WINTER SHOREBIRD COMMUNITIES OF HUMBOLDT BAY: SPECIES DIVERSITY, DISTRIBUTIONS, AND HABITAT CHARACTERISTICS
by
Tamar Danufsky
A Thesis
Presented to
The Faculty of Humboldt State University
In Partial Fulfillment
of the Requirements for the Degree
Master of Science
In Natural Resources: Wildlife
August, 2000 WINTER SHOREBIRD COMMUNITIES OF HUMBOLDT BAY: SPECIES DIVERSITY, DISTRIBUTIONS, AND HABITAT CHARACTERSITICS
By
Tamar Danufsky
Approved by the Master's Thesis Committee
Mark A. Colwell, Major Professor Date
Richard T. Golightly, Committee Member Date
Yoon G. Kim, Committee Member Date
Associate Dean,
College of Natural Resources and Sciences Date
00-W-428-06/07 Natural Resources Graduate Program Number
Approved by the Dean of Graduate Studies
Ronald A. Fritzsche Date ABSTRACT
Patterns of species assemblages are often influenced by habitat variables. I used nested subset analysis to determine if wintering shorebird assemblages at 19 sites on
Humboldt Bay have a significantly nested pattern. I then analyzed species diversity, individual species' densities, and species' incidences in relation to the following habitat characteristics: mudflat width, channelization, standing water, ebb height, and sediment particle size. An additional variable, length of tide-line (analogous to area surveyed) was analyzed for diversity and incidence, and variability of the sediment was added to the diversity analysis as a measure of habitat complexity.
I found a significantly nested pattern that was not caused by random sampling.
Killdeer (Charadrius vociferus), American Avocet (Recurvirostra americana), Spotted
Sandpiper (Actitis macularia), and Black Turnstone (Arenaria melanocephala) were species that detracted disproportionately from the nested pattern. These species have specialized habitat requirements suggesting a link between habitat use and nestedness.
Diversity was most strongly correlated with tide-line and substrate variability.
Standing water, ebb height, sediment particle size, and tide-line were the most frequently occurring variables in the density and incidence models. All species analyzed had significant (P < 0.1) incidence models. In the two most significant density
models yellowlegs (Tringa melanoleuca and T. flavipes) correlated negatively with ebb height (P = 0.0026) and Long-billed Curlew (Numenius americanus) correlated positively with channelization and ebb height (P = 0.014). Two of the most abundant
iii species, Marbled Godwit (Limosa fedoa) and Dunlin (Calidris alpina) had the poorest correlations with habitat variables.
I found that wintering shorebird communities using Humboldt Bay are influenced by physical characteristics of the mudflats. I recommend additional study on the mechanisms that affect shorebird use of sites to aid in protecting Humboldt Bay as an important wintering and migratory stopover site for large numbers of shorebirds.
iv ACKNOWLEDGEMENTS
My sincere gratitude goes to my major advisor, Dr. Mark Colwell, for his support, enthusiasm, guidance, patience, and humor throughout. My committee members Drs.
Richard Golightly and Yoon Kim provided valuable suggestions and guidance. Thanks also to Dr. Mark Rizzardi for his assistance. I am extremely grateful to Dr. Larry Fox, when he offered to assist me in remote sensing classification I suspect that neither of us anticipated how much of his time it would involve. Ryan Mathis, Georgia Trehey, Dr.
Steve Steinberg, and Martin Garrett helped me with GIS and remote sensing problems.
Dr. Jeff Borgeld kindly gave me access to the Telonicher Marine Laboratory, where he and Heather Carnocki guided me through sediment analysis.
I am indebted to Bo Brown who traveled so far, and to Mary Ortwerth, who
appeared out of nowhere, for their expert assistance in shorebird surveys. Joe Liebezeit helped with surveys, and Jeff Moore and Patrick Kleeman assisted with sediment collection. This project benefited from conversations with Dr. Rocky Gutierrez, Mylea
Petersburg, Monica Bond, Peter Carlson, Dr. Matt Johnson, and Carlin Finke. I thank
Sergio Henricy for assistance with GIS and remote sensing, editing, and his willingness
to get stuck in the mud. Special thanks go to Linda and Thomas Leeman, who assisted
me in most of the activities mentioned above. My graduate experience was greatly
enhanced through their friendship, support, and assistance. This study was funded in part by a generous scholarship from the Marin Rod and Gun Club. Finally, I am grateful
to my family for their unwavering support and encouragement in all my endeavors. TABLE OF CONTENTS
ABSTRACT iii
ACKNOWLEDGMENTS v
TABLE OF CONTENTS vi
LIST OF TABLES viii
LIST OF FIGURES ix
INTRODUCTION 1
METHODS 4
Study Area 4
Survey Methods 6
Habitat Characteristics 7
Site area 7
Mudflat width 9
Channelization 9
Timing of tidal movements 9
Substrate 9
Standing water 10
Mudflat variability 10
Data Summary and Analysis 12
Surveys 12
Nested subset analysis 12
Statistical analysis 14
vi RESULTS 16
Site Characteristics 16
Shorebird Observations 16
Nested Subset Analysis 16
Random Placement 19
Species Diversity 19
Species Incidence 19
Density 23
DISCUSSION 26
Nested Subsets 26
Species Diversity 28
Habitat Characteristics and Site Use 29
Conclusions and Conservation Implications 33
LITERATURE CITED 37
APPENDIXES 45
A. Dates of Each of Four Surveys at Humboldt Bay Study Sites 45
B. Remote Sensing Classification of Study Site Substrates 46
vii LIST OF TABLES
Table Page
1 Species' codes, common and scientific names, mean abundance, and relative abundance for all shorebird species observed 8
2 Mean abundances of shorebird species observed at study sites. Numbers presented are means of the high counts for each species from the four surveys at each site 13
3 Physical characteristics of 19 study sites on Humboldt Bay 17
4 Results of logistic regression analyses on presence/absence of species at sites in relation to habitat characteristics. There are two best models for Sanderling, combining all three variables (TIDE- LINE, EBB, and MUD) resulted in complete separation in sample points (see text for details) 22
5 Results of multiple regression analyses on species' densities in relation to habitat characteristics. Transformations are of the dependent variable (species density) 24
6 Total number of regression models in which habitat characteristics occurred for species' incidences (multiple logistic regressions), and densities (multiple regressions), and the number of models in which the coefficients for each habitat variable were significant 25
7 Spearman correlation matrix and P-values for shorebird habitat characteristics on Humboldt Bay 32
8 Expected (from previous studies) vs. observed (in this study) signs of coefficients for correlations between habitat variables and species' occurances (density or incidence analysis). * P < 0.1, ** P < 0.05. Boxes indicate agreement and underlines indicate discrepancy between expected and observed signs (significant results only) 34
9 Statistics of substrate classes from Landsat TM classification 48
viii LIST OF FIGURES
Figure Page
1 Locations of study sites (identified by site numbers) and extent of mudflats on Humboldt Bay. Water level shown is at mean lower low water (MLLW). Line-work is reproduced from a paper navigational map (Humboldt Bay 1993) 5
2 The relationship between the ratio of percent sand to percent clay from sediment samples and the log of Landsat TM band 4 reflectance values. y = -81.3x + 125.35, R2 = 0.83 11
3 Matrix depicting presence (shaded squares) of shorebird species at study sites and histograms showing idiosyncratic species (at bottom) and sites (on right). The lines through the histograms represent the matrix temperature. Species and sites that exceed the matrix temperature are considered idiosyncratic 18
4 The relationship between log of mean species abundance and species incidence at survey sites on Humboldt Bay. Two species (Spotted Sandpiper and Ruff) had identical incidences and abundances (single observations) resulting in overlapping points 20
5 Random placement model comparing the number of shorebird species with relative area (alpha) at 19 study sites on Humboldt Bay, CA, during November 1998 through February 1999. Solid line is (predicted number of species), dashed lines represent circles represent observed species diversity at study sites 21
6 Locations of 31 sediment samples collected from Humboldt Bay mudflats between 18 January 1999 and 30 April 1999 49
ix INTRODUCTION
The essence of community ecology is in identifying and explaining patterns in nature (Wiens 1989). Recognition of patterns of species diversity, composition, and distribution leads to an increased understanding of the underlying processes which create these patterns. Some of the factors that influence community patterns include habitat characteristics, interspecific interactions, and species' relative abundances,
(Wiens 1989). In particular, patterns of species diversity and composition are strongly influenced by habitat. For example, there is a well established positive correlation between species diversity and habitat complexity (MacArthur and MacArthur 1961,
Recher 1969, Jarvinen and Haila 1984). The explanation for this pattern is that as habitats increase in complexity, more niches are available to additional species.
