Ecological, Taphonomic, and Paleoecological Dynamics of An

ECOLOGICAL, TAPHONOMIC, AND PALEOECOLOGICAL DYNAMICS OF AN

OSTRACODE METACOMMUNITY

A Dissertation

Presented to

The Graduate Faculty of The University of Akron

In Partial Fulfillment

of the Requirements for the Degree

Doctor of Philosophy

Andrew V. Michelson

August, 2012

ECOLOGICAL, TAPHONOMIC, AND PALEOECOLOGICAL DYNAMICS OF AN

OSTRACODE METACOMMUNITY

Andrew V. Michelson

Dissertation

Approved: Accepted:

______Advisor Department Chair Dr. Lisa E. Park Dr. Monte E. Turner

______Committee Member Dean of the College Dr. Francisco B.-G. Moore Dr. Chand Midha

______Committee Member Dean of the Graduate School Dr. Jean J. Pan Dr. George R. Newkome

______Committee Member Date Dr. John M. Senko

______Committee Member Dr. Alison J. Smith

ABSTRACT

The modern synthesis of paleontology with evolutionary biology has successfully integrated population ecology into the study of the fossil record. While it may prove impossible to measure and account for the important processes that structure communities through time, the integration of community ecology into paleoecology remains to be done to further the modern synthesis. This dissertation attempts to integrate community ecology into the study of a lacustrine ostracode metacommunity across space today and through the mid Holocene on San Salvador Island, Bahamas.

Patterns of community change across space today are investigated by comparing the live/dead agreement in taxonomic composition and rank-abundance of species in seven lakes. This taphonomic study establishes that live/dead agreement of ostracode assemblages is high in all lakes save one. Therefore, sampling of death assemblages, as is common in many paleolimnolgical studies, can be used to investigate changes in alpha and beta diversity of assemblages across time and space.

Death assemblages were then sampled from thirty-two lakes on San Salvador to investigate the metacommunity dynamics that explain patterns of beta diversity of communities. I found that beta diversity was most strongly controlled by the local environment in which communities live with the change in communities most strongly correlated with changes in a complex hydrological gradient of: conductivity, dissolved oxygen, and alkalinity.

iii

After establishing that the metacommunity dynamics conformed to a species sorting model, I exploited the association between ostracode assemblages and conductivity to create a statistical model that used changes in ostracode assemblages to predict changes in conductivity within individual lakes on San Salvador. This model was then applied to archives of ostracode assemblages from the mid-Holocene to today to create a record of changing conductivity through time in three lakes. The model reveals large, high-frequency fluctuations in conductivity controlled by regional changes in precipitation/evaporation ratios, controlled by similarly high frequency climate oscillations.

Finally, I use the metacommunity concept of community ecology as a theoretical tool to explain how changes in communities through time are related to ecosystem dynamics. Ostracodes, as easily-dispersed organisms who respond to changes in their local environment through habitat-tracking, prove to be consistently useful proxies of environmental changes. In this way, neontological principles are successfully applied to the paleoecological record demonstrating the seamless application of community ecology to the fossil record.

DEDICATION

To my parents, Allan, Barbara, and Melonie for giving me the greatest gift, an education

and to René, the wish of my heart

ACKNOWLEDGEMENTS

First and foremost, I thank my adviser, Lisa E. Park. She guided this research from the start and has provided invaluable advice throughout its completion. I will forever be grateful for the many opportunities she has provided me to grow as a scientist, mentor, and teacher. I hope I will someday be as good a scientist as she is.

I also thank all the members of my committee: Francisco B.-G. Moore, Jean J.

Pan, John M. Senko, and Alison J. Smith. They all provided unique advice and constructive criticism. I could not have done this without them.

Stephen C. Weeks deserves credit for encouraging me to become a scientist. He saw potential in me I did not know was there.

Sara Bright, Mark Dalman, Emily Draher, and Emily Woodward provided enormous help collecting samples from lakes both pristine and foul. I am grateful for

Tom and Erin Rothfus’ support in the field. After a full day driving around San Salvador it is comforting to know you have a warm bed and a hot meal to come home to. Tom

Quick has provided innumerable hours of technical support, always with a smile and a kind word. Elaine Butcher continues to provide great support navigating through administration and organizational problems.

Finally, my family René Rivera, and Allan, Barbara, Megan, Melonie, and Sarah

Michelson, and have continually provided me with love and support, without which I never would have had the strength to finish this.

TABLE OF CONTENTS

Page

LIST OF TABLES ………………………………………………...…………………….ix

LIST OF FIGURES ……………………………………………………………...... x

CHAPTER

I. TAPHONOMIC DYNAMICS OF LACUSTRINE OSTRACODES ON SAN SALVADOR ISLAND, BAHAMAS: HIGH FIDELITY AND EVIDENCE OF ANTHROPOEGENIC MODIFICATION………………………………………...... 1

Introduction………………………………………………………………………..2

Methods……………………………………………………………………………7

Results……………………………………………………………………………10

Discussion……………………………………………………………………..…21

II. DISCERNING PATTERN OF DIVERSITY AND BIOGEOGRAPHICAL DISTRIBUTIONS: OSTRACODE METACOMMUNITY DYNAMICS ON SAN SALVADOR ISLAND, BAHAMAS…………………………….…………..………….25

Introduction………………………………………………………………………26

Methods…………………………………………………………………………..33

Results……………………………………………………………………………38

Discussion……………………………………………………………………..…50

III. TESTING THE ASSUMPTIONS OF HIGH RESOLUTION PALEOENVIRONMENTAL INFERENCE MODELS IN AN ECOLOGICAL CONTEXT…………………………………...... …………...... 59

Introduction………………………………………………………………………60

Methods…………………………………………………………………………..67 vii

Results……………………………………………………………………………72

Discussion……………………………………………………………………..…78

IV. A QUANTITATIVE INFERENCE MODEL FOR CONDUCTIVITY USING OSTRACODE ASSEMBLAGES ON SAN SALVADOR ISLAND, BAHAMAS………………….………………………………………………………...... 99

Introduction……………………………………………………………………..100

Methods…………………………………………………………………………104

Results…………………………………………………………………………..111

Discussion………………………………………………………………………131

Conclusions……………………………………………………………………..141

REFERENCES ………………………………………………………………………...142

APPENDICIES………………………………………………………………………....155

APPENDIX A. TAPHONOMIC DATA: LIVING ASSEMBLAGES..……………....156

APPENDIX B. TAPHONOMIC DATA: DEATH ASSEMBLAGES………………...165

APPENDIX C. GEOGRAPHIC, LIMNOLOGICAL, CHEMICAL, OSTRACODE DATA FROM ALL LAKES…………………………………………………………...174

APPENDIX D. FOSSIL OSTRCODE DATA FROM SALT POND CORE ………...182

APPENDIX E. FOSSIL OSTRCODE DATA FROM CLEAR POND CORE………..184

APPENDIX F. FOSSIL OSTRCODE DATA FROM NORTH STORRS CORE…….187

APPENDIX G. PHYSICAL DATA FROM SALT POND CORE……………………195

APPENDIX H. PHYSICAL DATA FROM CLEAR POND CORE………………….197

APPENDIX I. PHYSICAL DATA FROM NORTH STORRS CORE………………..201

APPENDIX J. POTASSIUM XRF DATA CLEAR POND CORE…………………...206

APPENDIX K. POTASSIUM XRF DATA NORTH STORRS CORE……………….314

viii

LIST OF TABLES

Table Page

1.1 Species list of the ostracodes from this study…………………………………...….. 11

2.1 Criteria for distinguishing metacommunity models…………………………..……..31

2.2 Species list of the ostracodes from this study…………………………………...…...39

2.3 Minimum, maximum, mean, and standard deviation of measured environmental variables in all lakes………………………………………………………………...... 44

3.1 The assumption of the transfer function method of paleoenvironmental reconstruction ……………………………………………………………………………………………62

3.2 Species list of the ostracodes from this study…………………………………..……73

3.3 Criteria for distinguishing metacommunity models…………………………………92

4.1 Minimum, maximum, mean, and standard deviation of measured environmental variables in all lakes………………………………………………………………...... 112

4.2 Pearson's correlation (r) of measured environmental variables with the first two axes of a three-dimensional non-metric multidimensional scaling plot……………………..115

4.3 Conductivity optima and tolerances for all species………………………………...118

4.4 Performance of apparent and cross-validated statistics of the conductivity transfer function…………………………………………………………………………………124

4.5 Results of radiocarbon dating of all three cores………………………………...... 126

LIST OF FIGURES

Figure Page

1.1 Map of San Salvador Island showing lakes sampled…………………………...... 8

1.2 Ostracode species encountered in this study…………………………………...... 12

1.3 Box plot of rho in all lakes sampled…………………………………………………16

1.4 Mean rho of each lake according to its mean species richness………………………17

1.5 Taxonomic similarity (Jaccard-Chao index) and rank-abundance correlation of 112 samples of living communities and associated death assemblages………………...... 18

1.6 First 2-dimensions of a 3-dimensional nonmetric multidimensional scaling ordination on the Bray-Curtis dissimilarity matrix of all samples with lakes overlain………...... 19

2.1 San Salvador Island, Bahamas……………………………………………………….34

2.2 Ostracode species encountered in this study…………………………………………40

2.3 Scatter plot of Euclidean distances of Z-scores of environmental variables between lakes and geographical distance between pairs of lakes ……………………..………….45

2.4 Moran’s I of environmental factors and species percent abundances………………..46

2.5 Effect sizes of correlation between environmental factors and ostracode assemblages ……………………………………………………………………………………………48

2.6 Scatter plot Bray-Curtis dissimilarity matrix of species assemblages and distances between lakes ………………………………………... ………………………………....49

2.7 Results of variance decomposition…………………………………………………..51

2.8 Correlograms of Moran’s I for selected species percent abundances…………...... 52

3.1 San Salvador Island, Bahamas……………………………………………………….69

3.2 Ostracode species encountered in this study…………………………………………74

3.3 Scatter plot Bray-Curtis dissimilarity matrix of species assemblages and distances between lakes…………………………………………………………………………….77

3.4 Moran’s I of environmental factors and species percent abundances…………...... 79

3.5- Correlograms of Moran’s I for species percent abundances………………………..81

3.6 Results of variance decomposition……………………………………………...... 84

3.7 Effect sizes of correlation between environmental factors and ostracode assemblages ………………………………………………………………………………...... 85

4.1 San Salvador Island, Bahamas…………………………………………………...... 105

4.2 First 2-dimensions of a 3-dimensional non-metric multidimensional scaling plot of all 32 ostracode assemblages collected on the Bray-Curtis dissimilarity matrix…………..113

4.3 Multidimensional Fuzzy Set Ordination…………………………………...……….116

4.4 Percent abundances of all species……………………………………………...... 119

4.5 Performance of ostracode-based transfer function for conductivity………………..125

4.6 Results of ostracode-based transfer function for conductivity as applied to the three cores……………………………………………………………………………...... 128

4.7 - Detrended results of ostracode-based transfer function for conductivity as applied to the three cores……………………………………………………………………...... 130

4.8- Results of ostracode-based transfer function for conductivity and potassium from Clear Pond and North Storrs……………………………………………………………132

CHAPTER I

TAPHONOMIC DYNAMICS OF LACUSTRINE OSTRACODES ON SAN

SALVADOR ISLAND, BAHAMAS: HIGH FIDELITY AND EVIDENCE OF

ANTHROPOGENIC MODIFICATION

Abstract

Paleobiological archives are essential for understanding long term and large scale diversity patterns. However, prior to using any taxa in these types of studies, the taphonomic processes by which they enter the fossil record should be well documented and understood. This study focuses on the precision and accuracy of the fossil record of

Ostracoda (Phylum: Arthropoda) on San Salvador Island, Bahamas. Ostracodes

(microcrustaceans) are an important proxy for reconstructing climate change and sea level fluctuations, but their taphonomy is not well known. We test the accuracy of the ostracode record on San Salvador by examining the correlations between rank-abundance and taxonomic composition of sixteen living communities and death assemblages in seven lakes on San Salvador Island. We also test the precision of this record by comparing the taxonomic composition and species abundances of sixteen death assemblages from each of the same seven lakes. In six out of seven of these lakes, the accuracy by which death assemblages record the taxonomic composition and rank-

abundance distributions of living communities is high. In these same six out of seven lakes, the within lakes precision of the record is also high since death assemblages recovered from individual lakes are more similar to assemblages from the same lake than to any other lake sampled. In the remaining lake tested, Watlings Blue Hole, the accuracy and precision of the record exhibited wide variation at individual sampling sites that appears to be related to anthropogenic activities—namely modification of a lake as a pen to store captured sea turtles. This study demonstrates the high fidelity of the ostracode fossil record and highlights the importance of site-specific taphonomic studies to understand physical and biological processes that may obscure the record.

Introduction

An omnipresent and vital question in paleoecological studies is to what degree does the fossil assemblage represent the biological community from which it was drawn?

Paleoecological archives provide important data on the state and natural variability of pristine ecosystems (Smol 2007). These archives can help identify areas of anthropogenic alteration (Kidwell, 2009) and provide long-term records of environmental change (Saros 2009; Woodbridge and Roberts, 2010). However, if paleobiological data is to be trusted as faithful recorders of conditions at the time of deposition, then the process by which organisms enter this record must be understood in order to eliminate or correct for sources of preservational bias.

In particular, studies of the accuracy by which living assemblages of organisms are recorded in associated death assemblages have been a classic problem in paleontology

(Johnson, 1965; Staff et al., 1986; Kidwell, 2001) and have been carried out for many taxonomic groups including: small mammals (Terry 2010a; Terry 2010b), diatoms

(Hassan et al., 2007; Pike et al., 2008), plants and pollen (Jackson and Kearsley 1998;

Simms and Cassara, 2009), mollusks (Zuschin et al., 2000; Lockwood and Chastant

2006; Ferguson and Miller, 2007; Ferguson 2008), foraminifera (Yordanova and

Hohengger 2002; Murray and Pudsey 2004), coral reefs (Greenstein and Pandolfi 1997), and ostracodes (Park et al., 2003; Alin and Cohen, 2004) among many others.

These live/dead studies are critical to questions in historical ecology, conservation paleobiology, paleoenvironmental reconstruction, and evolutionary studies since they help disentangle changes in diversity and species abundances due to time-averaging or differential preservation of species from biologically meaningful changes in diversity, species turnover, and invasion or extinction of taxa from particular environments

(Tomašových and Kidwell, 2009). Specifically, a quantitative approach to studies of the accuracy of the fossil record is necessary since relative abundance of taxa are required to address many questions of evolutionary dynamics including clade interaction and relative local ecological dominance over evolutionary timescales (McKinney et al., 1998;

Kidwell, 2001).

Live/dead studies also help to estimate the amount of time-averaging represented in fossil assemblages (Park et al., 2003). Understanding the amount of time represented by individual fossil deposits allows for estimates of the temporal resolution of samples.

Mixing of non-contemporaneous communities across time or space can lead to over-

estimation of species richness and exclusion of rare species (Fursich, 1990; Alin and

Cohen, 2004; Tomašových and Kidwell, 2009). While some time-averaging occurs with all paleoecolgical samples (Kidwell and Flessa, 1995; Erthal et al., 2011), a high live/dead agreement would indicate that changes in death assemblages through time should closely mirror biological changes in the living community.

Additionally, the subfossil record provides an important archive of ecological change over time-scales that exceed human monitoring which makes it possible to separate changes in diversity driven by natural causes from those driven by humans and provides data on natural variability in ecosystems (Alin and Cohen. 2004; Dietl and

Flessa, 2010). The understanding of how assemblages enter the fossil record will allow the effects of natural taphonomic processes, like transport or differential species preservation, to be separated from the effects of anthropogenic alteration on the accuracy with which death assemblages record the living community (Kidwell, 2007).

Ostracodes (Phylum: Arthropoda) have long been used as indicators of past environments due to their small size, allowing for collection of many individuals, and their sensitivity to environmental changes (Frenzel and Boomer, 2005). Most ostracode species live as benthic organisms and are sensitive to changes in the abiotic environment such as salinity, water depth, temperature or dissolved oxygen concentration (Frenzel and

Boomer, 2005). Their low-Mg calcite shells range from .5-2mm in size, can be preserved as fossils, and have long been used as biological proxies for past environments (Frenzel and Boomer, 2005), including such abiotic factors as electrical conductivity (Mischke et al, 2010a; Mischke et al., 2010b; Mischke et al., 2007; Mezquita et al., 2005), water depth (Mourguiart et al., 1996; Mourguiart and Carbonel 1994; Alin and Cohen, 2003),

and water temperature (Viehberg, 2006; Mezquita et al., 2005) Each individual secretes

8-9 molted shells over its lifetime with the adult stage containing definitive characteristics that often allow for species-level identification (Holmes, 2008). The changing abundances of species and geochemical variability in their carapaces through time in lake sediments on San Salvador Island, Bahamas have been used as indicators of past environments including changing salinity, sea level, and to identify ecosystem changes due to disturbances by hurricanes (Teeter, 1995; Teeter and Quick, 1990; Park and Trubee, 2008; Park et al., 2009). Yet, the taphonomic processes that affect the accuracy and precision of this record are not well understood. Thus, the purpose of this study is to understand the process by which lacustrine Ostracoda on San Salvador Island become part of the paleobiological record. The accuracy of this record will be assessed by examining the correlation between living communities and death assemblages and the precision of this record will be assessed by examining the variability in death assemblages within lakes.

Assemblages of ostracodes and other benthic organisms are also sensitive to modern pollution, such as heavy metals and anthropogenic eutrophication (Bergin et al.,

2006). For example, changes in ostracodes population densities and species richness have been shown to correlate with pollution load (Padmanabha and Belagali, 2008) and discrepancies in rank-abundance and taxonomic composition of living communities and death assemblages of mollusks correlate with human modification of their ecosystem

(Kidwell, 2007). Thus, assessing the accuracy of the paleobiological record of ostracode assemblages on San Salvador Island, Bahamas could be used in identifying areas of

recent human modification as well as to better interpret changes in this record through time.

Distribution of ostracode communities within lakes have also been described as

“patchy” with different species assemblages detected within a few meters of each other

(Muller et al., 2002). This would lead to assemblages with a high degree of spatial heterogeneity within a lake (Alin and Cohen, 2004). However, calibration studies of modern ostracode distribution often rely on one or a few samples of ostracode assemblages per lake (Mischke et al., 2010a, b). If ostracode assemblages are spatially heterogeneous within individual lakes on San Salvador due to multiple habitats within one lake or species-specific preferences such as substrate or macrophyte presence, then this method of sampling would miss key patterns in ostracode distribution. This study will evaluate the precision of the ostracode record. A lake with a high precision record will record similar assemblages within one lake. In a low-precision lake, the assemblages sampled will vary across fine spatial scales. Thus, this study will also examine multiple assemblages within lakes on San Salvador to evaluate the precision of the ostracode fossil record.

Study Area

San Salvador Island, Bahamas is an ideal place to examine the fidelity of the ostracode fossil record since ostracodes can be recovered from lakes in high numbers and many physically diverse lakes occur today on the island (Park and Trubee 1990; Park et al., 2009). San Salvador is a small (163 km2) carbonate island with many interior lakes

located within the Bahamian archipelago in the Southwestern Atlantic within the northern hemisphere (Davis and Johnson, 1989). During times of elevated sea level throughout the Pleistocene, dune sediments were deposited across the San Salvador platform. Many lakes occur on the island today between these ancient dunes, as karst dissolution features in the carbonate bedrock, or as ancient lagoons whose surficial connection to the ocean has been severed (Bain, 1991; Teeter, 1995; Park and Trubee, 2008). The lakes included in this study encompass all types of lake formation as well as associated gradients in salinity.

Methods- Field Methods

We collected sediment samples from seven lakes on San Salvador Island that represent the variation in salinity, dissolved oxygen, and alkalinity across the island

(Figure 1.1). These abiotic factors influence ostracode faunal distribution (Michelson and Park, in press). Sample collections were taken at 4, 9, 14, and 19 m from shore in two replicate transects perpendicular from shore. Individual samples were stained with

Rose Bengal to identify live individuals (Corrège, 1993). In all, sixteen sediment samples were collected and stained from each of the seven lakes for 112 total samples.

These samples were sieved with deionized water using 125 µm (φ-size 3) and 63 µm (φ- size 4) mesh sieves, and were dried and picked for ostracodes. In all cases, at least 400 ostracodes were picked from each sample and all adults were identified to species level using reference collections at the University of Akron (Trubee, 2002; Park and Trubee,

North Storrs Lake

Little Lake ¹ No Name Pond

Clear Pond Blue Hole 5 Watlings Blue Hole French Pond

Kilometers 0 1 2 4

Figure 1.1- Map of San Salvador Island showing lakes sampled. The seven lakes sampled for this study are indicated by black dots and labeled. The single headed arrow points north.

2008). Juveniles were not identified to species level because of the convergent morphology of juveniles in the Caribbean non-marine ostracode fauna (Park and Beltz,

1998). Stained individuals were counted as alive at the time of collection if they appeared pink and had noticeable soft parts attached.

Numerical Methods

The accuracy of the lacustrine ostracode record on San Salvador was tested in two ways. The correlation between the rank-abundance of living communities and death assemblages was tested using Spearman’s rho (Kidwell, 2001) as calculated in the software Minitab®. Rho measures whether the most (least) abundant species in the living community is also the most (least) abundant species in the death assemblage. The accuracy with which death assemblages reflect the taxonomic composition of the living community was tested using the Jaccard-Chao index of taxonomic similarity (Chao et al.,

2005). The R package “fossil” (Vavrek, 2011) was used to calculate the Jaccard-Chao index. Since living communities contained fewer individual valves than death assemblages, the Jaccard-Chao index was used to correct for the discrepancy in sample sizes. This index is the proportion of shared species between the living community and death assemblage with a correction for unseen shared species. Thus, while rho measures the correlation between rank-abundance of the same species in the living community to its rank abundance in the death assemblage, the Jaccard-Chao index measures the taxonomic similarity (presence/absence of species).

The precision of the ostracode fossil record was measured by comparing the variation in death assemblages sampled within lakes to the variation in death assemblages between lakes. A nonmetric multidimensional scaling (NMDS) ordination was carried out on the Bray-Curtis dissimilarity metric using PAST v. 191® (Hammer et al., 2001) to see which death assemblages were most similar. One-way Analysis of Similarity

(ANOSIM) was carried out in PAST v. 191® (Hammer et al., 2001) to test whether lakes contained assemblages distinct from each other.

Results

In total, nine ostracode species from nine genera and seven families were found in the seven lakes from this study. These species are members of the Caribbean non-marine fauna of Park and Beltz (1998) and include: Aurila floridana Benson and Coleman, 1963,

Cyprideis americana Sharpe, 1909, Dolerocypria inopinata Klie, 1939, Hemicyprideis setipunctata Brady, 1869, Loxonconcha pursubrhomboidea Edwards, 1944, Paramesidea harpago Kornicker, 1961, Perissocytheridea bicelliforma Swain, 1955, Reticulocythereis multicarinata Swain, 1955, and Xestoleberis curassavica Klie, 1939 (Table 1.1, Figure

1.2). Most of these species have conductivity ranges centered within the marine window, the only exceptions are Hemicypridesi setipunctata which has a wide conductivity tolerance, but reaches its maximum abundance in brackish waters, and Aurila floridana which has a somewhat narrower range, reaching its maximum abundance in hypersaline waters (Michelson and Park, 2011).

Table 1.1. Species list of the ostracodes from this study.

Podocopida Podocopina Bairdiidae Paranesidea harpago Kornicker, 1961 Candonidae Dolerocypria inopinata Klie, 1939 Cytherideidae Cyprideis americana Sharpe, 1909 Hemicyprideis setipunctata Brady, 1869 Perissocytheridea bicelliforma Swain, 1955 Hemicytheridae Aurila floridana Benson and Coleman, 1963 Loxoconchida Loxoconcha pursubrhomboidea Edwards, 1944 Trachyleberididae Reticulocythereis multicarinata Swain, 1955 Xestoleberideidae Xestoleberis curassavica Klie, 1939

A B

C D

E F

G H

Figure 1.2- Ostracode species encountered in this study: Aurila floridana Benson and Coleman, 1963 (A), Cyprideis americana Sharpe, 1909 (B), Dolerocypria inopinata Klie, 1939 (C), Hemicyprideis setipunctata Brady, 1869 (D), Loxoconcha pursubrhomboidea Edwards, 1944 (E), Paranesidea harpago Kornicker, 1961 (F), Perissocytheridea bicelliforma Swain, 1955 (G), Reticulocythereis multicarinata Swain, 1955 (H), Xestoleberis curassavica Klie, 1939 (I).

Accuracy of the Record

Across all seven lakes, ostracode death assemblages display high correlation to the living community (mean rho =0.854). Figure 1.3 shows the distribution of rho within all 7 lakes. Additionally, this correlation is robust with respect to species richness. The mean rho across all lakes, when weighted according to species richness of the sample, is close to the unweighted rho (mean rho weighted by species richness =0.826). Figure 1.4 displays mean rho within lakes according to the lake's mean richness. While low diversity lakes (French Pond, No Name Pond, North Storr’s Lake) display high correlation between the living community and the death assemblage, this correlation is based on only 2-3 species. However, Little Lake, the most diverse lake in this study, also displays high mean correlation of the living community and the death assemblage. This accounts for the low difference between the unweighted mean rho across all lakes and the weighted-by-richness mean rho across all lakes.

The only exception to the general high fidelity between the death assemblages to the living community is Watlings Blue Hole, where the mean rho is 0.450. Watlings

Blue Hole also displays a high variability in rho that is unrelated to the locations sampled, the water depth, or distance from shore. In fact, it is the only lake included in this study that displays a negative correlation between the living community and the death assemblage in any one sample. The large variation in Watlings’ rho can be seen in

Figures 1.3, 1.4, 1.5, and 1.6.

Figure 1.5 shows the relationship between rho and the Jaccard-Chao index for all

112 samples in this study. All samples, except for some of the samples from Watlings

Little North Storrs Blue Hole 5 Watlings Clear French No Name

Figure 1.3- Box plot of rho in all lakes sampled. The y-axis displays Spearman's rho (Pearson's r on rank-order), based on 16 samples per lake. A +1 indicates that the rank-abundances of the dead species match perfectly the rank-abundances of the living species; -1 indicates that the rank-abundances are reversed between dead and living species.

North Storrs 1 French Blue Hole 5 No Name Little 0.8 Clear

0.6

Rho Watlings 0.4

0.2

0 2 3 4 5 6 7 8 Richness

Figure 1.4- Mean rho of each lake according to its mean species richness. Each dot represents a lake's mean rho (live/dead rank-abundance correlation) and its mean species richness, based on 16 samples. Error bars are 95% confidence intervals about that mean.

0.8

0.6

Chao -

0.4 Jaccard

0.2

0 -0.8 -0.4 0 0.4 0.8 rho

Figure 1.5- Taxonomic similarity (Jaccard-Chao index) and rank-abundance correlation of 112 samples of living communities and associated death assemblages. The y-axis displays the Jaccard-Chao index for each comparison of living communities and death assemblages. The x-axis displays rho for each comparison of living communities and death assemblages. Dots indicate lakes as follows: Plus Sign: Little Lake, Filled Square: North Storrs Lake, Diamond: No Name Pond, Star: Clear Pond, Cross: Blue Hole 5, Unfilled Rectangle: Watling's Blue Hole, Unfilled Square: French Pond. Blue Hole, plot in the upper right quadrant of the biplot.

0.16

North Storrs Blue Hole 5 0.08 French

No Name Coordinate2 Clear -0.08 Watlings Little

-0.16

-0.16 -0.08 0 0.08 0.16 Coordinate 1

Figure 1.6- First 2-dimensions of a 3-dimensional nonmetric multidimensional scaling ordination on the Bray-Curtis dissimilarity matrix of all samples with lakes overlain. Stress = 8.8%. Ellipses are 95% confidence areas. Lakes are indicated near ellipse and symbols of lakes are the same as in Figure 1.5. Ranked Bray-Curtis distances of sampled from the same lake are smaller than ranked distances to samples from other lakes (ANOSIM P<.0001).

Blue Hole, plot in the upper right quadrant of the biplot. In previous work with molluscan assemblages, this biplot has been used to distinguish pristine ecological settings from those affected by anthropogenic disturbance (eutrophication or bottom trawling), with pristine environments plotting in the upper right quadrant and anthropogenically affected samples plotting elsewhere (Kidwell, 2007; 2009). Thus, samples from Little Lake, French Pond, No Name Pond, North Storr’s Lake, Blue Hole 5, and Clear Pond appear to represent environments undisturbed by humans, whereas

Watlings Blue Hole shows a live/dead pattern that could be associated with anthropogenic disturbance.

Precision of the Record

Intralake variability of death assemblages indicates that there is less variability within lakes than between lakes. Figure 1.6 shows the first two axes of a three- dimensional NMDS plot on the Bray-Curtis dissimilarity matrix of all 112 death assemblages with the lake taken from overlain and corresponding 95% confidence ellipses. The death assemblages from individual lakes plot closer to assemblages from the same lake and ranked-Bray-Curtis distances of assemblages to other assemblages from the same lake are significantly less than ranked-distances to assemblages from other lakes (ANOSM P<.0001). This indicates that lakes contain distinct death assemblages which are more similar to death assemblages from that lake than death assemblages from any other lake. While this is true of all lakes, Watlings Blue Hole displays much higher

variability in death assemblages than any other lake (the area occupied by its 95% confidence ellipse is larger than any other lake's ellipse).

Discussion

Changing abundances of ostracode species through time are often used as proxy records for past environments. The generally high accuracy by which death assemblages record living communities and the relatively low within-lake variability of ostracode death assemblages on San Salvador Island support their use in paleoenvironmental reconstruction. Specifically, the high accuracy by which death assemblages record living communities suggests that diversity changes through time captured in sediment cores should reflect biologically-meaningful changes in ostracode communities living at the time of deposition with little complications from post mortem taphonomic processes.

The burial of recently dead ostracode valves seems to occur on a rapid enough timescale not to be obscured by taphonomic processes.

The generally high live/dead correlation additionally allows the study of ostracode modern ecology across the island based on death assemblages since little new information would be gained from collection of living communities only. This result implies little seasonal change in ostracode communities within lakes on San Salvador

Island since death assemblages integrate variable living communities across seasons

(Park et al., 2003). If different species dominated the assemblage at different times of the year, a difference in the “snapshot” living community sampled in this study and its time- averaged death assemblage would be observed.

The generally low variability of ostracode death assemblages within lakes on San

Salvador means that any sampled location through time should be reflective of diversity changes in the lake as a whole. This also affirms the practice of taking one or a few samples of death assemblages from each lake to build a calibration model for paleoenvironmental reconstruction since little new information on ostracode diversity would be gained by sampling many recent assemblages from one lake. This conclusion applies to the small lakes sampled here and may not apply to larger lakes that may contain multiple habitats, such as embayments or other complex features.

Alin and Cohen (2004) reported higher levels of spatial variability in live ostracode assemblages (patchy distribution) in Lake Tanganyika, East Africa since living assemblages only resembled death and fossil assemblages when pooled. Also in Lake

Tanganyika, Park et al. (2003) reported sampled a larger proportion of dead individuals at deeper sampling sites. Except in Watlings Blue Hole, this spatial heterogeneity of assemblages and increase in proportion of dead individuals down slope are not seen from lakes on San Salvador. All of the lakes sampled on San Salvador are much smaller than

Lake Tanganyika. A bigger lake could have more varied habitats than the ones sampled on San Salvador which could lead to different communities characteristic of these different habitats, causing an increase in spatial heterogeneity of living assemblages.

Lake Tanganyika is also deeper than the sampled lakes on San Salvador. Park et al.

(2003) reported collecting samples at depths of greater than 15m, while the deepest sample collected in this study was from 3.55m. This increase in depth could lead to more downslope transport producing the increased taphonomic bias seen in Lake Tanganyika when compared to lakes on San Salvador.

Watlings Blue Hole (Figure 1.6) is the only lake sampled from San Salvador to show low rank-correlation and low taxonomic similarity between living communities of ostracodes and their associated death assemblages. Kidwell (2009) has shown that this discrepancy in rank-abundance and taxonomic composition of living communities and death assemblages is characteristic of ecosystems modified by humans such as eutrophication or bottom trawling. In fact, Watlings Blue Hole was used in the past as a turtle pen when the surrounding area was used to grow cotton (Gerace, 1982). This turtle pen probably involved bottom trawling of the sediment. This anthropogenic modification may explain the dual discrepancies in rank-abundance and taxonomic composition of the living communities and death assemblages seen only in this lake in the study. However,

Watlings Blue Hole also has a depth gradient (the deepest sample was taken at 3.35m water depth) and tidal fluctuation both of which could influence the taphonomic fidelity of sampled taken from Watlings. Before low taphonomic fidelity of ostracode assemblages can be taken as evidence of anthropologically modification, these causes other sources of taphonomic biases must be ruled out.

A comparison of Watlings to another physically similar blue hole, Blue Hole 5, without a history of human use does not support the hypothesis that the low fidelity of death assemblages in Watlings is caused by within-lake transport of dead valves down slope or by tidal fluctuations (Figures 1.3, 1.4, 1.5, 1.6). Blue Hole 5 has a similar depth gradient (the deepest sample was taken at 2.13m water depth) as Watlings and also shows substantial tidal fluctuation, but shows very high live/dead correlation. Its rank- abundance correlation and taxonomic similarity are indistinguishable from other lakes

without large depth gradients or affected by tidal fluctuations, suggesting that use by humans to be the causal force behind the low live/dead correlation in Watlings.

The high accuracy by which death assemblages from lakes on San Salvador Island record the rank-abundances and taxonomic composition of the living community mean that little information about the biological structure of the community is lost. The little variability of living communities and death assemblages within lakes allow for the history of lake communities and ecosystems can be reconstructed from one or a few records since spatial heterogeneity is low. Ostracode records should thus be exploited to provide background data on natural variability of ecosystems and to reconstruct past environments.

CHAPTER II

DISCERNING PATTERN OF DIVERSITY AND BIOGEOGRAPHICAL

DISTRIBUTIONS: OSTRACODE METACOMMUNITY DYNAMICS ON SAN

SALVADOR ISLAND, BAHAMAS

Abstract

The metacommunity framework is a powerful body of theory that explains changes in diversity and species composition across landscapes and environmental gradients as a combination of local and regional processes. However, empirical studies of natural metacommunities have lagged behind theoretical development, limiting the usefulness of the metacommuity framework. Here we distinguish among the four metacommuity models of Holyoak et al., (2005), which differ in the rate of species dispersal across the landscape and the response of communities to changes in the environment, using lacustrine ostracode communities across San Salvador Island,

Bahamas. This is particularly important since changes in preserved assemblages of ostracodes can be used to understand changes in past abiotic environmental factors such

as conductivity, water depth, or dissolved oxygen, but only if their modern day ecology is well understood.

We sampled thirty-two lakes on San Salvador Island for ostracode assemblages and nineteen physio-chemical variables that could influence the distribution of those assemblages in an explicitly spatial context. We found that the abiotic environment of lakes varies independently of space allowing for independent assessment of spatial and environmental factors. At the scale of the island, changes in assemblages were not spatially autocorrelated, precluding dispersal limitation for any species. Additionally, species abundances were not spatially autocorrelated at any scale indicating no species has very high dispersal across the landscape. Finally, we found that changes in ostracode assemblages strongly and independently correlate with changes in lake conductivity, alkalinity, and dissolved oxygen. The combination of moderate dispersal and a strong correlation between change in assemblages and the abiotic environment supports a species sorting metacommuity model. This means that preserved ostracode assmeblages should faithfully record the abiotic environment in which they lived.

Introduction

Determining the causes of community change (e.g. species richness and composition) across space, environmental gradients, and time is a central goal of ecology.

The processes maintaining biodiversity operate at several hierarchical scales, including local, within-locality interspecific interactions and regional, across locality processes like dispersal (Holyoak et al., 2005). Interspecific interactions can lead to loss of diversity

through competitive exclusion (Hardin, 1960), can increase diversity through mutualistic interactions (Hacker and Gaines, 1990), while predation can decrease diversity by excluding prey species or can increase diversity by suppressing dominant competitors

(Howeth and Leibold, 2010). Dispersal of individuals into habitats can affect diversity through introduction of new species to a community and can alter population dynamics of species already present in a community through introduction of conspecifics (Holyoak et al., 2005). Additionally, with the increasing fragmentation of habitats due to human habitation, migration and dispersal may become more important for the maintenance of populations and communities. For instance, empirical and theoretical work has shown that local populations connected by dispersal and migration may be less prone to extinction than a single large population (Hanski and Simberloff, 1997). Understanding the local and regional processes that maintain biodiversity and the interactions between these processes is critical to conservation efforts. This study focuses on understanding the forces maintaining diversity across a lacustrine ostracode metacommunity on San

Salvador Island, Bahamas.

A metacommunity is a set of local communities connected by dispersal of potentially interacting species (Gilpin and Hanski, 1991; Wilson, 1992). Metacommunity theory has contributed to the understanding of the processes that drive the dynamics of many taxa: from simple food webs of plants, butterflies, and their parasitoids (Nouhuys and Hanski, 2005), to more complex food webs of bacteria, protists, and insects in pitcher plants inquiline (Miller and Kneitel, 2005), to taxonomically defined competitive metacommunities such as beetles in a eucalyptus forest (Chase et al., 2005; Davies et al.,

2005).

Metacommunity theory considers the role of both local and regional processes in maintaining biodiversity in an explicitly spatial context (Holyoak et al., 2005). Patterns of biodiviersity in natural communities are inherently spatial and ignoring the geographical context of metacommunities may lead to oversimplification of the key processes that maintain diversity in metacommunities (Hanski and Gaggiotti, 2004). In particular, both experimental (Thompson and Townshed, 2006) and theoretical work

(Belyea, 2007; Lennon, 2000) have shown that spatial autocorrelation of the abiotic environment can lead to inaccurate conclusions about the relationship between β- diversity and changes in the local environment. Spatially structured populations of individual species can be an indicative of source-sink dynamics in metacommunities

(Cottenie, 2005; Ng et al., 2009).

Theoretical work on metacommunities has so far outpaced empirical studies such that theory is advanced without much empirical validation (Dorazio et al., 2010).

Holyoak et al., (2005) describes four models of metacommunity dynamics that are characterized by unique combinations of how dispersal and the environment influence local diversity. These models are: patch dynamics, species sorting, mass effects, and neutral. The patch dynamic model is characterized by low rates of dispersal between similar habitats such that species are dispersal-limited and community assembly in local habitats is determined by interspecific interactions and stochastic dispersal. The species sorting model involves moderate rates of dispersal between heterogeneous environments such that all species can reach all habitats, so that community assembly is driven principally by the abiotic environment. The mass effects model is similar to the species sorting model, except that some species have such high rates of dispersal that they may

be maintained regionally in source-sink dynamics. Finally, the neutral metacommunity dynamics model (sensu Hubbell, 2001) assumes that species within a metacommunity have evolved to be ecologically equivalent and so they have equivalent per capita birth and death rates and are dispersal limited. In this case, community assembly in individual environments is independent of the abiotic environment and is driven by the stochastic process of dispersal and ecological drift.

The rate of dispersal of species and how species respond to changes in the environment are the distinguishing factors between the four metacommunity models.

Species in metacommunities dominated by patch dynamics or neutral models have low dispersal between patches. In these models we expect communities to be positively spatially autocorrelated- more similar communities would be found closer together since species are more likely to disperse to nearby patches. Species in metcommunities dominated by species sorting have moderate rates of dispersal across the landscape, so that all species in the metacommunity can reach all patches. Thus, in this model we expect no spatial autocorrelation of communities. Some species in a metacommunity dominated by mass effects will have high rates of dispersal if they export individuals from environments where they have very high rates of growth to environments where they could not maintain self-sustaining populations. Other species, not engaged in source-sink dynamics, have moderate rates of dispersal as in the species sorting model.

Therefore, we expect to see negative spatial autocorrelation of species abundances if populations of those species are engaged in source-sink dynamics. Space independent of environment would explain a large amount of variation between communities that have either low dispersal (patch dynamics and neutral) or high dispersal (mass effects),

whereas the environment independent of space would explain a large proportion of variation between communities in a species sorting metacommunity model.

In species sorting and mass effects models, the local environment plays an important role in determining community composition. Thus, in these two models we expect to see a correlation between changes in the environment and community composition. In metacommunities dominated by patch dynamics, where local species interactions are paramount, we don’t expect a correlation between environmental changes across patches and community composition. Similarly, in metacommunities dominated by neutral processes where changes in community composition are driven by local stochastic processes we don’t expect to see a correlation between changes in the environments of local patches and community composition. Table 2.1 lists the criteria used for distinguishing among metacommunity models in this study.

Study System

San Salvador is a small (163 km2) carbonate island with many interior lakes located within the Bahamian archipelago in the northern hemisphere-southwest Atlantic

(Davis and Johnson, 1989). During times of elevated sea level throughout the

Pleistocene, dune sediments were deposited across the San Salvador platform. Many lakes occur on the island today between these ancient dunes, as cutoff-lagoons once open to the ocean, or as karst dissolution features in the carbonate bedrock (Bain, 1991; Teeter.

1995; Park and Trubee, 2008). Lacustrine ostracodes on San Salvador Island provide an ideal way to distinguish between these metacommunity models because they live in

Table 2.1. Criteria for distinguishing metacommunity models of Holyoak et al., (2005), based on Chase et al., (2005), Cotienne (2005), and Ng et al., (2009).

Patch Dynamics Species Sorting Mass effects Neutral

Environmental None or not Yes, strongly Yes, correlated None or not Heterogenity correlated with correlated with with abundances correlated with β- Across Pathces β-diversity of β-diversity of of some species diversity of communities communities communities or abundances of species

Spatial Positive or None None None or Positive or None Autocrrelation Negative of Communities

Spatial Positive None Negative Positive Autocrrelation of Species Abundances

Results of Space explains Environmental Space and Space explains large Variance large proportion factors explain environmental proportion of Decomposition of variance large factors explain variance among among proportion of large proportion communities or communities variance among of variance neither space nor communities among environmental communities factors do

discrete, environmentally-heterogenous habitats and populations of species in separate lakes are potentially linked by dispersal by birds, fish, or amphibians (Sohn and

Kornicker, 1979; Lopez et al., 1999), wind (Sohn, 1996), or occasionally flooding (Park and Downing, 2000; Finston, 2007).

Ostracodes are a class of bivalved microcrustaceans (Phylum Arthropoda) that live in all manner of aquatic habitats, from the deep ocean to ephemeral ponds (Horne et al., 2002). Most ostracode species live as benthic organisms and are sensitive to changes in the abiotic environment such as salinity, water depth, temperature or dissolved oxygen concentration (Delorme, 1969; Frenzel and Boomer, 2005). Their low-Mg calcite shells range from .5-2mm in size and are often preserved as fossils. Each individual ostracode secretes 8-9 molted shells over its lifetime with the adult stage containing definitive characteristics that allow for species-level identification (Holmes, 2008).

In addition to their potential tractability as a study system, ostracode assemblages and other taxonomically-defined organisms that exists in metacommunities and can be preserved as microfossils, such as diatoms, foraminifera, or chironomids are frequently used in paleontology as indictors of past environments (Saros, 2009; Woodridge and

Roberts, 2010) These microfossils have the potential to provide quantitative reconstruction of past terrestrial environments (Cronin, 1999). This method, however, assumes a direct relationship between the environment (and often only one aspect of the environment) and the organisms under study. Paleoenvironmental methods make the substantial assumption that ostracode metacommunity dynamics are dominated by species sorting such that the abiotic environment alone controls which species are found in which lakes and at what percent abundance. Equating assemblages of organisms

representing paleocommunities with the environment ignores variables other than those from the abiotic environment that might be influencing community assembly. These variables might include dispersal (Thompson and Townshed, 2006), historical effects

(Chase, 2003), neutral dynamics (Hubbel, 2001), biological interactions (Wellborn et al.,

1996), and complex or non-equilibrium dynamics between organisms and their abiotic environment (Belyea, 2007). This paper tests the four metacommitiy models of Holyoak et al., (2005): patch dynamics, species sorting, mass effects, and neutral using lacustrine ostracodes on San Salvador Island. Understanding the relative contributions of local environment and dispersal also determines if this system would respond in a predictable, systematic way to environmental gradients, and thus can be used as a reliable indicator of past environments (Belyea, 2007).

Methods- Field and Laboratory Methods

In June 2008, March 2009, and June 2009 we collected surface sediment, water samples, and measured field limnological data from thirty-two lakes on San Salvador

Island (Figure 2.1). We sampled as many accessible lakes as possible and randomly chose sample locations within lakes. Surface sediments were collected by sweeping a net with attached jar across the sediment-water interface, recovering the upper 1-2 cm of sediment. All samples were collected within the littoral margin, approximately ~10 m

Kilometers 0 2.5 5 10

Figure 2.1- San Salvador Island, Bahamas. Each dot indicates a lake sampled for ostracode assemblages and environmental variables.

from the shore. In a separate study, live/dead collections were made along a transect into each lake. Except for one lake, Watling’s Blue Hole, live/dead agreement was extremely high (Michelson and Park, in press; chapter one). Likewise, little or no variability was found along transects in these lakes (Michelson and Park, in press; chapter one). Thus, the ostracode death assemblages sampled in these samples were determined to represent

“modern” communities typical of the lake from which they were sampled.

Lake environmental factors: conductivity, salinity, total dissolved solids, dissolved oxygen, and water temperature were determined in the field with an YSI 556 model field meter. This field meter measures conductivity within ± 0.5% of the reading, it measures salinity within ± 1% of the reading, it measures total dissolved solids within

± 4 g/L, it measures dissolved oxygen within ± 2% of the reading, and water temperature within ± 0.15°C. Alkalinity was determined in the field using a Hach methyl orange and phenolphthalein (total) acidity digital titration kit. This titration kit measures alkalinity within ± 0.1 mg/L as CaCO3. Water depth was determined at the site of collection within

± 1 cm. Latitude and longitude at the site of collection as well as lake area were determined using the San Salvador GIS database (Robinson and Davis, 1999).

Sediments were sieved using 125 μm (φ-size 3) and 63 μm (φ-size 4) sieves with deionized water. Upon drying, ostracodes were picked from samples using a dissecting microscope. In all cases, at least 400 ostracodes were picked from each sample and all adults were identified to species level. All samples included in this analysis contained more juveniles than adults indicating that the assemblage accumulated where the ostracodes lived and were not transported from another location (Park et al., 2003;

Mischke et al., 2007).

Water samples were analyzed for the concentration of major cations using a

Perkin Elmer Analysis 700 atomic absorption (AA) spectrometer at the University of

Akron. Water samples were analyzed for chloride and sulfate anions using a Dionex DX-

120 ion chromatograph. All major ions concentrations were expressed as mg/L.

Sediment grain size was determined with a Malvern 2000 Mastisizer at Kent State

University. Sediment samples were sieved to < 1mm before grain size distribution was determined.

Numerical methods

All species abundances were expressed as percent of total adult ostracodes per sample. The dissimilarity of ostracodes assemblages was assessed using the Bray-Curtis distance between pairs of assemblages (Bray and Curtis, 1957; Cao et al., 2002). The

Bray-Curtis dissimilarity between pairs of assemblages was calculated using the “vegan” package in R (Okansen et al., 2005). This was compared to the distance between pairs of lakes to assess the relationship between change in ostracode communities across the island and space (after Thompson and Townshed 2006). A Mantel test using 9999 permutations was used to assess the strength and significance of this relationship.

Geographic distance between pairs of lakes was also compared to the multivariate

Euclidean distance of the Z-scores of all measured environmental variables between pairs of lakes to assess whether change in environment is independent of distance between lakes (after Thompson and Townshed 2006). A Mantel test using 9999 permutations was also used to assess the strength and significance of this relationship. Both Mantel tests

were done in the “ade4” package in R (Dray and Dufour, 2007). Z-scores were used for the environmental variables instead of raw data to make sure the Euclidean distance is not dominated by variables measured in large units.

Moran’s I was used to assess the spatial autocorrelation of individual environmental variables and species abundances. Moran’s I for environmental variables and species abundance was calculated using the “ape” package in R (Paradis et al., 2004).

Correlograms of Moran’s I across distance were calculated for species percent abundances using the “ncf” package in R (Bjornstad, 2009).

