ACKNOWLEDGEMENTS
I would like to thank my major advisor, Dr. Pam Cox Jutte, for unending support, guidance, and encouragement during the many phases of my graduate career. Although there aren’t enough words to express it here, I will simply state that without her, I know this thesis would have never been. In addition, I would like to thank committee members, Drs. Laura Kracker, Cass Runyon, and Bob Van Dolah, for many thought provoking discussions and helpful comments on this thesis. Their input and insights have provided direction that has shaped this project. Many others have also helped me gather, develop, analyze, and present this data: Mr. George Riekerk, Ms. Lynn Zimmerman, the
South Carolina Estuarine and Coastal Assessment Program crew, Mr. William Roumillat,
Dr. John Fauth, Dr. Allan Strand, Dr. Lesa Meng, Ms. Gretchen Hay, Dr. George
Sedberry, and Marine Resources Library staff. Each person has made various contributions that were pillars for this research.
This project definitely would not have been possible if it were not for funding from the College of Charleston, NASA Experimental Program to Stimulate Competitive
Research (EPSCoR) Program, South Carolina Department of Natural Resources, U.S.
Fish and Wildlife, Joanna Deep Water Fellowship, U.S. Environmental Protection
Agency, RGII Technologies, and National Oceanic and Atmospheric Administration.
- ii - I would also like to thank Dr. Chip Biernbaum, Dr. Scott France, Mr. Robert
Martore, Ms. Peko Tsuji, and the South Carolina Department of Natural Resources
Artificial Reef group for their time and effort on a previous project. Their enthusiasm for planning and developing a field intensive project was very much incorporated into this thesis and has also made me a better scientist.
I would like to acknowledge the support that I got from the Grice Marine
Laboratory family that has definitely helped me in more ways than I can describe during my graduate career. Finally, I would like to thank my family and friends for providing me love and laughter. Many people have been with me through thick and thin, including
Elizabeth Jones, Mark Renshaw, and Bohdan Kot. I truly have been blessed to have so many cheerleaders in my corner.
- iii -
TABLE OF CONTENTS
ACKNOWLEDGEMENTS………………………………………………………….... ii
TABLE OF CONTENTS...... iv
LIST OF FIGURES……………………………………………………………………. vii
LIST OF TABLES…………………………………………………………………...... ix
ABSTRACT……………………………………………………………………………. xi
INTRODUCTION……………………………………………………………...... 1
METHODS AND MATERIALS……………………………………………………..... 8
Sampling design and procedures……………………………………………..... 8
Candidate fish metrics………………………………………………...... 11
Life history metrics…………………………………………………………….. 13
Ecological and trophic metrics……………………………………………...... 14
Tolerance metrics………………………………………………………………. 15
Community structure metrics…………………………………………………... 19
Determining environmental quality…………………………………………..... 20
Water quality……………….………………………….……...... 20
Sediment quality……………………………………………………...... 21
Upland quality…………………………………………………...... 22
Overall quality…………………………………………………...... 23
- iv - METHODS AND MATERIALS (continued)
Physical features……………………………………………………………….. 24
Development of the estuarine biotic integrity (EBI) index…………………….. 25
One-way analyses...... 26
Stepwise discriminant analyses...…………….…………………………26
Previous studies………………………………………………………... 28
Composite and single metric analyses…………………………………. 28
Application and validation of the EBI index…………………………………... 29
Median analyses………………………………………………………... 29
Discriminant analyses………………...………….…………………….. 30
Evaluation and selection of the EBI index...... ….………………….………. 32
Stations with excellent environmental quality…………………………………. 33
RESULTS………………………………………………………………………...... 34
Environmental quality and physical features...... 34
Fish community………………………………………………………………... 38
Development of the estuarine biotic integrity (EBI) index…………………….. 40
One-way analyses...... 40
Stepwise discriminant analyses...... 41
Previous studies...... 42
Composite and single metric analyses...... 43
Application of the EBI index – Median analyses...... 46
EBI index Ax...... 46
- v - RESULTS (continued)
EBI index Bx...... 47
EBI index Cx...... 49
EBI index Dx...... 49
EBI index Ex...... 52
Application of the EBI index – Discriminant analyses...... 52
EBI index Ax...... 52
EBI index Bx...... 54
EBI index Cx...... 55
EBI index Dx...... 56
EBI index Ex...... 57
Evaluation and selection of the final EBI index……………………………….. 58
Stations with excellent environmental quality……………………...... 62
DISCUSSION...... 63
Environmental quality and physical features...... 63
Fish community...... 67
Development and evaluation of the final EBI index...... 70
Future directions and recommendations...... 77
SUMMARY AND CONCLUSIONS...... 86
LITERATURE CITED...... 89
FIGURES...... 187
TABLES...... 205
APPENDICES...... 230
- vi -
LIST OF FIGURES
Figure 1. Array of 97 tidal creek stations sampled in 1999-2002 used in the current study,
chosen from the larger South Carolina Estuarine Coastal Assessment Program
(SCECAP) sampling array..…………………………...... 187
Figure 2. Flowchart of methods for developing and evaluating an estuarine biotic
integrity (EBI) index for South Carolina tidal creeks.………………………..... 189
Figure 3. The two creeks that contained one marginal station located upstream relative to
one good station located downstream: a) Kiawah River and b) May River.…... 191
Figure 4. Box-plots of nine of the 73 candidate fish metrics that were significantly
different between good and marginal stations sampled in 1999-2001 (Wilcoxon
test, Dunn-Sidak test, k=73, α=0.10, p<0.0014).………………………...... 193
Figure 5. Total misclassification rates of EBI indices A1,2 and B1,2, based on the median
or discriminant analyses………………...... 195
Figure 6. Total misclassification rates for all EBI indices developed in the current study,
based on the median or discriminant analyses…………………………...... 197
Figure 7. Good and marginal station misclassification rates for all EBI indices developed
in the current study, based on the median analyses.………………...... 199
Figure 8. Good and marginal station misclassification rates for all EBI indices developed
in the current study, based on discriminant analyses...... 201
- vii - Figure 9. Estuarine biotic integrity (EBI) scores of marginal and good stations, calculated
by a) EBI index A3, b) EBI index C2, c) EBI index C3, d) EBI index D2, and e)
EBI index D6 (final EBI index)...... 203
- viii -
LIST OF TABLES
Table 1. Critical values of water, sediment, and upland quality parameters that were used
to classify 97 stations sampled in 1999-2002 for the South Carolina Estuarine and
Coastal Assessment Program (SCECAP) as good, marginal, or poor...... 205
Table 2. Fish metrics that described life history, ecological and trophic composition,
tolerance, and community structure (italicized metrics were not included as
candidate fish metrics in statistical analyses).……...... 207
Table 3. Average values (±1 standard deviation) of water, sediment, upland, and physical
parameters for marginal, good, and excellent stations sampled in 1999-2002.... 210
Table 4. Environmental and physical parameters of two creeks (May and Kiawah Rivers)
that each contained one good and one marginal station.…………………...... 212
Table 5. Average value (±1 standard deviation) of the 21 fish metrics selected by the one-
way analyses, stepwise discriminant analyses, or previous studies for marginal,
good, and excellent stations.....………………...... 214
Table 6. Summary of the 21 fish metrics included for each EBI index evaluated (boxed
X=not used in discriminant analyses)..………………...... 216
Table 7. Fish metrics that were significantly different between good and marginal stations
sampled in 1999-2001 (Wilcoxon test, Dunn-Sidak test, 61 stations=good, 8
stations=marginal, α=0.10, k=73, p<0.0014)...... 218
- ix - Table 8. Significant fish metrics selected by stepwise discriminant analyses, using a
subset of 50 candidate metrics and stations sampled in 1999-2001 (61
stations=good; 8 stations=marginal; p<0.15)...... 220
Table 9. Significant fish metrics selected by stepwise discriminant analyses, using a
subset of 50 candidate metrics and stations sampled in 1999-2002 (87
stations=good; 9 stations=marginal, p<0.15)...... 222
Table 10. Subset of fish metrics that were used in previously developed estuarine biotic
integrity indices (Deegan et al. 1997; Meng et al. 2002).……………………... 224
Table 11. Twenty-one candidate fish metrics that were selected by statistical analyses or
by previous studies.……………...... 226
Table 12. Nine fish metrics that were used in the final EBI index (EBI index D6)……. 228
- x -
ABSTRACT
Large-scale environmental monitoring studies require a great amount of time and energy to complete. Often, a more efficient method to monitor environmental condition is to concentrate on biological communities. Fish communities are desirable environmental indicators due to their ability to directly integrate physical, chemical, and biological conditions. Data collected in tidal creeks for the South Carolina Estuarine and Coastal
Assessment Program (SCECAP) during the 1999-2002 sampling seasons were used to determine the relationship between environmental quality and fish community measures.
Statistical analyses, previous studies, and ecological concepts directed the selection of fish metrics that were the best discriminators of environmental quality. Potential multimetric estuarine biotic integrity (EBI) indices used combinations of fish metrics to calculate a single score to predict environmental quality. Station classification results using median analyses were more conservative in having low error rates for classifying marginal stations, while results from discriminant analyses were most useful in determining the final EBI index that could discriminate between marginal and good stations without error. The final EBI index developed and evaluated for South Carolina tidal creeks used metrics that described fish life history, ecological composition, tolerance, and community structure. These metrics were sensitive in determining environmental quality as described by water, sediment, and upland quality parameters,
- xi - and should be among the primary metrics considered for the development of future
indices. The final EBI index presented in the current study should be considered as an
index in the developmental stage, due to the low number of marginal stations available
and the lack of a true validation dataset. While the final EBI index did not prove to be a
perfect tool for assessing environmental quality in South Carolina’s tidal creeks, it can
serve as a point of departure for continuing development of future indices. This study
was the first effort in South Carolina to develop and evaluate an estuarine index of biotic integrity using the fish community and was an important first step in understanding the relationships between fish metrics and environmental quality in tidal creeks.
- xii -
INTRODUCTION
The United States (US) Water Pollution Control Act of 1972, an amendment to
the Clean Water Act originally implemented in 1948, prompted biological assessment for
the restoration and maintenance of the biotic integrity of surface waters. The standard definition for biotic integrity was established as “the capability of supporting and maintaining a balanced, integrated, adaptive community of organisms having a species
composition, diversity, and functional organization comparable to that of natural habitat
of the region” (Karr and Dudley 1981). This definition is supported by the US
Environmental Protection Agency (EPA; Ohio EPA 1988; USEPA 1988) and has
influenced many ecological studies of least impacted and developed habitats.
Environmental parameters, such as dissolved oxygen, pH, sediment composition,
and human disturbances, can greatly affect the species composition of biological
communities in a given area. Since large-scale studies of an ecosystem require a great
amount of time and energy, many have recognized that concentrating on biological
communities is a more efficient method to monitor overall environmental condition (e.g.,
Chandler 1970; Winner et al. 1980; Ohio EPA 1988; Ramm 1988; Hughes 1989; Simon
and Lyons 1995; Yoder and Rankin 1998). For example, biological communities in
estuaries have been shown to predictably respond to anthropogenic pollution (e.g.,
Pearson and Rosenberg 1978; Leppakoski 1977; Wilson and Jeffrey 1987; Crawford et
al. 1994; Hartwell et al. 1997; Hyland et al. 1999; Schimmel et al. 1999). Both fish and
macroinvertebrate communities are desirable indicators, reflecting the quality of the
environment by directly integrating physical, chemical, and biological conditions
(Berkman 1986; Ohio EPA 1988; Cairns et al. 1993; Yoder and Rankin 1995; Cranston
et al. 1996; Mebane 2001). Historically, macroinvertebrates have been popular indicators
for surveying conditions because they incorporate various trophic levels, cannot escape
adverse environmental conditions quickly, and are highly sensitive to environmental
changes (Perry et al. 1984; Ohio EPA 1988; USEPA 1988; Rosenberg and Resh 1993;
Chessman 1995). Fish are also sensitive to environmental changes, and are arguably
more easily understood by the public as economically and recreationally important organisms (Hocutt 1981; Karr 1981; Berkman et al. 1986; Karr et al. 1986; USEPA
1988; Harris 1995; Blaber 1999; Hughes and Oberdoff 1999). As a result, many investigations have considered fish communities as the prime environmental indicator or as a supplement to macroinvertebrate community studies (Ohio EPA 1988; USEPA 1988;
Yoder and Rankin 1995; Snyder et al. 1999).
Gammon (1976) proposed a multi-parameter method to profile water quality using four measures (number of species, relative density, biomass, and diversity) of the fish community in an Index of well-being (Iwb). However, some fish communities may not reflect environmental degradation in the Iwb if a measurement of high biomass, associated as a positive trait, was dominated by tolerant species (Yoder and Smith 1999).
Consequently, Karr (1981) proposed an index of biotic integrity (IBI) using fish community metrics that included the presence of intolerant species, richness and composition of tolerant species, and the representation of different trophic levels. Each
- 2 - metric was rated (5=slight deviation from the undisturbed condition, 3=moderate deviation, and 1=strong deviation from the undisturbed condition). Sites were scored by the sum of the ratings, and the score placed each site into a category that explained its relative condition (excellent, good, fair, poor, very poor; Karr 1981; Karr et al. 1986).
Validation studies have demonstrated that the multimetric index approach proposed by Karr (1981) was more effective for environmental assessment than relying solely upon independent metrics (Angermeier and Schlosser 1987; Karr et al. 1987; Ohio
EPA 1988; Fausch et al. 1990; Hughes 1989, Karr 1991; Harris 1994; Barbour et al.
1995; Yoder and Rankin 1995; Lyons et al. 1996; Deegan et al. 1997; Boulton 1999) or multivariate analyses (Fausch et al. 1990; Hughes and Noss 1992; Fore et al., 1996; Van
Dolah et al. 1999). An independent metric, such as species diversity, may produce misleading interpretations of the environment. For example, Gray (1976) found a grossly polluted estuary and other less polluted estuaries to have comparable species diversity, a metric that was popularly associated with ecosystem health. On the other hand, multivariate analyses (e.g., clustering and ordination) enable the interactions among many variables to be considered while being objective (Zar 1984), and have been used successfully for communities with a limited amount of variables (Clarke 1993; Rosen
1995). However, fish and invertebrate community assessments involve many variables that may increase the complexity and decrease the power (the probability to reject a false null hypothesis) in multivariate analyses (Zar 1984; Fausch et al. 1990; Fore et al. 1996;
Reynoldson et al. 1997; Van Dolah et al. 1999).
The multimetric index approach shows a clear reflection of relationships among many variables that are simple to repeat and understand (Fausch et al. 1990; Fore et al.
- 3 - 1994; Hughes and Noss 1992; Gerritsen 1995; Fore et al. 1996; Karr and Chu 1997; Van
Dolah et al. 1999). However, the development and practical application of an index
depends greatly on the amount of knowledge available to resource managers on the
physical habitat quality, water quality, and natural fish community composition
(Bramblett and Fausch 1991; Fausch et al. 1984; Karr 1999). Key factors that contribute
to the accuracy and effectiveness of an index are: consistent sampling methods, high
quality data, and the identification of metrics that are closely related to environmental
quality (Angermeier and Karr 1986; Fausch et al. 1990; Karr 1999).
The IBI developed by the multimetric approach has been approved by the USEPA
(1988) to be used to monitor freshwater quality and the IBI continues to be modified as
numerous new indices are produced regionally inside and outside of the US (e.g., Saylor
and Scott 1987; Miller et al. 1988; Steedman 1988; Plafkin et al. 1989; Hughes 1989;
Hughes et al. 1998; Roth et al. 1998; Kleynhans 1999). Modified fish IBIs have expanded from the mid-western US to Canada and the northern regions of the US, but the technique has not yet become a popular application in the southeastern US (Hughes 1989;
Simon and Lyons 1995). In South Carolina, stream water quality monitoring programs are well established (Perry et al. 1984), but the biocriteria of fish and invertebrate
communities used in biological assessment programs are still in the developmental stage
(Southerland and Stribling 1995; Yoder and Rankin 1998). Paller et al. (1996) developed
a modified IBI for fish communities in South Carolina coastal plain streams 2-15 m wide.
However, an IBI modified for small streams cannot be directly applied to larger water
bodies because stream width and depth are the greatest influences on the fish community
structure (Fausch et al. 1984; Paller 1994; Hay 2001).
- 4 - Along with many of the modified IBIs, the original IBI (Karr et al. 1986) was developed for fish communities inhabiting freshwater streams and few multimetric indices have been applied directly to estuaries. Estuaries are ecosystems classified as semi-enclosed areas where freshwater and seawater mixes (Pritchard 1955), including tidal creeks, marshes, and bays. Beginning in 1983, US federal programs targeted estuaries to evaluate estuarine health by gathering baseline physical, hydrological, and biological data and information on anthropogenic and natural resources (Alexander and
Monaco 1994; USEPA 2001, 2004). Many smaller scale estuarine assessments have used individual metrics (e.g., Gray 1976; Harrel and Hall 1991; Crawford et al. 1994) or benthic macroinvertebrates indices (e.g., Engle et al. 1994; Fore et al 1996; Weisberg et al. 1997; Van Dolah et al. 1999) to determine estuarine quality.
Fish communities have been used to develop multimetric estuarine biotic integrity indices to determine the status of estuaries in the northeastern US (Deegan et al. 1993,
1997; Meng et al. 2002). In particular, Deegan et al. (1993, 1997) developed an estuarine biotic integrity index (EBI) that has been validated as a useful tool to monitor anthropogenic change in the Massachusetts region (Chun et al. 1996; Deegan et al. 1997;
Hughes et al. 2002). The EBI included fish metrics, similar to Karr et al. (1986), which were significantly different between areas of different habitat quality.
Fish metrics that were used or suggested in the development of other estuarine indices of biotic integrity (Thompson and Fitzhugh 1986; Deegan et al. 1993, 1997;
Guillen 2000; Meng et al. 2002) were evaluated as candidate metrics for the current study. Candidate metrics described fish in four broad categories: life history, trophic and ecological composition, tolerance, and community structure. Fish life history metrics
- 5 - characterize fish based on the habitat that they use to develop as juveniles, to spawn, and
to inhabit for the majority of their life. Trophic and ecological composition metrics
define fish based on diet and feeding behavior, as well as where fish reside relative to the
water column. Tolerance metrics are a measure of relative sensitivity of species to
environmental conditions and include metrics such as salinity independent fish
(Weinstein 1979), resilient fish (Musick 1999; Froese and Pauly 2000), and taxonomic designation. Community structure metrics include fish density, species richness, species evenness, species diversity, and species dominance. Statistical tests indicated preliminary metrics that were strong discriminators of environmental quality, while ecological principles guided the final selection of candidate metrics that were useful in a multimetric index.
The current study is the first to use fish metrics to develop and evaluate an estuarine biotic integrity (EBI) index for South Carolina tidal creeks. A benthic index of
biotic integrity (B-IBI) was successfully developed in South Carolina estuaries using benthic macroinvertebrates in large tidal rivers (tidally influenced rivers with detectable
2 tides >2.5 cm; area >260 km , and length/width aspect ratio >20), as well as areas that
contained more open water (area >2.6 km2 and length/width aspect ratio <20; Hyland et
al. 1998; Van Dolah et al. 1999). Although the B-IBI was developed to assess sediment
quality in the Carolinian Province (Hyland et al. 1998; Van Dolah et al. 1999), the
environmental conditions in tidal creek habitats vary greatly from large tidal rivers and
open water areas. Tidal creeks (defined in the current study as creeks <100 m wide) are
smaller bodies of water than tidal rivers or open water, and can provide an early
indication of habitat stress because they are the first point of entry for upland runoff
- 6 - (Holland et al. 1997; Sanger et al. 1999a, 1999b; Van Dolah et al. 2000). As part of a statewide monitoring program, Van Dolah et al. (2002) compared South Carolina tidal creeks (also defined as creeks <100 m wide) and open water stations and found significant differences in water quality parameters, sediment quality parameters, and density and biomass of fish and crustacean species. Based on these findings, Van dolah et al. (2002) suggested that tidal creeks should be evaluated as separate habitats from open water bodies.
The current study used the tidal creek fish community to develop and evaluate an
EBI index and to determine if: 1) fish communities adequately reflect the biotic integrity of creek habitats based on specific environmental parameters and 2) using the EBI index is an effective method for managers to determine critical sites to rehabilitate, monitor, and protect. The development of the EBI index used results from one-way analyses, stepwise discriminant analyses, previous studies (Deegan et al. 1997; Meng et al. 2002), and ecological principles to incorporate many parameters into a single multimetric index.
The evaluation of the EBI index involved median analysis and discriminant analysis. The current study was the first to use fish communities as a tool to discern estuarine biotic integrity when evaluating the quality of estuarine habitats in South Carolina.
- 7 -
MATERIALS AND METHODS
Sampling design and procedures
Sample collection for the current study was completed in 1999-2002 through the
South Carolina Estuarine and Coastal Monitoring Program (SCECAP; Van Dolah et al.
2002; Van Dolah et al. 2004a). SCECAP is an interagency program developed by the
South Carolina Department of Natural Resources (SCDNR) and the South Carolina
Department of Health and Environmental Control (SCDHEC), and a partner in the United
States Environmental Protection Agency (USEPA) National Coastal Assessment program
and Coastal 2000 program. Field sampling design and sampling procedures for the current study followed SCECAP protocols (Van Dolah et al. 2002).
During 1999-2002, SCECAP selected approximately 30 South Carolina tidal
creek stations to sample each year, with stations located in water bodies that had widths
of less than 100 m from marsh bank to marsh bank. Tidal creeks were defined using one
or more of the following geographic information system (GIS) coverages: United States
Geological Survey (USGS) 1994 hydrography digital line graphs (DLG), National
Wetland Inventory (NWI) 1989 and 1994 databases, digital 7.5’ topographic quadrangle maps (1994), and the Coastal Change Analysis Program (CCAP) 1995 database.
Additional stations were located in deeper open water sites, such as harbors, sounds, and large tidal rivers, but these data were not analyzed in the current study. To reduce the
effect of biological variation due to salinity (Weinstein 1979), only stations with salinities
greater than 18 ppt were selected for the current study, which excluded ten stations from
analysis.
Stations were located within the coastal zone extending from the saltwater–
freshwater interface to near the mouth of each estuarine drainage basin, and extending
from the Little River Inlet at the South Carolina-North Carolina border to the Wright
River near the South Carolina-Georgia border (Figure 1). Some portions of the state’s
coastal waters that were too shallow to sample at low tide were excluded from the station
selection process. Stations were part of a larger array of stations selected using a probability-based, random tessellation, stratified sampling design (Stevens 1997; Stevens and Olsen 1999), with new station locations picked each year for SCECAP. Five non- random stations sampled in 2001 and 2002 were also included: three stations (MR1-01-T,
MR3-03-T, and MR3-04-T) were sampled in the May River, a tidal creek area that is currently experiencing increased development pressure (Van Dolah et al. 2004b), and two stations (NT01598 and NT02301) were sampled in Shem Creek, a highly developed tidal creek area.
Tidal creek stations were sampled during the day, at low tide, during June through
August of 1999-2002. At low tide, fish are forced out of the shallow marsh banks into subtidal channels where they can be sampled. The majority of fish that take advantage of
South Carolina estuaries for food, spawning grounds, and nursery grounds usually migrate into the estuaries beginning in the spring and reside there through the summer
(Shealy et al. 1974; Cain and Dean 1976; Wenner et al. 1981, 1984, 1991; Allen and
Barker 1990). Natural stresses in estuaries, such as low dissolved oxygen levels and high
- 9 - temperatures, are more common during the summer season. The effects of anthropogenic
stress on biological communities in estuarine systems would be most apparent during the
summer if natural stresses are already present. Therefore, sampling during the summer
season maximized the likelihood of detecting anthropogenic stress acting on the estuarine
fish community (Deegan et al. 1997).
A subset of water and sediment parameters collected for SCECAP were selected
for the current study based on their ability to distinguish among stations based on differing levels of development and anthropogenic disturbance (Table 1; Appendix A).
At each station, a datasonde was deployed for at least 25 hours to continuously collect salinity, temperature, dissolved oxygen, and pH at 15-minute intervals. Near-surface
water samples were collected in bottles and used to determine biological oxygen demand,
fecal coliform bacteria concentration, total nitrogen, and total phosphorus (SCDHEC
1997, 1998b). Sediment samples were collected using a 0.04 m2 Young grab. Sediments were analyzed for inorganic and organic contaminants by the National Oceanic and
Atmospheric Administration – National Ocean Service Center for Coastal Environmental
Health and Biomolecular Research (NOAA-NOS CCEHBR; Van Dolah et al. 2002).
Physical features, such as latitude/longitude, and average depth at each station were also collected using a geological positioning system (GPS) and depth finder, respectively
(Appendix B).
Two standardized 0.25 km replicate tows were made at each station using a 4- seam bottom trawl (5.5 m foot rope, 4.6 m head rope, and 2 cm bar mesh throughout).
All animals were sorted to the lowest practical taxonomic level, counted, and checked for
gross pathologies, deformities or external parasites. Fish and crustaceans were measured
- 10 - to the nearest 1.0 cm, and when a species’ abundance exceeded 25 individuals, a subsample of 25 individuals from that species was measured. Species identification and measurements from random trawls were checked by a quality assurance and quality control program approved by the USEPA National Coastal Assessment Program.
Although crustaceans and squid were found in trawls, only fish data were used in the current study. Mean fish abundances were corrected for the total area swept (Krebs
1972; Appendix C): Distance (D) x 0.6 Head rope length (H) Area swept (A) = 10,000 m2 ha-1
Candidate fish metrics
The first step to developing and evaluating an estuarine biotic integrity (EBI) index was to compile fish community metrics (Figure 2). Metrics describing fish life history, ecological and trophic composition, relative fish tolerance, and community structure were compiled using literature and past observations of local fish experts (Table
2; Appendices D.1-5). Several candidate life history metrics evaluated in the current study described estuarine/tidal creek nursery fish, estuarine dependent fish, estuarine/tidal creek spawning fish, and estuarine/tidal creek residents. Candidate ecological and trophic metrics evaluated in the current study were benthic fish, benthic feeders, herbivores, carnivores, predators, and detritivores. Tolerance metrics considered in the current study included salinity independent fish (Weinstein 1979), resilient (Musick
1999; Froese and Pauly 2000), and taxonomic designation such as flatfish, flounder, sciaenids, bay anchovy, and shad. Complete profiles were created for all but five metrics
(tidal creek nursery fish, tidal creek spawning fish, tidal creek resident, salinity
- 11 - independent fish, and resilient fish) because detailed ecological and tolerance data were not available for all taxa. A conservative approach was used for these five metrics when data were not available by leaving taxa as unclassified (blank value; Appendices D.1-5).
All metrics were calculated for two replicate trawls and averaged for each station
(Appendices E.1-4; SAS Institute 2002b). All candidate life history, ecological and trophic composition, and tolerance metrics described the fish community in three ways:
1) density of fish, 2) percent of fish, and 3) number of taxa. Community structure metrics included overall density, overall number of taxa, dominance of the most abundant taxon, dominance of the two most abundant taxa, dominance of the three most abundant taxa, the number of taxa that composed 90% of the total abundance, the number of taxa that composed 95% of the total abundance, species diversity, species evenness, and species richness. Formulas included:
Nmax x 100 (1) Berger-Parker Dominance d (Berger and Parker 1970) = Nt
where Nmax=number of individuals of either the most abundant taxon, the
two most abundant taxa, or the three most abundant taxa, and
Ntotal=total number of individuals in a sample
(2) Shannon-Wiener Species Diversity H' (Shannon 1948) =
(N log2 N) - Σ(ni log2 ni) N where N=total number of individuals in a sample, and
th ni=the number of individuals in the i taxa H' (3) Pielou’s Species Evenness J' (Pielou 1966) = Log S where H'=Shannon-Wiener index, and
S=total number of taxa in the sample
- 12 - (4) Margalef’s Species Richness D (Margalef 1958) = (S-1) ln N where S=total number of taxa in the sample, and
N=total number of individuals in the sample
Species diversity (H'), species evenness (J'), and species richness (D) were not transformed for any analyses. The density of individuals and the number of species data were log transformed (ln[x+1]), while percents were converted to proportions and arcsine transformed (arcsin√x; Zar 1984). Non-transformed data were analyzed nonparametrically when statistical tests were available, as transformations were not successful in normalizing all data (Shapiro-Wilk test, p<0.05; Zar 1984).
Life history metrics
Fish life history metrics provided information on the life stage and the amount of time that a fish spends in an estuary. Costa and Cabral (1999) found that pollution in an estuary caused a decrease in the abundances of juvenile fish that used the estuary as a nursery. Fish living in adverse environmental conditions have also been found to have decreased fecundity and offspring survival, ultimately leading to decreased abundances
(Kime 1995). Diadromous and estuarine-dependent fish have high energy and oxygen demands during migration (Leonard 1997) and may be more sensitive to degraded conditions than fish that reside in the estuary for their entire life cycle. Furthermore,
Chittenden (1969) and Ellis et al. (1947) found that repeat estuarine spawners were especially prone to decreased abundances when pollution was high and dissolved oxygen was low.
- 13 - All of the candidate life history metric values evaluated for the current study were
expected to decrease in response to environmental degradation, except for the metrics
describing estuarine resident and tidal creek resident fish (Table 2). Resident fish species
may migrate within the estuary or tidal creek, but do not spend any part of their life cycle
in coastal areas (i.e., offshore, nearshore, surf zone). Long-term effects of degraded
environmental conditions may eventually lead to lowered abundances of resident fish
species. However, resident fish species are expected to dominate the fish community
because initial changes in the environmental quality will result in decreased abundances
of transient fish species, such as estuarine dependent fish, that have higher demands for
resources.
Ecological and trophic metrics
Ecological and trophic metrics integrated information on fish spatial distribution
and community interactions, indicating the degree to which a fish is exposed to poor quality conditions. Benthic fish and benthic feeders have contaminant levels in their tissues that are comparable to those found in the sediment they inhabit (Koli et al. 1977;
Yannai and Sachs 1978; McCain et al. 1996), which may lead to many negative lethal
and sublethal health effects (Sindermann 1995). Adverse environmental conditions may
also cause decreased prey quality as well as quantity for benthic feeders (Wedemeyer et
al. 1984; Meng et al. 2001; Swan and Palmer 2000). Some benthic feeders accumulate
contaminants at a higher rate when exposed to contaminated sediments because
contaminants are available through consumption of contaminated prey, as well as
absorption through the skin and gills (DiPinto and Coull 1997). Furthermore, most
- 14 - benthic estuarine fish and benthic feeders are able to detect and avoid hypoxic bottom waters (dissolved oxygen <1 mg/L; Pihl et al. 1991; Wannamaker and Rice 2000).
Additionally, piscivorous fish and other top predators are more sensitive to degraded environmental quality than invertivores, herbivores, or omnivores because of the effects from bioaccumulation and biomagnification of toxic chemicals, and populations of top predators respond negatively to decreased environmental quality (e.g., Koli et al. 1977;
Yannai and Sachs 1978; Karr et al. 1986; Paller et al. 1996; Ganasan and Hughes 1998;
Guillen 2000; Mol et al. 2001; Wilcox et al. 2002).
All of the candidate ecological and trophic metric values evaluated for the current study were expected to decrease in response to environmental degradation, except for herbivores (Table 2). Since carnivores are more sensitive to contaminants based on their food resources, relatively high abundances and number of species of herbivores are expected in degraded areas. Although the omnivore metric values were not evaluated because it was found to be redundant with the carnivore and herbivore metrics, it was also expected to increase because omnivorous fish are less sensitive to degraded conditions. Likewise, values of the pelagic metric were expected to increase in degraded conditions because pelagic fish are less sensitive than benthic fish. The pelagic fish metric was also excluded in statistical analyses because it correlated completely with the benthic fish metric after fish were categorized as being either pelagic or benthic.
Tolerance metrics
An organisms’ tolerance to stress has often been included as a metric in indices for freshwater quality (e.g., Karr 1981; Karr et al. 1986; Fausch et al. 1984; Angermeier
- 15 - and Karr 1986; Leonard and Orth 1986; Miller et al. 1988; Schleiger 2000), but tolerance remains a difficult metric to define when comparing across species in other ecological systems, such as estuaries. Although standardized methods to determine the effects of single and multiple stressors on different species are not well established, information on physiological functions, growth, and survival after exposure to stressor(s) is available. A review of literature provided supplemental information on South Carolina tidal creek fish species and was used in the current to compile metrics describing fish tolerance.
The ability of fish to be independent of salinity may allow for greater opportunities to exploit areas from which salinity dependent fish are restricted.
Weinstein (1979) studied fish tolerance in shallow marsh habitats and tidal creeks in
North Carolina and found that certain species were distributed independently of salinity.
Weinstein (1979) categorized dominant fishes found in North Carolina tidal creeks, seven of which were found in South Carolina tidal creeks. Out of the seven South Carolina species that Weinstein (1979) studied, six were defined as “salinity independent” and one was defined as “salinity dependent” (Appendix D.3). Fish found in tidal creeks that
Weinstein (1979) did not study or had termed as salinity dependent were categorized in the current study as “not salinity independent.” For the current study, the number of salinity independent fish was not expected to be significantly different among stations because of an a priori adjustment of sampled stations due to salinity (stations with salinities less than 18 ppt were not included in analyses). Salinity independent fish was still included as a candidate metric that would increase in value with lowered environmental quality because intrinsic physiological and behavioral differences in
- 16 - salinity independent taxa may result in advantages for tolerating the stress of
environmental degradation (Table 2).
Another tolerance metric, resilient fish, was derived from a review by Musick
(1999) on the capacity of certain marine animals (fish, turtles, birds, whales) to withstand
exploitation. Musick (1999) suggested that animals with low intrinsic rates of increase
(r) and low growth coefficients (k) were less resilient. Marine animals that had known growth rates (k) were categorized by Musick (1999) as having high, medium, low, or no resilience. Twenty-three fishes found in South Carolina tidal creeks belonged to families categorized by Musick (1999). In the current study, twenty-one fish species were defined as being “resilient” (having high or medium resilience), and two fish species were defined as being “not resilient” (having low or no resilience; Appendix D.3; Musick
1999; Froese and Pauly 2000). As a conservative approach, fish that were defined as
“not resilient” also included any fish not included in that Musick’s (1999) study. The resilient metric profiled tidal creek fishes by identifying fish that are highly resilient to fishing pressure and, therefore, might be expected to be capable of withstanding the stress of environmental conditions (Table 2).
Additional candidate tolerance metrics included flatfish (fishes that belong to the
Bothidae, Cynoglossidae, or Soleidae families) and flounders (recreationally important flatfish) because they incorporate life history, ecology and trophic behaviors that make them sensitive to pollution. The abundances of flatfish and flounders have been used as indicators of environmental quality in a variety of studies (e.g., Murchelano and Wolke
1985; Nelson et al. 1991a; Sindermann 1994; Araujo et al. 2000; Meng et al. 2001, Meng et al. 2002). High concentrations of contaminants in the sediment impair reproduction
- 17 - and suppress the immune systems of many flatfish, leading to increased incidence of disease and decreased abundances (Pulsford 1995; Johnson et al. 1998). Flounders not only have relatively high rates of contaminant uptake (Rogers et al. 1992), but are also subject to added fishing pressure due to their status as recreationally important species.
The potential of flounders to be overfished increases the population’s vulnerability to pollution (Sindermann 1996). Therefore, the flounder and flatfish metric values were expected to decrease with degraded environmental quality (Table 2).
The life history, ecology and trophic behaviors of fish in the family Sciaenidae make them sensitive to pollution and habitat degradation, and therefore they were included as a candidate tolerance metric for evaluation. Most juvenile sciaenids are dependent on tidal creeks as critical habitats after migrating from offshore areas.
Sciaenid presence within an estuary may indicate that a habitat is in good condition, since they are commonly found in zones with high dissolved oxygen (>10 mg/L; Gelwick et al.
2001). Guillen (2000) suggested that high numbers of sciaenids, in an index of biotic integrity developed for the Galveston Bay, indicated excellent habitat condition. Values for the sciaenid metric were expected to decrease with environmental degradation (Table
2).
Bay anchovy and shad were also included as tolerance metrics. These species have lower rates of contaminant bioaccumulation and biomagnification because they are filter feeders that consume short-lived planktonic prey at the bottom of the food chain.
Bechtel and Copeland (1970) found that bay anchovy dominance was related to anthropogenic stress in the Galveston Bay. Thompson and Fitzhugh (1986) and Guillen
(2000) have recommended the use of high abundances of bay anchovy as an indication of
- 18 - degradation in an index of biotic integrity for Galveston Bay. Guillen (2000) also has recommended the use of shad as an alternative metric when bay anchovies are not present. For the current study, bay anchovy (Anchoa mitchilli) and shad (Alosa sapidissima and Dorosoma sp.) were evaluated as candidate tolerance metrics because they are relatively insensitive to environmental degradation and are expected to be present in high numbers in degraded conditions (Table 2).
Community structure metrics
Community structure metrics that describe species composition have been historically used as individual tools to assess environmental quality (Gray 2000).
Although many studies have cautioned against the use of an individual community structure metric as the only indicator of environmental quality (e.g., Livingston 1976;
Angermeier and Schlosser 1987; Fausch et al. 1990; Van Dolah et al. 1999), community structure metrics have been useful when studied in conjunction with other metrics describing the fish community, life history, ecological, trophic, and/or tolerance (see
Deegan et al. 1993, 1997; Meng et al. 2002). Decreased water quality of estuaries has been found to correspond to decreased fish species diversity, richness, and evenness (e.g.,
Bechtel and Copeland 1970; Gray 1989; Tzeng and Wang 1992; Scott and Hall 1997).
Deegan et al. (1993, 1997) observed an increase in the number of species, the abundance of fish, and dominance with increased habitat quality and successfully incorporated these metrics into an index of biotic integrity for estuaries in Massachusetts. Meng et al.
(2002) also observed that overall abundance and species diversity, along with other fish metrics, were useful in determining differences in habitat quality. The density of
- 19 - individuals, number of taxa, dominance, number of taxa that composed 90% of the total
abundance, number of taxa that composed 95% of the total abundance, species richness,
species evenness, and species diversity were included in the current study as candidate community metrics.
All of the candidate community structure metric values evaluated for the current study were expected to decrease in response to environmental degradation, except for the metrics describing dominance (Table 2). In the current study, the dominance value explains the percent of the total abundance that is composed of the most abundant fish
taxon or taxa. High dominance values, or low variety of fish taxa, may be a result of
degraded conditions if tolerant fish are highly abundant and more sensitive fish are not
present. Therefore, an increase in the dominance value is expected in response to
environmental degradation (Table 2).
Determining environmental quality
Water quality
Six parameters were used to determine water quality in this study, including
dissolved oxygen, biological oxygen demand, fecal coliform bacteria concentration, total nitrogen, total phosphorus, and pH. At each station, average levels of dissolved oxygen, biological oxygen demand, fecal coliform bacteria, total nitrogen, and phosphorus were scored: 1=poor, either exceeded state water quality standards or the 90th percentile of
SCDHEC’s historical database, 3=marginal, either exceeded an intermediate water
quality standard or the 75th percentile of SCDHEC’s historical database, and 5=good,
either did not exceed state water quality standards or the 75th percentile of SCDHEC’s
- 20 - historical database (Table 1; Appendix F; SCDHEC 1998a, 2001). Average values for
pH were scored similarly, but good and marginal criteria were determined with pH values
measured for SCECAP in polyhaline (18-30 ppt) and euhaline (>25 ppt) tidal creeks and
open water estuarine stations during 1999-2000, instead of SCDHEC’s historical database (Van Dolah et al. 2002). Criteria for poor pH values were determined by using the SCDHEC standard for degraded pH conditions in polyhaline waters (Table 1; Van
Dolah et al. 2002). It should be noted that the SCDHEC historical database on water
quality was primarily obtained from larger open water bodies and these values were used
because, to date, no criteria specific to tidal creeks exist.
After scoring the six water quality parameters, average water quality scores were
calculated for each station, using a procedure similar to that described by Van Dolah et
al. (2002). Missing data were regarded as blank values and overall water quality was
averaged with the number of parameters available. Raw averages were adjusted with the
same criteria that were used to adjust overall average quality (see Table 1), as discussed
later in this section, to facilitate comparisons between water, sediment, and upland
quality: 1=poor, 3=marginal, and 5=good (Appendix F). However, raw averages were
ultimately used to calculate overall environmental quality averages, not adjusted scores
because an adjustment process was used in the final calculation of overall environmental
quality
Sediment quality
The Effects Range Median – Quotient (ERM-Q) score was the only sediment
quality parameter used to define sediment quality in this study. The ERM-Q score
- 21 - represented the overall contaminant exposure of trace metals and organic compounds in the sediment (Hyland et al. 1999), and was calculated by dividing the measured concentrations of 24 contaminants by their Effects Range-Median (ER-M) value (i.e., caused adverse effects in more than 50% of the studies; Long et al. 1995). The ERM-Q values were scored: 1=poor or high risk of observing degraded benthic communities,
3=marginal or moderate risk of observing degraded benthic communities, and 5=good or low risk of observing degraded benthic communities (Table 1; Appendix G; Hyland et al.
1999; Van Dolah et al. 2002).
Upland quality
Land use and land cover data of the area surrounding each station were obtained from NWI 1989 and 1994 databases, categorized using the Anderson classification system (Anderson et al. 1976; US Fish and Wildlife 1989, 1994; ESRI 1998). To date, there is no standardized method that describes significant effects on environmental quality based on the amount of physically altered land, although impervious surface has been shown to be a useful tool (e.g., Karr and Chu 1999; Holland et al. 1997; Lerberg et al. 2000; Elvidge et al. 2004; Holland et al. 2004). For this study, physically altered land was defined as land categorized as residential or cropland/pasture (agricultural) within a
100 m buffer zone of the station. Residential and agricultural areas are usually associated with increased amounts of surface water runoff and are sources of contaminants that include chemicals and high amounts of nitrogen and phosphorus. The presence of urban and industrial areas within a 500 m buffer zone of each station was also quantified, but found to be rare. Therefore, urban and industrial areas were not investigated in the
- 22 - current study. Some stations that were sampled for SCECAP could not be analyzed for land use and land cover because they were located in creeks that were not well-defined in the NWI database, or located in creeks that were not at least 1,000 m in length (1999, n=2; 2000, n=2; 2001, n=5; 2002, n=3). Stations where land use and land cover could not be quantified were eliminated from further analyses.
The percent of upland that was categorized as residential or agricultural was calculated for each station (ESRI 1998). However, this percentage indicated low levels of physical alteration from residential or agricultural development (average=2%).
Therefore, the presence/absence of physical alteration (i.e., residential or agricultural development) within a 100 m buffer zone was used to determine upland quality. Final upland quality was scored: 2=marginal-poor, presence of physical alteration, and 5=good, no physical alteration).
Overall environmental quality
For stations sampled in 1999-2001, the effects of individual and average scores from combined water, sediment, and upland quality parameters on the fish community were compared. Comparisons were made in an effort to eliminate the environmental parameters that had little to no effect on the fish community from being incorporated into the final calculation of overall environmental quality. Individual environmental parameters that showed the greatest amount of variability in the fish community
(quantified as the number of fish metrics with significant differences among poor, marginal, and good stations) included pH, dissolved oxygen, and physical alteration
(Kruskal-Wallis test, Dunn-Sidak test, k=73, p<0.0007). None of the environmental
- 23 - parameters was able to distinguish significant differences for every fish metric tested
when used individually. A number of combinations and subsets of water, sediment, and
upland parameters were used to classify stations as good, marginal, or poor based on the
average scores of water, sediment, and upland quality. Most combinations did not
classify any station as poor and most of the combinations classified the majority of
stations as good.
Based on these analyses, environmental quality was defined to include parameters
that reflect anthropogenic stress and were essentially associated with environmental
habitat important to biological communities, including fish. The overall environmental quality of stations was equally dependent on water, sediment, and upland quality, determined by the overall average of the water quality score, the sediment quality score, and the upland quality score (Table 1; Appendix G). Raw overall averages were adjusted: <2.334=poor, 2.334 - <3.667=marginal, and ≥3.667=good (Table 1).
The environmental quality of 97 tidal creek stations sampled in 1999-2002 was
evaluated. Eighty-seven stations were classified as good, nine were marginal, and one
station was classified as poor. Since only one station was classified as poor, efforts were
focused on developing an estuarine biotic integrity index (EBI) that could distinguish
between the good and marginal stations.
