: An assessment of economic intensification at the Yindayin rockshelter, Stanley Island
Martin Wright revised October 2018
Title page: Yindayin rockshelter, Stanley Island (left) facing southwest
(Photograph: Westaway, 2016)
NOTE: This thesis was updated to correct formatting issues and to adjust volume correction calculations for spits 11 and 12. These corrections have been approved by the Honours
Coordinator.
Martin Wright, 8 October 2018.
[i]
TABLE OF CONTENTS
List of Figures ...... v List of Tables ...... vii List of Appendices Tables ...... viii Acknowledgements ...... ix Abstract ...... xi Chapter 1 Thesis Overview ...... 13 1.1 Introduction and rationale for research ...... 13 1.2 Research question and aims ...... 18 1.3 Research design and thesis structure ...... 18 Chapter 2 Theory and identification of economic intensification ...... 21 2.1 What is economic intensification? ...... 21 2.2 Specialisation, diversification, and investment ...... 23 2.2.1 Archaeological correlates for specialisation, diversification, and investment ...... 25 2.3 Identifying diversification through Optimal Foraging Theory...... 27 2.3.1 The prey choice model ...... 27 2.3.2 The patch choice model ...... 29 2.4 Explaining economic intensification ...... 30 2.5 Criteria for economic intensification ...... 31 2.6 Summary ...... 31 Chapter 3 Background ...... 33 3.1 The Flinders Group ...... 33 3.2 Stanley Island and the Yindayin (Endaen) rockshelter ...... 33 3.3 Beaton’s Princess Charlotte Bay fieldwork ...... 36 3.4 Fieldwork conducted August 2016 ...... 39 3.4.1 Site integrity ...... 43 3.5 Trends in Holocene occupation of the Great Barrier Reef (GBR) ...... 43 3.5.1 Chronology ...... 45 3.5.2 Occupation data ...... 46 3.5.3 Explaining the initiation and fluctuation in island occupation ...... 47 3.5.4 Discussion ...... 50 3.6 Chapter Summary ...... 52 Chapter 4 Materials and Methods ...... 53 4.1 Introduction ...... 53 4.1.1 Preparation of Materials ...... 53 [ii]
4.1.2 Identification of faunal material ...... 54 4.1.3 Invertebrate Quantification ...... 54 4.1.4 Radiocarbon dating ...... 59 4.1.5 Sampling of the 3mm material ...... 59 4.2 Analytical methods ...... 60 4.2.1 Accumulation and Deposition Rates ...... 61 4.2.2 Fragmentation Rates ...... 63 4.2.3 Diversity Indices ...... 63 4.2.4 Patch Preferences ...... 67 4.3 Environment and Climate data ...... 69 4.3.1 Sea level variation ...... 69 4.3.2 Effective Precipitation and Temperature ...... 70 4.3.3 Fluctuations in the El-Nino Southern Oscillation (ENSO) ...... 70 4.3.4 Reef formation ...... 71 4.3.5 Extreme cyclonic events...... 71 4.4 Summary ...... 72 Chapter 5 Results ...... 73 5.1 Introduction ...... 73 5.2 Site occupation data ...... 73 5.2.1 Radiocarbon dating ...... 73 5.2.2 Accumulation rates ...... 75 5.2.3 Deposition rates ...... 77 5.2.4 Summary of site occupation trends ...... 85 5.3 Invertebrate quantification data...... 87 5.3.1 MNI count ...... 89 5.3.2 NISP count ...... 93 5.3.3 Weight ...... 97 5.3.4 Fragmentation rates ...... 101 5.3.5 Common, non-dominant taxa ...... 104 5.3.6 Summary of quantification trends ...... 112 5.4 Diversity Indices ...... 113 5.4.1 NTAXA - Richness ...... 113 5.4.2 Diversity—Simpson 1-D and Shannon H’ ...... 115 5.4.3 Summary of indices ...... 116 5.5 Patch preferences ...... 118 5.5.1 Phase I ...... 122
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5.5.2 Phase II ...... 122 5.5.3 Phase III ...... 123 5.5.4 Meat weight ...... 124 5.5.5 Changing preferences and economic intensification ...... 125 5.6 Environment and climate data ...... 127 5.6.1 Sea level variation ...... 129 5.6.2 Reef formation ...... 129 5.6.3 Effective precipitation and temperature ...... 130 5.6.4 Fluctuations in the El-Nino Southern Oscillation (ENSO) ...... 131 5.6.5 Extreme cyclonic events...... 131 5.6.6 Summary of environment and climate data ...... 133 5.7 Other cultural materials ...... 133 5.8 Summary ...... 134 Chapter 6 Discussion and Conclusions ...... 135 6.1 Introduction ...... 135 6.2 Testing the economic intensification criteria ...... 135 6.2.1 Assessment of economic intensification ...... 142 6.3 A model for change at Yindayin ...... 143 6.3.1 Phase I: ca. 6,286 cal BP to 3,000 BP ...... 145 6.3.2 Phase II: ca. 2,938 cal BP to ca. 2,073 cal BP ...... 145 6.3.3 Phase III: ca. 2000 BP to present ...... 149 6.4 Future directions ...... 151 6.5 Conclusions ...... 153 Chapter 7 References ...... 157 APPENDIX A Abundance data ...... 166 APPENDIX B Nestedness ...... 183 APPENDIX C 3mm Data ...... 185 APPENDIX D Patch Preference Data ...... 188
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LIST OF FIGURES
Figure 2.1: Production function curves describing specialisation, diversification, and investment ...... 24
Figure 3.1: Map of Flinders Group (Qld Department of Natural Resources, Mines, and Energy, 2018), inset map
of Australia (Google Maps, 2018) ...... 34
Figure 3.2: Topographic map of Stanley Island—Yindayin rockshelter (Qld Department of Natural Resources,
Mines and Energy, 2018) ...... 35
Figure 3.3: Beaton’s Yindayin floor plan with Griffith University 2016 excavation (adapted from Beaton, 1983b:
Figure 2)...... 40
Figure 3.4: Hale and Tindale's map of Yindayin (1933: Figure 162)...... 40
Figure 3.5: View across the front of Yindayin rockshelter, view facing East (Westaway, 2016) ...... 41
Figure 3.6: Yindayin excavation, view facing East (Westaway, 2016) ...... 41
Figure 3.7: Yindayin section facing west (source: Westaway, 2016) ...... 42
Figure 3.8: Great Barrier Reef - highlighted study areas (adapted from Great Barrier Reef Marine Park
Authority, 2004) ...... 44
Figure 5.1: Median and 2σ calibrated age Ranges for Yindayin ...... 74
Figure 5.2: Age-Depth Curve and interpretation ...... 76
Figure 5.3: Volume corrected deposition rates for stone, shell, charcoal, organic material, bone, and
coral/pumice ...... 79
Figure 5.4: Charcoal - Volume corrected weights by spit ...... 80
Figure 5.5: Stone - Volume corrected weights by spit ...... 81
Figure 5.6: Bone - Volume corrected weights by spit ...... 82
Figure 5.7: Shell - Volume corrected weights by spit ...... 83
Figure 5.8: Organic, Coral/Pumice—Volume corrected weights by spit ...... 84
Figure 5.9: MNI and NISP count by spit ...... 88
Figure 5.10: Volume corrected MNI and NISP by spit ...... 88
Figure 5.11: MNI and volume corrected MNI count by spit ...... 89
Figure 5.12: NISP and volume corrected NISP count by spit ...... 93
Figure 5.13: Raw and volume corrected weight by spit ...... 97
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Figure 5.14: Fragmentation rates (NISP:MNI) for all taxa ...... 101
Figure 5.15: Volume corrected NISP for Neritidae and Potamididae families ...... 102
Figure 5.16: Comparison of Neritidae and Potamididae fragmentation rates (note the different vertical scales
for each taxon) ...... 103
Figure 5.17: Fragmentation rates (NISP:MNI) for all taxa excluding Potamididae ...... 104
Figure 5.18: Volume corrected MNI for key Intertidal sand/mud species ...... 107
Figure 5.19: Volume corrected MNI for N. planospira relative to other prominent mangrove species ...... 109
Figure 5.20: Volume corrected MNI for N. planospira relative to other prominent Nerita species ...... 109
Figure 5.21: Volume corrected MNI for reef platform species ...... 110
Figure 5.22: NTAXA for taxa aggregated to family level or higher - NISP ...... 114
Figure 5.23: Species accumulation curve for groups aggregated to family or higher - NISP ...... 115
Figure 5.24: Diversity - Simpson 1-D Index vs Shannon H’ ...... 116
Figure 5.25: Volume corrected MNI for high and moderate abundance patches ...... 119
Figure 5.26: Volume corrected MNI for low abundance patches ...... 120
Figure 5.27: Percentage of MNI by patch ...... 121
Figure 5.28: Proportional contribution to meat weight by selected patches ...... 125
Figure 5.29: Comparison of dating between extreme cyclonic events and dating of Yindayin occupation (spit
numbers in callout boxes) ...... 132
[vi]
LIST OF TABLES
Table 3.1: Dating of select GBR sites established during the Holocene ...... 45
Table 4.1: Description of Patches for Yindayin Taxa ...... 68
Table 5.1: Radiocarbon dates and 2σ calibrated ages for Spits 1-11 from Yindayin rockshelter Midden ...... 74
Table 5.2: Accumulation rates ...... 76
Table 5.3: Weights for all material recovered from 5mm mesh...... 77
Table 5.4: Volume corrected (g/cm3) weights for all material recovered from 5mm mesh ...... 78
Table 5.5: Summary of raw and volume corrected (vc) quantification data for identified shell by spit ...... 87
Table 5.6: MNI count—Top 10 taxa aggregated to the lowest taxonomic level ...... 91
Table 5.7: MNI count—Top 10 taxa aggregated to family level or higher ...... 92
Table 5.8: NISP count—Top 10 taxa aggregated to the lowest taxonomic level ...... 95
Table 5.9: NISP count—Top 10 taxa aggregated to family level or higher ...... 96
Table 5.10: Weight—Top 10 taxa aggregated to the lowest possible taxonomic level ...... 99
Table 5.11: Weight—Top 10 taxa aggregated to family level or higher ...... 100
Table 5.12: MNI and volume corrected MNI for common, non-dominant taxa (high and low abundances have
been highlighted in blue and yellow respectively) ...... 106
Table 5.13: Diversity Indices—Family level or higher ...... 113
Table 5.14: Percentage of MNI by patch and spit (dominant patches highlighted in yellow) ...... 118
Table 5.15: Total meat weight (g) for selected patches ...... 124
Table 5.16: Comparison of climate/environmental variation with occupation at Yindayin (grey/orange =
amelioration/fluctuation or increasing instability) ...... 128
Table 6.1: Comparison of data with dates from Yindayin (yellow/blue shading = below/above average,
grey/orange = amelioration/fluctuation or increasing instability) ...... 144
[vii]
LIST OF APPENDICES TABLES
Table A-1: Proportion of shell from 5mm identified and indeterminant ...... 166
Table A-2: Spit weight (g) for invertebrates by lowest taxonomical level ...... 167
Table A-3: Spit weight (g) for invertebrates aggregated to family level or higher ...... 169
Table A-4: MNI of lowest taxonomic level ...... 170
Table A-5: MNI aggregated to family or higher ...... 172
Table A-6: NISP of lowest taxonomic level ...... 173
Table A-7: NISP aggregated to family or higher ...... 175
Table A-8: Volume Corrected MNI—Top 10 taxa aggregated to the lowest possible taxonomic level ...... 176
Table A-9: Volume Corrected NISP—Top 10 taxa aggregated to the lowest possible taxonomic level ...... 178
Table A-10: Volume Corrected Weight—Top 10 taxa aggregated to the lowest possible taxonomic level ...... 179
Table A-11: Foraging patch percentages—Volume corrected MNI ...... 180
Table A-12: Foraging patch percentages—Volume Corrected NISP ...... 181
Table A-13: Meat weight values for selected taxa ...... 182
Table B-1: Nestedness Measures ...... 183
Table B-2: Nestedness matrix for taxa identified to the lowest taxonomic level ...... 183
Table C-1: Comparative Data for 3mm and 5mm samples from spits 2 and 6 ...... 185
Table C-2: Rank comparison between taxa in 3mm and 5mm mesh from spits 2 and 6 ...... 185
Table C-3: Species/Family ranking 3mm vs 5mm - Spit 2 ...... 186
Table C-4: Species/Family ranking 3mm vs 5mm - Spit 6 ...... 187
Table D-1: Patch preferences for individual taxa ...... 188
[viii]
ACKNOWLEDGEMENTS
I would like to acknowledge my gratitude to the Cape Melville, Flinders and Howick Islands
Aboriginal Corporation for allowing myself and others to conduct research in their beautiful country. In particular, I’d like to thank Clarence Flinders and Danny Gordon for sharing their knowledge and guiding our group around the islands of the Flinders Group. This study is focused on malacology and I must give thanks to my supervisor, Pat Faulkner, for introducing me to the study of shells, for his guidance in the writing of this thesis, and his mentorship in developing me as an archaeologist. I must also give thanks to Katie Woo who has been so generous with her knowledge of malacology. Michael Westaway also deserves thanks for giving me the opportunity to work with the Yindayin assemblage and for his generosity with resources, time, and encouragement. Michael also facilitated my participation in field surveys of the Flinders Group in 2017 and additional thanks should be given to the members of that group, particularly Shaun Adams, Toni Massey, Lee Hess, and
Clayton Enoch. Without chronological context, this thesis would be difficult to comprehend so thanks also to Stewart Fallon from the ANU for his expertise and thanks again to Michael
Westaway for funding that work. I have had many people help me in the lab, namely, Lilly,
Paolo, Max, Sarah, Tailei, Caitie, Connor, Ebony, and Mitchell. Thank you for your help and I hope you gained a little knowledge out of the experience that will help with your future archaeological efforts. Also, I must thank Ina Kehrberg-Ostrasz for her words of encouragement and help around the lab.
Without the support of my friends and family, this thesis would have been impossible to write. Special thanks to Miranda, Alex, and Bek for numerous cups of coffee and conversation in the lab, to Meg for being my shell-buddy over the last couple of years, and
[ix] to Max for four years of archaeological discussion. Finally, the biggest thanks goes to my wife Katie – thank you for supporting me in my archaeological dreams (and helping in the lab)!
Martin Wright
2 June 2018
[x]
ABSTRACT
Economic intensification is a prominent concept in hunter-gatherer literature, being used to explain increasing hunter-gatherer complexity and the transition to domestication and permanent settlement. This study used invertebrate material from the Yindayin rockshelter to evaluate whether population driven economic intensification was present during the
Holocene. Environmental and climate data was also assessed to evaluate its impact on the observed subsistence patterns. An explanatory model describing the occupation at Yindayin was produced that incorporated the results of the economic intensification assessment, the environmental and climatic data, and data from Beaton’s original analysis of Princess
Charlotte Bay.
This study did not find a unidirectional increase in occupation during the Holocene. Instead, the results demonstrated that subsistence and occupation patterns at the site were complex and non-linear with periods of increased intensity interspersed with periods of stability and abandonment. Environmental and climate change had the most visible effect on subsistence behaviours while the potential for population induced economic intensification was only identified within the last 200 years of occupation.
The results emphasised that interactions between population, environment, and climate are complex, and that to presume there are singular explanations for variation in coastal occupation and subsistence is to deny this complexity. The study demonstrate how economic intensification can be deduced from archaeological correlates and how population driven effects may be separated from environmental effects under certain circumstances. Finally, this study demonstrated how valuable invertebrate assemblages can
[xi] be for understanding the responses of coastal foragers to environmental and population driven resource pressure.
[xii]
Chapter 1 THESIS OVERVIEW
1.1 INTRODUCTION AND RATIONALE FOR RESEARCH
Economic intensification is a prevalent concept in international hunter-gatherer literature
(Atici, 2009; Beaton, 1991; Betts and Friesen, 2004; Broughton, 1994; Jerardino, 2012;
Morgan, 2015; Stiner et al., 2000). Economic intensification refers to a sustained increase in the production of economic resources that is accompanied by a decrease in efficiency
(Butler and Campbell, 2004: 336). In its traditional form, it is caused by an increase in resource pressure that results from growing population or population density (Bouey, 1979:
163). Economic intensification is characterised by an increase in diversity, with smaller bodied but abundant prey being included in the diet, and decreased mobility as populations become tied to those resources (Codding and Bird, 2015: 13). By looking for signals of economic intensification, insight into hunter-gatherer population growth and densities can be derived.
The impacts of economic intensification have been noted for hunter-gatherer populations across the world (Morgan, 2015: 164). In the eastern Mediterranean during the
Epipalaeolithic, hunter-gatherers who had previously focused on large ungulates, diversified their foraging strategy to include lower yield small game and shifted to more sedentary modes of settlement (Atici, 2009: 1). Similarly, in California during the late Holocene, there is increased exploitation of shellfish, small fish, and acorns which was accompanied by increased settlement and population growth (Beaton, 1991: 948-949; Broughton, 1994:
372). Again, on the west coast of South Africa between 4,000–2,200 BP, there is an increase in reliance on both small terrestrial and marine prey coupled with reduced mobility
(Jerardino, 2010: 2298-2300).
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The problem for studies of economic intensification is that the decline in foraging efficiency that signals its presence can also be produced by other, often interrelated factors, such as environmental change and changing social dynamics (Morgan, 2015: 195). The challenge for archaeologists, therefore, is to separate the effects of population from other explanatory variables. One confounding issue is that the interpretation of economic intensification has become blurred, with some authors describing it as any increase in resource use rather than linking it to decreasing efficiency (Arnold et al., 2004: 15; Morgan, 2015: 165). This interpretation breaks the link between population and economic intensification and therefore it cannot be tested through reference to the effects of growing population on foraging behaviour. For this reason, a Boserupian approach is taken in this thesis where the mark of economic intensification is the combination of decreasing foraging efficiency and increasing resource abundance (see Chapter 2 for details).
