion research supported by CALFED will provide data on well-established marsh plants; the
proposed plant research for this grant will fill a major gap in understanding potential plant community
shifts by evaluating climate change impacts on plant establishment. Together, these projects will make
tremendous contributions to the development of long-term management and policy initiatives for BayDelta tidal marsh vegetation and the animals that are dependent on them.
13
Parker et al.
TABLES AND FIGURES
Table 1. Climate change scenarios to be evaluated using SDM techniques (combinations with gray
shading). GCM outputs will be based on IPCC Assessment Report 4 (AR4) simulations. GFDL =
Geophysical Fluid Dynamics Laboratory; PCM = Parallel Climate Model. See IPCC (2007) for
description of emissions scenarios.
Salinity Emissions Scenarios / GCMs (from CASCADE project
SLR Emissions
output,
based on IPCC 2007 AR4 simulations)
Scenarios (Cayan et al.
2005)
B1 / GFDL
B1 / PCM
A2 / GFDL
A2 / PCM
current
current
accretion rates
accretion rates
B1 / GFDL (13-62 cm)
max.
max.
accretion
accretion
current
current
accretion rates
accretion rates
A2 / GFDL (18-76 cm)
max.
max.
accretion
accretion
current
current
accretion rates
accretion rates
A1fi / GFDL (21-89 cm)
max.
max.
accretion
accretion
2000
species diversity
ANPP
6
4
1000
2
ANPP (g/m2)
species diversity
1500
500
0
0
0
2
4
6
8
10
12
average spring water salinity (psu)
14
16
B
Figure 1. Average plant species diversity per 3m-diameter plot and ANPP decrease with increasing
salinity in the San Francisco Bay-Delta (error bars = ±1 SE; number of random plots per site range from
151 to 447). Salinity data represent measurements averaged across spring months in 2004 (Wetlands and
Water Resources, unpublished data). ANPP values were derived from site-specific averages of total
standing biomass of individual dominant species that were scaled up to site-level estimates using
vegetation maps, and then adjusted by site area to obtain ANPP estimates at the g/m2 level.
14
Parker et al.
Figure 2. Locations of
existing extensive and
intensive sampling
locations in the San
Francisco-Bay Delta.
Additional sites will be
selected in the Delta region
if possible.
Figure 3. Potential effects
of sea-level rise (SLR) in
the San Francisco-Bay
Delta. Maps depict
potential extreme shifts in
tidal marsh habitat with a
1m rise in sea level, under
the assumption that
accretion rates do not keep
up with SLR. Potential
tidal marsh habitat is
estimated between 0 and 1
meter above sea level.
Current marsh includes
tidal and non-tidal (i.e.,
diked or leveed) marsh.
Figure 4. Preliminary distribution model
predictions for Suisun marsh aster
(Symphyotrichum lentum) under current and
potential future climate change scenarios, using the
MaxEnt modeling approach (Phillips et al. 2006).
This model uses 1 m sea-level rise and increased
mean annual salinity projections (- 0.09 to +1.83
PSU; data provided by Noah Knowles, USGS).
Levee locations and land use information were not
included in this example
15
Parker et al.
LITERATURE CITED
Araujo, M. B., R. G. Pearson, W. Thuiller, and M. Erhard. 2005. Validation of species-climate envelope
models under climate change. Global Change Biology 11:1504-1513.
Armstrong, W. 1979. Aeration in higher plants. Advances in Botanical Research 7:225-232. assessment:
Modeling the Sacramento Basin. Water Resources Research 23(6): 1049-1061.
Atwater B.F.S., G. Conard, J. N. Dowden, C. W. Hedel, R. L. MacDonald, and W. Savage. 1979. History,
landforms, and vegetation of the estuary’s tidal marshes. In: Conomos TJ (ed.) San Francisco
Bay: the urbanized estuary. Pacific Division AAAS, San Francisco, pp 347-385.
Atwater, B.F. and C.W. Hedel. 1976. Distribution of seed plants with respect to tide levels and water
salinity in the natural tidal marshes of the northern San Francisco Bay estuary. California
U.S.Geol. Surv. Open-File Rep. 76-389. 41 pp.
Austin, G. E., and M. M. Rehfisch. 2003. The likely impact of sea level rise on waders (Charadrii)
wintering on estuaries. Journal for Nature Conservation 11:43–58.
Ayres, D.R., D.L. Smith, K. Zaremba, S. Klohr, and D.R. Strong. 2004. Spread of exotic cordgrasses and
hybrids (Spartina sp.) in the tidal marshes of San Francisco Bay, California, USA. Biological
Invasions 6:221-231.