Physical features
Additional features were examined for all stations using GIS coverages to
determine if physical habitat characteristics were similar among tidal creeks (Appendix
B; ESRI 1998; Hay 2001; Jutte et al. 2004). Using a hydrography DLG (USGS 1994),
- 24 - the average width of the tidal creek was calculated by averaging the distance of five lines drawn perpendicular to the banks of the creek that intersected points located: 1) at the station, 2) 250 m upstream of the station, 3) 250 m downstream of the station, 4) 500 m upstream of the station, and 5) 500 m downstream of the station. The width to depth
(W/D) ratio was calculated by dividing the average width of the tidal creek by the average depth that was collected on site, at each station, with a depth finder. Sinuosity, or the bending and curving path of the tidal creek, was calculated by measuring the distance of a straight line that connected a point located 500 m upstream with a point located 500 m downstream. Shorter distances between the two points indicated high levels of sinuosity, or curviness. The number of rivulets, or small streams draining into the tidal creek, was quantified within a 500 m buffer zone of the station by using digital orthophoto quarter-quadrangle (DOQQ) images for each station (USGS 1994, 1999).
Stations that were contained in the same creek were also compared with regards to relative location within the creek (upstream or downstream), overall environmental quality, and fish community composition.
Development of the estuarine biotic integrity (EBI) index
The selection of a subset of metrics to develop candidate EBI indices was the second step to developing and evaluating an EBI index (Figure 2). Five approaches were used to select fish metrics: 1) one-way analysis, 2) stepwise discriminant analysis, 3) metrics selected by previous studies, 4) metrics selected by a composite of approaches, and 5) individual metrics historically used as indicators of environmental quality.
- 25 - One-way analyses
The first of three approaches used for the development of the EBI index used one- way analyses to evaluate which set of the 73 candidate metrics that described the fish community by fish density, percent of fish, and number of taxa that most strongly distinguished between good and marginal environmental quality (SAS Institute 2002a;
Appendices E.1-4). One-way analyses, such as analysis of variance (ANOVA), t-test, and Wilcoxon test, have been used successfully in other similar studies to select metrics for developing indices of biotic integrity (e.g., Deegan et al. 1993, 1997; Scott and Hall
1997; Schubauer-Berigan et al. 2000). In the current study, EBI indices developed with metrics selected by one-way analyses were designated with an “A” prefix (i.e., EBI index
Ax).
Although stations were sampled independently, fish community data were not normally distributed (Shapiro-Wilkes test, p<0.05). Therefore, the Wilcoxon test, a nonparametric one-way analysis that ranks variables and compares the medians of groups to determine if there are significant differences, was used. Since multiple one-way comparisons did not account for additive errors, it was necessary to adjust the critical value (α) to reduce the probability of committing a Type I error. The Dunn-Sidak test was used to adjust the significance level (critical α'=1 – [1 - α] 1/k, where k=the number of independent significance tests; Sokal and Rohlf 1995).
Stepwise discriminant analyses
Stepwise discriminant analysis was the second approach used to select metrics that were strong indicators of environmental quality (SAS Institute 2002b). A subset of
- 26 - 50 candidate metrics that described the fish community based on the density of
individuals and the number of taxa was included (Appendices E.1-4). Metrics based on
percent abundance were not found to be strong discriminators for environmental quality
after results from the one-way analyses and were eliminated from subsequent
discriminant analyses to avoid collinearity of the variables. The metrics describing shad
(Alosa sapidissima and Dorosoma sp.) density and the number of shad taxa were also
eliminated to avoid collinearity. In the current study, EBI indices developed with metrics
selected by stepwise discriminant analyses were designated with a “B” prefix (i.e., EBI
index Bx).
Stepwise discriminant analysis accounted for multiple comparisons of variables
that were dependent, redundant, and/or highly correlated (Khattree and Naik 2000).
Since fish metrics were not distributed normally (Shapiro-Wilkes test, p<0.05) and
covariance matrices were not equal between good and marginal stations (Bartlett’s
correction, χ2=24.95, p=0.05), results from stepwise discriminant analyses were regarded
with caution. Forward selection chose variables one at a time using squared partial
correlations, the Wilk’s lambda, and the partial F ratio; variables were selected for the
smallest lambda or the largest F, and the selection process ended when all of the remaining variables did not meet the criteria (F-test=0.15; Klecka 1980; SAS Institute
2002b). For example, the first step selected the most discriminatory variable based on the
F-test criteria, and each additional step selected variables that were the best discriminatory variable when combined with the already selected variable(s). It has been cautioned that stepwise discriminant analysis does not always select for the best combination of variables that can predict differences (Klecka 1980; Hawkins 1982).
- 27 - However, every possible combination would have to be tried to select the optimum set of
variables and this is not always feasible when evaluating large numbers of variables.
Therefore, selection of variables from a stepwise discriminant analysis was considered to
be a good compromise worth investigating.
Previous studies
Indices and metrics suggested from previous estuarine studies (Deegan et al.
1993, 1997; Meng et al. 2002) were included to determine the transferability of biotic integrity indices and metrics from other regions and biological systems. In this study, metrics selected by Deegan et al. (1997), metrics selected by Meng et al. (2002), and all metrics from both of these studies were used in the development and application of three additional EBI indices. In the current study, EBI indices developed with metrics selected by previous studies were designated with a “C” prefix (i.e., EBI index Cx).
Composite and single metric analyses
The methods of selecting metrics for inclusion in indices for composite and single
metric analyses were more subjective than the other approaches previously mentioned.
In order to determine if selecting metrics by using one approach (one-way analyses,
stepwise discriminant analyses, or previous studies) was better than using a combination
of the three approaches, composite indices were developed. Metrics that were predicted
as indicators of environmental quality, based on the expert knowledge of the local fish
community metrics, were included in composite indices. In the current study, eight
composite EBI indices, designated with a “D” prefix (i.e., EBI index Dx), included a
- 28 - combination of metrics selected by one-way analyses, stepwise discriminant analyses,
previous studies, and ecological principles. In addition, community structure metrics
(density of individuals, number of taxa, species diversity) were selected for three single
metric EBI indices to determine if environmental quality could be predicted accurately by
using individual metrics. In the current study, individual EBI metrics were designated
with an “E” prefix and labeled as an index for consistency (i.e., EBI index Ex).
Application of the EBI index
At the initiation of this study, stations sampled in 1999-2001 for SCECAP were planned for the development of an EBI index, while stations sampled in 2002 were set aside for application and validation of the EBI index. However, when the EBI index was not successfully validated after application to the original data set, combined data from
1999-2002 were used to develop the final EBI index.
The application of the metrics selected for 22 candidate EBI indices was the third step to developing and evaluating an EBI index (Figure 2). All candidate EBI indices were applied with two approaches: 1) median analysis and 2) discriminant analysis.
Median analyses
The median analysis used in this study followed the multimetric approach from previously developed biotic indices (e.g., Van Dolah et al. 1999; Meng et al. 2002;
Weisberg et al. 1997). Stations with good environmental quality were set aside to be analyzed (1999-2001, n=61; 2002, n=26; 1999-2002, n=87). The 50th percentile (median
value) of each selected metric for good stations was used as the critical value between
- 29 - good and marginal environmental quality. If the fish metric’s average or median value for good stations was lower than the average or median value for marginal stations, then a score of 5 was given to each fish metric that was below the determined critical value, while a score of 0 was given to each fish metric that was above the determined critical value. If the fish metric’s average or median value for good stations was higher than the average or median value for marginal stations, then a score of 5 was given to each fish metric that was above the determined critical value, while a score of 0 was given to each fish metric that was below the determined critical value. All metric scores were summed for an EBI score and the maximum EBI score for each index was 5i, where i=the number of metrics used for the index. Scores that were less than half of the maximum value indicated marginal environmental quality while scores that were equal to or more than half the maximum value indicated good environmental quality.
Indices that were developed with 1999-2001 data used the median value of each fish metric as the critical value, and were based on 61 good stations. These critical values were applied to three data sets: 1) 1999-2001 stations, 2) 2002 stations, and 3) 1999-2002 stations. Indices that were developed with the combined 1999-2002 data used the median value of each fish metric as the critical value, based on 87 good stations, and were applied only to the 1999-2002 data set.
Discriminant analyses
Discriminant analysis was the second approach used for the application of the developed EBI indices. Assumptions of discriminant analyses included normality of variables, homoscedasticity (equal covariance matrices), and non-collinearity of
- 30 - variables. However, a nonparametric discriminant analysis was applied to circumvent
problems associated with violating these assumptions. When variables in an index were
collinear, a correlation matrix was examined and the most highly correlated variable was
not entered into the analysis. Preliminary tests included a multivariate analysis of
variance (MANOVA) to determine if there were differences in the selected metrics with
environmental quality and a Bartlett's modification of the likelihood ratio test to examine
the homogeneity of the within-group covariance matrices (Morrison 1976; Anderson
1984; SAS Institute 2000b). Although the MANOVA was relatively robust with
variables that were not normal if the sample size was large (>20 for each category;
Mertler and Vannatta 2002), sample sizes were unequal and the sample size for marginal
stations was small (1999-2001, n=8; 1999-2002, n=9). Furthermore, the Bartlett’s test
was not robust to deviations from normality (Khattree and Naik 2000). Therefore, results from the MANOVA and Bartlett’s test were regarded with caution. When the Bartlett’s test did not show a significant difference (p>0.10) between covariance matrices, the matrices were pooled for classification and a linear discriminant analysis was used
(Morrison 1976; SAS Institute 2000b). When the Chi-square test showed a significant difference (p<0.10) between covariance matrices, the individual within-group covariance matrices were used for classification and a quadratic discriminant analysis was used
(Morrison 1976; SAS Institute 2000b). Nonparametric discriminant analysis used the kernel method to transform nonparametric data with a kernel function and a smoothing parameter (radius; Simonoff 1996; Khatree and Naik 2000). Since there was no universally accepted standard kernel function or smoothing parameter available (Hawkins
1982; Khatree and Naik 2000), five kernel functions (uniform, normal, Epanechnikov,
- 31 - biweight, triweight) and all possible smoothing parameters (1-10) were considered (SAS
2000b). A final standard kernel and smoothing parameter (normal and 1, respectively)
were chosen for comparison because these standards were applicable and feasible for all
indices and minimized the overall mean squared error.
Discriminant analysis used the Fisher’s approach of generalized square distances
to determine discriminant functions and estimated error rates by cross-validation (Khatree
and Naik 2000). Cross-validation (leave-one-out procedure) was used to decrease the
misclassification rate by minimizing the predicted residual sum of squares (Lachenbruch
1967; Lachenbruch and Mickey 1968; SAS Institute 2000b). Cross-validation was
similar to the jackknife and bootstrap procedures, where observations were left out one at
a time and fitted to the model until all observations were left out. The resulting error
rates were calculated using all models to combine into a larger sample size (Chernick
1999).
Indices that were developed using 1999-2001 data were applied to two data sets:
1) 1999-2001 stations, and 2) 1999-2002 stations. A discriminant analysis was not
applicable for a data set limited to 2002 stations because there was only one marginal station found (degree of freedom was less than one). Indices that were developed using the combined 1999-2002 data were applied only to the 1999-2002 data set.
Evaluation and selection of the EBI index
Evaluation and selection of the final EBI index were the last two steps to
developing and evaluating an EBI index (Figure 2). After the median and discriminant
analyses were used to classify stations, the EBI indices that had the lowest
- 32 - misclassification rate for good, marginal, and all stations were evaluated as candidates for
the final EBI index. For each of the selected EBI indices, fish metrics were scored 5 or 0,
using the same values and criteria established by the multimetric median analysis. EBI
scores were calculated by averaging fish metric scores for each index. EBI scores for all
stations were then plotted to determine if a new criteria, other than medians, was needed
to predict environmental quality of stations (good vs. marginal). New criteria, or
threshold values, were established based on EBI score ranges that could determine
environmental quality with low rates of error. The final EBI index was selected based on its ability to predict environmental quality without error, using the new criteria and the
EBI score.
Stations with excellent environmental quality
Stations sampled in 1999-2002 where all water, sediment, and upland quality parameters scored as good were analyzed a posteriori and categorized as excellent stations. Average values of environmental parameters and physical features were compared between stations classified as good and excellent. Stations predicted to have good environmental quality by the EBI index were also compared to excellent stations.
Finally, the median (50th percentile) value for all excellent stations was calculated for
select fish metrics to determine conservative critical values that indicate high
environmental quality in South Carolina tidal creeks.
- 33 -
RESULTS
Environmental quality and physical features
Average values for water and upland quality parameters were comparable between marginal stations and the one station that was classified as poor (NT02301).
Exceptions included a higher average fecal coliform bacteria concentration and ERM-Q value (1600 col/100mL and 0.1113, respectively) at NT02301 when compared to the maximum value observed at marginal stations (Appendix A). Since NT02301 was the only station classified as poor, a criterion could not be established for poor stations.
Therefore, NT02301 was eliminated from all further statistical analyses.
For the 96 good and marginal stations in 1999-2002, overall average values for individual water quality parameters were high for pH (7.54) and dissolved oxygen (4.31 mg/L), while biological oxygen demand (1.28 mg/L), total nitrogen (0.615 mg/L), total phosphorus (0.0888 mg/L), and fecal coliform bacteria concentration (32.4 col/100 mL) were low when compared to the criteria used for the current study (Table 1; Appendix A).
On average, all six water quality parameters for 1999-2002 were individually scored as good (Appendix F). Using the adjusted average water quality score, 78 stations were classified as good, 18 stations were marginal, and none was classified as poor. The overall average water quality of South Carolina tidal creeks was good for stations sampled in 1999-2001, 2002, and for the combined 1999-2002 data (Appendix F). It
should be noted that adjusted scores were not used to calculate overall environmental quality and were presented here to facilitate comparisons among sediment and upland quality.
The average sediment contaminant level was low (Effects Range-Median
Quotient [ERM-Q] score=0.0130) when compared to the criteria used for the current study (Table 1; Hyland et al. 1999), and there were no missing ERM-Q values for the 96 good and marginal stations sampled in 1999-2002 (Appendix A). The overall average sediment quality of South Carolina tidal creek stations sampled in 1999-2002 was good, based on the ERM-Q score. Seventy-seven stations classified as good, 19 stations classified as marginal, and no stations classified as poor (Appendix G).
The average percent of land that was physically altered was very low (2%) at good and marginal stations sampled in 1999-2002 when compared to the criteria developed for the current study, and there were no missing upland quality values (Table
1; Appendix A). The overall average upland quality of South Carolina tidal creek stations sampled in 1999-2002 was good, based on the presence/absence of physical alteration within a 100 m buffer zone. Seventy-four stations were classified as good and
22 stations were not good (Appendix G).
Using the overall environmental criteria developed for this study (Table 1), 91% of stations sampled in 1999-2002 were classified as good (Appendix G). For stations sampled in 1999-2001, 61 stations were classified as good, eight stations were classified as marginal, and none were classified as poor. For stations sampled in 2002, 26 stations were classified as good, one station was classified as marginal, and one station was classified as poor.
- 35 - The ranges, maximum, minimum, and average values for physical features
(temperature, salinity, width, depth, width/depth ratio, sinuosity, rivulets) measured at all
stations sampled in 1999-2002, reflected highly dynamic environments characteristic of
tidal creeks and estuaries (Appendix B). The number of rivulets for one station
(RT02160) was the only physical feature that was unattainable and was regarded as
blank. RT02160 was located in an area surrounded by mudflats and the number of
rivulets was difficult to assess from aerial photographs and the National Wetland
Inventory database.
Stations were initially split into two data sets as stations sampled in 1999-2001 for
development and 2002 for application purposes. Data were also analyzed using all stations sampled in 1999-2002 (Table 3). There was no significant difference in good and marginal stations sampled in 1999-2001, 2002, or in 1999-2002 with respect to all water quality, sediment quality, or most physical features (Wilcoxon test, Dunn-Sidak test, k=19, p>0.0027). For the 1999-2001 and 1999-2002 data sets, marginal stations were more shallow than good stations (Wilcoxon test, Dunn-Sidak test, k=19, p<0.0027).
For the 1999-2001, 2002, and 1999-2002 data sets, there was a higher percentage of physically altered land at marginal stations than at good stations (Wilcoxon test, Dunn-
Sidak test, k=19, p<0.0027).
When all 96 good and marginal stations were analyzed geographically, nine creeks contained more than one station. Each of the nine creeks contained two stations that were within 2.5 km of each other, and the 18 stations were identified as being located either upstream or downstream in relation to each other (Appendix B). Seven creeks contained two stations that were both classified as good while two creeks contained one
- 36 - station that was classified as good and the other as marginal. For all nine pairs of stations, none of the 73 candidate fish metrics were significantly different between the station located upstream and the stations located downstream (Wilcoxon test, Dunn Sidak test, p>0.0014).
The two creeks that contained two stations differing in environmental quality were located in the Kiawah River (RT00542 and RT99004; Figure 3a) and May River
(MR1-01-T and RT01602; Figure 3b). Although the small sample size may not allow statistical tests to detect differences because of a lack of power, preliminary comparisons between marginal and good stations were included (Table 4). The marginal stations had slightly lower numbers for pH, dissolved oxygen, ERM-Q values, salinity, width, depth, width/depth ratio, sinuosity, and rivulets. On the other hand, marginal stations had slightly higher numbers for biological oxygen demand and sinuosity when compared to good stations. However, for each pair of good and marginal stations, none of the water quality, sediment quality, and physical features were significantly different between good and marginal stations (analysis of variance [ANOVA], p>0.05; SAS Institute 2002a).
More obvious differences between good and marginal stations located in the
Kiawah and May Rivers were with respect to the year of sampling, location of the station within the tidal creek (upstream or downstream), and upland quality. For both pairs, the good station was sampled the year before the marginal station, located downstream, and the upland was not physically altered within a 100 m buffer (Figure 3; Table 4). The marginal station was sampled a year later, located upstream, and the upland was physically altered (Figure 3; Table 4). However, when the percent of physically altered
- 37 - land within the total 100 m buffer area was compared between the paired marginal and
good stations, there was no significant difference (ANOVA, p>0.10).
Fish community
A total of 53 fish taxa were collected at the 96 tidal creek stations sampled from
1999-2002 (Appendix C). The five most common species were spot (Leiostomus
xanthurus), silver perch (Bairdiella chrysoura), pinfish (Lagodon rhomboides), bay
anchovy (Anchoa mitchilli), and hogchoker (Trinectes maculates). These species
comprised 79% of the total abundance sampled for the current study, with spot
contributing to 24% of the total abundance, silver perch contributing 22%, and smaller
percentages of pinfish (14%), bay anchovy (14%), and hogchoker (4%). No fish were
found to have gross pathologies, deformities, or external parasites.
For good and marginal stations, 60 fish species were profiled with seven life history metrics, eight ecological and trophic metrics, and seven tolerance metrics
(Appendices D.1-5). Fish that were identified to taxonomic categories above the species
level included Blennidae, Citharichthys sp., Eucinostomus sp., and Menticirrhus sp. Fish species that were within these higher taxonomic categories that were likely to be present in tidal creek habitats were also profiled (n=7).
Preliminary comparisons showed that most (n=46) of the average and/or median fish metric values were higher for marginal stations when compared to good stations
(Tables 2 and 5; Appendices E.1-4). Exceptions to this trend included several metrics based on the percent abundances of fish (bay anchovy, benthic fish, benthic feeder, detritivore, estuarine nursery fish, estuarine spawner, flatfish, flounder, herbivore, shad,
- 38 - and the sum of bay anchovy and shad), density of fish (flatfish, flounder, herbivore, and shad), and number of taxa (flatfish, flounder, herbivore, and shad). In addition, the average and/or median values of species evenness and the three metrics describing dominance at good stations were equal to or higher than values at marginal stations.
Average values that were higher at good stations than at marginal stations were most often reflected in metrics that were based on the percent abundance of fish.
However, metric values that were based on percent abundances were not significantly different between marginal and good stations (Wilcoxon test, Dunn-Sidak test, k=19, p<0.0027). Metrics based on the number of taxa and density of fish showed greater differences between good and marginal stations (Wilcoxon test, Dunn-Sidak test, k=19, p<0.0027). Therefore, trends observed for the current study were generalized based on the density and number of taxa of fish (Table 2).
Most fish collected at good and marginal stations for the current study utilized the estuary (97% of fish) and tidal creeks (88% of fish) for nursery grounds and/or were estuarine dependent (81% of fish; Appendix E.1). Many of the fish were also transient
(59% of fish) and spawned offshore or nearshore (50% of fish; Appendix E.1). The majority of the fish identified were benthic (76% of fish), benthic feeders (83% of fish), detritivores (81% of fish), and/or carnivores that fed on invertebrates (69% of fish;
Appendix E.2).
Several metrics did not have heavy representation among the fish collected at good and marginal stations. Only 31% of the fish community were top predators
(piscivores), with relatively few omnivores (14%) or herbivores (<1%) collected
(Appendix E.2). Small percentages of the various taxonomic metrics were found at good
- 39 - and marginal stations, such as bay anchovy (15%), shad (<1%), flounder (<1%), and
flatfish (8%; Appendix E.3).
The resilient and salinity independent metrics described only 43% and 47%, respectively, of the fish community at good and marginal stations (Appendix E.3).
Although the resilient and salinity independent metrics are not ideal reflections of the fish community due to their inability to definitively categorize many fish, these studies were considered to gain insight on possible differences in the fish community that may vary with environmental quality.
Development of the estuarine biotic integrity (EBI) index
One-way analyses
For stations sampled in 1999-2001, nine of the 73 candidate metrics were significantly different between good and marginal stations (Wilcoxon test, Dunn-Sidak test, k=73, α=0.10, p<0.0014; Figure 4). The nine metrics described fish life history
(estuarine nursery taxa, tidal creek nursery taxa, tidal creek resident taxa), trophic level
(carnivorous taxa, top predator taxa), tolerance (salinity independent taxa, highly resilient taxa), and community structure (number of taxa, species richness). All of these nine
metrics were used to develop EBI index A1 (Table 6 and 7). After adjusting to a stricter
critical value (α=0.05; p<0.0007), a subset of the nine metrics were significantly different between marginal and good stations. This subset included six metrics (carnivorous taxa, estuarine nursery taxa, number of taxa, salinity independent taxa, tidal creek nursery taxa, and top predator taxa) that were used to develop EBI index A2 (Tables 6 and 7).
- 40 - For stations sampled in 1999-2002, the top predator taxa metric was the only
metric significantly different between good and marginal stations based on a conservative
(α=0.10) or strict (α=0.05) criteria (Wilcoxon test, Dunn Sidak test, χ2=11.3900,
p=0.0002). Although it is a single metric, the number of top predator taxa was designated
as EBI index A3 for consistency (Table 6).
Stepwise discriminant analyses
For stations sampled in 1999-2001, five of the 50 candidate metrics were selected
(forward stepwise discriminant analysis, p<0.15) describing fish trophic level (top predator taxa), life history (tidal creek nursery taxa), tolerance (flatfish density), and community structure (number of taxa that composed 90% of the total abundance, dominance of the most abundant taxon). All five of the metrics that were selected accounted for 46% of the total variation and were used to develop EBI index B1 (Tables 6
and 8). After adjusting to a stricter critical value (p<0.10), a subset of three of the five
metrics were significant discriminators, including flatfish density, number of taxa that
composed 90% of the total abundance, and dominance of the most abundant taxon. This
subset of three metrics accounted for 40% of the total variation and was used to develop
EBI index B2 (Tables 6 and 8).
For stations sampled in 1999-2002, seven of the 50 metrics were selected
(forward stepwise discriminant analysis, p<0.15) describing fish trophic level (top
predator taxa, detritivore density), life history (tidal creek nursery density, estuarine
dependent density), tolerance (flatfish density, salinity independent taxa), and community
structure (dominance of the most abundant taxon). All of the seven metrics that were
- 41 - selected accounted for 29% of the total variation and were used to develop EBI index B3
(Tables 6 and 9). After adjusting to a stricter critical value (p<0.10), a subset of four of the seven metrics were significant discriminators, including estuarine dependent density, salinity independent taxa, tidal creek nursery density, and top predator taxa. This subset of four metrics accounted for 22% of the total variation and was used to develop EBI index B4 (Tables 6 and 9). After a final adjustment of the critical value (p<0.05), a subset
of three of the four metrics remained as significant discriminators, excluding tidal creek
nursery density. This subset of three metrics accounted for 19% of the total variation and
was used to develop EBI index B5 (Tables 6 and 9).
Previous studies
A total of nine fish metrics that were used in previous estuarine biotic integrity indices (Deegan et al. 1993, 1997; Meng et al. 2002) were transferable to the current study (Table 10). These metrics were used to develop three EBI indices applicable to the
South Carolina tidal creek fish found in the current study.
Deegan et al. (1993, 1997) successfully developed an estuarine biotic integrity index (EBI) for estuaries near Massachusetts using metrics describing fish ecology
(proportion of benthic fishes), life history (number of estuarine nursery species, number of estuarine spawning species, number of resident species), tolerance (proportion of abnormal or diseased fishes), and community structure (number of species, dominance, abundance) Since the current study did not find any abnormal or diseased fishes, this metric was not examined. The seven remaining metrics selected by Deegan et al. (1993,
1997) were used to develop EBI index C1 (Tables 6 and 10).
- 42 - Meng et al. (2002) developed an estuarine index of biotic integrity for the
Narragansett Bay using metrics describing fish ecology (proportion of benthic species),
life history (number of estuarine spawning species), tolerance (proportion of killifish,
proportion of flounder), and community structure (abundance, species diversity [H']).
Since the current study did not find any killifish, this metric was not examined. The five remaining metrics selected by Meng et al. (2002) were used to develop EBI index C2
(Tables 6 and 10).
Finally, all nine metrics that have been selected and used successfully in other indices for estuarine fish communities (Deegan et al. 1993, 1997; Meng et al. 2002) were considered together. The nine metrics selected by either Deegan et al. (1993, 1997) or
Meng et al. (2002) were used to develop EBI index C3 (Tables 6 and 10).
Composite and single metric analyses
Eight composite indices were developed using a combination of results from the
one-way and stepwise discriminant analyses, previous studies, and the knowledge and
opinions of local fish scientists. For EBI index D1-5, metrics were included after
considering the number of times a metric was selected by one of the three analyses used
in the current study, the units of the metric (i.e., density of individuals, number of taxa, or
percent of individuals), and the aspect of the fish community the metric was describing.
Eight metrics that were selected more than twice for EBI indices A1-3, B1-5, and C1, 2 were
analyzed for five composite indices (Table 6). EBI indices D1-5 included the number of
top predator taxa, the number of tidal creek nursery taxa, and salinity independent taxa
because they were the three metrics that were selected most frequently by both the one-
- 43 - way and stepwise discriminant analyses. Both the number of taxa and flatfish density
were metrics that were also included in EBI index D1-5 because the metrics were easy to
identify and calculate, had units that can be clearly interpreted, and have historically been
used to indicate differences in environmental quality. The number of estuarine nursery
taxa was not included in EBI indices D1-5 because it was redundant with the metric
already selected to describe the number of tidal creek nursery taxa. As a result of
eliminating redundant metrics and retaining metrics that had broad groupings (which
would simplify identification procedures for future studies), five “core” metrics were
included for EBI indices D1-5 that described fish life history, trophic composition,
tolerance, and community structure.
In addition to these five core metrics, one additional metric was added to each of
indices D2-4 in an effort to discern the relative contribution of the three additional metrics
that were also selected frequently by the one-way analyses, stepwise discriminant
analyses, or previous studies. Metrics describing the dominance of the most abundant
taxon, density of individuals, and estuarine dependent taxa were added to EBI indices D2-
4, respectively (Table 6).
All metrics included in EBI index D1 were included in EBI index D5, with the
exception of the metric describing the number of salinity independent taxa. The metric
describing salinity independent taxa, as discussed previously, was derived from a study
on North Carolina fish communities (Weinstein 1979), but not all fish taxa found in this
study were definitively profiled as independent or dependent of salinity. Therefore, this
metric was not included in EBI index D5 as a conservative measure to avoid
misrepresentation of the fish community.
- 44 - EBI index C3, produced by using metrics selected by Deegan et al. (1993, 1997) or Meng et al. (2002), was modified slightly with closely related metrics for EBI indices
D6 and D7. EBI index D6 used the same metrics included in EBI index C3 with the substitution of the density of flatfish for the density of flounder (Tables 6 and 12).
Another example of two closely related metrics was the number of taxa that composed
90% of the total abundance and the dominance of the most abundant taxon. EBI index
D7 included the metrics selected for EBI index C3 with the substitution of the number of
taxa that composed 90% of the total abundance for the dominance of the most abundant
taxon (Table 6).
EBI index D8 was the result of determining key metrics that were predicted to be
useful, based on ecological principles and previous studies using estuarine fish as
indicators of environmental condition (see Thompson and Fitzhugh 1986; Deegan et al.
1993, 1997; Guillen 2000; Meng et al. 2002). EBI index D8 included metrics that
described fish life history (tidal creek nursery taxa), trophic composition (top predator
taxa), tolerance (flatfish density), and community structure (density of individuals,
number of taxa that composed 90% of the total abundance; Table 6).
Three community structure metrics historically used as individual indicators of
environmental quality (the density of individuals, number of taxa, and species diversity)
were designated as EBI indices E1-3, respectively, for consistency (Table 6).
- 45 - Application of the EBI index – Median analyses
EBI index Ax
Although EBI indices A1 and A2 were developed with different metrics (Table 6),
they had the same error rates when used to classify stations with the median analysis
(Figures 5-7). EBI index A1 had a maximum EBI score of 45, using the critical values
(Table 7) to score nine metrics as good (5) or marginal (0). For EBI index A1, stations
that scored above or equal to 22.5 were classified as good, and stations that scored below
22.5 were classified as marginal. EBI index A2 had a maximum EBI score of 30, using
the critical values (Table 7) to score six metrics as good (5) or marginal (0). For EBI
index A2, stations that scored above or equal to 15 were classified as good, and stations
that scored 15 or below were classified as marginal. For both EBI indices A1 and A2, 24 of the 69 (34.78%) stations sampled in 1999-2001 were incorrectly classified (Figure 5).
Twenty-four of the 61 (39.34%) good stations and none of the eight marginal stations were misclassified (Figure 5). When applied to stations sampled in 2002, EBI indices A1
and A2 incorrectly classified 18 of the 27 (66.67%) stations (Figure 5). Seventeen of the
26 (65.38%) good stations and the only marginal station were misclassified. For stations
sampled in 1999-2002, EBI indices A1 and A2 incorrectly classified 42 of the 96
(43.75%) stations (Figure 6). Forty-one of the 87 (47.13%) good stations and one marginal station were misclassified (Figure 7).
EBI index A3 scored stations sampled in 1999-2002 as good (5) or marginal (0) by
using the critical value for the number of top predator taxa (Table 7). EBI index A3 incorrectly classified 40 of the 96 (41.67%) stations (Figure 6). Forty of the 87 (45.98%) good stations and none of the marginal stations were misclassified (Figure 7).
- 46 -
EBI index Bx
EBI index B1 had a maximum EBI score of 25, using the critical values (Table 8)
to score five metrics as good (5) or marginal (0). Stations that scored above or equal to
12.5 were classified as good, and stations that scored below 12.5 were classified as marginal. EBI index B1 incorrectly classified 23 of the 69 (33.33%) stations sampled in
1999-2001 (Figure 5). Twenty-three of the 61 (37.70%) good stations and no marginal
stations were misclassified for stations sampled in 1999-2001. When applied to stations
sampled in 2002, EBI index B1 incorrectly classified 20 of the 27 (74.07%) stations
(Figure 5). Nineteen of the 26 (73.08%) good stations and the only marginal station were
misclassified. For stations sampled in 1999-2002, EBI index B1 incorrectly classified 43 of the 96 (44.79%) stations (Figure 6). Forty-two of the 87 (48.28%) good stations and one of the nine (11.11%) marginal stations were misclassified (Figure 7).
EBI index B2 had a maximum EBI score of 15, using the critical values (Table 8)
to score three metrics as good (5) or marginal (0). Stations that scored above or equal to
7.5 were classified as good, and stations that scored below 7.5 were classified as marginal. EBI index B2 incorrectly classified 24 of the 69 (34.78%) stations sampled in
1999-2001 (Figure 5). Twenty-four of the 61 (39.34%) good stations and no marginal
stations were misclassified. When applied to stations sampled in 2002, EBI index B2
incorrectly classified 19 of the 27 (70.37%) stations (Figure 5). Eighteen of the 26
(69.23%) good stations and the only marginal station were misclassified. For stations
sampled in 1999-2002, EBI index B2 incorrectly classified 43 of the 96 (44.79%) stations
- 47 - (Figure 6). Forty-two of the 87 (48.28%) good stations and one of the nine (11.11%)
marginal stations were misclassified (Figure 7).
EBI index B3 had a maximum EBI score of 35, using the critical values (Table 9)
to score seven metrics as good (5) or marginal (0). Stations sampled in 1999-2002 that
scored above or equal to 17.5 were classified as good, and stations that scored below 17.5
were classified as marginal. EBI index B3 incorrectly classified 39 of the 96 (40.63%) stations (Figure 6). Thirty-eight of the 87 (43.68%) good stations and one of the nine
(11.11%) marginal stations were misclassified (Figure 7).
EBI index B4 had a maximum EBI score of 20, using the critical values (Table 9)
to score four metrics as good (5) or marginal (0). Stations sampled in 1999-2002 that
scored above or equal to 10 were classified as good, and stations that scored below 10
were classified as marginal. EBI index B4 incorrectly classified 33 of the 96 (34.38%)
stations (Figure 6). Thirty-two of the 87 (36.78%) good stations and one of the nine
(11.11%) marginal stations were misclassified (Figure 7).
EBI index B5 had a maximum EBI score of 15, using the critical values (Table 9)
to score three metrics as good (5) or marginal (0). Stations sampled in 1999-2002 that
scored above or equal to 7.5 were classified as good, and stations that scored below 7.5
were classified as marginal. EBI index B5 incorrectly classified 34 of the 96 (35.42%)
stations (Figure 6). Thirty-four of the 87 (39.08%) good stations and one of the nine
(11.11%) marginal stations were misclassified (Figure 7).
- 48 - EBI index Cx
EBI index C1 had a maximum EBI score of 35, using the critical values (Table 10)
to score seven metrics as good (5) or marginal (0). Stations sampled in 1999-2002 that
scored above or equal to 17.5 were classified as good, and stations that scored below 17.5
were classified as marginal. EBI index C1 incorrectly classified 39 of the 96 (40.63%) stations (Figure 6). Thirty-eight of the 87 (43.68%) good stations and one of the nine
(11.11%) marginal stations were misclassified (Figure 7).
EBI index C2 had a maximum EBI score of 25, using the critical values (Table 10)
to score five metrics as good (5) or marginal (0). Stations sampled in 1999-2002 that
scored above or equal to 12.5 were classified as good, and stations that scored below 12.5
were classified as marginal. EBI index C2 incorrectly classified 32 of the 96 (33.33%)
stations (Figure 6). Thirty-one of the 87 (35.63%) good stations and one of the nine
(11.11%) marginal stations were misclassified (Figure 7).
EBI index C3 had a maximum EBI score of 45, using the critical values (Table 10)
to score nine metrics as good (5) or marginal (0). Stations that scored above or equal to
22.5 were classified as good, and stations that scored below 22.5 were classified as marginal. EBI index C3 incorrectly classified 38 of the 96 (39.58%) stations (Figure 6).
Thirty-seven of the 87 (42.53%) good stations and one of the nine (11.11%) marginal stations were misclassified (Figure 7).
EBI index Dx
Although EBI indices D1 and D6 included different metrics, they had the same
error rates when used to classify stations sampled in 1999-2002 with the median analysis.
- 49 - EBI index D1 had a maximum EBI score of 25, using the critical values (Table 11) to
score 5 metrics as good (5) or marginal (0). For EBI index D1, stations that scored above
or equal to 12.5 were classified as good, and stations that scored below 12.5 were
classified as marginal. EBI index D6 had a maximum EBI score of 45, using the critical values (Table 12) to score nine metrics as good (5) or marginal (0). For EBI index D6, stations that scored above or equal to 22.5 were classified as good, and stations that scored a 22.5 or below were classified as marginal. Both EBI indices D1 and D6 incorrectly classified 40 of the 96 (41.67%) stations (Figure 6). Thirty-nine of the 87
(44.83%) good stations and one of the nine (11.11%) marginal stations were misclassified
(Figure 7).
EBI index D2 had a maximum EBI score of 30, using the critical values (Table 11)
to score six metrics as good (5) or marginal (0). Stations sampled in 1999-2002 that
scored above or equal to 15 were classified as good, and stations that scored a 15 or
below were classified as marginal. EBI index D2 incorrectly classified 32 of the 96
(33.33%) stations (Figure 6). Thirty-one of the 87 (35.63%) good stations and one of the nine (11.11%) marginal stations were misclassified (Figure 7).
EBI index D3 had a maximum EBI score of 30, using the critical values (Table
11) to score six metrics as good (5) or marginal (0). Stations sampled in 1999-2002 that
scored above or equal to 15 were classified as good, and stations that scored a 15 or
below were classified as marginal. EBI index D3 incorrectly classified 34 of the 96
(35.42%) stations (Figure 6). Thirty-three of the 87 (37.93%) good stations and one of the nine (11.11%) marginal stations were misclassified (Figure 7).
- 50 - EBI index D4 had a maximum EBI score of 30, using the critical values (Table
11) to score six metrics as good (5) or marginal (0). Stations sampled in 1999-2002 that
scored above or equal to 15 were classified as good, and stations that scored a 15 or
below were classified as marginal. EBI index D4 incorrectly classified 33 of the 96
(34.38%) stations (Figure 6). Thirty-two of the 87 (36.78%) good stations and one of the nine (11.11%) marginal stations were misclassified (Figure 7).
EBI index D5 had a maximum EBI score of 20, using the critical values (Table 11)
to score four metrics as good (5) or marginal (0). For EBI index D5, stations that scored
above or equal to 10 were classified as good, and stations that scored a 10 or below were
classified as marginal. EBI index D5 incorrectly classified 36 of the 96 (37.50%) stations
(Figure 6). Thirty-five of the 87 (40.23%) good stations and one of the nine (11.11%)
marginal stations were misclassified (Figure 7).
EBI index D7 had a maximum EBI score of 45, using the critical values (Table
11) to score nine metrics as good (5) or marginal (0). Stations sampled in 1999-2002 that
scored above or equal to 22.5 were classified as good, and stations that scored a 22.5 or
below were classified as marginal. EBI index D7 incorrectly classified 42 of the 96
(43.75%) stations (Figure 6). Forty-one of the 87 (47.13%) good stations and one of the nine (11.11%) marginal stations were misclassified (Figure 7).
EBI index D8 had a maximum EBI score of 25, using the critical values (Table 11)
to score five metrics as good (5) or marginal (0). Stations sampled in 1999-2002 that
scored above or equal to 12.5 were classified as good, and stations that scored a 12.5 or
below were classified as marginal. EBI index D8 incorrectly classified 44 of the 96
- 51 - (45.83%) stations (Figure 6). Forty-three of the 87 (49.43%) good stations and one of the
nine (11.11%) marginal stations were misclassified (Figure 7).
EBI index Ex
EBI index E1 scored stations sampled in 1999-2002 as good (5) or marginal (0) by
using the critical value for the density of individuals (Table 11). EBI index E1 incorrectly
classified 44 of the 96 (45.83%) stations (Figure 6). Forty-three of the 87 (49.43%) good stations and one of the nine (11.11%) marginal stations were misclassified (Figure 7).
EBI index E2 scored stations sampled in 1999-2002 as good (5) or marginal (0) by
using the critical value for the number of taxa (Table 11). EBI index E2 incorrectly
classified 43 of the 96 (44.79%) stations sampled in 1999-2002 (Figure 6). Forty-two of
the 87 (48.28%) good stations and one of the nine (11.11%) marginal stations were misclassified (Figure 7).
EBI index E3 scored stations sampled in 1999-2002 as good (5) or marginal (0) by
using the critical value for the number of top predator taxa (Table 11). EBI index E3 incorrectly classified 45 of the 96 (46.88%) stations (Figure 6). Forty-three of the 87
(49.43%) good stations and two of the nine (22.22%) marginal stations were misclassified (Figure 7).
Application of the EBI index – Discriminant analyses
EBI index Ax
For EBI index A1, the number of estuarine nursery taxa and species richness (D)
were eliminated from the discriminant analysis because they were highly correlated
- 52 - (Spearman’s rank correlation, r>0.95, p<0.0001) with the number of taxa. EBI index A1 incorrectly classified four of the 69 (5.80%) stations sampled in 1999-2001 (Figure 5) using a nonparametric, quadratic discriminant analysis (multivariate analysis of variance
[MANOVA], p=0.0004; Bartlett’s test, p=0.0001). None of the good stations and four of
the eight (50%) marginal stations were misclassified. When applied to stations sampled
in 1999-2002, EBI index A1 incorrectly classified eight of the 96 (8.33%) stations (Figure
5) using a nonparametric, quadratic discriminant analysis (MANOVA, p=0.0057;
Bartlett’s test, p=0.0518). One of the 87 (1.15%) good stations and seven of the nine
(77.78%) marginal stations were misclassified (Figure 8).
For EBI index A2, the number of estuarine nursery taxa was eliminated from the
discriminant analysis because it was highly correlated (Spearman’s rank correlation,
r>0.95, p<0.0001) with the number of taxa. EBI index A2 incorrectly classified eight of
the 69 (11.59%) stations sampled in 1999-2001 (Figure 5) using a nonparametric, linear
discriminant analysis (MANOVA, p<0.0001; Bartlett’s test, p=0.1030). One of the 61
(1.64%) good stations and seven of the eight (87.50%) marginal stations were
misclassified. When applied to stations sampled in 1999-2002, EBI index A2 incorrectly
classified nine of the 96 (9.38%) stations (Figure 5) using a nonparametric, quadratic
discriminant analysis (MANOVA, p=0.0038; Bartlett’s test, p=0.0080). One of the 87
(1.15%) good stations and eight of the nine (88.89%) marginal stations were
misclassified (Figure 8).
EBI index A3 incorrectly classified nine of the 96 (9.38%) stations sampled in
1999-2002 (Figure 6) using a nonparametric, quadratic discriminant analysis
- 53 - (MANOVA, p=0.0009; Bartlett’s test, p=0.0539). None of the 87 good stations and all of
the nine marginal stations were misclassified (Figure 8).
EBI index Bx
EBI index B1 incorrectly classified five of the 69 (7.25%) stations sampled in
1999-2001 (Figure 5) using a nonparametric, quadratic discriminant analysis
(MANOVA, p<0.0001; Bartlett’s test, p=0.0506). For stations sampled in 1999-2001, one of the 61 (1.64%) good stations and four of the eight (50%) marginal stations were misclassified. When applied to stations sampled in 1999-2002, EBI index B1 incorrectly
classified nine of the 96 (9.38%) stations (Figure 5) using a nonparametric, quadratic
discriminant analysis (MANOVA, p<0.0001; Bartlett’s test, p=0.0006). Three of the 87
good stations (3.45%) and six of the nine (66.67%) marginal stations were misclassified
(Figure 8).
EBI index B2 incorrectly classified six of the 69 (8.7%) stations sampled in 1999-
2001 (Figure 5) using a nonparametric, linear discriminant analysis (MANOVA,
p<0.0001; Bartlett’s test, p=0.1406). None of the good stations and six of the eight
(75%) marginal stations were misclassified. When applied to stations sampled in 1999-
2002, EBI index B2 incorrectly classified eight of the 96 (8.33%) stations (Figure 5) using
a nonparametric, quadratic discriminant analysis (MANOVA, p<0.0001; Bartlett’s test,
p=0.0010). None of the good stations and eight of the nine (88.89%) marginal stations were misclassified (Figure 8).
EBI index B3 incorrectly classified six of the 96 (6.25%) stations sampled in
1999-2002 (Figure 6) using a nonparametric, quadratic discriminant analysis
- 54 - (MANOVA, p<0.0001; Bartlett’s test, p=0.0002). None of the good stations and six of
the nine (66.67%) marginal stations were misclassified (Figure 8).
EBI index B4 incorrectly classified eight of the 96 (8.33%) stations sampled in
1999-2002 (Figure 6) using a nonparametric, quadratic discriminant analysis
(MANOVA, p=0.0012; Bartlett’s test, p=0.0184). None of the 87 good stations and eight of the nine (88.89%) marginal stations were misclassified (Figure 8).
EBI index B5 incorrectly classified seven of the 96 (8.33%) stations sampled in
1999-2002 (Figure 6) using a nonparametric, quadratic discriminant analysis
(MANOVA, p=0.0004; Bartlett’s test, p=0.0016). None of the good stations and seven of the nine (77.78%) marginal stations were misclassified (Figure 8).