Another community pattern, nested subsets, occurs when species assemblages are subsets of progressively richer assemblages (Patterson 1987). In a perfectly nested system all species present in an assemblage are also present in more species-rich communities. Nesting of habitats has been identified as one of the factors causing a nested species assemblage pattern (Cutler 1991, Worthen 1996). Thus, detecting a nested pattern can shed light on how habitat characteristics may influence species distributions.
Many shorebirds show preferences for particular habitat characteristics (e.g.,
Colwell and Landrum 1993, Warnock and Takekawa 1995, Lopez-Uriarte et al. 1997).
Shorebird prey have a complex relationship with substrate characteristics (Goss-Custard
1 2 et al. 1977, Pienkowski 1981, Hicklin and Smith 1984, Yates et al. 1993a). Recher
(1966) found the highest shorebird species diversity was in areas with a variety of
substrate types. Shorebirds may choose foraging sites based on prey density (Boland
1988, Kalejta and Hockey 1994), substrate composition (Quammen 1982, Gerritsen and
van Heezik 1985), or substrate wetness (Ashmole 1970, Kelsey and Hassall 1989, Le
Drean-Quenec'hdu et al. 1995). Moisture may increase invertebrate activity, and
substrate penetrability, thus shorebirds may find more food at sites with large areas of
standing water (Bradstreet et al. 1977, Evans and Dugan 1984, Kelsey and Hassall
1989). Conversely, standing water may decrease the available foraging area if it is too
deep for birds. Another important habitat characteristic may be the presence of
channels, as some species appear to preferentially forage in or on the edge of channels
(Evans and Harris 1994), especially during periods of inclement weather (Townshend et
al. 1984). Timing of tidal movements may be important to shorebirds as earlier exposed
mudflats are available to them while most other foraging habitat is still flooded (Ramer
et al. 1991). Additional characteristics may include the width of the mudflat (Congdon
and Catterall 1994) and the presence of vegetation (Kalejta and Hockey 1994).
Humboldt Bay is an important wintering and migratory stopover site for large
numbers of shorebirds (Gerstenberg 1979, Senner and Howe 1984, Colwell 1994, Page
et al. 1999). However, few studies have analyzed shorebird use of Humboldt Bay
mudflats (Gerstenberg 1979, Holmberg 1975). In these studies mudflats were grouped in broad categories, thus the affect of small scale variation in Humboldt Bay mudflat characteristics on shorebird use has not been investigated. 3 In this study I analyzed wintering shorebird use of Humboldt Bay mudflats in relation to mudflat characteristics. First, I used nested subset analysis to determine if species assemblages at study sites had a significantly nested pattern. Then, I analyzed species diversity, individual species' densities, and species' incidences in relation to characteristics of the mudflats. METHODS
Study Area
Humboldt Bay is the largest estuary between San Francisco Bay, 372 km to the south, and Coos Bay, 335 km to the north. Humboldt Bay consists of two shallow tidal basins, Arcata Bay and South Bay, connected by a narrow passage and smaller bay,
Entrance Bay (Figure 1). Humboldt Bay is 22.5 km long and 7.2 km wide at its widest point. The bay is separated from the ocean by two long spits. Four watersheds empty into the bay, two into Arcata Bay, one into Entrance Bay, and one into South Bay.
Arcata and South bays contain extensive mudflats cut by drainage channels. The bay is characterized by two high and two low tides of unequal height each day, exposing variable amounts of intertidal habitat. Approximately 61 km2 of mudflats are exposed at mean lower low water (MLLW) (Barnhart et al. 1992). The tidal range during the period of this study was a low of —0.68 m and a high of 2.74 m (NOAA/NOS 1999).
Habitats in the bay have been described in detail by Gerstenberg (1979). Sediments in
Humboldt Bay have been described by Thompson (1971) as various combinations of sand, silt, and clay.
I selected 19 sites on Humboldt Bay for shorebird surveys based on accessibility, ability to delineate boundaries, and distribution of sites throughout the bay (Figure 1). I delineated site boundaries in the field using landmarks such as channels, navigation markers, and islands. At each site, I mapped the boundary of the survey area on high- resolution (0.3 m pixels) digital aerial photos (Terra-Mar 1997) taken on
4 5
Figure 1. Locations of survey sites (identified by site numbers), and extent of mudflats on Humboldt Bay. Water level shown is at mean lower low water (MLLW). Line-work is reproduced from a paper navigational map (Humboldt Bay 1993). 6 December 10, 1997. I measured site characteristics (see below) using ArcView 3.1 and ArcInfo 7.2.1 geographic information system (GIS) software (ESRI, Redlands, CA
1998).
Survey Methods
Six observers surveyed shorebirds on Humboldt Bay, from 13 November 1998 - 18
January 1999 (see Appendix A for dates of surveys, IACUC approval #98/99.W.41A).
This time interval is within the period of minimal migratory movement by shorebirds at
Humboldt Bay (Harris 1996). I modified survey methodology from Colwell (1994) who surveyed birds on a rising tide. At low tide, birds are widely dispersed and patchily distributed (Recher 1966). In addition, numbers of feeding shorebirds are highest in the two hours before and after low tide (Burger et al. 1977). Some investigators have found maximum habitat use during ebb tides (Holway 1990), others during flood tides (Burger et al. 1997). I was not able to survey on rising tides because few rising tides occurred during daylight hours during the period of this study. Therefore, I conducted surveys on falling tides. Observers surveyed while falling tides spanned the range of 0% - 100% mudflat exposure of a site. This range was 0.3 to 1.4 m tide height (above MLLW).
Observers began surveys when the site began to expose. Observers tallied all birds in the study site once every 0.5 hour (Colwell and Cooper 1993) until the site was completely exposed. Hereafter, I refer to each 0.5 hour tally as a count, and each series of counts at a site on a given day as a survey. Each site was surveyed four times
(Appendix A). Surveys consisted of four counts with the exception of one five-count survey and one six-count survey. Because estimates of numbers of birds in large flocks 7 can be extremely subjective, I rotated observers among sites to decrease the influence
of observer bias (Kavanagh and Recher 1983). Observers recorded the following
information for each sequential half hour count: start time, end time, % mudflat exposed
at start of count, species, abundance, behavior (number of individuals feeding or
roosting), and disturbances.