Bakkenes, M., J. R. M. Alkemade, F. Ihle, R. Leemans, and J. B. Latour. 2002. Assessing effects of
forecasted climate change on the diversity and distribution of European higher plants for 2050.
Global Change Biology 8:390-407.
Baldwin, A. H. and I. A. Mendelssohn. 1998. Effects of salinity and water level on coastal marshes: an
experimental test of disturbance as a catalyst for vegetation change. Aquatic Botany 61:255-268.
Baldwin, A. H., M. S. Egnotovich, and E. Clarke. 2001. Hydrologic change and vegetation of tidal
freshwater marshes: Field, greenhouse, and seed-bank experiments. Wetlands 21:519-531.
Baldwin, A. H., K. K. McKee, and I. A. Mendelssohn. 1996. The influence of vegetation, salinity, and
inundation on seed banks of oligohaline coastal marshes. American Journal of Botany 83:470479.
Basham, M. P., and L. R. Mewaldt. 1987. Salt water tolerance and the distribution of South San Francisco
Bay Song Sparrows. Condor 89:697-709.
Baskin, C. C. and J.M. Baskin. 1998. Seeds: Ecology, Biogeography, and Evolution of Dormancy and
Germination. San Diego: Academic Press
Baumann, R.H., J.W. Day, Jr., and C.A. Miller. 1984. Mississippi deltaic wetland survival: Sedimentation
versus coastal submergence. Science 224:1093-1095.
Bertness, M.D., 1991. Interspecies interactions among high marsh perennials in a New England salt
marsh. Ecology 72:125-137.
Bertness, M. D., and A. M. Ellison. 1987. Determinants of pattern in a New England salt marsh
community. Ecological Monographs 57:129-47.
Boesch, D.F., M.N. Josselyn, A.J. Mehta, J.T. Morris, W.K. Nuttle, C.A. Simenstad, and D.J.P. Swift.
1994. Scientific assessment of coastal wetland loss, restoration and management in Louisiana.
Journal of Coastal Research Special issue 20:1-103.
Breiman, L., J. H. Freidman, and R. A. Olshen. 1984. Classification and Regression Trees. Wadsworth
and Brooks/Cole, Monterey, CA.
Burkett, V.R., D. A. Wilcox, R. Stottlemyer, W. Barrow, D. Fagre, J. Baron, J. Price, J.L. Nielsen, C.D.
Allen, D.L. Peterson, G. Ruggerone and T. Doyle. 2005. Nonlinear dynamics in ecosystem
response to climatic change: case studies and policy implications. Ecological Complexity 2: 357394.
Busby, J. R. 1991. BIOCLIM – a bioclimate analysis and prediction system. – In: Margules, C. R. and
Austin, M. P. (eds), Nature conservation: cost effective biological surveys and data analysis.
CSIRO, pp. 64–68.
16
Parker et al.
Callaway, J. C. and M. N. Josselyn. 1992. The introduction and spread of smooth cordgrass (Spartina
alterniflora) in south San Francisco Bay. Estuaries. 15:218-226.
Callaway, J.C., J.A. Nyman, and R.D. DeLaune. 1996. Sediment accretion in coastal wetlands: A review
and a simulation model of processes. Current Topics in Wetland Biogeochemistry 2:2-23.
Callaway, R.M. and S.C. Pennings. 2000. Facilitation may buffer competitive effects: Indirect and
diffuse interactions among salt marsh plants. American Naturalist, 156, 416-424.
Carpenter, G., A. N. Gillson, and J. Winter. 1993. DOMAIN: a flexible modeling procedure for mapping
potential distributions of plants and animals. Biodiversity and Conservation:667-680.
Casanova, M. T. and M. A. Brock. 2000. How do depth, duration and frequency of flooding influence the
establishment of wetland plant communities? Plant Ecology 147:237-250.
Cayan, D., P. Bromirski, K. Hayhoe, M. Tyree, M. Dettinger, and R. Flick. 2005. Projecting Future Sea
Level. California Climate Change Center Report (CEC-500-2005-202-SD), December 2005.
http://www.energy.ca.gov/2005publications/2005_pubs_alpha_order.html.
Chapman, V.J. 1974. Salt marshes and salt deserts of the world, 2nd Ed. J. Cramer, Berlin.
Cohen, A. N. and J. T. Carlton. 1998. Accelerating invasion rate in a highly invaded estuary. Science
279:555-558.
