(22SU526) a Freshwater Mussel Shell Ring in the Mississippi Delta

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Theses and Dissertations Theses and Dissertations

8-1-2012

Addressing sample bias and representativeness at the Kinlock site (22SU526) a freshwater mussel shell ring in the Mississippi Delta

Joseph Alan Mitchell

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Addressing sample bias and representativeness at the Kinlock site (22SU526): a

freshwater mussel shell ring in the Mississippi Delta

Joseph Mitchell

A Thesis Submitted to the Faculty of Mississippi State University in Partial Fulfillment of the Requirements for the Degree of Masters of Arts in Applied Anthropology in the Department of Anthropology and Middle Eastern Cultures

Mississippi State, Mississippi

August 2012

Joseph Mitchell

2012

Addressing sample bias and representativeness at the Kinlock site (22SU526): a

freshwater mussel shell ring in the Mississippi Delta

Joseph Mitchell

Approved:

______Evan Peacock Janet Rafferty Professor and Graduate Coordinator of Professor of Anthropology Anthropology and Middle Eastern Cultures (Committee Member) (Major Professor)

______James Hardin Gary L. Myers Associate Professor of Anthropology Professor and Dean, College of Arts & (Committee Member) Sciences

Name: Joseph Mitchell

Date of Degree: August 11, 2012

Institution: Mississippi State University

Major Field: Applied Anthropology

Major Professor: Evan Peacock

Title of Study: Addressing sample bias and representativeness at the Kinlock site (22SU526): a freshwater mussel shell ring in the Mississippi Delta

Pages in Study: 105

Candidate for Degree of Applied Anthropology

Applied zooarchaeology provides baselines which can be used in modern conservation biology to better understand how faunal communities have changed over time. This goal can only be accomplished, however, by first accounting for the multiple biases present within the archaeological record, and how they may affect sample representativeness.

Taxonomic analysis was conducted on freshwater mussel shell from the late prehistoric (ca. A.D. 700 - 1200) Kinlock site, Sunflower County, Mississippi. Species- area curves and biodiversity indices demonstrate that random sampling of surface clusters of shell, up to about 4,000 valves, provides an adequate picture of the overall surface assemblage. Comparison of surface and subsurface contexts shows a highly significant difference in species numbers and proportions, indicating a need for multi-context sampling when dealing with archaeological shell deposits.

ACKNOWLEDGEMENTS

First and foremost, I would like to thank Dr. Evan Peacock, my major advisor, for his guidance and support throughout these past three years. Thank you for getting my attention back in undergrad, taking a chance on me, and enabling me to realize my interest in such a cool field as archaeology. Also, thank you for introducing me to the fascinating (and dusty) world of freshwater mussel shell; your encouragement and direction here has been truly invaluable, and is something I hope to take forward in future research. I would also like to thank my other committee members, Drs. Janet Rafferty and Jimmy Hardin, for your ideas, assistance, conversations, and timely humor throughout my coursework and thesis research. A big thanks to my friend Shon Myatt for writing the program that I used to conduct my redundancy and diversity analysis; your assistance with this no doubt saved me hours and hours of painful number crunching.

I would like to thank my family for their constant support throughout my collegiate experience, especially when I decided to take on this crazy career path. Lastly, many thanks go to my girlfriend, Claire, for motivating me during this entire process with persistent encouragement and moral support.

TABLE OF CONTENTS

ACKNOWLEDGEMENTS ...... ii

LIST OF TABLES ...... v

LIST OF FIGURES ...... vi

CHAPTER

I. INTRODUCTION ...... 1

Problem Statement ...... 1 The Kinlock Site (22SU526) ...... 5 Field Methods (2009 MSU Field School) ...... 7

II. FRESHWATER MUSSELS IN NORTH AMERICA ...... 10

Mussel Ecology and Morphology ...... 10 Mussel Conservation and Modern Applications...... 14

III. SAMPLING AND BIAS ...... 19

Archaeological Sampling Theory ...... 19 Plow-zone Archaeology ...... 22 Bias in Archaeology ...... 25 Sources of Bias ...... 25 To screen, or not to screen? That is the question...... 27 Taphonomy ...... 29 Taphonomy and Faunal Analysis ...... 31 Taphonomy of Shellfish ...... 33 The Wolverton et al. Model ...... 34

IV. MATERIALS & METHODS ...... 38

Redundancy Analysis & Species-Area Curves ...... 38 Select-Coverage Analysis ...... 39 Shannon-Weaver Diversity Index ...... 40 Test of the Wolverton et al. Model ...... 42

V. RESULTS ...... 46 iii

The Kinock Shell Assemblage...... 46 Redundancy Analysis and Species-Area Curves ...... 49 Preliminary Analysis ...... 49 Select-Coverage Analysis ...... 55 Shannon-Weaver Diversity Index ...... 58 Wolverton et al. Model...... 60 Shell Preservation at the Kinlock Site ...... 64

VI. DISCUSSION ...... 72

Biogeography ...... 74

VII. CONCLUSIONS ...... 76

Future Applications and Considerations ...... 78

REFERENCES...... 82

APPENDIX

A. SPECIES TABULATIONS AND VALVE COUNTS FOR EXCAVATION UNITS ...... 93

B. SPECIES TABULATION AND VALVE COUNTS FOR SURFACE UNITS...... 95

LIST OF TABLES

4.1 Modern specimens and provenience list for testing the Wolverton et al. (2010) Shell Preservation Model...... 44

5.1 Species list and valve counts for excavation units...... 47

5.2 Species list and valve counts for surface collection...... 48

5.3 Valve counts for select-coverage analysis (23 of 25 species), experiments 1-25...... 56

5.4 Descriptive statistics for select-coverage analysis (23 of 25 species), experiments 1-25...... 56

5.5 Valve counts for select-coverage analysis (25 of 25 species), experiments 26-50...... 57

5.6 Descriptive statistics for select-coverage analysis (25 of 25 species), experiments 26-50...... 58

5.7 Evenness results for experiments 1-25. Taxonomic richness is 23, at 92% select-coverage...... 59

5.8 Evenness results for experiments 26-50. Taxonomic richness is 25, at 100% select-coverage...... 60

5.9 Shell measurements with sphericity and density test results for the Wolverton et al. (2010) Shell Preservation Model...... 62

5.10 Species density and sphericity averages from Table 5.9; sorted according to sphericity. Both values were used in scatter-plot shown in Figure 5.9...... 63

5.11 Combination of valve counts by species for CSC data and excavation data; not including unidentified shell...... 67

LIST OF FIGURES

1.1 Location of the Kinlock site in Sunflower County near border with Washington County, on Big Sunflower River, Mississippi...... 6

1.2 Aerial photo of the Kinlock site (22SU526)...... 8

2.1 Freshwater mussel taxa distribution in the United States. Copied from Machtinger 2007:1...... 11

2.2 Freshwater Mussel Morphology ...... 14

2.3 Right valve of an Amblema plicata containing stamping holes from pearl button production. Copied from Watters et al. 2009:5...... 15

2.4 (Top) Total number of freshwater mussel species by state. (Bottom) Percentage of imperiled freshwater mussel species by state. Copied from Grabarkiewicz and Davis 2008:2...... 16

3.1 “A) conceptual model for predicting preservation relative to sphericity and density using quadrants as heuristic devices to explain each continuous variable and how it relates to preservation. B) The same conceptual model as portrayed above, but communicated topographically, which clarifies that sphericity and density vary as continua. Probability of preservation of remains of a species increases upward and right on the graph” (copied from Wolverton et al. 2010:166). This model specifically relates to freshwater mussel shell...... 35

5.1 Species-Area Curve-1 of preliminary redundancy analysis...... 51

5.2 Species-Area Curve-2 of preliminary redundancy analysis...... 52

5.3 Species-Area Curve-3 of preliminary redundancy analysis...... 52

5.4 Species-Area Curve-4 of preliminary redundancy analysis...... 53

5.5 Species-Area Curve-5 of preliminary redundancy analysis...... 53

5.6 Species-Area Curve-6 of preliminary redundancy analysis...... 54

5.7 Species-Area Curve-7 of preliminary redundancy analysis...... 54

5.8 Species-Area Curve-8 of preliminary redundancy analysis...... 55

5.9 Scatter plot showing relationship of shell density and sphericity values from specimens used during test of Wolverton et al. (2010) Shell Preservation model...... 64

5.10 Right and left valves of Toxolasma parvus. Specimens are from fauna comparative collection housed at MSU...... 69

5.11 (Top) Fractured right and left valves of Elliptio dilatata with worn lateral and pseudocardinal teeth. (Bottom) Intact right and left valves of Elliptio dilatata. Valves are from excavation unit 14s26w, Zone B Level 2, bag # 5023...... 70

5.12 (Top) Fractured right and left valves of Plectomerus dombeyanus. (Bottom) Intact right and left valves of Plectomerus dombeyanus. Valves are from excavation unit 14s26w, Zone B Level 2, bag # 5023...... 71

6.1 Left valve of Rangia cuneata, the Atlantic Rangia. Recovered from Kinlock CSC, unit 24n8w, bag # 1604...... 75

7.1 Model of how humans in North America altered the trajectory of mussel community change; copied from Lyman and Cannon 2004:10 and modified for mussel considerations...... 80

vii

CHAPTER I

INTRODUCTION

Problem Statement

Archaeology, perhaps more than any other field, is faced with the daunting task of explaining the human condition through both time and space. While most disciplines view the universe (more or less) synchronically, archaeology is the only scientific field that has the means of viewing the human condition and how it changes across not only multiple regions and locales, but also time scales. Observation is a primary step in any scientific method. Observation in archaeology, or any discipline, “is predicated upon certain fundamental assumptions relating to the investigator's ability to make valid observations about the phenomena constituting the subject matter of that discipline”

(Nance 1981:151). For archaeologists, the only way to explain the past is to go out in the field and either collect ‘it’ (i.e., the phenomenon of interest - artifacts) from the surface or dig it up.

The problem-oriented research topic that will be addressed with this thesis is a question of preservation and sampling bias and their influence on sample representativeness. This is an issue which has plagued archaeology for decades and, despite there being extensive study in the matter, there is much room for improvement

(Erlandson and Moss 2001; Lyman 2002; Lyman 2010; Peacock 2000). Yet, for one to conduct valuable research with testable (and replicable) hypotheses, one must always 1

begin with a problem-oriented question. This thesis is focused on quantitative measures of bivalve assemblages; specifically, on shell obtained from the freshwater mussel “shell ring” located at the Woodland to Mississippian period Kinlock site (22SU526), near

Belzoni, Mississippi. In terms of problem-oriented sampling strategy, a controlled surface collection (CSC) was conducted with consideration for not only ceramic and lithic artifacts, but also the recovery of freshwater mussel shell. This allows the potential for systematic spatial and statistical exploration of a shell assemblage across the ‘shell- ring’ portion of the site. The use of the CSC data, for this thesis, is to provide an estimation of just how much of a surface collection is necessary to get a representative sample (e.g., could a random collection of 10, 15, 20, or 25% of the surface shell reflect the entire surface assemblage?). That estimation is accomplished by randomly selecting numerous samples (2x2m squares) from the surface assemblage and employing multiple quantitative and statistical methods (e.g., redundancy analysis with the use of species area curves, and diversity indices) to gain an explicit “representative” value.

Recognizing the potential problems inherent in poor sampling, both in terms of representativeness and bias (sample and preservation), this thesis employs quantitative methods, in correlation with taxonomic bivalve classification, to analyze the Kinlock mussel shell assemblage for any preservation biases. Though the CSC provides the bulk of the data, especially regarding species representativeness and diversity within the assemblage, 3 excavation units make available the opportunity to gauge the degree of shell fragmentation within the plow-zone and how it differs from the sub-soil (e.g.,

Wolverton et al. 2010). Preservation also can be investigated by comparing the stratigraphic layers within the excavation units; i.e., plow-zone contexts vs. undisturbed

midden (Peacock and Chapman 2001; Wolverton et al. 2010). Comparisons between surface and subsurface collections will hopefully provide information on the degree of preservation biases present within shell deposits, and especially, as in this case, sites which are located on plowed fields (agricultural/cultivated).

The complexity of a site will ultimately determine sample strategy. “If a site consists of a single layer of shell – as opposed to a complex, multi-component shell- bearing site with numerous intrusive features – then a sample from anywhere in the shell stratum can be expected to provide a representative picture of the total assemblage as long as an adequate number of valves is recovered and analyzed” (Peacock and Chapman

2001:52). With a complex site, however, multiple sampling methods may need to be employed to ensure that all taxa present are accounted for, with none being more or less represented than others because of any bias, either sampling or preservation bias (i.e., whether or not the assemblage is indicative of the local mussel population or of site activity).

But why does this matter? What is the relationship between sampling and mussel shell? It is very important, when dealing with multiple shell taxa, that each species within a site is as accurately represented as possible (i.e., representativeness). If it can be demonstrated that a taxonomically representative sample of shell has been obtained, it can be argued that the same is true of associated artifacts. Ultimately, the representativeness of artifacts, regardless of “type” (whether lithic, bone, ceramics, mussel shell, etc.), within a site is absolutely fundamental to our understanding of the population (i.e., the collection of artifacts within the site) from which they were drawn.

In terms of applied faunal and zoological research, this project is of paramount importance; historically, mussel shell routinely has been discarded or ignored in favor of more ‘exotic’ and/or ‘exciting’ artifacts, and is rarely used in any archaeological capacity beyond species tabulations and gross paleo-environmental inquiries (Peacock 1997;

2000; 2008; Peacock and Jenkins 2010). This thesis is intended to provide a basis for an

“applied” aspect of archaeological mussel shell analysis, which can be used in modern conservation biology to better understand what mussel communities were like in the past

(Grayson 2001; Lyman 2006; Lyman and Cannon 2004; Peacock and Jenkins 2010;

Peacock et al. 2011). Using archaeological faunal data in modern wildlife-management and conservation decisions is predicated upon accurately establishing past community characteristics as ecological targets (Grayson 2001; Lyman and Cannon 2004:10; Stahl

1996). The archaeological shell sampled ideally can be assumed to represent what was available in the local prehistoric mussel community; thus, understanding how certain field and statistical methods may influence the obtaining of a representative sample is key to an applied zooarchaeology. Framing this analysis, for scale and approach, the prehistoric mussel community can be considered the universe, whereas the population, as mentioned above, is the collection of artifacts within the site, and the mussel samples are drawn from the population for analysis.

The future application of the approach developed in this thesis is to vet shell assemblages for adequacy in illustrating how mussel populations have changed over time and whether that change is a result of increased human ecological disturbance.

Ultimately, any environmental or landscape disruption which occurs over time will be represented by changes within the ecological community. Using archaeological

assemblages in this capacity potentially can give insight on the trajectory of ecological change within modern-day mussel communities, thus allowing for better protection and conservation of the world’s endangered bivalve species. In Mississippi alone, there are over 23 endangered freshwater mussel species (Mississippi National Heritage Program

2002:1). Assessing any historical, human-induced changes in a sensitive fauna requires establishing a baseline against which modern impacts can be measured. In order to argue that archaeological faunas represent such a baseline, it must be demonstrated that archaeological samples are robust, and that the various kinds of bias that might affect them have been considered and accounted for to the best of our ability.

The Kinlock Site (22SU526)

Kinlock (22SU526) is a site located in Sunflower County, near Belzoni,

Mississippi (see Figure 1.1). The site rests primarily on agricultural property which fronts the Big Sunflower River, and consists of a plaza and as many as 6 earthen mounds

(Phillips 1970).

Figure 1.1 Location of the Kinlock site in Sunflower County near border with Washington County, on Big Sunflower River, Mississippi.

Contained within the site are various styles of ceramic and lithic artifacts as well as a thick deposit of mounded mussel shell (in a circular orientation; i.e., “shell ring”) that defines the plaza. Kinlock was recorded in 1941 during the Lower Mississippi

Survey (LMS) by Phillips, Ford, and Griffin (1951), who gathered and tabulated over

8,000 pottery sherds from the site (Phillips 1970:438-439). Phillips (1970) associates the

Kinlock site with the “Wasp Lake phase” (ca. 1500 to 1650 CE), due to the presence of

Leland Incised and Kinlock Simple Stamped pottery (Phillips 1970:441). Late Woodland

Baytown period ceramics dominated a recent surface collection made at the site (Carlock, pers. comm. 2011).

For Phillips, Ford, and Griffin (1951) and Phillips (1970), the primary emphasis in collection and analysis was almost exclusively on ceramics, while non-pottery assemblages were largely excluded (Rafferty 2008:99). Like much of the archaeology done during this period, the ultimate goal was a recreated “culture history”.

Archaeologists often employed a strict presence/absence use of diagnostic artifact types to denote phases and components, based entirely on those artifacts’ occurrence within the site (Rafferty 2008:100). More comprehensive field work, including controlled surface collection and limited test excavations, was undertaken in the summer of 2009 by the

Mississippi State University field school.

Field Methods (2009 MSU Field School)

The field-study portion (containing the shell-ring; see Figure 1.2) of the site was gridded into 4x4 meter units, with each unit subdivided into separate 2x2 squares. All sherds larger than a U.S. quarter were collected and bagged in accordance to their specific 2x2 (i.e., NW, NE, SE, and SW). For mussel shell, only intact umbos (i.e., the beak portion) were collected as the umbo is generally diagnostic for identification, as well as ensuring that individual valves are only represented once. All other artifacts, of whatever size, were collected. Shell was gathered in one 2x2 m unit out of every 4x4 by alternating between NW, NE, SE, and SW clockwise with every pass, thus collecting

~25% of the surface shell. This strategy was used for the sake of time and, due to the sheer abundance of shell, was intended to reduce the sample size to a manageable level.