Observers identified birds to species except those too difficult to distinguish in the
field. Observers grouped Greater and Lesser Yellowlegs (Tringa melanoleuca and T.
flavipes) as "yellowlegs", and Short-billed and Long-billed Dowitchers (Limnodromus
griseus and L. scolopaceus) as "dowitcher". These groups were treated as species in all
analyses. In some cases, small sandpipers of the genus Calidris (C. mauri, C. minutilla,
and C. alpina) were indistinguishable due to distance or viewing conditions. In these
cases (5% of all observations of these species), I assigned unidentified birds to species
in proportion to other counts at that site (Page et al. 1999). For a list of species'
common and Latin names and species codes see Table 1.
Habitat Characteristics
Site area (TIDE-LINE). A well known relationship exists between area and number of species (Connor and McCoy 1979, Worthen 1996). However, since most shorebird species forage along the tide-line (Recher 1966), a more appropriate measure of sampling area is the length of the tide-line at each site. Since the length of the tide-
line changes as the tide ebbs, I estimated mean tide-lines. For each site I calculated the mean of the lengths (m) of the shoreline (the interface between mudflat and marsh or Table 1. Species' codes, common and scientific names, mean abundance, and relative abundance for all shorebird species observed.
Species % Relative Code Common Name Latin Name Abundance Abundance
BBPL Black-bellied Plover Pluvialis squatarola 438 1.2366 SEPL Semipalmated Plover Charadrius semipalmatus 304 0.8583 KILL Killdeer Charadrius vociferus 14.75 0.0416 AMAV American Avocet Recurvirostra americana 154.5 0.4362 YELL Greater & Lesser Yellowlegs Tringa melanoleuca & T. flavipes 25 0.0706 WILL Willet Catoptrophorus semipalmatus 704.25 1.9883 SPSA Spotted Sandpiper Actitis macularia 0.25 0.0007 WHIM Whimbrel Numenius phaeopus 5.25 0.0148 LBCU Long-billed Curlew Numenius americanus 152.5 0.4305 MAGO Marbled Godwit Limosa fedoa 2725.25 7.6941 RUTU Ruddy Turnstone Arenaria interpres 1.5 0.0042 BLTU Black Turnstone Arenaria melanocephala 22.75 0.0642 REKN Red Knot Calidris canutus 0.5 0.0014 SAND Sanderling Calidris alba 149 0.4207 WESA Western Sandpiper Calidris mauri 7542 21.2931 LESA Least Sandpiper Calidris minutilla 9011.5 25.4418 DUNL Dunlin Calidris alpina 14041 39.6414 DOWI Short- & Long-billed Dowitcher Limnodromus griseus & L. scolopaceus 127.75 0.3607 RUFF Ruff Philomachus pugnax 0.25 0.0007 8 9 dike) and the mudflat edge (the water line of the receding tide when the site is completely exposed).
Mudflat width (WIDTH). I calculated mean width of the mudflat at each site using
GIS. First I delineated the shoreline and the mudflat edge. I converted the shoreline to a series of points spaced one meter apart. Then I calculated the shortest distance (m) between each of the points (shoreline) and the mudflat edge. I took the mean of these distances as the mean width of the mudflat at each site.
Channelization (CHANNELS). On the digital air photos I measured the length of all channels 1-3 m wide that were discernible with the image displayed at a scale of
1:5,000. Channels narrower than 1 m were not readily discernible, and channels wider than 3 m were flooded during surveys. I calculated CHANNELS as the ratio of channel length to site area (m/ha).
Timing of tidal movements (EBB). As the tide ebbs, the timing of mudflat exposure depends on elevation and distance from the bay entrance. I recorded the time each site began to expose for each survey (excluding surveys where the observer estimated that more than 10% of the site was exposed at the start of the survey). I recorded the actual tide height at the north jetty for each survey start time (NOAA/NOS
1999), and calculated mean tide height (m; above MLLW) at which each site began to expose.
Substrate (MUD). Yates et al. (1993b) showed that it was possible to describe estuarine substrates using satellite imagery. I used a Landsat Thematic Mapper (TM) satellite image of the study area to analyze the relationship between sediment particle 10 size and Landsat reflectance values. Landsat sensors record reflectance in seven wavelength bands, which creates an image of Humboldt Bay composed of pixels 30 m on a side. Each pixel consists of a reflectance value (digital number) from each of the seven bands. I classified the satellite image into eleven substrate classes. I collected 31 sediment samples, and regressed particle size on mean reflectance value (digital number) of the substrate classes. The best regression model used the log of band 4 reflectance as the independent variable, and the ratio of percent sand to percent clay as the dependent variable (Figure 2, R2 = 0.83, P < 0.0001). I used this relationship to estimate the percentage sand to clay ratio for each site. For details of the satellite image classification, sediment sampling and analysis, and calculation of particle size at study sites, see Appendix B.
Standing water (% WATER). I measured the area of standing water at low tide by tracing the outline of pools on the digital air photos with the image displayed at a scale of 1:500. I divided the total area of pools at each site by the site area and multiplied this ratio by 100 for the percent of the site that was covered with standing water.
Mudflat variability (MUD SD). I used the particle size classification from the
Landsat image (30 m pixels) as a measure of substrate variability. I calculated the percentage of each site in each of the eleven mud classes. For each site I then calculated a standard deviation (in digital numbers [DN]) of the mean reflectance values for the mud classes. 11 = 0.83. 2 R Figure 2. The relationship between the ratio of percent sand to percent clay from sediment samples and the log of the ratio of percent sand to percent clay from sediment samples Figure 2. The relationship between values. The regression equation is: y = -81.3x + 125.35, Landsat TM band 4 reflectance 12 Data Summary and Analysis
Surveys. For each species, I estimated maximum use of a site as the highest of the
1 - 6 counts during each survey. For each site, I calculated mean species' abundance as the average of these high counts for each of the four surveys. I calculated species diversity as the total number of species observed at a site during all four surveys. I calculated species' densities by dividing the mean high abundance of each species at each site by the mean tide-line calculated for that site.
Nested Subset Analysis. I organized species incidence data in a matrix with sites in rows and species in columns (Table 2). I checked for a significant nested pattern using the Nestedness Temperature Calculator (AICS Research, Inc. 1995). In a perfectly nested matrix a diagonal line can be drawn from the upper right to the lower left corner, separating presences (in the upper left) from absences (in the lower right).
The program calculates a nestedness metric, T (system "temperature") (Atmar and
Patterson 1993). T is calculated from the distance between the diagonal and unexpected presences and absences. Using Monte Carlo simulations (1000 runs) of matrices with the same number of presences and the same dimensions as the matrix being tested, I calculated the probability that T (the degree of nestedness) was different from that expected by chance. In addition, the temperature calculator identifies
"idiosyncratic" sites and species, those that contribute disproportionately to raise the system temperature (reduce the degree of nestedness). Because idiosyncratic species do not fit the nested pattern, identifying them can help shed light on factors influencing the system as a whole. Table 2. Mean abundances of shorebird species observed at study sites. Numbers presented are means of the high counts for each species from the four surveys at each site.