Colorado and Sacramento-San Joaquin River Basins. Journal of the American Water
Colwell, R.K. 2006. EstimateS. http://viceroy.eeb.uconn.edu/EstimateSPages/AboutEstimateS.htm
Crain, C. M., B. R. Silliman, S. L. Bertness, and M. D. Bertness. 2004. Physical and biotic drivers of
plant distribution across estuarine salinity gradients. Ecology 85:2539-2549.
Cronk, J. K. and M. S. Fennessy. 2001. Wetland Plants: biology and ecology. Lewis Publishers, Boca
Raton, FL.
Dettinger, M. D. 2005. From climate change spaghetti to climate-change distributions for 21st Century
California. San Francisco Estuary and Watershed Science 3(1) Article 4.
http://repositories.cdlib.org/jmie/sfews/vol3.iss1.art4
Dettinger, M. D., D. R. Cayan, M. K. Meyer and A. E. Jeton. 2004. Simulated hydrologic responses to
climate variations and change n the Merced, Carson, and American River basins, Sierra Nevada,
California, 1900-2099. Climatic Change 62:283-317.
Donnelly, J.P. and M.D. Bertness. 2001. Rapid shoreward encroachment of salt marsh cordgrass in
response to accelerated sea-level rise. Proc. Nat. Acad. Sci. (USA) 98: 14218-14223.
Dunne, J.A., R.J. Williams, and N.D. Martinez. 2002a. Network structure and biodiversity loss in food
webs: robustness increases with connectance. Ecology Letters 5:558-567.
Dunne, J.A., R.J. Williams, and N.D. Martinez. 2002b. Food-web structure and network theory: the role
of connectance and size. Proceedings of the National Academy of Sciences USA 99:1291712922.
Elith, J., C. H. Graham, R. P. Anderson, M. Dudik, S. Ferrier, A. Guisan, R. J. Hijmans, F. Huettmann, J.
R. Leathwick, A. Lehmann, J. Li, L. G. Lohmann, B. A. Loiselle, G. Manion, C. Moritz, M.
Nakamura, Y. Nakazawa, J. McC. M. Overton, A. Townsend Peterson, S. J. Phillips, K.
Richardson, R. Scachetti-Pereira, R. E. Schapire, J. Soberon, S. Williams, M. S. Wisz, and N. E.
Zimmermann. 2006. Novel methods improve prediction of species' distributions from occurrence
data. Ecography 29:129-151.
estuary. Science 231:567-573.
Evens, J., and G. W. Page. 1986. Predation on black rails during high tides in salt marshes. Condor
88:107-109.Flick, R. E. and D. R. Cayan. 1984. Extreme seas levels on the coast of California.
ASCE 19th Coastal Engineering Conference Proceedings. Houston, TX, pp. 886-897.
Fenner, M. and K. Thompson. 2005. The Ecology of Seeds. Cambridge: Cambridge University Press.
Ferrier, S. et al. 2002. Extended statistical approaches to modelling spatial pattern in biodiversity: the
north-east New South Wales experience. II. Community-level modelling. Biodiversity and
Conservation 12: 2309-2338.
Flick, R. E. 1998. Comparison of California tides, storm surges, and mean sea level during the El Niño
winters of 1982–1983 and 1997–1998. Shore and Beach 66:7-11.
17
Parker et al.
Flynn, K. M., K. L. McKee, and I. A. Mendelssohn. 1995. Recovery of fresh-water marsh vegetation after
a saltwater intrusion event. Oecologia 103:63-72.
Folke, C., S. Carpenter, B. Walker, M. Scheffer, T. Elmqvist, L. Gunderson, and C. S. Holling. 2004.
Regime shifts, resilience, and biodiversity in ecosystem management. Annual Review of Ecology
Evolution and Systematics 35:557-581.
Foxgrover, A. C., S. A. Higgins, M. K. Ingraca, B. E. Jaffe, and R. E. Smith. 2004. Deposition, erosion,
and bathymetric change in South San Francisco Bay: 1858-1983: U.S. Geological Survey OpenFile Report 2004-1192.25 p.
functioning. Conservation Biology 19:1019-1024.
Galbraith, H., R. Jones, R. Park, J. Clough, S. Herrod-Julius, B. Harrington, and G. Page. 2002. Global
climate change and sea level rise: potential losses of intertidal habitat for shorebirds. Waterbirds
25:173-183.
Gleick, P.H. 1987a. The development and testing of a water-balance model for climate impact
assessment: Modeling the Sacramento Basin. Water Resources Research 23:1049-1061.
