Mesoscale spatial and temporal water quality trends in the Rookery Bay estuary

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Mesoscale spatial and temporal water quality trends in the Rookery Bay estuary

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Title:
Mesoscale spatial and temporal water quality trends in the Rookery Bay estuary
Creator:
Christenson, Todd.
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Tampa, Florida
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University of South Florida
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English
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vi, 157 leaves : ill. (some col.) ; 29 cm.

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Water quality -- Florida -- Rookery Bay ( lcsh )
Rookery Bay (Fla.) ( lcsh )
Dissertations, Academic -- Marine Science -- Masters -- USF ( FTS )

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General Note:
Thesis (M.S.)--University of South Florida, 1998. Includes bibliographical references (leaves 107-112).

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University of South Florida
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Universtity of South Florida
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All applicable rights reserved by the source institution and holding location.
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025854711 ( ALEPH )
41460380 ( OCLC )
F51-00135 ( USFLDC DOI )
f51.135 ( USFLDC Handle )

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l\IIESOSCALE SPATIAL AND TEMPORAL WATER QUALITY TRENDS IN THE ROOKERY BAY ESTUARY b y /TODD CHRISTENSON A thesis su bmitted in partial fulfillment of the requirements for the degree of Master of Science Department ofMarine Science University of South Florida December 1998 Major Professor : Gabriel Vargo, Ph.D

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Graduate School University of South Florida Tampa, Florida CERTIFICATE OF APPROVAL Master's Thesis Thi s is to certify that the Master's Th e s i s of TOBD CHRISTENSON with a major in Marine Science ha s been app r oved b y the Examining Committee on October 1 2 1 998 as s ati s f ac tory for the thesis requirem ent for the Master of Science degree Exami ning Committee: Memb er: Ph.D. Memb er: Todd Hoi5kfu'?,:Ph.'J:)..

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ACKNOWLEDGEMENTS The author gratefu lly acknowledges the Florida Department of Environmental Prot ection Rookery Bay National E st uarine Research Reserve for their generous s upport for this p roject, Rhonda Watkin s for her assistance, and, in particular, Drs Gabriel Var go and Todd Hopkin s for their continued guidance.

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TABLE OF CONTENTS LIST OF TABLES LIST OF FIGURES ABSTRACT INTRODUCTION STUDY AREA METHODS Data Collection Data Analysis RESULTS Correlation and Regression Spatial trends Temporal trends Principal components analysis Relationships Between Variables Correlation Multiple Regression Spatial Trends Temporal Trends Principal Components Analysis DISCUSSION CONCLUSION REFERENCES CITED APPENDICES Appendix I : Sununary Statistics by Parameter Appendix II: Box and Whiske r Plots (Raw Data) Appendix III: Results ofPCA Appendix IV: 3-D Contour Plot s b y Month Appendix V. Mean DO for Henderson Cr eek ii iii iv 1 6 9 9 13 13 15 16 17 19 19 19 29 32 51 67 80 100 107 113 114 117 123 138 157

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LIST OF TABLES Table 1: Student s t-test for Surface vs Bottom Values Table 2: Multiple regressions : Dependent and Independent Variables by Station and r2 Values for Each Regression Table 3 : ANOV A by Station Table 4: ANOVA by Station (Bay Transect) Table 5 : ANOVA by Station (Henderson Creek Transect) Table 6: Annual Means : Overall, Minimum and Maximum Table 7: Monthly Means : Minimum and Maximum 27 31 32 32 33 51 51 Table 8 : Contribution of Original Variables to Each PC (Based on Eigenvectors) 68 Table 9 : Percentage of Surface Light Reaching Bottom 94 Table 10: ANOV A by Month 96 11

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List of Figures Page Figure 1: Map ofRookery Bay showing sampling stations 10 Figure 2: Correlation matrices 19 Figure 3 : Mean depth of stations showing significant differences between surface and bottom parameters 35 Figure 4 : Bay Transect 37 Figure 5: Henderson Creek Transect 43 Figure 6 : Annual Averages 52 Figure 7: Monthly Averages 56 Figure 8 : Overall Station Averages 64 Figure 9 : Average DIN : DIP b y Month 66 Figu re 10: Plot of Eigenvectors of Station Averages-Analysis 1 69 Figure 11: Plot of Eigenvectors of Station Averages (phys variables)-Analysis 2 70 Figure 12: Plot ofEigenv ec tors of Station Averages (phys variables)-Analysis 2 71 F i g ure 13: Plot ofEigenvectors ofMonthly Avera gesAnalysis 3 72 Figure 14 : Plot ofEigenvectors of W e t/Dry Seasonal Data-Analysis 4 73 Figure 1 5 : Plot ofEigenvectors ofWet/Dry Seasonal Data Analysis 4 74 Figure 16: Plot ofEigenvectors of Wet / Dry Season Data for all Stns-Analysis 5 75 Figure 17a : Surface Salinity Gradient for Hender so n Creek 89 Figure 17b: Bottom Salinity Gradient for Henderson Creek 90 lll

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MESOSCALE SPATIAL AND TEMPORAL WATER QUALITY TRENDS IN THE ROOKERY BAY ESTUARY by TODD CHRJSTENSON An Abstract Of a thesi s submitted in partial fulfillment ofthe requir e ments for the degree of Master of Science Departm e nt o fMarine Science University of South F l orida D ece mber 1 998 Major Profe ssor : Gabriel Var g o Ph D I V

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Biweekly measurements of surface and bottom values for temperature, dissolved oxygen, pH, salinity conductivity, redox potential, and turbidity, as well as bottom depth and Secchi depth for 19 stations in Rookery Bay, FL between 1986 and 1992 and biweekly measurements of nitrate nitrite, ammonia, phosphate and chlorophyll for 7 of these stations from 1988 to 1992 were obtained from Dr. Tom Smith III, formerly of Rookery Bay National Estuarine Research Reserve A variety of statistical methods were used to analyze the data sets including correlation analyses t-tests, ANOV A, multiple regression, and principal components analysis The results indicate that the bay tends toward hypersalinity at certain times of the year with salinity values >43%o at many stations and shows some evidence of stratification in Henderson Creek, the main source of freshwater. Most of the bay is dominated by tidal exchange with Gulf waters but the profile changes upstream. Stations located in Henderson Creek demonstrate reduced and variable salinity and conductivity, low dissolved oxygen increased phytoplankton productivity and increased ammonia concentrations (station 12) Stations 1, 8, 10 and 12 were particularly prone to periods of low DO and anoxia Correlation between turbidity and nutrients suggest that the sediments may be a source of nutrients & may ultimately derive from the decomposition of mangrove litter. Nutrient values overall do not indicate that eutrophication has occurred during the study period Mean nutrient values were highest in 1988 and dropped precipitously thereafter. Furthermore, mean values were significantly lower overall than those reported by Grabe (1993) for 1988 and diverged even more sharply in 1989 Nitrate values were similar to those found by Thoernke and Gyorkos (1988) for the period of June 1984 to April 1985, v

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though other nutrient species were elevated relative to those of Thoemke and Gyorkos (1988) Relative to other estuaries nutrient v alues are very low suggesting that the impact of increased development through 1992 did not increase eutrophication in Rookery Bay. Abstract Major Professor : Gabri Vargo, Ph D Professor Departmen fMarine Science Date Approved : ........ Vl

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INTRODUCTION Situated equidistantly between Naples and Marco Island on Florida's west coast, Rookery Bay National Estuarine Research Reserve is located in a region of rapidly increasing population, both permanent and transitory, which the natural beauty of the area attracts Collier County was during the last decade the fastest growing county in the United States with a growth rate of > 120% (Smith, 1993) a trend mirrored in much of coastal Florida However based as they are on the 1990 Census, which was known to have g rossly underestimated population figures in many areas the actual numbers could indeed be far greater With this mass influ x came inevitable issues of wetland conservation and debate over where the line between the needs of the human population and those of the coastal ecosystem at large should be drawn Concern over anthropog e nic imp act on natural waters has become widespread in recent years both at home and abroad and water quality issues have been the source of much study According to Bricker & Stevenson (1996), "eutrophication is threatening the coastal waters of the U.S with varyin g degrees of urgency" Human activities in the Florida Keys for example have nearl y doubled in the last 20 years with an associated increase in nutrient input from septic s y s tems and cesspits (Lapointe & Clark 1992) This could lead to increased phytoplankt o n and epiphyte production and decline of hermatypic 1

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corals Chesapeake Bay, the largest and most productive estuary in the U.S and the subject of decades of study, has it is believed, experienced alteration of phytoplankton dynamics due, in part to increased nutrient input (Harding, 1994). Consequences of this alteration include a shift from diatom-zooplankton-fish food chain to a microbial-loop type system and shading of submerged vegetation due to increased phytoplankton biomass Anthropogenic nutrient input has been cited as a factor in the decline of seagrasses in Sarasota Bay (Tomasko et al. 1996) Tampa Bay (McPherson & Miller 1990), Chesapeake Bay (Stevenson et al. 1993), (Staver et al. 1996), St. Lucie Estuary (Chamberlain & Hayward 1996) and has been suggested as a factor in a seagrass die-off in Florida Bay (Fourqurean et al. 1993) although Florida Bay itself does not show evidence of eutrophication. Boyer et al. (1998) asserted that freshwater flow into the Everglades and, by association, into Florida Bay is almost entirely controlled by human activities. Bordering the Everglades to the north is the eutrophied Lake Okeechobee, a water body whose inflows and outflows have been significantly altered during the past century to meet human needs (James et al. 1995) which, in turn, has impacted other coastal water bodies Doering (1996), for example, cites these alterations as having altered the St. Lucie Estuary from a seagrass-dominated system where oysters were plentiful, to a phytoplankton based system characterized by high nutrient concentrations and periods of hypoxia Similarly, nutrient loading has been blamed for an increase in organic matter in Pamlico River Estuary, NC (Stanley and Nixon, 1992), the decomposition of which has caused periods of anoxia and associated fish kills Rizzo and Christian (1996) link 2

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eutrophication to loss of submerged vegetation and benthic fauna, as well as nuisance algal blooms and fish kills Much of the economic value of estuaries comes from their function as nursery to many coastal marine fish and habitat to numerous invertebrates, harboured by the seagrasses Studies such as that conducted by Moshiri et al. (1981) in Bayou Texar Florida suggest that reversal of eutrophication conditions should lead to recovery of the estuary Charlotte Harbor FL (McPherson & Miller, McPherson et al. 1991), relatively undeveloped in comparison with Tampa Bay and Sarasota Bay, is beginning to experience population growth of its own (McPherson and Miller, 1990) cite a 29% decline in seagrass between 1945 and 1982 McPherson et al. (1991) estimate that, if current population trends continue nitrogen loading will increase by more than 2 7 x 106 mg d -1 by the year 2020, an increase of 18 percent over the 1991 loading values. Given the ever expanding population such estimate s may have consequences for Rookery Bay as well Fortunately, efforts aimed at the protection of Rookery Bay began prior to much of this current growth Initial efforts at conservation of greater Rookery Bay date to 1964 when The Conservancy Inc and National Audubon Society purchased 1,620 ha of wetland area creating the reserve It was incorporated into the National Estuarine Research Reserve System (NERR) in 1978 This original area has since grown to 15,000 acres and further expansion is however this expansion is often at odds with the encroaching development. 3

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Research designed to und erstand ecosystem function has been underway virtually since the inception of the Reserve. Data encompassing a wide variety of ecosystem components were produced by these early surveys, much of it as yet unanalyzed and unpublished Among these studies have been surveys of biota such as the extensive mangrove system, as well as pesticide monitoring, bird censuses, and extensive water quality sampling. It is this latter aspect which will be the focus of the analyses which follow Several studies of Rookery Bay and its environs will, hopefully prove useful in establishing a historical context for this analysis Thoemke and Gyorkos (1988), for example, conducted a survey of nutrients and chlorophyll in Rookery Bay over tidal cycles from June 1984 to April 1985 and declared Rookery Bay a "relatively undisturbed estuary ", however the scope of their study, both spatially and temporally was smaller than the present study Grabe (1993) studied several basins in Collier County, including Henderson Creek (the main source of fresh water in Rookery Bay), Gordon River Extension, Cocohatchee River, Main Golden Gate, Faka-Union and Barron River from 1979 to 1989 He found nutrient le v els generally low in Henderson Creek relative to the other basins but noted a statistically significant increase in nutrients over the study period. Smith (1993) published an extensive survey report based on some of the past, unpublished data Yokel (1975) studied Rookery Bay between 1970 and 1975 in order to assist in development planning Twilley (1985) studied the extensive mangrove stands surrounding Rookery Bay The goal of this analysis is 1) to identify mesoscale spatial and temporal trends in the 21 water quality parameters at the location where they were 2) determine if 4

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these parameters are 3) assess if measurable eutrophication has occurred over the study and 4) make recommendations for the future study and management of the Rookery Bay estuary Thoemke and Gyorkos asserted that the nutrient levels found in Rookery Bay were on the whole, quite low compared with other, more disturbed estuaries found in this region (e g ., Tampa Bay and Sarasota Bay) and that such levels represent those typical of an undisturbed mangrove system My overall goal for this study is to provide a thorough analysis of the available data which will ultimately add to our knowledge of ecological relationships in the Rookery Bay estuary thereby providing at least a partial framework upon which sound estuarine management practices may be based 5

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STUDY AREA Rookery Bay can best be described as a seawater-dominated lagoon type estuary (Kno x, 1990) which experiences p e r i ods of hypersalinity at a number of stations Rookery Bay is a positive estuary for most of the year in which freshwater inflow and precipitation e x ce e d evaporation and salinit y increases downstream from head to mouth It becomes a negative estuary with evaporat i on e xc eeding freshwater inputs and occas i onal periods of hypersalinity in the late sprin g before the onset of summer rains The bay would likely be ne g ati v e from mid Spring throu g h mid Autumn but for the dramatic increase in rainfall typical of the coastal Florida summer Within the Reserve s bound aries lie a div ersity of habitats ranging from dry-zone scrub hardwood groves, oy ster r eefs and while not common, seagrass beds Most pre v alent however are the fresh and s a ltwater marshes and dense forests of mangroves co v erin g 1 454 ha (Twilley 1985) Th e mangro v e forest consists of a fringe forest along the bay margin in which red man g r ove s (Rhi zo phora mangle) predominates and a basin fore s t consisting ofblack (Avi ce nni a ge rminan s ) and red mangroves, which grades to pure black further inland White man g r oves (Lagun c ularia racemosa) are uncommon and are interspersed throughout the mixed assemblag es Hurricanes in 1918 and 1960 destroyed much of the then extant fore s ts and thu s most of what remains is secondary growth ran g ing from 30-100 yea r s m age (RBNERR Home Page 6

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http://inlet.geol.sc edu/RKB/home htrnl) Lugo & Snedaker (1974) studied several mangrove systems in south Florida including Rookery Bay, and asserted that the lower Faka-Union and the red mangrove forest of Rookery Bay represent the most favorable mangrove environments". Lu go et al. (1975) found the black mangrove forest at Rookery Bay to be more productive than the other two south Florida mangrove forests they studied Mangroves, in general, are acknowledged to be highly productive Clough (1992) reported that productivity of mangrove forests is comparable to terrestrial forests. Though the exact function of mangroves in nutrient and carbon flux is still not resolved, there is evidence that they may play a role in nutrient exchange (Alongi, 1990; Rivera-Monroy et al. 1995 ; Boto & Wellington 1988 ; Alongi et al. 1993;) As with most estuaries Rookery Bay serves a vital role as nursery and shelter for many fish and invertebrate spec ies. Numerous plant and animal species, many of which are endangered, are found within the Reserve including the West Indian manatee Kemp's Ridley and loggerhead sea turtles Florida panther, several ferns and plant species and, as its name implies roughly 150 species of birds many of which nest on the mangrove i slan ds in the bay Several workers have pointed to mangrove detritus as a major source of both food and shelter for juvenil e fish and invertebrates (Odum & Heald, 1975; Gong & On g 1990 ; John & Lawson 1 990). Freshwate r enters the bay from s everal sourc es ; the most important of which is Henderson Creek which supplies on average approximately 6 x 107liters/day Significant additional input derives from both s urface and subsurface sheet flow which, at one time, occurred along the entire land ward boundary (Thoernke and Gyorkos 1988) This pattern has now been altered by the construction of channels and levees (Smith, 1993) Plans cited 7

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by Thoemke and Gyorkos for respectively, 20,356 person and 6,800 person developments in the wetlands area to the north (the latter including a 1000 slip marina) would likely have had enormous consequences for freshwater input to the bay These projects are at least for the moment, on hold (Todd Hopkins RBNERR pers comm .). Still, extensive residential and agricultural areas exist along the headwaters of Henderson Creek and golf courses are abundant in Collier County 8

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METHODS Data Co llection: The data upon which this report is based were collected at a series of 19 stations numbered 1-10 12, 160 170 180 191, 192 205, 206, 210 covering not only Rookery Bay proper but also the upper reaches of Henderson Creek, Dollar Bay near Naples, Stopper Creek and areas surro undin g Marco Island (Figure 1) Some data existed from two other stations (11 and 190) which were part ofthe original data set but the stations were samp led too infrequently to be s t atist ically useful and were excluded from the analysis. All data included in this analysis were co llected on a biweekly basis by RBNERR staff The complete dat a set was made avai lable to Dr. Gabriel Vargo and subsequently to me by Dr. Tom Smith Ill, former research coordinator at the RBNERR. 9

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C\.ffiENT 1"'0\ITOHl'\G PROL-cTS Wi\lffi Cl.X.Jl'Y 0---ri r"r-' ,___.. 0 ,..,.... ,__. -'r t1.D OWl (l(XNlY SUM:1'S 1 .. -._I .,J--0 2 3 [ _ !._==r---==-=' MILES ROOKERY BAY NATIONAL ESTUARINE RESEARCH RESERVE 191 205 206 210 Figure 1 Map of Rookery Bay showing sampling stations 10 .... : ; :

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Physical and chemical parameters measured for the monitoring project included temperature COC), pH, dissolved oxygen (mg r\ conductivity (mSiemens/cm2), redox potential (volts), salinity (%o) and turbidity (Nephelometric Turbidity Units) at both the surface and the bottom, as well as depth overall (m) and Secchi depth (m). Dates of collection were from January 1986 through December 1992 at stations 1-10, from April 1987 through December 1992 at stations 160 170 and 180, from February 1988 through December 1992 at stations 191, 205, 206, and 210, and from April 1990 through December 1992 at Station 12 Physical data except Secchi depth were recorded using Hydrolab Sconde and Hydrolab Surveyor II data loggers placed both at the surface and on the bottom at each station Turbidity was determined using a HF Instruments Turbidometer Model DRT 200. In addition, data on monthly average rainfall for Naples was obtained from the National Weather Service through a link at NOAA's home page on the World Wide Web (www ncdc.noaa gov) Nutrient and chlorophyll data were collected at only 8 of the 19 stations Dates of measurement were from July 1987 through May 1992 at stations 2, 5 6, 160, 170, 180, from February 1988 through May 1992 at station 191, and from April 1990 through May 1992 at station 12. Samples were collected using a Niskin bottle and transferred to 0.5 1 polyethylene bottles which were rinsed twice with sample water before filling Bottles were kept in a cooler-but not frozenfor return to the lab Assays for nutrients and chlorophyll were run at USF Department of Marine Science, St. Petersburg, FL. All nutrient concentrations and chlorophyll r'). were determined spectrophotometrically in accordance with established techniques Nitrate assay followed 11

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the method developed by Morris and Riley (1963); nitrite was determined by a closely related procedure outlined by Bendschneider and Robinson (1952); ammonia according to Ivancic and Degobbis (1984) ; and phosphate the technique ofMurphy and Riley (1962). Three separate sub samples of each water sample was run The average of these three was the value used for a given date in this analysis For chlorophyll a analyses 0 5 1 seawater was filtered using a Whatman GF/C glass fiber filter and the filter placed in a snapcap vial containing 90% acetone to extract the chlorophyll Absorbance was measured at 450, 667 and 750 nm As with the nutrient analyses the assays were run in triplicate to produce an a v erage for each sampling date Although chlorophyll b and c and accessory pigments were also determined on a limited number of samples, only chlorophyll a data was sufficiently consistent, frequent and free from errors to be ofuse in this analysis A cautionary note is necessary from the outset. The data, the nutrient data in particular, suffered somewhat from numerous shortcomings which placed constraints on the types of analyses carried out and the conclusions to be drawn from them First, inclusive dates were highly inconsistent between stations sampled as detailed above In addition, within the sampling period there were numerous examples of missing data points. While the physical data suffered from this to a far lesser degree, redox potential and salinity data for both surface and the bottom was missing from January through October 1986 for all stations Missing values occur more frequently in the nutrient data with gaps of several months. At most stations at which nutrient data was collected only a few points exist prior to mid-1989, for nitrate in particular 12

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Much of this missing data derived from erroneous values which were excised from the data set prior to analysis These often took the fonn of clearly nonsensical values such as negative nutrient and chlorophyll values and redox potential values greater than 2, while others were theoretically possible but deemed to fall too far outside the mean to be likely. A degree of judgment had to be exercised in the removal of such points. For example, a nitrate value of 30 is not unusual in many estuaries but seemed quite anomalous for Rookery Bay and caused a great deal of what was probably unnecessary skew in the analyses On the other hand, many data points were included which were well outside the mean, though they were more reasonable and will have to be dealt with as outliers Data Analysis : Corre lation and regression Analyses were performed on the data in a number of fonns using Statgraphics (Manugistics, Inc ), SAS (SAS Institute) and Excel (Microsoft Corp.) Because of the problem of missing points data were collapsed into a "wet" season for each year, running from May through October and a dry season, comprised of the months November through April and averaged over these periods (Harcum et al. 1992). Correlation matrices were produced for these derivations to identify mathematical relationships between va riables (a.=0.05) 13

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The existence of correlation among variables guided the next phase of analysis which was to further examine the relationship between variables through multiple regression Because some degree of causality must be assumed for regression analysis (Neter, Wasserman, & Kutner 1990) only those intercorrelated variables to which the roles of dependence and independence could be reasonably assigned were subjected to regression. The only parameters which had causal relationships with a sufficient number of other variables for a multiple regression to be revealing were dissolved oxygen and chlorophyll a. In the multiple regression analysis, a model was constructed based on the combination of variables which explained the greatest proportion of the variability in the dependent variable as measured by the r2 value. The combination of independent variables which maximized the r2 value was selected Of concern in these types of regression analyses is the issue of multicollinearity which can have a profound effect on the interpretability of the regression results (Chatterjee and Price 1977). Such a condition arises when one or more of the independent variables are intercorrelated, making their individual effects on the dependent variable difficult to discern It is generally only problematic in cases of severe collinearity (r2 > 0.95) and few of the variables in this data set correlated to that great a degree. Those that did (such as surface and bottom values of the same variable, which generally fluctuate together and salinity and conductivity) were not used in the same regression model. For regression of chlorophyll only bottom values were used in the model since surface and bottom values were so closely correlated in most cases that virtually the same results were obtained regardless of whether surface or bottom values were used At station 5 different 14

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variables were factors in the regression depending on whether surface or bottom values were included in the regr ession. Also, since salinity and conductivity were so strongly collinear, only salinity was used as an indepen dent variable in the regressions For analysis of dissolve d oxygen, surface values were regressed against surface values of the independent variables, and bottom va lues aga inst bottom values Results are shown in Table I below. Spatial trends Variability between stati ons for each parameter was assessed by single factor analysis of variance (a = 0 05) While there were, as with most water quality data sets, departures from normality, the ANOV A is genera lly considered a robust enough test to overcome all but the most serious deviations from normality Box and whisker plots are also provided Student's t -test was used to assess differences, if any, between surface and bottom parameters. As with the ANOVA the t-test is robust and should not be adversely affected by any but the most serious violations of its underlying assumptions-that the data are normally distributed and that the variances of two samples being compared are equal To assess whether depth was a factor in the occurrence of significant differences stations were ranked into categories "no difference s in any parameter' ', differences in turbidity only" and differences in parameters beside s turbidity ", and the mean depth of these categories were plotted 15

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Two sets of data were grouped into transects. The first, running from the headwaters of Henderson Creek to its mouth in the southeast corner of Rookery Bay proper, is comprised of stations 10, 9, 12, 4 and 5 The other, the 'bay transect' is formed by stations 160, 170 180 2 6, and 191. Averages for the wet/dry seasons were plotted to visualize trends ANOV A were performed on each parameter for both the Henderson Creek transect and the bay transect. Nutrient data existed for only 2 stations in the Henderson Creek transect and thus were not included in the analysis. Wet/Dry season averages for all stations combined we re likewise plotted 3 dimensional surface graphs were produced for each parameter showing variability over the entire sampling area based on monthl y a v erages Temporal trends Temporal trends were assessed both graphically and by ANOV A. ANOV A was performed on the overall monthly averages and graphs were produced from the annual and monthly averages In order to assess nutrient limitation the DIN : DIP ratio was calculated b a sed on the raw data covering dates for which values of all nutrients were available. Summary statistics including the mean minimum and maximum, standard deviation and va riance for each parameter at each station were calculated and are included in Appendix I. 16