Total Site # WILL LBCU MAGO WESA LESA DUNL Yell BBPL AMAV SEPL DOWI BLTU SAND KILL WHIM RUTU REKN RUFF SPSA Species
11 13.75 2.50 211.25 55.75 70.75 432.75 0.25 15.75 1.00 3.25 24.25 2.75 11.50 0.25 0.75 0.25 16 5 75.50 9.75 192.00 148.25 134.25 327.75 0.25 20.00 5.75 0.25 0.50 1.50 5.25 2.50 14 13 66.25 4.25 143.25 692.50 636.75 1074.75 0.25 4.25 14.75 18.00 2.75 4.25 1.50 0.75 14 17 32.25 10.00 111.00 795.25 1087.75 1838.25 1.50 44.50 10.25 3.00 5.25 2.50 1.50 8.75 14 1 34.75 65.75 385.00 312.50 1245.00 690.00 0.50 95.50 24.50 18.25 1.25 0.25 1.25 13 3 6.25 4.75 212.75 1667.00 681.50 1298.50 0.25 42.00 100.25 1.50 3.25 35.75 0.25 13 7 12.25 2.50 326.75 472.00 432.25 1140.00 4.25 29.50 121.50 2.25 5.25 17.25 0.50 13 9 55.75 13.50 118.25 777.00 873.75 602.25 0.25 13.75 0.50 0.50 1.25 1.25 0.50 13 12 61.75 1.25 36.50 358.25 295.50 537.50 9.25 21.50 0.25 0.50 59.50 0.25 0.25 13 15 31.50 18.00 228.25 1167.50 890.00 2640.50 0.25 124.50 19.75 1.75 1.75 0.25 12 2 8.50 0.75 191.25 78.25 333.25 272.00 4.25 8.50 13.75 0.50 37.00 11 6 31.50 0.50 154.50 0.50 69.00 5.50 0.25 1.25 26.75 45.25 0.25 11 8 11.25 1.50 42.50 166.75 246.50 598.50 1.50 8.00 0.25 0.50 10.75 11 16 78.00 6.00 24.75 97.00 70.75 660.00 0.50 10.25 26.75 5.50 7.50 11 19 56.50 2.75 116.50 291.25 228.75 1290.00 3.25 8.75 2.75 1.00 1.25 11 10 38.50 2.00 55.50 448.00 627.75 363.00 1.25 2.00 2.75 2.25 10 14 11.00 1.00 27.00 7.75 786.25 14.25 1.50 1.50 10.75 0.25 10 18 12.25 0.50 79.00 5.00 189.25 11.25 1.00 0.25 3.75 0.50 10 4 66.75 5.25 69.25 1.50 112.50 244.25 3.75 3.00 8 Total 19 19 19 19 19 19 18 17 14 13 13 11 11 5 5 3 2 1 1 sites 13
14 A correlation between incidence and abundance is expected by chance (Wright
1991) and can cause a nested pattern (Cutler 1991, Worthen 1996). I tested for effects of passive sampling using the random placement model of Coleman et al. (1982). The random placement model uses the proportional area of each site and the species diversity and abundance data to calculate an expected species-area curve using the following equation:
where is the predicted mean number of species, a is the relative area of a site, S is the total number of species, and is the abundance of species i. I calculated n„ the abundance of each species, as the total of the mean high counts for that species at all the sites. The variance of is calculated as follows:
If at least 66% of the data points fall within one standard deviation of the expected species-area curve, then the data do not differ from a random assemblage (Coleman et al. 1982).
Statistical Analysis. I conducted analyses using NCSS 2000 (J. Hintze, Kaysville,
UT 1999) and SAS 6.12 (SAS Institute, Inc., Cary, NC 1996). I used multiple linear regressions to analyze relationships between habitat variables and species diversity, and species' densities. I used multiple logistic regressions to analyze relationships between species' incidences and habitat variables. Independent variables for all analyses consisted of the following habitat characteristics: WIDTH, CHANNELS, % WATER,
EBB, and MUD. In addition, I added the variable TIDE-LINE (which may affect 15 species' occurrences) to the diversity and incidence analyses, and since habitat variability is expected to affect species diversity, I added the variable MUD SD to the diversity analysis.
To select independent variables for inclusion in the models I performed all possible regressions, and based selection of the best model on MSE, adjusted R2, F value, collinearity, and outliers. Regression diagnostics indicated that multicollinearity was not a problem. I present adjusted R2 to control for the effects of multiple independent variables and small sample size (SAS Institute Inc. 1991).
I performed logistic regressions on those species with 5 - 15 (of 19) occurrences.
Three of the best incidence models showed complete (Sanderling and dowitchers) or quasi-complete (Whimbrel) separation of sample points. It is impossible to calculate a maximum likelihood estimator due to lack of overlap (complete separation), or insufficient overlap (quasi-complete separation) between presences and absences
(Hosmer and Lemshow 1989). In these cases I removed variables to get the next best model, which did not have complete or quasi-complete separation.
I analyzed species' densities of the eight species that were present at more than 15
(of 19) sites. I used the Box-Cox transformation to transform dependent variables as needed, determined by model diagnostic plots. RESULTS
Site Characteristics
The 19 study sites varied greatly in physical features (Table 3). Site areas ranged from 3.5 to 51 ha, mean tide-line ranged from 591 to 1574 m, and mudflat width varied from 45 to 780 m. Channelization ranged from 0 to 827 m of channel per ha, percent of the site covered with water ranged from 0 (seven sites) to 11, and ratio of percent sand to percent clay ranged from 0 to 8.55. The range of heights at which sites began to be exposed was from 0.74 to 1.34 m.
Shorebird Observations
I observed 19 species of shorebird (Table 1). Percent relative abundance ranged from 0.007 (single observations of Spotted Sandpiper and Ruff) to 39.6 (Dunlin).
Species diversity at sites ranged from eight to 16 (Table 2). Six species occurred at all sites and two occurred at single sites.
Nested Subset Analysis
The nested subset analysis resulted in a nestedness score (T) of 20.48 (on a scale of
0 to 100 where 0 is perfectly nested). This nestedness score was highly significant
(P < 0.0001). Idiosyncratic species included Killdeer, American Avocet, Spotted
Sandpiper, and Black Turnstone (Figure 3).