Gleick, P.H. 1987b. Regional hydrologic consequences of increases in atmospheric carbon dioxide and
other trace gases. Climatic Change 10: 137-161.
Gleick, P.H. and E.L. Chalecki. 1999. The impacts of climatic changes for water recourses of the
Colorado and Sacramento-San Joaquin river basins. Journal of the American Water Resources
Association 35:1429-1441.
Goals Project. 1999. Baylands ecosystem habitat goals: A report of habitat recommendations. U.S.
Environmental Protection Agency, San Francisco, CA.
Grace, J.B. and B.H. Pugesek. 1997. A Structural equation model of plant species richness and its
application to a coastal wetland. The American Naturalist 149:436-460.
Greenberg, R., and S. Droege. 1990. Adaptations to tidal marshes in breeding populations of the swamp
sparrow. Condor 92:393-404.
Greenberg, R.J. Maldonado, S. Droege, and M.V. McDonald. 2006. Tidal marshes: a global perspective
on the evolution and conservation of their terrestrial vertebrates. BioScience 56:675-685.
Grosse, W., H.B. Buchel, and H. Tiebel. 1991. Pressurized ventilation in wetland plants. Aquatic Botany
39:89-98.
Guisan, A., and N. E. Zimmermann. 2000. Predictive habitat distribution models in ecology. Ecological
Modelling 135:147-186.
Guntenspergen G. R., and J. C. Nordby. 2006. The impact of invasive plants on tidal marsh vertebrate
species: Phragmites australis and Spartina alterniflora as case studies. Studies in Avian Biology
32:229-237
Hastie, T. J., and R. J. Tibshirani. 1990. Generalized Additive Models. Chapman & Hall, London.
Hicks, P.L. and J.M. Hartman. 2004. Can natural colonization successfully restore salt marsh habitat?: a
three-year assessment (New Jersey). Ecological Restoration 22: 141-143.
Hirzel, A.H., J. Hausser, D. Chessel, and N. Perrin, 2002. Ecological-niche factor analysis: How to
compute habitat-suitability maps without absence data? Ecology 83:2027-2036.
Hopkins, D. R. and V. T. Parker. 1984. A study of the seed bank of a salt marsh in northern San Francisco
Bay. Amer. J. Bot. 71:348-355.
Howard, R.J., and I.A. Mendelssohn. 1999. Salinity as a constraint on growth of oligohaline marsh
macrophytes. II. Salt pulses and recovery potential. American Journal of Botany 86:795-806.
Howard, R.J., and I.A. Mendelssohn. 2000. Structure and composition of oligohaline marsh plant
communities exposed to salinity pulses. Aquatic Botany 68:143-164.
Howe, H. F. and J. Smallwood. 1982. Ecology of seed dispersal. Annual Review of Ecology and
Systematics, 13: 201-228.
Hubbell, S. P. 2001. The Unified Neutral Theory of Biodiversity and Biogeography. Princeton:
Princeton University Press.
Hughes, T. P., D. R. Bellwood, C. Folke, R. S. Steneck and J. Wilson. 2005. New paradigms for
supporting the resilience of marine ecosystems. Trends Ecol. Evol. 20:380-386.
18
Parker et al.
immersion. Journal of Ecology 64:811-819.
Hurtt, G. C. and S.W. Pacala. 1995. The consequences of recruitment limitation: reconciling chance,
history and competitive differences between plants. Journal of Theoretical Biology 176: 1–12.
Ingebritsen, S.E., M.E. Ikehara, D.L. Galloway, D.R. Jones. 2000. Delta subsidence in California: the
sinking heartof the state. U.S. Geological Survey FS-005-00. 4 p.
Inouye, D. W., B. Barr, K. B. Armitage, and B. D. Inouye. 2000. Climate change is affecting altitudinal
migrants and hibernating species. Proceedings of the National Academy of Sciences 97:16301633.
IPCC. 2007. Climate Change 2007, the Fourth Assessment Report (AR4) of the United Nations
Intergovernmental Panel on Climate Change. Working Group I (WGI): The Physical Science
Basis. Available at htpp://www.ipcc.ch/.
Iverson, L. R., and A. M. Prasad. 1998. Predicting abundance of 80 tree species following climate change
in the eastern United States. Ecological Monographs 68: 465-485.
James, F. C., and N. O. Wamer. 1982. Relationships between temperate forest bird communities and
vegetation structure. Ecology 63:159-171.
Johnston, R.F., 1956. Population structure in salt marsh Song Sparrows Part II: Density, age structure,
and maintenance. The Condor 58:254-274.