The decision to gather one 2x2 of shell out of every 4x4 was not made until after the second day of fieldwork, and up to that point, all shell had been collected from the first few transects. 7

Figure 1.2 Aerial photo of the Kinlock site (22SU526)

NOTE:Kinlock road is on the left, and the Big Sunflower River on right. Outlined area indicates the ‘shell-ring’ portion of site which was collected via CSC.

Three excavation units were dug, also within the field-study portion of the site. A

1x1m unit was placed in the suspected “plaza” area. Another 1x1 and a 50x50cm unit

were placed on two areas of high density shell (Carlock and Rafferty 2009). The excavation units were dug in accordance with standard archaeological practice, using a water screening station and ¼” and 1/16” nested wire mesh to remove the artifacts from the soil. Zone levels were dug in 10cm increments. All artifacts were bagged after washing according to provenienced unit, zone, and level and are being analyzed and sorted at the Cobb Institute of Archaeology, Mississippi State University. Employing excavation and a controlled surface collection, rather than one or the other, allowed the opportunity to gather shells from multiple contexts. “Sampling a variety of different contexts at such large, complex sites is far more important in obtaining a truly representative collection of shell than is overall sample size” (Peacock and Chapman

2001:45; see also Peacock 2000). Thousands of mussel valves were obtained from the site.

CHAPTER II

FRESHWATER MUSSELS IN NORTH AMERICA

Mussel Ecology and Morphology

Freshwater mussels (also called naiads, unionids, or clams) of the family

Unionidae are found throughout much of the world, inhabiting every continent on Earth except Antarctica. Freshwater mussel species reach their greatest diversity in North

America, with nearly 300 identified species of mussels being found in the U.S. alone

(Grabarkiewicz and Davis 2008; Peacock et al. 2011; Williams et al. 2003; see also

Machtinger 2007). The distribution of freshwater mussels in the United States is at its greatest in the Southeast, east of the Mississippi River, with the highest species richness in the states of Alabama, Tennessee, Kentucky, Georgia, Mississippi, West Virginia,

Illinois, Indiana, and Ohio (see Figure 2.1).

Figure 2.1 Freshwater mussel taxa distribution in the United States. Copied from Machtinger 2007:1.

Adult mussels can range in size from less than 4 cm to more than 30 cm, and occupy a broad assortment of habitats, but are usually associated with lotic waters

(Williams et al. 1993; Williams et al. 2008).

Freshwater mussels are an important ecological component in both rivers and lakes. As filter feeders, mussels “remove a variety of materials from the water column, including sediment, organic matter, bacteria, and phytoplankton” (Grabarkiewicz and

Davis 2008:4). Machtinger (2007) notes that “some mussels can filter up to 10 gallons of water per day, which helps improve water quality for other animals, including humans”

(Machtinger 2007:2). Mussels are also sensitive to contaminants and other types of human induced disturbance (i.e., sediment and run-off from livestock, agriculture, logging, construction, etc.), making them very useful in understanding water quality and habitat trajectory.

Mussels are often used as biological indicators for water quality in lakes and rivers. A rise in mussel mortality at a given locale is usually indicative of some sort of toxic contamination or other pollution. Considering this, “biologists can measure the amount of pollutants found in mussel shells and tissue to determine the type, extent, and even timing of water pollution events” present in given rivers, streams, and lakes

(Machtinger 2007:2). Studies (e.g., Badra 2005) have shown that the richness in species of mussels and fish are related. Waterways containing a high number of fish species tend also to have more mussel taxa, and vise versa. Since mussels are “sensitive to changes in habitat quality, the status of unionids can be indicative of the biological integrity of river ecosystems as a whole” (Badra 2005:3).

In order to accurately identify mussel species, a fairly thorough understanding of shell morphology is required. With archaeological mussels, we are only dealing with the valve remains, since the soft tissue has long since decomposed, so being able to identify shell species based solely on physical characteristics is essential. Some freshwater species can easily be distinguished from others, while others are much more difficult to identify. Young mussels can vary considerably in “shape, thickness, length, color, and inflation when compared to older individuals” (Grabarkiewicz and Davis 2008:8). Due to phenotypic plasticity, which is simply variation in how individuals adapt to their specific environment, mussels of the same species can contrast markedly across different water sources, and even within the same water source, varying “in their degree of lateral compression or inflatedness, in the strength of their sculpture, and in their color patterns”, potentially making identification very problematic (Watters et al. 2009:13; see also

Grabarkiewicz and Davis 2008; Williams et al. 2008).

Freshwater mussel physical morphology consists of two valves, a right and a left.

The oldest portion of a valve is the umbo (or beak), with the shell expanding along the margins as the animal grows. “Most freshwater mussels have a dorsal area called the hinge, which has interdigitating projections called teeth. (…) The anterior-most teeth are called the cardinal (or pseudocardinal) teeth, whereas the posterior teeth are the lateral (or pseudolateral) teeth” (Watters et al. 2009: 6). A valve’s interior construction and teeth orientation are basic tools in shell identification, along with exterior shell sculpture, which can vary significantly, especially among certain species. Quadrula pustulosa, for example, can differ drastically in external shell sculpture among specimens, depending on the water source. Other species, such as Obliquaria reflexa, are fairly consistent in appearance and can be more easily identified. General freshwater mussel interior and exterior morphology can be seen in Figure 2.2.

Figure 2.2 Freshwater Mussel Morphology

NOTE: (Top) External morphology. (Bottom) Internal morphology.

Mussel Conservation and Modern Applications

The commercial use of freshwater mussels undoubtedly had a profound effect on past communities. The “Pearl Rush” of the 1850’s and the button industry of the late

1800’s drastically diminished mussel beds in rivers all across the U.S. (Watters et al.

2009:5). Button factories were established along nearly every major waterway, where shells were gathered with the use of rakes and hooks, then ‘stamped’ for buttons (see

Figure 2.3) (Watters et al. 2009; Claassen 1994; Williams et al. 2008).

Figure 2.3 Right valve of an Amblema plicata containing stamping holes from pearl button production. Copied from Watters et al. 2009:5.

The pearl button industry lasted until the early 1900’s, by which time overharvesting had depleted most mussel beds beyond a sustainable point. Pearl buttons were eventually replaced by ones made from plastic, since they were more durable and far less expensive, thus ending the pearl button industry.

Despite being historically diverse and abundant throughout much of North

America, freshwater mussel taxa are in steep decline, and are now considered one of the most imperiled fauna groups nationwide (Grabarkiewicz and Davis 2008; Peacock et al.

2011). The decline in modern mussel populations is often attributed to “habitat destruction, water quality degradation, damming, exotic species [e.g., Dreissena polymorpha, the zebra mussel], and hydrologic changes” (Grabarkiewicz and Davis

2008:3; Strayer et al. 2004; Williams et al. 1993; Williams et al. 2008). Figure 2.4 shows the number of species (i.e., richness) by state in the U.S., as well as the percentage of those species which are considered imperiled. Unfortunately, the majority of states

containing multiple mussel taxa have an imperiled rate of at least 50%, some as high as

71% (West Virginia), revealing how dire the straits presently are.

Figure 2.4 (Top) Total number of freshwater mussel species by state. (Bottom) Percentage of imperiled freshwater mussel species by state. Copied from Grabarkiewicz and Davis 2008:2.

Only recently has there been a significant move by state and federal agencies to thwart the decline of freshwater mussel species in the United States. Efforts to restore areas where mussel populations have been seriously impacted have met limited success

(Machtinger 2007). Unfortunately, biologists and malacologists are still in the developmental stage in understanding how to “improve survival rate, population growth, and recruitment of relocated individuals” in affected mussel communities (Machtinger

2007:7). This lack of a true baseline in how we view and understand mussel communities, ultimately, results in the utility archaeological data provide modern conservation efforts. Archaeological mussel data can contribute a historical baseline for how mussel populations were organized prior to and after Native American contact, as well as prior to and after Euro American contact. Thus, to better understand how humans are affecting mussel communities today, we must first establish a trajectory from past to present.

Peacock and Chapman (2001) and Lyman (1996) note the value archaeological data can provide modern conservation, particularly with freshwater mussel species. Data obtained from shell bearing sites “can be used to establish the pre-industrial ranges and expected natural proportions of mussel species in river systems now extensively altered by impoundment and pollution. Such data may be useful for aquatic biologists and land managers charged with protecting rare and endangered species” (Peacock and Chapman

2001:51). The biogeography of mussel species is a very useful tool in understanding population dynamics at the macro scale. Studies have shown (e.g., Peacock and

Chapman 2001; Peacock and James 2002) that archaeological sites frequently contain mussel species not previously known to exist in that given area. At the Pocahontas site

(22HI500), for example, researchers were able to extend the known historical ranges of three species: Cyprogenia aberti, Plethobasus cyphyus, and Quadrula quadrula. Also, at site 16OU97, in Ouachita Parish, Louisiana, the presence of Arkansia wheeleri represents the southernmost range extension for that species in the Ouachita River (Peacock and

Chapman 2001). Given the findings at these two sites alone, the utility archaeological data provides in furthering our understanding of “biogeographic ranges and community characteristics [of mussel species], as they existed prior to extensive modern impact”, is unmistakably clear (Peacock et al. 2011). As noted by Peacock et al. (2011:3),

Mussel shell is a common constituent of the archaeological record of Mississippi, being found at sites of various sizes located along waterways ranging from major rivers, to what are today small channels carrying intermediate flow. This is one characteristic of archaeological shell that makes it so valuable for biogeographical studies, as data are available from locales where modern biological surveys have not been carried out (Peacock et al. 2011:3; see also Peacock 2010a; Peacock and Chapman 2010).

CHAPTER III

SAMPLING AND BIAS

Archaeological Sampling Theory

For decades, it seems, archaeologists have been mired in debate over which methods of sampling provide the ‘best’ and most accurate results (Mueller 1975; Plog

1978). Though far from perfect, much work has been done to enhance our understanding of both statistical methods and advances in survey/excavation sampling techniques.

Sampling theory in archaeology basically comes down to a ‘principle of representativeness’, where we assess what is present in the universe (i.e., artifacts) by looking at a portion of it (i.e., population). But how do we know if we have a representative sample of a given population? How much is enough? These are questions that archaeologists, and scientists of all disciplines, put their faith in statistical techniques to answer. As simple as these questions may appear, there are, in fact, a plethora of factors which must be taken into account before they can be answered. “The minimum sample size needed depends on interplay between the sampling element, the density of archaeological remains, the amount of sampling error that is tolerable, and the degree of confidence you would like to have in the results” (Banning 2002:124).

In order to collect a sample from a population, one must first have a sound classification, establishing classes based on attributes that are both comparable and exhaustive; this is known as a paradigmatic classification (Dunnell 1971). Without a 19

sound classification in place prior to any actual research, the archaeologist will not know what phenomena are necessary to record and which ones are not (let alone, what information an archaeologist hopes to gain from those phenomena). With floral and faunal remains, the “class” employed usually is the lowest taxonomic level that can be identified, e.g., the species. With any form of sampling, however, one must begin by asking a problem-oriented question; one which can be scientifically tested. What an archaeologist wants to know will undoubtedly affect the choices he/she makes in regard to sampling strategies. Though most archaeologists now cite problem-orientations in their reports, many times the issues specified “do not affect the way research is conducted, hence, they do not provide the advantage of defining specific data categories to be recovered that might otherwise be overlooked” (Redman 1987: 250-51).

Many archaeologists find themselves making inferences about the past without ever considering whether they have a true representation of the phenomenon under investigation. Nowhere is this more apparent in archaeology than in cultural resource management (CRM). Redman (1975, 1987) notes the importance of sampling strategies and a specific problem-oriented research topic in research designs, as well as some of the misuse and confusion associated with each. A recurring problem is that sampling strategies are often “presented in the proposal, sometimes even followed in the field, but are not tailored to the specific project, being a standard format used in many circumstances”, thus “not yielding the full potential of a project-specific research design”

(Redman 1987:251). In the simplest form, there are three main types of sampling: judgment (purposeful), haphazard (grab), and probability (Redman 1975:149). Judgment sampling is described as the “conscious selection of units to investigate based on what the

researcher considers to be the most productive or most representative examples”

(Redman 1975:149). As Redman notes, the most obvious shortcoming of judgment sampling is that it usually involves some level of preconceived notions held by the researcher, ultimately leading to its own bias (i.e., self-fulfilling prophecies). It is very difficult to observe a phenomenon without applying one’s own cultural norms and ideas to its meaning. This is one of the main problems with inductive reasoning. It allows the researcher to see a pattern and, without ever asking a problem-oriented question,

“explain” the pattern, usually from some preconceived notion.

Haphazard sampling, according to Redman, is mostly caused by archaeologists’ misconceptions regarding random sampling. He notes that many archaeologists believe they can haphazardly “grab” samples randomly across a site and acquire a random representative sample. Sometimes the result of randomly selecting one’s samples without any form of sampling strategy is that “biases are introduced implicitly by the manner in which the sample is chosen” (Redman 1975:149); these samples are not truly random.

There are numerous factors that could influence a researcher’s selection bias, including artifact size, shape, color, vegetation cover, site accessibility, etc. (A large painted ceramic sherd would most likely have a higher probability of being noticed than a smaller, more neutral-colored one, for instance).

Probability sampling, which is based on statistical theory, is meant to minimize sampling bias. According to Redman (1975), probability sampling helps estimate the

‘reliability’ of samples both in terms of bias and sampling error, as well as helping to display how close the samples “approximate the parameters of the entire population”

(Redman 1975:150). In this capacity, providing a quantification of population

parameters is, in essence, a description of that sample’s representativeness by giving information on a site’s ‘inventory’ (i.e., total artifact assemblage). Most work that has been done in this area involves assessment of sample adequacy in archaeological survey and site excavation methods.

To some, surface collections are believed to be a necessity in constructing predictive archaeological models (Redman 1975; Redman and Watson 1970; Kvamme

1988). Beginning with sound sampling strategy, artifact classification, and collection techniques, many scholars believe that predictive modeling can, in fact, provide insight into settlement patterns. Surface artifacts (i.e., surface “indicators”) are the central resources used to make calculations in many predictive models; unfortunately, some areas contain buried surfaces, making standard surface survey impossible. In the case of a buried site, however, coring and auguring, deep excavation units, and systematic back- hoe trenching can be used to find such deposits.

Plow-zone Archaeology

As discussed before, obtaining a representative sample may rely heavily on surface artifacts. But how representative are artifacts found in a plow-zone context? In parts of the Southeastern US, there has been landscape alteration through agriculture practices for over three hundred years. Since much of what archaeologists find on the surface is used to make inferences regarding what is under the surface, are plow-zone contexts reliable? Many archaeologists (Dunnell and Simek 1995; Navazo and Diez

2008; Odell and Cowen 1987) have discussed the effects of tillage on the reliability of surface artifact assemblages.

As Odell and Cowan (1987) note, it is obvious that “since a large percentage of surveyed sites in many regions exists on plowed fields, the effect of tillage on the archaeological record is enormous” (Odell and Cowan 1987:479-80). According to Odell and Cowan (1987), a site located within an agriculture field, being annually plowed, will effectively double in area, while gradually decreasing in artifact density. As Dunnell

(1990) notes, though it is apparent that artifacts are displaced by agricultural practices, it is not entirely known what mechanisms might be responsible for a correlation between artifact size and displacement. According to Dunnell, “artifact size must play a key role

… not only because of the potential for differential displacement by size but also because size changes through breakage under tillage” (Dunnell 1990:593; see also Lewarch and

O’Brien 1981; Nielsen 1991).

Up until recently, there have not been many successful case studies of the correlation between artifact size and breakage and artifact disturbance within the plow- zone. Navazo and Diez (2008) conducted an experiment in which they observed how farm machinery scatters artifacts in Burgos, Spain. They used an agricultural field, with

50 mock-artifacts placed in specific locations. The artifacts were labeled and mapped prior to any plowing activities, so they could monitor the disturbance over the course of several years (from original to disturbed locations). The authors define disturbance as

“the reorganization of archaeological materials, which in this case is primarily the horizontal and vertical movement of stone artifacts. Such disturbances differentially affect particles with variable shape, size, and mass, and produce inclination, orientation, and positional changes, thus eliminating original three-dimensional relationships”

(Nevazo and Diez 2008:324).

After several plowing episodes, the field only had 5 artifacts visible on the surface, “indicating that 10% of the evidence of this ‘settlement’ could be recorded by direct surface inspection” (Nevazo and Diez 2008:324). The artifacts were moved from north-south, following the path of the plowing. None of the artifacts visible on the surface were larger than 4 cm in diameter, “thereby not fulfilling the hypothetical rule that large materials tend to remain at the surface whereas artifacts with smaller dimensions sink to greater depths” (Nevazo and Diez 2008:327).