EBI index Cx
For EBI index C1, the number of estuarine nursery taxa was eliminated from the
discriminant analysis because it was highly correlated (Spearman’s rank correlation,
r>0.95, p<0.0001) with the number of taxa. EBI index C1 incorrectly classified 14 of the
96 (14.58%) stations sampled in 1999-2002 (Figure 6) using a nonparametric, quadratic
discriminant analysis (MANOVA, p=0.0189; Bartlett’s test, p=0.0562). Five of the 87
(5.75%) good stations and all nine marginal stations were misclassified (Figure 8).
EBI index C2 incorrectly classified 19 of the 96 (19.79%) stations sampled in
1999-2002 (Figure 6) using a nonparametric, quadratic discriminant analysis
(MANOVA, p=0.0191; Bartlett’s test, p=0.0052). Thirteen of the 87 (14.94%) good stations and six of the nine (66.67%) marginal stations were misclassified (Figure 8).
- 55 - For EBI index C3, the number of estuarine nursery taxa was eliminated from the
discriminant analysis because it was highly correlated (Spearman’s rank correlation,
r>0.95, p<0.0001) with the number of taxa. EBI index C3 incorrectly classified one of
the 96 (1.04%) stations sampled in 1999-2002 (Figure 6) using a nonparametric,
quadratic discriminant analysis (MANOVA, p=0.0484; Bartlett’s test, p<0.0001). One of
the 87 (1.15%) good stations and none of the marginal stations were misclassified (Figure
8).
EBI index Dx
EBI index D1 incorrectly classified six of the 96 (6.25%) stations sampled in
1999-2002 (Figure 6) using a nonparametric, quadratic discriminant analysis
(MANOVA, p=0.0008; Bartlett’s test, p=0.0023). Two of the 87 good stations (2.30%) and four of the nine (44.44%) marginal stations were misclassified (Figure 8).
EBI index D2 incorrectly classified five of the 96 (5.21%) stations sampled in
1999-2002 (Figure 6) using a nonparametric, quadratic discriminant analysis
(MANOVA, p=0.0017; Bartlett’s test, p=0.0155). None of the good stations and five of the nine (55.56%) marginal stations were misclassified (Figure 8).
EBI index D3 incorrectly classified seven of the 96 (7.29%) stations sampled in
1999-2002 (Figure 6) using a nonparametric, quadratic discriminant analysis
(MANOVA, p=0.0002; Bartlett’s test, p=0.0002). One of the 87 (1.15%) good stations and six of the nine (66.67%) marginal stations were misclassified (Figure 8).
EBI index D4 incorrectly classified seven of the 96 (7.29%) stations sampled in
1999-2002 (Figure 6) using a nonparametric, quadratic discriminant analysis
- 56 - (MANOVA, p=0.0003; Bartlett’s test, p=0.0006). One of the 87 (1.15%) good stations
and six of the nine (66.67%) marginal stations were misclassified (Figure 8).
EBI index D5 incorrectly classified four of the 96 (4.17%) stations sampled in
1999-2002 (Figure 6) using a nonparametric, quadratic discriminant analysis
(MANOVA, p=0.0003; Bartlett’s test, p=0.0004). None of the good stations and four of the nine (44.44%) marginal stations were misclassified (Figure 8).
EBI index D6 correctly classified all 96 stations sampled in 1999-2002 (Figures 6 and 8) using a nonparametric, quadratic discriminant analysis (Appendix H; MANOVA, p=0.0033; Bartlett’s test, p<0.0001).
EBI index D7 incorrectly classified six of the 96 (6.25%) stations sampled in
1999-2002 (Figure 6) using a nonparametric, quadratic discriminant analysis
(MANOVA, p=0.0062; Bartlett’s test, p<0.0001). Four of the 87 (4.60%) good stations and 2 of the nine (22.22%) marginal stations were misclassified (Figure 8).
EBI index D8 incorrectly classified six of the 96 (6.25%) stations sampled in
1999-2002 (Figure 6) using a nonparametric, quadratic discriminant analysis
(MANOVA, p=0.0001; Bartlett’s test, p=0.0003). One of the 87 (1.15%) good stations and five of the nine (55.56%) marginal stations were misclassified (Figure 8).
EBI index Ex
Although EBI indices E1-3 used different metrics, they all had the same error rates
when used to classify stations sampled in 1999-2002 with the discriminant analysis. EBI
index E1 used a nonparametric quadratic discriminant analysis (MANOVA, p=0.007;
Bartlett’s test, p=0.114) while EBI indices E2 and E3 used a nonparametric linear
- 57 - discriminant analysis (MANOVA, p<0.02; Bartlett’s test, p>0.30). EBI indices E1-3 incorrectly classified nine of the 96 (9.38%) stations (Figure 6). None of 87 good stations and all nine marginal stations were misclassified by EBI indices E1-3 (Figure 8).
Evaluation and selection of the final EBI index
Metrics selected by the one-way analyses, stepwise discriminant analyses, and previous studies differed greatly and not one metric was chosen by all three selection methods (Table 6). The indices that included metrics selected by the one-way analyses
(EBI indices Ax) and stepwise discriminant analyses (EBI indices Bx) were most closely related by having three metrics in common that described fish life history (tidal creek
nursery taxa), trophic composition (top predator taxa), and tolerance (salinity independent
taxa; Table 6). The one-way analyses and previous studies shared two metrics (estuarine
nursery taxa and number of taxa), while the stepwise discriminant analyses and previous
studies shared only one metric (dominance of the most abundant taxon). All three
selection methods chose metrics that described fish life history, tolerance, and
community structure (Table 6).
For both the median and discriminant analyses, EBI indices A1,2 and B1,2 had lower misclassification rates when applied back to the original data from which they were developed (Figure 5). For the median analysis, EBI indices A1,2 and B1,2 were not
effective in predicting the environmental quality of stations sampled in 2002 because of
extremely high error rates (>65%, Figure 5). Discriminant analysis was impossible for stations sampled in 2002 because stations showed little to no difference in water, sediment, or upland quality (degree of freedom less than one for marginal stations). Error
- 58 - rates were also higher for the combined 1999-2002 data than when compared to error
rates as a result of application to stations sampled in 1999-2001 (Figure 5).
High error rates obtained by the median analysis for good, marginal, and the total
number of stations did not necessarily correlate with high error rates obtained by the
discriminant analysis (Figure 6). The median analysis was more conservative in that this
technique misclassified good stations more often than marginal stations (Figure 7), while
the discriminant analysis had extremely high rates of error for marginal stations (Figure
8). Overall, the discriminant analysis had lower error rates using the cross-validation
method than when compared to the median analysis (Figure 6).
Five EBI indices with relatively low error rates were selected for further
consideration as the final EBI index. Results of the median analysis indicated that EBI
index A3 had the lowest misclassification rate for marginal stations (Figure 7) and EBI
indices C2 and D2 had the lowest misclassification rates for good and total number of
stations (Figures 6 and 7). Based on the discriminant analysis, EBI index D6 was the only
index that correctly classified all stations (Figures 6 and 8). Since EBI index C3 included similar metrics to EBI index D6, it was also considered.
The fish metrics that were incorporated, the number of metrics, and the maximum
score differed greatly among EBI indices A3, C2, C3, D2, and D6 (Table 10; Figure 9).
When the scores of these five EBI indices were plotted for stations classified a priori as
good or marginal, each of the five EBI indices had a large overlap in EBI scores for good
and marginal stations (Figure 9). Stations that were scored within the overlap were
labeled as “unknown” because the range of scores could not determine environmental
quality without error. For example, 70 good and marginal stations scored by EBI index
- 59 - D2 overlapped at scores that fell below 25, while 26 stations classified as good scored 25
or higher (Figure 9d). For EBI index D2, a solid vertical line representing the new
threshold value was drawn at 22.5 to distinguish the cutoff EBI score between good and
unknown stations.
EBI index A3 had the lowest number of stations labeled as unknown (n=49), when
the original criterion (2.5) was used to score stations, and EBI index A3 had the highest
number of stations correctly labeled as good (n=47; Figure 9a). On the other hand, EBI
indices C2 and D2 were composed of more than one metric, and threshold values were
increased from 12.5 and 15, respectively, to 22.5 for both indices. For EBI index C3, the threshold value was increased from 22.5 to 37.5. These threshold values were established because stations that scored above the new values were all classified a priori as good and stations that scored below the new values were labeled as unknown. As a result of the establishment of these new thresholds, which allowed no overlap of marginal and good stations, all of the marginal stations and most of the good stations were labeled as unknown. EBI indices C2, D2, and C3 had a total of 85, 70, and 73 of the 96 stations,
respectively, labeled as unknown. Unlike EBI indices A3, C2, and D2, the criterion for
EBI index D6 was adjusted by using two new thresholds. Stations labeled as unknown
(n=81) were bounded by upper and lower limit threshold values (37.5 and 2.5,
respectively). The upper threshold value separated 14 good stations from unknown stations while the lower threshold value separated one marginal station from unknown stations.
EBI index D6 was the only index that had scores that could classify stations as
good and marginal without error (Figure 9). Therefore, EBI index D6 was selected and
- 60 - named as the final EBI index for this study. However, EBI index D6 correctly classified a limited number of good and marginal stations. Since most of the stations (84.38%) were labeled as unknown, another review was necessary to assess the threshold values for marginal and good stations used for the final EBI index. The original threshold was modified with four threshold value adjustments (1, 2, 3, and 4; Figure 9). Adjustment 1 changed the marginal threshold value from 2.5 to 7.5 and correctly classified five unknown stations as marginal, while misclassifying five good stations that were labeled as unknown. Although this adjustment of the marginal threshold value decreased the percent of stations labeled as unknown from 84.38% to 73.96%, it increased the error rate from zero to 5.21%. Likewise, adjustment 2 changed the good threshold value from 37.5 to 32.5 and correctly classified 10 unknown stations as good, while misclassifying one marginal station that was labeled as unknown. Although this adjustment of the good threshold value decreased the percent of stations labeled as unknown from 84.38% to
72.92%, it also increased the error rate from zero to 10.42%. Adjustment 3 used both of the previously discussed adjustments of the marginal and good threshold values, and misclassified 15 of the 96 (15.63%) stations, while 61 of the 96 (63.54%) were labeled as unknown. Finally, adjustment 4 established a threshold value (17.5) that would result in the lowest error rate, while classifying all 96 stations as either marginal or good. With this threshold value, 34 of the 96 (35.42%) stations sampled in 1999-2002 were misclassified. Thirty-three of the 87 (37.93%) good stations and one of the nine
(11.11%) marginal stations were misclassified with a threshold value at 17.5.
- 61 - Stations with excellent environmental quality
A subset of 16 good stations sampled in 1999-2002 were classified as excellent as
a result of scoring good (5) for all parameters describing water, sediment, and upland
quality (Appendix G). Fish metric critical values for excellent stations overlapped with
critical values for good stations (Table 11). No consistent relationship existed between
environmental quality and the average values of all 21 fish metrics selected by one-way
analyses, stepwise discriminant analyses, and previous studies (Table 5). The final EBI
index predicted only three of the 16 (18.75%) excellent stations to have good
environmental quality, using the original threshold values (Appendix G). Using threshold values established by adjustment 1, three of the 16 (18.75%) excellent stations
were classified as good, while one of the 16 (6.25%) excellent stations was classified as
marginal. Threshold values that were established by adjustment 2 classified five of the
16 (31.25%) excellent stations as good, while none was classified as marginal. Threshold
values that were established by adjustment 3, which were the combined threshold values
of adjustments 1 and 2, classified five of the 16 (31.25%) excellent stations as good,
while one was classified as marginal. Finally, threshold values established by adjustment
4 classified nine of the 16 (56.25%) excellent stations as good, while seven of the 16
(43.75%) excellent stations were classified as marginal.
- 62 -
DISCUSSION
Environmental quality and physical features
Based on the criteria developed for this study, South Carolina tidal creeks in
1999-2002 had good overall environmental quality and were similar in water, sediment,
and upland quality. These results are comparable to a study done by Van Dolah et al.
(2002), which also used South Carolina Estuarine and Coastal Assessment Program
(SCECAP) data to determine the quality of South Carolina tidal creeks. Van Dolah et al.
(2002) included different parameters to define tidal creek quality, using integrated measures of water quality, sediment quality, and a benthic index of biotic integrity (B-
IBI). The overall estimate of the condition of creek quality was calculated by Van Dolah
et al. (2002) with a cumulative distribution function (CDF). Although Van Dolah et al.
(2002) used different methods to determine tidal creek quality, and analyzed data from
only 1999-2000, it was reported that 88% of South Carolina tidal creeks were in good
condition in 1999-2000, which is very similar to the value of 91% found in the current
study.
In contrast, studies in other areas of the United States (US) have often found
lower overall environmental quality and have developed indices of biotic integrity
without available reference sites (sites of good environmental quality) because most areas
had high levels of anthropogenic degradation (Karr 1981; Hughes et al. 1986; Deegan et
al. 1993, 1997; Meng et al. 2002). Dame et al. (2000) compared south Atlantic US estuaries to find that the coastal human population of South Carolina was one of the smallest in the country, suggesting relatively low levels of detrimental anthropogenic environmental impact. However, Kennish (2002) estimated that by 2020, 75% of the world’s population will live within 60 km of the coast and predicted that the growing human population will contribute significantly to habitat loss in estuaries. Although there are currently low levels of environmental degradation in South Carolina, an estuarine biotic integrity (EBI) index for South Carolina tidal creeks will become an increasingly important tool as coastal populations continue to increase.
Estuarine environmental quality was defined in the current study using many of the same parameters (dissolved oxygen, total nitrogen, sediment contaminants, and human disturbance) that have been used in previous studies to develop an index of estuarine biotic integrity based on invertebrates or fishes (e.g., Deegan et al. 1993, 1997;
Weisberg et al. 1997; Engle and Summers 1999; Van Dolah et al. 1999; Meng et al.
2002). Additional parameters, such as pH, biological oxygen demand, total phosphorus, and fecal coliform bacteria concentration, were incorporated in the current study because they are also indicators of anthropogenic pollution (e.g., Mallin et al. 1999a, 1999b;
Vernberg et al. 1992; Lebo and Sharp 1993; Ringwood and Keppler 2002; Ansari et al.
2003; Scott et al. 1996, 1998). The final combination of water, sediment, and upland quality parameters used in the current study were selected because of their ability to influence estuarine biological community structure.
Unlike previously developed fish indices of estuarine biotic integrity (see Deegan et al. 1993, 1997; Meng et al. 2002), the current study did not use the abundances of
- 64 - other biota, such as eelgrass and chlorophyll a, to define environmental quality. While
these biotic indicators have been proven to be useful in the development of some indices, provided that they are capable of identifying differences in environmental conditions, biotic indicators were not transferable to the current study for several reasons. In
northeastern estuaries, the decline of eelgrass is a response partly due to increased
anthropogenic development, organic loading, and eutrophication (Costa 1988; Short and
Burdick 1996; Short and Wyllie-Echeverria 1996; McClelland et al. 1997; Deegan et al.
2002; Meng et al. 2002; Hughes et al. 2002; Hauxwell et al. 2003). Therefore, eelgrass
abundance is a useful indicator of environmental quality in northeastern estuaries.
However, eelgrass and other sea grasses are very rare in South Carolina estuaries due to
naturally occurring high turbidity and tidal amplitude (Ernst and Stephan 1997; Thayer et
al. 1997). Therefore, abundance of sea grass is not a useful metric in South Carolina
estuarine systems. Likewise, high levels of chlorophyll a are not common in South
Carolina, with elevated levels found in only 13% of South Carolina tidal creeks in 1999-
2000 (Bricker et al. 1999; Van Dolah et al. 2002). Furthermore, Vernberg et al. 1992
found that chlorophyll a levels were not significantly different between a developed
estuary in South Carolina and a relatively pristine estuary in South Carolina, which
suggests that chlorophyll a is not a critical biotic indicator for the current study.
In the current study, the percent of physically altered land may have been
underestimated since outdated levels of physically altered land (Anderson et al. 1976; US
Fish and Wildlife 1989, 1994) were used to quantify upland quality. The presence of
industrial, urban, residential, and agricultural land within 500 m of a station was rare in
1989 and 1994. However, the amount of developed land (urban, built up, or
- 65 - transportation areas) in South Carolina rose by 21% between 1992-1997, mainly from
urbanization (USDA 2000; USDA 2003). South Carolina was ranked 10th, out of the 48
contiguous US, for having the most acres of land developed between 1992 and 1997
(USDA 2000). In 2001, 6% of the land within the 48 contiguous US was developed, a
23% increase from 1992 (USDA 2003). Although the use of 1989 or 1994 land use and
land cover data were out of date for the time period covered in the current study (1999-
2002), the inclusion of 1989 or 1994 data was better than leaving the effects of upland
quality on tidal creek environmental quality unexplored. Data from 1989 or 1994 showed
that the percent of physically altered land was significantly higher in areas surrounding
marginal stations sampled in 1999-2002 than when compared to good stations (Table 4).
The ability to detect environmental quality of tidal creeks, with low levels of development found in 1984 or 1994, emphasized the need to continue to monitor South
Carolina tidal creeks as levels of land development increase.
Physical features for all tidal creeks were similar except for depth and location of the station (Table 4). Marginal stations were significantly more shallow than good stations, although the average depth of marginal and good stations differed by only 1 m
(Table 4). Shallow areas are more vulnerable to anthropogenic influences because fine sediments that are associated with shallow areas may concentrate contaminants, such as trace metals and pesticides (Liu et al. 2003). Shallow areas are also usually found in upper reaches of the estuary and are in close proximity to pollutants, such as high levels of nitrogen and phosphorus that cause eutrophication (Staver et al. 1996; Mallin et al.
1999a). Stations classified as having marginal environmental quality had significantly
higher levels of physically altered land, which could increase the amount of surface water
- 66 - run-off and serve as a source of harmful contaminants. In addition, two shallow stations with marginal environmental quality were located upstream relative to two deeper stations with good environmental quality. However, the sampling protocol did not allow for strong statistical analyses to examine the relationship between tidal creek depth, relative location of the station within the tidal creek, and upland quality because of the low availability of marginal stations.
Fish community
The profiles of fish species that were completed for this study provide an overview of life history, trophic and ecological composition, and tolerance of South
Carolina tidal creek fishes. High numbers of juvenile transient fish were found since sampling occurred during the summer, when most juvenile fish move into the estuary after being spawned offshore (Shealy et al. 1974; Cain and Dean 1976; Wenner et al.
1981, 1984, 1991; Allen and Barker 1990). The trawl sampled at the bottom of the water column and collected many benthic organisms that fed mostly on detritus or benthic and demersal crustaceans (Shealy et al. 1974; Wenner et al. 1981, 1984, 1991). Gear selectivity resulted in high numbers of benthic fish and benthic invertivores. South
Carolina tidal creeks had low numbers of tolerant fish, as predicted for relatively pristine areas (see Karr 1981; Karr et al. 1986). Average community values were comparable to other tidal creek fish communities (Appendix E.4; see Wenner et al. 1981, 1984, 1991;
Van Dolah et al. 2002).
Many of the candidate fish metrics showed a statistically significant response to each of the environmental parameters evaluated for this study, but many of the metrics
- 67 - still had overlapping values between marginal and good stations (Figure 5). The overlap
in fish metric values for marginal and good stations may be explained by the fish community’s inability to detect environmental quality for the areas sampled. The small differences in environmental parameters found in the current study may have not been large enough to affect the fish community. For example, although some fish have been shown to respond quickly to degraded environments (i.e., fleeing areas of low dissolved
oxygen concentrations; Klauda and Bender 1987; Giattina and Garton 1983), other fish
are more tolerant and can remain in areas of poor condition because they have higher thresholds (Klauda and Bender 1987). The South Carolina tidal creek fish may not demand the same criteria, or threshold values, that were used in the current study to
classify good and marginal stations.
Another factor that may explain the similarity in fish metric values for marginal and good stations is that although the fish can detect differences in environmental quality, they are opportunistically utilizing marginal habitats (i.e., feeding or avoiding predators)
because the benefits outweigh the costs of being in an area that is less pristine, as suggested by Meng et al. (2002). These benefits may be strong enough to influence fish
to continue to seek out areas of lower environmental quality. Since fish have the
advantage of mobility, it is also possible that fish spend only limited amounts of time in
marginal habitats, while the majority of the time is spent in areas that are in good
condition. The behavior and residence times of fish in response to environmental quality
could not be examined in the current study because sampling provided only an isolated
point-in-time observation.
- 68 - Fish that were sensitive to poor conditions were predicted to be present in higher numbers in areas that had relatively high dissolved oxygen and low anthropogenic influence (Carmichael et al. 1992; Deegan et al. 1993, 1997). In contrast, the current study showed that higher numbers of fish that were sensitive to environmental degradation were generally found at marginal stations when compared to good stations
(Table 2). This interesting trend may be explained by the previously mentioned factors that affect the overlap in fish metric values for marginal and good stations. However, the current study was not able to determine the causes that directly influenced sensitive fish to be in higher abundances at lower quality stations.
Meng et al. (2002) also observed higher numbers and densities of fish sensitive to environmental degradation in low quality sites in Narragansett Bay. These unexpected results were attributed to the location of the station within the estuary, since the low quality sites that had higher numbers and densities of fish were generally located in the upper estuary (Meng et al. 2002). Depth may have also contributed to structuring the unexpected trends in fish density with environmental quality found in the study done by
Meng et al. (2002), since higher numbers of fish were located in more shallow areas.
Stations located in more shallow and protected areas can provide fish with more adequate habitats for food and shelter (Boesch and Turner 1984; McIvor and Odum 1988; Ruiz et al. 1993; Meng and Powell 1999; Meng et al. 2002) when compared to deeper tidal creek areas. Additionally, depth is closely associated with sediment type, and the interaction between the and sediment type is one of the major parameters that influence estuarine fish distribution (Araujo et al. 2002; Prista et al. 2003).
- 69 - Although targeting to sample stations at different locations within the tidal creek and at different depths was not within the scope of this study, a preliminary analysis was conducted to address this issue. For the 96 stations sampled in 1999-2002, there were nine creeks that allowed for comparisons between stations located upstream and downstream of each other. Two of the nine creeks (Kiawah River and May River) contained one marginal station that was located in the upper estuary while one good station was located downstream (Figure 3). While the general trend in these two station pairs follows results from the study done by Meng et al. (2002), the fish community was not significantly different between marginal and good stations. Furthermore, seven other creeks that contained two stations did not differ in environmental quality or any of the 73 candidate fish metrics. For the environmental criteria developed for this study, South
Carolina tidal creeks were very similar in water, sediment, and upland quality.
Therefore, stations located less than 2.5 km apart, and within the same creek, were not expected to differ greatly in environmental quality or fish community.
Development and evaluation of the final EBI index
The use of one-way analyses in the development of an EBI index had several advantages, including the basic interpretation and display of metrics that were significantly different between good and marginal stations (Figure 5). One-way analyses are also relatively common procedures that can be learned without the prerequisite of advanced statistical knowledge, which is appealing when presenting developmental procedures for future studies. On the other hand, with each additional metric that is evaluated, the statistical power of the one-way analyses decreases and the amount of time
- 70 - needed to run analyses increases. The one-way analyses were used in the current study as
a preliminary tool to evaluate fish metrics, and therefore, statistical power was not a
primary concern.
The use of the stepwise discriminant analyses in the development of an index had
the ability to combine a large number of redundant metrics without discounting the
relationships between metrics. Another advantage of stepwise discriminant analyses was
the ability to produce the cumulative percent of the total variation that the metrics
explained, by calculating the average squared canonical correlation (Tables 8 and 9). The cumulative percent of the total variation can then be used to guide the selection of metric combinations for further analyses. A disadvantage of the discriminant analyses was that
the results varied depending on how many metrics were entered into the initial analyses.
Another disadvantage was that proportions and densities of metrics could not be entered
simultaneously into analyses because of problems associated with collinearity. In
addition, stepwise discriminant analyses are less popular, and therefore, results from
stepwise discriminant analyses can be easily misinterpreted as the best combination of
metrics when, in fact, further analyses are required. Like the one-way analyses, the current study used results from stepwise discriminant analyses as a preliminary assessment of candidate metrics.
After compiling a list of 73 candidate fish metrics based on ecological principles
and the results from previous studies, statistical tests helped to indicate preliminary fish
metrics that may be strong discriminators of environmental quality. One-way and stepwise discriminant analyses proved to be easy to employ and produced straightforward results. A drawback to the use of statistical analyses for describing ecological systems is
- 71 - the common tendency to focus on the results without investigating if the results agree with established ecological principles (Yoccoz 1991; Hughes and Noss 1992; Fore et al.
1996). Therefore, comparisons between metrics that were selected by statistical tests in the current study and metrics that were selected or suggested by previous studies provided insight for the application of an EBI index in South Carolina tidal creeks.
Many of the metrics selected in the current study, as a result of the one-way and stepwise discriminant analyses, were similar to those chosen for other estuarine indices
(see Thompson and Fitzhugh 1986; Guillen 2000; Deegan et al. 1993, 1997; Meng et al.
2002). However, previous estuarine studies differed in fish species, sampling technique, environmental quality definition, and the method used to select fish metrics. Deegan et al. (1993, 1997) and Meng et al. (2002) developed estuarine indices of biotic integrity for northeastern US estuaries by modifying metrics of the freshwater index of biotic integrity
(IBI) developed by Karr et al. (1986). Deegan et al. (1993, 1997) developed and validated an estuarine biotic integrity index (EBI) for estuaries in the Massachusetts area, where habitat quality of stations were found to be marginal or poor based on parameters such as oxygen, physical alteration, dissolved inorganic nitrogen, disturbance, eelgrass abundance, chlorophyll a, and macroalgal abundance. Fish were sampled using a semi- balloon otter trawl and the metrics included in the final EBI were selected by using analysis of variance (ANOVA), Chi-square contingency tables, and Bonferroni test
(Deegan et al. 1997). Meng et al. (2002) developed an estuarine index of biotic integrity for estuaries in the Rhode Island area, where the habitat quality of stations was found to be high or low based on dissolved oxygen, total nitrogen concentration, human disturbance, abundance of macroalgae, and eelgrass presence. Fish were sampled using a
- 72 - beach seine deployed from a boat, and metrics included in the final estuarine index were selected using a stepwise discriminant analysis. Deegan et al. (1993) also used a stepwise discriminant analysis, but found that metrics selected with this technique were not useful in classifying stations, in contrast to Meng et al. (2002).
Metrics that were not directly transferable from previously developed estuarine indices included the proportion of abnormal or diseased fishes and the proportion of killifish. Deegan et al. (1993, 1997) did not find a high proportion of abnormal or diseased fishes, but the metric was included into their final index for future application.
Fishes that were abnormal or diseased have been associated with estuarine habitats of high anthropogenic stress (Mulcahy et al. 1987; Sindermann 1995; Moore et al. 1996).
There were no externally abnormal or diseased fish reported for the current study, but other studies on South Carolina tidal creeks should reconsider abnormal or diseased fishes as an indicator of environmental quality, when present. Based on fish sampled using a beach seine with a mesh size of 0.95 cm (Meng 2004), Meng et al. (2002) found the proportion of striped killifish (Fundulus majalis) to be a significant discriminator of fish habitat quality. High numbers of killifish were expected in degraded environmental conditions because they are relatively tolerant fish (Meng et al. 2002). In the current study, gear selectivity largely explains the absence of killifish (Fundulus spp.), since a bottom trawl with a larger mesh size (2 cm) was used to sample fish. In South Carolina, killifish are present in tidal creeks and coastal inlets (Ogburn-Matthews and Allen 1993), but killifish were rare in trawl surveys (Shealy et al. 1974; Wenner et al. 1981, 1984,
1991). The metric assessing killifish abundance was not directly transferable to this study, but other tolerant taxa that may be regarded as equivalent to the killifish metric
- 73 - were evaluated as candidate metrics. Bay anchovy (Anchoa mitchilli) and shad (Alosa
sapidissima and Dorosoma sp.) are tolerant taxa commonly found in bottom trawls, and
high abundances are expected in areas of degraded environmental conditions (Bechtel
and Copeland 1970; Thompson and Fitzhugh 1986; Guillen 2000).
EBI indices developed in the current study were composed of metrics that were selected through one-way analyses, stepwise discriminant analyses, and results from previous studies. When the EBI indices were used to predict environmental quality with the median and discriminant analyses, high error rates often resulted (Figures 6-8). These high error rates emphasized further evaluation of the selected metrics by incorporating established ecological principles that were specific to South Carolina tidal creek fish communities. Therefore, the development of composite indices (EBI indices D1-8) was a
more subjective approach that applied statistical analyses from the current study, results
from previous studies, and scientific knowledge.
When compared to indices developed using the one-way analyses, stepwise
discriminant analyses, or previous studies, most of the misclassification rates of composite indices were lower (Figures 6-8). For example, EBI index B1 was developed
using six metrics selected by discriminant analysis, and four of the six metrics
(dominance of the most abundant taxon, flatfish density, tidal creek nursery taxa, and top
predator taxa) were shared with EBI index D2 (Table 6). One metric that was included in
EBI index B1 and excluded in EBI index D2 was the metric describing 90% of the total
abundance, which may be redundant to the metric already incorporated into EBI index B1
that described the dominance of the most abundant taxon. EBI index D2 included two
metrics (number of taxa and salinity independent taxa) that were among the metrics that
- 74 - were most frequently selected. Based on results from the median and discriminant
analyses, overall misclassification rates were lower for EBI index D2 when compared to
EBI index B1 (Figure 6).
Individual fish community metrics that were traditionally used as indicators of habitat quality (EBI indices E1-3) had relatively high error rates when compared to other
indices that were composed of more than one metric (Figures 6-8). High error rates
confirmed that community metrics, such as density of individuals, number of taxa, species diversity, were not effective as individual indicators of environmental quality because they often missed many of the ecological and trophic interactions that are affected by environmental quality (Livingston 1976; Karr 1981; Angermeier and
Schlosser 1987; Ohio EPA 1987; Fausch et al. 1990; Hughes and Noss 1992; Van Dolah et al. 1999). In most cases, error rates decreased when individual community metrics used in EBI indices E1-3 were used in conjunction with other metrics as a multimetric
index (Figures 6-8). For example, results from the discriminant analyses showed that
EBI index E3, which uses only the species diversity metric, incorrectly classified all marginal stations (Figure 8). In comparison, EBI index C3, which uses the species
diversity metric in addition to eight other metrics, correctly classified all marginal
stations (Figure 8). Results from the current study clearly demonstrated the limited value of individual metrics and that the multimetric approach was a better methodology to determine environmental quality.
Median and discriminant analyses were useful tools in categorizing good and marginal stations because they had relatively simple application procedures that were rapid and repeatable. Ultimately, the evaluation of the range of EBI scores from potential
- 75 - EBI indices as selected by either discriminant or median analyses was needed before
choosing the final EBI index. When evaluating indices in the current study, discriminant analyses proved to be more helpful than the median analyses; in fact, results from the discriminant analyses were used to select EBI index D6 as a potential final EBI index.
Results from the discriminant analyses indicated that EBI index D6 was the only index to
correctly classify all stations (Figures 6 and 8), while results from the median analyses
indicated that no index was able to correctly classify all stations. Consequently, EBI
index D6 was the only index to have threshold values that could clearly distinguish
between good and marginal stations without error, and was determined as the final EBI
index in the current study.
The final EBI index was developed by applying knowledge of South Carolina
tidal creek habitats to modify EBI index C3, after finding that EBI index C3 had relatively low error rates when used to predict environmental quality (Table 12). The EBI index C3 included metrics used by Deegan et al. (1993, 1997) and Meng et al. (2002), and was modified into the final EBI index by substituting one metric (percent abundance of flatfish) for another metric (percent abundance of flounder). Meng et al. (2002) developed a fish index using flounder in the northeastern US, where winter flounder
(Pseudopleuronectes americanus) was the dominant flounder present. Winter flounder have been shown in a number of studies to be relatively sensitive to anthropogenic stress
(Sindermann 1996). However, summer flounder (Paralichthys dentatus) was one of the dominant recreationally important flounders in southeastern US estuaries (Nelson et al.
1991a; this study) and is relatively tolerant of sediment contaminants and pollution (Hoss et al. 1974; Schaaf et al. 1987). Therefore, the flounder metric used for northeastern US
- 76 - estuaries was not appropriate in the southeast. Results from the current study indicated that flounders (Paralichthys dentatus and P. lethostigma) were extremely rare (<1%) in the fish community, while the broader grouping of flatfish composed a slightly larger proportion (8%) of the overall abundance of fishes. The metric describing flatfish included flounder taxa, in addition to other flatfish taxa collected in South Carolina tidal creeks, such as whiffs (Citharichthys sp.), fringed flounder (Etropus crossotus), blackcheek tounguefish (Symphurus plagiusa) and hogchoker (Trinectes maculatus). The
flatfish metric made the final EBI index more sensitive in detecting environmental
conditions than the original flounder metric that was used in EBI index C3.
Although EBI index C3 and the final EBI index shared all but one metric,
misclassification rates differed greatly. Based on the discriminant analyses, EBI index C3
had higher error rates than the final EBI index for good, marginal, and across all stations,
while the median analyses showed slightly lower error rates (Figures 6-8). After the
distribution of EBI scores was plotted and new thresholds were considered, EBI index C3
was not able to distinguish between good and marginal stations without error and was not
as adequate as the final EBI index for determining environmental quality (Figure 9).
Future directions and recommendations
Based on the results from the current study, metrics describing fish life history
(estuarine nursery taxa, estuarine resident taxa, and estuarine spawner taxa), ecological
composition (percent abundance of benthic individuals), tolerance (density of flatfish),
and community structure (density of individuals, dominance of the most abundant taxon,
number of taxa, and species diversity) should be the primary metrics considered in future
- 77 - indices. Low values for estuarine nursery taxa, estuarine resident taxa, estuarine spawner
taxa, density of flatfish, density of individuals, number of taxa, and species diversity
indicated areas of good estuarine biotic integrity. High values for percent abundance of benthic individuals, dominance of the most abundant taxon, and density of flatfish
indicated areas of marginal estuarine biotic integrity. Since the trends found in the
current study were unexpected and could not be explained (Tables 2 and 12), the EBI
index needs to be validated as more datasets become available. Sampling for the South
Carolina Estuarine and Assessment Program (SCECAP) was continued in 2003-2004
(Van Dolah et al. 2004a) and is planned to continue through 2009. SCECAP data will
provide a good validation data set for the final EBI index, and/or could be used for future
development and evaluation of a new index based on the methods that were used in the
current study.
Validation data sets are also needed for the criteria used in the current study to
describe the South Carolina the tidal creek fish communities present in habitats with
excellent environmental quality. Although the final EBI index was not successful in
predicting excellent stations with the EBI score, as only three of the 16 excellent stations
were classified as good (Appendix G), these fish metric criteria were the first step to
establish important thresholds to be considered in future studies on estuarine biotic
integrity. At this time, fish metric criteria for marginal, good, and excellent
environmental quality can be used as a guide for resource managers as efforts continue to
identify and protect critical habitats.
Resource managers should consider final classification of stations based on the
median analyses as preferable to the discriminant analyses due to the more conservative
- 78 - approach. The median analyses was more conservative in that marginal stations were
generally misclassified at a lower rate than when compared to the discriminant analysis
approach (Figures 7 and 8). All but one index (EBI index E3) correctly identified eight
out of nine (88.88%) marginal stations using the median analysis, while average error
rate for marginal stations was 67.17% after using the discriminant analysis approach.
Resource managers would have the ability to detect marginal stations at a higher rate
using the median approach, which would be beneficial in targeting areas to maintain,
conserve, and protect.
For the current study, the ability to distinguish between marginal and good stations without error was the principal factor in selecting the final EBI index, which was the result of establishing EBI score threshold values at 2.5 and 37.5 (Figure 9). However, adjustments in the original threshold values that were established for the final EBI index resulted in lower numbers of unknown stations and increased error rates. These adjusted values are useful for future applications of the EBI index when the potential for classifying the environmental quality of stations is more essential than accuracy. The acceptable amount of error should be the guide that is considered when choosing the appropriate threshold values.
In addition, the levels for each parameter incorporated in the current study to define water and sediment quality should be reevaluated in regards to fish communities.
The critical values used in the current study may not have been biologically relevant, that is not strict enough for fish to detect degraded environments. Therefore, supplemental experiments to determine the critical threshold values of environmental parameters (i.e., pH, dissolved oxygen, and upland development) and observations on the behavior of
- 79 - local fish species could be beneficial in discerning the dynamics that structure fish
communities.
It is important to continue monitoring tidal creeks for changes in the fish
community, water, sediment, and upland quality, especially in areas that were classified
as marginal or poor in the current study. Using the original threshold values, only one
station (RT99017) that was classified a priori as marginal was predicted by the EBI score
(0) to have marginal estuarine biotic integrity. Replicate samples should focus on areas near RT99017 and other stations that had low EBI scores, such as the 10 stations had an
EBI score of 5. Additionally, two stations (NT02301 and NT01518) sampled for the current study were specifically placed in Shem Creek, a highly developed tidal creek area. NT02301 was classified as having poor environmental quality, but was ultimately eliminated from analyses because other stations classified as poor were unavailable for comparison. For future studies, Shem Creek and other areas of known anthropogenic stress should be targeted to increase the likelihood of detecting a fish community response to degraded conditions, if and when present. In the current study, large
differences in the fish community between good and marginal stations were not present
because of low numbers of marginal stations (n=9). However, monitoring water,
sediment, and upland quality of tidal creeks will help to determine if a greater amount of
variability among stations is reflected in the fish community.
As the development of land in South Carolina continues, upland quality criteria should be reevaluated as land cover and land use change in South Carolina. Wang et al.
(1996, 2000, 2001) studied the effects of upland development within a 100 m buffer of freshwater stream sites and found that there was a threshold percent of development
- 80 - (between 8-12%) that significantly affected biotic integrity (Wang et al. 1996, 2000,
2001). The use of a threshold suggested that fish species richness, biotic integrity, and habitat may still be high within areas that had levels of upland development below the threshold. In the current study, due to low levels of physical alteration from residential or agricultural development (average=2%), a presence/absence criteria for upland quality was used. As levels of physical alteration increase, a criteria based on the percent of upland development may be more practical than a presence/absence criteria. Although
South Carolina presently has a small coastal population compared to other states in the eastern US (Dame et al. 2000), the human population and the rate of land development continues to grow at a rapid pace. South Carolina’s human population growth rate was
15% in 1990-2000, 2% higher than the national growth rate (Perry and Mackun 2001).
Residential, urban, and agricultural developments were the major contributors to losses of wetlands and tidal creeks in South Carolina (Dardeau et al. 1992, Fulton et al. 1993).
The relationship between upland development and biological communities should be further investigated in South Carolina tidal creeks to determine if there is a threshold percent of development similar to that found in freshwater stream sites by Wang et al.
(1996, 2000, 2001).
Results from the current study suggested that future development and evaluation of EBI indices should not rely strictly on statistical analyses, but needs to incorporate scientific knowledge and local expertise. While statistical analyses were extremely useful in directing further investigation in this study, the statistically significant results must not be interpreted as the final solution to managing finfish and their habitats.
Knowledge of the local fish community and habitat is always of critical importance and
- 81 - should be expanded through monitoring and assessment programs to determine the types of fish that are sensitive to environmental degradation.
Results from the current study also found that metrics based on the number of taxa were the most common discriminators for environmental quality when compared to metrics based on percent abundances or density. This suggested that fish are more valuable as indicators of environmental quality when identified to the lowest practical taxonomic level. Although broad categories such as fish life history, ecological and trophic composition, and tolerance metrics are useful in understanding the fish community composition, it is critical for future sampling efforts to accurately identify fish at the lowest practical taxonomic level.
The examination of the relationship of each of the metrics used in the final EBI index will also help with detecting subtle differences in the environmental quality of future studies. For example, in the current study, estuarine nursery taxa and the number of taxa were highly correlated because 97% of the fish utilized the estuary as a nursery ground. Although the estuarine nursery taxa and the number of taxa may be redundant, both metrics were retained in the final EBI index. If future studies found that the number of taxa and estuarine nursery taxa differed greatly, this may indicate environmental change that has limited the fish community’s use of the estuary.
In addition, the physical condition of the fish should be considered as a metric of estuarine biotic integrity in future studies. In this study, there were very low occurrences of fish deviating from normal conditions, but if fish were found to have visible lesions, abnormalities, and/or disease, this would undoubtedly indicate that high stress was present in the environment (Sindermann 1994, 1995). Another interesting direction may
- 82 - be to test fish for possible sublethal effects from contaminants, since many stressors of
the environment may not manifest in obvious characteristics (Sindermann 1994, 1995).
SCECAP has analyzed fish tissue contaminant loads of select fish species sampled from
South Carolina tidal creeks (Van Dolah et al. 2002, 2004a) and these data may be useful
when incorporated into future indices. As more information and data become available,
the final EBI index for South Carolina tidal creeks may include additional fish metric(s) that describe the physical condition of fish.
Currently, there are still many gaps in the body of information available for South
Carolina tidal creek fish species, especially with respect to fish that are less commonly sampled and studied. For example, resilient and salinity independent metrics were limited in describing fish tolerance because information was available for only a subset of fish species found in the current study. It is possible that the number of highly resilient
and salinity independent fish were underestimated at marginal stations because the lack
of information caused fish to be conservatively categorized as not resilient or not salinity
independent. In addition, an increased amount of information available for future studies may conclude that additional fish metrics that were not evaluated in the current study,
such as contaminant loads in fish tissue, biomass, and fish physical condition are
significant discriminators of environmental quality. As more studies on tidal creek fish
life history, trophic and ecological composition, relative tolerance, and habitat
preferences become available, candidate fish metrics incorporated into a future index may
differ from those included in the final EBI index developed in the current study.
Supplemental studies on the effects of environmental parameters would be useful
for biological communities other than fish. Although some fish metrics evaluated for the
- 83 - current study were able to detect differences in environmental quality, other biological communities may be more reliable indicators of tidal creek quality, such as macroinvertebrates (e.g., Van Dolah et al. 1999). Detailed information on macroinvertebrate tolerance to low dissolved oxygen levels, sensitivity to sediment contaminants, and behavior in areas of high anthropogenic influence may allow for the development of a more accurate index. Future evaluations and comparisons are needed to determine if fish communities are an effective indicator of tidal creek environmental quality.
The purpose of the final EBI index developed in the current study was to distinguish differences in environmental quality, but future fish indices may be directed to detect differences in fish habitat preferences. Due to the random selection process used to establish sampling sites, there were not enough replicates of paired stations to explore the relationship between environmental quality and fish community response with regards to physical features such as the location of the station (upstream or downstream) and depth. However, preliminary analyses based on the two-paired stations showed that stations determined to have marginal quality had higher abundances of fish, were shallower, and were located relatively upstream in the tidal creek. An interesting approach for future studies would be to sample within single tidal creeks to examine the differences between shallow, upper reaches and deeper, lower reaches in relation to fish and environmental quality.
To date, indices developed in freshwater and estuarine habitats have usually been limited to specific regions because of regional differences in habitats and fish community composition (Hughes et al. 1986, Miller et al. 1988, Weisberg et al. 1997). The current
- 84 - study was the first to develop and evaluate an EBI index based on the tidal creek fish community in the southeastern US. General methods used in this study have benefited
greatly from the results of previous studies and are most adaptable to other estuarine
areas similar in habitat and fish community. The South Carolina tidal creek fish
community sampled for the current study was similar to other southeastern US and Gulf
of Mexico estuarine fish communities (e.g., Subrahmanyam and Drake 1975; Hackney et
al. 1976; Weinstein 1979; Bozeman and Dean 1980; Thompson and Fitzhugh 1986,
Miglarese and Sandifer 1982; Rogers and Herke 1985; Williams et al. 1990; Nelson et al.
1991b; Dardeau et al. 1992). Southeastern US and Gulf of Mexico estuarine fish
communities have also been used successfully as indicators of environmental changes
(Thompson and Fitzhugh 1986, Guillen 2000), but have not yet been used in a fully
developed multimetric index. Future research should also include testing the final EBI
index in other regions for applicability. For example, this study’s development and
evaluation methods can be applied to data, such as Georgia’s National Coastal
Assessment (NCA) Program, to determine if an EBI index is feasible for a larger
southeastern US region.