Josselyn M. 1983. The ecology of San Francisco Bay tidal marshes: a community profile. Biological
Report FWS/OBS-83/23. U.S. Fish and Wildlife Service, Division of Biological Services,
Washington, DC.
Josselyn, M., and J. Callaway. 1988. Ecological Effects of Global Climate Change: Wetland Resources of
San Francisco Bay. Prepared for the Environmental Research Laboratory, U.S. Environmental
Protection Agency. Romberg Tiburon Center for Environmental Studies, Tiburon, CA. 39 p.
Journal of Ecology 64:793-809.
Kearney, M.S., and J.C. Stevenson. 1991. Island land loss and marsh vertical accretion rate evidence for
historical sea-level changes in Chesapeake Bay. Journal of Coastal Research 7:403-415.
Keddy, P. A. and T. H. Ellis. 1985. Seedling recruitment of 11 wetland plant species along a water level
gradient: Shared or distinct responses? Canadian Journal of Botany 63: 1876-1879.
Knowles, K., M. D. Dettinger, and D. R. Cayan. 2006. Trends in Snowfall versus Rainfall in the Western
United States. Journal of Climate 19:4545-4559.
Knowles, N. and D. Cayan. 2004. Elevational dependence of projected hydrologic changes in the San
Francisco Estuary and watershed: Climatic Change 62:319-336.
Knowles, N. and D.R. Cayan. 2002. Potential effects of global warming on the Sacramento/San Joaquin
watershed and the San Francisco estuary: Geophysical Research Letters 29:1-5.
Lawler, J. J., D. White, R. P. Neilson, and A. R. Blaustein. 2006. Predicting climate-induced range shifts:
model differences and model reliability. Global Change Biology 12:1568-1584.
Lawrence, A. J., and J. M. Soame. 2004. The effects of climate change on the reproduction of coastal
invertebrates. Ibis 146:29-39.
Leathwick, J. R. et al. 2005. Using multivariate adaptive regression splines to predict the distributions of
New Zealand’s freshwater diadromous fish. Freshwater Biology 50: 2034-2052.
Leathwick, J.R., J. Elith, M.P. Francis, T. Hastie, and P. Taylor. 2006. Variation in demersal fish species
richness in the oceans surrounding New Zealand: an analysis using boosted regression trees.
Marine Ecology Progress Series 321:267-281
Leck, M. A., and R. L. Simpson. 1995. Ten-year seed bank and vegetation dynamics of a tidal freshwater
marsh. American Journal of Botany 82:1547-1557.
Leck, M.A., V.T. Parker and R.L. Simpson (eds). 1989. Ecology of Soil Seed Banks. Academic Press,
NY.
Leibold, M. A., M. Holyoak, N. Mouquet, P. Amarasekare, J.M. Chase, M.F. Hoopes, R.D. Holt, J.B.
Shurin, R. Law, D. Tilman, M. Loreau, and A. Gonzalez. 2004. The metacommunity concept: a
framework for multi-scale ecology. Ecology Letters 7:601-613.
Lettenmaier D.P. and T. Y. Gan. 1990. Hydrologic sensitivities of the Sacramento-San Joaquin
19
Parker et al.
Levine, J. M. and D.J. Murrell, D. J. 2003. The community-level consequences of seed dispersal
patterns. Annual Review of Ecology and Systematics 34:549-574.
Lyons, K. G., C. A. Brigham, B. H. Traut, and M. W. Schwartz. 2005. Rare species and ecosystem
functioning. Conservation Biology 19:1019-1024.
Mahall, B. E. and R. B. Park. 1976a. The ecotone between Spartina foliosa Trin. and Salicornia virginica
L. in salt marshes of northern San Francisco Bay. I. Biomass and production. J. Ecol.
64:421-433.
Mahall, B. E. and R. B. Park. 1976b. The ecotone between Spartina foliosa Trin. and Salicornia virginica
L. in salt marshes of northern San Francisco Bay. II. Soil water and salinity. J. Ecol. 64:793-809.
Mahall, B. E. and R. B. Park. 1976c. The ecotone between Spartina foliosa Trin. and Salicornia virginica
L. in salt marshes of northern San Francisco Bay. III. Soil aeration and tidal immersion. J. Ecol.
64:811-819.
McCullagh, P. and J. A. Neder. 1989. Generalized Liner Models. Chapman and Hall, London.