Variance decomposition was used to estimate the percent of the variation in assemblages that can be explained by space independent of environment ([S|E]), environment independent of space ([E|S]), and environment and space together ([E∩S],

Borcard et al., 1992; Legendre and Legendre, 1998). Latitude and Longitude were converted to x, y coordinates and a third order polynomial was used, only those spatial variables that were forward selected in a canonical correspondence analysis (CCA, ter

Braak, 1987) were used. Likewise, only environmental variables forward selected in a

CCA were used. Variance decomposition and CCAs were carried out in CANOCO ver.

4.5 (ter Braak, 1990).

Finally, we carried out forward-selected multidimensional fuzzy set ordination

strength and significance of each individual environmental variable in the dataset. The effect size and significance of each variable in correlating to change in assemblages was then assessed. The single variable with the highest effect size was then retained in the model. The residuals were used to test for correlation with other environmental variables. MFSO avoids model selection problems associated with multicolinearity of environmental variables. All remaining environmental variables were tested for effect size and significance. The process stopped when no other environmental variables significantly correlated to the residuals (Roberts, 2008; 2009).

Results

In total, eleven ostracode species from eleven genera and nine families were found in the thirty-two lakes from this study. These species are members of the

Caribbean non-marine fauna of Park and Beltz (1998) and include: Aurila floridana

Benson and Coleman, 1963, Cyprideis americana Sharpe, 1909, Cytherella arostrata

Kornicker, 1963, Dolerocypria inopinata Klie, 1939, Hemicyprideis setipunctata Brady,

1869, Loxonconcha pursubrhomboidea Edwards, 1944, Paramesidea harpago Kornicker,

1961, Perissocytheridea bicelliforma Swain, 1955, Physocypria denticulata Daday,

1905, Reticulocythereis multicarinata Swain, 1955, and Xestoleberis curassavica Klie,

1939 (Table 2.2, Figure 2.2).

The environments of lakes on San Salvador are not homogeneous since many variables known to influence ostracode communities, like conductivity (Mischke et al.,

2007), water depth (Alin and Cohen, 2004), dissolved oxygen (Mezquita et al., 2004),

Table 2.2. Species list of the ostracodes from this study.

Podocopida Podocopina Bairdiidae Paranesidea harpago Kornicker, 1961 Candonidae Dolerocypria inopinata Klie, 1939 Cyprididae Physocypria denticulata Daday, 1905 Cytherellidae Cytherella arostrata Kornicker, 1963 Cytherideidae Cyprideis americana Sharpe, 1909 Hemicyprideis setipunctata Brady, 1869 Perissocytheridea bicelliforma Swain, 1955 Hemicytheridae Aurila floridana Benson and Coleman, 1963 Loxoconchida Loxoconcha pursubrhomboidea Edwards, 1944 Trachyleberididae Reticulocythereis multicarinata Swain, 1955 Xestoleberideidae Xestoleberis curassavica Klie, 1939

A B

C D

E F

G H

I J

Figure 2.2- Ostracode species encountered in this study: Aurila floridana Benson and Coleman, 1963 (A), Cyprideis americana Sharpe, 1909 (B), Cytherella arostrata Kornicker 1963 (C), Dolerocypria inopinata Klie, 1939 (D), Hemicyprideis setipunctata Brady, 1869 (E), Loxoconcha pursubrhomboidea Edwards, 1944 (F), Paranesidea harpago Kornicker, 1961 (G), Perissocytheridea bicelliforma Swain, 1955 (H), Physocypria denticulata Daday, 1905 (I), Reticulocythereis multicarinata Swain, 1955 (J), Xestoleberis curassavica Klie, 1939 (K).

and solute concentrations (Forester, 1986) have high coefficients of variation across the environment (Table 2.3).

Before environmental factors can be related to change in ostracode communities across San Salvador, the degree of spatial autocorrelation of the environment must be assessed. Neither the multivariate environment (Figure 2.3), nor individual abiotic factors (Figure 2.4) are spatially autocorrelated at the scale of the island of San Salvador.

Therefore, the correlation between the abiotic environment and ostracode communities can be independently assessed without any complications from a spatially structured environment.

Three variables were selected that significantly and uniquely correlate with change in ostracode assemblages: conductivity, dissolved oxygen, and alkalinity, although the effect size of alkalinity is quite low (Figure 2.5). The constrained ordination with these three variables correlate strongly with the Bray-Curtis dissimilarity of the ostracode assemblages (r=0.826). Thus, there appears to be a strong link between the environment and ostracode communities in this study that is completely independent of space.

Next, the relationship between β-diversity and distance between pairs of lakes was examined in order to infer the role of dispersal in structuring communities. There is no relationship between community dissimilarity and distance (Figure 2.6).

In order to investigate a high rate of dispersal, we follow Cottenie (2005) and Ng et al.

(2009) by examining the spatial autocorrelation of individual species and the role of space in explaining the variance among all communities. Here, space independently accounts for only 3.3% of the variance in community structure and environment and

Table 2.3. Minimum, maximum, mean, and standard deviation of measured environmental variables in all lakes (N=32)

Variable Min Max Mean SD latitude 23.9536 24.11469 24.03478 0.055912 longitude -74.5501 -74.4499 -74.4915 0.035327 lake area (m2) 133.049 13722846 992658.3 2572089 depth (cm) 2 162 66.70675 45.52269

DO (mg/L) 1.9 12.8 7.03875 2.798656

Cond. (mS/cm) 4.94 124.7 59.9075 25.6214

T water (°C) 23.1 36 31.36 2.903568 alkalinity (mg/L) 56 373 189.1875 67.2806

Total Fe (mg/L) 0.006 0.65 0.139063 0.177358

Mn (mg/L) 0 0.162 0.030313 0.037477

Na (mg/L) 5881 90360 20025.91 19268.27

K (mg/L) 200 4093 859.675 874.6568

Ca (mg/L) 230.7 2184 628.5719 470.191

Mg (mg/L) 577.3 5261 1995.509 1237.719

Sr (mg/L) 8.1 60.4 24.38438 14.98234

Cl (mg/L) 4270 180020 36986.72 40194.3

SO4 (mg/L) 1302 17450 4507.719 3642.476

Mean grain size (μm) 25.62214 230.5701 116.3495 58.94476

c n

a 8

. v

n 6

d i

l 4

u E

0 0 5 10 15 20 Distance (km)

Figure 2.3- Scatter plot of Euclidean distances of Z-scores of environmental variables between lakes and geographical distance between pairs of lakes. Each point represents a comparison between 2 lakes. There are 496 possible pairwise comparisons among 32 lakes. The solid line represents the ordinary least squares regression line. There is no relationship between the environment and geographic distance between lakes (Mantel r = -0.02005979, p(9999 permutations)=0.5975).

1.0

0.5

' n

a 0.0

o M

-0.5

-1.0 . . . ] ] ] ] ] ] ] ] ] a th O d p lk e n a K a g r l 4 ze re p D n F N [ C S C i A e o m A [ [M [ [ [M [ [ O S D C e [S T in r ra te a G W Environmental Factor

1.0

0.5

' n

a 0.0

o M

-0.5

-1.0 CA PB HS DI RM CyA LP XC AF PH PD Species

Figure 2.4- Moran’s I of environmental factors (A) and species percent abundances (B). Moran’s I is a measure of spatial autocorrelation ranging from +1 to -1, calculated in this study based on distances between lakes. “Area” indicates lake area, “Depth” indicates water depth, “DO” indicates dissolved oxygen, “Cond.” indicates conductivity, “Alk.: indicates alkalinity, “Grain Size” indicates mean grain size, “CA” indicates Cyprideis americana, “PB” indicates Perissocytheridea bicelliforma, “HS” indicates Hemicyprideis setipunctata, “DI” indicates Dolerocypris inopinata, “RM” indicates Reticulocythereis multicarinata, “CyA” indicates Cytherella arostrata, “LP” indicates Loxonchoncha pursubrhomboidea, “XC” indicates Xestoleberis curassavica, “AF” indicates Aurila floridana, “PH” indicates Paranesidea harpago, “PD” indicates Physocypria denticulata. A value of ±0.08 was significant at p=0.05 based on 9999 permutations.

Figure 2.5- Effect sizes of correlation between environmental factors and ostracode assemblages. Multivariate Fuzzy Set Ordination is a technique used to measure the strength and significance of the correlation between environmental factors and species assemblages. Figure 3 represents the results of a multivariate fuzzy set ordination of ostracod assemblages and 19 associated environmental factors. Conductivity, dissolved oxygen (DO) and alkalinity were the only factors that significantly correlate with ostracod assemblages. The y-axis is the incremental increase in Pearson’s correlation (r) between the Bray-Curtis dissimilarity matrix of species assemblages and the indicated environmental factor resulting from its addition to a forward selection model. The final, 3-variable model correlates strongly (r=.86) to changes in species assemblages.

1.0

0.8

i m

i 0.6

t r

u 0.4

a r B 0.2

0.0

0 5 10 15 20 Distance (km)

Figure 2.6- Scatter plot Bray-Curtis dissimilarity matrix of species assemblages and distances between lakes. Each point represents a comparison between 2 lakes. There are 496 possible pairwise comparisons among 32 lakes. The solid line represents the ordinary least squares regression. There is no relationship of distance between sites of sampling and their species assemblages (Mantel r = 0.04239832, p(9999 permutations)= 0.225).

space jointly account for 8.39%, both being far lower than the more than 50% of the community structure that can be explained by the environment (Figure 2.7). None of the ostracode species abundances in this dataset displays high absolute values of spatial autocorrelation at the scale of the island (Figure 2.4) and correlograms reveal no high absolute values of Moran’s I at smaller spatial scales than the scale of the island (Figure

2.8).

Discussion

Our data support a metacommunity dominated by species sorting (sensu Holyoak et al., 2005). The lakes sampled are indeed different in key abiotic variables that may influence ostracode community assembly (Table 2.3). Conductivity, water depth, dissolved oxygen, lake area, and concentration of many cations have high coefficients of variation. Both the patch dynamics and neutral metacommunity models involve dispersal of species across homogeneous environments. However, the presence of environmental heterogeneity by itself does not rule out these two metacommunty models. Community structure must be correlated with change in environmental factors to provide support for species sorting and mass effects metacommunty models.

Figure 2.7- Results of variance decomposition. Latitude and Longitude were converted to x,y coordinates and a third order polynomial was used, only those spatial variables that were forward selected in a CCA were sued. Likewise, only environmental variables forward selected in a CCA were used. Variance decomposition was then used to calculate % variation in assemblages explained by space given environment ([S|E], marked “space”), environmental variables and space together ([E∩S]), and environment given space ([E|S], marked “environment”).

Figure 2.8- Correlograms of Moran’s I for species percent abundances: Aurila floridana (A), Cyprideis americana (B), Cytherella arostrata (C), Dolerocypris inopinata (D), Hemicyprideis setipunctata (E), Loxonchoncha pursubrhomboidea (F), Paranesidea harpago (G), Perissocytheridea bicelliforma (H), Physocypria denticulata (I), Reticulocythereis multicarinata (J), Xestoleberis curassavica (K). The middle line represents Moran’s I, while the upper and lower lines represent 95% confidence intervals around Moran’s I.

The abiotic environment of lakes on San Salvador does not vary in a systematic way with distance between lakes (Figure 2.3) and no individual abiotic factors are spatially autocorrelated (Figure 2.4). This allowed for the independent assessment of the relationship between environmental factors and change in ostracode assemblages at the scale of the island (Thompson and Townshed, 2006).

We found that conductivity, alkalinity, and dissolved oxygen are the most important abiotic factors in determining ostracode community assembly (Figure 2.5).

Conductivity has been commonly identified as correlated with ostracode assemblages in disparate environments (Delorme, 1969; Meztquta et al., 2004; Mischke et al., 2007;

Mischke et al., 2010). Given that the ostracodes found in lacustrine environments on San

Salvador may have invaded from the ocean (Park and Beltz 1998), it is reasonable to assume that conductivity would be a driver of ostracode diversity today. There are ample opportunities for ostracode niche-partitioning across a conductivity gradient since the processes responsible for the formation of lakes on San Salvador leads to lakes of differing salinities and hydrologic conditions (Park, et al., 2011).

Similarly, previous work (Delorme, 1969; Mezquita et al., 2004; Frenzel and

Boomer, 2005) has identified dissolved oxygen as a potential driver of diversity in ostracode assemblages since some species may have different oxygen requirements.

While alkalinity explains a significant and unique proportion of the Bray-Curtis dissimilarity matrix of ostracode assemblages, this proportion is quite small. Like conductivity, the processes responsible for lake formation on San Salvador have led to lakes with different alkalinities, so this may also be an important factor in the evolutionary ecology of ostracode species inhabiting San Salvador lakes.

The pattern of species assemblages between lakes indicates that communities are not dispersal-assembled (sensu Hubbell, 2001) because distance cannot explain why communities of ostracodes on San Salvador are different at all (Figure 2.6). This lack of relationship of distance between lakes and community dissimilarity means that communities are not spatially autocorrelated; both very dissimilar and very similar communities may be close together or far apart. If individual species were dispersal- limited, then we would expect to see more similar communities clustered together in geographic space and more dissimilar communities separated in space, a “distance- decay” pattern of community similarity (Thompson and Townshed, 2006). Instead, no relationship is observed between distance and community similarity. This indicates that on the spatial scale of this study, species are not dispersal limited.

Together with the lack of dispersal limitation of any species, this correlation between changes in the abiotic environment and changes in communities rules out the patch dynamics and neutral metacommunity models. Ostracode communities are strongly influenced by the abiotic environment and dispersal of individual species appears to be large enough so none are limited in reaching all lakes across the island.

However, the mass effects metacommunity model cannot yet be ruled out. Dispersal could still be sufficiently large so that some species are maintained in source-sink dynamics with populations with a very high rate of growth exporting propagules to populations with a low or declining rate of growth, consistent with a mass effects metacommunity model (Holyoak et al., 2005).

There is evidence that local species abundances are not influenced by source-sink dynamics (Figure 2.4, Figure 2.7, Figure 2.8). Coteeine (2005) and Ng et al., (2009)

show that a metacommunity dominated by mass effects would exhibit spatial structure and that space would explain a high percentage of the variation among communities. If species could not disperse very far (limited dispersal as under the patch dynamics and neutral models), then species abundances would be positively autocorrelated as would assemblages. Therefore, very high dispersal of species (as under the mass effects model) would result in spatial structure to species and assemblages as well (Ng et al., 2009), but with species abundances and assemblages negatively autocorrelated. Some species would disperse at a high rate to sites where the abiotic environment is unfavorable to the growth rate of populations of that species. No species abundance exhibits spatial autocorrelation (Figure 2.4, Figure 2.8), and very little of the variance between assemblages (3.3%) can be attributed to space alone (Figure 2.7). Therefore, since space cannot explain a high proportion of the variance in community structure and none of the individual species abundances are spatially autocorrelated, no species have sufficiently high rates of dispersal to show source-sink dynamics, ruling out the mass effects metacommunity model.

The combination of environmental heterogeneity correlated with changes in ostracode assemblages and lack of spatial pattern to species abundances and assemblages support the species sorting metacommunity for lacustrine ostracodes on San Salvador

Island, Bahamas. Species dispersal across San Salvador is high enough so all ostracode species can reach all lakes, but not so high that some species export individuals to unfavorable environments. Thus, the abiotic environment of each lake determines the species composition in that lake. Species sorting may be the dominant metacommunity dynamic of passively-dispersing aquatic taxa across distances on the order of tens of

kilometers. Hoverman et al. (2011) and Cottenie and De Meester (2005) both found species sorting to be the dominant metacommunity dynamic operating in the small aquatic organisms in their studies, while Cottenie (2005) found a plurality of surveyed studies had metacommunitiy dynamics dominated by species sorting. In landscapes with discrete, environmentally heterogeneous patches species may have evolved mechanisms for dispersal across the landscape, exposing the species to many different environments.

This may have led to species evolving to be better competitors in certain environments.

This moderate dispersal combined with niche-partitioning across environmental gradients results in a metacommuity governed by species sorting.

The dominance of species sorting in structuring the lacustrine ostracode community on San Salvador Island means that we can confidently use fossil assemblages of ostracodes to infer past environments. That dispersal is high enough to allow for all species to encounter lakes with conductivity, alkalinity, and dissolved oxygen suitable to maintain growing populations and since neither conductivity, alkalinity, nor dissolved oxygen are spatially autocorrelated at the scale of the island ostracodes assemblages should be faithful recorders of the abiotic environment. This understanding of the pattern of biodiversity and the biological processes that produce this pattern allows for the creation of models extending this correlation between assemblages and the abiotic environment backwards in time to predict past environments from preserved assemblages without any complications from the spatial structure of the environment or dispersal of species.

CHAPTER III

TESTING THE ASSUMPTIONS OF HIGH RESOLUTION

PALEOENVIRONMENTAL INFERENCE MODELS IN AN ECOLOGICAL

CONTEXT

Abstract

Organism-based calibration data sets have the potential to produce detailed, high- resolution records of past environments. These records provide important background information on environmental variability, insight into how global or regional climate changes affect local ecosystems and critical data for archaeologists to better interpret records of past human habitation. One way of producing such records is through transfer functions, which use changing abundances of microfossils through time to reconstruct a single environmental variable.

Transfer functions are constructed by understanding the correlation between modern communities and the abiotic environment. This correlation is then applied to preserved microfossil assemblages to produce a quantitative record of past abiotic factors.

However, organism-based paleoenvironmental inference models have been criticized as producing spurious results when the underlying ecology is misunderstood. Specifically, ecological processes such as dispersal limitation of species, neutral processes of

community assembly, or physical factors like spatial autocorrelation of the abiotic environment can lead to overestimation of the accuracy of transfer functions. In this paper, we discuss the limits of transfer function models in light of their assumptions as well as suggest ways to test these assumptions and correct for any violations discovered.

We studied an ostracode metacommuity on San Salvador Island, Bahamas to construct a transfer function in an ecological context. Thirty-two lakes were sampled for ostracode species abundances and nineteen abiotic variables. We demonstrate here that no physical or ecological barriers exist to the construction of a transfer functions in this system as neither species abundances, assemblages, nor individual abiotic factors were spatially autocorrelated and the metacommunity conforms to a species sorting model in which the abiotic environment is the main determinant of community structure. It is clear that with care in analyzing the geographic and ecological context of training sets, we can ensure that quantitative paleoenvironmental inference models reflect the underlying biology of the studied organisms and have high confidence in their results.

Introduction

There is a need to produce quantitative records of past environments both to test hypotheses regarding past climates and to understand how climate has changed in the past. High resolution, quantitative records of past abiotic factors can be used to understand how global climate events affect local environments (Edlund and Stoermer,

2000), how local biological communities are affected by a changing climate (Walker et

al., 1991), and even how human societies may affect the local environment and climate

(Bush et al., 2007).

A widely used method of producing such quantitative high-resolution records of past environments is through the construction of transfer functions (sensu Imbrie and

Kipp, 1979). The development of transfer functions proceeds by sampling assemblages of microfossils (chironomids, foraminfera, diatoms, ostracodes, testate amoebas, etc.) across environmental gradients and then employing multivariate statistics to measure the correlation between the assemblages and one or more abiotic variables of paleoenvionmental interest (Saros, 2009). Regression techniques then are used to construct a model designed to predict past abiotic environmental variables from preserved assemblages of microfossils (ter Braak and Juggins, 1993). This model then can be applied to preserved assemblages to produce such records of past environments.

The five assumptions of transfer function models and are enumerated in Birks

(1995) and summarized here in Table 3.1. Assumptions one, two, and five ensure that the proper environmental variables are measured and there exists a strong correlation between these variables and biological assemblages. Without such a strong correlation, a transfer function cannot be constructed. However there are many situations in which this correlation can be overestimated. These include: spatial autocorrelation of environmental variables, multicollinearity between measured environmental variables, unmeasured environmental variables affecting species distributions, and complex or non-linear relationships between species assemblages and the environment (McCune, 1997; Lennon,

2000; Belyea. 2007). Assumptions three is that the species’ niches across the environmental gradient of interest have not changed during the timeframe of the fossil

Table 3.1. The assumption of the transfer function method of paleoenvironmental reconstruction from Birks (1995) Assumption of Birks (1995: 168) Comments

1) The taxa in the modern training set (Y) are systematically related to the environment (X) in which they live.

2) The environmental variable(s) to be reconstructed (x) is, or is linearly related to, an ecologically The proper sampling of assemblages and important determinant in the ecological system of associated environmental variables and techniques to measure the relationships interest. between them ensure that assumptions 1 and 2 are adhered to.

3) The taxa in the training set (Y) are the same biological entities as in the fossil data (Y0 ) and their ecological responses (U) to the environmental variable(s) of interest have not changed significantly This is perhaps the most difficult assumption over the time span represented by the fossil to adhere to. Ensuring consistent and assemblage. Contemporary patterns of taxon accurate taxonomy between the modern and abundance in relating to X can thus be used to fossil assemblages is necessary but not reconstruct changes in X through time. adequate for this assumption.

Many methods of calibration contain ways of assessing the ability of the model to make predictions. The problem with these 4) The mathematical methods in regression and methods is that often the data sets used to calibration adequately model the biological responses test the predictive ability is often the same to the environmental variable(s) of interest and yield (or a subset) of the modern assemblages. calibration functions with sufficient predictive powers Other methods are discussed in this paper. to allow useful, accurate, and unbiased reconstructions of X

This assumption is difficult to fulfill. After all, 5) Other environmental variables than the one of the assemblages are being sampled to interest have negligible influence, or their joint produce a model to understand distribution with the environmental variable in the paleoenvironments, so reconstructing fossil set is the same as in the training set. another variable will be difficult.

data. This is perhaps the most difficult assumption to fulfill, but encapsulates the substitution of assemblages’ responses to an environmental gradient across space today for the response of those assemblages to the environment through time that is the cornerstone of the transfer function method. If however, species’ niches have evolved during the period under study in the fossil data, then the reconstructed environmental variable will become more inaccurate the further back in time the transfer function is used. Finally, assumption four ensures that the statistical procedures necessary to develop transfer function models accurately reflect species’ niches across environmnental gradients. This assumption can also be difficult to fulfill without independent data.

Additionally, many techniques assume species’ niches are unimodal across the environmental variable of interest. This can be problematic for generalist species or those species adapted to extreme environments. Adequate agreement with these assumptions ensures transfer functions are not applied inappropriately.

In light of the potential complications of the transfer function method of paleoenvironmental reconstruction, Belyea (2007) has criticized its theoretical underpinnings as neglecting developments in ecological theory by discussing the five assumptions of Birks (1995). In addition, Belyea (2007) claims the transfer function method is not robust to certain spatial patterns of the abiotic environment, assemblages, and species abundances, particularly spatial autocorrelation (Belyea, 2007; Telford and

Birks, 2005). In this paper, we test some of these assumptions and integrate the transfer function methodology with relevant ecological theory by treating assemblages as comprising a metacommunity (sensu Holyoak et al., 2005) sampled across environmental gradients.

Transfer Functions in a Metacommunity Context

Transfer functions are created by sampling many environments (often, but not always, bounded environments like lakes) for species assemblages and associated environmental variables. Groups of communities potentially connected by dispersal have been termed “metacommunities” and many studies have focused on sampling many assemblage to understand what drives the distribution of assemblages in metacommunities (Dorazio et al., 2010). In particular, the role of dispersal of species and the abiotic environment have proven critical in understanding metacommunity dynamics and four metacommunity models have been proposed in which the role of dispersal and the environment vary: patch dynamics, species sorting, mass effects, and neutral

(Holyoak et al., 2005).

The patch dynamic model is characterized by low rates of dispersal between similar habitats such that individual species are dispersal-limited and individual community assembly is influenced by stochastic dispersal and local biotic processes like competition, mutualism, or predation. A metacommunity conformed to this patch dynamic model would not be a good candidate for constructing a transfer function since the assemblages would not be distributed predictably across an environmental gradient, but rather be more influenced by the stochastic process of species dispersal and local biotic interactions. This would produce a low measured association between the abiotic environment and species assemblages because biotic factors would be more important in structuring communities.

The species sorting model involves moderate rates of dispersal between abiotically heterogeneous environments such that all species can reach all habitats. If all species in the metacommunity can reach all sites, then the local environment will determine which species will be found there. This would result in a high correlation between change in communities across the landscape and the abiotic environment. This situation would be ideal for the creation of a transfer function since the abiotic environment is the main determinant of community structure in a metacommunity dominated by species sorting dynamics.

The mass effects model is similar to the species sorting model, except that some species have such high rates of dispersal that they may be maintained regionally in source-sink dynamics whereby species in very favorable environments may export individuals to less favorable environments. This situation would complicate the construction of a transfer function since modeling species niches based on their distribution across environmental gradients today assumes that species abundances are a reliable function of the environment. If some species are only maintained in an environment because they receive individuals from other environments, then this niche modeling would be inaccurate. Constructing a transfer function using a metacommunity dominated by mass effects would not result in reliable predictions of past environments since species abundances would not reflect their local environment if they received individuals from more favorable environments or exported individuals to less favorable environments.

Finally, the neutral metacommunity dynamics model (sensu Hubbell, 2001) assumes that species have evolved to be ecologically equivalent and can be modeled as

having equivalent per capita birth and death rates and are dispersal limited. In this case, community assembly in individual environments is independent of the abiotic environment and is driven by the stochastic process of dispersal and ecological drift

(Chase et al., 2005). Like the patch dynamic model, this situation would not be conducive to the construction of a transfer function since the effect of dispersal and ecological drift would be more influential than the abiotic environment in determining the distribution of communities. If species were neutrally distributed across an environmental gradient of interest, then there would be no correlation between species assemblages and the abiotic environment and the construction of a transfer function would be impossible.

Thus, researchers who have constructed transfer functions have been making the implicit assumption that the metacommunity they are sampling conforms to species sorting metacommunity model since this is the only model in which the abiotic environment is the principle driver of community assembly. Luckily, the same data to test metacommunity models is also used to construct a transfer function. In this paper, we will tests the transfer function assumptions of Birks (1995) and Belyea (2007) in a metacommunity context by analyzing lacustrine ostracodes on San Salvador Island,

Bahamas.

Methods- Lacustrine Ostracodes on San Salvador Island, Bahamas

We studied a metacommunity of ostracodes (bivalved microcrustaceans) living in lakes on San Salvador Island, Bahamas. San Salvador is a small (163 km2) carbonate island with many physically and limnolgically diverse interior lakes located within the

Bahamian archipelago in the northern hemisphere-southwest Atlantic (Davis and

Johnson, 1989). During times of elevated sea level throughout the Pleistocene, dune sediments were deposited across the San Salvador platform. Many lakes occur on the island today between these ancient dunes, as cutoff-lagoons once open to the ocean, or as karst dissolution features in the carbonate bedrock (Bain, 1991; Teeter. 1995; Park and

Trubee, 2008).

2mm in size and are often preserved as fossils. Each individual ostracode secretes 8-9 molted shells over its lifetime with the adult stage containing definitive characteristics that allow for species-level identification (Holmes, 2008). Lacustrine ostracodes on San

Salvador Island provide a good way to evaluate the assumptions of transfer function models because they live in discrete, environmentally-heterogenous habitats and populations of species in separate lakes are potentially linked through dispersal by birds,

fish, or amphibians (Sohn and Kornicker, 1979; Lopez et al., 1999), wind (Sohn, 1996), or occasionally flooding (Park and Downing, 2000; Finston, 2007).

We sampled thirty-two lakes for ostracode assemblages and nineteen associated physical, geographical, and limnological variables including: water depth, water temperature, conductivity, alkalinity, dissolved oxygen, total dissolved solids, mean grain size of sediment, lake area, latitude, longitude, and concentrations of iron, manganese, sodium, potassium, calcium, magnesium, strontium, chloride, and sulfate ions (Figure

3.1).

In June 2008, March 2009, and June 2009 we collected surface sediment, water samples, and measured field limnological data from thirty-two lakes on San Salvador

Island. We sampled as many accessible lakes as possible and randomly chose sample locations in lakes. Surface sediments were collected by sweeping a net with attached jar across the sediment-water interface, recovering the upper 1-2 cm of sediment. All samples were collected within the littoral margin, approximately ~10 m from the shore.

In a separate study, live/dead collections were made along a transect into each lake.

Except for one lake, Watling’s Blue Hole, live/dead agreement was extremely high

(Michelson and Park, 2011; Chapter one). Likewise, little or no variability was found along transects in these lakes (Michelson and Park, 2011; Chapter one). Thus, the ostracodes in these samples were determined to represent “modern” assemblages, typical of the lakes from which they were sampled.

Kilometers 0 2.5 5 10

Figure 3.1- San Salvador Island, Bahamas. Each dot indicates a lake sampled for ostracode assemblages and environmental variables.

± 1 cm. Latitude and longitude at the site of collection as well as lake area were determined using the San Salvador GIS database (Robinson and Davis, 1999).

Sediments were sieved using 125 μm (φ-size 3) and 63 μm (φ-size 4) sieves with deionized water. Upon drying, ostracodes were picked using a dissecting microscope. In all cases, at least 400 ostracodes were picked from each sample and all adults were identified to species level. All samples included in this analysis contained more juveniles than adults indicating that the assemblage accumulated where the ostracodes lived and were not transported from another location (Park et al., 2003; Mischke et al., 2007;).

Water samples were analyzed for the concentration of major cations using a

Perkin Elmer Analysis 700 atomic absorption (AA) spectrometer at the University of

Akron. Water samples were analyzed for chloride and sulfate anions using a Dionex DX-

120 ion chromatograph. All major ions concentrations were expressed as mg/L.

Sediment grain size was determined with a Malvern 2000 Mastisizer at Kent State

University. Sediment samples were sieved to < 1mm before grain size distribution was determined.

Numerical Methods

We first examined the spatial autocorrelation of assemblages, environmental variables, and species percent abundances to look for geographical or physical barriers to the construction of a transfer function. To examine the spatial distribution of assemblages, we found the distance between all pairs of lakes using the San Salvador GIS database (Robinson and Davis, 1999) and the ecological (Bray-Curtis) distance between all pairs of assemblages was determined using the “vegan” package in R (Okansen et al.,

2005). A Mantel test using 9999 permutations was used to test the strength and significance of the relationship between ecological distance between pairs of lakes and geographical distance between pairs of lakes. The Mantel test was done in the “ade4” package in R (Dray and Dufour, 2007). Moran’s I was used to examine the spatial autocorrelation of environmental variables and species percent abundances; it was calculated using the “ape” package in R (Paradis et al., 2004). Correlograms of Moran’s

I across distance were calculated for species percent abundances using the “ncf” package in R (Bjornstad, 2009).

Variance decomposition was then used to estimate the percent of the variation in assemblages that can be explained by space independent of environment ([S|E]), environment independent of space ([E|S]), and environment and space together ([E∩S],

Braak, 1987) were used. Likewise, only environmental variables forward selected in a

CCA were used (Borcard et al., 1992). Variance decomposition and CCAs were carried out in CANOCO ver. 4.5 (ter Braak, 1990).

We then carried out forward selected multidimensional fuzzy set ordination

(MFSO, Roberts, 2009) in order to directly relate environmental variables to variation in ostracode assemblages by reducing the environmental dataset to only those variables that significantly and uniquely correlate to ostracode β-diversity. MFSO was done in R using the package MFO (Roberts, 2008). MFSO was chosen as the preferred method for directly relating environmental variables to change in assemblages since it tests the strength and significance of each individual environmental variable in the dataset. The effect size and significance of each variable in correlating to change in assemblages was then assessed. The single variable with the highest effect size was then retained in the model. The residuals were used to test for correlation with other environmental variables. MFSO avoids model selection problems associated with multicolinearity of environmental variables. All remaining environmental variables were tested for effect size and significance. The process stopped when no other environmental variables significantly correlated to the residuals (Roberts, 2008; 2009).

Results

We found eleven species of ostracodes from eleven genera and nine families in the thirty-two lakes sampled (Table 3.2, Figure 3.2).

Assemblages of ostracodes were not significantly spatially autocorrelated at the scale of San Salvador Island (Figure 3.3). Abiotic environmental variables did not

Table 3.2. Species list of the ostracodes from this study.

A B

C D

E F

G H

I J

Figure 3.2- Ostracode species encountered in this study: Aurila floridana Benson and Coleman, 1963 (A), Cyprideis americana Sharpe, 1909 (B), Cytherella arostrata Kornicker 1963 (C), Dolerocypria inopinata Klie, 1939 (D), Hemicyprideis setipunctata Brady, 1869 (E), Loxoconcha pursubrhomboidea Edwards, 1944 (F), Paranesidea harpago Kornicker, 1961 (G), Perissocytheridea bicelliforma Swain, 1955 (H), Physocypria denticulata Daday, 1905 (I), Reticulocythereis multicarinata Swain, 1955 (J), Xestoleberis curassavica Klie, 1939 (K).

1.0

0.8

i m

i 0.6

t r

u 0.4

a r B 0.2

0.0

0 5 10 15 20 Distance (km)

Figure 3.3- Scatter plot Bray-Curtis dissimilarity matrix of species assemblages and distances between lakes. Each point represents a comparison between 2 lakes. There are 496 possible pairwise comparisons among 32 lakes. The solid line represents the ordinary least squares regression. There is no relationship of distance between sites of sampling and their species assemblages (Mantel r = 0.04239832, p(9999 permutations)= 0.225).

display high absolute values of spatial autocorrelation at the island scale (Figure 3.4A).

Likewise, species percent abundances did not display high absolute values of spatial autocorrelation at the island scale (Figure 3.4B) or at smaller spatial scales (Figure 3.5).

In fact, space independently accounts for only 3.3% of the variance in community structure and environment and space jointly account for 8.39%, both being far lower than the more than 50% of the community structure that can be explained by the environment

(Figure 3.6).

The abiotic environment does correlate with changes in the assemblages of ostracodes across lakes on San Salvador with conductivity, dissolved oxygen, and alkalinity significantly and independently explaining changes in ostracode assemblages

(Figure 3.7).

Discussion- Transfer functions in a metacommunity context

The metacommunity concept provides a framework for analyzing data used to create transfer functions by understanding how dispersal and the local abiotic environment together influence community structure and can help identify systems when it may not be appropriate to create transfer functions. The species sorting metacommunity model is the only model for which it is appropriate to construct a transfer function.

1.0

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-1.0 . . . ] ] ] ] ] ] ] ] ] a th O d p lk e n a K a g r l 4 ze re p D n F N [ C S C i A e o m A [ [M [ [ [M [ [ O S D C e [S T in r ra te a G W Environmental Factor

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0.5

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-0.5

-1.0 CA PB HS DI RM CyA LP XC AF PH PD Species

Figure 3.4- Moran’s I of environmental factors (A) and species percent abundances (B). Moran’s I is a measure of spatial autocorrelation ranging from +1 to -1, calculated in this study based on distances between lakes. “Area” indicates lake area, “Depth” indicates water depth, “DO” indicates dissolved oxygen, “Cond.” indicates conductivity, “Alk.: indicates alkalinity, “Grain Size” indicates mean grain size, “CA” indicates Cyprideis americana, “PB” indicates Perissocytheridea bicelliforma, “HS” indicates Hemicyprideis setipunctata, “DI” indicates Dolerocypris inopinata, “RM” indicates Reticulocythereis multicarinata, “CyA” indicates Cytherella arostrata, “LP” indicates Loxonchoncha pursubrhomboidea, “XC” indicates Xestoleberis curassavica, “AF” indicates Aurila floridana, “PH” indicates Paranesidea harpago, “PD” indicates Physocypria denticulata. A value of ±0.08 was significant at p=0.05 based on 9999 permutations.

Figure 3.5- Correlograms of Moran’s I for species percent abundances: Aurila floridana (A), Cyprideis americana (B), Cytherella arostrata (C), Dolerocypris inopinata (D), Hemicyprideis setipunctata (E), Loxonchoncha pursubrhomboidea (F), Paranesidea harpago (G), Perissocytheridea bicelliforma (H), Physocypria denticulata (I), Reticulocythereis multicarinata (J), Xestoleberis curassavica (K). The middle line represents Moran’s I, while the upper and lower lines represent 95% confidence intervals around Moran’s I.

Figure 3.6- Results of variance decomposition. Latitude and Longitude were converted to x,y coordinates and a third order polynomial was used, only those spatial variables that were forward selected in a CCA were sued. Likewise, only environmental variables forward selected in a CCA were used. Variance decomposition was then used to calculate % variation in assemblages explained by space given environment ([S|E], marked “space”), environmental variables and space together ([E∩S]), and environment given space ([E|S], marked “environment”).

Figure 3.7- Effect sizes of correlation between environmental factors and ostracode assemblages. Multivariate Fuzzy Set Ordination is a technique used to measure the strength and significance of the correlation between environmental factors and species assemblages. Figure 3 represents the results of a multivariate fuzzy set ordination of ostracode assemblages and 19 associated environmental factors. Conductivity, dissolved oxygen (DO) and alkalinity were the only factors that significantly correlate with ostracode assemblages. The y-axis is the incremental increase in Pearson’s correlation (r) between the Bray-Curtis dissimilarity matrix of species assemblages and the indicated environmental factor resulting from its addition to a forward selection model. The final, 3-variable model correlates strongly (r=.86) to changes in species assemblages.

In both the patch dynamic and neutral metacommunity models, individual community assembly is independent of the abiotic environment and species have low rates of dispersal across the landscape. In patch dynamics, dispersal of species across aboiotically homogeneous patches influences which species colonize environments with a role for interspecific interactions in determining abundances of species and extinctions at local sites. On the other hand, in a neutral model the stochastic processes of dispersal and ecological drift determine community assembly at local sites. Dispersal-limited species are more likely to disperse to nearby sites, so positive spatial autocorrelation of communities is indicative of the low dispersal of species common to both patch dynamics and neutral metacommunity models. Our data did not display this positive spatial autocorrelation of assemblages (Figure 3.3). In addition to data on assemblages and associated environmental variables, geographic data on sites should also be collected including geographic coordinates and distance between sites to test for these two models.

Spatial autocorrelation of assemblages may be an important signal of underlying biological processes that structure these communities. Chase et al., (2005) has shown that positive spatial autocorrelation of communities such that more similar communities are found closer together can be produced by neutral processes of community assembly (sensu Hubbel, 2001). In particular, dispersal limitation of individual species ensures that communities will exhibit spatial autocorrelation since some species are not found at more distant sites just because they cannot reach them.

This would severely limit the predicative power and applicability of a transfer function since species niches would be inaccurate. By modeling species’ niches using data on observed abundances, transfer functions assume that if a species is not found in a

particular environment, this is evidence that environment is not part of its niche. If, however, some species have low dispersal ability, then they may not be sampled in an environment that could not yet reach even if they could maintain viable populations in that environment.

Even in the ocean, where you may expect little barrier to dispersal, assemblages can be spatially autocorrelated. For example, de Vernal et al., (2005) describes a transfer function using dinoflagellate cysts that cover a wide spatial range encompassing: the

Arctic Ocean, the Mediterranean, the Eastern seaboard of the U.S., the North American

Pacific Northwest, and the Bering Sea. While this transfer function has apparent predictive power, this could be an overestimation since taxa from one area of the globe may not be sampled in another area, not because the environment is not conducive to their growth, but because they have not yet colonized the area. Thus the niche model for these non-cosmopolitan species would be inaccurate since environments within their fundamental niche could be sampled, but dispersal limitation prevents them from reaching these sites. While positive spatial autocorrelation of communities can be indicative of this problem of dispersal limitation, it can be investigated further by ensuring that the spatial distribution of all taxa in the training set (the modern assemblages used to construct the transfer function) covers all or most of the study area.

Of course, spatial autocorrelation can be present in the environmental variables as well as the assemblages in the training set. In fact, spatial autocorrelation of the abiotic environment (a common property of many environmental variables (Lennon, 2000)) can often lead to spatial autocorrelation of assemblages if the environmental variable most influential in community structure is geographically structured. Telford and Birks (2009)

shows how to detect and overcome this problem in the environmental variables. First, they suggest ways to detect spatial autocorrelation including: Mantel tests, Moran’s I,

Geary’s C correlograms, and empirical variograms. Once detected, Telford and Birks

(2009) propose a general way to correct for the drop in predictive ability that comes with transfer functions based on spatially structured environmental data. In these cases, they propose a method based on h-block cross validation. In this method, all sites within h distance of a site are omitted and then the resulting subset of the model is used to predict the environment of the site of interest. Doing this for all sites in the training set produces a measure of the predictive power of the full model. A more general way to test the predictive power of a transfer function model vis a vis environmental variables is also recommended by Telford and Birks (2011). They recommend that a reconstruction should only be considered useful if it can explain more variation in a fossil dataset than a training set focused on random environmental data applied to the same fossil dataset.

This test seems prudent no matter if the environment in the original training set is spatially autocorrelated or not.

Additionally, dispersal limitation may not be the only process causing the positive spatial autocorrelation of assemblages as seen in Chase et al. (2005). Competitive exclusion of species best adapted to similar environments could also cause the pattern.

Species with similar fundamental niches are expected to compete such that one species may drive the other to local extinction (Hardin, 1960). This competitive exclusion of similarly-adapted species could cause problems for transfer function development since the abiotic environment is assumed to be the main driver of community structure

(assumptions one and two of Birks (1995)). Even if a species can disperse to a distant

site, if it is outcompeted by another species, then positive spatial autocorrelation of assemblages could result. This problem could be investigated in transfer function development by modeling species niches across the environmental gradient of interest through techniques like hierarchical modeling (Huisman et al., 1993) and logistic regression (ter Braak and Looman, 1986). If the model produces two species with similar niches, but their distributions are geographically separated, then this competitive exclusion could be the cause of positive autocorrelation of species abundances or assemblages. Of course, this niche modeling approach may not pick up this problem since these models can only describe the realized niches of species. If their fundamental niches did indeed overlap, then their realized distributions across the environmental gradient would most likely be different.

Both patch dynamics and neutral metacommunity models feature species dispersing across undifferentiated abiotic environments. This does not mean that each site needs to have the same abiotic environment, but rather that assemblages will not sort themselves across any measurable abiotic environmental gradients. Therefore, there will be no association between change in assemblages and their abiotic environment. Every attempt to construct a transfer function uses constrained ordination techniques such as

CCA (ter Braak, 1987), RDA (Stewart and Love, 1968), or MFSO (Roberts, 2008) to test for this association and our data clearly showed a relationship between assemblages and the abiotic environment (Figure 3.7).

In principle, if a strong correlation between assemblages across an environmental gradient is found, then a transfer function can be created. However, spatial autocorrelation of the environment and assemblages can lead to an overestimation of the

predictive power of the transfer function (Belyea 2007; Telford and Birks, 2005; Lennon,

2000). Our data displayed no such spatial autocorrelation of environmental variables

(Figure 3.4A) that could overestimate the correlation between species assemblages and the environment. This overestimation can be so debilitating as to render the model unusable (Guiot and de Vernal 2011; Telford and Birks, 2009; Telford, 2006).

If species do not display low dispersal across the study area the next problem is then recognizing mass effect metacommunity models from species sorting models since both models will feature associations of assemblages with the abiotic environment. Ng et al. (2009) offers a solution. Species engaging in source-sink dynamics are more likely than species that do not export or receive a high number of individuals from other environments to produce a spatial signal. Therefore, Ng et al., (2009) suggests a variance decomposition to rule out the possibility of a mass effect model. A mass effects metacommunity model is characterized by change in assemblages correlated with the abiotic environment while also exhibiting high amount of variance that can be explained by space. Our data do not show a high amount of variation among communities that can be explained by space (Figure 3.6).

Since source-sink dynamics are characteristics of populations, examining the spatial autocorrelation of species abundances may identify which species are engaged in these dynamics. Since species which have very low dispersal will tend to have positively autocorrelated percent abundances since they are more likely to disperse to nearby sites, species with very high rates of dispersal should have negatively spatially autocorrelated percent abundances. No ostracode species in this study displays such negative spatial autocorrelation indicative of source-sink dynamics (Figure 3.4B, Figure 3.5). If species

engaging in source-sink dynamics are found, it may be possible to exclude these species from a transfer function, or if independent data on their niche across the variable of interest is available, it may be possible to keep them in the model across only a limited range of the abiotic variable. The species sorting metacommunity model is the only model for which it would be appropriate to construct a transfer function since it is the only model in which the abiotic environment alone is responsible for the variation in species assemblages.

Metacommnity dynamics of lacustrine ostracodes on San Salvador Island, Bahamas

The metacommunity dynamics of lacustrine ostracodes on San Salvador Island,

Bahamas are dominated by species sorting (Table 3.3). In a metacommunity dominated by species sorting, changes in species assemblages would be strongly correlated with changes in the abiotic environment and all species would have a high enough dispersal rate to reach all environments, but not so high a dispersal rate to engage in source- sink dynamic. Species assemblages are strongly correlated with changes in conductivity, dissolved oxygen, and alkalinity of lakes (Figure 3.7). No individual abiotic variable is spatially autocorrelated (Figure 3.4A), so the correlation between species assemblages and the abiotic environment is robust to spatial structure. The lack of association between distance between lakes and ecological difference of assemblages demonstrates that species dispersal across San Salvador is high (Figure 3.3). Finally, the lack of spatial structure of individual species abundances shows that we cannot detect any very high dispersal that is characteristic of source-sink dynamics of individual populations (Figure

Table 3.3. Criteria for distinguishing metacommunity models of Holyoak et al., (2005), based on Chase et al., (2005), Cotienne (2005), and Ng et al., (2009).

Patch Dynamics Species Sorting Mass effects Neutral

Environmental None or not Yes, strongly Yes, correlated None or not Heterogeneity correlated with correlated with with abundances correlated with β- Across Patches β-diversity of β-diversity of of some species diversity of communities communities communities or abundances of species

Spatial Positive or None None None or Positive or None Autocorrelation Negative of Communities

Spatial Positive None Negative Positive Autocorrelation of Species Abundances

3.4B; Figure 3.5). Taken together, the lack of spatial autocorrelation of assemblages and species abundances and the association between change in assemblages and the abiotic environment is only consistent with a species sorting model (Table 3.3). Thus, the communities of lacustrine ostracodes on San Salvador are structured by the abiotic environment, demonstrating that this system would be a good candidate for the creation of a transfer function.

The assumptions of transfer function models

Assumptions one and two of Birks (1995) are fulfilled by ensuring the metacommunity dynamics of a system are dominated by species sorting since this ensures that the assemblages are related to the environment and the variable to be reconstructed has been correctly identified. Assumption three of Birks (1995) is arguably the most difficult to test. All transfer functions, and many other related techniques of paleoenvironmental reconstruction, rely on a space-for-time substitution whereby variation in space today represented by change in assemblages across environmental gradients is used to explain variation in fossil assemblages through time. Of course, if the fossil assemblages are not comparable to the modern assemblages, then the ability to substitute space-for-time falls apart. The most basic way to overcome this is to have consistent taxonomic identification between the modern and fossil assemblages. Species encountered in the fossil assemblage which are not encountered in the modern assemblage must be left out of the reconstruction since there is no way to model their niches.

Beyond ensuring accurate taxonomy, there are not many ways to make sure that the species in the fossil dataset respond the same way to environmental variation as the same species in the modern dataset. If reconstructions of other environmental variables associated with the fossil dataset are available, then one way would be to check if the species abundances vary with the second variable through time in the same way as they do today. Of course, this is weak evidence since a species’ response to two environmental variables may evolve independently of one another such that the species response to the variable of interest may have evolved while their response to the second variable may have remained constant.

Taking a morphometric approach, if a certain morphology today is correlated with an environmental variable, then if you reconstruct that variable through time you would expect to see a corresponding change in the morphology of the target species. For example shape of ostracode carapaces in asexual lineages is linked to salinity (Van der

Meeren et al., 2010), so if a researcher used ostracode assemblages to reconstruct salinity through time, you would expect to see a corresponding change in the carapace shape of asexual species. More work on the relationship between species’ responses to environmental variation and morphology, and the genetic underpinnings of this relationship is needed to ensure the validity of the constancy of species responses to changing environments through time. The only thing that can be said with absolute certainty is that we should be more cautions when interpreting reconstructions based on transfer functions the further back in time we extend them.

Birks (1995)’s assumption four is critical since it deals with the actual model to infer past abiotic factors from preserved assemblages. Many widely used regression

methods to create the predictive model, such as weighted averaging partial least squares

(WA-PLS) and related techniques, rely on unimodal distributions of species abundances across the environmental model of interest (ter Braak and Juggins, 1993). This assumption is easily checked by ranking environments according to the abiotic variable to be predicted and plotting the percent abundances of all species. A species exhibiting a unimodal distribution would reach its peak abundance at some value and decline on either side of that peak. This unimodal niche model may be inappropriate for generalist species or species adapted to extreme environments. For these species, more sophisticated methods of understanding species distributions across environmental gradients are available, including hierarchical niche modeling (Huisman et al., 1993) and logistic regression (ter Braak and Looman, 1986).

