ON THE VALIDITY OF ARCHAEOLOGICAL SHELLFISH METRICS
IN COASTAL CALIFORNIA
______
A Thesis
Presented
to the Faculty of
California State University, Chico
______
In Partial Fulfillment
of the Requirements for the Degree
Master of Arts
in
Anthropology
______
by
Arran M. Bell
Fall 2009 ON THE VALIDITY OF ARCHAEOLOGICAL SHELLFISH METRICS
IN COASTAL CALIFORNIA
A Thesis
by
Arran M. Bell
Fall 2009
APPROVED BY THE INTERIM DEAN OF THE SCHOOL OF GRADUATE, INTERNATIONAL, AND INTERDISCIPLINARY STUDIES:
______Mark J. Morlock, Ph.D.
APPROVED BY THE GRADUATE ADVISORY COMMITTEE:
______Frank E. Bayham, Ph.D., Chair
______Eric J. Bartelink, Ph.D. ACKNOWLEDGMENTS
First I would like to thank Dr. Frank Bayham for working with me on this project from its inception. Without his vision and ideas this thesis would never have taken place. I would also like to thank Eric Bartelink for all the time and hard work he put into making my thesis what it is.
I would especially like to thank my parents whose unconditional love and support have meant everything to me. They encouraged and sustained me through out this long process and I owe them a great deal.
All of the volunteers who participated in this study were a great service to me.
Their participation in this project was essential to its completion. I am very grateful for the time that they unselfishly gave to me to complete this study.
I would also like to thank Greg White for allowing me to use his template and providing me with all of his original notes and background research. This information was invaluable to me and very greatly appreciated.
Elena Nilsson and all of the URS team and URS Corporation have supported me through out my graduate experience. Their understanding and help have always been a great asset to me through out my time at CSU Chico. Without their support and acknowledgment this project could not have been completed.
I would like to thank Melinda Button for her encouragement and friendship during the completion of this project. Her constant support, advice and help through out
iii this entire experience was greatly appreciated. In addition, I would like to thank all of my graduate school cohorts from 2005 – 2007 who encouraged and supported me through out this process.
Finally, I would like to thank God. Without my parent’s strong religious background and my own experiences of blessing, this project most likely would never have been completed. Skye, I love you and I miss you. As always, this is dedicated to you.
iv TABLE OF CONTENTS
PAGE
Acknowledgments ...... iii
List of Tables...... vii
List of Figures...... viii
Abstract...... x
CHAPTER
I. Introduction...... 1
Archaeofaunal Shellfish Research...... 4 Project Goals ...... 8 Thesis Organization...... 9
II. The Archaeology of Human Impacts ...... 11
Human Behavioral Ecology ...... 13 Methods Used to Identify Ecological Patterns ...... 23 Summary...... 29
III. An Ecological Perspective on California Mussels ...... 30
The Natural History of the California Mussel...... 31 Ecology of the Coastline ...... 40 Rocky Shores...... 40 Discussion...... 47
IV. Research Design...... 49
Sample Loci...... 51 Phase I ...... 59 Phase II ...... 61 Discussion...... 62
v CHAPTER PAGE
V. Effectiveness of White Template: A Blind Study ...... 64
Stage I: Davenport Landing Pilot Study...... 65 Stage II: Punta Gorda Study...... 75 Summary...... 81
VI. Morphological Variation and Size Estimation in California mussels ...... 83
Descriptive Statistics of Mussel Shells...... 84 Basal Angle Measurements ...... 87 Incremental Height Measurements...... 92 Conclusions from Phase II...... 100
VII. Summary and Conclusions...... 103
Summary...... 103 Contributions to the Field of Archaeology...... 106
References Cited...... 110
Appendices
A. Ventral Side of the California Mussel...... 118 B. Mussel Size Class Template...... 120 C. Punta Gorda Length, Height and Width Measurements...... 122 D. Davenport Landing Length, Height and Width Measurements...... 125 E. Punta Gorda Incremental Height Measurements...... 130 F. Davenport Landing Incremental Height Measurements...... 133 G. Punta Gorda Basal Angle Measurements...... 138 H. Davenport Landing Basal Angle Measurements...... 140
vi LIST OF TABLES
TABLE PAGE
1. Distribution of Shellfish Fragments Size Classes from the First Blind Test, Stage I ...... 67
2. Distribution of Shellfish Fragments Size Classes from the Second Blind Test, Stage I...... 67
3. Distribution of Student 3 Shell Fragments When Using the Pre-Sorted Fragment Size Groups ...... 70
4. Distribution of Shellfish Fragments Size Classes, Stage II ...... 76
5. Stage II Correctly Identified Shells by Volunteer ...... 77
6. Pearson’s r Correlation Coefficient Using Results from Stage II ...... 77
7. Inter-observer Agreement Results Using Cohen’s Kappa...... 79
8. Punta Gorda and Davenport Landing: Descriptive Statistics for Maximum Length, Height and Width with Range and Standard Deviation ...... 86
9. Number of Shells Analyzed and Their Size Ranges for Punta Gorda and Davenport Landing ...... 101
vii LIST OF FIGURES
FIGURE PAGE
1. California Mussel Valve Illustration, Demonstrating Location of Length and Height Measurements...... 2
2. Map of Punta Gorda, Humboldt County, California...... 52
3. Map of Davenport Landing, Santa Cruz County, California...... 55
4. California Mussel Valve Illustration, Demonstrating Location of Length, Height, and Incremental Height Measurements...... 62
5. Comparison of Student Results to Actual Shell Measurements from the First Blind Test of Stage I ...... 68
6. Comparison of Student 1 Results from Blind Test 1, 2 and Actual Measurements ...... 68
7. Comparison of Student Results to Actual Shell Measurements from the Second Blind Test of Stage I...... 71
8. Comparison of Student 2 Results from Blind Test 1, 2 and Actual Measurements ...... 72
9. Comparison of Student 3 Results from Blind Test 1, 2 and Actual Measurements ...... 74
10. Range of Student Responses for Shells Belonging to the 6 cm Size Group ...... 80
11. Range of Student Responses for Shells Belonging to the 4 cm Size Group ...... 80
12. Location of Length, Height and Width Measurements on the California Mussel Valve ...... 84
13. Protractor Diagram...... 87
viii FIGURE PAGE
14. Ventral Side of California Mussel Shell Showing the Location of the Angle and Length Measurements Using AutoCAD 2005 ..... 89
15. Ventral Side of the California Mussel Shell Showing the Location of AutoCAD 2005 Length and Angle Measurements Using a Tangent...... 89
16. Davenport Landing: Basal Angle Measurements Correlated to Length ...... 90
17. Punta Gorda: Basal Angle Measurements As Correlated to Length ...... 91
18. Regression Analysis of Punta Gorda, Group 1 (0.5 cm) ...... 93
19. Regression Analysis of Punta Gorda, Group 2 (1 cm) ...... 94
20. Regression Analysis of Punta Gorda, Group 3 (2 cm) ...... 95
21. Regression Analysis of Punta Gorda, Group 4 (3 cm) ...... 95
22. Regression analysis for Punta Gorda: Group 5 (4 cm) ...... 96
23. Regression analysis for Davenport Landing: Group 1 (0.5 cm)...... 97
24. Regression analysis for Davenport Landing: Group 2 (1 cm)...... 97
25. Regression analysis for Davenport Landing: Group 3 (2 cm)...... 98
26. Regression analysis for Davenport Landing: Group 4 (3 cm)...... 99
27. Regression analysis for Davenport Landing: Group 5 (4 cm)...... 99
ix ABSTRACT
ON THE VALIDITY OF ARCHAEOLOGICAL SHELLFISH METRICS
IN COASTAL CALIFORNIA
by
Arran M. Bell
Master of Arts in Anthropology
California State University, Chico
Fall 2009
Shellfish middens represent highly visible, ubiquitous areas for interpreting prehistoric forager behavior. In California one of the most common species used in shell midden analysis is the California mussel (Mytilus californianus). The California mussel is of interest to archaeologists for two main reasons, one is their proportionate contribution to the prehistoric diet and the other is the effect of human predation on shellfish populations. Impacts of human exploitation of ancient marine ecosystems are studied by tracking changes in size or age composition of targeted resources. Unfortu- nately, most archaeological reconstructions are forced to use fragmentary remains. To compensate for this, a template was developed for use in size reconstructions by match- ing fragmentary specimens to drawn size classes. This study examines the basic as- sumptions on
x which the template operates and tests its precision level in a series of experiments using pre-measured, fragmented remains of California mussels.
Metrical analysis on modern shells was used to test the assumption that the basal angle of the hinge is correlated to shell length and to assess the degree of frag- mentation appropriate for template use. Experimental template data was generated by crushing pre-measured shells and conducting a series of blind tests with student volun- teers. Results from this study have indicated that the template is an imprecise and in- consistent method for determining shell length from fragmentary remains. Shell frag- ments must be over 2 cm for accurate use and the template operates on low levels of repeatability and precision. Application of a more precise method for reconstructing shell length would benefit the field by allowing more exact measurements of highly fragmented remains.
xi
CHAPTER I
INTRODUCTION
Archaeological shellfish analysis has become a part of a growing number of faunal studies interested in human interactions with the environment, and the ways that those interactions may have affected environmental health and maintenance (Broughton
1994; Erlandson et al. 2008; Rick and Erlandson 2008). These studies look at the impacts of human exploitation of ancient marine ecosystems and ancient shellfish species by studying the harvesting strategies and historical management practices of native people
(Erlandson et al. 2004, 2008; Whitaker 2008; White 1989). Because coastal shell middens are highly visible, much of the evidence for the reconstruction of past harvesting and management practices comes from quantitative and metric analysis of archaeological shellfish remains. One of the species commonly used in quantitative reconstructions in
California is the California mussel (Mytilus californianus) (Erlandson, Rick and
Vellanoweth 2004; Erlandson et al. 2008, Jones and Richman 1995; Whitaker 2008;
White 1989).
Interpretations of past harvesting strategies and management practices commonly use size and age reconstructions of archaeological shellfish assemblages to evaluate the level of exploitation or management affecting the population. Unfortunately, most of the archaeological remains used in reconstructions are fragmentary making reconstruction of size selective predation difficult. Due to the fragmentary nature of
1 2 shellfish collected from archaeological sites, methods used in analysis must compensate for the fragmented remains in order to reconstruct size and age profiles. One method that attempts to overcome this problem is to use a drawn template of size classes (White
1989; Jones and Richman 1995; Whitaker 2008). The breakage pattern of the California mussel is such that the anterior portion of the shell, which includes the hinge (umbo), is structurally stronger and tends to survive (Figure 1). The template operates on the assumption that the angle at the hinge or anterior portion of the shell is directly correlated to the length, making it possible to match fragmented hinges to drawn size classes and estimate length.
Figure 1. California mussel valve illustration, demonstrating location of length and height measurements.
The template operates on the logical assumption that a larger shell will have a larger hinge angle. However, no critical assessment of this assumption has been conducted, making the assumptions on which the template operates unknown.
3
Additionally, the level of accuracy and reliability of results obtained from the template have not been critiqued. Without testing the consistency and precision of results obtained from template use, it is difficult to say if the template offers robust and sound data.
Furthermore, the breakage pattern of California mussel shell is unpredictable and differential across the landscape of the shell. As no evaluation of the template has been conducted, it is unclear how these differing breakage patterns may affect template use.
Additionally variation in shell morphology between and within populations and its relation to template use has not been evaluated. Based on a visual assessment, it is likely that variation in shell morphology affects the template’s ability to generate accurate results.
Archaeologists should continually evaluate the methods used in analysis in order to provide the strongest data set possible. This project aims to investigate how reliable the template is for size estimations and if the assumption on which the template operates are correct. This problem is evaluated in two parts. The first part assesses the reliability and accuracy of results obtained from the template. The second part attempts to understand potential limitations inherent in template use and why those limitations exist.
To accomplish this, blind tests will be conducted that evaluate the template’s ability to estimate size from fragmented remains using a sample of modern shells whose original length is known. Based on those results, an examination into the assumptions upon which the template operates will be performed. This will include the following: how the basal angle is correlated to hinge length, if variation in shell structure affects template use, and if differing breakage patterns and fragment sizes affect template results. Metric analysis of samples of modern California mussels will be used to evaluate the degree of
4 correlation between hinge angle and shell length as well as assessing variation in
California mussel shell morphology and its affect on template accuracy in size estimates.
Archaeofaunal Shellfish Research
Archaeologists attempt to recreate size profiles using shellfish for a variety of reasons, including their proportionate contribution to the prehistoric diet and the effect of human predation on shellfish populations. Originally, shellfish analysis was mainly limited to discussions involving descriptions of food resources consumed and their placement within a site (Uhle 1907). Since then the field has moved away from simple site descriptions to concentrate on how faunal analysis and subsistence reconstructions can explain important relationships that existed in the past between humans and their environment (Classan 1998). The role of shellfish within these explanations has often been varied, from views which position them as minor seasonal dietary supplements, associated with poverty and low status people (Osborn 1977), to optimal, early-targeted resources, which helped to usher in cultural complexity and culture change (Raab et al.
2002; Moss 1993; Erlandson 1988). Today interpretations also address how shellfish are key players in disentangling the complex interaction between humans and the environment as well as highlighting the potential complexity of food subsistence techniques that precipitated the transition to agriculture. Within this academic context, shellfish size recreations take on an importance which warrants good, scientific methodology for unraveling how, when and why they were used.
Due to their visible nature at archaeological sites, and longevity within the human diet, shellfish offer a productive area of research. Research topics include the
5 identification of human depletion of marine species, and increases in forager populations coinciding with local resource depression (Jerardino, Branch and Navarro 2008;
Jerardino 1997; Erlandson, Rick and Vellanoweth 2004), as well as demonstrating when native Californian harvesting techniques led to increasing productivity rates among resources such as seeds and shellfish (Blackburn and Anderson 1993). Ethnographic data suggest that shellfish along the northern coast of California were in a state of
“semicultivation” because of management practices such as selective harvesting which helped to activate growth (Blackburn and Anderson 1993). An argument has been made that patterns like these can be recognized archaeologically by using size profiles of specimens through time (Erlandson et al. 2008). An increase in harvesting pressure should result in a reduction in the prey age or size, whereas sustainable management should demonstrate prey age or size stability over time (Erlandson, Rick and Vellanoweth
2004).
It is recognized that the archaeological analysis of mollusks can be an important source of inference and interpretation. Yet, progress in this area in California is limited in part by the methods commonly employed. Continual evaluation of methods used in this analysis generates new data and strengthens current research techniques.
Template Analysis of Fragmentary Remains
In 1989, a methodology was developed for recognizing size selective harvesting or predation pressure using fragmented California mussel remains by using a drawn template of size classes which allowed the length of broken shells to be estimated
(White 1989). The template consists of drawn, whole shells which increase by 1 cm increments. The angle at the hinge of the base of the shell is matched to the drawn
6 template size class. This is done for several reasons. First, the anterior portion of the shell is structurally the strongest and most often survives post depositional processes. Second, the hinge of the California mussel is a unique portion of the shell, which can be counted to calculate how abundant the species is within an entire faunal assemblage. Assessing the prevalence of a species within an assemblage is part of the ecological analysis commonly employed when conducting archaeological faunal analysis and will be discussed in greater detail in Chapter II. Finally the template operates on the assumption that the hinge angle is correlated to shell length.
This final assumption was partially investigated in 1979. Research conducted on California mussel growth found a correlation between maximum height and maximum length of a California mussel (Kopp 1979). The maximum height of a mussel is the greatest distance between the two lateral margins of the shell valve (Figure 1). The shell valve refers to one of two hinged halves of the California mussel. The maximum length measures the greatest distance between the anterior and posterior ends of the valve
(Figure 1). The study found a correlation between shell height and length suggesting that the fundamentals on which the template operates have a good foundation. The logical conclusion would be that as height increases, the angle at the base of the shell increases as well. However, California mussels are not triangular in shape. Instead, they have more of a wedge shape, meaning that the angle at the base and the height of the shell may not be perfectly aligned. Additionally the study conducted by Kopp (1979) was undertaken to assess how differing environmental conditions affect California mussel growth patterns.
His results indicated that differing environmental conditions can affect shell growth patterns causing variation between individuals based upon where they grow.
7
To use the template, fragmented shell hinges are matched with the drawn sizes classes and an estimated length is derived. Following White’s research, this template has continued to be used in archaeological shellfish analysis (Bouey and Basgall 1991; Jones and Richman 1995; Whitaker 2008). However, researchers who use the template have said little about the method’s reliability or the accuracy of its use (Bouey and Basgall
1991; Jones and Richman 1995; Whitaker 2008; White 1989). The fundamental assumptions on which it is based have not been tested, and therefore, the inferences and conclusions drawn from its use may be questionable.
This preliminary assessment of how template use is affected by California mussel morphology and their growth patterns suggests that a more complete examination of how California mussels grow and how this growth pattern affects the template’s ability to generate accurate results, would strengthen the data sets used by archaeologists when recreating size profiles from fragmented remains. In addition, other shellfish researchers have opted against the shell size template in favor of measuring “only whole (or nearly whole) shells for which total length could be accurately determined” (Erlandson et al.
2008:3). Whole or nearly whole shells were used as it was thought to reduce bias in shell size estimation (see Erlandson, Rick and Vellanoweth 2004:77).
Several points have been highlighted in this examination of how fragmented shellfish hinges are measured using a drawn template. First, it is currently unknown if the template accurately recreates size profiles from fragmented remains and what percentage of the time the template is incorrect or correct. Next it is unknown if the assumption upon which the template operates, that the angle at the base of the valve is correlated to the valve length, is correct. Finally, we do not know the extent of how morphological
8 variation of the California mussel may affect template use. After only a quick assessment of template use and shellfish growth patterns, it appears that a more in depth exploration of these issues would improve this analytical method used to recreate size profiles from fragmented California mussel remains.
Project Goals
To address the potential biases with the template methodology, research was designed first to investigate the efficacy and reliability of White’s template for mollusk size estimation and second, to explore the nature of morphological variation in the
California mussel and to outline and recommend improvements in the analytical methods used to study archaeological California mussel. The study was designed in two phases
(Phase I and Phase II) to address each of these issues separately. The first phase was to assess if the template generated reliable and accurate results and what percentage of the time it was able to do so. The second phase of the study was designed to assess potential limitations with use of the template and why those limitations might exist. In order to explore these two lines of inquiry, it was necessary to collect two samples of modern, whole California mussel shells.
Phase I explored how accurately the template reflects the real length measurements of fragmented shellfish by using it to measure samples of modern
California mussel fragments whose length was known. This was accomplished by measuring the shell valves when they were whole and then fragmenting them to resemble archaeological specimens. Once the shell valves were fragmented, they were re-measured
9 using the template in a series of blind tests and the results were compared and statistically analyzed.
The second phase explored potential template limitations by measuring the same populations of modern shells to explore possible variation in shell morphology due to geographic location; the degree of correlation between the angle at the base of the mussel valve and length; and finally if variation in fragment size affects the accuracy of the template. Measurements performed on the California mussels included length, height and width. To assess the degree of correlation between hinge angle and length, the angle was calculated and statistically analyzed. To assess how variation in fragmentation affected use of the template, incremental height measurements were analyzed against valve length.
It is hoped that by undertaking this study the current method of using a drawn template to estimate fragmented shell length will be improved. This is accomplished, first by exploring its accuracy and reliability and second by exploring potential limitations with its use. Given that the analysis of marine invertebrates is commonly used in archaeological reconstructions of prehistoric human impacts, identifying problem areas with the existing methodology increases the strength of current interpretations.
Thesis Organization
This thesis is organized as follows: Chapter II provides an in depth overview of how shellfish are currently used in interpretations of human impacts on the paleo- environment. It will also provide an examination of the different types of methodology used in the study of size selective predation on shellfish. Chapter III presents an
10 ecological background for understanding the natural history of the California mussel including where they grow and why those factors might affect shell size estimations with a template. Chapter IV provides the research design for the study. Chapter V explains the materials, methods and results for the testing phase of the template. Chapter VI summarizes the materials, methods and results of the metric analysis of the California mussel shell and its affect on use of the template. Chapter VII provides conclusions and offers suggestions for future research.