Mendelssohn, I.A., and J.T. Morris. 2000. Ecophysiological controls on the productivity of Spartina
alterniflora Loisel. In: Concepts and Controversies in Tidal Marsh Ecology. Eds. M.P. Weinstein
and D.A. Kreeger. pp 59-80.
Mendelssohn, I.A., K.L. McKee, and W.H. Patrick, Jr. 1981. Oxygen deficiency in Spartina alterniflora
roots: Metabolic adaptation to anoxia. Science 214:439-441.
Mitsch, W.J., and J.G. Gosselink. 2000. Wetlands, 3rd ed edition. John Wiley, New York, NY.
Morris, J. T. 2006. Competition among marsh macrophytes by means of geomorphological displacement
in the intertidal zone. Estuarine Coastal and Shelf Science 69:395-402.
Morris, J.T., P.V. Sundareshwar, C.T. Nietch, B. Kjerfve, and D.R. Cahoon. 2002. Responses of coastal
wetlands to rising sea level. Ecology 83: 2869-2877.
Mount, J. and R. Twiss. 2005. Subsidence, sea level rise, and seismicity in the Sacramento-San Joaquin
Delta. San Francisco Estuary and Watershed Science 3(1):Article 5,
http://repositories.cdlib.org/jmie/sfews/vol3/iss1/art5.
Nathan, R. and H.C. Muller-Landau. 2000. Spatial patterns of seed dispersal, their determinants and
consequences for recruitment. Trends in Ecology and Evolution 15: 278-285.
Neff, K. P., and A. H. Baldwin. 2005. Seed dispersal into wetlands: Techniques and results for a restored
tidal freshwater marsh. Wetlands 25:392-404.
Nichols, F. H., J. E. Cloern, S. N. Luoma, and D. H. Peterson. 1986. The modification of an Estuary.
Science 231:567-573
Noe, G. B., and J. B. Zedler. 2001. Spatio-temporal variation of salt marsh seedling establishment in
relation to the abiotic and biotic environment. Journal of Vegetation Science 12:61-74.
Odum, W. E. 1988. Comparative ecology of tidal fresh-water and salt marshes. Annual Review of
Ecology and Systematics 19:147-176.
Olofson, P. R., editor. 2000. Bayland ecosystem species and community profiles: Life histories and
environmental requirements of key plants, fish, and wildlife. San Francisco Bay Regional Water
Quality Control Board, Oakland, CA.
Orr, M., S. Crooks, and P. B. Williams. 2003. Will Restored Tidal Marshes Be Sustainable? San
Francisco Estuary and Watershed Science. 1:Article 5.
http://repositories.cdlib.org/jmie/sfews/vol1/iss1/art5.
Ostander, G. K., K. M. Armstrong, E.T. Knobbe, D. Gerace and E. P. Scully. 2000. Rapid transition in the
structure of a coral reef community: The effects of coral bleaching and physical disturbance.
Proceedings of the National Academy of Science (USA) 97:5297-5302.
Overpeck, J.T., B.L. Otto-Bliesner, G.H. Miller, D.R. Muhs, R.B. Alley, and J.T. Kiehl. 2006.
Paleoclimatic evidence for future ice-sheet instability and rapid sea-level rise. Science 311:17471750.
Parker, V. T., and M. A. Leck. 1985. Relationships of seed banks to plant-distribution patterns in a freshwater tidal wetland. American Journal of Botany 72:161-174.
Parmesan, C. 1996. Climate and species' range. Nature 382:765-766.
20
Parker et al.
Patrick, W. H., Jr., and R. D. DeLaune. 1990. Subsidence, accretion, and sea level rise in south San
Francisco Bay marshes. Limnology and Oceanography 35:1389-1395.
Pearcy, R.W. and S.L. Ustin. 1984. Effects of salinity on growth and photosynthesis of three California
tidal marsh species. Oecologia 62: 68-73.
Pearson, R. G., and T. P. Dawson. 2003. Predicting the impacts of climate change on the distribution of
species: are bioclimate envelope models useful? Global Ecology and Biogeography 12:361-371.
Pearson, R. G., T. P. Dawson, P. M. Berry, and P. A. Harrison. 2002. SPECIES: A spatial evaluation of
climate impact on the envelope of species. Ecological Modelling 154:289-300.
Peet, R.K., T.R. Wentworth, and P.S White. 1998. A flexible, multipurpose method for recording
vegetation composition and structure. Castanea 63:262-274.
Pennings, S. C. and R. M. Callaway. 1992. Salt marsh zonation: the relative importance of competition
and physical factors. Ecology 73: 681-690.