The validation procedure of creating predictive models is also problematic. Many methods of calibration produce subsets of the training set and leave them out and use the smaller model to predict the abiotic variable of interest (Kutner et al., 2004). Other more promising methods require producing totally independent datasets to measure the predictive value of a transfer function model (Belyea, 2007), but this may be impossible in some cases. Additionally, independent data collection must be restricted to environments from which species in the training set can disperse to in order to avoid the problems of dispersal limitation.

The last assumption of Birks (1995) is also difficult to fulfill. In ecological systems, many variables, both biotic and abiotic, influence the distribution of communities (Wilson, 1992), so it can be difficult to isolate the effects of only one variable. Shipley (2000) has developed a robust method to isolate the effects of

individual variables on ecological structure using structural equation modeling and PATH analysis. Using this method to determine which variables to model in a transfer function model would improve the understanding of systems used to construct transfer functions.

However, Shipley (2000)’s method can require extensive sampling that may be difficult to achieve. Raising proxy organisms in laboratories to ground truth proxy data is another promising avenue to fulfill assumption five of Birks (1995) but it can be difficult to achieve the precise combinations of biotic and abiotic variables in a laboratory that can influence the distribution of communities (Hintz et al., 2004).

In addition, it can be difficult or impossible to collect data on the distribution of abiotic variables in the fossil data set. After all, a transfer function is created to produce quantitative records of past environments, a whole new model would have to be created to produce as detailed a record of a second variable. However, by examining physical proxies available in the sedimentary record like those produced by loss-on-ignition analysis (Boyle, 2001), and examining lithology, at least a qualitative assessment of other confounding environmental variables through time can be produced. The records of the primary variable through time can then be compared to the distribution of this second, potentially confounding variable, in the training set and changes in abundances of species in the fossil dataset coincident with this second variable can be compared to the correlation in the training set to fulfill assumption five of Birks (1995).

This discussion of the assumptions of transfer functions and how to test them is not meant to be an indictment of previously published transfer functions. Studies using transfer functions would, however, benefit from a more robust discussion of whether the systems studied can fulfill these assumptions. In this way, Birks (1995), Belyea (2007),

and the methods discussed here provide a systematic way to ensure the integrity of transfer functions and thus increase the confidence in the records produced by this method. Indeed, by using an ostracode metacommnity on San Salvador as an example, we have shown that the assumptions of a transfer function can be checked through examination of the spatial structure of the data and evaluation of its metacommunity dynamics.

We found no physical or biological barrier to the construction of a transfer function in this system since the abiotic environment was consistently strongly associated with changes in biological assemblages. This association was robust to the spatial structure of the abiotic environment with very little spatial structure of species abundances and assemblages. Thus, in this system the spatial structure of the environment does not obscure the ability to use assemblages to reconstruct past environments. Also, dispersal of species does not seem to present a problem to modeling species’ niches since assemblages and species abundances do not exhibit spatial structure.

By examining the metacommunity structure of the system, we ensued that its dynamics are dominated by species sorting, thereby ruling out ecological processes that could obscure the association between the abiotic environment and ostracode assemblages and render any transfer function model inaccurate. Thus, the testable assumptions of Birks

(1995) and Belyea (2007) are consistent with using this system to create a transfer function model.

There remains however more work to be done to fully evaluate the assumptions of

Birks (1995) and Belyea (2007). In this paper, we have shown how modern data used to construct a transfer function can be analyzed in an ecological context to identify

geographical or biological barriers to using modern data on assemblages to interpret fossil assemblages. However, testing the constancy of the relationship between assemblages and the abiotic environment through time is more problematic.

Assumptions three and five of Birks (1995) are particularly vexing since it can be difficult to account for potentially confounding environmental variables and species’ responses to environmental gradients in the past. The common practice of a multiproxy approach to paleoenvironmental reconstruction is prudent in light of these limitations, but does not address the inherent uncertainty of the space for time substitution that underlies using biological assemblages to reconstruct the past. More work on the evolution of species’ environmental preferences through time is needed to increase our confidence in the results of studies using transfer functions. This study demonstrates, however, that with careful analysis of the geographic and ecological context of modern training sets, we can ensure quantitative paleoenvironmental models reflect the underlying biology of the organisms under study and have great confidence in their results.

CHAPTER IV

A QUANTITATIVE INFERENCE MODEL FOR CONDUCTIVITY USING

OSTRACODE ASSEMBLAGES ON SAN SALVADOR ISLAND, BAHAMAS

Abstract

Quantitative records of past environments are needed to understand natural variability in ecosystems and their responses to climate change. Changing ostracode assemblages through time can produce such records since ostracode species are often sensitive to changes in their local environments. Before they can be used to indicate past environments, is it necessary to understand how distributions of assemblages change across the modern landscape. Thirty-two lakes on San Salvador Island, Bahamas were sampled for both ostracodes and nineteen physio-chemical variables that may influence their distribution. Multivariate fuzzy set ordination indicated that change in ostracode assemblages was significantly and independently correlated with: electrical conductivity, dissolved oxygen, and alkalinity. A transfer function was then created to reconstruct past conductivity since changing conductivity of lakes on San Salvador has been linked to changes in climate and sea level fluctuations. A 2-component weighted-averaging partial least squares model performed best as a transfer function for conductivity with an

apparent r2 of 0.76 and an r2 of 0.69 between observed and predicted conductivity, as assessed by leave-one-out cross validation. The resulting transfer function was then applied to three San Salvador Island sediment cores from which ostracode paleoassemblages were sampled. The late Holocene paleoconductivity records show that changes in conductivity of lakes on San Salvador are broadly synchronous and show that lakes respond to regional climate changes. In particular, variation in the strength on El

Niño corresponds to changes in conductivity with times of strong El Niño events recorded as arid periods and times without strong El Niño events recorded as more humid periods. These results demonstrate that changing ostracode assemblages through time provide a reliable means to reconstruct past conductivity of lakes on San Salvador Island and can be used to understand how climate and sea level changes are reflected in local environments on the island.

Introduction

Sediments from coastal lakes are important archives of local and regional environmental changes. Many of these lakes sit at the interface between the terrestrial and marine environments and can record sea level changes and changes in the aridity of the local and regional climate (Tetter 1995; Peros et al., 2007). Thus, records from these lakes can provide provide important background information on natural environmental variability (Dietl and Flessa, 2011), insight into how global or regional climate changes affect local ecosystems (Edlund and Stoermer, 2000), and critical information that can

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help archaeologists interpret records of past human habitation (Berman and Gnivecki,

1995).

There are few such records of late Quaternary changes in sea level and climate from the Caribbean because of the paucity of sites for paleolimnological investigation

(Higuera-Gundy et al., 1999). Yet, this region is critically important for controlling climate since changing patterns of aridity control the strength of the North Atlantic Deep

Water formation (NADW), greatly affecting the climate of the North Atlantic and the globe (Schmidt et al., 2004). This study provides paleolimnological records from three coastal lakes on San Salvador Island, Bahamas using changing ostracode assemblages through the mid-late Holocene.

Microfossil assemblages are important archives that can yield information on changes in ecosystems over long time scales. Organism-based calibration datasets, coupled with precise age control have the potential to produce detailed, high-resolution records of past environments. Transfer functions based on these calibration datasets can provide high-resolution, quantitative records of past environments (Saros, 2009; Sayer et al., 2010). Transfer functions are regression models in which preserved assemblages of microfossils are used to predict one environmental variable of interest (Saros, 2009).

They proceed by understanding the nature and strength of the association between modern assemblages and the abiotic environment. If a correlation of sufficient strength and paleolimnological interest is found, then a model can be created to predict that variable from preserved assemblages (Mischke et al., 2007). These models have the potential to be of significant use to environmental managers or policy makers. For instance, Mischke et al. (2010a) produced quantitative records of conductivity from

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individual lakes in the Middle East that can be related to changing precipitation/evaporation regimes in the past and can thus aid in evaluating the historical risk of water shortages at fine temporal and local spatial scales.

This study utilizes Ostracoda (Phylum Arthropoda) from lakes and blue holes on

San Salvador Island, Bahamas to produce quantitative records of past conductivity.

Ostracodes are a class of bivalved microcrustaceans that live in all manner of aquatic habitats from the deep ocean to ephemeral ponds (Horne et al., 2002). Most ostracode species live as benthic organisms and are sensitive to changes in the abiotic environment such as salinity, water depth, temperature or dissolved oxygen concentration (Frenzel and

Boomer, 2005). Their low-Mg calcite shells range from .5-2mm in size, can be preserved as fossils, and long have been used as biological proxies (Frenzel and Boomer, 2005).

Each individual secretes 8-9 molted shells over its lifetime with the adult stage containing definitive characteristics that typically allow for species-level identification (Holmes,

2008). These organisms have already been used in transfer functions in lake sediments to reconstruct conductivity (Mischke et al., 2007; Mezquita et al., 2005; Mischke et al.,

2010a), water depth (Mourguiart et al., 1996; Mourguiart and Carbonel 1994; Alin and

Cohen, 2003; Mischke et al., 2010b), and temperature (Viehberg, 2006; Mezquita et al.,

2005).

San Salvador is a small (163 km2) carbonate island with many interior lakes located within the Bahamian archipelago in the northern hemisphere-southwest Atlantic

(Davis and Johnson, 1989). During times of elevated sea level throughout the

Pleistocene, dune sediments were deposited across the San Salvador platform. Many lakes occur on the island today between these ancient dunes, as cutoff-lagoons once open

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to the ocean, or as karst dissolution features in the carbonate bedrock (Bain, 1991; Teeter.

1995; Park and Trubee, 2008). The salinity of these lakes are controlled by basin geomorphology, degree of connection to the ocean, precipitation-evaporation balance, and climate (Tetter, 1995; Park et al., 2009). Lake sediments on the island are capable of preserving paleocommuniuties of ostracodes in sufficient abundance to be used in a transfer function (Tetter, 1995; Park et al., 2009).

The Bahamas receive airborne dust input from North Africa which forms the major sources of aluminosilcates on the islands (Muhs et al., 1990; Foos, 1990). Strong

El Niño events cause more of this African dust to be deposited across the Bahamas

(Prospero and Lamb, 2003; Evan et al., 2006). These strong El Niño events are also associated with less tropical cyclone activity in the Atlantic since high wind shear in the tropical Atlantic acts to disrupt cyclone formation (Gray, 1984). Hurricanes hitting San

Salvador lower the salinity of lakes on the island through input of freshwater in rainfall

(Park et al., 2009). Therefore, we hypothesize that times of strong El Niño events, indicated by increased African dust deposition, would raise the salinity of lakes on San

Salvador through less hurricane activity.

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Methods- Field and Laboratory Methods

In June 2008, March 2009, and June 2009 we collected surface sediment, water samples, and measured field limnological data from 32 lakes on San Salvador Island

(Figure 4.1). We sampled as many accessible lakes as possible and chose a location to sample in lakes randomly. Surface sediments were collected by sweeping a net with attached jar across the sediment-water interface, recovering the upper 1-2 cm of sediment. All samples were collected within the littoral margin, approximately ~10 m from the shore. In a separate study, live/dead collections were made along a transect into each lake. Except for one lake, Watling’s Blue Hole, live/dead agreement was extremely high (Michelson and Park, in press; Chapter one). Likewise, little or no variability was found along transects in these lakes (Michelson and Park, in press; Chapter one). Thus, the ostracodes in these samples were determined to represent “modern” assemblages, typical of the lakes from which they were sampled.

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Figure 4.1- San Salvador Island, Bahamas. Each black dot indicates a lake sampled for ostracode assemblages and environmental variables. Each grey dot with white border represents a lake cored for ostracode paleoassemblages.

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within ± 0.1 mg/L as CaCO3. Water depth was determined at the site of collection within

± 1 cm. Latitude and longitude at the site of collection as well as lake area were determined using the San Salvador GIS database (Robinson and Davis, 1999).

Water samples were analyzed for the concentration of major cations using a

Perkin Elmer Analysis 700 atomic absorption (AA) spectrometer at the University of

Akron. Water samples were analyzed for chloride and sulfate anions using a Dionex DX-

120 ion chromatograph. All major ions concentrations were expressed as mg/L.

Sediment grain size was determined with a Malvern 2000 Mastisizer at Kent State

University. Sediment samples were sieved to < 1mm before grain size distribution was determined.

Numerical Methods

Species were retained in the dataset if they were found in at least three lakes or their abundance exceeded 30% of total adult ostracodes in any one lake. All species abundances were expressed as percent of total adult ostracodes.

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We conducted non-metric multidimensional scaling (NMDS) on the Bray-Curtis dissimilarity matrix in order to understand relationships among ostracode assemblages independently of environmental variables. NMDS is an unconstrained ordination technique (sensu Richards (2008)) in that variation in assemblages is displayed according to biotic data only. That variation was then correlated to measured environmental variables as an exploratory method to understand the relationship between species and environment in the dataset. NMDS was done in PAST (Hammer and Harper, 2005).

We then carried out forward multidimensional fuzzy set ordination (MFSO) ordination (Richards, 2009) in order to directly relate environmental variables to variation in ostracode assemblages by reducing the environmental dataset to only those variables that significantly and uniquely correlate to ostracode β-diversity. These variables then became candidate variables to model in the transfer function. NMDS displays all variation in assemblages, while MFSO focuses only on that variation in assemblages that can be correlated to measured environmental variables. MFSO was done in R using the package MFO (Richards, 2008). MFSO was chosen as the preferred method for directly relating environmental variables to change in assemblages since it tests the strength and significance of each individual environmental variable in the dataset. The effect size and significance of each variable in correlating to change in assemblages was then assessed.

The single variable with the highest effect size was then retained in the model. The residuals were used to test for correlation with other environmental variables. In this way,

MFSO eliminates model selection problems caused by correlations among measured environmental variables. All remaining environmental variables were tested for effect

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size and significance. The process stopped when no other environmental variables significantly correlated to the residuals (Richards, 2008; 2009).

Taxon optima (niche position) and tolerances (niche breath) were calculated for all taxa using Gaussian logistic regression (GLR) in R. Ter Braak and Looman (1986) was used to convert the regression coefficients from GLR into estimates of taxon optima and tolerance.

The program C2 (Juggins, 2003) was also used to construct the transfer function through weighted averaging partial least squares regression (WA-PLS). Low root-mean- square-error of prediction (RMSEP), a low maximum bias (ter Braak and Juggins, 1993), and a high coefficient of determination (r2) between observed and predicted values, estimated by leave-one-out cross-validation (Birks, 1995), as well as the smallest number of 'useful' partial-least-squares components all contributed to the selection of the minimum adequate WA-PLS model. We assessed the predictive ability of the transfer function model by the correlation between the measured and inferred conductivity and the apparent root mean squared error (RMSE) of prediction and the equivalent jackknifed values. Jackknifing is used to predict the conductivity of each sample using only the other samples in the dataset. It proceeds by leaving one lake out of the dataset and then using all 31 other lakes in the dataset to create a model to predict the left out lake's conductivity from its ostracode assemblage only. It is thus a method to assess the predictive ability of the model without collecting independent data.

In order to use the resulting model to produce records of past environments, three sediment cores were extracted from lakes on San Salvador and assessed for ostracode assemblages. One 61cm core was extracted from Salt Pond (Metzger, 2007), one 1.23m

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core was extracted from Clear Pond (Dalman, 2009), and one 1.62m core was extracted from the north basin of Storrs Lake (Figure 4.1). Using three cores allows us to determine whether changes in conductivity are synchronous across lakes on the island and to relate changes in conductivity of individual lakes to sea level and/or changing climate (Teeter, 1995).

Salt Pond is a small (0.043km2 in surface area), shallow, hypersaline salina that can experience large fluctuations in salinity within a year and has substantial microbial mat growth (Park et al., 2009). Because this lake lacks a marine conduit to the ocean, its water depth or conductivity may be sensitive to regional changes in climate. Clear Pond is larger than Salt Pond (0.117 km2 in surface area), also shallow and its salinity is marine due to the presence of an ebb- and-flow spring on its eastern shore (Dalman, 2009).

Clear Pond was previously a lagoon with a surficial connection to the ocean, but today it is isolated from the ocean by mid-Holocene eolinite dunes to the north and unlithified dunes to the south (Park, in press). Storrs Lake is a larger (3.2 km2), also shallow, hypersaline lake that like Clear Pond was once an oceanic lagoon (Park, in press). The core used in this study was taken from the northern basin of Storrs Lake, the largest of its three basins.

All three cores were extracted by push-coring technique, were collected in 2-inch- diameter black acrylonitrile-butadiene-styrene (ABS) tubing, and split and stored in the

University of Akron's refrigeration facility. The cores were sampled at 1-cm intervals for ostracode assemblages. In each interval, 1cm3 of sediment was taken from the core and dissolved in water at 80°C. These sampled were then put through two cycles of freezing and thawing to break up the sediment. Finally, all adult ostracodes in the sample were

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identified to species using a dissecting microscope and all valves were expressed as percentages of the total valves in the assemblage. The program C2 (Juggins, 2003) was used to convert these assemblages in records of past conductivity using the transfer function model created. Spectral analysis was run on these results to test for cyclicity in changes in conductivity from individual San Salvador lakes using the program PAST

(Hammer and Harper, 2005).

To contextualize the ostracode-derived conductivity records for the three cores by providing records of the depositional environements, records of percent organic matter by weight and percent carbonate content by weight were determined for each core by loss- on-ignition (LOI) analysis (Heiri et al., 2001). 1cm3 of sediment from each core was extracted and subjected to three heat treatments to remove water content (105°C), organic content (550°C), and carbonate content (1000°C). Water content was removed by placing the samples in a drying oven for twenty-four hours, organic matter and carbonate matter were removed in a furnace by heating at four hours each. Samples were weighed after each heat treatment to note mass lost through each process. Organic matter and carbonate content were expressed as percentages of the dry sediment weight (Dalman,

2009; Metzger, 2007; Sipahioglu 2008).

Sediment grain size was also analyzed to contextualize the records of past conductivity. Sediment grain size was determined with a Malvern 2000 Mastisizer at

Kent State University by sampling 1cm3 of sediment continuously through the Salt and

Clear Pond cores at 1cm intervals (Metzger, 2007; Dalman, 2009). 1cm3of the North

Storrs core was sampled at 2cm intervals for grain size analysis (Sipahioglu 2008). Grain size was expressed as percent sand (62.5µm-2mm) of the sample by volume.

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Records of potassium (K) were produced by x-ray fluorescence of the Clear Pond and North Storrs cores at the University of Minnesota, Duluth using an ITRAX x-ray fluorescence core scanner. Potassium was chosen as the proxy for African dust since it is present in the airborne dust (Prospero and Carlson, 1972; Foos, 1990) and the dust is the only source of aluminosilicate deposition in the Bahamas (Foos, 1990; Muhs et al.,

1990). Both cores were scanned at .02cm intervals.

Organic matter in the form of allochthonous leaves were collected at 25cm depth in the Salt Pond core, at 20cm, 49.5cm, and 79cm depth in the Clear Pond core, and 1.2m in the North Storrs core and sent to Beta Analytic laboratories to determine their radiocarbon ages. These dates were then calendar-year calibrated using CALIB online

(http://calib.qub.ac.uk/calib/).

Results

Lakes in this study are alkaline (pH 7.2-8.7) and have a wide range of conductivity (4.94-152.2 mS/cm). Additional limnological and geographical characteristics of these lakes are listed in Table 4.1.

Results of non-metric multidimensional scaling (NMDS) show the first two dimensions of three-dimensional NMDS ordinations overlain with measured environmental variables (Figure 4.2). No single environmental variable, out of the nineteen measured, shows a high correlation with the first two NMDS (akin to loadings

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Table 4.1. Minimum, maximum, mean, and standard deviation of measured environmental variables in all lakes (N=32)

Variable Min Max Mean SD

Latitude 23.9536 24.11469 24.03478 0.055912

Longitude -74.5501 -74.4499 -74.4915 0.035327

Lake area (m2) 133.049 13722846 992658.3 2572089

Depth (cm) 2 162 66.70675 45.52269

Dissolved oxygen (mg/L) 1.9 12.8 7.03875 2.798656

Conductivity (mS/cm) 4.94 124.7 59.9075 25.6214

Water temperature (°C) 23.1 36 31.36 2.903568

Alkalinity (mg/L) 56 373 189.1875 67.2806

Total Fe (mg/L) 0.006 0.65 0.139063 0.177358

Mn (mg/L) 0 0.162 0.030313 0.037477

Na (mg/L) 5881 90360 20025.91 19268.27

K (mg/L) 200 4093 859.675 874.6568

Ca (mg/L) 230.7 2184 628.5719 470.191

Mg (mg/L) 577.3 5261 1995.509 1237.719

Sr (mg/L) 8.1 60.4 24.38438 14.98234

Cl (mg/L) 4270 180020 36986.72 40194.3

SO4 (mg/L) 1302 17450 4507.719 3642.476

Mean grain size (μm) 25.62214 230.5701 116.3495 58.94476

112

Figure 4.2- First 2-dimensions of a 3-dimensional non-metric multidimensional scaling plot of all 32 ostracode assemblages collected on the Bray-Curtis dissimilarity matrix. The correlations of both axes and all 19 environmental variables measured are overlain as arrow.

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of a PCA; Table 4.2). Additionally, many environmental variables show a high degree of pairwise correlation with each other.

Three variables were selected that significantly and uniquely correlate with change in ostracode assemblages: electrical conductivity, dissolved oxygen, and alkalinity, although the effect size of alkalinity is quite low (Figure 4.3). The constrained ordination with these three variables correlate strongly with the Bray-Curtis dissimilarity of the ostracode assemblages (r=0.826). Conductivity is highly correlated with concentrations of major ions; concentrations of Sr and Mn correlate with change in ostracode assemblages almost as well as conductivity. Individually, these three environmental variables have stronger effect size than other environmental variables in

MFSO with only one environmental variable.

Gaussian logistic regression of each species niche over the conductivity gradient with the regression coefficients converted to species' optimum and tolerance according to ter Braak and Looman (1986) reveals that most species have niche positions tightly clustered in the range of marine salinity (Table 4.3). Exceptions include Physocypria denticulata and Hemicyprideis setipunctata whose niches are in the brackish range and

Aurila floridana whose niche is positioned in slightly saline conductivity. The niche breaths', however, are widely divergent. Three species have noticeably broad niches

(Cyprideis americana, Perissocytheridea bicelliforma, and Hemicyprideis setipunctata), while other species have narrower niches. A few species display patterns of decreasing abundances away from their optima, but many species have similar abundances in marine conductivities (Figure 4.4).

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Table 4.2. Pearson's correlation (r) of measured environmental variables with the first two axes of a three-dimensional non-metric multidimensional scaling plot

Axis 1 Axis 2

Mn -0.6359 Alkalinity -0.2601

Sr -0.6161 K -0.2472

Mg -0.5829 Mn -0.2335

Ca -0.5253 Conductivity -0.2314

Fe -0.4994 Cl -0.22

SO4 -0.4476 Na -0.1862

Conductivity -0.4411 Sr -0.165

Cl -0.4309 Mg -0.1624

Na -0.4144 SO4 -0.1618

K -0.4108 Fe -0.1163

Longitude -0.1833 Ca -0.1078

DO -0.154 Longitude 0.03385

Alkalinity -0.1339 Depth 0.08696

Water Temp. -0.1131 Grain Size 0.09514

Latitude 0.0945 DO 0.11254

Lake Area 0.18235 Latitude 0.18454

Grain Size 0.46319 Water Temp. 0.18803

Depth 0.55538 Lake Area 0.18844

115

116

Figure 4.3- Multidimensional Fuzzy Set Ordination, axis 1: fuzzy set conductivity (cond), axis 2: fuzzy set dissolved oxygen (do), axis 3: fuzzy set alkalinity (alk). a) axis 1 and 2, b) axis 1 and 3, c) axis 2 and 3. Axes are not environmental variables, but rather fuzzy sets of environmental variables.

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Table 4.3. Conductivity optima and tolerances for all species as determined by Gaussian logistic regression according to the formulas in ter Braak and Looman (1986). Key to species abbreviations: PD= Physocypria denticulata, HS= Hemicyprideis setipunctata, CyA= Cytherella arostrata, DI= Dolerocypris inopinata, PH= Paranesidea harpago, RM= Reticulocythereis multicarinata, PB= Perissocytheridea bicelliforma, XC= Xestoleberis curassavica, LP= Loxonchoncha pursubrhomboidea, CA= Cyprideis Americana, AF= Aurila floridana

Species Optimum Tolerance

PD 17.64 2.7

HS 39.53 46

CyA 52.5 0.11

DI 54.09 13.79

PH 55.21 2.68

RM 55.47 10.1

PB 55.38 59

XC 56.49 9.97

LP 57.69 9.46

CA 58.26 189.2

AF 62.68 6.81

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Aurila floridana 1.0

0.8

e c n a 0.6 d n u b A t n e c 0.4 r e P

0.2

0.0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 3 3 3

Cyprideis americana

1.0

0.8

e c n a d n 0.6 u b A t n e c r 0.4 e P

0.2

0.0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 3 3 3

Loxonchoncha pursubrhomboidea 1.0

0.8

e c n a 0.6 d n u b A t n e c 0.4 r e P

0.2

0.0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 3 3 3

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Xestoleberis curassavica 1.0

0.8

e c n a 0.6 d n u b A t n e c 0.4 r e P

0.2

0.0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 3 3 3

Perissocytheridea bicelliforma 1.0

0.8

e c n a d 0.6 n u b A t n e c 0.4 r e P

0.2

0.0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 3 3 3

Reticulocythereis multicarinata 1.0

0.8

e c n a 0.6 d n u b A t n e c 0.4 r e P

0.2

0.0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 3 3 3

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Paranesidea harpago 1.0

0.8

e c n a 0.6 d n u b A t n e c 0.4 r e P

0.2

0.0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 3 3 3

Dolerocypris inopinata 1.0

0.8

e c n a 0.6 d n u b A t n e c 0.4 r e P

0.2

0.0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 3 3 3

Cytherella arostrata 1.0

0.8

e c n a 0.6 d n u b A t n e c 0.4 r e P

0.2

0.0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 3 3 3

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Hemicyprideis setipunctata 1.0

0.8

e c n a 0.6 d n u b A

t n e c 0.4 r e P

0.2

0.0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 3 3 3

Physocypria denticulata 1.0

0.8

e c n a 0.6 d n u b A

t n e c 0.4 r e P

0.2

0.0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 3 3 3

Figure 4.4- Percent abundances of all species. Lakes are arranged along the abscissa according to measured conductivity. Species are arranged from bottom to top in increasing order of their conductivity optima as assessed by Gaussian logistic regression according to the formulas in ter Braak and Looman (1986).

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A 2-component weighted-averaging partial least squares model performed best as a transfer function for conductivity (Table 4.4, Figure 4.5). This model performs well with an apparent r2 of 0.76 and an r2 of 0.69 between observed and predicted conductivity, as assessed by leave-one-out cross validation (Table 4.4; Figure 4.5A).

There is no systematic relationship between the residuals of the full model and the observed conductivity, although lakes in the middle of the sampled gradient tended to overestimate the conductivity of lower-conductivity lakes and underestimate higher- conductivity lakes (Figure 4.5B). Sampling across the conductivity gradient is not even.

Bamboo pond is noticeable; it is a high leverage point, but not a highly influential point.

Even though it is the only sample from the very low end of conductivity, its species composition is on-trend with the remainder of the dataset. Most of the samples fall in the mid-range of conductivity and display lowering residuals with increasing conductivity

(Figure 4.5B). Three samples fall on the highest end of the conductivity gradient and their residuals display no systematic relationship with observed conductivity.

Based on one radiocarbon date in the Salt Pond core, a sedimentation rate of 52 calendar years/cm was calculated (Table 4.5). Also based on one radiocarbon date for the

North Storrs core, a sedimentation rate of 32.25 calendar years/cm was calculated (Table

4.5). Based on three dates, a linear sedimentation rate of 41.9 calendar years/cm was calculated for the Clear Pond core (Table 4.5).

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Table 4.4. Performance of apparent and cross-validated statistics of the conductivity transfer function. WA-PLS weighted averaging partial least squares regression, RMSE(P) root mean square error (of prediction).

Model type WA-PLS No. of PLS components 2 Apparent r2 apparent 0.76 RMSE 12.24 Ave. bias -0.0007294 Max. bias 25.25 Cross-validation jack-kniffing r2 jack-knifed 0.68 RMSEP 14.47 Ave. bias -0.06 Max. bias 28.11

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1 40

) 1 20

c /

S 1 00

(

y t

i 80

t c

u 60

o C

t c

i 20

e r

P 0

0 20 40 60 80 100 120 140 Observed Conductivity (mS/cm)

a 10

u d

i s e 0 R -10

-20

-30

0 20 40 60 80 100 120 140 Observed Conductivity (mS/cm)

Figure 4.5- Performance of ostracode-based transfer function for conductivity. A: Relationship between measured and inferred conductivity using a two-component weighted averaging partial least squares (WA-PLS) regression and calibration model. B: residuals against measured conductivity.

125

Table 4.5. Results of radiocarbon dating of all three cores.

Radiocarbon date, calibrated to Core Depth (cm) calendar years before present (ybp)

Salt Pond 25 1300 +-40 ybp

Clear Pond 20 1250 +- 40 ybp

49.5 1850 +- 40 ybp

79 3350 +- 40 ybp

North Storrs 120 3870 +- 40 ybp

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According to their age models, the Salt Pond core extends to 3146 ybp, the Clear

Pond core extends to 5133 ybp, while the longest North Storrs core extends to 5208 ybp

(Figure 4.6). All three cores show large fluctuations in conductivity throughout the mid- late Holocene that are for the most part synchronous, while Clear Pond is the only core to show a secular trend towards lower conductivity (Figure 4.6)

In all three cores, percent organic matter and percent carbonate content vary inversely with each other, but peaks in these records do not seem to correspond with changes in the records of conductivity as determined by ostracode assemblages (Figure

4.6). Changes in percent sand by volume do correspond to changes in conductivity through time with peaks in the sand record coeveal with times of lowered conductivity

(Figure 4.6). This relationship can be seen in the Salt Pond core from approximately 500 to 800 years before present (Figure 4.6A) and the Clear Pond core for over the most recent approximately 500 years (Figure 4.6B) when noticeable dips in conductivity coincides with prominent peaks in the cores’ percent sand.

Spectral analysis reveals cyclical changes in conductivity in only the records from

Salt Pond with cycles of 140 year frequency. Only the Clear Pond core exhibited secular changes in the form of long-term freshening over the time recorded in these records. The detrended records of these cores show changes in lake conductivity related to climate

(Figure 4.7). This is simply the deviation from the mean conductivity over the entire record of the core for Salt Pond and North Storrs, while the long-term freshening trend has been eliminated from the record of Clear Pond by simply linear regression over time.

Plotting these detrended records allows for the identification of times of aridity and

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Conductivity (mS/cm) % Organic Matter % Carbonate % Sand

0 40 80 120 160 0 20 40 60 80 100 0 20 40 60 80 100 0 20 40 60 80 100 0

-500

-1000

-1500

-2000

Years beforeYears Present -2500

-3000

-3500

Conductivity (mS/cm) % Organic Matter % Carbonate % Sand

0 40 80 120 0 20 40 60 80 100 0 20 40 60 80 100 0 20 40 60 80 100 0

-1000

-2000

-3000

Years before Present Years -4000

-5000

-6000

128

Conductivity (mS/cm) % Organic Matter % Carbonate % Sand

0 40 80 120 0 20 40 60 80 100 0 20 40 60 80 100 0 20 40 60 80 100 0

-1000

-2000

-3000

Years BeforeYears Present -4000

-5000

-6000

Figure 4.6- Results of ostracode-based transfer function for conductivity as applied to the three cores from this study plotted according to the age models derived for Salt Pond (A), Clear Pond (B), and North Storrs (C). Also plotted for each core are percent organic matter and percent carbonate content by weight and percent sand by volume. Gaps in the North Storrs core represent depths at which no adult ostracode valves were recovered.

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Residuals Residuals Residuals Humid Arid Humid Arid Humid Arid

-40 0 40 80 -20 0 20 40 -40 0 40 0

-1000

-2000

-3000

Salt Pond

Years BeforeYears Present -4000

-5000

North Storrs Clear Pond -6000 Figure 4.7- Detrended results of ostracode-based transfer function for conductivity as applied to the three cores from this study plotted according to the age models derived for each core. Gaps in the record represent depths at which no ostracode or no adult ostracode valves were recovered.

130

humidity that recorded in these cores that may be related to regional or global climate changes.

Records of potassium from Clear Pond and North Storrs show good correspondence to the first 6,000 years of an independently-derived record of El Niño strength (Moy et al., 2002), times of increased potassium deposition are coincident with strong El Niño events (Figure 4.8). The ostracode-derived record of conductivity from these lakes also shows a positive correlation with the potassium record, peaks in the potassium record correspond to times of elevated conductivity in these lakes (Figure 4.8).

Discussion- Conductivity records from San Salvador lakes

Changes in conductivity in lakes on San Salvador caused by climate changes have been broadly synchronous across the late Holocene (Figure 4.7). Clear Pond, however is the only lake to shows a secular trend across the time period represented and only Salt

Pond shows cyclical changes, while the utility of the Storrs lake cores is limited by the paucity of assemblages recovered (Figure 4.6). Since the majority of the sections in the

Storrs lake core from which no adult ostracodes were recovered were associated with evaporite minerals (principally gypsum and halite), these times may represent lower lake levels and thus periods of aridity. Further and more extensive coring of Storrs lake is needed to more fully understand its depositional history.

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Conductivity (mS/cm) K (XRF counts)

0 40 80 120 0 400 800 1200 0

-1000

-2000

-3000

Year before Present Year -4000

-5000

-6000

132

Conductivity (mS/cm) K (XRF counts)

0 40 80 120 160 0 400 800 1200 0

-1000

-2000

-3000

-4000

Years before Years Present

-5000

-6000

Figure 4.8- Results of ostracode-based transfer function for conductivity from Clear Pond (A) and North Storrs (B) compared to the XRF-derived record of K for each core and El Niño proxy reconstruction from Laguna Pallcacocha Ecuador (Moy et al., 2002). Peaks in red color intensity represent allochtonous material washed into the lake during strong El Niño events.

133

Deposition of allochtonous sand in these lakes is due to infrequent large storms which contribute a large amount of fresh water to these lakes and alter their species composition (Park et al., 2009). Therefore, times of increased sand input should be linked to a drop in the conductivity of the lakes as determined by ostracode assemblages.

This pattern is evident in the Salt Pond as most sand peaks in the Salt Pond record correspond to times of lower conductivity. This relationship is inconsistent through the

Clear Pond records, but some periods, such as the most recent 500 years and at approximately 2000 years before present show decreased conductivity and peaks in the percent sand. This relationship is weakest in North Storrs lake. The weaker relationship between peaks in the sand record and lowered conductivity in Clear Pond and North

Storrs reflects the different degrees of time-averaging in the allochthonous sand records and autochthonous ostracode assemblages. The sand records are highly-pulsed while the ostracode records likely accrue more evenly through time. The lack of correspondence between the lithological records of organic matter and carbonate with the ostracode- derived records of conductivity probably reflect this differential degree of time averaging.

The weakest relationship between peaks in the sand content of the cores and changes in conductivity in North Storrs lake is because this core was taken furthest from the ocean, so sand deposition when hurricanes overwash the dune would only make its way to this position with the largest storms.

Clear Pond’s long term freshening trend may be caused by the gradual rise in sea level for the Caribbean basin (Lidz and Shinn, 1991). Before 3400 ybp, Clear Pond was a lagoon with a surficial connection to the ocean, similar to Pigeon Creek on San

134

Salvador today (Dalman, 2009). At 3400 years before present Clear Pond closed to the ocean through dune progradation, but seawater continued to flow to the pond through an ebb-and-flow spring on its eastern shore (Dalman, 2009; Park, in press). A rising sea level may have lessened the impact of this sea water on the pond’s water budget through a rising fresh groundwater lens (Carew and Mylorie, 1994), thus causing the observed freshening trend represented in this core. While variation in this long-term freshening trend is related to short-term climate fluctuations, these fluctuations are not as high in magnitude nor as regular as in Salt Pond (Figure 4.7).

In addition to it being the only lake exhibiting cyclical changes in conductivity,

Salt Pond also shows the largest changes in conductivity. Due to its nature as a closed basin (Metzger, 2007), Salt Pond may thus most conclusively capture changes in the precipitation/ evaporation balance of the area. The 140 year cycles of conductivity recorded in the core are harder to explain. Since precipitation in this region is strongly influenced by hurricanes with much of the annual precipitation falling during the hurricane season (Park et al., 2009), these cycles may be related to changing magnitudes of El Niño events. Times of strong El Niño result in high wind shear across the Atlantic, inhibiting hurricane formation (Gray, 1984), thus these cycles in the strength of El Niños.

Times of strong El Niño events would produce less precipitation, raising conductivity and times without strong El Niño events would be associated with more hurricanes striking

San Salvador, resulting in lowered conductivity.

This relationship between strong El Niño events and increased conductivity is also seen in the Clear Pond and North Storrs cores. The potassium records from these cores show good correspondence with an independently-derived record of El Niño

135

strength (Moy et al., 2002) indicating that is should faithfully record times of strong El

Niño events when more dust is blown from Africa and deposited in the Bahamas

(Prospero and Lamb, 2003; Evan et al., 2006). Many of these times of strong El Niño events correspond to times of increased conductivity in Clear Pond and North Storrs and times of decreased El Niño events correspond to times of decreased conductivity (Figure

4.8). Thus, the variation in hurricane activity in the Atlantic, driven by changes in El

Niño/ Southern Oscillation (ENSO) cycle, controls much of the variation in the salinity of lakes on San Salvador Island, Bahamas.

Performance of transfer function

Compared to other published ostracode-based inference models for conductivity

(Mezquita et al., 2004; Mishchke et al., 2007; Mishke et al., 2010), this transfer function performs comparably as assessed by the coefficient of determination between measured and inferred conductivity (0.69). This is surprising given that this transfer function has a lower sample size than other studies. This transfer function, does however, have noticeably higher average and maximum bias (Table 4.4). This measure of performance would probably improve with a higher sample size. Nevertheless, the ostracode assemblages of San Salvador Island, Bahamas do indeed show a strong relationship to conductivity and this study demonstrates their effectiveness as proxy indicators in tropical environments.

One lake (Bamboo) had higher leverage due to its position in brackish, almost freshwater salinity. Nonetheless, it contained an assemblage consistent with other lakes.

136

Two generalist species (Cyprideis americana and Perissocytheridea bicelliforma) were found in low abundances of this lake, but it was dominated by Physocypria denticulata which was found in low abundance in only one other lake. This resulted in Physocypria denticulata having much lower conductivity optima than all other species as assessed by

Gaussian logistic regression according to ter Braak and Looman (1986). This species may be evolutionarily unique in comparison with all other species sampled in this dataset.

Park and Beltz (1998) concluded that ostracode species recovered from non-marine samples in the Caribbean must have invaded from the marine realm since many species reached highest abundances in lakes of marine salinity. Physocypria denticulata provides a counter example to this general pattern. It may have exhibited more rapid evolution than other taxa to colonize and dominate brackish lakes or may have invaded via a different route than other species.

No other lakes were noticeable outliers, although lakes in the mid range of salinity may exhibit the edge effect common to regression techniques that model species with unimodal responses to environmental gradients (Mischke et al., 2007) since lakes in the middle of the sampled gradient tended to overestimate the conductivity of lower- conductivity lakes and underestimate higher-conductivity lakes. However, this effect was not seen in the one lake on the very lowest end of the gradient and the three lakes on the highest end of the gradient.

137

Abiotic factors controlling distribution of ostracode assemblages

The environmental variables identified as best able to explain the variation in ostracode assemblages included: conductivity, dissolved oxygen, and alkalinity.

Conductivity has been commonly identified as correlated with ostracode assemblages in disparate environments (Mezquita et al., 2004; Mischke et al., 2007; Mischke et al.,

2010). Given that the ostracodes found in lacustrine environments on San Salvador may have invaded from the ocean, it is reasonable to assume that salinity would be a driver of ostracode diversity today since there are ample opportunities for diversification in lakes of divergent conductivity found today on San Salvador. The processes responsible for the formation of lakes on San Salvador leads to lakes of differing salinities and hydrologic conditions (Park, et al. 2011).

Similarly, previous work (Delorme, 1969; Frenzel and Boomer, 2005) has identified dissolved oxygen as a potential driver of diversity in ostracode assemblages since some species may have differing oxygen requirements. While alkalinity explains a significant and unique proportion of the Bray-Curtis dissimilarity matrix of ostracode assemblages, this proportion is quite small. Like conductivity, the processes responsible for lake formation on San Salvador have led to lakes with different alkalinities, so this may also be an important factor in the evolutionary ecology of ostracodes inhabiting San

Salvador lakes.

138

Representativeness of samples

Previous work has demonstrated that ostracode death assemblages sampled from the upper-most centimeter of lake sediments on San Salvador Island faithfully represent the living ostracode community in the lake and vary little across space in individual lakes

(Michelson and Park, in press; Chapter one). Therefore, we regard the ostracode assemblages used in this study as representative of living communities and sampling living individuals in this case is not necessary for inference-model building.

However, Dalman (2009) and Sipahioglu (2008) have demonstrated that some lakes exhibit substantial seasonal variability in salinity. Studies are needed to estimate variability in salinity across all lakes to see if this could affect ostracode assemblage distribution. This variation in salinity could then be imputed as an environmental variable to see if it significantly correlated with ostracode β-diversity. It is possible that certain species may be better adapted to this disturbance and may come to dominate shallow, smaller lakes, or those that lack a robust connection to the ocean. In that case, analyses that use a one-point sample of salinity would miss this potential driver of β- diversity on San Salvador Island.

Modeling of species niches across the sampled conductivity gradient

This paper presents the first quantitative estimates of many of species sampled responses' across the sampled conductivity gradient. In general, it accomplishes this with mixed results. In some species it produces a significant and biologically meaningful

139

model, while it gives a misleading result in other cases. P. bicelliforma for instance is a generalist species found in many of the sampled lakes. However, a Gaussian logistic regression on these data produces a concave niche with increasing probability of being found in lakes on the extreme high and low ends of the sampled gradient. This is the opposite of theoretical unimodal gradients on which this analysis is based. More studies need to be done on the autecology of these species to produce more reasonable unidimensional models of individual species niches as unimodal modeling of the niches of generalist species or species adapted to extreme environments may be inappropriate.

Limits of quantitative paleoenvironmental reconstruction

Belyea (2007) has identified many potential problems with the transfer function method that could lead to spurious and unreliable results. In this dataset, many of those problems may be mute since neither abiotic factors sampled nor the Bray-Curtis dissimilarity matrix of ostracode assemblages are spatially autocorrelated. Thus spatial factors are unlikely to bias the results in this case. Additionally, MFSO avoids overfitting the relationship between ostracode assemblages and the measured environmental variables, since after a variable has entered the model, only the residuals are used to fit the model. In this way, the correlation of environmental variables themselves will not bias the ordination results. A more thorough understanding of potential problems in quantitative paleoenvironmental reconstruction using these data will be a subject of later work.

140

Conclusions

A transfer function for conductivity with reasonable predictive ability was developed using ostracodes on San Salvador Island, Bahamas, again demonstrating their ability to produce records of past environments. This model would be improved by further sampling of lakes and estimation of seasonal variation in conductivity in all lakes.

Changes in the conductivity of lakes on San Salvador were broadly synchronous and related to climate change, with times of increased conductivity corresponding to times of strong El Niño events when there are fewer hurricanes providing freshwater input to these lakes.