CHAPTER II
THE ARCHAEOLOGY OF HUMAN
IMPACTS
Archaeological analysis of faunal data has the potential to inform in important ways about the nature of ancient human-environmental interactions. One important area is the manner in which humans interacted with shellfish as a resource from the Paleolithic to the historic period. Faunal analysis and shellfish analysis has shifted away from simple dietary reconstructions and has become more focused on how human behavior affected the environments they inhabited and the resources they consumed (Claassen 1998;
Erlandson and Rick 2008; Erlandson et al. 2008; Whitaker 2008; Broughton 2002b,1994;
Blackburn and Anderson 1993). Within this context a new archaeological framework has emerged termed “the archaeology of human impacts” (Erlandson and Rick 2008:3). The archaeology of human impacts allows researchers to examine the role early humans may have played in species extinction, large-scale habitat changes and even the collapse of state-level societies (Erlandson and Rick 2008).
By focusing on changes to the natural environment which were likely human induced, the archaeology of human impacts contributes to modern conservation management for both terrestrial and coastal environments (Broughton 2002b; Erlandson and Rick 2008). Researchers attempt to draw conclusions about prehistoric and historic conservation practices, overexploitation, and the development of sustainable economies
11 12 by studying how prehistoric humans interacted with their environment. It is now recognized that prehistoric people had the capacity for overexploitation and conservation management, displaying a range of behaviors determined by the type of environment they inhabited. The recognition of environmental decisions made by prehistoric people gives modern policy makers tools to make better informed decisions about current conservation management.
Modern policies used in the management of natural resources often disregard that prehistoric North Americans shaped and affected their environment. This faulty reasoning leads natural resource managers to the belief of a “natural regulation” that does not require human intervention (Alvard 2002:30). However, many of these so-called
“natural” environments owed their productivity and existence to their human counterparts
(Blackburn and Anderson 1993). One example is the effect clam harvest regulations had on the clambeds of Tomales Bay in Marin County, California. Starting in the Great
Depression, field biologists limited the number of clams that could be harvested in order to “protect” the environment. It was thought that by limiting the number of clams harvested, an increase in their overall abundance would occur. Unfortunately, the opposite happened, something which did not surprise the native Coast Miwok. The Coast
Miwok had argued against harvest limitations insisting “it was the act of harvesting that was keeping the clambeds healthy” (Baker 1992:29). In 1980, when biologists reviewed the situation they came to the conclusion that it was the act of digging and disturbing the beds that had kept them flourishing. Without regular disturbance, the clams had no room to grow (Baker 1992). The actions of the biologists relied on the view of the environment as natural and self-sufficient, with out realizing it owed its composition and structure to
13 the humans who had managed it for thousands of years. This example demonstrates how archaeological faunal research can contribute to maintaining the health of the environment today.
By focusing on the following lines of inquiry: resource depression or the depletion or shifting of focus from one prey item to another, reductions in prey size or age profiles, changes in the abundance and geographic distribution of prey, trophic cascades or changes in the average trophic level of the species harvested, archaeological analysis demonstrates past human induced environmental change (Erlandson and Rick
2008: 14).
Archaeological analysis uses two ways to decipher prehistoric human- environmental interactions. These include creating theoretical models which attempt to capture the most likely outcome given a certain scenario and then testing that hypothesis against recovered data from the field. While models help archaeologists interpret the data collected, interpretations are limited by methods used in laboratory analysis. This chapter provides the background theoretical context of human-environmental research primarily employed in the analysis of shellfish subsistence strategies, as well as providing a close examination of the methodologies currently employed for reconstruction of the impacts on shellfish harvesting.
Human Behavioral Ecology
Human behavioral ecology (HBE) is an evolutionary approach that is often used to explore human adaptionist strategies within an ecological and social context
(Smith and Winterhalder 1981; Bettinger 1991). It is provides a contextual framework for
14 the methodological approach used in the current study. HBE provides a method for analyzing human behavior through a set of developed models that study different human adaptations to a range of ecological conditions (Kennett 2005). The theory has a foundation in neo-Darwinism, primarily in the theory of natural selection (Broughton and
O’Connell 1999). It is also fundamentally aligned with the principles of microeconomics where individuals will make logical decisions regarding limited resources and unlimited needs (Bettinger 1991:83). This theoretical approach offers a series of models that can be used to test archaeologic and ethnographic data (Bettinger 1991; Broughton and
O’Connell 1999). Early questions which sought to use the framework provided by HBE were mainly concerned with prey selection and settlement patterns (Kennett 2005).
However, as mentioned above, foraging studies using HBE as a guiding theory have changed through the years and expanded to include those topics mentioned under the
“Archaeology of Human Impacts.” Questions regarding the effects of human interaction with local environments are particularly applicable to HBE because of its focus on ecological contexts within an evolutionary framework. Essentially, it provides the mechanism that shapes the decisions an individual or group will make (Erlandson and
Rick 2008; Kennett 2005; Broughton and O’Connell 1999). Several models used to identify patterns in the archaeological record and formulate hypotheses regarding human behavior, include the diet breadth model, patch choice model, the marginal value theorem, and central place foraging (Bettinger 1991). While all models are used with the interpretation of past human behavior, the models which are most closely aligned with questions regarding human impacts to paleo-environments include the diet breadth model and the patch choice model. Those models will be discussed in greater detail.
15
Diet Breadth Model
The diet breadth model was developed to understand why different prey items are selected by foragers. The diet breadth model has been used to identify changes in subsistence through time, and is often used to examine changes in resource intensification and the expansion of diet breadth (Broughton 1994; Broughton and O’Connell 1999;
Raab 1992; Hildebrandt and Jones 1992). Within the context of the model, it is assumed that foragers will have a variety of choices at their disposal, but those choices will vary according to abundance, energy provided by the prey, and energy expanded to capture the prey (Bettinger 1991:84). Based on microeconomic principles, it is assumed that the forager will make the logical decision to capture the prey that provides the highest level of energy intake while expending the least amount of energy (Bettinger 1991). The selection of prey based on these criteria is referred to as the optimal diet.
The model bases the “optimal diet” on the ranking of prey in terms of dietary gain, which is typically measured in kilocalories (Glassow and Wilcoxon 1988). The highest ranked prey items are those which provide the greatest amount of energy (calories or protein) with the least amount of energy cost. Ranked prey also refers to preference for a given resource in an optimized diet. It is assumed in this theory that predators will attempt to optimize their diet to ensure the species’ survival (Smith 1983). This is also known as the “fine-grained prey choice model” and allows researchers to make predictions about what prey will be targeted on the landscape first (Broughton 1994:
373).
Researchers develop prey ranks empirically through experimental data measuring the actual pursuit and processing times of different types of prey. However,
16 estimates of prey ranks are also used. One of the most reliable ways to estimate prey rank is to use size as a proxy measurement (Broughton 1994), as preferred or higher-ranked resources are usually larger and require less energy to extract from the environment.
However, the assumption that larger prey items are always preferred because of size ignores the hidden costs associated with extraction, processing and manufacturing activities. These costs can only be inferred through experimental or ethnographic data
(Kennett 2005). Although researchers examining prey ranks based on size have estimated that handling costs for small-bodied animals are between 25-800 percent higher than large bodied game, some smaller bodied animals, such as invertebrates (e.g., grasshoppers) are ranked higher than large bodied prey (Ugan 2005). Based on harvesting strategy, invertebrates taken by mass collection have higher energetic returns because of their relatively minimal processing costs (Ugan 2005).
Once return rates are established for prey items, the model can be used to make assumptions about when a prey item will enter the diet. The underlying assumption of the prey rank model is that the highest ranked prey resources will enter the diet first, while lower-ranked prey resources enter the diet when the abundance of higher-ranked resources declines. Additionally, the diet breadth will be relatively narrow when high ranked resources are abundant and inversely will expand when those resources become more scarce and lower-ranked prey items are added to the diet (Kennett 2005). Changes in the abundance of high ranked prey resources are thought to be caused by both cultural and environmental agents. Climate change may have an effect on the abundance of high ranked prey (Broughton and Bayham 2002), but increasingly it has been demonstrated
17 that human impacts are often the catalysts for changes in prey distribution (Erlandson et al. 2008; Raab 1992; Hildebrandt and Jones 1992; Broughton 1994).
Established return rates for the various available prey to foragers are a main component of the diet breadth model and allow for interpretations about changes in subsistence practices that may be either human or environmentally induced. Return rates for shellfish are a debated topic (Erlandson 2001; Glassow and Wilcoxon 1988). In general, shellfish refer to bivalves and gastropods within a prehistoric midden context
(Kennett 2005). Part of the debate about the return rate or ranking of shellfish is their relatively low caloric content, but high protein content (Erlandson 1988). Based on work exploring harvesting technology, Jones and Richman (1995) established that the caloric return rate, measured in kilocalories per hour, was between 214-543 kcal for shellfish
(Jones and Richman 1995). This ranks shellfish well behind the more substantial acorn
(1073-1488 kcal/hr) and mule deer (17,971 kcal/hr) (Jones and Richman 1995). In terms of protein, shellfish rank more favorably. Ethnographic data from the Tolowa on the
Northwest Coast of California suggest that around 100 mussels per hour could be collected, which totals around 76 g of protein per hour of work. Using these figures, a collector could harvest the protein needs for a nuclear family after only a few hours of work and the protein requirements of two people could be met after only an hour
(Glassow and Wilcoxon 1988). Additionally, shellfish beds provide stable, predictable resources available to all members of a foraging party (Kennett 2005; Jones and Richman
1995; Jones 1991). As such, they can often be seen as preferred resources, despite their low caloric content and small size.
18
Patch Choice Model
The heterogeneous distribution of resources across the landscape is known as patches (Smith 1983). Because coastal environments are often clumped and irregular, the patch choice model is often relied upon to determine the types of prey selected for in the prehistoric diet (Kennett 2005). This is because coastal habitats have resources, such as shellfish beds or sea mammal rookeries, which are associated with single, distinct habitats whose productivity may change seasonally (Kennett 2005). Under this model, resource environments (or patches) are assessed in terms of their value to prehistoric people in the same way that prey items are ranked in the diet breadth model. Patches are ranked according to their net energy intake, minus time spent foraging and processing. It is assumed that the patches are selected which offer the highest returns with the least amount of time spent foraging, both between patches and within patches (Smith 1983).
Higher ranked patches on the landscape become suboptimal when the time spent traveling between the highest ranked patches causes the rate of energetic return to drop
(Bettinger 1991). According to the model then, patches are exploited in the order of decreasing rates of energetic return rates per time spent foraging. Patches are utilized up to the point when the energetic return rate, per time spent foraging, is less than the overall rate of energetic return for traveling to and foraging in all higher ranked patches
(Bettinger 1991:89).
Unlike prey ranks which remain constant, patch ranking is linked to the type and behavior of the resources located within it. Foragers are frequently confronted with a variety of patches in terms of productivity, type, and size, and they must make decisions about which patch to utilize and for how long. Therefore, the amount of time a forager
19 will spend within a given patch is dependent upon the composition of the resources within it. Foragers may stay within a patch until all available resources have been harvested, as in the case of certain plants or sessile animals. Additionally, foragers may choose to occupy a patch only seasonally when high numbers of prey are expected, such as sea lion rookeries established during breading season. Finally, foragers may choose to exploit the resource environment of highest quality first, and then later exploit additional resource environments as the higher quality patches become depleted. The increase in costs would make their overall energetic efficiency lower (Smith 1983). Resource patches which enter the diet later in time may be just as valuable in terms of caloric gain, but only after increased labor inputs, also known as “resource intensification.”
Resource Intensification and Resource Depression
Resource intensification is a process where the overall productivity of a patch has increased, but at the cost of declining foraging efficiency (Kennett 2005:33). This is due to several co-occurring phenomenon. First, resource intensification is considered to be population dependent. Second, increases in human populations cause declines in the abundance of higher ranked resources, resulting in a focus on lower ranked, more costly
(in terms of foraging time) prey items. Resource intensification is a measure of foraging efficiency on a given landscape. When preferred, larger bodied prey items become scarce with the result that encounter rate increases and foraging efficiency decreases, then the predator’s diet expands to smaller, less favored resources. In California, less favored resources include small fishes, acorns and mollusks (Broughton 1994).
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Local declines in higher ranked prey, is also referred to as localized resource depression (Broughton 1994). Resource depression is a phenomenon which examines declines in prey capture rates attributable to human predation (Broughton 2002a).
Declines in prey capture rates attributable to human hunting predicts that a forager should stay in a patch only until the preferred prey item is no longer abundant. Therefore, the longer a predator stays in a local environment, the scarcer the preferred prey item becomes. Once the abundance of the preferred prey item has declined, the forager must make the decision to either leave the patch or pursue other sources of prey (Charnov
1976).
Researchers measure foraging efficiency by examining the ratios of differently ranked prey resources on the landscape and changes in age composition of the exploited prey (Broughton 1994, 2002a). Evidence of resource depression is measured by both declines in species abundance and changes in the age composition of prey populations (Broughton 2002a). Resource intensification is well documented in the archaeological record and a growing body of research suggests that these declines in foraging efficiency are most likely human induced (Broughton 1994, 2002a; Canon 2002;
Hildebrandt and Jones 1992).
One example using shellfish to study this phenomenon comes from San
Clemente Island in Southern California. The data indicate human impacts as the casual link in the shifting abundance of two exploited marine resources. Changes in weight ratios between the larger, preferred black abalone (Haliotis cracherodii) and the smaller, less desirable black turban snail (Tegula funebralis) indicate that the composition of shellfish populations are susceptible to human overexploitation (Raab 1992).
21
According to the diet breadth model, an increased reliance on or increased abundance of mollusks in the prehistoric diet is a hallmark of resource intensification, suggesting declining foraging efficiency due to human induced changes to the landscape.
That is because within this ecological model shellfish appear as a low ranked or less desirable resource due to their low meat yield and perceived high processing costs
(Broughton 1994; Glassow and Wilcoxon 1988; Jones and Richman 1995; Waselkov
1987). Although these hypotheses predict that higher ranked resources are targeted first and are more susceptible to overexploitation, the same principles can affect smaller, less desirable prey as well.
Within the context of resource intensification declines in higher ranked resources result in greater impacts on mussel populations, because these resources are targeted in greater proportions. Studies have demonstrated resource intensification for both freshwater and marine mollusk species. For instance, evidence supports the notion that declines in large bodied prey resources in the Sacramento Valley were followed by an increased focus on freshwater mussels. This increased focus resulted in changes to the age structure and growth rates of the mussel population (Chatters 1987).
Research on changes in prey composition examines the age (or size) composition of prey species in the same way that prey abundance is studied. It is assumed that foragers should favor larger (adult) individuals which provide higher-return rates, thus a reduction in prey age or size profiles also indicates a decline in foraging efficiency.
Changes in prey age and size profiles affect foraging efficiency of a specific prey item because larger, mature individuals offer a more robust prey package while also being responsible for reproduction. A decline in the mean age (or size) is therefore one of the
22 predictions of resource intensification (Broughton 2002a). Historical data has demonstrated that heavy harvest pressure on regional shellfish and fish populations effectively reduces the mean age and size of the targeted resource (Erlandson and Rick
2008). Inversely it could be hypothesized that a sustainable economy predicts a population of abundant, mature or large prey species through time at a given locality.
Studies which focus on changes in prey size and age composition are especially useful for species that grow continuously throughout their lifetime, such as shellfish. Changes in the age or size profiles of a prey items, such as shellfish are particularly easy to see and useful for interpretations of overexploitation or sustainable management.
Archaeological research on resource exploitation and subsistence change uses the theoretical ideas behind resource intensification to analyze shellfish data and interpret how prehistoric humans shaped the environment. These studies are part of a broader range of ideas which are used as casual links to human settlement patterns, technological change, and cultural complexity (Broughton 2002; Mannino and Thomas 2002). This research has also been used to make suggestions for modern natural resource management based on evidence that the structuring of the prehistoric landscape was produced by humans (Broughton 2002b; Erlandson and Rick 2008).
The models provided by HBE allow researchers to predict how human behavior adapts to changing environmental and social conditions. These models allow interpretations of both overexploitation and sustainable management because it provides a framework for understanding variation in human behavior. Regardless of the foundations upon which the models rest, methods used during data collection have the
23 potential to bias predictions and outcomes based upon these models. As a result, methods should continuously be reviewed and revised.
Methods Used to Identify Ecological Patterns
Using the ecological models discussed above, archaeologists have examined shellfish species at archaeological sites and offered interpretations that run the gamut from human overexploitation to sustainable management and incipient aquaculture
(Botkin 1980; Erlandson et al. 2008; Shawcross 1975; Raab 1992; Whitaker 2008; White
1989). Additionally, the knowledge has been presented as a tremendous opportunity for archaeologists to contribute to current research and policy relating to marine conservation and restoration (Erlandson and Rick 2008:3). As discussed above most of these data are derived from examining size and age profiles of the shellfish species examined because changes in size composition are one of the assumptions of resource depression which allows archaeologists to make interpretations about the type of harvesting pressure or strategy employed (Erlandson and Rick 2008).
Unfortunately, archaeological sites are subject to taphonomic processes and differential preservation, meaning that researchers recreate age and size profiles from remains that have been either highly fragmented or have undergone decomposition.
Taphonomic processes which affect shells include fragmentation during both the actual shell processing and meat extraction activities, and later due to trampling, rodent burrowing or excavation techniques. Additional taphonomic processes affecting shells include dissolution and chemical conversion (Classeen 1998). Shells are considered more resistant to decay than other organic materials because they are comprised of calcium
24 carbonate (Claassen 1998). However, chemical alteration does occur due to acid deterioration. Acid deterioration or leaching causes the destruction of the calcium carbonate in the shell. This process can cause a significant weight loss for the entire shell
(Waselkov 1987). The calcium carbonate can be dissolved in both salt and freshwater.
The process occurs rapidly in settings with high salinity, low temperatures and high bioturbation (Claassen 1998:59). Shells which decay in a terrestrial environments are subject to various acids including carbonic acid, from rain mixing with carbonic dioxide found in the atmosphere, as well as sulfuric acid, nitric acid and others. The rate of the decay is dependent upon the type of soil a shell is deposited in and how acidic the environment is (Claassen 1998).
Due to the post depositional processes that affect shellfish remains, archaeologists use estimated recreations of the original population, consequently analytical methods should to be scientific and thorough. Although much has been said about avoiding broad or biased interpretations of the data examined, not all methods used to obtain and recreate the data have been closely critiqued. While the theoretical framework allows research questions to be developed, the analytical methodology should be reviewed to prevent unwarranted or biased interpretations.
Current methods used to study harvesting pressure on shellfish beds include a quantative measurement of species’ age (or size) composition and abundance. Size profiles are then used to study the effect of human harvesting pressure on shellfish populations. Decreases in shell size through time have been interpreted as an increase in human harvesting pressure (White 1989). Size profiles are obtained by either measuring complete or nearly whole shells, using fragmentary individuals with a drawn template of
25 size classes, or by combining weight and known length data to determine the length of the fragmentary specimens (Erlandson et al. 2008; Glassow 2000; Whitaker 2008; White
1989). New methods using morphometric equations have been developed for several mollusk species and are currently growing in popularity. Morphometric equations use an element on the species’ shell which survives taphonomic processes as a proxy measure for the maximum shell length (Randklev et al. 2008). To date no equation has been developed for the California mussel, the species targeted in this investigation.
Template Use
Zooarchaeological remains are usually poorly preserved and fragmentary as discussed above. One method for overcoming this problem was to develop a template of drawn size classes aimed at measuring the fragmented remains of California mussel shells (White 1989). The method has continued to be used in recent studies (Whitaker
2008). The template consists of drawn mussel size classes ranked in 1 cm increments from 0-2 cm to 9-10 cm. The template was designed to measure the angle of the most anterior portion of the shell (the umbo or hinge) and height (distance between the two lateral margins of the shell) as proxy measures for shell length. This portion of the shell was targeted because it is the most likely to survive post depositional processes.
Fragmented mussel hinges or umbos are matched to a drawn size class and then grouped
(White 1989).
The template operates on the assumption that the basal angle of the hinge of the California mussel is directly correlated to the overall length of the shellfish (Jones and
Richman 1995; White 1989; Whitaker 2008). Following White’s study, additional researchers have put his methodology to use in order to examine California mussel
26 harvesting strategies in California (Bouey and Basgall 1991; Codding and Jones 2007;
Jones and Richman 1995; Whitaker 2008) and have argued for the long-term productivity of Native American management practices and incipient aquaculture.