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Principal Components Analysis Principal Component Analysis was performed using SAS/Insight v 6.12 (SAS Institute, Cary, NC) on five different derivations ofthe data set : 1) Overall averages for all stations (physical variables only); 2) Overall averages for all variables (Stations 2, 5, 6, 12, 160 170 180, 191 only) ; 3) Monthly averages for all variable (all stations); 4) Wetldry season averages over time for all variables (Stations 2, 5, 6, 12, 160, 170, 180, 191 only); and 5) Wet/dry season averages over time for all stations (physical variables only) These will be referred to hereafter as Analyses 1 through 5 The goal of this multivariate technique is to identify trends in the data by transforming the original variables into new "variables" known as principal components (PCs) Each PC is the linear combination of the original variables with the largest possible variance thereby accounting for the greatest proportion of the original variability, and is orthogonal to all other PCs (Harris 1975) (Ludwig & Reynolds, 1988). The analysis produces a Table of Ei g envalue s representing the portion of the original variance attributed to each PC, in descending order as well as a Table of Eigenvectors, which are the coefficients for the original variables in each PC. Eigenvalues > 1 generally define which PCs are noteworthy (Manly 1986) although some that were marginal (Eigenvalues < 1.2) were i g nored if a large majority of the variance ( > 75%) was already accounted for by the earlier PCs (Mcintire 1973) Eigenvectors (coefficients) range from -1 to +I. Interpretation of PCs is usually based on the magnitude of the eig envectors which is a measure of the importance of the 1 7

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original variables in each principal component (Chatfield & Collins, 1980) Those variable with eigenvectors nearest to 1 or -1 are the most important and the analyst tries to determine what, if anything tho se variables might have in common Some workers have been fortunate enough to get eigenvectors > 0.5 (Bulger et al. 1993) but such was not the case here Most variables in this analysis had eigenvectors between 0 2 and 0 3 or between -0 2 and -0 3 so a more liberal criteria had to be used A value > 0.2 or < -0 2 was deemed to be significant for the purposes of this analysis Data points for each analysis were then plotted against the first two PCs effectively reducing the dimensionality to two while retaining all of the original variables 1 8

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RESULTS Rel ationships betwee n variab l es: Correlations Figure 2. Correlation Matrices Figure 2b. Station 2 10000 09981 1000< i ,. -'""""' fNOl ''""'''' '' I)J007 TIJilB 180" IOOIX .. ''!' 19

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Figure 2. Correlation Matrices (Cont.) Figure 2e Station 5 BOT SURF O()T 1110< 0 : .. i:;: -".'." 0 3 ., _., .. ,. --".!!! ... ._, 0 OlOt: . .. .o,.., __:! 0 ., 0 .. ., 0>4 o 510! ..... ,., .. o .... -04611 ,_,o .... -"'-".'. -009!6 .,,., """' 20

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Figure 2 Correlation Matrices (Cont.) Figure 2g. Station 7 -.D?_ DO C ONI RDOX RDOx SAL SAL T\JRB. TURB. BOT I SECCHI SURF. ""8'T'f 001 1 BC SURI. BC SURF. BC SURF. BOT SURF. B OT Dboll Ul< D Dseec .DODO T surf -0.019 5 1 0000 F' h s 1 g ure 2 tatlon 8 eo SECCHI SURF. DEPTH DEPTH TEMP. IDbott .00( IDsecc a 72: 1.10C I T s u r 0 4 5D6 2 1D2 !.DODD Tbott ) 4139 1905 0 9955 loHsurf ..( OD5( 0 4 ::':::i!JO!l39( eo SURF. TEMP. ot'l roooc 1.0000 ,,_ """' :::rt ,_;, .lW -... rt ii. asi [RdibOU 0 .39 10 1 .. o 1412[ 0. 1401 0 152' ISsurt . 0 2482 ISbott : .. : -:::,:. . 1 2329 [ Turbsurt ,::::. : : 24iiff":0:1998 -(.28: Turbbott :' ,: :.: .7'}Sf; -=o:2589 -0. 2232 T 4768 0 2915 !23 1 416 9 -:o:2543 l26 0 5178 0 2 446 -0.262 4 "0:2439 -0 : 2365 :.-0;7612 2 1 80' &uiF & SURF. BOT SURF. & ROOX ROOX SAL SAL TURB. TURB. 1.0000 0000 -=<1.2148 0 997! 1 0000 ""0:9826 -0 .1816 1.0000 ([9799 -0 198 7 ).9862 1 0000 0 2 418 -( !2Z -0 .30 1 1081 0 0669 1 0000 Q.2507 -0. 3659 -0 3 47: 0 .087 0 0 0552 1 .9565 1 0000 :::-O.n-. 1454 0 -0 3220 -0.34 02 R ai n IRoln TOOOO

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Figure 2. Correlation Matrices (Cont.) Figure 2i. Station 9 BOT SECCHI SURF. 801 SURF. BOT' SURF __ B9 _SURF. BC SURF. BOT SURF. BOT SURF. BOT DEPTH DEPTH TEMP, TEMP. pH pH DO ONI ONI SAL SAL TURB. URB. IRa .. lbott .ODD surf -0.02: 1000 'bott 139 1811 .ODD 1 .87< 1 ,0000 0 .3971 DODD 0.8129 .. 1 .166 : 150 1 .oooo Rdxsuo -0 4496 -0 3329 -0 3589 -0 4 080 -0 .1541 -0 0539 -0 .3461 -0.1120 Rdlsurf -0 4685 -0 4112 0 .2214 0 2170 0.0017 0.1630 -0.3339 -0. 0707 0 4 8 78 1 0000 :_,:_._:_:_._; _ :_._ . .. -0 4933 0 1285 0 1566 0 1717 0 3850 -0.0876 0 .2087 0 4 726 0 4 235 .,., ...,.... -0 3007 -a. zs29 -0 3065 -0 2627 -0 0623 -0 4 504 .o.ms o .9601 0 4390 -0 4136 -0.4648 -0 1eo4 -0. 0 12 1 0 .27 68 -0.oo14 ... ,.1,W35 .,.,.,J.:='---.-=:At-----t----1 r urbsurf -0. 4 790 0 .2918 o o584 .o. 1054 .o.3932 -0.3101 !---o::-.,.,31;;;3i;-l7 .o:zs.5 .. -0.3421 -0 .147 6 -0. 1ss Figure 2k. Station 12 SURf ... .,... """ .. : 01$01( . I :!!;! ... : .... 0117< 11 ,.,. ... .;; .... ,, 0<661 : 22

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Figure 2. Correl ation Matrices (Cont.) Figure 21. Station 1 60 ... ::'::Oio[ :: Eio..1. r.e 2m <;:tati : m 170 ,. 0311 .. ..... ... ...... :. ,,.., :<>1m .. 0011 SI ""' PO< ... t 0 13 DO 1M .. :::,, F i g ure 2n. Station 18 0 oor SUAF 0 1011 0111 o,. .. o: .. "-',., ..'.'?.' -0>7J4 o:,JOI o : 0181 .;;: ..... : :=. .o ... bt.: ... 00500 -01 .. ; :.'Oi6(>1. -OOH: -00001 0 . 0 "" .... .o .. .o "" ..... ,..,, ODIIJ ..... .. l>tl .. DOlO .(JJJ&/1 :"l iDO -CIHIS1 -00510 .o, 23 o 10 'oooa o 12 1 1 aooa """ '"" .. 1 OJolfl I OOOD lOU 4 I ...03)41 _,, -

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Figure 2. Correlation Matrices (Cont.) Figure 2o. Station 191 I= 1Figure 2p Station 192 ONC OATE "' sec :Ht BOT SURF BOT SURF 80 SURF. eo c IOboll lome Tsur1 lroon toMball too IOObott ICnd btt (Rdnuff (Sboll : ; ... . ;: lili1ilii 8 3 5 :Jl .. ft070 .<:: : :.' J .OO.i 3 .0516 3 .6; 24 :::eo SUR F BO C SURF eo 1.0001 0.1 240 = aln 1 .0000

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Figure 2. Cor r elation Matrices (Cont ) 1gure 2 St f r a 10n 206 SOl SEC CHI SURF. son SURF. son SURF son SURF BOl SURF. 8011 SURF. BOlT. SURF. BOTT. DEPTH DEPTH rEMP rEMP oH oH DO DO COND. COND. RDOX RD SAL SAL URB. Rlin llOott DODD Dsecc I.DODD 0.0315 .5656 1 1 00 rbdl 0 .0174 0.561' 0 .9998 1 .000 0 oHS
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Correlation results were noteworthy as much for the relationships which did not exist as for those that did and e xc eptions were often geographically based. Both DO and salinity/conductivity correlated strongly and inversely with temperature at most stations. This is not unexpected due to the strong seasonality of these parameters : DO and salinity are low in summer when rainfall and temperature are high, while the reverse is true in winter For DO, no significant relationship with temperature at all occurred at stations 10, 12 and 206 Stations 10 (Figure 2j) and 12 (Figure 2k) are far up Henderson Creek where DO was generally lower and periods of anoxia occurred at certain times of the year These anoxic events were not coincidental with the highest temperatures Station 206 (Figure 2r) occurs adjacent to Marco Island near the main entrance to Rookery Bay and DO may be more influenced by tidal exchange with Gulf waters than strictly by temperature pH and DO, which might both be affected b y respiration mostly correlated at upstream locations (Stations 1 4 9 10 12 191) (Figure 2) usually either surface or bottom values only pH was lower (more acidic) at these stations as was DO. Salinity and conductivity showed a similar geographic pattern in its correlation with temperature Significant correlation did not occur at station 10 (the most upstream station) nor at stations 205 and 206 which, again, occur near the entrance to the estuary Station 10 was the station which experienced the lowest mean salinity and the site of the greatest variability. The correlation between rain and temperature was not as strong as at most other stations Rain correlated strongly with temperature at most stations with r-values exceeding 0 9 at all stations except station 10 (Figure 2j) (r = 0 86 and 0 87 for surface 26

PAGE 36

and bottom temperature respectively) bottom temperature at station 12 (Figure 2k) (r = 0 88) and bottom temperature at station 180 (r = 88) (Figure 2n) This is not surprising since temperature was not highly variable between stations & rainfall was considered to be the same for all stations Rainfall correlated negatively with salinity and conductivity at all stations at all stations except 2 (Figure 2b ) 160 (Figure 21) and 192 (Figure 2p ), where rainfall correlated with surface values of sa linit y and conductivity only These stations were not amo ng the deeper stations (d < 3m) and these results might indicate calm, poorly mix ed waters Station 205, 206 and 210 meanwhile, demonstrated no significant correlation between rainfall and salinity/conductivity Salinity and conductivity were consistently high relative to other stations (mean surface salinity 35.52 35.62 and 34.68 for stations 205, 206 and 210 respectively ; mean bottom salinity 35 59, 35 .65 and 35.15 for stations 205, 206 and 210 respectively) This would tend to suggest that rainfall is not of significant influence over salinity relative to Gulf waters at these stations and that these stations are well mixed by tides and/or winds or that rainfall has only a short term influence over salinity Table 1 : Student's ttests for Surface vs Bottom Values of Each Parameter for all Stations Variable ta(2) oo P-Value Temperature 0 .08662 0 93097 pH 0 23459 0.81452 Dissolved Oxygen 6 11592 0 .00000 Redox Potential 0.45538 0 64884 Salinity 2 26982 0 02322 Conductivity 6.06683 0 00000 Turbidity 7 1686 0 00000 27

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A strong negative correlation between turbidity and secchi depth occurred at stations 2 7 8, 160 and 205, and between secchi depth and surface turbidity only at 3, 180 and 206. Chlorophyll a and secchi depth were negatively correlated at stations 2 and 6 What little relationship existed between turbidity and chlorophyll a was only with bolLom turbidity values. Chlorophyll was positively correlated with bottom turbidity at station 2 whereas the relationship was strongly negative at station 12, the site of highest chlorophyll and second lowest turbidity Chlorophyll a thus appears to be a minor component in turbidity or water transparency except in isolated areas. Turbidity appears to affect secchi depth more frequently but does so at only 8 of the 19 stations Humic material derived from swamps was cited by McPherson et al. (1990) as a factor in the water clarity in Charlotte Harbor as well as adjacent areas in Florida. Rookery Bay is characterized by highly colored waters as well Mangroves, Rhizophora in particular, are known sources of tannins (Robertson et al. 1992) and this may be also be a factor in secchi depth Nutrients generally showed a high degree of correlation with each other, in particular N02 and P04 which correlated strongly at all stations for which nutrient data were available (2 5 6 12, 160, 170, 180 191). Nfl4 likewise was correlated with P04 at all ofthese stations except 160 and 170. N03 correlated with all other nutrient species at stations 2 and 180 with N02 and P04 at station 170 with P04 at stations 5 and 191, with Nfl4 at 6, and with no other nutrient species at station 160. An unexpected result of this analysis is that nutrients correlated so inconsistently with chlorophyll. Chlorophyll was positively correlated with N03 at stations 2 5 and 170 and with no nutrient species at 6 28

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12, 160 180 191. This suggests that nutrients may not have been the major factor limiting phytoplankton growth/biomass as one would expect a negative correlation between nutrients and chlorophyll ; however it is possible that nutrient regeneration may take place too rapidly to be reflected in these measurements. Turbidity demonstrated one of the strongest and most consistent relationships with nutrients Strong positive relationships existed between turbidity with all nutrient species at all stations for which data was collected with the following exceptions : Station 12 showed no statistically significant relationship because of the smaller number of "samples" at that station once they were collapsed into wet/dry seasons (while the r values were high they were still not considered statistically significant); the relationship existed with all nutrients except N03 at station 5 with all except N"R. at station 170, with P04 (surface turbidity only) and N03 at station 160 ; the relationship between turbidity and N03 was negative at station 6 and station 191 (bottom turbidity only) This tends to indicate that resuspension of sediments may be a signi ficant source of most nutrients, with the possible exception of nitrate There was a frequent occurrence of negative correlation between pH and nutrients at many of the stations. pH correlated negatively with at least 2 nutrient species, particularly P04,at all sampled stations with the exception of stations 160 and 191. Multiple regression Regression models for both surface and bottom DO as well as chlorophyll a were selected based on the combination of variables which maximized the value of ? (Zar, 1 984) at each station (Table I). Temperature and pH both most frequently impacted the 29

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regression models for both surface and bottom DO. Temperature was a factor in the bottom DO regression models at all stations except 12 and in the surface DO regression models at all stations but 10 and 12 pH was a factor in all DO regressions except surface DO at station 160 and bottom DO at station 180 Salinity was a factor in DO regressions for stations in Henderson Creek and in Rookery Bay proper both for surface and bottom values (stations 4 5 7 9, 10, 12) for surface DO (station 6), and bottom DO only (stations 3 and 170). Turbidity was a factor at most of these same stations, though, counter to expectations, more frequently at the surface than the bottom, where turbidity was higher. Nutrients were a factor at all stations at which they were assayed for except station 6 and surface DO at station 5. Neither N02 nor P04 was a factor in any regression Chlorophyll a was a factor at stations 2 160 170, 180 and 191, as well as station 6 (bottom DO only) For Chlorophyll a temperature affected the regression at all stations a factor at station 2, 160 and 170 N03 at stations 5, 170 180 and 191, N02 at station 160 and P04 at station 2 DO was a factor at stations 12, 160, 170 and 180. As mentioned previously, station 5 was the one station where different results were obtained depending on whether surface or bottom values for a given independent variable were used in the regression given the stratification at this station When bottom values were used temp, pH, turbidity redox potential, N03 and P04 influenced the regression with an r2 value of 48.4786% While stations 160 and 191 also exhibited a degree of stratification, the results were not significantly different whether bottom or surface values were used for a given independent variable 30

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Table 2 Multiple r egression : Depend ent and i ndependent v ariables by station and r values f o r e a ch regression Independent v ariable s shown are the combination which maximized the r2 for the regression (ex plai n ed the g reat e st portion of the variability) at that station. Dep Variable Station Ind. Variables r Surface DO 1 Temp pH, Turb 69 378 % 2 T emp, pH N03 Chla Turb 75.487% 3 Temp pH, Turb 56 874% 4 Temp Salinity pH, Turb 64.054% 5 Temp, Salinity pH 43.961% 6 Temp Salinity, pH, Turb 61.875% 7 Temp Salinity pH, Turb 61.828% 8 Temp Salinity p H Turb 65 902% 9 T e mp Salinity pH, Turb 51.026% 10 Salinity pH 51.694% 12 Salinity pH, NH 4 Redo x 71.186% 160 Temp N03 Chla 65 915% 170 Temp pH N03 NH 4 Redo x, Chla 62.804% 180 Temp pH N03, NH 4 Chla 76.266% 191 Temp pH N03, NH4 Chla 71. 839% 192 Temp pH 52 381% 205 Temp pH 25 997% 206 Temp pH 42. 381% 210 Temp, pH 51.626% Bottom DO 1 Temp pH Turb 65 556% 2 Temp pH NH4 N03 chla 70.258% 3 Temp Salinity pH, Turb 66.507% 4 Temp S a lin i ty pH, Turb 62. 912% 5 Temp Salin i ty pH, NH4. N03 71. 280% 6 T e mp pH, Chl a 60 049% 7 Temp Salin i ty, pH, Turb 65 630% 8 Temp Salinity, pH 73 578% 9 T e mp S a l i nity, pH, Turb 71. 703% 10 T e mp S alin ity pH 56 060% 12 pH, N03, NH4 Redo x 89 712% 160 Temp N03 Turb Chla 72 215% 170 Temp, Salinity pH NH 4 Chla Redox 66 903% 1 80 T emp, N03 NH 4 Chla 63 310% 191 Temp, pH Chla 51. 038% 192 Temp pH 39. 720% 205 Temp pH 35.221% 206 Temp pH 40.769% 210 Temp p H 46 646% Chlorophyll a 2 T e mp NH 4 P04. pH 57 .601% 5 Temp N03 Redo x 48.479% 6 T emp, R e do x 43.487 % 12 Temp DO 44.592% 160 Temp, DO Salinity N02 NH4 75.553 % 170 Temp, Salinity DO pH, NH 4 N03. Redox 65.703% 180 Temp DO N03 Turb 37.551% 19 1 Temp N03 43 505% 3 1

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Spatial trends Table 3 : Variability between stations sampled in this study as indicated by ANOV A performed on the raw data. Statistically significant differences between stations are d t d b P I 0 05 m 1ca e >Y -va ues < Variable 1 :F-Ratio:'P-Value Depth 563 82 0 0000 Secchi Depth 17 70 0.0000 Surf Temperature 1 90 0 0124 Btm Temperature 1 70 0.0329 Surface pH 47 27 0 0000 Bottom pH 66 53 0 0000 Surface DO 13 28 0 0000 Bottom DO 24. 02 0.0000 Surf Conductivity 104 82 0 0000 Btm Conductivity 100 74 0 0000 Surface Redox 0 55 0 9352 Bottom Redox 1 14 0 3045 Surface Salinity 86.15 0 0000 Bottom Salinity 87.23 0 0000 Surface Turbidity 17 37 0 0000 Bottom Turbidity 14.40 0 0000 Nitrate 1 .61 0 0000 Nitrite 1 70 0 0956 Ammonia 4 06 0 0001 Phosphate 1 27 0 .2 579 Chlorophyll 2 99 0 0029 Table 4 : Variab i lity between stati o n s comprising the Bay Transect as indicated by ANOVA performed on the ra w data. Statis tically significan t differences between stations d d b p al < 0 05 are m r eate JY v ues Variable F-Ratio P-Value Depth 1321 82 0 0000 Secchi Depth 16 65 0 0000 Surf Temperature 1 73 0 1257 Btm Temperature 1 30 0 2621 Surface pH 2 .47 0 0312 Bottom pH 6 8 0 0 0000 Surface DO 6 15 0 0000 Bottom DO 7.89 0 0000 Surf Conductivity 5.42 0 0001 Btm Conductivity 2.55 0 0 2 68 Surface Redox 0.27 0 9306 Bottom Redox 0 23 0 9465 Surface Salinity 4.44 0.0006 Bottom Salinity 3.71 0 0026 Surface Turbidity 2 7 4 0 0125 Bottom Turbidity 3 0 2 0 0107 Nitrate 1 62 0 154 2 Nitrite 0 22 0.9530 Ammonia 0 62 0 6855 Phosphate 0.4 3 0 8292 Chlorophyll 1 3 2 0 2571 3 2

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Table 5 : Variability between stations comprising the Henderson Creek Transect as indicated by ANOV A performed on the raw data Statistically significant differences bet t f d t db P l 0 05 ween s a Ions are m 1ca e )y -va ues < Variable F -Ratiq p:. Va{ue ( Depth 497.09 0.0000 Secchi Depth 50 58 0.0000 Surf Temperature 1.55 0.1853 Btm Temperature 1.98 0.0963 Surface pH 52.33 0.0000 Bottom pH 79 09 0 0000 Surface DO 19 40 0 0000 Bottom DO 40 .81 0.0000 Surf Conductivity 89 63 0 .0000 Btm Conductivity 102.48 0 0000 Surface Redox 0.07 0 9922 Bottom Redox 2 16 0.0731 Surface Salinity 67.92 0 0000 Bottom Salinity 76.81 0 .0000 Surface Turbidity 60 33 0 .0000 Bottom Turbidity 29 .73 0.0000 Analysis of variance (Table 3 ) performed on the raw data revealed significant differences between stations for most but not all parameters Dissolved oxygen, pH, conductivity and salinity and turbidity were all significantly different with the most striking contrast between upstream s t a tions (9 10 and 12) and those situated downstream Of the nutrients only ammonia differed between stations (Fo. o 5 (1)8 ,353 = 4.06), however only station 12 differed from the others. The situation was much the same for chlorophyll a (Fo.os( t ) 8 419 = 2 99) except that differences also existed between station 191 and stations 5 and 6. ANOV A was also run on the two transects. Dissolved oxygen salinity turbidity, and conductiv i ty differed signific antly between stations for the Bay transect (Table 4), alt hough the value ofF for bottom conductivity (Fo. o s( t )5,66 4 = 2.55) exceeded the critical v alue (Zar 1984) by a mere 0 02 Bottom pH was shown to differ between stations in this 33

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transect (Fo. os(I)5,621 = 6 80), but surface pH did not (F0 .05c1>5 627 = 2.47) For the Henderson Creek transect (Table 5) only temperature which did not differ between stations overall, and redox potential were not significantly different while conductivity, dissolved oxygen, pH, secchi depth (depths), salinity and turbidity proved different. Differences between surface and bottom values were assessed using Student's ttest (Table 1 ) 11 Stations demonstrated a significant difference between the surface and the bottom for at least one paramet e r At stations 1, 2, 3 10, 180 and 206 only turbidity was significantly different. The r es t were as follows: station 4 : salinity, conductivity and station 5 : salinity and station 9 : salinity, conductivity, DO, and station 160 : pH and and station 191: salinity, turbidity, conductivity and pH. Differences between surface and bottom values at station 9 were the greatest of any of the stations and indicate moderate to strong stratification during the wet season Likewise, the existence of differences in surface and bottom values for salinity at the mouth of Henderson Creek (stations 4 and 5) are suggestive of the formation of a salt wedge in this area To test if the occurrence of significant differences might be dependent on overall depth at that station, the stations were grouped into three categories : no differences in any parameter' 'difference in turbidity only (because turbidity was frequently the only parameter which differed) and differences in other parameters' The mean depth for the stations in each category was charted (Figure 3). The result proved opposite to what might be expected The stations where 'no differences' occurred were deeper (mean = 2.3m) 34

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w V\ Figure 3. Mean depth of stations showing significant differences* between surface and bQttom parameters 2 5 e 2 o -,.c a 1 5 ($) a 1 0 1! Q> 0 5 0 .0 No Difference Turb Only Other Differences Based on Student's t-test

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The two transects show identifiable geographic patterns as can be seen in the charts for the average of each parameter Figures 4 and 5 For the Bay Transect, temperature dissolved oxygen and surface salinity remained relatively flat, while pH (except surface, wet season pH) and bottom salinity, which was more highly variable than surface salinity, reached their minimum at station 180 The maximum value for pH occurred at station 191, and for bottom salinity at station 2. Turbidity maxima occurred for both surface and bottom also occurred at station 180 for the dry season and station 6 for the wet season Turbidity fluctuated more between stations during the dry season. Wet season values remained relatively uniform between stations but for the higher observed average at station 6 Inorganic nutrient levels were not significantly different (a. = 0 05) between stations along the Bay transect (Figure 4) N03 N02 and P04 were higher in the dry season at most stations while + did not demonstrate a clear seasonal pattern between stations. The Henderson Creek transect showed distinctive upstream/downstream behavior, in particular during the dry season (Figure 5) Both surface and bottom pH for the dry season increased quite uniformly from 7 .23 and 7 14 respectively at station 10 (upstream) to 7 70 and 7 60 respectively at station 5 at the mouth of Henderson Creek Dry season dissolved oxygen increased generally, downstream but the peak occurred at station 4 with 5 slightly lower. Wet season DO was lower than the dry season and showed a slight, but steady increase downstream at the surface and the increase was larger for bottom values. Bottom redox potential increased very mildly downstream but surface redox showed a different pattern with values decreasing slightly downstream Surface redox 36