After the surface artifacts were collected, all the topsoil was sieved through .5 cm screens. Artifacts were classified according to size with the “Bagolini” method by measuring length/width ratios and size categories (artifacts up to 4 cm, over 4 up to 6 cm, over 6 to 8 cm, and >8). According to the authors, smaller artifacts have a greater tendency to remain in the ‘plowslice’, and artifacts visible on the surface never exceed

10% of the sample population (Nevazo and Diez 2008; see also Lewarch and O’Brian

1981). With these results, the authors conclude that in areas “prone to agricultural disturbance, surface artifact assemblages are in secondary contexts (if not tertiary) and may underrepresent the true occupational history of the site”, especially when subsurface artifacts are ignored (Nevazo and Diez 2008:331). This study by Nevazo and Diez provides good information as to the nature of archaeological disturbance within agricultural areas, and it is imperative that archaeologists understand these mechanisms and processes in order to prevent bias in their interpretations, especially when results are being generalized.

Bias in Archaeology

The potential for bias (whether methodological or preservation) within a sample is one of the many factors that can influence a study, and thus must be one of the first things for which an archaeologist accounts. Assessing which types of bias are present can prove very difficult at times, especially considering the scales at which archaeologists work. In an archaeological context, there are countless variables that can affect data and results, many of which can go unnoticed by the researcher. With all of the potential variables that can arise in any particular archaeological study, it is imperative that the ones within the researcher’s control are recognized and limited accordingly. Sample biases attributed to collection and sampling strategies (such as the use of screening) are within the researcher’s control, and many problems with data interpretation usually can be accounted for by examining one’s own methods. Understanding the extent to which sample biases affect the data is vital if interpretations “are to reflect actual human behavior as represented in the archaeological record” (Peacock 2000:183) rather than a research error.

Sources of Bias

There are three main categories of bias inherent in archaeology. The first is preservation bias, which is influenced by countless variables, ranging from chemical composition within the soil, to artifact damage threshold (dependent on material, size, shape, etc.), to bioturbation, diagenesis, pedogensis, etc. (the list goes on and on). The second bias, and probably the most difficult to account for, is cultural bias. Cultural bias can manifest in the form of any item (food species or raw material) that was specifically selected for or, conversely, some items may have been avoided. Cultural and 25

preservation bias can both work simultaneously as well. The third type of bias is caused by the researcher. This type of bias can come in many forms: it can be the result of accidental sampling error, or it can be the product of a flawed research design.

Concerning these types of bias, it is always important to keep equifinality in mind.

In terms of faunal remains, particularly freshwater mussel shell, different taxa at a given site will be represented more or less depending not only on the relative degree of preservation, but also the specific context in which the shells were collected prehistorically (i.e., Native American food procurement). Within a stream or river, mussel beds can be very large in extent, and not necessarily homogenous in terms of species make up (Parmalee 1990:68). Particular shell species often are clustered together, so shell gathered randomly in a small part of a large mussel bed will not show a representative sample of available species; this presents a “skewed picture of what the overall aquatic environment was like” (Peacock 2000:187). Considering context, the shell assemblage at a given site is either representative of a cultural bias (i.e., prehistoric populations only selecting certain shell species) or it is not (i.e., mussels were gathered without any preference to shell size, shape, species, etc.). Typically, it has been shown that at shell-bearing sites, a large number of species are recovered, indicating that Native

Americans gathered whatever was available (Parmalee 1988; Parmalee and O’Hare 1989;

Peacock 2000).

The presence of any cultural bias initially can be assessed by comparing a shell assemblage to known historic shell representation and relative abundance (Peacock

2000:187; Teledyne Brown Engineering 1975). Time – and space – averaging can be used to assess the problem of cultural/collection biases. With repeated collection over

time and space at a given locale, the representativeness of species is (more or less) balanced over the entire assemblage (Peacock 2000:187; see also Lyman 2003). In order to account for cultural bias it is wise to recover shell from several contexts at a given site,

“with the assumption that these different contexts represent different episodes of shellfish gathering” (Peacock 2000:187; see also Claassen 1991). Ultimately, when shell is recovered from multiple contexts, the result can be considered a “time – and space – averaged sample that accurately reflects the aquatic environment in the vicinity of the site” (Peacock 2000:187).

To screen, or not to screen? That is the question.

For many years, few archaeologists screened the dirt they excavated and, not surprisingly, they found few small artifacts. Today, for the most part, screening has been accepted as a necessary method in archaeology. Methods such as water-screening and flotation greatly increase the number of small artifacts found in a site, whether small lithic flakes, fish bones, or plant seeds and charcoal. Flotation did not catch on as quickly as general screening and water-screening, but it has gradually shown its value, especially in recovering micro-botanical remains and charcoal. Micro-botanical remains like plant seeds give enormous insight into human subsistence and seasonality (Dincauze 2000), as well as providing information to environmental archaeologists concerned with paleoenvironmental modeling. As for charcoal remains, flotation is invaluable. Charcoal is one (if not the most) important tools for obtaining absolute dates within sites, as it is the primary item used in radiocarbon dating (14C) as well as contributing to various environmental and cultural studies.

Screening as a useful and necessary method in collecting micro-fauna (and botanical samples: Fritz 2008) has been a topic discussed at great length (Jackson 2008;

Muckle 1994; Payne 1975; Shaffer and Sanchez 1994; Sparks 1961). Jackson (2008) notes that, while excavating at Winterville (Greenville, MS), Brain (1989) did not use mesh screening during collection. Only a “meager sample (NISP=553-animal bone) was recovered and retained” (Jackson 2008:285), and interestingly Brain did not report finding any fish bones, despite the site being in close proximity to the Mississippi River.

As Jackson (2008) and Fritz (2008) both note, the use of screening, with ¼” as the standard, as well as fine-mesh (i.e., window screen), greatly increases the representation of small taxa such as fish, mollusks, etc. (see also Koloseike 1970). Because the choice of screen size will undoubtedly affect the size of material recovered (Gordon 1993;

Grayson 1984), Shaffer and Sanchez (1994) believe it important to have a ‘standard’ screen size used by all archaeologists. They note that the differentiation of artifacts recovered between 1/8” and ¼ ” inch screens was very apparent (Shaffer and Sanchez

1994:528), and was responsible for researcher bias (especially in faunal recovery), as the smaller screen size could capture more artifacts (both smaller in size and fractured). The importance of screening as a recovery method, however, is obvious. The use of screening, with appropriate sizes, will likely maximize the potential that assemblages will

“include the critical elements to differentiate species” (Jackson 2008:297), as well as gathering an accurate depiction of floral representation within the assemblage (Fritz

2008). Such caveats apply to all artifact classes.

Sparks (1961) and Muckle (1994) have noted the importance of screening methods when gathering mollusk samples from an archaeological context. Generally,

freshwater mollusk species will differ markedly by size class, ranging from 10 mm to 300 mm, as well as by shell density (Williams et al. 2008). With such variation in mollusk- shell size, as well as the likelihood of shell fragmentation as an omnipresent variable

(e.g., Wolverton et al. 2010), it is reasonable to assume that unscreened samples will tend to have more specimens representing large size classes (Lyman 2008; Peacock 2000).

Sparks states that “any attempt to pick out shells by eye from a deposit (rather than employing screening methods) is bound to lead to distortion in the frequencies of species” (Sparks 1961:72). This concern raised by Sparks (1961), as mentioned before, was avoided in this project, by collecting all the intact umbos on the surface, and screening (both ¼” and fine) the excavation material.

Taphonomy

Understanding how artifacts come to reside in a particular context, and what effects certain conditions can have, naturally occurring or human induced, is very important to our understanding of the archaeological record. The term “taphonomy” was coined by Russian paleontologist I.A. Efremov as the study of the transition (in all its details) of organic remains from the biosphere into the lithosphere (Efremov 1940:85;

Lyman 2010:2). Basically, taphonomy refers to how organisms decay, become fossilized, and the physical and chemical interactions that affect the organism from the time it is buried until the time it is collected in the field. Obviously, being able to distinguish between natural processes and anthropogenic activities is a primary issue with taphonomy, and it is a problematic one, given that equifinality can play a role in much of the archaeological record (Lyman 2004a).

Taphonomy is historically rooted in paleontology, with interests being focused on the many kinds of biases in the fossil record (Martin 1999), but as a mode of inquiry for archaeologists, its utility arose with problems distinguishing bone and other organic material (e.g., shell, plants, etc.) that were modified through natural processes (e.g., bioturbation, decomposition, diagenesis, pedogenesis, etc.), from those modified by hominid activity (Lyman 2010:4; Shipman and Phillips 1976). This early interest in taphonomy was later expanded to an overall study of site formation processes, and how such mechanisms ultimately function in creating the archaeological record, both past and present (e.g., Schiffer 1972, 1975, 1983, 1987). Concerning taphonomy, which entails aspects of many disciplines, archaeologists must be actively aware not only of the nuances of each field they reference, but also of the inherent problems and biases which accompany other disciplines. Dincauze (2000) goes to great lengths to stress the importance of education regarding natural processes and mechanisms which shape the archaeological record, as well as the need for a basic understanding of each discipline’s primary goals and capabilities. It seems that, in archaeology, many use elaborate terms and techniques, not to provide themselves or the community with a better understanding of what is being studied, but rather to present a sense of legitimacy, whether fueled by pride or academic elitism. This, as noted by Dincauze, is very harmful to the goals of archaeology.

How does one known if an artifact was altered prior to, or after, deposition?

Since the archaeological record occurs within a constantly changing environment, at whatever scale that may be, recognizing formation and taphonomic processes can be quite difficult. As Schiffer (1983) notes, “even when multiple lines of evidence are

brought into the analysis, the genesis of complex deposits formed by many processes may, in our current state of knowledge, remain uncertain” (Schiffer 1983:680). There is a considerable degree of variability in preservation, depending on the artifact, site structure, landscape, etc. Obviously, some artifacts survive better than others over time; thus, accounting for and understanding that fact as a potential bias within the archaeological record is always a concern. As Kidwell and Holland (2002) note, “much taphonomic research has been concerned with the rates and variability of postmortem processes, and the challenge now is to move from a phenomenology of the modification and accumulation of organic remains to quantitative models of bias in paleobiological data” (Kidwell and Holland 2002:565). The state of burial plays a very important role in

‘potential preservation’ of organic matter and any consequent bias. Once buried, organic material is subjected to diagenesis, which is any physical, chemical, or biological change undergone by sediment after its initial deposition within the soil (Gastald et al. 1996), and any subsequent changes during or after lithification (i.e., becoming stone). Bone, for example, requires very specific chemical and physical conditions in the burial environment to allow a high rate of preservation. Bone, is preserved very well at sites containing shell deposits, since the calcium carbonate, of which the shells are composed, neutralizes destructive acids within the soil.

Taphonomy and Faunal Analysis

Bias within archaeology is an overarching problem, but it seems to be a particular problem with faunal analysis (Grayson 1978). Taphonomic factors, such as soil chemistry and bioturbation, take their toll on stone and other artifacts, but not nearly as much as on bone and mussel shell. In the Black Prairie of Mississippi and Alabama, for instance, 31

constant movement of shrink-swell clays will ‘grind’ bone fragments into dust (Peacock

2010b). As for freshwater mussel-shell assemblages, which are very useful for paleoenvironmental modeling, soil chemistry has a profound effect on shell preservation, making some specimens unidentifiable. Though bioturbation, cultivation activities, and other disturbances do have an effect on faunal assemblages, the true extent of these processes is case-specific.

Gordon and Buikstra (1981) note a significant correlation between soil acidity

(measured by pH levels) and osseous bone deterioration. They believe that pH levels alone can be a good indicator of the “preservational potentials” at any given site (Gordon and Buikstra 1981:589; see also Grayson 1979). They note that, depending on a bone’s age (in terms of lifespan, not archaeologically), soil acidity will have different effects.

Gordon and Buikstra go on to say that soil pH as a predictor for bone preservation can be a factor used to assess the significance of sites. They note that in the “era of problem oriented research and the selection of sites for protection due to their potential in future archaeological study, an ability to predict recovery of significant data sets is of crucial importance” (Gordon and Buikstra 1981:570).

It is not discussed in the Gordon and Buikstra study, but bone fragmentation would potentially coincide with its susceptibility to both acidic soils and soil turbulence

(Lyman and O’Brien 1987). Bone fragmentation also plays a major role in faunal analysis. Lyman and O’Brien note that “factors controlling the identifiability of skeletal elements represented by fragments also control taxonomic identification” (Lyman and

O’Brien 1987:96). Considering this, many avenues of analysis will be contingent on bone preservation both in terms of soil chemistry and fragmentation. Since species

identification is a major part in faunal analysis, archaeologists must take extra precaution when selecting and evaluating faunal assemblages.

Taphonomy of Shellfish

There has been much previous work in assessing taphonomic bias in faunal assemblages. However, little consideration has been directed towards invertebrates, particularly shellfish (Erlandson and Moss 2001; Lyman 2002, 2010; Peacock 2000;

Roberts et al. 2008). For archaeological shellfish, as with other artifacts, there is quite a diverse array of taphonomic agents and processes which can influence a particular assemblage. These can include, but are not limited to:

the handling and processing for food, the discard process (e.g., height from which shells are dropped by people), orientation of deposition, disparity in burning among shellfish remains of different species, rate of disarticulation of valves, exposure to trampling [e.g., Nielson 1991], chemical weathering in acidic soils, soil formation processes, rates of deposition, sedimentation and erosion, archaeological recovery methods, modification of shells by predators, and shell shape and microstructure (Wolverton et al. 2010:165).

Characteristics of mussel shell assemblages differ drastically depending on the particular context. For example, the taxonomic composition of an archaeological site which contains shell deposits that are a product of human activities (e.g., food waste, building material, etc.) will contrast considerably with a fossilized mussel bed accumulation (i.e., a natural, nonhuman produced assemblage) (Wolverton et al.

2010:165). Sites containing shell are typically very complex, both in terms of represented taxa and the sites’ particular depositional and stratigraphic characteristics.

As Peacock and Chapman (2001) note, archaeological shell sites can contain very diverse strata which can “vary from horizontal to steeply sloping, depending on their location within the site, and which can be interspersed with large numbers of juxtaposed pits and 33

other intrusive features” (Peacock and Chapman 2001:45; see also Marquardt and

Watson 1983, Marquardt 2010). With such complexity and variability, it is important when dealing with shell deposits to sample multiple contexts within the site. As noted earlier, Peacock and Chapman (2001) state that sample diversity and representativeness are far more important than overall sample size (Peacock 1998; Peacock and Chapman

2001:45). The number of shell taxa (i.e., richness), and how they are distributed (i.e., evenness), can vary considerably throughout a site. It is important for archaeologists to be mindful of the many formation processes and taphonomic agents that can influence a shell deposit, as well as the disparity in taxonomic representativeness if only limited areas within a mussel community were exploited prehistorically.

The Wolverton et al. Model

Wolverton et al. (2010) have presented a model for assessing preservation biases caused by fracturing within freshwater mussel-shell assemblages. The authors focus on two mussel-shell parameters which coincide with preservation: shell microstructure (i.e., density) and shell shape (i.e., sphericity). Shell size is not a factor. This model considers round “cup-shaped” specimens, with high density relative to size (as measured via specific gravity), as more likely to preserve, while longer “rod-shaped” specimens, having lower density, are preserved more poorly (Wolverton et al. 2010:165-66) (See

Figure 3.1).

Figure 3.1 “A) conceptual model for predicting preservation relative to sphericity and density using quadrants as heuristic devices to explain each continuous variable and how it relates to preservation. B) The same conceptual model as portrayed above, but communicated topographically, which clarifies that sphericity and density vary as continua. Probability of preservation of remains of a species increases upward and right on the graph” (copied from Wolverton et al. 2010:166). This model specifically relates to freshwater mussel shell.

The goal of this model is to allow for predictions of mussel shell preservation depending on the particular species. Different species will have different preservational probabilities, which is based on the density and shape of shells. As the authors note,

“microstructural strength and shape are often cited as physical characteristics of shells that mediate preservation in a variety of settings”, and “thickness is the single size measure that [traditionally] appears to relate to preservation” (Wolverton et al.

2010:165). Interestingly, the authors are not concerned with “whether or not complete shells preserve, but that diagnostic features of shells preserve, such as external morphology, pseudocardinal and lateral teeth, and/or the umbo” (Wolverton et al.

2010:165). Diagnostic features are the most important aspect of mussel shells, and are basically a necessity unless the research is ignoring species identification and focusing on other assemblage features such as weight (Mason et al. 1998).

Though some ‘long/thin’ shells may be more prone to fragmentation (Wolverton et al. 2010), that does not mean they will be unidentifiable. Elliptio dilatata, for example is a long, relatively thin-shelled species. However, though its posterior margin may be prone to fracturing, the umbo portion of the shell is still identifiable at very small scales

(even when the lateral and pseudocardinal teeth are worn).

Peacock and Chapman (2001) did a taphonomic study that focused on taxonomic representation at a prehistoric shell midden site on the Ouachita River, in Louisiana.

They found that “shell preservation, as indicated by the proportion of identifiable valves, increases with depth”, with valves becoming more fragmented closer to and within the plow zone (Peacock and Chapman 2001:48). Foreshadowing the Wolverton et al. (2010) model, Peacock and Chapman note that in the plow zone, two shell species were

represented at disproportionately higher numbers than others: Pleurobema rubrum and

Obliquaria reflexa – “the shells of these taxa are thick and, thus, relatively resistant to fragmentation in the plow zone” (Peacock and Chapman 2001:48; see also Zugasti 2011).