141

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APPENDICES

155

APPENDIX A

TAPHONOMIC DATA: LIVING ASSEMBLAGES

Position (Transect Cyprideis Perissocytheridea Dolerocypris Station americana bicelliforma inopinata Lake .Replicate) (count) (count) (count) Little A1.1 6 4 94 Little A1.2 11 0 82 Little B1.1 16 4 50 Little B1.2 8 8 78 Little A2.1 12 5 38 Little A2.2 4 0 20 Little B2.1 16 4 32 Little B2.2 3 0 35 Little A3.1 2 4 72 Little A3.2 3 1 64 Little B3.1 3 0 96 Little B3.2 4 1 89 Little A4.1 11 2 75 Little A4.2 13 2 92 Little B4.1 12 4 90 Little B4.2 18 0 53 No Name A1.1 16 48 0 No Name A1.2 18 32 0 No Name B1.1 2 137 0 No Name B1.2 0 136 0 No Name A2.1 0 101 0 No Name A2.2 0 105 0 No Name B2.1 0 104 0 No Name B2.2 0 68 0 No Name A3.1 2 79 0 No Name A3.2 0 96 0 No Name B3.1 5 102 0 No Name B3.2 1 105 0 No Name A4.1 3 105 0 No Name A4.2 6 42 0 No Name B4.1 2 84 0 No Name B4.2 0 90 0 North Storrs A1.1 21 0 0 North Storrs A1.2 32 0 0 North Storrs B1.1 36 0 0 North Storrs B1.2 42 0 0 North Storrs A2.1 49 0 0 North Storrs A2.2 17 3 0 156

North Storrs B2.1 28 0 0 North Storrs B2.2 36 2 0 North Storrs A3.1 75 2 0 North Storrs A3.2 68 1 0 North Storrs B3.1 75 5 0 North Storrs B3.2 48 13 0 North Storrs A4.1 47 2 0 North Storrs A4.2 73 9 0 North Storrs B4.1 128 0 0 North Storrs B4.2 118 0 0 Blue Hole 5 A1.1 61 0 8 Blue Hole 5 A1.2 55 0 14 Blue Hole 5 B1.1 58 0 6 Blue Hole 5 B1.2 45 0 8 Blue Hole 5 A2.1 87 0 1 Blue Hole 5 A2.2 170 2 8 Blue Hole 5 B2.1 140 2 6 Blue Hole 5 B2.2 122 3 12 Blue Hole 5 A3.1 70 0 2 Blue Hole 5 A3.2 66 0 11 Blue Hole 5 B3.1 67 0 22 Blue Hole 5 B3.2 70 0 16 Blue Hole 5 A4.1 73 2 6 Blue Hole 5 A4.2 66 4 14 Blue Hole 5 B4.1 27 6 18 Blue Hole 5 B4.2 86 6 18 Clear A1.1 48 6 39 Clear A1.2 37 7 34 Clear B1.1 45 9 36 Clear B1.2 50 3 43 Clear A2.1 49 10 13 Clear A2.2 49 2 41 Clear B2.1 46 8 57 Clear B2.2 47 6 30 Clear A3.1 37 4 62 Clear A3.2 39 4 44 Clear B3.1 27 8 53 Clear B3.2 24 1 43 Clear A4.1 49 2 54 Clear A4.2 39 6 57 Clear B4.1 41 2 74 Clear B4.2 36 0 64 Watlings A1.1 4 0 4 Watlings A1.2 15 7 8 Watlings B1.1 24 14 9 Watlings B1.2 19 30 8 Watlings A2.1 19 2 2 Watlings A2.2 0 4 38 Watlings B2.1 36 39 8 157

Watlings B2.2 28 42 6 Watlings A3.1 2 2 2 Watlings A3.2 5 2 2 Watlings B3.1 5 0 0 Watlings B3.2 18 0 0 Watlings A4.1 11 2 2 Watlings A4.2 20 2 2 Watlings B4.1 0 0 0 Watlings B4.2 4 4 0 French A1.1 41 78 2 French A1.2 24 41 0 French B1.1 25 44 0 French B1.2 12 58 0 French A2.1 15 35 0 French A2.2 24 50 0 French B2.1 15 27 0 French B2.2 19 38 0 French A3.1 15 22 0 French A3.2 12 52 0 French B3.1 4 15 0 French B3.2 10 34 0 French A4.1 8 53 0 French A4.2 12 70 0 French B4.1 17 28 0 French B4.2 8 28 0

Position Hemicyprideis Reticulocythereis Xestoleberis Lake (Transect setipunctata multicarinata curassavica 158

Station (count) (count) (count) .Replicate) Little A1.1 10 15 71 Little A1.2 16 11 55 Little B1.1 26 19 54 Little B1.2 23 18 52 Little A2.1 19 17 54 Little A2.2 18 21 39 Little B2.1 32 15 98 Little B2.2 8 11 68 Little A3.1 5 7 79 Little A3.2 8 11 75 Little B3.1 19 16 78 Little B3.2 17 6 94 Little A4.1 34 13 94 Little A4.2 37 20 113 Little B4.1 38 34 108 Little B4.2 27 16 105 No Name A1.1 0 0 0 No Name A1.2 0 0 0 No Name B1.1 0 0 0 No Name B1.2 0 0 0 No Name A2.1 0 0 0 No Name A2.2 0 0 0 No Name B2.1 0 0 0 No Name B2.2 0 0 0 No Name A3.1 0 0 0 No Name A3.2 0 0 0 No Name B3.1 0 0 0 No Name B3.2 0 0 0 No Name A4.1 0 0 0 No Name A4.2 0 0 0 No Name B4.1 0 0 0 No Name B4.2 0 0 0 North Storrs A1.1 0 0 0 North Storrs A1.2 0 0 0 North Storrs B1.1 0 0 0 North Storrs B1.2 0 0 0 North Storrs A2.1 0 0 0 North Storrs A2.2 0 0 0 North Storrs B2.1 0 0 0 North Storrs B2.2 0 0 0 North Storrs A3.1 0 0 0 North Storrs A3.2 0 0 0 North Storrs B3.1 0 0 0 North Storrs B3.2 0 0 0 North Storrs A4.1 0 0 0 North Storrs A4.2 0 0 0 North Storrs B4.1 0 0 0 159

North Storrs B4.2 0 0 0 Blue Hole 5 A1.1 238 2 0 Blue Hole 5 A1.2 219 0 0 Blue Hole 5 B1.1 248 0 0 Blue Hole 5 B1.2 221 0 0 Blue Hole 5 A2.1 100 2 0 Blue Hole 5 A2.2 114 0 0 Blue Hole 5 B2.1 190 0 0 Blue Hole 5 B2.2 153 2 0 Blue Hole 5 A3.1 202 0 0 Blue Hole 5 A3.2 214 0 0 Blue Hole 5 B3.1 170 0 0 Blue Hole 5 B3.2 176 0 0 Blue Hole 5 A4.1 35 0 0 Blue Hole 5 A4.2 35 0 0 Blue Hole 5 B4.1 32 0 0 Blue Hole 5 B4.2 109 0 0 Clear A1.1 17 5 0 Clear A1.2 14 13 2 Clear B1.1 11 6 2 Clear B1.2 46 23 4 Clear A2.1 54 9 0 Clear A2.2 18 6 0 Clear B2.1 21 12 2 Clear B2.2 60 12 0 Clear A3.1 68 11 0 Clear A3.2 28 12 1 Clear B3.1 22 11 0 Clear B3.2 60 7 0 Clear A4.1 20 15 2 Clear A4.2 63 15 0 Clear B4.1 24 19 0 Clear B4.2 64 6 0 Watlings A1.1 2 0 0 Watlings A1.2 12 0 0 Watlings B1.1 11 0 0 Watlings B1.2 5 0 0 Watlings A2.1 14 0 0 Watlings A2.2 0 0 0 Watlings B2.1 22 0 0 Watlings B2.2 19 0 0 Watlings A3.1 0 0 0 Watlings A3.2 11 0 0 Watlings B3.1 2 0 0 Watlings B3.2 5 0 0 Watlings A4.1 5 0 0 Watlings A4.2 19 0 0 Watlings B4.1 0 0 0 Watlings B4.2 0 0 0 160

French A1.1 0 0 0 French A1.2 0 0 0 French B1.1 0 0 0 French B1.2 0 0 0 French A2.1 0 0 0 French A2.2 0 0 0 French B2.1 0 0 0 French B2.2 0 0 0 French A3.1 0 0 0 French A3.2 0 0 0 French B3.1 0 0 0 French B3.2 0 0 0 French A4.1 0 0 0 French A4.2 0 0 0 French B4.1 0 0 0 French B4.2 0 0 0

Lake Position Aurila Paranesidea Loxonchoncha 161

(Transect floridana harpago pursubrhomboidea Station (count) (count) (count) .Replicate) Little A1.1 0 0 1 Little A1.2 2 2 2 Little B1.1 0 1 5 Little B1.2 4 2 4 Little A2.1 4 2 6 Little A2.2 0 0 2 Little B2.1 4 0 6 Little B2.2 2 0 3 Little A3.1 0 0 0 Little A3.2 0 0 1 Little B3.1 3 0 2 Little B3.2 0 0 2 Little A4.1 3 2 3 Little A4.2 2 0 1 Little B4.1 4 0 2 Little B4.2 2 0 2 No Name A1.1 0 0 0 No Name A1.2 0 0 0 No Name B1.1 0 0 0 No Name B1.2 0 0 0 No Name A2.1 0 0 0 No Name A2.2 0 0 0 No Name B2.1 0 0 0 No Name B2.2 0 0 0 No Name A3.1 0 0 0 No Name A3.2 0 0 0 No Name B3.1 0 0 0 No Name B3.2 0 0 0 No Name A4.1 0 0 0 No Name A4.2 0 0 0 No Name B4.1 0 0 0 No Name B4.2 0 0 0 North Storrs A1.1 0 0 0 North Storrs A1.2 0 0 0 North Storrs B1.1 0 0 0 North Storrs B1.2 0 0 0 North Storrs A2.1 0 0 0 North Storrs A2.2 0 0 0 North Storrs B2.1 0 0 0 North Storrs B2.2 0 0 0 North Storrs A3.1 0 0 0 North Storrs A3.2 0 0 0 North Storrs B3.1 0 0 0 North Storrs B3.2 0 0 0 North Storrs A4.1 0 0 0 North Storrs A4.2 0 0 0

162

North Storrs B4.1 0 0 0 North Storrs B4.2 0 0 0 Blue Hole 5 A1.1 0 0 0 Blue Hole 5 A1.2 0 0 0 Blue Hole 5 B1.1 0 0 0 Blue Hole 5 B1.2 0 0 0 Blue Hole 5 A2.1 0 0 0 Blue Hole 5 A2.2 0 0 0 Blue Hole 5 B2.1 0 0 0 Blue Hole 5 B2.2 0 0 0 Blue Hole 5 A3.1 0 0 0 Blue Hole 5 A3.2 0 0 0 Blue Hole 5 B3.1 0 0 0 Blue Hole 5 B3.2 0 0 0 Blue Hole 5 A4.1 0 0 0 Blue Hole 5 A4.2 0 0 0 Blue Hole 5 B4.1 0 0 0 Blue Hole 5 B4.2 0 0 0 Clear A1.1 0 0 0 Clear A1.2 0 0 0 Clear B1.1 0 0 0 Clear B1.2 0 0 0 Clear A2.1 0 0 0 Clear A2.2 0 0 0 Clear B2.1 0 0 0 Clear B2.2 0 0 0 Clear A3.1 0 0 0 Clear A3.2 0 0 0 Clear B3.1 0 0 0 Clear B3.2 0 0 0 Clear A4.1 0 0 0 Clear A4.2 0 0 0 Clear B4.1 0 0 0 Clear B4.2 0 0 0 Watlings A1.1 0 0 0 Watlings A1.2 0 0 0 Watlings B1.1 0 0 0 Watlings B1.2 0 0 0 Watlings A2.1 0 0 0 Watlings A2.2 0 0 0 Watlings B2.1 0 0 0 Watlings B2.2 0 0 0 Watlings A3.1 0 0 0 Watlings A3.2 0 0 0 Watlings B3.1 0 0 0 Watlings B3.2 0 0 0 Watlings A4.1 0 0 0 Watlings A4.2 0 0 0 Watlings B4.1 0 0 0 163

Watlings B4.2 0 0 0 French A1.1 0 0 0 French A1.2 0 0 0 French B1.1 0 0 0 French B1.2 0 0 0 French A2.1 0 0 0 French A2.2 0 0 0 French B2.1 0 0 0 French B2.2 0 0 0 French A3.1 0 0 0 French A3.2 0 0 0 French B3.1 0 0 0 French B3.2 0 0 0 French A4.1 0 0 0 French A4.2 0 0 0 French B4.1 0 0 0 French B4.2 0 0 0

164

APPENDIX B

TAPHONOMIC DATA: DEATH ASSEMBLAGES

Position (Transect Cyprideis Perissocytheridea Dolerocypris Station americana bicelliforma inopinata Lake .Replicate) (count) (count) (count) Little A1.1 22 2 68 Little A1.2 19 0 46 Little B1.1 43 2 19 Little B1.2 44 0 33 Little A2.1 48 0 27 Little A2.2 50 2 27 Little B2.1 55 0 21 Little B2.2 27 3 29 Little A3.1 13 1 71 Little A3.2 13 0 58 Little B3.1 8 4 72 Little B3.2 9 2 66 Little A4.1 12 7 63 Little A4.2 17 0 33 Little B4.1 15 2 33 Little B4.2 14 2 60 No Name A1.1 66 83 0 No Name A1.2 75 76 0 No Name B1.1 3 81 0 No Name B1.2 6 38 0 No Name A2.1 1 65 0 No Name A2.2 5 67 0 No Name B2.1 1 56 0 No Name B2.2 1 54 0 No Name A3.1 16 50 0 No Name A3.2 6 52 0 No Name B3.1 12 53 0 No Name B3.2 10 54 0 No Name A4.1 4 56 0 No Name A4.2 9 48 0 No Name B4.1 3 42 0 No Name B4.2 1 40 0 165

North Storrs A1.1 60 0 0 North Storrs A1.2 54 0 0 North Storrs B1.1 101 0 0 North Storrs B1.2 88 0 0 North Storrs A2.1 75 0 0 North Storrs A2.2 36 1 0 North Storrs B2.1 14 0 0 North Storrs B2.2 28 3 0 North Storrs A3.1 90 1 0 North Storrs A3.2 58 3 0 North Storrs B3.1 68 10 0 North Storrs B3.2 39 16 0 North Storrs A4.1 72 1 0 North Storrs A4.2 93 13 0 North Storrs B4.1 98 0 0 North Storrs B4.2 89 0 0 Blue Hole 5 A1.1 70 4 13 Blue Hole 5 A1.2 52 2 13 Blue Hole 5 B1.1 44 0 4 Blue Hole 5 B1.2 49 0 10 Blue Hole 5 A2.1 91 0 2 Blue Hole 5 A2.2 94 2 16 Blue Hole 5 B2.1 93 0 2 Blue Hole 5 B2.2 103 4 7 Blue Hole 5 A3.1 101 1 12 Blue Hole 5 A3.2 90 4 16 Blue Hole 5 B3.1 98 4 29 Blue Hole 5 B3.2 47 2 25 Blue Hole 5 A4.1 102 10 25 Blue Hole 5 A4.2 88 12 26 Blue Hole 5 B4.1 45 11 20 Blue Hole 5 B4.2 88 10 18 Clear A1.1 94 19 20 Clear A1.2 45 19 26 Clear B1.1 36 14 20 Clear B1.2 30 29 30 Clear A2.1 58 15 14 Clear A2.2 75 16 25 Clear B2.1 28 18 28 Clear B2.2 29 19 16 Clear A3.1 89 16 51 Clear A3.2 109 28 36 166

Clear B3.1 58 21 48 Clear B3.2 38 10 16 Clear A4.1 103 14 18 Clear A4.2 106 23 25 Clear B4.1 116 16 45 Clear B4.2 117 8 35 Watlings A1.1 102 68 28 Watlings A1.2 75 122 35 Watlings B1.1 56 87 22 Watlings B1.2 29 91 24 Watlings A2.1 131 26 14 Watlings A2.2 51 106 46 Watlings B2.1 42 99 10 Watlings B2.2 45 88 19 Watlings A3.1 32 8 20 Watlings A3.2 60 30 11 Watlings B3.1 25 172 21 Watlings B3.2 89 25 14 Watlings A4.1 32 0 3 Watlings A4.2 78 0 4 Watlings B4.1 49 18 19 Watlings B4.2 51 27 23 French A1.1 61 95 0 French A1.2 82 80 0 French B1.1 27 72 0 French B1.2 57 110 0 French A2.1 25 47 0 French A2.2 82 89 0 French B2.1 22 38 0 French B2.2 60 91 0 French A3.1 20 71 0 French A3.2 30 66 0 French B3.1 4 16 0 French B3.2 2 19 0 French A4.1 16 50 0 French A4.2 22 51 0 French B4.1 3 12 0 French B4.2 9 16 0

Position Hemicyprideis Reticulocythereis Xestoleberis (Transect setipunctata multicarinata curassavica Lake Station (count) (count) (count) 167

.Replicate) Little A1.1 19 13 112 Little A1.2 18 13 102 Little B1.1 47 12 67 Little B1.2 67 18 90 Little A2.1 66 21 120 Little A2.2 50 24 157 Little B2.1 56 9 153 Little B2.2 26 9 178 Little A3.1 18 9 162 Little A3.2 16 8 116 Little B3.1 11 18 111 Little B3.2 25 13 122 Little A4.1 28 5 128 Little A4.2 24 11 125 Little B4.1 60 20 162 Little B4.2 49 26 174 No Name A1.1 0 0 0 No Name A1.2 0 0 0 No Name B1.1 0 0 0 No Name B1.2 0 0 0 No Name A2.1 0 0 0 No Name A2.2 0 0 0 No Name B2.1 0 0 0 No Name B2.2 0 0 0 No Name A3.1 0 0 0 No Name A3.2 0 0 0 No Name B3.1 0 0 0 No Name B3.2 0 0 0 No Name A4.1 0 0 0 No Name A4.2 0 0 0 No Name B4.1 0 0 0 No Name B4.2 0 0 0 North Storrs A1.1 0 0 0 North Storrs A1.2 0 0 0 North Storrs B1.1 0 0 0 North Storrs B1.2 0 0 0 North Storrs A2.1 0 0 0 North Storrs A2.2 0 0 0 North Storrs B2.1 0 0 0 North Storrs B2.2 0 0 0 North Storrs A3.1 0 0 0 168

North Storrs A3.2 0 0 0 North Storrs B3.1 0 0 0 North Storrs B3.2 0 0 0 North Storrs A4.1 0 0 0 North Storrs A4.2 0 0 0 North Storrs B4.1 0 0 0 North Storrs B4.2 0 0 0 Blue Hole 5 A1.1 254 0 0 Blue Hole 5 A1.2 208 0 0 Blue Hole 5 B1.1 145 0 0 Blue Hole 5 B1.2 167 0 0 Blue Hole 5 A2.1 164 0 0 Blue Hole 5 A2.2 144 0 0 Blue Hole 5 B2.1 120 0 0 Blue Hole 5 B2.2 127 0 0 Blue Hole 5 A3.1 295 0 0 Blue Hole 5 A3.2 173 0 0 Blue Hole 5 B3.1 176 0 0 Blue Hole 5 B3.2 186 0 0 Blue Hole 5 A4.1 44 0 0 Blue Hole 5 A4.2 75 0 0 Blue Hole 5 B4.1 33 0 0 Blue Hole 5 B4.2 60 0 0 Clear A1.1 32 20 8 Clear A1.2 14 14 3 Clear B1.1 20 15 4 Clear B1.2 15 8 3 Clear A2.1 72 3 2 Clear A2.2 53 8 2 Clear B2.1 23 13 2 Clear B2.2 23 9 1 Clear A3.1 76 11 4 Clear A3.2 61 7 1 Clear B3.1 38 20 1 Clear B3.2 32 15 2 Clear A4.1 26 7 2 Clear A4.2 47 5 5 Clear B4.1 44 17 4 Clear B4.2 45 11 5

Watlings A1.1 24 0 0

169

Watlings A1.2 27 0 0 Watlings B1.1 34 0 0 Watlings B1.2 6 0 0 Watlings A2.1 126 0 0 Watlings A2.2 30 0 0 Watlings B2.1 36 0 0 Watlings B2.2 58 0 0 Watlings A3.1 191 0 0 Watlings A3.2 137 0 0 Watlings B3.1 25 0 0 Watlings B3.2 171 0 0 Watlings A4.1 260 0 0 Watlings A4.2 317 0 0 Watlings B4.1 185 0 0 Watlings B4.2 173 0 0 French A1.1 0 0 0 French A1.2 0 0 0 French B1.1 0 0 0 French B1.2 0 0 0 French A2.1 0 0 0 French A2.2 0 0 0 French B2.1 0 0 0 French B2.2 0 0 0 French A3.1 0 0 0 French A3.2 0 0 0 French B3.1 0 0 0 French B3.2 0 0 0 French A4.1 0 0 0 French A4.2 0 0 0 French B4.1 0 0 0 French B4.2 0 0 0

Position Aurila Paranesidea Loxonchoncha Lake (Transect floridana harpago pursubrhomboidea 170

Station (count) (count) (count) .Replicate) Little A1.1 2 2 2 Little A1.2 2 2 6 Little B1.1 2 0 1 Little B1.2 3 0 3 Little A2.1 0 0 6 Little A2.2 4 2 2 Little B2.1 4 0 1 Little B2.2 4 0 4 Little A3.1 2 2 1 Little A3.2 2 0 2 Little B3.1 5 1 7 Little B3.2 5 1 5 Little A4.1 7 1 2 Little A4.2 3 2 4 Little B4.1 6 1 6 Little B4.2 6 1 6 No Name A1.1 0 0 0 No Name A1.2 0 0 0 No Name B1.1 0 0 0 No Name B1.2 0 0 0 No Name A2.1 0 0 0 No Name A2.2 0 0 0 No Name B2.1 0 0 0 No Name B2.2 0 0 0 No Name A3.1 0 0 0 No Name A3.2 0 0 0 No Name B3.1 0 0 0 No Name B3.2 0 0 0 No Name A4.1 0 0 0 No Name A4.2 0 0 0 No Name B4.1 0 0 0 No Name B4.2 0 0 0 North Storrs A1.1 0 0 0 North Storrs A1.2 0 0 0 North Storrs B1.1 0 0 0 North Storrs B1.2 0 0 0 North Storrs A2.1 0 0 0 North Storrs A2.2 0 0 0 North Storrs B2.1 0 0 0 North Storrs B2.2 0 0 0 171

North Storrs A3.1 0 0 0 North Storrs A3.2 0 0 0 North Storrs B3.1 0 0 0 North Storrs B3.2 0 0 0 North Storrs A4.1 0 0 0 North Storrs A4.2 0 0 0 North Storrs B4.1 0 0 0 North Storrs B4.2 0 0 0 Blue Hole 5 A1.1 0 0 0 Blue Hole 5 A1.2 0 0 0 Blue Hole 5 B1.1 0 0 0 Blue Hole 5 B1.2 0 0 0 Blue Hole 5 A2.1 0 0 0 Blue Hole 5 A2.2 0 0 0 Blue Hole 5 B2.1 0 0 0 Blue Hole 5 B2.2 0 0 0 Blue Hole 5 A3.1 0 0 0 Blue Hole 5 A3.2 0 0 0 Blue Hole 5 B3.1 0 0 0 Blue Hole 5 B3.2 0 0 0 Blue Hole 5 A4.1 0 0 0 Blue Hole 5 A4.2 0 0 0 Blue Hole 5 B4.1 0 0 0 Blue Hole 5 B4.2 0 0 0 Clear A1.1 0 0 0 Clear A1.2 0 0 0 Clear B1.1 0 0 0 Clear B1.2 0 0 0 Clear A2.1 0 0 0 Clear A2.2 0 0 0 Clear B2.1 0 0 0 Clear B2.2 0 0 0 Clear A3.1 0 0 0 Clear A3.2 0 0 0 Clear B3.1 0 0 0 Clear B3.2 0 0 0 Clear A4.1 0 0 0 Clear A4.2 0 0 0 Clear B4.1 0 0 0 Clear B4.2 0 0 0

172

Watlings A1.1 0 0 0 Watlings A1.2 0 0 0 Watlings B1.1 0 0 0 Watlings B1.2 0 0 0 Watlings A2.1 0 0 0 Watlings A2.2 0 0 0 Watlings B2.1 0 0 0 Watlings B2.2 0 0 0 Watlings A3.1 0 0 0 Watlings A3.2 0 0 0 Watlings B3.1 0 0 0 Watlings B3.2 0 0 0 Watlings A4.1 0 0 0 Watlings A4.2 0 0 0 Watlings B4.1 0 0 0 Watlings B4.2 0 0 0 French A1.1 0 0 0 French A1.2 0 0 0 French B1.1 0 0 0 French B1.2 0 0 0 French A2.1 0 0 0 French A2.2 0 0 0 French B2.1 0 0 0 French B2.2 0 0 0 French A3.1 0 0 0 French A3.2 0 0 0 French B3.1 0 0 0 French B3.2 0 0 0 French A4.1 0 0 0 French A4.2 0 0 0 French B4.1 0 0 0 French B4.2 0 0 0

173

APPENDIX C

GEOGRAPHIC, LIMNOLOGICAL, CHEMICAL, OSTRACODE DATA FROM ALL LAKES

Dissolved Latitude Longitude Lake Area Depth Oxygen Lake (°N) (°W) (m2) (cm) (mg/L) Bamboo Pond 24.045948 74.53048 38654.166 62 1.9 Blue Hole #5 23.96387 74.54559 1048.195 162 10.7 Central Storr's 24.03918 74.44985 684959.6652 26 8.2 Clear Pond 23.97319 74.54771 117468.258 62 5.8 Columbus Landing Blue Hole 23.95953 74.54795 668.641 160 8.5 Crescent Pond 24.11323 74.45668 33824.696 66 5.2 Flamingo 24.06982 74.51022 1010170.16 150 7.23 French Pond 23.95360 74.53937 51446.581 2 6.5 Granny 24.043638 74.48653 3184901.929 70 6.88 Great Lake 24.041552 74.51141 13722845.66 136 4.2 Little Lake 24.04959 74.51705 1841919.026 102 8.8 Little Salt Pond 24.02062 74.45216 14623.536 5.08 7.77 Long Lake 24.04650 74.49813 4574281.885 88 5.6 Mermaid Pond 23.96502 74.51473 147394.721 97.536 5.3 Moon Rock Pond 24.11035 74.45875 14283.091 64 5.9 Nasty Pond 23.97434 74.49898 15387.39 2 12.8 New Blue Hole 23.959888 74.53728 176.313 98 5.8 No Name 24.01164 74.46019 6800.179 2 12.6 North Storr's 24.05496 74.45245 2568598.745 26 9.5 Osprey Lake 24.11082 74.46432 89936.72 68 4.8 Oyster Pond 24.10925 74.46239 78825.952 96 5.6 Pain Pond 24.11190 74.45680 5728.046 72 6.7 Peel 23.97742 74.49899 9971.38 34 6.7 Plantation Pond 24.03353 74.45786 20758.71 82 6.8 Reckley Hill Settlement Pond 24.11469 74.45934 104959.465 78 6.5 Salt Pond 24.02339 74.45161 43155.125 24 2.45 Six Pack 24.052494 74.48767 837466.268 100 3.11 South Storr's 24.02962 74.45013 171239.9163 22 6.5 Stouts 23.985504 74.49688 2212710.109 74 6.4 Triangle Pond 24.10415 74.51638 265312.843 24 12.8 Watlings Blue Hole 23.95381 74.55013 3692.672 20 12 Wild Dilly Pond 24.10980 74.46056 7977.104 60 5.7 174

Conductivity Salinity Water Total Alkalinity Lake (mS/cm) (ppt) Temperature Dissolved (mg/L) 175

(°C) Solids (g/L) Bamboo Pond 4.94 2.6 30.1 2.47 373 Blue Hole #5 30.7 19.1 33.5 15.35 233.2 Central Storr's 75.6 28.7 37.8 190 Clear Pond 50.2 33.1 34.8 25.2 274.4 Columbus Landing Blue Hole 32.1 20 32.4 16.01 240 Crescent Pond 49.6 32.5 27.5 25 166 Flamingo 62 32.2 39.7 150 French Pond 75 36 39.6 56 Granny 59.9 31.86 38.5 132 Great Lake 58.9 31.8 29.4 105 Little Lake 54.7 36.3 31.1 27.4 192 Little Salt Pond 118.6 26 59.4 172 Long Lake 61.1 41.1 28.6 30.5 115 Mermaid Pond 46.6 30.2 23.1 23.3 208 Moon Rock Pond 52.8 34.8 30.1 26.4 160 Nasty Pond 55.1 35.3 27.6 260 New Blue Hole 30.8 19 31 298.4 No Name 118.3 35 59.3 300 North Storr's 77 32.9 38.4 170 Osprey Lake 73.2 32 36.6 140 Oyster Pond 53.7 35.6 31.8 26.9 160 Pain Pond 52.2 34.6 29.2 26.2 166 Peel 25.8 15.7 32.3 12.9 123 Plantation Pond 53.1 35.1 26.5 220 Reckley Hill Settlement Pond 67 30.4 33.5 148 Salt Pond 124.7 28.7 62.3 180 Six Pack 43.9 30.86 28.1 120 South Storr's 152.2 29.8 76.1 196 Stouts 55.4 36.4 33.5 27.4 144 Triangle Pond 90 35.8 45.3 184 Watlings Blue Hole 34.5 21.6 32.6 17.2 290 Wild Dilly Pond 52.2 34.5 29.5 26.2 188

Lake Total Fe Mn Na K Ca 176

(mg/L) (mg/L) (mg/L) (mg/L) (mg/L) Bamboo Pond 0.006 0.08 11080 432 381.5 Blue Hole #5 0.006 0.019 5881 305.8 252.1 Central Storr's 0.160 0.010 14590 696.3 537.5 Clear Pond 0.054 0.020 10290 505.6 365.7 Columbus Landing Blue Hole 0.032 0.007 6422 320.7 239.2 Crescent Pond 0.055 0.041 10800 508.7 275.8 Flamingo 0.006 0.000 22700 721.0 898.5 French Pond 0.234 0.037 15110 744.3 667.8 Granny 0.006 0.000 19170 625.0 596.8 Great Lake 0.006 0.000 20780 772.0 603.3 Little Lake 0.124 0.018 10130 532 389.3 Little Salt Pond 0.006 0.030 83250 4093 1068.0 Long Lake 0.006 0.000 17250 571 630.1 Mermaid Pond 0.006 0.000 10760 317 381.6 Moon Rock Pond 0.119 0.019 10560 509 368.4 Nasty Pond 0.253 0.079 11250 528.5 480.3 New Blue Hole 0.006 0.000 7465 200.0 230.7 No Name 0.484 0.162 31370 1057 1017 North Storr's 0.193 0.038 15150 742.8 616.2 Osprey Lake 0.207 0.022 14890 733.2 536.0 Oyster Pond 0.101 0.004 11030 522.3 383.7 Pain Pond 0.121 0.013 14840 732.6 559.7 Peel 0.650 0.060 90360 3911.0 1536.0 Plantation Pond 0.058 0.020 10830 549.6 410.0 Reckley Hill Settlement Pond 0.177 0.027 13690 656.5 479.7 Salt Pond 0.644 0.116 24960 1174 1978 Six Pack 0.006 0.000 15970 471 381.2 South Storr's 0.354 0.074 39200 1350 2184.0 Stouts 0.006 0.000 35570 1506 432 Triangle Pond 0.267 0.048 18660 883.1 601.2 Watlings Blue Hole 0.060 0.006 6311 333.8 251.2 Wild Dilly Pond 0.037 0.020 10510 504.8 381.8

177

Mean Mg Sr Cl SO4 Grain Size Lake (mg/L) (mg/L) (mg/L) (mg/L) (μm) Bamboo Pond 1029.0 13.6 17060 2430 25.622136 Blue Hole #5 697.2 14.6 9010 1302 189.23477 Central Storr's 1930.0 22.4 27140 3737 149.48356 Clear Pond 1261.0 14.3 17660 2470 141.62195 Columbus Landing Blue Hole 747.8 12.6 11070 1419 179.17626 Crescent Pond 1326.0 17.3 17400 2555 38.792055 Flamingo 2761.0 28.3 38260 4680 103.13339 French Pond 1872.0 58.2 28730 3259 40.278019 Granny 1709.0 41.9 32510 4270 102.5714 Great Lake 2051.0 20.0 35640 4430 230.5701 Little Lake 1392.0 16.5 17980 2773 180.97129 Little Salt Pond 5261.0 60.4 175690 14930 37.579387 Long Lake 2104.0 20.8 29970 4020 129.96871 Mermaid Pond 1225.0 13.1 16860 2290 107.5088 Moon Rock Pond 1319.0 15.8 17240 2586 28.965886 Nasty Pond 1414.0 21.2 18970 2742 90.485074 New Blue Hole 577.3 8.1 11400 1710 125.35573 No Name 3909.0 36.7 53910 6590 56.055472 North Storr's 2048.0 24.4 28900 3997 142.47506 Osprey Lake 1989.0 19.1 28390 3912 197.89986 Oyster Pond 1373.0 16.4 18580 2767 199.99145 Pain Pond 1965.0 18.6 27730 3852 87.727795 Peel 4682.0 50.9 180020 17450 38.312305 Plantation Pond 1358.0 14.7 18710 2730 93.721647 Reckley Hill Pond 1756.0 19.7 24220 3481 124.71989 Salt Pond 3835.0 53.2 58100 7780 178.81881 Six Pack 1128.0 17.5 25210 3350 189.90664 South Storr's 4925.0 51.5 72325 10000 75.632343 Stouts 1424.0 12.2 65020 7600 111.86344 Triangle Pond 2690.0 17.0 37760 5162 90.79782 Watlings Blue Hole 782.0 11.7 4270 1497 57.229201 Wild Dilly Pond 1316.0 17.6 17840 2476 176.71285

178

Cyprideis Perissocytheridea Hemicyprideis Dolerocypris americana bicelliforma setipunctata inopinata Lake (count) (count) (count) (count) Bamboo Pond 24 74 0 0 Blue Hole #5 105 2 114 16 Central Storr's 264 0 0 0 Clear Pond 136 15 135 43 Columbus Landing Blue Hole 26 0 321 0 Crescent Pond 12 0 0 11 Flamingo 214 0 0 29 French Pond 127 61 0 0 Granny 92 0 6 26 Great Lake 47 6 53 116 Little Lake 26 9 88 122 Little Salt Pond 7 239 0 0 Long Lake 53 4 143 48 Mermaid Pond 40 2 110 21 Moon Rock Pond 0 0 137 0 Nasty Pond 232 35 0 0 New Blue Hole 190 16 126 0 No Name 85 166 2 0 North Storr's 186 2 0 0 Osprey Lake 75 4 143 44 Oyster Pond 25 2 589 32 Pain Pond 50 15 52 11 Peel 157 55 0 0 Plantation Pond 105 32 42 1 Reckley Hill Settlement Pond 165 11 38 11 Salt Pond 22 225 0 0 Six Pack 87 14 67 32 South Storr's 129 0 0 0 Stouts 21 0 134 40 Triangle Pond 121 12 0 0 Watlings Blue Hole 121 2 614 1 Wild Dilly Pond 39 0 45 16

179

Reticulocythereis Cytherella Loxonchoncha Xestoleberis multicarinata arostrata pursubrhomboidea curassavica Lake (count) (count) (count) (count) Bamboo Pond 0 0 0 0 Blue Hole #5 0 0 0 0 Central Storr's 0 0 0 0 Clear Pond 21 0 0 3 Columbus Landing Blue Hole 0 0 0 0 Crescent Pond 0 0 0 0 Flamingo 0 0 0 2 French Pond 0 0 0 0 Granny 7 0 0 61 Great Lake 3 0 4 119 Little Lake 8 0 6 83 Little Salt Pond 0 0 0 0 Long Lake 8 0 6 90 Mermaid Pond 15 0 30 69 Moon Rock Pond 19 10 14 57 Nasty Pond 0 0 0 0 New Blue Hole 0 0 0 0 No Name 0 0 0 0 North Storr's 0 0 0 0 Osprey Lake 34 0 15 107 Oyster Pond 8 2 50 8 Pain Pond 10 2 5 2 Peel 0 0 0 0 Plantation Pond 0 0 0 0 Reckley Hill Settlement Pond 0 0 0 0 Salt Pond 0 0 0 0 Six Pack 24 0 0 88 South Storr's 0 0 0 0 Stouts 15 0 193 73 Triangle Pond 0 0 0 0 Watlings Blue Hole 0 0 0 0 Wild Dilly Pond 33 3 86 2

180

Aurila Paranesidea Physocypria floridana harpago denticulata Lake (count) (count) (count) Bamboo Pond 0 0 240 Blue Hole #5 0 0 0 Central Storr's 0 0 0 Clear Pond 0 0 0 Columbus Landing Blue Hole 0 0 0 Crescent Pond 0 0 0 Flamingo 0 0 0 French Pond 0 0 0 Granny 8 0 0 Great Lake 7 4 0 Little Lake 10 2 0 Little Salt Pond 0 0 0 Long Lake 24 0 0 Mermaid Pond 0 0 0 Moon Rock Pond 265 6 0 Nasty Pond 0 0 0 New Blue Hole 0 0 3 No Name 0 0 0 North Storr's 0 0 0 Osprey Lake 28 0 0 Oyster Pond 0 13 0 Pain Pond 0 0 0 Peel 0 0 0 Plantation Pond 0 0 0 Reckley Hill Settlement Pond 0 0 0 Salt Pond 0 0 0 Six Pack 0 0 0 South Storr's 0 0 0 Stouts 20 0 0 Triangle Pond 0 0 0 Watlings Blue Hole 0 0 0 Wild Dilly Pond 0 2 0

181

APPENDIX D

FOSSIL OSTRCODE DATA FROM SALT POND CORE

Cyprideis Perissocytheridea Hemicyprideis Dolerocypris americana bicelliforma setipunctata inopinata Depth (cm) (percent) (percent) (percent) (percent) 0.5 2 98 0 0 1.5 43 14 0 43 2.5 55 0 0 45 3.5 25 67 0 8 4.5 100 0 0 0 5.5 40 55 1 4 6.5 25 64 1 10 7.5 6 87 1 6 8.5 25 49 1 25 9.5 49 39 0 12 10.5 68 19 2 12 11.5 88 0 6 6 12.5 72 4 3 21 13.5 20 37 2 42 14.5 75 21 0 4 15.5 75 21 0 4 16.5 25 75 0 0 17.5 10 89 1 0 18.5 26 74 0 0 19.5 27 73 0 0 20.5 24 76 0 0 21.5 47 53 0 0 22.5 13 87 0 0 23.5 63 38 0 0 24.5 83 17 0 0 25.5 29 71 0 0 26.5 90 3 0 7 27.5 13 87 0 0 28.5 100 0 0 0 29.5 7 88 0 5 30.5 5 94 0 1 31.5 86 14 0 0 32.5 97 3 0 0 33.5 88 7 0 5 34.5 30 49 0 21 35.5 29 54 0 16 36.5 50 40 0 11 182

37.5 100 0 0 0 38.5 11 88 0 1 39.5 68 18 0 14 40.5 67 33 0 0 41.5 100 0 0 0 42.5 92 8 0 0 43.5 5 95 0 0 44.5 90 8 0 3 45.5 51 41 0 8 46.5 18 82 0 0 47.5 70 30 0 0 48.5 52 48 0 0 49.5 21 79 0 0 50.5 98 2 0 0 51.5 47 53 0 0 52.5 29 71 0 0 53.5 93 6 1 0 54.5 28 72 0 0 55.5 93 7 1 0 56.5 88 13 0 0 57.5 32 68 0 0 58.5 89 0 0 11 59.5 18 82 0 0 60.5 16 84 0 0

183

APPENDIX E

FOSSIL OSTRCODE DATA FROM CLEAR POND CORE

Cyprideis Perissocytheridea Hemicyprideis Dolerocypris Reticulocythereis americana bicelliforma setipunctata inopinata multicarinata Depth (cm) (percent) (percent) (percent) (percent) (percent) 0.5 44 6 28 19 3 1.5 23 0 73 4 0 2.5 35 6 59 0 0 3.5 71 5 24 0 0 4.5 25 9 35 27 4 5.5 26 16 21 37 0 6.5 64 0 27 9 0 7.5 42 0 25 33 0 8.5 36 0 18 36 9 9.5 65 0 6 29 0 10.5 61 11 0 28 0 11.5 44 6 11 39 0 12.5 67 0 17 17 0 13.5 50 5 35 10 0 14.5 64 9 9 18 0 15.5 73 0 0 27 0 16.5 56 6 13 25 0 17.5 71 7 0 21 0 18.5 69 3 17 10 0 19.5 69 0 23 8 0 20.5 64 0 18 18 0 21.5 83 17 0 0 0 22.5 62 15 15 8 0 23.5 75 13 13 0 0 24.5 84 3 6 6 0 25.5 85 0 8 8 0 26.5 96 0 0 4 0 27.5 90 0 10 0 0 28.5 86 0 14 0 0 29.5 72 0 28 0 0 30.5 89 0 11 0 0 31.5 100 0 0 0 0 32.5 88 0 12 0 0 33.5 96 0 4 0 0 34.5 89 0 11 0 0 35.5 100 0 0 0 0 36.5 100 0 0 0 0 184

37.5 100 0 0 0 0 38.5 88 0 13 0 0 39.5 100 0 0 0 0 40.5 88 0 13 0 0 41.5 79 0 21 0 0 42.5 89 0 11 0 0 43.5 100 0 0 0 0 44.5 78 22 0 0 0 45.5 100 0 0 0 0 46.5 67 33 0 0 0 47.5 71 29 0 0 0 48.5 76 24 0 0 0 49.5 90 10 0 0 0 50.5 86 11 4 0 0 51.5 86 11 4 0 0 52.5 100 0 0 0 0 53.5 92 0 8 0 0 54.5 96 4 0 0 0 55.5 100 0 0 0 0 56.5 72 6 22 0 0 57.5 71 6 23 0 0 58.5 68 24 8 0 0 59.5 67 33 0 0 0 60.5 94 6 0 0 0 61.5 90 10 0 0 0 62.5 91 9 0 0 0 63.5 88 12 0 0 0 64.5 86 11 4 0 0 65.5 94 6 0 0 0 66.5 95 5 0 0 0 67.5 89 11 0 0 0 68.5 78 16 7 0 0 69.5 84 14 2 0 0 70.5 76 12 0 0 12 71.5 96 4 0 0 0 72.5 91 9 0 0 0 73.5 77 14 9 0 0 74.5 97 3 0 0 0 75.5 96 3 1 0 0 76.5 96 0 4 0 0 77.5 100 0 0 0 0 78.5 92 8 0 0 0 79.5 97 3 0 0 0 80.5 91 3 6 0 0 81.5 92 8 0 0 0 82.5 100 0 0 0 0 83.5 100 0 0 0 0 84.5 100 0 0 0 0 85.5 100 0 0 0 0 185

86.5 92 8 0 0 0 87.5 64 27 9 0 0 88.5 90 10 0 0 0 89.5 67 33 0 0 0 90.5 88 8 4 0 0 91.5 75 13 0 13 0 92.5 86 14 0 0 0 93.5 25 44 13 19 0 94.5 48 12 2 38 0 95.5 34 28 6 31 0 96.5 59 15 10 15 0 97.5 65 13 11 11 0 98.5 81 11 4 4 0 99.5 58 21 5 16 0 100.5 77 23 0 0 0 101.5 64 28 4 0 4 102.5 75 19 0 6 0 103.5 67 17 17 0 0 104.5 54 43 4 0 0 105.5 86 14 0 0 0 106.5 82 18 0 0 0 107.5 61 39 0 0 0 108.5 75 25 0 0 0 109.5 50 33 0 0 17 110.5 78 22 0 0 0 111.5 38 50 0 13 0 112.5 31 62 0 0 8 113.5 39 36 11 0 14 114.5 67 20 7 0 7 115.5 64 24 4 0 8 116.5 65 27 0 4 4 117.5 65 17 13 0 4 118.5 67 8 17 0 8 119.5 83 7 2 0 7 120.5 58 19 4 4 15 121.5 59 35 0 0 6 122.5 56 29 6 0 9

186

APPENDIX F

FOSSIL OSTRCODE DATA FROM NORTH STORRS CORE

Cyprideis Perissocytheridea Hemicyprideis Dolerocypris Reticulocythereis americana bicelliforma setipunctata inopinata multicarinata Depth (cm) (percent) (percent) (percent) (percent) (percent) 0.5 100 0 0 0 0 1.5 100 0 0 0 0 2.5 100 0 0 0 0 3.5 100 0 0 0 0 4.5 100 0 0 0 0 5.5 100 0 0 0 0 6.5 100 0 0 0 0 7.5 100 0 0 0 0 8.5 100 0 0 0 0 9.5 100 0 0 0 0 10.5 100 0 0 0 0 11.5 66.66666667 0 0 33.33333333 0 12.5 100 0 0 0 0 13.5 100 0 0 0 0 14.5 100 0 0 0 0 15.5 100 0 0 0 0 16.5 100 0 0 0 0 17.5 100 0 0 0 0 18.5 100 0 0 0 0 19.5 100 0 0 0 0 20.5 100 0 0 0 0 21.5 100 0 0 0 0 22.5 100 0 0 0 0 23.5 50 50 0 0 0 24.5 100 0 0 0 0 25.5 50 0 0 50 0 26.5 100 0 0 0 0 27.5 100 0 0 0 0 28.5 100 0 0 0 0 29.5 50 50 0 0 0 30.5 66.66666667 33.33333333 0 0 0 31.5 100 0 0 0 0 32.5 11.11111111 88.88888889 0 0 0 33.5 66.66666667 0 0 0 0 34.5 60 40 0 0 0 35.5 100 0 0 0 0 36.5 33.33333333 66.66666667 0 0 0 187

37.5 0 100 0 0 0 38.5 100 0 0 0 0 39.5 75 25 0 0 0 40.5 100 0 0 0 0 41.5 66.66666667 33.33333333 0 0 0 42.5 64.28571429 35.71428571 0 0 0 43.5 16.66666667 33.33333333 0 0 0 44.5 0 0 0 0 0 45.5 33.33333333 66.66666667 0 0 0 46.5 0 0 0 0 0 47.5 66.66666667 33.33333333 0 0 0 48.5 0 0 0 0 0 49.5 0 0 0 0 0 50.5 0 0 0 0 0 51.5 0 100 0 0 0 52.5 0 0 0 0 0 53.5 0 0 0 0 0 54.5 0 0 0 0 0 55.5 0 0 0 0 0 56.5 0 0 0 0 0 57.5 0 0 0 0 0 58.5 0 0 0 0 0 59.5 0 0 0 0 0 60.5 0 0 0 0 0 61.5 0 0 0 0 0 62.5 0 0 0 0 0 63.5 0 0 0 0 0 64.5 0 0 0 0 0 65.5 0 0 0 0 0 66.5 0 0 0 0 0 67.5 0 0 0 0 0 68.5 0 0 0 0 0 69.5 0 0 0 0 0 70.5 0 0 0 0 0 71.5 0 0 100 0 0 72.5 0 0 0 0 0 73.5 0 0 0 0 0 74.5 0 0 0 0 0 75.5 0 0 0 0 0 76.5 0 0 0 0 0 77.5 0 0 0 0 0 78.5 0 0 0 0 0 79.5 0 0 0 0 0 80.5 14.28571429 0 85.71428571 0 0 81.5 0 0 0 0 0 82.5 0 0 0 0 0 83.5 0 0 0 0 0 84.5 50 0 50 0 0 85.5 0 0 0 0 0 188

86.5 0 0 0 0 0 87.5 0 0 0 0 0 88.5 0 0 0 0 0 89.5 0 0 0 0 0 90.5 0 0 0 0 0 91.5 0 0 0 0 0 92.5 0 0 0 0 0 93.5 0 0 0 0 0 94.5 0 0 0 0 0 95.5 0 0 0 0 0 96.5 0 0 0 0 0 97.5 0 0 0 0 0 98.5 0 0 0 0 0 99.5 0 0 0 0 0 100.5 66.66666667 0 33.33333333 0 0 101.5 0 0 0 0 0 102.5 0 0 0 0 0 103.5 0 0 0 0 0 104.5 0 0 0 0 0 105.5 0 0 0 0 0 106.5 0 0 0 0 0 107.5 0 0 0 0 0 108.5 0 0 0 0 0 109.5 0 0 0 0 0 110.5 0 0 0 0 0 111.5 0 0 0 0 0 112.5 0 0 0 0 0 113.5 100 0 0 0 0 114.5 0 0 0 100 0 115.5 100 0 0 0 0 116.5 0 0 0 100 0 117.5 10 20 0 70 0 118.5 0 100 0 0 0 119.5 0 0 0 100 0 120.5 50 0 21.42857143 28.57142857 0 121.5 66.66666667 0 16.66666667 16.66666667 0 122.5 11.11111111 0 33.33333333 55.55555556 0 123.5 24.76190476 2.857142857 51.42857143 20.95238095 0 124.5 14.44444444 0 58.88888889 20 0 125.5 9.302325581 1.550387597 42.63565891 44.18604651 0.775193798 126.5 1.183431953 0 59.17159763 38.46153846 0 127.5 0.909090909 0 89.09090909 9.090909091 0.909090909 128.5 4.301075269 1.075268817 3.225806452 91.39784946 0 129.5 29.57746479 1.408450704 19.71830986 49.29577465 0 130.5 43.20987654 4.938271605 25.92592593 24.69135802 0 131.5 46.77419355 3.225806452 6.451612903 43.5483871 0 132.5 53.57142857 0 7.142857143 39.28571429 0 133.5 50 6.25 0 43.75 0 134.5 0 50 0 50 0 189

135.5 47.05882353 0 5.882352941 47.05882353 0 136.5 22.22222222 11.11111111 0 66.66666667 0 137.5 9.677419355 0 6.451612903 83.87096774 0 138.5 47.61904762 9.523809524 9.523809524 33.33333333 0 139.5 0 66.66666667 0 33.33333333 0 140.5 13.33333333 6.666666667 6.666666667 60 0 141.5 25 25 0 50 0 142.5 0 0 0 100 0 143.5 0 0 0 0 0 144.5 9.090909091 0 0 36.36363636 0 145.5 0 0 0 0 0 146.5 11.11111111 0 0 22.22222222 0 147.5 0 0 0 33.33333333 0 148.5 0 0 0 0 0 149.5 0 0 0 0 0 150.5 0 0 0 0 0 151.5 0 0 0 0 0 152.5 0 0 0 0 0 153.5 0 0 0 0 0 154.5 0 25 0 0 0 155.5 0 0 0 25 0 156.5 0 0 0 0 0 157.5 0 0 0 0 0 158.5 0 0 0 0 0 159.5 0 0 0 0 0 160.5 0 0 0 0 0 161.5 0 0 0 0 0

190

Cytherella Xestoleberis Aurila Paranesidea arostrata curassavica floridana harpago Depth (cm) (percent) (percent) (percent) (percent) 0.5 0 0 0 0 1.5 0 0 0 0 2.5 0 0 0 0 3.5 0 0 0 0 4.5 0 0 0 0 5.5 0 0 0 0 6.5 0 0 0 0 7.5 0 0 0 0 8.5 0 0 0 0 9.5 0 0 0 0 10.5 0 0 0 0 11.5 0 0 0 0 12.5 0 0 0 0 13.5 0 0 0 0 14.5 0 0 0 0 15.5 0 0 0 0 16.5 0 0 0 0 17.5 0 0 0 0 18.5 0 0 0 0 19.5 0 0 0 0 20.5 0 0 0 0 21.5 0 0 0 0 22.5 0 0 0 0 23.5 0 0 0 0 24.5 0 0 0 0 25.5 0 0 0 0 26.5 0 0 0 0 27.5 0 0 0 0 28.5 0 0 0 0 29.5 0 0 0 0 30.5 0 0 0 0 31.5 0 0 0 0 32.5 0 0 0 0 33.5 0 33.33333333 0 0 34.5 0 0 0 0 35.5 0 0 0 0 36.5 0 0 0 0 37.5 0 0 0 0 38.5 0 0 0 0 39.5 0 0 0 0 40.5 0 0 0 0 41.5 0 0 0 0 42.5 0 0 0 0 43.5 0 0 50 0 44.5 0 0 0 0 45.5 0 0 0 0

191

46.5 0 0 0 0 47.5 0 0 0 0 48.5 0 0 0 0 49.5 0 0 0 0 50.5 0 0 0 0 51.5 0 0 0 0 52.5 0 0 0 0 53.5 0 0 0 0 54.5 0 0 0 0 55.5 0 0 0 0 56.5 0 0 0 0 57.5 0 0 0 0 58.5 0 0 0 0 59.5 0 0 0 0 60.5 0 0 0 0 61.5 0 0 0 0 62.5 0 0 0 0 63.5 0 0 0 0 64.5 0 0 0 0 65.5 0 0 0 0 66.5 0 0 0 0 67.5 0 0 0 0 68.5 0 0 0 0 69.5 0 0 0 0 70.5 0 0 0 0 71.5 0 0 0 0 72.5 0 0 0 0 73.5 0 0 0 0 74.5 0 0 0 0 75.5 0 0 0 0 76.5 0 0 0 0 77.5 0 0 0 0 78.5 0 0 0 0 79.5 0 0 0 0 80.5 0 0 0 0 81.5 0 0 0 0 82.5 0 0 0 0 83.5 0 0 0 0 84.5 0 0 0 0 85.5 0 0 0 0 86.5 0 0 0 0 87.5 0 0 0 0 88.5 0 0 0 0 89.5 0 0 0 0 90.5 0 0 0 0 91.5 0 0 0 0 92.5 0 0 0 0 93.5 0 0 0 0 94.5 0 0 0 0 192