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w ---l Figure 4. Bay Transect Figure 4a. Average Depth for Bay Transect 7 6 .. 4 s:: Q. 3 Ql 0 2 1 0 Stn Stn 160 1 70 Stn Stn 180 2 S t n 6 Stn 1 9 1 II Avg 11\.et I]Avg Dry Figure 4c. Average Surface Temperature for Bay Transect 35 <,> 25 20t 15 : Ql @1 e 1 0 I I i:L I I I I I IIIIWetAvg b mDry Avg I! 5 0 St n Stn Stn Stn Stn Stn 160 17 0 180 2 6 191 I .r: c. Ql 0 :c u u Ql 1/) F igur e 4b. Avera g e Secchi Depth for Bay Transect 1. 6 1.4 1 2 1 0 8 0 6 0 4 0 2 0 St n 160 S t n 1 7 0 Stn 180 S t n 2 Sin 6 S t n 191 Figure 4d. Average Bottom Temperature for Bay Transect 35r--------------------------3 0 25 +I I I I I 1 1 W etAvg .a 20 .. 15 m aDy A v g . c. 10 E Ql 5 1-0 0 0 0 N <0 ..-<0 ,.._ co .6 .6 0) ..-..-..-..-.6 .6 .6 (/) (/) (/) (/) (/)

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VJ 00 Figure 4. Bay Transect (Cont.) Figure 4e. Average Surface pH for Bay Transect 8 7 9 7 8 7.7 =a 7 6 7 5 7 4 7 3 7.2 Stn 160 8 7 Stn Stn Stn 170 180 2 Stn 6 Stn 191 aWetAvg mDry Avg Figure 4g. Average Surface Dissolved Oxygen for Bay Transect t; f I W I I 1111 Wet Avg E 4 :r::; .._ ""' OrA 0 3 :>.l Iii) y vg c 2 1 0 0 0 0 (\I <0 .....
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w \D Figure 4. Bay Transect (Cont.) f"' Figure 4i. Average Surface Conductivity for Bay Transect E 60 0 50 G.l E 40 c;; 0 H G.l fl'i:':'l E 3 1!1!1 1'''' ' 1 11m1 1 1 aWetAvg it: rn ory Avg 20 ;; 10 ::I "0 0 c 0 Stn Stn Stn Stn Stn Stn u 1 60 1 7 0 180 2 6 19 1 Figure 8k. Average Surface Redox Potential for Bay Transect 0 3 -.------------------, E o .2s iU .. c a.. >< 0 0 2 0 .15 0.1 0 05 0 0 0 (() 1'--..-c c Ci5 Ci5 0 N (() .- ..-c C/) C/) c -C/) -C/) aWetAvg rn Dry Avg Figure 4j. Average Bottom Conductiv ity for f"' Bay Transect 60 .... 50 E .9.! en g ::I "0 c 0 u 40 30 20 10 0 0 (0 .,.... .s C/) 0 0 ('\1 ,..... co c .,.... .,.... U5 .s c en U5 (0 c U5 .-0> .,.... .s C/) aWetAvg GOry Avg Figure 41. Average Bottom Redox Potent i al for Bay Transect 0 25 z: 0 .2 iU c 0 15 + ..I..J... a.. 0.1 t lilill 0 05 0 0 (() .-IIIIRI;b Blim ;:!: 0 0 1'-co .,... .-i en i C/) m: 1 I WetAvg I, lli11 I IEl Dry Avg .. N (() .-i i 0> .C/) C/) i C/)

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-+:>. 0 Figure 4. Bay Transect (Cont.) 4 0 35 30 i 25 2 0 : 1 5 C1l (/) 1 0 5 0 Figu r e 4m Average Surface Salinity for Bay Transect Stn Stn S t n Stn Stn Stn 1 60 170 180 2 6 191 & W etAvg Eil Dry A v g Figure 4o. Average Surface Turbidity for Bay Transect 1 6 0 -r------------------, 14. 0 s-1 2 0 1-10 0 t I .TI ll::t::: 8 0 T : ::: :c 6 0 ... ::I 4 0 1-2 0 0 0 Stn Stn Stn Stn Stn Stn 160 170 1 80 2 6 191 Figure 4n. Average Bottom Salinity for Bay Transect 35 3 0 l 25 i!' 20 : E 1 5 1 0 5 0 g ...-f2 ...-.5 (/) ...-N .5 (/) (0 c U5 o; ...-c U5 & We t A v g g Dry Avg Figure 4p. Average Bottom Turbidity for Bay Transect 2 5 0 s-2 0 0 1-15 .0 :2 10 0 .c ... ::I 5 0 1-0 0 0 0 <0 ,..... ...... .... .E (/) .E (/) 0 N co .E .... .E C/) (/) (0 .... .E Cl) .... (/) .E C/) WetAvg oo Dry A v g

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Figure 4. Bay Transect (Cont.) Figure 4q Average Nitrate for Bay Transect 8 7 6 5 4 :::t. .!! 3 "' !: 2 z 1 0 -1 -2 Stn Stn Stn Stn Stn Stn 160 170 180 2 6 191 Figure 4s. Average Ammonia for Bay Transect 2 5 r------------------, 2 :::t. 1 5 "' 'E 0 0 5 0 -0. 5 ,L_ _______________ __J Stn 160 S tn 170 Stn 180 Stn 2 Stn 6 Stn 191 BWetAvg mDry A v g Figure 4r. Average Nitrite for Bay Transect 1 6 1 4 1.2 1 :::t. 0.8 ..!! 0 6 E 0.4 z 0.2 0 0.2 -0.4 Stn Stn Stn Stn Stn S t n 160 170 18 0 2 6 19 1 Figure 4t. Average Phosphate for Bay Transect 2.5 I 2 3 1 s I IV c. :s 0.. 0 5 0 0 5 0 0 <0 ,..._ ....-j; (/) j; (/) 0 N <0 ..-a) j; j; 0> ..-,.... j; (/) (/) j; (/) (/) WetAvg m D'y Avg WetAvg EJ Dry Avg

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.j:::.. I N 9 f"'B 7 Figure 4. Bay Transect (Cont.) Figure 4u. Average Chlorophyll a for Bay Transect 3 t <<: lr IL *i ll 1m l lmWetAvg 1iJ Cry Avg 1-.2 2 1 (.) 0 0 0 0 N <0 ..--<0 1'co c c 0> ..--..--..--..... ..... ..--c c c (/) (/) c ..... ..... ..... ..... (/) (/) (/) (/)

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w Figure 5. Henderson Creek Transect Figure 5a. Average Depth for Henderson Creek Transect 4 3 5 3 : 2 5 EJ Dry Average 11 Wet Average .c -2 fr 1 .5 Cl 1 0 5 0 Stn Stn Stn Stn Stn 10 9 12 4 5 Figure 5c. Avg Surface Temp for Henderson Creek 35.00 30.00 25 00 -20 00 :::1 iU 15 00 E {!!. 1 0 00 5 00 0 00 Stn 10 Stn9 Stn 12 Stn4 Stn5 Figure 5b. Average Secchi depth for Henderson Creek Transect 1.4 1 2 .. 1 i 0 8 t 1;:;:::::1 0 6 ;:::;m: a rti I IIII : :::r::: .c g 0.4 Ql (/) 0.2 0 Stn Stn Stn Stn Stn 10 9 12 4 5 Figure 5d. Avg Bottom Temp for Henderson Creek Transect 35 00 30 00 y 25 00 l!! 20.00 :I 15 00 !. E 10 00 5 00 0.00 Stn 10 Stn 9 Stn 12 Stn 4 Stn 5 []Avg I:Xy AvgWet

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..j:::. ..j:::. 8 .00 7 .80 7 .60 7.40 =[ 7 .20 7 .00 6 80 6 60 6.40 8 .00 7 00 6 00 'C" 5 00 Cl .E. 4 00 8 3.00 2 00 1 00 0 00 Figure 5. Henderson Creek Transect (Cont.) Figure 5e. Avg Surface pH for Henderson Creek Transect Stn 1 0 Stn 9 Stn 12 Stn4 Stn5 Figure 5g. Avg Surface DO for Henderson Creek Transect Stn 10 Stn9 Stn 12 Stn4 Stn 5 mAvg Dry mAvg Wet Figure Sf. Avg Bottom pH for Henderson Creek Transect 8 .00 7 .80 7 .60 7.40 =[ 7 20 7 00 6 80 6 60 6.40 Stn 1 0 Stn 9 Stn 12 Stn 4 Stn5 mAv g Dry mAvgWet Figure Sh. Avg Bottom DO for Henderson Creek Transect 8 00 7 00 6 00 5 00 lll i lrnAvg Dry :.:.;-.:..: E 4 00 d : ':.: AvgWet 0 3 00 c 2 .00 1.00 0 00 Stn Stn Stn Stn Stn 10 9 12 4 5

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Vl Figure 5. Henderson Creek Transect (Cont.) 'l' E 60 00 0 II) c: 50 00 Q) E Q) 40.00 00 E 30 00 :;: 20. 00 :;:; 0 ::J 10 00 "0 c: 0 0 00 (.) Figu r e 5i. Avg Surface Conducti vi t y for Henderson Creek Transect Stn 10 Stn 9 Stn 12 Stn 4 Stn 5 QAvg Dry aAvgWet Figure 5k Avg Surface Redox Potential for Henderson Creek Transect 0 300 0 250 0 200 c: Q) 0 0 150 Q. )( 0.100 0 ] 0 050 a::: 0 000 Stn Stn 10 9 Stn St n 12 4 Stn 5 mAvg Dry IIAvgWet 'l' E 60.00 0 50.00 Q) E 40.00 Q) 30. 00 >. 20. 00 +' :;: 10. 00 :;; 0 ::J 0 00 "0 c: 0 (.) Figure 5j. Avg Bottom Conductivity for Henderson Creek Transect Stn Stn Stn Stn Stn 10 9 12 4 5 Dry gAvg Wet Figu re 51. Avg Bottom R edox Potential for Hend e rson Creek Transect 0.300 0 250 0.200 0 Q. 0 150 )( 0 0.100 0 050 0 000 Stn 10 Stn9 S t n 12 Stn4 Stn5

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.j::.. 0\ Figure 5. Henderson Creek Transect (Cont.) Figure 5m. Avg Surface Salinity for Henderson Creek Transect 40.00 35.00 30.00 ..;. 25.00 20.00 '2 1 5 .00 (/) 10 .00 5 00 0.00 20 00 1 8 00 16 00 5 14 00 1-12 .00 10 .00 :c :a 8 .00 6 .00 4 .00 2 00 0 00 Stn 1 0 Stn 9 Stn 12 Stn 4 Stn 5 rnAvg Dry III!AvgWet Figure 5o Avg Surface Turbidity for Hende r son Creek Transect Stn1 0 Stn9 Stn1 2 Stn4 Stn5 [)Avg Dry II Avg \/I.e I Figure 5n. Average Bottom Salinity for Henderson Creek Transect 40 00 35. 00 30. 00 ;. 25 00 20 00 I: 1 5 .00 (/) 10 00 5 00 0 00 20. 00 18 00 16.00 5 14 00 1-12 00 10. 00 "C :a 8.00 ... :I 1-6 .00 4 .00 2 .00 0.00 Sin 10 Stn9 Stn 12 Stn4 S t n 5 0Avg Dry II Avg Wet Figure 5p. Average Bottom Turbidity for Henderson Creek Tansect Stn 10 Stn9 Stn 12 Stn 4 Stn5 mAvg Dry aAvg Wet

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potential at station 10, however was 0 012 for the dry season, while its wet season equivalent was 0 180 and the former value should perhaps, be questioned given the overall pattern and mean for Rookery Bay. Wet season surface salinity (Figure ) showed a marked increase downstream from a mean of 5 .90%o at station 10 to 27 .7%o at station 5, although salinity was lower at station 12 than at station 9 In fact, a second smaller, peak occurred at station 9 both for surface and bottom salinity and for both wet and dry season With the exception of stations 4 and 5, significant differences existed between surface and bottom values and between wet and dry season (Table 1 ), as would be expected The sharp salinity gradient which exists year round in Henderson Creek is itself, subject to fluctuation. Salinities fall nearly to zero at station 1 0 in August, when the maximum bottom salinity gradient of 28 07 %o is reached The maximum surface salinity gradient is 22 .92%o, observed in November. Smith (1993) observed an actual reversal of the gradient in Henderson Creek during April and May which were generally the months of lowest rainfall Such does not appear to be the case with this data set however the gradient does narrow considerably, particularly in the month of May when the observed gradient fell to minimum values of 11. 09%o and 12.44 o/oo respecti v ely for bottom and surface salinity Given the generally high salinities in the Bay, however, the estuary appears to be tide and not river-dominated Henderson Creek appears to contain many of the areas of the greatest variability in the Rookery Bay estuary (Appendix I) In addition to salinity, maxima and/or minima for pH, conductivity turbidity, and secchi depth were observed at station 10, though the Secchi depth minimum ofO .lm i s actually shared with station 12. pH at station 10 reached 47

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a minimum of 5 1 at the surface and 5. 5 at the bottom, though station 5 and station 8 were close behind with a surface pH of5.43 and a bottom pH of5. 54 respectively pH values as high as 8 5 at the surface and 8 6 at the bottom were observed at station 6 Conductivity fell as low as 0.3 mSiemens/cm2 at station 10 for both the surface and bottom, while turbidity was 0.3 NTU for the surface and 0.4 NTU for the bottom. In addition, a value for bottom redox potential of -0.031 v was observed at station 10 which, while not the lowesfvalue, was second only to station 8 at -0 041v Negative redox potential values are indicative of a lack of oxygen. Dissolved oxygen also fell below 1 mg r1 numerous times at this station, not always in the summer. A bottom DO reading ofO mg r1 was recorded on October 5, 1988 and a surface value of 0.7 mg r1 was observed in July 1988 Both represent the lowest observed values in Rookery Bay during the study period. A bottom value of 0.53 mg r1 was also observed in October, 1991 Station 10 was not alone in experiencing periods of hypoxia and anoxia A number of other stations including 8, 9, and 1 experienced periods at or near anoxia and all stations but 205 and 206 had minimum oxygen readings below 4 mg r1 at both the surface and the bottom Station 9, also part ofHenderson Creek, as well as station 8 experienced bottom DO concentrations below 1 mg r1 several times during the sampling period. Readings were taken during periods of daylight, though each station was not necessarily sampled at the same time of day. Since respiration by phytoplankton causes DO to drop at night periods of anoxia may have been more severe and frequent than reflected here A dissolved oxygen concentration below 3 mg r1 can place severe stress on organisms (Stickney, 1984) DO below 10% saturation often leads to widespread death of 48

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fauna, increasing the BOD and compounding the anoxia However, given the Bay's high salinities and its lack of a sill, it apparently flushes well and such periods of anoxia appear to be short-lived For comparison a recent data set from the upper Henderson Creek, roughly coinciding with station 9 in the present study, was obtained from Dr. Todd Hopkins of RBNERR This data was recorded on a semi-hourly basis using an in situ data logger covering the period from January 1997 to February, 1998; however, 4 of the data files, representing January and March-May, 1997 could not be opened These results are shown in Appendix 5 An inspection of this data revealed hypoxic and anoxic conditions to be as pervasive as in the present study Eight examples of anoxia occurred between June, 1997 and February, 1998, six of them in July, 1997. Oddly, the other anoxic values were measured in February 1998. February was generally among the colder months and was the month of the second highest DO reading (5. 5 mg r1 for the bottom) The mean for February 1998 was 4.0 mg r1 ; however that for February 1997 was 5 5 mg r1 in exact agreement with the mean for February at station 9 in the present study (Figure 8e) The mean for July 1997 was 2 2 mg r1 and that for September, 2.0 mg r1 again, similar to mean values at station 9 for July and September, respectively, of 1.8 mg r1 and 2 5 mg r1 The mean for August, 1997, on the other hand was significantly higher at 5.0 mg r1 Taken in the context of both the present study's results and those of Yokel (1975), who measured mean DO values of 3.89 mg r1 upstream in Stopper Creek and 3 .93 mg r1 in upper Henderson Creek for the period from June 1970 to December, 1972 suggest that 49

PAGE 59

low DO is simply a innate feature of this estuary rather than an induced or anthropogenic phenomenon Salinities fell to O%o not only at station 10 but stations 9 and 12 as well Rookery Bay, particularly in comparison to other estuaries, tends toward hypersalinity and these latter three were the only stations which did not experience maximum observable salinity values in excess of 40%o at any time Hypersaline conditions were not uncommon, particularly in the March and April, prior to the onset of the rainy season The stations where maximum salinity was observed were stations 5 and 1 where salinity reached 43.8%o at the surface Stations 1 and 191 reached a bottom salinity of 43.9%o, the maximum for Rookery Bay during this sampling period, however eleven other stations experienced salinity >43%o at least once 50

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Temporal Trends Table 6 Overall mean for Rookery Bay based on raw data for each parameter and rrurumum and maximum annual means for each parameter and ear in which they occurred :':::::: ... :::.: overall Mean Miri>::):'''\YEiaf:. Depth 2.0m 1.7m 1989 2.3m 1986 Secchi Depth 0.9m 0 .8m 1 986 1.1m 1991 Surf Temperature 25.97C 25.37 C 1988 27 00C 1990 Btm Temperature 25.97 C 25.34C 1988 27.00C 1990 Surface pH 7.61 7.42 1987 7.83 1991 Bottom pH 7.61 7 .40 1987 7.86 1991 Surface DO 5 69 mg r1 5.33 mg r1 1989 6 .30 mg r1 1986 Bottom DO 5.37 mg r1 5 .00 mg r1 1989 6 10 mg r1 1986 Surf Conductivity 45. 05 mS 39.49 1991 51.72 mS 1989 Btm Conductivity 47.53 mS 43.73 1991 53.46 mS 1989 Surface Salinity 29.52 %o 25 .52 %o 1991 34.53 %o 1989 Bottom Salinity 31.19 %o 28 .35 % o 1991 35.44 o/oo 1989 Surface Turbidity 6.9 NTU 3.1 NTU 1991 9.8 NTU 1988 Bottom Turbidity 8 9 NTU 4 3 NTU 1991 12.5 NTU 1988 Nitrate 1 .06 J..LM 0 .46 J..LM 1991 3 27 J..LM 1988 Nitrite 0.60 J..LM 0 .10 J..LM 1990 1 .82 J..LM 1988 Ammonia 1.23 J..LM 0.87 J..LM 1991 1 .89 J..LM 1988 Phosphate 0.89 J..LM 0 .32 J..LM 1991 2 .73 J..LM 1988 Chlorophyll 4.57J..Lgl-l 4 .14J..Lgr1 1989 5 .11 J.lgr1 1990 Rainfall 424 mm 298 mm 1988 557 mm 1991 Table 7. Minimum and maxrmum monthly means and month in which thev occurred . Vatfa.bfe'::::::::t M;n > IYJontnti:' Depth 1 .9m J-M M,D 2 3m Sept Secchi Depth 0 .8m F-A 1 1 m Oct-Dec Surf Temperature 20.69C Dec 29 .99 C July Btm Temperature 20.63C Dec 30.07 C July Surface pH 7.54 July 7 76 March Bottom pH 7.57 July 7.75 March Surface DO 4.47 mg r1 Aug 6 72 mg r1 March Bottom DO 4 .04 mg r1 Aug 6.43 mg r1 March Surf Conductivity 35. 24 mS Aug 53.57 mS May Btm Conductivity 39.51 mS Sept 53.76 ms May Surface Salinity 22.32 %o Sept 35.33 o/oo May Bottom Salinity 26.01 %o Sept 35. 69 o/oo May Surface Turbidity 3 7 NTU Sept 1 0 7 NTU Feb Bottom Turbidity 5.5 NTU Sept 13 5 NTU Feb Nitrate 0.33 J..LM April 1 72 J..LM Oct Nitrite 0.10 J..LM April 1 17 J..LM Sept Ammonia 0.63 J..LM Feb 1 67 J..LM Dec Phosphate 0.22 J..LM Oct 2 83 J..LM Jan Chlorophyll 1.63 J.lg r1 Jan 7 37 J.lg r1 Sept Rainfall 94 mm Dec 837 mm August 51

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Vl N -E .c: Q. (I) 0 :::s e Q) c. E Q) 1Figure 6. Annual Averages Figure 6a Ave rage Annua I Depth 4 0 3 5 3 0 2 5 2 0 1 5 1.0 0 5 0 0 3 0 00 28 00 2 6 .00 2 4.00 22.00 2 0 .00
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Vl w Figure 6. Annual Averages (Cont.) Figure 6e. Average Annual DO 8 .00 7 .00 6 .00 .:... 5 00 Cl .. 4 00 0 3.00 c 2 .00 1 00 0 .00 CD 1'-co co 0) 0) ...... ...... co 0) 0 co co 0) 0) 0) 0) ...... ...... ...... ...... N 0) 0) 0) 0) ...... m DO Surf II DOBtm Figure 6g. Average Annual Redox Potential > 0 3 0 25 'i6 :;; 02 f c: I I I I !Em RdxSurf Cl.l 0 .15 ... 0 tiiilii IIRdxBtm D. )( 0.1 0 0 .05 "C Cl.l r:k: 0 1'-co 0) 0 ...... N co co co 0) 0) 0) 0) 0) 0) 0) 0) 0) ...... ...... ...... ...... ...... ...... Figure Sf. Average Annual Conductivity 60.00 ..---------=;:::::;:::------, >-e so.oo 40.00 :;; g Cl.l 30.00 g 20.00 (..) 10.00 0 .0 0 CD 1'--CO 0) 0 ..N co co co co 0) 0) 0) 0) 0) 0) 0) 0) 0) 0) ..... ...... ..... ...... ...... 1m CndSurf 11CndBtm Figure 6h Average Annual Salinity 40 "0' 3 0 i 20tl I I I I l l lmSaiSurf IISaiBtm 'i6 10 en 0 1'-co 0) 0 ...... N co co co 0) 0) 0) 0) 0) 0) 0) 0) 0) ...... ...... ...... ...... ...... ......