In theory, this method provides researchers the chance to use all faunal remains, including fragmented specimens, preventing potential bias in subsistence recreations that may favor larger or better preserved specimens. However, no critical evaluation of the template’s accuracy has been conducted, nor has there been an assessment of the assumption that the basal angle of the hinge and overall shell length are correlated. While it seems logical to assume that larger individuals would have a larger basal angle, there may be more plausibility than truth in this line of reasoning. Without a close examination of both the level of accuracy on which the template operates and a critique of the assumptions on which it is based, it is impossible to know the accuracy of results derived from its use. Additionally no assessment of shell morphology has been conducted to see if the level of variability that exists between individuals could affect the template’s ability to measure fragmented specimens. It is also possible that location may affect the growth pattern and morphology of specific mussel populations, making it necessary to develop templates which target mussel populations based on location.
Furthermore, the degree of fragmentation of archaeological specimens is highly variable. Currently it is unknown whether the degree of fragmentation may have an effect on the template’s ability to accurately measure length. By visually assessing the
California mussel shell it appears that the height of the shell becomes less variable closer to the umbo or hinge. Currently no study has been conducted to see if this statement is true and whether that would have an effect on the template’s ability to capture the hinge
27 angle. However, it does demonstrate the lack of standardization associated with template use where the degree of fragmentation of a specimen used with the template is subject to the analyst. This type of treatment suggests that all shells, no matter how fragmentary, are as likely to yield accurate results as those which are whole.
Counts and Weight Ratios
The second common way researchers have chosen to analyze fragmentary shellfish remains is to use shell weight and non-repetitive element (NRE) counts to complement one another. NRE counts refer to an element on the shell which is unique, such as the shell hinge or umbo. Weight and NRE counts used in combination are considered more valuable because researchers have recognized several areas of potential bias when only one form of measurement is chosen for analysis. Critiques include the possibility of ignoring potential data because shellfish remains extracted from archaeological sites are too fragmented to identify the original length. This occurs when using only NRE counts because the sample tends to be biased towards those shells which are larger and survive better.
Shell weight is generally used to assess the importance of a specific taxa in the prehistoric diet, or the relative abundance of that species to other identified species in the assemblage. For those researchers who rely solely upon shell weight measurement, it has been pointed out that the chemical dissolution of shell material may throw off the abundance index of the taxa by under representing it. Additionally it is often difficult to identify highly fractured specimens to the species level, affecting how a species is represented in the assemblage. Critiques have also recognized that both of these techniques are not suitable for all the questions regarding shellfish in the prehistoric diet,
28 as they are both indications of a shellfish species’ abundance within the diet and alone do not address questions about changes in age or size (Glassow 2000).
In response to criticism over shellfish measurement techniques at the time,
Glassow (2000) derived a method that combined the two analyses. He used both NRE counts and weight to estimate the average valve length in addition to being able to use both as measurements of species abundance. In order to calculate the shell length, he first derived weights from a collected sample of 319 umbo fragments and calculated the average weight per shell umbo or hinge. He then compared the average weight from his shells to the weight of a second population of well preserved, whole shells to see if he could determine the average length of his original sample, based on their represented weight. He used the least-squares regression equation of length/weight on the second, well preserved population. Based on the results from the statistical analysis, he was able to calculate the average valve length for his original, fragmented sample.
Calculating the average length based on two forms of analysis, counts and weights has a bonus in that it appears more rigorous. However, it does not control for excavation methods which obscure the actual amount of California mussel present because many specimens are too broken and slip through the screen mesh. In addition, cleaning, sorting and storage in the lab all create potential for further dissolution of the shellfish remains. All of these processes create a scenario where the actual weight of a taxa is under represented and interpretations based upon this calculation have the potential to be biased.
Both methods have been used to study prehistoric subsistence strategies for mollusks. Of the two, application of the template has been the more common method
29 used in analysis (Bouey and Basgall 1991; Jones and Richman 1992; Whitaker 2008;
White 1989). Despite the method’s popularity, no critical evaluation of its accuracy has been evaluated. Because archaeological interpretations of prehistory rest on the methods used in analysis, an evaluation of the template would enhance its use in the field. In order to advance the field of ecological analysis of California mussel presented here, this study is concerned with critically assessing the accuracy of template use and exploring the morphological relationships upon which its use is based.
Summary
Past research on archaeological shellfish remains has done much to open doors into our understanding of human-environmental interactions in the past. These data not only help archaeologists understand our past, but present the opportunity to help with current ecological management techniques. Ecological models used in this analysis create a basis for archaeologists to make predictions about sites as well as interpretations of patterns witnessed in the data. However, all interpretations are subject to the methods used to obtain the data analyzed. Methods for interpreting archaeological samples need to attempt to be as free from potential bias or inaccuracy as possible. One way in which to help prevent this situation is to continually re-evaluate the methods used by archaeologists in the field and in the lab.
CHAPTER III
AN ECOLOGICAL PERSPECTIVE ON
CALIFORNIA MUSSELS
This study is a critical evaluation of the methodology used to measure fragmented California mussel shells. Currently one method used is a template which measures shell size by placing fragmented shell valves into a drawn template and matches the shell hinge angle to drawn size classes. An in depth examination of the biology the California mussel valve is important to understand how shell morphology varies and if this variation has an effect on template accuracy. A comprehensive investigation of how shellfish grow and which factors affect their growth provides a foundation for understanding if shellfish morphology affects how the template is used.
Factors such as differing environmental conditions, including wave action, tidal depth, shell bed density, and biodiversity of specific intertidal zones may all have an effect on shell growth patterns. A foundation on the biology of the California mussel highlights potential factors effecting template use. This chapter will provide an overview of the natural history of the California mussel and an in depth examination of the environments in which they grow.
30 31
The Natural History of the California Mussel
The California mussel is found along the Pacific Coast from the Aleutian
Islands to Mexico (Suchanek 1981). In fact, mussels are one of the most successful members of rocky intertidal communities. Members of the mussel family (Mytilidae) tend to dominate rocky shorelines on most continents within the temperate zone
(Suchanek 1986). The California mussel has a relatively limited geographic niche. It is most prolific along rocky coasts with constant wave action. Wave action prevents the possibility that silt will accumulate and suffocate juvenile mussels, which tends to occur in quiet areas such as bays and estuaries. Although the California mussel does occasionally inhabit harbors, it prefers exposed coastlines (Harger 1970). The mussel generally favors the mid-intertidal zone where temperature and salinity levels stay relatively constant, and frequent wave action occurs. California mussels may also occur sub-tidally at depths of up to 73 meters. Due to their constant submersion, the sublitorrial individuals are generally more robust than their intertidal counterparts (Suchanek 1986).
The California mussel is one of the largest mussel species; the maximum length was recorded at >266 mm (Suchanek 1986). It is considered readily identifiable from other Mytilus species due to the existence of radiating ribs on its shell (Gosling
1992). The umbo is located at the anterior end and the shell is easily distinguished by a heavy blue-black periostracum (Jones and Richman 1995). The mussels attach to both the substrate and each other by way of byssal threads, hair-like filaments that bivalves secrete from their foot. Due in part to its large size and heavy, thick, durable shell, Mytlius californianus has a competitive edge over other intertidal species in terms of defense
32 against predators, the ability to crush competition from smaller mussel species, and wave action (Suchanek 1981).
Growth Rate
The growth rate for the California mussel is mainly determined by tidal height and food availability but is also influenced by age and size. California mussels tend to grow continuously, but slowly in comparison to other mussel species. Their growth rate is on average 2-5 mm/month. Suchanek (1981) tracked their growth at 5 mm/month initially, with a decline to 2 mm/month after 33 months. It has been found that growth is most rapid among individuals less than three years of age. After this stage, growth slows due to a decrease in metabolic activity and efficient feeding (Seed and Suchanek 1992).
Additionally Seed and Suchanek (1992) found that during the third month of life, growth was greatly amplified with length increased by two-thirds, and volume increased four- fold. Their capacity for rapid early growth suggests their suitability as an aquaculture species, as yearly harvesting would keep the population in a state of constant, optimal growth.
In addition to age, water temperature also affects the rate of growth among
California mussel. The optimal seawater temperature for growth is between 15-19ºC.
Water temperatures that reach 20ºC and above, demonstrate a strong correlation with a decrease in the growth rate of mussels. Additionally mortality rates climb to 89-100 percent in water temperatures of 25ºC and higher (Jones and Richman 1995). Similarly, growth rates decrease in waters of 14ºC and cooler. Absolute seawater temperature not only affects the rate of growth for California mussel, but sets limits on their ability to reproduce (Suchanek 1986).
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Salinity has been demonstrated to have an effect on growth rate as well.
Lowered levels of salinity appear to cause a decrease in growth rate. Additionally in some cases, it has been observed to be deadly. This is in part because Mytlius species will close their valves in conditions of low salinity in order to maintain a high osmotic concentration within the mantle fluid. As a result, feeding is delayed while the animal’s valves are closed (Seed and Suchanek 1992:117-118). Lowered levels of salinity also seem to have a dampening effect on metabolic efficiency. Lowered salinity has been observed to generate smaller oxygen to nitrogen ratio which is considered unfavorable for energetic productivity (Seed and Suchanek 1992:118).
Another important factor affecting the rate of growth is food supply. Mussels are highly efficient filter feeders, which feed only when submerged. Their utilizable food types include bacteria, phytoplankton, as well as organic and inorganic material. The contribution to the diet for each of these food types is dependent upon season and mussel size (Seed and Suchanek 1992). Seasonal and regional variations in the quality and quantity of available food play an important role in growth rate. Studies conducted in the lab indicate that growth rate is always higher for mussels in the field. This is thought to be caused by the nutrient value found in resuspended materials which are caused by wild surf, upwelling and even hurricanes and vary according to region and season (Seed and
Suchanek 1992). However, mussels can also cushion their growth rate during short-term deviations in food availability by utilizing stored glycogen reserves (Seed and Suchanek
1992).
Changes in environmental conditions, such as food availability, salinity and water temperature affect the rate of growth among mussel populations. Due to the
34 template’s inability to compensate for environmental variation, recreations of age estimates or population structures could be comprised. Much research has used size as an approximate measure of age to recreate harvesting strategies (Whitaker 2008; White
1989). However, this approach does not compensate for changes in environmental conditions which would affect growth rate making interpretations of yearly growth patterns difficult.
Additionally, mussels only feed when submerged; therefore a relationship exists between tidal depth and growth rate. Or in other words an increase in tidal exposure decreases the growth curve. Tidal height also affects the type of growth experienced by the shell or the shell’s growth pattern. Both hinge height and hinge growth are hindered when emersed. However, the growth area which is most affected by time spent emersed is shell width or thickness. In other words, mussels which are located in shallow tidal areas are more likely to have thicker shells than their deeper counterparts.
These thicker shells exhibit differing weight and shape data which is dependent on the amount of time spent emersed (Kopp 1979). The template is dependent on the hinge angle to capture the appropriate size of the fragment. As explained above, hinge width and morphology are affected by tidal location depth. However, the template relies on the assumption that shell morphology is a constant, reflecting similar growth patterns and discounting environmental variation.
Growth rate is also affected by tidal depth because the density of a mussel bed is negatively correlated with tidal height, meaning that the density of a shellfish bed affects the growth pattern. Mussel height and weight (also known as mussel girth when combined) are both affected by density levels, because mussel girth decreases with an
35 increase in shellfish bed density (Kopp 1979). Shells which grow in less crowded beds exhibit a more robust shell appearance, while shells which grow in dense beds will exhibit lean, narrower appearance. These environmental changes to shell morphology likely affect the template’s ability to capture length using the basal angle. This is because a leaner shell will have a narrow hinge angle making it appear smaller than it actually is.
In the study performed by Kopp (1979), he found indicated a negatively correlated relationship between mussel width and tidal height; mussels located in shallow areas tended to be more robust with heavier shells, whereas their counterparts in deeper water put more energy into length production. Interestingly, however, all height measurements
(or maximum width measurements) did not show a statistical difference by location, indicating the various ways shells grow. Because the average emersion time and population density affect the shell morphology, researchers using the template to measure fragmented specimens should be cautious in their determinations.
Mortality
Natural mortality among California mussels is determined by competition, predation, and temperature. One of the main predators affecting the California mussel is the purple ochre sea star. Other predators include the small snails Nucella canaliculata and N. emarginata. However, there is a size limit on individuals consumed by sea stars.
Both a minimum and a maximum size threshold seem to exist, although the maximum size threshold increases as the predator’s size increases. In experiments where the genus
Pisaster, a type of sea star, was removed California mussels colonized lower down in the littoral zone and increase in their overall size (Paine 1976).
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In addition to predation by the sea star, competition affects the success of
California mussel. In areas of extreme cold weather, such as Alaska, the California mussel beds are replaced by the more tolerant M. edulis in its expected habitat in the littoral zone. California mussels appear unable to tolerate freezes, and therefore usually lose out to the more resilient M. edulis in areas with a colder climate (Seed and Suchanek
1992). California mussels are out-competed by the smaller M. edulis when they are removed by winter storms or occasionally by biologists (Suchanek 1986). California mussel must also compete with the sea palm along some coastlines in California. The sea palm out competes California mussel in Bodega Bay in terms of both food acquisition and occupation of favored niches (Seed and Suchanek 1992).
Tidal height also has an effect on predation rates. Predation pressure decreases with increase in tidal exposure (Seed and Suchanek 1992; Kopp 1979). The increase in life expectancy due to a decrease in natural predators may make these shells targets for human hunters because: 1) the shells would survive into adulthood and likely be larger and 2) the shells could still be accessed by human hunters. However, as stated above these shells have a different morphology than their more exposed brethren. The template operates on the theory that shell growth is universal for all California mussels, and does not show variation based on location of harvesting. As shown here, it is impossible to accurately measure a shellfish fragment without knowing the location from which it was harvested, as many factors can affect the appearance of the shell and the shell hinge.
Therefore, an understanding of the biological character of the exploited coastline is important for understanding the shell growth pattern and growth rate.
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Reproduction
Reproduction or spawning occurs when eggs and sperm are released from the excurant chamber into the water to be fertilized. The spawning period of mytilid mussels is variable, both between and within species as well as localities. Researchers have disagreed on the spawning period for the California mussel for some time. Young (1942) states that the spawning period begins in September, reaches a maximum in mid-winter and declines between May and August. Coe and Fox (1942) make the argument that two spawning periods occur, one in November and one in March. However, Suchanek (1981) has argued that the California mussel has no definite spawning period and has been observed to mildly recruit gametes throughout the year. This was supported by the size- frequency of individuals observed from a stand located along the Washington coast. The sample indicated a continuous even size distribution, suggesting that rather than a single episode of massive spawning, the population had been spawning continuously throughout the year (Suchanek 1981). This is important to note, because the ability to spawn throughout the year appears to be linked to episodic periods of disturbance which create
“available settling sites” for the gametes (Suchanek 1981:148).
Temperature also plays an important role in setting the limits for reproduction in California mussel. Temperatures are thought to stimulate nerve responses in mature adults which “trigger the release of neurosecretions from cerebral ganglia and associated spawning events” (Suchanek 1986: 83). Often the thermal limits are set by long-term average temperatures, which then influence the geographic distribution of the species
(Suchanek 1986: 83). Thermal limits of long-term averages may also affect the variation in spawning period length and season.
38
Colonization
Little is known about mussel recruitment, or larval settlement patterns.
Following the spawning period, the larvae spend an unspecified amount of time drifting in the water with currents, sometimes even for considerable distances (Seed and
Suchanek 1992). It is thought that California mussel larvae survive for up to three weeks and that, like spawning, recruitment occurs year around with peaks in spring and fall
(Suchanek 1986). Recruitment refers to the time after an initial successful settlement period, when it is possible for some postsettlement mortality to have taken place (Seed and Suchanek 1992).
Colonization of disturbed beds, caused by either naturally occurring storms or human intervention usually follows a prescribed pattern: the cleared patches are filled by algae, then M. edulis mussels, and then the California mussel. At this point, the more competitive California mussel overtakes M. edulis in the patch. California mussel will not settle on patches of only bare rock or barnacle-covered rock that has not been previously colonized, preferring to colonize where either extant mussel populations occur, or where the byssal threads of past mussel colonization still exist (Peterson 1984).
After the complete destruction of a mussel bed, it takes at least two years to begin to re-establish itself (Jones and Richman 1995). However, the complete recovery of the bed has been estimated at anywhere between five to 100 years. This involves the re- establishment of the mussel bed in terms of productivity, size, and horizontal distribution and vertical distribution. Yamadas and Peters (1989) studied a mussel bed which had been completely cleared along the southern Oregon coast. Based on their observations,
39 they felt it would take between 15-30 years for the bed to achieve its prior condition and support a commercial harvest.
The slow recovery of a cleared mussel bed is likely related to the larvae’s preference for colonizing areas of established mussel beds, or areas where byssal threads from previous mussels still exist. Yamada and Peters (1989) used their observations of the completely cleared mussel bed to conduct a study which aimed at looking at the type of harvesting strategy most beneficial for the growth patterns of the California mussel.
Their results indicated that conservative harvesting of the mussels, which required workers to leave one layer of mussels attached to the rocks, or to hand pick individual market-sized mussels, actually produced a stimulatory effect on the growth and condition of the mussels. This pattern was thought to occur because densely populated mussels must compete for space, as well as deal with decreases in oxygen and food availability
(Yamadas and Peters 1989). Additionally as noted before, California mussels have the ability to spawn continuously throughout the year and there is some evidence to support the notion that spawning periods and intensity are in fact linked to the occurrence of
“available settling spots” caused by episodic disturbances through out the year (Suchanek
1981: 148). Evolutionally, this behavior may have originally evolved because of naturally occurring disturbances caused by storms, but human induced disturbances would have the same biological reaction and may have reinforced this evolutionary trait. Thus for the
California mussel, a low level of disturbance would stimulate reproduction and increase overall productivity. This supports the concept that continual sustained harvesting of mussels by Native Americans was a form of aquaculture.
40
Ecology of the Coastline
The biological character of any exploited coastline is defined by the nature of that coastal habitat. There are three main types of coastal habitats for almost any temperate coastline, include rocky shores, sandy beaches, and tidal flats, which are protected areas of mud and sand (Brusca and Brusca 1979). California mussels grow along rocky shores within the intertidal zone. This examination of rocky shores is presented here to display how rocky shores vary from one another and effect growth rate and patterns, as well as species survival. The two additional coastal habitats will not be discussed, as they have been examined in more depth elsewhere (Brusca and Brusca
1979).
Rocky Shores
The environmental conditions affecting growth patterns are mainly linked to where on the rocky coastline mussels choose to colonize. Variation among rocky coastlines includes the substrate type and the types of tidal zonation created by differences in the formation of the substrate. Rocky coastlines include steep cliffs overlooking sandy beaches with numerous offshore and near-shore rocks and boulders, flat pavements, and jagged rocks (Boaden and Seed 1985). These substrate differences affect the amount of ocean water exposure to the rocky shore, which provides the constant replenishing of the nutrients that affect growth rates and pattern. Both the exposure to offshore waters, as well as seasonal episodes of upwelling, contribute much needed dietary nourishment. Seasonal episodes of upwelling refer to the process where nutrient-rich, deeper waters driven by wind currents are exposed to the rocky shoreline.
41
The combination of both of these factors influences species biodiversity and the health.
Seasonal upwelling waters are high in dissolved oxygen and are known to greatly increase the productivity of mussel beds (Brusca and Brusca 1979). Understanding the environmental factors that affect shell morphology and growth rate, require an understanding of the environment in which it grew, including those mentioned above such as substrate type, and exposure to ocean waters. These factors will be discussed below.
Substrate Type
Within the rocky shore species diversity is affected by the types of rocks comprising the shoreline. Most animals within the rocky shoreline habitat live on the surface where they are constantly barraged by the physical environment and moving waters of the ocean, so the types of rock present greatly influences the diversity of species found within the given environment (Boaden and Seed 1985; Brusca and Brusca
1979). Porous rocks, such as granite or sandstone can host a larger number of seashore species because of the number of crevices and holes that animals can move into. Rocky shorelines which consist of smooth, hard rocks, such as basalt tend to have lower levels of species diversity. The type of rocks which make up a seashore substrate is what is known as the beach’s rugosity (Brusca and Brusca 1979). In addition to rugosity, what is known as the seashore’s geologic stability affects its productivity rates and species biodiversity. Shorelines dominated by large, stable boulders tend to support larger numbers of sea life. Shores that primarily comprise cobbles or small, loose rocks tend to have smaller species biodiversity (Brusca and Brusca 1979). Therefore, rocky shores
42 which are dominated by large, porous boulders will tend to have the highest levels of productivity and species diversity.