- 85 -
SUMMARY AND CONCLUSIONS
Fish are valuable environmental indicators because they are sensitive to physical, chemical, and biological stress, and are relatively easy to sample and identify. In addition, fish communities are likely to be assessed in future studies because they continue to be widely recognized as recreationally and economically important by resource managers and the general public. A multimetric estuarine biotic integrity (EBI) index was developed in the current study with the goal of creating a simple tool to quickly assess the South Carolina tidal creek environmental quality using fish communities as indicators.
Methods in this study provided the groundwork for development and evaluation of future EBI indices in this and other regions (see Figure 2). Statistical analyses, previous studies, and ecological concepts directed the selection of fish metrics that were the best discriminators of environmental quality. Potential multimetric estuarine biotic integrity (EBI) indices used combinations of fish metrics to calculate a single score to predict environmental quality. Station classification results from the median analyses were more conservative in having low error rates for classifying marginal stations, while results from the discriminant analyses were most useful in determining the final EBI index that could discriminate between marginal and good stations without error. The final EBI index used nine fish metrics that described fish life history, ecological
composition, tolerance, and community structure (Table 12). These metrics were
sensitive in determining environmental quality as described by water, sediment, and
upland quality parameters, and should be among the primary metrics considered for the
development of future indices.
The fish metrics incorporated into the final EBI index were useful indicators of
environmental quality. However, fish metric values that were predicted to be low in response to degraded conditions, such as estuarine nursery fish, benthic fish, and species diversity, were found to be high at stations that were classified as having marginal environmental quality, relative to stations of good environmental quality. The unexpected response of these and other fish metrics revealed that more information on fish habitat preferences and research on the criteria fish require for specific water,
sediment, and upland parameters are necessary.
The multivariate discriminant analysis showed that the nine metrics used in the
final EBI index (Table 12) correctly classified the environmental quality of all stations
(Figure 8). However, the multimetric approach of scoring metrics based on the criteria
established by the median of 87 good stations and the original thresholds, showed that
metrics used in the final EBI index did not adequately reflect estuarine biotic integrity for
all stations (Figure 9). Using the original thresholds, the EBI index correctly classified
14 of the 87 (16.09%) good stations and one of the nine (11.11%) marginal stations.
However, values of the EBI scores overlapped for the majority of stations, which made
the environmental quality of 81 of the 96 (84.38%) stations unknown. The inability of
the EBI index to consistently distinguish between good and marginal stations using a
multimetric approach was due to the lack of variation in environmental quality among
- 87 - South Carolina tidal creek stations sampled in 1999-2002. Preliminary analyses indicated
that tidal creeks that were shallow, near headwaters, and in close proximity to upland that
is highly developed are areas that warrant future monitoring and assessment.
As US coastal regions become more developed in the future, South Carolina tidal
creek habitats will become more susceptible to degradation. Future projections for South
Carolina in the next 20 years include increases in residential, urban, and agricultural
development of land and high rates of human population growth. The final EBI index
presented in the current study should be considered as an index in the developmental
stage, due to the low number of marginal stations available and the lack of a true
validation dataset. While the final EBI index did not prove to be a perfect tool for
assessing environmental quality in South Carolina’s tidal creeks, it can serve as a point of
departure for continuing development of future indices. It is highly recommended that
future efforts of monitoring and assessment work towards understanding and protecting
estuarine biotic integrity. The EBI index developed and evaluated for South Carolina
tidal creeks has the potential to be an effective tool for resource managers to determine
critical areas to rehabilitate, monitor, and protect. This study was the first effort to
develop and evaluate an estuarine index of biotic integrity using the fish community and
was an important first step in understanding the relationships between fish metrics and environmental quality in South Carolina tidal creeks.
- 88 -
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- 186 - Figure 1. Array of 97 tidal creek stations sampled in 1999-2002 used in the current study, chosen from the larger South Carolina Estuarine Coastal
Assessment Program (SCECAP) sampling array. Tidal creeks were defined as tidally influenced water bodies that were less than 100 m wide from marsh bank to marsh bank. Stations that had salinities greater than 18 ppt were selected for the current study. Environmental quality of each station was determined by using water, sediment, and upland quality parameters. Estuarine biotic integrity (EBI) score was calculated using the final EBI index (EBI index D6).
- 187 - N 0 20406080100Kilometers W E #S#S#S
S
#S#S
#S
LEGEND #S#S Environmental Quality #S#S #S#S #S S Good #S#S #S Marginal #S #S#S â #S#S T Poor #SÊÚ #S $T#S #S EBI Score #S %U #S 0 #S %U 5 #SÊÚ#SÊÚ#S#S#S#S #S #S #S#SÊÚ #S#S %U 10 #S #SÊÚ #S#S#S#S #S#S #S#S %U 15 #SS##S #S #S #SÊÚ %U #S #S #S #S 20 #S #SÊÚ#S#S#S %U #S#SÊÚ 25 #S#S#S#S %U 30 #S #S#S#S#S#S#S#S #S %U 35 #S#SÊÚ #S#S %U 40 #S#S #S#S #S#S %U 45 #S %U N/A
Figure 2. Flowchart of methods for developing and evaluating an estuarine biotic integrity (EBI) index for South Carolina tidal creeks. General steps are boxed or italicized; details of each step taken in the current study are adjacent; steps that led to the selection of the final EBI index (EBI index D6) are in bold font. See text for details.
- 189 -
Compile Seventy-three Candidate Metrics Candidate - Life history Fish Metrics - Trophic and ecological composition - Tolerance - Community structure
Develop Five Approaches to Select Subset of Fish Candidate Metrics Used to Develop 22 Candidate Indices with Indices 1) One-way analyses (EBI Index Ax) Subset of Fish 2) Stepwise discriminant analyses (EBI Metrics Index Bx) 3) Previous studies (EBI Index Cx) 4) Composite analyses (EBI Index Dx) 5) Individual metrics (EBI Index Ex)
Apply Two Application Approaches Candidate 1) Median Analyses Indices 2) Discriminant Analyses
Choose indices with lowest misclassification rates
Evaluate Plot EBI Scores for a Subset of Five Subset of Candidate Indices Candidate 1) EBI Index A3 2) EBI Index C Indices 2 3) EBI Index C3 4) EBI Index D2 5) EBI Index D6
Choose index that can determine environmental quality, without error
Select One Final EBI Index of Nine Metrics Final EBI - EBI Index D6 Index
Figure 3. The two creeks that contained one marginal station located upstream
relative to one good station located downstream: a) Kiawah River and b) May
River. Land use and land cover data surrounding each station were obtained
from National Wetland Inventory (NWI) 1989 and 1994 databases, categorized
by using the Anderson classification system (Anderson et al. 1976; US Fish and
Wildlife 1989, 1994; ESRI 1998). A 100 m buffer for each station was used to
determine upland quality. Environmental quality of each station was determined
by using water, sediment, and upland quality parameters. Estuarine biotic
integrity (EBI) score was calculated using the final EBI index (EBI index D6). For environmental and physical parameters at each station, see Table 4.
- 191 - LEGEND Land Use/Land Cover EBI score Residential %U 5 Cropland/Pasture %U 10 Transportation/Utility %U 25 Mixe d Forest %U 35 Forested Wetland Environmental Quality Evergreen Forest Marginal Planted Pine â S Good Non-forested Wetland Sand Buffer (100 m) Herbaceous Rangeland N Tida l Cre ek Open Water W E 01234KilometersS a)
ÊÚ#S #S
b)
ÊÚ#S
#S
Figure 4. Box-plots of nine of the 73 candidate fish metrics that were significantly different between good and marginal stations sampled in 1999-2001 (one-way analyses, Wilcoxon test, Dunn-Sidak test, k=73, α=0.10, p<0.0014).
- 193 - Resilient (# of taxa) Number of Taxa Estuarine Nursery (# of taxa) Carnivore (# of taxa) -0.5 0.5 1.5 2.5 3.5 10 11 11 11 -1 -1 -1 9 0 1 2 3 4 1 3 5 7 9 1 3 5 7 9 0 1 2 3 4 5 6 7 8 odMarginal Good Marginal Good Marginal Good Marginal Good Environmental Quality Environmental Quality Environmental Quality Environmental Quality
Salinity Independent (# of taxa) 0 1 2 3 4 odMarginal Good Environmental Quality
Top Predator (# of taxa) Tidal Creek Resident (# of taxa) Tidal Creek Nursery (# of taxa) Species Richness (Margalef's D) -0.5 0.5 1.5 0.5 1.5 2.5 3.5 10 -1 0 1 2 3 4 0 1 2 3 4 5 6 7 8 9 0 1 0 1 2 3 odMarginal Good odMarginal Good Marginal Good odMarginal Good Environmental Quality Environmental Quality Environmental Quality Environmental Quality
Figure 5. Total misclassification rates of EBI indices A1,2 and B1,2, based on the
median or discriminant analyses. EBI indices A1,2 incorporated metrics selected
by the one-way analyses, while EBI indices B1,2 incorporated metrics selected by
the stepwise discriminant analyses. All indices were developed using 1999-2001
data. For the median analyses, indices were applied to three data sets: 1) 1999-
2001 stations, 2) 1999-2002 stations, and 3) 2002 stations. For the discriminant
analyses, indices were applied to two data sets: 1) 1999-2001 stations, and 2)
1999-2002 stations. The discriminant analyses were not applicable for the data
set limited to 2002 stations because there was only one marginal station found
(degree of freedom was less than one) in 2002.
- 195 - Median Analysis (99-01) Discriminant Analysis (99-01)
Median Analysis (99-02) Discriminant Analysis (99-02)
Median Analysis (2002)
80
70
60
50
40
30 Total Misclassified (%) 20
10
0 A1 A2 B1 B2 One-Way Analysis Stepwise Discriminant Analysis
Estuarine Biotic Integrity Index
Figure 6. Total misclassification rates for all EBI indices developed in the current study, based on the median or discriminant analyses. EBI indices Ax incorporated metrics selected by one-way analyses; EBI indices Bx incorporated metrics selected by stepwise discriminant analyses; EBI indices Cx incorporated metrics selected by previous studies; EBI indices Dx incorporated metrics selected by a combination of methods; EBI indices Ex included single community structure metrics. All indices were developed with and applied to stations sampled in 1999-2002.
- 197 - 50 Discriminant Analysis Median Analysis
45
40
35
30
25
20 Total Misclassified (%) 15
10
5
0 A1 A2 A3 B1 B2 B3 B4 B5 C1 C2 C3 D1 D2 D3 D4 D5 D6 D7 D8 E1 E2 E3 One-Way Stepwise Discriminant Previous Studies Composite Analysis Single Analysis Analysis Community Metrics Estuarine Biotic Integrity Index
Figure 7. Good and marginal station misclassification rates for all EBI indices
developed in the current study, based on the median analyses. EBI indices Ax incorporated metrics selected by one-way analyses; EBI indices Bx incorporated
metrics selected by stepwise discriminant analyses; EBI indices Cx incorporated
metrics selected by previous studies; EBI indices Dx incorporated metrics
selected by a combination of methods; EBI indices Ex included single community
structure metrics. All indices were developed with and applied to stations
sampled in 1999-2002.
- 199 - Good Marginal 100
90
80
70
60
50
40 Total Misclassified (%)
30
20
10
0 A1 A2 A3 B1 B2 B3 B4 B5 C1 C2 C3 D1 D2 D3 D4 D5 D6 D7 D8 E1 E2 E3 One-Way Analysis Stepwise Discriminant Analysis Previous Studies Composite Analysis Single Community Metrics Estuarine Biotic Integrity Index
Figure 8. Good and marginal station misclassification rates for all EBI indices
developed in the current study, based on discriminant analyses. EBI indices Ax incorporated metrics selected by one-way analyses; EBI indices Bx incorporated
metrics selected by stepwise discriminant analyses; EBI indices Cx incorporated
metrics selected by previous studies; EBI indices Dx incorporated metrics
selected by a combination of methods; EBI indices Ex included single community
structure metrics. All indices were developed with and applied to stations
sampled in 1999-2002.
- 201 - Good Marginal 100
90
80
70
60
50
40
Total Misclassified (%) 30
20
10
0 A1 A2 A3 B1 B2 B3 B4 B5 C1 C2 C3 D1 D2 D3 D4 D5 D6 D7 D8 E1 E2 E3
One-Way Analysis Stepwise Discriminant Analysis Previous Studies Composite Analysis Single Community Metrics Estuarine Biotic Integity Index
Figure 9. Estuarine biotic integrity (EBI) scores of marginal and good stations,
calculated by a) EBI index A3, b) EBI index C2, c) EBI index C3, d) EBI index D2,
and e) EBI index D6 (final EBI index). The EBI score range that contained good
and marginal stations was labeled as “unknown” because the range of scores could not determine environmental quality without error. A solid vertical line represented the new threshold value that distinguished the cutoff EBI score between unknown stations and good or marginal stations. A dashed vertical line represented threshold values that were adjusted from original values (1=lower boundary adjustment from 2.5 to 7.5; 2=upper boundary adjustment from 37.5 to
32.5; 3=combined upper and lower boundary adjustments of 1 and 2, and
4=adjustment to one threshold value at 17.5).
- 203 - Marginal (n=9) Threshold value Good (n=87) Adjusted threshold value
Unknown Good Unknown Good a) b) Station Station
-5 0 5 10 0 5 10 15 20 25 30 Estuarine Biotic Integrity Score (A 3) Estuarine Biotic Integrity Score (C 2)
Unknown Good Unknown Good c) d) Station Station
0 5 10 15 20 25 30 35 40 45 50 -5 0 5 10 15 20 25 30 35
Estuarine Biotic Integrity Score (C 3) Estuarine Biotic Integrity Score (D 2)
Unknown Marginal1, 34 2, 3 Good e) Station
-5 0 5 10 15 20 25 30 35 40 45 50
Estuarine Biotic Integrity Score (D6)
Table 1. Critical values of water, sediment, and upland quality parameters that
were used to classify 97 stations sampled in 1999-2002 for the South Carolina
Estuarine and Coastal Assessment Program (SCECAP) as good, marginal, or
poor. Each water and sediment quality parameter was scored: 5=good,
3=marginal, or 1=poor. The upland parameter was scored: 5=good, or
2=marginal/poor. Overall environmental quality was determined by averaging the scores of the parameters within each of the three quality categories (water, sediment, and upland) and then adjusting the average score.
- 205 - Environmental Quality Good (5) Marginal (3) Poor (1) Water quality parameters pH ≥7.4 7.1 - <7.4 <7.1 Dissolved oxygen (mg/L) ≥4 3 - 4 <3 Biological oxygen demand (mg/L) ≤1.8 1.8 - 2.6 >2.6 Total nitrogen (mg/L) ≤0.95 >0.95 - 1.29 >1.29 Total phosphorus (mg/L) ≤0.09 >0.09 - 0.17 >0.17 Fecal coliform bacteria (col/100mL) ≤43 >43 - 400 >400 Sediment quality parameter Effects range median-quotient (score) <0.020 0.020 - 0.058 >0.058 Upland quality parameter Physically altered (within 100 m buffer) No Yes Yes Overall environmental quality Average quality ≥3.667 2.334 - <3.667 <2.334
Table 2. Fish metrics that described life history, ecological and trophic composition, tolerance, and community structure (italicized metrics were not included as candidate fish metrics in statistical analyses). The expected fish metric responses to degraded environmental quality were based on a review of literature and ecological principals. The actual fish response was based on observations from the current study.
- 207 - Response to degraded environmental quality Metric Definition Expected Actual Life history metrics Estuarine dependent Spawns offshore and larva actively or passively immigrates to estuaries to settle out as a Decrease Increase juvenile (pre-adult, immature non-spawning recruits) or spawns in estuary and remains as a juvenile; juvenile is not found nearshore, offshore, near the coast, or at the surf zone (McHugh, J.L. 1966; Blaber and Blaber 1980; Lenanton 1982; Lenanton and Potter 1987; Blaber et al . 1989; Forward et al . 1999) Estuarine nursery Juvenile (pre-adult, immature non-spawning recruit) uses estuary as a nursery ground Decrease Increase (develop, forage, reside); larva may have spawned offshore and recruit into estuary or juvenile may move out of the estuary after being spawned and developed in estuary and continue as juveniles offshore; do not include development of juvenile at sea or offshore Estuarine resident Lives in estuary year round and are not diadromous or marine; uses estuary for all life Increase Increase stages and does not move offshore, nearshore, near the coast, or in the surf zone at any time Estuarine spawner Uses estuary (including most bays and sounds) as spawning ground; larva found in Decrease Increase estuaries along with gravid adults; gravid adult does not spawn offshore, near shore, along the coast, or in the surf zone Tidal creek nursery* Same as estuarine nursery, but specifically utilizes the tidal creek habitat, when Decrease Increase information was available Tidal creek resident* Same as estuarine resident, but specifically utilizes the tidal creek habitat Increase Increase Tidal creek spawner* Same as estuarine spawner, but specifically utilizes the tidal creek habitat Decrease Increase
Ecological and trophic composition metrics Benthic Typically found near, dwells on, or is associated with the bottom; demersal Decrease Increase Benthic feeder Diet includes benthic infauna and/or demersal epifauna; diet typically includes Decrease Increase invertebrates not found in the water column (i.e. crabs, mollusks, penaeid shrimp) Carnivore Depends on "animal" material for the majority (>60%) of diet; cannot mechanically or Decrease Increase chemically digest incidental plant material (Stickney and Shumway 1974) Detritivore Diet includes detritus (may be significant proportion or incidental) Decrease Increase Herbivore Depends on "plant" material for the majority (>60%) of diet; can mechanically or Increase Decrease chemically digest plant material (Stickney and Shumway 1974) Omnivore Depends on "animal" and "plant" material for diet; usually about 50/50 animal/plant but up Increase Decrease to 40/60 or 60/40 animal/plant; have been found to sometimes have all animal or all plant diets in an individual; usually generalistic opportunistic feeders dependent on environmental conditions Pelagic Typically found in/related to/associated with the water column; Living in open waters Increase Increase away from the bottom; bathy/epi/mesopelagic; was not used in statistical analyses Top predator Top predator; subset of carnivores that includes fish in their diet Decrease Increase Response to degraded environmental quality Metric Definition Expected Actual Tolerance metrics Bay anchovy Anchoa mitchilli (bay anchovy) Increase Increase Bay anchovy and shad Alosa sapidissima (American shad) and Anchoa mitchilli (bay anchovy) Increase Increase Flatfish Belongs to the Bothidae, Cynoglossidae, or Soleidae family Decrease Decrease Flounder Recreationally important flatfish Decrease Decrease Resilient* Resilience to fishing pressure/productivity (Musick 1999); in this study, high and medium Increase Increase resilience are termed as "resilient" and low and very low resilience are termed "not Salinity independent* Independent of salinity within the range 1-32 ppt (Weinstein 1979) Increase Increase Sciaenid Belongs in the Sciaenidae family Decrease Increase Shad Alosa sapidissima (American shad) Increase Decrease
Community structure metrics Density of fish Number of individuals per hectare Decrease Increase Dominance Described with three submetrics (Berger and Parker 1970): 1) the percent of the fish Increase Decrease population that is made up of the most abundant taxon 2) the percent of the fish population that is made up of the two most abundant taxa, and 3) the percent of the fish population that is made up of the three most abundant taxa Percent abundance Described with two submetrics: 1) the number of taxa that it takes to make up a Decrease Increase cumulative abundance 90% of the total fish abundance, and 2) the number of taxa that it takes to make up a cumulative abundance of 95% of the total fish abundance Species diversity H' (Shannon-Wiener species diversity Index; Shannon 1948) Decrease Increase Species evenness J' (Pielou's species evenness Index; Pielou 1966) Decrease Decrease Number of Taxa Average number of fish species/taxa per sample Decrease Increase Species richness D (Margalef's species richness index; Margalef 1958) Decrease Increase *Incomplete profile of community; available information was compiled for certain taxa while other taxa were conservatively left as blank
Table 3. Average values (±1 standard deviation) of water, sediment, upland, and physical parameters for marginal, good, and excellent stations sampled in 1999-
2002. Excellent stations were a subset of good stations. *Depth and the percent of physically altered land within a 100 m buffer were the only parameters significantly different between good and marginal stations (Wilcoxon test, Dunn-
Sidak test, α=0.05, k=19, p<0.0027). All other parameters shown and not shown
(latitude, longitude, month, and year) were not significantly different between good and marginal stations (Wilcoxon test, Dunn-Sidak test, α=0.05, k=19, p>0.0027). For a complete list of water, sediment, upland, and physical parameter values for each stations, refer to Appendices A and B.
- 210 - Environmental Quality Marginal (n = 9) Good (n = 87) Excellent (n=16) Water quality parameters pH 7.41 ±0.11 7.55 ±0.20 7.70 ±0.069 Dissolved oxygen (mg/L) 3.56 ±0.93 4.39 ±0.73 4.97 ±0.55 Biological oxygen demand (mg/L) 2.58 ±2.02 1.15 ±1.56 0.06 ±0.24 Total nitrogen (mg/L) 0.78 ±0.25 0.59 ±0.29 0.42 ±0.17 Total phosphorus (mg/L) 0.11 ±0.02 0.09 ±0.05 0.06 ±0.02 Fecal coliform bacteria (col/100 mL) 172.44 ±289.00 17.73 ±45.62 4.13 ±5.66 Sediment quality parameter Effects range median-quotient (score) 0.02 ±0.01 0.01 ±0.01 0.01 ±0.00 Upland quality parameter Physically altered (%) *9.47 ±9.88 *1.38 ±3.86 0.00 ±0.00 Physical parameters Temperature (degrees C) 30.11 ±1.16 29.67 ±1.35 29.82 ±1.40 Salinity (ppt) 29.58 ±4.63 32.55 ±4.06 34.42 ±1.73 Width (m) 77.88 ±34.53 71.06 ±28.51 72.32 ±38.19 Depth (m) *1.74 ±0.54 *2.60 ±0.97 3.00 ±1.02 Width/depth ratio (m) 50.38 ±38.84 30.96 ±17.06 25.01 ±10.60 Sinuousity (m) 821.75 ±79.32 791.20 ±181.45 793.90 ±154.69 Rivulets 15.50 ±8.45 21.19 ±9.78 24.06 ±7.11
Table 4. Environmental and physical parameters of two creeks (May and Kiawah
Rivers) that each contained one good and one marginal station. Numbers in parenthesis were scored parameters; italicized parameters showed a significant difference between good and marginal stations (analysis of variance [ANOVA], p>0.05). The small sample size (n=2) does not allow statistical tests to detect differences because of a lack of power.
- 212 - May River Kiawah River Station code RT01602 MR1-01-T RT99004 RT00542 Overall environmental quality Good Marginal Good Marginal Creek May River May River Kiawah River Kiawah River Month of sampling July (7) July (7) August (8) July (7) Year of sampling 2001 2002 1999 2000 Water quality parameters pH 7.63 (5) 7.37 (3) 7.61 (5) 7.47 (5) Dissolved oxygen (mg/L) 4.59 (5) 3.79 (3) 3.70 (3) 3.68 (3) Biological oxygen demand (mg/L) 0 (5) 1.20 (5) 1.80 (5) 3.20 (1) Total nitrogen (mg/L) N/A 1.20 (3) 1.12 (3) 0.83 (5) Total phosphorus (mg/L) 0.073 (5) 0.082 (5) 0.11 (3) 0.11 (3) Fecal coliform (col/100mL) 22 (5) 15 (5) 8 (5) 110 (3) Sediment quality parameter Effects range median-quotient (score) 0.0017 (5) 0.0013 (5) 0.0071 (5) 0.0056 (5) Upland quality parameter Physically altered (%) 0 (5) 8.10 (2) 0 (5) 3.51 (2) Physical parameters Temperature (degrees C) 31.81 29.69 29.23 29.79 Salinity (ppt) 30.74 28.91 34.35 33.66 Width (m) 192.70 77.58 89.75 35.76 Depth (m) 3.6 2.4 1.1 0.60 Width/depth ratio (m) 53.5331.99 80.13 59.60 Sinuousity (m) 808.90 830.50 656.98 903.42 Rivulets (#) 33 31 18 12 Latitude (decimal degrees) 32.2166 32.2237 32.6439 32.6465 Longitude (decimal degrees) -80.9158 -80.9256 -80.0437 -80.0576 Relative location Downstream Upstream Downstream Upstream EBI score 25 35 10 5
Table 5. Average value (±1 standard deviation) of the 21 fish metrics selected by the one-way analyses, stepwise discriminant analyses, or previous studies for marginal, good, and excellent stations. Excellent stations were a subset of good stations. For a complete list of fish metric averages and values for each station, refer to Appendix E.
- 214 - Environmental Quality Marginal (n = 9) Good (n = 87) Excellent (n = 16) Life history metrics Estuarine dependent (density of individuals) 485.37 ±352.30 214.81 ±191.36 169.97 ±154.29 Estuarine nursery (# of taxa) 6.44 ±2.23 4.07 ±2.16 4.28 ±1.38 Estuarine resident (# of taxa) 2.11 ±0.70 1.34 ±0.88 1.41 ±1.00 Estuarine spawner (# of taxa) 3.06 ±1.10 2.00 ±1.29 2.00 ±1.11 Tidal creek nursery (# of taxa) 5.56 ±2.30 3.34 ±1.70 3.72 ±1.30 Tidal creek nursery (# of individuals/hectare) 513.63 ±388.02 226.20 ±206.35 195.22 ±182.90 Tidal creek resident (# of taxa) 1.78 ±0.83 1.04 ±0.73 1.09 ±0.74
Ecological and trophic metrics Benthic (% of individuals) 77.86 ±14.16 75.13 ±27.06 80.94 ±22.37 Carnivore (# of taxa) 5.78 ±1.86 3.57 ±1.97 3.63 ±1.22 Detritivore (# of individuals/hectare) 488.05 ±386.67 219.54 ±200.06 190.73 ±182.07 Top predator (# of taxa) 2.39 ±0.49 1.35 ±0.89 1.34 ±0.77 Tolerance metrics Flatfish (# of individuals/hectare) 14.49 ±19.17 17.90 ±43.39 17.19 ±31.19 Flounder (% of individuals) 0.17 ±0.50 0.85 ±3.08 1.36 ±3.22 Resilient (# of taxa) 2.50 ±0.83 1.51 ±0.95 1.31 ±0.51 Salinity independent (# of taxa) 2.28 ±1.09 1.34 ±0.72 1.44 ±0.60 Community structure Density of individuals (# of individuals/hectare) 536.72 ±385.60 246.77 ±216.90 207.45 ±183.95 Dominance of most abundant taxon (%) 48.45 ±11.97 56.26 ±17.01 53.52 ±16.20 Number of taxa 6.44 ±2.23 4.07 ±2.16 4.28 ±1.38 Species diversity (H') 1.91 ±0.50 1.38 ±0.64 1.57 ±0.52 Species richness (D) 0.90 ±0.33 0.57 ±0.33 0.63 ±0.23 90% abundance (# of taxa) 7.22 ±2.86 5.45 ±2.68 6.13 ±1.93
Table 6. Summary of the 21 fish metrics included for each EBI index evaluated
(boxed X=not used in discriminant analyses). EBI indices Ax incorporated
metrics selected by one-way analyses; EBI indices Bx incorporated metrics
selected by stepwise discriminant analyses; EBI indices Cx incorporated metrics selected by previous studies; EBI indices Dx incorporated metrics selected by a
combination of methods; EBI indices Ex included single community structure
metrics. All metric scores were summed for an EBI score for each station and the maximum EBI score for each index was 5i, where i=the number of metrics used for the index. Selection frequency was based on the number of times a metric was selected for EBI indices A1-3, B1-3, and C1, 2.
- 216 - EBI Index One-way Stepwise discriminant Previous Composite Individual Selection A1 A2 A3 B1 B2 B3 B4 B5 C1 C2 C3 D1 D2 D3 D4 D5 D6 D7 D8 E1 E2 E3 frequency Life history metrics Estuarine dependent (# of individuals/hectare) X X X X 3 Estuarine nursery (# of taxa) X X X X X X 3 Estuarine resident (# of taxa) X X X X 1 Estuarine spawner (# of taxa) X X X X X 2 Tidal creek nursery (# of taxa) X X X X XXXXX X 4 Tidal creek nursery (# of individuals/hectare) X X 2 Tidal creek resident (# of taxa) X 1 Ecological and trophic composition metrics Benthic (% of individuals) X X X X X 2 Carnivore (# of taxa) X X 2 Detritivore (# of individuals/hectare) X 1 Top predator (# of taxa) X X X X X X X XXXXX X 7 Tolerance metrics Flatfish (# of individuals/hectare) X X X XXXXXXXX 3 Flounder (% of individuals) X X 1 Resilient (# of taxa) X 1 Salinity independent (# of taxa) X X X X X XXXX 5 Community stucture metrics Density of individuals (# of individuals/hectare) X X X X X X X X 2 Dominance of most abundant taxon (%) X X X X X X 3 Number of taxa X X X X XXXXXXX X 3 Species diversity (H') XX XX X 1 Species richness (D) X 1 90% abundance (# of taxa) X X X X 2 Total number of metrics selected 9 6 1 53743759 56664995 111 Maximum EBI score 45 30 5 25 15 35 20 15 35 25 45 25 30 30 30 20 45 45 25 5 5 5
Table 7. Fish metrics that were significantly different between good and marginal stations sampled in 1999-2001 (Wilcoxon test, Dunn-Sidak test, 61 stations=good, 8 stations=marginal, α=0.10, k=73, p<0.0014). Critical value for good quality was the 50th percentile of 61 good stations sampled in 1999-2001.
All metrics used in estuarine biotic integrity (EBI) index A1; metrics that were significant at α<0.05 (p<0.0007) were used in EBI index A2; one metric (top predator taxa) was significantly different for stations sampled in 1999-2002 and used in EBI index A3 (Wilcoxon test, Dunn-Sidak test, 87 stations=good, 9 stations=marginal, α=0.10, k=73, χ2=11.3900, p=0.0002). For box-plots of fish metrics, see Figure 2.
- 218 - Critical value for good Metric χ2 p environmental quality Tidal creek nursery (# of taxa) 14.1900 0.0002 ≤3.0 Top predator (# of taxa) 13.9363 0.0002 ≤1.0 Salinity independent (# of taxa) 12.8602 0.0003 ≤1.5 Carnivore (# of taxa) 12.2417 0.0005 ≤3.0 Estuarine nursery (# of taxa) 11.8483 0.0006 ≤3.5 Number of taxa 11.8483 0.0006 ≤3.5 Tidal creek resident (# of taxa) 10.9643 0.0009 ≤1.0 Resilient (# of taxa) 10.3191 0.0013 ≤1.5 Species richness (D) 10.1565 0.0014 ≤0.46
Table 8. Significant fish metrics selected by stepwise discriminant analyses, using a subset of 50 candidate metrics and stations sampled in 1999-2001 (61 stations=good; 8 stations=marginal; p<0.15). Critical values were determined by using the 50th percentile of 61 good stations sampled in 1999-2001. All fish metrics were used in EBI index B1; three of the five metrics that were significant at p<0.10 were used in EBI index B2.
- 220 - Average squared Critical value for good Step Metric Partial r 2 χ2 p canonical correlation environmental quality 1 Tidal creek nursery (# of taxa) 0.2600 23.71 <0.0001 0.2614 ≤3.00 2 Flatfish (# of individuals/hectare) 0.1015 7346.00 0.0081 0.3364 ≥7.25 3 90% abundance (# of taxa) 0.0993 7317.00 0.0094 0.4023 ≤5.00 4 Top predator (# of taxa) 0.0526 3.55 0.0640 0.4337 ≤1.00 5 Dominance of most abundant taxon (%) 0.0467 3.09 0.0837 0.4602 ≥61.95
Table 9. Significant fish metrics selected by stepwise discriminant analyses,
using a subset of 50 candidate metrics and stations sampled in 1999-2002 (87
stations=good; 9 stations=marginal, p<0.15). Critical values were determined by
th using the 50 percentile of 87 good sites. All metrics were used in EBI index B3; four of the metrics that were significant at p<0.10 were used in EBI index B4;
three of the metrics that were significant at p<0.05 were used in EBI index B5.
- 222 - Average squared Critical value for good Step Metric Partial r 2 χ2 p canonical correlation environmental quality 1 Estuarine dependent (# of individuals/hectare) 0.1260 13.55 0.0004 0.1260 ≤152.17 2 Salinity independent (# of taxa) 0.0455 4.44 0.0379 0.1658 ≤1.50 5 Top predator (# of taxa) 0.0458 4.32 0.0405 0.2510 ≤1.00 4 Tidal creek nursery (# of individuals/hectare) 0.0353 3.33 0.0712 0.2150 ≤166.66 6 Detritivore (# of individuals/hectare) 0.0262 2.40 0.1250 0.2706 ≤159.42 3 Flatfish (# of individuals/hectare) 0.0246 2.32 0.1312 0.1863 ≥7.25 7 Dominance of most abundant taxon (%) 0.0236 2.13 0.1479 0.2879 ≥55.95
Table 12. Nine fish metrics that were used in the final EBI index (EBI index D6).
Metrics were selected by applying expert knowledge of the local habitat to modify metrics selected by previous studies (i.e., Deegan et al. 1997; Meng et al. 2002).
Good estuarine biotic integrity (EBI) was determined by using the critical values for good quality, which were calculated using the 50th percentile for 87 good stations sampled in 1999-2002. The expected fish metric responses to good EBI were based on a review of literature and ecological principals. The actual fish response was based on observations from the current study. See Table 2 or text for more details.
- 228 - Critical value for good Metric Reference environmental quality Benthic (% of individuals) Deegan et al . 1997; Meng et al . 2002 ≥85.83 Density of individuals (# of individuals/hectare) Deegan et al . 1997; Meng et al . 2002 ≤181.15 Dominance of most abundant taxon (%) Deegan et al . 1997 ≥55.95 Estuarine nursery (# of taxa) Deegan et al . 1997 ≤3.5 Estuarine resident (# of taxa) Deegan et al . 1997 ≤1.5 Estuarine spawner (# of taxa) Deegan et al . 1997; Meng et al . 2002 ≤1.5 Flounder (% of individuals) Meng et al . 2002 ≥0 Number of taxa Deegan et al . 1997 ≤3.5 Species diversity (H') Meng et al . 2002 ≤1.41
Table 10. Subset of fish metrics that were used in previously developed estuarine biotic integrity indices (Deegan et al. 1997; Meng et al. 2002). Critical values were determined by using the 50th percentile of 87 good sites sampled in 1999-
2002 for the current study. Metrics selected by Deegan et al. (1997) were used in EBI index C1; metrics selected by Meng et al. (2002) were used in EBI index
C2; metrics selected by either Deegan et al. (1997) or Meng et al. (2002) were used in EBI index C3.
- 224 - Used in final Critical Value EBI index Metric Good Excellent Life history metrics Estuarine dependent (# of individuals/hectare) ≤152.17 ≤110.51 X Estuarine nursery (# of taxa) ≤3.5 ≤4 X Estuarine resident (# of taxa) ≤1.5 ≤1.5 X Estuarine spawner (# of taxa) ≤1.5 ≤1.5 Tidal creek nursery (# of individuals/hectare) ≤166.66 ≤124.86 Tidal creek nursery (# of taxa) ≤3 ≤3.75 Tidal creek resident (# of taxa) ≤1 ≤1 Ecological and trophic metrics X Benthic (% of individuals) ≥85.83 ≥89.23 Carnivore (# of taxa) ≤3.5 ≤3.5 Detritivore (# of individuals/hectare) ≤159.42 ≤119.56 Top predator (# of taxa) ≤1 ≤1.5 Tolerance metrics X Flatfish (# of individuals/hectare) ≥7.25 ≥7.25 Flounder (% of individuals) ≥0 ≥0 Resilient (# of taxa) ≤1.5 ≤1.25 Salinity independent (# of taxa) ≤1.5 ≤1.5 Community metrics X Density of individuals (# of individuals/hectare) ≤181.15 ≤130.43 X Dominance of most abundant taxon (%) ≥55.95 ≥49.11 X Number of taxa ≤3.5 ≤4 X Species diversity (H') ≤1.41 ≤1.66 Species richness (D) ≤0.56 ≤0.64 90% abundance (# of taxa) ≤5 ≤6.5
Table 11. Twenty-one candidate fish metrics that were selected by statistical
analyses or by previous studies. Subsets of metrics and critical values were
used for EBI indices D1-8 and E1-3 (see Table 6 for details). Critical values for
good quality were calculated using the 50th percentile for 87 good stations
sampled in 1999-2002. Critical values for excellent quality were calculated using
the 50th percentile for 16 stations sampled in 1999-2002. Excellent stations were
a subset of good stations. Critical values for good quality were used in the
current study for the final EBI index, while critical values for excellent quality are suggested for future resource managers.
- 226 - Good EBI Expected? Life history metrics Estuarine nursery (# of taxa) ≤3.5 Estuarine resident (# of taxa) ≤1.5 9 Estuarine spawner (# of taxa) ≤1.5 Ecological metric Benthic (% of individuals) ≥85.83 9 Tolerance metric Flatfish (# of individuals/hectare) ≥7.25 9 Community metrics Density of individuals (# of individuals/hectare) ≤181.15 Dominance of most abundant taxon (%) ≥55.95 Number of taxa ≤3.5 Species diversity (H') ≤1.41
APPENDICES
Appendix A. Water, sediment, and upland quality parameters and overall
environmental quality of 97 stations sampled in 1999-2002. Missing data (n=38) were regarded as blank values for analyses. Minimum, maximum, range, and
average values were calculated using 96 good and marginal stations. *Poor
station (NT02301) was not included in calculating minimum, maximum, range,
and average values and was eliminated in final analysis. See text for details.
Dissolved Biological Oxygen Total Total Fecal Effects Range- Physically Oxygen Demand Nitrogen Phosphorus Coliform Median Quotient Altered Station Quality pH (mg/L) (mg/L) (mg/L) (mg/L) (col/100mL) (score) (%) MR1-01-T Marginal 7.3669 3.7906 1.2 1.200 0.082 15 0.0013 8.10 MR3-03-T Good 7.6632 4.9143 1.2 1.200 0.077 3 0.0023 0.00 MR3-04-T Good 7.6425 4.2332 1.2 0.520 0.112 1 0.0067 0.00 NT01598 Good 7.5505 4.7216 2.2 0.360 0.083 280 0.0168 17.75 NT02301* Poor 7.6081 3.9555 2.4 0.829 0.060 1601 0.1113 2.38 RT00501 Good 7.4535 4.0121 0.0 0.540 0.100 0 0.0088 0.00 RT00502 Good 7.0268 3.2466 0.0 0.610 0.200 23 0.0023 0.00 RT00503 Good 7.7132 3.8296 0.0 0.500 0.060 22 0.0140 17.08 RT00504 Good 7.3150 3.8462 1.2 17 0.0048 0.00 RT00505 Good 7.4572 3.9476 0.0 0.530 0.060 0 0.0153 0.00 RT00517 Good 7.6952 4.1598 1.0 0.610 0.040 2 0.0053 0.00 RT00518 Marginal 7.2270 2.9052 2.5 0.970 0.110 80 0.0279 0.00 RT00519 Good 7.2430 4.4668 0.0 0.800 0.100 2 0.0126 0.00 RT00520 Good 7.6969 4.8099 0.0 0.350 0.080 0 0.0113 0.00 RT00521 Good 7.5387 4.5978 0.0 0.470 0.060 2 0.0355 0.00 RT00523 Marginal 7.3878 3.5726 0.0 0.800 0.140 900 0.0199 7.18 RT00525 Good 7.4158 3.4996 0.0 0.590 0.070 0 0.0087 0.00 RT00528 Good 7.1719 4.1348 2.5 1.110 0.200 50 0.0168 0.00 RT00531 Good 7.3754 5.0289 2.6 0.660 0.060 23 0.0040 0.00 RT00541 Good 7.6668 4.6337 0.0 0.420 0.060 0 0.0171 0.00 RT00542 Marginal 7.4687 3.6751 3.2 0.830 0.110 110 0.0056 3.51 RT00543 Good 7.4436 4.2582 1.7 90 0.0049 0.00 RT00544 Good 7.7588 4.2980 3.4 0.550 0.080 2 0.0028 0.00 RT00545 Good 7.9086 5.4874 3.3 0.180 0.060 0 0.0003 12.11 RT00546 Good 7.4891 3.9120 2.1 0.580 0.100 0 0.0043 0.00 RT00547 Good 7.5463 3.8778 0.0 0.660 0.070 14 0.0121 0.00 RT00550 Good 7.7530 5.1940 4.3 0.380 0.050 20 0.0031 8.25 RT00554 Good 7.0884 3.7856 0.0 0.670 0.090 22 0.0078 0.00 RT00557 Good 7.4283 4.9142 0.0 0.740 0.160 30 0.0087 4.90 RT00558 Good 7.3350 4.4964 0.0 0.490 0 0.0307 0.00 RT01602 Good 7.6346 4.5880 0.0 0.073 22 0.0017 0.00 RT01603 Good 7.0639 3.0640 0.0 1.395 0.250 70 0.0029 0.00 Dissolved Biological Oxygen Total Total Fecal Effects Range- Physically Oxygen Demand Nitrogen Phosphorus Coliform Median Quotient Altered Station Quality pH (mg/L) (mg/L) (mg/L) (mg/L) (col/100mL) (score) (%) RT01604 Good 7.4688 3.8864 0.0 0.097 23 0.0102 9.95 RT01606 Good 7.7957 5.3392 0.0 2 0.0328 0.00 RT01619 Good 7.7201 4.5603 0.0 0.110 0 0.0080 0.00 RT01624 Good 7.7640 4.9898 0.0 0.074 0 0.0040 0.00 RT01642 Good 7.7933 6.0240 0.0 7 0.0096 0.00 RT01643 Good 7.3705 3.7358 0.0 1.088 0.230 2 0.0101 0.00 RT01645 Good 7.7141 4.5024 2.9 3 0.0035 0.00 RT01646 Good 7.6314 5.3986 0.0 2 0.0292 0.00 RT01647 Marginal 7.5638 2.4214 2.0 0.531 0.060 4 0.0104 0.55 RT01648 Good 7.4470 4.3489 0.0 0.584 0.160 0 0.0354 0.00 RT01649 Good 7.8086 5.0826 0.0 7 0.0066 0.00 RT01650 Good 7.8539 5.4825 0.0 0.061 11 0.0072 1.04 RT01652 Good 7.5958 4.7481 0.0 11 0.0138 0.00 RT01653 Good 7.4197 4.1756 2.3 0.089 4 0.0131 0.00 RT01655 Good 7.9497 4.0821 2.4 4 0.0052 0.65 RT01664 Good 7.7059 5.2513 0.0 0.272 0.065 4 0.0094 2.42 RT01668 Good 7.7292 5.2503 2.0 0.0333 0.00 RT02002 Good 7.6872 4.0957 0.0 0.450 0.054 0 0.0079 0.00 RT02006 Good 7.9439 5.6993 0.0 0.140 0.041 21 0.0070 7.79 RT02007 Good 7.7068 5.2119 0.0 0.528 0.063 2 0.0373 0.00 RT02008 Good 7.8480 5.3193 0.0 0.170 0.052 7 0.0062 0.00 RT02009 Good 7.6617 5.3801 0.0 0.554 0.084 2 0.0114 0.00 RT02013 Good 7.7070 5.1120 0.0 0.677 0.058 8 0.0011 3.42 RT02015 Good 7.5836 2.7070 0.0 0.360 0.066 22 0.0080 0.00 RT02016 Good 7.5319 4.5088 0.0 0.450 0.053 0 0.0247 0.00 RT02019 Good 7.6338 4.8002 0.0 0 0.0114 0.00 RT02021 Good 7.2127 3.9020 0.0 300 0.0201 0.00 RT02027 Good 7.4668 4.6233 4.3 0.980 0.089 11 0.0144 0.00 RT02030 Good 7.2179 4.4529 0.0 0.390 0.028 9 0.0071 0.00 RT02152 Good 7.2159 2.8955 0.0 0.616 0.056 7 0.0434 0.00 RT02153 Good 7.4225 3.9418 0.0 0.600 0.059 50 0.0229 0.00 RT02154 Good 7.6501 5.4139 0.0 0.703 0.081 2 0.0098 0.00 RT02155 Good 7.6283 3.9590 0.0 0.440 0.110 4 0.0109 0.00 RT02156 Good 7.7102 4.3539 0.0 0.360 0.042 2 0.0058 0.00 RT02157 Good 7.5057 4.3446 2.3 0.193 0.062 4 0.0054 0.00 Dissolved Biological Oxygen Total Total Fecal Effects Range- Physically Oxygen Demand Nitrogen Phosphorus Coliform Median Quotient Altered Station Quality pH (mg/L) (mg/L) (mg/L) (mg/L) (col/100mL) (score) (%) RT02160 Good 7.6408 5.9813 0.0 0.190 0.048 0 0.0044 0.00 RT02162 Good 7.3798 4.3490 2.2 0.220 0.030 0 0.0099 0.00 RT02164 Good 7.6971 4.7694 2.7 0.230 0.047 2 0.0200 0.00 RT02165 Good 7.4775 5.2735 0.0 0.676 0.099 13 0.0203 0.00 RT02167 Good 7.3698 3.5423 0.0 0.518 0.061 7 0.0230 0.00 RT02171 Good 7.6727 5.1096 0.0 4 0.0033 0.00 RT99001 Good 7.5530 3.9111 1.9 1.270 0.120 13 0.0365 0.00 RT99003 Good 7.4610 3.6832 1.0 0.670 0.000 22 0.0171 0.00 RT99004 Good 7.6089 3.6987 1.8 1.120 0.110 8 0.0071 0.00 RT99005 Marginal 7.5094 3.9554 7.2 0.630 0.120 0 0.0230 7.33 RT99006 Good 7.7774 4.8492 2.3 0.860 0.160 70 0.0075 0.00 RT99008 Good 7.5014 1.4 0.800 0.100 2 0.0137 0.00 RT99009 Marginal 7.2840 2.5346 1.6 0.800 0.100 130 0.0293 7.29 RT99010 Good 7.3692 4.4555 5.5 0.570 0.110 8 0.0186 0.00 RT99012 Good 7.5025 3.8820 3.8 0.440 0.100 0 0.0080 0.00 RT99013 Good 7.5877 3.7479 1.3 0.790 0.000 4 0.0328 0.00 RT99017 Marginal 7.3801 5.5448 3.4 0.890 0.110 300 0.0148 29.98 RT99019 Good 7.4491 4.0051 2.3 0.700 0.070 4 0.0036 15.51 RT99022 Good 7.6338 3.5227 1.3 0.530 0.100 30 0.0139 10.08 RT99024 Good 7.3986 3.4727 1.3 0.350 0.080 11 0.0062 0.00 RT99026 Good 7.3060 2.6517 2.2 0.860 0.000 0 0.0082 0.00 RT99027 Good 7.3031 4.7376 2.2 0.050 13 0.0066 8.85 RT99028 Good 7.7196 4.4916 1.2 0.210 0.100 0 0.0135 0.00 RT99029 Good 7.6012 4.5 0.840 0.080 8 0.0060 0.00 RT99030 Marginal 7.4935 3.6781 2.1 0.350 0.120 13 0.0142 21.25 RT99036 Good 7.4770 4.0399 1.4 1.210 0.130 8 0.0367 0.00 RT99037 Good 7.1212 3.0551 3.6 0.440 0.100 60 0.0053 0.00 RT99038 Good 7.8472 4.7741 4.1 0.190 0.230 0 0.0157 0.00 RT99039 Good 7.6538 3.6321 1.1 0.540 0.120 4 0.0063 0.00 RT99040 Good 7.4809 4.3865 7.7 1.050 0.110 8 0.0075 0.00 Minimum 7.0268 2.4214 0.0 0.1400 0.000 0 0.0003 0.00 Maximum 7.9497 6.0240 7.7 1.3950 0.250 900 0.0434 29.98 Range 0.9229 3.6026 7.7 1.2550 0.250 900 0.0431 29.98 Overall Average 7.5359 4.3153 1.3 0.6151 0.089 32 0.0130 2.14
Appendix B. Physical features and overall environmental quality of 97 stations
sampled in 1999-2002. Data that were not available or applicable were left
blank. Minimum, maximum, range, and average values were calculated using 96
good and marginal stations. *Poor station (NT02301) was not included in calculating minimum, maximum, range, and average values and was eliminated in final analysis. See text for details.