This thesis uses midden material from the Yindayin rockshelter on Stanley Island as a case study to test for the presence of economic intensification. In doing so, this study will assess how occupation at Yindayin compares with observations of late-Holocene intensification within the Great Barrier Reef (GBR). Our knowledge of island occupation within the GBR is drawn from work conducted in the central and southern regions, namely, the Whitsunday
Islands (Barker, 2004), the Keppel Islands (Rowland, 1980; Rowland, 1985; Rowland, 1999),
Shoalwater Bay (McNiven et al., 2014), and the Northumberland group (Border, 1999). Sea levels around Australia began rising during the Pleistocene and are thought to have peaked in many areas around 7,000 BP (Hiscock, 2008: 22, Lewis et al., 2008: 79). The islands off the east coast of Queensland remained mostly uninhabited prior to 6,000 BP. Low level
[14] occupation was noted until the late Holocene with an intensification of activity beginning around 3,000 BP and peaking from 1,000 BP onwards (Hiscock, 2008: 162).
Multiple explanations have been offered for the late Holocene island intensification including changes in climate and the environment (Rowland, 1999; Rowland et al., 2015), increasing population sizes (Beaton, 1985), changing demographic structures (McNiven et al., 2014), and increasing complexity in social and exchange systems (Barker, 2004).The diversity of opinion shown here reflect the specific behavioural, environmental, and ecosystem variables for the region and the theoretical and methodological position taken by individual researchers. For this reason, it is necessary to undertake specific research on the patterns of occupation and economic structures for a given location rather than to presume that generalisations of behaviour developed elsewhere will apply.
The northern section of the GBR has not been investigated to the same extent as the central and southern section, with most of the work being conducted by Beaton (1985) within
Princess Charlotte Bay (although see Lentfer et al., 2013: and ; Mills, 1992: for work on
Lizard Island). This thesis will add to our knowledge of the northern section of the GBR through an evaluation of the effects of population growth and climate/environmental change on intertidal foraging at the Yindayin (Endaen) rockshelter on Stanley Island.
Population growth and climate/environmental change are often intertwined, with populations capable of growth or decline in response to environmental conditions, and growing populations capable of exerting pressure on local environments. The close connection between these factors makes it difficult to discern whether there is a primary influence on hunter-gatherer behaviour at any point in time (Morgan, 2015: 168). Data on
Holocene environmental and climate conditions are readily available for north Queensland
[15]
(see Chapter 5 section 5.6 Environment and Climate data). Conversely, while attempts have been made to model continent wide population trajectories, their low resolution can tell us little about local populations (for examples, see Beaton, 1985; Williams, 2013; and for a critique see also Attenbrow and Hiscock, 2015).
The challenge for this research is to identify a model that can isolate the effects of local population growth from the influence of environmental and climate change. To that effect, the Boserupian model of economic intensification has been used in conjunction with
Optimal Foraging Theory (OFT) to highlight how foraging efficiency decreases in the face of population driven resource stress. Both population growth and environmental deterioration can produce resource stress that may impact foraging behaviour. While both scenarios should produce an increased diversity of taxa being incorporated into the diet, environmental depression should produce patterns of decreasing abundance, while population growth should produce increasing abundance (sensu Stiner and Munro, 2002:
196). This difference provides a method for differentiating between the two scenarios— zooarchaeological data from Yindayin will be evaluated to see if this signal is discernible (see
Chapter 2 for more information).
Zooarchaeological data allow us to examine the connection between subsistence, environment, and the demography of hunter-gatherers (Reitz and Wing, 2008: 1). Standard zooarchaeological methods can produce descriptive data from which trends and anomalies in the behaviour of foragers can be discerned (Lyman, 2008: 172). This thesis focuses on data generated from invertebrate material excavated from the Yindayin rockshelter in 2016.
In the past, marine invertebrate taxa and coastal economic structures have been interpreted as inferior to terrestrial resources, however, in more recent times their
[16] importance to hunter-gatherer populations has been acknowledged (Braje and Erlandson,
2009: 270; Harris and Weisler, 2018; Thomas, 2015; also see Barker, 2004: 6 for a more critical assessment of the importance of invertebrate resources). The ubiquity of shellfish in coastal sites across the GBR, suggests they were important resources for coastal foragers and therefore, changes in their composition over time can provide valuable insight into how people reacted to climate or human driven change in the environment (sensu Codding et al.,
2014b: 145-146). Regardless of their economic role, invertebrates provide critical information on the dietary structure in addition to creating links with past environmental and habitat structures.
Yindayin rockshelter is one of three rockshelters that formed part of Beaton’s (1985) influential study of Princess Charlotte Bay, however, the specifics of the invertebrate material used to develop his observations of littoral foraging were not included in his published material. This current study provides an opportunity to systematically examine the invertebrate material from Yindayin rockshelter, and by doing so provide a more detailed understanding of the nature of subsistence on Stanley Island.
Finally, in addition to adding to our understanding of occupation at Yindayin, the findings from this project will provide new insights into patterns of coastal subsistence in the mid to late-Holocene. In particular, this study will provide insight into whether there was one phase of economic intensification as has been previously suggested, or whether the trajectory of change was complex and multidirectional.
[17]
1.2 RESEARCH QUESTION AND AIMS
This thesis will address the following research question:
How have trends in intertidal foraging at the Yindayin rockshelter been influenced by:
Economic intensification driven by population growth, and/or Environmental and climate change.
Stemming from this overarching question, the broad aim of this research is to develop an understanding of intertidal subsistence behaviour at Yindayin through the analysis of invertebrate material. This understanding will allow us to determine whether environmental/climate change and/or population growth can explain trends in subsistence and occupation at Yindayin. This research aims demonstrate how an economic intensification framework can be used to detect its presence within an invertebrate assemblage and as a result, to demonstrate the value of archaeomalacology in assessing subsistence behaviour in coastal societies. Finally, this work aims to determine whether occupation at Yindayin exhibits similar patterns of occupation and intensification as that witnessed elsewhere in the GBR.
1.3 RESEARCH DESIGN AND THESIS STRUCTURE
The zooarchaeological and occupational data generated from the Yindayin assemblage and the environmental and climate data collated for north Queensland will be used to address the research question. Economic intensification theory and OFT are used to develop a set of criteria to evaluate the zooarchaeological data for the presence of population driven economic intensification. Trends in intertidal foraging behaviour are also compared to the available environmental and climate data to evaluate their strength of association. Beaton’s
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(1985) work in Princess Charlotte Bay will also be considered to assess if changes on the mainland align with subsistence and occupation trends at Yindayin.
Chapter 2 begins by defining economic intensification, its relationship with population growth, and the Optimal Foraging Theory models that underly it. A framework is presented that describes the effects of economic intensification on foraging behaviour and criteria for detecting its presence is identified.
Chapter 3 provides background on the Yindayin case study, including site location and excavation data, as well as a review of the work conducted by Beaton (1985) in Princess
Charlotte Bay. The chapter concludes with a review of the chronology and explanations given for the late-Holocene intensification in the GBR.
Chapter 4 details the methods used to process the material from Yindayin, the process for collecting the primary data, and the methods and theoretical basis used to analyse that data.
Chapter 5 describes the results of the study, detailing the site occupation data, invertebrate quantification data, and the collated environmental and climate data.
Finally, chapter 6 contains a discussion of the results, beginning with an evaluation of the
Yindayin assemblage against the economic intensification criteria set out in Chapter 2. This is followed by a synthesis of the economic intensification results, the environmental and climate data, and Beaton’s chronology of Princess Charlotte Bay, to produce a model of occupation at Yindayin. The future directions for research in the region are then presented, followed by conclusions about this research and how they relate to broader questions in coastal archaeology.
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A set of appendices have been included to supplement this work. Detailed quantification data is provided in APPENDIX A. APPENDIX B details the nestedness data generated to test the representativeness of the Yindayin material. APPENDIX C sets out the quantification, diversity, and ranking data for the 3mm sample material relative to the 5mm material for corresponding spits. Finally, APPENDIX D provides the patch preference data for each taxon that was used in the analysis of patch preference.
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Chapter 2 THEORY AND IDENTIFICATION OF ECONOMIC INTENSIFICATION
The identification of economic intensification is central to the research question addressed in this study. This chapter will define economic intensification and its relationship with population growth. A framework will be developed that can describe the effects of economic intensification on foraging behaviour and archaeological correlates will be identified that can be used as criteria for detecting its presence.
2.1 WHAT IS ECONOMIC INTENSIFICATION?
For the purposes of this study, economic intensification is defined as:
a sustained increase in the production/procurement of economic resources that is
accompanied by a decrease in foraging efficiency (Butler and Campbell, 2004: 336).
This definition entails an increase in total economic production but a decrease in productivity per capita. This interpretation is derived from Esther Boserup’s work on the effects of increasing agricultural production in response to an increase in population size/density (Boserup cited in Beaton, 1991: 951). Boserup argued that, as the most productive agricultural resources became fully utilised, any additional input of labour or materials would result in decreased productivity, as the increased effort could only be added to less productive resources (Beaton, 1991: 951). Boserup’s interpretation assumes that rising population is at the root of increased demand for subsistence. This is an important idea for this study—if other factors remain stable (e.g. distribution and density of resources, environmental and climate) then this pattern of increased abundance and decreased foraging efficiency provides a method to test for population driven resource pressure.
[21]
Boserup’s model has been adapted by archaeologists studying the transition from hunter- gatherer to agriculturally based societies, and to describe complex hunter gatherers who displayed characteristics usually associated with agriculturalists (Morgan, 2015: 166-167).
The value of this model was that it could explain why marginal areas and marginal resources were exploited, and what motivated foragers to develop and adopt agricultural innovations
(Morgan, 2015: 166). In transposing these ideas to hunter-gatherers or small-scale societies, agricultural resources were exchanged for wild animal and plant resources, however the central concept remains the same—increased exploitation will eventually lead to decreased productivity unless an innovation in organisation, technique, or technology is developed that increases yields (Earle, 1980: 19-21; Thakar, 2011: 2597). Some authors conflate economic intensification with any change that results in the increased use of a resource
(Arnold et al., 2004: 15; Morgan, 2015: 165). These types of observations are not economic intensification in the Boserupian sense—they describe an increase in resource production but not necessarily an increase in foraging effort (Arnold et al., 2004: 15). It is by identifying increasing production in conjunction with decreasing productivity that population driven resource pressure may be detected.
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2.2 SPECIALISATION, DIVERSIFICATION, AND INVESTMENT
Evaluating foraging efficiency in an archaeological setting is difficult as direct observation is not possible. Instead we must develop frameworks and proxies that can be operationalised to detect trends in subsistence activities (Morrison, 1994: 136; Codding and Bird, 2015).
Kaiser and Voytek (1983: 329) argue that the effects of economic intensification (i.e. increased yields) can be achieved by way of specialisation, diversification, and investment.
Betts and Friesen (2004: 358-359) have employed Kaiser and Voytek’s components as a framework with which to search for economic intensification. They define the components as follows:
specialisation refers to the procurement of a narrow group of taxa and an associated increase in the quantities of those taxa; diversification refers to an increasing reliance on a broad range of taxa; and investment refers to the input of labour into technology, techniques, or labour organisation that improve productivity (Betts and Friesen, 2004: 358-359).
These definitions have been adopted for this study as they are useful for describing the choices available to foragers to increase production. Specialisation is a coherent strategy when people are trying to maximise their return on effort and there are sufficient taxa to support this approach. As resource pressures are encountered and the abundance of the most productive resources can no longer supply demand, a diversification of diet is likely unless there is an investment in technology, foraging techniques, or labour organisation
(Kelly, 2013: 48-50).
[23]
The interactions of these components can be described using production functions.
Figure 2.1: Production function curves describing specialisation, diversification, and investment
Figure 2.1 denotes two production functions (curves i and ii) that describe the relationship between inputs and outputs for a hypothetical environment. The x-axis plots the number of units of input (labour and materials) and the y-axis plots output (yield). Curve i plots a situation where the most productive resources are initially exploited (specialisation) but are exhausted after two units of input (x1). To raise production after this point, foragers must either diversify their diet to include less productive taxa (moving to the right on curve i to y1) or increase yields through investment to increase unit productivity (x2 on curve ii).
These strategies are not mutually exclusive however interpretations of diet-breadth tend to treat specialisation and diversification as opposites (Atici, 2009: 2). Earle (1980: 20) demonstrates that specialisation and diversification can occur together but once specialisation and investment options are exhausted, only diversification will meet demand.
In the face of increased demand for resources, specialisation and diversification will occur
[24] sequentially—it is at this point where Boserupian economic intensification occurs and therefore this sequence may provide a signal that can be traced in the archaeological record.
2.2.1 ARCHAEOLOGICAL CORRELATES FOR SPECIALISATION, DIVERSIFICATION, AND INVESTMENT
The identification of the specialisation—diversification signal can be conducted through a diachronic analysis of zooarchaeological quantitative data (Butler and Campbell, 2004;
Munro and Atici, 2009; Thakar, 2011: 2597). Ecological measures of richness and diversity can be applied to zooarchaeological data to test for specialisation and diversification (Betts and Friesen, 2004: 370-371; Magurran, 2004: 64). The methods used in this study to derive richness and diversity are outlined in Chapter 4 (section 4.2.3). At its most basic level, specialisation becomes visible when there is an increasing trend in the procurement of a limited range of species (Betts and Friesen, 2004: 371). Diversification implies a broadening of the number of taxa incorporated into the diet, however, its presence does not automatically imply economic intensification—a diversified diet may also be indicative of foragers buffering themselves from risk of failing food stocks due to unpredictable foraging conditions (Binford, 2001: 402). For Boserupian economic intensity to be present, diversity should be accompanied by increasing diachronic abundance following a period of specialisation.
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In the absence of investment or extensification1, economic intensification should produce longer periods of site occupation, as specialisation will tie foragers to resource patches (Betts and Friesen, 2004: 358-359; Kaiser and Voytek, 1983: 326). If foragers are bound to locations, measures such as accumulation and deposition rates may be used to assess the extent to which this has occurred.
Investment tells a different story to that of specialisation or diversification. The concept of investment is complex due to the question of whether investment (innovation) begets population growth, or whether it is a response to the resource stress (Morrison, 1994: 135).
Investment can increase diversity by allowing access to new prey or decrease diversity by allowing increased specialisation. In this study, it is not expected that investment will play a large role in the harvesting of invertebrates. Molluscan resources are often simple to collect and process and there is little need for technological investment to improve yields or to improve processing costs (Bourke, 2015: 10; Thomas, 2001: 87). Whitaker and Byrd (2012:
208) have identified that foraging efficiency can be increased if boats are used as platforms allow divers to access invertebrates in deeper waters. This may be appropriate where boat use is an innovation, however, as Yindayin is an island, boats would already have been in use. Another potential investment would be to dry shellfish for later consumption, however this would not require much by way of technology and is unlikely to be discernible from regular consumption (Henshilwood et al., 1994: 107). Investment can also refer to changes
1 Extensification is the opposite of intensification. It refers to the option of expanding production in a new location rather than continuing to expend resources in the current location (Boserup cited in Beaton, 1991: 951). [26] in foraging techniques such as mass harvesting. Identification of mass harvesting may be detectable at Yindayin through an examination of species behaviour and evidence of increased abundance (Whitaker, 2008: 1116).
2.3 IDENTIFYING DIVERSIFICATION THROUGH OPTIMAL FORAGING THEORY
This study, while not undertaking a formalised optimal foraging theory (OFT) analysis, makes use of the underlying principles of the OFT prey choice and patch-choice models. OFT is predicated on the Darwinian concept of natural selection. It is inferred that natural selection pressures will force foragers to seek optimal behaviours in terms of energy maximisation within the physical and social constraints of their environment (Codding and Bird, 2015: 9-
10). In simpler terms, this means that foragers want to maximise the amount of food they can obtain from any given environment, relative to the time and effort they put in to securing it (Kelly, 2013: 47). If optimisation pressure is present, predictable patterns of behaviour will occur and signatures for its presence may be found in the archaeological record (Codding and Bird, 2015: 9; Nagaoka, 2002b: 98). This idea of optimality is inherent to the adoption of specialisation and the transition to diversification. The following sections describe how the prey choice and patch choice models function, what behaviour they predict, and how this relates to ideas of specialisation and diversification.
2.3.1 THE PREY CHOICE MODEL
The prey choice model addresses how foragers decide which prey types to pursue within a given patch of resources. The decision to pursue or collect prey is based on maximising the relationship between energy expended to obtain (search) and process (handling) prey, relative to the energy derived from consuming that prey (Thomas, 2007: 180). Those prey types that provide the greatest net return on investment (energy/search and handling time)
[27] will be ranked the highest and pursued whenever encountered (Nagaoka, 2000: 423).
Decisions to pursue certain prey types are based on the opportunity cost of pursuing that prey relative to other prey types (Smith et al., 1983: 626). Prey will only be included in the diet once its net energy return rate becomes higher than the average return rates for higher ranked prey (Kelly, 2013: 48).
In periods of low resource stress, the prey choice model predicts that assemblages would be dominated by high ranked prey, in other words, foragers will tend towards specialisation.