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VI Figure 6. Annual Averages (Cont.) ::J z >o :0 :.0 .... ::J :e -Figure 6i. Average Annual Turbidity 20 0 .....------------, 15.0 10 0 tl 1 I T l lmTurbSurf IIIII TurbBtm 5 0 0 0 (!) ,.._ CX) 0) 0 N CX) CX) CX) CX) (j) (j) (j) (j) (j) (j) 0) (j) (j) (j) ..--..-Figure 6k. Average Annual Nitrite 2 1 5 1 c) 2 0 5 0 1988 1989 1990 1991 Figure 6j. Average Annual Nitrate 6 -4 :e .33 d z 2 0 1988 1989 1990 1991 Figure 61. Average Annual Ammonia 2 5 -r----------------..., 2 i' 1 5 .5 'I..., 1 z 0 5 0 1988 1989 1990 1991

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V1 V1 3 2 5 -2 :!: .3 1 5 .... 0 1 a.. 0 5 0 Figure 6. Annual Averages (Cont.) Figure Sm. Average Annual Phosphate Figure 6n Average Annual Chlorophyll a 1988 1989 1990 1000 800 600 = ... c-400 200 0 200 1991 6 Cl .34 .s= 2 (.) 0 F igure 6o. Average Annual Rainfall 1988 1986 1987 1988 1989 1990 1991 1992 Owrall A\g = 424 mm 1989 1990 1991

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Vl 0\ -E .r:. a. Q) Cl 35 30 25 20 E" 15 Q) 1-10 5 0 Figure 7. Monthly Averages 2 5 2 1 5 1 0.5 0 Figure 7a. Average Monthly Depth for Rookery Bay c ro ...., .a .. c c.. ro ::J <( ...., .... .... >.. 0> 0.. (.) s ::::'1 0 z (.) (.) ro C1l .., a. ro ::J ::J ::J C1l 0 o C1l """) u.. <( ::!: """) """) <( w z 0 lllTrrpSurf TrrpBtm .. ..c Q. Q) "CI :.c (.) (.) Q) (/) Figure 7b. Average Monthly Secchi Depth for Rookery Bay 1 2 1 0 8 0 6 0.4 0.2 0 8 7 8 7 6 I i I I c .a L.. ro .. 0> 0.. (.) "3 ::::'1 .. c o. ro ::J <( ...., > 0 z Figure 7d. Average Monthly pH for All Stations Combined :a 7 4 7 2 7 6 8 c .a L.. a. c C) D. 0 !) &l to Q) to to ::J 3 """) u.. ::!: <( ::!: """) Q) 0 z 0 """) (JJ (.)
PAGE 66

Vl -.1 Figure 7. Monthly Averages (Cont.) Figure 7e. Average Monthly DO for All Stations Combined 7 6 5 =a, .. 4 0 0 3 2 0 !'''''Jill : '1111111''''J!!!II c Ill --, .J:I Q) u._ lii ::2 a. <( >. Ill ::2 c :J --, >. ::; --, 0) c. 0 :J Q) 0 <( (/) z 0 Q) 0 !lJ DO Surf IIIIIIDOBtm Figure 7g. Average Monthly Turbidity for Rookery Bay 20 18 16 ::J 14 1-i t T Ill TI _T I I m Tur bSurf T T -T &TurbBtm .c .. 6 ::I 14 2 0 c .J:J ..... ..... >. c C) 0 > 0 111 Ql 111 a. 111 ::J a. 0 Ql --, LL ::2 <( ::2 --, ::J Ql 0 z Cl --, (/) Figure 7f. Average Monthly Salinity for Rookery Bay 40 35 30 0 25 20 .5 15 iii (/) 10 5 0 c .J:J . c >. C) a. 0 > 0 111 Ql a. 111 :J ::; ::J 0 Ql --, LL ::2 <( ::2 --, <( Ql 0 z 0 --, (/) Figure 7h. Average Monthly Nitrate for Rookery Bay 3.50 3 .00 2 .50 2 .00 ;:[; z 1 .00 0 .50 1' 3 150 l I 0 .00 i . ; -0.50 c Cll ...., .J:I ..... Cll :::!! ..... a. < i;' :::!! c :J ...., j?_. :J ...., Cl a Q) (/) z 8 EJ Sal Surf aSaiBtm

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V'l 00 Figure 7. Monthly A v erages (Cont.) Figure 7 i. Average Monthly Nitrite for Rookery Ba y 1 60 ......-----------------, 1.40 1 .20 1.00 3 0.80 O N .. ::,:: ;:: I z 0.60 QOO i I I I I I I c: ro ...., .0 u.. iii .... a. <( >ro c: ::l ...., >-C) a o 0 1) z Figure 7k Average Monthly Phosphate for Rookery Bay 4 0 0 T 3.50 3 .00 :w. 200 [ -; 0 '"' D. 1 .50 :si' 1 .00 0 .50 0 00 l-1 c: ro ..., .0 iii .... Q) u.. >-c: >-Ol 15.. 0 i) co ::l "3 ..., 0 z ..., en 0 0 Figure 7j. A verage Monthly Ammonia for Rookery Bay 2 5 -r----------------------, 2 151 lill :::: :;t: :: I -r 1 j: z :-::<: : .:::-:-:.: :-: ;11 :.: : ::::: 0 .5-1; .. ; ::::.; ::: ;: 11 ::::: ::: :: :-:-: .;.;. i!li! :.::: :: ;.;.;. ::: =:: :-::: :.::-===-;.;.; ::::;:; 0 ; .;.:-;.;.; c: .0 iii a. >-c: >-Ol 15.. 0 > 0 co ro ::l "3 ::l 0 0 ...., u.. <( ...., ...., <( en z 0 Figure 7m. Average Monthly Rainfall for Rookery Bay 1200 1 000 800 e .5. 600 .. ::: I l 400 c ii l l a:: 200 0 ,F.;;."'!;:J I -200 c .0 iii .... >. c >. Ol 15.. 0 i) co ro ::l "3 ::l ..., u.. ...., <( Cll 0 z 0 ...., en

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Vl \() Figure 7. Monthly Averages (Cont.) Figure 7m. Average Monthly Rai nfall for Rookery Bay 1986 1992 800 700 E .. 600 500 = 400 c 300 Ill 200 100 0 c Ill ...., .c (1) lJ.. ._ Ill :2 >-C) a :; :::1 (1) ...., -c c. Ill :::1 0 z () (1) Q

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Minimum and mruamum annual means and plots of annual means for each parameter are shown in Table 6 and Figure 6, respectively Most parameters showed variability by year. Salinity (Figure 6h) and conductivity (Figure 6f) peaked in 1989 and were at their minimum in 1991. 1991 was the year of the highest rainfall (Figure 6o) at 557 mm with 1989 second highest at 474 mm indicating, again, that salinity was not strictly a function of rainfall. 1990 was the warmest year and 1988 the coolest (Figure 6c ) Turbidity (Figure 6i) and nutrients (Figure 6j-m) were maximal in 1988 and declined sharply thereafter. Chlorophyll a (Figure 6n), on the other hand, showed very little variability. Most parameters showed a rather distinct seasonality as illustrated in the monthly average charts for each parameter (Figure 7). Temperature, DO, and salinity all behave in a smooth sinusoidal manner. Temperature peaks in July on average, followed closely by August and September, with surface temperature slightly lower (29.99 C vs 30.07 C) than bottom (Figure 7c). Minimum temperatures occur in December; 20.69 oc and 20.63 C for surface and bottom respectively. The dissolved oxygen curve followed that of temperature closely but minimum values were reached in August (Figure 7e). DO dropped to 4.47 mg r1 at the surface and 4.07 mg r1 at the bottom DO peaked in March at 6.72 mg r1 and 6.43 mg r1 for the surface and bottom Salinity was maximum in May at 35.33%o at the surface prior to the onset of the summer rainy season, and fell to 22.32o/oo in September (Figure 7f). Bottom salinity was less variable, with a May peak of 35.69%o and a minimum in September of26.01% o 60

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Rainfall, pH, turbidity, and chlorophyll followed a similar seasonal pattern but demonstrated greater variability in the curve. Rainfall fell into a somewhat discrete pattern throughout the year (Figure 7m) From November through April, rainfall remained low, varying between 94 mrn in December the annual low, and 284 mrn in March with a mean of 197 mrn. From June through September rainfall was consistently high and varied between 693 mrn in June and the annual high in August of837 mrn for a mean of776 mrn. May averaging 454 mrn, and October which averaged 401 mrn, represent transitional months during which the mean was 428 mrn, only 4 mrn off the overall mean rainfall for this sampling period of 424 mrn pH peaked in March at 7 .75 and 7 76 respectively for surface and bottom and with a low in September of 7 .53 and 7.56 (Figure 7d). There was, however, secondary fluctuation between months within that general pattern Likewise, turbidity which peaked in February at 10. 7 NTU and 13. 5 NTU and bottomed out in September at 3 7 NTU and 5.5 NTU, fluctuated slightly between months within the overall curve (Figure 7g). Maximum chlorophyll occurred in September at 7 37 r1 followed by July at 6.98 r \but dropped in August to 5 5 r1 (Figure 71). A second peak occurred in November at 4.40 r \ perhaps coinciding with a secondary fall bloom The minimum average, 1.63 r1 occurred in January. With a coefficient of 0.83 chlorophyll correlated well with rainfall. Chlorophyll increased 61% in June coinciding with the 56% increase in rainfall for these months. It is hard to imagine a causality in this relationship outside of the potential for rain-borne nutrient input which, as will be shown momentarily did not appear to occur 61

PAGE 71

Nutrients behaved far more erratically than did the physical parameters Nitrate showed two very distinct peaks in October and February of 1.72 J.lM and 1.65 J.lM respectively (Figure 7h). Another secondary peak of 1 36 J.1M was observed in October Between these months there was a great deal of variability although January and April June averaged between 0.33 and 0.42 11M. Nitrite also showed two peaks, 1.17 J.1M in September and 1.10 11M in January (Figure 7i) The lowest value, 0 .10 J.1M occurred in April. Ammonia showed a more distinct season pattern with a peak in September of 1.63 11M and more regular variability throughout the year (Figure 7j) The highest value, however occurred in December at 1.67 Phosphate showed quite a different pattern (Figure 7k). The spike in January must be regarded with some suspicion Values for all stations were greatly elevated on January 14, 1989 and may be causing erroneous inflation of the average. No sewage spills were reported on or near that date (Rhonda Watkins Collier Cty Poll. Control Dept, pers. comm ); however, since there was no viable reason to reject these points, they were allowed to remain in the data set. Barring the January spike the highest average was in June at 1.29 11M. This represents a 54 % increase over the previous month agreeing very closely with the increase in mean rainfall between May and June and matching the observations of Smith (1993) who c ited a similar increase for Rookery Bay coinciding with the summer rain onset. Despite this however, correlation between rainfall and nutrients was rather poor (Figure 2) Phosphate in fact, had the least significant correlation coefficient (-0 10) and exclusion of January from the calculation proved negligibly better (0.2344) The 62

PAGE 72

coincidence, therefore, between the onset of the ramy season and the increase in phosphate may be simply that. Nitrate's coefficient was negative (-0.19) indicating an inverse, albeit insignificant, relationship. Chlorophyll, as mentioned above, correlated well with rainfall but not with nutrients. The most significant relationship was a negative one with phosphate (-0.39) and the correlation with nitrate was similarly negative. Correlation between chlorophyll and phosphate with January excluded resulted in a positive, but less significant correlation (0 1 0). Differences in many parameters between upstream stations (1, 9, 10, 12) and the rest can be seen in plots of each parameter for all of Rookery Bay (Figure 8). Stations 1, 9, 10, and 12 were lower in pH (Figure 8d), DO (Figure 8e), Salinity (Figure 8h), conductivity (Figure 8f), and turbidity (Figure 8i). Nitrite (Figure 8k) and phosphate (Figure 8m) were lower at station 12, the only upstream station where nutrient data was collected, while ammonia (Figure 81) was higher at station 12 than any other station and nitrate (Figure 8j) was higher than all stations except 160 Chlorophyll a (Figure 8n) was also highest at station 12 Station 6 was the deepest station (Figure 8a) followed by stations 210 and 205. 63

PAGE 73

0\ $lJII Sin 2 Sin 3 stn-4 $lJI5 sine $lJI7 Sin 8 In g Stn 10 Stn 12 STniBO Stnl70 Stn 180 Stn 191 stn 192 ""205 stn 206 stn 210 stn1 stn2 stnl 11n4 11115 11116 ""' otnl ""' Sin 10 Sin 12 STn150 stntJO Sln1 .. Sin 111 lln1t2 101205 101206 *'210 ;,. Redox Pot M o e e f ---rnl ltlJ laJinlty('J.4 0 bt 1!; t [ij t; !! i> .:p .. iii '8 &' i!: !!. g !! ., f il 'i .. !. 2: '< "' '< c 0 stnl stn2 ,.,, s1n4 stn5 DO(mgt l stn6 ..... ......................... .' ............... .. stn7 stn B stnB Stn 1 0 Sin 12 STn160 ....... ... ,,v;; Stn170 Stn 180 ...... .. ,v., S'b1191 stn 192 ,,.;....-.... . ....... "' stn205 stn206 sb-1210 stnl sln2 sin 3 stn4 st n 5 stn e 11n7 llnB ltng Sin 10 Sin 12 STniBO Stn170 Stn lBO Sin lgl ltnltn ltn 205 ltn 20e ltn 210 rnl l!lJ Conductivity 'mSiemens em ') 0 :!! !!: l!: [[I] g ., "' c ; ;; "' 8 .![ "' ;;: 0 !! "' !Ill il "' n 0 i!.. .i "' It .. 0 Temp ('C) 0 Sin I stn 2 stn3 stn4 stn5 stn8 stn7 stn 8 stn 9 Sin 10 Sin 12 STnl60 Sln170 Stn 180 Stn 19 1 stn 192 stn 205 Sin 206 sin 210 tn l otn2 ... s .... ltn6 me stn7 ma ..,g Sin 10 Sin 12 STn180 S1n170 S1n lBO S1n lgl stnl112 Wt206 Wt206 otn210 [E =I pH e : : : ..., i:! i: [i]J !!: ,.. .. .... N !! "' c a If iii "' .. 3 "i i> g !! "' il "' ... :z: .!l !1 .. 0 Depth(m) 0 stnl Stn 191 stn 206 .. stn 210 J.-.w. ....w...w.w.J I I S e cehl Depth (m) 0000 -.... ..._ C.. Co .... N ..._ Ot slnl : I : I I stn 2 ... . .. ..... .. stn 3 .. ............... . ,, . . stn-4 ...................... .... stn 5 ttn e ltn7 ............. ., lin 8 ,. ,. .,. ltni ......... ,., Sin I 0 VWNw Stn l 2 v srnuso "' . .... .. Stnl70 Stn 1BO l Sin 1gl "' 11111112 llln205 oln 206 " "' llln210 iii c: iii !1i iii "' .. :r g C!. 0 ::> ., iii !; ,. "' J7 ,. il "' "' n n =: c :; "' :0 0 ::1 = ., QO 0 < ., ..... ..... 00. 0 = ., IZl

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Figure 8. Overall Station Averages Fig .. o 81. Avera go TUrbidity by Station Figure Bj. Average Nitrate by Station 30 6 25 5 4 i' 3 .3 2 fi : : : 6' 1 z 0 1 -2 -3 -5 N "' ., N 0 lil s ,._ tli c .. Iii tli Figure Bk. A ve rage Nitrite by Station Figure 81. A v erage Ammonia by Station 1 6 6 1.4 1.2 5 1 4 :? 0 8 .3 0 6 ;.;:;: c5' 0.4 n z 0 2 0 : .. -:: 0 2 i' 3 .3 :r; 2 z 0 -0. 4 1 N "' ., 0 0 ,._ s "' s c c c en Iii Ui Ui "' .., CD !il 0 ,_ c t; tli tli Figure Sm. Average Ph osp h ate by Station Figure Bn. Average Chlorophyll a by Station 3 2 5 14 2 12 :? 1.5 ..3 o 0.. 0.5 ::. 10 Ill 8 .3 .. 6 :;: 0 0 4 -0.5 2 1 0 "' "' CD !il 0 ,_ s s c c s "' "' Iii f-"' "' N "'
PAGE 75

35.00 30 00 25 .00 D.. 20 00 I I c 0\ . 0\ z -1s.oo I c 10 00 5 00 0 00 Figure 9. Average DIN:DIP by Month c:: ro -, ..c (1) u. L.... ro Phosphorus limitation Nitrogen limitation L.... c:: c. ro ::J <( -, ::J -, 0> 0. +-' ::J (1) () <( (/) 0 > () 0 (1) z 0

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In order to assess nutrient limitation patterns in Rookery Bay the monthly mean DIN: DIP was plotted (Figure 9) Only dates for which data for all nitrogen species was available were used to calculate the mean Based on the Redfield ratio of 16: 1, a general threshold between nitrogen and phosphorus limitation, Rookery Bay apparently becomes phosphorus limited in August (33. 08) and again though to a lesser degree, in October (21.27) and November (17.40) while remaining nitrogen limited the rest ofthe year For the remainder of the year the ratio remains between 5 and 10, with the exception of February (2 33) and September (3. 86) Principal Components Analysis Four principal components-three in the case of analysis 2 were sufficient to explain a majority of the original variability : 95% and 88% respectively for analyses 1 and 3 and 79% for analyses 4 and 5 The first three components in analysis 2 explained 81% of the variability The first PC in most cases, was a composite of most of the variables, predominantly pH, DO, conductivity salinity and turbidity with Eigenvectors between 0 2 and 0 3. Table 8 summarizes the contribution ofthe original variables to each PC. Points were plotted against the first two PCs for each analysis With the exception of analyses 4 and 5, the first two PCs accounted for a large fraction (>70%) of the total variability and therefore provide a sound graphic model for understanding the Rookery Bay system Eigenvalues and Eigenvectors for the PCA analyses are shown in Appendix 3. 67

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Table 8 Results ofPCA (based on Eigenvectors) showing impact (positive or negative) of h . l bl h . l u all al t e ongma vana es to eac pnnctpa component or an lyses. Contribution of Original Variables to Each Principal Component Analysis 1 Station for all variables Positive Negative PCI pH, DO, Cond, Sal Turb, N02, P04 NH4 Chla PC II Temp, SurfRedox Depth, SurfDO, Btm Redox, N03 PC III Depth (strongly) Temp, Btm Redox N03 (strongly) PCIV Temp, Secchi, Btm pH, DO Redox, Turb Analysis 2-Station averages for physical variables only (all stations) Pos itive Negative PCI pH, DO Btm Redox Cond, Sal Turb none signif PC II Temp (strongly), SurfRedo x Depth, Secchi PC III Depth SurfTemp SurfDO Redox (strongly) Analysis 3 Monthly averages for all stations Positive Negative PCI pH, DO, Cond Sal, Turb Depth Temp, Chla, Rain PC II Secchi Redox N03, NH4 Turb PC III Secchi, N02, P04 SurfRedox, Turb PCIV Cond, Btm Redox, Sal Btm Turb, N02, P04 Analysis 4 Wet/dry season averages over time for all variables Positive Negative PCI DO Cond, Sal Turb Temp Chla PC II Tu r b N03, N02, NH4, P04 Secchi pH PC III Temp, Redo x (Strongly) pH PCIV Depth Secchi Btm Sal, N02, P04 DO Redox Chla Analysis 5-Wet/dry season averages over time for all stations (Physical only) Positiv e Negative PCI pH, DO Cond, Sal Turb Temp PC II Temp, pH Redo x Turb PC III Temp, Cond Sal Turb Secchi DO, Redox PCIV Redox (strongly) Turb Depth Secchi (strongly) pH 68

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0\ \0 -(.) a.. Positions of Stations on PC I and PC II 191 and 160 do not best-fit _l+ Stn 191 lme for the Bay Transect. Therr pos1t10ns would ---sect be expected to be reversed .., ... '\ <,, \.,.".., '\ 1 ,, '\ \. \. 2 Stn 170 --------J?est-fit line for bay tr . 180 ., ... ;..,, -8 -6 -4 -2 Stn 12 -1 -2 PC I PC I and II represent 7 6% of original variability

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-.l 0 Figure 11. Plot of Eigenvectors of Station Averages (Physical (Analysis 2) (..) 0.. Position of Stations on PC I and PC II Upstream Stations -6 -4 9 3 1 PC I Rookery Bay Near Marco Island PC I and II represent 7 1% of original variability

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-..) ....... Figure 12. Plot of Eigenvectors of Station Averages (Physical Variables) (Analysis 2 ) --(.) n. Position of Stations on PC I and PC II 12 il 3 + 191 2 + 192 4 .r 206 ......... B.e.s.t:fit...line. .for. .. ... .. \ + 205 1 o 1 I ..... f'\ \ 210 -6 -4 +9 .... .,2 \ -2 8 80 J -1 7 \ 3 -2 _!2.. """fiOT PC I .s + Bes t -fit linelfor bay transect PC I and II represent 7 1 % of original variability

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-J N Figure 13. Plot of Eigenvectors of Monthly Averages Analvsis 3 Position of Months on PC I and PC II Fall Winter A "T --(.) a.. -17 . December ...... J v ....... V,."'vv 3 + ..... //./ \ _..-. /October -.,.Vv"J __ ..... ____ ..... / ..... 2 . ...___"'v"h.,h_,+.l Nove In be r .......... '.. ./ ... 1 August .. ,_"---..... "" Feb ., _ .._ __ h_,__ Mar 3 1 1 3 / 5 .,,, -i July.,.,_ -1 __ / .... / .,,"-----_,____ -2 _/_ .. / .. _ ______ . +-M-ay-- / April ........... ,..... ,/, .. 3 ............. ......... Jun-e .. ,.-----' A Summer PC I Spring PC I and II represent 71% of original variability

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-....J w (.) c.. Figure 14. Plot of Eigenvectors of Wet/Dry Seasonal Data (Analysis 4: Stations 2, 5, 6, 12, 160, 170, 180, 191) Positions of Seasonal Data (Physical and Nutrients) on PC I and PC II Station 12 Station 2 Station 5 D Station 12 Station 6 D Station 160 Station 170 Station 180 1 ..., ..1k : 9 1 7'1"' ... :i ... ', 1 Station 191 -8 W e lfseason 1991 -2 ., 991 -3 Dry Season 9 PC 1 PC I and II represent 56% of oiriinal variability

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-.l .+::>. Figure 15. Plot of Eigenvectors of Wet/Dry Seasonal Data (Analysis 4: Stations 2, 5, 6 12, 160 170, 180 191) Positions of Seasonal Data (Physical and Nutrients) on PC I Wet Season A vgs and PC 11 Dry S e a s on Avgs Predominant Predominant 5 4 3 .2 (.) a.. 1 1'\ .... '. .... 0 -8 6 4 .-. -1 (I i. -2 :' .. 3 A PCI PC I and II represent 56% of original variability

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-....J Vl Figure 16. Plot of Eigenvectors of Wet/Dry Season Data for All (Analysis 5) 0 D. Position of Seasonal Data (all Stations) on PC I and PC II Wet Season 1992 -6 -4 Stations 10, 9, and 12 (Upstream locations) PC I -3 Dry Season 1988 PC I and II represent 57% of original variability

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Analysis 1 (Figure 1 0) The plot for the station averages against PC I and PC IT shows a sharp, and distinct negative trend for PC II vs PC I which, but for the reversal of stations 160 and 191, coincides with the bay transect. Depth is the most obvious factor in this trend for it is highly variable and has a negative impact on PC II. Station 1 is the deepest with a mean of 5 7 M and station 191 among the shallowest with a mean of 1.0 M In addition, surface redox potential impacted PC II strongly and was somewhat elevated at station 6 Ammonia and nitrate levels were also negatively correlated with PC IT and were lower at stations 191 and 170 than at stations 5 and 6 There was little variability in PC I for these stations with the very significant exception of station 12 which lies deep in the negative region for PC I. Environmentally speaking, station 12 is significantly different from the other stations included in this plot by virtue of its upstream location PC I is influenced positively by salinity, conductivity pH, DO nitrite and phosphate which are all significantly lower at station 12 than at all other stations Likewise, ammonia and chlorophyll negatively impacted PC I and were highest at station 12. Analysis 2 (Figures 11 & 12) Patterns in the stations are more apparent in the plot of PCs I and IT for the physical data for all stations The upstream stations 10, 9, 12 and 1 are clustered at the negative end of PC I due to lower pH DO turbidity bottom and conductivity and salinity, which are highly variable at these stations (Figure 11). The stations comprising the 76

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Henderson Creek transect occur sequentially moving from negative to positive on PC I, corresponding to the increasing influence of the pH, DO, turbidity, and salinity regime of the bay waters (Figure 12). Dispersion on PC II is largely a function of depth and Secchi depth. Along the Henderson Creek transect Stations 5 and 9 are deeper than the mean and stations 4 and 12 are much shallower. Station 12 has the lowest Secchi depth of any station while station 9 is among the highest. A similar line can be drawn although not as distinctly, through the points comprising the bay transect. While the best-fit line on this plot for the Henderson Creek transect had a slope close to zero, the corresponding line for the Bay transect was negative on PC II. From stations 170 and 180, which lie very closely together on the plot, to station 6, depth increases to the maximum of all the stations Again, stations 160 and 191 do not fit sequentially along the best-fit line due to the shallowness and wanner temperature of station 191 and the greater Secchi depth at station 160. Stations lying close to Marco Island in the south occur as a group in the positive range for both PC I and II, reflecting the higher pH DO, salinity and conductivity (PCI) found at those stations Temperatures were slightly higher at these stations as well which, while not highly variable, had a significant impact (Eigenvector > 0 56) on PC II, therefore magnifying differences between sta tions. Stations 205 and 210 were also deeper (Figure 8a), and station 206 showed an elevated redo x potential (Figure 8g) Analysis 3 (Figure 13) The seasonal variability in the original variables is clearly reflected in the plot of the monthly averages on PC I and II which follows a nearly perfect elliptical pattern. PC I 77

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is positively influenced by pH, DO, turbidity salinity and conductivity, which would be e x pected to be higher in winter and lower in summer and negatively by temperature, chlorophyll a and rain, which are higher in summer and lower in winter PC II in tum, is impacted positively by Secchi depth, redox potential, nitrate and ammonia, which are higher in October and December and lower in the spring Maximum values for PC II are reached in December, when values for ammonia and Secchi depth are maximal, with minimum values occurring in June when nitrate and ammonia are relatively low A slight deviation from the overall elliptical pattern occurs in November when Secchi depth nitrate and ammonia values drop relative to October and December, and turbidity is slightly higher Similarly, July is less negative in PC II than the pattern would otherwise suggest due to lower turbidity and higher nitrate and ammonia values than those in June Analysis 4 (Figures 14 & 15) A number of obvious trends largely temporal ones, are apparent from the plot of the wet/dry seasonal averages for all parameters The main spatial trends were the clustering of points from station 12 in the negative region of PC I, again a function of the lower upstream DO, turbidity, salinity and conductivity regime and the higher chlorophyll values found at that station In addition station 12 was also shallower and had both lower pH and higher nutrient values causing it to be positive for PC II. An annual clustering phenomena was also apparent, with values of both PC I and II increasing from 1992 to 1988 and points of different years tending to cluster together (Figure 14) In addition, wet season averag es tended to be negative, and dry season averages positive for PC I following the seasonal fluctuations in temperature, DO, 78

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salinity and chlorophyll (Figure 15) Most negative for both PCs was 1991 which was both the wettest year and that with the lowest nutrient values, Turbidity was also low and pH and Secchi va lue s high Values for 1992 a year of lower nutrients, and higher pH and Secchi depth tended to be the most tightly clustered in the region positive for PC I and negative for PC IT. 1988 was the coolest and driest year characterized by high turbidity and significantly higher nutrient values Analysis 5 (Figure 16) Few patterns revealed themselves in the plot of wet/dry season averages for physical variables, however several trends are worth noting. Values for the wet season of 1992 tended to cluster in the most positive region for PC II due to the high redox potential and pH values and lower turbidity (Figure 16) Values for 1988, the coolest year, the year for which redox potential was lowest and, by far, the year with the highest nutrients, clustered in the lower right quadrant. Upstream stations 10, 9, and 12, with lower DO pH, salinity, and redox po t ential tended to be negative for both PCs, clustering in the lower left quadrant. Stations 191, 192, 205 206 and 210, situated near Marco Island and adjacent to the southernmost entrance to Rookery Bay, tended to be more positive on PC IT due to the increased salinity pH, turbidity and DO found at those stations The remaining stations clustered closer to the origin reflecting salinity pH, DO redox potential and turbidity values closer t o the mean 79