CHAPTER IV

MATERIALS & METHODS

As mentioned earlier, this thesis is intended to provide a basis for an “applied” aspect of archaeological mussel shell, which can be used in modern conservation biology

(Lyman and Cannon 2004) to better understand what mussel communities were like in the past. The methods portion of this thesis is focused on quantitative measures of the bivalve assemblage at the Kinlock site. Also, comparisons between surface and subsurface collections will provide information on the degree of preservation bias present within these shell deposits, hopefully providing guidance for future shell research.

Redundancy Analysis & Species-Area Curves

The technique of ‘sampling to redundancy’ in correlation with species-area curves provides an estimation of just how much of a surface collection is necessary to get a representative sample. This is accomplished by randomly selecting numerous samples

(2x2m squares) from the surface assemblage and displaying the data in an X:Y chart, with ‘NTAXA’ (number of taxa) on the Y-axis, and ‘Number of Valves’ represented on the X-axis. Lyman and Ames (2007) note the importance of an explicit justification for the order in which samples are added in the species-area curve. The order, whether from largest to smallest (number of specimens per bag) or in numerical order of collection, likely will influence the sample size in number of units sampled at which the model curve

levels off (Lyman and Ames 2007:1987; Kerrich and Clarke 1967). The number of shell specimens per collection bag can vary dramatically, with larger bags obviously containing a higher number of valves than smaller ones. With this in mind, samples were taken from the data set using a random number generator.

Lyman (2004, 2008) explains how species-area curves are used to analyze sample representation/redundancy in faunal assemblages using NTAXA (i.e., sample richness).

When displayed in a X:Y chart, taxonomic frequency (NTAXA) will either level off

(plateau, more or less) or continue to climb as samples are added. The “point” of plateau is important because it, in essence, indicates a sample which has reached appropriate representativeness. By adding incremental samples on the X-axis, while cumulative taxonomic richness is plotted on the Y-axis, the curve will become progressively less steep as fewer new taxa are added with new samples and the graph will ‘level off’ once all taxa have been encountered (Lyman and Ames 2004:334). A distribution that depicts a continued climb in frequency indicates the need for more data to obtain a representative sample (Beck and Jones 1989; Lyman 1995, 2008).

Select-Coverage Analysis

This method was devised specifically for this thesis. Select-coverage analysis allows for measuring redundancy by accounting for a specific percentage of the available taxa. If there are 25 available taxa at a site, with some having an extremely rare occurrence within the assemblage, can 90, 92, or 95% of the taxa present be considered an adequate representation of the overall taxa population? Ultimately, there always is the possibility for a rare species to turn up. However, without looking at every single shell at a site, we cannot possibly know if there are in fact rare taxa present. With this in mind, 39

the goal of select-coverage analysis is to provide a means to generally characterize the overall mussel community and the basic makeup of the Kinlock assemblage, by accounting for the most common taxa within a sample. This allows for measuring redundancy without having to analyze every shell at a site. The select-coverage analysis will provide an estimate of the minimum number of mussel valves required for a redundant sample in a given surface collection. This requires one to measure the shell in a random order, inputting the results continuously into the method until the curve plateaus.

Shannon-Weaver Diversity Index

The Shannon-Weaver Diversity Index (Lyman 2008; Shannon and Weaver 1949; see also Magurran 1988) was employed to gauge the taxonomic evenness of the Kinlock shell assemblage. This index is calculated using two separate quantitative values; richness and evenness. Taxonomic richness is “the wealth or variety of species in a collection of individuals” (Bobrowsky and Ball 1989:5), or simply, the direct species count. Taxonomic evenness, which is sometimes referred to as “equitability” (Magurran

1998), is a measure of how individuals are distributed across multiple categories (e.g., taxa). “Faunas are taxonomically even if each taxon has the same number of individuals as every other taxon, regardless of richness. Faunas are taxonomically uneven if each taxon has a different number of individuals than every other taxon, regardless of richness” (Lyman 2008:175-76).

First, one must acquire a Shannon ‘diversity’ (i.e., heterogeneity) value.

The diversity value is calculated using the formula:

H = -∑ Pi (ln Pi) (4.1) where Pi is the proportion (P) of taxon (i) in the assemblage. This value is then multiplied by the natural log (ln) of that proportion. Values of the products of the multiplication are summed, and then converted from a negative value to a positive value

(Lyman 2008:192). The index value generally falls between 1.5 and 3.5 (Lyman 2008;

Magurran 1988:35), with larger values signifying greater heterogeneity (i.e., more diverse) and lower values representing homogeneity (i.e., less diverse).

After the Shannon diversity value is obtained, the second part of this process is measuring evenness.

Evenness is calculated using the formula:

e = H / InS, (4.2) where H represents the Shannon-Weaver diversity value, which is divided by natural log

(ln) multiplied by taxonomic richness (S). With this formula, the lower the value of e, the less even the assemblage. The evenness value is constrained to fall between 0 and 1, with the value of 1 indicating an even fauna, or that all taxa are equally abundant (Lyman

2008:195). Evenness is contingent upon sample size, as larger samples will generally be less even and smaller samples more even. This is because larger samples generally exhibit a higher probability of having greater taxonomic richness, making it less likely that individuals are equally distributed across all species.

Test of the Wolverton et al. Model

The Wolverton et al. (2010) model is predicated on the assumption that shells which are long, thin, and less dense will be more prone to fracturing over time, thus affecting the population’s diversity and representativeness. This is the first independent test of the Wolverton et al. (2010) model.

The right valves of thirteen species (see Table 4.1) were selected for analysis from the mussel collections housed at the Museum of Natural Science, Jackson Mississippi.

There are 8 taxa not present within the surface assemblage that were identified within the excavation shell. These species (highlighted in Table 4.1) were chosen for testing in the

Wolverton model to gauge whether their absence in the surface assemblage is a result of rarity or preservation factors. The other 5 species included in the test are present in both surface and subsurface collections, and are intended to represent a variety of shapes and sizes. Small, medium, and large specimens, identified subjectively from the available specimens, were selected for each taxon (a total of 39 specimens). This was done to provide a size scale for each taxon, which is intended to limit error in calculating each average.

As mentioned above, Wolverton et al. apply the measurements of four dimensions to their sphericity formula: shell height, length, breadth, and density. I used these dimensions as well, but expanded the authors’ model to include the category “beak cavity”. Beak cavity dimensions are classified as N-none, S-shallow, and D-deep. A shell’s umbo, which contains both the pseudocardinal teeth and lateral teeth, is generally the densest area on a valve. The Wolverton model does not distinguish areas of high density on a shell (e.g., the umbo) as being relevant to its identification. Since valves

with a beak cavity tend to have denser umbos, this category is intended to gauge if there is any relationship between shell preservation and the presence of a beak cavity.

Table 4.1 Modern specimens and provenience list for testing the Wolverton et al. (2010) Shell Preservation Model.

Specimen Provenience Panola Co. Amblema plicata Little Tallahatchie Washington Eliptio dilatata Co. Bogue Phalia Creek Ballard Co. Ellipsaria lineolata (Ky) Ohio R.

Rankin Co. Fusconia ebena Pearl R.

Montgomery Cardium complex* Co. Big Black R. Lafayette Co. Lampsilis siliquoidea Yocona R. Trib. Yazoo Co. Big 0bovaria subrotunda Black R.

Sharkey Co. Plectomerous dombeyanus Big Sunflower R. Prairie du Quadrula metenevra Chien (WI) MS R. Prairie du Strophitus undulatus Chien (WI) MS R. Noxubee Co. Toxolasma parvus Macedonia Creek Jackson Co. Toxolasma texasiensis Big Black Creek Jackson Co. Vilosa lienosa Big Black Creek NOTE: *Cardium Complex is used to label both the species Lampsilis cardium and Lampsilis ovata, both of which are commonly confused in archaeological specimens from the Sunflower River and its surrounding drainages (Parmalee and Bogan 1998:126-128). Specimens highlighted indicate taxa which were present in the excavation data, but absent in the surface collections. All specimens are from Mississippi, unless otherwise noted. 44

With every specimen, digital calipers were used to take three separate measurements for height, length, and breadth, averaging them together for a single value, and rounding to the nearest hundredth millimeter; this was done, as noted by Wolverton et al. (2010:168), to minimize error. For weight, each valve was measured on a digital scale and the value rounded to the nearest hundredth gram. For volume, I followed the

Wolverton et al. method of using two graduated cylinders, the first to submerge the valve in 1000mL of water, the second to measure the amount of displacement. When a valve was submerged in the first cylinder, the displaced water was siphoned off and measured in the second cylinder with finer graduations for reading. Due to our lack of a large, finely graduated cylinder, I was forced to round volume to the nearest milliliter. As mentioned above, density values are generated by dividing valve weight by valve volume. Beak cavity dimensions were simply visually recorded as none, shallow, or deep. Though somewhat subjective, this was done to gauge any potential significant relationship between a valve’s sphericity and the presence of any beak cavity.

The authors generate a sphericity score using the formula:

[(bc/a 2 ) 0.33 ] (4.3) where ‘a’ is the average maximum length between the anterior and posterior margin of each valve (shell length) for a species, ‘b’ is the average maximum length between the dorsal and ventral margin of each valve (shell height), and ‘c’ is average maximum length between the interdentum and topmost surface of the shell (shell breadth)

(Wolverton et al. 2010:167-8). Shell density is calculated by dividing valve weight by valve volume.

CHAPTER V

RESULTS

The Kinock Shell Assemblage

Shell was analyzed to the genus and species level in accordance with present freshwater mussel shell guides (Williams et al. 2008) and with the assistance of the faunal comparative collection housed at the Cobb Institute of Archaeology, MSU. The

Kinlock site yielded a total of 33 separate shell species, only 25 of which, however, were identified from the surface samples. Tables 5.1 and 5.2 show the species tabulations and valve counts for the excavation units and the surface collection.

Table 5.1 Species list and valve counts for excavation units.

Species Zone A totals Zone B totals Zone C totals Total Amblema plicata 727 1542 27 2296 Arcidens confragosus 6 24 0 30 Cyprogenia aberti 12 41 1 54 Ellipsaria lineolata 5 0 0 5 Eliptio dilatata 98 338 3 439 Fusconaia ebena 131 498 6 635 Fusconaia flava 379 940 14 1333 Glebula rotundata 0 0 1 1 Lampsilis cardium 5 34 3 42 Lampsilis hydiana 29 234 4 267 Lampsilis ovata 0 8 0 8 Lampsilis siliquoidea 5 40 0 45 Lampsilis teres 7 261 0 268 Ligumia recta 1 20 3 24 Meglonaia nervosa 32 59 0 91 Oboquaria reflexa 199 563 4 766 Obovaria subrotunda 0 1 0 1 Plectomerous dombeyanus 687 2102 35 2824 Plethobasus cyphyus 1 6 0 7 Pleurobema rubrum 571 956 18 1545 Potamilus purpuratus 0 2 1 3 Quadrula apiculata 2 54 0 56 Quadrula cylindrica 10 27 0 37 Quadrula metanevra 18 0 0 18 Quadrula nodulata 29 242 0 271 Quadrula postulosa 199 503 7 709 Quadrula quadrula 99 467 5 571 Strophitus undulatus 0 1 0 1 Toxolasma parvus 0 17 0 17 Toxolasma texasiensis 1 0 0 1 Tritogonia verrucosa 8 47 1 56 Truncilla truncata 25 71 1 97 Vilosa lienosa 3 1 0 4 Unidentifiable 500 1766 17 2283 TOTALS 3789 10865 151 14805

Table 5.2 Species list and valve counts for surface collection.

Species Total Amblema plicata 4478 Arcidens confragosus 16 Cyprogenia aberti 119 Ellipsaria lineolata 0 Elliptio dilatata 165 Fusconaia ebena 3079 Fusconaia flava 2370 Glebula rotundata 10 Lampsilis cardium 30 Lampsilis hydiana 160 Lampsilis ovata 0 Lampsilis siliquoidea 2 Lampsilis teres 83 Ligumia recta 79 Meglonaia nervosa 75 Oboquaria reflexa 1964 Obovaria subrotunda 0 Plectomerous dombeyanus 2238 Plethobasus cyphyus 5 Pleurobema rubrum 5423 Potamilus purpuratus 4 Quadrula apiculata 60 Quadrula cylindrica 19 Quadrula metanevra 0 Quadrula nodulata 376 Quadrula postulosa 1170 Quadrula quadrula 874 Strophitus undulatus 0 Toxolasma parvus 0 Toxolasma texasiensis 0 Tritogonia verrucosa 30 Truncilla truncata 52 Vilosa lienosa 0 Unidentifiable 9745 TOTAL 32626

A grand total of 47,035 valves were analysed, 32,233 from the surface (see

Appendix B) and 14,802 from the excavation units (see Appendix A). Of the surface valves, 22,653 were identified to 25 species, and 9,580 (~30% of total) were found unidentifiable. Of the excavation valves, 12,519 were identified to 33 species, and 2,283

(~15% of total) were found unidentifiable.

The presence of identifiable shell is central to our understanding of the assemblage. In their study, Peacock and Chapman (2001) note that the percentage of identifiable shell significantly increases with depth. There are numerous “post- depositional agents” that disproportionately impact shell on the surface and in the plow zone (Peacock and Chapman 2001:48), while shell species located in midden generally have greater preservation. This is of key importance with this study, and future applications, given that the surface collections from Kinlock were undoubtedly impacted by years of agricultural activity; thus, understanding the differences in taxonomic characteristics between surface and subsurface collections is key.

Redundancy Analysis and Species-Area Curves

Preliminary Analysis

The preliminary redundancy trials include the collected sample (~25%) of the surface assemblage: 597 sample bags, 25 identified species (i.e., 100% species coverage), and a total of 22,653 valves; unidentified valves were not included. Eight redundancy experiments were conducted, with data being tabulated using Microsoft Excel. Species- area curves were constructed using a running total of the number of valves (x-axis) and the number of taxa, or NTAXA (y-axis). Each preliminary trial represents a different random order of the 597 sample bags. This was done with the help of a custom program 49

which randomizes the samples and constructs the tables of the valve running totals and taxa occurrence.

Species-area curves 1-8 (Figures 5.1 – 5.8) show how the number of species

(NTAXA) levels off as more valves are added. The “point” where NTAXA levels off is considered here as signifying redundancy; in other words, a generally representative sample has been reached.

The preliminary analysis yielded these results:

Species-area curve #1 (Figure 5.1) meets redundancy at 23 taxa (2,747

valves), requiring over 9,000 additional valves to account for all 25

taxa.

Species-area curve #2 (Figure 5.2) meets redundancy at 24 taxa (1,721

valves), but required less than 3,000 additional valves to account

for all 25 taxa.

Species-area curve #3 (Figure 5.3) meets redundancy at 23 taxa (4,252

valves), requiring over 14,000 additional valves to account for all

25 taxa.

Species-area curve #4 (Figure 5.4) meets redundancy at 23 taxa (1,201

valves), requiring over 11,000 additional valves to account for all

25 taxa.

Species-are curve #5 (Figure 5.5) meets redundancy at 24 taxa (4,863

valves), requiring over 11,000 additional valves to account for all

25 taxa.

Species-area curve #6 (Figure 5.6) meets redundancy at 23 taxa (4,202

valves), requiring over 12,000 additional valves to account for all

25 taxa.

Species-area curve #7 (Figure 5.7) meets redundancy at 23 taxa (1,741

valves), requiring over 10,000 additional valves to account for all

25 taxa.

Species-area curve #8 (Figure 5.8) meets redundancy at 23 taxa (4,369

valves), requiring over 8,000 additional valves to account for all 25

taxa.

Figure 5.1 Species-Area Curve-1 of preliminary redundancy analysis.

Figure 5.2 Species-Area Curve-2 of preliminary redundancy analysis.

Figure 5.3 Species-Area Curve-3 of preliminary redundancy analysis.

Figure 5.4 Species-Area Curve-4 of preliminary redundancy analysis.

Figure 5.5 Species-Area Curve-5 of preliminary redundancy analysis.

Figure 5.6 Species-Area Curve-6 of preliminary redundancy analysis.

Figure 5.7 Species-Area Curve-7 of preliminary redundancy analysis.

Figure 5.8 Species-Area Curve-8 of preliminary redundancy analysis.

Select-Coverage Analysis

Preliminary analysis showed that, with multiple trials, redundancy is generally met at 23 taxa. For the select-coverage analysis, 25 experiments were conducted (Table

5.3) focusing on the point of redundancy when based on 92% (23 out of 25 taxa) of

NTAXA. The coverage experiments for trials 1-25 yielded normal descriptive statistics

(see Tables 5.4) which demonstrated that an average of 2,961 valves accounts for 92% of the total NTAXA. A minimum valve count of 968 was recorded for experiment #6, while the maximum valve count was 8,805 for experiment #11. The standard deviation

(average) is 809.74.

Table 5.3 Valve counts for select-coverage analysis (23 of 25 species), experiments 1-25.

Experiment # of Valves Coverage (%) 1 2830 92 2 2942 92 3 2317 92 4 2982 92 5 2656 92 6 968 92 7 2751 92 8 8524 92 9 2328 92 10 1455 92 11 8805 92 12 3138 92 13 1048 92 14 2592 92 15 1036 92 16 1006 92 17 3331 96 18 2234 92 19 6822 92 20 1378 92 21 1486 92 22 2045 92 23 5415 92 24 2451 92 25 1489 92

Table 5.4 Descriptive statistics for select-coverage analysis (23 of 25 species), experiments 1-25.