Year of Month of Temperature Salinity Width Depth W/D Ratio Sinuosity Rivulets Latitude Longitude Relative Station Quality Sampling Samping (degrees C) (ppt) (m) (m) (m) (m) (number) (decimal decrees (decimal decrees Location MR1-01-T Marginal 2002 July 29.69 28.91 77.58 2.43 31.99 830.50 31 32.2237 -80.9256 Upstream MR3-03-T Good 2002 August 28.97 33.51 73.55 3.88 18.98 900.89 19 32.2112 -80.8152 MR3-04-T Good 2002 July 29.52 33.20 72.87 2.20 33.12 922.68 22 32.2223 -80.8078 NT01598 Good 2001 July 30.13 29.57 44.10 3.80 11.61 754.70 19 32.7995 -79.8708 NT02301* Poor 2002 August 28.38 27.00 90.11 4.25 21.18 735.63 12 32.7913 -79.8830 RT00501 Good 2000 August 30.50 30.00 69.65 3.10 22.47 989.55 17 32.0896 -80.9150 RT00502 Good 2000 July 28.67 25.64 126.36 1.80 70.20 918.63 25 32.6066 -80.5369 Upstream RT00503 Good 2000 July 29.46 34.71 88.17 1.90 46.40 875.83 14 32.5996 -80.2028 RT00504 Good 2000 June 29.36 33.21 124.07 1.50 82.70 929.45 26 32.4153 -80.5978 RT00505 Good 2000 July 30.81 36.23 76.86 3.40 22.60 734.54 13 33.0360 -79.3952 Upstream RT00517 Good 2000 June 29.51 35.90 43.03 1.70 25.30 548.02 15 32.3015 -80.5842 RT00518 Marginal 2000 July 28.66 28.56 56.45 1.90 29.70 860.13 24 32.6068 -80.2737 RT00519 Good 2000 July 30.46 33.80 38.53 2.10 18.30 748.15 12 32.5506 -80.8343 RT00520 Good 2000 July 30.57 35.27 85.49 2.90 29.50 963.36 27 32.8143 -79.7547 RT00521 Good 2000 July 30.56 36.49 63.16 2.00 31.60 915.41 15 33.0378 -79.4919 RT00523 Marginal 2000 July 28.92 33.11 40.93 1.50 27.30 900.86 8 32.5042 -80.3058 RT00525 Good 2000 July 30.47 37.11 36.42 2.40 15.20 738.71 28 32.9037 -79.6263 RT00528 Good 2000 June 29.38 26.62 45.20 1.00 45.20 910.68 31 32.5884 -80.4494 RT00531 Good 2000 July 29.63 23.63 100.75 2.40 42.00 829.34 19 32.8994 -79.9011 Downstream RT00541 Good 2000 August 30.13 34.47 92.89 3.60 25.80 780.74 26 32.1581 -80.8428 RT00542 Marginal 2000 July 29.79 33.66 35.76 0.60 59.60 903.42 12 32.6465 -80.0576 Upstream RT00543 Good 2000 June 29.59 31.78 85.86 2.40 35.80 885.59 24 32.4717 -80.5082 RT00544 Good 2000 July 29.36 34.68 62.62 3.00 20.90 732.16 15 32.6466 -79.9880 RT00545 Good 2000 August 28.39 36.55 84.24 1.90 44.34 986.02 8 33.8437 -78.6066 RT00546 Good 2000 August 30.03 34.82 60.95 3.00 20.30 947.04 14 32.1808 -80.8215 RT00547 Good 2000 July 29.42 34.73 54.21 1.60 33.90 495.71 9 32.5833 -80.1873 Upstream RT00550 Good 2000 August 29.37 36.16 71.75 2.10 34.20 854.88 16 33.5658 -79.0210 RT00554 Good 2000 August 29.70 23.89 113.73 2.50 45.50 919.59 44 32.1558 -80.9517 RT00557 Good 2000 July 30.80 33.86 58.61 0.85 69.00 781.34 23 32.5057 -80.7580 RT00558 Good 2000 July 30.59 35.44 43.49 2.50 17.40 379.82 6 33.0466 -79.5350 Year of Month of Temperature Salinity Width Depth W/D Ratio Sinuosity Rivulets Latitude Longitude Relative Station Quality Sampling Samping (degrees C) (ppt) (m) (m) (m) (m) (number) (decimal decrees (decimal decrees Location RT01602 Good 2001 July 31.81 30.74 192.70 3.60 53.53 808.90 33 32.2166 -80.9158 Downstream RT01603 Good 2001 August 29.91 26.10 94.40 4.40 21.45 743.80 17 32.5920 -80.5387 Downstream RT01604 Good 2001 August 30.35 34.41 103.60 1.80 57.56 919.10 10 32.4341 -80.8618 RT01606 Good 2001 July 28.83 34.97 105.10 3.30 31.85 598.20 16 33.0399 -79.3781 Downstream RT01619 Good 2001 August 27.97 35.68 43.40 2.30 18.87 243.30 32 32.3134 -80.5794 RT01624 Good 2001 August 27.85 35.91 86.30 4.00 21.58 933.30 21 32.3173 -80.5195 RT01642 Good 2001 August 30.36 33.70 32.30 2.00 16.15 971.10 18 32.6211 -80.0011 RT01643 Good 2001 August 30.08 30.06 73.50 5.30 13.87 729.62 35 32.5209 -80.5778 RT01645 Good 2001 July 28.70 35.90 96.40 3.50 27.54 790.90 26 33.3494 -79.1760 RT01646 Good 2001 July 30.88 31.19 104.00 2.30 45.22 997.30 11 32.1621 -80.8672 RT01647 Marginal 2001 August 31.60 31.74 78.44 1.30 60.34 761.47 24 32.6327 -80.0854 RT01648 Good 2001 August 29.97 24.24 58.80 3.80 15.47 772.80 29 32.4892 -80.5288 RT01649 Good 2001 August 30.52 33.80 58.20 3.90 14.92 719.10 18 32.6601 -79.9765 RT01650 Good 2001 July 28.38 29.16 84.30 1.40 60.21 947.60 13 33.8571 -78.5748 Downstream RT01652 Good 2001 August 30.54 33.29 48.50 3.30 14.70 598.10 23 32.5649 -80.2251 RT01653 Good 2001 July 29.29 32.57 102.90 2.10 49.00 866.30 19 32.4197 -80.5719 RT01655 Good 2001 July 29.04 36.59 50.20 1.20 41.83 857.40 38 33.5318 -79.0531 RT01664 Good 2001 August 27.61 34.70 124.70 4.10 30.41 979.50 9 32.3247 -80.4873 RT01668 Good 2001 July 29.31 34.93 56.40 2.40 23.50 942.30 22 32.9605 -79.6152 RT02002 Good 2002 August 29.61 36.37 72.69 4.40 16.52 977.12 28 32.3065 -80.5479 Upstream RT02006 Good 2002 July 30.32 33.40 87.01 2.55 34.12 914.12 10 32.7750 -79.8241 RT02007 Good 2002 July 30.18 35.92 77.16 1.87 41.15 915.79 40 32.4872 -80.8039 RT02008 Good 2002 July 30.82 35.45 69.05 1.97 34.96 934.56 16 32.7010 -79.9145 RT02009 Good 2002 July 30.59 36.04 94.46 2.54 37.23 818.81 41 32.5032 -80.8458 RT02013 Good 2002 July 29.99 34.42 77.11 1.40 55.08 862.14 30 32.4624 -80.6649 RT02015 Good 2002 June 27.24 36.28 50.66 2.40 21.11 591.79 45 32.5186 -80.5855 RT02016 Good 2002 July 31.77 33.13 41.07 2.88 14.29 505.15 13 33.0418 -79.3933 RT02019 Good 2002 June 27.84 31.97 49.35 3.58 13.77 819.24 25 32.5045 -80.3774 RT02021 Good 2002 June 26.16 24.93 26.51 2.52 10.53 637.48 45 32.6179 -80.3324 RT02027 Good 2002 June 28.02 36.86 51.56 2.94 17.55 375.19 18 32.4444 -80.5971 RT02030 Good 2002 August 28.63 24.09 35.67 1.68 21.29 444.01 18 32.9419 -79.7884 RT02152 Good 2002 August 29.36 27.53 97.80 4.14 23.64 950.06 30 32.1260 -81.0041 Year of Month of Temperature Salinity Width Depth W/D Ratio Sinuosity Rivulets Latitude Longitude Relative Station Quality Sampling Samping (degrees C) (ppt) (m) (m) (m) (m) (number) (decimal decrees (decimal decrees Location RT02153 Good 2002 July 29.69 33.51 99.33 1.29 77.15 637.14 32 32.3056 -80.9284 RT02154 Good 2002 July 31.80 33.46 60.06 1.95 30.80 926.28 28 32.7874 -80.0808 RT02155 Good 2002 July 31.33 33.73 54.33 3.35 16.22 652.04 15 32.6252 -80.0253 RT02156 Good 2002 August 29.52 37.15 84.56 4.37 19.33 762.91 25 32.3061 -80.5571 Downstream RT02157 Good 2002 June 28.05 37.19 118.31 4.33 27.35 998.86 31 32.4228 -80.6026 RT02160 Good 2002 August 28.88 34.22 58.05 3.05 19.01 598.91 32.2616 -80.7959 RT02162 Good 2002 August 28.30 25.61 72.69 2.65 27.43 934.18 18 32.8598 -79.8510 RT02164 Good 2002 August 28.36 37.37 38.36 1.92 19.93 865.61 15 32.9084 -79.6400 RT02165 Good 2002 July 30.58 34.28 44.08 1.95 22.60 788.27 57 32.5284 -80.7937 RT02167 Good 2002 June 27.22 33.85 95.03 2.64 36.03 989.47 29 32.5778 -80.5137 RT02171 Good 2002 June 26.80 32.94 29.51 1.09 27.13 542.00 17 32.5774 -80.2209 Downstream RT99001 Good 1999 July 27.50 33.76 49.55 2.81 17.63 908.67 14 33.0261 -79.4613 RT99003 Good 1999 July 28.76 32.35 76.92 3.89 19.77 980.55 20 32.3310 -80.4985 RT99004 Good 1999 August 29.23 34.35 89.75 1.12 80.13 656.98 18 32.6439 -80.0437 Downstream RT99005 Marginal 1999 July 31.11 28.71 132.13 0.88 150.14 946.88 6 32.4404 -80.6522 RT99006 Good 1999 August 28.91 32.34 61.53 1.00 61.53 837.15 12 33.8526 -78.5840 Upstream RT99008 Good 1999 July 29.11 32.09 67.05 2.75 24.38 770.62 23 32.3626 -80.4768 RT99009 Marginal 1999 August 31.66 32.61 92.51 1.60 57.82 722.31 14 32.5579 -80.3618 RT99010 Good 1999 August 32.53 24.64 40.31 2.78 14.50 794.72 14 32.5063 -80.8020 RT99012 Good 1999 August 31.17 35.28 82.78 2.32 35.68 910.03 19 32.2953 -80.6201 RT99013 Good 1999 July 29.48 33.61 94.13 2.94 32.02 972.05 20 32.3358 -80.5599 RT99017 Marginal 1999 July 29.91 18.78 51.29 1.34 38.28 735.17 13 32.8247 -79.8667 RT99019 Good 1999 July 32.10 31.48 38.37 1.99 19.28 780.64 21 32.5622 -80.2441 RT99022 Good 1999 July 32.43 30.08 28.54 1.56 18.29 368.74 17 32.1578 -80.7882 RT99024 Good 1999 August 32.57 25.69 93.14 4.50 20.70 883.12 9 32.4523 -80.8365 RT99026 Good 1999 July 27.51 32.95 66.96 2.17 30.86 974.20 14 33.0843 -79.4201 RT99027 Good 1999 July 29.87 20.13 73.05 2.66 27.46 823.65 24 32.8934 -79.9069 Upstream RT99028 Good 1999 August 30.90 35.89 88.85 3.00 29.62 703.45 16 32.3462 -80.5566 RT99029 Good 1999 July 31.66 32.33 28.49 1.29 30.90 523.85 20 32.5762 -80.2242 Upstream RT99030 Marginal 1999 August 31.40 32.75 123.61 1.63 27.46 821.16 11 32.3885 -80.6334 RT99036 Good 1999 July 27.41 32.38 42.13 2.16 75.83 997.55 9 33.0894 -79.3643 RT99037 Good 1999 August 29.26 20.35 102.93 1.63 16.72 932.79 14 32.9418 -79.7725 RT99038 Good 1999 August 30.83 35.95 47.73 2.00 23.87 920.07 17 32.3436 -80.5464 RT99039 Good 1999 August 31.09 34.25 54.84 3.24 16.93 479.45 10 32.5822 -80.1862 Downstream RT99040 Good 1999 August 31.29 33.43 38.10 2.90 13.10 313.42 8 32.3929 -80.6413 Minimum 1999 July 26.16 18.78 26.51 0.60 10.53 243.30 6 32.0896 -81.0041 Maximum 2002 August 32.57 37.37 192.70 5.30 150.14 998.86 57 33.8571 -78.5748 Range 3 3 6.41 18.59 166.19 4.70 139.61 755.56 51 1.7675 2.4294 Overall Average 2001 July 29.73 32.30 71.57 2.50 33.08 794.96 21 32.6229 -80.2379
Appendix C. Fish density (# of individuals/hectare) for two trawls and average fish density (# of individuals/hectare) at 97 stations sampled in 1999-2002. Zero values were left blank. *Poor station (NT02301) was eliminated in final analysis.
See text for details.
Percent Total
Taxon Common Name Abundance Abundance MR1-01-T MR3-03-T MR3-04-T NT01598 Alosa sapidissima American Shad 0.03 14.49 Aluterus schoepfi Orange Filefish 0.03 14.49 Anchoa hepsetus Striped Anchovy 0.69 362.32 Anchoa mitchilli Bay Anchovy 14.29 7519.07 28.99 Archosargus probatocephalus Sheepshead 0.03 14.49 14.49 Arius felis Sea Catfish 0.08 43.48 Astroscopus y-graecum Stargazer 0.03 14.49 Bagre marinus Gafftopsail Catfish 0.14 72.46 Bairdiella chrysoura Silver Perch 21.91 11523.54 86.96 115.94 Blenniidae Combtooth Blennies 0.03 14.49 Brevoortia tyrannus Atlantic Menhaden 0.69 362.32 Centropristis philadelphica Rock Sea Bass 0.27 143.37 Centropristis striata Black Sea Bass 0.03 14.49 Chaetodipterus faber Atlantic Spadefish 1.67 880.95 Chilomycterus schoepfi Striped Burrfish 0.46 243.27 Chloroscombrus chrysurus Atlantic Bumper 0.28 144.93 14.49 Citharichthys macrops Spotted Whiff 0.08 43.48 Citharichthys sp. Whiff 0.08 43.48 Citharichthys spilopterus Bay Whiff 0.72 376.81 14.49 Cynoscion nebulosus Spotted Sea Trout 0.17 86.96 Cynoscion regalis Weakfish 3.39 1782.61 14.49 14.49 Dasyatis sabina Atlantic Stingray 0.11 57.97 Dorosoma cepedianum Gizzard Shad 0.03 14.49 Dorosoma petenense Threadfin Shad 0.03 14.49 Elops saurus Ladyfish 0.14 72.46 Etropus crossotus Fringed Flounder 0.58 304.35 43.48 Eucinostomus gula Silver Jenny 0.74 391.30 Eucinostomus sp. Mojarra 0.33 173.91 14.49 Common Name NT02301* RT00501 RT00502 RT00503 RT00504 RT00505 RT00517 RT00518 RT00519 RT00520 RT00521 RT00523 American Shad Orange Filefish Striped Anchovy Bay Anchovy 57.97 333.33 492.75 86.96 173.91 318.84 14.49 231.88 28.99 Sheepshead Sea Catfish Stargazer Gafftopsail Catfish Silver Perch 28.99 28.99 391.30 57.97 536.23 28.99 158.39 202.90 231.88 Combtooth Blennies 14.49 Atlantic Menhaden Rock Sea Bass 14.49 Black Sea Bass 14.49 Atlantic Spadefish 14.49 43.48 25.88 28.99 Striped Burrfish 28.99 14.49 Atlantic Bumper 28.99 Spotted Whiff Whiff Bay Whiff 14.49 Spotted Sea Trout 28.99 Weakfish 57.97 14.49 14.49 Atlantic Stingray 14.49 Gizzard Shad Threadfin Shad Ladyfish 43.48 Fringed Flounder 14.49 Silver Jenny Mojarra 14.49 14.49 Common Name RT00525 RT00528 RT00531 RT00541 RT00542 RT00543 RT00544 RT00545 RT00546 RT00547 RT00550 RT00554 American Shad Orange Filefish 14.49 Striped Anchovy Bay Anchovy 72.46 27.43 536.23 115.94 72.46 72.46 Sheepshead Sea Catfish 14.49 Stargazer Gafftopsail Catfish Silver Perch 57.97 27.43 376.81 14.49 101.45 Combtooth Blennies Atlantic Menhaden 260.87 14.49 Rock Sea Bass 12.94 28.99 14.49 Black Sea Bass Atlantic Spadefish 14.49 Striped Burrfish 14.49 25.88 14.49 14.49 Atlantic Bumper Spotted Whiff 14.49 Whiff Bay Whiff 57.97 14.49 Spotted Sea Trout 14.49 Weakfish 14.49 72.46 144.93 Atlantic Stingray Gizzard Shad Threadfin Shad Ladyfish 14.49 Fringed Flounder 14.49 14.49 14.49 28.99 Silver Jenny 28.99 43.48 202.90 14.49 Mojarra Common Name RT00557 RT00558 RT01602 RT01603 RT01604 RT01606 RT01619 RT01624 RT01642 RT01643 RT01645 RT01646 American Shad Orange Filefish Striped Anchovy Bay Anchovy 101.45 72.46 188.41 57.97 14.49 14.49 14.49 Sheepshead Sea Catfish Stargazer Gafftopsail Catfish Silver Perch 144.93 478.26 67.93 14.49 14.49 14.49 57.97 Combtooth Blennies Atlantic Menhaden 14.49 14.49 Rock Sea Bass Black Sea Bass Atlantic Spadefish 43.48 43.48 Striped Burrfish 14.49 Atlantic Bumper Spotted Whiff 28.99 Whiff Bay Whiff 14.49 28.99 Spotted Sea Trout Weakfish 43.48 14.49 43.48 72.46 Atlantic Stingray Gizzard Shad Threadfin Shad Ladyfish Fringed Flounder 43.48 14.49 Silver Jenny 14.49 Mojarra Common Name RT01647 RT01648 RT01649 RT01650 RT01652 RT01653 RT01655 RT01664 RT01668 RT02002 RT02006 American Shad Orange Filefish Striped Anchovy Bay Anchovy 14.49 14.49 14.49 42.36 57.97 Sheepshead Sea Catfish Stargazer Gafftopsail Catfish 14.49 14.49 Silver Perch 173.91 724.64 188.41 217.39 43.48 14.49 139.35 28.99 Combtooth Blennies Atlantic Menhaden Rock Sea Bass 14.49 Black Sea Bass Atlantic Spadefish 86.96 14.49 14.49 Striped Burrfish 14.49 14.49 Atlantic Bumper 14.49 28.99 Spotted Whiff Whiff Bay Whiff 14.49 14.49 Spotted Sea Trout Weakfish Atlantic Stingray Gizzard Shad Threadfin Shad Ladyfish Fringed Flounder Silver Jenny Mojarra Common Name RT02007 RT02008 RT02009 RT02013 RT02015 RT02016 RT02019 RT02021 RT02027 RT02030 RT02152 RT02153 American Shad Orange Filefish Striped Anchovy Bay Anchovy 57.97 86.96 28.99 57.97 159.42 304.35 Sheepshead Sea Catfish Stargazer Gafftopsail Catfish Silver Perch 159.42 130.43 14.49 246.38 72.46 14.49 Combtooth Blennies Atlantic Menhaden 14.49 14.49 14.49 Rock Sea Bass Black Sea Bass Atlantic Spadefish 14.49 14.49 28.99 14.49 Striped Burrfish Atlantic Bumper 14.49 14.49 Spotted Whiff Whiff 14.49 Bay Whiff 14.49 14.49 14.49 Spotted Sea Trout 14.49 Weakfish 173.91 14.49 130.43 14.49 14.49 Atlantic Stingray 14.49 Gizzard Shad Threadfin Shad Ladyfish Fringed Flounder Silver Jenny Mojarra Common Name RT02154 RT02155 RT02156 RT02157 RT02160 RT02162 RT02164 RT02165 RT02167 RT02171 RT99001 American Shad Orange Filefish Striped Anchovy Bay Anchovy 28.99 57.97 86.96 130.43 Sheepshead Sea Catfish 14.49 Stargazer Gafftopsail Catfish Silver Perch 14.49 101.45 28.99 1028.99 144.93 72.46 14.49 Combtooth Blennies Atlantic Menhaden 14.49 Rock Sea Bass 14.49 14.49 14.49 Black Sea Bass Atlantic Spadefish 14.49 57.97 43.48 144.93 14.49 Striped Burrfish Atlantic Bumper Spotted Whiff Whiff 14.49 Bay Whiff 144.93 Spotted Sea Trout 28.99 Weakfish 14.49 28.99 72.46 14.49 43.48 Atlantic Stingray 14.49 14.49 Gizzard Shad Threadfin Shad Ladyfish Fringed Flounder 101.45 14.49 Silver Jenny Mojarra 14.49 115.94 Common Name RT99003 RT99004 RT99005 RT99006 RT99008 RT99009 RT99010 RT99012 RT99013 RT99017 RT99019 American Shad Orange Filefish Striped Anchovy 86.96 101.45 14.49 28.99 14.49 Bay Anchovy 28.99 318.84 188.41 217.39 28.99 14.49 101.45 130.43 Sheepshead Sea Catfish Stargazer Gafftopsail Catfish 14.49 28.99 Silver Perch 28.99 57.97 550.72 1101.45 14.49 72.46 28.99 318.84 86.96 57.97 Combtooth Blennies Atlantic Menhaden Rock Sea Bass 14.49 Black Sea Bass Atlantic Spadefish 14.49 14.49 Striped Burrfish 57.97 28.99 Atlantic Bumper Spotted Whiff Whiff 14.49 Bay Whiff 14.49 Spotted Sea Trout Weakfish 86.96 28.99 14.49 173.91 144.93 Atlantic Stingray Gizzard Shad 14.49 Threadfin Shad Ladyfish Fringed Flounder Silver Jenny 28.99 14.49 Mojarra Common Name RT99022 RT99024 RT99026 RT99027 RT99028 RT99029 RT99030 RT99036 RT99037 RT99038 RT99039 RT99040 American Shad 14.49 Orange Filefish Striped Anchovy 14.49 72.46 28.99 Bay Anchovy 115.94 579.71 43.48 202.90 289.86 623.19 28.99 391.30 Sheepshead Sea Catfish 14.49 Stargazer 14.49 Gafftopsail Catfish Silver Perch 57.97 507.25 28.99 231.88 57.97 840.58 463.77 115.94 14.49 101.45 Combtooth Blennies Atlantic Menhaden Rock Sea Bass Black Sea Bass Atlantic Spadefish 14.49 57.97 28.99 57.97 14.49 Striped Burrfish Atlantic Bumper 28.99 Spotted Whiff Whiff Bay Whiff Spotted Sea Trout Weakfish 246.38 28.99 14.49 Atlantic Stingray Gizzard Shad Threadfin Shad 14.49 Ladyfish 14.49 Fringed Flounder Silver Jenny 43.48 Mojarra Percent Total
Taxon Common Name Abundance Abundance MR1-01-T MR3-03-T MR3-04-T NT01598 Gobiidae Goby 0.00 0.00 Gymnura micrura Smooth Butterfly Ray 0.39 202.90 43.48 Hypsoblennius hentzi Feather Blenny 0.06 28.99 14.49 Lagodon rhomboides Pinfish 14.44 7595.63 637.68 Leiostomus xanthurus Spot 23.79 12513.33 188.41 159.42 Lepisosteus osseus Longnose Gar 0.17 86.96 Lutjanus synagris Lane Snapper 0.03 14.49 Menticirrhus americanus Southern Kingfish 0.03 14.49 Menticirrhus sp. Kingfish 0.14 72.46 Micropogonias undulatus Atlantic Croaker 3.88 2043.48 14.49 Mugil cephalus Striped Mullet 0.13 70.91 Opsanus tau Oyster Toadfish 0.91 478.26 14.49 Orthopristis chrysoptera Pigfish 1.63 855.07 14.49 14.49 Paralichthys dentatus Summer Flounder 0.25 130.43 Paralichthys lethostigma Southern Flounder 0.30 158.86 Peprilus alepidotus Harvestfish 0.06 28.99 Prionotus scitulus Leopard Searobin 0.03 14.49 Prionotus tribulus Bighead Searobin 0.03 14.49 Rhizoprionodon terraenovae Atlantic Sharpnose Shark 0.06 28.99 Scomberomorus maculatus Spanish Mackerel 0.08 43.48 14.49 Selene vomer Lookdown 1.12 590.54 14.49 28.99 Stellifer lanceolatus Star Drum 0.52 275.36 Stephanolepis hispidus Planehead Filefish 0.17 86.96 Symphurus plagiusa Blackcheek Tounguefish 0.22 115.94 Synodus foetens Inshore Lizardfish 0.36 188.41 14.49 14.49 Trinectes maculatus Hogchoker 4.19 2202.90 14.49 Overall Total 100.00 52601.80 86.96 449.28 333.33 695.65 Average Density (n=2) 26300.90 43.48 224.64 166.67 347.83 *Excluded from analysis Common Name NT02301* RT00501 RT00502 RT00503 RT00504 RT00505 RT00517 RT00518 RT00519 RT00520 RT00521 RT00523 Goby 14.49 Smooth Butterfly Ray Feather Blenny Pinfish 14.49 12.94 14.49 43.48 Spot 57.97 14.49 347.83 43.48 14.49 57.97 347.83 43.48 173.91 Longnose Gar 14.49 Lane Snapper Southern Kingfish 14.49 Kingfish Atlantic Croaker 14.49 28.99 86.96 28.99 Striped Mullet Oyster Toadfish 14.49 14.49 43.48 Pigfish 14.49 Summer Flounder Southern Flounder 14.49 Harvestfish 14.49 Leopard Searobin 14.49 Bighead Searobin Atlantic Sharpnose Shark Spanish Mackerel Lookdown Star Drum Planehead Filefish Blackcheek Tounguefish 28.99 Inshore Lizardfish Hogchoker 14.49 86.96 28.99 Overall Total 173.91 405.80 0 898.55 565.22 202.90 260.87 1536.23 86.96 197.20 521.74 695.65 Average Density (n=2) 86.96 202.90 0 449.28 282.61 101.45 130.43 768.12 43.48 98.60 260.87 347.83 *Excluded from analysis Common Name RT00525 RT00528 RT00531 RT00541 RT00542 RT00543 RT00544 RT00545 RT00546 RT00547 RT00550 RT00554 Goby Smooth Butterfly Ray 14.49 28.99 Feather Blenny 14.49 Pinfish 28.99 14.49 157.87 231.88 14.49 217.39 Spot 391.30 57.97 14.49 269.67 188.41 101.45 43.48 362.32 Longnose Gar Lane Snapper 14.49 Southern Kingfish Kingfish Atlantic Croaker 14.49 86.96 43.48 14.49 28.99 Striped Mullet 70.91 Oyster Toadfish 14.49 14.49 Pigfish 28.99 14.49 Summer Flounder 43.48 14.49 14.49 Southern Flounder 14.49 Harvestfish Leopard Searobin Bighead Searobin 14.49 Atlantic Sharpnose Shark 14.49 14.49 Spanish Mackerel Lookdown 25.88 Star Drum 86.96 Planehead Filefish 57.97 Blackcheek Tounguefish 14.49 14.49 Inshore Lizardfish 14.49 28.99 14.49 Hogchoker 14.49 14.49 86.96 14.49 Overall Total 144.93 768.12 72.46 144.93 661.49 913.04 1086.96 43.48 86.96 1086.96 333.33 304.35 Average Density (n=2) 72.46 384.06 36.23 72.46 330.75 456.52 543.48 21.74 43.48 543.48 166.67 152.17 *Excluded from analysis Common Name RT00557 RT00558 RT01602 RT01603 RT01604 RT01606 RT01619 RT01624 RT01642 RT01643 RT01645 RT01646 Goby Smooth Butterfly Ray 14.49 Feather Blenny Pinfish 57.97 376.81 308.88 86.96 289.86 28.99 72.46 14.49 Spot 101.45 43.48 376.81 113.22 246.38 43.48 28.99 28.99 14.49 101.45 Longnose Gar Lane Snapper Southern Kingfish Kingfish Atlantic Croaker 43.48 43.48 Striped Mullet Oyster Toadfish 14.49 14.49 Pigfish 43.48 86.96 14.49 14.49 Summer Flounder Southern Flounder 14.49 Harvestfish 14.49 Leopard Searobin Bighead Searobin Atlantic Sharpnose Shark Spanish Mackerel Lookdown 14.49 14.49 14.49 14.49 Star Drum Planehead Filefish Blackcheek Tounguefish Inshore Lizardfish 14.49 Hogchoker 28.99 101.45 14.49 14.49 14.49 Overall Total 492.75 666.67 898.55 43.48 504.53 913.04 405.80 144.93 101.45 173.91 101.45 159.42 Average Density (n=2) 246.38 333.33 449.28 21.74 252.26 456.52 202.90 72.46 50.72 86.96 50.72 79.71 *Excluded from analysis Common Name RT01647 RT01648 RT01649 RT01650 RT01652 RT01653 RT01655 RT01664 RT01668 RT02002 RT02006 Goby Smooth Butterfly Ray 28.99 14.49 Feather Blenny Pinfish 1362.32 188.41 144.93 188.41 1115.94 43.48 Spot 797.10 57.97 43.48 565.22 14.49 14.49 463.77 942.03 14.49 14.49 Longnose Gar Lane Snapper Southern Kingfish Kingfish 14.49 Atlantic Croaker 101.45 246.38 14.49 86.96 Striped Mullet Oyster Toadfish 14.49 14.49 14.49 Pigfish 57.97 28.99 14.49 14.49 Summer Flounder 14.49 Southern Flounder 14.49 13.94 Harvestfish Leopard Searobin Bighead Searobin Atlantic Sharpnose Shark Spanish Mackerel Lookdown 14.49 28.99 14.49 28.43 Star Drum Planehead Filefish 14.49 Blackcheek Tounguefish Inshore Lizardfish 57.97 14.49 Hogchoker 43.48 260.87 28.99 14.49 28.99 289.86 Overall Total 2652.17 927.54 463.77 173.91 1536.23 101.45 1188.41 637.68 1376.81 253.07 144.93 Average Density (n=2) 1326.09 463.77 231.88 86.96 768.12 50.72 594.20 318.84 688.41 126.53 72.46 *Excluded from analysis Common Name RT02007 RT02008 RT02009 RT02013 RT02015 RT02016 RT02019 RT02021 RT02027 RT02030 RT02152 RT02153 Goby Smooth Butterfly Ray 14.49 14.49 Feather Blenny Pinfish 275.36 318.84 101.45 43.48 28.99 28.99 Spot 28.99 115.94 391.30 115.94 57.97 753.62 86.96 28.99 Longnose Gar 14.49 Lane Snapper Southern Kingfish Kingfish 14.49 14.49 Atlantic Croaker 202.90 57.97 173.91 14.49 Striped Mullet Oyster Toadfish 14.49 28.99 14.49 14.49 14.49 Pigfish 28.99 43.48 43.48 28.99 57.97 Summer Flounder Southern Flounder 14.49 43.48 Harvestfish Leopard Searobin Bighead Searobin Atlantic Sharpnose Shark Spanish Mackerel 14.49 Lookdown 43.48 14.49 Star Drum 159.42 Planehead Filefish Blackcheek Tounguefish 14.49 14.49 14.49 Inshore Lizardfish Hogchoker 28.99 14.49 43.48 Overall Total 86.96 666.67 362.32 28.99 826.09 637.68 347.83 1463.77 391.30 28.99 231.88 449.28 Average Density (n=2) 43.48 333.33 181.16 14.49 413.04 318.84 173.91 731.88 195.65 14.49 115.94 224.64 *Excluded from analysis Common Name RT02154 RT02155 RT02156 RT02157 RT02160 RT02162 RT02164 RT02165 RT02167 RT02171 RT99001 Goby Smooth Butterfly Ray 14.49 Feather Blenny Pinfish 130.43 86.96 14.49 217.39 72.46 14.49 28.99 Spot 14.49 14.49 28.99 159.42 14.49 14.49 72.46 144.93 14.49 Longnose Gar 43.48 14.49 Lane Snapper Southern Kingfish Kingfish 14.49 14.49 Atlantic Croaker 14.49 28.99 217.39 72.46 Striped Mullet Oyster Toadfish 28.99 14.49 14.49 57.97 14.49 Pigfish 28.99 14.49 14.49 86.96 43.48 Summer Flounder 14.49 Southern Flounder 14.49 14.49 Harvestfish Leopard Searobin Bighead Searobin Atlantic Sharpnose Shark Spanish Mackerel Lookdown 28.99 14.49 Star Drum Planehead Filefish 14.49 Blackcheek Tounguefish 14.49 Inshore Lizardfish 14.49 Hogchoker 28.99 14.49 405.80 28.99 86.96 Overall Total 260.87 333.33 231.88 130.43 565.22 217.39 2130.43 449.28 463.77 202.90 202.90 Average Density (n=2) 130.43 166.67 115.94 65.22 282.61 108.70 1065.22 224.64 231.88 101.45 101.45 *Excluded from analysis Common Name RT99003 RT99004 RT99005 RT99006 RT99008 RT99009 RT99010 RT99012 RT99013 RT99017 RT99019 Goby Smooth Butterfly Ray Feather Blenny Pinfish 57.97 28.99 57.97 115.94 14.49 Spot 710.14 130.43 28.99 28.99 202.90 637.68 57.97 376.81 57.97 115.94 Longnose Gar Lane Snapper Southern Kingfish Kingfish Atlantic Croaker 115.94 14.49 72.46 86.96 Striped Mullet Oyster Toadfish 14.49 14.49 Pigfish 43.48 Summer Flounder Southern Flounder Harvestfish Leopard Searobin Bighead Searobin Atlantic Sharpnose Shark Spanish Mackerel Lookdown 101.45 14.49 43.48 Star Drum Planehead Filefish Blackcheek Tounguefish Inshore Lizardfish Hogchoker 130.43 72.46 173.91 Overall Total 1173.91 478.26 782.61 420.29 1623.19 1159.42 159.42 144.93 1101.45 463.77 318.84 Average Density (n=2) 586.96 239.13 391.30 210.14 811.59 579.71 79.71 72.46 550.72 231.88 159.42 *Excluded from analysis Common Name RT99022 RT99024 RT99026 RT99027 RT99028 RT99029 RT99030 RT99036 RT99037 RT99038 RT99039 RT99040 Goby Smooth Butterfly Ray 14.49 Feather Blenny Pinfish 115.94 130.43 28.99 14.49 Spot 57.97 884.06 159.42 72.46 14.49 43.48 28.99 Longnose Gar Lane Snapper Southern Kingfish Kingfish Atlantic Croaker 14.49 57.97 14.49 Striped Mullet Oyster Toadfish 14.49 14.49 14.49 Pigfish 14.49 28.99 14.49 Summer Flounder 28.99 Southern Flounder Harvestfish Leopard Searobin Bighead Searobin Atlantic Sharpnose Shark Spanish Mackerel 14.49 Lookdown 14.49 43.48 57.97 Star Drum 28.99 Planehead Filefish Blackcheek Tounguefish Inshore Lizardfish Hogchoker 57.97 Overall Total 318.84 1101.45 1347.83 739.13 405.80 115.94 1623.19 594.20 14.49 797.10 144.93 217.39 Average Density (n=2) 159.42 550.72 673.91 369.57 202.90 57.97 811.59 297.10 7.25 398.55 72.46 108.70 *Excluded from analysis
Appendix D.1. Life history classification compiled for fish taxa caught and
identified in trawls at tidal creek stations sampled in 1999-2002 and species that comprise taxonomic categories that were higher than species level, but were not identified in trawls (1=Yes; 0=No; blank=no information available, treated as a 0 in the final analysis). For fish metric definitions, refer to Table 2.
Life History Metrics Estuarine Estuarine Tidal Creek Estuarine Tidal Creek Estuarine Tidal Creek Taxon Common Name Dependent Nursery Nursery Resident Resident Spawner Spawner Alosa sapidissima American Shad 110011 Aluterus schoepfi Orange Filefish 011001 Anchoa hepsetus Striped Anchovy 1110010 Anchoa mitchilli Bay Anchovy 1111110 Archosargus probatocephalus Sheepshead 1110000 Arius felis Sea Catfish 11 0010 Astroscopus y-graecum Stargazer 1 0000 Bagre marinus Gafftopsail Catfish 1 0000 Bairdiella chrysoura Silver Perch 1111111 Blenniidae Combtooth Blennies 111 11 Brevoortia tyrannus Atlantic Menhaden 1110000 Centropristis philadelphica Rock Sea Bass 11 0000 Centropristis striata Black Sea Bass 0100000 Chaetodipterus faber Atlantic Spadefish 110000 Chasmodes bosquianus 1 * Striped Blenny 111111 Chilomycterus schoepfi Striped Burrfish 11 0000 Chloroscombrus chrysurus Atlantic Bumper 1 0000 Citharichthys macrops 2 Spotted Whiff 1 0000 Citharichthys sp. Whiff 1000 Citharichthys spilopterus 2 Bay Whiff 1110010 Cynoscion nebulosus Spotted Sea Trout 11 0010 Cynoscion regalis Weakfish 11 0010 Dasyatis sabina Atlantic Stingray 11 0011 Dorosoma cepedianum Gizzard Shad 110000 Dorosoma petenense Threadfin Shad 1 0000 Elops saurus Ladyfish 1110000 Etropus crossotus Fringed Flounder 1110000 Eucinostomus argenteus 3 * Spotfin Mojarra 0 1 1 0000 Eucinostomus gula 3 Silver Jenny 01 0000 Eucinostomus melanopterus 3 * Flagfin Mojarra 11 0000 Eucinostomus sp. Mojarra 01 0000 Gymnura micrura Smooth Butterfly Ray 11 1 11 Life History Metrics Estuarine Estuarine Tidal Creek Estuarine Tidal Creek Estuarine Tidal Creek Taxon Common Name Dependent Nursery Nursery Resident Resident Spawner Spawner Hypleurochilus geminatus 1 * Crested Blenny 111111 Hypsoblennius hentzi 1 Feather Blenny 111111 Hypsoblennius ionthas 1 * Freckled Blenny 1 1 1111 Lagodon rhomboides Pinfish 1110000 Leiostomus xanthurus Spot 1110000 Lepisosteus osseus Longnose Gar 111111 Lutjanus synagris Lane Snapper 1 0000 Menticirrhus americanus 4 Southern Kingfish 1110000 Menticirrhus littoralis 4 * Gulf Kingfish 0 0000 Menticirrhus saxatalis 4 * Northern Kingfish 11 0000 Menticirrhus sp. Kingfish 11 0000 Micropogonias undulatus Atlantic Croaker 1110000 Mugil cephalus Striped Mullet 1110000 Opsanus tau Oyster Toadfish 111110 Orthopristis chrysoptera Pigfish 0110010 Paralichthys dentatus Summer Flounder 1110000 Paralichthys lethostigma Southern Flounder 1110000 Peprilus alepidotus Harvestfish 01 0000 Prionotus scitulus Leopard Searobin 1 0000 Prionotus tribulus Bighead Searobin 110000 Rhizoprionodon terraenovae Atlantic Sharpnose Shark 11 001 Scomberomorus maculatus Spanish Mackerel 110000 Selene vomer Lookdown 110000 Stellifer lanceolatus Star Drum 11 0010 Stephanolepis hispidus Planehead Filefish 01 0000 Symphurus plagiusa Blackcheek Tounguefish 1110010 Synodus foetens Inshore Lizardfish 110000 Trinectes maculatus Hogchoker 111 1 *species within higher taxonomic categories found in trawl, but were not reported as catch in 1999-2002 1species that comprise Blenniidae 2species that comprise Citharichthys sp. 3species that comprise Eucinostomus sp. 4species that comprise Menticirrhus sp.
Appendix D.2. Ecological and trophic classification compiled for fish taxa caught and identified in trawls at tidal creek stations sampled in 1999-2002 and species that comprise taxonomic categories that were higher than species level, but were not identified in trawls (1=Yes; 0=No; blank=no information available, treated as a
0 in the final analysis). For definitions of fish metrics, refer to Table 2.