Tidal Zonation
Rocky coasts are divided by a zonation framework that is based on vertical environmental changes that place caps on the distribution and frequency of various species which inhabit the shoreline. The original zonation scheme was modified several times, with three zones ultimately defined, known as the supralittoral, the littoral
(composed of both the littoral fringe and the eulittoral) and the sublittoral (Boaden and
Seed 1985). These zones are also referred to as the splash zone (supralitorral), the high intertidal zone (littoral fringe), mid intertidal zone (eulittoral) and low intertidal zone
(sublittoral) (Brusca and Brusca 1979).
The supralittoral zone comprises the last of the terrestrial vegetation, which usually includes orange or gray lichens. The animals which inhabit this zone are mainly small snails (Littorina sp., Ligia sp.) and barnacles (Family Balanomorpha). The littoral fringe is considered the transitional zone between the arid terrestrial environment and the ocean. Common species found here include tufted red alga (Endocladia muricata), two species of acorn barnacle (Chthamalus dalli and Balanus glandula), and the high tide limpit (Collisella digitalis).
The eulittoral zone mainly refers to rocky, exposed shorelines. The eulittoral comprises several sessile species. Some of the more common species include barnacles, mussels (Mytilus sp.), rock snails (Nucella sp.), black turban snails (Tegula funebralis), lined shore crab (Pachygrapsus crassipes), purple ochre sea star (Pisaster ochraceus), and the goose barnacle (Pollicipes polymerus).
43
The sublittoral zone is rarely uncovered by ocean water, except in late spring.
It is typified by cold temperatures, and is usually dominated by kelp species, such as feather-boa kelp (Egregia menziesii) (Boaden and Seed 1985). Some species which also inhabit this zone include the purple sea urchin (Strongylocentrotus purpuratus), the many-rayed seastar (Pycnopodia helianthroides), and the giant green anemone
(Anthopleura xanthogrammica) (Brusca and Brusca 1979).
Two main factors influence the vertical zonation of rocky coasts. These are the physical factors of the shoreline, and the biological factors of the species (Boaden and
Seed 1985). Physical factors include wave action, topography, type of substratum, aspect, climate, and timing of low tides. Wave action is especially important because it can keep normally exposed rocks wet, and increases the eulittoral zone beyond the high water mark. However, it is the littoral fringe that experiences the greatest enhancement from wave action. Along shores with high levels of wave action, the littoral fringe can expand to twice the size of the eulittoral, allowing for higher species productivity as species found in lower zones can expand their environmental niche. The degree of wave action can also affect the foraging time and efficiency of a well-known Mytilus predator, the whelk snail (Nucella lapillus). In areas of less wave action, the degree of foraging efficiency increases for the predatory snail (Etter 1996). Depending on the type of environmental conditions, any number of inter-connected ecological considerations must be acknowledged in order to understand the way in which a species may reproduce and grow.
44
Wave Action
Rocky, open coasts are exposed to extreme wave action and ocean swell. As a result, the species which inhabit these surfaces are exposed to a rigorous physical environment and have therefore developed specific adaptations which help them compete in these environments. These adaptations include the following: strong attachment devices, the ability to bore into the rock surface, the ability to use crevices, the ability to form impenetrable clusters, flexibility, the ability to use rough surface contours, and the ability to change orientation in order to lessen shear stress (Boaden and Seed 1985:36).
Most of these species are sessile feeders. This means that most of the rocky intertidal species can only feed when submersed, which effectively limits the type of species inhabiting the inter-tidal zone.
Wave action is a key element in contributing to the biological character of a rocky shore because it provides many of the substrate dwelling animals with their nutrients, and shapes and re-shapes the shoreline and environmental types (Brusca and
Brusca 1979). The constantly moving water allows a greater range of nutrients to reach the various animals inhabiting the coast. Water currents also affect settlement and recruitment patterns, because many coastal species release their eggs into the water where the currents affect the distribution and range of animals (Brusca and Brusca 1979).
Species Biodiversity
In addition to wave action, the distribution of species within vertical zones is greatly influenced by sea surface temperature. Sea surface temperature is a leading factor in the distribution of various species throughout the zones because many species have adapted to specific temperatures and cannot tolerate temperatures which exceed or fall
45 below those limits. Additionally the steepness of the coast, the aspect and degree of shading, and the type of rocks and timing of low tides all play a part in species diversity and productivity. In general, the idea is that when the environment is cooler and wetter, the upper zone levels expand, allowing for greater species biodiversity (Boaden and Seed
1985).
Biological factors which influence zonation are primarily a function of between species interaction. Within each zone, species compete with one another for space and food, as well as fighting off hostile predators. Although some species have specially adapted features for upper or lower zonal limits, research has indicated that often species which inhabit more marginal, upper zones grow much faster in lower zones and are limited by constraints other than the physical environment Boaden and Seed
1985). It is likely that the species which dominate the upper zones have been outcompeted by other species in areas which they might have preferred, (Boaden and
Seed 1985). For example, along the California coast the hardier California mussels
(Mytlius californianus) repeatedly outcompete the smaller M. edulis mussel for prime spots along shorelines (Boaden and Seed 1985). Similarly, when predators are experimentally removed, upper zone species will often expand their territory to lower zones. However, this type of predation-regulated distribution mainly affects those species which are sessile; the distribution of more mobile species is primarily a function of between-species competition for food (Boaden and Seed 1985).
Species within the littoral zone were important resources to native people inhabiting coastal environments, with the primary focus on bivalves and gastropods
(Kennett 2005). Ethnographically it has been documented that people in these areas
46 regularly used the marine resources available to them. Archaeological evidence suggests that in areas of coastal California, the number one exploited resource within the tidal zone was the California mussel (Whitaker 2008).
Sea Surface Temperature
Water temperature also affects species biodiversity. Individual species have specific temperature zones where they thrive. As water temperature changes from south to north, so do does the distribution of species. The distribution is also affected by water depth; in warmer areas with deeper water, species have been known to move down in the tidal zones in order to submerge themselves in the cooler, deeper water (Brusca and
Brusca 1979). Thus, it is often possible to find cold weather animals far south of their expected distribution. This is also true for areas which experience higher than normal occurrences of upwelling; the cooler, deeper water can support a greater number of species (Brusca and Brusca 1979). Additionally, cooler water can also hold greater quantities of dissolved oxygen. Dissolved oxygen is essential for the fitness of the marine animals inhabiting the open coastline. When water warms during the summer months, tide pools are often completely depleted of the necessary oxygen (Brusca and Brusca
1979).
Salinity
Salinity levels also affect the health and productivity of coastal habitats. Most animals which live in deeper waters are adapted to a constant level of salt in the sea water, and changes in the level of salinity can greatly affect their ability to survive.
However, most inter-tidal animals who are exposed constantly to harsh conditions have adapted to withstand stressful situations where salinity levels can rise or fall. Salinity
47 levels tend to rise in warmer water temperatures and drop when heavy rainfall and water runoff causes an influx of fresh water (Brusca and Brusca 1979).
Marine coasts are highly productive communities with high levels of species diversity. The composition of each of these communities is dependent on many factors, both physical and biological. Coastal habitats are broadly defined by the type of shoreline
(topography and climate) and also more narrowly by the physical characteristics of that habitat (substrate, sea surface temperature, salinity, wave action, species biodiversity, etc.). Understanding the biological community is important because factors such as degree of wave action, species biodiversity, salinity, and temperature all affect the ways shells grow.
Discussion
The natural history of rocky coasts and the California mussel suggests that shell growth patterns are linked to environmental conditions. Shells which grow along coasts that experience a higher level of disturbance, either natural or human-induced, have different growth patterns than those which do not. This is because when shell bed density is higher, shell height becomes more narrow. Additionally, tidal depth and wave action play a role in affecting growth patterns, because shell width increases when tidal depth is more shallow. Furthermore, reproduction and growth are also affected by the level of disturbance; it appears that some mild disturbance may stimulate reproduction and growth, while large disturbances can cause severe damages that may require several years of repair. Essentially, the growth patterns of the California mussel are linked to the environmental conditions under which the shell grows. Attempts to recreate size profiles
48 from fragmented remains should consider the different variables that contribute to variation in size and shape.
CHAPTER IV
RESEARCH DESIGN
It has been established that environmental factors can cause variation in shell morphology which may affect the template’s ability to estimate the size of a mussel from fragmented specimens. Currently there is no baseline for how accurately the template can be used to size fragmented mussel remains. As a result, a study designed to critically evaluate the template’s level of accuracy would establish that control. To accomplish this, the template will be tested using a modern sample of mussel shells which have been fragmented to resemble archaeological specimens. Measurements will be performed on the mussel shells prior to fragmentation so that template results can be compared against actual size measurements. Given the possibility that template results may not reflect real measurements, additional measurements will be obtained from the shells to broaden our understanding of the variables affecting size estimates from fragmented remains.
Therefore, a two-part study has been designed to establish a baseline of template accuracy, while also increasing our knowledge of the factors that affect size estimations from fragmented remains. The first phase will be concerned with testing the template’s accuracy and the second phase will concentrate on understanding what conditions affect the template’s ability to accurately reflect real measurements.
The first part of the study evaluates how the template operates. A series of blind tests using pre-measured shells are used to establish if the template can precisely
49 50 size fragmented mussel remains. Sample populations of modern California mussel shells will be measured, fragmented and re-measured using the template. Two types of analysis will be conducted on the template: a coarse-grained pilot study and a finer-grained second study. The coarser grained study establishes the template’s accuracy level using lumped data comparisons. The finer grained study examines how frequently the template measures individual shells correctly. Both studies will be performed using student volunteers and modern populations of California mussel shells collected by the author.
The comparison of the results from the template and the real shell measurements will be used to examine the template’s accuracy and reliability. This critical evaluation of the template’s accuracy level will establish its use as a methodology for sizing fragmented remains.
The second part is concerned with dissecting the assumptions on which the template operates by establishing the degree of correlation between hinge angle, valve length, and portion of the valve available for analysis. Therefore, in addition to length, width and thickness, measurements will include calculating the basal angle of the hinge, and taking five different height measurements starting from the umbo and moving toward the terminal growing edge. The basal angle of the hinge will be used to evaluate the assumption that this angle is directly correlated to valve length. The degree of correlation between the two measurements has a direct impact on understanding what part of the fragmented valve should be measured. The five height measurements will be used to assess the degree of correlation valve height has to valve length when less of the shell is present. These measurements will help to establish the amount of shell valve needed when using a template for size estimation. The second part of the study will determine
51 what factors, if any, increase or decrease the template’s chance of correctly identifying shell valve size. Based on the results of the two part study, the use of this analytic methodology should be strengthened.
Sample Loci
Because the literature review revealed that shell growth patterns are subject to environmental conditions, two locations were chosen to assess how different geographical sites affect shell growth patterns and morphology. Identifying variation due to geographical location is important because variation in shell structure might affect the template’s ability to accurately recreate size profiles. The two locations selected for mussel collecting were Punta Gorda in Humboldt County and Davenport Landing in
Santa Cruz County. The locations were chosen based on their intertidal productivity, accessibility to shellfish beds and environmental distinctiveness. A brief environmental outline is provided for each of the loci chosen for mussel collection. For each location environmental factors with the potential to cause changes in shell morphology, such as sea surface temperature, tidal height, substrate formation, climate, precipitation and tidal biodiversity, are presented.
Punta Gorda, Humboldt County and Davenport Landing, Santa Cruz County
Punta Gorda is located within the King Range National Conservation Area
(KRNCA) in southern Humboldt County, California (Figure 2). The KRNCA lies in the most northern part of the North Coast Mountain Range. Punta Gorda is located 3 miles south of the Mattole River drainage in a region known as the Mouth of the Mattole.
52
Petrolia
Pacific Ocean
Shellfish Beds Punta Gorda
Humboldt County
1,250 Meters
Figure 2. Map of Punta Gorda, Humboldt County, California.
Coastal and inland habitats are both represented at Punta Gorda. Of the marine habitats, tide pools, kelp beds, offshore rocks, and sandy and rocky beaches are represented. The offshore rocks are a noted important habitat for large sea mammals like stellar and
California sea lions as well as harbor seals. These offshore rocks also provide habitat for coastal birds. The area around Punta Gorda is important to large sea mammals in part because of the interaction between the kelp beds and the tidal zone. The kelp beds support large numbers of abalone, sea urchins, and mollusks, as well as Dungeness and rock crabs which live in the tidal habitat. Species within the rocky intertidal zone at Punta
Gorda include California mussels, sea urchins, limpits (Collisella sp. and Diodara
53 aspera), black katy chiton (Katherina tunicate), gumboot chiton (Cryptochiton stellari), barnacles and rock snails. In turn, these species are popular food items for the seals and sea lions (BLM 1984). However, it has been noted that the combination of high wave action and inadequate kelp beds restrict otter population density near the vicinity of Punta
Gorda.
The seawater temperature around Punta Gorda is highly affected by near shore currents. During the winter months, the Davidson current brings warmer water up from the south. The National Oceanic and Atmospheric Administration (NOAA) recorded a range of average monthly temperatures between the years 2004-2006 from 12°C and
14°C from November to February. During the summer, the temperature is affected by the increase in solar energy, and also by the south flowing California current which brings cooler water down from the north. The average monthly temperatures from July to
October in the years 2004 through 2006 ranged from 11°C and 14°C. Powerful northwest winds during the summer and spring months also produce some of the strongest upwelling currents along the Pacific coast of California which contribute toward maintaining cooler temperature (Goddard 1987).
In May, 2006 when shellfish collection occurred, the average monthly sea surface temperature was recorded by the NOAA at 11.04°C (SD= 1.19). These data were retrieved from the nearest NOAA data buoy at Cape Mendocino located eleven miles to the north. The average tidal height was recorded by the NOAA at Shelter Cove twenty- one miles to the south. The average tidal height in May, 2006 was 5.19 ft with a minimum tidal depth of 3.99 ft and a maximum tidal depth of 6.72 ft. The mean tidal height for low tides during the same month was 1.08 ft with a minimum of -1.46 ft and a
54 maximum of 3.44 ft. Generally two high tides and two low tides occur every 24 hours, each which lasts approximately six hours.
The area of Punta Gorda where mussel samples were collected is characterized by an expanse of black sand beach and rocky intertidal habitat. The beach is narrow and adjacent to steep, uplifted cliffs. The rocky intertidal habitat comprises several large boulder outcrops which contain numerous California mussel beds, in addition to several tide pool habitats. Based on a visual estimate the area is approximately
15% cobbles, 35% sand, 15% bedrock, and 35% boulders. The nearshore water here is noted for its high turbidity, such that during periods of high surf the low intertidal zone is inaccessible, even during low tide (Goddard 1987). However, during May at low tide, the mussel beds located on the various boulder outcrops are easily accessible from the beach.
The second site selected for shellfish collection was Davenport Landing, located in Santa Cruz County, California (Figure 3). It is approximately 15 kilometers north of the city of Santa Cruz. The shoreline that stretches along this section of the coast is characterized by steep sea cliffs of Santa Cruz mudstone with sandstone intrusions.
Much of the coastline here includes sea cliffs fronted by beaches and exposed rocky coasts with no beaches (Griggs, Patsch and Savoy 2005).
Due to the hard Santa Cruz mudstone that comprises this coastline, Davenport
Landing has a very productive intertidal zone. However, because of the prolific, dense mussel beds located at the site, the species biodiversity is limited in comparison to other locations along the Central California coastline (Pearse 1997). Common species found within the mid-intertidal zone where the California mussel grows include the purple ochre sea star, limpits, several species of chiton, barnacles and rock snails.
55
Highway 1 Davenport Landing Road
Shellfish Beds
Santa Cruz Scale 180 m
Figure 3. Map of Davenport Landing, Santa Cruz County, California.
The NOAA recorded average winter sea surface temperatures along this portion of the central coast between 13°C and 12°C. Average summer sea surface temperatures were recorded between 13.5°C and 14.5C° demonstrating slight differences from the colder northern waters at Punta Gorda. The average sea surface temperature for
September between 1987 and 2001 was recorded by the NOAA as ranging from 11°C and 19°C. Monterey Bay is noted as a region of numerous, small upwelling centers which create variation in ocean circulation hundreds of miles out into the ocean. Additionally these pockets of upwelling centers have an important effect on the health and maintenance of the various ecosystems and help to control the sea surface temperature
(Blanchette et al 2008).
56
The tidal height recorded by the NOAA for high tide in September, 2008 ranged between 3.97 ft and 5.38 ft. The tidal height recorded for low tide during
September, 2008 ranged from -0.2 ft and 1.5 ft. The tidal fluctuations at Davenport
Landing are slightly less extreme than that those recorded for Punta Gorda. The tides occur approximately twice every 24-hour period and generally last around six hours. The strong wave action noted along this stretch of the coastline by the NOAA likely affects the emersion time of the mid-intertidal zone by increasing the splash zone.
The area of Davenport Landing used for mussel sampling is located approximately 100 m from the entrance to the beach. Based on a visual estimate the substrate is approximately 35% sand, 5% cobbles, and 60% bedrock. The area used for the collection is characterized by large expanses of flat, boulder outcrops with productive
California mussel beds and tidepool areas. The area is a noted place for California mussel collecting (Jones and Richman 1995), so the level of human disturbance to the beds is high. The ocean water around this area is typified by choppy, stormy waters, but the rocks are accessible at low tide. The beds were selected due to their accessibility from the shoreline and the noted productivity of the mussel beds.
Punta Gorda represents a coastal habitat on the northern coast of California.
Like most of the north coast of California, it experiences a Mediterranean climate with mild summers and rainy, cool winters. Most of the rain falls from October through May.
This section of the north coast experiences some of the highest annual precipitation in
California. The average rainfall is between 40-100 inches per year. In addition, the summer months experience fog almost daily. The climate is fairly mild with little temperature extremes due to its proximity to the ocean. Temperature highs range from
57
18-21° C in the summer, with lows of 10-12°C. Temperatures vary only about 10 degrees from summer to winter. During the winter, average temperature highs range from 12°C to
14°C, while lows range from 5°C to 7°C. Punta Gorda also experiences extreme wind action. During the spring and summer months the wind blows in a northeasterly direction, while during the fall and winter months it blows in a southeasterly direction. Winds have been recorded up to 50-60 mph through the KRNCA. In part, this is due to its steep topography cut by channels and drainages (Elford 1974).
Davenport Landing represents a central coast habitat. While it also shares a
Mediterranean climate like Punta Gorda, there are slight differences between the regions.
Annual precipitation is somewhat less, usually averaging around 30 inches per year. The rainy season begins around December and lasts through April. The climate is mild with minimal changes between seasons. The average high during the rainy season is 18°C and the average low is 4°C. During the dry season the average high is 26°C, while the average low is around 9°C. Like Punta Gorda, fog is common during the summer months, especially in the morning (Felton 1965).
The main difference between the two sites is their substrate formation.
Davenport Landing supports large, flat bedrock outcrops, whereas Punta Gorda is characterized by large boulders and some bedrock outcrops. Additionally the degree of precipitation is quite different, with Punta Gorda residing in one of the highest areas of precipitation in California. In addition, wind action is much stronger at Punta Gorda, which helps to increase wave action. The two sites are spatially and environmentally distinct and can be used to identify any potential variation based on geographic setting.
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Mussel Collection
Twenty modern California mussel shells were collected by the author from one of the near shore rocks at Punta Gorda during low tide in May, 2006. The mussel bed was completely exposed and located approximately 15 m from the sandy beach. The average sea surface temperature was 11°C. Although the level of human or natural disturbance affecting the bed was unknown, the mussels were intact and densely grouped.
The site was selected due to its proximity to the shore and the presence of several large, intact mussel colonies. Shells were collected using a trowel. No time limit was imposed on the harvesting period. Shells were not selected for specific attributes; they were collected as randomly as possible to represent a diverse population in terms of size and shape. Once removed, the shells were placed into a one-gallon Ziploc bag for transportation back to Chico, CA. The shells were then placed in a small amount of water in a large roasting pan and steamed until opened. The meat was removed and then discarded. No data was collected on shell meat as the focus of the study is concerned with morphological characteristics of the shell. The shells were placed on several baking sheets and allowed to dry in the sun. Each of the shells was labeled with a number and letter to designate the shell hinge as either the right or left valve. All labels were assigned using an archival ink pen.
The second sample of shells was collected in September, 2008 from
Davenport Landing, Santa Cruz County, California. The shells were collected from a large bedrock outcrop located approximately 100 m from the entrance to the beach. The beds were selected due to their accessibility from the shoreline and the productivity of the
59 mussel beds. One hundred eight modern California mussel shells were collected using a trowel. Once collected the shells were processed and labeled as discussed above.