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DIS CUSSION Nutrient values are somewhat difficult to reconcile with those of Grabe (1993) given that his sampling occurred above the Henderson Creek weir (Rhonda Watkin, Collier County Pollution Control Dept. pers comrn ) while station 12 lies downstream of the weir structure Grabe (1993) gives mean nutrient values of0. 56 mg r1 N03 0 007 mg l-1 N02, 0 038 mg r1 and O-P04 of 0 008 mg r1 which, when converted to j..tM (Head 1979) yields 4 00 j..tM N03, 0 50 uM N02 2 .71 j..tM and 0 08 j..tM O-P04 Mean values for Rookery Bay based on the present data (Table 6) are 1 06 j..tM for N03 0 60 N02, 1.23 and 0 .89 P04 N03 and NH4 values for the period 1988-1992 were a factor of 2 -4 times below those quoted by Grabe ( 1993) for the period 1979-1989 Grabe s (1993) mean O -P04, however was an order of magnitude below that in the present study Mean N02 va lues were in near perfect agreement. Grabe (1993) cited an overall increase both in inor g anic nitrogen and in O-P04 through 1989 whereas the present study shows a peak in 1988 and a sharp decline thereafter (Figure 6). Station 12, presumably the closest geographically to Grabe's (1993) sampling point in Henderson Creek shows the highest mean NH4 and the second highest N03 values (1.67 and 1.96 respectively) in Rookery Bay (Figure 8) but are still lower than those of Grabe (1993) Mean N02 and P04 at station 12 (0 .14 and 0 22 j..tM respectively) are the lowe s t of any of th e sampled stations in Rookery Bay Though Grabe (1993) reports a slight overall increase in nutrients in Henderson Creek (strong in the case of phosphorus) the present data do not support the continuation of such an increase, for mean concentrations for all species fall off precipitously after 1988 (Figure 6) 80

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Mean nitrate for Rookery Bay (1.06 is almost precisely that reported by Thoemke and Gyorkos (1988) for the period June 1984-April 1985 (1.03 mM) Mean N02 and P04, however exceeded that reported by Thoemke and Gyorkos (1988) (0. 063 1-lM and 0 .07 1-lM respectively) by nearly an order of magnitude while values were roughly twice that of Thoernke and Gyorkos' (1988) reported level of 0 53 J.lM for Rookery Bay. These result are suggestive of an overall increase in nutrients other than N03 but it may be difficult to draw conclusions on the basis of an 11 month sampling period The closest agreement in data between Grabe (1993) and the present study was in chlorophyll a values. The mean chlorophyll a value found by Grabe (1993) for Henderson Creek was 8 911 J.lg r1 while at station 12 the mean was 7.47 J.lg r1 The mean for all of Rookery Bay was 4 .57 r1 (Table 6) Mean chlorophyll a concentrations found by Thoernke and Gyorkos (1988) is 6 3 mg r1 which was higher than that in the present study (4 57 mg l"1 ) At their furthest upstream station which appears to closely coincide with station 12 in the current study the concentration was 12 8 mg l"1 It is possible that the lower nutrients reported by Thoemke and Gyorkos (1988) were due to uptake by the larger reported algal biomass Nutrient concentrations reported in the present study are simi l ar to those reported for such low-nutrient regimes as Florida Bay (Fourqurean et al. 1993; Boyer et al 1997) Fourqurean et al (1993) reported concentrations of0.59 1-lM for N03, 0 .13 J.I.M N02, 1.89 J.I.M and 0 .37 J.I.M P04 Boyer et al. (1997) found median levels of 0.47 1-lM for N03, 0 19 J.I.M N02 and 2 .09 J.I.M NH4 N03 concentrations were much lower than those 81

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reported by McPherson & Miller (1991) of 29 7 mM and 42.2 mM for the Caloosahatchee and Peace Rivers respectively P04levels in the Peace River after 1981 were similarly high (35 9 !J.M) due in large part to local phosphate mining (McPherson & Miller, 1991) Despite these inputs McPherson et al. (1990) found N03 + N02 concentrations to be near the detection limit of 0.07 !J.M (0 .001 mg r1 ) most of the time significantly less than Rookery Bay's-though levels at or exceeding 21.42 !J.M (0.3 mg r1 ) were found near the tidal rivers Nutrient concentrations in Rookery Bay were lower than those reported for St. Lucie Estuary by Doering (1996) who found mean NOx concentrations of 4.9 !J.M, concentration of 2 7 llM and DIP values of 3.6 IJ.M. Mean NOx and concentrations as high as 65. 0 J..lM and 10. 2 !J.M, respectively, were found by Christian et al. (1991) in the Neuse River Estuary, NC, which led to mean chlorophyll a concentrations in excess of25 IJ.g r1 Many of Florida s west coast estuaries exhibit strong nitrogen limitation due to input of phosphate from mining operations and erosion of natural deposits. The high mean P04 concentrations cited by McPherson & Miller (1991) in the Peace River result in N limitation in Charlotte Harbor. Similarly Tomasko et al. (1996) found mean N : P ratios in Sarasota Bay < 5 making it similar to both Charlotte Harbor and Tampa Bay in N limitation. To the south, Florida Bay is largely P limited (Fourqurean et al. 1991; Fourqurean et al. 1993; Boyer et al. 1997 ; Boyer et al. 1998) Rookery Bay apparently tends toward N limitation (Figure 9) most times of the year based on the Redfield ratio (N : P) of 16: 1 though it does become strongly P limited in August and less so in October and November. Though there was not enough data to evaluate each station individually, 82

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there is reason to believe N : P limitation may vary spatially. Station 12 shows lower phosphate and significantly higher nitrate and ammonia than other stations, suggesting that, at this station at least, phosphorus limitation may be more commonplace. Boto & Wellington (1983) found that P limitation may be a significant control on mangrove growth in Australian mangroves but it is not known ifthis is a factor influencing mangrove growth in Florida Input of 'new' nutrients to Rookery Bay are likely to be agricultural and stormwater runoff and septic systems (Grabe 1993) Mean NHt concentrations are highest and N03 the second highest at station 12 in Henderson Creek, suggesting it to be a source of nitrogen P04 is lower (Figure 8m) at station 12 than all other stations indicating that Henderson Creek is not a significant source of phosphorus as is Charlotte Harbor. The phosphorus that is derived from upstream sources may be utilized by phytoplankton or mangroves upstream. The highest N03 concentration is at station 160, situated just south of Naples (Figure 8j), which may also be a significant source of nitrogen. Precipitation has been studied as a source of nutrients Scudlark and Church (1993) report that atmospheric deposition may comprise up to 25% of DIN input to Delaware Bay during the summer when fluvial input is at a minimum Correll & Ford (1982) concluded precipitation can be a significant source of N when land runoff is low and when rainfall is below average during the growing season While leaching of fertilizers from grassy areas such as golf courses may still affect groundwaters Rookery Bay should be far enough removed from these areas so that leaching should not significantly impact its water quality and runoff has been dismissed as a significant nitrogen source (Petrovic 1990) 83

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Numerous authors emphasize the importance of nutrient regeneration to the ecology of the estuaries they studied. Nixon (1981) found significant fluxes of both phosphorus and inorganic nitrogen from the sediments in Narragansett Bay during the summer and asserted that benthic recycling may be responsible for observed differences in the N : P ratio found between the input sources and the bay itself Stanley and Hobbie (1981) found that the release ofNH/ from the sediments in the Chowan River, NC helped sustain a large algal biomass throughout the summer though N inputs were low and that the sediments represented a sink for organic N during the winter. Rizzo & Christian (1996) asserted that recycling may even sustain eutrophic conditions after reductions of external loading It seems likely that nutrient cycling plays an important role in the dynamics of Rookery Bay For example, there exists no clear correlation between nutrients and chlorophyll. Chlorophyll did correlate with N03 at stations 2, 5 and 170 (Figure 2) but at no other stations nor with any other nutrient species. Studies in d icate that the cycling of water column nutrients may be extremely rapid. Suttle et al. (1990) obs erved turnover times for 'NiiJ + pools on the order of 13 minutes and found that small changes in concentration 'Nl-LJ+ and P04 had considerable impact on uptake. Haertel et al. (1969) found no correlation between N03 and chlorophyll a concentration. They also reported that these pools were often undetectable by standard methods Thus uptake may be rapid enough to preclude accurate measurement particularly at times of high chlorophyll concentrations This may be particularly true of'NI-LJ +, which is preferentially taken up by phytoplankton (Dortch 1990), which may explain why a correlation did exist between 84

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chlorophyll a and N03 at several stations but none with NRt + Secondly, a close correlation between nutrients and turbidity (Figure 2) exists at every station except 12 (where turbidity was minimal) suggesting release of nutrients when turbidity is highest. This can also be seen clearly in the plots of annual averages of turbidity and nutrients, which both show a clear decline from 1988 onward (Figure 6) Lugo and Snedaker (1974) found mangrove muds in south Florida to have a high affinity for nutrients, phosphorus in particular. While these tend to be utilized by the mangroves themselves they could also be released by resuspension There is ample evidence that mangrove forests can nourish adjacent areas through export of litter detritus or DOC (Rivera-Monroy et al. 1995; Lugo et al. 1975, Lugo & Snedaker 1974 ; Odum & Heald 1975 Twilley 1985) However, the amount exported fluctuates depending on rainfall (Twilley 1985) and tides (Twilley, 1985; Gong & Ong, 1990) and large quantities remain within the estuary to be recycled by any of several mechanisms : decomposition b y the microbial community (Odum & Heald 1975), leaching of partly decomposed litter (Twille y 1985) and direct grazing by crabs and other epifauna (Gong & Ong, 1990) POC liberated b y these processes is either deposited consumed by fish and invertebrates or exported Twilley (1985) found that only 22% of the leaf fall to the forest floor was exported from Rookery Bay While Alongi (1990) found much of mangrove litter to be refractory he acknowledged that it is deposited in significant enough volumes to enrich the sediments Avicennia litter, however, was found by Alongi et al (1992) to decompose more rapidl y and to be more nutritionally available 85

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The decomposition of mangrove litter has also been shown to release humic material (Alongi et al 1992) as well as tannins (Robertson & Blaber, 1992) and the presence of these substances ma y be a major factor in the secchi depths found in Rookery Bay. Turbidity and secchi depth do not appear to correlate well nor do chlorophyll a and secchi depth (Figure 2). Nutrients a nd secchi depth are, however negatively correlated at stations 2, 5 and 6 and it is pos s ibl e this flux of tannin and humus also carries nutrients with it from the mangrove forests at these stations. pH and secchi depth are positively correlated at stations 1, 2, 3, 5 6 7 and 180 This would be consistent with a mechanism of humic material release either from the mangroves themselves or from the peaty soils found in Rookery Bays environs (Rookery Bay Web page). Humic material deriving from plants grown in relatively nutrient poor soil (mor) is known to be acidic (Purves et al 1992) an d also to color nat ural waters in Florida (McPherson et al 1990) The presence of this material would account both for low pH and the colored waters There are distincti v e upstream/downstream trends to be observed in many of the parameters measured in this study Stations 9, 10 and 12, and to a lesser extent, station 1, were characterized by significantly lower (a = 0 05) DO, salinity, pH and turbidity than the remainder of Rookery Bay (Figure 8) Station 12 was also significantly higher (a = 0.05) in NH/ and in chlorophyll a than all other stations (Figure 8n). Station 12 was also second highest i n N03 (although not significantly so) after station 160 (Figure 8j) P04 and N02 were lowest at station 12 but these too were significantly different. As nutrient data was not available for any ofthe other upstream stations it is not known if this nutrient profile is characteristic of upstream locations or endemic to that particular station The 86

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lower DO, pH and higher NH/ and chlorophyll found at station 12 are indicative of more highly productive environment in which the hig her algal biomass is deposited and consumed by the benthic microbial en v ironment resulting in lower DO. N"}-4+ and organic acids derived from the reduction of organic matter under anoxic conditions are released from the sediments causing a drop in pH and supporting the phytoplankton Again, while nutrient and chlorophyll d at a do not exist at the other upstream stations the data that do exist (DO, pH, turbidity and redox potential) are consistent with the scenario outlined above. Secchi depth, however was higher at stations 9 and 10 than at station 12 (Figure 8b) Miles & Brezonik (1981) o utlined a mechanism by which humic substances could contribute to the consumption of oxygen by a photoreduction ofFe(ill) to Fe(ill) and this could explain the lower DO values found there Dissolved oxygen concentrations below the recommended EPA minimum of 4 mg r1 were not infrequent in Rookery Bay This was particularly true at the upstream locations Bottom DO values at sta tion 10 fell to 0 .53 mg r1 in October 1991, to 0 07 mg r1 in July 1988 and to 0 mg r1 in October 1988 Stations 8 9 and 1 also experienced hypoxic or even anoxic conditions at times (Appendix I) and all stations except 205 and 206 had minimum oxygen values < 4 mg r1 at both surface and bottom DO at stations 8 and 9 also fell below 1 mg r1 during the sampling period By comparison, 13 percent of DO values measured in the St. Lucie Estuary were below 2 mg r1 (Chamberlain and Hayward, 1996). Stanley and Nixon ( 1 992), meanwhile, reported DO < mg r1 in one third of the samples from the upper reaches of the Pamlico River Estuary, NC leading to frequent fish kills. Periods of hypo xia and anoxia in Rookery Bay do not appear to be long 87

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lived ; however, since DO undoubtedly drops considerab le overnight due to community respiration night time sampling might re veal the problem to be more widespread and severe While most ofRookery Bay is hypersaline stations 1, 9, 10 and 12 show lower va lues overall (a.= 0 05) as well as greater variability Mean surface salinity at station 10 the furthest upstream (Figure 1) was 5 .90%o and increased downstream to 27.70%o at station 5, though surface salinity was lower at statio n 12 than at station 9. Salinities at 88

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Figure 17a. Surface Salinity Gradient for Henderson Creek 40 35-30-1 I 1035-40 J 25 J I "Y I 30-35 i 20 I ....... I "1--------r I 7W I 1 25-30 c 1/ I........<..._ / \\I I I I -,-.v I 1-25 00 I (ij \0 en IV v I \ /\ V I I I 015-20 Stn 5 I 0 1 0 -15 5-10 0-5 J a n Feb Mar Month July Aug Sept Oct Nov Dec

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Figure 17b. Bottom Salinity Gradient for Henderson Creek Jan Feb Mar Apr May Jun July Aug Sept Oct Nov Dec Month

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station 10 fall nearly to O%o in August (Figure 17a) A distinctive salinity gradient exists year round in the Henderson Creek transect though it fluctuates seasonally. Smith (1993) observed an actual reversal of the salinity gradient in (April and May) preceding the commencement of the summer rainy season While this was not observed in the present study the salinity gradient did narrow considerably The maximum salinity gradient was 22 .92%o for the surface (observed in November) while that for the bottom was 28.07%o (observed in August) The gradient narrows to 12.44%o and 11.09%o respectively for surface and bottom salinity in the month of May which was the month of maximal salinity in Rookery Bay (Figure 7f) While Rookery Bay has been considered an unstratified estuary (Twilley, 1985) ttests (Table 1) of surface vs bottom data suggest that it may, in fact, become stratified in some locations at certain times of the year A combination of DO, salinity, conductivity and particularly turbidity was shown to be significantly different (a= 0 05) At stations 1, 2 3, 10, 180 and 206 only turbidity was significantly different. Salinity and conductivity, a better measure of true stratification was significantly different at stations 4, 5, 9 160 and 191. pH was significantly different at stations 160 and 191 and DO was significantly different at station 9, which had the lowest mean bottom DO (Table 3) and was apparently the most strongly stratified (Figure 8h) Station 9 lacks data on productivity and nutrients so it is not known if higher organic matter is to blame for the difference in DO. pH was lower, consistent with this scenario but other data are lacking Salinity differences at stations 4 and 5 suggest the formation of a mild salt wedge at the mouth of Henderson 91

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Creek. Stratification was apparently not strictly depth related as the deeper stations showed no significant differences in any parameter (Figure 3). Mult i ple regressions elucidated the effects of the other parameters on dissolved oxygen with temperature and pH being the most common factors in regressions of both surface and bottom DO. Temperature was a factor in the DO regressions at all stations with the exception of station 12 and surface DO at station 10. Its effect on DO was most likely due not only to its inverse impact on the solubility of dissolved gases but to its effect on metabolism as well (Kennish 1986) pH was not a factor in DO at station 160 and in bottom DO station 180 but impacted the regressions at all other stations. pH may not directly affect DO as much as fluctuate with both DO and C02 as functions of respiration, and the regression, as mentioned before does not imply causality C02 and pH are interrelated by the carbonate system given by the following equation (Day et al. 1989): Addition of C02 will drive this reaction to the right lowering pH. The relative amounts of C02 and 02 will vary inversely as functions of respiration and photosynthetic rates (Day et al. 1989) Given this it is not surprising that chlorophyll a affected the regression at stations 2 6 (bottom DO only), 160, 170 180 and 191. Dissolved oxygen can be influenced significantly by salinity (Head 1985) and it was a factor in both surface and bottom salinity in particular where salinity was more variable stations 4 5 7 9 10 12 as well as stations 3 and 170 (bottom DO only) and station 6 ' ' 92

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(surface DO only) Turbidity was also a factor at stations 1 2 3, 4, 6, 7, 8 and 9 for surface DO and stations 1, 3 4 7 and 9 for bottom DO. It would be expected that the impact wou ld be more widespread for bottom DO as turbidity was higher at the bottom but such did not appear to be the case. Sulfide compounds and other products of anaerobic metabolism may be mobilized from the sediments causing a drop in DO. Likewise, organic matter may be liberated and metabolized, thereby consuming oxygen N03 were included in the regression at all stations where they were assayed for except station 6 and surface DO at station 5. + may also be released from the sediments during sediment resuspension and consume oxygen during its oxidization to N03. Eppley (1972) has dismissed temperature as a factor controlling phytoplankton growth within a range of approximately 1 0 C to 40C Day et al. s (1989) conclusion was that temperature affected but did not control growth in phytoplankton. From the regression data on Chlorophyll a, temperature appears to at least affect phytoplankton biomass, as temperature is a factor in the regression at all stations where data was taken In an apparent contradiction to the correlation results, nutrients of one species or another appear to affect the regression for chlorophyll a and at least one species was a factor at stations 2, 5 6, 160 170 180 and 191. The reason for the apparent dichotomy is unknown, however the regressions were based on the raw data while the correlations were performed after the data had been compressed to wet/dry seasons. The compression, however, should generally have led to a high e r not lower degree of correlation. As previously mentioned, Haertel et a!. (1969) did not find a correlation between N03 and 93

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biomass in the Columbia River estuary, Oregon, however, Mallin et al. (1993) found a strong relationship between surface N03 le vels and chlorophyll a concentrations in the Neuse River estuary, NC. Light intensity, one of the primary factors regulating phytoplankton g rowth (Day et al. 1989; Kennish, 1986) was not measured directly in this study. However light intensity can be estimated from Secchi depth (Zs) measurements by the following expression: K = c!Zs where K is the combined extinction coefficient and the coefficient c varies from 1.4 to 1.8 (Day et al. 1989) The percentage of surface light (Io) reaching the bottom can subsequently be estimated by the Beer-Lambert equation: where Z = depth (Day et al. 1989) and I o = 100% These results are shown in Table 9 Table 9: Percentage of surface light reaching the bottom based on mean depth and Secchi d th t h t ( ep a eac sa ton. : : Station Depth .. SecchtDifptb : J::,J ;., ,i t< 1 1.5 0.9 10% 2 2.9 1.1 2% 3 1.5 0 9 10% 4 0.9 0.7 17% 5 3.5 0.9 0% 6 5.7 0.9 0% 7 2.2 1.0 5% 8 1.2 0.9 15% 9 2.0 1.1 8% 10 1.2 1.1 22% 12 0.7 0.7 25% 160 1.3 1.1 19% 170 0.9 0.8 21% 180 1.0 0.8 17% 191 1 0 0 9 21% 192 1 2 0.9 15% 205 2.7 0.9 1% 206 1.1 0.8 15% 210 4 1 1 0 0% 94

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A maximum of25% of surface light penetrated to the bottom, even at the shallowest stations, although it should be pointed out that at station 12, the least turbid station, the Secchi depth equaled the bottom depth, but could have been deeper. These figures agree overall with values given by Pickard and Emery (1990) for turbid coastal water. Given that little or no light penetrates to the bottom at some stations, the lack of seagrasses and other benthic macrophytes is understandable Light limitation might tend to lead to more nutrient enriched conditions on the bottom. Most parameters demonstrated a strong seasonality as can be seen in the plots for monthly means (Figure 7) ANOVA also showed significant differences (a = 0.05) between months for all parameters with the exception of depth (Table 1 0) pH and DO fluctuate in a very similar pattern (Figure 7) because of respiration and the commensurate production of C02 and are completely out of phase with temperature, with which both are inversely correlated (Figure 2) Salinity and conductivity dip significantly in the summer months as a result of rainfall while turbidity increases from February through April, likely as a function of increased wind stress 95

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Table 10: Seasonal variability as indicated by ANOVA based on monthly averages for each parameter. P-values < 0.05 indicate statistically significant differences between months Variable F-Ratio Depth 0.18 0.9986 Secchi Depth 4.25 0.0000 Surf Temperature 320.16 0.0000 Btm Temperature 364 .87 0.0000 Surface pH 2.87 0 0016 Bottom pH 2.03 0.0274 Surface DO 21.35 0.0000 Bottom DO 17.01 0.0000 Surf Conductivity 7.34 0.0000 Btm Conductivity 6.10 0 0000 Surface Redox 3.64 0.0001 Bottom Redox 2 76 0 0023 Surface Salinity 7 .2 1 0.0000 Bottom Salinity 6.16 0 0000 Surface Turbidity 15 52 0.0000 Bottom Turbidity 11.67 0.0000 Nitrate 3.56 0.0005 Nitrite 19.82 0.0000 Ammonia 7.22 0.0000 Phosphate 21.08 0.0000 Chlorophyll 23.22 0 0000 Rainfall 7.41 0 0000 Chlorophyll a reaches a ma x imum of 7 37 J..lg r' in September after increasing steadily from January on-though l evels dip slightly in August-after which the bloom apparently terminates, falling to 3 .51 llg r' in October. Values increase again to 4.40 J..t.g r' in November, consistent with a smaller Autumn bloom Nutrients, meanwhile, show a somewhat erratic pattern N03 has several peaks throughout the year, the highest of which is in October (1. 72 J..t.M) w ith a second strong peak in February of 1.65 J..lM and a third in December (1.36 IJ.M). This is not consistent with a mechanism of runoff from precipitation because N03 is so strongly out of phase with and does not correlate with 96

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rainfall (Figure 2). In fact none of the aforementioned peaks occur in the wetter summer months (Figure 7). NH/ reaches a maximum in December (1.67 a second peak in September of (1.63 J..LM) after a steady increase throughout the summer. This may be related to increased rates of anaerobic metabolic within the benthic community associated with warmer temperatures. P04, with the exception of a possibly anomalous spike in January shows little variability with the exception of a small but noticeable peak in June and a steady decline thereafter. Smith (1993) notes that the June peak, which he also observed, occurs concurrently with the onset of the summer rainy season and may represent a pulse of nutrients due to runoff The increase is most pronounced at stations 160 and 191 which occur at opposite ends of the estuary (Figure 1) near the more developed areas ofNaples and Marco Island, respectively Results ofPCA tends to reflect seasonal, annual and spatial trends demonstrated in Rookery Bay with stations clustering based either on geographic location or time period. For Analysis 1 (using station means for physical parameters only, Figure 10) stations fall roughly into 3 different groups: stations in Rookery Bay proper, stations nearest Marco Island and the upstream stations PC I in this case was loaded with pH, DO, salinity and turbidity while PC II was loaded with temperature (which did not differ significantly between stations) and depth and the clustering reflects these different zones Upstream stations cluster on the negative end of PC I due to lower salinity, pH, DO and turbidity while those near Marco Island were higher in these parameters. Similarly, for Analysis 2 (station means for all variablesstns 2 5 6, 12 160 170, 180, 191, Figures 11 & 12) The Bay transect is reflected in this plot moving positive to negative on PC II due to N03 97

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and NH4. Stations 160 and 191 are reversed on the plot due to high N03 at station 160 and low N03 at station 191. The other stations show slight increase in N03 along the transect from NW to SE. While all other stations on this plot are positive for PC I (loaded for pH DO, salinity turbidity and nutrients) station 12 is isolated far in the negative region due to lower DO turbidity salinity and higher NH4 + the coefficient for which is negative. Analysis 3 (based on monthly averages for all parameters, Figure 13) clearly demonstrates the seasonality of the parameters. PC I is loaded for pH, temperature, DO, salinity, turbidity, nutrients chlorophyll and rain, most of which vary seasonally. The dip along PC I in November most likely is due to the lower N03 concentration relative to October and December. Analysis 4 (wet/dry season data for each year for all parameters, Figures 14 & 15) showed distinctive clustering by both season and by year. There was a tendency for dry season points to cluster in the positive region for PC I due to higher temperatures and chlorophyll and lower salinity and DO for which PC I was loaded. Wet season points which exhibited lower temperatures and chlorophyll concentrations and higher DO clustered in the negative region. Years were separated by variability in these parameters as well. 1991 was the year of the highest pH and secchi depth which were negatively loaded in PC II and the lowest for most nutrients and turbidity which were positively loaded in PC II. Temperature was likely the most important factor in the separate clustering of the wet season and dry season for 1991. 1988 was highest in nutrients and turbidity and lower in pH than most stations. 1990 was too widely dispersed 98