Min Valve Count: 968 Max Valve Count: 8805 Mean Valve Count: 2961 Median Valve Count: 2451 Std. Dev. : 809.74 Average Percent Coverage: 0.92

Select-coverage experiments were also conducted (Table 5.5) focusing on the point of redundancy when based on 100% (25 out of 25) NTAXA. Descriptive statistics for experiments 26-50 (Table 5.6) reveal an average of 10,302 valves is required for redundancy at 100% NTAXA. A minimum valve count of 3,492 was recorded in experiment #36, while the maximum valve count was 17,040 for experiment #37. The standard deviation (average) for experiments 26-50 was 2937.32.

Table 5.5 Valve counts for select-coverage analysis (25 of 25 species), experiments 26-50.

Experiment # of Valves Coverage 26 15809 100 27 9021 100 28 10205 100 29 4151 100 30 8715 100 31 7290 100 32 9814 100 33 13639 100 34 11523 100 35 11036 100 36 3492 100 37 17040 100 38 8834 100 39 10207 100 40 5871 100 41 4187 100 42 14543 100 43 7367 100 44 16966 100 45 7940 100 46 13255 100 47 11305 100 48 10335 100 49 14310 100 50 10698 100

Table 5.6 Descriptive statistics for select-coverage analysis (25 of 25 species), experiments 26-50.

Min Valve Count: 3492 Max Valve Count: 17040 Mean Valve Count: 10302 Median Valve Count: 10207 Std. Dev. : 2937.32 Average Percent Coverage: 100

Shannon-Weaver Diversity Index

Shannon-Weaver analysis was conducted on the same samples used in the select- species coverage. Diversity and evenness analysis was applied to each experiment’s data by combining the sample units for each trial, with the collective species and valve counts for each being input into the respective formula. Results for experiments 1-25 (Table

5.7) show that the minimum evenness is .6709 for trial #17, and the maximum is .7244 for trial #13. Richness is a constant at 23 taxa for experiments 1-25 (92% coverage).

Results for experiments 26-50 (Table 5.8) show a minimum evenness value is

.6671 for experiment #32, and the maximum is .68 for experiment #29. Richness is a constant at 25 taxa for experiments 26-50 (100% coverage).

Table 5.7 Evenness results for experiments 1-25. Taxonomic richness is 23, at 92% select-coverage.

Experiment Evenness Richness

1 0.6961 23 2 0.6736 23 3 0.6821 23 4 0.677 23 5 0.7039 23 6 0.6857 23 7 0.6964 23 8 0.6818 23 9 0.7022 23 10 0.7083 23 11 0.6949 23 12 0.6948 23 13 0.7244 23 14 0.691 23 15 0.6907 23 16 0.6971 23 17 0.6709 23 18 0.6794 23 19 0.6936 23 20 0.7047 23 21 0.6921 23 22 0.6931 23 23 0.6927 23 24 0.7016 23 25 0.6844 23

Table 5.8 Evenness results for experiments 26-50. Taxonomic richness is 25, at 100% select-coverage.

Experiment Evenness Richness

26 0.6705 25 27 0.6731 25 28 0.6673 25 29 0.68 25 30 0.678 25 31 0.6708 25 32 0.6671 25 33 0.6743 25 34 0.6762 25 35 0.6746 25 36 0.6777 25 37 0.6741 25 38 0.6705 25 39 0.6703 25 40 0.6676 25 41 0.6777 25 42 0.6752 25 43 0.6693 25 44 0.6745 25 45 0.6739 25 46 0.6775 25 47 0.6717 25 48 0.6766 25 49 0.676 25 50 0.6745 25

Wolverton et al. Model

This test began by selecting modern specimens of numerous shell species for measurement, all with varied sizes and densities, with the goal of comparing their representation within the Kinlock assemblage, between the surface and subsurface

collections. Wolverton et al. (2010) list many shell taxa they believe fit their model, either as breakage-prone or breakage-resistant shells. I selected specimens that are shared between their list and the Kinlock assemblage, as well as some species that are only included at Kinlock. As mentioned before, the specimens only present at Kinlock are of a variety of shapes and sizes, and were intended to act as a control for the test.

Modern shell specimens were obtained from the Museum of Natural Science in Jackson,

Mississippi, with the help of biologist Bob Jones.

Sphericity and density measurements (Table 5.9) were calculated for 39 specimens, three for each species (13 in total). Results show that Obovaria subrotunda yielded the highest sphericity value at .70 (density of 1.03), while the lowest value was

.42 for Elliptio dilatata (density of .87). That is, Obovaria subrotunda is highly spherical, while Elliptio dilatata is relatively flat and rod-shaped. The highest density recorded was for the Plectomerus dombeyanus at 4.30 (sphericity of .51), while the lowest value was Toxolasma parvus at .37 (sphericity of .46).

Table 5.9 Shell measurements with sphericity and density test results for the Wolverton et al. (2010) Shell Preservation Model.

BEAK CAVITY Specimen Provenience HIEGHT LENGTH BREADTH DENSITY D-avg N S D S-VALUE S-av S) 29.8 38 11.7 0.6 * 0.63 Panola Co. Little A. plicata M) 59.4 76.4 23.1 1.4 1.43 * 0.62 0.61 Tallahatchie R. L) 73 101.3 28.9 2.3 * 0.59 Washington Co. S) 31 61.9 8.3 0.8 * 0.41 E. dilatata Bogue Phalia M) 30.6 65.7 9.9 0.9 0.87 * 0.42 0.42 Creek L) 36.2 73.5 11.9 0.9 * 0.43 S) 39.3 50.5 6.4 2 * 0.47 Ballard Co. (Ky) E. lineolata M) 57.7 76.2 17.7 4.8 3.73 * 0.56 0.53 Ohio R. L) 60.9 81.6 19.5 4.4 * 0.57 S) 41.4 52 14.9 1.9 * 0.61 Rankin Co. Pearl F. ebena M) 47.8 58 16.6 1.7 1.90 * 0.62 0.62 R. L) 52.6 66.8 20.9 2.1 * 0.63 S) 61.1 86.8 27.8 2.9 * 0.61 Montgomery Co. Cardium complex* M) 67 106.2 30.3 3.5 3.37 * 0.57 0.57 Big Black R. L) 73.4 119.4 29.6 3.7 * 0.54 S) 31 53.6 11.4 0.9 * 0.50 Lafayette Co. L. siliquoidea M) 36.6 61.9 15.3 1 1.40 * 0.53 0.52 Yocona R. Trib. L) 48.4 85.7 22.7 2.3 * 0.53 S) 30.4 34 11 1.1 * 0.66 Yazoo Co. Big 0. subrotunda M) 30.8 34.7 15.8 0.9 1.03 * 0.74 0.70 Black R. L) 38.9 41.2 14.6 1.1 * 0.70 S) 28.9 55.7 10.5 1.2 * 0.46 Sharkey Co. Big P. dombeyanus M) 65.1 98.4 22.1 5.2 4.30 * 0.53 0.51 Sunflower R. L) 73.2 119 26.6 6.5 * 0.52 S) 40.1 42.8 13.2 2.6 * 0.66 Prairie du Chien Q. metenevra M) 51 56.6 18.3 3 3.03 * 0.67 0.65 (WI) MS R. L) 57 72 21.9 3.5 * 0.63 S) 41.7 69.4 12 1.4 * 0.47 Prairie du Chien S. undulatus M) 41.9 72.2 12.9 1 1.53 * 0.47 0.48 (WI) MS R. L) 49 85.2 17.7 2.2 * 0.50 Noxubee Co. S) 11.6 20.8 4 0.3 * 0.48 T. parvus Macedonia M) 14.9 26.4 5.3 0.4 0.37 * 0.49 0.46 Creek L) 12 29.2 5.3 0.4 * 0.42 S) 17.1 29 6 0.9 * 0.50 Jackson Co. Big T. texasiensis M) 20.3 36.2 8.6 1.4 1.40 * 0.51 0.50 Black Creek L) 24.6 48.8 10.6 1.9 * 0.48 S) 22.9 39.7 7.7 1.6 * 0.49 Jackson Co. Big V. lienosa M) 26.3 45.4 9.3 3 2.37 * 0.49 0.49 Black Creek L) 32.3 58.5 11.9 2.5 * 0.49 NOTE: All are modern specimens, obtained from the Museum of Natural Science, Jackson, Mississippi.

The averages for both sphericity and density (S-avg & D-avg: see Table 5.10) were calculated and plotted in an X: Y scatter chart. This was done to gauge the correlation between density and sphericity as analytical variables used in the Wolverton et al. (2010) preservation model. Correlation (i.e., relationship between variables) is determined based on how the R2 value is distributed between 0 and 1. The closer to 0, the more independent the variables, while a value closer to 1 suggest a more significant correlation. The R2 results (Figure 5.9) show a value of .019, indicating that a correlation between sphericity and density is nearly nonexistent.

Table 5.10 Species density and sphericity averages from Table 5.9; sorted according to sphericity. Both values were used in scatter-plot shown in Figure 5.9.

Sphericity & Density Averages Specimen Density Sphericity Eliptio dilatata 0.87 0.42 Toxolasma parvus 0.37 0.46 Strophitus undulatus 1.53 0.48 Vilosa lienosa 2.37 0.49 Toxolasma texasiensis 1.4 0.5 Plectomerus dombeyanus 4.3 0.51 Lampsilis siliquoidea 1.4 0.52 Ellipsaria lineolata 3.73 0.53 Cardium complex* 3.37 0.57 Amblema plicata 1.43 0.61 Fusconaia ebena 1.9 0.62 Quadrula metenevra 3.03 0.65 0bovaria subrotunda 1.03 0.7

Figure 5.9 Scatter plot showing relationship of shell density and sphericity values from specimens used during test of Wolverton et al. (2010) Shell Preservation model.

Shell Preservation at the Kinlock Site

Upon completing the shell analysis of both the surface and excavation assemblages, it was apparent there are some interesting taxonomic and preservational characteristics within the Kinlock site. In previous chapters, there was much discussion on ‘surface archaeology’ and the notable difference in preservation probabilities among artifacts within the plow-zone as compared to undisturbed midden. As mentioned before, of the 32,233 surface valves, 9,580 were found to be unidentifiable, while of the 14,805 excavation valves, 2,283 were unidentifiable. This is significant because the unidentified surface shell represent nearly 30% of all the valves collected from the surface, while only

15% of the excavation shell was found unidentifiable; this would seemingly indicate an obvious preservation bias within the Kinlock surface assemblage. A Chi-square test comparing plow-zone (zone A) to undisturbed midden (zones B and C) within the 64

excavation units, showed that the difference between the two is highly significant at the

.05 level.

Along with the unidentified shell, the difference in number of taxa between the surface and excavation assemblages is also a concern. As noted before, a total of 33 shell taxa were identified within the three excavation units. Of those 33 taxa, only 25 were identified from the surface. The absence of 8 taxa (see Table 5.11) from the surface is most likely the result of preservation bias due to shell fracturing. The 8 taxa not present within the surface assemblage were included, along with another five species, in testing the Wolverton model to gauge the level (if any) of ‘preservational probabilities’ contingent on shell sphericity and density.

When comparing sphericity and density values of the taxa used in testing the

Wolverton model to the ones identified in the Kinlock assemblage, it appears that shell density (not sphericity) is the primary factor governing shell fracturing and preservation.

The R2 value provided in Figure 5.9 indicates that density and sphericity have basically no relationship with one another. The results provided in Tables 5.9 and 5.10 seem to support this assertion, as the specimens which are more spherical are not also denser, and vice versa. When considering the preservation of shell within the Kinlock site, it most always was contingent upon density, particularly around the umbo, not how ‘cup-shaped’ or ‘rod-shaped’ the shell is.

Of the shell included in the test, only six were identified at a high rate within the

Kinlock shell assemblage. Table 5.11 displays the grand total of valves per taxa that were collected from the Kinlock site. Amblema plicata, Elliptio dilatata, Fusconia ebena, the Cardium complex, Lampsilis siliquoidea, and Plectomerous dombeyanus all

frequently occur, and interestingly, appear in both the surface and excavation units. All of these species, with the exception of Elliptio dilatata, possess a beak cavity, and are all relatively dense for their size, especially around the anterior margin and pseudocardinal teeth.

Table 5.11 Combination of valve counts by species for CSC data and excavation data; not including unidentified shell.

Species Total Obovaria subrotunda* 1 Strophitus undulatus* 1 Toxolasma texasiensis* 1 Villosa lienosa* 4 Ellipsaria lineolata* 5 Potamilus purpuratus 7 Lampsilis ovata* 8 Glebula rotundata 11 Plethobasus cyphyus 12 Toxolasma parvus* 17 Quadrula metanevra* 18 Arcidens confragosus 46 Lampsilis siliquoidea 47 Quadrula cylindrica 56 Lampsilis cardium 72 Tritogonia verrucosa 86 Ligumia recta 103 Quadrula apiculata 116 Truncilla truncata 149 Meglonaia nervosa 166 Cyprogenia aberti 173 Lampsilis teres 351 Lampsilis hydiana 427 Elliptio dilatata 604 Quadrula nodulata 647 Quadrula quadrula 1445 Quadrula postulosa 1879 Oboquaria reflexa 2730 Fusconaia flava 3703 Fusconaia ebena 3714 Plectomerus dombeyanus 5062 Amblema plicata 6774 Pleurobema rubrum 6968 Totals 35403 NOTE: Valve counts for Lampsilis cardium and Lampsilis ovata are combined and labeled ‘Cardium Complex’ for Wolverton et al. (2010) test. Species included in Wolverton test are highlighted. Taxa notated with an asterisk (*) represent the 8 species not present within the surface assemblage. 67

The other taxa from the model, Ellipsaria lineolata (5), Obovaria subrotunda (1),

Quadrula metanevra (18), Strophitus undulatus (1), Toxolasma parvus (17), Toxolasma texasiensis (1), and Villosa lienosa (4) all occur at low rates, and only within the excavation units. Of these specimens, only Obovaria subrotunda and Quadrula metanevra have a high level of sphericity, and interestingly both are fairly dense for their size. It would seem that Obovaria subrotunda is, in fact, a rare species in this portion of the Sunflower River, having only 1 identified valve from the Kinlock site. Quadrula metanevra, though present in the subsurface, interestingly, is completely absent from the surface assemblage. During analysis, many valves were identified to the genus

Quadrula, but the exact species could not be ascertained, so the valves were labeled unidentifiable.

It is also interesting that Toxolasma parvus was not identified in the surface, despite there being 17 valves from the three excavation units. This species was found to have the lowest density on average and has a shallow beak cavity. For this species, lateral teeth are very thin and blade-like, while the pseudocardinal teeth are nearly nonexistent and the anterior margin is very thin and fragile (see Figure 5.10). Since

Toxoplasma parvus lacks density near the anterior margin and beak cavity, the data seem to support the idea that a dense beak/umbo is indeed necessary for better preservation.

Figure 5.10 Right and left valves of Toxolasma parvus. Specimens are from fauna comparative collection housed at MSU.

Curiously Elliptio dilatata is both one of the least dense, and the only shell in this test that is without a beak cavity. However, despite not having a beak cavity, Elliptio dilatata are generally denser around the pseudocardinal and lateral teeth (see Figure

5.11), and can easily be identified when there is an intact umbo (165 valves identified on the surface and 439 in the excavation units). The same can be said for Plectomerus dombeyanus, which were frequently identified at extremely small scales, usually with the posterior margin and lateral teeth completely absent (see Figure 5.12).

Figure 5.11 (Top) Fractured right and left valves of Elliptio dilatata with worn lateral and pseudocardinal teeth. (Bottom) Intact right and left valves of Elliptio dilatata. Valves are from excavation unit 14s26w, Zone B Level 2, bag # 5023.

Figure 5.12 (Top) Fractured right and left valves of Plectomerus dombeyanus. (Bottom) Intact right and left valves of Plectomerus dombeyanus. Valves are from excavation unit 14s26w, Zone B Level 2, bag # 5023.

CHAPTER VI

DISCUSSION

The preliminary results provide a valuable picture of the overall taxonomic structure of the Kinlock shell assemblage. Knowing which taxa are most common, or the least common, is key to understanding when redundancy is reached (Lyman and Ames

2007). The results show that redundancy is met at 23 taxa in six of the eight species-area curves, where occurrence begins to ‘level off’ considerably, as thousands of valves are continually added to the running total. As said before, these results are used to justify the approach in the select-coverage analysis, as well as to provide an indication as to the taxonomic structure of the site.

The results generated from the select-coverage analysis offer an interesting perspective for future shell analysis. As mentioned before, one of the goals of this thesis is to determine a quantitative baseline for sample representativeness (i.e., how much is enough?). The descriptive statistics for trials 1-25 show that the average number of valves needed to reach redundancy (at 92% coverage, i.e., the most common species) is

2,961. This value is important because it indicates a sample size significantly smaller than the overall collected surface assemblage at Kinlock (fewer than 3,000 compared to nearly 33,000). Also of importance is the difference between the results shown in the

92% and 100% select-coverage analysis. The average valve count necessary for redundancy at 100% coverage is 10,302, which is considerably larger than 2,961 valves 72

at 92%. This is significant because the difference between 100% and 92% coverage,

7,341 valves, represents the number of valves needed to account for the rare taxa present within the surface assemblage. The rare taxa in question are Lampsilis siliquoidea (2 valves), Plethobasus cyphyus (5 valves), and Potamilus purpuratus (4 valves).