Pennings, S.C., E.R. Selig, L.T. Houser, and M.D. Bertness, 2003. Geographic variation in positive and
negative interactions among salt marsh plants. Ecology 84.
Pennings, S.C., M-B Grant and M.D. Bertness. 2005. Plant zonation in low-latitude salt marshes:
disentangling the roles of flooding, salinity and competition. J. Ecol. 93: 159-167.
Peterson, A. T. 2001. Predicting species' geographic distributions based on ecological niche modeling.
Condor:599-605.
Peterson, J. E., and A. H. Baldwin. 2004. Seedling emergence from seed banks of tidal freshwater
wetlands: response to inundation and sedimentation. Aquatic Botany 78:243-254.
Phillips, S. J., R. P. Anderson, and R. E. Schapire. 2006. Maximum entropy modeling of species
geographic distributions. Ecological Applications 190:231-259.
Porter, W. P., S. Budaraju, W. E. Stewart, and N. Ramankutty. 2000. Calculating climate effects on birds
and mammals: impacts on biodiversity, conservation, population parameters, and global
community structure. American Zoologist 40:597-630.
Ralph, C. J., G. R. Geupel, P. Pyle, T. E. Martin, and D. F. DeSante. 1993. Handbook of field methods for
monitoring landbirds. Gen. Tech. Rep., USDA Forest Service, Pacific Southwest Research
Station 144.
Rasse, D.P., G. Peresta and B. G. Drake. 2005. Seventeen years of elevated CO2 exposure in a
Chesapeake Bay wetland: sustained by contrasting responses of plant growth and CO2 uptake.
Global Change Biology 11: 369-377.
Reed, D.J. 1995. The response of coastal marshes to sea-level rise: Survival or submergence. Earth
Surface Processes and Landforms 20:39-48.
Rehfeldt, G.E., N.L. Crookston, M.V. Warwell, and J.S. Evans. 2006. Empirical analyses of plant-climate
relationships for the western United States. International Journal of Plant Sciences 167: 1123–
1150
Rehfisch, M. M., G. E. Austin, S. N. Freeman, M. J. S. Armitage, and N. H. K. Burton. 2004. The
possible impact of climate change on the future distributions and numbers of waders on Britain's
non-estuarine coast. Ibis 146:70-81.
Resources Association 35: 1429-1441.
Rignot, E., and P. Kanagaratnam. 2006. Changes in the velocity structure of the Greenland ice sheet.
Science 311:986-990.
Ripley, B. D. 1996. Pattern recognition and neural networks. Cambridge University Press, Cambridge.
River basin, California, to global warming. Water Resour Res 26:69-86.
Roos, M. 1989. Possible climate change and its impact on water supply in California. Oceans ’89
Conference, Inst. Of Electr. And Electron. Eng., Seattle, WA.
Root, T. L., J. T. Price, K. R. Hall, S. H. Schneider, C. Rosenzweig, and A. Pounds. 2002. 'Fingerprints'
of global warming on wild animals and plants. Nature 421:57-59.
Rotenberry, J. T., and J. A. Wiens. 1980. Habitat structure, patchiness, and avian communities in North
American steppe vegetation: a multivariate analysis. Ecology 61:1228-1250.
San Francisco Estuary Project. 1991. Status and trends report on wetlands and related habitats in
21
Parker et al.
Sanderson, E.W., S.L. Ustin, T.C. Foin. 2000. The influence of tidal channels on the distribution of salt
marsh plant species in Petaluma Marsh, CA, USA. Plant Ecology 146: 29-41.
Scavia, D., J.C. Field, D.F. Boesch, R.W. Buddemeier, V. Burkett, D.R. Cayan, M. Fogarty, M.A.
Harwell, R.W. Howarth, C. Mason, D.J. Reed, T.C. Royer, A.H. Sallenger, and J.G. Titus. 2002.
Climate Change Impacts on U.S. Coastal and Marine Ecosystems. Estuaries 25: 149-164.
Scheffer, M. and S.R. Carpenter. 2003. Catastrophic regime shifts in ecosystems: linking theory to
observation. Trends Ecol. Evol. 18: 648-656.
Scheffer, M., S. Carpenter, J.A. Foley, C. Folkes, and B. Walker. 2001. Catastrophic shifts in ecosystems.
Nature 413:591-596.
Seabloom, E. W., K. A. Moloney, and A. G. van der Valk. 2001. Constraints on the establishment of
plants along a fluctuating water-depth gradient. Ecology 82:2216-2232.