95.5 0 0 0 0 96.5 0 0 0 0 97.5 0 0 0 0 98.5 0 0 0 0 99.5 0 0 0 0 100.5 0 0 0 0 101.5 0 0 0 0 102.5 0 0 0 0 103.5 0 0 0 0 104.5 0 0 0 0 105.5 0 0 0 0 106.5 0 0 0 0 107.5 0 0 0 0 108.5 0 0 100 0 109.5 0 0 0 0 110.5 0 0 0 0 111.5 0 0 0 0 112.5 0 0 0 0 113.5 0 0 0 0 114.5 0 0 0 0 115.5 0 0 0 0 116.5 0 0 0 0 117.5 0 0 0 0 118.5 0 0 0 0 119.5 0 0 0 0 120.5 0 0 0 0 121.5 0 0 0 0 122.5 0 0 0 0 123.5 0 0 0 0 124.5 0 2.222222222 4.444444444 0 125.5 0 1.550387597 0 0 126.5 0 0 1.183431953 0 127.5 0 0 0 0 128.5 0 0 0 0 129.5 0 0 0 0 130.5 1.234567901 0 0 0 131.5 0 0 0 0 132.5 0 0 0 0 133.5 0 0 0 0 134.5 0 0 0 0 135.5 0 0 0 0 136.5 0 0 0 0 137.5 0 0 0 0 138.5 0 0 0 0 139.5 0 0 0 0 140.5 0 0 0 0 141.5 0 0 0 0 142.5 0 0 0 0 143.5 0 0 0 0 193

144.5 0 36.36363636 0 0 145.5 0 100 0 0 146.5 0 22.22222222 0 0 147.5 0 0 0 0 148.5 0 0 0 0 149.5 0 0 0 0 150.5 0 0 0 11.11111111 151.5 0 0 0 0 152.5 0 0 0 0 153.5 0 0 0 42.85714286 154.5 0 0 0 12.5 155.5 0 0 0 25 156.5 0 0 0 0 157.5 0 0 0 22.22222222 158.5 0 0 0 0 159.5 0 0 0 0 160.5 0 0 0 0 161.5 0 0 0 0

194

APPENDIX G

PHYSICAL DATA FROM SALT POND CORE

Depth (cm) % Organic Matter % Carbonate % Sand 0.5 20.26913373 24.22203532 17.18 1.5 11.97703035 22.47744053 10.78 2.5 11.97183099 15.66901408 15.39 3.5 14.15441176 15.44117647 19.65 4.5 13.63636364 19.94434137 23.87 5.5 15.79476861 27.56539235 10.1 6.5 14.46028513 32.99389002 6.82 7.5 14.71652593 33.17249698 4.46 8.5 12.21001221 32.47863248 5.56 9.5 10.98039216 35.58823529 6.54 10.5 5.777166437 36.93259972 66.41 11.5 15.10729614 25.40772532 40.85 12.5 10.40061633 24.8844376 18.59 13.5 8.786936236 25.58320373 54.75 14.5 10.71729958 14.17721519 14.87 15.5 10.290652 17.83189317 62.32 16.5 9.865092749 15.59865093 24.91 17.5 10.94170404 23.13901345 35.32 18.5 16.97916667 26.14583333 6.17 19.5 13.46830986 31.42605634 6.41 20.5 9.794238683 23.04526749 31.37 21.5 12.40242845 19.42758023 52.78 22.5 12.64274062 22.18597064 13.83 23.5 9.202453988 20.77125329 35.34 24.5 13.67313916 11.08414239 18.94 25.5 16.02895553 23.47466391 32.91 26.5 8.92053973 21.13943028 38.41 27.5 9.710258418 21.5348473 65.14 28.5 10.29411765 19.44444444 30.47 29.5 11.34453782 10.50420168 63.39 30.5 13.39869281 12.99019608 26.6 31.5 11.45662848 21.19476268 43.08 32.5 100 0 9.19 195

33.5 -4.84496124 25.87209302 6.24 34.5 21.5320911 32.40165631 2.43 35.5 -6.841505131 45.15393387 10.9 36.5 -4.052780396 16.21112158 12.93 37.5 23.36065574 20.6557377 50.5 38.5 2.406159769 16.36188643 8.93 39.5 35.16386182 15.67759079 42.62 40.5 -9.462616822 24.64953271 11.45 41.5 2.561669829 15.65464896 38.25 42.5 6.272084806 18.99293286 46.13 43.5 18.89830508 24.57627119 33.3 44.5 14.41860465 35.44186047 28.4 45.5 7.907425265 31.72613308 13.3 46.5 3.717472119 26.57992565 11.92 47.5 16.93617021 20 36.52 48.5 100 0 14.97 49.5 12.5 30.74074074 19.23 50.5 11.26126126 33.06306306 37.44 51.5 12.25364182 16.88089117 42.88 52.5 10.03490401 14.22338569 26.72 53.5 13.32644628 26.75619835 26.98 54.5 13.85416667 27.8125 7.57 55.5 16.69960474 23.02371542 14.53 56.5 16.73819742 20.42918455 5.69 57.5 16.80194805 26.70454545 7.41 58.5 18.55955679 27.1468144 1.57 59.5 22.03567681 29.06610703 3.87

196

APPENDIX H

PHYSICAL DATA FROM CLEAR POND CORE

Depth (cm) % Organic Matter % Carbonate % Sand 0.5 46.11111111 5.277777778 73.562081 1.5 46.61508704 3.094777563 64.279088 2.5 46.50205761 3.086419753 68.919291 3.5 45.92445328 2.982107356 63.03999 4.5 45.47368421 4 70.85499 5.5 45.80896686 2.923976608 60.332263 6.5 45.84070796 2.300884956 64.235901 7.5 45.45454545 1.96969697 69.321534 8.5 45.20766773 1.597444089 69.481439 9.5 44.96551724 1.655172414 59.71282 10.5 45.18072289 1.325301205 30.897774 11.5 44.78527607 1.595092025 81.487895 12.5 44.75218659 1.020408163 64.015675 13.5 44.88977956 1.402805611 34.795249 14.5 44.92900609 1.825557809 45.479352 15.5 44.78527607 1.329243354 62.099553 16.5 44.78897502 1.378122308 52.933608 17.5 45.16806723 1.890756303 64.67255 18.5 44.71992654 1.469237833 52.227525 19.5 44.94219653 2.74566474 51.121567 20.5 45.3125 2.232142857 44.321638 21.5 45.42410714 1.897321429 27.130938 22.5 46.73539519 2.405498282 26.356041 23.5 45.49918167 3.109656301 56.726311 24.5 45.26315789 2.556390977 67.882311 25.5 44.89795918 1.913265306 35.0399 26.5 44.72769409 1.853997683 67.057995 27.5 44.66750314 1.631116688 64.762076 28.5 45.49763033 3.001579779 68.451988 29.5 45.57823129 3.628117914 67.374017 30.5 45.11434511 4.781704782 61.941957 31.5 45.49450549 3.736263736 57.241747

197

32.5 45.65701559 4.231625835 75.389901 33.5 45.78059072 3.58649789 61.482749 34.5 45.35104364 4.933586338 51.855924 35.5 44.96124031 4.069767442 53.751805 36.5 44.9197861 4.991087344 52.203702 37.5 44.9122807 4.736842105 63.81116 38.5 45.24959742 3.8647343 62.150019 39.5 45.05862647 4.020100503 53.617667 40.5 14.98559078 30.47550432 64.640571 41.5 32.5203252 15.24390244 66.638163 42.5 39.05511811 9.133858268 47.676802 43.5 40.32549729 9.222423146 44.926357 44.5 31.14754098 16.93989071 53.159967 45.5 43.04029304 5.860805861 59.726991 46.5 45.02712477 4.701627486 58.427044 47.5 44.85981308 3.894080997 64.196505 48.5 44.50171821 4.982817869 56.73399 49.5 38.8004896 7.466340269 71.222674 50.5 43.99421129 3.762662808 61.427856 51.5 43.99323181 4.060913706 56.224179 52.5 43.75772559 2.719406675 62.273027 53.5 40.88983051 4.766949153 87.439343 54.5 39.16423712 6.802721088 89.146429 55.5 43.82911392 4.905063291 64.097194 56.5 43.78531073 3.672316384 68.220153 57.5 44.17670683 3.012048193 69.143463 58.5 43.61413043 3.39673913 36.147088 59.5 44.46254072 4.071661238 43.537996 60.5 44.59459459 4.054054054 50.645052 61.5 44.99252616 3.587443946 54.776979 62.5 44.72049689 3.726708075 59.761168 63.5 44.40497336 5.150976909 66.980259 64.5 20.6943499 24.64261402 79.938855 65.5 44.51827243 4.318936877 69.447196 66.5 45.09803922 4.411764706 65.000304 67.5 44.40619621 5.507745267 57.141032 68.5 25.5 24.5 54.581395 69.5 25.26158445 23.01943199 55.026426 70.5 27.97704448 20.08608321 62.966012 71.5 30.43478261 18.56187291 52.818506 72.5 20 28.26446281 54.540345 73.5 14.70985155 33.73819163 50.019764 198

74.5 44.97681607 2.627511592 49.240518 75.5 44.68085106 2.127659574 70.036208 76.5 45.27439024 2.743902439 72.627567 77.5 46.20253165 3.797468354 65.495137 78.5 45.1183432 2.662721893 39.300791 79.5 44.87179487 2.421652422 44.873436 80.5 45.34534535 3.303303303 47.447188 81.5 44.90674319 2.869440459 52.53032 82.5 44.15954416 2.184235518 64.04236 83.5 45.10869565 2.065217391 51.129737 84.5 44.55172414 3.724137931 66.925366 85.5 43.50649351 4.545454545 51.965369 86.5 44.41964286 3.125 43.21385 87.5 44.02877698 2.158273381 67.613456 88.5 43.89937107 2.893081761 62.74576 89.5 44.33453237 1.888489209 59.03801 90.5 44.36997319 2.949061662 66.208161 91.5 44.82269504 4.113475177 59.53778 92.5 44.48529412 3.553921569 58.249932 93.5 44.04624277 2.89017341 57.630377 94.5 43.08617234 4.609218437 57.613389 95.5 43.80264741 2.647412756 54.798749 96.5 43.45323741 3.021582734 58.927972 97.5 42.06896552 4.022988506 51.362202 98.5 34.53815261 12.31593039 56.29634 99.5 38.6627907 9.302325581 60.026246 100.5 42.88617886 7.113821138 48.534214 101.5 30.47858942 16.62468514 56.044467 102.5 43.66666667 2.555555556 55.80436 103.5 43.65821095 2.803738318 39.751195 104.5 44.01913876 2.153110048 35.101096 105.5 44.38356164 2.876712329 48.07701 106.5 44.08014572 3.096539162 46.713075 107.5 43.6827957 2.553763441 42.627847 108.5 43.58974359 3.353057199 48.57965 109.5 44.70404984 2.024922118 41.372904 110.5 44.96567506 2.402745995 48.967269 111.5 44.66666667 1.80952381 47.209807 112.5 44.61181924 2.549246813 58.494747 113.5 44.61045891 2.241195304 51.721194 114.5 44.71299094 2.265861027 56.358835 115.5 44.04761905 2.678571429 53.26156 199

116.5 43.847487 2.59965338 46.304547 117.5 43.67541766 2.625298329 48.092154 118.5 43.83116883 2.272727273 51.538817 119.5 41.35338346 4.511278195 51.69641 120.5 38.55165069 7.667731629 54.256564 121.5 28.42227378 18.21345708 44.290934 122.5 30.3172738 16.45123384 49.846631

200

APPENDIX I

PHYSICAL DATA FROM NORTH STORRS CORE

Depth (cm) % Organic Matter % Carbonate % Sand 0.5 42.11576846 24.5508982 1.5 38.64541833 25.89641434 2.5 37.62376238 26.98019802 3.5 40.81632653 24.19825073 4.5 40.27972028 26.15384615 5.5 36.84210526 26.6002845 6.5 44.29429429 22.67267267 7.5 49.61538462 20.64102564 70.283584 8.5 50.55401662 19.66759003 9.5 45.67375887 22.69503546 72.683211 10.5 47.1636953 21.88006483 11.5 41.86746988 24.69879518 70.385266 12.5 51.15089514 20.20460358 13.5 43.68811881 23.76237624 73.029298 14.5 50.51150895 20.71611253 15.5 41.58767773 25.71090047 77.396803 16.5 45.42897328 21.94092827 17.5 51.27819549 20.60150376 73.971798 18.5 52.71966527 18.82845188 19.5 49.00398406 19.7875166 72.2677 20.5 48.62804878 22.25609756 21.5 50.22900763 20.61068702 70.763131 22.5 42.03910615 24.44134078 23.5 51.46666667 20.66666667 64.884567 24.5 50.21276596 18.58156028 25.5 49.08256881 19.72477064 70.456935 26.5 46.1212976 20.31029619 27.5 46.96969697 22.03856749 62.161513 28.5 49.74093264 21.11398964 29.5 44.12903226 23.61290323 60.356571 30.5 42.44833068 24.96025437 31.5 35.9832636 25.80195258 69.538662

201

32.5 48.50065189 21.64276402 33.5 44.21052632 23.94736842 51.577302 34.5 45.25810324 23.04921969 35.5 13.19910515 38.03131991 67.176681 36.5 14.00437637 36.3238512 37.5 15 37.17391304 58.12029 38.5 13.75770021 38.6036961 39.5 14.60674157 38.20224719 76.090624 40.5 12.71929825 37.93859649 41.5 10.26058632 39.57654723 63.594784 42.5 13.96508728 35.91022444 43.5 12.97709924 38.74045802 70.806287 44.5 10.79136691 39.02877698 45.5 6.117353308 40.69912609 61.408142 46.5 5.089408528 41.40302613 47.5 5.462184874 40.86134454 69.254907 48.5 12.68656716 38.05970149 49.5 14.49579832 37.60504202 66.718593 50.5 12.6 38.8 51.5 10.31613977 39.60066556 53.932732 52.5 9.400544959 38.96457766 53.5 14.04399323 37.05583756 59.999174 54.5 11.78247734 38.21752266 55.5 7.008086253 22.77628032 39.935965 56.5 8.607021518 39.07134768 57.5 20.77562327 26.59279778 77.849661 58.5 8.781362007 18.27956989 59.5 4.387990762 19.16859122 84.734666 60.5 3.036238981 14.00587659 61.5 2.608695652 7.391304348 93.802331 62.5 7.525083612 16.22073579 63.5 4.472049689 15.77639752 77.521901 64.5 3.988326848 11.18677043 65.5 8.707482993 15.10204082 37.792996 66.5 13.13969571 30.01383126 67.5 6.976744186 14.6878825 78.103138 68.5 4.742547425 6.910569106 69.5 17.54716981 30.94339623 37.851533 70.5 2.276176024 8.497723824 71.5 4.266958425 11.70678337 81.176613 72.5 8.439306358 14.10404624 73.5 28.02197802 23.48901099 58.045656 202

74.5 19.93214589 8.651399491 75.5 19.11402789 9.762100082 70.633688 76.5 20.36697248 12.66055046 77.5 29.20918367 22.95918367 64.221461 78.5 19.35483871 12.61859583 79.5 27.30263158 22.03947368 47.437907 80.5 15.85714286 15.71428571 81.5 19.36720997 9.108341323 62.701797 82.5 20.12248469 7.436570429 83.5 17.16961498 15.08844953 81.184154 84.5 16.11650485 12.23300971 85.5 20.10954617 7.824726135 63.889146 86.5 19.75841242 7.592752373 87.5 31.55893536 22.43346008 79.116148 88.5 16.37744035 22.12581345 89.5 22.25672878 14.59627329 79.279784 90.5 21.77942539 9.638554217 91.5 33.68283093 19.26605505 70.019006 92.5 18.13125695 12.6807564 93.5 20.12711864 11.12288136 75.911212 94.5 20.15775635 7.624890447 95.5 20.43010753 9.76702509 73.037569 96.5 13.72912801 24.76808905 97.5 18.65443425 10.90723751 84.184484 98.5 20.61248528 5.653710247 99.5 5.658436214 18.00411523 70.210619 100.5 0 23.85321101 101.5 7.373271889 13.82488479 82.273626 102.5 6.892655367 16.61016949 89.383633 103.5 6.915477497 13.28210757 104.5 6.276150628 11.71548117 77.567064 105.5 7.665505226 21.02206736 106.5 6.718924972 29.33930571 69.493271 107.5 6.432748538 18.3625731 108.5 4.83175151 10.69887834 75.226201 109.5 11.15241636 23.17224287 110.5 7.81938326 13.43612335 75.241417 111.5 9.479166667 13.95833333 112.5 9.500959693 14.87523992 82.030861 113.5 7.818532819 7.142857143 114.5 13.85350318 38.85350318 73.829867 115.5 12.93532338 40.13266998 203

116.5 9.908536585 40.54878049 79.067597 117.5 12.5382263 38.53211009 118.5 11.61103048 40.34833091 62.771228 119.5 16.63837012 37.01188455 120.5 9.3270366 34.7107438 62.160035 121.5 4.8298573 23.27113063 122.5 9.24784217 35.26510481 65.326711 123.5 10.5914718 39.61485557 124.5 4.520990312 42.94940797 73.502471 125.5 5.36159601 43.14214464 126.5 11.71032357 38.98305085 63.183452 127.5 5.706874189 43.19066148 128.5 9.45083014 40.35759898 57.086472 129.5 11.34846462 37.65020027 130.5 10.16518424 38.11944091 74.294409 131.5 8.068783069 40.47619048 132.5 9.179926561 39.90208078 51.845664 133.5 7.319952774 39.78748524 134.5 7.926829268 40.24390244 37.875204 135.5 9.628610729 39.75240715 136.5 11.38888889 38.47222222 43.408226 137.5 11.20107962 37.9217274 138.5 11.63101604 35.29411765 40.280909 139.5 9.57309185 37.38680466 140.5 9.320905459 39.01464714 39.4298 141.5 8.396946565 41.34860051 142.5 9.98552822 39.79739508 32.458572 143.5 13.81692573 35.57858377 144.5 15.46391753 35.91065292 25.649194 145.5 22.75 52.75 146.5 7.792207792 38.57142857 24.871932 147.5 13.83537653 36.77758319 148.5 22.25201072 35.12064343 33.092591 149.5 13.71900826 37.19008264 150.5 10.56661562 38.89739663 31.371429 151.5 7.06940874 38.56041131 152.5 24.38478747 34.00447427 47.651953 153.5 9.090909091 37.1007371 154.5 9.07960199 37.68656716 34.827711 155.5 6.775956284 39.45355191 156.5 10.92043682 39.46957878 30.552966 157.5 35.23131673 27.75800712 204

158.5 33.203125 34.765625 28.66118 159.5 51.79487179 27.17948718

160.5 58.65921788 27.37430168 26.580637 161.5 60.30150754 26.63316583

205

APPENDIX J

POTASSIUM XRF DATA CLEAR POND CORE

K (XRF Depth (cm) counts) 2.502 733 3.001 933 3.501 693 4.001 628 4.501 510 5.001 501 5.501 469 6.001 608 6.501 600 7.001 428 7.501 498 8.001 467 8.501 286 9.001 317 9.501 142 10.001 53 10.501 29 11.001 163 11.501 0 12.001 0 12.501 6 13.001 0 13.501 0 14.001 0 14.501 50 15.001 0 15.501 0 16.001 0 16.501 193 17.001 361 17.501 252 18.001 286 206

18.501 0 19.001 38 19.501 0 20.001 0 20.501 0 21.001 66 21.501 228 22.001 486 22.501 296 23.001 588 23.501 644 24.001 558 24.501 546 25.001 367 25.501 435 26.001 300 26.501 182 27.001 121 27.502 441 28.001 557 28.501 102 29.001 22 29.504 49 30.001 156 30.501 42 30.521 202 30.541 112 30.561 140 30.581 137 30.601 179 30.621 88 30.641 52 30.661 46 30.681 91 30.701 73 30.721 35 30.741 86 30.761 0 30.781 107 30.801 25 30.821 50 30.841 71 207

30.861 40 30.881 83 30.901 0 30.921 94 30.941 31 30.961 42 30.981 31 31.001 35 31.021 41 31.041 98 31.061 135 31.081 38 31.101 91 31.121 75 31.141 14 31.161 30 31.181 171 31.201 61 31.221 130 31.241 133 31.261 137 31.281 91 31.301 22 31.321 5 31.341 0 31.361 0 31.381 0 31.401 0 31.421 0 31.441 0 31.461 0 31.481 11 31.501 13 31.521 99 31.541 90 31.561 144 31.581 119 31.601 103 31.621 126 31.641 189 31.661 186 31.681 123 208

31.701 100 31.721 181 31.741 172 31.761 159 31.781 115 31.801 130 31.821 106 31.841 47 31.861 80 31.881 39 31.901 50 31.921 87 31.941 125 31.961 145 31.981 122 32.001 170 32.021 145 32.041 213 32.061 227 32.081 226 32.101 125 32.121 149 32.141 244 32.161 123 32.181 139 32.201 262 32.221 194 32.241 191 32.261 295 32.281 227 32.301 257 32.321 233 32.341 230 32.361 214 32.381 195 32.401 148 32.421 175 32.441 204 32.461 211 32.481 228 32.501 236 32.521 269 209

32.541 241 32.561 213 32.581 268 32.601 273 32.621 222 32.641 162 32.661 201 32.681 190 32.701 116 32.721 206 32.741 213 32.761 245 32.781 272 32.801 198 32.821 288 32.841 295 32.861 269 32.881 347 32.901 234 32.921 314 32.941 221 32.961 198 32.981 279 33.001 285 33.021 219 33.041 329 33.061 283 33.081 348 33.101 276 33.121 185 33.141 208 33.161 236 33.181 226 33.201 186 33.221 176 33.241 145 33.261 124 33.281 84 33.301 96 33.321 128 33.341 173 33.361 67 210

33.381 190 33.401 92 33.421 138 33.441 195 33.461 167 33.481 129 33.501 148 33.521 134 33.541 193 33.561 219 33.581 217 33.601 156 33.621 189 33.641 145 33.661 121 33.681 163 33.701 123 33.721 209 33.741 241 33.761 234 33.781 194 33.801 213 33.821 141 33.841 217 33.861 240 33.881 200 33.901 220 33.921 250 33.941 200 33.961 306 33.981 266 34.001 264 34.021 184 34.041 229 34.061 180 34.081 208 34.101 267 34.121 206 34.141 151 34.161 249 34.181 271 34.201 179 211

34.221 261 34.241 248 34.261 220 34.281 287 34.301 280 34.321 325 34.341 259 34.361 369 34.381 458 34.401 411 34.421 383 34.441 388 34.461 340 34.481 270 34.501 225 34.521 216 34.541 310 34.561 236 34.581 259 34.601 257 34.621 271 34.641 226 34.661 220 34.681 218 34.701 303 34.721 225 34.741 290 34.761 303 34.781 183 34.801 218 34.821 327 34.841 248 34.861 226 34.881 196 34.901 215 34.921 167 34.941 205 34.961 189 34.981 204 35.001 263 35.021 265 35.041 284 212

35.061 409 35.081 321 35.101 296 35.121 370 35.141 380 35.161 321 35.181 299 35.201 340 35.221 346 35.241 296 35.261 290 35.281 323 35.301 247 35.321 227 35.341 246 35.361 278 35.381 301 35.401 319 35.421 320 35.441 349 35.461 327 35.481 331 35.501 343 35.521 224 35.541 281 35.561 308 35.581 276 35.601 297 35.621 274 35.641 219 35.661 154 35.681 208 35.701 303 35.721 279 35.741 344 35.761 353 35.781 246 35.801 267 35.821 311 35.841 212 35.861 419 35.881 286 213

35.901 298 35.921 250 35.941 286 35.961 261 35.981 248 36.001 210 36.021 198 36.041 113 36.061 126 36.081 115 36.101 114 36.121 74 36.141 191 36.161 204 36.181 34 36.201 168 36.221 50 36.241 126 36.261 109 36.281 44 36.301 59 36.321 94 36.341 159 36.361 172 36.381 141 36.401 167 36.421 228 36.441 170 36.461 244 36.481 208 36.501 207 36.521 201 36.541 135 36.561 183 36.581 200 36.601 255 36.621 105 36.641 148 36.661 178 36.681 114 36.701 103 36.721 97 214

36.741 149 36.761 201 36.781 104 36.801 124 36.821 196 36.841 111 36.861 194 36.881 132 36.901 194 36.921 221 36.941 174 36.961 281 36.981 187 37.001 218 37.021 220 37.041 198 37.061 251 37.081 309 37.101 286 37.121 212 37.141 173 37.161 198 37.181 184 37.201 91 37.221 176 37.241 190 37.261 256 37.281 288 37.301 110 37.321 218 37.341 188 37.361 188 37.381 199 37.401 196 37.421 200 37.441 254 37.461 212 37.481 216 37.501 290 37.521 179 37.541 154 37.561 147 215

37.581 183 37.601 194 37.621 203 37.641 223 37.661 141 37.681 234 37.701 284 37.721 189 37.741 256 37.761 114 37.781 243 37.801 233 37.821 197 37.841 211 37.861 226 37.881 269 37.901 251 37.921 138 37.941 274 37.961 246 37.981 274 38.001 272 38.021 314 38.041 185 38.061 155 38.081 149 38.101 210 38.121 197 38.141 194 38.161 235 38.181 172 38.201 212 38.221 181 38.241 191 38.261 188 38.281 175 38.301 236 38.321 270 38.341 266 38.361 283 38.381 234 38.401 294 216

38.421 387 38.441 420 38.461 432 38.481 377 38.501 294 38.521 392 38.541 184 38.561 250 38.581 218 38.601 160 38.621 215 38.641 108 38.661 211 38.681 142 38.701 206 38.721 149 38.741 155 38.761 131 38.781 119 38.801 152 38.821 297 38.841 269 38.861 330 38.881 265 38.901 282 38.921 286 38.941 400 38.961 288 38.981 326 39.001 377 39.021 371 39.041 284 39.061 311 39.081 298 39.101 268 39.121 291 39.141 303 39.161 265 39.181 333 39.201 229 39.221 245 39.241 300 217

39.261 350 39.281 247 39.301 352 39.321 328 39.341 336 39.361 315 39.381 327 39.401 397 39.421 312 39.441 298 39.461 354 39.481 328 39.501 312 39.521 285 39.541 253 39.561 280 39.581 259 39.601 333 39.621 306 39.641 322 39.661 253 39.681 242 39.701 251 39.721 249 39.741 232 39.761 308 39.781 316 39.801 269 39.821 269 39.841 375 39.861 273 39.881 334 39.901 304 39.921 323 39.941 262 39.961 263 39.981 274 40.001 262 40.021 304 40.041 260 40.061 198 40.081 268 218

40.101 192 40.121 151 40.141 156 40.161 111 40.181 187 40.201 293 40.221 229 40.241 229 40.261 213 40.281 275 40.301 298 40.321 289 40.341 230 40.361 160 40.381 130 40.401 107 40.421 163 40.441 137 40.461 190 40.481 170 40.501 209 40.521 209 40.541 158 40.561 224 40.581 172 40.601 153 40.621 118 40.641 223 40.661 231 40.681 150 40.701 98 40.721 147 40.741 224 40.761 155 40.781 175 40.801 290 40.821 200 40.841 194 40.861 188 40.881 201 40.901 233 40.921 158 219

40.941 199 40.961 106 40.981 109 41.001 140 41.021 25 41.041 57 41.061 158 41.081 174 41.101 172 41.121 140 41.141 102 41.161 74 41.181 143 41.201 201 41.221 156 41.241 179 41.261 155 41.281 143 41.301 206 41.321 236 41.341 224 41.361 314 41.381 221 41.401 191 41.421 186 41.441 226 41.461 197 41.481 169 41.501 173 41.521 120 41.541 148 41.561 222 41.581 205 41.601 167 41.621 282 41.641 259 41.661 122 41.681 140 41.701 212 41.721 166 41.741 142 41.761 148 220

41.781 191 41.801 203 41.821 121 41.841 215 41.861 116 41.881 171 41.901 152 41.921 114 41.941 117 41.961 107 41.981 130 42.001 183 42.021 212 42.041 138 42.061 103 42.081 181 42.101 171 42.121 167 42.141 174 42.161 116 42.181 184 42.201 110 42.221 153 42.241 130 42.261 158 42.281 206 42.301 177 42.321 224 42.341 196 42.361 314 42.381 194 42.401 166 42.421 167 42.441 162 42.461 194 42.481 137 42.501 162 42.521 157 42.541 146 42.561 175 42.581 129 42.601 154 221

42.621 157 42.641 165 42.661 88 42.681 97 42.701 54 42.721 0 42.741 0 42.761 0 42.781 0 42.801 0 42.821 0 42.841 0 42.861 0 42.881 0 42.901 0 42.921 0 42.941 0 42.961 0 42.981 0 43.001 0 43.021 0 43.041 0 43.061 0 43.081 0 43.101 0 43.121 0 43.141 0 43.161 0 43.181 0 43.201 0 43.221 0 43.241 0 43.261 0 43.281 0 43.301 0 43.321 0 43.341 0 43.361 0 43.381 0 43.401 0 43.421 0 43.441 0 222

43.461 0 43.481 0 43.501 0 43.521 0 43.541 0 43.561 122 43.581 124 43.601 143 43.621 129 43.641 102 43.661 97 43.681 108 43.701 45 43.721 22 43.741 60 43.761 80 43.781 0 43.801 17 43.821 16 43.841 45 43.861 19 43.881 105 43.901 120 43.921 156 43.941 212 43.961 120 43.981 212 44.001 141 44.021 145 44.041 101 44.061 102 44.081 177 44.101 142 44.121 171 44.141 158 44.161 187 44.181 155 44.201 123 44.221 118 44.241 139 44.261 65 44.281 170 223

44.301 153 44.321 57 44.341 95 44.361 42 44.381 58 44.401 148 44.421 65 44.441 73 44.461 76 44.481 165 44.501 76 44.521 103 44.541 17 44.561 68 44.581 88 44.601 156 44.621 112 44.641 156 44.661 169 44.681 180 44.701 158 44.721 164 44.741 207 44.761 154 44.781 207 44.801 250 44.821 190 44.841 203 44.861 197 44.881 172 44.901 212 44.921 184 44.941 111 44.961 95 44.981 90 45.001 157 45.021 178 45.041 180 45.061 87 45.081 44 45.101 191 45.121 182 224

45.141 90 45.161 195 45.181 55 45.201 120 45.221 96 45.241 143 45.261 132 45.281 172 45.301 180 45.321 211 45.341 184 45.361 155 45.381 161 45.401 199 45.421 201 45.441 179 45.461 244 45.481 310 45.501 310 45.521 271 45.541 246 45.561 233 45.581 231 45.601 176 45.621 131 45.641 105 45.661 187 45.681 108 45.701 197 45.721 116 45.741 106 45.761 137 45.781 144 45.801 265 45.821 293 45.841 183 45.861 251 45.881 135 45.901 258 45.921 216 45.941 262 45.961 134 225

45.981 231 46.001 260 46.021 201 46.041 242 46.061 260 46.081 217 46.101 191 46.121 289 46.141 211 46.161 294 46.181 259 46.201 307 46.221 296 46.241 277 46.261 347 46.281 228 46.301 339 46.321 275 46.341 289 46.361 266 46.381 409 46.401 376 46.421 288 46.441 325 46.461 362 46.481 381 46.501 373 46.521 308 46.541 352 46.561 358 46.581 410 46.601 356 46.621 350 46.641 345 46.661 342 46.681 458 46.701 398 46.721 282 46.741 407 46.761 416 46.781 341 46.801 396 226

46.821 397 46.841 214 46.861 166 46.881 392 46.901 282 46.921 380 46.941 426 46.961 323 46.981 329 47.001 268 47.021 271 47.041 288 47.061 331 47.081 276 47.101 332 47.121 451 47.141 442 47.161 485 47.181 520 47.201 501 47.221 487 47.241 395 47.261 353 47.281 372 47.301 399 47.321 392 47.341 500 47.361 432 47.381 410 47.401 377 47.421 335 47.441 314 47.461 423 47.481 443 47.501 407 47.521 518 47.541 460 47.561 431 47.581 487 47.601 459 47.621 409 47.641 385 227

47.661 422 47.681 410 47.701 431 47.721 237 47.741 201 47.761 97 47.781 106 47.801 186 47.821 209 47.841 151 47.861 270 47.881 356 47.901 300 47.921 329 47.941 357 47.961 466 47.981 456 48.001 468 48.021 383 48.041 445 48.061 373 48.081 409 48.101 438 48.121 467 48.141 384 48.161 300 48.181 286 48.201 262 48.221 361 48.241 362 48.261 278 48.281 292 48.301 299 48.321 316 48.341 304 48.361 347 48.381 317 48.401 368 48.421 368 48.441 365 48.461 329 48.481 365 228

48.501 320 48.521 368 48.541 296 48.561 327 48.581 360 48.601 333 48.621 339 48.641 219 48.661 299 48.681 328 48.701 322 48.721 266 48.741 265 48.761 293 48.781 295 48.801 280 48.821 329 48.841 317 48.861 304 48.881 277 48.901 270 48.921 255 48.941 255 48.961 203 48.981 336 49.001 253 49.021 229 49.041 237 49.061 314 49.081 298 49.101 441 49.121 434 49.141 383 49.161 289 49.181 319 49.201 272 49.221 336 49.241 311 49.261 354 49.281 276 49.301 276 49.321 372 229

49.341 320 49.361 276 49.381 306 49.401 251 49.421 306 49.441 277 49.461 215 49.481 240 49.501 216 49.521 247 49.541 274 49.561 333 49.581 275 49.601 192 49.621 262 49.641 245 49.661 184 49.681 167 49.701 150 49.721 152 49.741 191 49.761 194 49.781 237 49.801 185 49.821 164 49.841 239 49.861 127 49.881 262 49.901 274 49.921 154 49.941 258 49.961 203 49.981 169 50.001 190 50.021 158 50.041 207 50.061 208 50.081 263 50.101 137 50.121 137 50.141 121 50.161 145 230

50.181 158 50.201 178 50.221 228 50.241 221 50.261 171 50.281 264 50.301 173 50.321 197 50.341 169 50.361 177 50.381 193 50.401 139 50.421 195 50.441 264 50.461 163 50.481 181 50.501 202 50.521 203 50.541 211 50.561 95 50.581 156 50.601 134 50.621 184 50.641 269 50.661 177 50.681 200 50.701 119 50.721 239 50.741 167 50.761 108 50.781 114 50.801 101 50.821 105 50.841 62 50.861 117 50.881 97 50.901 81 50.921 94 50.941 195 50.961 117 50.981 94 51.001 115 231

51.021 29 51.041 185 51.061 24 51.081 0 51.101 0 51.121 0 51.141 0 51.161 0 51.181 0 51.201 0 51.221 0 51.241 0 51.261 0 51.281 0 51.301 0 51.321 0 51.341 0 51.361 0 51.381 0 51.401 0 51.421 0 51.441 0 51.461 0 51.481 0 51.501 0 51.521 0 51.541 0 51.561 0 51.581 71 51.601 82 51.621 6 51.641 0 51.661 0 51.681 0 51.701 0 51.721 0 51.741 0 51.761 0 51.781 0 51.801 18 51.821 33 51.841 42 232

51.861 76 51.881 134 51.901 97 51.921 173 51.941 194 51.961 210 51.981 199 52.001 301 52.021 249 52.041 251 52.061 183 52.081 280 52.101 261 52.121 164 52.141 241 52.161 217 52.181 345 52.201 374 52.221 322 52.241 324 52.261 303 52.281 288 52.301 257 52.321 256 52.341 238 52.361 243 52.381 224 52.401 280 52.421 236 52.441 240 52.461 292 52.481 359 52.501 234 52.521 302 52.541 363 52.561 383 52.581 292 52.601 253 52.621 238 52.641 243 52.661 223 52.681 235 233

52.701 239 52.721 313 52.741 211 52.761 256 52.781 228 52.801 219 52.821 188 52.841 209 52.861 134 52.881 158 52.901 171 52.921 167 52.941 168 52.961 214 52.981 168 53.001 234 53.021 202 53.041 304 53.061 369 53.081 349 53.101 283 53.121 232 53.141 242 53.161 164 53.181 278 53.201 200 53.221 200 53.241 190 53.261 182 53.281 118 53.301 241 53.321 125 53.341 143 53.361 231 53.381 239 53.401 319 53.421 283 53.441 302 53.461 283 53.481 323 53.501 281 53.521 189 234

53.541 219 53.561 257 53.581 288 53.601 225 53.621 193 53.641 154 53.661 106 53.681 203 53.701 163 53.721 130 53.741 70 53.761 134 53.781 158 53.801 148 53.821 168 53.841 169 53.861 181 53.881 137 53.901 275 53.921 257 53.941 225 53.961 219 53.981 200 54.001 309 54.021 290 54.041 258 54.061 271 54.081 196 54.101 242 54.121 227 54.141 224 54.161 203 54.181 179 54.201 169 54.221 223 54.241 181 54.261 160 54.281 215 54.301 196 54.321 185 54.341 186 54.361 156 235

54.381 174 54.401 286 54.421 113 54.441 225 54.461 226 54.481 111 54.501 226 54.521 280 54.541 127 54.561 214 54.581 147 54.601 243 54.621 353 54.641 265 54.661 260 54.681 190 54.701 323 54.721 128 54.741 228 54.761 142 54.781 91 54.801 144 54.821 272 54.841 361 54.861 323 54.881 311 54.901 397 54.921 326 54.941 341 54.961 353 54.981 202 55.001 239 55.021 57 55.041 0 55.061 0 55.081 0 55.101 0 55.121 0 55.141 0 55.161 0 55.181 0 55.201 0 236

55.221 0 55.241 0 55.261 0 55.281 0 55.301 0 55.321 0 55.341 0 55.361 0 55.381 0 55.401 0 55.421 0 55.441 0 55.461 0 55.481 0 55.501 0 55.521 0 55.541 0 55.561 0 55.581 0 55.601 0 55.621 0 55.641 0 55.661 0 55.681 0 55.701 0 55.721 0 55.741 0 55.761 0 55.781 0 55.801 69 55.821 0 55.841 0 55.861 0 55.881 0 55.901 0 55.921 0 55.941 0 55.961 0 55.981 0 56.001 0 56.021 0 56.041 0 237

56.061 0 56.081 0 56.101 0 56.121 0 56.141 0 56.161 0 56.181 0 56.201 0 56.221 0 56.241 0 56.261 0 56.281 0 56.301 0 56.321 0 56.341 0 56.361 0 56.381 0 56.401 0 56.421 0 56.441 0 56.461 0 56.481 0 56.501 0 56.521 0 56.541 0 56.561 0 56.581 0 56.601 0 56.621 0 56.641 0 56.661 0 56.681 0 56.701 0 56.721 0 56.741 0 56.761 0 56.781 0 56.801 0 56.821 0 56.841 0 56.861 0 56.881 0 238

56.901 0 56.921 0 56.941 0 56.961 0 56.981 0 57.001 0 57.021 0 57.041 0 57.061 247 57.081 244 57.101 324 57.121 474 57.141 421 57.161 434 57.181 433 57.201 480 57.221 487 57.241 401 57.261 405 57.281 360 57.301 349 57.321 381 57.341 373 57.361 465 57.381 422 57.401 380 57.421 444 57.441 389 57.461 407 57.481 439 57.501 452 57.521 406 57.541 260 57.561 376 57.581 356 57.601 407 57.621 349 57.641 350 57.661 376 57.681 402 57.701 348 57.721 362 239

57.741 417 57.761 374 57.781 430 57.801 459 57.821 482 57.841 477 57.861 389 57.881 390 57.901 425 57.921 362 57.941 372 57.961 472 57.981 384 58.001 346 58.021 333 58.041 417 58.061 420 58.081 394 58.101 476 58.121 454 58.141 503 58.161 381 58.181 492 58.201 519 58.221 494 58.241 393 58.261 465 58.281 491 58.301 395 58.321 354 58.341 330 58.361 375 58.381 260 58.401 359 58.421 379 58.441 486 58.461 561 58.481 528 58.501 546 58.521 510 58.541 657 58.561 611 240

58.581 493 58.601 522 58.621 468 58.641 468 58.661 489 58.681 287 58.701 312 58.721 260 58.741 297 58.761 251 58.781 264 58.801 285 58.821 242 58.841 234 58.861 223 58.881 194 58.901 261 58.921 179 58.941 116 58.961 191 58.981 173 59.001 197 59.021 250 59.041 298 59.061 317 59.081 239 59.101 173 59.121 225 59.141 215 59.161 174 59.181 262 59.201 317 59.221 374 59.241 316 59.261 360 59.281 286 59.301 380 59.321 405 59.341 250 59.361 331 59.381 402 59.401 438 241

59.421 355 59.441 322 59.461 237 59.481 164 59.501 236 59.521 232 59.541 246 59.561 244 59.581 353 59.601 438 59.621 337 59.641 387 59.661 302 59.681 283 59.701 420 59.721 313 59.741 227 59.761 310 59.781 364 59.801 298 59.821 309 59.841 391 59.861 331 59.881 391 59.901 291 59.921 319 59.941 308 59.961 330 59.981 327 60.001 355 60.021 353 60.041 339 60.061 288 60.081 270 60.101 281 60.121 310 60.141 346 60.161 405 60.181 358 60.201 410 60.221 425 60.241 362 242

60.261 498 60.281 449 60.301 337 60.321 404 60.341 395 60.361 422 60.381 355 60.401 337 60.421 378 60.441 401 60.461 320 60.481 355 60.501 381 60.521 305 60.541 414 60.561 414 60.581 296 60.601 411 60.621 395 60.641 385 60.661 398 60.681 409 60.701 350 60.721 321 60.741 375 60.761 361 60.781 395 60.801 446 60.821 391 60.841 416 60.861 363 60.881 330 60.901 414 60.921 429 60.941 355 60.961 363 60.981 346 61.001 307 61.021 359 61.041 408 61.061 293 61.081 371 243

61.101 348 61.121 355 61.141 312 61.161 381 61.181 357 61.201 336 61.221 299 61.241 356 61.261 406 61.281 363 61.301 327 61.321 339 61.341 318 61.361 371 61.381 306 61.401 369 61.421 318 61.441 328 61.461 310 61.481 350 61.501 378 61.521 362 61.541 335 61.561 327 61.581 257 61.601 323 61.621 287 61.641 294 61.661 413 61.681 344 61.701 336 61.721 371 61.741 335 61.761 349 61.781 288 61.801 369 61.821 303 61.841 314 61.861 366 61.881 391 61.901 290 61.921 307 244

61.941 302 61.961 227 61.981 228 62.001 228 62.021 261 62.041 284 62.061 272 62.081 202 62.101 316 62.121 312 62.141 288 62.161 289 62.181 292 62.201 342 62.221 285 62.241 282 62.261 321 62.281 334 62.301 267 62.321 297 62.341 319 62.361 294 62.381 182 62.401 254 62.421 279 62.441 265 62.461 363 62.481 413 62.501 412 62.521 239 62.541 366 62.561 284 62.581 288 62.601 318 62.621 288 62.641 241 62.661 249 62.681 212 62.701 284 62.721 237 62.741 272 62.761 257 245

62.781 306 62.801 277 62.821 290 62.841 283 62.861 287 62.881 259 62.901 465 62.921 449 62.941 344 62.961 347 62.981 338 63.001 337 63.021 374 63.041 320 63.061 325 63.081 307 63.101 285 63.121 297 63.141 222 63.161 235 63.181 213 63.201 201 63.221 114 63.241 152 63.261 153 63.281 187 63.301 211 63.321 161 63.341 245 63.361 247 63.381 249 63.401 268 63.421 205 63.441 143 63.461 214 63.481 206 63.501 263 63.521 261 63.541 341 63.561 346 63.581 342 63.601 215 246

63.621 227 63.641 258 63.661 263 63.681 223 63.701 259 63.721 417 63.741 433 63.761 377 63.781 259 63.801 334 63.821 354 63.841 294 63.861 337 63.881 347 63.901 302 63.921 332 63.941 281 63.961 198 63.981 192 64.001 169 64.021 205 64.041 154 64.061 237 64.081 185 64.101 156 64.121 159 64.141 227 64.161 211 64.181 271 64.201 363 64.221 274 64.241 254 64.261 192 64.281 274 64.301 216 64.321 307 64.341 248 64.361 175 64.381 205 64.401 162 64.421 179 64.441 163 247

64.461 189 64.481 273 64.501 342 64.521 358 64.541 413 64.561 404 64.581 464 64.601 434 64.621 372 64.641 223 64.661 268 64.681 216 64.701 343 64.721 525 64.741 448 64.761 226 64.781 304 64.801 408 64.821 537 64.841 516 64.861 440 64.881 382 64.901 439 64.921 297 64.941 281 64.961 384 64.981 284 65.001 351 65.021 213 65.041 232 65.061 301 65.081 417 65.101 589 65.121 463 65.141 418 65.161 265 65.181 346 65.201 296 65.221 333 65.241 446 65.261 437 65.281 551 248

65.301 537 65.321 504 65.341 407 65.361 408 65.381 417 65.401 420 65.421 363 65.441 457 65.461 407 65.481 387 65.501 300 65.521 300 65.541 363 65.561 410 65.581 355 65.601 373 65.621 331 65.641 316 65.661 336 65.681 293 65.701 331 65.721 354 65.741 427 65.761 323 65.781 307 65.801 334 65.821 275 65.841 86 65.861 128 65.881 62 65.901 45 65.921 45 65.941 20 65.961 14 65.981 0 66.001 0 66.021 0 66.041 0 66.061 0 66.081 0 66.101 0 66.121 0 249

66.141 0 66.161 0 66.181 0 66.201 0 66.221 0 66.241 0 66.261 0 66.281 0 66.301 0 66.321 0 66.341 0 66.361 0 66.381 0 66.401 0 66.421 0 66.441 0 66.461 0 66.481 0 66.501 0 66.521 0 66.541 0 66.561 0 66.581 187 66.601 221 66.621 227 66.641 214 66.661 318 66.681 390 66.701 369 66.721 405 66.741 428 66.761 392 66.781 445 66.801 345 66.821 352 66.841 341 66.861 309 66.881 437 66.901 377 66.921 386 66.941 410 66.961 354 250

66.981 319 67.001 278 67.021 280 67.041 346 67.061 353 67.081 293 67.101 202 67.121 251 67.141 248 67.161 267 67.181 213 67.201 273 67.221 288 67.241 278 67.261 237 67.281 286 67.301 240 67.321 234 67.341 244 67.361 180 67.381 153 67.401 128 67.421 212 67.441 209 67.461 257 67.481 276 67.501 213 67.521 228 67.541 218 67.561 131 67.581 221 67.601 227 67.621 207 67.641 164 67.661 168 67.681 149 67.701 135 67.721 236 67.741 286 67.761 232 67.781 342 67.801 247 251

67.821 228 67.841 233 67.861 258 67.881 251 67.901 275 67.921 206 67.941 218 67.961 294 67.981 195 68.001 285 68.021 266 68.041 257 68.061 338 68.081 245 68.101 304 68.121 279 68.141 311 68.161 308 68.181 266 68.201 220 68.221 262 68.241 295 68.261 218 68.281 252 68.301 299 68.321 336 68.341 281 68.361 269 68.381 159 68.401 256 68.421 208 68.441 309 68.461 265 68.481 207 68.501 214 68.521 156 68.541 84 68.561 112 68.581 233 68.601 174 68.621 210 68.641 220 252

68.661 256 68.681 166 68.701 206 68.721 199 68.741 237 68.761 248 68.781 185 68.801 219 68.821 204 68.841 137 68.861 81 68.881 157 68.901 164 68.921 103 68.941 115 68.961 68 68.981 95 69.001 114 69.021 126 69.041 92 69.061 138 69.081 201 69.101 198 69.121 123 69.141 177 69.161 204 69.181 124 69.201 179 69.221 195 69.241 199 69.261 170 69.281 129 69.301 129 69.321 121 69.341 182 69.361 189 69.381 188 69.401 141 69.421 155 69.441 182 69.461 111 69.481 197 253