16 Table 3. Physical characteristics of 19 study sites on Humboldt Bay.
MUD Area TIDE-LINE WIDTH CHANNELS EBB (%sand/ MUD SD Site # Site Name (ha) (m) (m) (m/ha) % WATER (m) %clay) (DN)
1 Woodley Island 24.2 951 387 224.8 0.99 1.32 0.61 2.63 2 Mad River Slough at Lanphere 20.7 1170 415 48.5 0.00 0.93 0.96 3.76 3 Elk River Mouth 9.3 1574 225 267.2 0.00 1.28 3.53 7.81 4 CR Exit 21.9 1035 350 192.0 4.32 1.09 0.00 2.30 5 Indian Island North Shore 24.7 1025 230 218.6 1.44 1.21 0.81 2.98 6 W. Arcata Marsh 32.3 1017 489 136.7 0.00 0.95 0.71 3.39 7 Bucksport 11.7 711 237 0.0 0.00 0.74 8.55 4.93 8 South Spit, The Narrows 3.5 796 45 0.0 0.00 1.13 4.47 4.01 9 Indian Island West Shore 8.2 810 240 95.5 0.07 1.34 1.57 3.18 10 Bracut 51.0 641 780 384.7 11.06 0.82 0.48 0.26 11 Bayshore 13.1 924 183 7.1 0.06 1.10 2.27 6.14 12 South Spit, The Den 7.5 1089 70 15.5 0.00 1.10 8.43 6.15 13 Bridge Flats 12.0 719 197 9.7 9.37 1.06 1.87 3.70 14 North Arcata Redwoods 33.7 591 602 124.8 6.56 0.97 0.42 0.00 15 Vance Road 36.2 680 466 87.1 1.30 1.18 2.44 4.90 16 South Arcata Redwoods 31.5 600 640 144.3 1.10 1.25 0.66 1.17 17 Manila 40.1 1094 458 827.3 1.90 0.97 0.73 2.04 18 Oxidation Ponds 5.4 632 99 49.5 0.00 1.00 2.61 3.64 19 Mad River Slough at 255 27.1 904 258 168.3 0.15 0.83 0.43 0.00 Mean 21.8 893 335 158.0 2.02 1.07 2.19 3.32 Standard Deviation 13.3 248 201 192.9 3.37 0.17 2.51 2.13 17
18
Figure 3. Matrix depicting presence (shaded squares) of shorebird species at study sites and histograms showing idiosyncratic species (at bottom) and sites (on right). The lines through the histograms represent the matrix temperature. Species and sites that exceed the matrix temperature are considered idiosyncratic. 19 Random Placement
As expected, there was a strong correlation between species' abundances and incidences at study sites (for log of abundance: rs = 0.92, P < 0.0001, Figure 4). The random placement model resulted in 42% (8 of 19) of the data points falling within one standard deviation of the predicted number of species (Figure 5). This is considerably less than the minimum of 66% expected in a random system (Coleman et al. 1982), indicating that despite the correlation between incidence and abundance, species diversity at sites of varying size is not likely a result of passive sampling.
Species Diversity
The best regression equation for species diversity was:
Number of species = 8.65 + 0.0026 x TIDE-LINE + 0.3684 x MUD SD
(model adjusted R2 = 0.30, P = 0.02). MUD SD (partial R2 = 0.20, P = 0.06) explained more of the variation in species diversity than TIDE-LINE (partial R2 = 0.16, P = 0.11).
None of the other habitat variables correlated well with species diversity. In addition to
TIDE-LINE and MUD SD, the next best regression model included EBB, but its contribution to the model was negligible (partial R2 = 0.04, P = 0.42).
Species Incidence
I analyzed the seven species that occurred at 5 - 15 sites for incidence (Table 2).
Of these seven species, three (Semipalmated Plover, Killdeer, and Black Turnstone) had significant models at P < 0.1, and the remaining four were significant at P < 0.05 (Table
4). There were two best models for Sanderling: EBB and MUD or TIDE-LINE and
MUD. When all three variables were included in the model there was complete Figure 4. The relationship between log of mean species abundance and species incidence at survey sites on Humboldt Bay. Two species (Spotted Sandpiper and Ruff) had identical incidences and abundances (single observations) resulting in overlapping points. 2 0 Figure 5. Random placement model comparing the number of shorebird species with relative area (alpha) at 19 study sites on Humboldt Bay, CA, during November 1998 through February 1999. Solid line is s (predicted number of species), dashed lines represent s +1- 1 SD, circles represent observed species diversity at study sites. 21 Table 4. Results of logistic regression analyses on presence/absence of species at sites in relation to habitat characteristics. There are two best models for Sanderling, combining all three variables (TIDE-LINE, EBB, and MUD) resulted in complete separation in sample points (see text for details).
Variables Coefficients Model 2 R 2 Species in model Coefficient SE X P value X2 P value % correct SEPL TIDE-LINE 0.01 0.003 4.19 0.04 0.27 5.95 0.05 73.68 CHANNELS -0.01 0.005 1.03 0.31
KILL TIDE-LINE 0.01 0.004 2.11 0.15 0.31 6.74 0.08 78.95 % WATER 0.21 0.18 1.27 0.26 MUD -0.53 0.54 0.96 0.33
AMAV MUD -0.95 0.52 3.35 0.07 0.35 9.22 0.002 94.74
WHIM WIDTH -0.01 0.01 1.76 0.18 0.41 10.32 0.02 89.47 % WATER 0.58 0.43 1.81 0.18 EBB 17.54 9.22 3.62 0.06
BLTU CHANNELS 0.01 0.01 1.31 0.25 0.24 5.03 0.08 63.16 MUD 0.66 0.45 2.15 0.14
SAND EBB 9.91 5.40 3.37 0.07 0.45 13.00 0.002 84.21 MUD 1.15 0.72 2.55 0.11 OR TIDE-LINE 0.01 0.004 2.54 0.11 0.45 13.04 0.001 84.21 MUD 1.67 0.92 3.32 0.07
DOWI TIDE-LINE 0.01 0.01 1.43 0.23 0.52 16.05 0.001 89.47 % WATER -1.56 1.39 1.25 0.26 EBB 12.65 8.09 2.44 0.12 22 23 separation of sample points. Including all three variables is probably the best model, but is impossible to quantify.
Density
Species densities varied greatly among study sites (Table 2). The strongest relationships from the multiple regressions on species' densities were seen in yellowlegs
(adjusted R2 = 0.39, P = 0.003) and Long-billed Curlew (adjusted R2 = 0.34, P = 0.01)
(Table 5). The species that showed the weakest relationships were Marbled Godwit and
Dunlin.
The following characteristics appeared most frequently in the incidence models:
% WATER (n = 3), EBB (n = 3), MUD (n = 4), and TIDE-LINE (n = 4) (Table 6).
MUD was positively correlated with Sanderling and Black Turnstone, and negatively correlated with Killdeer and American Avocets. EBB was positively correlated with
Whimbrel, Sanderling, and dowitcher. Percent water was positively correlated with
Killdeer, Whimbrel and dowitcher incidences, but none of these correlations was significant. In the significant density models two habitat characteristics, EBB and
MUD, showed up most frequently (n = 5 and n = 4 respectively) (Table 6). In all the models that included MUD (n = 4), species densities increased with particle size. In 4 of 5 models, densities increased with higher ebb; yellowlegs alone had a negative correlation with EBB. Table 5. Results of multiple regression analyses on species' densities in relation to habitat characteristics. Transformations are of the dependent variable (species density).