Ecological and Trophic Metrics Benthic Top Taxon Common Name Pelagic Benthic Feeder Carnivore Predator Detritivore Herbivore Omnivore Alosa sapidissima American Shad 100 1 1 0 0 Aluterus schoepfi Orange Filefish 010 0 0 1 0 Anchoa hepsetus Striped Anchovy 100 1 0 0 0 Anchoa mitchilli Bay Anchovy 10010100 Archosargus probatocephalus Sheepshead 011 1 0 0 0 Arius felis Sea Catfish 01110100 Astroscopus y-graecum Stargazer 011 1 1 0 0 Bagre marinus Gafftopsail Catfish 01111100 Bairdiella chrysoura Silver Perch 01111100 Blenniidae Combtooth Blennies 011 0 0 0 1 Brevoortia tyrannus Atlantic Menhaden 10000101 Centropristis philadelphica Rock Sea Bass 011 1 0 0 0 Centropristis striata Black Sea Bass 01111100 Chaetodipterus faber Atlantic Spadefish 10110100 Chasmodes bosquianus 1 * Striped Blenny 011 0 0 0 1 Chilomycterus schoepfi Striped Burrfish 011 1 0 0 0 Chloroscombrus chrysurus Atlantic Bumper 10010100 Citharichthys macrops 2 Spotted Whiff 011 1 1 0 0 Citharichthys sp. Whiff 011 1 1 0 0 Citharichthys spilopterus 2 Bay Whiff 011 1 1 0 0 Cynoscion nebulosus Spotted Sea Trout 01111100 Cynoscion regalis Weakfish 011 1 1 0 0 Dasyatis sabina Atlantic Stingray 01110100 Dorosoma cepedianum Gizzard Shad 10000101 Dorosoma petenense Threadfin Shad 10000101 Elops saurus Ladyfish 10111100 Etropus crossotus Fringed Flounder 01110000 Eucinostomus argenteus 3 * Spotfin Mojarra 0 1 1 1 0 1 0 0 Eucinostomus gula 3 Silver Jenny 01110100 Eucinostomus melanopterus 3 * Flagfin Mojarra 01110100 Eucinostomus sp. Mojarra 01110100 Gymnura micrura Smooth Butterfly Ray 011 1 1 0 0 Ecological and Trophic Metrics Benthic Top Taxon Common Name Pelagic Benthic Feeder Carnivore Predator Detritivore Herbivore Omnivore Hypleurochilus geminatus 1 * Crested Blenny 011 0 0 0 1 Hypsoblennius hentzi 1 Feather Blenny 011 0 0 0 1 Hypsoblennius ionthas 1 * Freckled Blenny 0 1 1 0 0 0 1 Lagodon rhomboides Pinfish 01100101 Leiostomus xanthurus Spot 01110100 Lepisosteus osseus Longnose Gar 101 1 1 0 0 Lutjanus synagris Lane Snapper 011 1 0 0 0 Menticirrhus americanus 4 Southern Kingfish 01110100 Menticirrhus littoralis 4 * Gulf Kingfish 011 1 0 0 0 Menticirrhus saxatalis 4 * Northern Kingfish 01110100 Menticirrhus sp. Kingfish 01110100 Micropogonias undulatus Atlantic Croaker 01110100 Mugil cephalus Striped Mullet 10100101 Opsanus tau Oyster Toadfish 011 1 1 0 0 Orthopristis chrysoptera Pigfish 01110100 Paralichthys dentatus Summer Flounder 011 1 1 0 0 Paralichthys lethostigma Southern Flounder 011 1 1 0 0 Peprilus alepidotus Harvestfish 10010100 Prionotus scitulus Leopard Searobin 011 1 0 0 0 Prionotus tribulus Bighead Searobin 01110100 Rhizoprionodon terraenovae Atlantic Sharpnose Shark 011 1 1 0 0 Scomberomorus maculatus Spanish Mackerel 100 1 1 0 0 Selene vomer Lookdown 101 1 1 0 0 Stellifer lanceolatus Star Drum 011 1 0 0 0 Stephanolepis hispidus Planehead Filefish 10110100 Symphurus plagiusa Blackcheek Tounguefish 01100101 Synodus foetens Inshore Lizardfish 010 1 1 0 0 Trinectes maculatus Hogchoker 01110100 *species within higher taxonomic categories found in trawl, but were not reported as catch in 1999-2002 1species that comprise Blenniidae 2species that comprise Citharichthys sp. 3species that comprise Eucinostomus sp. 4species that comprise for Menticirrhus sp.
Appendix D.3. Relative tolerance classification compiled for fish taxa caught and
identified in trawls at tidal creek stations sampled in 1999-2002 and species that comprise taxonomic categories that were higher than species level, but were not identified in trawls (1=Yes; 0=No; blank=no information available, treated as a 0 in the final analysis). For definitions of fish metrics, refer to Table 2.
Tolerance Metrics Bay Salinity Taxon Common Name Anchovy Shad Flatfish Flounder Resilient Independent Sciaenid Hypleurochilus geminatus 1 * Crested Blenny 000 0 0 Hypsoblennius hentzi 1 Feather Blenny 000 0 0 Hypsoblennius ionthas 1 * Freckled Blenny 0 0 0 0 0 Lagodon rhomboides Pinfish 000 0 1 0 Leiostomus xanthurus Spot 000 0 1 1 Lepisosteus osseus Longnose Gar 000 0 0 0 Lutjanus synagris Lane Snapper 000 0 1 0 Menticirrhus americanus 4 Southern Kingfish 000 0 1 Menticirrhus littoralis 4 * Gulf Kingfish 000 0 1 1 Menticirrhus saxatalis 4 * Northern Kingfish 000 0 1 1 Menticirrhus sp. Kingfish 000 0 1 Micropogonias undulatus Atlantic Croaker 000 0 0 1 Mugil cephalus Striped Mullet 000 0 1 0 Opsanus tau Oyster Toadfish 000 0 0 Orthopristis chrysoptera Pigfish 000 0 0 Paralichthys dentatus Summer Flounder 001 1 0 Paralichthys lethostigma Southern Flounder 001 1 0 Peprilus alepidotus Harvestfish 000 0 1 0 Prionotus scitulus Leopard Searobin 000 0 0 Prionotus tribulus Bighead Searobin 000 0 0 Rhizoprionodon terraenovae Atlantic Sharpnose Shark 000 0 1 0 Scomberomorus maculatus Spanish Mackerel 000 0 1 0 Selene vomer Lookdown 000 0 0 Stellifer lanceolatus Star Drum 000 0 1 Stephanolepis hispidus Planehead Filefish 000 0 0 Symphurus plagiusa Blackcheek Tounguefish 001 0 1 0 Synodus foetens Inshore Lizardfish 000 0 0 Trinectes maculatus Hogchoker 001 0 0 *species within higher taxonomic categories found in trawl, but were not reported as catch in 1999-2002 1species that comprise Blenniidae 2species that comprise Citharichthys sp. 3species that comprise Eucinostomus sp. 4species that comprise for Menticirrhus sp. Tolerance Metrics Bay Salinity Taxon Common Name Anchovy Shad Flatfish Flounder Resilient Independent Sciaenid Alosa sapidissima American Shad 010 0 0 0 Aluterus schoepfi Orange Filefish 000 0 1 0 Anchoa hepsetus Striped Anchovy 000 0 1 1 0 Anchoa mitchilli Bay Anchovy 100 0 1 0 Archosargus probatocephalus Sheepshead 000 0 0 Arius felis Sea Catfish 000 0 0 Astroscopus y-graecum Stargazer 000 0 0 Bagre marinus Gafftopsail Catfish 000 0 0 Bairdiella chrysoura Silver Perch 000 0 1 1 1 Blenniidae Combtooth Blennies 000 0 0 Brevoortia tyrannus Atlantic Menhaden 000 0 1 0 Centropristis philadelphica Rock Sea Bass 000 0 0 Centropristis striata Black Sea Bass 000 0 1 0 Chaetodipterus faber Atlantic Spadefish 000 0 0 Chasmodes bosquianus 1 * Striped Blenny 000 0 0 Chilomycterus schoepfi Striped Burrfish 000 0 0 Chloroscombrus chrysurus Atlantic Bumper 000 0 1 0 Citharichthys macrops 2 Spotted Whiff 001 0 0 Citharichthys sp. Whiff 001 0 0 Citharichthys spilopterus 2 Bay Whiff 001 0 0 Cynoscion nebulosus Spotted Sea Trout 000 0 1 1 Cynoscion regalis Weakfish 000 0 1 1 Dasyatis sabina Atlantic Stingray 000 0 1 0 Dorosoma cepedianum Gizzard Shad 010 0 1 0 Dorosoma petenense Threadfin Shad 010 0 1 0 Elops saurus Ladyfish 000 0 1 0 Etropus crossotus Fringed Flounder 001 0 0 Eucinostomus argenteus 3 * Spotfin Mojarra 0 0 0 0 0 Eucinostomus gula 3 Silver Jenny 000 0 0 Eucinostomus melanopterus 3 * Flagfin Mojarra 000 0 0 Eucinostomus sp. Mojarra 000 0 0 Gymnura micrura Smooth Butterfly Ray 000 0 0
Appendix D.4. Fish metric references, by number. For full list of references, refer to Appendix D.5.
Taxon Common Name Reference Number Alosa sapidissima American Shad 19, 24, 50, 51, 88, 89, 95, 106, 114, 152, 153, 163, 170, 174, 195, 197, 199, 239, 245, 268, 293, 306, 315, 315, 316, 317, 320, 321 Aluterus schoepfi Orange Filefish 60, 99, 125, 157, 190, 195, 205, 213, 228, 302 Anchoa hepsetus Striped Anchovy 8, 24, 31, 50, 51, 60, 109, 110, 121, 170, 188, 191, 192, 197, 205, 238, 251, 252, 270, 284, 288, 289, 317
Anchoa mitchilli Bay Anchovy 8, 31, 50, 51, 55, 65, 73, 105, 109, 110, 114, 121, 125, 130, 131, 140, 174, 188, 197, 199, 204, 239, 251, 270, 282, 284, 289, 317 Archosargus probatocephalus Sheepshead 50, 112, 125, 157, 197, 204, 239, 252, 253, 266, 280 Arius felis Sea Catfish 50, 54, 60, 65, 100, 116, 144, 192, 204, 205, 212, 239, 251, 252, 272, 303, 324 Astroscopus y-graecum Stargazer 24, 34, 50, 65, 70, 145, 187, 239, 252 Bagre marinus Gafftopsail Catfish 50, 51, 65, 91, 93, 144, 197, 204, 218, 235, 239, 260, 273, 324 Bairdiella chrysoura Silver Perch 7, 24, 30, 36, 38, 50, 51, 52, 55, 110, 126, 157, 173, 174, 185, 195, 197, 202, 205, 225, 235, 242, 243, 251, 260, 261, 269, 270, 282, 284, 286, 303, 309 Blenniidae Combtooth Blennies 200, 239, 250 Brevoortia tyrannus Atlantic Menhaden 6, 19, 24, 25, 50, 63, 71, 76, 79, 94, 107, 114, 125, 133, 134, 135, 142, 148, 150, 164, 165, 174, 195, 197, 199, 205, 216, 251, 260, 268, 269, 276, 282, 284, 308, 309, 314, 316, 319 Centropristis philadelphica Rock Sea Bass 155, 205, 244, 251, 252, 261, 284 Centropristis striata Black Sea Bass 2, 19, 24, 51, 138, 139, 157, 174, 195, 196, 197, 205, 251, 253, 284 Chaetodipterus faber Atlantic Spadefish 27, 50, 65, 103, 114, 125, 157, 174, 205, 229, 235, 253, 274, 282, 288 Chasmodes bosquianus 1 * Striped Blenny 47, 50, 98, 125, 127, 219, 239, 250 Chilomycterus schoepfi Striped Burrfish 114, 157, 191, 192, 205, 213, 253, 269, 274 Chloroscombrus chrysurus Atlantic Bumper 7, 24, 50, 51, 66, 195, 197, 220, 239, 284 Citharichthys macrops 2 Spotted Whiff 8, 222, 252, 261 Citharichthys spilopterus 2 Bay Whiff 7, 32, 39, 93, 197, 232, 251, 252, 261, 285, 300, 301 Cynoscion nebulosus Spotted Sea Trout 7, 11, 16, 31, 36, 50, 51, 55, 65, 83, 92, 94, 114, 120, 157, 158, 173, 186, 194, 195, 197, 202, 205, 214, 215, 227, 243, 251, 258, 269, 282, 284, 304 Cynoscion regalis Weakfish 19, 24, 36, 50, 52, 73, 87, 92, 101, 115, 146, 157, 159, 166, 166, 173, 174, 177, 178, 194, 195, 197, 205, 227, 261, 269, 270, 282, 284, 286, 299, 312, 318 Dasyatis sabina Atlantic Stingray 19, 50, 81, 93, 117, 195, 205, 230, 239, 251, 252, 265, 276, 277, 284 Dorosoma cepedianum Gizzard Shad 20, 51, 55, 62, 63, 64, 68, 69, 86, 118, 174, 183, 184, 193, 195, 197, 205, 239, 251, 264, 268, 316, 323
Dorosoma petenense Threadfin Shad 55, 57, 86, 118, 132, 170, 184, 193, 195, 205, 251, 315, 316 Elops saurus Ladyfish 22, 34, 39, 50, 51, 55, 65, 86, 92, 94, 107, 108, 114, 157, 162, 169, 179, 195, 197, 199, 204, 252, 267, 273, 282 Etropus crossotus Fringed Flounder 39, 104, 125, 157, 174, 197, 205, 231, 232, 236, 251, 252, 261, 282, 285 Eucinostomus argenteus 3 * Spotfin Mojarra 5, 50, 51, 77, 167, 168, 204, 205, 228, 249, 262, 278, 280, 282, 288, 289, 295, 303 Eucinostomus gula 3 Silver Jenny 22, 31, 157, 168, 191, 192, 197, 204, 205, 228, 233, 236, 239, 249, 280, 282, 295, 303, 325 Eucinostomus melanopterus 3 * Flagfin Mojarra 5, 66, 249, 255, 256, 295 Eucinostomus sp. Mojarra 21, 220, 261, 289, 295, 305 Gymnura micrura Smooth Butterfly Ray 50, 51, 66, 172, 193, 205, 218, 239, 251, 252, 284 Taxon Common Name Reference Number Hypleurochilus geminatus 1 * Crested Blenny 154, 239 Hypsoblennius hentzi 1 Feather Blenny 47, 50, 97, 114, 125, 154, 157, 205, 218, 239, 250 Hypsoblennius ionthas 1 * Freckled Blenny 125, 154, 239, 250 Lagodon rhomboides Pinfish 4, 7, 25, 31, 55, 65, 76, 93, 94, 96, 112, 125, 134, 141, 157, 191, 192, 197, 199, 204, 205, 220, 233, 258, 271, 274, 282, 287, 289, 290, 297, 303, 308 Leiostomus xanthurus Spot 7, 24, 25, 36, 46, 50, 55, 58, 65, 76, 94, 96, 105, 110, 114, 115, 125, 126, 133, 134, 145, 148, 151, 157, 174, 185, 195, 197, 199, 202, 205, 208, 209, 213, 214, 234, 242, 243, 261, 269, 270, 272, 282, 283, 284, 286, 289, 308, 309, 310 Lepisosteus osseus Longnose Gar 23, 48, 51, 65, 92, 117, 119, 128, 149, 174, 193, 195, 201, 205, 257, 291 Lutjanus synagris Lane Snapper 4, 9, 22, 49, 78, 195, 197, 199, 205, 237, 284 Menticirrhus americanus 4 Southern Kingfish 17, 24, 36, 46, 50, 76, 94, 114, 124, 129, 157, 174, 194, 239, 269, 270, 275, 284 Menticirrhus littoralis 4 * Gulf Kingfish 60, 111, 148, 175, 182, 187, 188, 194, 195, 239, 249, 259, 275, 294, 296, 324, 325 Menticirrhus saxatalis 4 * Northern Kingfish 24, 50, 56, 111, 126, 148, 174, 182, 195, 197, 213, 239, 263, 269, 284, 298, 312 Micropogonias undulatus Atlantic Croaker 7, 25, 36, 45, 46, 50, 55, 65, 76, 83, 94, 96, 102, 105, 110, 114, 115, 125, 133, 134, 145, 157, 173, 174, 181, 197, 201, 202, 205, 214, 227, 234, 235, 240, 242, 243, 269, 270, 271, 272, 282, 283, 284, 286, 308, 309, 311 Mugil cephalus Striped Mullet 12, 13, 15, 25, 35, 50, 55, 93, 94, 114, 147, 162, 170, 174, 189, 197, 199, 203, 205, 213, 221, 226, 239, 269, 282, 284, 289, 309 Opsanus tau Oyster Toadfish 4, 7, 19, 50, 51, 156, 157, 174, 197, 205, 213, 251, 253, 260, 270, 282, 284 Orthopristis chrysoptera Pigfish 4, 24, 31, 53, 110, 114, 122, 157, 197, 205, 251, 252, 290, 303 Paralichthys dentatus Summer Flounder 1, 3, 7, 19, 24, 25, 28, 29, 50, 76, 90, 92, 94, 110, 114, 136, 148, 160, 180, 185, 197, 205, 206, 213, 223, 232, 254, 261, 269, 271, 292 Paralichthys lethostigma Southern Flounder 7, 25, 28, 29, 50, 65, 76, 92, 94, 162, 174, 180, 197, 205, 232, 235, 242, 261, 269, 271, 289 Peprilus alepidotus Harvestfish 24, 27, 118, 174, 195, 197, 205, 269, 284 Prionotus scitulus Leopard Searobin 7, 50, 118, 157, 205, 233, 239, 246, 247, 248, 280 Prionotus tribulus Bighead Searobin 7, 50, 118, 125, 157, 174, 205, 233, 239, 246, 248, 256, 280, 289 Rhizoprionodon terraenovae Atlantic Sharpnose Shark 18, 24, 26, 33, 43, 44, 84, 195, 205, 210, 211, 239, 252, 279 Scomberomorus maculatus Spanish Mackerel 24, 40, 42, 72, 74, 112, 125, 143, 171, 174, 176, 195, 198, 199, 205, 224, 284, 322 Selene vomer Lookdown 51, 59, 114, 125, 137, 157, 174, 197, 205, 251, 273 Stellifer lanceolatus Star Drum 7, 50, 65, 75, 111, 197, 205, 225, 227, 239, 251, 261, 270, 282, 284, 286, 313 Stephanolepis hispidus Planehead Filefish 4, 24, 41, 67, 117, 123, 205, 222, 241, 253, 278 Symphurus plagiusa Blackcheek Tounguefish 10, 50, 65, 85, 110, 114, 157, 161, 174, 180, 197, 205, 207, 232, 261, 269, 270, 273, 281, 282, 284, 289, 300, 307, 309 Synodus foetens Inshore Lizardfish 14, 24, 31, 51, 82, 92, 114, 125, 157, 174, 174, 185, 197, 205, 213, 228, 281, 282, 284, 289 Trinectes maculatus Hogchoker 7, 31, 50, 54, 55, 61, 65, 73, 105, 114, 115, 180, 204, 205, 217, 232, 242, 270, 284, 313 *possible species within higher taxonomic categories considered, but were not reported as catch in 1999-2002 1species considered for Blennidae 2species considered for Citharichthys sp. 3species considered for Eucinostomus sp. 4species considered for Menticirrhus sp.
Appendix D.5. List of fish metric references. For full citations, refer to literature cited section.
Reference Number Reference Number Able and Kaiser 1994 1 Darovec 1983 56 Able et al . 1995 2 Davis and Foltz 1991 57 Able et al . 1990 3 Dawson 1958 58 Adams 1976 4 Deegan et al . 1993 59 Aguirre-Leon and Yanez Arancibia 1986 5 Delancey 1989 60 Ahrenholz 1991 6 Derrick and Kennedy 1997 61 Allen and Barker 1990 7 Dettmers and Stein 1992 62 Allen et al . 1995 8 Dettmers and Stein 1996 63 Allen 1985 9 Devries and Stein 1992 64 Allen and Baltz 1997 10 Diener 1974 65 Alshuth and Gilmore 1993 11 Diouf 1996 66 Alvarez-Lanjonchere 1976 12 Dooley 1972 67 Anderson 1958 13 Drenner et al . 1982a 68 Anderson et al . 1966 14 Drenner et al . 1982b 69 Arnold and Thompson 1958 15 Duarte Lopes and Tavares de Oliveira Silva 1999 70 Baltz et al . 1998 16 Durbin and Durbin 1988 71 Bearden 1963 17 Earll 1882 72 Bigelow and Schroeder 1948 18 Ferraro 1980 73 Bigelow and Schroeder 1953 19 Finucane et al . 1990 74 Bodola 1966 20 Flores-Coto et al . 1998 75 Bohlke and Chaplin 1968 21 Forward et al . 1999 76 Bohlke and Chaplin 1993 22 Franks 1970 77 Bonham 1941 23 Franks and Vanderkooy 2000 78 Bowman et al . 2000 24 Friedland et al . 1989 79 Bozeman and Dean 1980 25 Friedland et al . 1996 80 Branstetter 1981 26 Funicelli 1975 81 Buckel et al . 1999 27 Garcia-Abad et al . 1999 82 Burke 1995 28 Geary et al . 2001. 83 Burke et al . 1991 29 Gelsleichter et al . 1999 84 Cain and Dean 1976 30 Ginsburg 1951 85 Carr and Adams 1973 31 Gomez Gasper 1981 86 Castillo-Rivera et al . 2000 32 Goshorn and Epifanio 1991 87 Castro 1993 33 Grabe 1996 88 Cervigon et al . 1992 34 Grecco and Blake 1983 89 Chang et al . 2000 35 Grover 1998 90 Chao and Musick 1977 36 Gudger 1916 91 Chapman 1978 37 Guillen 2000 92 Chavance et al . 1984 38 Gunter 1945 93 Chaves and Serenato 1998 39 Gusey 1981 94 Chittenden et al . 1993 40 Hammann 1981 95 Clements and Livingston 1983 41 Hansen 1969 96 Collette and Nauen 1983 42 Harding 1999 97 Compagno 1984 43 Harding and Mann 2000 98 Cortes 1999 44 Harmelin-Vivien and Quero 1990 99 Cowan and Birdsong 1988 45 Harris and Rose 1968 100 Cowan and Shaw 1988 46 Hartman and Brandt 1995 101 Crabtree and Middaugh 1982 47 Haven 1959 102 Crumpton 1970 48 Hayse 1987 103 Cueller et al . 1996 49 Hensley 1995 104 Dahlberg 1972 50 Hester and Copeland 1975 105 Dahlberg 1980 51 Hildebrand 1963a 106 Daniel and Graves 1994 52 Hildebrand 1963b 107 Darcy 1985 53 Hildebrand 1963c 108 Darnell 1958 54 Hildebrand 1963d 109 Darnell 1961 55 Hildebrand and Cable 1930 110 Reference Number Reference Number Hildebrand and Cable 1934 111 Massmann et al . 1958 166 Hildebrand and Cable 1938 112 Matheson and Gilmore 1995 167 Hildebrand and Schroeder 1927 113 Matheson and McEachran 1984 168 Hildebrand and Schroeder 1928 114 McBride et al . 2001 169 Hines et al . 1990 115 McDowall 1988 170 Hoese 1966 116 McEachran et al . 1980 171 Hoese 1973 117 McEachran and Seret 1990 172 Hoese and Moore 1977 118 McGovern 1986 173 Holloway 1954 119 McHugh 1967 174 Holt and Holt 2000 120 McMichael and Ross 1987 175 Houde and Lovdal 1984 121 Menezes 1970 176 Howe 2001 122 Merriner 1975 177 Irlandi and Mehlich 1996 123 Merriner 1976 178 Irwin 1970 124 Migdalski 1958 179 Jackson 1990 125 Miller et al . 1991 180 Jannke 1971 126 Miller and Able 2002 181 Javonillo, R. in review 127 Miller et al . 2002 182 Johnson and Noltie 1997 128 Miller 1960 183 Johnson 1978 129 Miller 1963 184 Johnson et al . 1990 130 Miltner et al . 1995 185 Jones et al . 1978 131 Minello et al . 1989 186 Jorgensen 1979 132 Modde 1980 187 Joyeux 1998 133 Modde and Ross 1983 188 Joyeux 1999 134 Moore 1974 189 June and Carlson 1971 135 Morrow 1980 190 Keefe and Able 1994 136 Motta et al . 1995 191 Keith et al . 2000 137 Motta et al . 1993 192 Kendall 1972 138 Murdy et al . 1997 193 Kendall 1977 139 Music and Pafford 1984 194 Kimura et al . 2000 140 Musick 1999 195 Kjelson and Johnson 1976 141 Musick and Mercer 1977 196 Kjelson et al . 1975 142 NOAA/NOS 2002 197 Klima 1959 143 Naughton and Saloman 1981 198 Kobelkowsky D. and Castillo Rivera 1995 144 Nelson et al . 1991a 199 Kobylinski and Sheridan 1979 145 Nelson 1994 200 Lankford and Targett 1994 146 Netsch and Witt 1962 201 Larson and Shanks 1996 147 Ocana-Luna and Sanchez-Ramirez 1999 202 Layman 2000 148 Odum 1970 203 Lee et al . 1981 149 Odum and Heald 1972 204 Lewis 1966 150 Ogburn et al . 1988 205 Lewis and Mann 1971 151 Olla et al . 1972 206 Limburg 1996a 152 Olney and Grant 1976 207 Limburg 1996b 153 Owen et al . 1984 208 Lindquist and Dillaman 1986 154 Pacheco 1962 209 Link 1980 155 Parsons 1981 210 Linton 1901 156 Parsons 1983 211 Linton 1904 157 Pattillo et al . 1997 212 Llanso et al . 1998 158 Pearcy and Richards 1962 213 Luczkovich et al . 1999 159 Pearson 1928 214 Manderson et al . 2000 160 Peebles and Tolley 1988 215 Martin and Drewery 1978 161 Peters and Schaaf 1981 216 Martore 1986 162 Peterson 1996 217 Massmann 1952 163 Pew 1971 218 Massmann 1954 164 Phillips 1977 219 Massmann et al . 1954 165 Pierce and Mahmoudi 2001 220 Reference Number Reference Number Potter et al . 1983 221 Snelson and Williams 1981 276 Powell and Robbins 1998 222 Snelson et al . 1988 277 Powell and Schwartz 1979 223 Soares et al . 1993 278 Powell 1975 224 Springer 1967 279 Powles 1980 225 Springer and Woodburn 1960 280 Powles 1981 226 Stickney 1976 281 Powles and Stender 1978 227 Stickney 1984 282 Randall 1967 228 Stickney and McGeachin 1978 283 Randall and Hartman 1968 229 Stickney and Shumway 1974 284 Rasmussen and Heard 1995 230 Stickney et al . 1974 285 Reichert 2002 231 Stickney et al . 1975 286 Reichert and Van der Veer 1991 232 Stoner 1980 287 Reid 1954 233 Stoner 1986 288 Reid 1955 234 Subrahmanyan and Drake 1975 289 Reid et al . 1956 235 Sutter and McIlwain 1987 290 Rivas et al . 1999 236 Suttkus 1963 291 Riviera-Arriaga et al . 1994 237 Szedlmayer and Able 1993 292 Robinette 1983 238 Talbot and Sykes 1958 293 Robins and Ray 1986 239 Teixeira and Almeida 1998 294 Roelofs 1954 240 Teixeira and Helmer 1997 295 Rogers et al . 2001 241 Teixeira et al . 1992 296 Rogers et al . 1984 242 Thayer et al . 1999 297 Rooker et al . 1998 243 Thomas 1971 298 Ross et al . 1989 244 Thorrold et al . 1998 299 Ross et al . 1997 245 Toepfer and Fleeger 1995 300 Ross 1977 246 Tucker 1982 301 Ross 1978 247 Tyler 1978 302 Ross 1983 248 Vega-Cendejas et al . 1994 303 Ross and Lancaster 2002 249 Vetter 1982 304 Roumillat 2002a 250 Vieira and Musick 1994 305 Roumillat 2002b 251 Walburg and Nichols 1967 306 Roumillat 2003 252 Walsh et al . 1999 307 Rountree 1990 253 Warlen and Burke 1990 308 Rountree and Able 1992 254 Weinstein 1979 309 Roux 1986 255 Weinstein et al . 1984 310 Roux 1990 256 Weinstein et al . 1980 311 Rozas and Hackney 1984 257 Welsh and Breder 1923 312 Rozas and Minello 1998 258 Wenner et al . 1981 313 Ruple 1984 259 Werner et al . 1999 314 Ryder 1993 260 White 1970 315 Sandifer et al . 1980 261 Whitehead 1985 316 Sazima 1986 262 Whitehead et al . 1988 317 Schaefer 1965 263 Wilk 1979 318 Schaus et al . 2002 264 Wilkens and Lewis 1971 319 Schwartz and Dahlberg 1978 265 Williams and Brager 1972 320 Sedberry 1987 266 Witherall and Kynard 1990 321 Sekavec 1974 267 Wollam 1970 322 Setzler et al . 1981 268 Yako et al . 1996 323 Setzler-Hamilton 1987 269 Yanez-Arancibia and Lara-Dominguez 1988 324 Shealy et al . 1974 270 Zahorcsak et al . 2000 325 Shenker and Dean 1979 271 Sheridan et al . 1984 272 Sierra et al . 1994 273 Smith 1907 274 Smith and Wenner 1985 275
Appendix E.1. Life history fish metrics calculated for 96 good and marginal stations sampled in 1999-2002 (metrics in normal and bold font = used in one- way analyses; metrics in bold font = used in discriminant analyses; italicized metrics = not used in statistical analyses). *Average/median value at good stations equal to or lower than average/median value at marginal stations. For fish metric definitions, refer to Table 2.
Life History Metrics Estuarine Estuarine Estuarine Estuarine Estuarine Estuarine Tidal Creek Tidal Creek Tidal Creek Estuarine Estuarine Estuarine Dependent Dependent Dependent Nursery Nursery* Nursery Nursery Nursery Nursery Resident Resident Resident Station Quality (#) (%) (taxa) (#) (%) (taxa) (#) (%) (taxa) (#) (%) (taxa) MR-101-T Marginal 36.23 83.33 2 43.47 100 2.5 14.49 33.33 1 28.98 66.67 1.5 MR-303-T Good 173.9 78.46 3 224.61 100 6.5 202.88 78.08 5 57.96 39.61 2 MR-304-T Good 152.17 80.83 1.5 166.66 100 2.5 166.66 100 2.5 72.46 53.33 1 NT01598 Good 326.08 93.3 1.5 347.82 100 3 347.82 100 300 0 RT00501 Good 202.89 100 2.5 202.89 100 2.5 202.89 100 2.5 181.15 88.46 1.5 RT00502 Good 0 0 000 00 0 000 0 RT00503 Good 427.52 96.43 4.5 449.25 100 6 398.54 90.36 4 217.39 56.67 2.5 RT00504 Good 268.11 94.84 2 282.6 100 3 275.35 97.62 2.5 253.62 90.08 1.5 RT00505 Good 101.44 100 3.5 101.44 100 3.5 86.95 91.67 2.5 72.46 83.34 1.5 RT00517 Good 115.94 88.89 1.5 130.43 100 2.5 123.19 94.45 2 86.96 66.67 1 RT00518 Marginal 688.38 93.89 6.5 768.08 100 8.5 731.85 92.08 6.5 478.25 62.36 3 RT00519 Good 43.47 100 2 43.47 100 2 43.47 100 2 21.74 70 1.5 RT00520 Good 85.65 92.31 1.5 98.59 100 2 98.59 100 2 79.19 88.47 1 RT00521 Good 224.63 87.5 2.5 260.85 100 4.5 239.12 91.67 3 231.87 87.5 2.5 RT00523 Marginal 297.08 87.58 6 347.79 100 8 340.55 98.49 7.5 152.17 44.55 2 RT00525 Good 50.72 78.57 2 72.45 100 3 57.96 85.71 2.5 36.23 45.24 1.5 RT00528 Good 384.04 100 3.5 384.04 100 3.5 376.8 96.15 300 0 RT00531 Good 36.23 100 1.5 36.23 100 1.5 36.23 100 1.5 0 0 0 RT00541 Good 65.21 94.44 2.5 72.46 100 3 57.97 88.89 2 36.23 27.78 0.5 RT00542 Marginal 310.51 93.53 7 330.69 100 8 311.29 93.18 7 27.42 8.39 2 RT00543 Good 449.26 99 4 456.51 100 4.5 413.03 88.31 3 275.36 38 1 RT00544 Good 456.49 88.89 6.5 543.44 100 10 442 84.13 6 268.1 56.09 3.5 RT00545 Good 7.25 25 0.5 21.74 100 1.5 21.74 100 1.5 0 0 0 RT00546 Good 43.47 100 2 43.47 100 2 43.47 100 2 7.25 16.67 0.5 RT00547 Good 376.78 78.41 8.5 543.44 100 11 420.26 83.25 9 137.67 21.4 2.5 RT00550 Good 130.43 72.32 2.5 166.65 100 5 144.92 82.59 3.5 21.74 17.41 1.5 RT00554 Good 152.17 100 2.5 152.17 100 2.5 36.23 26.39 1 36.23 26.39 1 RT00557 Good 195.63 80.4 3.5 246.35 100 6 231.86 95.24 5.5 123.18 50.73 2 RT00558 Good 333.32 100 3.5 333.32 100 3.5 333.32 100 3.5 275.36 81.34 2 RT01602 Good 427.53 95.12 3.5 449.26 100 4.5 427.53 92.5 400 0 RT01603 Good 7.25 16.67 0.5 21.74 50 1 14.49 33.33 0.5 14.49 33.33 0.5 RT01604 Good 245.01 75 2 252.26 100 2.5 252.26 100 2.5 33.97 7.14 0.5 Life History Metrics Estuarine Estuarine Estuarine Estuarine Estuarine Estuarine Tidal Creek Tidal Creek Tidal Creek Estuarine Estuarine Estuarine Dependent Dependent Dependent Nursery Nursery* Nursery Nursery Nursery Nursery Resident Resident Resident Station Quality (#) (%) (taxa) (#) (%) (taxa) (#) (%) (taxa) (#) (%) (taxa) RT01606 Good 326.06 70.53 5.5 456.48 100 9 427.5 94.02 8 159.41 35.45 3 RT01619 Good 181.15 87.12 2.5 202.88 100 4 188.39 89.39 3 14.49 16.67 1 RT01624 Good 43.47 60 2 72.45 100 4 72.45 100 4 50.72 70 2.5 RT01642 Good 43.47 90 3 50.72 100 3.5 50.72 100 3.5 21.74 30 1.5 RT01643 Good 86.95 100 3.5 86.95 100 3.5 50.72 62.5 2.5 28.98 31.25 1 RT01645 Good 50.72 100 2 50.72 100 2 50.72 100 2 7.25 12.5 0.5 RT01646 Good 72.46 91.67 2.5 79.7 100 3 72.46 90 2.5 7.25 10 0.5 RT01647 Marginal 1181.14 89.63 4 1326.05 100 9 1318.81 99.61 8.5 123.18 11.18 3 RT01648 Good 449.26 96.61 3.5 463.75 100 4.5 449.26 96.61 3.5 362.31 78.16 1 RT01649 Good 224.63 98.39 2.5 231.87 100 3 224.63 98.39 2.5 101.45 22.58 1 RT01650 Good 72.46 81.25 1 86.95 100 2 86.95 100 200 0 RT01652 Good 608.67 80.32 3.5 768.08 100 6 768.08 100 6 246.37 32.18 2.5 RT01653 Good 28.98 33.33 1 50.72 100 2.5 50.72 100 2.5 36.23 83.33 1.5 RT01655 Good 579.71 96.3 2 594.2 100 3 579.71 96.3 2.5 28.98 7.41 1.5 RT01664 Good 282.59 81.66 4 318.82 100 6.5 289.84 88.8 4.5 21.74 9.85 1.5 RT01668 Good 536.22 77.5 3 688.39 100 4.5 681.14 99.09 4 152.17 22.84 1.5 RT02002 Good 105.07 71.43 2.5 126.52 100 4 126.52 100 4 90.86 55.36 1.5 RT02006 Good 65.21 92.86 3 72.45 100 3.5 57.96 76.19 2.5 50.72 69.05 2 RT02007 Good 43.47 100 1.5 43.47 100 1.5 43.47 100 1.5 28.98 75 1 RT02008 Good 268.1 80.44 3.5 333.31 100 7 326.06 97.83 6.5 144.92 43.48 3 RT02009 Good 166.66 90.08 4.5 181.15 100 5.5 166.66 90.08 4.5 101.44 47.62 3 RT02013 Good 0 0 0 14.49 50 0.5 14.49 50 0.5 14.49 50 0.5 RT02015 Good 405.78 98.49 4.5 413.02 100 5 318.83 76.89 3.5 21.74 5.68 1.5 RT02016 Good 268.1 84.16 4 318.82 100 6.5 311.57 97.62 6 50.72 16.46 1.5 RT02019 Good 123.18 72.22 3 173.9 100 5.5 159.41 93.33 4.5 0 0 0 RT02021 Good 695.63 94.54 6.5 731.85 100 8.5 652.15 87.32 6.5 130.43 15.47 1.5 RT02027 Good 152.17 76.99 3.5 195.64 100 5.5 188.39 95.45 5 86.96 38.92 1 RT02030 Good 14.49 50 0.5 14.49 50 0.5 14.49 50 0.5 0 0 0 RT02152 Good 115.93 100 2 115.93 100 2 36.23 35 1 36.23 35 1 RT02153 Good 217.38 90 4.5 224.62 100 5 210.13 96.15 4 159.42 50.39 1.5 RT02154 Good 94.2 73.22 3 130.42 100 5 115.93 83.93 4 36.23 26.78 2 Life History Metrics Estuarine Estuarine Estuarine Estuarine Estuarine Estuarine Tidal Creek Tidal Creek Tidal Creek Estuarine Estuarine Estuarine Dependent Dependent Dependent Nursery Nursery* Nursery Nursery Nursery Nursery Resident Resident Resident Station Quality (#) (%) (taxa) (#) (%) (taxa) (#) (%) (taxa) (#) (%) (taxa) RT02155 Good 79.7 47.22 3 166.65 100 6.5 137.67 81.75 4.5 21.74 14.68 1 RT02156 Good 86.95 84.62 3 115.93 100 4 94.19 88.46 3 57.97 56.41 1.5 RT02157 Good 50.72 67.86 1.5 65.21 100 2.5 28.98 64.28 2 7.25 7.14 0.5 RT02160 Good 202.89 70.32 3 282.59 100 5.5 210.13 72.59 3.5 0 0 0 RT02162 Good 86.95 70 2.5 108.68 100 3 108.68 100 3 65.21 60 1.5 RT02164 Good 717.37 69.08 5.5 1065.18 100 9 1050.69 98.51 8 797.09 70.52 3.5 RT02165 Good 217.38 95 5.5 224.62 100 6 202.89 90.24 4.5 144.92 58.1 2.5 RT02167 Good 224.62 96.88 4 231.86 100 4.5 202.88 87.5 3.5 0 0 0 RT02171 Good 57.97 55 2 101.44 100 3.5 101.44 100 3.5 57.97 55 1.5 RT99001 Good 57.97 48.48 2 101.44 100 3 94.2 95.46 2.5 50.72 56.06 1.5 RT99003 Good 514.47 88.33 6 586.93 100 7.5 514.47 88.06 5.5 101.44 16.67 3.5 RT99004 Good 195.64 77.27 4.5 239.11 100 7 202.89 81.82 4.5 28.99 9.09 0.5 RT99005 Marginal 340.57 87.64 3 391.29 100 4 391.29 100 4 275.36 68.61 1 RT99006 Good 202.89 95.83 3.5 210.13 100 4 210.13 100 4 159.42 76.96 1 RT99008 Good 811.57 100 6 811.57 100 6 789.83 97.35 4.5 644.92 79.47 2 RT99009 Marginal 565.2 97.46 5 579.69 100 6 478.25 82.22 4 115.94 21.59 1.5 RT99010 Good 79.71 50 1.5 79.71 50 1.5 79.71 50 1.5 50.72 31.82 1 RT99012 Good 21.74 18.75 1 72.46 100 3 57.97 68.75 2 57.97 68.75 2 RT99013 Good 463.75 83.15 4 550.7 100 5 478.24 87.89 4 246.37 42.29 2 RT99017 Marginal 181.14 78.02 4 231.86 100 7 224.61 97.83 6.5 101.44 40.58 2.5 RT99019 Good 159.42 100 2.5 159.42 100 2.5 159.42 100 2.5 94.2 57.14 1 RT99022 Good 123.18 71.18 3 159.4 100 5 130.42 81.18 3.5 86.95 49.41 1.5 RT99024 Good 543.47 98.49 2 550.72 100 2.5 550.72 100 2.5 550.72 100 2.5 RT99026 Good 666.65 99.07 5 673.89 100 5.5 550.71 81.41 4.5 36.23 6.05 1.5 RT99027 Good 362.31 97.22 3.5 369.55 100 4 369.55 100 4 217.39 60.61 1.5 RT99028 Good 152.17 63.75 1.5 202.88 100 3.5 202.88 100 3.5 144.92 61.25 1 RT99029 Good 57.96 100 2 57.96 100 2 57.96 100 2 28.98 46.67 1 RT99030 Marginal 768.1 94.31 3 811.57 100 5 811.57 100 5 739.12 90.84 2.5 RT99036 Good 297.09 100 3.5 297.09 100 3.5 268.11 92.31 2.5 246.37 83.72 1.5 RT99037 Good 7.25 50 0.5 7.25 50 0.5 7.25 50 0.5 0 0 0 RT99038 Good 318.83 80.37 4 398.53 100 6.5 369.55 92.53 5 253.62 65.7 1.5 RT99039 Good 28.98 41.67 1.5 72.45 100 3.5 72.45 100 3.5 43.47 62.5 2 RT99040 Good 94.19 85 3 108.68 100 4 94.19 80 3 57.97 55 1.5 Average Good 214.81 79.77 2.91 246.77 95.98 4.07 226.20 87.07 3.34 103.29 40.09 1.34 Average Marginal 485.37 89.49 4.50 536.72 100.00 6.44 513.63 88.53 5.56 226.87 46.09 2.11 Average All 240.18 80.68 3.06 273.95 96.35 4.29 253.15 87.21 3.55 114.88 40.65 1.41 Median Good 152.17 87.12 3.00 181.15 100* 3.50 166.66 93.33 3.00 50.72 39.61 1.50 Median Marginal 340.57 89.63 4.00 391.29 100.00 7.00 391.29 97.83 6.50 123.18 44.55 2.00 Median All 177.52 87.61 3.00 202.89 100.00 4.00 202.88 93.68 3.50 57.97 41.44 1.50 *Average/median value at good stations equal to or lower than average/median value at marginal stations Life History Metrics Tidal Creek Tidal Creek Tidal Creek Estuarine Estuarine Estuarine Tidal Creek Tidal Creek Tidal Creek Resident Resident Resident Spawner Spawner* Spawner Spawner Spawner Spawner Station Quality (#) (%) (taxa) (#) (%) (taxa) (#) (%) (taxa) MR-101-T Marginal 0 0 0 36.23 83.33 2 21.74 50 1 MR-303-T Good 57.96 39.61 2 79.7 53.46 3.5 50.72 37.69 1.5 MR-304-T Good 72.46 53.33 1 79.71 55.83 1.5 57.97 20 0.5 NT01598 Good 0 0 000 00 0 0 RT00501 Good 181.15 88.46 1.5 181.15 88.46 1.5 14.49 6.67 0.5 RT00502 Good 0 0 000 00 0 0 RT00503 Good 202.9 54.29 1.5 246.37 62.74 3.5 202.9 54.29 1.5 RT00504 Good 253.62 90.08 1.5 253.62 90.08 1.5 0 0 0 RT00505 Good 72.46 83.34 1.5 79.71 87.5 2 28.99 16.67 0.5 RT00517 Good 86.96 66.67 1 86.96 66.67 10 0 0 RT00518 Marginal 434.77 59.03 2.5 514.47 70.28 5 282.6 42.22 2 RT00519 Good 21.74 70 1.5 21.74 70 1.5 14.49 60 1 RT00520 Good 79.19 88.47 1 79.19 88.47 1 79.19 88.47 1 RT00521 Good 217.38 83.34 2 231.87 87.5 2.5 101.45 31.25 1 RT00523 Marginal 152.17 44.55 2 173.9 52.73 3.5 115.94 33.34 1 RT00525 Good 28.98 38.1 1 36.23 45.24 1.5 28.98 38.1 1 RT00528 Good 0 0 0 7.25 3.85 0.5 0 0 0 RT00531 Good 0 0 000 00 0 0 RT00541 Good 36.23 27.78 0.5 43.48 33.33 10 0 0 RT00542 Marginal 27.42 8.39 2 27.42 8.39 2 13.71 4.19 1 RT00543 Good 268.12 37 0.5 318.84 49.69 2.5 0 0 0 RT00544 Good 260.86 55.16 3 282.59 59.39 4.5 202.89 37.57 2 RT00545 Good 0 0 000 00 0 0 RT00546 Good 7.25 16.67 0.5 14.49 33.33 1 7.25 16.67 0.5 RT00547 Good 94.2 16.56 2 195.63 33.93 5.5 50.72 11.72 1 RT00550 Good 0 0 0 36.23 27.68 2.5 14.49 10.27 1 RT00554 Good 36.23 26.39 1 152.17 100 2.5 0 0 0 RT00557 Good 123.18 50.73 2 123.18 50.73 2 72.46 28.2 1 RT00558 Good 275.36 81.34 2 282.6 84.28 2.5 239.13 70.29 1 RT01602 Good 0 0 0 43.47 12.38 1.5 0 0 0 RT01603 Good 0 0 0 21.74 50 10 0 0 RT01604 Good 33.97 7.14 0.5 33.97 7.14 0.5 33.97 7.14 0.5 Life History Metrics Tidal Creek Tidal Creek Tidal Creek Estuarine Estuarine Estuarine Tidal Creek Tidal Creek Tidal Creek Resident Resident Resident Spawner Spawner* Spawner Spawner Spawner Spawner Station Quality (#) (%) (taxa) (#) (%) (taxa) (#) (%) (taxa) RT01606 Good 108.69 24.84 2 239.11 53.74 5.5 7.25 1.92 0.5 RT01619 Good 0 0 0 21.74 18.94 1.5 7.25 8.33 0.5 RT01624 Good 43.47 60 2 57.96 80 3 7.25 10 0.5 RT01642 Good 14.49 20 1 21.74 30 1.5 7.25 10 0.5 RT01643 Good 28.98 31.25 1 65.21 68.75 2 28.98 31.25 1 RT01645 Good 7.25 12.5 0.5 7.25 12.5 0.5 0 0 0 RT01646 Good 7.25 10 0.5 7.25 10 0.5 0 0 0 RT01647 Marginal 101.44 9.01 2 159.4 14.15 4.5 86.95 7.72 1 RT01648 Good 362.31 78.16 1 362.31 78.16 1 362.31 78.16 1 RT01649 Good 101.45 22.58 1 108.69 72.58 1.5 94.2 20.97 0.5 RT01650 Good 0 0 000 00 0 0 RT01652 Good 115.94 16.49 1.5 260.86 34.27 3 108.69 15.45 1 RT01653 Good 21.74 25 0.5 43.47 91.67 2 21.74 25 0.5 RT01655 Good 7.25 1.85 0.5 28.98 7.41 1.5 14.49 3.7 0.5 RT01664 Good 7.25 1.35 0.5 21.74 9.85 1.5 0 0 0 RT01668 Good 7.25 1.25 0.5 152.17 22.84 1.5 7.25 1.25 0.5 RT02002 Good 90.86 55.36 1.5 90.86 55.36 1.5 69.68 35.72 0.5 RT02006 Good 43.47 52.38 1.5 57.96 76.19 2.5 21.74 30.95 1 RT02007 Good 28.98 75 1 28.98 75 10 0 0 RT02008 Good 130.43 39.13 2.5 166.65 50 4.5 79.71 23.91 1 RT02009 Good 86.95 42.06 2 115.93 57.54 4 86.95 42.06 2 RT02013 Good 14.49 50 0.5 14.49 50 0.5 0 0 0 RT02015 Good 14.49 3.6 1 115.93 28.22 3 14.49 4.17 1 RT02016 Good 28.99 9.52 0.5 86.95 27.74 3.5 0 0 0 RT02019 Good 0 0 0 28.98 15.55 10 0 0 RT02021 Good 130.43 15.47 1.5 217.38 29.42 3.5 123.19 14.66 1 RT02027 Good 86.96 38.92 1 130.43 61.93 30 0 0 RT02030 Good 0 0 000 00 0 0 RT02152 Good 36.23 35 1 115.93 100 2 36.23 35 1 RT02153 Good 159.42 50.39 1.5 166.66 52.31 2 7.25 1.92 0.5 RT02154 Good 21.74 10.71 1 43.47 39.28 2.5 14.49 16.07 1 Life History Metrics Tidal Creek Tidal Creek Tidal Creek Estuarine Estuarine Estuarine Tidal Creek Tidal Creek Tidal Creek Resident Resident Resident Spawner Spawner* Spawner Spawner Spawner Spawner Station Quality (#) (%) (taxa) (#) (%) (taxa) (#) (%) (taxa) RT02155 Good 14.49 11.11 0.5 50.72 28.97 2.5 0 0 0 RT02156 Good 57.97 56.41 1.5 72.46 64.1 2 50.72 52.57 1 RT02157 Good 7.25 7.14 0.5 50.72 67.86 1.5 0 0 0 RT02160 Good 0 0 0 14.49 5.21 1 7.25 2.94 0.5 RT02162 Good 65.21 60 1.5 65.21 60 1.5 36.23 40 1 RT02164 Good 586.95 52.64 2 913.02 81.98 5.5 521.73 45.29 1.5 RT02165 Good 144.92 58.1 2.5 166.66 67.86 4 79.71 34.05 1.5 RT02167 Good 0 0 0 28.98 12.5 10 0 0 RT02171 Good 43.48 30 1 79.7 70 2 36.23 25 0.5 RT99001 Good 7.25 4.54 0.5 50.72 56.06 1.5 7.25 4.54 0.5 RT99003 Good 36.23 6.11 2.5 144.91 23.89 4.5 14.49 2.5 1 RT99004 Good 28.99 9.09 0.5 115.93 45.45 3.5 28.99 9.09 0.5 RT99005 Marginal 275.36 68.61 1 326.08 83.81 2 275.36 68.61 1 RT99006 Good 159.42 76.96 1 166.66 79.9 1.5 0 0 0 RT99008 Good 644.92 79.47 2 666.66 82.06 3 550.72 67.95 1 RT99009 Marginal 115.94 21.59 1.5 210.14 38.25 3 7.25 1.43 0.5 RT99010 Good 50.72 31.82 1 50.72 31.82 1 36.23 22.73 0.5 RT99012 Good 21.74 18.75 1 57.97 68.75 2 14.49 12.5 0.5 RT99013 Good 159.42 25.45 1 318.83 54.41 3 159.42 25.45 1 RT99017 Marginal 101.44 40.58 2.5 101.44 40.58 2.5 43.48 19.81 1 RT99019 Good 94.2 57.14 1 94.2 57.14 1 28.99 25 0.5 RT99022 Good 86.95 49.41 1.5 86.95 49.41 1.5 28.98 25.88 1 RT99024 Good 550.72 100 2.5 550.72 100 2.5 253.62 46.34 1 RT99026 Good 36.23 6.05 1.5 166.66 25.57 3 14.49 2.21 1 RT99027 Good 217.39 60.61 1.5 217.39 60.61 1.5 115.94 24.24 0.5 RT99028 Good 144.92 61.25 1 152.17 63.75 1.5 0 0 0 RT99029 Good 28.98 46.67 1 28.98 46.67 1 28.98 46.67 1 RT99030 Marginal 739.12 90.84 2.5 753.61 92.08 3 420.29 45.76 1 RT99036 Good 246.37 83.72 1.5 275.35 91.41 2.5 231.88 79.87 1 RT99037 Good 0 0 000 00 0 0 RT99038 Good 253.62 65.7 1.5 297.09 75.68 3 57.97 12.5 0.5 RT99039 Good 14.49 25 1 50.72 70.83 2.5 7.25 12.5 0.5 RT99040 Good 50.72 45 1 86.95 80 3 65.21 60 2 Average Good 90.47 34.32 1.04 123.36 49.11 1.99 53.99 18.68 0.59 Average Marginal 216.41 38.07 1.78 255.85 53.73 3.06 140.81 30.34 1.06 Average All 102.27 34.67 1.11 135.78 49.55 2.09 62.13 19.77 0.64 Median Good 36.23 30.00 1.00 79.70 53.46* 1.50 14.49 10.27 0.50 Median Marginal 115.94 40.58 2.00 173.90 52.73 3.00 86.95 33.34 1.00 Median All 43.48 30.63 1.00 83.33 53.10 2.00 14.49 12.11 0.50 *Average/median value at good stations equal to or lower than average/median value at marginal stations
Appendix E.2. Ecological and trophic composition fish metrics calculated for 96 good and marginal stations sampled in 1999-2002 (metrics in normal and bold font = used in one-way analyses; metrics in bold font = used in discriminant analyses; italicized metrics = not used in statistical analyses). *Average/median value at good stations equal to or lower than average/median value at marginal stations. For fish metric definitions, refer to Table 2.