Conversely, in periods of high resource stress, the prey choice model predicts a diversification of prey types within the assemblage (Jones, 2004: 315-316; Nagaoka, 2002b:
89; Smith et al., 1983: 628). In archaeological assemblages, prey rankings often use size as a proxy measure, the premise being that larger bodied taxa provide a higher calorific return rate (Broughton, 1994: 374-375; see Hockett and Haws, 2009: for an alternative approach).
The use of size to rank prey often leads to small taxa like invertebrates being interpreted as low ranked and their consumption a signal for resource stress (Braje and Erlandson, 2009:
270). The characteristics of small taxa should be considered before assigning ranks as some small invertebrates have the potential to produce high meat yields when mass harvested
(Braje and Erlandson, 2009: 282). In addition to body size, consideration of search time and handling costs affect how prey is ranked. Prey that is highly evasive or requires extensive processing before consumption incurs a higher energy cost and will be ranked lower than highly visible, less mobile, and easily consumed prey (Beaton, 1991: 949; Stiner et al., 2000:
49; Wolverton et al., 2015).
Economic intensification implies that an imbalance between the demand and supply of high ranked prey will be addressed through the addition of lesser ranked prey (diversification). In
[28] essence, this scenario mirrors that seen in the prey choice model during periods of high resource stress. In practical terms, this assessment requires both quantitative and qualitative analysis of assemblages. Assemblages where economic intensification is occurring should display the following characteristics:
increasing overall resource abundance, and decreasing prey package size, and/or increasing abundances of prey that are difficult to capture and/or process (Broughton cited in Thakar, 2011: 2597; also see Mannino and Thomas, 2002: 458 for additional criteria that can be applied to identify shellfish over-exploitation).
2.3.2 THE PATCH CHOICE MODEL
Patch choice is similar to prey choice but substitutes resource patches for individual prey.
Like the prey choice model, patch choice ranks patches according to their return on energy investment—once a patch is chosen, selection of prey conforms to the prey choice model
(Kelly, 2013: 62). As patches are exploited, resources can become depressed and therefore the return on energy investment also decreases. Patch choice employs the Marginal Value
Theorem (MVT) to identify when foragers should move from one patch to another.
According to the MVT, foragers will move out of a patch when the net return rate of that patch decreases below the average return rates for all other patches, provided that movement costs are not prohibitive (Kelly, 2013: 65; Nagaoka, 2002a: 422).
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Patch choice reveals economic intensification through the identification of increasing diversification of patch use. Increased patch switching is an indication of declining return rates and resource stress that can result from predation or environmental pressure. In addition to the points discussed in the previous section, assemblages where economic intensification is occurring should display the following patch-related characteristics:
a decrease in the dominance of previously productive patches, an increase in the evenness of patch contributions, and an increase in the use of more marginal or difficult to access patches.
2.4 EXPLAINING ECONOMIC INTENSIFICATION
While Boserupian economic intensification focuses on the decrease in foraging efficiency caused by population growth, variations in foraging efficiency can be created by other factors. Morgan (2015: 168) identifies that the Boserupian interpretation is
“vexed by the chicken-or-the-egg problem of whether environmental productivity
results in opportunities for intensification, or whether environmental perturbations or
increasing population densities (i.e., scarcity) leads to intensification processes.”
This study will test for population driven signature economic intensification through the identification of the standardisation—diversification signal. As Morgan (2015: 168) has suggested, environmental productivity and perturbations can also generate intensification of production, therefore the environmental conditions will also need to be assessed to see if they align with trends at Yindayin. Social change arguments may be appropriate if environmental, climate, or population-based arguments prove insufficient; however, they will need to be corroborated with data from other regional sites to assess the similarities and differences in groups and group identities that may be indicative of increasing
[30] complexity in social and economic system. A comprehensive assessment of the styles and chronology of rock art within the Flinders Islands and on the mainland may be one avenue of investigation (see Hale and Tindale, 1933-1934: for descriptions of rock art in the islands and at Bathurst Head).
2.5 CRITERIA FOR ECONOMIC INTENSIFICATION
The previous discussion has defined economic intensification and the archaeological correlates that can be used to detect its presence. Criteria for assessing economic intensification for this study have been derived from this discussion and are listed below:
EIC1 Evidence for increased intensity of site occupation and deposition of cultural material (site occupation).
EIC2 Evidence of increased quantities of subsistence material being deposited (deposition).
EIC3 Evidence for increased diversity in both taxa and patches being exploited (diversity).
EIC4 Evidence for a decline in foraging efficiency (foraging efficiency).
A final check must also be conducted to assess the prevalent environmental conditions during the period being assessed. If the criteria above are met during a period where the effects of climate and environment change are negligible, then population driven resource pressure may be in effect.
2.6 SUMMARY
This chapter has defined economic intensification, the standardisation—diversification signature, and the Optimal Foraging Theory that underlies it. Boserupian economic intensification is present if there is an increasing abundance and increasing diversity of
[31] subsistence material, a decrease in foraging efficiency, and evidence for increased site occupation. If these trends can be identified in archaeological assemblages, then they may be evidence for economic intensification, providing competing explanations such as environmental and climate change are not present.
The following chapter provides an overview of the site location and excavation data for the
Yindayin case study, and reviews Beaton’s (1985) work in Princess Charlotte Bay. The trends in Holocene GBR occupation are identified and the explanations provided for the late-
Holocene intensification are reviewed.
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Chapter 3 BACKGROUND
This chapter describes the study area and the excavation undertaken in 2016 to recover the material used in this research, and reviews the work undertaken by Beaton in Princess
Charlotte Bay. The chapter will conclude with a review of the trends in Holocene coastal and island archaeology in the GBR and will highlight the arguments used to explain the late-
Holocene intensification observed in this area.
3.1 THE FLINDERS GROUP
The Flinders Group is a set of islands located in Princess Charlotte Bay in far north
Queensland (Figure 3.1). The group consists of Flinders, Stanley, Denham, Blackwood, King,
Clack, and MacLear Islands. This group is the northern edge of the plateau formed by
Bathurst Head and Cape Melville that is bisected by the Coral Sea (Hale and Tindale, 1933-
1934: 64). Archaeological and ethnographic research in the area has been dominated by the work of Hale and Tindale in the 1930s, with Sutton and Beaton undertaking further investigations in last forty years (Beaton, 1981; Beaton, 1983b; Beaton, 1985; Hale and
Tindale, 1933-1934; Sutton, 2016).
3.2 STANLEY ISLAND AND THE YINDAYIN (ENDAEN) ROCKSHELTER
Stanley Island is the second largest island in the Flinders Group and is situated approximately 9 km from the mainland. It is roughly triangular in shape and measures 6km from its apex to base and 4km across its base. The Yindayin rockshelter is situated in the north of the Island and is currently 30–40 metres above sea level (Figure 3.2). Previous publications have referred to this rockshelter as Endaen—this thesis uses its traditional name, Yindayin. The rockshelter is a long, wide overhang of sandstone that faces to the
[33]
Figure 3.1: Map of Flinders Group (Qld Department of Natural Resources, Mines, and Energy, 2018), inset map of Australia (Google Maps, 2018)
34
Legend Yindayin rockshelter
Contour lines—10 metre increments with darker lines indicating 50 metre increments.
Mangrove
Reef
Figure 3.2: Topographic map of Stanley Island—Yindayin rockshelter (Qld Department of Natural Resources, Mines and Energy, 2018)
[35] north with several large boulders enclosing the front of the shelter—see Beaton’s and Hale and Tindale’s maps (Figure 3.3 and Figure 3.4).
Yindayin is surrounded by intertidal habitats with mangroves to the north, reef flats to the north and north-west, and a long white sandy beach to the northwest. All these habitats are within a kilometre radius of the shelter. Hale and Tindale (1933-1934: 125) noted that
Yindayin was the principal camp of the Walmbaria tribe “within recent times”.
3.3 BEATON’S PRINCESS CHARLOTTE BAY FIELDWORK
In 1979/80 John Beaton conducted fieldwork in Princess Charlotte Bay with the goal of describing and explaining its archaeological record (Beaton, 1981: 1; Sutton, 2016: 87). This work included surveying and excavating shell mounds and rockshelters within the bay and in the Flinders Island Group. The oldest site identified was Walaemini rockshelter, the base of which was radiocarbon dated to 5,556 cal BP2 (Beaton, 1985: 7). Beaton excavated midden material from this rockshelter, noting a stratigraphic break just after initial occupation as evidence for a hiatus—no other hiatuses were noted nor was there occupation prior to the marine transgression (Beaton, 1981: 12). Twenty kilometres inland, shellmidden material in
Alkaline Hill rockshelter produced a basal date of 3846 cal BP3. The earliest faunal remains recovered were from the intertidal zone, indicating that occupation began after sea level
2 A radiocarbon date of 4760±90 is listed in Beaton (1985: 7), however all figures in that paper included a correction for what Beaton refers to as the environmental reservoir effect. The uncalibrated date of 5210±80 cited by Williams and Ulm (2014) has been calibrated using OxCal 4.3, Marine13.14c curve (Reimer PJ, Bard E, Bayliss A, et al., 2013) and ∆R values of 12±10 (Ulm, 2006). 3 As above, 3,440±80 was recorded in Beaton (1985: 7), 3,890±80 in Williams and Ulm (2014). It was re- calibrated using OxCal 4.3, Marine13.14c curve (Reimer PJ, Bard E, Bayliss A, et al., 2013) and ∆R values of 12±10 (Ulm, 2006). 36 stabilisation but before progradation had pushed the coastline further west (Beaton, 1981:
10). Beaton (1985: 7) noted an increase in diet breadth in the mid to upper levels at
Walaemini, while conversely at Alkaline Hill he noted a decrease in diversity in the upper portions of the midden.
Beaton (1981: 13) excavated two shell mounds and sectioned four additional mounds located on the chenier plains between Walaemini and Alkaline Hill—these mounds are dominated by Tegillarca (= Anadara) granosa. At North Mound, three separate episodes of occupation were noted, with rapid accumulations of T. granosa separated by sediment rich strata (Beaton, 1981: 13). At South Mound, five pulses of T. granosa deposition intercalated with periods of abandonment occur over a 1,000-year period (Beaton, 1985: 8).
As part of this work, Beaton excavated a 1x2m pit in the Yindayin midden (Figure 3.3).
Beaton discovered cultural material to a depth of 45–50cm that overlaid 100cm of sterile sand (Beaton, 1983b: 15, 17). A date of 2,547 cal BP4 was produced from marine shell approximately 45cm below the surface and at the bottom of the cultural material excavated
(Beaton, 1985: 6). By weight, Terebralia palustris composed ca. 40% of the midden with
Lambis lambis contributing ca. 20%. There was evidence of sea turtle throughout all levels, while in the second, third, and fourth spits, remains of wallaby and giant clam (Tridacninae spp.) were recorded—dugong was also present. No fish hooks or lithic material was recovered, however the robust Geloina coaxans shell that can be used as a substitute for
4 Recorded as 2,370±100 by Beaton (1985: 6) but adjusted to 2,820±90 in Williams and Ulm (2014). It was re=calibrated using OxCal 4.3, Marine13.14c curve and ∆R values of 12±10. [37] lithic material was found throughout the midden (Beaton, 1983b: 16-17). Based on the quantities and composition of the shellfish species, Beaton did not see significant changes in the economic base, despite noting increasing mangrove species in the more recent spits— importantly Beaton (1983b: 16-17) did not provide quantitative data to clarify this point.
Beaton’s chronology of the Princess Charlotte Bay area is as follows: Initial ephemeral occupation on the mainland after the height of the marine transgression (Walaemini rockshelter). Formation of chenier plains from ca. 4,000 BP. Flinders Group first occupied ca. 2,500 BP (Yindayin). Occupation of chenier plains begin ca. 2,000 BP (South Mound). Peak mound building occurs ca. 1,000 BP (South Mound). Chenier formation and mound building cease ca. 500–600 BP (Beaton, 1985: 9).
In his assessment of occupation at Princess Charlotte Bay, Beaton identified a time-lag between sea level stabilisation and the earliest evidence for occupation in the area. He posited that oscillating sea levels in the mid–late Holocene had prevented the establishment of marine habitats capable of supporting large populations (Hiscock, 2008: 163). In addition, he identified a lag between mainland occupation and occupation of the Flinders Group of islands—this he attributed to a lack of watercraft (Beaton, 1985: 18). While these ideas were influential when published, they have proven to be unsustainable as further information about coastal colonisation has been revealed (Hiscock, 2008: 165-166; McNiven et al., 2014). Beaton’s ideas are discussed further in section 3.5.3.
[38]
3.4 FIELDWORK CONDUCTED AUGUST 2016
In August 2016, a team from Griffith University re-excavated the shell midden at Yindayin. A
1m x 1m square was excavated in arbitrary spits (5cm spits for spits 1 to 8, 10cm for spits 9 to 14). The square is situated approximately 3–4m to the west of Beaton’s 1980 excavation
(Figure 3.3). Excavation notes indicate that soil alkalinity (pH 8–10) increased with depth.
Figure 3.7 shows the west-facing section of the excavation—a section drawing of the excavation was not available. Sediment at the top of the square was grey to light brown and contained high concentrations of ash and became darker below approximately 20 cm
(below spit 4). From approximately 30 cm (spit 6) there are lenses of ash and dark charcoal.
At approximately 60 cm (spit 10), a large slab of sandstone roof fall was encountered within the northern two-thirds of the trench. Sediment to the south of this roof fall was lighter in colour than that above. Excavation ceased at a depth of 1m where concentrations of shell and bone had continued to decrease. The deposit was probed for a further 50 cm until the probe hit rock. The excavated material was sieved through nested 6mm/3mm mesh and bagged on site. Some bone and charcoal samples were bagged separately during excavation.
[39]
Griffith University excavation 2016 Beaton excavation 1980
Figure 3.3: Beaton’s Yindayin floor plan with Griffith University 2016 excavation (adapted from Beaton,
1983b: Figure 2)
Figure 3.4: Hale and Tindale's map of Yindayin (1933: Figure 162)
[40]
Figure 3.5: View across the front of Yindayin rockshelter, view facing East (Westaway, 2016)
Figure 3.6: Yindayin excavation, view facing East (Westaway, 2016)
[41]
Figure 3.7: Yindayin section facing west (source: Westaway, 2016)
42
3.4.1 SITE INTEGRITY
There are several characteristics of the Yindayin midden deposit that suggest it has not undergone substantial disturbance. Firstly, the rockshelter is situated 30–40m above sea level, therefore the chance of tidal processes causing erosion, redeposition and/or accumulation of natural materials is unlikely. The site appears to have been subjected to minimal significant natural disturbance, however there has been some human disturbance relating to the charcoal lenses at a depth of approximately 30cm—this is discussed further at section 5.2.4.
3.5 TRENDS IN HOLOCENE OCCUPATION OF THE GREAT BARRIER REEF (GBR)
This section discusses the chronological trends for Holocene occupation of the GBR area.
Studies from the Whitsunday Islands, Shoalwater Bay, the Northumberland Group, the
Keppel Islands, and Princess Charlotte Bay will be used to demonstrate how data has been used to assess occupation and what explanations have been given for the initiation and fluctuations in occupation during the mid to late-Holocene. Figure 3.8 highlights the study areas.
43
1
2 1 = Princess Charlotte Bay
2 = Whitsundays 3 3 = Northumberland Group and Shoalwater Bay
4 = Keppels 4
Figure 3.8: Great Barrier Reef — highlighted study areas (adapted from Great Barrier Reef Marine Park
Authority, 2004)
[44]
3.5.1 CHRONOLOGY
This section identifies the chronology for the four regions noted above. Table 3.1 lists the sites discussed further and identifies periods of intensification noted by the original researcher at ca. 3,000 cal BP and ca. 1,000 cal BP, and dates of known abandonment.
Where possible, the dates have been sourced from Willams and Ulm (2014) and re- calibrated using OxCal 4.3, marine curve 13 (Reimer et al., 2013), and ∆R values of 12±10 for sites in Princess Charlotte Bay and 11±15 for the other three central Queensland regions
(Ulm, 2006a).
Table 3.1: Dating of select GBR sites established during the Holocene
Site/Island Region Established (BP) Intensification 1 (BP) Abandonment Intensification 2 (BP) Nara Inlet 1, Hook Is. Whitsundays ca. 9,000 ca. 3,008 ca. 520 South Molle Is. Whitsundays ca. 9,000 Border Island 1 Whitsundays ca. 6,917 ca. 3,069 Walaemini rockshelter PCB ca. 5,557 ca. 5557? Otterbourne Is. Shoalwater Bay ca. 5,167 ca. 3,200 ca. 2,800 ca. 1,400 Mazie Bay, North Keppel The Keppels ca. 4,749 ca. 3,500 Alkaline Hill PCB ca. 3,846 St Bees Is. Northumberland ca. 3,339 ca. 3,339 High Peak Is. Shoalwater Bay ca. 3,267 ca. 3,267 ca. 506 Curlew Is. Northumberland ca. 3,039 ca. 1,000? Spur Bay East Midden, Middle Percy Is. Northumberland ca. 2,818 Hill Inlet rockshelter 1, Hook Is. Whitsundays ca. 2,799 Yindayin PCB ca. 2,547 Nara Inlet Art Site 1, Hook Is. Whitsundays ca. 2,444 Castle Rock Cave, Middle Percy Is. Northumberland ca. 2,253 South Mound PCB ca. 2,000 ca. 657 ca. 1,000? Mother Mound on Ridge 3 PCB ca. 1,414
The earliest sites in the GBR are found in the Whitsundays, with sites on Hook, South Molle, and Border Islands occupied while they were still part of the mainland (Barker, 1991: 102,
107, 139). Walaemini rockshelter, Otterbourne Island, and Mazie Bay are the only other sites dated before 4,000 cal BP. Sites in the Whitsundays, the Keppels, Shoalwater Bay, and the Northumberland group have noted periods of increased or changed activity between
3,500–3,000 cal BP. There is also a growth in the number of sites established between 3,500
BP and 2,400 BP. Finally, a second period of growth is noted in several sites after 1,000 BP—
[45] this correlates with what has been interpreted as an intensification of economic activity at coastal sites across north-eastern Australia (Ulm, 2011: 450).