Phase I
As discussed before, this investigation is aimed at determining if the template is an accurate and reliable method for measuring fragmented shells. Therefore, the first phase of the study will test the template by using it to calculate the size of pre-measured shell fragments.
The first phase tests the template’s ability to measure fragmented specimens by applying it to samples of collected, modern shells which have been fragmented. Two stages of analysis were used during this phase. The first stage is a pilot study that used the shells collected from Davenport Landing. The pilot study is a coarse-grained analysis which tests the accuracy of the template for classifying fragmented shells size groups in comparison to actual shell measurements. Rather than tracking results for each individual valve, measurements from the template were lumped. Critical evaluation of the template was limited to comparing means and general trends. Results demonstrated a broad overview of template accuracy, but did not indicate the level of precision or repeatability of the template. After establishing that a finer-grained analysis was necessary to assess the template’s accuracy, the second stage tracked the size classifications by individual hinges. This finer-grained analysis used shells from both Punta Gorda and Davenport
Landing. The finer-grained analysis allowed results to be tallied and the level of precision and repeatability to be calculated.
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Phase I, Stage I
The first stage used three student volunteers to classify 100 fragmented shells from Davenport Landing. Each student was asked to measure the shells twice with the template. Individual shells were not labeled and the students evaluated the shells as a whole population. The results from the six blind tests were compared to the size classes derived from the real measurements and compared to one another. General trends could be outlined by comparing the results, but finer-grained analysis, such as how frequently the template accurately measured an individual valve, was impossible. Inter-observer error also could not be determined during the pilot study. The analysis during the first stage indicated that a more precise method for evaluating template accuracy was warranted. General trends indicated a discrepancy between student results, and actual measurements, indicating that further analysis was necessary. The results from the six blind tests were graphed to present a visual aid in interpreting the data.
Phase I, Stage II
The second stage is a finer grained analysis which tracked results for each individual fragmented hinge. This phase used five student volunteers. Students were asked to measure labeled, fragmented specimens and record their measurements on an individual valve basis. The results from each of the five blind tests were compared to real shell measurements and to each other. The results were analyzed statistically using
Cohen’s kappa and correlation coefficients, and graphed to present a visual aid. This analysis allowed the accuracy level and repeatability to be calculated for the template indicating its strength as a method for measuring fragmented shell specimens.
61
Phase II
Based on results from the first phase of the study, discrepancies between template measurements and actual measurements indicated that the template had areas of weakness with its application. Therefore, the second phase of the study sought to examine how morphological characteristics and physical variability of California mussel shells might affect estimations of length based on fragmentary remains. To address potential weak areas of template application, the following lines of inquiry were examined: 1) the degree of correlation between the basal angle of the hinge to hinge length; 2) the degree of variation in shell morphology due to collection site; and 3) the degree of fragmentation appropriate for template use. To strengthen our knowledge of how these factors might affect template application, shell width, length, and height were measured in addition to the calculation of the degree of correlation between hinge angle, valve length, and height at various locations along the valve.
Analytical Methods
All collected shell specimens were measured and used in analysis.
Measurements included calculating the basal angle of the hinge, measuring maximum hinge height, width and length, as well as measuring incremental height measurements along the shell from the umbo towards the terminal growing edge (Figure 4).
Measurements for length, height and width were taken using sliding calipers and were performed in the CSU Chico Archaeological laboratory. The basal angle of the hinge was calculated using a protractor diagram designed by the author and using uploaded pictures of individual shell valves in AutoCAD 2005. Recorded measurements were analyzed statistically using descriptive statistics, regression analysis, and correlation
62
Figure 4. California mussel valve illustration, demonstrating location of length, height, and incremental height measurements.
coefficients. Results from the statistical analysis were used to broaden our knowledge of factors that play a role in the template’s ability to correctly measure fragmented specimens. Based on the critical analysis of shell morphology recommendations for template use were also provided.
Discussion
The overall design of this study was constructed to critically analyze template applicability by assessing its precision level, repeatability, and limitations based on morphological shell characteristics. It was recognized during the background research that several aspects of the template needed to be evaluated. The first was that no assessment of template accuracy had been conducted. This needed to be evaluated so research using this method would have an understanding of the strength of the data generated. The second area was the lack of data supporting the assumption that the basal
63 angle is correlated to valve length, and the lack of standardization in the size of the shell fragment used in template application. This analysis highlighted template limitations and evaluated the appropriateness of its use with different sizes of fragmented specimens.
In order to rigorously examine the application of the template to measure fragmented specimens, it was necessary to conduct a two-part study. Results from Phase I provided an understanding of data strength when the template is used. Results from Phase
II indicated the limitations associated with its use. The interpretations of both of these results should be considered in future studies which use the template as a method for determining shell size from fragmented remains. Detailed results and interpretations of the data from both Phase I and Phase II will be presented in the subsequent chapters.
CHAPTER V
EFFECTIVENESS OF WHITE TEMPLATE:
A BLIND STUDY
The background research provided in the previous chapters has outlined the need for an evaluation of the template methodology. As stated in the research design, this research is interested in critiquing two aspects of the template. The first phase is to assess how the accurately the template estimates shell length from fragmented specimens. The second phase is an attempt to understand what limitations exist when using the template.
The first stage of testing began with a preliminary study aimed at identifying the template’s ability to recreate the original size groups from a sample of modern California mussel shells. This stage was conducted as a blind test using student volunteers to measure a sample of modern California mussel shells that were fragmented to resemble archaeological specimens. Following the completion of the preliminary study, results indicated that a second analysis was necessary. The second stage of testing was aimed at identifying how accurately the template estimated the length of individual shell valves.
This stage also used fragmented modern California mussel shells in blind tests with student volunteers. The methods, materials and analysis of the testing phase of this study are presented here.
64 65
Stage I: Davenport Landing Pilot Study
Methods
Template investigations began with a preliminary study focused on identifying similarities between size groups derived from measurements of whole shells and size groups obtained from template estimates. Initial investigations began by measuring the length of a sample of 200 shell valves collected from Davenport Landing.
Length was calculated by measuring the greatest distance between the anterior and posterior ends of each individual valve (Figure 1). Based on the length measurements, size groups were created that corresponded to those drawn on the template.
The shell valves were then prepared for the fragmentation process. First, a sample of the shells was photographed and recorded. Next, all of the selected shell valves were placed on a sheet of plastic on the concrete flooring in the CSU, Chico
Archaeological laboratory. A second layer of plastic was laid over the top of shells and the author and an assistant walked across the shells to fragment them. Upon completion of the fragmentation process, the valve hinges were collected and the fragmented remains were discarded. A total of 183 shell umbos were collected after the crushing took place.
17 shells were considered too fragmented to be of use with the template and were discarded. The discarded shell valves were those specimens which did not have a complete umbo or hinge. Prior to testing, the collected shell valves were sorted into groups based on fragment size to examine if this had an effect on how the template was used to sort the shells (e.g. a larger shell fragment placed in a larger template size class).
The shell fragments were placed into groups that ranged from .5 cm-1cm, 1-2 cm, 2-3 cm, 3-4 cm, 4-5 cm, 5-6 cm, and 6-7 cm.
66
The testing was accomplished through the use of blind studies using three student volunteers (student 1, student 2 and student 3). The level of laboratory experience varied among the volunteers (student 1 had greater than 3 years of laboratory experience, student 2 had less than 1 year of laboratory experience, and student 3 had at least 2 years of laboratory experience and had worked with shellfish.) The students were instructed how to use the template and were given the sorted shell fragments and a copy of the template and asked to measure the shell fragments.
During the initial test the fragments were not labeled which resulted in discrepancy between results. Student 2 over counted the shell umbos, while student 1 discarded two umbos as too fragmented for use with the template. As the study was aimed at comparing the size groups between the original measurements and the student volunteers, all of the data was used.
Upon completion of the first blind study, the sorted shell valves were removed from their groups. The same three student volunteers were then asked to re-measure the shells. This part of the pilot study was aimed at exploring how template use differed when the volunteer was no longer forced to analyze the fragment according to size.
Results
As stated above this investigation was aimed at comparing size groups derived from length measurements of the whole valves, with size groups derived using the template to measure the fragmented remains. Investigations yielded interesting results
(Tables 1 and 2; Figures 5 and 6).
Results from student 1’s first blind study indicated that the template was best at estimating the length of larger shells. Original measurements indicated that 30 shells
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Table 1. Distribution of shellfish fragments size classes from the first blind test, Stage I.
Mussel Size Class Original Student 1 Student 2 Student 3 0-2 cm 0 2 0 2 2-3 cm 2 9 7 19 3-4 cm 22 36 13 28 4-5 cm 64 50 20 53 5-6 cm 61 35 24 44 6-7 cm 21 9 8 21 7-8 cm 13 17 64 13 8-9 cm 11 19 34 2 9-10 cm 6 4 16 1 Total 200 181 186 183
Table 2. Distribution of shellfish fragments size classes from the second blind test, Stage I.
Mussel Size Class Original Student 1 Student 2 Student 3 0-2 cm 0 3 0 0 2-3 cm 2 19 32 0 3-4 cm 22 39 17 4 4-5 cm 64 47 42 17 5-6 cm 61 32 41 33 6-7 cm 21 16 4 36 7-8 cm 13 19 20 45 8-9 cm 11 8 18 28 9-10 cm 6 0 9 20 Total 200 183 183 183
belonged in the size range between 7-8 cm and 9-10 cm, and Student 1 placed 40 shells into this size range, demonstrating comparable results. The results were less comparable for smaller size groups. Student 1 placed 47 valves in the 0-2 cm to 3-4 cm range. The
68
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50
Original 40 Student 1(1) Student 2(1) 30 Student 3(1)
Number of Shells of Number 20
10
0 0-2 cm 2-3 cm 3-4 cm 4-5 cm 5-6 cm 6-7cm 7-8 cm 8-9 cm 9-10 cm Shell Size Class
Figure 5. Comparison of student results to actual shell measurements from the first blind test of Stage I.
70
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50 Student 1 (1) 40 Student 1 (2) 30 Original 20 Number of Shells of Number 10
0 0-2 cm 2-3 cm 3-4 cm 4-5 cm 5-6 cm 6-7cm 7-8 cm 8-9 cm 9-10 cm Siz e Cla ss Group
Figure 6. Comparison of Student 1 results from blind test 1, 2 and actual measurements.
69 original measurements indicated that only 24 shells belonged in this category. Results were also biased for the size groups 4-5 cm to 6-7 cm. The original measurements indicated that 146 valves belonged in this category. Student 1 grouped only 94 valves in this size range. Based on the results from student 1, the template was better at correctly identifying larger shells, e.g., 7.1 cm and 10 cm. Separating the hinges into groups based on size did not appear to have an effect on student 1’s use of the template.
Investigations yielded different results for student 2 with the same conclusions. These results indicated that the template had difficulty in correctly identifying smaller shells, by misclassifying them into larger shell size groups. The majority of student 2’s shells were grouped between 7-8 cm and 8-9 cm (98 hinges) categories, rather than the actual 24 hinges which belonged in this category. However, the distribution of shells into length classes based on the hinge size was fairly even across the entire sample indicating that the volunteer was not more likely to put a larger shell hinge into a larger length group or vice versa.
Student 3’s results from the first blind test also indicated differing results from the previous two experiments. These results were fairly comparable to the original size classes obtained from length measurements of the whole valves. However, when student
3 used the template, it was more likely to inaccurately identify larger shells by placing them into a smaller category. In the size class range between 8-9 cm and 9-10 cm, student
3 placed three shells. The actual amount of shells within this category was 17.
Additionally, student 3 placed 21 shells into the size class range between 0-2 cm and 2-3 cm, when the original measurements indicated that only 2 shells belonged in the category. However, for shells ranging between 3-4 cm and 7-8 cm, student 3 placed 159
70 valves, original measurements indicated that 181 shells belonged between these size ranges. These results indicated that student 3 could correctly identify medium sized shells using the template, but that very large shells and very small shells were incorrectly identified.
Results also indicated that the pre-sorted fragment size groups had an effect on student 3’s categorizations (Table 3). In general, student 3 placed smaller hinges into smaller size groups, indicating that forcing an observer to concentrate on a particular aspect of a fragment may have an effect on the template’s results.
Table 3. Distribution of Student 3 shell fragments when using the pre-sorted fragment size groups.
Pre-sorted fragment size classes Mussell .5-1 cm 1-2 cm 2-3 cm 3-4 cm 4-5 cm 5-6 cm 6-7 cm Size Class 0-2 cm 2 0 0 0 0 0 0 2-3 cm 1 17 0 1 0 0 0 3-4 cm 3 20 4 1 0 0 0 4-5 cm 0 11 22 14 6 0 0 4-5 cm 0 4 22 13 4 1 0 5-6 cm 0 0 12 6 3 0 0 6-7 cm 0 0 5 6 0 1 0 7-8 cm 0 0 1 0 0 0 0 8-9 cm 0 0 1 1 0 0 0 9-10 cm 0 0 0 1 0 0 1 Total 6 52 66 43 13 2 1
Results obtained from the first blind study indicated discrepancies between users. Additionally two out of the three times the template was used, it was more likely to correctly estimate the length of a larger shell, rather than a smaller valve. The third time the template was used, the results indicated that size outliers, those who are very small or
71 very large, have an increased chance of being misclassified. Data displayed here is naturally affected by differing numbers of valves between the original sample and the sample used for the blind tests. Results do indicate room for additional investigation into the template’s accuracy and reliability.
The second blind study returned the same shell fragments to the same three students. The only difference was that the fragmented valves were removed from the pre- sorted size groups. Each student used the same template for the second blind test as they did in the first. Results from the three students volunteers were compared against the original measurements as occurred during the first study (Figure 7).
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50
Original 40 Student 1 (2) Student 2 (2) 30 Student 3 (2)
Number ofShells 20
10
0 0-2 cm 2-3 cm 3-4 cm 4-5 cm 5-6 cm 6-7cm 7-8 cm 8-9 cm 9-10 cm Shell Size Class
Figure 7. Comparison of student results to actual shell measurements from the second blind test of Stage I.
Results recovered from the second blind test using student 1 demonstrate fairly good consistency (Figure 6). The main difference between the two tests was a
72 higher number of shells placed into a smaller size category. Student 1 placed 22 shells into the size categories ranging from 0-2 cm and 2-3 cm, while the original measurements indicated only two shells belong in this category. Student 1 adequately identified medium sized shells, but large shells were either underrepresented or neglected (Table 2). Student
1 placed only eight shells into the size range between 8-9 cm and 9-10 cm, rather than the original measurements which indicated that 17 shells belonged in this category. These results indicate that student 1 had a fairly high level of consistency, but that as indicated by previous results, the template had trouble distinguishing shell size outliers by either over estimating or underestimating shell size.
Unlike student 1, student 2 did not have a high level of user consistency
(Figure 8). Results from the second blind test indicated a reversal from the previous
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50 Student 2 (1) 40 Student 2 (2) 30 Original 20 Number of Shells of Number 10
0 0-2 cm 2-3 cm 3-4 cm 4-5 cm 5-6 cm 6-7cm 7-8 cm 8-9 cm 9-10 cm Siz e Cla ss Group
Figure 8. Comparison of Student 2 results from blind test 1, 2 and actual measurements.
73 recorded results. The first time student 2 used the template, it overestimated the number of large shells in the population. The second time student 2 used the template, results overestimated the amount of small shells within the population (Table 2). The curve of the graphed line from student 2’s results for the second blind test, more closely replicates the line representing the actual shell size groups represented by the test population.
However, during the second test, student 2 placed 32 shells into the 2-3 cm size group, when the actual measurements indicated only two shells belonged in that classification.
These results may indicate that continued familiarity with the template improves user accuracy, as the results of the second blind test more closely mimic the actual size profile
(Figure 8). Despite an increase in user accuracy during the second test, the results from student 2 continue to demonstrate that the template has a difficult time distinguishing outliers, by either overrepresenting or underrepresenting the outer size classes.
Results from the second blind test conducted with student 3 indicated a low level of user consistency. Results obtained from the second blind test indicated that student 3 tended to overestimate the shell size by placing the majority of shells into 6-7 cm and 7-8 cm size groups (81 hinges). The actual shell size profile for the groups 6-7 cm and 7-8 cm should have only contained 17 shells (Table 2). The second time student 2 measured the shells, the template overestimated shell size and the student volunteer’s graphed size profile was less accurate than during the first blind test (Figure 9). These results indicate that continued familiarity with the template does not always increase the accuracy level. They also continue to indicate the trend witnessed for all three volunteers during both tests: the template adequately captures medium sized shells, but has difficulty distinguishing those shells that fall on the outside of the size spectrum.
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50 Student 3 (1) 40 Student 3 (2) 30 Original 20 Distrubtion of Shells of Distrubtion 10
0 0-2 cm 2-3 cm 3-4 cm 4-5 cm 5-6 cm 6-7cm 7-8 cm 8-9 cm 9-10 cm Size Class Group
Figure 9. Comparison of Student 3 results from blind test 1, 2 and actual measurements.
Additionally, the change in the experimental design may in part have affected student 3’s results. During the first blind study, student 3 was more likely to place small fragments into small shell size groups. The removal of the fragments from their size groups, may have affected where on the template the fragment was matched.
Preliminary results indicate that the template has the most difficulty in distinguishing shell size on either end of the spectrum, but can adequately capture medium sized shells. Results also indicate that conclusions can vary between different users and for the same user. Only student 1 had a fairly high level of consistency between tests. Both student 2 and student 3 had results that were nearly reversed from the first classifications. This preliminary study has indicated a need for a more in depth analysis of the accuracy and reliability of the template. These results have so far only indicated that the template can have varied responses, but the level of precision and repeatability is currently unknown.
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Stage II: Punta Gorda Study
Methods
In order to examine the template’s level of accuracy in more depth, a fine grained analysis was designed using a second population of modern California mussel shells mainly collected from Punta Gorda. This analysis was aimed at assessing how the template classified fragmented shells on an individual basis. During Stage II results were tracked by shell in order to see how the template classified an individual fragment, rather than tracking total shell size class number. Each labeled hinge was assigned a size class providing a way to track how the template measured each shell fragment.
Stage II used twenty shells from Punta Gorda, plus eight additional shells from Davenport Landing, to create a larger sample. Each shell hinge was placed in a zip lock bag and crushed individually to avoid potentially losing any of the shell hinges during the crushing process. After each shell valve was crushed it was assigned a new number and letter which corresponded to the original label for which length measurements were previously recorded. The shell hinges or umbos were collected and bagged first. The remaining shell fragments were collected and bagged for possible future analysis. Five new student volunteers were asked to measure the sample using the template. All five student volunteers had varying levels of laboratory expertise. Student 2 and student 3 had several years worth of experience (>3 years). Student 4 had more than two years of lab experience, while student 1 and student 5 had minimal laboratory experience (<1 year). The students were instructed on how to use the template and were individually given the shell fragments to measure. All results were kept confidential.
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Results
The results were first tallied by size class and compared with the results from the known size classes derived from length measurements of the whole shells to compare with results obtained from Stage I (Table 4).Graphing the data by shell size class indicated low levels of comparability. Results from Stage II, indicated less of an agreement between users and original data, than those obtained during Stage I.
Table 4. Distribution of shellfish fragments size classes, Stage II.
Mussel Original Student 1 Student 2 Student 3 Student 4 Student 5 Size Class 0-2 cm 0 0 0 0 1 1 2-3 cm 9 4 3 4 7 6 3-4 cm 7 8 8 13 19 13 4-5 cm 11 13 6 7 11 10 5-6 cm 17 7 6 12 9 13 6-7 cm 12 4 8 9 4 3 7-8 cm 0 13 11 3 5 7 8-9 cm 0 6 9 3 0 3 9-10 cm 0 1 5 5 0 0 Total 56 56 56 56 56 56
Individual shell template classifications from each student volunteer were then compared against the known measurements. The student volunteer was allowed some leeway with their responses. A correct response was awarded if the volunteer placed the fragmented hinge into the correct size category or one size category above or below the true category it. This was done as it was felt that results would average around the mean, allowing the template to represent correct size profiles, even if each hinge was not correctly classified each time. The results were tallied and the percent correct was calculated (Table 5).
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Table 5. Stage II correctly identified shells by volunteer.