Trans- Independent Coefficients Model Species formation Variables Coefficient SE T value P- value Partial R 2 Intercept R 2 MSE Adj. R 2 F value P- value
BBPL 0.25a CHANNELS 0.0003 0.0002 1.44 0.17 0.12 -0.15 0.29 0.14 0.15 2.08 0.15 EBB 0.36 0.19 1.94 0.07 0.20 MUD 0.02 0.01 1.73 0.10 0.17
YELL 0.25 EBB -0.20 0.06 -3.53 0.003 0.42 0.40 0.42 0.04 0.39 12.48 0.003
WILL none % WATER 0.004 0.002 1.83 0.09 0.17 -0.04 0.23 0.03 0.13 2.38 0.12 EBB 0.07 0.04 1.63 0.12 0.14
LBCU log CHANNELS 0.001 0.001 1.65 0.12 0.15 -4.52 0.41 0.45 0.34 5.66 0.01 EBB 1.86 0.61 3.03 0.01 0.37
MAGO none MUD 0.01 0.01 1.02 0.32 0.06 0.14 0.06 0.12 0.003 1.04 0.32
WESA 0.25 CHANNELS 0.001 0.000 1.73 0.11 0.18 -0.30 0.36 0.27 0.18 1.99 0.15 % WATER 0.03 0.02 1.37 0.19 0.12 EBB 0.64 0.39 1.64 0.12 0.16 MUD 0.07 0.03 2.43 0.03 0.30
LESA none % WATER 0.06 0.03 1.89 0.08 0.17 0.45 0.17 0.44 0.12 3.57 0.08
DUNL none MUD 0.05 0.09 0.63 0.54 0.02 0.75 0.02 0.92 0.00 0.40 0.54 a Transformation: y0.25 2 4 Table 6. Total number of regression models in which habitat characteristics occurred for species' incidences (multiple logistic regressions), and densities (multiple regressions), and the number of models in which the coefficients for each habitat variable were significant.
Incidence Density
Characteristic # models # P < 0.1 # P < 0.05 # models # P < 0.1 # P < 0.05
WIDTH 1 0 0 0 0 0
CHANNELS 2 0 0 3 0 0
% WATER 3 0 0 3 2 0
EBB 3 2 0 5 1 2
MUD 4 2 0 4 0 1
TIDE-LINEa 4 0 1
aTIDE-LINE was included in incidence analyses only. 25 DISCUSSION
Nested Subsets
Nested subset analysis has been used increasingly to analyze patterns of community assemblages as they relate to conservation issues such as predicting the sequence of species extinctions and determining refuge size requirements (Patterson
1987, Soule et al. 1988, Wright and Reeves 1992, Cook 1995, Worthen et al. 1996).
The focus of these analyses has been on the isolated communities found on islands or in fragmented (insular) habitats (Cutler 1991, Wright and Reeves 1992, Kadmon 1995). In fact, nested subset analysis has been described as applying only to assemblages composed of non-vagile species in isolated habitats (Atmar and Patterson 1993).
Conversely, nestedness also has been declared common to most systems (Simberloff and Martin 1991, Wright et al. 1998).
The factors causing a nested pattern are immigration, extinction, nesting of habitats and passive sampling (Cutler 1991, Worthen 1996). Only the first two of these four factors apply solely to isolated communities. The application of nested subset analysis to non-isolated species assemblages has been only recently approached (Patterson and
Brown 1991, Patterson et al. 1996, Worthen et al. 1996, Day and Colwell 1998). Thus, the presence and causal factors of nestedness in vagile species assemblages in non- fragmented habitats have been largely unexplored.
26 27 Shorebirds at coastal wetlands during winter and migration are extremely vagile, forming mixed-species flocks and moving among habitats in response to tides
(Gerstenberg 1979, Ramer et al. 1991, Burger et al. 1997). I found highly significant nestedness in shorebird species assemblages at sites on Humboldt Bay. In addition, a random placement analysis indicated that the nested pattern was not likely due to passive sampling. The remaining explanation is that the nested pattern resulted from nesting of habitats, however, it is difficult to directly test for significant nestedness of habitats. Indeed, the contribution of habitat distribution to nestedness has been unclear
(Cutler 1994, Wright et al. 1998), although Honnay et al. (1999) showed that nestedness of plant communities in Belgium resulted from nesting of habitats.
An additional explanation for the significant nestedness I found in this study may be statistical error. Recently, some authors have criticized the methodology used in nested subset analysis. Brualdi and Sanderson (1999) showed that the methods commonly used for calculating nestedness tend to inflate the nestedness metric, exaggerating the degree of nestedness. In addition, Cook and Quinn (1998) showed that all of the null models currently used in nested subset analyses are biased toward low levels of nestedness. As a result, the significance of the analysis is generally overestimated, and there is an increased likelihood of Type I error.
The real strength of nested subset analysis may be in identifying those sites and species that deviate from the nested pattern (Simberloff and Martin 1991, Kadmon
1995). Understanding deviations from a pattern can help explain the pattern itself. The four idiosyncratic species in this study all have unique habitat associations. Killdeer 28 forage more frequently in pastures than on mudflats, comprising 13% of all shorebirds recorded in pastures (Colwell and Dodd 1997), but only 0.04% of all observations on mudflats in the present study. Spotted Sandpipers are commonly found along rivers and lakes, and only more rarely on mudflats (Oring et al. 1997). In this analysis at selected sites on Humboldt Bay, American Avocets showed a preference for small particle size. However, on a bay-wide scale the distribution of avocets has changed over the past two decades, indicating that there are dynamic factors influencing avocet distribution on Humboldt Bay (Colwell et al. in review [a]). Black Turnstones are usually found in non-mudflat estuarine habitat. Turnstones prefer rocky intertidal areas, and when using mudflat, tend to forage on and around debris (Paulson 1993). My finding that the species that deviate from the nested pattern have specialized habitat associations may support the hypothesis that nestedness is caused by habitat associations.
Species diversity
Two well-established patterns in community ecology are the relationships of species diversity with area and habitat diversity. The first of these, the species-area relationship (Connor and McCoy 1979, Wiens 1989), predicts increasing diversity with increasing area surveyed. Shorebird diversity at sites on Humboldt Bay was most strongly correlated with the length of the tide-line and mudflat variability. The positive correlation with tide-line is as expected from the species-area relationship, indicating a sampling artifact (the larger the sampling area -- length of the tide-line -- the greater the likelihood of finding more species). The second relationship, between habitat 29 complexity and increasing species diversity (MacArthur and MacArthur 1961, Recher
1966), explains the correlation between shorebird species diversity and mudflat variability. Increasingly variable mudflats may provide additional niches for more species of shorebird.
I successfully used satellite imagery to relate shorebird species diversity to habitat characteristics on Humboldt Bay mudflats. This could be extremely useful in predicting loss of shorebird species diversity resulting from habitat alteration. However, more data need to be gathered on the relationship between satellite reflectance variability and estuarine sediment characteristics.
Habitat Characteristics and Site Use
Other than mud variability, none of the habitat characteristics analyzed correlated significantly with species diversity. This may be due to the scale at which I conducted the analysis (Wiens 1989), or there may be habitat descriptors other than those I measured that influence foraging site selection and species diversity. Additional influential habitat characteristics may include: water chemistry (Hill et al. 1993), salinity
(Wolff 1969), presence of other birds (Myers 1984), human disturbance (Goss-Custard and Verboven 1993), pollution (van Impe 1985), distance to roost sites (Goss-Custard et al. 1982, Le Drean-Quenec'hdu et al. 1995), and presence of vegetation (Kalejta and
Hockey 1994).
Sediment particle size has been identified as an important habitat characteristic in many shorebird studies (Quammen 1982, Gerritsen and van Heezik 1985, Boland 1988,
Yates et al. 1993a, Le Drean-Quenec'hdu et al. 1995). I found that particle size, ebb 30 height, and percent water were the most important characteristics influencing shorebird incidence and density at sites on Humboldt Bay. All the significant coefficients for particle size were positive except for American Avocets, which had a negative relationship with particle size. This is to be expected due to the unique way in which avocets forage, sweeping in water or wet mud (Hamilton 1975).