3.5.2 OCCUPATION DATA
Assessment of economic intensity across Queensland is based on observations of deposition rates, changes in faunal assemblages, increasing technological complexity, and patterns in the timing of occupation or re-occupation of sites within geographic regions.
In the Whitsundays, Barker (2004: 110) uses increased deposition of cultural material, variation in the type of fauna exploited, and an increase in marine procurement technology to signify a change in intensity and therefore a change in cultural phases at Nara Inlet 1 at ca. 3,000 BP and again at ca. 520 BP. The timing of the establishment of sites at Hill Inlet 1 and Nara Inlet Art Site 1 provide supporting evidence for increased activity during this period (Barker, 2004: 147-148).
At Mazie Bay, Rowland et al. (2015) uses shifts in faunal composition to suggest a change in occupation strategies. Rowland et al. (2015: 161) note that after ca. 3,500 BP the number of spear-caught fish decrease while hook-caught fish species increase—overall fish numbers also increase. At the same time there is a reduced emphasis on turtle and crustaceans, and increased volumes of Saccostrea cucullata and Polyplacophora (chiton) (Rowland et al.,
2015: 161). Lithic technology also varied between the pre– and post–3,500 BP periods although details of this change are not provided (Rowland et al., 2015: 161).
McNiven et al. (2014: 31) suggest that the horizontal expansion of the midden deposit on
Otterbourne Island at ca. 3,200 cal BP represents a spatial increase in activity. The concurrence of this expansion with the occupation of High Peak Island is seen as significant
[46] and in line with expanding occupation patterns in the Whitsundays and the Northumberland
Group (McNiven et al., 2014: 31).
Border’s (1999: 133) analysis of the Northumberland Group identified 77 sites in the region.
Dating of the islands was limited, however Border (1999: 135) noted that on Curlew Island, while initial occupation had dated to ca. 3,000 BP, the most intense deposition of cultural material had occurred in the last 1,000 years.
Beaton uses deposition rates to define the intensity of occupation within Princess Charlotte
Bay. Beaton (1985: 11-12) uses the low levels of deposition in the earliest levels of the mainland rockshelters to suggest that initial occupation was limited. As people moved from the mainland rockshelters to the chenier plains and islands, deposition rates increase, implying increased activity across the region. Beaton (1985: 11-12) suggests that the focus in occupation and resource use moved away from the area because of the failure of the
Anadara beds, which he surmises from the cessation of mound building.
3.5.3 EXPLAINING THE INITIATION AND FLUCTUATION IN ISLAND OCCUPATION
The trends in occupation across the GBR region have been attributed to one or many of the following factors: changes in climate and the environment, increasing population sizes, changing demographic structures, and increasing complexity in social and exchange systems
(Hiscock, 2008: 162; Lourandos, 1997: 21-23; Rowland et al., 2015). Many of these explanations are drawn from the Australian ‘Intensification’ debate, and as such the discussion surrounding them can be polemic (Lourandos and Ross, 1994: 55-57). To suggest there is a single explanation for these changes ignores the complex interaction between climate, environment, culture, and demographics (Hiscock, 2008: 162). To search for a primary driver is more realistic, but again, timing and location is crucial—the driver in one
[47] place in one instance may not be the driver at different times and in different locations. The following sections look at how these arguments have been made with respect to
Queensland coastal archaeology.
POPULATION GROWTH
In assessing the occupation of Princess Charlotte Bay, Beaton saw its increased use over time as the natural product of population growth during a period of stable sea-levels and therefore productive coastlines (Beaton, 1985: 18). While he acknowledged the presence of significant change during the mid-Holocene and that a continent-wide explanation was required, Beaton did not see social restructuring as a valid explanation, instead positing that population growth provided a simpler and more satisfactory model (Beaton, 1983a: 94). In
Beaton’s model, population growth rates in Australia remained low until the mid-Holocene, after which there was exponential growth that generated the archaeological patterns seen in the late-Holocene (Beaton, 1983a: 94). While Beaton focused on population, he also cited sea level stability as a factor affecting the growth of populations, both human and animal.
He argued that a pre-requisite for the growth of a species is the existence of a physical niche for it to occupy—the previous instability of sea levels meant that prior to the mid-late
Holocene, growth in coastal regions for humans and biota was restricted (Beaton, 1985: 13).
While the simplicity of Beaton’s argument is alluring, others have argued that it is difficult to securely connect population increase to the archaeological record (Rowland et al., 2015:
158). Barker (2004: 16) also dismisses simple population growth explanations, arguing that they do not address the causes for growth. He contends that hunter-gather populations are socially and culturally controlled, and therefore any explanation for intensification must
[48] start with understanding why cultural controls on population sizes are loosened in the late-
Holocene (Barker, 2004: 16-18).
SOCIAL AND DEMOGRAPHIC DYNAMICS
In his work in the Whitsundays, Barker (2004: 148) views the archaeological patterns that appear and intensify from 3,000 BP as depicting a regional and local restructuring of populations and social dynamics. He uses a range of evidence, such as the increasing use of marine technology and decreasing lithic deposition, to argue that there was a transition from generalised coastal foragers to island specialists (Barker, 2004: 89). In this instance he argues that cultural change drove economic change (Hiscock, 2008: 170). Barker rejects the environmental context as the determining factor in influencing foraging behaviour, citing a mismatch in chronologies between intensification and environmental patterns (Barker,
2004: 148). He argues that because we still find the same range of marine fauna now as we did at Nara Inlet 1 9,000 years ago, there has been little change in broader climatic patterns
(Barker, 2004: 143). Barker argues that while environmental change may provide foraging opportunities, the decision to take advantage of those opportunities is socially and/or politically mediated, and therefore there is no guarantee that people will always react in predictable ways (Barker, 2004: 149).
Barker’s view of the Whitsundays archaeological record is only one of the possible interpretations for what occurred there. Hiscock (2008: 169-170) has argued that the Border
Island site shows that exploitation of marine resources was greater in the early Holocene and that the increased presence of molluscs in the late-Holocene was indicative of a coastal rather than open sea economy. Barker’s argument that changes in fish, crustacean, and mollusc exploitation were the result of procurement choice, while plausible, is a perhaps
[49] weak as it is not a statement that can be tested. It could equally be valid, and perhaps more theoretically sound, to invoke arguments that link foraging decisions to the density and distribution of these types of resources.
CLIMATE AND ENVIRONMENT
Rowland et al. (2015) have identified that occupational trends in the Keppel Islands align with fluctuations in sea levels, precipitation, and temperature. The strength of their argument lies in their ability to provide environmental and climate data to support their statements. They uses data from relic S. cucullata beds to assess sea level at Great Keppel, noting that at key periods sea levels were at least 1m higher than present (Rowland et al.,
2015: 161). They also uses data to show that effective precipitation and temperature variations align with both sea level changes and changes in foraging preferences seen at
Mazie Bay (Rowland et al., 2015: 161). Rowland et al. (2015: 161) argue that the alignment of occupational changes with climate and environmental data at Mazie Bay represents an adaptive response to those changes.
3.5.4 DISCUSSION
Barker’s interpretations and Hiscock’s critique provide valuable discussion points for this study. Firstly, connecting cultural change to settlement patterns is a difficult prospect even when substantial archaeological correlates are available. For Yindayin, there is little physical evidence that can be used to support arguments relating to social or political factors.
Secondly, part of Barker’s dismissal of environmentally driven explanations is chronologically driven, and therefore, any reliance on environmental/climate arguments must demonstrate timelines that align with environmental timescales.
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Arguments relating to population increase are an easy option for interpreting the archaeological record as increasing people have increasing material needs and therefore may leave more evidence of their presence. The problem with this argument is that we have very little knowledge of population in any area of Australia during the Pleistocene or
Holocene and many of the correlates used require qualification before they can be linked to increased population (Attenbrow, 2004: 26-27). Attempts by Williams (2013) to associate the chronologies of site establishment with population levels are currently too flawed to provide valid data (see Attenbrow and Hiscock, 2015; Hiscock and Attenbrow, 2016: for a critique of this method and ; Torfing, 2015: for a critique of similar international approaches). While this does not remove population as a factor, the absence of reliable data makes population-driven arguments harder to sustain.
The arguments for climate and environment driven change have an important advantage over cultural and population driven explanations—that advantage is data. Scientific research on climate patterns, sea level fluctuations, precipitation rates, etc provide a corpus of data that can provide a framework for understanding the conditions faced by past foragers. A scientific approach provides models that can be tested against evidence and therefore provides a starting point to examine past behaviours. Finally, climate and environmental data provide a starting point for examining behaviour—deviations from the expected response to these conditions can be the impetus to search for more socially driven explanations.
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3.6 CHAPTER SUMMARY
This chapter set out the geographical and archaeological context for the Yindayin site and surrounding Princess Charlotte Bay. It provided an outline of key Holocene occupation within the GBR, highlighting the chronology, types of data utilised, and the explanations given for the identified trends. This information provides the context for occupation at
Yindayin and the common trends and methods used to assess the intensity of occupation within the Australian coastal context.
The following chapter will discuss the methods used to process the material from Yindayin, the process for collecting the primary data, and the methods and theoretical basis used to analyse that data.
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Chapter 4 MATERIALS AND METHODS
4.1 INTRODUCTION
This chapter discusses the methods used to collect and analyse data on the invertebrate faunal material from Yindayin. The techniques and theoretical issues relating to the preparation, identification, and quantification of the material will be discussed. This will be followed by a similar discussion relating to the analytical approaches and will address issues relating to:
accumulation rates, deposition rates, diversity indices, patch preference, and environmental and climate data.
The methods discussed in this chapter are based on standard zooarchaeological practice and are commonly used in discussions regarding Holocene economic intensification. As such these methods have been selected to address the economic intensification criteria set out in section 3.5 (Grayson, 1984; Morgan, 2015: 176).
4.1.1 PREPARATION OF MATERIALS
The excavated materials were initially sieved through nested 6mm and 3mm mesh screens on site before being bagged and transported to the University of Sydney. Between
November 2016 and February 2017, all the material was re-sieved through nested 5mm and
3mm mesh. The 5mm material was subsequently wet sieved to removed fine sediment to enable easier identification of the molluscan material—this material was left to air dry for at least 24 hours before being re-bagged. Weights were recorded before and after dry and wet sieving for each of the mesh sizes. A primary sort was then undertaken to separate the 5mm [53] material into the constituent components: namely shell, bone, stone, charcoal, pumice/coral, organic material (plant matter, scats, seeds), and indeterminant material
(fine-grained dust/shell waste). Each component was bagged, labelled according to the spit and initial bag identification, and then weighed.
4.1.2 IDENTIFICATION OF FAUNAL MATERIAL
The assemblage has been separated into vertebrate and invertebrate taxa, with only the invertebrate taxa being examined as part of this study. Vertebrate material has been sent to
Griffith University for identification, however, the material is highly fragmented, and it is estimated that for most of this material, identification to species, family, or even order may not be possible (J Louys, 2018, pers. comm, 24 April). All the invertebrate material taken from the 5mm mesh has been identified to the lowest possible taxonomic level based on visible diagnostic elements, with all barring the Echinoidea (sea urchin), Brachyura (crab), and Sigmurethra (land snail) being identified to at least family level. Identifications were made with reference to published literature (Eichhorst, 2016; Lamprell et al., 1992; Lamprell and Healy, 1998; Robin, 2008; Robin, 2011; Stanisic et al., 2010; Wilson et al., 1993; Wilson,
2002) and the University of Sydney molluscan reference collection. The nomenclature of marine taxa have been standardised with reference to the World Register of Marine Species
(WoRMS Editorial Board, 2016).
4.1.3 INVERTEBRATE QUANTIFICATION
This study uses three basic quantification measures to assess relative frequencies of taxa— the minimum number of individuals (MNI), the number of identified specimens (NISP), and weight. These three methods are commonly used as measures of abundance in zooarchaeological assemblages, however, the descriptive power and accuracy of each
[54] quantifier continues to be debated (Giovas, 2009; Grayson, 1984; Lyman, 1994; Mason et al., 1998; Mason et al., 2000). The following sections describe each of these methods, how they are used in this study, and includes a brief discussion on the merits of each measure.
NISP
NISP represents a count of all the identifiable remains that can be attributed to a specific taxon or taxonomic category within an analytical unit (Reitz and Wing, 2008: 167). It is a simple and replicable measure, however using it to assess the abundance of taxa across time and relative to other taxa can be problematic. Issues that affect NISP include: the impact of processing fauna for consumption; variation in identifiability between groups of taxa; differential fragmentation rates, preservation, and collection techniques; and interdependence between specimens (Grayson, 1984: 20-23). Of these issues, interdependence is the most concerning (Grayson, 1984: 24, 27). NISP counts cannot distinguish between individuals therefore statistical methods that rely on independence cannot be used (Grayson, 1984: 24). These criticisms highlight the tendency for NISP to overestimate abundances (particularly with increasing fragmentation) and its problems in accurately estimating the relative abundance of taxa within an assemblage. Due to these limitations, NISP counts have only been used to calculate fragmentation rates and, as MNI can exclude taxa that is present but have no identifiable non-repetitive elements, NISP has also been used to calculate the number of taxa (NTAXA) as a measure of assemblage richness.
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MNI
MNI is a measure of the minimum number of individuals per taxon required to account for all the diagnostic elements found within an assemblage (Shotwell cited in Reitz and Wing,
2008: 205). To calculate MNI, non-repetitive elements (NREs) for each taxon are identified, quantified, and then combined to analytical units to identify the highest NRE value
(Grayson, 1984: 28-29). One criticism of MNI counts is that they are influenced by the method of aggregation used to group taxa (Grayson, 1984: 34). The use of small, arbitrary analytical units can lead to inflated MNI counts, therefore NREs should be aggregated to the most appropriate and justifiable analytical unit in view of the structure and integrity of the deposit (Harris et al., 2015: 169). For this study, given that the deposit was excavated in arbitrary units, counts of non-repetitive elements (NREs) have been aggregated to the spit level before MNI was calculated. The effects of aggregation are minimised for invertebrates compared to vertebrates due to structural/morphological differences which necessitate a reduced range of NRE.
The tMNI method developed by Harris et al. (2015) has been used to identify NREs for this study. This method improves on common MNI protocols by increasing the range of NREs while also tailoring them to a set of broad but common molluscan morphologies (Harris et al., 2015: 169). This method is more time intensive, however, using a wider range of NREs increases the accuracy of MNI for a wider range of taxa (Harris et al., 2015: 169).
MNI is the primary measure used in this study as it, unlike NISP, does not incur issues such as interdependence. Additionally, the use of tMNI and aggregation of NREs, will moderate some of the weaknesses of this measure. While a number of enhancements have been suggested in addition to those by Harris et al. (2015) (see Giovas, 2009: and; Zugasti, 2011),
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MNI is the dominant measure for invertebrates in local and International studies (for examples see McNiven et al., 2014; Morrison and Cochrane, 2008; Szabó, 2009).
WEIGHT
Weights for all taxa have been measured for each spit. Weights have not been used for analytical purposes as they can be affected by specimen size and age, recovery techniques
(e.g. mesh sizes), fragmentation, and taphonomic issues such as chemical dissolution
(Flessa, Chave cited in Mason et al., 1998: 310; Faulkner, 2010: 1942; Reitz and Wing, 2008:
180).
MEAT WEIGHT
A count such as NISP and MNI does not always reveal the relative dietary importance of different taxa (Thomas and Mannino, 2015: 57). If we are to understand the foraging value of different taxa there needs to be consideration of its subsistence value—one method for this is to assess the meat yields for different taxa.
Only a very simplified assessment of meat weights has been conducted for this study and only for the dominant taxa within the assemblage. Meat weights are calculated by multiplying the total MNI by the estimated average meat weights derived from published literature for the central and west Pacific. The calculated values have been aggregated by family and by patch. This simple method has been undertaken to demonstrate how MNI can sometimes be misleading when comparing MNI for taxa of differing size (Thomas and
Mannino, 2015: 57). There is the assumption that the weight of one taxon has the same value as any other—this may not be the case but is an assumption regularly made when comparing meat weights (Thomas and Mannino, 2015: 56). This analysis has also assumed that the meat weights recorded in other locations are valid approximations of the meat
[57] weights for taxa at Yindayin, despite their different geographic locations. A more robust strategy would have been to conduct actualistic study of living taxa from the Flinders Group, however this was beyond the scope of the current study (Thomas and Mannino, 2015: 47).
IDENTIFIABILITY OF ASSEMBLAGE
Pre and post depositional factors such as fragmentation, weathering, and dissolution remove features required to accurately identify invertebrate material. As a result, a portion of invertebrate material could not be identified—weights and percentages of the total shell weight that could not be identified are listed in Table A-1 in APPENDIX A.
VOLUME CORRECTION
Volume correction standardises data to allow density comparisons to be made between excavation units of different sizes. Volume correction was necessary for the Yindayin excavation due to inconsistent spit depths and reduced spit excavation for spits 11 and 12.