Student Number of Correctly Total Number of Percent Accurately Volunteer Identified Shells Shells Identified Student 1 33 56 59% Student 2 27 56 48% Student 3 33 56 59% Student 4 30 56 54% Student 5 38 56 70% Total 57.5%
Results indicated that the student volunteers correctly identified shell size using the template 48-70% of the time. When all of the results were aggregated, the accuracy level of using the template was calculated at 57.5%. The aggregated results indicate the template estimates shell length correctly a little more than half the time.
These results suggest that the template does not provide robust data and size estimations derived from its use are biased.
The data was then analyzed to assess the degree of correlation between the size groups based on length measurements of whole shells, and those assigned by students using the template. Statistical measurements included Pearson’s R correlation coefficient. Statistical analysis indicated moderate levels of correlation between student volunteer size estimations and actual shell length (Table 6). Statistical analysis was performed using SPSS Version 15.0 with an alpha level of 0.05. Results with an p value of 0.05 or less were considered statistically significant.
Table 6. Pearson’s r correlation coefficient using results from Stage II.
Student 1 Student 2 Student 3 Student4 Student 5 n 56 56 56 56 56 Pearson’s r 0.498 0.557 0.443 0.268 0.487 P 0 0 0 .045 0
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All R values were significant. The R value refers to the strength of the relationship between the two variables, in this case between the student results and the results from length measurements. The average R value was 0.45 (r=0.48; r-=0.27; r=0.44; r=0.55; r=0.5). This indicates that there is a moderate correlation between the two variables. The lowest level of correlation was between student 4 (r=.22; p=.045) and the original measurements. Student 2’s results had the highest correlation value (r=.56; p= 0). According to the R values generated during the statistical analysis, about 20% of the variability in the students’ results is predicted by the original measurements. This indicates that a weak relationship between the two variables, and suggests that the accuracy of the template is low.
After analyzing how accurately the template performed, the next part of the study aimed at investigating the replicablility of the template. This was performed by analyzing the level of inter-observer agreement between users. Cohen’s kappa is a conservative measure of inter-observer agreement. It was used to assess the degree of conformity between student results. Cohen’s kappa measures agreement between only two observers.
Statistical analysis indicated no consistent pattern of agreement between volunteers. Most students had a fair level of agreement to one another (Appendix VIIII).
Student 2 had the most varied results with the least agreement between all other participants. Student 2 and Student 4, Student 2 and Student 5 and Student 1 and Student
4 had no agreement with their results, while Student 2 and Student 3 had a slight agreement (Table 7). Aggregated results indicated that about 30% of the time students would agree. The low level of student agreement indicates template results are likely to
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Table 7. Inter-observer agreement results using Cohen’s kappa.
Student # Student # ĸ P Interpretation Student 1 Student 2 .219 .093 Fair Agreement Student 1 Student 3 .336 .012 Fair Agreement Student 1 Student 4 .023 .861 No Agreement Student 1 Student 5 .351 .007 Fair Agreement Student 2 Student 3 .148 .256 Slight Agreement Student 2 Student 4 -.104 .432 No Agreement Student 2 Student 5 .048 .698 No Agreement Student 3 Student 4 .313 .019 Fair Agreement Student 3 Student 5 .275 .036 Fair Agreement Student 4 Student 5 .340 .008 Fair Agreement Average ĸ .195
be biased according to the user. This indicates that the template operates on a low level of reliability.
As a final analysis, shell size classifications determined by student volunteers were studied using bar graphs. This aimed at understanding where individual shells were placed on the template. The data are presented for each template size group. The graphs were designed to plot the known measured shell against the classified size class assigned by the student volunteers. If there was perfect agreement between the student assigned size classes and the length measurements only one size designation should be represented in the bar graph, the group which corresponded to the length measurement. Graphed results indicated that this was not the case (Figures 10 and 11). With the exception of group 0-2 cm, the majority of shells were incorrectly categorized. This was particularly true for the shell size class “6 cm” as shown below.
In this instance, the actual size class was the smallest chosen response with 4 cm, 5 cm and 7 cm more frequently chosen. While, all three of the size categories
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14
12
10
8
6
4 2
0 Frequency of Chosen Response Chosen of Frequency 123456789 Range of Possible Responses
Figure 10. Range of student responses for shells belonging to the 6 cm size group.
20 18 16 14 12 10 8 6 4 2 0 Frequency of Chosen Response Chosen of Frequency 123456789 Range of Possible Responses
Figure 11. Range of student responses for shells belonging to the 4 cm size group.
81 selected are close to the known measurement, the size data are incorrect. Results of this analysis indicate that the template is able to classify a shell as small, medium or large, but has trouble assigning the correct length.
Results obtained during Stage II have several important conclusions. First the template correctly identified shell length around 60% of the time, even when allowing for leeway on either side of the correct classification. Evaluation of the correlation between student results and length measurements indicates a significant, but only moderate relationship, suggesting weak agreement between length measurement and size assigned by the template. Statistical analysis of inter-observer agreement indicates that the template has a low level of reliability, suggesting that data obtained from template use is biased according to the user. Each of these forms of analysis demonstrated that the template performs inadequately in terms of precision and repeatability.
After analyzing how the template measured individual shells by shell size class, it became apparent that the template is often unable to assign the correct shell length. However, results indicated that shells were often assigned size classes near their actual length suggesting that the template is able to summarize or lump data into larger assignations such as “small,” “medium” or “large.” This suggests that the template does not determine actual measurements of shell fragments.
Summary
This investigation has suggested that the template has only limited use when attempting to reconstruct size profiles from fragmentary remains. As the conclusions demonstrate limitations with template use, investigations aimed at understanding those
82 limitations and how they might be improved or refined is the next step in critically evaluating template methodology. Metric measurements aimed at studying the constraints highlighted in this first analysis will hopefully expand and improve upon template use.
CHAPTER VI
MORPHOLOGICAL VARIATION AND
SIZE ESTIMATION IN CALIFORNIA
MUSSELS
It was established in the previous chapter that the template operates on low levels of accuracy and reliability. Currently those limitations are poorly understood. This next stage of analysis is aimed at identifying potential problem areas in using the template. By highlighting and broadening our understanding of the limitations associated with template use in reconstructing size profiles from fragmented shellfish remains, recommendations for improvements with the methodology can be made. Several areas were chosen to understand the identified limitations in template use. These include the basic assumption on which the template operates: that the angle at the base of the hinge is correlated to valve length, and the effect of shell morphology on template use.
To investigate suspected limitations with template use, measurements taken from both populations of whole shells were aimed at dissecting the assumptions on which the template operates by establishing the degree of correlation between hinge angle, valve length, and the portion of the valve necessary for analysis. Measurements included calculating the basal angle of the hinge, measuring maximum valve height, width and length, as well as incremental height measurements on the shell starting at the umbo and moving towards the terminal growing edge (Figure 12).
83 84
Scale 1:1
height 4 cm 3 cm 2 cm 1 cm .5 cm
width length
Figure 12. Location of length, height and width measurements on the California mussel valve.
The same shells and shell fragments were used in this analysis, as in Phase I.
They were harvested from two geographically distinct loci along the California coast,
Punta Gorda in Humboldt County and Davenport Landing in Santa Cruz County. One hundred twenty eight shells (256 valves) were studied. All measurements were performed at the CSU, Chico Archaeology Laboratory. Measurements were analyzed statistically using SPSS Version 15.0 with an alpha level of 0.05. Results with a p-value of 0.05 or less were considered statistically significant. The various analyses used included descriptive statistics and regression analysis.
Descriptive Statistics of Mussel Shells
Methods
Analysis began with measurements aimed at investigating how different geographical locations affect shell growth patterns. In order to investigate this problem,
85 length, height and width measurements of all shells were statistically analyzed. These variables were chosen to assess potential morphological differences between populations collected at two geographically different locations, Punta Gorda and Davenport Landing.
These variables were chosen because varying environmental conditions can affect shell growth pattern and rate, including differences between height, width and length (Kopp
1979, Yamadas and Peters 1989, Jones and Richman 1995).
All measurements were taken using sliding calipers, including maximum length, height and width. Maximum length was calculated by measuring the greatest distance between the anterior and posterior valve ends on the ventral size of the valve
(Figure 12). Maximum height measurements were taken by measuring the greatest distance between the dorsal and ventral sides of the shell (Figure 12). Width was calculated by measuring hinge thickness (Figure 12). All measurements were taken in mm (Appendix C and D). The layout of the measurements was guided by previous mussel shell measurement analysis (Kopp 1979). Recorded measurements were put into spread sheets to be analyzed statistically.
Results
Forty shell valves were analyzed from Punta Gorda. Two hundred sixteen shell valves were analyzed from Davenport Landing. The range between length, height and width is greater for the Davenport Landing population than Punta Gorda (Table 8).
This is likely a function of the larger number of individuals included in the Davenport
Landing sample.
The two samples were analyzed statistically using mean length, width and height to determine significant differences between the two populations (Table 8). Mean
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Table 8. Punta Gorda and Davenport Landing: Descriptive statistics for maximum length, height and width with range and standard deviation. Measurements are in mm.
Maximum Length Maximum Height Maximum Width Punta Davenport Punta Davenport Punta Davenport Gorda Landing Gorda Landing Gorda Landing N 40 216 40 216 40 216 Mean + SD 54.6 ± 8.05 53.1 ± 15.64 22.5 ± 2.54 25.3 ± 5.46 10.5 ± 1.56 11 ± 3.38 Variance 64.8 244.7 6.5 29.8 2.4 11.4 Range 28.8 75.4 9.6 23.7 5.7 15
length for the Punta Gorda sample is 54.6 mm ± 8.05. Davenport Landing had a mean of
53.1 mm ± 15.64. (t = -.901, df = 103.473, p = .370). Mean width for Punta Gorda is 10.5 mm ± 1.56. Davenport Landing had a mean of 1 mm ± 3.38, a difference that is not significant, (t=1.613, df = 119.749, p= .109). The average height is significantly different
(t=5.155, df =118.322, p=.000). However, this difference is only 2.82 mm which is a relatively small disparity in size. Davenport Landing is known to be an area of frequent shellfish collecting (Jones and Richman 1995), which reduces the density of the mussel beds and allows the mussels more room to grow. The level of human disturbance is unknown at Punta Gorda, although due to its isolated location, collection is likely to be infrequent. Therefore, some difference in height between the two populations is not unexpected.
Discussion
Statistical analysis did not demonstrate substantial differences between the two populations. Mean width and length were not significantly different. Mean height was significantly different, the mean difference was only 2.82 mm. It is likely that growing conditions in these two geographically distinct areas along the California coast are similar enough to establish similar morphological populations of California mussel.
The one difference between the two populations was mean height. Davenport Landing, an
87 area of frequent human harvesting, had a larger mean height than Punta Gorda. The level of disturbance associated with this location is likely responsible for the difference in mean heights. Despite this difference, it appears that for these two locations, geographical difference does not change morphological characteristics substantially.
Basal Angle Measurements
Methods
Two methods were used to calculate the basal angle of the mussel hinge. The first method used a protractor diagram created by the author (Figure 13). This method
Figure 13. Protractor diagram.
used a drawn protractor with interval lines marked every 5 degrees. The shell valve was aligned on the diagram by matching the umbo to a pre-designated mark at the center of the protractor. To standardize the measurements, all were taken 1 cm above the umbo.
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The angle was calculated by aligning both lateral margins of the shell with the degrees on the protractor. Due to the almost homogenous results obtained from these measurements, only 128 hinges from the Davenport Landing sample were measured.
Due to the imprecision of results obtained using the protractor diagram, basal angle measurements of the smaller sample of shells from Punta Gorda were re-measured using AutoCAD 2005, which should capture more precise measurements and distinguish more variation between shell valves. AutoCAD is a computer aided design and drafting software program that calculates angles and lengths using lines. Photographs of the labeled shell hinges from Punta Gorda were uploaded into AutoCAD 2005. A line was drawn dividing the shell into two symmetrical halves (Figure 14, A). A second line was drawn bisecting the valve with a perpendicular line 1 cm from the base of the valve at the hinge or umbo (Figure 14, B). This was to keep the same standard as measurements taken
4using the protractor diagram. Lines were then drawn from the base of the hinge (umbo) at the point of symmetrical bisection to the point of intersection at the 1 cm mark (Figure
14, C). The angle created at the umbo was then calculated using the computer software.
Preliminary results indicated that angle measurements were nearly all identical. Therefore, a second measurement using AutoCAD 2005 was designed. Because the California mussel has a wedge-shaped valve, rather than a true triangle, the second measurement calculated the angle of the tangent at the 1 cm mark. This measurement was aimed at adjusting for the curve along the dorsal side of the hinge. By measuring the tangent at the 1 cm mark, it was felt that only the basal angle measurement at the hinge umbo was captured (Figure 15).
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Figure 14. Ventral side of California mussel shell showing the location of the angle and length measurements using AutoCAD 2005.
Figure 15. Ventral side of the California mussel shell showing the location of AutoCAD 2005 length and angle measurements using a tangent.
90
Results
One of the primary assumptions associated with template use is that the basal angle at the valve hinge and the length of the valve are highly correlated (Jones and
Richman 1995; Whitaker 2008; White 1989). This investigation was aimed at assessing if that assumption is correct. Statistical analysis was used to investigate the degree of correlation using regression analysis, which evaluates how an independent variable, in this case the basal angle, is related to a dependent variable, length. A positive relationship was found between the basal angle of the hinge and length for both samples. This means that the variables form a positive linear relationship (Figure 16). The results from the first sample from Davenport Landing using the protractor diagram demonstrated a low correlation between the two variables (r = .221, r² = .042, p =.011). This means that only around 4% of the hinge length is predicted by the basal angle.
R2 = 0.049 100 90 80 70 60 50
Angle 40 30 20 10 0 0 20 40 60 80 100 120 Length
Figure 16. Davenport Landing: basal angle measurements correlated to length.
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The second sample from Punta Gorda, using AutoCAD 2005 to measure the basal angle 1 cm from base of the umbo is not significant (r=.191, r²=.037, p=.238).
Results measuring the angle of the tangent were significant (Figure 17). These results demonstrated a positive relationship, with a low level of correlation (r=.331, r²=.110, p=.037). Statistical analysis indicates that about 11% of the valve length is predicted by the hinge angle. This is a weak correlation and indicates that the basal angle is not a strong predictor of hinge length.
R2 = 0.1097 60
50
40
30 Angle 20
10
0 0 10203040506070
Length
Figure 17. Punta Gorda: basal angle measurements as correlated to length. Measurements taken for tangents 1cm out from the edge of the shell hinge using AutoCAD 2005.
Discussion
Results based on statistical analysis aimed at evaluating the level of correlation between the basal angle and valve length indicate only a low level of correlation exists. Three measurement methods were used to calculate the basal angle of the mussel hinge. Only two of the methods had significant statistical results when using
92 regression analysis to evaluate the level of correlation. Of the two methods that indicated significant results, both only reported low levels of correlation. The evaluation of the assumption that the basal angle of the mussel hinge is a good predictor of valve length has indicated that this assumption is incorrect. These conclusions appear to support the notion that the predictive quality of shellfish fragments is not the basal angle of the hinge.
Past research indicating that shell height is good predictor of length indicates that a more in depth evaluation of how height and length are related (Kopp 1979) may be necessary for increasing our knowledge of template methodology. Investigations aimed at evaluating if all heights along the mussel hinge are correlated to length would identify the size of the mussel fragment necessary for template use.
Incremental Height Measurements
Methods
Because the basal angle of the hinge is not a good indicator of length, an alternative predictor for measuring fragmentary hinges is shell height. The design of the template allows for length estimates to be made based on how well the shell height fits into the drawn size class. Previous research indicates that the maximum height is highly correlated to the maximum length (Kopp 1979). The next part of this study is aimed at identifying the point when a shell fragment has become too small for analysis, because the height no longer predicts length. This investigation should indicate the necessary fragment size for accurate template use.
All shells from both samples were used in the analysis. Five incremental height measurements were taken starting from the shell umbo at 0.5 cm, 1 cm, 2 cm, 3
93 cm and 4 cm away from the hinge (Figure 12). Measurements were sorted as Group 1
(0.5 cm), Group 2 (1 cm), Group 3(2 cm), Group 4 (3 cm) and Group 5 (4 cm). The umbo at the very anterior of the hinge was laid flat onto a metric ruler and each incremental measurement was marked on the shell. Shell heights were assigned using sliding calipers.
All measurements were taken in mm. Linear regression analysis was used to investigate the strength of the relationship between each of the recorded shell heights and length.
Results
Punta Gorda. Results from Punta Gorda, Group1 (r=.255, r²=.065, p=.000) demonstrated a significant relationship, but a low correlation between the two variables
(length and height). Statistical analysis indicated that around only 7% of the length is predicted by shell height measured 0.5 cm away from the umbo. This indicates that a very small fragment of shell is not a good specimen for predicting length from height
(Figure 18).
R2 = 0.0651 18 16 14 12 10 8 Length 6 4 2 0 0 10203040506070 Height
Figure 18. Regression Analysis of Punta Gorda, Group 1 (0.5 cm).
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Results from Group 2 were significant. The correlation between the two variables was a moderate relationship (r=.547, r²=.299, p=.000). The regression analysis indicated that 30% of shell length was predicted by height. These results demonstrate that with more shell present, the height becomes a stronger predictor of length (Figure 19).
R2 = 0.2989 25
20
15
Length 10
5
0 0 10203040506070 Height
Figure 19. Regression Analysis of Punta Gorda, Group 2 (1 cm).
Regression analysis results for Group 3 (r=.601, r²=.361, p=.000) were significant. Results also indicated a moderate correlation existed between shell height taken 2 cm away from the umbo and shell length. The use of regression analysis demonstrates that around 36% of shell length is predicted from shell height 2 cm away from the umbo (Figure 20).
Results from Group 4 began to demonstrate a much stronger relationship between the two variables. Regression analysis indicated a significant and strong
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R2 = 0.3612 70
60
50
40
Length 30
20
10
0 0 5 10 15 20 25 30 Height
Figure 20. Regression Analysis of Punta Gorda, Group 3 (2 cm).
correlation (r=.850, r²=.723, p=.000), indicating that 72% of length is predicted by shell height (Figure 21).
R2 = 0.7227 70
60
50
40
Length 30
20
10
0 0 5 10 15 20 25 30 35 Height
Figure 21. Regression Analysis of Punta Gorda, Group 4 (3 cm).
Results from Group 5 also indicated a strong relationship. Statistical analysis demonstrates (r=.887, r²=.787, p=.000). These results indicate that nearly 80% of the length is predicted by height (Figure 22).
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R2 = 0.7866 70
60
50
40
Length 30
20
10
0 0 5 10 15 20 25 30 35 Height
Figure 22. Regression analysis for Punta Gorda: Group 5 (4 cm).
Results from the statistical analysis of shell gathered at Punta Gorda indicate that height measurements taken closer to the shell umbo had the weakest relationship with length. When more of the shell was available for measurement, the predictive relationship between height and length became stronger. Shell heights taken at least 3 cm away from the shell umbo had a strong relationship to length. If more of the shell is present then the predictive quality becomes stronger, indicating that when using the template a shell fragment should be at least 3 cm.
Davenport Landing. Results from statistical analysis of shells collected from
Davenport Landing indicated a similiar pattern as the results gathered from Punta Gorda.
Results from Group 1 are significant. Analysis indicated that Group 1 (r=.491, r²=.241, p =.000) had a moderate relationship between the two variables. Around 24% of the shell length is predicted by height (Figure 23).
97
R2 = 0.2415 120
100
80
60 Length
40
20
0 0246810121416 Height
Figure 23. Regression analysis for Davenport Landing: Group 1 (0.5 cm).
Results from Group 2 are also significant. Statistical analysis indicated a moderate relationship between height and length (r=.551, r²=.303, p=.000. Around 30% of the shell length was predicted by height taken 1 cm from the umbo (Figure 24).
R2 = 0.3033 120
100
80
60 Length
40
20
0 0 5 10 15 20 25 Height
Figure 24. Regression analysis for Davenport Landing: Group 2 (1 cm).
98
Results from Group 3 indicate a stronger correlation (r= .748, r²= .560, p=.000). These results demonstrated that nearly 60% of the length was predicted from shell height measured 2 cm away from the umbo (Figure 25).
R2 = 0.5597 120
100
80
60 Length
40
20
0 0 5 10 15 20 25 30 35 Height
Figure 25. Regression analysis for Davenport Landing: Group 3 (2 cm).
Regression analysis also indicated a strong correlation between the two variables in Group 4 (r=.820, r²=.673, p=.000). These results indicate that around 67% of the length is predicted by shell height measured 3 cm from the umbo (Figure 26).