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on this plot to be identified as a separate cluster Once again, station 12 was isolated due to its particularly low salinity and turbidity Upstream stations 9, 10 and 12 form a distinct cluster in the plot for Analysis 5 (wet/dry data for all physical parameters Figure 16), again due to lower DO, pH, salinity and turbidity. Stations 191-210, adjacent to Marco Island clustered more positively on PC I reflecting increased salinity, pH DO and turbidity associated with the influence of tidal exchange with the Gulf Wet season 1992 forms a cluster far in the positive region of PC II the most likely reason for which is the elevated redox potential, which is heavily loaded in PC II Lower temperatures redox potential and pH and the highest mean turbidity of any year (Table 6) caused the dry season 1988 to cluster in the positive region for PC I and the negative region for PC II The remaining points, representing years 1987, 1989, 1990 and 1991, and stations not located upstream or near Marco Island clustered near the origin reflecting salinity, pH, DO and turbidity close to the mean. 99

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CONCLUSION Based on the goals of this analysis set forth above, the following may be concluded : I) Temporal and Spatial Trends Strong seasonal trends are evident in most parameters DO, pH and salinity reached their lowest values in the summer months as temperature rainfall, primary production and community respiration increased Turbidity varies as a function of wind stress associated with the cooler months reaching a maximum in February and a minimum in September. Correlation between nutrients and turbidity suggest that recycling by the benthos and subsequent release by resuspension of sediments may supply a relatively large percentage of nutrients P04 appears to increase coincidentally with the onset of summer rams, particularly at station 160 which lies just south of Naples suggesting a pulse ofP04 due to runoff Chlorophyll reaches its maximum in September, dips in October and increases again in November sug gestiv e of a smaller fall bloom 100

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The N : P ratios varied seasonally with the bay becoming strongly phosphorus limited in August and less so in October and November. Parameters were highly variable by station The bay is prone to strong salinity gradients from upstream to downstream stations throughout the year and periods of hypersalinity at certain locations prior to the onset of summer rains due to evaporation exceeding freshwater input. As such tidal exchange with the Gulf waters probably exerts the greatest influence over the salinity regime. The bay is also prone to periods of anoxia in the driest months particularly at the upstream stations, and most stations at some point experience DO levels below 4 mg r1 the minimum safe level based EPA guidelines. Based on previous studies by Yokel (1975) as well as more recently collected data (RBNERR, 1998. unpublished data), this appears to be an endemic characteristic of this estuary, likely a function of its shallow depth, its relative lack ofbenthic macrophytes and seasonally high temperatures. Strong upstream/downstream trends in many of the parameters were evident. pH, DO, salinity and turbidity were lower at stations 9, 10 and 12 which occur upstream in Henderson Creek. Station 12 also experienced significantly higher chlorophyll a as well as higher N03 and NIL.+ suggesting this may be an area of greater productivity. Lower levels of DO and pH found at these stations are consistent with a model of increased organic matter leading to greater oxygen 101

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consumption during decomposition and increased respiration of both phytoplankton and the benthic community. More data would be needed to confirm this however. Spatial trends were also reflected in the results of principal components analysis. Stations tended to cluster together based on the environmental co nditions at those stations. For example, stations near Marco Island tended to cluster togethe r as did the upstream sta tions as a function of the DO, salinity, turbidity and pH profiles at those stations. 2) Relationships between variables Based on the PCA and multiple regressions, a high degree of inter-relatedness existed between variables Multiple regressions demonstrated that dissolved oxygen and chlorophyll a concentrations were affected by a wide variety of factors though temperature was the most common factor in all regressions In the PC A, likewise, many of the original variables impacted the PCs indicating that Rookery Bay is a complex system requiring a variety of variables to adequately describe Factors tended to vary together based on seasonality and spatial trends in both the PCA and regression analyses 3) Evidence for eutrophication over the study period Nutrie nt levels found in the present study are not consistent with those reported by Grabe (1993), whose study period briefly coincided with the present study in 1988 and 1989 Most species were lower than those of Grabe (1993) even for the same year, and 102

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while Grabe (1993) showed a steady increase in nutrient levels through 1989, the present data show a peak for nutrients in 1988 and a sharp decline afterward This does not support the notion of increased nutrient l oading (though slight) proposed by Grabe (1993) or of eutrophication which has plagued some estuaries worldwide. 4) Management Recommendation s C ontinued monitoring: Though Rookery Bay appears to be a relatively undisturbed system to other systems such as Tampa Bay and Sarasota Bay continued monitoring is essential in light of continued developmental pressure in the region surrounding the Reserve. Due to the inter-relatedness of many of the variables measured in the present study most of them should be retained in a future sampling strategy Measured parameters should include temp e rature, salinity dissolved oxygen, pH and turbidity all of which may be measured using a data logger with a minimum of effort. Conductivity which was almost perfectly collinear with salinity, and redox potential, which at best only served to confirm hypoxic events added little to the study and can be dropped from the sampling strategy. Rainfall, which can be highly v ariable g eographically should be measured daily at the Reserve. While there is a great deal of short term variability in nutrient and chlorophyll data only the assessment of long term water quality trends can identify if 103

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eutrophication is occurring Water samples should be collected for nitrate, ammonia, phosphate and chlorophyll assays. Accurate nutrient limitation data is vital to understanding the ecology of the system Given the seasonal nature of these variables sampling for all parameters should be conducted year round The number of stations involved in sampling should be reduced. Results of the principal component analysis suggest that Rookery Bay may be adequately represented by fewer stations than used in the present study as stations tended to cluster based on similar values for the original variables. Henderson Creek may be effectively represented by station 12, the Marco Island vicinity by Station 205 the mouth of Henderson Creek by station 5, the area near Naples by station 160 and Rookery Bay proper by station 8. Station 1, up Stopper Creek should be included to verify if conditions especially for nutrients, are similar to those found in Henderson Creek. Of these stations, stations 160 and 5 showed some degree of stratification and, therefore, data loggers should be placed at both the surface and the bottom at these stations Previous studies have suggested that mangroves can play a major role in estuarine nutrient dynamics ; however, such data is lacking for Rookery Bay. A short-term sampling program should be instituted in order to address this Identify possible sources of sinks of nutri ents in order to develop nutrient budget: An assessment of sources and sinks for nutrients and an overall nutrient budget in Rookery Bay is vital to informed management decisions. Possible sources of 104

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nutrient input to Rookery Bay might include agricultural runoff, spills from the campground near the head of Henderson Creek, stormwater runoff from urban areas, septic systems and marinas These should be identified and recorded If increases in nutrients in Rookery Bay can be traced directly to such sources, this might lay the groundwork for regulation of those sources. RBNERR personnel should maintain close contact with the Collier County Pollution Control Department to be apprised of spills or other events and the schedule for the Henderson Creek weir should be obtained from the South Florida Water Management District. Likewise it is necessary to identify sinks for nutrients within the estuary The mangrove biomass may be perhaps, the most important and this needs to be assessed Estuarine sediments may act as a source or sink of nutrients and nutrient flux to or from the sediment both in the mangrove forests and areas not covered by mangroves should be determined This would allow the role of the mangroves themselves versus the benthos to be further resolved Assessment of mangrove coverage: Acreage of the mangrove forests should be determined through the use of remote sensing data and aerial assessment on a biannual basis. Rookery Bay's mangrove forest represent an important and, so far, relatively undisturbed nursery and habitat for fish, birds and invertebrates of many species and this resource should be closely monitored This, as 105

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well as other data, should be maintained in a GIS data base for use in management decision making 106

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REFERENCES CITED Alon g i D. M 1990. Effect of mangrove detrital outwelling on nutrient regeneration and oxygen fluxes in coastal sediments of the central Great Barrier Reef lagoon Estuarine Coastal and Shelf Science 31: 581-598 Alongi D M Christofferson, P ., and Tirendi F 1993 The influence offorest type on microbial-nutrient relationships in tropical mangrove sediments Journal of E x perimental Marine Biology and Ecology. 171 : 201-223 Alongi, D M., Boto, K. G ., and Robertson AI. Nitrogen and phosphorus cycles In A. I. Robertson and D M Along i eds Tropical Mangrov e Ecosystems American Geophysical Union. Washington DC. 1992 Bendschneider, K. and Robinson R. A 1952 A new spectrophotometric method for the determination of nitrite in seawater. Journal of Marine Research. 11: 87-96 Boto, K.G. and Wellington, J. T. 1983 Phosphorus and nutritional status of a northern Australian mangrove forest. Marine Ecology Progress Series 11 : 63-69 Boto, K.G. and Wellington, J. T. 1988 Seasonal variations in concentrations and fluxes of dissolved organic and inorganic materials in a tropical, tidally-dominated, mangrove waterway Marine Ecology Progress Series. 50: 151-160 Boyer, J. N ., Fourqurean, J.W and Jones R.D 1998. Seasonal and long-term trends in water quality ofFlorida Bay (1989-1997). (In press) Estuaries. Boyer, J. N ., Fourqurean J.W and Jones R.D 1997 Spatial characterization ofwater quality in Florida Bay and Whitewater Bay by multivariate analyses : Zones of Similar Influence Estuar i es 20 : 743-758 Bricker S B and Stevenson J.C 1996. Nutrients in coastal waters : A chronology and synopsis ofresearch Estuari es. 19: 337341 Bulger A. J. Hayden, B P ., Monaco M E Nelson D M ., and McCormick-Ray, M G 1993 Biologically-based estuarine salinity zones derived from a multivariate analysis. Estuaries. 16 : 3 11-32 2 107

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Chamberlain R. and Hayward, D. 1996 Evaluation ofwater quality and monitoring in the St. Lucie Estuary Florida. Water Resources Bulletin. 32: 681-696 Chapman, D and Kimstach, V. Selection ofWater Quality Variables. In D Chapman ed. Water Quality Assessments 2nd ed London : 1996 Chatfield, C. and Collins A J. Introduction to multivariate analysis. London: Chapman and Hall 1980 Chatterjee S., and Price B Regres s i o n Analy s is by Exampl e New York: John Wiley & Sons, 1977 Clough, B J. Primary productivity and growth of mangrove forests. In AI. Robertson and D. M. Alongi eds. Tropi cal Mangrov e Ecosystems. American Geophysical Union. Washington, DC. 1992 Correll D.L. and Ford D 1982 Comparison of precipitation and land runoff as sources of estuarine nitrogen. E s t u arine, Co astal and Shelf Science. 15 : 45-56 Davison I. R. 1991. Environmental effects on algal photosynthesis : Temperature Journal of Phycology. 27 : 2-8 Day, J.W Jr. Hall, C.AS, Kemp W M and Yanez-Arancibia, A Estuarine Ecology. New York : John Wiley and Sons. 1989 Doering, P.H. 1996 Temporal variabilit y ofwater quality in the St. Lucie Estuary South Florida Water Resour ce s Bull e tin 32: 1293-1306 Dortch Q. 1990 The interaction between ammonium and nitrate uptake in phytoplankton. Marine Ecology Progr ess S e ri es. 61: 183-201. Eppley R. W 1972. Temperature and phytoplankton growth in the sea. Fishery Bulletin 70: 1063-1080 Fourqurean, J.W., Jones RD. and Zieman J.C 1993. Processes influencing water column nutrient characteristics and phosphorus limitation of phytoplankton biomass in Florida Bay FL USA: Inferences from spatial distribution E s tuarine, Coastal and S h elf Science. 36: 2 9 5314 Fourqurean, J.W., Zieman, J.C. and P owell G V.N 1992 Phosphorus limitation of primary production in F lorida Bay: Evidence from C : N : P ratios of the dominant seagrass Thalassia t e studinum. Limn o logy and Oceanography. 37 : 162-171 Gong W.K and Ong J.E 199 0 Plant bioma s s and nutrient flux in a managed mangrove forest in Mala y s i a Estuarine, Coasta l and Shelf Sci e nc e. 31: 519 530 108

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Grabe, S A. 1993. Assessment report: Inland surface-w ater quality monitoring network January 1979 to December 1989 Collier County Environmental Services Division Pollution Control Department. PC-AR-91-02 Grasshoff, K. Determination of Oxygen In Grasshoff, K., Ehrhardt, M Kremling, K., eds. Methods of Seawater Analysis 2nd ed. Weinheim: Verlag Chemie, 1983 Haertel L. Osterberg, C., Curl H. Jr. and Park P .K. 1969 N ut rient and plankton ecology of the Columbia Ri ver Estuary Ecology 50 No.6 Harcum J.B., Loftis, J.C and W ard, R.C 19 92. Selecting trend tests for water quality series with serial correlation and missin g values. Water Resources Bulletin. 28: 469-478 Harding L. W Jr. 1994 Long-term trends in the distribution ofphytoplankton in Chesapeake Bay : Roles of light, nutrients and streamflow Marine Ecology Progress Series 104: 267 -291 Harris R.J. A primer of multivariate statistics. NY: Academic Press 1975 Head P.C. Data presentation and interpretation In P .C. Headed. Practical Estuarine Chemistry: A Handbook. Cambridge : Cambridge University Press. 1985 I v ancic I. and Degobbi s D 1 984 An optima l manual procedure for ammonia analysis in natural waters by the indophenol blue method Water Research. 18 : 1143 -1147 James R T., Smith, V. H ., and Jones B L. 1995 Historical trends in the Lake Okeechobee ecosystem III Water Quality. Archiv Hydrobiol/Suppl 107. 46-69 John D M., and Lawson G W 1 990 E cosys tems in West Africa and their po ssible relationships. Estuarine, C oastal and Sh elf Science 31 : 505-518 Kennish M J Ecology of Estuaries, Vol I. Boca Raton: CRC Press, 1986 Kno x, G A. Estuarine Ecosystems: A Systems Approach, Vol. I. Boca Raton : CRC Press 1990 Lapointe B E. and Clark, M W 1992. Nutrient inputs from the watershed and coastal eutrophication in the Florida Keys Est uari es. 15: 465-476 Ludwi g, J.A. and Reynolds J.F. Statistical Eco l ogy: A Primer on M e thods & Computing New York: Wiley 1 988 10 9

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Lugo, A.E., and Snedaker, S.C. The ecology of mangroves. In Johnston, R.F., Frank, P W and Michener, C.D eds Annual Review of Ecology and Systematics. Palo Alto : Annual Reviews Inc 1974 Lugo, A. E Evink, G., Brinson M.M., Bruce, A., and Snedaker, S .C. Diurnal rates of photosynthesis respiration and transpiration in mangrove forests in South Florida In Galley, F. B. and Medina, E eds. Tropical Ecological Systems New York: SpringerVerlag. 197 5 Mallin M.A. Paerl H.W Rudek J., and Bates, P W 1993 Regulation of estuarine primary production by watershed rainfall and river flow Marine Ecology Progress Series 93: 199-203 Manly, B.F J. Multivariate Statistical Methods: A Primer London: Chapman and Hall. 1986. Mcintire, C D 1973. Diatom associations in Yaquina Estuary Oregon : A multivariate analysis Journal of Phycology 9 : 254-259 McPherson, B. F and Miller R. L. 1990 Nutrient distribution and variability in the Charlotte Harbor Estuarine system Florida Water Resources Bulletin. 26 : 67-80 McPherson B F., Montgomery R T ., and Emmons E E 1990. Phytoplankton productivity and biomass in the Charlotte Harbor Estuarine System, Florida Water Resources Bulletin. 26: 787-800 Miles C J and Brezonik P.L. 1981. Oxygen consumption in humic colored waters by a photochemical ferrous-ferric catalytic cycle. Environmental Science and Technology. 15: 1089-1095 Morris, A. W. and Riley, J.P. 1963. The determination of nitrate in seawater. Analytica Chimica Acta. 29: 272-279 Moshiri, G.A., Aumen, N G. and Crumpton W G Reversal ofthe eutrophication process : A case study In Nielson B.J and Cronin, L.E eds Estuaries and Nutrients New Jersey : Humana 1981 Murphy J. and Riley, J.P. 1962 A modified single solution method for the determination of phosphate in natural waters Analytica C himica Acta. 27 : 31-36 Neter, J., Wasserman, W and Kutner M H Applied Linear Statistical Models 3rd ed. Boston: Irwin 1990 110

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Nixon S W Remineralization and nutrient cycling in coastal marine ecosystems. In Nielson, B.J. and Cronin L.E. eds Estuaries and Nutrients. New Jersey: Humana 1981 Odum, W E and Heald, E .J. The detritus based food web of an estuarine mangrove community. In Cronin, L.E. ed. Estuarine Research. Vol 1: Chemistry, Biology and the Estuarine System New York : Academic Press, Inc. 1975 Petrovic, A.M. 1990. The fate of nitrogenous fertilizers applied to turfgrass. Journal of Environmental Quality 19 : 1-15 Pickard, G L. and Emery W .J. D e scriptive physi cal oceanography: An introduction. 5th (Sf) Ed Oxford UK. Pergamon Press. 1990 Purves, W K., Orians, G. H. and Heller, H. C Life: The science of biology. 3rd Ed. Sunderland, MA: Sinauer and Associates 1992 Rivera-Monroy, V.H., Day, J.W., Twilley R R. Vera-Herrera, F and Coronado-Molina, C 1995. Flux of nitrogen and sediment in a fringe mangrove forest in Tenninos Lagoon, Mexico Estuarine, Coastal and Shelf Science 40 : 139-160 Rizzo, W M and Christian, R R. 1996. Significance of subtidal sediments to heterotrophically mediated oxygen and nutrient dynamics in a temperate estuary Estuaries. 19 (No 2B) : 475-487 Robertson, A.I., and Blaber S. J. M 1992 Epibenthos and fish communities. In AI. Robertson and D. M. Alongi eds. Tropical Mangrove Ecosystems American Geophysical Union. Washington, DC. 1992 RBNERR Home Page http :// inlet.geol.sc edu/RKB / home.html SAS/Insight V 6 12. Cary NC: SAS Institute Scudlark JR. and Church T.M. 1993. Atmospheric input of inorganic nitrogen to Delaware Bay. Estuaries. 16: 747-759 Seitzinger, S P 1991. The effect of pH on the release of phosphorus from Potomac Estuary sediments : Implications for blue-green algal blooms Estuarine, Coastal and Shelf Science. 33 : 409-418 Smith T J. 1993. An estuarine characterization of the Rookery Bay National Estuarine Research Reserve: Pha s e 1. Final Report NOAA Grant# NA170R0326-01 Stanley, D W and Hobbie J.W 1981. Nitrogen cycling in a North Carolina coastal river Limnology and Oceanography. 26: 30-42 111

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Stanley D W and Nixon, S W 1992. Stratification and bottom water hypoxia in the Pamlico River Estuary Estuaries. 15 : 270-281 Statgraphics Plus for Windows 1.11. Maryland: Manugistics, Inc 1994 Staver L. W., Staver, K.W and Stevenson, J.C 1996. Nutrient inputs to the Choptank River Estuary: Implications for watershed management. Estuaries. 19: 342-358 Stevenson J. C., Staver L. W and Staver K. W 1993 Water quality associated with survival of submerged aquatic vegetation along an estuarine gradient. Estuaries 16: 346-361 Stickney, R. R. Estuarine Ecology of the Southeastern United States and Gulf of Mexico. College Station : Texas A&M University Press, 1984. Suttle C.A. Fuhrman J.A. and Capone, D G 1990 Rapid ammonium cycling and concentration-dependent partitioning of ammonium and phosphate: Implications for carbon transfer in planktonic communities Limnology and Oceanography. 35 : 424-433 Tomasko, D .A., Dawes, C .J., and Hall M.O 1996 The effects of anthropogenic nutrient enrichment of turtle grass (Thalassia testudinum) in Sarasota Bay, Florida Estuaries. 19 (No 2B): 448-456 Thoemke, K. W. and Gyorkos K. P. An analysis of nutrient, chlorophyll, heavy metal and pesticide levels in Rookety Bay National Estuarine Research Reserve. Final Report NOAA Grant# NA83AA-D-CZ060. 1988 Twilley R R 1985. The exchange of organic carbon in basin mangrove forests in a southwest Florida estuary. Estuarine, Coas tal and Shelf Science. 20: 543-557 Yokel, B .J. 197 5 Rookery Bay land use stud ies. Environmental planning strategies for the development of a mangrov e shoreline. Study No. 3. Estuarine water quality. The Conservancy Foundation Washington, D.C Zar, J. H., Biostatistical Analysis 2nd ed. Englewood Cliffs: Prentice Hall, 1984 112

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APPENDICES 113

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Appendix I Summary Statis tic s by Parameter SUrmwy Sl&.&:1JC$ f ot [)q;U'CK -"""' A--------------------,.._ """ -v..--"""' -121 09-t%91) 009tJOU ------... 1_., OIUJtz "' ,...., IUOl 120 IUOJI 0291175 IJO OS62JOI 007021)1 ... :U:5724 IUPOt "' U41111 "' 067S61 OOJ.WtU "' UQJZ 1 151921 1491)1 0111126 "' OSW-1606 009014U 1)4 """ 149UI IJI 090$).W 007112)1 "' 0,.]796 0 1040VJ ... 1516)1 IZI ) z 14556 "' I \06)6 0116116 "' :U165Z \46)4) 126 12.1)4 018927 1 0 "" 101196 00956216 I) I Z60l29 7 .01409 12> 20144 OZ714l6 12 0(.6)1)8 10 119 26.5042 ..... I 10 "' 124444 010000$ 1 60 1 0 1 107601:4 01200) II %81462. UIQA 12 ,. 070)12 1 00704991 "' 101 0818812 00419426 160 IOZ 151961 U0101 160 .. 129479 01152.)6 110 100 0121 oonlJU "' 102 1UZSI4 16014 no 100 091) 0111112 191 0 170667 OOUPIIl 110 101 ..... ,. ,.,.., 180 .. I 00)0) 0161919 192 Ol>oon 004U4)9 If I n 261442 1)7111 191 ,. 09126)1 006-IIUJ "' 091SJtS 006Utl IH .. zteJe 1601 In I'"'' 01U9U .. on6m OOUI4 U '" 266911 IUJOl .. 241462 0941112 ZIO .. IOUQ 01459<17 ,.. zuon 161409 .. I 101t1 ,,.., .. --------------------ZIO .. 2"-J)OJ IUOZ ZIO .. o1m 0991101 T .... 1900 omm 010))) T .... "" Z l-971 6 I4)1Jt T .... Jtl) 2001, 19901 1 ""'M .... .......... --------------------WnD:uo -).Cftm.m .......,.., 0 4 ----------0407826 04 JO J SlNJ no no 0)4)) .. Ol6S092 OJ ...... 160 JLO OSU2:41 .. 0 01 4 01402 160 uo OJJJJ)' 0 1 u 0 ) 00402 OJ zo J064J no )40 0 271714 O J .. 0}22641 04 Jtl)l 160 ,., 0&48701 .. 81 0306737 0 4 zo 4119'2. 1 160 no OSI17tl6 71 0 11\907 OJ lUJl4 160 )10 O))oot .. Ol41784 OJ )t211 160 uo O J ,. 10 0)09227 01 166139 "' )10 OS267U o> .. OZA0960 0 1 10 2.44))f zoo JlO 10 OJ216ot) OJ 160 03464)} OJ " UlUI ... JOt> 0%6Uil 01 "' 0211?11 oz 160 )174).6 no uo 160 OU7'J44 .. .. 110 02J92JI 02 "' 11) 160 no O)AJfl 02 u ltl 02011)1 .. 110 ... ,., 160 J10 110 oozos 14 192 OZ0626Z o 191 Jf')JII 170 '" 191 OUJUZ 04 .. 01)620) .. In ooau 110 ,., 192 01 04 .. 011746) o ,.. .. 04106 160 no '" 09?0624 .. .. ZIO 018'20 ) 04 10 .. 40J 110 no .. O-Il .. -------------------------210 J9'JJI) 170 no 210 0991)49 .. T .... 01214) 01 JO --------------T .... )11121 160 J>O T .... 141011 01 8 1 :iUmwya&I&Ja:lorpltltm Sl.mNty Sta!Slla:lcrTtnf'&Uom ""'M """' -""'M """" A ...... """'' c ..... ......... --------------------------------124 ,.., OON06fl IJI lltlll 116 74l(loU OM'\1\11( "' ,.., OOotl)lo M 14> zs )02) 11 )704 IJ9 7'JOII1 00ot6))97 "' 771)61 004J'JJlJ "' "'"" "61U "' 1nn 0044)6)1 124 '""' 00121.44 1 IJO 24)Q4 UU67 ... 1 )6612 009JJ21 ... 76611) 001W04 ... 2.)6297 .. -1'0 7662.1 ,_,, "' 774 T11 00441tU 14> 2)61)1 1721 .. "' lnZ41 OOUH4f "' 761SOl 00)91JI) "' Ult """ "' 76Jl4 OO>U607 IU 7.>0789 00Jf'JU4 "' 2:59641 1]112 128 1 )60)) OO,lll l11UJ ....... "' %601'JI 116144 IZ> 72Zll6 00764011 10 109 7 1)45 00P1)2:61 10 114 267011 62)912 10 114 llJ791 OIUI)) " 7480)1 006.)7121 " 26 5171 II 0161 " 7UOS4 00)...,..1 160 .. 779484 00)06274 100 IOZ UZHI 169ti:J 160 .. llo.t)l 001)4)0of 170 7.7 1441 OO)IJJ)I no 100 2' ) 461 16 196 no .. 7UI11 0 06)19(.4 110 .. 7 6534 00400UI ISO 101 U51 Zl 16))2 1 00 .. 7 64761 0046147 1 191 ..... OOJ1'Ml4 191 71JZ.5-t 00))119'2 ... 7 .1801 OOJilflP 191 267211 1)2))1 192 116119 oousro:n .. 7 .1)224 00194)94 192 .. 261'0H 141626 ZO> 1 10169 00616024 .. 1 &4)92 0022NtJ ,., ,. ... 1$719 .. 7111104 00209147 "' ., 77114) OCIU1Uf .. 2:,.,, 14 ))9? ZIO 77692) 002J61J7 ZIO .. 26))4 1))61 ---------------------""" "" ... OOHJIH ------------------T"" "" 1611U om102:1 T"" 1,., 2$9?44 1491)) ,.._ ......................... -,...., ....,_,. ,..,M '""aaon Mnm.m .......... -------------------OtfJJ1) ... -------------02:911) ... 02061P ,., ou )62%) 9 160 '"' 021)7Jl 70 '" OU01S4 6.91 OJI 419111 ... nn 021))11 70 '" OlAlll .. '" 40762: 1 ... ,,., 0)0}49) 6J .. 0241fl 701 ,,. )940)9 Ill " 0}140-I Z ... "' OZIINI HI .. 40$117 "' ))12 02)(1.6)2 .. 014JJ" '" 414891 ... JJI9' 02422 .. 0)1)19 "' 14J ,. .... "' "" 01)2)78 '" 0297tm ., 10 8 J1'161S "' '" 021(;401 61 tn 10 0Jil29J .. ) 4 1091 IU6 '" 1 0 0})0)07 OU6).45 7 0 OOJ 10 Z49"J,... 211 ))]6 OZJ8SZ 5 10 001 100 o u .... ,,.. 011 J J ISIOS lf41 llJl 160 0211017 .. no 0226514 ., '" 160 412016 "'' .... "' OU:JH6 .. "' 110 OZOOOlJ .... ... no 40244) In )115 110 0UC82 .. ... ltl Olf09 .. u 1 00 ..... 1 101 1211 191 0216161 ... '" 192 0111)14 '" 191 )90)11 1114 JJl "' 0'160?') .. 20> 01)"'91 ... "" 111)2 1602 ))42 .. 0241191 ... "' ... 01)111J UJ 01 ,., 19?221 IH 1 2..14 .. 01.U619 '" ... ZIO 0 1604tf "' ... .. JIU12 llt JH ZIO 0 1))617 .. "' ] 92021 In JZIJ ------------------------T .... 0110))4 .. ---------------------T"" O lotJ86 .. TOUI Jl?ot) ... ,,. 1 1 4