Spits 1–8 were excavated in arbitrary 5cm spits, while spits 9–12 were excavated in 10cm spits. The presence of a hard, concreted surface in the northern section of spits 11 and 12 meant only a reduced portion in their southern section was excavated. Excavation notes do not identify the precise measurements for the area excavated, however measurements from spit 13 have been compared to photographs of the site and the dimensions appear applicable to spits 11 and 12. Spit 13 measured 10cm on the western edge, 30cm on the eastern edge, and 100cm across the southern edge. The northern edge was assumed to be a straight line between the eastern and western edges, forming a trapezoid with an area of
2,000cm2. By multiplying by the depth of the spit (10cm), a volume figure of 20,000 cm3 was calculated. This figure lacks absolute precision; however, it provides a reasonable estimate in the absence of direct measurements.
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4.1.4 RADIOCARBON DATING
Standard 14C radiocarbon dating was conducted in November 2017 on shell specimens from
11 spits by Associate Professor Stewart Fallon of the ANU Radiocarbon Laboratory. Dates were obtained from Conomurex luhuanus specimens for all spits except spit 11, where in the absence of C. luhuanus a single specimen of Pinctada albina was used. No specimens were dated from lower spits due to the lack of quality samples for dating. The exact location of specimens was not recorded during excavation (having been recovered from the sieved samples); therefore, the position of the specimens can only be identified to the 5cm (spits
1–8) or 10cm (spits 9–11) ranges of the spits from which they were taken.
The dates were calibrated using OxCal 4.3 and the Marine13.14c curve with a ∆R of 12+/-10
(Bronk Ramsey, 2009; Reimer et al., 2013; Ulm, 2006a: 58). The dating results are listed in
Table 5.1 while Figure 5.1 graphs the median age and the 2–sigma range.
4.1.5 SAMPLING OF THE 3MM MATERIAL
The data for this study is based on material retrieved from the 5mm mesh. The decision to exclude 3mm material from the study was driven primarily by the research question. While there is the potential to recover more diagnostic elements from the 3mm material, and therefore boost the overall number of individuals for the assemblage, following visual inspection of the 3mm samples it was determined that the additional data would not affect the overall trends relating to economic intensity at the site. Additionally, as mesh sizes decrease, the identifiability of diagnostic elements also decreases, therefore there is a diminishing return on effort as shell fragments decline in size (sensu Campbell, 2017: 17).
The large quantities of material processed from the 5mm mesh were deemed to be a
[59] representative sample of the invertebrate material linked to economic and subsistence behaviours (see section 5.4.1 for information on representativeness of the assemblage).
To ensure that these assumptions were founded, two samples from the 3mm material from spits 2 and 6 were analysed. These spits are situated near the middle portion of the two younger phases of occupation at the rockshelter. A visual inspection was conducted on the
3mm from the oldest phase of occupation (spits 11 and 12) however the shell content for these two spits was minimal and it was deemed unlikely to impact on observations relating to economic structures.
Approximately 22% of the 3mm material for spit 2 and 30% for spit 6 was analysed. The material was re-sieved through 3mm mesh to remove sediment and smaller material.
Flotation was used to separate most of the lighter material such as charcoal, wood, and other organics, from the shell, rock, bone, and heavier organics. A primary sort was conducted and then the molluscan material was identified to the lowest taxonomic level possible. MNI and NISP quantification was then conducted as per the processes used for the
5mm material.
This analysis did not identify additional species or significant variations in diversity.
Additionally, there was little movement in terms of the taxonomic rankings between the
3mm and 5mm material. APPENDIX C 3mm Data contains the comparative data between the 3mm sample and the 5mm material for spits 2 and 6.
4.2 ANALYTICAL METHODS
This section outlines the analytical methods applied to the quantification data. These methods include the calculation of accumulation, fragmentation, and deposition rates as
[60] well as calculations of diversity indices and patch preference. Methods relating to testing representativeness (sampling to redundancy and nestedness) are dealt with as part of the assessment of NTAXA.
4.2.1 ACCUMULATION AND DEPOSITION RATES
Accumulation and deposition rates have been used to describe the type and intensity of occupation in sites across Australia (Attenbrow, 2004; Barker, 2004; McNiven et al., 2014;
Rowland, 1999; Ulm, 2006b) and internationally (Jerardino, 1995; Mannino and Thomas,
2001; Morrison and Cochrane, 2008). Accumulation rates describe the speed at which cultural deposits have accumulated and are calculated by comparing the depth of a sequence to the age of dated materials within that sequence (Holdaway et al., 2017: 6; Stein et al., 2003: 298). Deposition rates for this study refer to the quantity (grams) of material deposited relative to the volume of the analytical unit. Variations in the quantities of cultural and natural material can provide information on both the intensity of site use and variations in site activities.
These types of measures make assumptions that require testing. They both assume that the rate of matrix deposition is constant, and therefore the only variable is cultural material
(Jerardino, 1995: 21). Accumulation and discard rates can be affected by site disturbance and therefore an understanding of site formation and integrity is required to assess the validity of this data. The resulting values also do not explain the mechanisms behind deposition, therefore they need to be evaluated holistically to place them within their proper context (Attenbrow, 2004: 13; Hall and Hiscock, 1988: 11; Hiscock, 1986: 40;
Jerardino, 1995: 21-22). Interpreting increased accumulation or deposition rates as the product of increased group size or increased frequency of visits to the site is difficult based
[61] on rates alone. For example, changes in hunting techniques may change the quantities of material deposited without affecting group size or frequency of visitation. Similarly, field processing can reduce the amount of material returned to a ‘home base’, meaning discard rates may underestimate broader elements of the economy. The unique characteristics of shell middens also need to be considered when assessing deposition. In some cases, where shell to meat weight is high, large amounts of shellfish is required to produce an adequate meal. This can lead to large amounts of midden material being produced over short periods of time, a process that can make deposition appear to be continuous and rapid (Holdaway et al., 2017: 5-6). Shell diagenesis can also alter the thickness of deposits over time, thereby affecting estimates of accumulation (Holdaway et al., 2017: 6). Arguments that use accumulation data need to be coupled with supporting information relating to site formation, taphonomic assessments, and qualitative assessments of the cultural material to address these shortcomings (Hiscock, 1986: 48; Holdaway et al., 2017).
Accumulation rates have been calculated according to the methodology set out for unit accumulation by Stein et al. (2003). As this study deals with only a single excavated area, unit accumulation calculations rather than site calculations are applicable (Stein et al., 2003:
298). Unit accumulation rates are calculated by dividing the thickness of accumulation
(centimetres) by the duration of the accumulation (years). Stein et al. (2003: 298) use mean calibrated radiocarbon ages in their assessment but, to ensure consistent dating throughout this paper, median calibrated dates have been used as point measurements for calculating accumulation.
Deposition rates have been identified for each of the groups of material identified during the primary sort, that is, shell, stone, charcoal, bone, coral and pumice, and miscellaneous
[62] organic material. Weights for each category have been adjusted to account for changing spit volume (see section 4.1.3 Volume correction).
4.2.2 FRAGMENTATION RATES
Fragmentation rates are the ratio of NISP to MNI for an analytical unit (Morrison and
Cochrane, 2008: 2393). A fragmentation rate equal to 1 represents a one-to-one ratio between MNI and NISP—as the ratio increases, so too does fragmentation (Faulkner, 2010:
1946). For shell material, fragmentation rates have been used to understand the effects of post-depositional impacts such as trampling and weathering (Morrison and Cochrane, 2008:
2393; Wolverton et al., 2009: 165). The link between increased fragmentation and site intensity is complex. Low level occupation and therefore low deposition rates can result in higher rates of fragmentation due to material being exposed to weathering for long periods of time (Faulkner, 2010: 1946; Hiscock, 1985: 89). Alternatively, high fragmentation can also be caused by increased trampling due to increased foot traffic—human or otherwise.
4.2.3 DIVERSITY INDICES
Measures of species richness, evenness, and overall diversity are used to describe assemblages and to detect chronological variations (Hammer et al., 2006: 187; Magurran and McGill, 2011: 64). Diversity indices provide a simple summary of changing assemblage composition over time and provide an assessment of relative abundance that raw abundance data does not (Lyman, 2008: 172). Diversity indices provide intuitive visualisations of the complex chronological variations in taxonomic composition, thereby allowing changes to be seen in the context of the entire assemblage rather than in isolation.
There are many diversity indices and they vary in their power to accurately describe assemblages (Hammer et al., 2006: 186; Magurran, 1988: 71-75). The best indices are those
[63] that provide statistically representative results, that are representative of their biological and sampling context, and are simple to calculate and interpret (Hammer et al., 2006: 189;
Magurran, 1988: 80). This study uses three common indices—NTAXA, the Simpson Index of
Diversity (1-D), and the Shannon Index of Diversity (H’). These indices are simple to calculate with NTAXA and the Simpson 1-D being easy to interpret. Individual scores for the Shannon
H’ are difficult to interpret, however, it has been included in this study as it can be used to confirm movements within the Simpson 1-D index and, as it is one of the most commonly used indices it can be used as a point of comparison with other studies (Lyman, 2008: 192).
The Simpson 1-D and Shannon H’ indices have been calculated using MNI data while NTAXA has been based on NISP. All measures have been aggregated to at least family taxonomic level—collapsing the taxonomic groups allows us to avoid exaggerating richness through double counting of species/genus/family groups (Lyman, 2008: 174). These indices have been calculated using Palaeontological Statistics (PAST) version 3.15 analysis package
(Hammer et al., 2001).
NTAXA—RICHNESS
NTAXA is a count of the number of taxa within an assemblage and is a simple expression of taxa richness. In its basic form, richness describes the breadth of variation within a community (Magurran and McGill, 2011: 56; Reitz and Wing, 2008: 110). Measures of richness provide a numerical description of the diet-breadth of assemblages (Nagaoka,
2001: 104). Changes in richness can indicate variation in encounter rates with high ranked prey, changes in technology, or in some cases, an inadequate sampling methodology
(Magurran and McGill, 2011: 41; Wolverton et al., 2015: 504).
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Grayson (1984: 132) has identified that taxonomical richness is positively affected by the size of the sample population. Richness can therefore be reflective of the sample rather than the overarching population. Samples should therefore be tested to ensure the data is representativeness of the population from which it was drawn and not an artefact of sampling (Grayson, 1984: 132). This study has used nestedness and sampling to redundancy analyses to test the representativeness of the material from Yindayin.
Sampling to redundancy involved plotting the cumulative increase in NTAXA against cumulative sub-samples of NISP. When this data is plotted, the curve will initially incline rapidly as common taxa are counted and then flatten as fewer new taxa are identified. At the point where the plotted curve becomes asymptotic, all species have been identified and the sampling regime will be representative of the population’s richness (Leonard cited in
Lyman, 2008: 143; Magurran and McGill, 2011: 42-43). Due to the sample sizes required, achieving an asymptotic curve can be difficult for archaeological assemblages. In practice, if a visual inspection shows that the curve is at least approaching an asymptote then the sample is deemed to be a valid representation of the greater population. This measure can be enhanced when combined with a nestedness analysis.
Nestedness measures the likelihood that samples with different taxonomic richness are nested subsets of one another (Lyman, 2008: 167; Wolverton et al., 2015: 502). If taxa are from the same community then composition of samples with lower richness will nest within those of higher richness (Wolverton et al., 2015: 502). In other words, the most common taxa should be present in smaller samples while both common and rarer taxa will be present in species rich samples (Wright and Reeves, 1992: 416). A nestedness matrix has been developed that compares taxa (rows) with spits (columns) with the data either signifying the
[65] taxonomic presence or absence (Ulrich et al., 2009: 3). The matrix is packed with common taxa at the top of the matrix and the spits with the greatest taxonomic richness to the left.
Samples that are highly nested will group to the top left of the matrix—summary statistics have been generated to test the strength of this grouping. Nestedness is scored using a temperature value between 0 and 100°, with 0° being a fully nested sample and 100° representing a complete absence of nesting (Lyman, 2008: 168; Wolverton et al., 2015:
502). Nestedness has been assessed using Static NeD for Windows (Strona and Fattorini,
2014).
If the species accumulation curve is asymptotic and the samples are sufficiently nested, the assemblage is likely to be representative of the overall animal community.
SIMPSON’S INDEX OF DIVERSITY (1-D)
Simpson’s Index (D) measures the likelihood of selecting two identical species from an infinitely sized population and is often described in its 1-D form ensuring that, more intuitively, diversity increases as the index value increases (Magurran, 2004: 114-115). The
1-D index generates values between 1/S and 1 where S is the number of taxa recorded for the assemblage—as diversity increases, so too does the index (Magurran, 2004: 115; Woo et al., 2015: 733). Unlike the Shannon-Weiner Index, the Simpson Index is not sensitive to richness, being mostly influenced by the most numerous species in the assemblage
(Magurran, 2004: 115).
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SHANNON-WEINER INDEX OF DIVERSITY (H’)
The Shannon-Weiner Index of Diversity measures the probability of randomly selecting a species from a population (Magurran, 2004: 107-108). Generally, scores range between 1.5 and 3 with higher scores reflecting more evenly distributed populations (Magurran, 1988:
35). This index incorporates both richness and evenness, therefore sample sizes and the expected distribution influence its outcomes (Henderson and Southwood, 2000: 478;
Magurran, 1988: 79). As already noted, this index is not intuitive and comparisons between results can only be done between populations that have similar richness. As a result, it is common to include additional measures, notably, Simpson’s Index (Magurran, 2004: 108).
4.2.4 PATCH PREFERENCES
Due to tidal changes, wave action, and topographic factors, coastal environments tend to develop clear vertical zonation along shorelines, with different zones exhibiting different fauna and flora (Knox, 2001: 16). The composition of taxa on shorelines is also dependent upon the available substrates such as sandy beaches, mudflats, rock platforms, and coral platforms (Knox, 2001: 13-14).
The definition of patches for this study has been based on zonation and substrate composition as these two factors play a large role in determining the composition of taxa and the amount of effort required to extract resources from that patch (Harris and Weisler,
2017: 206). To determine the Yindayin patches, habitat preferences for each have been identified and then aggregated to create eight patches plus one miscellaneous category (see
APPENDIX D Patch Preference Data). Table 4.1 lists the patches, common taxa, and patch characteristics. It is acknowledged that these aggregations may oversimplify more complex interactions between taxa and environments, however, it is a common approach to
[67] molluscan analysis (see Bourke et al., 2007; Szabó, 2009; Harris and Weisler, 2017) and it is anticipated that these simplifications will not overly affect the identification of long-term trends.
Table 4.1: Description of Patches for Yindayin Taxa
Patch/Zone Zone description Substrate Inundation Foraging method Common Species Terebralia spp. Mangrove forest closest Mangrove Terebralia palustris, Mangrove-Landward Low Surface collection to land. roots, mud Nerita planospira, Geloina coaxans Mid zone between Mangrove Telescopium Mangrove-Mid zone Moderate Surface collection Landward and Seaward. roots, mud telescopium Mangrove forest closest Mangrove Mangrove-Seaward High Surface collection Terebralia sulcata, to sea. roots, mud Asaphis violascens Soft or firm substrates in Atactodea striata, Intertidal-sand/mud Sand, mud Low Digging the intertidal. Gafrarium pectinatum, Anadara spp. Nerita albicilla, Nerita costata, Hard substrates in the Rocks, coral Intertidal-hard Low Surface collection Nerita polita, intertidal. rubble Lunella cinerea, Monodonta labio Hard substrate in the Supralittoral-hard splash zone (above the Rocks Very low Surface collection Nerita undata spring high tide line). Horizontal section of the Seagrass, Conomurex luhuanus, fringing reef that grows coral sands, Surface collection, Lambis lambis, Reef flat Moderate-High directly from the sand flats, wading, diving Tridacna spp., shoreline. pavement Hippopus Hippopus Camaenidae c.f., Terrestrial Non-aquatic areas Varied N/A Surface collection Sigmurethra May apply where taxonomic identification is to Refers to taxa that cross Varied Varied Varied Varied genus/family level and multiple zones sub-categories of taxa occupy multiple patches.
As this research aims to assess possible economic intensification at Yindayin, changes or stability in patch preference may be indicative of the intensity of occupation. Chronological variations in patch preference will be assessed by applying theory derived from the patch choice model from Optimal Foraging Theory (see Chapter 2 Theory and Identification of
Economic Intensification).
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4.3 ENVIRONMENT AND CLIMATE DATA
North Queensland climatic and environmental data has been collated to test for correlations with occupation intensity at Yindayin and Princess Charlotte Bay. Where possible, data specific to the GBR region or Princess Charlotte Bay has been sourced. The collated data refers to:
sea level variations, effective precipitation, temperature, fluctuations in the El-Nino Southern Oscillation (ENSO), reef formation, and extreme cyclonic events.
4.3.1 SEA LEVEL VARIATION
There is ongoing debate about when sea levels peaked, the height and duration of that zenith, the extent of fluctuations thereafter, and when sea levels reached their modern levels (Lewis et al., 2008: 74). Variability is attributable to the different locations data has been drawn from and the reliability of the proxies that have been used to date sea level fluctuation (see Lewis et al., 2013: for a review of these issues in Australia). Perry and
Smithers (2011: Supplement S2) have argued microatolls are the most reliable proxy while
Lewis et al. (2008: 74) have argued that fixed biological indicators such as barnacles, oyster beds and tubeworms, in addition to microatolls, provide reasonable data for resolving sea level issues. For the sake of consistency, sea level data will be sourced from Lewis et al.
(2008) and Lewis et al. (2013).
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4.3.2 EFFECTIVE PRECIPITATION AND TEMPERATURE
Precipitation and temperature data have been used as a proxy for the productivity of environments in many archaeological studies (Attenbrow, 2004: 207; Hiscock, 2008: 56).