Group 5 (r= .823, r² = .678, p = .000) also demonstrated a strong correlation between height and length measurements. The results from Group 5 indicate that nearly
70% of length is predicted from height measured 4 cm away from the shell umbo (Figure
27).
Results gathered from statistical analysis of shells collected at Davenport
Landing indicated that height measurements taken closer to the shell umbo had a lower correlation value to length. When height was measured 2-3 cm above the umbo the predictive relationship between the two variables (height and length) became stronger.
99
R2 = 0.6728 120
100
80
60 Length
40
20
0 0 5 10 15 20 25 30 35 40 Height
Figure 26. Regression analysis for Davenport Landing: Group 4 (3 cm).
R2 = 0.7151 120
100
80
60 Length
40
20
0 0 5 10 15 20 25 30 35 40 Height
Figure 27. Regression analysis for Davenport Landing: Group 5 (4 cm).
These results suggest that the larger fragment has a better predictive relationship between height and length.
100
Conclusions from Phase II
Phase II of this study was designed to enhance our current understanding of the morphological characteristics that affect shell size estimates when using fragmentary remains. The three lines of inquiry investigated included: the degree of variation in shell morphology due to location, the degree of correlation between the basal angle of the shell and length, and the degree of fragmentation appropriate for template use. Measurements used in this analysis included length, width and thickness, as well as calculating the basal angle of the hinge, and also taking five different height measurements starting from the umbo and moving toward the terminal growing edge.
Based on statistical analysis, the study indicated that there is no a significant difference between the two samples harvested along the California coast. The minor difference observed in height is likely a function of the difference in the level of human disturbance between the two sites. However, the difference is not substantial and likely would not change outcomes predicted by the template.
Analysis of the degree of correlation between the basal angle of the shell and length determined that the basal angle is a poor predictor of length. The correlation analysis demonstrated almost no relationship. It appears that the assumption that the basal angle of the hinge and length are correlated is incorrect.
The investigation into the degree of fragmentation appropriate for template use indicated that the larger a shell fragment, the stronger the relationship between height and length becomes. Shell height measured closer to the umbo had only a moderate correlation to length. Shell height measured further away (representing more of the shell present) had a stronger predictive relationship. Height groups greater than 3 cm had the
101 strongest predictive relationship, indicating that when using the template a fragmentary specimen should be at least 3 cm in size. The average size of the mussels used in analysis was 5.4 cm (Table 9). This analysis indicates that approximately half of the mussel shell
Table 9. Number of shells analyzed and their size ranges for Punta Gorda and Davenport Landing.
No. of Length (mm) Height (mm) Width (mm) Shells Min Max Min Max Min Max Location Punta Gorda 40 37.6 66.4 18.5 28.1 7 12.7 Davenport Landing 216 24.8 100.2 14.4 38.1 5.1 20.1
is necessary for height to have a substantial correlation to length. The morphology of the
California mussel shell indicates that the maximum height is located at approximately the midsection. Given that past research indicated that maximum mussel height is highly correlated to mussel length, these results are unsurprising. Essentially, analysis has revealed that approximately half of a mussel shell is necessary for accurate length estimates using the template.
This phase was aimed at identifying and exploring limitations with template use discovered during Phase I. Metric analysis of morphological variation between two shellfish samples did not provide new information regarding template use. However, the investigation into the assumption upon which the template is based, that the angle at the base of the shell and length are correlated, provided new insight. It was found during statistical analysis that the hinge angle and length are not correlated, indicating that more than just the umbo of the shell must be present to be used in template analysis. The next part of this analysis was aimed at identifying when a fragmented specimen is too small for analysis. This was accomplished by measuring height at various locations along the
102 shell valve from umbo toward terminal growing edge. It was found that height measured closer to the base of the valve (umbo) had only a moderate correlation to length, but height measured at least 3 cm out had a strong predictive relationship. For the sample of shell used, 3 cm indicates approximately half of the valve. This study indicates that the template can use shell height to predict length, but that the fragmentary specimen must represent at least half of the valve or results may be biased.
CHAPTER VII
SUMMARY AND CONCLUSIONS
This study provided a critical evaluation of the method for estimating
California mussel length from fragmentary specimens using a drawn template of size classes. The California mussel was chosen because of its ubiquitous presence in many coastal archaeological sites through out California and because of its susceptibility to size selective predation pressure (Erlandson et al. 2008). The California mussel was heavily utilized by pre-historic cultures on the Pacific Coast, and there is support for the concept that incipient aqua-culture was practiced (Blackburn and Anderson 1993; Whitaker
2008).
Summary
This study was interested in evaluating the drawn template developed by
White in 1989 that has since been referenced and used in additional studies (e.g. Bouey and Basgall 1991; Jones and Richman 1995; Whitaker 2008). Although there is a level of intuitive logic in using the existing template, the down side is that false interpretations may be derived from its use, taking later examiners in wrong directions. Despite its use, the template as a method for estimating length had never been critically evaluated. The research presented here aimed at understanding how accurately the template estimated size length, potential limitations with its use and why those limitations might exist. In
103 104 order to accomplish these research goals, the study was designed in two phases. Phase I tested the template’s accuracy level in a series of blind tests using student volunteers.
Phase II examined how morphological variation in the California mussel may affect template use. It also examined the primary assumption of the template, that the basal angle is correlated to shell length. In addition, it established some parameters for minimum size of fragments used with the template.
Phase I indicated that the template had only a moderate level of accuracy.
When results from the tests using student volunteers were aggregated, the analysis demonstrated that the template could accurately estimate length around 60 percent of the time. Additionally when the subjects were analyzed for inter-observer agreement, it was found that only a fair level of agreement existed between volunteers, or between 20 – 40 percent of the time volunteers were likely to agree. These results suggest that data generated from template use is not robust and is likely biased depending on the level of experience.
Results from Phase II demonstrated that the assumption that the basal angle of the hinge is correlated to length was incorrect. Statistical analysis found only a weak relationship between angle and length. This suggests that when matching the fragment to the template, the angle was not a reliable indicator of size. However, regression analysis indicated that height is correlated to length. Although height is correlated to length, not all heights measured along the shell have a strong relationship. Heights measured closer to the shell umbo had only a moderate relationship to length, indicating that some of the predictive value was lost. This analysis implied that a larger fragmentary specimen is better for estimating shell length. The strength of the relationship increased when
105 fragments were at least 2 cm and was strong for both samples of shells when fragments were larger than 3 cm. These results demonstrate that almost half of the shell is necessary for a strong predictive relationship to exist between mussel height and length, as the mean shell length for the samples studied was 5.4 cm (Table 8). These results indicate that fragments representing less than half a shell have limited value.
These results have important implications for the field of shellfish faunal analysis. The results indicate that data collected using this methodology should be cautiously interpreted. The critical evaluation of the template’s accuracy and reliability indicated that the template operates on only a moderate accuracy level and fair level of reliability. The template was better at designating shellfish fragments as large, medium or small, then estimating the precise size. The aggregated accuracy of the template, which was found to be at around 60 percent, may be improved by applying results discovered during Phase II of the study. The results from Phase II indicated that shell fragments larger than 3 cm are the best specimens to use because the correlation between height and length is the strongest at this point. Results have also indicated that essentially half of the mussel shell must be present for accurate use with the template. Unfortunately this study indicates that the template cannot capture the length of very small mussel fragments, indicating that its use in the field of shellfish faunal analysis is limited. Sample sizes would have to be restricted and the likelihood of ignoring large quantities of data is expected. Although it is disappointing that this analysis showed that the templates drawn by White have limitations with their use in predicting shell fragment size, the results from this analysis provide a future opportunity to make improvements with the methodology
106 used to estimate length from fragments. These results also provide opportunities to re- examine with greater accuracy studies based on the template.
Contributions to the Field of Archaeology
This study has highlighted the need for improved methodology for estimating length from fragmented California mussel remains in future studies. Biometric analysis of shellfish remains has begun to grow in popularity in recent years. Biometric analysis relies upon morphomentric equations derived for specific species. These morphmetric equations are tested using statistical analysis and then frequently used to reconstruct size estimates and assess size-age distribution. This method can usually be used with highly fragmented remains and operates on a tested assumption.
Morphometric equations might provide an alternative method for recreating size profiles of fragmented shellfish remains. Morphometric equations are derived from the measurements of morphological attributes of shells. Measurements of unique elements on the shell are correlated to length, which can be calculated using the derived morphometric equation. So far, morphometric equations have been established for two species of mussels, one species of crustacean and two species of limpits (Buchanan 1980;
Hall 1986; Jeradino and Navarro 2008). Metric data obtained from two different species of mussels (Chromomytilus meridionalis and Perna perna) have also demonstrated the applicability of measuring anterior portions of the mussel and deriving indices for estimating shell length (Buchanan 1986; Hall 1980).
The benefits of using morphometric equations are that they use unique elements of the shell which tend to survive taphonomic processes. This allows
107 researchers to use unbiased samples that do not favor larger shells or shells which survive post depositional processes the best. In a case study using two species of limpits, the size threshold for their samples was increased three-fold for one species and two-fold for the second (Jeradino and Navarro 2008). This study also demonstrated that a smaller sample of shellfish could be used to conduct faunal analysis, rather than excavating bulk samples to obtain enough well preserved shells for size recreations.
Based on these studies, morphometric equations are opening new avenues for obtaining shell length from fragmentary remains and should be explored using species found along the Pacific coast in California. It is likely that morphometric equations could be used with the California mussel, based on a method used for the morphologically similar North American zebra mussel (Dreissena polymorpha). This biometric method uses the length of the internal septum, or umbonal length to predict overall shell length from shell fragments. The technique demonstrated that the umbonal length was correlated to the overall length (Hamiliton 1992). Additionally this method has also been used to predict length in European zebra mussel with similar results. The internal septum is a region of the mussel that generally does not break down, making it applicable for fragmentary remains (Hamilton 1992).
Future experiments to determine if similar statistical results would occur when used with the California mussel may provide researchers with another good alternative method for estimating size profiles from fragmentary remains. The use of these equations may solve the problems that are currently associated with template use, including the size restriction of specimens used and the imprecision of length estimates derived from the methodology.
108
Not only does this study encourage re-evaluation of methodology for future studies, but should be used to re-evaluate past studies which sought either whole or nearly whole shells or those which used the template to estimate length from fragmentary remains. This study has highlighted that data generated from using the present template has a 40 percent chance of being biased. This means that past studies whose interpretations have leaned solely upon this methodology may be improved if they were re-evaluated with a more accurate morphomentric equation.
The results of this study have four implications for the field of archaeology.
First and foremost, results obtained from the template have a 40 percent chance of being inaccurately classified. Results may also vary according to the analyst, making it difficult to compare data sets equally. Data which are generated from research using this methodology should be used cautiously. Second, because of the morphological characteristics of the California mussel, specimens used with the template need to be between 2-3 cm in size or need to consist of at least one half of the mussel shell. This limitation means that archaeological samples need to be restricted, potentially ignoring valuable data. By restricting archaeological samples, the data generated by the template is also compromised because of preservation bias. Fragmentation has been mainly shown to affect larger shells, while preserving the smaller specimens (Jerardino and Navarro
2008).
The third contribution of this study is the identification of the need for improved methodology in the field of California mussel faunal analysis. The foundation upon which the template rests, that morphological features on the shell are correlated to size is solid. The application of this foundation may be better used in the field of
109 morphometric equations, which have been demonstrated to be useful with other shellfish species. This new style of analysis has resolved several of the problems highlighted with template use in this study; namely, small, restricted sample sizes and inaccurate results.
Finally the fourth contribution of this study is that past research which used the template should be re-evaluated. The possibility of both analyst bias and inaccurate methodology may warrant a second evaluation of the data generated.
Hopefully future research will take this study one step further by attempting to refine the methodology so that even severely fragmented samples can be used in analysis.