Three other taxa, Arcidens confragosus (16 valves), Glebula rotundata (10 valves), and

Quadrula cylindrica (19 valves), also are not very common within the surface assemblage.

The results for the Shannon-Weaver evenness values are intended to provide information on the taxonomic makeup within the Kinlock assemblage, and how it may change across different sample sizes. Experiments 1-25 show evenness values that are all relatively high. Evenness values for trials 26-50, at 100% coverage, are also all relatively high, but slightly lower on average than experiments 1-25. This was expected, since evenness is contingent on sample size. As stated earlier, larger samples generally exhibit a higher probability of having greater taxonomic richness, making it less likely that individuals are equally distributed across all species.

Interestingly, however, the evenness results for experiments 1-25 and 26-50 are both relatively high, despite the average valve count differing considerably. These results show there is little difference in evenness between samples containing 2,961 and 10,302 valves. Considering this, as well as the redundancy results, it would appear that a 92% select-coverage (i.e., the most common species) does, in fact, meet an adequate level of redundancy, and can be considered representative of the general surface assemblage.

Biogeography

As mentioned earlier, archaeological sites frequently contain mussel species not previously known to exist in that given area. The Kinlock site contained several mussel species that are considered extremely rare or were previously believed as nonexistent in the Big Sunflower River. Glebula rotundata, Lampsilis cardium, and Quadrula metanevra are all considered stable in Mississippi (Williams et al. 1993) and generally widespread throughout most of the state (Jones et al. 2005). However, they are all extremely rare in the Big Sunflower River (Peacock et al. 2011).

Strophitus undulatus is considered stable nationally by Willams et al. (1993), but is currently listed as imperiled in Mississippi (Jones et al. 2005), being especially rare in the Big Sunflower River, having been identified archaeologically only once, at 22CO503

(Peacock et al. 2011:53).

Ellipsaria lineolata is considered a “species of concern” nationally (Williams et al. 1993) and very rare in Mississippi (Peacock et al. 2011). The limited excavation data at Kinlock yielded 5 Ellipsaria lineolata valves, which constitutes the only known prehistoric occurrence of the species in the Big Sunflower River.

Most notably, perhaps, is the presence of a single Rangia cuneata (the Atlantic

Rangia) valve (see Figure 6.1) from the Kinlock surface assemblage.

Figure 6.1 Left valve of Rangia cuneata, the Atlantic Rangia. Recovered from Kinlock CSC, unit 24n8w, bag # 1604.

This valve was identified near the end of shell analysis. The fact that this single valve was collected at all is improbable. Prior research on the geographic extent of

Rangia cuneata, which is a brackish-water clam, shows that it is primarily associated with coastal sites, with the most northern known occurrence along the Gulf of Mexico being from the Lower Tombigbee River, slightly north of the Mobile-Tensaw Delta of south Alabama (Peacock et al. 2011; see also Cobb 2009). Considering this, the identification of Rangia cuneata in the upper Sunflower River is quite significant, which could provide a basis for further studies ranging from biogeographic and chemical analysis, to paelo-climate inquiries and species range extensions.

CHAPTER VII

CONCLUSIONS

The results from redundancy and evenness testing all indicate that limited sampling can still provide an adequate representation of a shell deposit. This is key for future research, since shell assemblages, like the one at Kinlock, are quite vast, and require an abundance of resources to not only collect, but to wash, analyze, and house.

Given the results included in this paper, it seems reasonable when dealing with surface assemblages, to focus on the areas of higher artifact frequency, since redundancy testing and Shannon-Weaver analysis are contingent upon sample size.

As mentioned earlier, at sites where shell deposits are a result of human activity, there does not seem to be any form of prehistoric collection bias present. Peacock (2000) notes that with comparisons of prehistoric collections and historic period mussel surveys,

“studies have shown that shell-bearing sites tend to produce a good approximation of the range of species that would have been expected prior to extensive modern environmental change” (Peacock 2000:186; see also Rudolf 1983; Warren 1975; White 1977). With such large numbers of mussel species being recovered from archaeological sites, as well as the variety in shape and size among shells collected, this indicates that prehistoric

Native Americans gathered whatever was available from their adjacent water sources

(Peacock 2000:186).

At archaeological sites containing sizable shell deposits, taxonomic richness tends to be greater. This fact is also apparent at smaller scales, where a sample from a surface collection that contains more valves, has a higher probability of containing more taxa, and vise-versa. This fact is supported by the valve counts per Kinlock sample bag, since smaller samples had fewer taxa present, while larger bags containing hundreds of valves, generally accounted for most of the species identified within the surface assemblage (see

Appendix B). Given the nature of how mussel communities are structured, and since archaeological deposits can generally be considered representative of what was prehistorically available at a given locale (Peaock 2000:186; see also Atkinson, Phillips, and Walling 1980; Nance 1987), focusing on multiple shell clusters within surface assemblages is just as likely to provide an adequate sample as collecting the whole surface.

Still, however, one can never be sure that all taxa have been accounted for. This thesis has well documented the problems that arise when dealing with preservation biases, taphonomic processes, and research error. Admittedly, the excavation data are rather limited, and cannot be considered a representative picture of the subsurface structure at the Kinlock site. However, the subsurface data’s application here has been primarily that of a test of the Wolverton et al. model, providing comparisons between low and high density shell values between the surface and subsurface collections. Most importantly, despite its presumed shortcomings, the excavation data included 8 taxa that would have gone completely unnoticed if only a CSC was carried out. This is very important going forward, especially concerning the results of the Wolverton et al. test, since preservation biases obviously play a critical role in the representation of shell taxa

on the surface of sites, especially ones impacted by agricultural activities. Given this, for one to gain a truly representative assessment of all the taxa present at a site, some form of subsurface sampling, however limited, would appear necessary. As mentioned earlier, despite multiple sampling strategies being carried out, a shell’s sheer rarity can cause problems.

Future Applications and Considerations

In light of the results presented in this thesis, much can be gained by promoting the use and concepts of redundancy sampling and diversity analysis in future shell research. General surface collections, specifically ones dealing with archaeological mussel shell, can benefit greatly from taking note of the findings here. As mentioned earlier, shell clusters containing large numbers of valves have a high probability of containing all of the taxa available within the site, especially in contexts where preservation can be considered homogeneous throughout the entire surface (e.g., a plowed field). Thus, thoroughly collecting ‘X’ number (obviously case specific) of shell clusters at a site will result in a high likelihood that general redundancy can be reached.

Knowing and understanding the taxonomic structure within a faunal assemblage, like a shell midden, would allow archaeologists to be more strategic in their sampling methods.

This would not only cut down on sample sizes and operation costs, but would allow archaeologists to avoid collecting the entirety of a site, never knowing when or if a truly representative sample is obtained. Considering this, a statistically valid sample always should be the aim, regardless of the methods used.

A representative shell sample could then be applied to all associated artifacts within an assemblage. Using shell in this capacity, as a ‘measuring stick’ of sorts, would 78

allow archaeologists to employ the same collection methods when gathering samples, regardless of type (bone, shell, stone, ceramic, etc.), yielding the same benefits of efficiency and cost. Artifacts like mussel shell, ceramics, bone, and lithics are all products of human life at a given locale. Given this, mussel shell and other artifacts often were “deposited by a group of people over a period of continuous site use”, and thereby constitute an assemblage representing historical continuity (Rafferty 2001: 347; see also

Dunnell 1971:181).

A controlled surface collection’s primary goal is to obtain information on the spatial structure of the deposits within an assemblage (Sullivan 1998). CSC’s have been used extensively in the past, but as mentioned before, like most of archaeology, the primary emphasis in collection and analysis was almost exclusively on ceramic and stone artifacts, while non-pottery assemblages, especially shell, were largely excluded (Rafferty

2008:99). Random samples taken from multiple shell clusters at a site, could be used to inform us as to the degree of change over time within the local prehistoric mussel community, and if that change is associated with (or a result of) any activity or fluctuation within the resident human population. If prehistoric people gathered mussels from a river section until it was depleted, and then moved on to another section, there would likely be a fluctuation in the taxonomic richness or even a change in overall taxonomic structure.

As mentioned before, there are numerous endangered mussel species in

Mississippi alone, thus new avenues for mollusk research and conservation could prove highly beneficial. The many shell-bearing sites throughout the Southeast provide a useful

picture of past mussel communities, and how they have changed over time through multiple phases of human contact (e.g., Figure 7.1).

Figure 7.1 Model of how humans in North America altered the trajectory of mussel community change; copied from Lyman and Cannon 2004:10 and modified for mussel considerations.

The potential for an expanded field and knowledge base is significant, and archaeological shell studies provide a unique perspective going forward. Recently, the biogeography of freshwater mussels has become a valuable research avenue employing archaeological collections (Peacock et al. 2011; Peacock 2009; Peacock and Chapman

2001; Peacock and James 2002). The geographic ranges of mollusks throughout the

Southeast are visibly changing, and this provides a catalyst for adjusting how we look at shell communities, both past and present. This project alone yielded the presence of two mussel species (Ellipsaria lineolata and Rangia cuneata) not previously known to exist in the Big Sunflower River. As noted earlier, mollusks are extremely sensitive to their particular environments, which is why they are excellent indicators of ecological change.

Though archaeology is the dominant topic of this paper, the implications, however, far transcend it. Using archaeological data to generate solutions for modern day ecological problems, ones which can preserve endangered species, promote conservation, and help better understand the trajectory of human environment impact, is a worthy function for future zooarchaeological studies.

In conclusion, this thesis is intended to provide a basis for an “applied” aspect of archaeological mussel shell analysis. Hopefully, the results achieved here can help advance archaeological methods and theory which deal with mussel shell, and promote its value in modern day wildlife conservation as an essential tool in understanding past and modern mussel communities and their trajectory for the future.

REFERENCES

Atkinson, James R., John C. Phillips, and Richard Walling 1980. The Kellogg Village Site Investigations, Clay County, Mississippi. Mississippi State: Department of Anthropology, Mississippi State University.

Badra, Peter J. 2005. Freshwater Mussels of Michigan. Michigan Natural Features Inventory. Michigan State University Extension Center, Michigan State University.

Beck, Charlotte and George T. Jones 1989. Bias and Archaeological Classification. American Antiquity 54:244-262.

Brain, Jeffrey P. 1989. Winterville: Late Prehistoric Culture Contact in the Lower Mississippi Valley. Mississippi Department of Archives and History, Archaeological Report 23, Jackson.

Bobrowsky, Peter T. and Bruce F. Ball 1989. The Theory and Mechanics of Ecological Diversity in Archaeology. In: Leonard, Robert D. and George T. Jones (Eds.), Quantifying Diversity in Archaeology. Cambridge University Press, Cambridge. Pg. 4-12.

Carlock, Bradley 2011. Personal Communication.

Carlock, Bradley and Janet Rafferty 2009. Spatially Extensive Examination of the Kinlock Site (22SU526): Using Controlled Surface Collection, Magnetometry, Excavation, and Soil Cores. Paper Presented at 66th Southeastern Archaeological Conference, Mobile.

Claassen, Cheryl 1991. Normative Thinking and Shell-Bearing Sites. In: Michael B. Schiffer (Ed.), Archaeological Method and Theory, 3. Tucson: The University of Arizona Press, 249-298.

1994. Washboards, Pigtoes, and Muckets: Historic Musseling Industries in the Mississippi Watershed. Historic Archaeology 28:1-145.

Cobb, Robin 2009. Rangia cuneata Shells as an Environmental Proxy: Variations in Elemental Concentrations within Populations. The University of Alabama McNair Journal 9:45-58.

Dincauze, Dina F. 2000. Environmental Archaeology: Principles and Practice. Cambridge University Press.

Dunnell, Robert C. 1971. Systematics in Prehistory. Blackburn Press. New Jersey.

1990. Artifact Size and Lateral Displacement under Tillage: Comments on the Odell and Cowan Experiment. American Antiquity 55:592-594.

Dunnell, Robert C. and J.F Simek 1995. Artifact Size and Plowzone Processes. Journal of Field Archaeology 22:305-319.

Efremov, I.A. 1940. Taphonomy: a New Branch of Paleontology. Pan American Geologist 74:81-93.

Erlandson, J.M., and M.L. Moss 2001. Shellfish Feeders, Carrion Eaters, and the Archaeology of Aquatic Adaptations. American Antiquity 66:413-32.

Fritz, Gayle J. 2008. Paleoethnobotanical Information and Issues Relevant to the I-69 Overview Process, Northwest Mississippi. In: Rafferty J., and Peacock E., (Eds.), Times River. University of Alabama Press, Tuscaloosa. Pp. 299-343.

Gastaldo, Robert A., Charles E. Savrda, and Ronald D. Lewis 1996. Deciphering Earth History. Contemporary Publishing Company of Raleigh.

Gordon, Claire C. and Jane E. Buikstra 1981. Soil pH, Bone Preservation, and Sampling Bias at Mortuary Sites. American Antiquity 46:566-571.

Gordon, E.A. 1993. Screen Size and differential Faunal Recovery: A Hawaiian Example. Journal of Field Archaeology 20:453-460.

Grabarkiewicz, Jeffery D., and Wayne S. Davis 2008. An Introduction to Freshwater Mussels as Biological Indicators. EPA-260- R-08-015. United States Environmental Protection Agency, Office of Environmental Information, Washington, DC.

Grayson, D.K. 1978. Minimum Numbers and Sample Size in Vertebrate Faunal Analysis. American Antiquity 43:53-65.

1979. On the Quantification of Vertebrate Archaeofaunas. In: Schiffer M.B., (Ed.), Advances in Archaeological Method and Theory, Vol. 2. Academic Press, Yew York. Pp. 199-237.

1984. Quantitative Zooarchaeology: Topics in the Analysis of Archaeological Faunas. Academic Press, Orlando.

2001. The Archaeological Record of Human Impacts on Animal Populations. Journal of World Prehistory 15:1-68.

Jackson, H. Edwin 2008. Faunal Research in the Yazoo Basin and Lower Mississippi Valley. In: Rafferty J., and Peacock E., (Eds.), Times River. University of Alabama Press, Tuscaloosa. Pp. 274-298.

Jones, Robert L., William T. Slack, and Paul D. Hartfield 2005. The Freshwater Mussels (Mollusca: Bivalvia: Unionidae) of Mississippi. Southeastern Naturalist 4:77-92.

Kerrich, J.E., and D.L. Clarke 1967. Notes on the Possible Misuse and Errors of Cumulative Percentage Frequency Graphs for the Comparison of Prehistoric Artifact Assemblages. Proceedings of the Prehistoric Society 33:73-85

Kidwell, Susan M., and Steven M. Holland 2002. The Quality of the Fossil Record: Implications for Evolutionary Analyses. Annual Review of Ecology and Systematics 33:561-88.

Koloseike, Alan 1970. Costs of Shell Analysis. American Antiquity 35:475-480.

Kvamme, Kenneth L. 1988. Development and Testing of Quantitative Models. In: Judge, James and Sebastian, Lynne (Eds.), Quantifying the Present and Predicting the Past: Theory, Method, and Application of Archaeological Predictive Modeling. U.S. Department of the Interior, Bureau of Land Management Service Center. Denver, Co.

Lewarch, D.E., and M.J. O’Brien, 1981. Effect of Short Term Tillage on Aggregate Provenience Surface Pattern. In: M.J. O’Brien and D.E. Lewarch (Eds.), Plowzone Archaeology: Contributions to Theory and Technique. Vanderbilt University Publications in Anthropology 27:8- 49.

Lyman, R. Lee 1995. Determining when Rare (Zoo) Archaeological Phenomena are Truly Absent. Journal of Archaeological Method and Theory 2:369-424.

1996. Applied Zooarchaeology: The Relevance of Faunal Analysis to Wildlife Management. World Archaeology 28:110-125.

2002. Taphonomic Agents and Taphonomic Signatures. American Antiquity 67:361-65.

2003. The Influence of Time Averaging and Space Averaging on the Application of Foraging Theory in Zooarchaeology. Journal of Archaeological Science 30:595-610.

2004a. The Concept of Equifinality in Taphonomy. Journal of Taphonomy 2:15- 26.

2004b. Aboriginal Overkill in the Intermountain West of North America: Zooarchaeological Tests and Implications. Human Nature 15:169-208.

2006. Paleozoology in the Service of Conservation Biology. Evolutionary Anthropology 15:11-19.

2008. Quantitative Paleozoology. Graeme B., (Ed.), Cambridge Manuals in Archaeology. Cambridge University Press.

2010. What Taphonomy is, What it Isn’t, and Why Taphonomists Should Care About the Difference. Journal of Taphonomy 8:1-16.

Lyman, R. Lee and Kenneth M. Ames 2004. Sampling to Redundancy in Archaeology: Lessons from the Portland Basin, Northwestern Oregon and Southwestern Washington. Journal of Ethnobiology 24:329-46.

2007. On the Use of Species-Area Curves to Detect the Effects of Sample Size. Journal of Archaeological Science 34:1985-90.

Lyman, R. Lee and Kenneth P. Cannon (Eds) 2004. Zooarchaeology and Conservation Biology. University of Utah Press, Salt Lake City.

Lyman, R. Lee and Michael J. O’Brien 1987. Plow-zone Zooarchaeology: Fragmentation and Identifiability. Journal of Field Archaeology 14:493-498.

Magurran, A. E. 1988. Ecological Diversity and its Measurement. Princeton University Press, Princeton.

Marquardt, William H. 2010. Shell Mounds in the Southeast: Middens, Monuments, Temple Mounds, Rings, or Works? American Antiquity 75:551-70.