Pollen analysis, studies of Porites, and palaeohydrological studies have been used to assess the effective precipitation in Northern Australia (see Shulmeister, 1999: 81-82 for an overview). Data relating to precipitation and temperature have been sourced from
Shulmeister (1999) and Shulmeister and Lees (1995). Additional information regarding recent trends in precipitation have been sourced from Burrows et al. (2014).
4.3.3 FLUCTUATIONS IN THE EL-NINO SOUTHERN OSCILLATION (ENSO)
The El-Nino Southern Oscillation (ENSO) is the main determinant of global temperature variability and influences global weather patterns (Russell and Gnanadesikan, 2014) 2. ENSO produces dry and warm events (El-Nino) and wet and cold events (La-Nina) that occur at irregular intervals (Sarachik and Cane, 2010: 4-6). The frequency and amplitude of ENSO events has implications for the productivity of environments in Northern Queensland with increased El-Nino events commonly resulting in drought (Sarachik and Cane, 2010: 5).
Turney and Hobbs (2006: 1747) have suggested that adaptive responses to more variable climatic regimes may explain the economic intensification and expansion into coastal regions witnessed in the mid to late-Holocene—less predictable resources may have generated technological, social, and demographic responses that allowed for more effective resource extraction. While drier conditions would have impacted carrying capacity for some areas, it would also have opened others to development and exploitation. Drier El-Nino events would have improved accessibility to mangrove areas and increased the creation of habitats preferred by large marsupials (Turney and Hobbs, 2006: 1747).
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Data on ENSO frequency and amplitude is sourced from work by Carré et al. (2014), Leonard et al. (2016) and Moy et al. (2002).
4.3.4 REEF FORMATION
The timing of the formation of fringing reefs at Stanley Island would have dictated the availability of reef related taxa such as fish, sea turtle, dugong, and shellfish. Data on the timing of reef formation has been sourced from Smithers et al. (2006). Smithers et al. (2006:
175) have argued that while reef formation in the GBR is influenced by local conditions, the synchronous growth across the broader GBR indicates that meta-influences, such as sea level and climate change, play a more significant role. McNiven et al. (2014) have argued that occupation at Otterbourne and Border Islands after sea level stabilisation may have been delayed until fringing reefs could support sufficient sea turtle biomass around 5,000
BP. The presence of turtle throughout Beaton’s excavation of Yindayin suggests it was an import resource, and therefore the use of Yindayin may have been influenced by fluctuations in reef development (Beaton, 1985: 6).
4.3.5 EXTREME CYCLONIC EVENTS
The North Queensland coast is highly susceptible to cyclonic activity. These extreme events can devastate or destabilise coral reefs making them susceptible to further damage while also disrupting mangrove habitats (Nott and Hayne, 2001: 510; Paling et al., 2008: 604). The ecological damage from storms on coral environments can be extreme, recovery slow and can result in massive declines in richness and abundance of marine taxa (Cheal et al., 2017:
1511-1513). Data on extreme cyclones in Princess Charlotte Bay dating to 2,525 cal BP has been sourced from Nott and Hayne (2001).
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4.4 SUMMARY
This chapter has identified the practical and analytical methods used to analyse the Yindayin midden material. The chapter addressed the methods used to prepare, identify, and quantify invertebrate material and the methods used to analyse data derived from that material. Environmental and climate data collated was also presented. This chapter has identified how site occupation, diversity indices, and patch preferences can be used to assess economic intensification.
The following chapter describes the results of this research, detailing the site occupation data, invertebrate quantification data, and the collated environmental and climate data.
[72]
Chapter 5 RESULTS
5.1 INTRODUCTION
In this chapter the results of the data analyses discussed in Chapter 6 are presented. There are five main sections to this chapter, presenting results on broad trends in site occupation
(including a summary of the range of recovered cultural material), assemblage structure and mollusc relative abundance, assemblage richness and diversity, an examination of habitat representation to assess trends in patch preference through time, and finally, environmental and climate data relevant to the study area.
5.2 SITE OCCUPATION DATA
This section explores the data relating to radiocarbon dating, accumulation rates, and deposition rates for the main categories of midden material, with the causes for trends in the data briefly examined. A summary of these issues and how they relate to the research question is included at the end of the section.
5.2.1 RADIOCARBON DATING
The 2σ calibrated radiocarbon dates outlined in Table 5.1 and plotted in Figure 5.1 reveal three distinct chronological groups within the data:
Phase I (spit 11)—a single median age of ca. 6,286 cal BP. Phase II (spits 4-10)—seven median ages sit within the range of ca. 2,938 cal BP and 2,073 cal BP. Phase III (spits 1-3)—three median ages sit within the range of ca. 179 cal BP and ca. 51 cal BP.
[73]
Table 5.1: Radiocarbon dates and 2σ calibrated ages for Spits 1-11 from Yindayin rockshelter Midden
Phase Spit Depth Lab Code 14C Age ± Lower Cal Median Mean Upper Cal (Cm) Error Range BP Probability Probability Range BP 2σ 2σ 1 0-5 ANU-55227 475±25 ... 66 71 189 III 2 6-10 ANU-55230 456±26 ... 51 58 130 3 11-15 ANU-55229 549±26 63 179 172 265 4 16-20 ANU-55231 2455±28 1976 2083 2083 2193 5 21-25 ANU-55232 2636±29 2186 2312 2305 2400 6 26-30 ANU-55318 2446±28 1969 2073 2071 2167 II 7 31-35 ANU-55319 2705±28 2306 2384 2391 2485 8 36-40 ANU-55320 2783±28 2366 2505 2509 2654 9 41-50 ANU-55321 3076±26 2750 2825 2827 2915 ANU-55325 3057±25 2738 2806 2809 2890 10 51-60 ANU-55323 3167±27 2843 2938 2939 3046 I 11 61-70 ANU-55324 5881±31 6207 6286 6287 6375 Note: ANU-55227 (spit 1) and ANU-55230 (spit 2) may be out of range for this calibration curve; ANU-55325 is a duplicate specimen tested for spit 9
Age cal BP 0 1000 2000 3000 4000 5000 6000 7000
1 2 Phase III 3 4 5 6
Spits 7 Phase II 8 9 9 Dup 10 11 Phase I
Figure 5.1: Median and 2σ calibrated age Ranges for Yindayin
The calibration of the radiocarbon ages obtained for spits 1 and 2 has resulted in probable dates beyond the scope of the Marine13.14c calibration curve, indicating that they are modern. Given the degree of overlap in the 2σ age ranges, and the fact that they are tightly bounded by the dating of spit 3, the radiocarbon ages for spits 1 and 2 cannot be
[74] distinguished and are viewed as being contemporaneous. There is a slight inversion in the sequence with spit 6 predating spits 4 and 5. This inversion may have resulted from the digging of a firepit through spit 4 into spit 6—this issue is discussed further in the summary at the end of this section. Overall, the number and consistency of the calibrated radiocarbon ages provides a relatively robust sequence for the deposit, therefore observations relating to long term trends should be relatively secure.
5.2.2 ACCUMULATION RATES
There are some issues affecting the calculation of accumulation rates at Yindayin. Firstly, the precise location of samples used for dating was not recorded, and therefore samples are assigned to the middle of their respective spits. Inversions in the median dates between spits 1 and 2 and between spits 5 and 6 mean that accumulation rates have been calculated between Spits 1-3 and 5-7 (as combined units) to omit the inverted dates. Accumulation rates below spit 11 have been not been calculated due to the absence of dating and a lack of clarity on the depth at which cultural material ceased to appear. The data on spits 12 to
14 suggests a substantial decline in the discard of material with spit 12 containing limited amounts of material relative to spit 11 (see section 5.2.3), spit 13 containing small amounts of shell (8.5g) and bone (4g), and spit 14 containing only traces of bone (10g).
The phasing demonstrated in the radiocarbon dating is further emphasised when deposit accumulation rates are calculated, and an age-depth curve is plotted (Table 5.2:
Accumulation rates and Figure 5.2). There are four areas in the data that designate periods of low deposit formation that may indicate a hiatus in occupation. Accumulation rates between spits 3 and 4 are twenty times lower than the average rate, indicating a hiatus in use of this part of the midden. The same pattern occurs between spits 10 and 11, again
[75] indicating abandonment or very low-level occupation. Declining accumulation rates are also recorded between spits 4 and 5 and again between spit 8 and 9. While the low accumulation rates between spits 4 and 5 may be the product of the stratigraphic disruption to spit 6, the low accumulation rate between 8 and 9 may reflect a reduction in occupation. The peak periods of accumulation occur between spits 9 and 10 and then between 1 and 3. If these peaks in accumulation rates are related to increased intensity of site use then these periods may meet EIC1 (site intensity—see 2.5 Criteria for Economic Intensification).
Table 5.2: Accumulation rates
Spit 1-3 3-4 4-5 5-7 7-8 8-9 9-10 10-11 11 Median age (upper spit) 66 179 2083 2312 2384 2505 2825 2938 6286
Difference in depth 10 5 5 10 5 7.5 10 10
Difference in median cal BP age 113 1904 229 72 121 320 113 3348
Accumulation rate (cm/100yr) 13.27 0.26 2.18 13.88 4.13 3.13 8.85 0.30
Age (cal BP) 0 1000 2000 3000 4000 5000 6000 7000 0 1 10 2 4 Stratigraphic disruption 20 3 5 30 6 Possible hiatus Hiatus 7 40 8 9 Depth (cm) Depth 50 Hiatus
60 10 11
70
Figure 5.2: Age-Depth Curve and interpretation
[76]
5.2.3 DEPOSITION RATES
While the radiocarbon and deposit accumulation data provide an indication of the timing and nature of site formation and occupation, deposition rates are a more common proxy for assessing the intensity of occupation (see 3.5.2 Occupation Data for examples in Australian coastal contexts). Table 5.3 identifies the weights (g) for the excavated components, while
Table 5.4 displays the volume corrected (weight/m3) relationships between components for each spit.
Table 5.3: Weights for all material recovered from 5mm mesh
Depth Coral & Spit Bone Organic Stone Charcoal Shell Spit Totals (cm) Pumice 1 0-5 300.4 146.5 86.2 3577.2 1077.0 20870.5 26057.8 2 5-10 170.8 43.1 24.8 1533.5 716.3 13743.4 16231.9 3 10-15 105.9 26.5 25.2 2103.0 628.7 14938.9 17828.2 4 15-20 67.7 3.4 15.8 2632.9 569.3 10654.5 13943.6 5 20-25 162.0 8.8 35.6 4818.5 889.1 13135.5 19049.5 6 25-30 106.2 1.3 53.4 5765.5 262.2 6808.4 12997.0 7 30-35 101.6 1.1 25.2 4288.6 74.0 7195.6 11686.1 8 35-40 47.1 0.8 19.5 4451.8 54.1 9443.6 14016.9 9 40-50 65.5 1.4 38.5 2505.6 165.0 10404.4 13180.4 10 50-60 339.8 0.6 38.8 16035.0 99.8 18879.1 35393.1 11 60-70 17.2 0.0 0.7 4597.6 1.1 564.7 5181.3 12 70-80 58.0 0.0 0.0 2548.0 0.2 19.4 2625.6 TOTAL 1542.2 233.5 363.7 54857.2 4536.8 126658 188191.4
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Table 5.4: Volume corrected (g/cm3) weights for all material recovered from 5mm mesh
Depth Coral & Spit Bone Organic Stone Charcoal Shell Spit Totals (cm) Pumice 1 0-5 0.00601 0.00293 0.00172 0.07154 0.02154 0.41741 0.52116 2 5-10 0.00342 0.00086 0.00050 0.03067 0.01433 0.27487 0.32464 3 10-15 0.00212 0.00053 0.00050 0.04206 0.01257 0.29878 0.35656 4 15-20 0.00135 0.00007 0.00032 0.05266 0.01139 0.21309 0.27887 5 20-25 0.00324 0.00018 0.00071 0.09637 0.01778 0.26271 0.38099 6 25-30 0.00212 0.00003 0.00107 0.11531 0.00524 0.13617 0.25994 7 30-35 0.00203 0.00002 0.00050 0.08577 0.00148 0.14391 0.23372 8 35-40 0.00094 0.00002 0.00039 0.08904 0.00108 0.18887 0.28034 9 40-50 0.00066 0.00001 0.00039 0.02506 0.00165 0.10404 0.13180 10 50-60 0.00340 0.00001 0.00039 0.16035 0.00100 0.18879 0.35393 11 60-70 0.00086 0.00000 0.00004 0.22988 0.00006 0.02824 0.25907 12 70-80 0.00290 0.00000 0.00000 0.12740 0.00001 0.00097 0.13128 TOTAL 0.02905 0.00465 0.00652 1.12611 0.08813 2.25785 3.51230
Figure 5.3 plots deposition rates for each of the identified components by depth. The material is dominated by shell (67.3%) and stone (29.15%), with only small contributions of charcoal (2.41%), bone (0.82%), coral and pumice (0.19%), and organic material (0.12%)5. In the volume corrected data, spit 1 is the densest unit followed by spits 5, 3, 10 and 2. Spit 12 contains the least amount of volume corrected material suggesting that occupation was less intense than other periods. Spit 9 records similar densities to that of spit 12, highlighting the potential for reduced occupation intensity during this period.
The following sub-sections detail the deposition rate trends for the six midden components.
5 Note that only the material for spits 1-12 analysed. For spit 13 this material consisted of five fragments of Terebralia, and one each of Neritidae, and Tridacna spp., and weighed 8.5g in total. 4g of bone was also noted for spit 13 and 10g for spit 14. [78]
Phase III Phase II Phase I 0.45 0.025 0.40 0.35 0.020 0.30 0.015 0.25 0.20 0.010 0.15 0.10 wt/cm3 Charcoal Stone & Shell wt/cm3 Shell& Stone 0.005 0.05 0.00 0.000 1 2 3 4 5 6 7 8 9 101112 Spit
Stone Shell Charcoal
Phase III Phase II Phase I 0.007
0.006
0.005
0.004
0.003 Weight/cm3 0.002
0.001
0.000 1 2 3 4 5 6 7 8 9 101112 Spit
Organic Bone Coral/Pumice
Figure 5.3: Volume corrected deposition rates for stone, shell, charcoal, organic material, bone, and coral/pumice
[79]
CHARCOAL DEPOSITION
Charcoal deposition is displayed in Figure 5.4. Charcoal was found in greater densities in the upper five spits. Excavation notes identify high concentrations of burnt wood charcoal in these spits, with hearths potentially present in spits 3,4, and 5. Excavation notes also identify small quantities of wood charcoal in association with shell in spits 8, 9, and 10.
Charcoal deposition in spits 11 and 12 is negligible. The trend across the assemblage resembles a decay curve, suggesting that the pattern may relate to decreasing preservation rather than increased deposition over time.
Charcoal g/cm3 0.000 0.005 0.010 0.015 0.020
1 2 PHASE III 3 4 5 6
Spit 7 8 9 PHASEII 10 11 12 PHASE I
Figure 5.4: Charcoal—Volume corrected weights by spit
[80]
STONE DEPOSITION
The stone deposition data in Figure 5.5 includes all stone recovered from the site, both natural and anthropogenic in origin (see section 5.7 for details of knapped material). There is no obvious directional trend in deposition with the highest densities occurring in spits 6,
10, 11, and 12. In spit 6, excavation notes identify increased concentrations of stone and the presence of potential heat retainer stones. The excavation notes also identify the presence of a hard red, potentially burnt surface, in the north-east of spit 10 that extends beyond the depth of excavation. The notes suggest it is rockfall from the roof of the shelter—the increased amounts of bauxite from the ceiling in spits 10-12 support this idea. In addition, the peak in stone deposition in spit 10 occurs concurrently with a peak in accumulation and deposition rates for bone and shell, perhaps indicating that some of the stone has accumulated through anthropogenic deposition.
Stone/cm3 0.000 0.050 0.100 0.150 0.200 0.250
1 2 PHASE III 3 4 5 6
Spit 7 8 9 10 PHASEII 11 12 PHASE I
Figure 5.5: Stone—Volume corrected weights by spit
The presence of stone artefacts in these spits supports the link between increased deposition and increased occupation, however, further analysis of the stone from these
[81] spits is required to confirm such a relationship. The low concentrations of stone in spit 9 relative to other spits is notable and is a pattern repeated for charcoal, shell, and bone deposition.
BONE DEPOSITION
A little over a kilogram and a half of bone was recovered from the site (Figure 5.6). Little can be said with certainty until the material is identified and a taphonomic assessment conducted to identify the agents of accumulation. While it is likely that a proportion of the material is anthropogenic, making connections between its deposition and any potential economic intensification purely speculative. High concentration of bone is apparent in spit
1, while spits 2, 5, 10 and 12 have similarly high densities. Of note are the low levels recorded for spits 8, 9, and 11.
Bone/cm3 0.000 0.001 0.002 0.003 0.004 0.005 0.006 0.007
1 2 3 PHASE III 4 5 6
Spit 7 8 9 10 PHASEII 11 12 PHASE I
Figure 5.6: Bone—Volume corrected weights by spit
[82]
SHELL DEPOSITION
Figure 5.7 illustrates the changing concentrations of mollusc shell by spit. There is an upwards trend in shell deposition over time, with the highest densities in spits 1 to 3 (Phase
III). Of note is the similar trend in decreased deposition within spit 9 as seen in the other excavated components. Spits 11 and 12 also demonstrate very low relative concentrations of shell. There is a broad correlation between peaks in shell density in spits 1–3, 5, 8 and 10 and increases in accumulation rates, perhaps indication pulses in discard at the site. While a portion of the upward trend may be related to preservation, the condition of the shells within Phases I and II (while fragmented) do not demonstrate evidence of substantial breakdown, pH levels become increasingly alkaline (pH 8.5–10) with depth, and the weight of material classified as “indeterminant shell” shows no obvious correlation with depth (see
Table A-1 APPENDIX A).