Ecological and Trophic Metrics Benthic Benthic Benthic Top Top Top Pelagic Pelagic Pelagic Benthic Benthic* Benthic Feeder Feeder* Feeder Carnivore Carnivore Carnivore Predator Predator Predator Station Quality (#) (%) (taxa) (#) (%) (taxa) (#) (%) (taxa) (#) (%) (taxa) (#) (%) (taxa) MR-101-T Marginal 0.00 0.00 0.00 43.47 100 2.5 43.47 100 2.5 43.47 100 2.5 28.98 66.67 1.5 MR-303-T Good 21.74 5.77 1.50 202.88 94.23 5 210.12 96.15 5.5 217.37 98.08 6 79.7 53.46 3.5 MR-304-T Good 14.49 33.33 0.50 152.17 66.67 2 144.93 50 1.5 166.66 100 2.5 65.22 36.67 1 NT01598 Good 14.49 5.27 1.00 333.33 94.73 2 340.57 98.57 2.5 28.98 8.13 2 21.74 6.7 1.5 RT00501 Good 166.66 81.80 1.00 36.23 18.2 1.5 36.23 18.2 1.5 202.89 100 2.5 14.49 6.67 0.5 RT00502 Good 0.00 0.00 0.00 0 0 000 00 0 000 0 RT00503 Good 0.00 0.00 0.00 449.25 100 6 449.25 100 6 442.01 98.81 5.5 231.88 60.36 2.5 RT00504 Good 246.37 87.30 1.00 36.23 12.7 2 36.23 12.7 2 282.6 100 3 14.49 5.16 1 RT00505 Good 43.48 66.67 1.00 57.97 33.33 2.5 57.97 33.33 2.5 94.2 95.83 3 36.23 20.83 1 RT00517 Good 94.20 72.22 1.50 36.23 27.78 1 43.48 33.33 1.5 130.43 100 2.5 0 0 0 RT00518 Marginal 188.40 22.15 2.00 579.68 77.85 6.5 608.66 80.07 7.5 768.08 100 8.5 304.34 43.89 3 RT00519 Good 7.25 10.00 0.50 36.23 90 1.5 36.23 90 1.5 43.47 100 2 14.49 60 1 RT00520 Good 12.94 7.69 0.50 85.65 92.31 1.5 98.59 100 2 92.12 96.16 1.5 79.19 88.47 1 RT00521 Good 137.67 60.42 2.50 123.18 39.58 2 123.18 39.58 2 253.61 95.83 4 101.45 31.25 1 RT00523 Marginal 50.72 19.70 1.50 297.08 80.3 6.5 333.3 93.33 7.5 311.57 90.61 6.5 166.66 49.4 2.5 RT00525 Good 0.00 0.00 0.00 72.45 100 3 72.45 100 3 57.96 66.67 2.5 28.98 38.1 1 RT00528 Good 130.43 32.89 1.00 253.61 67.11 2.5 253.61 67.11 2.5 246.37 63.27 2 7.25 3.85 0.5 RT00531 Good 0.00 0.00 0.00 36.23 100 1.5 36.23 100 1.5 36.23 100 1.5 0 0 0 RT00541 Good 36.23 27.78 0.50 36.23 72.22 2.5 36.23 72.22 2.5 72.46 100 3 7.25 5.56 0.5 RT00542 Marginal 69.34 20.63 3.00 261.35 79.37 5 309.74 93.88 6.5 216.32 68.53 6 41.14 12.59 2.5 RT00543 Good 268.12 37.00 0.50 188.39 63 4 188.39 63 4 456.51 100 4.5 65.21 14.69 2 RT00544 Good 86.95 21.30 1.50 456.49 78.7 8.5 478.23 81.48 8.5 413.01 78.97 8 217.38 39.42 3 RT00545 Good 0.00 0.00 0.00 21.74 100 1.5 7.25 25 0.5 14.49 75 1 14.49 75 1 RT00546 Good 0.00 0.00 0.00 43.47 100 2 43.47 100 2 36.23 83.33 1.5 7.25 16.67 0.5 RT00547 Good 43.48 4.84 1.00 499.96 95.16 10 499.96 95.16 10 528.95 98.39 10 108.68 24.25 4 RT00550 Good 7.25 3.12 0.50 159.41 96.88 4.5 159.41 96.88 4.5 57.96 41.07 4 28.98 16.52 2 RT00554 Good 36.23 26.39 1.00 115.94 73.61 1.5 115.94 73.61 1.5 152.17 100 2.5 72.46 48.61 1 RT00557 Good 79.70 34.98 2.50 166.65 65.02 3.5 188.38 75.09 4.5 246.35 100 6 101.44 39.19 2.5 RT00558 Good 36.23 11.05 1.00 297.1 88.95 2.5 297.1 88.95 2.5 304.34 93.1 3 246.38 73.23 1.5 RT01602 Good 7.25 1.19 0.50 442.02 98.81 4 442.02 98.81 4 253.61 67.86 3.5 21.74 7.5 0.5 RT01603 Good 0.00 0.00 0.00 21.74 50 1 21.74 50 1 21.74 50 1 7.25 16.67 0.5 RT01604 Good 7.25 25.00 0.50 245.01 75 2 252.26 100 2.5 97.82 44.05 1.5 41.21 32.14 1 Ecological and Trophic Metrics Benthic Benthic Benthic Top Top Top Pelagic Pelagic Pelagic Benthic Benthic* Benthic Feeder Feeder* Feeder Carnivore Carnivore Carnivore Predator Predator Predator Station Quality (#) (%) (taxa) (#) (%) (taxa) (#) (%) (taxa) (#) (%) (taxa) (#) (%) (taxa) RT01606 Good 123.18 26.97 2.00 333.31 73.03 7 355.04 77.08 7.5 413.01 89.6 8 50.72 11.17 2.5 RT01619 Good 0.00 0.00 0.00 202.88 100 4 202.88 100 4 57.96 36.36 3 7.25 8.33 0.5 RT01624 Good 43.47 60.00 2.00 28.98 40 2 36.23 50 2.5 65.21 90 3.5 21.74 30 1.5 RT01642 Good 7.25 10.00 0.50 43.47 90 3 43.47 90 3 36.23 65 2.5 7.25 10 0.5 RT01643 Good 0.00 0.00 0.00 86.95 100 3.5 86.95 100 3.5 86.95 100 3.5 72.46 81.25 2.5 RT01645 Good 7.25 12.50 0.50 43.47 87.5 1.5 43.47 87.5 1.5 14.49 29.17 100 0 RT01646 Good 14.49 18.33 1.00 65.21 81.67 2 72.46 90 2.5 72.46 91.67 2.5 7.25 8.33 0.5 RT01647 Marginal 65.21 5.04 2.50 1260.84 94.96 6.5 1282.58 97.14 7.5 644.9 55 8 137.67 11.47 3 RT01648 Good 7.25 1.22 0.50 456.5 98.78 4 463.75 100 4.5 463.75 100 4.5 369.56 80.33 1.5 RT01649 Good 14.49 3.23 1.00 217.38 96.77 2 224.63 98.39 2.5 137.67 79.03 2.5 101.45 70.97 1 RT01650 Good 14.49 18.75 1.00 72.46 81.25 1 86.95 100 2 14.49 18.75 1 14.49 18.75 1 RT01652 Good 0.00 0.00 0.00 768.08 100 6 760.84 99.14 5.5 673.88 86.46 5.5 123.18 17.35 2 RT01653 Good 0.00 0.00 0.00 50.72 100 2.5 50.72 100 2.5 50.72 100 2.5 21.74 25 0.5 RT01655 Good 0.00 0.00 0.00 594.2 100 3 594.2 100 3 36.23 9.26 2 21.74 5.55 1 RT01664 Good 28.98 11.20 2.00 289.84 88.8 4.5 297.08 90.15 5 297.08 84.36 5.5 7.25 1.35 0.5 RT01668 Good 0.00 0.00 0.00 688.39 100 4.5 688.39 100 4.5 688.39 100 4.5 28.98 4.66 2 RT02002 Good 42.64 48.21 2.50 83.89 51.79 1.5 105.34 80.36 3 126.52 100 4 90.85 55.36 2 RT02006 Good 28.98 38.10 1.00 43.47 61.9 2.5 43.47 61.9 2.5 72.45 100 3.5 21.74 30.95 1 RT02007 Good 28.98 75.00 1.00 14.49 25 0.5 14.49 25 0.5 43.47 100 1.5 0 0 0 RT02008 Good 72.46 21.74 2.50 260.85 78.26 4.5 282.59 84.78 5.5 195.63 58.69 6 115.93 34.78 3 RT02009 Good 21.74 17.06 1.50 159.41 82.94 4 166.66 90.08 4.5 173.9 97.22 5 86.95 37.7 2 RT02013 Good 0.00 0.00 0.00 14.49 50 0.5 14.49 50 0.5 14.49 50 0.5 14.49 50 0.5 RT02015 Good 0.00 0.00 0.00 413.02 100 5 413.02 100 5 405.78 98.49 4.5 108.69 26.7 2.5 RT02016 Good 43.48 13.87 1.50 275.34 86.13 5 282.59 88.3 5.5 144.91 46.38 4.5 7.25 2.38 0.5 RT02019 Good 28.98 17.78 2.00 144.92 82.22 3.5 159.41 93.33 4.5 115.93 68.89 4 21.74 14.44 1.5 RT02021 Good 21.74 2.89 1.50 710.12 97.11 7 724.61 99.19 8 702.87 96.3 7 202.89 26.86 3 RT02027 Good 86.96 38.92 1.00 108.68 61.08 4.5 108.68 61.08 4.5 181.15 92.33 4.5 28.98 18.18 2 RT02030 Good 0.00 0.00 0.00 14.49 50 0.5 14.49 50 0.5 0 0 000 0 RT02152 Good 0.00 0.00 0.00 115.93 100 2 115.93 100 2 115.93 100 2 36.23 35 1 RT02153 Good 159.42 58.46 1.50 65.21 41.54 3.5 72.45 51.54 4 224.62 100 5 36.23 25.77 2 RT02154 Good 7.25 3.57 0.50 123.18 96.43 4.5 130.42 100 5 65.21 58.93 4 21.74 10.71 1 Ecological and Trophic Metrics Benthic Benthic Benthic Top Top Top Pelagic Pelagic Pelagic Benthic Benthic* Benthic Feeder Feeder* Feeder Carnivore Carnivore Carnivore Predator Predator Predator Station Quality (#) (%) (taxa) (#) (%) (taxa) (#) (%) (taxa) (#) (%) (taxa) (#) (%) (taxa) RT02155 Good 65.21 42.06 2.00 101.44 57.94 4.5 144.91 83.33 5.5 123.17 74.6 5.5 28.98 20.24 1.5 RT02156 Good 21.74 11.54 0.50 94.19 88.46 3.5 115.93 100 4 108.68 96.15 3.5 72.46 64.1 2 RT02157 Good 0.00 0.00 0.00 65.21 100 2.5 65.21 100 2.5 65.21 100 2.5 43.48 42.86 1 RT02160 Good 7.25 2.94 0.50 275.34 97.06 5 282.59 100 5.5 173.9 64.57 4.5 21.74 8.82 1.5 RT02162 Good 50.72 50.00 1.00 57.96 50 2 79.7 80 2.5 72.46 65 2 36.23 40 1 RT02164 Good 123.18 14.17 2.00 942 85.83 7 1014.46 93.27 8 1050.69 98.08 8 623.18 54.84 3 RT02165 Good 72.46 29.05 1.50 152.16 70.95 4.5 159.41 75.95 5 224.62 100 6 101.44 43.81 3 RT02167 Good 7.25 3.12 0.50 224.62 96.88 4 231.86 100 4.5 224.62 96.88 4 28.98 12.5 1 RT02171 Good 0.00 0.00 0.00 101.44 100 3.5 101.44 100 3.5 86.95 82.5 2.5 50.72 42.5 1.5 RT99001 Good 0.00 0.00 0.00 101.44 100 3 101.44 100 3 101.44 100 3 7.25 4.54 0.5 RT99003 Good 14.49 2.50 1.00 572.44 97.5 6.5 572.44 97.5 6.5 586.93 100 7.5 65.21 10.83 2.5 RT99004 Good 50.72 18.18 1.00 188.39 81.82 6 195.64 86.36 6.5 210.13 90.91 6.5 50.72 20.45 2 RT99005 Marginal 101.44 27.56 2.00 289.85 72.44 2 340.57 84.8 3 391.29 100 4 326.08 80.97 2 RT99006 Good 173.91 84.07 2.00 36.23 15.93 2 43.47 20.1 2.5 195.64 94.12 3.5 7.25 4.17 0.5 RT99008 Good 108.69 13.25 1.50 702.88 86.75 4.5 702.88 86.75 4.5 782.59 96.36 5 557.97 68.81 1.5 RT99009 Marginal 115.94 21.59 1.50 463.75 78.41 4.5 463.75 78.41 4.5 579.69 100 6 108.69 19.21 2.5 RT99010 Good 14.49 9.09 0.50 65.22 40.91 1 65.22 40.91 1 79.71 50 1.5 36.23 22.73 0.5 RT99012 Good 7.25 6.25 0.50 65.21 93.75 2.5 65.21 93.75 2.5 72.46 100 3 28.98 43.75 1.5 RT99013 Good 0.00 0.00 0.00 550.7 100 5 550.7 100 5 550.7 100 5 231.88 37.56 2 RT99017 Marginal 86.95 36.23 3.00 144.91 63.77 4 173.89 79.23 5.5 166.65 77.05 5.5 72.46 35.27 2.5 RT99019 Good 65.22 32.14 0.50 94.2 67.86 2 94.2 67.86 2 152.17 96.43 2 28.99 25 0.5 RT99022 Good 72.46 36.47 1.50 86.94 63.53 3.5 101.43 76.47 4.5 159.4 100 5 43.47 31.76 2 RT99024 Good 289.85 52.15 1.00 260.87 47.85 1.5 260.87 47.85 1.5 550.72 100 2.5 260.87 47.85 1.5 RT99026 Good 21.74 3.85 0.50 652.16 96.15 5 652.16 96.15 5 615.93 90.81 4.5 137.68 20.8 2 RT99027 Good 108.69 39.14 1.50 260.86 60.86 2.5 268.11 63.64 3 304.34 85.1 3 123.19 27.02 1 RT99028 Good 202.88 100.00 3.50 0 0 0 50.72 36.25 2 202.88 100 3.5 21.74 15 1 RT99029 Good 0.00 0.00 0.00 57.96 100 2 57.96 100 2 57.96 100 2 28.98 46.67 1 RT99030 Marginal 333.33 46.32 2.00 478.25 53.68 3 492.74 54.92 3.5 811.57 100 5 434.78 48.98 2 RT99036 Good 14.49 3.85 0.50 282.6 96.15 3 282.6 96.15 3 282.6 93.33 3 246.37 83.72 1.5 RT99037 Good 0.00 0.00 0.00 7.25 50 0.5 7.25 50 0.5 0 0 000 0 RT99038 Good 311.58 81.25 4.50 86.95 18.75 2 144.92 32.47 3.5 391.28 98.44 6 101.44 23.1 2 RT99039 Good 0.00 0.00 0.00 72.45 100 3.5 72.45 100 3.5 72.45 100 3.5 14.49 25 1 RT99040 Good 28.98 25.00 1.50 79.7 75 2.5 86.95 85 3 108.68 100 4 72.46 70 2.5 Average Good 47.53 20.85 0.90 199.24 75.13 3.17 205.72 78.01 3.44 209.77 81.32 3.57 73.06 28.98 1.35 Average Marginal 112.37 22.14 1.94 424.35 77.86 4.50 449.86 84.64 5.33 437.06 87.91 5.78 180.09 40.94 2.39 Average All 53.61 20.97 0.99 220.34 75.38 3.30 228.60 78.63 3.61 231.08 81.94 3.78 83.09 30.10 1.45 Median Good 14.49 11.05 0.50 108.68 85.83* 2.50 115.94 90.00* 3.00 137.67 96.15 3.50 28.99 25.00 1.00 Median Marginal 86.95 21.59 2.00 297.08 78.41 4.50 340.57 84.80 5.50 391.29 100.00 6.00 137.67 43.89 2.50 Median All 21.74 12.02 1.00 123.18 82.02 3.00 144.92 89.48 3.00 155.79 96.16 3.50 36.23 25.00 1.50 *Average/median value at good stations equal to or lower than average/median value at marginal stations Ecological and Trophic Metrics
Detritivore Detritivore* Detritivore Herbivore* Herbivore* Herbivore* Omnivore* Omnivore* Omnivore* Station Quality (#) (%) (taxa) (#) (%) (taxa) (#) (%) (taxa) MR-101-T Marginal 14.49 33.33 10 00 0.00 0.00 0.00 MR-303-T Good 159.41 74.62 30 00 7.25 1.92 0.50 MR-304-T Good 159.42 83.33 20 00 0.00 0.00 0.00 NT01598 Good 318.84 91.87 10 00 318.84 91.87 1.00 RT00501 Good 202.89 100 2.5 0 0 0 0.00 0.00 0.00 RT00502 Good 0 0 00 00 0.00 0.00 0.00 RT00503 Good 384.05 87.98 30 00 7.25 1.19 0.50 RT00504 Good 275.35 97.22 2.5 0 0 0 0.00 0.00 0.00 RT00505 Good 86.95 91.67 2.5 0 0 0 7.25 4.17 0.50 RT00517 Good 130.43 100 2.5 0 0 0 0.00 0.00 0.00 RT00518 Marginal 739.1 95.21 6.5 0 0 0 0.00 0.00 0.00 RT00519 Good 43.47 100 20 00 0.00 0.00 0.00 RT00520 Good 98.59 100 20 00 6.47 3.84 0.50 RT00521 Good 260.85 100 4.5 0 0 0 7.25 4.17 0.50 RT00523 Marginal 311.57 92.43 6.5 0 0 0 36.23 9.39 1.50 RT00525 Good 72.45 100 30 00 14.49 33.33 0.50 RT00528 Good 376.8 96.15 30 00 137.68 36.73 1.50 RT00531 Good 28.98 83.33 10 00 0.00 0.00 0.00 RT00541 Good 50.72 38.89 1.5 0 0 0 0.00 0.00 0.00 RT00542 Marginal 283.87 84.79 5.5 0 0 0 114.38 31.47 2.00 RT00543 Good 391.3 85.31 2.5 0 0 0 0.00 0.00 0.00 RT00544 Good 470.99 89.29 5.5 7.25 0.93 0.5 123.18 20.11 1.50 RT00545 Good 7.25 25 0.5 0 0 0 7.25 25.00 0.50 RT00546 Good 43.47 100 20 00 7.25 16.67 0.50 RT00547 Good 463.74 82.01 70 00 14.49 1.61 1.00 RT00550 Good 137.67 83.48 30 00 108.69 58.93 1.00 RT00554 Good 36.23 26.39 10 00 0.00 0.00 0.00 RT00557 Good 195.63 81.87 40 00 0.00 0.00 0.00 RT00558 Good 326.08 97.06 30 00 28.99 6.90 0.50 RT01602 Good 427.53 92.5 40 00 195.65 32.14 1.00 RT01603 Good 14.49 33.33 0.5 0 0 0 0.00 0.00 0.00 RT01604 Good 245.01 75 20 00 154.44 55.95 1.00 Ecological and Trophic Metrics
Detritivore Detritivore* Detritivore Herbivore* Herbivore* Herbivore* Omnivore* Omnivore* Omnivore* Station Quality (#) (%) (taxa) (#) (%) (taxa) (#) (%) (taxa) RT01606 Good 413.01 90.75 70 00 43.48 10.40 1.00 RT01619 Good 188.39 83.33 30 00 144.92 63.64 1.00 RT01624 Good 57.96 80 30 00 7.25 10.00 0.50 RT01642 Good 50.72 100 3.5 0 0 0 14.49 35.00 1.00 RT01643 Good 43.47 50 20 00 0.00 0.00 0.00 RT01645 Good 50.72 100 20 00 36.23 70.83 1.00 RT01646 Good 65.21 81.67 20 00 7.25 8.33 0.50 RT01647 Marginal 1275.33 96.25 70 00 681.16 45.00 1.00 RT01648 Good 463.75 100 4.5 0 0 0 0.00 0.00 0.00 RT01649 Good 224.63 50 2.5 0 0 0 94.20 20.97 0.50 RT01650 Good 72.46 81.25 10 00 72.46 81.25 1.00 RT01652 Good 753.59 98.1 50 00 94.20 13.54 0.50 RT01653 Good 50.72 100 2.5 0 0 0 0.00 0.00 0.00 RT01655 Good 572.46 94.45 20 00 557.97 90.74 1.00 RT01664 Good 297.08 95.95 50 00 21.74 15.64 1.00 RT01668 Good 673.9 97.5 3.5 0 0 0 0.00 0.00 0.00 RT02002 Good 105.35 80.36 2.5 0 0 0 0.00 0.00 0.00 RT02006 Good 57.96 76.19 2.5 0 0 0 0.00 0.00 0.00 RT02007 Good 43.47 100 1.5 0 0 0 0.00 0.00 0.00 RT02008 Good 297.08 89.13 50 00 137.68 41.31 1.00 RT02009 Good 159.41 87.3 40 00 7.25 2.78 0.50 RT02013 Good 0 0 00 00 0.00 0.00 0.00 RT02015 Good 311.58 75.38 30 00 7.25 1.51 0.50 RT02016 Good 311.57 97.62 60 00 173.91 53.62 2.00 RT02019 Good 152.16 85.56 40 00 57.97 31.11 1.50 RT02021 Good 659.4 88.61 70 00 28.98 3.70 1.50 RT02027 Good 166.66 81.82 3.5 0 0 0 14.49 7.67 1.00 RT02030 Good 14.49 50 0.5 0 0 0 14.49 50.00 0.50 RT02152 Good 36.23 35 10 00 0.00 0.00 0.00 RT02153 Good 195.64 76.15 3.5 0 0 0 0.00 0.00 0.00 RT02154 Good 108.69 89.29 40 00 65.22 41.07 1.00 Ecological and Trophic Metrics
Detritivore Detritivore* Detritivore Herbivore* Herbivore* Herbivore* Omnivore* Omnivore* Omnivore* Station Quality (#) (%) (taxa) (#) (%) (taxa) (#) (%) (taxa) RT02155 Good 137.67 79.76 50 00 43.48 25.40 1.00 RT02156 Good 86.95 84.62 2.5 0 0 0 7.25 3.85 0.50 RT02157 Good 21.74 57.14 1.5 0 0 0 0.00 0.00 0.00 RT02160 Good 260.85 91.18 40 00 108.69 35.43 1.00 RT02162 Good 86.95 70 2.5 0 0 0 36.23 35.00 1.00 RT02164 Good 898.53 82.24 60 00 14.49 1.92 1.00 RT02165 Good 202.89 87.62 4.5 0 0 0 0.00 0.00 0.00 RT02167 Good 202.88 87.5 3.5 0 0 0 7.25 3.12 0.50 RT02171 Good 86.95 82.5 2.5 0 0 0 14.49 17.50 1.00 RT99001 Good 101.44 100 30 00 0.00 0.00 0.00 RT99003 Good 507.23 86.95 50 00 0.00 0.00 0.00 RT99004 Good 166.66 70.46 4.5 0 0 0 28.99 9.09 0.50 RT99005 Marginal 289.85 72.44 20 00 0.00 0.00 0.00 RT99006 Good 195.64 92.89 30 00 14.49 5.88 0.50 RT99008 Good 775.34 95.63 40 00 28.98 3.64 1.00 RT99009 Marginal 478.25 82.22 40 00 0.00 0.00 0.00 RT99010 Good 79.71 50 1.5 0 0 0 0.00 0.00 0.00 RT99012 Good 72.46 100 30 00 0.00 0.00 0.00 RT99013 Good 478.24 87.89 40 00 0.00 0.00 0.00 RT99017 Marginal 202.88 84.54 5.5 0 0 0 65.21 22.95 1.50 RT99019 Good 159.42 100 2.5 0 0 0 7.25 3.57 0.50 RT99022 Good 152.16 97.06 4.5 0 0 0 0.00 0.00 0.00 RT99024 Good 543.47 98.49 20 00 0.00 0.00 0.00 RT99026 Good 550.71 81.41 4.5 0 0 0 57.97 9.19 1.00 RT99027 Good 362.31 97.22 3.5 0 0 0 65.22 14.90 1.00 RT99028 Good 173.9 82.5 20 00 0.00 0.00 0.00 RT99029 Good 57.96 100 20 00 0.00 0.00 0.00 RT99030 Marginal 797.08 96.77 40 00 0.00 0.00 0.00 RT99036 Good 268.11 92.31 2.5 0 0 0 14.49 6.67 0.50 RT99037 Good 7.25 50 0.5 0 0 0 7.25 50.00 0.50 RT99038 Good 318.83 80.98 40 00 7.25 1.56 0.50 RT99039 Good 65.21 87.5 30 00 0.00 0.00 0.00 RT99040 Good 72.46 65 20 00 0.00 0.00 0.00 Average Good 219.54 81.27 2.99 0.08* 0.01* 0.01* 36.92 14.65* 0.49 Average Marginal 488.05 82.00 4.67 0.00 0.00 0.00 99.66 12.09 0.67 Average All 244.72 81.34 3.15 0.08 0.01 0.01 42.80 14.41 0.51 Median Good 159.42 87.50* 3.00 0.00* 0.00* 0.00* 7.25* 3.57* 0.50* Median Marginal 311.57 84.79 5.50 0.00 0.00 0.00 0.00 0.00 0.00 Median All 170.28 87.40 3.00 0.00 0.00 0.00 7.25 3.35 0.50 *Average/median value at good stations equal to or lower than average/median value at marginal stations
Appendix E.3. Tolerance fish metrics calculated for 96 good and marginal stations sampled in 1999-2002 (metrics in normal and bold font = used in one- way analyses; metrics in bold font = used in discriminant analyses; italicized metrics = not used in statistical analyses). *Average/median value at good stations equal to or lower than average/median value at marginal stations. For fish metric definitions, refer to Table 2.