PAGE 124

Appendi x I (continued) ....._.,&l&&lllcsforConaa """" """" --... """" ...... -,.._ """" -------------------. ..,_ ........ """' -. ..... IJO ) 10Sol6 ..... IZ t IIJI Z ) 26111 "' 07114 "' U1471 161)0'2. 1<1 $714).1 201)12 l) Ill 46,,.., IUJ7l "' 6 ) 0Ul l ) IOU "' '"Jl 2.111)2 )09))1 ,...., 1<$ )I"'' llUN I )I 6 1 0JOS 29179'1 "' ,,.., l )6 .. 116125 II. '" 142)07 "' .. 1014 1 2 I ... 1274 76 "' 14"1") '" .. 5 1SJ1 1 "' .. 41tl'l no .. I 1""15 .. ltm 111 101 5 4 051] JU .,. 46fUI IU .,. 170119 10> 1):!161 .. U16oW "' I 4 S721 '" 12)6tot 161 I ))<&lJ Ill 101 10114) .. .. "'m I" ... 12Jl)) "' 1 "'" "' 101 611n ,., ... 4 16445 "' ... 1Ufl6 Ul ""' on ., 6))ts1 100 .,, Sl27 44 10< .. o 111J6J Ill 9 0 16JUI 007 .,.. 1 .. .., ... ,, 160 .. ... .. IU 10 2.102:62 00 109 11"70) 9 o II"' Ol 110 10 lllll) O> ,., 10 11))64 0 1 600 10 .. _, 11 11J111 IU , 11 IJif') u '" 11 110).4 O> ,., 01 160 I ZOAn 191 101 11 lltJ19 10 ,., 160 101641 "' ., llO 1 ..... "' 101 160 111599 I< I .... 160 U U I J Ul 6)1 llO 12-tSSZ 1 1 100 100 IU)Ol II 110 llO >90Ul U l <10 no ..... 1 I" .. o 1 06 1!22}4 "' 101 J4)1tl "' .. 12241) 'II IO 110 I OJ)) HI 106 )))761 47 5 ... 106 ,,,.,, '" ..I 110 I 07127 "' 10> 210 4 57911 .. .. I 110 11])2 .. ... ------------------------------.. -------------------..... 1 $114) o> 161 Totol I 69?49 00 110 ..... 14214) 01 .. ,...., "'' 01 ... &lmwyllll&ubcsfornknl ""'"' c .... -. ....... -----------------....... c .... -Vrunct ....... .,.,. -...... .. 01100)4 0 0<. ,.._ """' -....... 015101 000,..0616 .. 0111761 OO&JUH ,.,. . ,. .... 01))1)7 OOOUUZJ 016)UJ 00111t91 191011 .. ., 106 )4JJI'J ........ .. 0 14t216 ooosmJ.I ., 0 1 06 IU .. ..., .. nmt li1J11 OU4SU OOO>t6ot6 014SIC OOOU)..II I )19111 IS-""14 .. 10>001 ..... .. 0 1U165 00061 ... ll OUJJ21 OOJ14UI .. n"n ..... 1 .. ,..., ...., ., 01_, 000> -" Olt)01 4 006U065 107 10111J S9otA 9 106 ))-nom .. ou .. n 0..,.,.., J ., OUS) JZI914 UOlT> .. 1UJot 11026 OU)4t-l 0010.7 4 6 .. 011)14 ........ .. )2.7021 tf1UZ ., """ 201111 10 ,, 01)UII 0011'2611 .. ouon n.Mn zo1m .. .. ,., ...... " OIS*1 000111)49 10 0111)92 OOIOltH .. 1)06) 1 410&6 1 0 nm1 IJ1tl6 Ito 0151241 OOOSH011 )I O*lliSl 1 0 1oaon uHn " IN 111171 llO ., Ulltf nam 191 .. 000541441 100 0111)4>' 04>'1ZI IJO 12.2 1 62 190471 100 ]2)641 164001 "' 01)1016 000145]6'7 191 015U2J 00050)114 1 00 .. Jl..J%65 ... ,., 191 )42.t5J II 9f11 ,., >O 014hS 000]]0469 "' 000)916)) 19 1 )li'6 41)101 192 ,. .. ,. .. IZ.Un 206 0 14)194 000))))0, 10) 10 014)()t 1 91 JJ59':!9 261111 to> J))POl '"" ZIO .. 01406)6 OOOJ81S6J .. .. 014271 1 000))0844 20> JSSI H 106 ,,,. 69111) -----------------110 0 160J11 00209826 .. ))61Sl ...... 110 ,,,.. 1151)6 T""' .... 0 000681129 --------------------------110 }46'746 '"" --------..... 1411 016JOZ.Z 001:1714 -----------------------T""' .... JII":J 61.1JtJ ....... Sl.Naoddt'lllt.on Mntn.m """""" ..... 1 601 2PSI6 9S161S -----------------------------...... Mnrrun -........ ........ .................. ......... 007))17 OO> 0166 --------------------------""'"' Wlrwnm ......... ------001U21 0016 0)65 0101912 00) ,. 1074)1 ... 0014141 1 0021 0169 01-002) 0"' '"'I U061 lJI '" 001666)1 00)2 ., .. OO"JUJH 0029 01., 11211 "' 1 4160) lJO '" 00165)62 OOJ) OJlZ 001S0612 OOJ6 OI ... 641111 .. ., 001U401 0024 01<> 01ll414 OOJl I 9 2 4 0 1 ... 4)4)1 IH "' 0016514 o .. 0166 OU4S21 OOJI 10 ,. .. ., '" I.,,., ttl ,. 00"1))6)6 00<1 011J 0 16419) 0 ... "' 4 69J)l "' '" 424511 IP> 01 0101).46 00<1 0>6 0201 412 -0<>-1 1 01 4 .... 59'1 ... '" 441:))4 20> '" 10 0 1])1).4 .001 001 OOH:J1S1 0001 0)<6 44\)Ul 201 '" f)fl) 00 "' 001)5]07 0016 0)<1 1 0 0101 401 .oou 062 121161 00 16> 10 114)1f 00 Ul 160 001120)6 0012 on 12 OOilJJZ 0 .. 1 01'> 10 11))}6 00 "' 12 llW 0 0 J>l no 0106,1 OOIS 0901 160 0 1 61 41 0022 1))2 12 12))69 0 0 J>l 160 -1 Ill 100 001410)6 0021 0)6) no 01)4J)2 00] IZJS 160 146919 "' llO J61UZ u.o "' 191 OOUftH o .. on 100 OZOJ001 001 I Vl< llO 4 ] 64)1 100 100 4 049'11 11> 01 191 00NJ)A6 OOJl 0)01 191 00109)12 o .. OJ61 100 4 JJt "' ... ,,. 1.., no "' 20> oon OJOJ 192 0062Sto6 OOJJ 0)11 191 .,,., "' .,. 1n 1>06<4 nt '" .. 00Sf4)41 OOJJ .,. ,., 00641192: o o n OJ1J 192 '1160) 101 '" ,., UJll4 ,., "' 110 00611101 0016 OJIS 106 OOS151f OOJ6 0 ... ,., 2)5142 110 ... ,.. 16101 4 ... .,. -----------110 0 1utS4 OOlJ Ot9 .. 2 64166 ,., .,. 110 16f)9) 1 0 0 .,. Totol 001:!).40) 001 0901 ----------------110 )0910) '" 01 T"" 0 1 4 1904 .00<1 10 T .... '""' "' ..... f 1 1111 00 00 115

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Appendi x I (continued) "'"'" C4JZ\ ..., 100 .. IOIZIJ 901195 191 12].492 J2 9404 192 1014 U oi00\ 25 ,., . 1015-12 2l\S8J 106 ,. ..... 424671 110 6017J7 ---------------------T )6)46' 110 ..... ... ) 1612:2 JlO 1 0 2-4).4'"' . 1)0 22}0)2 .. ... awr. ., ... 170 4 4 12 94 170 180 10 ... )1]51]7 110 102 6J25S4 ... 20> 4812)1 170 206 6)161) lOO ZIO 180216 ... --------------------------------TOUI 129065 . 1000 """" c .... ....... 2 . I IIUS2 U0074 IOJJH 2S416 S 011)094 118)48 II 12 0)6) 0091 1909 12 2J 02))6)2 00)94166 .. 086UZ9 1596)1 110 1 0 1)09 2 160)1 ... 091942) 10S661 "' .. 016S581 2088\) ------------------------TOUI ... 0894U5 112,61 ""'"' Q.lf'lda'l1 dc-rillllJon Mlnuru:n ....,.., ---------------------------I ... "' 1594ZS .. ,., I 0688 002 "' II 0)02)09 0 0 111 12 0241 755 OOJ ... 160 126nJ 001 ,. 110 1 .. 6989 001 ,., 180 1 .. J.409 001 ... 1 44)().4 001 81 -------------------------TOUI IHII5 00 ,., S tunmary Statistics f or r.li.n Count83 A vcrogc 423.639 Variance 129.500.0 S l a ndard d ciati o n = 359.861 Minimtml o O Maximum 1415.0 Sum 35162. 0 116 ""'"' """' 1 )4 I S0511 ,.,.., ........ 0 5)0601 , OQUll on02se II 11 1 2 1)1) OltJ6l7 11 140)1) onttJe ... '""" 1))9()9 110 .. I.OCW5 17)1U ... .. 1 ""' )40014 191 OlOOll 2UJ11 TOUI m 1 01f76 4 1 46N ""'"' .......... 1 2.94459 O .Ol ,,, 074 101.5 0.0) om64J ... l9l II 094001 4 ., lOI 11 0 .111441 ., l04 !<10 )6)9]) 001 111 110 IUffi 001 "' 180 1t4l!n 001 tll1 19 1 '6011' 001 ... T .... ZOJIJI 001 11.2 """" """' -,. = 061114 .. 041117) 0 '""' Ot$4516 011P01.5 II II 0 106164 000)]1)4) 11 OU91jfl 00112611 160 .,., ....... 110 OUI214 ..,, 180 60 ....., 06112\S 191 o'nm 06911, TOUI OS91J 064)101 ""'"' ......... 0826946 001 .. O .tJ66U 001 191 0&479S4 001 ... II OOS76661 ... ., 0 1061)) 002 ... 160 01)5&61 001 ... 110 07920l 001 ,. 190 012jJ51 001 ... OIUJI 0 0 1 .. ------ToUI 080194 0 0 1 '" a_, aawcs ror Olb -Coon -....... 2 4211)6 101014 , """ ,.,., ..,. "'" II S .SIJ)) l94Ul 11 II ..... ll.ll2f 160 ,. 41)414 IJ)J94 110 ,. 42JII' , .., 110 4 J1011 t .02:UJ '" .. '))604 764)1 TOUI "' ... 101261 ....... Wlnm.m ......... 1n12t ... "" 2.)7 464 0 7 1 11948] ,. ... II 611141 00 2106 12 5)9151 20> 2).42 ... ""' on "" 110 11)111 1166 110 2.1)]14 ... 1474 '" 1.76)1 "" 1109 ToUI ,,, .. 0 0 ,

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........ ........ -..l c: 0 ..... .-j 0 +=J Cj .;......> M 1 2 3 4 5 6 7 8 9 10 12 160 170 18 0 191 192 205 206 210 1 2 3 4 5 6 7 8 9 i8 180 191 192 205 206 210 .... 0 --II 1 6 Appendi x II. Box and W hisker Plots (Raw Data) Box-and\Vlrisker Plot 0 .5 1.5 2 2.5 3 DenthSec Box-and -VJhisker P lot and-\Vhisker Plot l ""' 3 4 ,. .:. 6 i:1 7 0 8 -I 9 l g ro .......,;. {/.2 160 l'iO 180 191 192 205 206 210 36 1 5 I u ---20 24 28 32 1 9 3 1 23 27 TernpSurf TetnpBottotn 35 3 9

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Appendi x II (con t inued) 1 1 .... 2 3 3 4 4 5 5 6 6 c 7 7 c 8 0 8 9 9 .p...l 10 ig ro 12 160 160 U'2 170 V2 170 180 180 191 191 192 10') 205 205 206 206 210 210 5.1 6.1 7.1 8.1 9.1 5..5 6 . 1.5 8.5 9.5 -._: -pl-Isurf phbtln 00 --1 1 2 2 3 3 4 4 5 5 ti 6 7 7 0 8 0 8 9 9 'ri 10 -10 12 t'O 12 160 160 VI) po 170 80 180 191 191 192 192 205 205 206 206 210 210 0 3 6 9 12 15 18 0 2 4 6 8 10 12 DOsutf' DObtm

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Appendix II (continued) 1 1 . .:. 3 . 4 4 5 6 7 7 -8 0 8 0 9 9 - 'i""l *-' 10 10 12 12 160 ..j....) 160 0') 170 170 180 191 l92 192 205 ]05 206 206 210 210 0 20 40 60 80 0 20 40 60 80 ..... CondBtn1 ........ conclsurf \0 1 l 2 2 3 3 4 4 5 5 6 6 \=l 7 ...... 7 0 8 c 8 .-:1 9 9 -a 10 ......,: 10 12 1.2 l60 160 V1 170 170 1 80 180 19 1 191 192 192 285 205 2 6 206 210 210 .02 0 18 038 0.58 0 .78 0 .98 -0.1 0.3 0 7 1.1 1.5 1.9 2 3 redoxsurf redoxbtm

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Appendi x II (continued) 1 2 1 3 2 4 3 5 4 5 6 7 -< 7 ,...., 8 8 '-" 0 c .p 10 9 2 ..,_;. 10 C'i: 1 "'! 1 2 -!-' 160 oo 1 m 170 !RO 180 9 1 19] i92 o' ..... ... ?06 206 210 n -o ?n 40 5u ) ln TJ 40 0 10 .:: "" l . ' J ..... salsmf salbtrn 1 1 2 3 4 4 5 5 6 7 M g 0 8 0 9 ri 9 '.D lr. 10 . ? ('d 12 160 160 if.i 170 w 170 180 180 191 191 192 J 205 -206 2 g 210 0 1 0 20 30 0 ?.0 40 60 80 100 TurbSnr.f TurbBin1

PAGE 130

2 5 6 11 0 ...... .......,;. l2 cd 160 170 180 l91 II' : 0 4 8 ,....... N 2 5 6 ,...... 1-; ll -r4 12 ........ OJ 160 i70 180 191 0 2 4 Appendix II (continued) 2 .5 6 1:: 11 0 ---..;..,) 12 r.::3 ..,;. V':: 160 170 180 191 12 16 20 24 0 OA N,-,."lo 2 5 6 c: 11 0 12 1 60 170 1 80 191 6 8 10 0 2 hlfi4 0.8 1.2 N 4 6 P04 1.6 . 8 10

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,.-.. "'0 = ........, = 0 --.. [) = 0 00 ...... ..... ......... I "'0 ..... (;3 I X -0 "'0 o:l = c. c. < t. i .o :.: E?; ,..... ..... 122

PAGE 132

........ N w Appendi x III Eigenvalues for Station Av e rages for all V ariables Analvsis 1: Stations 21 5, 6, 170, 180_-'1__191 Eigenvalues (CORA) .... .......... -... ........................................................ 0 ................... 0 .......... ) ................................ ................. 0 ............. 0. 0 ......... ......... 0 .................................................................. 0 ............................................. Component E i genva 1 ue 1 D i f f ere nee j Propor t i on l Cumu 1 at i ve .... ............... ............ ......... ................................................ ........................................ .................................... ............... 1.. ............................. ..... PCR 1 l 1 3 2 8 8 7 l 1 0 64 31 l 0 6 3 2 8 1 0 6 3 2 8 PCR 2 1 2 64 56 0 4 7 9 0 j 0 1 2 6 0 1 0 7 58 8 PCR 3 l 2 1 6 6 7 l 0 3 3 0 0 0 1 0 3 2 j 0 8 6 2 0 PCR4 1 8 3 6 7 1 4 0 2 8 1 0 0 8 7 5 l 0 94 94 PCR 5 0 4 3 3 9 1 0 0 8 8 2 l 0 0 2 0 7 0 9 7 0 1 PCR 6 j 0 34 57 j 0 0 6 31 1 0 0 1 6 5 j 0 9 8 6 5 i i 1 Lgggg PCR 9 0 0 0 1 0 0 0 0 PCR 1 0 j 0 0 1 0 l 1 0 0 0 0 PCR 1 1 j 0 1 0 1 0 j 1 0 0 0 0 PCR 1 2 0 0 l 0 1 0 0 0 0 PCR 1 3 l 0 0 l 0 1 0 0 0 0 PCR 14 l 0 1 0 0 1 0 0 0 0 PCR 1 5 j 0 1 0 j 0 1 0 0 0 0 PCR 1 6 l 0 1 0 ; 0 l 1 0 0 0 0 PCR 1 7 l 0 l 0 0 1 0 0 0 0 PCR 1 8 1 0 0 i 0 1 0 0 0 0 PCR 1 9 l 0 0 1 0 l 1 0 0 0 0 PCR 2 0 1 0 l 0 l 0 l 1 0 0 0 0 PCR 21 l 0 1 0 1 0 0 0 0 .. .. 0

PAGE 133

....... N Appendix III (continued) Eigenvectors for S tation Average s for all Variables (Anal y sis 1: Stations 2 5 6, 12 160, 170 180 191) ....................................................................... .. ..... ........................ ..................... : ...................... .. Var i ab 1 e 1 PCR 1 PCR 2 PCR 3 PCR4 ..................................... .............................................. ............................................. ......................... ..................... ............................................ DPTHB 0.1433 1 -0.2684 0.4772 1 -0.0366 DP THS 1 0 1 81 2 1 -0 1 8 3 2 -0 1 9 8 2 l 0 3 61 9 TMPS -0 1 3 84 j 0 3 8 6 2 l 0 31 11 1 0 2 2 0 8 TMPB l -0 1 81 2 0 3 2 3 7 1 0 2 54 8 l 0 2 6 3 6 PHS 0 2 50 2 i 0 1 1 34 j 0 0 8 7 8 1 0 1 8 2 7 PHB 0 2 24 8 i 0 1 4 2 1 j -0 0 55 7 0 3 3 7 9 DOS j 0 2 0 9 2 -0 2 814 l 0 14 34 i 0 2 9 2 0 gg:os i I I i I I : RDXB l 0. 1 086 j -0. 2635 0. 4172 1 -0. 3468 SALS 1 0. 2662 1 0. 1281 -0. 0435 l -0. 0721 SALB 0. 2665 l 0. 1120 l -0. 0739 0. 0466 TURBS 1 0 24 9 6 1 -0 04 0 3 0 1 6 58 l -0 2 3 6 2 TURBB 0 2 3 61 l -0 0 0 54 j 0 0 24 3 l -0 2 0 64 N03 -o. 111s -o. 3066 -o. 481 1 o. ooo1 N02 1 o. 2696 o. 0243 -o. 011 6 -o. o 1 o3 NH4 l -0 24 2 7 l -0 21 0 8 1 0 14 7 2 1 -0 0 1 8 3 P04 l o. 2422 l o. 1 o1s j -o. 1641 1 -o. 1145 CHLA j -0 2 6 2 1 j 0 0 1 1 9 l 0 0 50 2 j 0 1 9 8 0

PAGE 134

N Vl Appendix III (continued) Pattern Matrix for Station Averages for all Variables D e monstrating correlations be tw een original variables and PCs Anal ys is 1 I::IJ. ............................................. .... ........................................ ... Variable l PCR1 PCR2 PCR3 0 0 ll t :0 0 S O l 0 0 0 J 00 0 0 1t 0 I 0 5 0 Ot 0 0 I I 0 0 .... 0 I 0 OC 00 Cl 0 t 1 0 t 0 0 0 I 00. 0 O t 0 0 0 0 o o o o o o 0 J < OOI 0 0 ,., 00 0 Oo > ot O o 0 0 0 .. 100 0100 00 10 oil I 00 O o O t 00 10 Ot.l o 0 J 0 0 I 0 0 0 o oooo"'o o o o o 0 0 o 0 0 010 0 o t o o 1 0 o 1 o o 1 :OOC.I o o 0 I I TMPB -0. 6605 0. 5265 0. 3750 PHS l 0 91 21 I 0 1 844 0 1 2 9 3 PHB 1 0 8 1 9 3 0 2 3 1 2 -0 0 8 2 0 i : REDXS i -0 0 1 3 2 0 8 1 6 7 -0 0 52 6 RDXB l 0 3 9 59 l -0 4 2 8 7 0 614 1 SALS 1 0 9 7 0 6 1 0 2 0 8 3 -0 0 64 1 i i TURBB 1 0.8606 -0.0087 0.0357 i NH4 -0 8 84 7 j -0 34 2 8 0 21 6 7 P04 l 0 8 8 2 9 0 1 7 4 9 -0 24 1 6 CHLA l -0 9 55 6 j 0 0 1 9 3 0 0 7 3 8

PAGE 135

-N 0\ Appendix III (continued) Eigenvalues for Stations Averages (Physical Variables) (Analysis 2) Eigenvalues (CORA) .. PcRl' ...................... T .................. 8 .. .. 2 . T .................. 6 .. .................. .. 9 ... I' .................. o .. .. 9 .. PCR 2 l 2 34 9 3 0 6 714 j 0 14 6 8 1 0 7 0 8 8 PCR 3 1 1 6 7 7 9 1 0 71 8 5 j 0 1 04 9 1 0 8 1 3 6 PCR4 0 9 5 94 0 144 0 0 0 6 0 0 1 0 8 7 3 6 PCR 5 0 8 1 54 0 2 9 51 0 0 51 0 0 9 24 6 PCR 6 i 0 52 0 3 0 1 1 6 7 1 0 0 3 2 5 i 0 9 5 71 PCR7 l 0. 4037 1 0. 2841 l 0. 0252 I 0. 9823 PCR 8 l 0 1 1 9 6 0 04 51 0 0 0 7 5 j 0 9 8 9 8 PCR 9 l 0 0 7 4 5 0 0 3 3 8 0 0 04 7 0 9 944 PCR 1 0 l 0 04 0 6 0 0 14 3 0 0 0 2 5 0 9 9 7 0 PCR 1 1 l 0 0 2 6 3 1 0 0 1 1 1 l 0 0 0 1 6 l 0 9 9 8 6 PCR 1 2 l 0 0 1 52 l 0 0 1 0 1 0 0 0 0 9 1 0 9 9 9 6 PCR 1 3 0 0 0 5 1 0 0 0 3 5 0 0 0 0 3 0 9 9 9 9 PCR 14 0 0 0 1 6 1 0 0 0 1 5 1 E04 j 1 0 0 0 0 PCR 1 5 1 0 0 0 0 1 0 0 0 0 1 1 7 E0 6 1 1 0 0 0 0 PCR 1 6 j 1 4 3 3E0 6 l l 9 E0 8 j 1 0 0 0 0