I expected a positive correlation between species' incidences and densities and ebb height. The greater the ebb height of a site, the earlier it will be exposed and available to shorebirds for feeding while most mudflats are still flooded. Indeed, most (7 of 8) of the species that had EBB in their best regression models had higher incidences and densities at higher elevation sites. Gerstenberg (1979) found Greater Yellowlegs using high and low mudflats with about equal frequency, yet I found that yellowlegs had a significantly negative correlation with ebb height. Later ebbing sites may have habitat characteristics associated with them that make them more attractive to yellowlegs. The
Bucksport site (#7) is the lowest ebbing site in this study. It is also the only site that has large areas of eelgrass, which may provide a unique assemblage of prey for yellowlegs.
Long-billed Curlews had a strong positive correlation with ebb height. This relationship may be related to the curlews' habit of roosting in salt marshes adjacent to high mudflats. As the tide starts to ebb at these sites, birds come directly off roosts and onto mudflats to feed, dispersing as more foraging area becomes available. I have observed as many as 63 curlews (21% of the estimated Humboldt Bay winter population, Colwell unpub. data) roosting at a single site. 31 Marbled Godwits showed no strong preferences for any of the habitat variables I measured. Indeed, Gerstenberg (1979) found them using a variety of habitats on
Humboldt Bay. Marbled Godwits were the most abundant species observed after the calidrids (Least Sandpipers, Western Sandpipers, and Dunlin). Dunlin are the most abundant shorebird on the bay (Colwell 1994), and along with godwits showed the lowest correlations with habitat variables. The lack of strong relationships between these two abundant species and habitat characteristics may be a result of their abundance. Due to their large numbers, they may be forced to spread out and occupy all habitats. Conversely, it may be their ability to forage in diverse habitats that enables these species to winter in Humboldt Bay in such large numbers.
Each of the habitat characteristics (with the exception of WIDTH) showed some strong relationships with species' densities. Although the regression diagnostics indicated that multicollinearity was not a problem, some of these results are difficult to interpret due to correlations between habitat variables (Table 7). For example, there were significant correlations with % WATER for both Willet and Least Sandpiper densities. However, there is also a strong correlation between % WATER and MUD
(rs = -0.65, P = 0.003) confounding interpretation of these results.
I used mean tide-line to measure density of shorebirds because of their habit of following the ebbing tide, rather than using exposed mudflat evenly (Recher 1966).
However, some species, notably Black-bellied Plover and Long-billed Curlew, do not follow the ebbing tide (Recher 1966, Townshend et al. 1984). These two species are Table 7. Spearman correlation matrix and P -values for shorebird habitat characteristics on Humboldt Bay.
TIDE-LINE WIDTH CHANNELS % WATER EBB MUD MUD SD
r 1.00 TIDE-LINE s P-value 0.00
rs -0.24 1.00 WIDTH P-value 0.32 0.00
r 0.26 0.54 1.00 CHANNELS s P-value 0.28 0.02 0.00
r s -0.30 0.50 0.46 1.00 % WATER P-value 0.21 0.03 0.05 0.00
r s 0.13 -0.24 0.11 -0.03 1.00 EBB P-value 0.61 0.32 0.64 0.89 0.00
r s 0.09 -0.70 -0.63 -0.65 0.16 1.00 MUD P-value 0.72 0.001 0.004 0.003 0.52 0.00
r s 0.33 -0.64 -0.58 -0.65 0.22 0.89 1.00 MUD SD P-value 0.17 0.003 0.01 0.002 0.37 0.00 0.00 32
33 often territorial on winter foraging sites (Myers et al. 1979, Colwell et al. in review
[b]). Results of analyses for these two species using density calculated from the tide- line did show some significant correlations. However, a better analysis for these territorial species may be to calculate density using site area rather than tide-line.
Studies of shorebird/habitat relationships define and measure habitat variables differently based on the unique characteristics of the study area and the scale at which the study is being conducted. This complicates my ability to make exact comparisons between my results and similar studies. Therefore, I compare the signs of the coefficients for the correlations between habitat variables and species' occurances from similar studies to the signs of the coefficients from this study (Table 8). The expected relationships for % WATER are based on Recher's (1966) predicted relationship between feeding mode and substrate. My results matched those from the literature in 5 of 6 (83%) instances where I both found a significant correlation between habitat characteristics and shorebird use, and had an expected relationship from the literature.
The sole discrepancy is for Least Sandpipers and % WATER, which may be explained by the inverse correlation between % WATER and MUD (see above).
Conclusions and Conservation Implications
Humboldt Bay has been designated a site of international importance by the
Western Hemisphere Shorebird Reserve Network (Bildstein et al. 1991). A number of activities within Humboldt Bay pose potential threats to shorebirds, including oysterculture, environmental contaminants from agricultural runoff, oil spills, dredging, Table 8. Expected (from previous studies) vs. observed (in this study) signs of coefficients for correlations between habitat variables and species' occurances (density or incidence analysis). * P < 0.1, ** P < 0.05. Boxes indicate agreement and underline indicates a discrepancy between expected and observed signs (significant results only).
aRecher 1966, bTownshend et al. 1984, cPage et al. 1979, dQuammen 1982, eEvans and Harris 1994, fTibbitts and Moskoff 1999, gSkeel and Mallory 1996, hGerstenberg 1979. 34 35 and disturbance from recreational activities (Shapiro and Associates 1980, Colwell
1994). Less than 3% of the bay is protected habitat (Colwell 1994), thus understanding habitat requirements is essential for conserving shorebird habitat at Humboldt Bay.
This study shows that physical characteristics of Humboldt Bay mudflats influence the distribution patterns of wintering shorebirds. These habitat characteristics correlate with species' densities and incidences for most of the shorebird species observed in this study. Thus, alterations to Humboldt Bay that affect these physical characters are likely to impact shorebirds using the bay. The importance of Humboldt Bay to shorebirds was noted in a report on the effects of proposed dredging (now completed) of navigational channels (Humboldt Bay Harbor Recreation and Conservation District and U. S. Army
Corps of Engineers 1994). The report predicted no net loss of habitat for shorebirds, yet defined habitat homogeneously as mudflat. The information gained in this study of small scale variability in mudflats and its impact on shorebird use (incidence and density) should be taken into account in future assessments of this kind.
The main conservation concern for shorebirds is loss and degradation of wetland habitats used during winter and as migratory staging areas (Senner and Howe 1984).
Page and Gill (1994) included Killdeer, Marbled Godwit, Long-billed Curlew, and
Willet in a list of shorebirds that rely on the most threatened habitats. Recent analyses of shorebird populations indicate declines for a number of species including Killdeer,
American Avocet, and Long-billed Curlew (Howe et al. 1989, Morrison et al. 1994, Gill et al. 1995). For the previous five species, three (Marbled Godwit, Long-billed Curlew, and Willet) occurred at all sites in this study. Of the remaining two species, Killdeer 36 use upland habitat more frequently than mudflat, and American Avocets have expanded their distribution in Humboldt Bay in the past decade. Thus, based on this study it is not possible to determine the most important sites for protection.