Seabloom, E. W., J. W. Williams, D. Slayback, D. M. Stoms, J. H. Viers, and A. P. Dobson. 2006.
Human impacts, plant invasion, and imperiled plant species in California. Ecological
Applications 16:1338–1350.
Shriver, W. G., T. P. Hodgman, J. P. Gibbs, and P. D. Vickery. 2004. Landscape context influences salt
marsh bird diversity and area requirements in New England. Biological Conservation 119:545553.
Simenstad, C., J. Toft, H. Higgins, J. Cordell, M. Orr, P. Williams, L. Grimaldo, Z. Hymanson, and D.
Reed. 2000. Sacramento/San Joaquin Delta Breached Levee Wetland Study (BREACH).
Unpulished report to CALFED. [URL: http://depts.washington.edu/calfed/breachii.htm]
Spautz, H., N. Nur, D. Stralberg, and Y. Chan. 2006. Multiple-scale habitat relationships of tidal marsh
breeding birds in the San Francisco Bay estuary. Environmental threats to tidal marsh vertebrates
of the San Francisco Estuary. Proceedings of the Vertebrates of Tidal Marshes Symposium,
October 24-26, 2002, Patuxent, MD. Studies in Avian Biology No. 32:247-269.
Stevenson, J.C., L.G. Ward, and M.S. Kearney. 1986. Vertical accretion in marshes with varying rates of
sea level rise. Pages 241-259 in D.A. Wolfe, editor. Estuarine variability. Academic Press, San
Diego, CA.
the San Francisco Estuary : Public report. San Francisco Estuary Project, Oakland, Calif.
Stohlgren, T.J. 2007. Measuring plant diversity: lessons from the field. New York: Oxford University
Press. 390p.
Stohlgren, T.J., M.B. Falkner, and L.D. Schell. 1995. A modified-Whittaker nested vegetation sampling
method. Vegetatio 117:113-121.
Thomson, D.M., G.P. Shaffer, and J.A. McCorquodale. 2001. A potential interaction between sea-level
rise and global warming: implications for coastal stability on the Mississippi River Deltaic Plain.
Global and Planetary Change 32:49-59.
Thuiller, W. 2004. Patterns and uncertainties of species' range shifts under climate change. Global
Change Biology 10:2020-2027.
Turner, R.E., E.M. Swenson, C.S. Milan, J.M. Lee and T.A. Oswald. 2004. Below-ground biomass in
healthy and impaired salt marshes. Ecological Research 19:29-35.
virginica L. in salt marshes of northern San Francisco Bay: II. Soil water and salinity.
virginica L. in salt marshes of northern San Francisco Bay: III. Soil aeration and tidal
Visser, J.M., C.E. Sasser, R.H. Chabreck, and R.G. Linscombe. 2002. The impact of a severe drought on
the vegetation of a subtropical estuary. Estuaries 25:1184-1195.
Wang, F. C. 1988. Dynamics of saltwater intrusion in coastal channels. Journal of Geophysical Research
93:6937-6946.
Warren, R.W., and W.A. Niering. 1993. Vegetation change on a northeast tidal marsh: Interaction of sealevel rise and marsh accretion. Ecology 74:96-13.
Wiens, J. A., and J. T. Rotenberry. 1981. Habitat associations and community structure of birds in
shrubsteppe environments. Ecological Monographs 51:21-41.
Williams, P.B. 1989. The impacts of climate change on the salinity of San Francisco Bay. In: SmithJB,
Tirpak DA, editors. The potential effect of global climate change on the United States. Appendix
22
Parker et al.
A, Water Resources. Prepared for the United States Environmental Protection Agency, Office of
Policy Planning and Evaluation. EPA-230-05-89-051. Washington DC: US EPA. p 3-1 to p 3-29.
Wintle, B., J. Elith, and J.M. Potts. 2005. Fauna habitat modelling and mapping: A review and case study
in the Lower Hunter Central Coast region of NSW. Austral Ecology 30:719-738.
Wooton, J.T. 1994. Predicting direct and indirect effects: An integrated approach using experiments and
path analysis. Ecology 75:151-165.
Young, J.A., C.E. Turner, and L.F. James. 1995. Perennial pepperweed. Rangelands 17:121-123.
Zedler, J. B., J. C. Callaway, J. S. Desmond, G. Vivian-Smith, G. D. Williams, G. Sullivan, A. E.
Brewster, and B. K. Bradshaw. 1999. Californian salt marsh vegetation: An improved model of
spatial pattern. Ecosystems 2:19-35.
23