Marguardt, William H., and Patty Jo Watson 1983. Excavation and Recovery of Biological Remains from Two Archaic Shell Middens in Western Kentucky. Southeastern Archaeological Conference Bulletin 20:112-29.

Martin, R. E. 1999. Taphonomy: A Process Approach. Cambridge: Cambridge University Press.

Mason, Roger D., Mark L. Peterson, and Joseph A. Tiffany 1998. Weighing vs. Counting: Measurement Reliability and the California School of Midden Analysis. American Antiquity 63:303-324.

Machtinger, Erika T. 2007. Native Freshwater Mussels. Natural Resources Conservation Service, Wildlife Habitat Council. Fish and Wildlife Habitat Management Leaflet 46: 1- 16.

Mississippi National Heritage Program 2002. Endangered Species of Mississippi. Museum of Natural Science, Mississippi Dept. of Wildlife, Fisheries, and Parks, Jackson, MS.

Muckle, Robert J. 1994. Differential Recovery of Mollusk Shell from Archaeological Sites. Journal of Field Archaeology 21:129-31.

Mueller, James W. (Ed.) 1975. Sampling in Archaeology. The University of Arizona Press, Tucson Arizona.

Nielsen, Axel E. 1991. Trampling the Archaeological Record: an Experimental Study. American Antiquity 56:483-503.

Nance, Jack D. 1981. Statistical Fact and Archaeological Faith: Two Models in Small-Sites Sampling. Journal of Field Archaeology 8:151-165.

1987. Research in the Lower Tennessee-Cumberland-Ohio Region. Southeastern Archaeology 6:93-100.

Navazo, Marta and Carlos Diez 2008. Redistribution of Archaeological Assemblages in Plowzones. Geoarchaeology: An International Journal 23:323-333.

Odell, George H. and Frank Cowan 1987. Estimating Tillage Effects on Artifact Distributions. American Antiquity 52:456-484.

Parmalee, Paul W. 1988. A Comparative Study of Late Prehistoric and Modern Molluscan Faunas of the Little Pigeon River System, Tennessee. American Malacological Bulletin 6:165-78.

Parmalee, Paul W., and Arthur E. Bogan, 1998. The Freshwater Mussels of Tennessee. University of Tennessee Press, Knoxville.

Parmalee, Paul W., and Constance O’Hare 1989. Snails and Freshwater Mussels from the Anderson Site. In: John T. Dowd, The Anderson Site: Middle Archaic Adaptation in Tennessee’s Central Basin. Tennessee Anthropological Association Miscellaneous Paper 13. Knoxville: Tennessee Anthropological Association, 37-42.

Payne, S. 1975. Partial Recovery and Sample Bias. In: Clason, A.T., (Ed.), Archaeozoological Studies. North Holland Press, Amsterdam. Pp. 7-17.

Peacock, E. 1997. Current and Future Directions in the Analysis of Freshwater Bivalves in Archaeology. In Charles M. McNutt (ed.), Results of Recent Archaeological Investigations in the Mid-South: Proceedings of the 17th Annual Mid-South Conference, Memphis, Tennessee: June 29-30, 1996, pp. 71-93. The University of Memphis, Anthropological Research Center, Occasional Paper No. 18.

1998. Fresh-Water Mussels as Indicators of Prehistoric Human Environmental Impact in the Southeastern United States. Unpublished PhD thesis, Department of Archaeology and Prehistory, University of Sheffield, Sheffield, UK.

2000. Assessing Bias in Archaeological Shell Assemblages. Journal of Field Archaeology 27:183-196.

2008. Paleoenvironmental Modeling in the Lower Mississippi River Valley: Past and Future Approaches. In: Rafferty J., and Peacock E., (Eds.), Times River. University of Alabama Press, Tuscaloosa. Pp. 69-98.

2009. Analysis of Molluscan Remains from 1CK56. In: Sarah E. Price (Ed.), Archaeology on the Tombigbee River: Phase III Data Recovery at 1CK56, the Corps Site, Clarke County, Alabama, pp. 196-216. Report submitted to the U.S. Army Corps of Engineers, Mobile District, by the University of South Alabama Center for Archaeological Studies.

2010a. Archaeological Freshwater Mussel Remains and Their Use in the Conservation of an Imperiled Fauna. Paper presented at the 33rd Annual Meeting of the Society of Ethnobiology, Victoria, British Columbia. Under review in: Zooarchaeological Perspectives on Issues in Conservation Biology. In Steve Wolverton, R. Lee Lyman, and Charles R. Randklev (Eds.). University of Arizona Press.

2010b. Personal Communication.

Peacock, E., and Cliff Jenkins 2010. The Distribution and Research Value of Archaeological Mussel Shell: An Overview from Mississippi. Midcontinental Journal of Archaeology 35:91-116.

Peacock, E., and Shawn Chapman 2001. Taphonomic and Biogeographic Data from a Plaquemine Shell Midden on the Ouachita River, Lousiana. Southeastern Archaeology 20:44-55.

Peacock, E., and Jennifer L. Seltzer 2008. A Comparison of Multiple Proxy Data Sets for Paleoenvironmental Conditions as Derived from Freshwater Bivalve (Unionid) Shell. Journal of Archaeological Science 35:2557-2565.

Peacock, E., and Thomas R. James 2002. A Prehistoric Unionid Assemblage from the Big Black River Drainage in Hinds County, Mississippi. Journal of the Mississippi Academy of Science 47:119-23.

Peacock, E., Cliff Jenkins, Paul F. Jacobs, and Joseph Greenleaf 2011. Archaeology and Biogeography of Prehistoric Freshwater Mussel Shell in Mississippi. BAR International Series 2297. Archaeopress, British Archaeological Reports. Oxford, England.

Peacock, Evan, Stuart W. McGregor, and Ashley A. Dumas Under review. Behavioral, Environmental, and Applied Aspects of Molluscan Assemblages from the Lower Tombigbee River, Alabama. In: Tanya M. Peres Lemons (Ed.), Trends and Traditions in Southeastern Zooarchaeology. Newfound Press, University of Tennessee Libraries.

Phillips, Philip 1970. Archaeological Survey in the Lower Mississippi Alluvial Valley, 1940- 1947. Papers of the Peabody Museum of Archaeology and Ethnology. Harvard University, Vol. 25, Cambridge.

Philips, Philip, James A. Ford, and James B. Griffin 1951. Archaeological Survey in the Lower Mississippi Alluvial Valley, 1940- 1947. Papers of the Peabody Museum of Archaeology and Ethnology. Harvard University, Vol. 25, Cambridge.

Plog, Stephen 1978. Sampling in Archaeological Surveys: a Critique. American Antiquity 43:280-285.

Rafferty, Janet 2001. Determining Duration at Prehistoric Sites: Short-Term Sedentary Settlement at Josey Farm, NE Mississippi. Journal of Field Archaeology 28:347- 366.

2008. Settlement Patterns, Occupations, and Field Methods. In: Rafferty J., and Peacock E., (Eds.), Times River. University of Alabama Press, Tuscaloosa. Pp. 99-124.

Redman, Charles L. 1975. Productive Sampling Strategies for Archaeological Sites. In Mueller, James W. (Ed.). Sampling in Archaeology. The University of Arizona Press, Tucson Arizona.

1987. Surface Collection, Sampling, and Research Design: A Retrospective. American Antiquity 52:249-265.

Redman, Charles L. and Watson, Patty Jo 1970. Systematic, Intensive Surface Collection. American Antiquity 35:279-291.

Ringrose, T.J. 1993. Bone Counts and Statistics: A Critique. Journal of Archaeological Science 20:121-157.

Roberts, Eric M., Leif Tapanila, and Brandon Mijal 2008. Taphonomy and Sedimentology of Storm-Generated Continental Shell Beds: A Case Example from the Cretaceous Western Interior Basin. The Journal of Geology 116:462-79.

Rudolph, James L. 1983. Lamar Period Exploitation of Aquatic Resources in the Middle Oconee River Valley. Early Georgia 11:86-103.

Shaffer, Brian S. and Julia L. J. Sanchez 1994. Comparison of 1/8”- and ¼”- Mesh Recovery of Controlled Samples of Small-to-Medium-Sized Mammals. American Antiquity 59:525-530.

Shannon, Claude E. and Warren Weaver 1949. The Mathematical Theory of Communication. The University of Illinois Press, Illinois.

Shipman, P. and J. Phillips 1976. On Scavenging by Hominids and Other Carnivores. Current Anthropology 17:170-172.

Schiffer, Michael B. 1972. Archaeological Context and Systemic Context. American Antiquity 37:156- 65.

1975. Archaeology as behavioral Science. American Anthropologist 77:836-48.

1983. Toward the Identification of Formation Processes. American Antiquity 48:675-706.

1987. Formation Processes in the Archaeological Record. University of New Mexico Press.

Sparks, B.W. 1961. The Ecological Interpretation of Quaternary Non-Marine Mollusca. Proceedings of the Linnean Society of London 172:71-80.

Stahl, Peter W. 1996. Holocene Biodiversity: An Archaeological Perspective from the Americas. Annual Review of Anthropology 25:105-26.

Strayer, D.L., J.A. Downing, W.R. Haag, T.L. King, J.B. Layer, T.J. Newton, and S.J. Nichols 2004. Changing Perspectives on Pearly Mussels, North America’s Most Imperiled Animals. Bioscience 54:429-39.

Sullivan, Alan P. 1998. Surface Archaeology. University of New Mexico Press.

Teledyne Brown Engineering 1975. A Preimpoundment Study of Macrobenthos on the River Section of the Tennessee-Tombigbee Waterway. Huntsville, Al: Teledyne Brown Engineering Environmental Services.

Thomas, David Hurst 1986. Refiguring Anthropology. First Principles of Probability and Statistics. American Museum of Natural History. Waveland Press, Illinois.

Watters, G. Thomas, Michael A. Hoggarth, and David H. Stansbery 2009. The Freshwater Mussels of Ohio. The Ohio State University Press. Columbus, Ohio.

Warren, Robert E. 1975. Prehistoric Unionaccean (Freshwater Mussel) Utilization at the Widows Creek Site (1JA305), Northeast Alabama. Unpublished M.A. thesis, University of Nebraska, Lincoln.

White, David S. 1977. Changes in the Freshwater Mussel Populations of the Poteau River System, Le Flore County, Oklahoma. Proceedings of the Oklahoma Academy of Science 57:103-5

Willams, J., A Bogan, and J. Garner 2008. Freshwater Mussels of Alabama and the Mobile Basin in Georgia, Mississippi, and Tennessee. University of Alabama Press, Tuscaloosa.

Williams, J., M. Warren, K. Cummings, J. Harris, and R. Neves 1993. Conservation Status of Freshwater Mussels of the United States and Canada. Fisheries 9: 6-22.

Wolverton, Steve, Charles R. Randklev, and James H. Kennedy 2010. A conceptual model for freshwater mussel (family: Unionidae) remain preservation in zooarchaeological assemblages. Journal of Archaeological Science 37:164-173.

Zugasti, F.I. Gutierrez 2011. Shell Fragmentation as a Tool for Quantification and Identification of Taphonomic Processes in Archaeomalacological Analysis: the Case of the Cantabrian Region (Northern Spain). Archaeometry 53:614-30.

APPENDIX A

SPECIES TABULATIONS AND VALVE COUNTS FOR EXCAVATION UNITS

APPENDIX B

SPECIES TABULATION AND VALVE COUNTS FOR SURFACE UNITS

P. d o m b ey an u s u aney b P.m o d A . p lic ata. lic A p . fragA n coosus . C ertiab E. taeo lin E. dilatata F.nabe e F. vafla da. n G tu ro L. cardm iu L. hydiana L. ovata L. oid siliqeau L. res te L. recta sa. erv o M n . O reflexa . O brosustra ta yuP.s yp h c P.rum b ru P.rpuratu u s p . Q icuaplata . Q lindcy rica tanraev. e Q m ta la . odu Q n . ustuQ p losa . druuaQ laq S.dulatu s n u T. rvu pas T. sienxa te sis T. rruc ve osa T.ta ca n tru . V lienosa identifiabn U le Prov. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 Totals 100n40w 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 100n36w 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 100n32w 2 0 0 0 0 2 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8 14 100n28w 9 0 0 0 0 2 0 0 0 1 0 0 0 0 0 0 0 1 0 6 0 0 0 0 1 3 0 0 0 0 0 0 0 4 27 100n24w 5 0 0 0 0 3 0 0 0 0 0 0 0 1 0 5 0 2 0 2 0 0 0 0 1 1 3 0 0 0 0 0 0 9 32 100n20w 2 1 0 0 0 8 3 0 0 0 0 0 0 0 0 5 0 4 0 5 0 0 0 0 1 6 1 0 0 0 0 0 0 8 44 100n16w 15 0 0 0 0 12 8 0 1 1 0 0 0 1 1 2 0 9 0 15 0 0 0 0 3 5 3 0 0 0 0 1 0 14 91 100n12w 3 0 0 0 0 4 4 0 0 0 0 0 0 0 0 0 0 1 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 4 19 100n8w 5 1 0 0 0 6 2 0 0 0 0 0 0 0 0 3 0 2 0 5 0 0 0 0 1 1 0 0 0 0 0 0 0 5 31 100n4w 8 0 0 0 0 14 7 0 0 0 0 0 1 1 0 7 0 3 0 31 0 1 1 0 2 4 5 0 0 0 0 0 0 16 101 96n32w 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 96n28w 2 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 10 96n24w 0 0 0 0 0 2 2 0 0 0 0 0 0 0 0 3 0 0 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 3 15 96n20w 11 0 0 0 0 3 1 0 0 0 0 0 0 0 0 8 0 4 0 2 0 0 0 0 0 2 0 0 0 0 0 0 0 18 49 96n16w 1 0 0 0 0 1 4 0 0 0 0 0 0 0 0 5 0 3 0 5 0 0 0 0 0 0 2 0 0 0 0 0 0 5 26 96n16w 11 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 6 0 3 0 0 0 0 0 1 1 0 0 0 0 0 0 5 33 96n12w 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 2 0 4 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 9 19 96n8w 2 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 14 20 96n4w 6 0 1 0 0 10 13 0 0 1 0 0 1 2 0 7 0 9 0 11 0 0 0 0 1 0 5 0 0 0 0 0 0 47 114 92n32w 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 98n28w 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 92n24w 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 4 92n20w 2 0 0 0 0 2 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 8 92n16w 3 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 3 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 16 25 92n12w 4 0 0 0 0 2 3 0 0 0 0 0 0 0 0 2 0 5 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 36 54 92n8w 12 0 2 0 0 6 6 0 0 0 0 0 0 0 0 11 0 11 0 10 0 0 0 0 0 1 0 0 0 0 0 0 0 36 95 92n4e 10 0 0 0 0 14 7 0 0 0 0 0 0 0 0 9 0 2 0 19 0 0 0 0 3 6 3 0 0 0 0 0 0 25 98 88n40w 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 88n36w 2 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 2 7 88n36w 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 88n32w 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 3 88n28w 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 88n24w 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 88n20w 2 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 5 88n16w 1 0 0 0 0 1 0 0 1 0 0 0 0 0 0 2 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 13 20 88n12w 2 0 0 0 0 1 2 0 0 0 0 0 0 0 0 1 0 0 0 2 0 0 0 0 0 0 1 0 0 0 0 0 0 8 17 88n8w 21 0 1 0 0 10 6 0 0 0 0 0 0 1 0 16 0 15 0 26 0 0 0 0 1 0 1 0 0 0 0 0 0 34 132 88n4w 40 0 1 0 1 19 16 0 0 1 0 0 0 0 0 17 0 12 0 55 0 0 0 0 3 8 6 0 0 0 1 0 0 31 211 88n28e 41 0 1 0 0 27 25 0 0 0 0 0 1 0 1 20 0 10 0 47 0 0 0 0 3 23 8 0 0 0 0 0 0 54 261 88n0e 12 0 0 0 1 15 9 0 0 0 0 0 0 0 0 4 0 3 0 13 0 0 0 0 1 8 0 0 0 0 0 0 0 33 99 84n40w 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 3 84n36w 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 84n32w 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 84n28w 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 3 84n24w 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 3 84n20w 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 5 84n16w 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 3 6