Because this study has illustrated problem areas when using a template with fragmentary remains, future research should concentrate on developing methodology which would allow for the use of highly fragmented archaeological remains.
Even if archaeological research moves away from studying human impacts on the paleo-environment, archaeological reconstruction of marine resource procurement is likely to continue. Because measurements of shellfish are often used in these reconstructions, this study was aimed at identifying areas of potential bias which can now be addressed. These results have indicated the need for a more rigorous methodology when analyzing highly fragmented archaeological shell remains in future studies and the need to re-evaluate those studies which have used this methodology in the past.
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APPENDIX A
VENTRAL SIDE OF THE CALIFORNIA MUSSEL (Drawing is not to scale)
119
APPENDIX B
MUSSEL SIZE CLASS TEMPLATE
Source: Redrawn from White, G., 1989, A Report of Archaeological Investigations at Eleven Native American Coastal Sites, MacKerricher State Park, Mendocino County, California. Sacramento: California State Parks. Reproduced with permission.
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APPENDIX C
PUNTA GORDA LENGTH, HEIGHT
AND WIDTH MEASUREMENTS
Shell# Max Length Max Height Max Width Weight 1AL 29.7 16.9 6.2 1.09 1AR 29.5 16.6 5.1 0.93 2AL 41.2 22.9 7.9 2.15 2AR 41.3 22.7 8.2 2.22 3AL 29.8 18.1 7 1.15 3AR 30 18.1 7.2 1.17 4AL 31.3 17.9 5.6 1.06 4AR 31.5 18 5.7 1.08 5AL 24.8 14.4 5.4 0.6 5AR 28.4 16.1 6.2 0.61 6AL 28.4 16.1 6.2 0.85 6AR 28.7 16.2 6.8 0.9 7AL 34.8 17.7 7.5 1.52 7AR 34.2 17.7 6.9 1.47 8AL 30.2 15.2 6.9 1.25 8AR 29.2 15 7 1.24 L01 62.5 21.8 11.8 7.61 R01 63 25.8 11.4 7.09 L02 66 20.7 12.7 8.3 R02 66.4 21.9 12.1 9.01 L03 47.2 18.9 8.7 3.46 R03 47 18.5 9.3 3.27 L04 52.3 19.1 9 3.59 R04 53 18.5 9.9 3.95 L05 64.7 25.7 11.9 8.01 R05 60.8 22.7 12.4 7.91 L06 58.8 22.8 9.6 4.8
123 124
Shell# Max Length Max Height Max Width Weight R06 59.1 23.6 10.2 5.36 L07 44.2 20 8.8 2.15 R07 44.5 20.4 7.6 2.62 L08 60 21.8 11.7 6.92 R08 59.9 23 11.1 7.12 L09 61.1 21.7 11.8 7.36 R09 60.8 27.1 12.5 7.89 L10 65.3 22 11.2 6.07 R10 65.4 22 10.9 6.36 L11 50.8 20.7 11.7 4.61 R11 51.3 20.2 10.5 4.43 L12 64.8 28.1 12.4 7.52 R12 64.8 28 12 7.07 L13 56.8 21.2 11.4 4.93 R13 56.9 21.2 10.3 4.37 L14 51.8 25.1 12 5.28 R14 52.8 25.2 12 4.53 L15 54.5 24 10.2 3.54 R15 53.9 24.9 9.5 4.19 L16 45.6 21.3 9.3 3.19 R16 45.2 21.3 9.2 3.26 L17 37.6 19.5 7.5 1.6 R17 37.8 19.3 7 1.51 L18 53.1 24.9 10 3.64 R18 53.2 24.5 10.2 3.91 L19 41.9 21.8 11.9 4.07 R19 50.5 21.3 9.9 3.51 L20 49.3 23.9 8.2 3.01 R20 48.3 24.5 8.1 2.94
APPENDIX D
DAVENPORT LANDING LENGTH, HEIGHT
AND WIDTH MEASUREMENTS
Shell# Length Height Width Shell# Length Height Width 1L 57.4 27.1 11.5 14R 49.7 24.6 8.5 1R 58.3 27 13 15L 50.3 22.6 9.5 2R 62.7 26.6 14.6 15R 50.3 22.9 11.1 2L 62.2 27 14.7 16L 86.6 35.7 16.3 3L 54.5 24.4 11.3 16R 87.7 35.5 15.9 3R 54.9 24.1 12.2 17L 48.6 23.6 7.9 4L 42.3 19.6 7.9 17R 47.7 24.4 7.4 4R 42.3 19.6 7.7 18L 97.7 35.9 17 5L 54.2 25.8 10.2 18R 98.8 35.5 19.1 5R 55.8 25.7 11.2 19L 85.3 35.7 18.4 6L 85.5 36.7 17.1 19R 87.1 36.3 18.4 6R 85.2 36.9 17.6 20L 73.7 35.9 15.2 7L 67.8 33.7 12.8 20R 74.2 36.2 15.6 7R 68.1 33.9 13.2 21L 62.2 28.9 13.1 8L 80.8 36.8 15.6 21R 63.4 29.3 13.4 8R 82.8 36.7 16.1 22L 39.9 21.9 10.1 9L 60.9 28.2 14.1 22R 40.1 21.2 9.3 9R 59.3 28.3 14 23L 91.7 34.3 18 10L 63.2 29.5 15.5 23R 91.8 35.1 19.1 10R 62.9 29.6 15 24L 61.1 28 11.6 11L 27.6 14.8 6.5 24R 61 28 11.6 11R 27.9 15.5 6 25L 79.8 32.8 15.1 12L 44.4 21.2 9.9 25R 80.7 32.6 16.5 12R 44.4 21.2 9.3 26L 53.7 26.4 10 13L 59.4 30.5 11.3 26R 53.7 26.5 8.9 13R 59.8 30.7 11.7 27L 50.9 25.4 11.1 14L 49.3 24.9 10.1 27R 51 25.5 11.6
126 127
Shell# Length Height Width Shell# Length Height Width 28L 79.2 27.6 16.5 41R 31.7 15.4 6.7 28R 79.1 28.1 18.5 42L 50.1 22.9 9.6 29L 49.1 23.2 8.1 42R 49.7 22.8 8.9 29R 49.1 23.1 8 43L 52.3 25.9 8.9 30L 43.4 23.1 8.1 43R 52.5 25.6 10.7 30R 43.1 23.1 7.2 44L 61.8 29.4 17 31L 57.5 25.2 11.1 44R 61.4 29.4 14 31R 57.7 25.3 10.5 45L 65.8 30.8 12.2 32L 41.5 20.7 9.8 45R 65.9 30.4 12.9 32R 41.3 20.3 8.4 46L 39.2 21.6 6.5 33L 59.3 29.4 11 46R 39.5 21.8 7.1 33R 59.6 29.1 10.4 47L 49.1 25.4 9.3 34L 45.4 24.1 8.1 47R 49.7 25.2 8.9 34R 45.6 23.6 8.6 48L 57.7 22.9 14.6 35L 57.9 29.6 10.2 48R 57.2 24.2 12.9 35R 58.2 29.5 11.2 49L 57.1 26.9 10.4 36L 78.4 32.6 19.4 49R 57 27.2 9.5 36R 79.3 33 20.1 50L 45.4 24.6 12.8 37L 59.6 27.9 13 50R 45.6 24.6 11.4 37R 59.5 28.5 12.6 51L 47.1 23.7 11.6 38L 42.3 24.3 7.5 51R 47.7 23.9 11.6 38R 42.5 23.8 8.4 52L 49.1 24.5 12.4 39L 55.3 29 11.2 52R 49.2 24.7 11.5 39R 56 28.9 11.9 53L 44.4 22 11.9 40L 73.1 33.7 12.9 53R 44.5 21.7 11.9 40R 72.7 33.8 12.3 54L 47.4 22.6 9.2 41L 31.7 15 6.9 54R 47.4 23.1 9.4
128
Shell# Length Height Width Shell# Length Height Width 55L 54.2 27.5 11.5 68R 38.7 20 8.8 55R 55 27.6 10.8 69L 41.9 20.9 9.2 56L 51.6 25.2 10.6 69R 42.4 21.1 9 56R 52.1 25.3 11.3 70L 54.1 23.6 13 57L 38.9 18 7.8 70R 55.3 23.8 12.9 57R 38.8 18.2 8.3 71L 42.6 22 8.9 58L 51.2 23.3 13.4 71R 42.2 22.2 9.2 58R 51.8 24.6 13.3 72L 45.9 22.1 8.5 59L 70.5 34.5 14.9 72R 45.7 22.2 7.7 59R 70.9 34.7 13.9 73L 47.8 24.2 11.9 60L 73.6 37.3 14.8 73R 49.1 24 12.1 60R 73.6 38.1 16 74L 68.3 31.7 13.4 61L 52.6 29.1 11.8 74R 68 31.9 14 61R 53 29.1 12.4 75L 40.8 20.8 7.2 62L 50.9 26.5 11.3 75R 40.9 20.2 7.1 62R 50.7 26.5 10.6 76L 48.8 24 10.1 63L 58.5 27.3 11.8 76R 49 23.5 10.2 63R 58.5 26.7 11.2 77L 55.7 29 10.3 64L 52.6 26.9 11 77R 56.5 29 11.4 64R 53 27.1 10.7 78L 46.5 22.4 9.5 65L 85.7 28.8 18.3 78R 47.1 22.2 9.7 65R 85.5 27.8 17.7 79L 50.7 29.2 11.4 66L 36.7 20.5 5.9 79R 50.8 29.3 11.7 66R 36.7 20.5 7.4 80L 41.3 20.6 8.2 67L 50.8 26.7 9.2 80R 41.6 20.7 8 67R 50.9 25.9 9.1 81L 32.8 17.9 6.9 68L 38.7 19.7 8.5 81R 33.9 18 6.8
129
Shell# Length Height Width Shell# Length Height Width 82L 40 20.9 8.3 91R 56.5 29.1 9.9 82R 40.4 20.8 7.6 92L 41.9 21.3 7.5 83L 48.6 21.7 10.5 92R 42.7 20.8 7.9 83R 48.6 21.2 12.5 93L 52.4 26.2 9.4 84L 56.3 24.8 12.5 93R 52.9 25.9 8.6 84R 56.7 24.8 13 94L 67.4 31.7 13.5 85L 32 16.7 5.8 94R 69.2 32.5 15 85R 32.3 17.2 6.3 95L 43.3 22.5 8.6 86L 45.8 23 11 95R 43.4 22.8 9.4 86R 46.6 23.2 10.3 96L 35.6 20 7.6 87L 48.6 22.7 11.2 96R 35.5 20.2 7.6 87R 48.6 23.2 10.5 97L 43.2 20.4 9.4 88L 100.2 36.8 19.8 97R 43.2 20.5 7.9 88R 100.1 36.6 19.1 98L 49.9 24 11.1 89L 67.7 27.2 16.1 98R 51.2 24 12 89R 68 27.9 15.7 99L 38.7 21.5 7.9 90L 50 24.7 13.3 99R 39.5 21.4 8.2 90R 49.6 24.6 11.6 100L 33.8 18.3 7.3 91L 56.7 29.4 11.1 100R 33.7 18 7.2
APPENDIX E
PUNTA GORDA INCREMENTAL
HEIGHT MEASUREMENTS
Shell# Height1 Height2 Height3 Height4 Height5 L01 14.5 18.6 23.9 26.5 28.9 R01 14 23.1 24.8 28.6 28.5 L02 12.1 17.5 22.6 25.9 26.8 R02 12.7 18 22.2 25.8 26 L03 10.5 16 20.6 21.4 19.5 R03 11.1 15.5 20.3 21.6 19.9 L04 10.7 14 20.3 21.4 19.5 R04 11.2 15.4 21 21.1 20.4 L05 11.7 18.4 22.8 26.5 25.3 R05 11.5 18.6 23 25.4 25.7 L06 13.1 18.5 26.1 25.9 24.4 R06 12.6 17.6 24 27 24.5 L07 10.7 15.4 20.8 20.3 16.1 R07 12.5 15.5 20.6 20.6 15.8 L08 12 19 24 28 26 R08 12.8 16.9 23.6 27.6 26.7 L09 13.8 17.9 23.2 26.6 25.7 R09 13.5 18 23.6 27.2 25.8 L10 12.1 17.8 23.2 26.6 25.7 R10 12 16.8 21.8 25.4 26.1 L11 10.5 13.9 18.1 19.2 19.1 R11 10.5 14.5 18.7 18.7 18.2 L12 13.6 18.9 24.1 28 26.2 R12 13.1 18.9 23.9 28 25.9 L13 11.6 16.6 20.8 20.5 20.3 R13 11.3 15.5 20 20.8 20.1 L14 16.1 20 24.4 24.3 23.5
131 132
Shell# Height1 Height2 Height3 Height4 Height5 R14 13.6 19.5 24.8 24.7 23.9 L15 12.1 16.9 22.2 23.8 22 R15 10.9 16.7 21 24.3 22.6 L16 12.2 17.2 21.2 19.6 15.4 R16 11.8 16.9 21.2 20 15.6 L17 12.3 15.5 19.1 17.2 . R17 12.2 15.3 19.3 17.6 . L18 14.3 17.5 22.9 23.7 22 R18 14.6 19.2 23 23.7 22.2 L19 12.9 16.2 20.8 21 18.9 R19 10.5 15.5 19.2 21.2 19.7 L20 12 17 23.1 23.3 21 R20 12.2 17.2 23 23.3 21.6
APPENDIX F
DAVENPORT LANDING INCREMENTAL HEIGHT MEASUREMENTS
Shell# Height 1 Height 2 Height 3 Height 4 Height 5 Shell# Height 1 Height 2 Height 3 Height 4 Height 5 1R 10.9 15.3 22.6 27 24.3 15R 12.3 15.8 21.1 23 20 1L 12.1 17.1 23 27.1 24.1 16L 12.5 18.8 26.5 32.3 36 2R 11.3 16.8 23.1 26.6 26.3 16R 12.5 19.5 29 32.4 35.3 2L 11.3 17.1 25.7 26.7 26.2 17L 10.8 17.6 23 23.5 20.2 3R 10.3 17.1 24.1 23.8 22.6 17R 11.3 16.3 21.3 24.1 19.8 3L 10.1 17 24.6 23.3 22.6 18L 11.2 16.9 22 27.9 30.7 4R 9.5 14.3 18.6 18.3 . 18R 12.3 17.5 23.6 28.6 31.5 4L 10.8 15.1 19.2 18.3 . 19L 14 22.3 29.5 33.6 36.7 5R 10.9 17.7 22.4 25.5 23 19R 11.6 18 26.5 31.8 35.5 5L 9.2 15.1 21 25.7 23.5 20L 12.5 20.1 28.1 34.6 35 6R 13.1 20.6 30 34.7 36.3 20R 11.4 19.5 27 32 37 134 6L 12.8 19.4 29 33.3 31.1 21L 11.9 16.6 24.2 28.2 28.4 7R 10.9 18.1 27 33 31.1 21R 11.4 17.3 24.3 28.2 28.7 7L 12.3 19.6 29 33.3 31.1 22L 12.6 17.9 20.7 19.7 . 8R 13.5 20.5 28.9 35.4 36.6 22R 8.7 16.2 20.8 18.5 . 8L 12.5 18.6 27.2 31.3 36.7 23L 13.4 18 26 31.8 33.9 9R 12.2 19.4 24.6 27.9 26.2 23R 10 17 24.5 29.6 33.5 9L 11.3 18.9 24.6 28.3 26.4 24L 12.5 16.9 23.2 27.5 26.6 10L 12.6 19.1 26.7 29.6 29.9 24R 11 17.3 24.9 27.9 26.7 10R 12.9 18.9 26.3 29 30 25L 14.7 19.7 25.5 29.7 32.4 11L 9.8 13.4 15 . . 25R 11.5 17.7 24.7 30.2 32.9 11R 9.7 13.5 14.5 . . 26L 12.1 18.7 23.3 26.3 24.5 12L 10.3 15.6 21.3 19.6 . 26R 11.2 17.6 23.3 26.6 25.6 12R 10 16 20.7 19.4 . 27L 11.9 16.7 23.3 24.6 21.7 13L 10.8 17.1 25 30.8 29.7 27R 12.1 16.9 24.3 24.6 22.3 13R 12.7 19.5 25.4 30.5 28.7 28L 11.6 16.6 23.5 27 27.3 14L 10.2 15.2 23.3 24.7 . 28R 11.7 15.2 23 27.5 27.3 14R 11.2 15.4 20.8 25 . 29L 11.9 16.3 21.5 23.1 19.7 15L 10.6 15.5 22.4 22.2 20 29R 11 16.5 21.1 22.8 19.4
Shell# Height 1 Height 2 Height 3 Height 4 Height 5 Shell# Height 1 Height 2 Height 3 Height 4 Height 5 30L 10 16.9 21.6 22.6 . 45L 13 17.9 25.4 29.1 30 30R 10.6 15.4 22.8 22 . 45R 13 17.8 25.8 29.7 29.6 31L 13 17.5 23.5 25.3 23.9 46L 11.2 16.8 21.8 . . 31R 12.6 16.1 23.2 25.4 23.8 46R 11 16.1 21.5 . . 32L 9.2 12.4 20 19.3 . 47L 11.4 18.4 23 24.7 . 32R 9.7 14.4 20.5 18.3 . 47R 11.4 18 23.3 24.2 . 33L 10 15.7 23.7 29.1 27.8 48L 10.2 14.7 21.1 24.2 21.7 33R 10.3 14.6 23.7 29.1 27.3 48R 10.3 14.8 20.8 24.4 20.8 34L 11.2 15.8 23.5 23.3 . 49L 10.4 16.1 23.6 26.9 26.1 34R 11.1 16.7 23.3 22.3 . 49R 10.2 16.4 24.3 27.2 25.8 35L 10.6 15.5 23 28.9 28.3 50L 11.5 19.1 24 23.3 . 35R 11.1 15.9 24 29.1 28.4 50R 11.5 19.1 24.7 23 . 36L 13.2 19.9 26.2 31.1 32.5 51L 10.4 15.3 21.3 22.4 19.1 36R 13.4 18.3 26.6 31.9 32.9 51R 9.4 15.2 21.7 23.5 18.7 37L 11.7 17 24.2 27.2 27.5 52L 11.6 16.2 23.9 25.2 21.8 37R 12 17 23.8 27.6 27.8 52R 10.5 16.5 24.2 24.3 20.7 38L 11.4 16.7 23 22.6 . 53L 9.9 13.4 22.5 . . 38R 11.7 16.4 22.8 22.5 . 53R 10.2 13.6 22.1 . . 39L 12.8 17.4 24.4 28.7 26 54L 10.2 15.2 21.5 21.7 . 39R 12.5 17 24.3 28.6 26 54R 10.8 15.9 22 21.4 . 40L 11.3 17.7 25.8 32.8 33.2 55L 10.4 16.3 24.1 27.9 25.1 40R 11.2 17.1 25.8 32.1 33.1 55R 10.2 16.8 25.4 27.3 25.8 41L 9.6 12 14.7 . . 56L 10.2 14.7 21.6 25.1 22.3 41R 9 13 13.7 . . 56R 10.9 14.2 23.4 24.4 21.8 42L 10 14.2 23.1 23 . 57L 6.5 11.4 17.6 16.8 . 42R 10.2 16.7 23.3 22.8 . 57R 7 15 17.9 16.3 . 43L 9.8 14.4 20.8 25.6 22.7 58L 10.8 15.4 21.8 23.8 21 43R 10 15.1 21.6 25.6 22.7 58R 10.7 15.1 21 24.8 20.7 44L 12.1 16.6 23.7 29.4 27.9 59L 12.5 18.7 26.8 32.5 34.6 44R 12 15.4 22.9 29.5 27.7 59R 12.4 19.4 27.9 33.4 34.2
135
Shell# Height 1 Height 2 Height 3 Height 4 Height 5 Shell# Height 1 Height 2 Height 3 Height 4 Height 5 60L 13.1 20.7 28.1 34.4 37.4 75R 10.2 14.7 21 21.1 . 60R 12.9 20.5 28.7 37.7 36.4 76L 9.5 14.1 20.3 24.1 20.9 61L 12.1 18.9 27.2 29.3 24.7 76R 10.5 14.5 21.3 22.7 19.3 61R 12.3 19.6 28.2 27.2 24.2 77L 12 18.5 24.8 28.6 27.5 62L 10 16.9 23.2 26.3 23.6 77R 10.8 18.2 24.9 28.8 26.5 62R 10.3 17.3 23.7 25.1 23.3 78L 10.5 17 22 22 . 63L 11.3 16.3 21.9 25.9 25.2 78R 10.7 16.1 22.2 21.7 . 63R 10.7 16.3 20.7 25.7 25.2 79L 13.5 20.7 27.1 28.3 24.6 64L 13 19.3 25.2 27 24.3 79R 13.7 19.4 26.9 27.4 23.2 64R 13.2 18.3 23.8 26.7 24 80L 11.3 15.4 20.3 19.8 . 65L 9.9 15 20.9 25.7 26.9 80R 10.4 14.6 19.4 19.7 . 65R 10.3 15.3 22.3 27 28.1 81L 9.8 14.5 17.7 . . 66L 10.3 15.8 20.7 17.9 . 81R 9.2 15 17.7 . . 66R 10.6 15.5 20.5 17.3 . 82L 10.5 14.9 20.8 20.2 . 67L 11.3 16.6 22.5 26.2 23.6 82R 10.5 14.9 20.8 20 . 67R 11.5 17 23 25.3 23.1 83L 8.5 12.7 20.2 21.8 19.4 68L 11 15.4 20.1 18.9 . 83R 8.3 12.4 20.3 21.4 19.1 68R 10.3 15.9 20.2 18 . 84L 10.7 16.8 22.8 24.2 22.9 69L 12 16.9 20.8 19.8 . 84R 10.5 16.7 23.3 23.8 22.3 69R 10.6 16 20.4 19.5 . 85L 10.3 13.6 16.5 . . 70L 11.3 17.2 21.2 23.9 22 85R 10.3 13.6 16.3 . . 70R 10.1 16.3 21.1 23.2 22 86L 11.5 15.5 22.2 22.9 20.9 71L 10.3 16 21.5 22.1 . 86R 11.1 16.2 22.9 22.3 20.2 71R 10.2 15.5 21.8 21.7 . 87L 11.1 14.9 20.6 22.1 20.6 72L 12 16.6 21.1 21 18.3 87R 11.1 16.6 23 21.2 19.5 72R 11.5 16 22.4 20.9 17.2 88L 10.5 15.6 23 28.7 33.4 73L 9.7 16 22.4 20.9 17.2 88R 11 16.5 24.6 30.8 33.2 73R 7.9 18.2 22.7 23.6 20 89L 11.7 17.3 23.7 27.5 27 74L 11.4 16.1 23 27.5 31.7 89R 11.6 16.9 23.6 27.3 27.3 74R 11.5 17.1 24.2 29.5 30.5 90L 12.9 18.2 23.2 24.2 20.1 75L 9.1 14.5 21.4 19.2 . 90R 11.8 18 24.2 23.8 18.5 136
137
Shell# Height1 Height 2 Height 3 Height 4 Height 5 91L 10.7 15.4 23.9 28.4 27.4 91R 11.4 16.2 23.7 28.9 26.5 92L 10.6 16.5 20.7 20.3 . 92R 10.2 15.8 21 20.1 . 93L 12 17.7 25.2 24.9 23.3 93R 10.6 16.6 25 24.9 23.3 94L 13.2 20.3 25.4 31.7 29.8 94R 12.4 18.8 25.7 32 30.5 95L 11 16.7 21.7 21.7 . 95R 10.5 17.1 22.8 21.6 . 96L 10 14.2 20.4 . . 96R 10.5 15.9 19.5 . . 97L 9 14.2 20.3 19 . 97R 9.7 16.7 20.2 18.3 . 98L 11.7 16.9 21.2 23.8 22.2 98R 11.7 17.6 22.7 23.3 22.2 99L 9.9 14.2 21.1 20.1 . 99R 10.6 15.7 21.6 19.4 . 100L 10.4 16.2 18.5 . . 100R 10.6 16.8 17.8 . . 1AL 10.6 16 15.8 . . 1AR 10.9 15.7 16.8 . . 2AL 12.2 17.1 22.6 19.4 . 2AR 10.2 15.2 20.7 21.1 . 3AL 11.5 17.6 16.7 . . 3AR 10.1 16.5 16.5 . . 4AL 11.2 16.3 17.7 . . 4AR 10 14.7 17.8 . . 5AL 11.3 14.1 13.3 . . 5AR 9.7 13 13 . . 6AL 10.8 14.3 14.7 . . 6AR 10.1 14.1 14.7 . . 7AL 10.4 14.9 17.5 16.3 . 7AR 9.7 14.8 17.6 16.3 . 8AL 11 15.1 14.7 . . 8AR 10.2 14.7 14.5 . .
APPENDIX G
PUNTA GORDA BASAL ANGLE
MEASUREMENTS
Tangent Tangent Shell# Angle ° Angle ° Length Shell# Angle ° Angle ° Length L01 82 53 62.5 L11 68 41 50.8 R01 81 50 63 R11 73 41 51.3 L02 73 50 66 L12 74 45 64.8 R02 72 46 66.4 R12 74 40 64.8 L03 74 45 47.2 L13 70 46 56.8 R03 72 44 47 R13 67 45 56.9 L04 67 41 52.3 L14 82 45 51.8 R04 70 48 53 R14 88 42 52.8 L05 78 48 64.7 L15 76 42 54.5 R05 75 55 60.8 R15 75 51 53.9 L06 79 55 58.8 L16 75 34 45.6 R06 78 45 59.1 R16 69 37 45.2 L07 73 49 44.2 L17 73 43 37.6 R07 73 54 44.5 R17 75 49 37.8 L08 79 56 60 L18 80 52 53.1 R08 79 46 59.9 R18 79 48 53.2 L09 77 51 61.1 L19 74 48 41.9 R09 80 48 60.8 R19 66 45 50.5 L10 73 48 65.3 L20 82 44 49.3
139
APPENDIX H
DAVENPORT LANDING BASAL
ANGLE MEASUREMENTS
Shell# Angle ° Length Shell# Angle ° Length 1L 80 57.4 22R 80 40.1 1R 80 58.3 23L 65 91.7 2L 80 62.2 23R 75 91.8 2R 80 62.7 24L 80 61.1 3L 75 54.5 24R 75 61 3R 70 54.9 25L 70 79.8 4L 75 42.3 25R 75 80.7 4R 80 42.3 26L 75 53.7 5L 75 54.2 26R 80 53.7 5R 75 55.8 27L 80 50.9 6L 85 85.5 27R 75 51 6R 90 85.2 28L 65 79.2 7L 90 67.8 28R 70 79.1 7R 80 68.1 29L 80 49.1 8L 80 80.8 29R 80 49.1 8R 85 82.8 30L 75 43.4 9L 80 60.9 30R 80 43.1 9R 70 59.3 31L 80 57.5 10L 80 63.2 31R 70 57.7 10R 80 62.9 32L 70 41.5 11L 75 27.6 32R 65 41.3 11R 80 27.9 33L 75 59.3 12L 80 44.4 33R 75 59.6 12R 75 44.4 34L 75 45.4 13L 80 59.4 34R 75 45.6 13R 75 59.8 35L 75 57.9 14L 65 49.3 35R 70 58.2 14R 70 49.7 36L 75 78.4 15L 70 50.3 36R 80 79.3 15R 80 50.3 37L 80 59.6 16L 75 86.6 37R 80 59.5 16R 80 87.7 38L 75 42.3 17L 85 48.6 38R 75 42.5 17R 85 47.7 39L 85 55.3 18L 75 97.7 39R 85 56 18R 85 98.8 40L 70 73.1
141 142
Shell# Angle ° Length Shell# Angle ° Length 19L 85 85.3 40R 75 72.7 19R 85 87.1 41L 70 31.7 20L 80 73.7 41R 65 31.7 20R 80 74.2 42L 75 50.1 21L 80 62.2 42R 75 49.7 21R 80 63.4 43L 75 52.3 22L 75 39.9 43R 70 52.5
143
Shell# Angle ° Length 44L 70 61.8 44R 75 61.4 45L 80 65.8 45R 85 65.9 46L 80 39.2 46R 75 39.5 47L 80 49.1 47R 75 49.7 48L 65 57.7 48R 65 57.2 49L 80 57.1 49R 80 57 50L 85 45.4 50R 85 45.6 51L 75 47.1 51R 70 47.7 52L 75 49.1 52R 75 49.2 53L 60 44.4 53R 60 44.5 54L 80 47.4 54R 75 47.4 55L 80 54.2 55R 70 55 56L 70 51.6 56R 70 52.1 57L 60 38.9 57R 60 38.8 58L 70 51.2 58R 65 51.8 59L 85 70.5 59R 95 70.9 60L 90 73.6 60R 85 73.6 61L 85 52.6 61R 90 53 62L 90 50.9 62R 75 50.7 63L 75 58.5 63R 80 58.5 64L 85 52.6 64R 85 53 65L 70 85.7 65R 70 85.5