69.501 163 69.521 146 69.541 85 69.561 128 69.581 140 69.601 174 69.621 153 69.641 205 69.661 181 69.681 105 69.701 132 69.721 233 69.741 88 69.761 154 69.781 169 69.801 223 69.821 187 69.841 242 69.861 233 69.881 195 69.901 213 69.921 169 69.941 161 69.961 150 69.981 163 70.001 190 70.021 135 70.041 125 70.061 241 70.081 237 70.101 223 70.121 188 70.141 134 70.161 214 70.181 121 70.201 238 70.221 173 70.241 174 70.261 224 70.281 179 70.301 204 70.321 197 254

70.341 165 70.361 177 70.381 159 70.401 183 70.421 222 70.441 246 70.461 267 70.481 173 70.501 265 70.521 212 70.541 259 70.561 194 70.581 195 70.601 239 70.621 166 70.641 177 70.661 184 70.681 201 70.701 153 70.721 240 70.741 169 70.761 168 70.781 210 70.801 192 70.821 184 70.841 198 70.861 116 70.881 182 70.901 76 70.921 97 70.941 14 70.961 0 70.981 0 71.001 0 71.021 0 71.041 0 71.061 0 71.081 0 71.101 0 71.121 0 71.141 0 71.161 0 255

71.181 0 71.201 0 71.221 0 71.241 17 71.261 50 71.281 119 71.301 123 71.321 163 71.341 128 71.361 198 71.381 97 71.401 190 71.421 135 71.441 117 71.461 102 71.481 230 71.501 171 71.521 104 71.541 142 71.561 144 71.581 116 71.601 175 71.621 94 71.641 153 71.661 201 71.681 154 71.701 119 71.721 162 71.741 61 71.761 113 71.781 174 71.801 158 71.821 160 71.841 122 71.861 135 71.881 90 71.901 132 71.921 188 71.941 121 71.961 118 71.981 130 72.001 153 256

72.021 155 72.041 200 72.061 162 72.081 64 72.101 153 72.121 157 72.141 145 72.161 105 72.181 205 72.201 224 72.221 136 72.241 203 72.261 135 72.281 152 72.301 157 72.321 190 72.341 224 72.361 267 72.381 237 72.401 238 72.421 194 72.441 139 72.461 125 72.481 85 72.501 91 72.521 93 72.541 88 72.561 96 72.581 8 72.601 111 72.621 5 72.641 17 72.661 61 72.681 81 72.701 48 72.721 105 72.741 84 72.761 109 72.781 89 72.801 157 72.821 123 72.841 89 257

72.861 34 72.881 191 72.901 152 72.921 197 72.941 203 72.961 189 72.981 197 73.001 169 73.021 171 73.041 210 73.061 105 73.081 176 73.101 55 73.121 117 73.141 98 73.161 113 73.181 74 73.201 23 73.221 62 73.241 108 73.261 117 73.281 162 73.301 143 73.321 108 73.341 169 73.361 220 73.381 264 73.401 124 73.421 176 73.441 187 73.461 169 73.481 241 73.501 180 73.521 83 73.541 0 73.561 162 73.581 134 73.601 161 73.621 120 73.641 125 73.661 91 73.681 96 258

73.701 100 73.721 150 73.741 90 73.761 58 73.781 80 73.801 0 73.821 62 73.841 38 73.861 50 73.881 96 73.901 96 73.921 125 73.941 123 73.961 119 73.981 125 74.001 41 74.021 101 74.041 77 74.061 104 74.081 103 74.101 88 74.121 123 74.141 48 74.161 107 74.181 117 74.201 118 74.221 56 74.241 111 74.261 29 74.281 60 74.301 143 74.321 72 74.341 101 74.361 102 74.381 148 74.401 98 74.421 65 74.441 164 74.461 135 74.481 123 74.501 85 77.802 82 259

77.822 145 77.842 88 77.862 53 77.882 123 77.902 99 77.922 14 77.942 112 77.962 138 77.982 151 78.002 188 78.022 171 78.042 252 78.062 276 78.082 235 78.102 140 78.122 178 78.142 255 78.162 134 78.182 192 78.202 205 78.222 183 78.242 161 78.262 153 78.282 203 78.302 192 78.322 195 78.342 61 78.362 193 78.382 256 78.402 215 78.422 254 78.442 259 78.462 241 78.482 228 78.502 174 78.522 94 78.542 155 78.562 186 78.582 247 78.602 221 78.622 130 78.642 234 260

78.662 147 78.682 143 78.702 107 78.722 124 78.742 105 78.762 24 78.782 0 78.802 0 78.822 0 78.842 0 78.862 0 78.882 0 78.902 0 78.922 0 78.942 0 78.962 0 78.982 0 79.002 0 79.022 0 79.042 0 79.062 0 79.082 0 79.102 43 79.122 16 79.142 0 79.162 0 79.182 0 79.202 0 79.222 20 79.242 104 79.262 73 79.282 85 79.302 41 79.322 55 79.342 97 79.362 115 79.382 70 79.402 48 79.422 139 79.442 65 79.462 130 79.482 130 261

79.502 39 79.522 158 79.542 204 79.562 110 79.582 141 79.602 115 79.622 204 79.642 149 79.662 113 79.682 63 79.702 156 79.722 194 79.742 165 79.762 172 79.782 220 79.802 199 79.822 164 79.842 182 79.862 98 79.882 85 79.902 148 79.922 175 79.942 119 79.962 176 79.982 159 80.002 127 80.022 122 80.042 142 80.062 90 80.082 80 80.102 74 80.122 81 80.142 117 80.162 112 80.182 146 80.202 119 80.222 61 80.242 81 80.262 105 80.282 75 80.302 66 80.322 99 262

80.342 83 80.362 70 80.382 54 80.402 51 80.422 0 80.442 92 80.462 80 80.482 105 80.502 158 80.522 70 80.542 57 80.562 52 80.582 71 80.602 25 80.622 121 80.642 90 80.662 101 80.682 30 80.702 54 80.722 72 80.742 29 80.762 63 80.782 171 80.802 60 80.822 106 80.842 40 80.862 95 80.882 13 80.902 0 80.922 14 80.942 88 80.962 72 80.982 14 81.002 31 81.022 54 81.042 59 81.062 65 81.082 54 81.102 57 81.122 146 81.142 88 81.162 128 263

81.182 83 81.202 148 81.222 114 81.242 110 81.262 58 81.282 127 81.302 93 81.322 103 81.342 156 81.362 76 81.382 163 81.402 59 81.422 121 81.442 103 81.462 144 81.482 147 81.502 114 81.522 67 81.542 79 81.562 114 81.582 100 81.602 79 81.622 103 81.642 116 81.662 99 81.682 23 81.702 175 81.722 94 81.742 39 81.762 64 81.782 114 81.802 133 81.822 38 81.842 136 81.862 102 81.882 173 81.902 7 81.922 9 81.942 96 81.962 37 81.982 46 82.002 33 264

82.022 0 82.042 132 82.062 25 82.082 117 82.102 101 82.122 105 82.142 71 82.162 92 82.182 109 82.202 0 82.222 42 82.242 0 82.262 27 82.282 24 82.302 55 82.322 40 82.342 26 82.362 121 82.382 90 82.402 54 82.422 70 82.442 48 82.462 57 82.482 35 82.502 71 82.522 36 82.542 83 82.562 103 82.582 25 82.602 71 82.622 8 82.642 76 82.662 55 82.682 13 82.702 71 82.722 80 82.742 50 82.762 39 82.782 30 82.802 95 82.822 68 82.842 131 265

82.862 123 82.882 85 82.902 135 82.922 110 82.942 71 82.962 141 82.982 114 83.002 60 83.022 70 83.042 119 83.062 87 83.082 118 83.102 120 83.122 60 83.142 116 83.162 56 83.182 145 83.202 40 83.222 78 83.242 41 83.262 75 83.282 80 83.302 40 83.322 108 83.342 9 83.362 33 83.382 27 83.402 20 83.422 9 83.442 90 83.462 145 83.482 140 83.502 81 83.522 72 83.542 36 83.562 111 83.582 125 83.602 174 83.622 115 83.642 70 83.662 62 83.682 124 266

83.702 163 83.722 157 83.742 86 83.762 153 83.782 80 83.802 123 83.822 94 83.842 46 83.862 102 83.882 90 83.902 90 83.922 36 83.942 92 83.962 115 83.982 37 84.002 62 84.022 116 84.042 109 84.062 63 84.082 150 84.102 89 84.122 108 84.142 130 84.162 88 84.182 199 84.202 306 84.222 228 84.242 248 84.262 195 84.282 226 84.302 209 84.322 200 84.342 148 84.362 85 84.382 143 84.402 71 84.422 79 84.442 94 84.462 38 84.482 95 84.502 41 84.522 11 267

84.542 66 84.562 56 84.582 64 84.602 58 84.622 32 84.642 54 84.662 65 84.682 69 84.702 120 84.722 0 84.742 30 84.762 75 84.782 61 84.802 115 84.822 160 84.842 132 84.862 115 84.882 76 84.902 64 84.922 219 84.942 94 84.962 54 84.982 140 85.002 164 85.022 172 85.042 110 85.062 223 85.082 149 85.102 145 85.122 204 85.142 125 85.162 166 85.182 183 85.202 135 85.222 97 85.242 80 85.262 65 85.282 28 85.302 0 85.322 86 85.342 104 85.362 43 268

85.382 47 85.402 24 85.422 0 85.442 0 85.462 0 85.482 0 85.502 0 85.522 0 85.542 0 85.562 0 85.582 0 85.602 0 85.622 0 85.642 0 85.662 0 85.682 0 85.702 0 85.722 0 85.742 0 85.762 0 85.782 0 85.802 0 85.822 0 85.842 0 85.862 0 85.882 0 85.902 0 85.922 0 85.942 0 85.962 0 85.982 0 86.002 0 86.022 0 86.042 0 86.062 0 86.082 0 86.102 0 86.122 0 86.142 6 86.162 11 86.182 19 86.202 76 269

86.222 0 86.242 87 86.262 61 86.282 48 86.302 41 86.322 111 86.342 78 86.362 113 86.382 81 86.402 92 86.422 55 86.442 17 86.462 11 86.482 16 86.502 0 86.522 60 86.542 63 86.562 65 86.582 61 86.602 101 86.622 106 86.642 94 86.662 64 86.682 17 86.702 57 86.722 0 86.742 0 86.762 60 86.782 122 86.802 40 86.822 85 86.842 87 86.862 81 86.882 95 86.902 125 86.922 99 86.942 93 86.962 129 86.982 147 87.002 180 87.022 180 87.042 152 270

87.062 67 87.082 115 87.102 190 87.122 127 87.142 57 87.162 157 87.182 71 87.202 76 87.222 0 87.242 0 87.262 21 87.282 56 87.302 20 87.322 31 87.342 0 87.362 9 87.382 59 87.402 71 87.422 48 87.442 12 87.462 0 87.482 68 87.502 105 87.522 54 87.542 43 87.562 33 87.582 35 87.602 6 87.622 73 87.642 40 87.662 115 87.682 79 87.702 68 87.722 156 87.742 171 87.762 107 87.782 143 87.802 93 87.822 210 87.842 160 87.862 126 87.882 252 271

87.902 119 87.922 137 87.942 139 87.962 148 87.982 220 88.002 196 88.022 198 88.042 278 88.062 263 88.082 273 88.102 237 88.122 197 88.142 236 88.162 209 88.182 218 88.202 181 88.222 228 88.242 196 88.262 198 88.282 215 88.302 274 88.322 234 88.342 319 88.362 249 88.382 315 88.402 368 88.422 503 88.442 578 88.462 609 88.482 618 88.502 592 88.522 667 88.542 688 88.562 617 88.582 625 88.602 628 88.622 680 88.642 618 88.662 678 88.682 560 88.702 738 88.722 598 272

88.742 561 88.762 547 88.782 544 88.802 509 88.822 605 88.842 687 88.862 501 88.882 462 88.902 542 88.922 558 88.942 479 88.962 517 88.982 453 89.002 450 89.022 466 89.042 455 89.062 388 89.082 396 89.102 394 89.122 365 89.142 478 89.162 503 89.182 472 89.202 462 89.222 424 89.242 341 89.262 399 89.282 375 89.302 383 89.322 423 89.342 362 89.362 531 89.382 622 89.402 591 89.422 498 89.442 481 89.462 431 89.482 392 89.502 412 89.522 509 89.542 552 89.562 576 273

89.582 588 89.602 483 89.622 319 89.642 503 89.662 557 89.682 551 89.702 626 89.722 538 89.742 555 89.762 572 89.782 528 89.802 599 89.822 518 89.842 435 89.862 376 89.882 391 89.902 386 89.922 342 89.942 139 89.962 233 89.982 262 90.002 239 90.022 186 90.042 207 90.062 162 90.082 9 90.102 25 90.122 0 90.142 61 90.162 98 90.182 155 90.202 30 90.222 25 90.242 65 90.262 0 90.282 129 90.302 119 90.322 35 90.342 78 90.362 125 90.382 108 90.402 122 274

90.422 192 90.442 186 90.462 160 90.482 107 90.502 89 90.522 171 90.542 198 90.562 234 90.582 166 90.602 262 90.622 296 90.642 233 90.662 256 90.682 261 90.702 271 90.722 282 90.742 299 90.762 302 90.782 334 90.802 214 90.822 271 90.842 337 90.862 334 90.882 308 90.902 287 90.922 295 90.942 262 90.962 319 90.982 247 91.002 299 91.022 332 91.042 292 91.062 282 91.082 136 91.102 313 91.122 209 91.142 173 91.162 232 91.182 151 91.202 262 91.222 223 91.242 331 275

91.262 247 91.282 280 91.302 266 91.322 263 91.342 220 91.362 299 91.382 277 91.402 234 91.422 296 91.442 385 91.462 292 91.482 276 91.502 177 91.522 237 91.542 157 91.562 188 91.582 235 91.602 265 91.622 289 91.642 327 91.662 408 91.682 443 91.702 438 91.722 442 91.742 417 91.762 391 91.782 451 91.802 500 91.822 558 91.842 533 91.862 481 91.882 489 91.902 454 91.922 443 91.942 331 91.962 398 91.982 373 92.002 285 92.022 207 92.042 264 92.062 315 92.082 239 276

92.102 233 92.122 272 92.142 195 92.162 153 92.182 207 92.202 186 92.222 163 92.242 228 92.262 158 92.282 339 92.302 387 92.322 291 92.342 354 92.362 389 92.382 407 92.402 349 92.422 303 92.442 334 92.462 323 92.482 186 92.502 138 92.522 154 92.542 188 92.562 168 92.582 164 92.602 74 92.622 166 92.642 196 92.662 238 92.682 194 92.702 204 92.722 230 92.742 205 92.762 277 92.782 220 92.802 311 92.822 457 92.842 349 92.862 350 92.882 374 92.902 293 92.922 352 277

92.942 274 92.962 412 92.982 331 93.002 365 93.022 350 93.042 368 93.062 431 93.082 311 93.102 328 93.122 320 93.142 365 93.162 406 93.182 431 93.202 364 93.222 490 93.242 436 93.262 392 93.282 461 93.302 448 93.322 461 93.342 524 93.362 377 93.382 345 93.402 353 93.422 419 93.442 358 93.462 353 93.482 349 93.502 361 93.522 327 93.542 379 93.562 348 93.582 305 93.602 236 93.622 305 93.642 328 93.662 300 93.682 346 93.702 319 93.722 304 93.742 296 93.762 258 278

93.782 329 93.802 327 93.822 291 93.842 161 93.862 267 93.882 302 93.902 254 93.922 269 93.942 401 93.962 330 93.982 323 94.002 260 94.022 354 94.042 337 94.062 351 94.082 305 94.102 365 94.122 388 94.142 205 94.162 226 94.182 241 94.202 229 94.222 210 94.242 184 94.262 239 94.282 213 94.302 191 94.322 265 94.342 244 94.362 328 94.382 249 94.402 212 94.422 307 94.442 249 94.462 328 94.482 262 94.502 273 94.522 262 94.542 219 94.562 294 94.582 288 94.602 305 279

94.622 241 94.642 261 94.662 356 94.682 190 94.702 227 94.722 215 94.742 253 94.762 282 94.782 302 94.802 340 94.822 244 94.842 343 94.862 283 94.882 322 94.902 184 94.922 298 94.942 293 94.962 201 94.982 198 95.002 263 95.022 274 95.042 235 95.062 293 95.082 194 95.102 191 95.122 211 95.142 201 95.162 199 95.182 228 95.202 266 95.222 322 95.242 334 95.262 327 95.282 336 95.302 355 95.322 332 95.342 261 95.362 357 95.382 360 95.402 353 95.422 376 95.442 482 280

95.462 479 95.482 376 95.502 380 95.522 277 95.542 304 95.562 356 95.582 247 95.602 322 95.622 339 95.642 496 95.662 484 95.682 405 95.702 447 95.722 471 95.742 458 95.762 417 95.782 322 95.802 401 95.822 406 95.842 456 95.862 453 95.882 482 95.902 528 95.922 499 95.942 430 95.962 398 95.982 357 96.002 358 96.022 342 96.042 376 96.062 306 96.082 312 96.102 317 96.122 257 96.142 253 96.162 218 96.182 324 96.202 304 96.222 363 96.242 338 96.262 372 96.282 372 281

96.302 376 96.322 439 96.342 313 96.362 353 96.382 458 96.402 468 96.422 410 96.442 510 96.462 392 96.482 286 96.502 256 96.522 205 96.542 232 96.562 327 96.582 302 96.602 393 96.622 448 96.642 463 96.662 438 96.682 439 96.702 468 96.722 391 96.742 362 96.762 347 96.782 354 96.802 319 96.822 393 96.842 361 96.862 449 96.882 463 96.902 343 96.922 428 96.942 351 96.962 362 96.982 338 97.002 423 97.022 376 97.042 423 97.062 451 97.082 385 97.102 345 97.122 235 282

97.142 284 97.162 359 97.182 379 97.202 337 97.222 363 97.242 286 97.262 334 97.282 272 97.302 347 97.322 348 97.342 415 97.362 429 97.382 284 97.402 270 97.422 295 97.442 318 97.462 284 97.482 390 97.502 432 97.522 400 97.542 309 97.562 418 97.582 353 97.602 434 97.622 307 97.642 392 97.662 382 97.682 388 97.702 457 97.722 352 97.742 380 97.762 325 97.782 406 97.802 384 97.822 350 97.842 380 97.862 417 97.882 397 97.902 376 97.922 388 97.942 361 97.962 324 283

97.982 377 98.002 454 98.022 363 98.042 398 98.062 303 98.082 385 98.102 405 98.122 377 98.142 351 98.162 424 98.182 363 98.202 372 98.222 465 98.242 414 98.262 423 98.282 448 98.302 364 98.322 396 98.342 454 98.362 467 98.382 409 98.402 366 98.422 485 98.442 417 98.462 384 98.482 326 98.502 436 98.522 398 98.542 357 98.562 370 98.582 391 98.602 354 98.622 318 98.642 374 98.662 339 98.682 369 98.702 366 98.722 365 98.742 449 98.762 511 98.782 454 98.802 387 284

98.822 375 98.842 336 98.862 370 98.882 366 98.902 287 98.922 234 98.942 276 98.962 297 98.982 260 99.002 274 99.022 309 99.042 285 99.062 312 99.082 305 99.102 315 99.122 321 99.142 330 99.162 308 99.182 284 99.202 318 99.222 347 99.242 310 99.262 193 99.282 220 99.302 194 99.322 301 99.342 335 99.362 267 99.382 104 99.402 216 99.422 145 99.442 124 99.462 125 99.482 33 99.502 83 99.522 71 99.542 16 99.562 136 99.582 118 99.602 85 99.622 152 99.642 132 285

99.662 169 99.682 203 99.702 235 99.722 141 99.742 196 99.762 215 99.782 149 99.802 252 99.822 333 99.842 243 99.862 215 99.882 273 99.902 277 99.922 241 99.942 214 99.962 201 99.982 152 100.002 256 100.022 256 100.042 170 100.062 199 100.082 189 100.102 227 100.122 204 100.142 188 100.162 195 100.182 281 100.202 217 100.222 278 100.242 229 100.262 254 100.282 329 100.302 285 100.322 327 100.342 339 100.362 276 100.382 365 100.402 322 100.422 345 100.442 335 100.462 276 100.482 278 286

100.502 272 100.522 280 100.542 287 100.562 437 100.582 356 100.602 371 100.622 348 100.642 275 100.662 420 100.682 360 100.702 296 100.722 338 100.742 336 100.762 421 100.782 424 100.802 439 100.822 349 100.842 396 100.862 393 100.882 409 100.902 398 100.922 354 100.942 459 100.962 433 100.982 426 101.002 394 101.022 419 101.042 500 101.062 411 101.082 461 101.102 316 101.122 388 101.142 356 101.162 421 101.182 318 101.202 301 101.222 256 101.242 391 101.262 409 101.282 402 101.302 342 101.322 391 287

101.342 304 101.362 294 101.382 362 101.402 323 101.422 332 101.442 357 101.462 358 101.482 345 101.502 293 101.522 313 101.542 296 101.562 337 101.582 305 101.602 263 101.622 326 101.642 333 101.662 304 101.682 389 101.702 343 101.722 355 101.742 366 101.762 299 101.782 349 101.802 353 101.822 446 101.842 432 101.862 406 101.882 395 101.902 356 101.922 406 101.942 369 101.962 401 101.982 353 102.002 412 102.022 400 102.042 416 102.062 284 102.082 286 102.102 269 102.122 373 102.142 358 102.162 265 288

102.182 320 102.202 326 102.222 314 102.242 341 102.262 330 102.282 324 102.302 373 102.322 288 102.342 373 102.362 339 102.382 427 102.402 368 102.422 429 102.442 389 102.462 336 102.482 372 102.502 345 102.522 251 102.542 349 102.562 311 102.582 340 102.602 286 102.622 310 102.642 333 102.662 270 102.682 325 102.702 338 102.722 301 102.742 355 102.762 425 102.782 268 102.802 323 102.822 279 102.842 287 102.862 278 102.882 352 102.902 328 102.922 333 102.942 366 102.962 362 102.982 359 103.002 363 289

103.022 344 103.042 283 103.062 313 103.082 304 103.102 379 103.122 343 103.142 334 103.162 372 103.182 466 103.202 407 103.222 420 103.242 380 103.262 310 103.282 402 103.302 354 103.322 420 103.342 359 103.362 321 103.382 376 103.402 366 103.422 433 103.442 424 103.462 405 103.482 395 103.502 361 103.522 389 103.542 337 103.562 372 103.582 311 103.602 403 103.622 359 103.642 282 103.662 383 103.682 288 103.702 241 103.722 254 103.742 300 103.762 303 103.782 385 103.802 460 103.822 462 103.842 359 290

103.862 416 103.882 374 103.902 294 103.922 373 103.942 431 103.962 357 103.982 402 104.002 368 104.022 356 104.042 308 104.062 379 104.082 356 104.102 316 104.122 343 104.142 288 104.162 446 104.182 391 104.202 424 104.222 364 104.242 299 104.262 328 104.282 408 104.302 450 104.322 362 104.342 374 104.362 288 104.382 283 104.402 268 104.422 211 104.442 247 104.462 221 104.482 277 104.502 214 104.522 213 104.542 232 104.562 238 104.582 140 104.602 199 104.622 277 104.642 316 104.662 255 104.682 330 291

104.702 380 104.722 357 104.742 328 104.762 324 104.782 379 104.802 280 104.822 379 104.842 362 104.862 397 104.882 426 104.902 381 104.922 373 104.942 362 104.962 373 104.982 380 105.002 374 105.022 416 105.042 398 105.062 319 105.082 527 105.102 445 105.122 436 105.142 405 105.162 467 105.182 322 105.202 331 105.222 321 105.242 266 105.262 279 105.282 300 105.302 371 105.322 434 105.342 332 105.362 385 105.382 494 105.402 503 105.422 510 105.442 398 105.462 469 105.482 411 105.502 426 105.522 398 292

105.542 264 105.562 257 105.582 241 105.602 205 105.622 244 105.642 223 105.662 311 105.682 245 105.702 239 105.722 233 105.742 380 105.762 390 105.782 340 105.802 346 105.822 352 105.842 323 105.862 445 105.882 334 105.902 263 105.922 339 105.942 377 105.962 320 105.982 357 106.002 378 106.022 328 106.042 385 106.062 415 106.082 437 106.102 464 106.122 525 106.142 508 106.162 510 106.182 440 106.202 427 106.222 401 106.242 511 106.262 471 106.282 487 106.302 423 106.322 286 106.342 400 106.362 421 293

106.382 398 106.402 446 106.422 413 106.442 501 106.462 428 106.482 431 106.502 430 106.522 401 106.542 312 106.562 384 106.582 411 106.602 359 106.622 326 106.642 300 106.662 347 106.682 309 106.702 208 106.722 121 106.742 203 106.762 120 106.782 177 106.802 177 106.822 286 106.842 247 106.862 202 106.882 136 106.902 231 106.922 270 106.942 228 106.962 164 106.982 193 107.002 285 107.022 240 107.042 282 107.062 283 107.082 293 107.102 237 107.122 281 107.142 289 107.162 336 107.182 302 107.202 342 294

107.222 300 107.242 384 107.262 366 107.282 309 107.302 283 107.322 329 107.342 282 107.362 318 107.382 306 107.402 335 107.422 332 107.442 321 107.462 407 107.482 343 107.502 408 107.522 279 107.542 233 107.562 224 107.582 318 107.602 297 107.622 266 107.642 331 107.662 341 107.682 312 107.702 354 107.722 293 107.742 355 107.762 339 107.782 342 107.802 305 107.822 372 107.842 275 107.862 300 107.882 390 107.902 324 107.922 301 107.942 372 107.962 317 107.982 274 108.002 401 108.022 327 108.042 386 295

108.062 339 108.082 355 108.102 320 108.122 386 108.142 330 108.162 272 108.182 392 108.202 377 108.222 300 108.242 269 108.262 367 108.282 315 108.302 348 108.322 324 108.342 378 108.362 384 108.382 331 108.402 284 108.422 357 108.442 363 108.462 337 108.482 409 108.502 364 108.522 319 108.542 375 108.562 388 108.582 376 108.602 360 108.622 337 108.642 334 108.662 324 108.682 304 108.702 265 108.722 277 108.742 360 108.762 396 108.782 492 108.802 362 108.822 397 108.842 317 108.862 353 108.882 398 296

108.902 391 108.922 509 108.942 491 108.962 450 108.982 478 109.002 412 109.022 407 109.042 308 109.062 402 109.082 359 109.102 364 109.122 304 109.142 394 109.162 432 109.182 431 109.202 345 109.222 389 109.242 357 109.262 285 109.282 194 109.302 274 109.322 272 109.342 197 109.362 230 109.382 199 109.402 174 109.422 285 109.442 273 109.462 358 109.482 350 109.502 329 109.522 398 109.542 381 109.562 409 109.582 492 109.602 362 109.622 408 109.642 389 109.662 368 109.682 346 109.702 363 109.722 259 297

109.742 246 109.762 382 109.782 384 109.802 373 109.822 414 109.842 391 109.862 384 109.882 384 109.902 469 109.922 499 109.942 503 109.962 507 109.982 460 110.002 488 110.022 402 110.042 521 110.062 441 110.082 467 110.102 473 110.122 455 110.142 448 110.162 412 110.182 475 110.202 414 110.222 364 110.242 509 110.262 393 110.282 484 110.302 412 110.322 483 110.342 438 110.362 470 110.382 461 110.402 530 110.422 540 110.442 440 110.462 444 110.482 476 110.502 530 110.522 531 110.542 467 110.562 542 298

110.582 475 110.602 442 110.622 446 110.642 525 110.662 482 110.682 458 110.702 488 110.722 366 110.742 446 110.762 408 110.782 360 110.802 489 110.822 401 110.842 407 110.862 493 110.882 340 110.902 491 110.922 505 110.942 452 110.962 366 110.982 381 111.002 378 111.022 450 111.042 422 111.062 279 111.082 244 111.102 218 111.122 266 111.142 177 111.162 272 111.182 224 111.202 191 111.222 175 111.242 155 111.262 346 111.282 210 111.302 259 111.322 197 111.342 177 111.362 206 111.382 179 111.402 156 299

111.422 199 111.442 221 111.462 230 111.482 139 111.502 215 111.522 206 111.542 209 111.562 218 111.582 244 111.602 185 111.622 125 111.642 241 111.662 192 111.682 171 111.702 244 111.722 217 111.742 217 111.762 213 111.782 233 111.802 209 111.822 246 111.842 180 111.862 251 111.882 219 111.902 331 111.922 278 111.942 282 111.962 262 111.982 313 112.002 223 112.022 295 112.042 133 112.062 205 112.082 182 112.102 169 112.122 296 112.142 316 112.162 362 112.182 352 112.202 227 112.222 259 112.242 188 300

112.262 305 112.282 352 112.302 325 112.322 317 112.342 233 112.362 248 112.382 336 112.402 273 112.422 305 112.442 284 112.462 293 112.482 274 112.502 200 112.522 308 112.542 319 112.562 274 112.582 245 112.602 257 112.622 246 112.642 216 112.662 194 112.682 273 112.702 292 112.722 286 112.742 290 112.762 281 112.782 314 112.802 349 112.822 172 112.842 265 112.862 275 112.882 269 112.902 287 112.922 262 112.942 250 112.962 205 112.982 319 113.002 335 113.022 245 113.042 314 113.062 235 113.082 300 301

113.102 246 113.122 278 113.142 228 113.162 214 113.182 237 113.202 269 113.222 203 113.242 206 113.262 149 113.282 137 113.302 88 113.322 42 113.342 21 113.362 129 113.382 83 113.402 113 113.422 116 113.442 69 113.462 87 113.482 99 113.502 196 113.522 309 113.542 356 113.562 312 113.582 275 113.602 210 113.622 305 113.642 215 113.662 242 113.682 246 113.702 342 113.722 271 113.742 324 113.762 397 113.782 322 113.802 294 113.822 271 113.842 232 113.862 321 113.882 265 113.902 251 113.922 266 302

113.942 309 113.962 241 113.982 327 114.002 331 114.022 312 114.042 318 114.062 315 114.082 352 114.102 390 114.122 411 114.142 425 114.162 443 114.182 430 114.202 476 114.222 407 114.242 462 114.262 459 114.282 391 114.302 311 114.322 382 114.342 338 114.362 387 114.382 368 114.402 417 114.422 368 114.442 351 114.462 371 114.482 355 114.502 366 114.522 315 114.542 234 114.562 342 114.582 195 114.602 200 114.622 182 114.642 99 114.662 226 114.682 167 114.702 158 114.722 90 114.742 157 114.762 128 303

114.782 71 114.802 93 114.822 147 114.842 136 114.862 120 114.882 206 114.902 181 114.922 156 114.942 167 114.962 165 114.982 170 115.002 165 115.022 161 115.042 170 115.062 149 115.082 127 115.102 82 115.122 120 115.142 148 115.162 126 115.182 85 115.202 192 115.222 117 115.242 94 115.262 140 115.282 56 115.302 153 115.322 176 115.342 173 115.362 114 115.382 145 115.402 234 115.422 106 115.442 137 115.462 92 115.482 163 115.502 108 115.522 143 115.542 135 115.562 133 115.582 126 115.602 127 304

115.622 148 115.642 215 115.662 204 115.682 159 115.702 151 115.722 163 115.742 146 115.762 115 115.782 208 115.802 175 115.822 160 115.842 221 115.862 238 115.882 220 115.902 304 115.922 297 115.942 260 115.962 274 115.982 193 116.002 168 116.022 188 116.042 185 116.062 304 116.082 203 116.102 271 116.122 228 116.142 233 116.162 230 116.182 169 116.202 156 116.222 258 116.242 258 116.262 214 116.282 192 116.302 279 116.322 177 116.342 169 116.362 201 116.382 231 116.402 284 116.422 248 116.442 265 305

116.462 281 116.482 216 116.502 245 116.522 198 116.542 218 116.562 234 116.582 215 116.602 327 116.622 267 116.642 231 116.662 293 116.682 253 116.702 297 116.722 254 116.742 176 116.762 191 116.782 289 116.802 213 116.822 170 116.842 200 116.862 248 116.882 304 116.902 287 116.922 270 116.942 264 116.962 253 116.982 119 117.002 186 117.022 193 117.042 226 117.062 225 117.082 244 117.102 277 117.122 296 117.142 201 117.162 233 117.182 307 117.202 162 117.222 295 117.242 285 117.262 204 117.282 194 306

117.302 100 117.322 172 117.342 201 117.362 153 117.382 123 117.402 207 117.422 228 117.442 289 117.462 206 117.482 286 117.502 248 117.522 181 117.542 235 117.562 267 117.582 219 117.602 267 117.622 294 117.642 202 117.662 224 117.682 288 117.702 307 117.722 267 117.742 272 117.762 155 117.782 240 117.802 313 117.822 218 117.842 275 117.862 297 117.882 273 117.902 248 117.922 271 117.942 285 117.962 267 117.982 297 118.002 285 118.022 302 118.042 270 118.062 302 118.082 249 118.102 260 118.122 320 307

118.142 279 118.162 256 118.182 267 118.202 320 118.222 296 118.242 255 118.262 285 118.282 233 118.302 331 118.322 222 118.342 284 118.362 287 118.382 287 118.402 300 118.422 344 118.442 242 118.462 206 118.482 241 118.502 267 118.522 272 118.542 252 118.562 318 118.582 320 118.602 181 118.622 234 118.642 293 118.662 225 118.682 157 118.702 23 118.722 63 118.742 95 118.762 42 118.782 0 118.802 114 118.822 137 118.842 76 118.862 55 118.882 83 118.902 142 118.922 238 118.942 283 118.962 291 308

118.982 159 119.002 155 119.022 212 119.042 199 119.062 219 119.082 297 119.102 290 119.122 236 119.142 183 119.162 294 119.182 247 119.202 275 119.222 284 119.242 287 119.262 252 119.282 228 119.302 231 119.322 282 119.342 206 119.362 225 119.382 280 119.402 303 119.422 367 119.442 238 119.462 284 119.482 278 119.502 281 119.522 332 119.542 239 119.562 214 119.582 322 119.602 260 119.622 258 119.642 351 119.662 262 119.682 354 119.702 270 119.722 258 119.742 284 119.762 189 119.782 308 119.802 326 309

119.822 294 119.842 343 119.862 252 119.882 209 119.902 257 119.922 228 119.942 217 119.962 229 119.982 284 120.002 226 120.022 319 120.042 413 120.062 324 120.082 275 120.102 309 120.122 268 120.142 257 120.162 284 120.182 272 120.202 287 120.222 285 120.242 217 120.262 180 120.282 244 120.302 247 120.322 236 120.342 300 120.362 280 120.382 277 120.402 254 120.422 288 120.442 236 120.462 282 120.482 220 120.502 206 120.522 245 120.542 214 120.562 264 120.582 302 120.602 319 120.622 275 120.642 239 310

120.662 293 120.682 307 120.702 265 120.722 325 120.742 272 120.762 256 120.782 283 120.802 228 120.822 293 120.842 277 120.862 296 120.882 288 120.902 277 120.922 241 120.942 270 120.962 203 120.982 248 121.002 249 121.022 314 121.042 276 121.062 278 121.082 210 121.102 236 121.122 187 121.142 212 121.162 274 121.182 222 121.202 181 121.222 211 121.242 257 121.262 283 121.282 272 121.302 235 121.322 209 121.342 204 121.362 259 121.382 240 121.402 223 121.422 313 121.442 216 121.462 157 121.482 134 311

121.502 250 121.522 292 121.542 340 121.562 209 121.582 257 121.602 268 121.622 287 121.642 263 121.662 201 121.682 181 121.702 182 121.722 227 121.742 224 121.762 207 121.782 159 121.802 206 121.822 271 121.842 222 121.862 184 121.882 200 121.902 281 121.922 222 121.942 231 121.962 244 121.982 166 122.002 178 122.022 172 122.042 236 122.062 158 122.082 164 122.102 15 122.122 94 122.142 68 122.162 112 122.182 131 122.202 127 122.222 162 122.242 161 122.262 216 122.282 226 122.302 164 122.322 239 312

122.342 138 122.362 176 122.382 164 122.402 128 122.422 230 122.442 166 122.462 167 122.482 219 122.502 150

313

APPENDIX K

POTASSIUM XRF DATA NORTH STORRS CORE

Depth K (XRF (cm) counts) 4.001 375 4.021 382 4.041 344 4.061 323 4.081 318 4.101 397 4.121 325 4.141 369 4.161 346 4.181 345 4.201 316 4.221 310 4.241 385 4.261 354 4.281 367 4.301 312 4.321 340 4.341 334 4.381 341 4.401 410 4.421 349 4.441 356 4.461 291 4.481 335 4.501 314 4.521 365 4.541 388 4.561 370 4.581 301 4.601 292 4.621 276 4.641 305 314

4.661 317 4.681 322 4.701 345 4.721 307 4.741 356 4.761 377 4.781 352 4.801 335 4.821 332 4.841 312 4.861 287 4.881 379 4.901 347 4.921 345 4.941 315 4.961 379 4.981 291 5.001 332 5.021 418 5.041 380 5.061 367 5.081 315 5.101 306 5.121 354 5.141 394 5.161 400 5.181 315 5.201 369 5.221 338 5.241 445 5.261 358 5.281 338 5.301 403 5.321 367 5.341 422 5.361 371 5.381 369 5.401 362 5.421 368 5.441 353 5.461 363 5.481 368 315

5.501 349 5.521 336 5.541 393 5.561 364 5.581 385 5.601 449 5.621 365 5.641 342 5.661 359 5.681 342 5.701 356 5.721 427 5.741 439 5.761 371 5.781 396 5.801 378 5.821 388 5.841 359 5.861 386 5.881 409 5.901 359 5.921 348 5.941 340 5.961 401 5.981 416 6.001 361 6.021 288 6.041 407 6.061 301 6.081 296 6.101 389 6.121 300 6.141 356 6.161 430 6.181 348 6.201 344 6.221 268 6.241 343 6.261 388 6.281 250 6.301 390 6.321 351 316

6.341 347 6.361 269 6.381 271 6.401 400 6.421 374 6.441 322 6.461 382 6.481 371 6.501 404 6.521 368 6.541 292 6.561 392 6.581 374 6.601 295 6.621 316 6.641 335 6.661 349 6.681 349 6.701 359 6.721 363 6.741 409 6.761 385 6.781 347 6.801 403 6.821 377 6.841 390 6.861 415 6.881 376 6.901 400 6.921 404 6.941 371 6.961 362 6.981 370 7.001 392 7.021 371 7.041 451 7.061 404 7.081 359 7.101 388 7.121 446 7.141 365 7.161 372 317

7.181 389 7.201 381 7.221 426 7.241 328 7.261 386 7.281 328 7.301 349 7.321 402 7.341 384 7.361 414 7.381 455 7.401 379 7.421 368 7.441 351 7.461 427 7.481 385 7.501 434 7.521 369 7.541 308 7.561 313 7.581 378 7.601 428 7.621 358 7.641 357 7.661 385 7.681 421 7.701 416 7.721 360 7.741 363 7.761 404 7.781 467 7.801 410 7.821 352 7.841 358 7.861 340 7.881 344 7.901 317 7.921 316 7.941 373 7.961 306 7.981 360 8.001 315 318

8.021 358 8.041 327 8.061 289 8.081 320 8.101 289 8.121 343 8.141 360 8.161 333 8.181 291 8.201 362 8.221 340 8.241 391 8.261 299 8.281 345 8.301 307 8.321 341 8.341 335 8.361 355 8.381 334 8.401 307 8.421 393 8.441 331 8.461 379 8.481 356 8.501 344 8.521 420 8.541 378 8.561 333 8.581 350 8.601 336 8.621 411 8.641 336 8.661 422 8.681 351 8.701 293 8.721 361 8.741 323 8.761 343 8.781 326 8.801 357 8.821 368 8.841 306 319

8.861 283 8.881 343 8.901 326 8.921 372 8.941 325 8.961 378 8.981 393 9.001 394 9.021 347 9.041 250 9.061 312 9.081 348 9.101 340 9.121 340 9.141 337 9.161 377 9.181 354 9.201 327 9.221 393 9.241 359 9.261 367 9.281 361 9.301 410 9.321 352 9.341 343 9.361 346 9.381 328 9.401 322 9.421 361 9.441 369 9.461 350 9.481 367 9.501 387 9.521 403 9.541 395 9.561 470 9.581 420 9.601 436 9.621 400 9.641 400 9.661 334 9.681 396 320

9.701 330 9.721 322 9.741 279 9.761 300 9.781 394 9.801 296 9.821 321 9.841 320 9.861 325 9.881 308 9.901 326 9.921 316 9.941 361 9.961 322 9.981 392 10.001 326 10.021 399 10.041 387 10.061 354 10.081 436 10.101 358 10.121 390 10.141 366 10.161 415 10.181 362 10.201 379 10.221 329 10.241 270 10.261 388 10.281 321 10.301 379 10.321 325 10.341 376 10.361 368 10.381 376 10.401 363 10.421 390 10.441 384 10.461 334 10.481 428 10.501 378 10.521 360 321

10.541 401 10.561 416 10.581 382 10.601 498 10.621 405 10.641 379 10.661 336 10.681 325 10.701 369 10.721 381 10.741 386 10.761 422 10.781 418 10.801 341 10.821 413 10.841 370 10.861 394 10.881 361 10.901 300 10.921 297 10.941 371 10.961 326 10.981 318 11.001 325 11.021 332 11.041 306 11.061 293 11.081 287 11.101 382 11.121 323 11.141 404 11.161 358 11.181 400 11.201 381 11.221 391 11.241 333 11.261 364 11.281 319 11.301 402 11.321 338 11.341 311 11.361 316 322

11.381 317 11.401 352 11.421 322 11.441 265 11.461 306 11.481 315 11.501 336 11.521 274 11.541 296 11.561 279 11.581 311 11.601 297 11.621 321 11.641 339 11.661 292 11.681 295 11.701 279 11.721 241 11.741 238 11.761 316 11.781 301 11.801 319 11.821 261 11.841 326 11.861 273 11.881 312 11.901 262 11.921 324 11.941 312 11.961 298 11.981 335 12.001 373 12.021 299 12.041 358 12.061 353 12.081 340 12.101 369 12.121 392 12.141 261 12.161 308 12.181 374 12.201 360 323

12.221 359 12.241 276 12.261 402 12.281 480 12.301 358 12.321 399 12.341 422 12.361 441 12.381 335 12.401 342 12.421 335 12.441 334 12.461 259 12.481 356 12.501 274 12.521 265 12.541 255 12.561 307 12.581 272 12.601 285 12.621 258 12.641 309 12.661 270 12.681 267 12.701 273 12.721 311 12.741 230 12.761 205 12.781 208 12.801 221 12.821 206 12.841 253 12.861 276 12.881 303 12.901 278 12.921 250 12.941 248 12.961 319 12.981 307 13.001 226 13.021 285 13.041 204 324

13.061 257 13.081 232 13.101 229 13.121 252 13.141 257 13.161 272 13.181 283 13.201 209 13.221 211 13.241 282 13.261 268 13.281 257 13.301 257 13.321 283 13.341 217 13.361 297 13.381 256 13.401 281 13.421 298 13.441 282 13.461 297 13.481 252 13.501 271 13.521 289 13.541 270 13.561 239 13.581 253 13.601 263 13.621 237 13.641 327 13.661 242 13.681 211 13.701 230 13.722 259 13.742 248 13.762 248 13.782 184 13.802 197 13.822 235 13.842 227 13.862 239 13.882 213 325

13.902 209 13.922 189 13.942 149 13.961 151 13.985 181 14.001 133 14.022 150 14.041 129 14.065 197 14.081 279 14.105 246 14.122 287 14.141 242 14.165 246 14.182 325 14.202 312 14.222 233 14.242 167 14.262 245 14.282 218 14.301 251 14.321 193 14.341 174 14.361 235 14.381 163 14.401 198 14.421 294 14.441 308 14.461 320 14.481 292 14.501 369 14.521 356 14.541 399 14.561 356 14.581 364 14.601 456 14.621 353 14.641 418 14.661 373 14.681 445 14.701 406 14.721 333 326

14.741 367 14.761 414 14.781 399 14.801 378 14.821 438 14.841 417 14.861 400 14.881 414 14.901 309 14.921 336 14.941 340 14.961 367 14.981 392 15.001 389 15.021 394 15.041 403 15.061 404 15.081 412 15.101 455 15.121 341 15.141 360 15.161 399 15.181 416 15.201 444 15.221 415 15.241 443 15.261 418 15.281 408 15.301 439 15.321 375 15.341 367 15.361 383 15.381 362 15.401 400 15.421 400 15.441 372 15.461 430 15.481 389 15.501 407 15.521 345 15.541 409 15.561 413 327

15.581 389 15.601 402 15.621 451 15.641 473 15.661 359 15.681 374 15.701 334 15.721 380 15.741 305 15.761 393 15.781 391 15.801 326 15.821 298 15.841 395 15.861 396 15.881 402 15.901 380 15.921 398 15.941 348 15.961 357 15.981 369 16.001 419 16.021 386 16.041 470 16.061 376 16.081 363 16.101 366 16.121 402 16.141 398 16.161 347 16.181 400 16.201 361 16.221 314 16.241 337 16.261 358 16.281 353 16.301 333 16.321 315 16.341 360 16.361 344 16.381 379 16.401 371 328

16.421 408 16.441 414 16.461 405 16.481 323 16.501 392 16.521 415 16.541 350 16.561 398 16.581 410 16.601 455 16.621 375 16.641 341 16.661 440 16.681 438 16.701 358 16.721 364 16.741 369 16.761 443 16.781 403 16.801 374 16.821 332 16.841 396 16.861 438 16.881 381 16.901 404 16.921 280 16.941 363 16.961 387 16.981 319 17.001 339 17.021 271 17.041 358 17.061 367 17.081 359 17.101 387 17.121 336 17.141 373 17.161 307 17.181 355 17.201 366 17.221 373 17.241 287 329

17.261 349 17.281 335 17.301 325 17.321 282 17.341 288 17.361 303 17.381 313 17.401 349 17.421 333 17.441 285 17.461 334 17.481 406 17.501 368 17.521 262 17.541 348 17.561 350 17.581 390 17.601 374 17.621 414 17.641 392 17.661 345 17.681 310 17.701 350 17.721 350 17.741 331 17.761 372 17.781 374 17.801 354 17.821 312 17.841 271 17.861 278 17.881 338 17.901 325 17.921 293 17.941 292 17.961 315 17.981 235 18.001 308 18.021 216 18.041 339 18.061 278 18.081 304 330

18.101 297 18.121 323 18.141 339 18.161 303 18.181 302 18.201 274 18.221 358 18.241 357 18.261 339 18.281 412 18.301 300 18.321 343 18.341 366 18.361 313 18.381 305 18.401 268 18.421 316 18.441 311 18.461 353 18.481 363 18.501 387 18.521 403 18.541 328 18.561 365 18.581 352 18.601 321 18.621 341 18.641 350 18.661 312 18.681 312 18.701 292 18.721 412 18.741 321 18.761 332 18.781 335 18.801 336 18.821 357 18.841 380 18.861 360 18.881 283 18.901 366 18.921 345 331

18.941 323 18.961 334 18.981 361 19.001 316 19.021 313 19.041 349 19.061 338 19.081 343 19.101 314 19.121 354 19.141 341 19.161 315 19.181 375 19.201 303 19.221 306 19.241 307 19.261 365 19.281 334 19.301 276 19.321 326 19.341 266 19.361 308 19.381 337 19.401 357 19.421 364 19.441 285 19.461 242 19.481 261 19.501 225 19.521 305 19.541 310 19.561 296 19.581 274 19.601 235 19.621 295 19.641 272 19.661 197 19.681 261 19.701 240 19.721 276 19.741 207 19.761 179 332

19.781 247 19.801 205 19.821 235 19.841 257 19.861 275 19.881 207 19.901 296 19.921 283 19.941 285 19.961 317 19.981 302 20.001 251 20.021 293 20.041 253 20.061 321 20.081 228 20.101 233 20.121 284 20.141 294 20.161 196 20.181 205 20.201 269 20.221 222 20.241 219 20.261 169 20.281 167 20.301 170 20.321 249 20.341 221 20.361 234 20.381 307 20.401 300 20.421 253 20.441 265 20.461 281 20.481 323 20.501 274 20.521 278 20.541 222 20.561 227 20.581 283 20.601 229 333