Tolerance Metrics Bay Bay Bay Bay Anchovy / Bay Anchovy / Bay Anchovy / Anchovy Anchovy* Anchovy Shad* Shad* Shad* Shad Shad* Shad Flatfish* Flatfish* Flatfish* Station Quality (#) (%) (taxa) (#) (%) (taxa) (#) (%) (taxa) (#) (%) (taxa) MR-101-T Marginal 0 0 0 000 0 0 0 7.25 16.67 0.5 MR-303-T Good 0 0 0 000 0 0 0 28.98 7.69 1 MR-304-T Good 14.49 33.33 0.5 000 14.49 33.33 0.5 0 0 0 NT01598 Good 0 0 0 000 0 0 000 0 RT00501 Good 166.66 81.8 1 000166.66 81.8 100 0 RT00502 Good 0 0 0 000 0 0 000 0 RT00503 Good 0 0 0 000 0 0 0 7.25 1.19 0.5 RT00504 Good 246.37 87.3 1 000246.37 87.3 100 0 RT00505 Good 43.48 66.67 1 000 43.48 66.67 100 0 RT00517 Good 86.96 66.67 1 000 86.96 66.67 100 0 RT00518 Marginal 159.42 19.93 1 000159.42 19.93 1 57.97 4.44 1.5 RT00519 Good 7.25 10 0.5 000 7.25 10 0.5 0 0 0 RT00520 Good 0 0 0 000 0 0 000 0 RT00521 Good 115.94 52.08 1 000115.94 52.08 1 14.49 4.17 0.5 RT00523 Marginal 14.49 6.67 0.5 000 14.49 6.67 0.5 21.74 6.36 1.5 RT00525 Good 0 0 0 000 0 0 0 7.25 7.14 0.5 RT00528 Good 0 0 0 000 0 0 000 0 RT00531 Good 0 0 0 000 0 0 0 7.25 16.67 0.5 RT00541 Good 36.23 27.78 0.5 000 36.23 27.78 0.5 7.25 50 0.5 RT00542 Marginal 13.71 4.19 1 000 13.71 4.19 1 7.25 1.92 0.5 RT00543 Good 268.12 37 0.5 000268.12 37 0.5 28.98 4 1 RT00544 Good 57.97 17.59 1 000 57.97 17.59 1 14.49 1.85 1 RT00545 Good 0 0 0 000 0 0 000 0 RT00546 Good 0 0 0 000 0 0 0 7.25 16.67 0.5 RT00547 Good 36.23 4.03 0.5 000 36.23 4.03 0.5 108.68 18.18 3.5 RT00550 Good 0 0 0 000 0 0 0 14.49 10.27 1 RT00554 Good 36.23 26.39 1 000 36.23 26.39 100 0 RT00557 Good 50.72 22.53 1 000 50.72 22.53 1 36.23 11.9 1 RT00558 Good 36.23 11.05 1 000 36.23 11.05 1 7.25 2.94 0.5 RT01602 Good 0 0 0 000 0 0 000 0 RT01603 Good 0 0 0 000 0 0 0 14.49 33.33 0.5 RT01604 Good 0 0 0 000 0 0 000 0 Tolerance Metrics Bay Bay Bay Bay Anchovy / Bay Anchovy / Bay Anchovy / Anchovy Anchovy* Anchovy Shad* Shad* Shad* Shad Shad* Shad Flatfish* Flatfish* Flatfish* Station Quality (#) (%) (taxa) (#) (%) (taxa) (#) (%) (taxa) (#) (%) (taxa) RT01606 Good 94.2 21 1 000 94.2 21 1 65.21 13.88 2 RT01619 Good 0 0 0 000 0 0 0 14.49 16.67 1 RT01624 Good 28.98 40 1 000 28.98 40 1 7.25 10 0.5 RT01642 Good 7.25 10 0.5 000 7.25 10 0.5 7.25 10 0.5 RT01643 Good 0 0 0 000 0 0 0 7.25 12.5 0.5 RT01645 Good 7.25 12.5 0.5 000 7.25 12.5 0.5 0 0 0 RT01646 Good 7.25 10 0.5 000 7.25 10 0.5 0 0 0 RT01647 Marginal 7.25 0.89 0.5 000 7.25 0.89 0.5 28.98 3.07 1.5 RT01648 Good 0 0 0 000 0 0 000 0 RT01649 Good 7.25 1.61 0.5 000 7.25 1.61 0.5 7.25 50 0.5 RT01650 Good 0 0 0 000 0 0 000 0 RT01652 Good 0 0 0 000 0 0 0 130.43 15.7 1 RT01653 Good 0 0 0 000 0 0 0 14.49 58.33 1 RT01655 Good 0 0 0 000 0 0 0 7.25 1.85 0.5 RT01664 Good 7.25 1.35 0.5 000 7.25 1.35 0.5 14.49 8.49 1 RT01668 Good 0 0 0 000 0 0 0 159.41 24.09 2 RT02002 Good 21.18 19.64 1 000 21.18 19.64 1 6.97 3.57 0.5 RT02006 Good 28.98 38.1 1 000 28.98 38.1 100 0 RT02007 Good 28.98 75 1 000 28.98 75 100 0 RT02008 Good 43.48 13.04 1 000 43.48 13.04 1 21.74 6.52 1 RT02009 Good 14.49 9.92 1 000 14.49 9.92 1 21.74 8.33 1.5 RT02013 Good 0 0 0 000 0 0 000 0 RT02015 Good 0 0 0 000 0 0 0 7.25 1.51 0.5 RT02016 Good 28.99 9.52 0.5 000 28.99 9.52 0.5 28.98 9.11 1.5 RT02019 Good 0 0 0 000 0 0 0 14.49 8.89 1 RT02021 Good 0 0 0 000 0 0 000 0 RT02027 Good 79.71 34.38 0.5 000 79.71 34.38 0.5 7.25 4.55 0.5 RT02030 Good 0 0 0 000 0 0 000 0 RT02152 Good 0 0 0 000 0 0 000 0 RT02153 Good 152.17 48.46 1 000152.17 48.46 1 21.74 21.92 1 RT02154 Good 0 0 0 000 0 0 0 14.49 16.07 1 Tolerance Metrics Bay Bay Bay Bay Anchovy / Bay Anchovy / Bay Anchovy / Anchovy Anchovy* Anchovy Shad* Shad* Shad* Shad Shad* Shad Flatfish* Flatfish* Flatfish* Station Quality (#) (%) (taxa) (#) (%) (taxa) (#) (%) (taxa) (#) (%) (taxa) RT02155 Good 14.49 11.11 0.5 000 14.49 11.11 0.5 7.25 3.57 0.5 RT02156 Good 0 0 0 000 0 0 000 0 RT02157 Good 0 0 0 000 0 0 000 0 RT02160 Good 0 0 0 000 0 0 0 14.49 5.88 1 RT02162 Good 28.99 20 0.5 000 28.99 20 0.5 0 0 0 RT02164 Good 43.48 5.77 0.5 000 43.48 5.77 0.5 326.08 31.52 2.5 RT02165 Good 65.22 24.05 1 000 65.22 24.05 1 7.25 5 0.5 RT02167 Good 0 0 0 000 0 0 0 14.49 6.25 1 RT02171 Good 0 0 0 000 0 0 0 21.74 37.5 1 RT99001 Good 0 0 0 000 0 0 0 43.48 51.52 1 RT99003 Good 14.49 2.5 1 000 14.49 2.5 1 65.21 10.55 1 RT99004 Good 0 0 0 000 0 0 0 7.25 4.55 0.5 RT99005 Marginal 0 0 0 000 0 0 000 0 RT99006 Good 159.42 76.96 1 000159.42 76.96 100 0 RT99008 Good 94.2 11.53 1 000 94.2 11.53 100 0 RT99009 Marginal 108.7 20.16 1 000 108.7 20.16 1 7.25 1.11 0.5 RT99010 Good 14.49 9.09 0.5 000 14.49 9.09 0.5 0 0 0 RT99012 Good 7.25 6.25 0.5 000 7.25 6.25 0.5 36.23 50 1 RT99013 Good 0 0 0 000 0 0 0 86.95 16.85 1 RT99017 Marginal 50.72 18.6 1 7.25 2.17 0.5 57.97 20.77 1.5 0 0 0 RT99019 Good 65.22 32.14 0.5 000 65.22 32.14 0.5 0 0 0 RT99022 Good 57.97 23.53 0.5 000 57.97 23.53 0.5 0 0 0 RT99024 Good 289.85 52.15 1 000289.85 52.15 100 0 RT99026 Good 21.74 3.85 0.5 000 21.74 3.85 0.5 0 0 0 RT99027 Good 101.45 36.36 1 000101.45 36.36 100 0 RT99028 Good 144.92 61.25 1 000144.92 61.25 100 0 RT99029 Good 0 0 0 000 0 0 000 0 RT99030 Marginal 311.59 43.47 1 000311.59 43.47 100 0 RT99036 Good 14.49 3.85 0.5 000 14.49 3.85 0.5 0 0 0 RT99037 Good 0 0 0 000 0 0 000 0 RT99038 Good 195.65 53.19 1 7.25 1.56 0.5 202.9 54.75 1.5 14.49 3.12 0.5 RT99039 Good 0 0 0 000 0 0 0 28.98 37.5 1 RT99040 Good 0 0 0 7.25 5 0.5 7.25 5 0.5 0 0 0 Average Good 35.56 15.20* 0.41 0.17 0.08 0.01 35.73 15.27* 0.42 17.90* 8.64* 0.51 Average Marginal 73.99 12.66 0.67 0.81 0.24 0.06 74.79 12.90 0.72 14.49 3.73 0.67 Average All 39.16 14.96 0.43 0.23 0.09 0.02 39.39 15.05 0.45 17.58 8.18 0.52 Median Good 7.25 2.50 0.50 0.00* 0.00* 0.00* 7.25 3.85 0.50 7.25* 1.85 0.50* Median Marginal 14.49 6.67 1.00 0.00 0.00 0.00 14.49 6.67 1.00 7.25 1.92 0.50 Median All 7.25 3.85 0.50 0.00 0.00 0.00 7.25 3.94 0.50 7.25 1.89 0.50 *Average/median value at good stations equal to or lower than average/median value at marginal stations Tolerance Metrics Salinity Salinity Salinity Flounder* Flounder* Flounder* Resilient Resilient Resilient Independent Independent Independent Sciaenid Sciaenid Sciaenid Station Quality (#) (%) (taxa) (#) (%) (taxa) (#) (%) (taxa) (#) (%) (taxa) MR-101-T Marginal 0 0 0 7.25 16.67 0.5 0 0 0 14.49 33.33 1 MR-303-T Good 0 0 0 159.41 74.62 3 43.47 35.77 1 144.92 70.77 2 MR-304-T Good 0 0 0 137.68 47.5 1 72.46 53.33 1 137.68 47.5 1 NT01598 Good 0 0 000 0 318.84 91.87 100 0 RT00501 Good 0 0 0 21.74 10.51 1 181.15 88.46 1.5 36.23 18.2 1.5 RT00502 Good 0 0 000 00 0 000 0 RT00503 Good 0 0 0 398.54 91.67 3 195.65 53.1 1 405.78 92.86 3.5 RT00504 Good 0 0 0 28.98 9.92 1.5 246.37 87.3 1 21.74 7.54 1 RT00505 Good 0 0 0 43.48 25 1.5 79.71 87.5 2 43.48 25 1.5 RT00517 Good 0 0 0 28.99 22.22 0.5 86.96 66.67 1 28.99 22.22 0.5 RT00518 Marginal 0 0 0 471 61.81 3.5 427.53 58.47 2 507.23 69.72 4 RT00519 Good 0 0 0 36.23 90 1.5 21.74 70 1.5 36.23 90 1.5 RT00520 Good 0 0 0 79.19 88.47 1 85.65 92.31 1.5 79.19 88.47 1 RT00521 Good 0 0 0 123.18 39.58 2.5 224.63 87.5 2.5 101.45 31.25 1 RT00523 Marginal 7.25 1.51 0.5 224.63 65.15 2.5 166.66 49.4 3 217.38 60 3 RT00525 Good 0 0 0 28.98 38.1 1 43.47 71.43 1.5 36.23 45.24 1.5 RT00528 Good 0 0 0 333.32 88.65 2.5 7.25 3.85 0.5 246.37 63.27 2 RT00531 Good 0 0 0 28.98 83.33 10 0 0 28.98 83.33 1 RT00541 Good 0 0 0 14.49 11.11 1 36.23 27.78 0.5 7.25 5.56 0.5 RT00542 Marginal 0 0 0 155.78 49.13 2.5 141.8 39.86 4 148.54 47.21 2 RT00543 Good 21.74 3 0.5 137.68 47.46 2.5 268.12 37 0.5 152.17 55.15 2.5 RT00544 Good 7.25 0.93 0.5 253.61 45.5 3 362.3 72.49 3 239.12 43.65 2 RT00545 Good 0 0 000 0 7.25 25 0.5 0 0 0 RT00546 Good 0 0 0 28.98 66.67 1 14.49 33.33 1 36.23 83.33 1.5 RT00547 Good 14.49 4.65 1 246.37 51.74 3 94.2 16.56 2 253.61 55.58 3.5 RT00550 Good 0 0 000 0 108.69 58.93 100 0 RT00554 Good 0 0 0 72.46 48.61 1 36.23 26.39 1 115.94 73.61 1.5 RT00557 Good 0 0 0 123.18 50.73 2 123.18 50.73 2 123.18 50.73 2 RT00558 Good 0 0 0 260.87 79.11 1.5 304.34 88.24 2.5 260.87 79.11 1.5 RT01602 Good 0 0 0 217.39 59.29 2 188.41 30.95 0.5 231.88 62.98 2.5 RT01603 Good 0 0 0 7.25 16.67 0.5 0 0 0 7.25 16.67 0.5 RT01604 Good 0 0 0 90.58 19.05 1 188.4 63.1 1.5 90.58 19.05 1 Tolerance Metrics Salinity Salinity Salinity Flounder* Flounder* Flounder* Resilient Resilient Resilient Independent Independent Independent Sciaenid Sciaenid Sciaenid Station Quality (#) (%) (taxa) (#) (%) (taxa) (#) (%) (taxa) (#) (%) (taxa) RT01606 Good 0 0 0 159.41 33.73 2.5 144.92 33.32 2.5 173.9 35.86 2.5 RT01619 Good 0 0 0 21.74 6.82 0.5 144.92 63.64 1 21.74 6.82 0.5 RT01624 Good 0 0 0 14.49 20 1 36.23 50 1.5 7.25 10 0.5 RT01642 Good 0 0 0 21.74 45 1.5 28.98 55 2 21.74 45 1.5 RT01643 Good 7.25 12.5 0.5 79.7 87.5 3 28.98 31.25 1 79.7 87.5 3 RT01645 Good 0 0 0 7.25 16.67 0.5 43.47 83.33 1.5 7.25 16.67 0.5 RT01646 Good 0 0 0 50.72 63.34 1 14.49 18.33 1 50.72 63.34 1 RT01647 Marginal 0 0 0 492.74 43.24 2.5 775.35 53.61 2.5 485.5 42.85 2 RT01648 Good 0 0 0 391.29 83.99 2 362.31 78.16 1 449.26 96.61 3.5 RT01649 Good 0 0 0 115.94 25.81 1 195.65 43.55 1.5 115.94 25.81 1 RT01650 Good 0 0 000 0 72.46 81.25 100 0 RT01652 Good 0 0 0 391.29 51.58 2 202.89 28.99 1.5 514.47 66.77 3 RT01653 Good 0 0 0 28.98 33.33 1 21.74 25 0.5 28.98 33.33 1 RT01655 Good 0 0 0 7.25 1.85 0.5 557.97 90.74 1 7.25 1.85 0.5 RT01664 Good 0 0 0 246.37 69.11 2 28.98 16.99 1.5 239.12 61.97 1.5 RT01668 Good 14.49 2.5 1 478.26 67.5 1.5 7.25 1.25 0.5 521.73 75 2 RT02002 Good 6.97 3.57 0.5 76.92 48.22 1 90.86 55.36 1.5 76.92 48.22 1 RT02006 Good 0 0 0 21.74 30.95 1 43.47 52.38 1.5 21.74 30.95 1 RT02007 Good 0 0 0 14.49 25 0.5 28.98 75 1 14.49 25 0.5 RT02008 Good 0 0 0 86.95 26.09 1.5 260.86 78.26 3 79.71 23.91 1 RT02009 Good 7.25 2.78 0.5 130.43 71.83 2 86.95 37.7 2 123.19 64.69 1.5 RT02013 Good 0 0 000 00 0 000 0 RT02015 Good 0 0 0 289.84 71.97 2.5 14.49 3.6 1 391.29 94.89 3.5 RT02016 Good 0 0 0 72.46 23.19 2 195.65 60.98 2 65.21 21.01 1.5 RT02019 Good 0 0 0 43.48 28.89 1.5 50.72 27.78 1 57.97 35.56 1 RT02021 Good 0 0 0 579.7 77.83 4 144.92 17.56 2 666.65 90.84 5 RT02027 Good 0 0 0 57.97 34.94 2 94.2 42.05 1.5 50.72 30.4 1.5 RT02030 Good 0 0 000 0 14.49 50 0.5 0 0 0 RT02152 Good 0 0 0 36.23 35 1 36.23 35 1 115.93 100 2 RT02153 Good 21.74 21.92 1 28.98 7.69 1.5 159.42 50.39 1.5 43.47 19.61 2.5 RT02154 Good 0 0 0 14.49 16.07 1 72.46 44.64 1.5 14.49 16.07 1 Tolerance Metrics Salinity Salinity Salinity Flounder* Flounder* Flounder* Resilient Resilient Resilient Independent Independent Independent Sciaenid Sciaenid Sciaenid Station Quality (#) (%) (taxa) (#) (%) (taxa) (#) (%) (taxa) (#) (%) (taxa) RT02155 Good 0 0 0 14.49 7.14 1 57.97 36.51 1.5 14.49 7.14 1 RT02156 Good 0 0 0 72.46 76.92 2 57.97 56.41 1.5 72.46 76.92 2 RT02157 Good 0 0 0 50.72 67.86 1.5 0 0 0 50.72 67.86 1.5 RT02160 Good 7.25 2.94 0.5 86.95 31.95 1.5 108.69 35.43 1 79.71 29.01 1 RT02162 Good 0 0 0 21.74 15 1 79.7 65 2 21.74 15 1 RT02164 Good 0 0 0 528.98 46.69 2 565.21 51.5 2 521.73 45.73 1.5 RT02165 Good 0 0 0 130.43 61.19 3.5 137.68 53.1 2 144.92 65.95 4 RT02167 Good 7.25 3.12 0.5 94.19 40.62 1.5 7.25 3.12 0.5 210.13 90.63 3 RT02171 Good 7.25 12.5 0.5 36.23 25 0.5 50.72 42.5 1.5 36.23 25 0.5 RT99001 Good 0 0 0 14.49 21.21 1 7.25 4.54 0.5 57.97 48.48 2 RT99003 Good 0 0 0 413.04 71.67 3 28.98 5 2 471 81.11 4 RT99004 Good 0 0 0 152.17 59.09 3 101.45 31.82 1.5 108.69 45.46 2.5 RT99005 Marginal 0 0 0 340.57 87.64 3 326.08 83.81 2 289.85 72.44 2 RT99006 Good 0 0 0 21.74 10.05 1.5 181.15 85.79 2 21.74 10.05 1.5 RT99008 Good 0 0 0 673.9 83.05 3 688.39 84.84 3.5 659.41 81.32 2.5 RT99009 Marginal 0 0 0 420.28 71.11 3 123.19 23.02 2 449.26 75.87 3.5 RT99010 Good 0 0 0 65.22 40.91 1 50.72 31.82 1 65.22 40.91 1 RT99012 Good 0 0 0 14.49 12.5 0.5 21.74 18.75 1 14.49 12.5 0.5 RT99013 Good 0 0 0 420.28 75.48 3 159.42 25.45 1 463.75 83.15 4 RT99017 Marginal 0 0 0 79.7 40.82 2.5 152.16 59.18 3 72.46 38.65 2 RT99019 Good 0 0 0 86.96 64.29 1.5 101.45 60.71 1.5 86.96 64.29 1.5 RT99022 Good 0 0 0 65.21 47.65 2.5 86.95 49.41 1.5 57.96 44.7 2 RT99024 Good 0 0 0 253.62 46.34 1 543.47 98.49 2 253.62 46.34 1 RT99026 Good 0 0 0 579.7 85.12 3 94.19 15.24 2.5 586.95 86.04 3.5 RT99027 Good 0 0 0 195.65 45.96 1.5 282.6 75.51 2.5 195.65 45.96 1.5 RT99028 Good 0 0 0 7.25 2.5 0.5 152.17 63.75 1.5 0 0 0 RT99029 Good 0 0 0 28.98 46.67 1 28.98 46.67 1 57.96 100 2 RT99030 Marginal 0 0 0 463.76 52.45 2.5 731.88 89.23 2 456.51 50.84 2 RT99036 Good 0 0 0 246.37 83.72 1.5 260.86 90.39 2 268.11 89.49 2.5 RT99037 Good 0 0 000 0 7.25 50 0.5 0 0 0 RT99038 Good 14.49 3.12 0.5 123.18 28.4 3 289.85 74.12 2.5 65.22 14.06 1 RT99039 Good 0 0 0 28.98 41.67 1.5 7.25 12.5 0.5 28.98 41.67 1.5 RT99040 Good 0 0 0 86.95 75 2.5 65.21 55 1.5 72.46 65 2 Average Good 1.58* 0.85* 0.09* 125.10 41.64 1.51 123.55 46.83 1.34 132.35 44.63 1.55 Average Marginal 0.81 0.17 0.06 295.08 54.22 2.50 316.07 50.73 2.28 293.47 54.55 2.39 Average All 1.51 0.78 0.08 141.04 42.82 1.60 141.60 47.20 1.43 147.45 45.56 1.63 Median Good 0.00* 0.00* 0.00* 65.22 40.91 1.50 79.71 50.00 1.50 65.22 45.00 1.50 Median Marginal 0.00 0.00 0.00 340.57 52.45 2.50 166.66 53.61 2.00 289.85 50.84 2.00 Median All 0.00 0.00 0.00 74.69 44.12 1.50 86.96 50.00 1.50 72.46 45.35 1.50 *Average/median value at good stations equal to or lower than average/median value at marginal stations
Appendix E.4. Fish community structure metrics calculated for 96 good and marginal stations sampled in 1999-2002 (metrics in normal and bold font = used in one-way analyses; metrics in bold font = used in discriminant analyses; italicized metrics = not used in statistical analyses). *Average/median value at good stations equal to or lower than average/median value at marginal stations.
For fish metric definitions, refer to Table 2.
Community Structure Metrics 90% 95% Species Species Species Abundance Abundance Density Dominance* Dominance* Dominance* Diversity Evenness* Richness Taxa Station Quality (taxa) (taxa) (#) (top taxon) (top 2 taxa) (top 3 taxa) (H') (J') (D) (#) MR-101-T Marginal 5 5 43.47 50 83.33 100 1.25 0.96 0.4 2.5 MR-303-T Good 10 11 224.61 55 70.77 86.54 1.93 0.81 0.99 6.5 MR-304-T Good 3 4 166.66 60.83 97.5 100 1.07 0.84 0.31 2.5 NT01598 Good 2 3 347.82 91.87 97.14 98.57 0.47 0.34 0.34 3 RT00501 Good 4 4 202.89 81.8 96.15 100 0.78 0.6 0.28 2.5 RT00502 Good 0000000000 RT00503 Good 6 9 449.25 65 85.6 91.67 1.51 0.61 0.8 6 RT00504 Good 3 5 282.6 87.3 94.84 100 0.67 0.42 0.35 3 RT00505 Good 6 7 101.44 66.67 83.34 87.5 1.13 0.44 0.48 3.5 RT00517 Good 4 5 130.43 66.67 94.45 100 0.95 0.69 0.31 2.5 RT00518 Marginal 8 11 768.08 38.54 60.7 80.63 2.34 0.78 1.16 8.5 RT00519 Good 3 3 43.47 80 90 100 0.69 0.43 0.23 2 RT00520 Good 3 4 98.59 88.47 96.16 100 0.5 0.31 0.2 2 RT00521 Good 5 7 260.85 64.59 83.34 91.67 1.44 0.66 0.63 4.5 RT00523 Marginal 10 12 347.79 33.34 58.49 69.7 2.54 0.85 1.21 8 RT00525 Good 5 6 72.45 54.76 85.71 92.86 1.38 0.92 0.46 3 RT00528 Good 5 5 384.04 51.92 84.81 96.15 1.51 0.85 0.44 3.5 RT00531 Good 2 2 36.23 83.33 100 100 0.46 0.46 0.13 1.5 RT00541 Good 5 6 72.46 77.78 83.33 88.89 0.94 0.4 0.41 3 RT00542 Marginal 11 14 330.69 48.78 66.79 79.03 2.22 0.74 1.21 8 RT00543 Good 5 6 456.51 67.77 84.31 92.15 1.39 0.65 0.59 4.5 RT00544 Good 13 17 543.44 35.72 64.42 77.12 2.5 0.79 1.41 10 RT00545 Good 2 2 21.74 75 100 100 0.5 0.5 0.15 1.5 RT00546 Good 4 4 43.47 66.67 83.33 100 0.79 0.5 0.27 2 RT00547 Good 13 17 543.44 38.4 57.38 66.07 2.71 0.81 1.6 11 RT00550 Good 7 8 166.65 58.93 69.2 79.47 1.72 0.74 0.8 5 RT00554 Good 3 4 152.17 52.78 95.83 100 1.16 0.91 0.3 2.5 RT00557 Good 9 11 246.35 32.05 54.58 73.26 2.35 0.91 0.91 6 RT00558 Good 5 6 333.32 70.29 86.01 97.06 1.24 0.69 0.44 3.5 RT01602 Good 5 6 449.26 68.45 89.05 93.93 1.32 0.61 0.58 4.5 RT01603 Good 2 2 21.74 33.33 50 50 0.46 0.46 0.13 1 RT01604 Good 3 4 252.26 55.95 92.86 100 1.16 0.92 0.31 2.5 Community Structure Metrics 90% 95% Species Species Species Abundance Abundance Density Dominance* Dominance* Dominance* Diversity Evenness* Richness Taxa Station Quality (taxa) (taxa) (#) (top taxon) (top 2 taxa) (top 3 taxa) (H') (J') (D) (#) RT01606 Good 12 14 456.48 27.76 46.83 63.2 2.79 0.88 1.31 9 RT01619 Good 6 7 202.88 63.64 78.79 89.39 1.44 0.72 0.6 4 RT01624 Good 7 8 72.45 40 60 80 1.85 0.93 0.7 4 RT01642 Good 5 5 50.72 35 70 80 1.66 1 0.62 3.5 RT01643 Good 5 6 86.95 37.5 68.75 87.5 1.7 0.94 0.58 3.5 RT01645 Good 4 4 50.72 70.83 100 100 0.86 0.86 0.26 2 RT01646 Good 5 6 79.7 63.34 81.67 100 1.31 0.83 0.46 3 RT01647 Marginal 6 9 1326.05 54.82 80.13 87.85 1.91 0.6 1.13 9 RT01648 Good 5 6 463.75 78.16 89.56 95.39 1.1 0.51 0.57 4.5 RT01649 Good 3 5 231.87 70.97 91.94 96.77 0.85 0.37 0.33 3 RT01650 Good 2 3 86.95 81.25 100 100 0.68 0.68 0.23 2 RT01652 Good 6 8 768.08 36.14 65.37 90.99 1.98 0.77 0.76 6 RT01653 Good 4 4 50.72 75 83.33 91.67 0.9 0.45 0.34 2.5 RT01655 Good 2 3 594.2 90.74 94.45 96.3 0.52 0.23 0.34 3 RT01664 Good 7 9 318.82 60.62 76.26 84.75 1.62 0.68 0.96 6.5 RT01668 Good 4 4 688.39 66.25 87.84 96.25 1.3 0.6 0.54 4.5 RT02002 Good 7 8 126.52 48.22 67.86 83.93 1.65 0.82 0.65 4 RT02006 Good 6 7 72.45 38.1 69.05 92.86 1.71 0.96 0.59 3.5 RT02007 Good 2 2 43.47 75 100 100 0.5 0.5 0.12 1.5 RT02008 Good 9 11 333.31 41.31 67.39 80.44 2.24 0.8 1.03 7 RT02009 Good 7 8 181.15 53.57 71.83 81.75 1.9 0.8 0.86 5.5 RT02013 Good 0.5 0.5 14.49 50 50 50 0 0 0 0.5 RT02015 Good 6 8 413.02 48.86 75.95 92.8 1.76 0.76 0.67 5 RT02016 Good 9 11 318.82 49.28 67.91 81.78 2.02 0.75 0.95 6.5 RT02019 Good 9 10 173.9 38.89 63.34 78.89 2.17 0.88 0.88 5.5 RT02021 Good 8 11 731.85 50.97 72.05 84.58 2.11 0.69 1.14 8.5 RT02027 Good 8 9 195.64 57.11 71.02 78.69 1.86 0.76 0.87 5.5 RT02030 Good 1 1 14.49 50 50 50 0 0 0 0.5 RT02152 Good 4 4 115.93 65 100 100 0.86 0.86 0.21 2 RT02153 Good 7 9 224.62 58.46 72.31 84.23 1.61 0.73 0.77 5 RT02154 Good 8 9 130.42 41.07 60.72 76.79 1.98 0.88 0.84 5 Community Structure Metrics 90% 95% Species Species Species Abundance Abundance Density Dominance* Dominance* Dominance* Diversity Evenness* Richness Taxa Station Quality (taxa) (taxa) (#) (top taxon) (top 2 taxa) (top 3 taxa) (H') (J') (D) (#) RT02155 Good 11 12 166.65 25.4 50.8 69.05 2.51 0.93 1.08 6.5 RT02156 Good 7 8 115.93 52.57 80.77 88.46 1.6 0.9 0.61 4 RT02157 Good 4 4 65.21 60.72 92.86 100 1.07 0.86 0.36 2.5 RT02160 Good 8 10 282.59 47.19 73.26 85.96 1.95 0.81 0.8 5.5 RT02162 Good 5 6 108.68 50 85 95 1.41 0.95 0.42 3 RT02164 Good 9 11 1065.18 44.77 63.08 71.48 2.38 0.75 1.17 9 RT02165 Good 8 10 224.62 39.05 68.1 77.86 2.17 0.84 0.94 6 RT02167 Good 6 8 231.86 46.88 78.13 90.63 1.72 0.8 0.64 4.5 RT02171 Good 5 6 101.44 50 77.5 95 1.59 0.89 0.55 3.5 RT99001 Good 5 6 101.44 56.06 90.91 95.46 1.3 0.88 0.43 3 RT99003 Good 8 10 586.93 61.95 72.5 81.95 1.84 0.63 1.02 7.5 RT99004 Good 9 11 239.11 31.82 52.27 65.91 2.52 0.9 1.11 7 RT99005 Marginal 4 5 391.29 68.61 90.06 96.17 1.24 0.62 0.5 4 RT99006 Good 5 6 210.13 76.96 87.01 94.12 1.13 0.57 0.56 4 RT99008 Good 5 6 811.57 67.95 81.32 91.99 1.51 0.59 0.75 6 RT99009 Marginal 6 8 579.69 53.02 75.4 88.41 1.81 0.7 0.79 6 RT99010 Good 3 3 79.71 22.73 40.91 50 0.75 0.47 0.2 1.5 RT99012 Good 5 5 72.46 50 87.5 93.75 1.37 0.94 0.46 3 RT99013 Good 7 9 550.7 51.26 71.43 83.55 1.89 0.81 0.64 5 RT99017 Marginal 11 13 231.86 31.88 56.04 75.85 2.42 0.89 1.1 7 RT99019 Good 3 4 159.42 57.14 96.43 100 1.1 0.88 0.29 2.5 RT99022 Good 7 8 159.4 43.53 62.35 78.24 2.04 0.9 0.8 5 RT99024 Good 4 4 550.72 52.15 98.49 100 1.08 0.86 0.24 2.5 RT99026 Good 5 7 673.89 64.32 82.91 92.1 1.53 0.63 0.69 5.5 RT99027 Good 5 6 369.55 57.58 80.81 94.19 1.53 0.76 0.51 4 RT99028 Good 5 6 202.88 61.25 82.5 97.5 1.2 0.7 0.47 3.5 RT99029 Good 3 3 57.96 63.33 100 100 0.94 0.94 0.25 2 RT99030 Marginal 4 7 811.57 57.05 89.23 94.31 1.49 0.64 0.61 5 RT99036 Good 5 6 297.09 79.87 90.39 94.23 0.97 0.58 0.43 3.5 RT99037 Good 1 1 7.25 50 50 50 0 0 0 0.5 RT99038 Good 8 10 398.53 53.19 70.04 80.64 1.91 0.7 0.91 6.5 RT99039 Good 6 7 72.45 37.5 66.67 87.5 1.73 0.96 0.59 3.5 RT99040 Good 7 8 108.68 45 65 85 1.84 0.92 0.65 4 Average Good 5.45 6.55 246.77 56.26* 77.28* 86.45* 1.38 0.69 0.57 4.07 Average Marginal 7.22 9.33 536.72 48.45 73.35 85.77 1.91 0.75 0.90 6.44 Average All 5.62 6.81 273.95 55.53 76.91 86.39 1.43 0.70 0.60 4.29 Median Good 5.00 6.00 181.15 55.95* 80.81* 91.67* 1.41 0.76* 0.56 3.50 Median Marginal 6.00 9.00 391.29 50.00 75.40 87.85 1.91 0.74 1.10 7.00 Median All 5.00 6.00 202.89 54.79 80.45 91.33 1.47 0.76 0.58 4.00 *Average/median value at good stations equal to or lower than average/median value at marginal stations
Appendix F. Individual water quality parameter scores, overall average water quality, and adjusted average water quality for 97 stations sampled in 1999-2002.
*Poor station (NT02301) was eliminated from final analysis. See text for details.
Dissolved Biological Oxygen Total Nitrogen Total Phosphorus Fecal Coliform Water Quality Water Quality Station pH Oxygen (mg/L) Demand (mg/L) (mg/L) (mg/L) (col/100mL) (Average) (Adjusted Average) MR1-01-T 3 3 5 3 5 5 4.000 5 MR3-03-T 5 5 5 3 5 5 4.667 5 MR3-04-T 5 5 5 5 3 5 4.667 5 NT01598 5 5 3 5 5 3 4.333 5 NT02301* 5 3 3 5 5 1 3.667 3 RT00501 5 5 5 5 3 5 4.667 5 RT00502 1 3 5 5 1 5 3.333 3 RT00503 5 3 5 5 5 5 4.667 5 RT00504 3 3 5 5 4.000 5 RT00505 5 3 5 5 5 5 4.667 5 RT00517 5 5 5 5 5 5 5.000 5 RT00518 3 1 3 3 3 3 2.667 3 RT00519 3 5 5 5 3 5 4.333 5 RT00520 5 5 5 5 5 5 5.000 5 RT00521 5 5 5 5 5 5 5.000 5 RT00523 3 3 5 5 3 1 3.333 3 RT00525 5 3 5 5 5 5 4.667 5 RT00528 3 5 3 3 1 3 3.000 3 RT00531 3 5 3 5 5 5 4.333 5 RT00541 5 5 5 5 5 5 5.000 5 RT00542 5 3 1 5 3 3 3.333 3 RT00543 5 5 5 3 4.500 5 RT00544 5 5 1 5 5 5 4.333 5 RT00545 5 5 1 5 5 5 4.333 5 RT00546 5 3 3 5 3 5 4.000 5 RT00547 5 3 5 5 5 5 4.667 5 RT00550 5 5 1 5 5 5 4.333 5 RT00554 1 3 5 5 5 5 4.000 5 RT00557 5 5 5 5 3 5 4.667 5 RT00558 3 5 5 5 5 4.600 5 RT01602 5 5 5 5 5 5.000 5 RT01603 1 3 5 1 1 3 2.333 3 RT01604 5 3 5 3 5 4.200 5 RT01606 5 5 5 5 5.000 5 RT01619 5 5 5 3 5 4.600 5 Dissolved Biological Oxygen Total Nitrogen Total Phosphorus Fecal Coliform Water Quality Water Quality Station pH Oxygen (mg/L) Demand (mg/L) (mg/L) (mg/L) (col/100mL) (Average) (Adjusted Average) RT01624 5 5 5 5 5 5.000 5 RT01642 5 5 5 5 5.000 5 RT01643 3 3 5 3 1 5 3.333 3 RT01645 5 5 1 5 4.000 5 RT01646 5 5 5 5 5.000 5 RT01647 5 1 3 5 5 5 4.000 5 RT01648 5 5 5 5 3 5 4.667 5 RT01649 5 5 5 5 5.000 5 RT01650 5 5 5 5 5 5.000 5 RT01652 5 5 5 5 5.000 5 RT01653 5 5 3 5 5 4.600 5 RT01655 5 5 3 5 4.500 5 RT01664 5 5 5 5 5 5 5.000 5 RT01668 55 3 4.333 5 RT02002 5 5 5 5 5 5 5.000 5 RT02006 5 5 5 5 5 5 5.000 5 RT02007 5 5 5 5 5 5 5.000 5 RT02008 5 5 5 5 5 5 5.000 5 RT02009 5 5 5 5 5 5 5.000 5 RT02013 5 5 5 5 5 5 5.000 5 RT02015 5 1 5 5 5 5 4.333 5 RT02016 5 5 5 5 5 5 5.000 5 RT02019 5 5 5 5 5.000 5 RT02021 3 3 5 3 3.500 3 RT02027 5 5 1 3 5 5 4.000 5 RT02030 3 5 5 5 5 5 4.667 5 RT02152 3 1 5 5 5 5 4.000 5 RT02153 5 3 5 5 5 3 4.333 5 RT02154 5 5 5 5 5 5 5.000 5 RT02155 5 3 5 5 3 5 4.333 5 RT02156 5 5 5 5 5 5 5.000 5 RT02157 5 5 3 5 5 5 4.667 5 RT02160 5 5 5 5 5 5 5.000 5 RT02162 3 5 3 5 5 5 4.333 5 RT02164 5 5 1 5 5 5 4.333 5 Dissolved Biological Oxygen Total Nitrogen Total Phosphorus Fecal Coliform Water Quality Water Quality Station pH Oxygen (mg/L) Demand (mg/L) (mg/L) (mg/L) (col/100mL) (Average) (Adjusted Average) RT02165 5 5 5 5 3 5 4.667 5 RT02167 3 3 5 5 5 5 4.333 5 RT02171 5 5 5 5 5.000 5 RT99001 5 3 3 3 3 5 3.667 3 RT99003 5 3 5 5 5 5 4.667 5 RT99004 5 3 5 3 3 5 4.000 5 RT99005 5 3 1 5 3 3.400 3 RT99006 5 5 3 5 3 3 4.000 5 RT99008 5 5 5 3 5 4.600 5 RT99009 3 1 5 5 3 3 3.333 3 RT99010 3 5 1 5 3 5 3.667 3 RT99012 5 3 1 5 3 5 3.667 3 RT99013 5 3 5 5 5 5 4.667 5 RT99017 3 5 1 5 3 3 3.333 3 RT99019 5 5 3 5 5 5 4.667 5 RT99022 5 3 5 5 3 5 4.333 5 RT99024 3 3 5 5 5 5 4.333 5 RT99026 3 1 3 5 5 5 3.667 3 RT99027 3 5 3 5 5 4.200 5 RT99028 5 5 5 5 3 5 4.667 5 RT99029 5 1 5 5 5 4.200 5 RT99030 5 3 3 5 3 5 4.000 5 RT99036 5 5 5 3 3 5 4.333 5 RT99037 3 3 1 5 3 3 3.000 3 RT99038 5 5 1 5 1 5 3.667 3 RT99039 5 3 5 5 3 5 4.333 5 RT99040 5 5 1 3 3 5 3.667 3
Appendix G. Water, sediment, upland, and overall quality and final estuarine biotic integrity (EBI) scores for 97 stations sampled in 1999-2002 (5=good;
3=marginal; 1=poor; e=excellent). Excellent stations were a subset of good stations. EBI scores were determined using the final EBI index (EBI index D6). A station that classified as good was correctly predicted if it had an EBI score
≥37.5; a station that classified as marginal was correctly predicted if it had an EBI score ≤2.5.
Environmental Quality Station Year Water Sediment Upland Overall EBI score Predicted MR1-01-T 2002 4.000 5 2 3 35 MR3-03-T 2002 4.667 5 5 5 10 MR3-04-T 2002 4.667 5 5 5 35 NT01598 2001 4.333 5 2 5 35 NT02301* 2002 3.667 1 2 1 N/A RT00501 2000 4.667 5 5 5 30 RT00502 2000 3.333 5 5 5 30 RT00503 2000 4.667 5 2 5 15 RT00504 2000 4.000 5 5 5 30 RT00505 2000 4.667 5 5 5 30 RT00517 2000 5.000 5 5 5 e 35 RT00518 2000 2.667 3 5 3 5 RT00519 2000 4.333 5 5 5 40 Yes RT00520 2000 5.000 5 5 5 e 40 Yes RT00521 2000 5.000 3 5 5 10 RT00523 2000 3.333 5 2 3 5 RT00525 2000 4.667 5 5 5 40 Yes RT00528 2000 3.000 5 5 5 20 RT00531 2000 4.333 5 5 5 45 Yes RT00541 2000 5.000 5 5 5 e 40 Yes RT00542 2000 3.333 5 2 3 5 RT00543 2000 4.500 5 5 5 20 RT00544 2000 4.333 5 5 5 5 RT00545 2000 4.333 5 2 5 40 Yes RT00546 2000 4.000 5 5 5 45 Yes RT00547 2000 4.667 5 5 5 10 RT00550 2000 4.333 5 2 5 25 RT00554 2000 4.000 5 5 5 25 RT00557 2000 4.667 5 2 5 5 RT00558 2000 4.600 3 5 5 30 RT01602 2001 5.000 5 5 5 e 25 RT01603 2001 2.333 5 5 5 35 RT01604 2001 4.200 5 2 5 30 RT01606 2001 5.000 3 5 5 5 RT01619 2001 4.600 5 5 5 25 RT01624 2001 5.000 5 5 5 e 10 RT01642 2001 5.000 5 5 5 e 35 RT01643 2001 3.333 5 5 5 30 RT01645 2001 4.000 5 5 5 40 Yes RT01646 2001 5.000 3 5 5 35 RT01647 2001 4.000 5 2 3 10 RT01648 2001 4.667 3 5 5 25 RT01649 2001 5.000 5 5 5 e 40 Yes RT01650 2001 5.000 5 2 5 35 RT01652 2001 5.000 5 5 5 e 10 RT01653 2001 4.600 5 5 5 40 Yes RT01655 2001 4.500 5 2 5 40 Yes RT01664 2001 5.000 5 2 5 25 RT01668 2001 4.333 3 5 5 30 Environmental Quality Station Year Water Sediment Upland Overall EBI score Predicted RT02002 2002 5.000 5 5 5 e 15 RT02006 2002 5.000 5 2 5 15 RT02007 2002 5.000 3 5 5 35 RT02008 2002 5.000 5 5 5 e 5 RT02009 2002 5.000 5 5 5 e 10 RT02013 2002 5.000 5 2 5 30 RT02015 2002 4.333 5 5 5 15 RT02016 2002 5.000 3 5 5 15 RT02019 2002 5.000 5 5 5 e 20 RT02021 2002 3.500 3 5 5 10 RT02027 2002 4.000 5 5 5 15 RT02030 2002 4.667 5 5 5 30 RT02152 2002 4.000 3 5 5 35 RT02153 2002 4.333 3 5 5 15 RT02154 2002 5.000 5 5 5 e 15 RT02155 2002 4.333 5 5 5 15 RT02156 2002 5.000 5 5 5 e 15 RT02157 2002 4.667 5 5 5 40 Yes RT02160 2002 5.000 5 5 5 e 20 RT02162 2002 4.333 5 5 5 30 RT02164 2002 4.333 5 5 5 10 RT02165 2002 4.667 3 5 5 5 RT02167 2002 4.333 3 5 5 20 RT02171 2002 5.000 5 5 5 e 30 RT99001 1999 3.667 3 5 5 45 Yes RT99003 1999 4.667 5 5 5 15 RT99004 1999 4.000 5 5 5 10 RT99005 1999 3.400 3 2 3 15 RT99006 1999 4.000 5 5 5 20 RT99008 1999 4.600 5 5 5 10 RT99009 1999 3.333 3 2 3 10 RT99010 1999 3.667 5 5 5 30 RT99012 1999 3.667 5 5 5 30 RT99013 1999 4.667 3 5 5 10 RT99017 1999 3.333 5 2 3 0 Yes RT99019 1999 4.667 5 2 5 35 RT99022 1999 4.333 5 2 5 15 RT99024 1999 4.333 5 5 5 15 RT99026 1999 3.667 5 5 5 15 RT99027 1999 4.200 5 2 5 15 RT99028 1999 4.667 5 5 5 30 RT99029 1999 4.200 5 5 5 40 Yes RT99030 1999 4.000 5 2 3 5 RT99036 1999 4.333 3 5 5 30 RT99037 1999 3.000 5 5 5 30 RT99038 1999 3.667 5 5 5 10 RT99039 1999 4.333 5 5 5 25 RT99040 1999 3.667 5 5 5 10 *Eliminated from analyses
Appendix H. The SAS procedure for applying the metrics selected for EBI index
D6 in a non-parametric, quadratic discriminant analysis, with cross-validation
(SAS Institute 2000b). Preliminary tests included a multivariate analysis of variance (MANOVA) and a Bartlett's modification of the likelihood ratio test
(Morrison 1976; Anderson 1984; SAS Institute 2000b). The standard kernel was normal and the smoothing parameter was 1. Metrics for EBI index D6 correctly classified all 96 stations sampled in 1999-2002 (MANOVA, p=0.0033; Bartlett’s test, p<0.0001). See text for more details.
data indxall2; input Station$ Category ABUN_M BENTH_PM DOM1_M ERES_NM ESPAW_NM FLAT_AM H_PRI_M NSPP_M; cards; MR1-01-T 3 43.47 100 50 1.5 2 7.25 1.25 2.5 MR3-03-T 5 224.61 94.23 55 2 3.5 28.98 1.93 6.5 MR3-04-T 5 166.66 66.67 60.83 1 1.5 0 1.07 2.5 NT01598 5 347.82 94.73 91.87 0 0 0 0.47 3 RT00501 5 202.89 18.2 81.8 1.5 1.5 0 0.78 2.5 RT00502 5 0 0 0 0 0 0 0 0 RT00503 5 449.25 100 65 2.5 3.5 7.25 1.51 6 RT00504 5 282.6 12.7 87.3 1.5 1.5 0 0.67 3 RT00505 5 101.44 33.33 66.67 1.5 2 0 1.13 3.5 RT00517 5 130.43 27.78 66.67 1 1 0 0.95 2.5 RT00518 3 768.08 77.85 38.54 3 5 57.97 2.34 8.5 RT00519 5 43.47 90 80 1.5 1.5 0 0.69 2 RT00520 5 98.59 92.31 88.47 1 1 0 0.5 2 RT00521 5 260.85 39.58 64.59 2.5 2.5 14.49 1.44 4.5 RT00523 3 347.79 80.3 33.34 2 3.5 21.74 2.54 8 RT00525 5 72.45 100 54.76 1.5 1.5 7.25 1.38 3 RT00528 5 384.04 67.11 51.92 0 0.5 0 1.51 3.5 RT00531 5 36.23 100 83.33 0 0 7.25 0.46 1.5 RT00541 5 72.46 72.22 77.78 0.5 1 7.25 0.94 3 RT00542 3 330.69 79.37 48.78 2 2 7.25 2.22 8 RT00543 5 456.51 63 67.77 1 2.5 28.98 1.39 4.5 RT00544 5 543.44 78.7 35.72 3.5 4.5 14.49 2.5 10 RT00545 5 21.74 100 75 0 0 0 0.5 1.5 RT00546 5 43.47 100 66.67 0.5 1 7.25 0.79 2 RT00547 5 543.44 95.16 38.4 2.5 5.5 108.68 2.71 11 RT00550 5 166.65 96.88 58.93 1.5 2.5 14.49 1.72 5 RT00554 5 152.17 73.61 52.78 1 2.5 0 1.16 2.5 RT00557 5 246.35 65.02 32.05 2 2 36.23 2.35 6 RT00558 5 333.32 88.95 70.29 2 2.5 7.25 1.24 3.5 RT01602 5 449.26 98.81 68.45 0 1.5 0 1.32 4.5 RT01603 5 21.74 50 33.33 0.5 1 14.49 0.46 1 RT01604 5 252.26 75 55.95 0.5 0.5 0 1.16 2.5 RT01606 5 456.48 73.03 27.76 3 5.5 65.21 2.79 9 RT01619 5 202.88 100 63.64 1 1.5 14.49 1.44 4 RT01624 5 72.45 40 40 2.5 3 7.25 1.85 4 RT01642 5 50.72 90 35 1.5 1.5 7.25 1.66 3.5 RT01643 5 86.95 100 37.5 1 2 7.25 1.7 3.5 RT01645 5 50.72 87.5 70.83 0.5 0.5 0 0.86 2 RT01646 5 79.7 81.67 63.34 0.5 0.5 0 1.31 3 RT01647 3 1326.05 94.96 54.82 3 4.5 28.98 1.91 9 RT01648 5 463.75 98.78 78.16 1 1 0 1.1 4.5 RT01649 5 231.87 96.77 70.97 1 1.5 7.25 0.85 3 RT01650 5 86.95 81.25 81.25 0 0 0 0.68 2 RT01652 5 768.08 100 36.14 2.5 3 130.43 1.98 6 RT01653 5 50.72 100 75 1.5 2 14.49 0.9 2.5 RT01655 5 594.2 100 90.74 1.5 1.5 7.25 0.52 3 RT01664 5 318.82 88.8 60.62 1.5 1.5 14.49 1.62 6.5 RT01668 5 688.39 100 66.25 1.5 1.5 159.41 1.3 4.5 RT02002 5 126.52 51.79 48.22 1.5 1.5 6.97 1.65 4 RT02006 5 72.45 61.9 38.1 2 2.5 0 1.71 3.5 RT02007 5 43.47 25 75 1 1 0 0.5 1.5 RT02008 5 333.31 78.26 41.31 3 4.5 21.74 2.24 7 RT02009 5 181.15 82.94 53.57 3 4 21.74 1.9 5.5 RT02013 5 28.98 100 100 1 1 0 0 1 RT02015 5 413.02 100 48.86 1.5 3 7.25 1.76 5 RT02016 5 318.82 86.13 49.28 1.5 3.5 28.98 2.02 6.5 RT02019 5 173.9 82.22 38.89 0 1 14.49 2.17 5.5 RT02021 5 731.85 97.11 50.97 1.5 3.5 0 2.11 8.5 RT02027 5 195.64 61.08 57.11 1 3 7.25 1.86 5.5 RT02030 5 14.49 50 50 0 0 0 0 0.5 RT02152 5 115.93 100 65 1 2 0 0.86 2 RT02153 5 224.62 41.54 58.46 1.5 2 21.74 1.61 5 RT02154 5 130.42 96.43 41.07 2 2.5 14.49 1.98 5 RT02155 5 166.65 57.94 25.4 1 2.5 7.25 2.51 6.5 RT02156 5 115.93 88.46 52.57 1.5 2 0 1.6 4 RT02157 5 65.21 100 60.72 0.5 1.5 0 1.07 2.5 RT02160 5 282.59 97.06 47.19 0 1 14.49 1.95 5.5 RT02162 5 108.68 50 50 1.5 1.5 0 1.41 3 RT02164 5 1065.18 85.83 44.77 3.5 5.5 326.08 2.38 9 RT02165 5 224.62 70.95 39.05 2.5 4 7.25 2.17 6 RT02167 5 231.86 96.88 46.88 0 1 14.49 1.72 4.5 RT02171 5 101.44 100 50 1.5 2 21.74 1.59 3.5 RT99001 5 101.44 100 56.06 1.5 1.5 43.48 1.3 3 RT99003 5 586.93 97.5 61.95 3.5 4.5 65.21 1.84 7.5 RT99004 5 239.11 81.82 31.82 0.5 3.5 7.25 2.52 7 RT99005 3 391.29 72.44 68.61 1 2 0 1.24 4 RT99006 5 210.13 15.93 76.96 1 1.5 0 1.13 4 RT99008 5 811.57 86.75 67.95 2 3 0 1.51 6 RT99009 3 579.69 78.41 53.02 1.5 3 7.25 1.81 6 RT99010 5 79.71 40.91 22.73 1 1 0 0.75 1.5 RT99012 5 72.46 93.75 50 2 2 36.23 1.37 3 RT99013 5 550.7 100 51.26 2 3 86.95 1.89 5 RT99017 3 231.86 63.77 31.88 2.5 2.5 0 2.42 7 RT99019 5 159.42 67.86 57.14 1 1 0 1.1 2.5 RT99022 5 159.4 63.53 43.53 1.5 1.5 0 2.04 5 RT99024 5 550.72 47.85 52.15 2.5 2.5 0 1.08 2.5 RT99026 5 673.89 96.15 64.32 1.5 3 0 1.53 5.5 RT99027 5 369.55 60.86 57.58 1.5 1.5 0 1.53 4 RT99028 5 202.88 0 61.25 1 1.5 0 1.2 3.5 RT99029 5 57.96 100 63.33 1 1 0 0.94 2 RT99030 3 811.57 53.68 57.05 2.5 3 0 1.49 5 RT99036 5 297.09 96.15 79.87 1.5 2.5 0 0.97 3.5 RT99037 5 7.25 50 50 0 0 0 0 0.5 RT99038 5 398.53 18.75 53.19 1.5 3 14.49 1.91 6.5 RT99039 5 72.45 100 37.5 2 2.5 28.98 1.73 3.5 RT99040 5 108.68 75 45 1.5 3 0 1.84 4 run; Proc print; run;
Proc DISCRIM POOL=NO METHOD=NPAR KERNEL=NORMAL R=1 WCOV PCOV BCOV MANOVA crosslist; Class CATEGORY; Priors proportional;
Var ABUN_M BENTH_PM DOM1_M ERES_NM ESPAW_NM FLAT_AM NSPP_M H_PRI_M; run;