Shell/cm3 0.000 0.100 0.200 0.300 0.400 0.500
1 2 3 PHASE III 4 5 6
Spit 7 8 9 10 PHASEII 11 12 PHASE I
Figure 5.7: Shell—Volume corrected weights by spit
[83]
ORGANIC, CORAL/PUMICE DEPOSITION
Organic material is limited and the correlation of decreasing quantities with spit depth is likely to reflect a decay curve like that seen for charcoal (Figure 5.8). Coral and pumice deposition peaks in spit 1 and 6. These peaks align with the peak in shell in spit 1 and a peak in stone in spit 6. The peak in spit 1 may reflect incidental collection with shell while the peak in spit 6 may be related to the heat retainer stones discussed in the excavation notes.
Most particles of pumice are small (<5mm diameter) and may have been carried in by the wind.
Coral/pumice & organic g/cm3 0.000 0.001 0.001 0.002 0.002 0.003 0.003 0.004
1
2
3 PHASE III
4
5
6
Spit 7
8
9
10 PHASEII
11
12 PHASE I
Figure 5.8: Organic, Coral/Pumice—Volume corrected weights by spit
ECONOMIC INTENSITY AND DEPOSITION RATES
The deposition data supports EIC2 (deposition—see 2.5 Criteria for Economic
Intensification) for Phase III in terms of sheer volume of material. Spit 1 contains the greatest densities of most components except stone, while spits 2 and 3 are also dense in
[84] most material. Spit 10 is also significant, not in terms of the density values, but for density relative to other spits in Phases I and II. The density of shell, bone, and stone in spit 10 is anomalously high, and not surpassed until 600 years later (spit 5).
There are some difficulties in directly relating deposition rates to occupation, particularly with the trends in charcoal and organic material resembling a decay curve, and the question of the anthropogenic nature of bone and stone deposition being unresolved. These issues make the assessment of the shell component even more important for the evaluation of economic intensification for the Yindayin site.
5.2.4 SUMMARY OF SITE OCCUPATION TRENDS
The data on accumulation and deposition rates suggests the following patterns. Initial occupation in spits 11 and 12 was of a relatively low intensity, with very little in the way of charcoal, shell, or bone deposited. Relatively high concentrations of stone in these two spits and in spit 10 may have resulted from ceiling rockfall, however further work is required to assess the proportion of anthropogenic stone in these spits. After a hiatus of approximately
3,000 years, use of this part of the midden starts again around 2,938 cal BP (spit 10) where high densities of shell, stone, and bone have been noted. Low concentrations of all materials in spit 9, coupled with low rates of accumulation indicates decreased intensity of occupation or a short abandonment after 2,825 cal BP. The intensity of occupation increases in spits 7 and 8 albeit at a slower rate than in spit 10 (ca. 2,505–2,384 cal BP). The stratigraphic integrity of spits 4–6 (ca. 2,312–2,073 cal BP) have likely been affected by an event that has moved material from spit 4 to spit 6. The observation of heat retainer stones in spit 6 coupled with increased charcoal in spit 5 suggest that a small fire pit was dug through a portion of this part of the deposit. This process may have resulted in older material from
[85] spit 6 moving up while the pit was being dug and then the younger material being deposited lower in the sequence. The reduction in shell in spit 6 and increase in spit 5 may add further weight to the idea that shell was dug out of spit 6 to create a hearth. While it is difficult to ascertain how much these layers have been affected, mixing of material across these spits is probable. The substantial increase in charcoal in the last five spits could suggest increased fire use, however the overall trend suggests that the pattern is preservational rather than cultural. The last phase of occupation contains the highest concentration of total material, and Spit 1 contains the highest concentration of all components except stone. The invertebrate and non-invertebrate material for Phase III is substantial and have accumulated at rates higher than in the other two periods.
This section partially addresses EIC1 (site intensity) and EIC2 (deposition) relating to increased accumulation and deposition rates. Across the entire assemblage there are trends for increasing accumulation and deposition and therefore site occupation. Two points in time, ca. 0–172 cal BP (spits 1–3) and ca. 2,938 cal BP (spit 10), exhibit trends that could be interpreted as an intensification of occupation relative to other periods at Yindayin.
[86]
5.3 INVERTEBRATE QUANTIFICATION DATA
The results and trends for the quantification of invertebrates described in Chapter 6 are outlined in the following section. The three basic measures, MNI, NISP, and weight provide three slightly differing perspectives on the composition of the Yindayin midden. Over 126kg of material was processed, of which over 121kg could be identified to a taxonomic level of class or higher. From this group, 162,344 individual specimens (NISP) were counted, and
13,107 individuals were identified through MNI counts. Summary data for raw and volume corrected measures are displayed in Table 5.5 and comparison between NISP and MNI counts are plotted in Figure 5.9. A comparison on volume corrected NISP and MNI is shown at Figure 5.10. The trends in these graphs are described in the sections that follow.
Note that weights listed in these tables are for identifiable taxa only. Weights for the non- identifiable portion of the assemblage can be found in Table A-1 in APPENDIX A.
Table 5.5: Summary of raw and volume corrected (vc) quantification data for identified shell by spit
1 2 3 4 5 6 7 8 9 10 11 12 TOTAL
MNI 1310 670 784 621 1012 847 959 802 1186 4761 150 5 13107 NISP 29421 15324 17109 12026 15864 10332 11484 12969 10288 26745 743 39 162344 Weight (g) 20385 13386 14595 10408 12560 6375 6745 9028 10068 17837 440 18 121845 VC MNI 0.0262 0.0134 0.0157 0.0124 0.0202 0.0169 0.0192 0.0160 0.0119 0.0476 0.0075 0.0003 N/A VC NISP 0.5884 0.3065 0.3422 0.2405 0.3173 0.2066 0.2297 0.2594 0.1029 0.2675 0.0372 0.0020 N/A VC Weight 0.4077 0.2677 0.2919 0.2082 0.2512 0.1275 0.1349 0.1806 0.1007 0.1784 0.0220 0.0009 N/A
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35000 5000 Phase III Phase II Phase I 4500 30000 4000 25000 3500 20000 3000 2500 MNI NISP 15000 2000 10000 1500 1000 5000 500 0 0 1 2 3 4 5 6 7 8 9 101112 Spit
NISP MNI
Figure 5.9: MNI and NISP count by spit
0.7 0.05 Phase III Phase II Phase I 0.045 0.6 0.04 0.5 0.035 0.4 0.03 0.025 0.3 0.02 0.2 0.015 0.01 Volume corrected MNI corrected Volume Volume corrected NISP corrected Volume 0.1 0.005 0 0 1 2 3 4 5 6 7 8 9 101112 Spit
Volume Corrected NISP Volume Corrected MNI
Figure 5.10: Volume corrected MNI and NISP by spit
[88]
5.3.1 MNI COUNT
Raw and volume corrected MNI data is shown in Figure 5.11. Low MNI for spits 11 and 12 suggest only limited activity for this phase of occupation. There is a significant peak in MNI in spit 10 driven by the small bodied Nerita spp. and Nerita undata. This peak is substantial, indicative of mass harvesting and, despite their small size, may be an indication of increased economic intensity. This extreme high is followed in spit 9 by the lowest volume corrected
MNI value recorded in Phases II and III. This correlates with similar observations of accumulation and deposition rates for spit 9.
0.06 Phase III Phase II Phase I 5000 4500 0.05 4000 3500 0.04 3000 0.03 2500 MNI 2000 0.02 1500 1000 Volume Corrected MNI Corrected Volume 0.01 500 0 0 1 2 3 4 5 6 7 8 9 101112 Spit
Volume Corrected MNI MNI
Figure 5.11: MNI and volume corrected MNI count by spit
There is a gradual increase in MNI until spit 5, after which there is a decline in spit 4, almost to the same level as spit 9. When Phase III occupation begins in spit 3, there is a small decline in spit 2 before a significant increase in spit 1. This degree of variability is relatively minor, however, and raw and volume corrected MNI between spits 8 and 2 suggest there is a level of consistency in total invertebrate abundance over this 2,500-year period.
[89]
The MNI is heavily skewed towards a small group of taxa, with over 97% of the MNI coming from the top 10 families, and the remaining 30 family/class groups providing just under 3%.
Data for taxa identified to their lowest taxonomic level show that 82.3% of the MNI comes from the top 10 taxa, 15.4% from the next 10, and 2.3% from the remaining 49 taxa. Two families, Neritidae and Potamididae, dominate the MNI count. Approximately 50% of all
MNI is attributed to the Neritidae, while just over 18% is attributed to the Potamididae. Of the 50% contributed by the Neritidae family, over 22% is attributed to N. undata, 17% to
Nerita spp., and just under 7% to Nerita polita. In the Potamididae family, the main contributors are Terebralia spp. at 8.9% and T. palustris at 5.4%. The dominance of these two families changes over time with the Neritidae dominating earlier spits (5–12) and
Potamididae dominating later spits (1–4). The dominance of Neritidae and Potamididae on
MNI numbers heavily influences the analyses relating to diversity and patch preference (see
5.5 Patch preferences).
Table 5.6 displays the top 10 taxa aggregated to the lowest possible taxonomic level by MNI, while Table 5.7 shows the top 10 taxa aggregated to family or higher by MNI. A complete list of invertebrate taxa and MNI counts can be found in Table A-4 in APPENDIX A.
[90]
Table 5.6: MNI count—Top 10 taxa aggregated to the lowest taxonomic level
Spit 1 Spit 2 Spit 3 Spit 4 Spit 5 Spit 6 Rank Taxa MNI Taxa MNI Taxa MNI Taxa MNI Taxa MNI Taxa MNI 1 Terebralia spp. 301 Terebralia spp. 145 Terebralia spp. 192 Terebralia spp. 130 Nerita undata 211 Nerita undata 189 2 Atactodea striata 274 Nerita undata 103 Nerita undata 126 Nerita undata 114 Nerita spp. 155 Nerita spp. 151 3 Nerita spp. 130 Terebralia palustris 91 Terebralia palustris 126 Terebralia palustris 88 Terebralia spp. 152 Pinctada albina 87 4 Potamididae 96 Nerita spp. 66 Potamididae 61 Nerita spp. 69 Terebralia palustris 132 Nerita polita 71 5 Polyplacophora 95 Atactodea striata 64 Nerita spp. 59 Potamididae 46 Potamididae 51 Asaphis violascens 57 6 Terebralia palustris 90 Potamididae 50 Polyplacophora 42 Polyplacophora 43 Polyplacophora 37 Lunella cinerea 53 7 Nerita undata 86 Polyplacophora 39 Nerita costata 29 Nerita costata 13 Asaphis violascens 34 Terebralia spp. 49 8 Nerita polita 44 Monodonta labio 18 Atactodea striata 28 Nerita polita 11 Nerita polita 31 Potamididae 24 9 Nerita costata 43 Nerita polita 18 Monodonta labio 28 Monodonta labio 10 Nerita planospira 28 Monodonta labio 22 10 Nerita planospira 25 Nerita costata 18 Nerita polita 23 Nerita planospira 10 Pinctada albina 27 Polyplacophora. 19
Spit 7 Spit 8 Spit 9 Spit 10 Spit 11 Spit 12 Rank Taxa MNI Taxa MNI Taxa MNI Taxa MNI Taxa MNI Taxa MNI 1 Nerita undata 246 Nerita spp. 155 Nerita spp. 243 Nerita undata 1416 Nerita undata 30 Nerita spp. 2 2 Nerita spp. 181 Nerita undata 149 Nerita undata 240 Nerita spp. 1037 Nerita spp. 28 Terebralia spp. 1 3 Asaphis violascens 86 Lunella cinerea 71 Nerita polita 86 Nerita polita 500 Monodonta labio 22 Nerita polita 1 4 Pinctada albina 85 Terebralia spp. 59 Lunella cinerea 77 Monodonta labio 288 Saccostrea cucullata 15 Nerita planospira 1 5 Lunella cinerea 80 Nerita polita 55 Monodonta labio 74 Lunella cinerea 269 Asaphis violascens 9 6 Nerita polita 60 Polyplacophora 45 Terebralia palustris 69 Nerita planospira 210 Polyplacophora 9 7 Monodonta labio 38 Pinctada albina 43 Terebralia spp. 64 Atactodea striata 184 Lunella cinerea 9 8 Terebralia spp. 32 Asaphis violascens 38 Gafrarium pectinatum 52 Polyplacophora 177 Nerita polita 7 9 Lambis lambis 24 Monodonta labio 37 Polyplacophora 48 Asaphis violascens 138 Nerita planospira 6 10 Polyplacophora 24 Terebralia palustris 31 Nerita planospira 46 Pinctada albina 94 Gafrarium pectinatum 3
91
Table 5.7: MNI count—Top 10 taxa aggregated to family level or higher
Spit 1 Spit 2 Spit 3 Spit 4 Spit 5 Spit 6 Rank Taxa MNI Taxa MNI Taxa MNI Taxa MNI Taxa MNI Taxa MNI 1 Potamididae 511 Potamididae 295 Potamididae 389 Potamididae 273 Neritidae 447 Neritidae 424 2 Neritidae 328 Neritidae 211 Neritidae 242 Neritidae 217 Potamididae 346 Potamididae 107 3 Mesodesmatidae 274 Mesodesmatidae 64 Polyplacophora 42 Polyplacophora 43 Polyplacophora 37 Pteriidae 87 4 Polyplacophora 95 Polyplacophora 39 Mesodesmatidae 28 Strombidae 16 Psammobiidae 34 Psammobiidae 57 5 Trochidae 25 Trochidae 20 Trochidae 28 Trochidae 11 Pteriidae 28 Turbinidae 53 6 Strombidae 19 Tegulidae 9 Strombidae 11 Turbinidae 9 Trochidae 24 Strombidae 36 7 Turbinidae 14 Strombidae 9 Turbinidae 7 Mesodesmatidae 9 Turbinidae 24 Trochidae 22 8 Tegulidae 13 Turbinidae 7 Psammobiidae 7 Ostreidae 7 Strombidae 21 Polyplacophora 19 9 Psammobiidae 8 Psammobiidae 4 Littorinidae 4 Psammobiidae 7 Ostreidae 16 Veneridae 11 10 Arcidae 5 Cardiidae 3 Cardiidae 3 Pteriidae 6 Arcidae 10 Mesodesmatidae 6
Spit 7 Spit 8 Spit 9 Spit 10 Spit 11 Spit 12 Rank Taxa MNI Taxa MNI Taxa MNI Taxa MNI Taxa MNI Taxa MNI 1 Neritidae 494 Neritidae 372 Neritidae 617 Neritidae 3175 Neritidae 72 Neritidae 4 2 Psammobiidae 86 Potamididae 105 Potamididae 151 Trochidae 290 Trochidae 22 Potamididae 1 3 Pteriidae 85 Turbinidae 71 Turbinidae 77 Turbinidae 271 Ostreidae 15 4 Turbinidae 82 Polyplacophora 45 Trochidae 74 Mesodesmatidae 184 Turbinidae 9 5 Potamididae 65 Pteriidae 43 Veneridae 52 Polyplacophora 177 Polyplacophora 9 6 Trochidae 39 Trochidae 38 Strombidae 49 Psammobiidae 138 Psammobiidae 9 7 Strombidae 36 Psammobiidae 38 Polyplacophora 48 Potamididae 129 Veneridae 3 8 Polyplacophora 24 Strombidae 35 Mesodesmatidae 37 Strombidae 117 Potamididae 3 9 Veneridae 14 Veneridae 16 Pteriidae 34 Pteriidae 94 Mesodesmatidae 2 10 Mesodesmatidae 6 Mesodesmatidae 16 Psammobiidae 23 Veneridae 82 Arcidae 2
[92]
5.3.2 NISP COUNT
NISP data is shown in Figure 5.12. As with the MNI figures, low NISP counts for spits 11 and
12 suggests a low level of activity for Phase I. The peak in MNI for spit 10 is absent in the
NISP data. The MNI peak was driven by large volumes of Nerita spp. and N. undata—due to their morphology and robusticity these taxa do not produce large numbers of fragments
(see section 5.3.4 Fragmentation Rates). The decline recognised in accumulation, deposition, and MNI for spit 9 is also present in the NISP count. NISP counts after spit 9 are relatively stable, with a slight increasing trend until a substantial increase into spit 1.
0.6 Phase III Phase II Phase I 35000
0.5 30000 25000 0.4 20000 0.3 15000 NISP 0.2 10000
Volume Corrected NISP Corrected Volume 0.1 5000
0 0 1 2 3 4 5 6 7 8 9 101112 Spit
Volume Corrected NISP NISP
Figure 5.12: NISP and volume corrected NISP count by spit
Like the MNI count, the majority of the NISP count is produced by a small number of taxa, with 96.6% coming from the top 10 families/classes and the rest from the remaining 32 groups. At the lowest identifiable taxonomic level, 86% of total NISP comes from the top 10 taxa, 11.7% from the next 10, and 2.3% from the remaining 63 taxa. NISP counts are primarily dominated by the Potamididae, with the Neritidae and Strombidae the next
[93] largest contributors. The shell of the Strombidae family is distinctive and the high NISP count may be related to its high identifiability. These three families account for 77.3% of the total NISP count with the Potamididae at 58.4%, Neritidae at 13%, and Strombidae at 5.9%.