20.621 311 20.641 248 20.661 313 20.681 316 20.701 319 20.721 301 20.741 278 20.761 308 20.781 294 20.801 365 20.821 277 20.841 355 20.861 256 20.881 282 20.901 283 20.921 269 20.941 294 20.961 228 20.981 331 21.001 307 21.021 308 21.041 290 21.061 284 21.081 352 21.101 320 21.121 350 21.141 320 21.161 312 21.181 340 21.201 274 21.221 387 21.241 309 21.261 289 21.281 279 21.301 322 21.321 286 21.341 293 21.361 281 21.381 344 21.401 274 21.421 308 21.441 344 334

21.461 308 21.481 332 21.501 294 21.521 279 21.541 321 21.561 332 21.581 320 21.601 259 21.621 337 21.641 321 21.661 307 21.681 278 21.701 365 21.721 291 21.741 282 21.761 309 21.781 361 21.801 426 21.821 366 21.841 322 21.861 360 21.881 305 21.901 341 21.921 276 21.941 303 21.961 309 21.981 269 22.001 291 22.021 277 22.041 298 22.061 363 22.081 322 22.101 314 22.121 349 22.141 359 22.161 251 22.181 343 22.201 333 22.221 206 22.241 313 22.261 316 22.281 264 335

22.301 312 22.321 319 22.341 339 22.361 241 22.381 309 22.401 275 22.421 305 22.441 279 22.461 296 22.481 262 22.501 281 22.521 238 22.541 318 22.561 317 22.581 332 22.601 284 22.621 270 22.641 329 22.661 298 22.681 312 22.701 279 22.721 320 22.741 280 22.761 261 22.781 333 22.801 304 22.821 311 22.841 290 22.861 264 22.881 271 22.901 207 22.921 217 22.941 261 22.961 288 22.981 284 23.001 310 23.021 280 23.041 276 23.061 269 23.081 233 23.101 249 23.121 212 336

23.141 322 23.161 327 23.181 356 23.201 292 23.221 263 23.241 233 23.261 371 23.281 284 23.301 343 23.321 317 23.341 353 23.361 293 23.381 250 23.401 278 23.421 357 23.441 249 23.461 274 23.481 314 23.501 328 23.521 297 23.541 285 23.561 290 23.581 285 23.601 269 23.621 243 23.641 295 23.661 327 23.681 296 23.701 324 23.721 285 23.741 358 23.761 315 23.781 358 23.801 365 23.821 346 23.841 277 23.861 319 23.881 294 23.901 285 23.921 402 23.941 323 23.961 307 337

23.981 309 24.001 286 24.021 308 24.041 293 24.061 302 24.081 352 24.101 346 24.121 358 24.141 368 24.161 307 24.181 303 24.201 351 24.221 305 24.241 259 24.261 342 24.281 416 24.301 333 24.321 304 24.341 328 24.361 394 24.381 342 24.401 362 24.421 346 24.441 348 24.461 404 24.481 368 24.501 321 24.521 348 24.541 388 24.561 349 24.581 400 24.601 353 24.621 322 24.641 330 24.661 343 24.681 389 24.701 316 24.721 349 24.741 353 24.761 395 24.781 366 24.801 393 338

24.821 364 24.841 286 24.861 364 24.881 329 24.901 430 24.921 417 24.941 371 24.961 367 24.981 400 25.001 382 25.021 412 25.041 399 25.061 395 25.081 390 25.101 356 25.121 313 25.141 349 25.161 333 25.181 389 25.201 365 25.221 400 25.241 372 25.261 377 25.281 440 25.301 407 25.321 392 25.341 365 25.361 373 25.381 310 25.401 288 25.421 288 25.441 327 25.461 350 25.481 360 25.501 334 25.521 304 25.541 356 25.561 357 25.581 409 25.601 347 25.621 360 25.641 323 339

25.661 383 25.681 363 25.701 357 25.721 319 25.741 335 25.761 339 25.781 321 25.801 381 25.821 301 25.841 355 25.861 331 25.881 270 25.901 340 25.921 367 25.941 332 25.961 399 25.981 378 26.001 342 26.021 299 26.041 319 26.061 299 26.081 367 26.101 332 26.121 298 26.141 342 26.161 349 26.181 325 26.201 317 26.221 343 26.241 293 26.261 362 26.281 450 26.301 448 26.321 415 26.341 349 26.361 335 26.381 316 26.401 284 26.421 377 26.441 363 26.461 346 26.481 318 340

26.501 348 26.521 333 26.541 304 26.561 379 26.581 318 26.601 383 26.621 281 26.641 395 26.661 361 26.681 286 26.701 276 26.721 315 26.741 299 26.761 353 26.781 280 26.801 332 26.821 345 26.841 308 26.861 326 26.881 201 26.901 249 26.921 280 26.941 280 26.961 198 26.981 296 27.001 320 27.021 295 27.041 255 27.061 293 27.081 381 27.101 337 27.121 298 27.141 263 27.161 225 27.181 304 27.201 322 27.221 330 27.241 311 27.261 370 27.281 353 27.301 308 27.321 328 341

27.341 284 27.361 329 27.381 353 27.401 278 27.421 291 27.441 283 27.461 344 27.481 340 27.501 320 27.521 315 27.541 393 27.561 302 27.581 352 27.601 323 27.621 261 27.641 296 27.661 361 27.681 305 27.701 311 27.721 345 27.741 344 27.761 284 27.781 360 27.801 325 27.821 289 27.841 324 27.861 325 27.881 339 27.901 304 27.921 352 27.941 351 27.961 339 27.981 395 28.001 259 28.021 230 28.041 318 28.061 236 28.081 366 28.101 284 28.121 297 28.141 348 28.161 298 342

28.181 298 28.201 328 28.221 261 28.241 288 28.261 296 28.281 288 28.301 289 28.321 318 28.341 298 28.361 341 28.381 278 28.401 267 28.421 340 28.441 338 28.461 330 28.481 273 28.501 309 28.521 324 28.541 289 28.561 273 28.581 259 28.601 315 28.621 393 28.641 332 28.661 401 28.681 322 28.701 262 28.721 296 28.741 346 28.761 300 28.781 357 28.801 300 28.821 359 28.841 279 28.861 300 28.881 346 28.901 367 28.921 346 28.941 297 28.961 283 28.981 352 29.001 348 343

29.021 309 29.041 334 29.061 323 29.081 303 29.101 312 29.121 388 29.141 334 29.161 369 29.181 354 29.201 368 29.221 307 29.241 330 29.261 323 29.281 332 29.301 353 29.321 330 29.341 308 29.361 362 29.381 326 29.401 293 29.421 293 29.441 304 29.461 360 29.481 379 29.501 361 29.521 351 29.541 343 29.561 419 29.581 353 29.601 317 29.621 336 29.641 359 29.661 393 29.681 390 29.701 411 29.721 388 29.741 299 29.761 405 29.781 285 29.801 442 29.821 395 29.841 438 344

29.861 359 29.881 395 29.901 310 29.921 388 29.941 329 29.961 302 29.981 368 30.001 304 30.021 373 30.041 318 30.061 329 30.081 325 30.101 352 30.121 465 30.141 385 30.161 403 30.181 390 30.201 370 30.221 395 30.241 378 30.261 371 30.281 397 30.301 424 30.321 409 30.341 368 30.361 351 30.381 353 30.401 330 30.421 340 30.441 311 30.461 359 30.481 274 30.501 355 30.521 381 30.541 314 30.561 383 30.581 268 30.601 371 30.621 319 30.641 406 30.661 365 30.681 283 345

30.701 325 30.721 350 30.741 367 30.761 330 30.781 309 30.801 366 30.821 433 30.841 429 30.861 413 30.881 363 30.901 392 30.921 456 30.941 490 30.961 446 30.981 450 31.001 415 31.021 424 31.041 456 31.061 459 31.081 429 31.101 409 31.121 388 31.141 355 31.161 484 31.181 433 31.201 447 31.221 425 31.241 450 31.261 351 31.281 490 31.301 480 31.321 444 31.341 460 31.361 419 31.381 369 31.401 477 31.421 441 31.441 495 31.461 410 31.481 416 31.501 483 31.521 433 346

31.541 422 31.561 391 31.581 494 31.601 432 31.621 366 31.641 404 31.661 412 31.681 423 31.701 382 31.721 422 31.741 437 31.761 417 31.781 427 31.801 522 31.821 471 31.841 499 31.861 387 31.881 514 31.901 463 31.921 458 31.941 447 31.961 467 31.981 450 32.001 474 32.021 499 32.041 459 32.061 526 32.081 531 32.101 474 32.121 484 32.141 381 32.161 468 32.181 417 32.201 426 32.221 434 32.241 490 32.261 465 32.281 487 32.301 502 32.321 409 32.341 355 32.361 524 347

32.381 383 32.401 445 32.421 401 32.441 377 32.461 386 32.481 442 32.501 439 32.521 269 32.541 382 32.561 429 32.581 413 32.601 498 32.621 411 32.641 430 32.661 338 32.681 382 32.701 441 32.721 373 32.741 441 32.761 426 32.781 366 32.801 446 32.821 408 32.841 428 32.861 447 32.881 410 32.901 446 32.921 452 32.941 398 32.961 390 32.981 429 33.001 509 33.021 402 33.041 440 33.061 429 33.081 399 33.101 433 33.121 477 33.141 418 33.161 464 33.181 477 33.201 527 348

33.221 468 33.241 468 33.261 471 33.281 402 33.301 459 33.321 468 33.341 504 33.361 449 33.381 507 33.401 472 33.421 440 33.441 441 33.461 470 33.481 507 33.501 442 33.521 475 33.541 500 33.561 503 33.581 480 33.601 484 33.621 454 33.641 444 33.661 459 33.681 382 33.701 437 33.721 431 33.741 390 33.761 478 33.781 499 33.801 429 33.821 460 33.841 374 33.861 458 33.881 452 33.901 464 33.921 489 33.941 453 33.961 456 33.981 399 34.001 319 34.021 428 34.041 389 349

34.061 430 34.081 480 34.101 467 34.121 393 34.141 426 34.161 439 34.181 381 34.201 418 34.221 453 34.241 437 34.261 500 34.281 383 34.301 504 34.321 448 34.341 375 34.361 455 34.381 511 34.401 454 34.421 430 34.441 403 34.461 373 34.481 403 34.501 425 34.521 409 34.541 442 34.561 439 34.581 455 34.601 414 34.621 440 34.641 407 34.661 529 34.681 387 34.701 458 34.721 505 34.741 458 34.761 356 34.781 438 34.801 398 34.821 448 34.841 332 34.861 441 34.881 362 350

34.901 428 34.921 418 34.941 360 34.961 420 34.981 444 35.001 434 35.021 475 35.041 468 35.061 425 35.081 437 35.101 428 35.121 426 35.141 451 35.161 381 35.181 466 35.201 422 35.221 403 35.241 414 35.261 456 35.281 421 35.301 446 35.321 438 35.341 476 35.361 405 35.381 475 35.401 469 35.421 413 35.441 479 35.461 341 35.481 464 35.501 332 35.521 357 35.541 410 35.561 352 35.581 442 35.601 408 35.621 368 35.641 407 35.661 394 35.681 318 35.701 404 35.721 377 351

35.741 400 35.761 355 35.781 380 35.801 429 35.821 394 35.841 449 35.861 417 35.881 367 35.901 447 35.921 456 35.941 467 35.961 408 35.981 420 36.001 401 36.021 469 36.041 479 36.061 498 36.081 461 36.101 455 36.121 395 36.141 385 36.161 473 36.181 442 36.201 410 36.221 431 36.241 454 36.261 431 36.281 404 36.301 396 36.321 414 36.341 415 36.361 384 36.381 376 36.401 372 36.421 394 36.441 378 36.481 362 36.501 438 36.521 416 36.541 391 36.561 397 36.581 378 352

36.601 388 36.621 403 36.641 421 36.661 412 36.681 460 36.701 401 36.721 404 36.741 388 36.761 414 36.781 351 36.801 388 36.821 361 36.841 386 36.861 348 36.881 401 36.901 380 36.921 500 36.941 439 36.961 401 36.981 368 37.001 381 37.021 452 37.041 415 37.061 354 37.081 370 37.101 447 37.121 492 37.141 400 37.161 351 37.181 364 37.201 383 37.221 419 37.241 348 37.261 395 37.281 374 37.301 354 37.321 465 37.341 398 37.361 378 37.381 391 37.401 379 37.421 288 353

37.441 401 37.461 394 37.481 314 37.501 297 37.521 389 37.541 286 37.561 338 37.581 301 37.601 453 37.621 308 37.641 341 37.661 356 37.681 333 37.701 286 37.721 389 37.741 290 37.761 295 37.781 256 37.801 270 37.821 281 37.841 319 37.861 381 37.881 278 37.901 285 37.921 265 37.941 278 37.961 277 37.981 193 38.001 161 38.021 166 38.041 212 38.061 219 38.081 189 38.101 225 38.121 211 38.141 160 38.161 194 38.181 178 38.201 201 38.221 215 38.241 226 38.261 280 354

38.281 181 38.301 189 38.321 236 38.341 271 38.361 228 38.381 220 38.401 210 38.421 293 38.441 246 38.461 188 38.481 281 38.501 192 38.521 197 38.561 288 38.581 212 38.601 303 38.621 251 38.641 253 38.661 228 38.681 306 38.701 289 38.721 366 38.741 304 38.761 300 38.781 313 38.801 297 38.821 311 38.841 302 38.861 346 38.881 363 38.901 368 38.921 337 38.941 368 38.961 383 38.981 301 39.001 277 39.021 396 39.041 355 39.061 414 39.081 396 39.101 309 39.121 377 355

39.141 328 39.161 339 39.181 313 39.201 327 39.221 310 39.241 334 39.261 345 39.281 367 39.301 326 39.321 354 39.341 281 39.361 436 39.381 376 39.401 366 39.421 386 39.441 413 39.461 360 39.481 345 39.501 414 39.521 339 39.541 388 39.561 386 39.581 360 39.601 398 39.621 392 39.641 444 39.661 415 39.681 365 39.701 322 39.721 354 39.741 375 39.761 440 39.781 345 39.801 354 39.821 388 39.841 390 39.861 398 39.881 451 39.901 331 39.921 357 39.941 462 39.961 422 356

39.981 408 40.001 414 40.021 390 40.041 378 40.061 417 40.081 437 40.101 416 40.121 326 40.141 409 40.161 394 40.181 429 40.201 489 40.221 392 40.241 354 40.261 351 40.281 435 40.301 336 40.321 426 40.341 305 40.361 351 40.381 292 40.401 283 40.421 256 40.441 327 40.461 230 40.481 258 40.501 283 40.521 373 40.541 349 40.561 319 40.581 335 40.601 303 40.621 254 40.641 276 40.661 392 40.681 335 40.701 339 40.721 344 40.741 358 40.761 404 40.781 343 40.801 374 357

40.821 363 40.841 376 40.861 301 40.881 390 40.901 385 40.921 364 40.941 307 40.961 312 40.981 356 41.001 268 41.021 301 41.041 352 41.061 340 41.081 334 41.101 316 41.121 352 41.141 381 41.161 341 41.181 452 41.201 325 41.221 302 41.241 398 41.261 328 41.281 257 41.301 339 41.321 312 41.341 247 41.361 365 41.381 341 41.401 271 41.421 360 41.441 326 41.461 316 41.481 236 41.501 266 41.521 295 41.541 319 41.561 253 41.581 265 41.601 297 41.621 335 41.641 230 358

41.661 287 41.681 337 41.701 244 41.721 231 41.741 203 41.761 198 41.781 233 41.801 254 41.821 288 41.841 224 41.861 211 41.881 242 41.901 159 41.921 233 41.941 204 41.961 264 41.981 240 42.001 239 42.021 297 42.041 230 42.061 161 42.081 150 42.101 124 42.121 107 42.141 192 42.161 178 42.181 221 42.201 179 42.221 159 42.241 117 42.261 41 42.281 96 42.301 100 42.321 149 42.341 142 42.361 115 42.381 152 42.401 156 42.421 151 42.441 142 42.461 142 42.481 140 359

42.501 63 42.521 98 42.541 90 42.561 143 42.581 107 42.601 178 42.621 156 42.641 115 42.661 102 42.681 152 42.701 77 42.721 102 42.741 182 42.761 125 42.781 159 42.801 69 42.821 174 42.841 188 42.861 217 42.881 205 42.901 150 42.921 73 42.941 97 42.961 95 42.981 192 43.001 118 43.021 143 43.041 222 43.061 130 43.081 145 43.101 108 43.121 203 43.141 145 43.161 106 43.181 130 43.201 120 43.221 80 43.241 91 43.261 62 43.281 93 43.301 0 43.321 0 360

43.341 45 43.361 38 43.381 39 43.401 37 43.421 36 43.441 43 43.461 6 43.481 0 43.501 6 43.521 0 43.541 0 43.561 0 43.581 0 43.601 0 43.621 0 43.641 0 43.661 0 43.681 0 43.701 0 43.721 0 43.741 0 43.761 0 43.781 0 43.801 0 43.821 0 43.841 0 43.861 0 43.881 0 43.901 0 43.921 0 43.941 0 43.961 10 43.981 11 44.001 45 44.021 68 44.041 37 44.061 69 44.081 91 44.101 120 44.121 85 44.141 95 44.161 95 361

44.181 113 44.201 64 44.221 87 44.241 76 44.261 100 44.281 92 44.301 65 44.321 97 44.341 85 44.361 83 44.381 104 44.401 68 44.421 61 44.441 12 44.461 15 44.481 33 44.501 38 44.521 127 44.541 128 44.561 140 44.581 135 44.601 180 44.621 139 44.641 133 44.661 195 44.681 131 44.701 184 44.721 233 44.741 243 44.761 288 44.781 258 44.801 294 44.821 320 44.841 312 44.861 299 44.881 352 44.901 297 44.921 338 44.941 290 44.961 355 44.981 307 45.001 255 362

45.021 298 45.041 212 45.061 301 45.081 279 45.101 230 45.121 271 45.141 319 45.161 361 45.181 297 45.201 220 45.221 246 45.241 232 45.261 197 45.281 236 45.301 148 45.321 203 45.341 223 45.361 147 45.381 205 45.401 230 45.421 166 45.441 196 45.461 199 45.481 249 45.501 144 45.521 84 45.541 113 45.561 117 45.581 231 45.601 205 45.621 194 45.641 275 45.661 252 45.681 197 45.701 225 45.721 221 45.741 191 45.761 135 45.781 165 45.801 159 45.821 173 45.841 210 363

45.861 201 45.881 158 45.901 254 45.921 258 45.941 250 45.961 253 45.981 238 46.001 226 46.021 223 46.041 236 46.061 197 46.081 146 46.101 220 46.121 283 46.141 218 46.161 210 46.181 368 46.201 249 46.221 282 46.241 218 46.261 284 46.281 256 46.301 247 46.321 297 46.341 319 46.361 241 46.381 246 46.401 265 46.421 308 46.441 271 46.461 283 46.481 277 46.501 310 46.521 234 46.541 247 46.561 232 46.581 320 46.601 274 46.621 264 46.641 349 46.661 248 46.681 299 364

46.701 288 46.721 301 46.741 244 46.761 343 46.781 359 46.801 286 46.821 337 46.841 364 46.861 303 46.881 241 46.901 218 46.921 269 46.941 265 46.961 270 46.981 256 47.001 285 47.021 281 47.041 193 47.061 321 47.081 338 47.101 305 47.121 317 47.141 336 47.161 396 47.181 256 47.201 272 47.221 188 47.241 172 47.261 224 47.281 177 47.301 200 47.321 155 47.341 249 47.361 166 47.381 212 47.401 195 47.421 181 47.441 88 47.461 177 47.481 195 47.501 136 47.521 147 365

47.541 212 47.561 194 47.581 178 47.601 197 47.621 167 47.641 252 47.661 153 47.681 154 47.701 190 47.721 185 47.741 216 47.761 163 47.781 161 47.801 116 47.821 115 47.841 167 47.861 120 47.881 220 47.901 223 47.921 183 47.941 158 47.961 151 47.981 227 48.001 179 48.021 254 48.041 147 48.061 166 48.081 169 48.101 228 48.121 204 48.141 247 48.161 195 48.181 139 48.201 306 48.221 323 48.241 348 48.261 244 48.281 245 48.301 264 48.321 308 48.341 309 48.361 244 366

48.381 357 48.401 288 48.421 264 48.441 276 48.461 73 48.481 155 48.501 136 48.521 59 48.541 74 48.561 102 48.581 153 48.601 115 48.621 191 48.641 171 48.661 230 48.681 221 48.701 229 48.721 159 48.741 247 48.761 201 48.781 186 48.801 247 48.821 231 48.841 304 48.861 229 48.881 279 48.901 283 48.921 217 48.941 330 48.961 313 48.981 327 49.001 307 49.021 374 49.041 323 49.061 381 49.081 241 49.101 339 49.121 243 49.141 268 49.161 308 49.181 300 49.201 289 367

49.221 305 49.241 274 49.261 301 49.281 228 49.301 276 49.321 280 49.341 266 49.361 292 49.381 254 49.401 313 49.421 299 49.441 244 49.461 318 49.481 242 49.501 323 49.521 311 49.541 262 49.561 248 49.581 307 49.601 298 49.621 349 49.641 386 49.661 376 49.681 338 49.701 428 49.721 351 49.741 266 49.761 347 49.781 292 49.801 377 49.821 370 49.841 370 49.861 414 49.881 373 49.901 452 49.921 382 49.941 417 49.961 422 49.981 481 50.001 481 50.021 506 50.041 507 368

50.061 525 50.081 470 50.101 413 50.121 527 50.141 591 50.161 595 50.181 497 50.201 489 50.221 534 50.241 465 50.261 503 50.281 592 50.301 511 50.321 415 50.341 514 50.361 511 50.381 433 50.401 546 50.421 484 50.441 494 50.461 496 50.481 526 50.501 524 50.521 496 50.541 497 50.561 383 50.581 509 50.601 410 50.621 384 50.641 365 50.661 323 50.681 338 50.701 314 50.721 399 50.741 381 50.761 222 50.781 325 50.801 382 50.821 328 50.841 384 50.861 406 50.881 318 369

50.901 301 50.921 400 50.941 370 50.961 392 50.981 373 51.001 422 51.021 354 51.041 347 51.061 312 51.081 386 51.101 398 51.121 403 51.141 456 51.161 351 51.181 472 51.201 373 51.221 363 51.241 322 51.261 370 51.281 379 51.301 419 51.321 415 51.341 426 51.361 514 51.381 383 51.401 472 51.421 383 51.441 433 51.461 442 51.481 445 51.501 473 51.521 423 51.541 464 51.561 387 51.581 395 51.601 485 51.621 520 51.641 340 51.661 499 51.681 444 51.701 435 51.721 380 370

51.741 454 51.761 429 51.781 472 51.801 426 51.821 424 51.841 375 51.861 392 51.881 420 51.901 382 51.921 323 51.941 449 51.961 371 51.981 338 52.001 321 52.021 392 52.041 327 52.061 403 52.081 391 52.101 428 52.121 366 52.141 464 52.161 402 52.181 298 52.201 432 52.221 314 52.241 368 52.261 360 52.281 384 52.301 309 52.321 437 52.341 262 52.361 283 52.381 290 52.401 326 52.421 351 52.441 338 52.461 384 52.481 358 52.501 315 52.521 365 52.541 319 52.561 371 371

52.581 293 52.601 279 52.621 301 52.641 234 52.661 271 52.681 294 52.701 375 52.721 352 52.741 371 52.761 328 52.781 328 52.801 327 52.821 372 52.841 359 52.861 391 52.881 356 52.901 334 52.921 330 52.941 318 52.961 375 52.981 447 53.001 454 53.021 435 53.041 472 53.061 388 53.081 465 53.101 508 53.121 420 53.141 578 53.161 536 53.181 506 53.201 457 53.221 371 53.241 299 53.261 268 53.281 252 53.301 222 53.321 289 53.341 331 53.361 243 53.381 297 53.401 286 372

53.421 337 53.441 365 53.461 257 53.481 309 53.501 310 53.521 287 53.541 233 53.561 285 53.581 238 53.601 267 53.621 218 53.641 250 53.661 276 53.681 279 53.701 210 53.721 283 53.741 235 53.761 259 53.781 240 53.801 284 53.821 185 53.841 209 53.861 234 53.881 192 53.901 195 53.921 206 53.941 263 53.961 238 53.981 280 54.001 270 54.021 255 54.041 205 54.061 269 54.081 266 54.101 321 54.121 272 54.141 260 54.161 319 54.181 400 54.201 397 54.221 424 54.241 389 373

54.261 459 54.281 403 54.301 347 54.321 411 54.341 447 54.361 395 54.381 457 54.401 398 54.421 461 54.441 498 54.461 448 54.481 355 54.501 415 54.521 446 54.541 504 54.561 509 54.581 546 54.601 505 54.621 538 54.641 570 54.661 533 54.681 603 54.701 624 54.721 649 54.741 638 54.761 582 54.781 476 54.801 440 54.821 434 54.841 415 54.861 465 54.881 389 54.901 370 54.921 237 54.941 198 54.961 131 54.981 203 55.001 163 55.021 81 55.041 134 55.061 124 55.081 152 374

55.101 82 55.121 14 55.141 40 55.161 140 55.181 133 55.201 90 55.221 301 55.241 345 55.261 471 55.281 444 55.301 586 55.321 535 55.341 598 55.361 617 55.381 643 55.401 660 55.421 776 55.441 838 55.461 750 55.481 552 55.501 319 55.521 194 55.541 112 55.561 130 55.581 152 55.601 138 55.621 102 55.641 67 55.661 0 55.681 21 55.701 18 55.725 0 55.742 0 55.762 59 55.781 101 55.802 89 55.822 96 55.845 87 55.862 42 55.882 27 55.902 7 55.921 12 375

55.941 0 55.965 0 55.981 26 56.002 0 56.025 0 56.042 0 56.061 0 56.082 0 56.101 0 56.122 0 56.141 20 56.162 47 56.181 27 56.202 77 56.221 0 56.245 0 56.261 54 56.282 27 56.301 23 56.322 20 56.341 73 56.361 0 56.381 0 56.401 0 56.421 10 56.441 0 56.461 0 56.481 0 56.501 0 56.521 27 56.541 46 56.561 94 56.581 48 56.601 0 56.621 0 56.641 0 56.661 0 56.681 0 56.701 0 56.721 0 56.741 17 56.761 26 376

56.781 14 56.801 0 56.821 0 56.841 0 56.861 11 56.881 12 56.901 0 56.921 0 56.941 0 56.961 0 56.981 0 57.001 0 57.021 0 57.041 24 57.061 25 57.081 0 57.101 0 57.121 0 57.141 0 57.161 0 57.181 0 57.201 0 57.221 0 57.241 0 57.261 0 57.281 0 57.301 0 57.321 0 57.341 0 57.361 0 57.381 0 57.401 0 57.421 0 57.441 5 57.461 0 57.481 0 57.501 7 57.521 17 57.541 5 57.561 0 57.581 0 57.601 28 377

57.621 0 57.641 0 57.661 0 57.681 0 57.701 0 57.721 0 57.741 0 57.761 36 57.781 32 57.801 19 57.821 0 57.841 0 57.861 0 57.881 0 57.901 0 57.921 12 57.941 5 57.961 0 57.981 23 58.001 13 58.021 0 58.041 0 58.061 0 58.081 0 58.101 43 58.121 0 58.141 0 58.161 0 58.181 0 58.201 0 58.221 0 58.241 0 58.261 0 58.281 0 58.301 0 58.321 25 58.341 0 58.361 0 58.381 0 58.401 0 58.421 0 58.441 0 378

58.461 0 58.481 0 58.501 0 58.521 23 58.541 0 58.561 0 58.581 0 58.601 0 58.621 0 58.641 0 58.661 0 58.681 0 58.702 0 58.722 0 58.742 0 58.762 0 58.781 0 58.801 0 58.821 64 58.845 11 58.862 0 58.881 30 58.901 0 58.922 0 58.945 0 58.965 0 58.981 0 59.005 0 59.021 0 59.041 0 59.065 0 59.081 0 59.102 0 59.121 0 59.141 0 59.161 0 59.181 0 59.201 0 59.221 0 59.242 0 59.262 0 59.282 0 379

59.302 0 59.322 0 59.342 0 59.362 0 59.382 0 59.402 0 59.422 0 59.442 0 59.462 0 59.482 0 59.501 0 59.521 0 59.541 0 59.561 0 59.581 0 59.601 0 59.621 0 59.641 0 59.661 0 59.681 0 59.701 0 59.721 0 59.741 0 59.761 0 59.781 0 59.801 0 59.821 0 59.841 0 59.861 0 59.881 0 59.901 0 59.921 0 59.941 0 59.961 0 59.981 0 60.001 0 60.021 0 60.041 0 60.062 0 60.082 0 60.102 0 60.122 0 380

60.142 0 60.162 12 60.181 0 60.201 0 60.221 0 60.242 0 60.261 0 60.285 0 60.302 0 60.322 0 60.342 0 60.361 0 60.385 0 60.402 0 60.425 0 60.441 0 60.465 0 60.482 0 60.501 0 60.525 0 60.541 0 60.565 0 60.581 0 60.605 0 60.621 0 60.645 0 60.661 0 60.682 0 60.701 0 60.725 0 60.76 0 60.761 0 60.782 0 60.801 0 60.822 0 60.841 0 60.862 0 60.881 0 60.902 0 60.922 0 60.942 0 60.961 0 381

60.985 0 61.001 0 61.025 0 61.042 23 61.062 0 61.082 0 61.101 0 61.125 0 61.142 5 61.161 0 61.185 0 61.201 0 61.225 0 61.241 0 61.265 0 61.281 0 61.305 0 61.321 0 61.345 0 61.361 0 61.385 0 61.402 0 61.421 0 61.445 0 61.465 0 61.482 0 61.502 0 61.522 0 61.542 0 61.561 0 61.581 0 61.605 0 61.622 0 61.641 0 61.662 0 61.681 0 61.702 0 61.722 0 61.742 0 61.762 0 61.782 0 61.801 0 382

61.825 0 61.841 0 61.865 0 61.881 0 61.901 0 61.925 0 61.941 0 61.965 0 61.981 0 62.005 0 62.021 0 62.045 0 62.061 0 62.085 0 62.101 0 62.125 0 62.142 0 62.162 0 62.181 0 62.205 0 62.221 26 62.245 0 62.262 21 62.285 35 62.305 31 62.321 55 62.345 55 62.365 86 62.381 114 62.405 143 62.422 91 62.445 160 62.461 143 62.485 90 62.501 140 62.521 124 62.542 150 62.561 186 62.581 171 62.601 186 62.622 179 62.641 104 383

62.661 96 62.681 186 62.701 147 62.721 165 62.741 135 62.761 169 62.781 142 62.801 203 62.821 183 62.841 232 62.861 190 62.881 193 62.901 99 62.921 136 62.941 120 62.961 141 62.981 178 63.001 155 63.021 189 63.041 146 63.061 138 63.081 175 63.101 195 63.121 176 63.141 237 63.161 197 63.181 235 63.201 198 63.221 211 63.241 242 63.261 182 63.281 183 63.301 221 63.321 209 63.341 240 63.361 261 63.381 213 63.401 197 63.421 208 63.441 192 63.461 220 63.481 196 384

63.501 180 63.521 103 63.541 137 63.561 106 63.581 126 63.601 156 63.621 75 63.641 132 63.661 32 63.681 80 63.701 78 63.721 103 63.741 0 63.761 23 63.781 35 63.801 35 63.821 0 63.841 13 63.861 9 63.881 46 63.901 7 63.921 62 63.941 58 63.961 15 63.981 134 64.001 62 64.021 45 64.041 56 64.061 0 64.081 44 64.101 150 64.121 223 64.141 126 64.161 38 64.181 0 64.201 90 64.221 24 64.241 0 64.261 0 64.281 0 64.301 28 64.321 24 385

64.341 8 64.361 55 64.381 62 64.401 16 64.421 0 64.441 49 64.461 80 64.481 10 64.501 51 64.521 0 64.541 0 64.561 0 64.581 0 64.601 31 64.621 21 64.641 29 64.661 0 64.681 0 64.701 0 64.721 35 64.741 53 64.761 0 64.781 0 64.801 24 64.821 0 64.841 65 64.861 0 64.881 5 64.901 47 64.921 52 64.941 0 64.961 0 64.981 0 65.001 32 65.021 0 65.041 100 65.061 0 65.081 15 65.101 29 65.121 35 65.141 52 65.161 43 386

65.181 86 65.201 42 65.221 47 65.241 76 65.261 81 65.281 84 65.301 10 65.321 15 65.341 35 65.361 124 65.381 66 65.401 61 65.421 41 65.441 72 65.461 107 65.481 140 65.501 119 65.521 216 65.541 198 65.561 133 65.581 78 65.601 193 65.621 138 65.641 228 65.661 102 65.681 142 65.701 29 65.721 183 65.741 109 65.761 129 65.781 112 65.801 134 65.821 265 65.841 196 65.861 215 65.881 236 65.901 191 65.921 210 65.941 196 65.961 166 65.981 278 66.001 332 387

66.021 284 66.041 284 66.061 291 66.081 237 66.101 330 66.121 347 66.141 330 66.161 306 66.181 350 66.201 315 66.221 303 66.241 329 66.261 327 66.281 340 66.301 314 66.321 363 66.341 342 66.361 310 66.381 296 66.401 281 66.421 304 66.441 327 66.461 345 66.481 405 66.501 382 66.521 369 66.541 328 66.561 349 66.581 316 66.601 358 66.621 404 66.641 373 66.661 358 66.681 346 66.701 289 66.721 375 66.741 404 66.761 469 66.781 390 66.801 438 66.821 432 66.841 364 388

66.861 316 66.881 261 66.901 231 66.921 298 66.941 273 66.961 238 66.981 289 67.001 296 67.021 259 67.041 217 67.061 234 67.081 219 67.101 166 67.121 199 67.141 63 67.161 95 67.181 112 67.201 125 67.221 101 67.241 89 67.261 97 67.281 88 67.301 59 67.321 47 67.341 5 67.361 64 67.381 0 67.401 0 67.421 30 67.441 0 67.461 6 67.481 0 67.501 0 67.521 0 67.541 0 67.561 25 67.581 0 67.601 0 67.621 0 67.641 0 67.661 82 67.681 0 389

67.701 65 67.721 0 67.741 18 67.761 7 67.781 34 67.801 69 67.821 62 67.841 63 67.861 0 67.881 12 67.901 39 67.921 40 67.941 27 67.961 119 67.981 198 68.001 136 68.021 111 68.041 194 68.061 255 68.081 145 68.101 88 68.121 201 68.141 189 68.161 155 68.181 179 68.201 121 68.221 184 68.241 204 68.261 173 68.281 284 68.301 309 68.321 224 68.341 307 68.361 359 68.381 396 68.401 371 68.421 449 68.441 459 68.461 479 68.481 643 68.501 497 68.521 334 390

68.541 352 68.561 308 68.581 208 68.601 305 68.621 300 68.641 293 68.661 331 68.681 387 68.701 400 68.721 507 68.741 427 68.761 386 68.781 269 68.801 227 68.821 228 68.841 173 68.861 157 68.881 179 68.901 296 68.921 217 68.941 166 68.961 212 68.981 211 69.001 184 69.021 97 69.041 61 69.061 93 69.081 84 69.101 75 69.121 130 69.141 194 69.161 172 69.181 277 69.201 230 69.221 177 69.241 236 69.261 246 69.281 156 69.301 266 69.321 303 69.341 192 69.361 299 391

69.381 357 69.401 346 69.421 302 69.441 368 69.461 389 69.481 402 69.501 356 69.521 344 69.541 396 69.561 436 69.581 373 69.601 403 69.621 378 69.641 381 69.661 378 69.681 432 69.701 429 69.721 455 69.741 391 69.761 458 69.781 438 69.801 513 69.821 494 69.841 525 69.861 528 69.881 527 69.901 565 69.921 564 69.941 479 69.961 460 69.981 546 70.001 476 70.021 445 70.041 441 70.061 358 70.081 381 70.101 309 70.121 236 70.141 164 70.161 190 70.181 125 70.201 98 392

70.221 120 70.241 45 70.261 0 70.281 0 70.301 0 70.321 0 70.341 14 70.361 0 70.381 0 70.401 11 70.421 0 70.445 5 70.461 17 70.482 18 70.501 0 70.525 29 70.545 38 70.561 0 70.581 5 70.601 17 70.625 19 70.642 0 70.661 0 70.685 0 70.701 0 70.725 42 70.741 0 70.765 0 70.781 22 70.805 0 70.821 55 70.845 18 70.861 19 70.885 10 70.901 36 70.925 21 70.942 71 70.962 14 70.981 14 71.005 25 71.021 49 71.045 55 393

71.061 37 71.082 0 71.102 40 71.122 0 71.142 0 71.162 55 71.182 63 71.202 18 71.221 41 71.241 22 71.261 0 71.281 0 71.301 0 71.321 0 71.341 0 71.361 0 71.381 0 71.401 0 71.421 16 71.441 0 71.461 0 71.481 0 71.501 0 71.521 15 71.541 52 71.561 0 71.581 10 71.601 29 71.621 26 71.641 31 71.661 57 71.681 47 71.701 52 71.721 82 71.741 100 71.761 120 71.781 65 71.801 35 71.821 45 71.841 44 71.861 47 71.881 8 394

71.901 79 71.921 46 71.941 47 71.961 0 71.981 52 72.001 125 72.021 82 72.041 22 72.061 39 72.081 109 72.101 79 72.121 36 72.141 145 72.161 36 72.181 126 72.201 125 72.221 65 72.241 83 72.261 116 72.281 142 72.301 69 72.321 107 72.341 234 72.361 141 72.381 131 72.401 163 72.421 167 72.441 153 72.461 216 72.481 220 72.501 234 72.521 192 72.541 116 72.561 184 72.581 178 72.601 129 72.621 184 72.641 120 72.661 111 72.681 109 72.701 67 72.721 82 395

72.741 68 72.761 116 72.781 80 72.801 123 72.821 165 72.841 169 72.861 198 72.881 177 72.901 185 72.921 242 72.941 199 72.961 160 72.981 289 73.001 301 73.021 265 73.041 186 73.061 187 73.081 235 73.101 212 73.121 243 73.141 240 73.161 211 73.181 253 73.201 226 73.221 279 73.241 250 73.261 286 73.281 343 73.301 354 73.321 279 73.341 394 73.361 346 73.381 386 73.401 367 73.421 328 73.441 401 73.461 301 73.481 281 73.501 246 73.521 353 73.541 247 73.561 257 396

73.581 248 73.601 231 73.621 217 73.641 224 73.661 304 73.681 280 73.701 216 73.721 276 73.741 306 73.761 310 73.781 281 73.801 299 73.821 337 73.841 275 73.861 305 73.881 254 73.901 254 73.921 309 73.941 245 73.961 361 73.981 330 74.001 302 74.021 244 74.041 316 74.061 224 74.081 349 74.101 307 74.121 259 74.141 256 74.161 219 74.181 189 74.201 55 74.221 94 74.241 17 74.261 31 74.281 22 74.301 0 74.321 0 74.341 0 74.361 0 74.381 8 74.401 0 397

74.421 0 74.441 0 74.461 0 74.481 0 74.501 0 74.521 0 74.541 0 74.561 0 74.581 0 74.601 33 74.621 0 74.641 0 74.661 0 74.681 10 74.701 0 74.721 0 74.741 0 74.761 0 74.781 0 74.801 19 74.821 0 74.841 0 74.861 13 74.881 0 74.901 54 74.921 56 74.941 116 74.961 110 74.981 118 75.001 197 75.021 52 75.041 97 75.061 52 75.081 75 75.101 90 75.121 84 75.141 72 75.161 46 75.181 97 75.201 118 75.221 144 75.241 121 398

75.261 147 75.281 89 75.301 83 75.321 114 75.341 160 75.361 141 75.381 102 75.401 164 75.421 208 75.441 169 75.461 74 75.481 110 75.501 173 75.521 173 75.541 213 75.561 226 75.581 183 75.601 63 75.621 36 75.641 62 75.661 86 75.681 59 75.701 78 75.721 139 75.741 46 75.761 122 75.781 96 75.801 117 75.821 151 75.841 81 75.861 100 75.881 63 75.901 63 75.921 123 75.941 96 75.961 187 75.981 116 76.001 46 76.021 186 76.041 101 76.061 176 76.101 79 399

76.121 82 76.141 138 76.161 18 76.181 29 76.201 48 76.221 54 76.241 35 76.261 58 76.281 69 76.301 49 76.321 96 76.341 57 76.361 102 76.381 87 76.401 86 76.421 107 76.441 68 76.461 122 76.481 100 76.501 159 76.521 101 76.541 109 76.561 147 76.581 162 76.621 186 76.641 241 76.661 171 76.681 206 76.701 164 76.721 243 76.741 223 76.761 227 76.781 246 76.801 213 76.821 195 76.841 153 76.861 175 76.881 207 76.901 228 76.921 174 76.941 258 76.961 212 400

76.981 188 77.001 207 77.021 198 77.041 249 77.061 209 77.081 216 77.101 258 77.121 318 77.141 292 77.161 215 77.181 255 77.201 232 77.221 288 77.241 349 77.261 260 77.281 234 77.301 286 77.321 251 77.341 341 77.361 310 77.381 287 77.401 304 77.421 265 77.441 371 77.461 368 77.481 419 77.501 410 77.521 467 77.541 344 77.561 328 77.581 266 77.601 347 77.621 384 77.641 311 77.661 276 77.681 343 77.701 337 77.721 365 77.741 316 77.761 323 77.781 291 77.801 364 401

77.821 334 77.841 349 77.861 327 77.881 295 77.901 329 77.921 296 77.941 268 77.961 343 77.981 323 78.001 335 78.021 324 78.041 291 78.061 364 78.081 373 78.101 313 78.121 247 78.141 230 78.161 339 78.181 262 78.201 269 78.221 160 78.241 185 78.261 151 78.281 46 78.301 84 78.321 98 78.341 148 78.361 117 78.381 119 78.401 82 78.421 76 78.441 79 78.461 45 78.481 87 78.501 127 78.521 63 78.561 75 78.581 49 78.601 65 78.621 86 78.641 13 78.661 77 402

78.681 46 78.701 41 78.721 0 78.741 42 78.761 52 78.781 62 78.801 130 78.821 33 78.841 102 78.861 99 78.881 0 78.901 93 78.921 115 78.941 164 78.961 166 78.981 126 79.001 161 79.021 159 79.041 176 79.061 178 79.081 143 79.101 207 79.121 152 79.141 195 79.161 204 79.181 191 79.201 193 79.221 224 79.241 207 79.261 252 79.281 145 79.301 172 79.321 239 79.341 202 79.361 248 79.381 189 79.401 198 79.421 195 79.441 197 79.461 232 79.481 246 79.501 305 403

79.521 265 79.541 298 79.561 326 79.581 386 79.601 261 79.621 310 79.641 267 79.661 210 79.681 258 79.701 327 79.721 332 79.741 289 79.761 261 79.781 292 79.801 226 79.821 245 79.841 225 79.861 293 79.881 261 79.901 389 79.921 289 79.941 266 79.961 330 79.981 298 80.001 351 80.021 335 80.041 388 80.061 325 80.081 339 80.101 309 80.121 312 80.141 369 80.161 216 80.181 268 80.201 185 80.221 114 80.241 195 80.261 162 80.281 87 80.301 68 80.321 84 80.341 55 404

80.361 159 80.381 123 80.401 146 80.421 120 80.441 105 80.461 185 80.481 199 80.501 138 80.521 132 80.541 56 80.561 165 80.581 179 80.601 241 80.621 192 80.641 192 80.661 213 80.681 233 80.701 285 80.721 315 80.741 261 80.761 280 80.781 243 80.801 224 80.821 242 80.841 233 80.861 233 80.881 230 80.901 199 80.921 216 80.941 199 80.961 126 80.981 93 81.001 202 81.021 199 81.041 229 81.061 223 81.081 262 81.101 224 81.121 191 81.141 181 81.161 154 81.181 119 405

81.201 118 81.221 90 81.241 95 81.261 107 81.281 87 81.301 157 81.321 131 81.341 203 81.361 121 81.381 138 81.401 91 81.421 31 81.441 79 81.461 8 81.481 24 81.501 60 81.521 24 81.541 64 81.561 21 81.581 64 81.601 132 81.621 104 81.641 150 81.661 130 81.681 149 81.701 157 81.721 61 81.741 72 81.761 137 81.781 42 81.801 136 81.821 69 81.841 83 81.861 70 81.881 106 81.901 57 81.941 149 81.961 111 81.981 101 82.001 107 82.021 53 82.041 121 406

82.061 161 82.081 161 82.101 66 82.121 0 82.141 50 82.161 10 82.181 55 82.201 30 82.221 80 82.241 18 82.261 0 82.281 14 82.301 21 82.321 48 82.341 64 82.361 35 82.381 21 82.401 100 82.421 120 82.441 54 82.461 93 82.481 63 82.501 28 82.521 85 82.541 46 82.561 0 82.581 32 82.601 8 82.621 78 82.641 86 82.661 46 82.681 0 82.701 51 82.721 35 82.741 85 82.761 0 82.781 0 82.801 0 82.821 34 82.841 55 82.861 85 82.881 65 407

82.901 120 82.921 167 82.941 89 82.961 90 82.981 184 83.001 232 83.021 165 83.041 251 83.061 246 83.081 259 83.101 275 83.121 267 83.141 334 83.161 278 83.181 242 83.201 124 83.221 144 83.241 153 83.261 101 83.281 87 83.301 66 83.321 4 83.341 65 83.361 46 83.381 0 83.401 68 83.421 57 83.441 24 83.461 127 83.481 48 83.501 28 83.521 109 83.541 30 83.561 39 83.581 59 83.601 163 83.621 106 83.641 109 83.661 109 83.681 115 83.701 202 83.721 199 408

83.741 216 83.761 291 83.781 319 83.801 218 83.821 247 83.845 292 83.861 300 83.885 240 83.901 286 83.925 163 83.941 161 83.965 177 83.981 120 84.005 175 84.021 163 84.045 150 84.061 127 84.085 125 84.101 139 84.125 80 84.141 57 84.165 0 84.181 0 84.205 0 84.221 0 84.245 0 84.261 0 84.285 0 84.301 0 84.325 0 84.341 0 84.365 0 84.381 0 84.405 0 84.421 0 84.445 0 84.461 0 84.485 0 84.501 0 84.522 0 84.542 0 84.562 0 409

84.581 0 84.601 0 84.621 0 84.641 0 84.661 0 84.681 0 84.701 0 84.721 0 84.741 5 84.761 0 84.781 0 84.801 0 84.821 0 84.841 0 84.861 0 84.881 0 84.901 7 84.921 18 84.941 0 84.961 33 84.981 0 85.001 47 85.021 32 85.041 66 85.061 10 85.081 107 85.101 56 85.121 35 85.141 55 85.161 52 85.181 89 85.201 123 85.221 94 85.241 47 85.261 31 85.281 100 85.301 88 85.321 28 85.341 55 85.361 27 85.381 39 85.401 96 410

85.421 18 85.441 84 85.461 124 85.481 65 85.501 97 85.521 20 85.541 110 85.561 134 85.581 152 85.601 131 85.621 79 85.641 73 85.661 129 85.681 52 85.701 125 85.721 29 85.741 53 85.761 57 85.781 47 85.801 148 85.821 131 85.841 119 85.861 24 85.881 31 85.901 0 85.921 32 85.941 45 85.961 22 85.981 56 86.001 0 86.021 118 86.041 84 86.061 68 86.081 110 86.101 81 86.121 111 86.141 152 86.161 161 86.181 168 86.201 116 86.221 92 86.241 107 411

86.261 83 86.281 138 86.301 205 86.321 101 86.341 88 86.361 134 86.381 116 86.401 100 86.421 161 86.441 156 86.461 37 86.481 67 86.501 91 86.521 52 86.541 74 86.561 70 86.581 65 86.601 124 86.621 33 86.641 0 86.661 17 86.681 60 86.701 0 86.721 0 86.741 44 86.761 77 86.781 12 86.801 46 86.821 11 86.841 0 86.861 25 86.881 6 86.901 38 86.921 29 86.941 66 86.961 45 86.981 0 87.001 34 87.021 90 87.041 81 87.061 70 87.081 28 412