PAGE 136

........ N -....) Appendix III (continued) Eigenvectors for Station Averages (Physical Variables) (Analysis 2) ................................................ .. .... J .. .............................................. Var i ab 1 e l PCR 1 1 PCR 2 l PCR 3 1o e c 10e C loe C II OC 11:1101111 llloO C C e CCioe c loe c co:' loCI <1: :tO C loe c :loe C O C cloe C C10e C Clo e C lo ) 0 C !O:Io e c 1oe Clo a: lo e C C loOCC :lo ltC ce'l ll:lo OC I :loe C. :lie C :loeC JI e C C e C Cll O C C -;-ec lo :ll e C :loO C C :lo e01 :loO Clle C C lO OCCio eC lleiiiiC:IoeCCioe Clo) e DPTHB j 0. 11 05 -0. 2930 0. 2411 DPTHS -0. 0778 -0. 3872 1 0. 1 826 TMPS -0 0 1 8 0 l 0 56 9 8 i 0 24 1 9 . TMPB -0 0 7 7 0 0 58 9 3 0 1 8 2 2 . PHS 0. 31 24 l 0. 0831 j 0. 0534 PHB 1 0. 3097 l 0. 0601 1 0. 0959 . DOS 0. 2881 1 0. 0043 l 0. 231 8 DOB 0. 3053 i 0. 0760 i 0. 0868 . CONDS 0. 3168 -0. 0432 i -0. 0706 CONDB 0. 3123 -0. 0845 -0. 0136 REDXS j 0 0 52 8 j 0 2 1 1 5 -0 6 6 0 1 RDXB 0 1 9 55 0 0 1 9 0 -0 5 1 8 2 . SALS 1 0. 31 69 1 -0. 0352 1 -0. 0775 . SALB l 0 3 1 2 6 l -0 0 7 6 8 l -0 0 2 3 5 . TURBS l 0 3 14 2 0 0 8 0 5 0 0 6 58 TURBB 0 2 64 6 0 0 9 04 0 1 5 24

PAGE 137

Appendix III (continued) Pattern Matrix for Station Averages (Physical Variables) Demonstrating correlations between original variables and PCs (Analysis 2) Pattern Matrix (CORA) .. .............. ................................ "' ............... "' .............................................................. II' ..................... ......................................................................... ..................................................................................... ,. .............................. .. Var i ab 1 e PCR 1 PCR 2 PCR 3 .............................................................. ........ .......... ,. ...... ., ...................................................................... ........................... .... ......................................................... ...................................................................................... .. . DPTHB 0. 3312 -0.4490 0. 3123 . DPTHS 1 -0. 2332 -0. 5935 0. 2366 TMPS -0. 0538 i 0. 8734 j 0. 31 34 . TMPB l 0 2 3 0 8 0 9 0 3 3 0 2 3 6 0 ,...... I : : : 1 PHS 0. 9366 0. 1 274 ; 0. 0692 PHB j 0. 9287 1 0. 0920 0. 1242 DOS l 0. 8639 1 0. 0066 0. 3003 DOB j 0 91 55 j 0 1 1 6 6 0 1 1 24 CONDS 0. 9500 -0. 0663 -0. 0914 CONDB 0. 9364 -0. 1296 -0. 0177 REDXS 1 0. 1585 1 0. 3242 1 -0. 8550 RDXB 0 58 61 0 0 2 91 l 0 6 7 1 2 SALS 0. 9502 l -0. 0540 -0. 1 004 SALB 1 0. 9373 j -0. 1178 -0. 0304 TURBS 0. 9422 0. 1233 0. 0852 TURBB 1 0. 7933 1 0. 1385 0. 1974

PAGE 138

Appendix III (continued) Eigenvalues for Overall Monthly Averages (All Stations) sis 3 ........................................................................... .. ..... ......................................................................... .. .. ... .. ... l . .. .. . l . .. .. .. .. PCR 1 j 11 1 2 7 3 l 6 6 2 2 8 j 0 50 58 l 0 50 58 PCR2 \ 4.5045 2.4326 i 0.2047 l 0.7105 PCR 3 1 2 0 71 9 j 0 3 9 7 7 0 0 94 2 j 0 8 04 7 PCR4 1 1 6 7 4 2 0 7 6 6 9 l 0 0 7 61 1 0 8 8 0 8 PCR 5 0 9 0 7 3 i 0 2 6 7 5 1 0 04 1 2 i 0 9 2 21 PCR 6 1 0 6 3 9 8 1 0 1 8 81 1 0 0 2 91 1 0 9 51 1 PCR7 ) 0. 451 7 1 0. 1 649 i 0. 0205 1 0. 971 7 -N \0 PCR8 l 0. 2868 1 0. 0570 1 0. 0130 1 0. 984 7 PCR 9 0 2 2 9 8 1 0 1 50 9 l 0 0 1 04 1 0 9 9 52 PCR 1 0 1 0 0 7 9 0 j 0 0 514 1 0 0 0 3 6 j 0 9 9 8 7 PCR 1 1 i 0 0 2 7 6 l 0 0 2 7 6 1 0 0 0 1 3 l 1 0 0 0 0 PCR 12 1 0 j 0 1 0 j 1 0000 PCR 1 3 j 0 1 0 j 0 1 1 0 0 0 0 PCR 14 1 0 l 0 1 0 l 1 0 0 0 0 PCR 1 5 1 0 0 1 0 j 1 0 0 0 0 PCR 1 6 0 1 0 j 0 1 1 0 0 0 0 PCR17 0 i 0 j 0 i 1. 0000 PCR 1 8 1 0 l 0 l 0 l 1 0 0 0 0 PCR 19 0 l 0 1 0 l 1 0000 PCR20 \ 0 l 0 0 l 1 0000 PCR 21 0 l 0 1 0 l 1 0 0 0 0 PCR22 0 l 0 l 1 0000

PAGE 139

-w 0 Appendix III (continued) Eigenvectors for Monthly Averages (All Stations) . (Analysis 3) ___ ............................... :-----...................... .. .. ....................... :-............. = ... l .............. ............... l .............. .......... ..... L. ........... ............... 1 .............. DPTHB j -0. 2128 0. 1213 j -0. 0284 1 0. 011 0 DPTHS l -0. 1022 l 0. 2801 l 0.4012 j 0. 2636 TMPS 1 -0.2616 -0.1999 1 -0.0738 1 0.0943 TMPB -0. 2650 -0. 1887 -0. 074 7 l 0. 0890 PHS j 0. 2733 j 0. 1 076 j -0. 0067 1 -0. 1229 PHB 1 0. 2369 l 0. 1 931 1 0. 0177 1 -0. 2029 DOS j 0. 2732 j 0. 1436 j -0. 0195 j -0. 0672 008 i 0 2 7 50 i 0 144 6 1 0 0 0 1 8 i -0 0 8 s 2 CONDS 1 0 2 57 1 1 0 1 3 3 3 1 0 1 3 1 8 1 0 2 8 1 7 CONDB 0. 2492 -0. 1 554 0. 1 330 l 0. 3006 REDXS 1 0. 0462 1 0. 3899 1 -0. 2426 1 0. 1854 RDXB l 0. 0211 l 0. 3316 l -0. 1889 0. 3501 SALS j 0. 2567 j -0. 1539 j 0. 0837 1 0. 2806 SALB 1 0. 2452 l -0. 1712 1 0. 0889 1 0. 3143 TURBS l 0 2 2 3 2 l -0 21 0 0 j -0 3 0 50 j -0 14 9 2 TURBB 0. 2116 -0. 2139 -0. 2659 i -0. 291 0 N03 0. 01 07 j 0. 2863 -0. 2885 j -0. 1993 N02 l -0. 1295 l 0. 1 711 l 0. 4436 l -0. 2649 NH4 l -0. 1643 l 0. 3063 1 -0. 0462 1 0. 0389 P04 l 0. 0985 l -0. 0923 0. 4584 l -0. 3393 CHLA 1 -0.2388 1 -0. 1895 1 -0. 1606 1 0. 0791 RA IN 1 -0 2 50 2 1 -0 21 1 3 i 0 0 1 2 2 1 -0 03 9 6

PAGE 140

........ w ........ Appendix III (continued) Pattern Matrix for Monthly Averages (All Stations) Demonstrating correlations between original variables and PCs (Analysis 3) Pattern Matrix (CORA) DPTHB l -0. 7099 0. 2575 1 -0. 0408 DPTHS -0. 3408 0. 5945 0. 5775 TMPS j -0.8726 l -0.4243 i -0. 1062 TMPB 0 8 84 0 j 0 4 0 04 0 1 0 7 5 PHS 0 9 1 1 8 l 0 2 2 84 0 0 0 9 7 PHB 0 7 9 0 2 l 0 4 0 9 9 0 0 2 55 DOS 0.9113 0.3047 -0.0281 DOB 0 91 7 2 l 0 3 0 6 9 1 0 0 0 2 6 CONDS 0. 8577 i -0. 2830 0. 1897 CONDB 1 0.83141 -0.3298 i 0.1914 REDXS 0. 1540 j 0. 8275 i -0. 3492 RDXB 0. 0705 1 0. 7037 l -0. 2720 SALS j 0. 8562 1 -0. 3266 1 0. 1 205 SALB 1 0 81 7 8 j -0 3 6 34 1 0 1 2 8 0 TURBS 0. 7444 j -0.4457 -0.4390 TURBB j 0. 7058 1 -0.4540 1 -0.3827 NO 3 1 0 0 3 56 0 6 0 7 6 1 -0 4 1 53 NO 2 0 4 3 1 9 j 0 3 6 3 2 0 6 3 B 6 NH4 -0. 5480 1 0. 6500 -0. 0665 P04 1 0 3 2 8 5 -0 1 9 58 l 0 6 59 8 CHLA 1 -0.7964 -0.4021 l -0.2312 RA I N j -0 8 34 7 1 -0 44 85 0 0 1 7 6

PAGE 141

........ w N Appendi x III (continued) Eigen v alues for Wet/Dry A v erage s V ariables (Analysis 4: Stations 2, 5, 6 12, 160, 170, 180, 191) Eigenvalues (CORA) .................. ..... ................... ,. ........ ................... 1 ............. ...... ...... ...... ..... ..... ....... .... s ... ..... Component l E i genva 1 ue D i f f erence l Propor t ion Cumu 1 at i ve ................... ......................................... PCR 1 6 84 6 2 j 2 0 144 0 3 2 6 0 1 0 3 2 6 0 PCR 2 l 4 8 31 8 2 0 7 4 3 0 2 3 0 1 0 55 61 PCR 3 l 2 7 57 5 1 0 53 8 2 0 1 31 3 0 6 8 7 4 PCR4 l 2. 2193 1 0874 l 0. 1 057 i 0. 7931 PCR5 l 1 1 319 l 0. 0680 0. 0539 l 0. 84 70 PCR 6 j 1 0 6 3 9 0 5 04 5 0 0 50 7 i 0 8 9 7 7 PCR7 i 0. 5594 0. 0970 0. 0266 j 0. 9243 PCR 8 \ 0 4 6 2 3 l 0 1 51 9 j 0 0 2 2 0 0 94 6 3 PCR9 1 0. 31 04 j 0. 1 014 0. 0148 l 0. 9611 PCR 1 0 1 0. 2091 0. 0179 1 0. 01 00 i 0. 971 0 PCR 11 \ 0. 1912 l 0. 0574 0 0091 1 0. 9801 i : I : I : I : PCR 14 j 0 0 6 54 j 0 0 2 04 l 0 0 0 31 1 0 9 94 8 PCR 1 5 0 04 50 l 0 0 14 1 0 0 0 21 l 0 9 9 6 9 PCR 1 6 1 0 0 3 0 9 0 0 1 6 0 0 0 0 1 5 i 0 9 9 84 PCR 1 7 j 0 0 14 9 1 0 0 0 6 7 j 0 0 0 0 7 1 0 9 9 91 PCR 1 8 i 0 0 0 8 2 0 0 0 0 9 0 0 0 04 0 9 9 9 5 PCR 1 9 l 0 0 0 7 3 0 0 04 9 l 0 0 0 0 3 j 0 9 9 9 9 PCR 2 0 l 0 0 0 24 0 0 0 21 0 0 0 0 1 1 0 0 0 0 PCR 21 j 0 0 0 0 3 1 l 1 E0 5 l 1 0 0 0 0

PAGE 142

Appendix III (continued) Eigenvectors for Wet/Dry Averages Variables (Analysis 4: Stations 2, 5, 6, 12, 160, 170, 180, 191) 100 E i genvec tors (CORA ) .. DPTHB l 0. 0765 -0. 1424 -0. 0489 i 0. 2368 -0. 5819 i 0. 4615 DP THS [ 0 0 51 6 -0 2 58 7 j 0 1 84 5 f 0 2 7 3 3 j 0 3 0 9 8 l 0 21 59 TMPS l -0 2 81 0 -0 0 6 7 3 l 0 214 8 j 0 2 59 3 1 0 21 7 0 l 0 2 3 9 2 1 : ci 1 : : 1 : I : l : I : gg! 1 :i:Hi: I I :tUH! 1 i:!f!! _. I CONDS [ 0 3 3 9 7 : -0 1 1 64 l 0 1 31 8 0 1 3 52 0 0 7 8 0 0 0 9 9 3 1 Jl!ii SALB l 0.3066 1 -0.1378 j 0.1740 l 0.2042) 0.1928 l -0.0160 TURBS 1 0 24 6 5 0 3 0 3 9 0 04 2 9 1 0 0 9 3 9 -0 04 71 l 0 1 3 2 0 TURBB 0 2 2 58 j 0 3 0 9 0 j 0 0 0 2 3 0 0 9 3 5 j 0 0 51 8 l 0 1 8 57 N03 l o. 0874 o. 2927 1 -o. 1640 -o. 0757 i o. 0626 l o. 3939 N02 i o. 1538 o. 3309 j -o. 1725 1 o. 2305 1 o. 1588 1 o. oss8 NH4 1 -o. 0904 o. 3126 1 -o. 0573 o. 1521 1 -o. 3483 i -o. 1876 I I I I I I

PAGE 143

-w Appendix III (continued) Pattern Matrix for Wet/Dry Averages Variables Analysis 4: Stations 2, 5, 6, 12, 160, 170, 180, 191 Pattern Matrix (CORA) .. r--lic:-i=i. 2 ............... T ............ iicfia-. .................... ................. ..... ..................... :. ..... .. ...... < ....... o-......... "''"''............ ........... DP THB j 0 2 0 0 2 i -0 31 3 0 l -0 0 81 2 DP THS 1 0 1 3 51 1 -0 56 8 7 j -0 3 0 64 TMPS \ -0.7351 -0.1478 1 0.3566 TMPB l -0. 7364 j -0. 1533 1 0. 3693 i i DOS 0 6 3 6 9 -0 0 3 61 -0 3 3 2 0 i ; CONDB 0. 8014 l -0.3255 1 0. 2867 REDXS l 0. 4148 i -0. 0128 l 0. 8121 RDXB 1 0.4074 j -0.0422 0. 7681 SALS 1 0 8 8 55 1 -0 2 3 53 j 0 21 61 SALB i 0 8 0 2 3 l -0 3 0 3 0 1 0 2 8 9 0 TURBS i 0 64 50 0 6 6 8 0 l 0 0 71 2 TURBB 1 0. 5907 1 0. 6792 -0.0039 NO 3 0 2 2 8 7 0 64 34 l -0 2 7 2 3 N02 o .4023 1 o. 7274 i -o. 2865 NH4 1 -o. 2364 1 o. 6871 1 -o. 0951 P04 1 o. 4354 o. 71 87 l -o. 1 914 CHLA 1 -0 6 0 9 6 l 0 2 2 55 l 0 14 3 5

PAGE 144

Appendix III (continued) Eigenvalues for Physical Variables (Wet/Dry Season Averages for All Stations) 5 ....... ......... ... ... .............. ...................................... .. .... .. .... .............................................. ....................... . .. .. .. .. .. J . ... PcR 1 1 6 6 0 2 0 4 0 55 6 0 4 1 2 6 0 4 1 2 6 PCR 2 1 2 54 64 0 6 50 2 1 0 1 59 2 l 0 5 71 8 PCR3 1 1.8962 1 0.2524 1 0.1185 l 0.6903 PCR4 1 1 6438 j 0. 4491 j 0. 1 027 j 0. 7930 PCR5 j 1 1948 j 0. 1988 j 0. 074 7 i 0. 8677 w Vl PCR 6 l 0 9 9 59 l 0 52 50 1 0 0 6 2 2 1 0 9 2 9 9 PCR7 i 0. 4 709 0. 1913 0. 0294 0. 9594 PCR 8 l 0 2 7 9 7 1 0 1 5 51 j 0 0 1 7 5 0 9 7 6 9 PCR 9 1 0 1 24 6 1 0 0 34 2 1 0 0 0 7 8 0 9 84 6 PCR 1 0 l 0 0 9 04 1 0 0 1 8 1 0 0 0 57 1 0 9 9 0 3 PCR 1 1 1 0 0 7 2 3 1 0 0 1 7 3 0 0 04 5 1 0 9 94 8 PCR 1 2 0 0 55 0 0 0 3 7 8 l 0 0 0 34 1 0 9 9 8 3 PCR 1 3 l 0 0 1 7 2 l 0 0 1 14 l 0 0 0 1 1 l 0 9 9 9 3 PCR 14 l 0 0 0 58 1 0 0 0 1 8 0 0 0 04 1 0 9 9 9 7 PCR 1 5 1 0 0 0 3 9 j 0 0 0 2 9 l 0 0 0 0 2 l 0 9 9 9 9 PCR 1 6 1 0 0 0 1 0 1 6 E0 5 j 1 0 0 0 0

PAGE 145

Appendix III (continued) Eigenvectors for Physical Variables (Wet/Dry Season Averages for All Stations) (Analysis 5) titl .. ... oo'"'''ooooooooooooo I Vari ab 1 e j PCR 1 j PCR2 PCR3 j PCR4 PCA5 r
PAGE 146

Appendix I I I (continued) Pattern Matrix for Physical Variables (Wet/Dr y Season A v erages for All Stations) (Anal y sis 5) Pattern Matrix (CORA) ......................................................... ............. ....................... .... .. ........................................................................... ........................................................................................................ .................................... .. .. .. .l. ............. .... . . ....... l.. ............ ............................ .... ......... .. DP THB 0 2 1 2 6 0 14 7 5 0 0 6 52 DPTHS l -0. 1754 l 0. 3023 -0. 2759 TMPS l -0 54 6 7 0 5 71 1 0 4 544 TMPB -0.5731 l 0. 5555 0.4455 PHS 0 6 9 56 0 3 6 6 7 0 2 3 59 w -...J PHB 0 6 9 7 7 0 4 1 8 3 -0 21 0 9 DOS 1 0. 7054 1 -0. 1 967 -0. 5450 DOB 0 8 0 7 5 -0 1 0 8 1 -0 4 3 53 CONDS l 0.89091 0.1949 0.3024 CONDB 1 0.8644 0.2252 0.3341 REDXS l 0.0463 l 0.6517 -0.2960 RDXB j 0. 1152 0. 6611 -0. 2835 SALS j 0. 8878 0. 1953 0. 3143 SALB 1 0 8 6 7 3 i 0 2 3 0 6 0 34 1 8 TURBS 0. 6201 l -0.4208 0. 3408 TUABB l 0. 5452 j -0.4688 0. 3485

PAGE 147

Appendix IV Average Monthly Surface Temperatur e w I ;jul ...... :. :1 ""-' I, .. I 1 30-35 00 ''--" 25 -30 20-25 0 IT" __ [J 1 5-20 'L. -_, ; r: [J10-15 ..-,Jioo '., I 20 'j .. ("' ,,..lr( .. l #-. 5 10 :I , I Jo, r iii .... ..... \:if,.: ..J .!1 '11 o-5 E CD 1-15 g z 0

PAGE 148

w '-0 0 'L. Gl .. ::s Cii 8. 1 E Gl 1Appendix IV (continued} Average Monthly Bottom Temp z Stn 206 30 35 25 20 015 0 10 51 0 o-s

PAGE 149

Appendix IV (continued) Ave r age Monthly Surface pH 8 -I 1 0 7 8-8 I 7 .8J ": ... .7. 6 7 8 "' ;,;_ )."..;: 0 7.4-7 6 ., I 7 2-7 4 7 .6-l 0 7 7 2 ,, I Jo6. 8-7 7.4-l -Stn 206 6 6-6 8 .6.4-6. 6 z

PAGE 150

Appendix IV (continued) Average Monthly Bottom pH z Stn 206 7 8 8 [J7.6-7. 8 7.4-7 6 7.2-7 4 7-7 .2 C 6.8-7 [J6 6-6 8 6.4-6 .6 6.2-6 4

PAGE 151

Appendix IV (continued) Average Monthly Surface DO I 7-j r '1 I 1 0 7-8 N ,.. .;; ... ,.- .# 6 -7 s 6 -5 0 3-4 0 2-3 1-2 o-1 1; z

PAGE 152

I w .. 4 0 0 Appendix IV (continued) Average Monthly Bottom DO > 0 z "; I -" J 0 7-8 6-7 s-6 4-5 0 3 4 Stn 206 0 2-3 -2 o-1

PAGE 153

"'e c: Gl E Gl 'tJ c: 0 0 Appendix IV (continued) Average Monthly Surface Conductivity > 0 z Stn 206 so-so 40-50 0 30 -40 0 20-30 10 -20 o-10

PAGE 154

Appendix IV (continued) Average Monthly Bottom Conductivity 60 -I Vl so.,. I I I I I I ' I so-6o 40-50 ..-. .. 030-40 E oj .. [' _.._.., 0 20-30 c Stn 206 10-20 Ql E Ql 30 o-10 (ij .:!. '0 c 0 0 i) z

PAGE 155

0'1 c 0 D.. >< 0 'tl Cll a: Appendix IV (continued) Average Monthly Surface Redox Potential z Stn 206 0.25 0.3 0 2 0 25 0 0 1 5 0.2 0 0.1-0 15 o o5-0 1 o-o o5

PAGE 156

(") lO "' lO ,.... 0 "' 0 0 lO .;, 0 .;, 9 .;, 0 "' ,.... 0 0 0 0 0 0 0 6 c c "'0 UIS Q) :::J
a: E 0 X = 0 "'0 Ill c >-:E Q) a. 1: 0 a. :E <( Q) C) < (A) IBIIU610d xopa1::1 1 4 7

PAGE 157

00 t .E iii In 40 Appendix IV (continued) Average Monthly Surface Salinity fi z S tn 206 C 35-40 30 35 25 3 0 20-25 C 15 -20 [J 10 15 5-1 0 o-5

PAGE 158

Appendix IV (continued) Avg Monthly Bottom Sa l inity -+>-I 351 I 1 0 35-40 \0 r . 30-35 .25-30 20-25 0 15-20 s::i .: Stn 206 0 10-15 ,, 5-10 .. ... o 5 (/) ei z

PAGE 159

VI 0 20 15 ::2. '0 :c :; 1 ... Appendix IV (continued) Average Monthly Surface Turbidity > 0 z Stn 206 .2025 015-20 0101 5 10 o 5

PAGE 160

VI s .... ::2. .D 1 Appendix IV (continued) Average Monthly Bottom Turbidity > 0 z S tn 206 20-25 0 15 -20 010 15 5-10 o-5

PAGE 161

VI N i' .:; ... 0 z 4 4 3 5 3 2 5 Appendix IV (continued) Average Monthly Nitrate Nov Stn 1 9 1 Dec 4-4 5 0 3.5-4 -3.5 2 .5 2 2. 5 [J 1 .5 [J 1-1.5 0.51 o-o. 5

PAGE 162

Vl w i' 0 z 1 Appendix IV (continued) Average Monthly Nitrite Nov Stn 191 Dec 1 .8-2 1.6 1 8 [] 1 .41 .6 1 2 1 4 .1-1. 2 o 8 1 []0 6-0.8 []0.4-0 6 0 2-0 .4 o o 2

PAGE 163

Vl i' .:;. -I z 2 5 Appendix IV (continued) Average Monthly Ammonia Nov Dec Stn 191 2-2 5 0 1.5-2 01-1. 5 0.5-1 o o 5

PAGE 164

VI VI i' ..:!. 0 D. Appendix IV (continued) Average Monthly Phosphate N o v Dec Stn 191 4-4 5 0 3 5 4 3 3.5 2 5-3 2 2 5 01. 5 2 01-1. 5 o 51 oo 5

PAGE 165

Appendix IV (continued) Average Mont h ly Chlorophyll a I 10 VI 0\ .10 8 8 -10 Cl -[]6 8 ..:; til .[]4-6 >. 2 1\ a. o-2 0 .. .... 0 :c 0 I -' I Stn 1 9 1 Nov Dec

PAGE 166

V'l -...J Appendix V. Mean DO for Henderson Creek by Month for Feb 1997 -Feb 1998 06.0 05.0 :2 04.0 0) 03 0 0 c 02 0 01. 0 00.0 00 00 m m m m m m m m m m --------N 00 m 0 N N Date


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