Additional study is needed of how variation in habitat characteristics on a small scale influences species' incidences and abundances, what the mechanisms are that affect bird use of a site, how habitat characteristics on Humboldt Bay relate to prey communities, and how satellite reflectance variability relates to substrate variability. A thorough understanding of shorebird habitat requirements is necessary to aid in management and conservation planning for both the birds and the bay. LITERATURE CITED
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Dates of Each of Four Surveys at Humboldt Bay Study Sites
Survey
Site # 1 2 3 4
1 11/14/98 12/26/98 12/31/98 1/9/99 2 11/13/98 12/17/98 1/1/99 1/11/99 3 11/17/98 12/16/98 1/2/99 1/12/99 4 11/15/98 12/17/98 1/1/99 1/13/99 5 11/16/98 12/15/98 12/24/98 1/11/99 6 11/18/98 12/14/98 1/2/99 1/16/99 7 11/15/98 12/15/98 1/1/99 1/16/99 8 11/28/98 12/18/98 12/30/98 1/18/99 9 11/19/98 12/16/98 1/3/99 1/12/99 10 11/18/98 12/15/98 1/8/99 1/14/99 11 11/29/98 12/20/98 12/27/98 1/13/99 12 12/4/98 12/18/98 12/30/98 1/18/99 13 12/1/98 12/18/98 12/26/98 1/14/99 14 12/3/98 12/19/98 12/28/98 1/2/99 15 12/11/98 12/19/98 12/28/98 1/15/99 16 12/10/98 12/25/98 12/29/98 1/10/99 17 12/14/98 12/19/98 12/31/98 1/10/99 18 12/12/98 12/27/98 12/31/98 1/9/99 19 12/14/98 12/25/98 12/29/98 1/8/99
45 APPENDIX B
Remote Sensing Classification of Study Site Substrates
I classified sediments on Humboldt Bay using a Landsat Thematic Mapper (TM) image (path 46, row 32), taken on 15 July 1994. The tide was low (approximately 0.46 m) when the image was acquired. I classified the image using bands 4, 5, and 7 (the near infrared and two short wave infrared bands). These bands are especially sensitive to soil moisture content and discrimination of mineral and rock types (Lillesand and
Kiefer 1994). Pixel size was 30 m by 30 m. I used Imagine 8.3 (ERDAS, Atlanta, GA
1997) to classify the image, and Arc/Info and ArcView to summarize classes within each study site.
I restricted the classification to Humboldt Bay mudflats by drawing a 6,142 hectare area of interest (AOI) around the bay (excluding areas of marsh vegetation) with the image displayed at a scale of 1:12,500. The AOI included eelgrass, channels, open water, and mudflat. I identified non-mudflat surfaces (eelgrass, channels, and open water) using Imagine's region growing tool (spectral Euclidean distance = 10).
Identifying these (training) areas creates a file of their reflectance values (spectral signatures). By identifying these features I could ensure my ability to separate and exclude them from the mud classes.
I ran an unsupervised classification (ISODATA clustering) which groups pixels into a specified number of classes (20) by the similarity of their reflectance values. I then combined the signature file from the training areas with the unsupervised
46 47 classification signature file. I merged classes that had a pairwise transformed divergence separability less than 1500 (Lillesand and Kiefer 1994). This resulted in a signature file with thirteen classes: eleven mud classes, one channel/water class, and one eelgrass class. I performed a supervised classification (maximum likelihood classifier) using this signature file. Mean reflectance values for each class are shown in
Table 9.
Between 18 January 1999 and 30 April 1999 I collected 31 sediment samples from
Humboldt Bay mudflats (see Figure 6 for sampling locations). Using a 2.5 cm diameter corer, I collected the top 7-10 cm of sediment. Each sample consisted of five subsamples collected from an area no larger than 5 m2 (Kramer et al. 1994). I quantified samples as percent sand, percent silt, and percent clay using standard wet sieve and pipette techniques (Kramer et al. 1994).
I regressed particle size class percentages of the samples against the mean reflectance values of the class for the pixel at the sampling location and its adjacent pixels. The best regression equation was:
%sand/%clay = (-81.3) x log(reflectance band 4) + 125.35
(R2 = 0.83, P < 0.0001, Figure 2). I dropped one sample from the calculation as an outlier.
For each of the 19 study sites, I used GIS to calculate a weighted mean reflectance value based on the proportion of the site in each of the mud classes. I excluded channel and eelgrass classes from this calculation. I used the regression equation above to calculate the ratio of percent sand to percent clay at each study site. 48 Table 9. Statistics of substrate classes from Landsat TM classification.
Band Minimum Maximum Mean Sigma
Mud class 1 4 12 20 16.42 1.72 5 7 15 9.90 1.75 7 2 9 4.68 1.14 Mud class 2 4 18 22 20.01 1.37 5 3 10 7.11 1.16 7 1 6 3.06 0.77 Mud class 3 4 23 27 24.41 1.14 5 4 10 7.46 1.20 7 1 6 3.03 0.78 Mud class 4 4 20 27 23.50 1.82 5 9 17 12.18 1.78 7 2 9 5.24 1.10 Mud class 5 4 27 33 28.92 1.53 5 4 12 8.59 1.47 7 1 6 3.41 0.85 Mud class 6 4 24 35 28.69 2.37 5 12 21 16.33 2.05 7 4 11 6.65 1.17 Mud class 7 4 9 23 18.29 2.70 5 13 26 17.63 2.37 7 4 15 8.04 1.54 Mud class 8 4 16 29 24.72 2.41 5 18 35 25.41 3.51 7 6 21 10.87 1.98 Mud class 9 4 28 35 31.45 1.80 5 24 32 28.63 1.73 7 6 16 10.90 1.14 Mud class 10 4 34 61 38.79 4.13 5 22 39 30.90 2.84 7 7 16 10.99 1.36 Mud class 11 4 21 75 34.40 3.79 5 30 98 39.54 5.17 7 9 54 16.10 2.80 Eelgrass 4 21 59 36.41 6.06 5 5 25 10.35 2.79 7 1 10 4.15 1.34 Channel/Water 4 6 35 11.13 3.23 5 2 37 6.70 1.56 7 1 21 3.39 0.97 49
Figure 6. Locations of 31 sediment samples collected from Humboldt Bay mudflats between 18 January 1999 and 30 April 1999. 50 One potential problem with my methodology was the difference in season and year between the date of the satellite image (July 1994) and the collection of field samples (January — April 1999). There is no information available on how surface sediments in Humboldt Bay vary temporally, either seasonally or annually. The summer 1994 image was the only image readily available to me that was free of clouds, and taken during low tide.
An additional problem with the use of remotely sensed images for determining sediment composition is that at certain times of the year mudflat surfaces on Humboldt
Bay may be partially covered with algal growth. The amount of algae varies seasonally and peaks in summer when the scouring effect of storms is minimal (Barnhart et al.
1992). Algae can be expected to alter the reflectance properties of the mudflat surface.
Yates et al. (1993b) used a similar method to characterize sediments in The Wash,
England. They performed multiple regressions with the amount of sediment in various particle size classes as their dependent variables, and Landsat TM band reflectance values as their independent variables. Their best resulting regression equation with an
R2 value of 0.52, did not have as strong a correlation as I found in this study. Thus, despite the potential problems with the methodology I have used here, the strength of the relationship I found between sediment particle size and reflectance values indicates that this is a valid approach to classification of sediments on Humboldt Bay.