84n12w 4 0 0 0 0 3 1 0 0 0 0 0 0 0 0 3 0 5 0 5 0 0 0 0 0 3 3 0 0 0 0 0 0 10 37 80n40w 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 80n36w 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 80n32w 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 80n36w 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 3 80n28w 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 3 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 6 80n24w 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 12 20 80n20w 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 80n16w 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 2 5 80n12w 8 0 0 0 1 4 3 0 0 0 0 0 0 0 0 3 0 1 0 7 0 0 0 0 0 0 4 0 0 0 0 0 0 18 49 80n8w 43 0 1 0 1 21 25 0 0 0 0 0 2 2 1 12 0 30 1 64 0 0 0 0 4 15 7 0 0 0 1 0 0 86 316 80n4w 47 0 1 0 2 26 23 1 1 1 0 0 4 1 3 38 0 35 0 39 0 0 0 0 5 29 10 0 0 0 0 0 0 107 373 80n0e 5 0 0 0 0 3 3 0 0 0 0 0 0 0 1 1 0 6 0 5 0 0 0 0 0 3 0 0 0 0 0 0 0 4 31 80n4e 13 1 0 0 0 19 10 0 0 0 0 0 0 0 0 19 0 3 0 31 0 0 1 0 12 3 2 0 0 0 2 0 0 27 143 80n8e 22 0 1 0 0 12 5 0 0 0 0 0 0 0 0 4 0 2 0 28 0 1 0 0 3 10 4 0 0 0 0 0 0 22 114 80n12e 13 0 0 0 1 7 5 0 1 0 0 0 0 0 0 2 0 8 0 5 0 0 0 0 0 0 4 0 0 0 0 0 0 23 69 80n16e 10 0 0 0 0 15 27 0 0 0 0 0 0 0 0 2 0 5 0 11 0 0 0 0 0 0 3 0 0 0 0 0 0 35 108 80n20e 14 0 0 0 3 7 5 0 0 0 0 0 0 0 0 1 0 2 0 12 0 0 0 0 0 1 1 0 0 0 0 1 0 48 95 84n8w 20 0 1 0 1 13 4 0 0 0 0 0 0 0 0 14 0 11 0 16 0 0 0 0 4 2 4 0 0 0 0 0 0 23 113 84n4w 39 0 1 0 1 33 25 0 0 1 0 0 0 1 0 19 0 11 0 44 0 2 0 0 7 15 7 0 0 0 0 0 0 38 244 76n40w 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 4 76n36w 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 2 76n32w 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 1 3 76n28w 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 3 76n24e 2 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 2 0 0 0 0 0 6 6 0 0 0 0 0 0 8 26 76n20w 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 3 76n16w 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 6 9 76n12w 14 0 0 0 1 10 4 0 0 0 0 0 1 1 0 4 0 6 0 10 0 0 0 0 0 2 1 0 0 0 1 0 0 25 80 76n8w 29 0 0 0 1 18 17 0 0 1 0 0 1 0 1 26 0 6 1 40 0 1 0 0 4 0 5 0 0 0 0 0 0 55 206 76n4w 14 0 1 0 1 0 0 0 0 0 0 0 0 1 0 8 0 6 0 24 0 1 0 0 3 5 6 0 0 0 0 0 0 19 89 76n0e 2 0 0 0 0 4 3 0 0 1 0 0 0 0 0 2 0 1 0 5 0 0 0 0 1 0 5 0 0 0 0 0 0 14 38 76n4e 9 0 0 0 1 5 6 0 0 0 0 0 0 0 0 5 0 5 0 6 0 0 0 0 1 6 0 0 0 0 0 0 0 6 50 76n8e 9 0 1 0 0 10 5 0 0 0 0 0 0 0 0 3 0 2 0 17 0 0 0 0 2 4 2 0 0 0 0 0 0 10 65 76n12e 22 0 1 0 0 19 11 0 0 0 0 0 0 0 0 2 0 3 0 23 0 0 0 0 0 6 2 0 0 0 0 0 0 24 113 76n16e 12 0 1 0 0 17 2 0 0 0 0 0 0 0 1 3 0 2 0 13 0 0 0 0 1 2 1 0 0 0 0 0 0 11 66 76n20e 3 0 0 0 0 7 2 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 3 21 76n24e 11 0 0 0 0 19 2 0 0 0 0 0 0 1 0 2 0 4 0 11 0 0 0 0 0 2 2 0 0 0 0 1 0 31 86 76n28e 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 2 8 72n36w 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 3 72n32w 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 3 72n28w 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 3 72n24w 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 9 12 72n20w 3 0 1 0 0 20 14 0 0 0 0 0 0 0 0 1 0 2 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 15 58 72n16w 1 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 1 0 0 0 0 0 0 0 2 8 72n12w 13 0 1 0 1 7 3 0 0 0 0 0 0 0 0 5 0 4 0 8 0 0 0 0 2 4 2 0 0 0 0 0 0 19 69 72n8w 15 0 2 0 1 14 7 0 0 0 0 0 2 0 1 8 0 9 0 22 0 0 0 0 2 6 0 0 0 0 0 0 0 19 108 72n4w 39 0 0 0 4 34 20 0 0 0 0 0 0 0 2 11 0 14 0 37 0 2 0 0 0 0 0 0 0 0 0 1 0 40 204 60n4w 39 0 1 0 1 33 22 0 0 1 0 0 0 0 0 23 0 21 0 56 0 3 0 0 7 17 9 0 0 0 0 0 0 56 289 72n0e 7 0 0 0 0 3 3 0 0 0 0 0 0 0 0 2 0 1 0 7 0 0 0 0 1 17 8 0 0 0 0 0 0 8 57 72n4e 7 0 0 0 0 8 6 0 0 0 0 0 0 0 1 0 0 2 0 12 0 0 0 0 0 9 0 0 0 0 0 0 0 7 52 72n8e 40 0 0 0 0 37 23 0 0 1 0 0 1 0 0 24 0 12 0 47 0 0 0 0 1 20 6 0 0 0 0 1 0 60 273 72n12e 55 1 0 0 0 45 18 0 0 1 0 0 1 0 3 18 0 26 0 54 0 0 0 0 0 12 8 0 0 0 0 0 0 54 296 72n16e 38 0 0 0 1 21 19 0 0 0 0 0 1 1 0 10 0 10 0 49 0 1 0 0 3 15 9 0 0 0 0 1 0 40 219 72n20e 36 0 1 0 0 41 15 0 0 0 0 0 0 0 0 15 0 16 0 32 0 0 1 0 1 9 3 0 0 0 0 0 0 40 210 72n28e 4 0 1 0 0 9 3 0 0 0 0 0 0 0 0 2 0 3 0 15 0 0 0 0 0 2 4 0 0 0 0 0 0 11 54 72n32e 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 68n40w 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 68n32w 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 68n28w 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 2 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 6 15 68n24w 2 0 0 0 0 2 0 0 0 0 0 0 0 0 1 2 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 21 30 68n20w 3 0 0 0 0 2 0 0 0 0 0 0 0 0 0 1 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 6 14 68n16w 2 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 1 0 2 0 0 0 0 0 0 1 0 0 0 0 0 0 1 11 68n12w 3 0 0 0 0 2 0 0 0 0 0 0 0 0 0 1 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 2 11 68n8w 18 0 0 0 0 14 10 0 0 1 0 0 0 0 0 14 0 17 0 31 0 0 0 0 1 9 2 0 0 0 0 0 0 28 145 68n4w 32 0 0 0 2 39 20 0 0 1 0 0 0 0 0 21 0 13 0 47 0 1 0 0 0 19 5 0 0 0 0 0 0 46 246

68n0e 13 0 1 0 3 21 22 1 0 0 0 0 0 1 0 26 0 11 0 23 0 0 0 0 0 6 4 0 0 0 0 0 0 75 207 68n4e 44 0 0 0 1 14 16 0 0 1 0 0 1 0 0 13 0 13 2 54 0 0 0 0 3 13 6 0 0 0 0 0 0 34 215 68n8e 32 1 0 0 3 33 16 1 1 1 0 0 0 1 0 19 0 18 0 48 0 0 0 0 3 10 13 0 0 0 1 0 0 48 249 68n12e 38 0 0 0 0 27 24 0 0 0 0 0 0 1 0 21 0 18 0 46 0 0 0 0 4 14 13 0 0 0 0 0 0 52 258 68n16e 22 0 0 0 0 25 18 0 0 1 0 0 0 0 0 14 0 11 0 29 0 0 0 0 2 10 6 0 0 0 0 0 0 39 177 68n20e 27 0 0 0 1 20 19 0 0 0 0 0 0 0 1 11 0 15 0 33 0 0 0 0 1 3 2 0 0 0 0 0 0 49 182 68n24e 39 0 0 0 1 32 17 0 0 0 0 0 0 0 0 12 0 20 0 35 0 0 0 0 2 12 6 0 0 0 1 0 0 54 231 68n28e 13 0 0 0 0 8 4 0 0 0 0 0 0 2 1 3 0 3 0 11 0 0 1 0 0 2 3 0 0 0 0 0 0 8 59 64n36w 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 3 64n28w 2 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 12 17 64n24w 2 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 4 9 64n20w 4 0 0 0 0 7 0 0 0 0 0 0 0 0 0 0 0 1 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 34 50 64n16w 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 2 9 64n8w 30 0 1 0 1 17 27 0 0 1 0 0 0 3 1 16 0 27 0 30 0 0 0 0 3 7 5 0 0 0 0 0 0 100 269 64n4w 29 0 1 0 0 17 24 0 0 0 0 0 3 0 1 25 0 21 0 50 0 0 0 0 4 17 10 0 0 0 0 1 0 45 248 64n0e 6 0 0 0 0 4 2 0 0 0 0 0 0 0 0 1 0 5 0 9 0 0 0 0 0 1 0 0 0 0 0 0 0 17 45 64n8e 37 0 1 0 3 27 26 0 0 2 0 0 0 0 0 10 0 5 0 54 0 0 0 0 6 9 8 0 0 0 0 0 0 45 233 64n12e 56 0 2 0 2 57 31 0 1 3 0 0 1 0 0 20 0 12 0 59 0 1 1 0 0 5 13 0 0 0 0 1 0 84 349 64n20e 43 0 2 0 0 28 19 0 0 0 0 0 2 0 0 19 0 22 0 37 0 0 0 0 5 12 8 0 0 0 0 0 0 29 226 64n24e 20 0 0 0 0 23 19 0 0 0 0 0 0 0 1 20 0 15 0 27 0 0 0 0 3 11 5 0 0 0 0 0 0 36 180 64n28e 50 0 0 0 0 17 15 0 1 0 0 0 0 0 0 12 0 6 0 63 0 0 0 0 1 4 15 0 0 0 0 3 0 33 220 64n16e 22 0 1 0 1 20 13 0 0 2 0 0 1 0 1 19 0 14 0 27 0 0 0 0 1 10 8 0 0 0 0 0 0 29 169 64n32e 15 0 0 0 0 8 2 0 0 0 0 0 0 0 1 1 0 2 0 13 0 0 0 0 0 0 0 0 0 0 0 0 0 20 62 64n36e 6 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 26 37 60n40w 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 4 60n36w 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 6 60n32w 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 5 60n28w 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 13 15 60n24w 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 9 60n20w 5 0 0 0 0 6 1 0 0 0 0 0 0 0 0 0 0 3 0 4 0 0 1 0 0 0 0 0 0 0 0 0 0 15 35 60n16w 0 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 52 59 60n12w 8 0 0 0 0 5 4 0 1 0 0 0 0 0 0 5 0 6 0 15 0 0 0 0 0 0 0 0 0 0 1 0 0 33 78 60n8w 36 1 1 0 3 21 19 0 0 2 0 0 2 2 2 26 0 30 0 28 0 1 0 0 2 13 7 0 0 0 0 1 0 77 274 60n0e 29 0 2 0 2 4 9 0 1 2 0 0 0 0 0 11 0 16 0 27 0 0 1 0 0 0 3 0 0 0 0 1 0 108 216 60n4e 32 0 0 0 0 19 14 0 1 2 0 0 0 3 0 13 0 33 0 41 0 0 0 0 2 5 6 0 0 0 0 2 0 86 259 60n8e 55 0 2 0 7 42 31 1 0 9 0 0 2 2 4 34 0 27 0 60 0 2 1 0 5 19 20 0 0 0 0 1 0 63 387 60n12w 21 0 1 0 1 4 16 0 0 3 0 0 0 1 0 7 0 16 0 20 0 0 1 0 1 7 4 0 0 0 0 2 0 38 143 60n16e 36 0 0 0 3 25 16 0 0 1 0 0 0 0 1 21 0 20 0 39 0 0 0 0 1 10 5 0 0 0 0 1 0 42 221 0n20e 37 0 2 0 5 20 20 1 0 5 0 0 0 0 0 16 0 22 0 42 0 2 1 0 5 20 13 0 0 0 1 1 0 41 254 60n24e 31 0 1 0 0 35 19 0 0 2 0 0 0 0 0 10 0 3 0 51 0 2 0 0 5 17 9 0 0 0 0 1 0 34 220 60n28e 36 0 1 0 2 36 28 0 0 0 0 0 1 1 0 17 0 15 0 45 0 1 0 0 2 10 16 0 0 0 1 0 0 56 268 60n32e 31 0 1 0 2 31 16 0 0 0 0 0 0 0 0 20 0 5 0 37 0 0 0 0 3 15 3 0 0 0 0 0 0 60 224 60n36e 18 0 0 0 0 25 4 0 0 0 0 0 0 0 0 3 0 1 0 20 0 0 0 0 1 1 2 0 0 0 0 0 0 28 103 56n36w 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 2 4 56n32w 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 2 56n28w 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 3 4 56n24w 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 18 22 56n20w 8 0 0 0 0 4 0 0 0 0 0 0 0 0 0 0 0 2 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 55 72 56n16w 5 0 0 0 0 3 0 0 0 0 0 0 0 0 0 2 0 3 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 50 68 56n12w 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 2 0 4 0 0 0 0 0 0 0 0 0 0 0 0 0 17 26 56n8w 18 0 1 0 0 10 14 0 0 0 0 0 0 0 0 19 0 12 0 18 0 0 0 0 2 6 2 0 0 0 1 0 0 54 157 56n4w 24 0 0 0 1 14 9 0 0 0 0 0 0 1 1 7 0 7 0 19 0 0 0 0 1 2 3 0 0 0 0 0 0 39 128 56n0e 19 0 1 0 0 17 9 0 0 0 0 0 1 1 1 16 0 14 0 21 0 0 1 0 0 8 4 0 0 0 0 1 0 51 165 56n4e 15 0 0 0 0 10 8 0 0 2 0 0 0 0 2 8 0 5 0 18 0 0 0 0 1 2 3 0 0 0 0 0 0 29 103 56n8e 55 0 1 0 0 16 21 0 0 5 0 0 0 1 3 14 0 33 0 44 0 0 0 0 3 19 12 0 0 0 0 2 0 57 286 56n12e 46 1 3 0 7 44 29 0 1 7 0 0 2 0 2 22 0 29 0 51 0 2 0 0 5 18 14 0 0 0 0 0 0 45 328 56n16e 60 0 3 0 4 48 39 0 0 4 0 1 4 2 2 35 0 36 0 56 0 3 0 0 7 18 15 0 0 0 0 2 0 63 402 56n20e 24 0 1 0 1 15 5 0 0 0 0 0 0 1 0 10 0 13 0 31 0 1 0 0 3 6 6 0 0 0 1 0 0 30 148 56n24e 31 0 0 0 0 20 20 0 0 1 0 0 2 0 0 22 0 13 0 37 0 0 0 0 3 15 5 0 0 0 0 0 0 39 208 56n28e 53 0 1 0 1 28 28 0 0 1 0 0 1 0 0 17 0 15 0 45 0 2 0 0 5 16 12 0 0 0 0 1 0 32 258 56n32e 8 0 0 0 0 22 3 0 0 0 0 0 0 0 0 1 0 4 0 5 0 0 0 0 1 0 2 0 0 0 0 0 0 58 104 56n36e 21 0 2 0 0 37 4 0 0 0 0 0 0 1 1 4 0 11 0 19 0 1 1 0 2 0 2 0 0 0 0 0 0 122 228 52n40w 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1

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36s12e 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7 8 36s16e 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 36s20e 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 6 36s24e 3 0 0 0 0 0 2 0 0 0 0 0 0 0 0 1 0 2 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 6 15 36s28e 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 4 36s32e 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 36s36e 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 36s40e 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 4 36s44e 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 36s48e 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 4 7 36s52e 3 0 0 0 0 8 5 0 0 1 0 0 0 0 0 0 0 1 0 6 0 0 0 0 0 2 2 0 0 0 0 0 0 6 34 32s52e 11 0 0 0 0 5 6 0 0 0 0 0 1 0 0 3 0 2 0 12 0 0 0 0 0 0 2 0 0 0 0 0 0 13 55 40s28w 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 15 16 40s24w 3 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 20 27 40s20w 0 0 0 0 0 2 2 0 0 0 0 0 0 0 0 4 0 0 0 8 0 0 0 0 0 1 1 0 0 0 0 0 0 10 28 40s12w 3 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 9 13 40s4w 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 3 40s0e 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 6 40s4e 3 0 0 0 0 0 3 0 0 0 0 0 0 0 0 2 0 2 0 2 0 0 0 0 0 0 2 0 0 0 0 0 0 8 22 40s8e 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8 11 40s12e 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 40s16e 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 40s28e 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 4 8 40s32e 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 3 40s36e 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 4 40s40e 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 40s44e 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 40s48e 1 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 1 7 40s52e 9 0 0 0 1 2 5 0 0 1 0 0 0 0 1 3 0 1 0 6 0 0 1 0 0 0 3 0 0 0 0 0 0 13 46 48n28e 17 0 0 0 0 2 11 0 0 1 0 0 0 0 0 6 0 3 0 17 0 0 0 0 2 3 1 0 0 0 0 0 0 34 97 48n28e 14 0 1 0 1 13 15 0 0 1 0 0 1 0 0 2 0 5 0 28 0 0 0 0 2 2 5 0 0 0 0 1 0 28 119 48n28e 17 0 0 0 2 4 5 0 0 0 0 0 0 0 0 9 0 8 0 12 0 1 0 0 1 2 4 0 0 0 0 0 0 24 89 48n28e 21 0 0 0 1 7 10 0 0 2 0 0 0 0 1 9 0 16 0 29 0 1 0 0 2 8 8 0 0 0 0 0 0 21 136 Total Valves 32233

105