87.101 6 87.121 38 87.141 76 87.161 8 87.181 60 87.201 75 87.221 45 87.241 70 87.261 39 87.281 108 87.301 95 87.321 42 87.341 127 87.361 27 87.381 17 87.401 73 87.421 35 87.441 75 87.461 119 87.481 125 87.501 62 87.521 79 87.541 70 87.561 46 87.581 89 87.601 129 87.621 78 87.641 67 87.661 20 87.681 90 87.701 192 87.721 143 87.741 110 87.761 0 87.781 184 87.801 187 87.821 226 87.841 219 87.861 125 87.881 253 87.901 181 87.921 159 413

87.941 226 87.961 217 87.981 163 88.001 134 88.021 134 88.041 136 88.061 91 88.081 125 88.101 149 88.121 186 88.141 130 88.161 144 88.181 112 88.201 54 88.221 64 88.241 94 88.261 16 88.281 72 88.301 36 88.321 73 88.341 97 88.361 90 88.381 14 88.401 59 88.421 119 88.441 74 88.461 73 88.481 137 88.501 79 88.521 45 88.541 30 88.561 69 88.581 131 88.601 70 88.621 108 88.641 45 88.661 74 88.681 68 88.701 115 88.721 106 88.741 53 88.761 107 414

88.781 148 88.801 150 88.821 131 88.841 98 88.861 80 88.881 30 88.901 16 88.921 60 88.941 102 88.961 69 88.981 73 89.001 16 89.021 19 89.041 0 89.061 49 89.081 53 89.101 57 89.121 101 89.141 133 89.161 0 89.181 61 89.201 19 89.221 12 89.241 17 89.261 7 89.281 0 89.301 27 89.321 89 89.341 78 89.361 105 89.381 68 89.401 159 89.421 122 89.441 45 89.461 69 89.481 90 89.501 116 89.521 115 89.541 115 89.561 52 89.581 72 89.601 136 415

89.621 122 89.641 73 89.661 189 89.681 180 89.701 83 89.721 130 89.741 143 89.761 41 89.781 104 89.801 150 89.821 135 89.841 147 89.861 64 89.881 115 89.901 64 89.921 117 89.941 78 89.961 60 89.981 127 90.001 78 90.021 140 90.041 173 90.061 81 90.081 121 90.101 151 90.121 156 90.141 74 90.161 44 90.181 125 90.201 126 90.221 68 90.241 73 90.261 191 90.281 156 90.301 87 90.321 24 90.341 48 90.361 61 90.381 39 90.401 74 90.421 78 90.441 70 416

90.461 122 90.481 141 90.501 118 90.521 40 90.541 55 90.561 47 90.581 64 90.601 21 90.621 7 90.641 89 90.661 51 90.681 153 90.701 135 90.721 107 90.741 140 90.761 171 90.781 211 90.801 265 90.821 145 90.841 106 90.861 199 90.881 214 90.901 258 90.921 234 90.941 140 90.961 230 90.981 199 91.001 208 91.021 189 91.041 206 91.061 205 91.081 199 91.101 243 91.121 176 91.141 193 91.161 159 91.181 139 91.201 238 91.221 268 91.241 236 91.261 237 91.281 261 417

91.301 293 91.321 188 91.341 204 91.361 340 91.381 343 91.401 326 91.421 237 91.441 310 91.461 252 91.481 367 91.501 245 91.521 272 91.541 280 91.561 285 91.581 334 91.601 285 91.621 206 91.641 250 91.661 131 91.681 136 91.701 124 91.721 96 91.741 93 91.761 109 91.781 107 91.801 203 91.821 119 91.841 171 91.861 133 91.881 198 91.901 163 91.921 123 91.941 248 91.961 210 91.981 222 92.001 181 92.021 235 92.041 289 92.061 310 92.081 350 92.101 300 92.121 171 418

92.141 231 92.161 181 92.181 33 92.201 54 92.221 0 92.241 0 92.261 0 92.281 0 92.301 15 92.321 50 92.341 103 92.361 138 92.381 154 92.401 162 92.421 165 92.441 151 92.461 217 92.481 264 92.501 259 92.521 182 92.541 238 92.561 223 92.581 45 92.601 64 92.621 48 92.641 74 92.661 4 92.681 23 92.701 103 92.721 81 92.741 98 92.761 125 92.781 82 92.801 73 92.821 53 92.841 37 92.861 74 92.881 42 92.901 26 92.921 0 92.941 55 92.961 0 419

92.981 0 93.001 42 93.021 52 93.041 51 93.061 71 93.081 68 93.101 71 93.121 92 93.141 63 93.161 71 93.181 16 93.201 76 93.221 45 93.241 71 93.261 106 93.281 91 93.301 75 93.321 12 93.341 49 93.361 43 93.381 66 93.401 9 93.421 0 93.441 55 93.461 71 93.481 94 93.501 0 93.521 7 93.541 33 93.561 51 93.581 31 93.601 113 93.621 67 93.641 104 93.661 52 93.681 94 93.701 80 93.721 79 93.741 151 93.761 217 93.781 84 93.801 64 420

93.821 29 93.841 54 93.861 82 93.881 123 93.901 115 93.921 88 93.941 113 93.961 78 93.981 22 94.001 0 94.021 127 94.041 56 94.061 147 94.081 62 94.101 146 94.121 51 94.141 0 94.161 18 94.181 50 94.201 80 94.221 80 94.241 0 94.261 12 94.281 4 94.301 9 94.321 0 94.341 18 94.361 0 94.381 0 94.401 0 94.421 19 94.441 0 94.461 94 94.481 15 94.501 0 94.521 31 94.541 72 94.561 37 94.581 0 94.601 12 94.621 0 94.641 0 421

94.661 0 94.681 19 94.701 76 94.721 68 94.741 63 94.761 79 94.781 61 94.801 57 94.821 89 94.841 0 94.861 50 94.881 23 94.901 47 94.921 56 94.941 5 94.961 0 94.981 56 95.001 0 95.021 0 95.041 0 95.061 0 95.081 0 95.101 6 95.121 0 95.141 0 95.161 22 95.181 23 95.201 8 95.221 0 95.241 0 95.261 0 95.281 5 95.301 18 95.321 0 95.341 0 95.361 0 95.381 0 95.401 4 95.421 35 95.441 0 95.461 0 95.481 0 422

95.501 0 95.521 4 95.541 44 95.561 51 95.581 65 95.601 88 95.621 69 95.641 103 95.661 130 95.681 120 95.701 231 95.721 270 95.741 319 95.761 335 95.781 348 95.801 370 95.821 385 95.841 400 95.861 392 95.881 453 95.901 386 95.921 390 95.941 359 95.961 370 95.981 371 96.001 457 96.021 470 96.041 421 96.061 397 96.081 482 96.101 306 96.121 272 96.141 350 96.161 269 96.181 292 96.201 258 96.221 174 96.241 122 96.261 76 96.281 82 96.301 87 96.321 117 423

96.341 45 96.361 18 96.381 62 96.401 134 96.421 183 96.441 144 96.461 173 96.481 158 96.501 265 96.521 267 96.541 215 96.561 279 96.581 227 96.601 243 96.621 311 96.641 278 96.661 328 96.681 258 96.701 168 96.721 174 96.741 206 96.761 257 96.781 199 96.801 125 96.821 106 96.841 165 96.861 148 96.881 205 96.901 160 96.921 190 96.941 55 96.961 69 96.981 45 97.001 112 97.021 81 97.041 187 97.061 178 97.081 196 97.101 129 97.121 187 97.141 262 97.161 274 424

97.181 390 97.201 342 97.221 288 97.241 311 97.261 167 97.281 234 97.301 293 97.321 448 97.341 413 97.361 325 97.381 243 97.401 242 97.421 156 97.441 43 97.461 33 97.481 8 97.501 0 97.521 41 97.541 39 97.561 0 97.581 0 97.601 0 97.621 28 97.641 13 97.661 31 97.681 0 97.701 13 97.721 7 97.741 0 97.761 0 97.781 0 97.801 0 97.821 0 97.841 0 97.861 0 97.881 0 97.901 0 97.921 0 97.941 0 97.961 6 97.981 10 98.001 0 425

98.021 0 98.041 63 98.061 45 98.081 0 98.101 37 98.121 86 98.141 9 98.161 104 98.181 103 98.201 109 98.221 105 98.241 32 98.261 0 98.281 87 98.301 0 98.321 62 98.341 53 98.361 81 98.381 121 98.401 128 98.421 120 98.441 109 98.461 175 98.481 164 98.501 126 98.521 119 98.541 100 98.561 154 98.581 148 98.601 92 98.621 110 98.641 112 98.661 199 98.681 165 98.701 199 98.721 132 98.741 109 98.761 203 98.781 193 98.801 115 98.821 201 98.841 176 426

98.861 25 98.881 61 98.901 70 98.921 67 98.941 50 98.961 45 98.981 69 99.001 63 99.021 90 99.041 146 99.061 52 99.081 38 99.101 109 99.121 113 99.141 39 99.161 72 99.181 62 99.201 22 99.221 75 99.241 40 99.261 79 99.281 88 99.301 0 99.321 52 99.341 31 99.361 67 99.381 77 99.401 63 99.421 48 99.441 13 99.461 86 99.481 0 99.501 0 99.521 0 99.541 0 99.561 0 99.581 0 99.601 14 99.621 0 99.641 0 99.661 0 99.681 0 427

99.701 0 99.721 0 99.741 0 99.761 0 99.781 0 99.801 0 99.821 0 99.841 0 99.861 0 99.881 0 99.901 0 99.921 0 99.941 0 99.961 0 99.981 0 100.001 0 100.021 0 100.041 0 100.061 0 100.081 0 100.101 0 100.121 0 100.141 0 100.161 0 100.181 0 100.201 0 100.221 0 100.241 0 100.261 36 100.281 0 100.301 0 100.321 0 100.341 0 100.361 0 100.381 0 100.401 0 100.421 0 100.441 0 100.461 0 100.481 0 100.501 28 100.521 23 428

100.541 0 100.561 0 100.581 4 100.601 8 100.621 52 100.641 0 100.661 90 100.681 74 100.701 180 100.721 259 100.741 325 100.761 363 100.781 338 100.801 484 100.821 462 100.841 422 100.861 441 100.881 509 100.901 416 100.921 409 100.941 425 100.961 287 100.981 213 101.001 300 101.021 203 101.041 188 101.061 116 101.081 190 101.101 232 101.121 152 101.141 212 101.161 182 101.181 143 101.201 194 101.221 214 101.241 107 101.261 163 101.281 187 101.301 271 101.321 311 101.341 490 101.361 516 429

101.381 403 101.401 287 101.421 237 101.441 239 101.461 187 101.481 105 101.501 141 101.521 163 101.541 88 101.561 163 101.581 156 101.601 109 101.621 119 101.641 193 101.661 233 101.681 152 101.701 177 101.721 271 101.741 191 101.761 165 101.781 246 101.801 236 101.821 190 101.841 197 101.861 37 101.881 88 101.901 18 101.921 59 101.941 63 101.961 0 101.981 0 102.001 0 109.702 0 109.722 0 109.742 0 109.762 0 109.782 0 109.802 0 109.822 0 109.842 0 109.862 0 109.882 0 430

109.902 0 109.922 0 109.942 0 109.962 0 109.982 0 110.002 0 110.022 0 110.042 0 110.062 0 110.082 0 110.102 0 110.122 0 110.142 0 110.162 0 110.182 0 110.202 0 110.222 0 110.242 0 110.262 0 110.282 0 110.302 0 110.322 0 110.342 0 110.362 0 110.382 0 110.402 0 110.422 0 110.442 0 110.462 0 110.482 0 110.502 0 110.522 0 110.542 0 110.562 0 110.582 0 110.602 0 110.622 0 110.642 0 110.662 0 110.682 0 110.702 0 110.722 0 431

110.742 0 110.762 0 110.782 0 110.802 0 110.822 0 110.842 0 110.862 0 110.882 14 110.902 0 110.922 0 110.942 0 110.962 0 110.982 9 111.002 0 111.022 0 111.042 93 111.062 24 111.082 86 111.102 52 111.122 29 111.142 64 111.162 0 111.182 0 111.202 15 111.222 0 111.242 0 111.262 0 111.282 0 111.302 0 111.322 0 111.342 0 111.362 0 111.382 0 111.402 0 111.422 0 111.442 0 111.462 22 111.482 11 111.502 0 111.522 0 111.542 0 111.562 0 432

111.582 0 111.602 0 111.622 19 111.642 0 111.662 0 111.682 104 111.702 23 111.722 38 111.742 22 111.762 0 111.782 0 111.802 0 111.822 0 111.842 0 111.862 0 111.882 0 111.902 0 111.922 0 111.942 0 111.962 0 111.982 0 112.002 0 112.022 33 112.042 0 112.062 0 112.082 0 112.102 0 112.122 18 112.142 0 112.162 0 112.182 0 112.202 0 112.222 0 112.242 0 112.262 0 112.282 0 112.302 0 112.322 0 112.342 0 112.362 0 112.382 0 112.402 0 433

112.422 0 112.442 0 112.462 0 112.482 0 112.502 0 112.522 0 112.542 0 112.562 0 112.582 0 112.602 0 112.622 0 112.642 0 112.662 0 112.682 0 112.702 0 112.722 0 112.742 0 112.762 0 112.782 0 112.802 0 112.822 11 112.842 0 112.862 0 112.882 0 112.902 0 112.922 0 112.942 62 112.962 18 112.982 0 113.002 5 113.022 0 113.042 0 113.062 42 113.082 31 113.102 40 113.122 58 113.142 104 113.162 96 113.182 74 113.202 114 113.222 204 113.242 156 434

113.262 103 113.282 91 113.302 95 113.322 45 113.342 0 113.362 0 113.382 0 113.402 0 113.422 0 113.442 87 113.462 155 113.482 55 113.502 82 113.522 0 113.542 0 113.562 0 113.582 0 113.602 0 113.622 6 113.642 0 113.662 0 113.682 0 113.702 0 113.722 0 113.742 0 113.762 0 113.782 0 113.802 0 113.822 0 113.842 0 113.862 0 113.882 0 113.902 0 113.922 0 113.942 0 113.962 0 113.982 0 114.002 0 114.022 0 114.042 0 114.062 0 114.082 0 435

114.102 0 114.122 0 114.142 0 114.162 0 114.182 0 114.202 0 114.222 0 114.242 0 114.262 0 114.282 0 114.302 0 114.322 0 114.342 0 114.362 0 114.382 0 114.402 0 114.422 0 114.442 0 114.462 0 114.482 33 114.502 101 114.522 0 114.542 0 114.562 0 114.582 0 114.602 0 114.622 0 114.642 0 114.662 0 114.682 0 114.702 0 114.722 0 114.742 0 114.762 0 114.782 0 114.802 0 114.822 0 114.842 0 114.862 0 114.882 6 114.902 65 114.922 0 436

114.942 0 114.962 0 114.982 0 115.002 23 115.022 0 115.042 54 115.062 0 115.082 103 115.102 49 115.122 12 115.142 15 115.162 80 115.182 67 115.202 42 115.222 39 115.242 18 115.262 0 115.282 0 115.302 0 115.322 49 115.342 72 115.362 0 115.382 15 115.402 8 115.422 12 115.442 81 115.462 43 115.482 52 115.502 0 115.522 0 115.542 0 115.562 0 115.582 0 115.602 0 115.622 0 115.642 0 115.662 0 115.682 10 115.702 40 115.722 0 115.742 33 115.762 61 437

115.782 117 115.802 154 115.822 123 115.842 138 115.862 204 115.882 138 115.902 132 115.922 80 115.942 27 115.962 9 115.982 64 116.002 12 116.022 0 116.042 52 116.062 49 116.082 90 116.102 103 116.122 44 116.142 160 116.162 94 116.182 118 116.202 192 116.222 159 116.242 172 116.262 166 116.282 24 116.302 127 116.322 59 116.342 95 116.362 84 116.382 15 116.402 46 116.422 0 116.442 51 116.462 7 116.482 105 116.502 16 116.522 0 116.542 0 116.562 0 116.582 0 116.602 12 438

116.622 24 116.642 0 116.662 0 116.682 0 116.702 0 116.722 0 116.742 0 116.762 0 116.782 0 116.802 0 116.822 0 116.842 0 116.862 0 116.882 0 116.902 0 116.922 0 116.942 0 116.962 0 116.982 0 117.002 0 117.022 0 117.042 0 117.062 0 117.082 0 117.102 0 117.122 0 117.142 0 117.162 0 117.182 0 117.202 0 117.222 0 117.242 0 117.262 0 117.282 0 117.302 0 117.322 0 117.342 0 117.362 0 117.382 0 117.402 0 117.422 0 117.442 0 439

117.462 0 117.482 0 117.502 0 117.522 0 117.542 0 117.562 0 117.582 0 117.602 0 117.622 0 117.642 0 117.662 0 117.682 0 117.702 0 117.722 0 117.742 0 117.762 0 117.782 52 117.802 60 117.822 0 117.842 0 117.862 0 117.882 0 117.902 0 117.922 0 117.942 0 117.962 0 117.982 0 118.002 0 118.022 0 118.042 0 118.062 0 118.082 0 118.102 0 118.122 0 118.142 0 118.162 0 118.182 0 118.202 0 118.222 0 118.242 38 118.262 73 118.282 22 440

118.302 45 118.322 58 118.342 92 118.362 142 118.382 190 118.402 112 118.422 97 118.442 198 118.462 308 118.482 278 118.502 330 118.522 221 118.542 67 118.562 100 118.582 136 118.602 225 118.622 182 118.642 286 118.662 220 118.682 190 118.702 247 118.722 192 118.742 122 118.762 0 118.782 5 118.802 0 118.822 0 118.842 0 118.862 15 118.882 0 118.902 0 118.922 0 118.942 0 118.962 0 118.982 0 119.002 0 119.022 0 119.042 0 119.062 12 119.082 0 119.102 8 119.122 63 441

119.142 67 119.162 50 119.182 0 119.202 0 119.222 0 119.242 0 119.262 37 119.302 15 119.322 0 119.342 39 119.362 0 119.382 40 119.402 55 119.422 0 119.442 10 119.462 5 119.482 0 119.502 88 119.522 106 119.542 0 119.562 76 119.582 23 119.602 89 119.622 40 119.642 101 119.662 45 119.682 63 119.702 0 119.722 0 119.742 34 119.762 0 119.782 14 119.802 87 119.822 21 119.842 0 119.862 35 119.882 144 119.902 51 119.922 116 119.942 187 119.962 78 119.982 119 442

120.002 71 120.022 67 120.042 55 120.062 92 120.082 114 120.102 55 120.122 4 120.142 0 120.162 0 120.182 0 120.202 0 120.222 0 120.242 0 120.262 0 120.282 0 120.302 0 120.322 51 120.342 0 120.362 24 120.382 51 120.402 48 120.422 46 120.442 97 120.462 0 120.482 0 120.502 0 120.522 7 120.542 46 120.562 6 120.582 0 120.602 0 120.622 0 120.642 0 120.662 0 120.682 0 120.702 25 120.722 0 120.742 0 120.762 0 120.782 5 120.802 0 120.822 0 443

120.842 0 120.862 0 120.882 16 120.902 0 120.922 0 120.942 0 120.962 0 120.982 0 121.002 0 121.022 0 121.042 0 121.062 0 121.082 0 121.102 0 121.122 0 121.142 0 121.162 0 121.182 48 121.202 0 121.222 35 121.242 5 121.262 0 121.282 0 121.302 0 121.322 0 121.342 0 121.362 0 121.382 0 121.402 0 121.422 0 121.442 0 121.462 0 121.482 0 121.502 0 121.522 50 121.542 0 121.562 67 121.582 159 121.602 215 121.622 209 121.642 385 121.662 432 444

121.682 235 121.702 173 121.722 55 121.742 53 121.762 29 121.782 21 121.802 0 121.822 17 121.842 25 121.862 33 121.882 9 121.902 0 121.922 0 121.942 0 121.962 17 121.982 90 122.002 64 122.022 25 122.042 0 122.062 0 122.082 0 122.102 53 122.122 48 122.142 42 122.162 0 122.182 7 122.202 0 122.222 0 122.242 0 122.262 0 122.282 14 102.001 122.322 0 122.342 0 122.362 0 122.382 0 122.402 0 122.422 0 122.442 0 122.462 0 122.482 0 122.502 0 445

122.522 0 122.542 0 122.562 0 122.582 0 122.602 0 122.622 0 122.642 0 122.662 0 122.682 0 122.702 0 122.722 0 122.742 0 122.762 0 122.782 0 122.802 0 122.822 0 122.842 0 122.862 0 122.882 0 122.902 0 122.922 0 122.942 6 122.962 0 122.982 0 123.002 0 123.022 0 123.042 0 123.062 0 123.082 0 123.102 0 123.122 12 123.142 0 123.162 14 123.182 7 123.202 33 123.222 56 123.242 40 123.262 82 123.282 158 123.302 94 123.322 109 123.342 173 446

123.362 197 123.382 149 123.402 245 123.422 244 123.442 246 123.462 243 123.482 328 123.502 305 123.522 345 123.542 347 123.562 325 123.582 313 123.602 379 123.622 414 123.642 368 123.662 412 123.682 338 123.702 378 123.722 394 123.742 421 123.762 396 123.782 439 123.802 362 123.822 357 123.842 385 123.862 298 123.882 352 123.902 403 123.922 427 123.942 329 123.962 377 123.982 392 124.002 447 124.022 430 124.042 398 124.062 462 124.082 387 124.102 332 124.122 381 124.142 419 124.162 395 124.182 449 447

124.202 370 124.222 314 124.242 409 124.262 464 124.282 361 124.302 468 124.322 371 124.342 465 124.362 450 124.382 482 124.402 390 124.422 345 124.442 378 124.462 424 124.482 489 124.502 467 124.522 352 124.542 345 124.562 295 124.582 367 124.602 373 124.622 404 124.642 421 124.662 368 124.682 380 124.702 422 124.722 391 124.742 355 124.762 395 124.782 434 124.802 387 124.822 388 124.842 387 124.862 350 124.882 301 124.902 436 124.922 436 124.942 373 124.962 310 124.982 448 125.002 417 125.022 402 448

125.042 374 125.062 387 125.082 373 125.102 297 125.122 295 125.142 299 125.162 386 125.182 280 125.202 367 125.222 338 125.242 316 125.262 355 125.282 365 125.302 381 125.322 312 125.342 326 125.362 368 125.382 331 125.402 438 125.422 455 125.442 483 125.462 445 125.482 503 125.502 374 125.522 434 125.542 341 125.562 389 125.582 433 125.602 405 125.622 425 125.642 454 125.662 441 125.682 356 125.702 384 125.722 383 125.742 389 125.762 463 125.782 451 125.802 458 125.822 416 125.842 447 125.862 378 449

125.882 419 125.902 434 125.922 436 125.942 499 125.962 485 125.982 490 126.002 426 126.022 420 126.042 334 126.062 440 126.082 395 126.102 310 126.122 429 126.142 380 126.162 360 126.182 395 126.202 393 126.222 401 126.242 339 126.262 308 126.282 357 126.302 235 126.322 409 126.342 335 126.362 395 126.382 365 126.402 408 126.422 419 126.442 370 126.462 435 126.482 399 126.502 354 126.522 322 126.542 354 126.562 341 126.582 384 126.602 436 126.622 419 126.642 358 126.662 333 126.682 374 126.702 416 450

126.722 356 126.742 358 126.762 392 126.782 321 126.802 328 126.822 357 126.842 378 126.862 369 126.882 328 126.902 387 126.922 437 126.942 366 126.962 371 126.982 414 127.002 350 127.022 439 127.042 354 127.062 345 127.082 302 127.102 298 127.122 376 127.142 373 127.162 381 127.182 291 127.202 395 127.222 345 127.242 280 127.262 325 127.282 390 127.302 309 127.322 431 127.342 350 127.362 402 127.382 367 127.402 331 127.422 353 127.442 273 127.462 324 127.482 365 127.502 304 127.522 382 127.542 303 451

127.562 344 127.582 333 127.602 310 127.622 373 127.642 347 127.662 258 127.682 354 127.702 374 127.722 271 127.742 289 127.762 415 127.782 351 127.802 432 127.822 446 127.842 505 127.862 516 127.882 414 127.902 344 127.922 393 127.942 430 127.962 422 127.982 396 128.002 421 128.022 413 128.042 341 128.062 409 128.082 370 128.102 418 128.122 423 128.142 444 128.162 363 128.182 394 128.202 380 128.222 375 128.242 372 128.262 365 128.282 375 128.302 362 128.322 348 128.342 357 128.362 362 128.382 361 452

128.402 300 128.422 323 128.442 343 128.462 314 128.482 336 128.502 336 128.522 340 128.542 312 128.562 436 128.582 360 128.602 419 128.622 335 128.642 342 128.662 328 128.682 314 128.702 399 128.722 231 128.742 260 128.762 179 128.782 95 128.802 175 128.822 130 128.842 144 128.862 102 128.882 39 128.902 72 128.922 113 128.942 134 128.962 39 128.982 62 129.002 92 129.022 122 129.042 91 129.062 24 129.082 58 129.102 43 129.122 100 129.142 153 129.162 106 129.182 41 129.202 106 129.222 126 453

129.242 108 129.262 128 129.282 134 129.302 138 129.322 159 129.342 156 129.362 110 129.382 92 129.402 185 129.422 187 129.442 150 129.462 136 129.482 127 129.502 48 129.522 55 129.542 51 129.562 123 129.582 129 129.602 77 129.622 109 129.642 43 129.662 28 129.682 85 129.702 0 129.722 60 129.742 31 129.762 99 129.782 75 129.802 72 129.822 0 129.842 101 129.862 31 129.882 21 129.902 74 129.922 75 129.942 55 129.962 23 129.982 122 130.002 70 130.022 0 130.042 22 130.062 0 454

130.082 0 130.102 0 130.122 36 130.142 79 130.162 27 130.182 29 130.202 50 130.222 0 130.242 8 130.262 45 130.282 40 130.302 77 130.322 14 130.342 45 130.362 0 130.382 8 130.402 0 130.422 11 130.442 10 130.462 19 130.482 0 130.502 40 130.522 0 130.542 17 130.562 0 130.582 43 130.602 25 130.622 17 130.642 0 130.662 0 130.682 0 130.702 0 130.722 0 130.742 0 130.762 14 130.782 0 130.802 0 130.822 0 130.842 0 130.862 0 130.882 0 130.902 0 455

130.922 5 130.942 32 130.962 100 130.982 52 131.002 20 131.022 46 131.042 112 131.062 0 131.082 15 131.102 123 131.122 28 131.142 5 131.162 66 131.182 97 131.202 134 131.222 151 131.242 152 131.262 217 131.282 193 131.302 134 131.322 86 131.342 42 131.362 0 131.382 83 131.402 136 131.422 115 131.442 111 131.462 224 131.482 228 131.502 90 131.522 83 131.542 101 131.562 156 131.582 172 131.602 175 131.622 233 131.642 222 131.662 255 131.682 245 131.702 191 131.722 300 131.742 212 456

131.762 234 131.782 250 131.802 270 131.822 278 131.842 230 131.862 248 131.882 280 131.902 282 131.922 252 131.942 161 131.962 274 131.982 221 132.002 174 132.022 154 132.042 176 132.062 98 132.082 143 132.102 84 132.122 141 132.142 102 132.162 0 132.182 186 132.202 182 132.222 179 132.242 184 132.262 170 132.282 153 132.302 176 132.322 327 132.342 137 132.362 0 132.382 34 132.402 0 132.422 64 132.442 0 132.462 0 132.486 0 132.502 0 132.526 0 132.542 0 132.566 0 132.582 0 457

132.606 0 132.622 0 132.642 0 132.666 12 132.683 0 132.702 0 132.726 0 132.742 0 132.766 0 132.782 0 132.806 0 132.822 0 132.846 0 132.862 0 132.886 0 132.902 0 132.926 0 132.942 0 132.966 0 132.982 0 133.006 0 133.022 0 133.046 0 133.062 0 133.086 31 133.102 8 133.126 0 133.142 0 133.166 0 133.182 0 133.206 0 133.222 0 133.246 0 133.263 0 133.283 0 133.303 0 133.322 0 133.342 0 133.362 0 133.382 0 133.402 0 133.422 0 458

133.442 71 133.462 0 133.482 0 133.502 35 133.522 0 133.542 0 133.562 55 133.582 19 133.602 30 133.622 64 133.642 105 133.662 31 133.682 25 133.702 28 133.722 21 133.742 36 133.762 62 133.782 114 133.802 109 133.822 0 133.842 0 133.862 100 133.882 101 133.902 109 133.922 86 133.942 181 133.962 174 133.982 143 134.002 192 134.022 367 134.042 239 134.062 331 134.082 372 134.102 407 134.122 321 134.142 300 134.162 297 134.182 302 134.202 353 134.222 205 134.242 257 134.262 243 459

134.282 261 134.302 335 134.322 333 134.342 423 134.362 352 134.382 266 134.402 200 134.422 185 134.442 286 134.462 146 134.482 283 134.502 420 134.522 376 134.542 380 134.562 206 134.582 216 134.602 251 134.622 262 134.642 296 134.662 159 134.682 233 134.702 312 134.722 290 134.742 320 134.762 227 134.782 287 134.802 289 134.822 367 134.842 427 134.862 353 134.882 528 134.902 660 134.922 687 134.942 555 134.962 553 134.982 655 135.002 724 135.022 670 135.042 674 135.062 673 135.082 591 135.102 564 460

135.122 660 135.142 588 135.162 472 135.182 507 135.202 514 135.222 581 135.242 605 135.262 599 135.282 564 135.302 508 135.322 461 135.342 515 135.362 554 135.382 557 135.402 534 135.422 537 135.442 606 135.462 534 135.482 574 135.502 597 135.522 513 135.542 462 135.562 413 135.582 357 135.602 316 135.622 290 135.642 288 135.662 305 135.682 279 135.702 334 135.722 366 135.742 436 135.762 380 135.782 323 135.802 288 135.822 180 135.842 248 135.862 274 135.882 225 135.902 227 135.922 328 135.942 368 461

135.962 324 135.982 245 136.002 351 136.022 245 136.042 287 136.062 218 136.082 205 136.102 212 136.122 89 136.142 109 136.162 123 136.182 137 136.202 131 136.222 68 136.242 174 136.262 110 136.282 228 136.302 122 136.322 117 136.342 87 136.362 57 136.382 17 136.402 31 136.422 148 136.442 162 136.462 152 136.482 232 136.502 48 136.522 61 136.542 68 136.562 129 136.582 69 136.602 112 136.622 73 136.642 64 136.662 52 136.682 35 136.702 0 136.722 58 136.742 45 136.762 44 136.782 70 462

136.802 134 136.822 28 136.842 0 136.862 0 136.882 0 136.902 0 136.922 0 136.942 0 136.962 0 136.982 0 137.002 0 137.022 0 137.042 0 137.062 0 137.082 0 137.102 0 137.122 0 137.142 0 137.162 21 137.182 0 137.202 0 137.222 0 137.242 0 137.262 0 137.282 0 137.302 5 137.322 0 137.342 28 137.362 39 137.382 0 137.402 5 137.422 26 137.442 0 137.462 0 137.482 38 137.502 77 137.522 53 137.542 37 137.562 72 137.582 8 137.602 71 137.622 80 463

137.642 75 137.662 82 137.682 39 137.702 122 137.722 199 137.742 147 137.762 238 137.782 193 137.802 219 137.822 167 137.842 334 137.862 326 137.882 375 137.902 422 137.922 445 137.942 440 137.962 516 137.982 505 138.002 515 138.022 677 138.042 586 138.062 595 138.082 612 138.102 519 138.122 545 138.142 611 138.162 537 138.182 524 138.202 464 138.222 548 138.242 595 138.262 540 138.282 546 138.302 487 138.322 509 138.342 604 138.362 458 138.382 503 138.402 561 138.422 527 138.442 542 138.462 514 464

138.482 582 138.502 613 138.522 522 138.542 435 138.562 423 138.582 347 138.602 393 138.622 454 138.642 419 138.662 304 138.682 238 138.702 369 138.722 308 138.742 477 138.762 404 138.782 374 138.802 408 138.822 215 138.842 273 138.862 226 138.882 151 138.902 220 138.922 183 138.942 299 138.962 295 138.982 277 139.002 402 139.022 378 139.042 422 139.062 397 139.082 383 139.102 430 139.122 387 139.142 447 139.162 435 139.182 403 139.202 431 139.222 448 139.242 448 139.262 432 139.282 417 139.302 435 465

139.322 494 139.342 489 139.362 495 139.382 542 139.402 512 139.422 474 139.442 515 139.462 370 139.482 461 139.502 293 139.522 389 139.542 200 139.562 295 139.582 195 139.602 149 139.622 254 139.642 312 139.662 301 139.682 292 139.702 344 139.722 400 139.742 328 139.762 392 139.782 287 139.802 324 139.822 377 139.842 387 139.862 385 139.882 337 139.902 347 139.922 379 139.942 400 139.962 440 139.982 442 140.002 340 140.022 431 140.042 524 140.062 446 140.082 361 140.102 299 140.122 329 140.142 268 466

140.162 366 140.182 357 140.202 446 140.222 402 140.242 420 140.262 363 140.282 250 140.302 295 140.322 257 140.342 305 140.362 394 140.382 436 140.402 415 140.422 272 140.442 338 140.462 311 140.482 294 140.502 279 140.522 259 140.542 322 140.562 400 140.582 337 140.602 329 140.622 360 140.642 301 140.662 297 140.682 468 140.702 478 140.722 365 140.742 356 140.762 276 140.782 371 140.802 331 140.822 340 140.842 419 140.862 391 140.882 309 140.902 258 140.922 328 140.942 320 140.962 390 140.982 353 467

141.002 319 141.022 352 141.042 370 141.062 348 141.082 299 141.102 287 141.122 243 141.142 153 141.162 308 141.182 280 141.202 271 141.222 284 141.242 273 141.262 141 141.282 234 141.302 221 141.322 279 141.362 336 141.382 374 141.402 346 141.422 286 141.442 237 141.462 218 141.482 269 141.502 229 141.522 245 141.542 369 141.562 463 141.582 428 141.602 395 141.622 415 141.642 456 141.662 570 141.682 455 141.702 422 141.722 419 141.742 373 141.762 400 141.782 321 141.802 429 141.822 395 141.842 483 468

141.862 450 141.882 493 141.902 511 141.922 407 141.942 563 141.962 530 141.982 456 142.002 463 142.022 522 142.042 365 142.062 493 142.082 385 142.102 374 142.122 404 142.142 447 142.162 431 142.182 447 142.202 449 142.222 460 142.242 464 142.262 483 142.282 455 142.302 361 142.322 376 142.342 380 142.362 367 142.382 389 142.402 354 142.422 350 142.442 366 142.462 369 142.482 355 142.502 416 142.522 355 142.542 408 142.562 335 142.582 372 142.602 302 142.622 231 142.642 378 142.662 311 142.682 341 469

142.702 308 142.722 312 142.742 225 142.762 324 142.782 324 142.802 362 142.822 270 142.842 386 142.862 336 142.882 299 142.902 380 142.922 319 142.942 363 142.962 317 142.982 342 143.002 349 143.022 405 143.042 384 143.062 380 143.082 416 143.102 366 143.122 403 143.142 383 143.162 368 143.182 387 143.202 502 143.222 372 143.242 332 143.262 370 143.282 331 143.302 351 143.322 402 143.342 358 143.362 384 143.382 385 143.402 361 143.422 388 143.442 382 143.462 494 143.482 359 143.502 348 143.522 494 470

143.542 366 143.562 311 143.582 400 143.602 456 143.622 322 143.642 461 143.662 365 143.682 436 143.702 359 143.722 389 143.742 409 143.762 367 143.782 390 143.802 275 143.822 356 143.842 371 143.862 388 143.882 434 143.902 371 143.922 338 143.942 385 143.962 317 143.982 422 144.002 344 144.022 368 144.042 364 144.062 353 144.082 373 144.102 357 144.122 311 144.142 360 144.162 398 144.182 345 144.202 333 144.222 338 144.242 403 144.262 364 144.282 326 144.302 350 144.322 381 144.342 365 144.362 321 471

144.382 384 144.402 348 144.422 396 144.442 314 144.462 376 144.482 396 144.502 342 144.522 306 144.542 394 144.562 399 144.582 351 144.602 294 144.622 357 144.642 407 144.662 409 144.682 339 144.702 451 144.722 403 144.742 396 144.762 431 144.782 354 144.802 374 144.822 352 144.842 395 144.862 385 144.882 327 144.902 333 144.922 346 144.942 387 144.962 381 144.982 399 145.002 399 145.022 391 145.042 429 145.062 366 145.082 418 145.102 384 145.122 368 145.142 343 145.162 404 145.182 363 145.202 338 472

145.222 391 145.242 353 145.262 314 145.282 393 145.302 299 145.322 357 145.342 363 145.362 348 145.382 322 145.402 323 145.422 439 145.442 365 145.462 400 145.482 407 145.502 339 145.522 360 145.542 380 145.562 375 145.582 277 145.602 443 145.622 320 145.642 361 145.662 403 145.682 330 145.702 371 145.722 308 145.742 352 145.762 349 145.782 376 145.802 400 145.822 439 145.842 323 145.862 381 145.882 415 145.902 394 145.922 368 145.942 377 145.962 318 145.982 380 146.002 423 146.022 363 146.042 355 473

146.062 422 146.082 313 146.102 414 146.122 404 146.142 333 146.162 386 146.182 441 146.202 331 146.222 435 146.242 387 146.262 369 146.282 466 146.302 373 146.322 424 146.342 456 146.362 459 146.382 348 146.402 381 146.422 393 146.442 472 146.462 395 146.482 400 146.502 435 146.522 461 146.542 465 146.582 462 146.602 370 146.622 438 146.642 445 146.662 483 146.682 407 146.702 424 146.722 441 146.742 506 146.762 502 146.782 485 146.802 435 146.822 458 146.842 505 146.862 474 146.882 427 146.902 539 474

146.922 488 146.942 422 146.962 524 146.982 488 147.002 549 147.022 362 147.042 460 147.062 530 147.082 485 147.102 456 147.122 489 147.142 585 147.162 455 147.182 507 147.202 485 147.222 559 147.242 449 147.262 545 147.282 532 147.302 519 147.322 525 147.342 453 147.362 501 147.382 543 147.402 563 147.422 503 147.442 521 147.462 546 147.482 639 147.502 508 147.522 554 147.542 578 147.562 480 147.582 582 147.602 519 147.622 458 147.642 497 147.662 440 147.682 553 147.702 552 147.722 518 147.742 487 475

147.762 530 147.782 433 147.802 540 147.822 512 147.842 488 147.862 439 147.882 459 147.902 489 147.922 481 147.942 471 147.962 416 147.982 500 148.002 527 148.022 512 148.042 505 148.062 405 148.082 436 148.102 540 148.122 520 148.142 587 148.162 489 148.182 424 148.202 469 148.222 495 148.242 560 148.262 589 148.282 430 148.302 521 148.322 517 148.342 450 148.362 393 148.382 476 148.402 531 148.422 491 148.442 496 148.462 514 148.482 517 148.502 509 148.522 450 148.542 543 148.562 516 148.582 516 476

148.602 448 148.622 497 148.642 497 148.662 549 148.682 546 148.702 491 148.722 394 148.742 506 148.762 494 148.782 499 148.802 470 148.822 451 148.842 458 148.862 521 148.882 471 148.902 379 148.922 441 148.942 399 148.962 503 148.982 418 149.002 527 149.022 496 149.042 367 149.062 464 149.082 498 149.102 493 149.122 533 149.142 440 149.162 509 149.182 447 149.202 468 149.222 480 149.242 493 149.262 416 149.282 389 149.302 456 149.322 491 149.342 429 149.362 492 149.382 448 149.402 492 149.422 539 477

149.442 537 149.462 554 149.482 562 149.502 522 149.522 539 149.542 593 149.562 492 149.582 595 149.602 562 149.622 568 149.642 562 149.662 535 149.682 598 149.702 603 149.722 515 149.742 622 149.762 654 149.782 626 149.802 545 149.822 745 149.842 598 149.862 798 149.882 736 149.902 556 149.922 676 149.942 686 149.962 663 149.982 646 150.002 433 150.022 532 150.042 475 150.062 460 150.082 499 150.102 435 150.122 407 150.142 326 150.162 363 150.182 426 150.202 335 150.222 316 150.242 323 150.262 391 478

150.282 343 150.302 346 150.322 345 150.342 336 150.362 331 150.382 351 150.402 275 150.422 334 150.442 323 150.462 350 150.482 318 150.502 321 150.522 258 150.542 326 150.562 371 150.582 365 150.602 395 150.622 385 150.642 537 150.662 430 150.682 519 150.702 410 150.722 380 150.742 350 150.762 368 150.782 360 150.802 353 150.822 357 150.842 341 150.862 371 150.882 320 150.902 314 150.922 375 150.942 428 150.962 373 150.982 421 151.002 387 151.022 393 151.042 485 151.062 382 151.082 346 151.102 282 479

151.122 385 151.142 258 151.162 322 151.182 316 151.202 357 151.222 311 151.242 268 151.262 274 151.282 321 151.302 280 151.322 177 151.342 278 151.362 303 151.382 291 151.402 294 151.422 222 151.442 308 151.462 261 151.482 353 151.502 289 151.522 353 151.542 358 151.562 311 151.582 380 151.602 389 151.622 329 151.642 321 151.662 356 151.682 371 151.702 466 151.722 464 151.742 411 151.762 372 151.782 427 151.802 386 151.822 362 151.842 387 151.862 528 151.882 462 151.902 488 151.922 426 151.942 436 480

151.962 493 151.982 444 152.002 490 152.022 526 152.042 479 152.062 567 152.082 580 152.102 527 152.122 492 152.142 559 152.162 475 152.182 455 152.202 467 152.222 451 152.242 508 152.262 559 152.282 508 152.302 501 152.322 497 152.342 595 152.362 543 152.382 551 152.402 531 152.422 650 152.442 646 152.462 654 152.482 541 152.502 416 152.522 470 152.542 538 152.562 491 152.582 624 152.602 523 152.622 552 152.642 655 152.662 537 152.682 549 152.702 585 152.722 520 152.742 634 152.762 653 152.782 615 481

152.802 567 152.822 632 152.842 543 152.862 616 152.882 600 152.902 558 152.922 596 152.942 529 152.962 607 152.982 582 153.002 670 153.022 542 153.042 602 153.062 558 153.082 448 153.102 654 153.122 682 153.142 544 153.162 448 153.182 546 153.202 534 153.222 488 153.242 442 153.262 477 153.282 412 153.302 465 153.322 501 153.342 489 153.362 470 153.382 360 153.402 369 153.422 441 153.442 572 153.462 594 153.482 539 153.502 563 153.522 625 153.542 555 153.562 574 153.582 630 153.602 623 153.622 587 482

153.642 593 153.662 562 153.682 618 153.702 624 153.722 541 153.742 592 153.762 624 153.782 611 153.802 663 153.822 625 153.842 727 153.862 678 153.882 773 153.902 676 153.922 561 153.942 689 153.962 710 153.982 747 154.002 699 154.022 753 154.042 817 154.062 787 154.082 678 154.102 661 154.122 677 154.142 630 154.162 644 154.182 715 154.202 704 154.222 655 154.242 640 154.262 722 154.282 777 154.302 755 154.322 730 154.342 733 154.362 782 154.382 659 154.402 542 154.422 574 154.442 602 154.462 710 483

154.482 691 154.502 747 154.522 725 154.542 743 154.562 689 154.582 653 154.602 717 154.622 622 154.642 614 154.662 604 154.682 612 154.702 655 154.722 732 154.742 651 154.762 623 154.782 600 154.802 669 154.822 641 154.842 580 154.862 667 154.882 634 154.902 667 154.922 703 154.942 718 154.962 822 154.982 798 155.002 732 155.022 743 155.042 779 155.062 806 155.082 712 155.102 628 155.122 561 155.142 659 155.162 615 155.182 600 155.202 650 155.222 641 155.242 659 155.262 766 155.282 698 155.302 631 484

155.322 613 155.342 583 155.362 499 155.382 677 155.402 673 155.422 588 155.442 632 155.462 653 155.482 596 155.502 421 155.522 511 155.542 416 155.562 521 155.582 434 155.602 455 155.622 379 155.642 325 155.662 362 155.682 360 155.702 335 155.722 363 155.742 390 155.762 429 155.782 381 155.802 435 155.822 391 155.842 345 155.862 366 155.882 386 155.902 388 155.922 347 155.942 330 155.962 333 155.982 415 156.002 497 156.022 454 156.042 439 156.062 393 156.082 423 156.102 400 156.122 506 156.142 539 485

156.162 630 156.182 537 156.202 579 156.222 585 156.242 566 156.262 567 156.282 538 156.302 461 156.322 513 156.342 526 156.362 565 156.382 587 156.402 537 156.422 579 156.442 629 156.462 653 156.482 681 156.502 796 156.522 685 156.542 697 156.562 702 156.582 829 156.602 820 156.622 732 156.642 708 156.662 693 156.682 687 156.702 790 156.722 754 156.742 792 156.762 703 156.782 728 156.802 828 156.822 849 156.842 790 156.862 835 156.882 777 156.902 603 156.922 743 156.942 721 156.962 652 156.982 661 486

157.002 568 157.022 588 157.042 505 157.062 504 157.082 623 157.102 747 157.122 687 157.142 572 157.162 547 157.182 575 157.202 500 157.222 381 157.242 398 157.262 393 157.282 618 157.302 593 157.322 487 157.342 412 157.362 451 157.382 504 157.402 517 157.422 635 157.442 554 157.462 532 157.482 539 157.502 552 157.522 529 157.542 557 157.562 559 157.582 409 157.602 381 157.622 543 157.642 398 157.662 506 157.682 546 157.702 470 157.722 483 157.742 446 157.762 416 157.782 322 157.802 326 157.822 387 487

157.842 347 157.862 347 157.882 472 157.902 472 157.922 352 157.942 429 157.962 512 157.982 509 158.002 502 158.022 505 158.042 441 158.062 481 158.082 448 158.102 340 158.122 329 158.142 336 158.162 313 158.182 410 158.202 387 158.222 429 158.242 421 158.262 438 158.282 420 158.302 386 158.322 362 158.342 407 158.362 494 158.382 577 158.402 562 158.422 536 158.442 555 158.462 526 158.482 397 158.502 470 158.522 528 158.542 486 158.562 451 158.582 390 158.602 492 158.622 439 158.642 576 158.662 570 488

158.682 537 158.702 468 158.722 519 158.742 385 158.762 391 158.782 347 158.802 273 158.822 404 158.842 499 158.862 356 158.882 415 158.902 537 158.922 439 158.942 560 158.962 503 158.982 435 159.002 351 159.022 443 159.042 457 159.062 413 159.082 367 159.102 404 159.122 444 159.142 431 159.162 501 159.182 371 159.202 457 159.222 543 159.242 428 159.262 479 159.282 560 159.302 653 159.322 463 159.342 449 159.362 334 159.382 440 159.402 298 159.422 490 159.442 499 159.462 492 159.482 484 159.502 525 489

159.522 534 159.542 541 159.562 490 159.582 551 159.602 404 159.622 396 159.642 401 159.662 354 159.682 309 159.702 310 159.722 367 159.742 361 159.762 316 159.782 348 159.802 348 159.822 427 159.842 344 159.862 368 159.882 384 159.902 427 159.922 446 159.942 347 159.962 407 159.982 365 160.002 420 160.022 385 160.042 423 160.062 510 160.082 448 160.102 476 160.122 517 160.142 489 160.162 528 160.182 510 160.202 457 160.222 543 160.242 514 160.262 587 160.282 514 160.302 497 160.322 497 160.342 505 490

160.362 479 160.382 480 160.402 508 160.422 468 160.442 439 160.462 415 160.482 490 160.502 362 160.522 539 160.542 547 160.562 504 160.582 424 160.602 534 160.622 407 160.642 477 160.662 452 160.682 479 160.702 401 160.722 446 160.742 376 160.762 468 160.782 474 160.802 439 160.822 429 160.842 407 160.862 450 160.882 385 160.902 418 160.922 404 160.942 355 160.962 412 160.982 480 161.002 526 161.022 493 161.042 484 161.062 474 161.082 465 161.102 538 161.122 518 161.142 557 161.162 555 161.182 485 491

161.202 504 161.222 531 161.242 519 161.262 551 161.282 501 161.302 476 161.322 461 161.342 540 161.362 560 161.382 500 161.402 528 161.422 582 161.442 552 161.462 574 161.482 641 161.502 672

492