The contribution of the Potamididae family to the overall NISP is considerably larger than its contribution to MNI. In addition, the NISP dominance of the Potamididae occurs from spit 9 as opposed to spit 4 for MNI, with the Neritidae only dominating spits 10–12. The NISP dominance of the Potamididae is related to increased fragmentation (see 5.3.4
Fragmentation Rates). The overall shape, size and robusticity of the Potamididae species relative to the Neritidae makes it more likely to produce a greater number of fragments.
Extraction techniques for Potamididae potentially involve cooking in hot coals and fracturing of the aperture to withdraw the meat—this would increase the fragility of the shell and increase fragmentation (Bourke, 2015: 8; Currey, 1979: 307).
Table 5.8 displays the top 10 taxa aggregated to the lowest possible taxonomic level by
NISP, while Table 5.9 shows the top 10 taxa aggregated to family or higher by NISP. A complete list of taxa and the associated NISP counts can be found at Table A-6 in APPENDIX
A.
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Table 5.8: NISP count—Top 10 taxa aggregated to the lowest taxonomic level
Spit 1 Spit 2 Spit 3 Spit 4 Spit 5 Spit 6 Rank Taxa NISP Taxa NISP Taxa NISP Taxa NISP Taxa NISP Taxa NISP 1 Terebralia spp. 19469 Terebralia spp. 10362 Terebralia spp. 11643 Terebralia spp. 7787 Terebralia spp. 9168 Terebralia spp. 2848 2 Potamididae 2927 Potamididae 1263 Potamididae 1557 Potamididae 1038 Potamididae 1128 Asaphis violascens 1505 3 Terebralia palustris 1100 Terebralia palustris 808 Terebralia palustris 876 Terebralia palustris 655 Lambis lambis 815 Lambis lambis 1177 4 Lambis lambis 1056 Lambis lambis 434 Nerita undata 421 Polyplacophora 394 Terebralia palustris 762 Pinctada albina 1015 5 Polyplacophora 973 Polyplacophora 429 Polyplacophora 421 Lambis lambis 366 Asaphis violascens 646 Nerita spp. 667 6 Atactodea striata 809 Nerita undata 276 Lambis lambis 391 Nerita undata 358 Nerita spp. 598 Potamididae 416 7 Telescopium telescopium 444 Nerita spp. 272 Nerita spp. 305 Nerita spp. 227 Nerita undata 492 Nerita undata 415 8 Nerita spp. 385 Telescopium telescopium 228 Monodonta labio 282 Asaphis violascens 184 Telescopium telescopium 402 Telescopium telescopium 358 9 Nerita undata 290 Echinoidea 177 Telescopium telescopium 214 Echinoidea 176 Polyplacophora 349 Nerita polita 299 10 Asaphis violascens 265 Atactodea striata 158 Asaphis violascens 106 Telescopium telescopium 147 Pinctada albina 201 Lunella cinerea 294
Spit 7 Spit 8 Spit 9 Spit 10 Spit 11 Spit 12 Rank Taxa NISP Taxa NISP Taxa NISP Taxa NISP Taxa NISP Taxa NISP 1 Terebralia spp. 2379 Terebralia spp. 5989 Terebralia spp. 3637 Nerita undata 4641 Nerita spp. 128 Nerita spp. 8 2 Asaphis violascens 2059 Potamididae 800 Nerita undata 747 Nerita polita 2677 Monodonta labio 124 Tectus niloticus 8 3 Lambis lambis 1306 Asaphis violascens 771 Pinctada albina 672 Terebralia spp. 2380 Asaphis violascens 90 Asaphis violascens 6 4 Pinctada albina 1144 Pinctada albina 741 Potamididae 625 Pinctada albina 2221 Polyplacophora 80 Terebralia spp. 6 5 Nerita undata 650 Lambis lambis 610 Polyplacophora 575 Nerita spp. 2155 Nerita undata 47 Potamididae 3 6 Nerita spp. 620 Polyplacophora 522 Nerita spp. 514 Asaphis violascens 2079 Lunella cinerea 45 Lunella cinerea 2 7 Telescopium telescopium 462 Nerita undata 466 Nerita polita 468 Polyplacophora 1944 Saccostrea cucullata 41 Brachyura 1 8 Potamididae 407 Nerita polita 359 Monodonta labio 445 Monodonta labio 1930 Terebralia spp. 36 Tridacna spp. 1 9 Nerita polita 396 Lunella cinerea 347 Asaphis violascens 404 Lambis lambis 1385 Nerita polita 31 Lambis lambis 1 10 Lunella cinerea 364 Nerita spp. 344 Lunella cinerea 397 Lunella cinerea 1356 Lambis lambis 23 Pinctada albina 1
[95]
Table 5.9: NISP count—Top 10 taxa aggregated to family level or higher
Spit 1 Spit 2 Spit 3 Spit 4 Spit 5 Spit 6 Rank Taxa NISP Taxa NISP Taxa NISP Taxa NISP Taxa NISP Taxa NISP 1 Potamididae 23947 Potamididae 12664 Potamididae 14293 Potamididae 9635 Potamididae 11480 Potamididae 3753 2 Strombidae 1210 Neritidae 722 Neritidae 933 Neritidae 749 Neritidae 1422 Psammobiidae 1505 3 Neritidae 1100 Strombidae 477 Strombidae 437 Strombidae 435 Strombidae 941 Neritidae 1430 4 Polyplacophora 973 Polyplacophora 429 Polyplacophora 421 Polyplacophora 394 Psammobiidae 646 Strombidae 1325 5 Mesodesmatidae 809 Echinoidea 177 Trochidae 282 Psammobiidae 184 Polyplacophora 349 Pteriidae 1015 6 Psammobiidae 265 Mesodesmatidae 158 Cardiidae 111 Echinoidea 176 Pteriidae 202 Turbinidae 295 7 Echinoidea 216 Cardiidae 143 Psammobiidae 106 Trochidae 99 Trochidae 147 Polyplacophora 264 8 Cardiidae 215 Tegulidae 137 Mesodesmatidae 103 Brachyura 66 Echinoidea 131 Trochidae 216 9 Trochidae 183 Brachyura 131 Tegulidae 103 Tegulidae 52 Turbinidae 127 Echinoidea 133 10 Tegulidae 172 Trochidae 131 Echinoidea 103 Turbinidae 44 Veneridae 89 Veneridae 124
Spit 7 Spit 8 Spit 9 Spit 10 Spit 11 Spit 12 Rank Taxa NISP Taxa NISP Taxa NISP Taxa NISP Taxa NISP Taxa NISP 1 Potamididae 3345 Potamididae 7278 Potamididae 4775 Neritidae 9971 Neritidae 217 Neritidae 10 2 Psammobiidae 2059 Neritidae 1217 Neritidae 1843 Potamididae 3639 Trochidae 124 Potamididae 9 3 Neritidae 1694 Strombidae 905 Pteriidae 672 Pteriidae 2221 Psammobiidae 90 Tegulidae 8 4 Strombidae 1520 Psammobiidae 771 Polyplacophora 575 Psammobiidae 2079 Polyplacophora 80 Psammobiidae 6 5 Pteriidae 1144 Pteriidae 741 Strombidae 471 Polyplacophora 1944 Potamididae 61 Turbinidae 2 6 Turbinidae 366 Polyplacophora 522 Trochidae 445 Trochidae 1933 Turbinidae 45 Brachyura 1 7 Trochidae 359 Turbinidae 347 Psammobiidae 404 Strombidae 1841 Ostreidae 41 Strombidae 1 8 Polyplacophora 348 Trochidae 345 Turbinidae 397 Turbinidae 1358 Strombidae 26 Cardiidae 1 9 Echinoidea 210 Echinoidea 223 Veneridae 267 Veneridae 512 Veneridae 21 Pteriidae 1 10 Veneridae 146 Veneridae 169 Cardiidae 106 Mesodesmatidae 401 Arcidae 13
[96]
5.3.3 WEIGHT
Raw weights and volume corrected weights are shown in Figure 5.13. In alignment with the
MNI and NISP count, spits 11 and 12 recorded low volume corrected weights relative to other spits. The peak seen in MNI and NISP in spit 10 is apparent in the raw weight data but is eliminated once volume correction is applied. Weights for spit 9 also drop markedly once volume corrections are applied. While there are fluctuations in the distribution by weight, there is a general upward trend in Phase II, with weights peaking in spit 5. At the beginning of Phase III (spit 3) the weight of shell is higher than at any previous stage in the raw or volume corrected data—this continues with spit 1 recording the highest shell weight in the entire assemblage.
0.45 Phase III Phase II Phase I 25000 0.4 0.35 20000 0.3 15000 0.25 0.2 10000 Weight 0.15
0.1 5000
Volume Corrected Weight Corrected Volume 0.05 0 0 1 2 3 4 5 6 7 8 9 101112 Spit
Volume Corrected Weight Weight
Figure 5.13: Raw and volume corrected weight by spit
Most of the weight recorded is produced by a small number of taxa with 94.1% coming from the top 10 families/classes and the rest from the remaining 34 groups. At the lowest taxonomic level, 82.9% of the total weight comes from the top 10 taxa, 13% from the next
10, and 4.1% from the remaining 65 taxa. The same three families that dominate the NISP
[97] count account for almost 78% of the total shell weight with the Potamididae at 59.7%,
Strombidae at 12.5%, and Neritidae at 5.7%. The dominance of the Potamididae is the product of high weights recorded for Terebralia spp. (30.9%) and T. palustris (22.9%). For most spits, Terebralia spp. or T. palustris were the highest ranked taxa, with L. lambis also ranked highly.
Table 5.10 displays the top 10 taxa aggregated to the lowest possible taxonomic level by weight, while Table 5.11 shows the top 10 taxa aggregated to family or higher by weight. A complete list of taxonomic categories and their recorded weights can be found at Table A-10 in APPENDIX A.
[98]
Table 5.10: Weight—Top 10 taxa aggregated to the lowest possible taxonomic level
Spit 1 Spit 2 Spit 3 Spit 4 Spit 5 Spit 6 Rank Taxa Weight Taxa Weight Taxa Weight Taxa Weight Taxa Weight Taxa Weight 1 Terebralia spp. 9405.8 Terebralia spp. 5005.2 Terebralia spp. 5849.3 Terebralia spp. 3835.7 Terebralia palustris 4155.8 Lambis lambis 1477.9 2 Terebralia palustris 4373.5 Terebralia palustris 4958.8 Terebralia palustris 5326.6 Terebralia palustris 3516.5 Terebralia spp. 4131.2 Terebralia spp. 1268.0 3 Lambis lambis 1205.4 Lambis lambis 685.6 Lambis lambis 725.3 Lambis lambis 645.6 Lambis lambis 919.4 Terebralia palustris 589.7 4 Potamididae 925.4 Bivalvia 450.0 Potamididae 480.6 Bivalvia 400.6 Gastropoda 456.5 Asaphis violascens 429.7 5 Bivalvia 783.9 Potamididae 350.8 Bivalvia 413.3 Potamididae 373.4 Potamididae 385.2 Telescopium telescopium 285.4 6 Atactodea striata 605.1 Telescopium telescopium 327.5 Telescopium telescopium 301.4 Telescopium telescopium 244.7 Anadara trapezia 281.8 Gastropoda 241.4 7 Polyplacophora 587.5 Tridacna spp. 324.9 Polyplacophora 235.5 Polyplacophora 235.2 Telescopium telescopium 263.4 Conomurex luhuanus 240.6 8 Telescopium telescopium 561.5 Polyplacophora 243.7 Nerita undata 209.1 Tectus niloticus 160.4 Nerita undata 215.3 Pinctada albina 197.6 9 Tridacna spp. 429.2 Tectus niloticus 184.2 Tridacna spp. 197.9 Nerita undata 146.3 Saccostrea cucullata 211.0 Lunella cinerea 159.9 10 Tectus niloticus 197.0 Nerita undata 132.0 Tridacna crocea 99.9 Conomurex luhuanus 125.5 Polyplacophora 202.7 Nerita undata 159.8
Spit 7 Spit 8 Spit 9 Spit 10 Spit 11 Spit 12 Rank Taxa Weight Taxa Weight Taxa Weight Taxa Weight Taxa Weight Taxa Weight 1 Lambis lambis 1818.2 Terebralia spp. 3184.0 Terebralia spp. 2320.0 Lambis lambis 2150.1 Tectus niloticus 119.4 Terebralia spp. 7.7 2 Terebralia spp. 1058.7 Terebralia palustris 1532.3 Terebralia palustris 2196.4 Terebralia spp. 1692.3 Saccostrea cucullata 65.9 Nerita spp. 2.5 3 Asaphis violascens 648.4 Lambis lambis 754.7 Gafrarium pectinatum 804.7 Nerita undata 1630.2 Asaphis violascens 53.8 Asaphis violascens 2.0 4 Terebralia palustris 422.0 Tridacna spp. 382.2 Conomurex luhuanus 756.4 Conomurex luhuanus 1476.3 Monodonta labio 44.7 Potamididae 1.2 5 Telescopium telescopium 347.9 Conomurex luhuanus 320.9 Lambis lambis 724.0 Lunella cinerea 1176.8 Polyplacophora 39.2 Tridacna spp. 1.1 6 Gastropoda 251.2 Asaphis violascens 287.5 Lunella cinerea 357.7 Asaphis violascens 1103.8 Nerita spp. 37.6 Nerita planospira 0.7 7 Nerita undata 225.2 Gastropoda 275.7 Tridacna spp. 326.2 Polyplacophora 1083.8 Lunella cinerea 28.6 Bivalvia 0.7 8 Pinctada albina 214.0 Polyplacophora 254.2 Polyplacophora 318.9 Nerita polita 998.7 Nerita undata 28.3 Nerita polita 0.6 9 Lunella cinerea 203.0 Potamididae 238.9 Nerita undata 269.7 Terebralia palustris 952.5 Terebralia spp. 27.0 Lunella cinerea 0.6 10 Geloina coaxans 201.6 Lunella cinerea 213.6 Asaphis violascens 225.2 Monodonta labio 785.8 Lambis lambis 13.1 Lambis lambis 0.4
[99]
Table 5.11: Weight—Top 10 taxa aggregated to family level or higher
Spit 1 Spit 2 Spit 3 Spit 4 Spit 5 Spit 6 Rank Taxa Weight Taxa Weight Taxa Weight Taxa Weight Taxa Weight Taxa Weight 1 Potamididae 15296.3 Potamididae 10677.4 Potamididae 11958.9 Potamididae 8024.4 Potamididae 9020 Potamididae 2345.9 2 Strombidae 1434.9 Strombidae 833 Strombidae 819.5 Strombidae 788.2 Strombidae 1157.7 Strombidae 1757.8 3 Gastropoda 783.9 Gastropoda 450 Gastropoda 413.3 Gastropoda 400.6 Neritidae 491.7 Psammobiidae 429.7 4 Mesodesmatidae 605.1 Cardiidae 369.1 Neritidae 357.7 Neritidae 271 Gastropoda 456.5 Neritidae 427.2 5 Polyplacophora 587.5 Neritidae 271.1 Cardiidae 303.7 Polyplacophora 235.2 Arcidae 300.9 Cardiidae 263.3 6 Cardiidae 478.6 Polyplacophora 243.7 Polyplacophora 235.5 Tegulidae 160.4 Ostreidae 211.4 Gastropoda 241.4 7 Neritidae 426 Tegulidae 184.2 Tegulidae 79.7 Ostreidae 112.8 Cardiidae 202.7 Pteriidae 197.6 8 Tegulidae 197 Mesodesmatidae 127.9 Trochidae 78.7 Cardiidae 82.4 Polyplacophora 202.7 Turbinidae 160.1 9 Trochidae 95.2 Trochidae 54.1 Mesodesmatidae 68.2 Arcidae 75.5 Psammobiidae 172.4 Polyplacophora 140.6 10 Arcidae 91.8 Arcidae 40.9 Cyrenidae 53.8 Bivalvia 53.8 Tegulidae 83.7 Trochidae 89
Spit 7 Spit 8 Spit 9 Spit 10 Spit 11 Spit 12 Rank Taxa Weight Taxa Weight Taxa Weight Taxa Weight Taxa Weight Taxa Weight 1 Strombidae 2094.4 Potamididae 5182.9 Potamididae 4849.1 Strombidae 3720.4 Tegulidae 119.4 Potamididae 8.9 2 Potamididae 1972 Strombidae 1149.9 Strombidae 1505.6 Potamididae 3559 Neritidae 79.7 Neritidae 3.8 3 Psammobiidae 648.4 Cardiidae 427.8 Veneridae 805.7 Neritidae 3217.9 Ostreidae 65.9 Psammobiidae 2 4 Neritidae 512.1 Neritidae 373.6 Neritidae 590.3 Turbinidae 1177.1 Potamididae 54 Cardiidae 1.1 5 Gastropoda 251.2 Psammobiidae 287.5 Cardiidae 413.6 Psammobiidae 1103.8 Psammobiidae 53.8 Gastropoda 0.7 6 Pteriidae 214 Gastropoda 275.7 Turbinidae 357.7 Polyplacophora 1083.8 Trochidae 44.7 Turbinidae 0.6 7 Turbinidae 203.6 Polyplacophora 254.2 Polyplacophora 318.9 Trochidae 786.7 Polyplacophora 39.2 Strombidae 0.4 8 Cyrenidae 201.6 Veneridae 227.7 Psammobiidae 225.2 Veneridae 751.9 Turbinidae 28.6 Tegulidae 0.3 9 Polyplacophora 163 Turbinidae 213.6 Gastropoda 211.4 Ostreidae 544 Strombidae 14.1 Brachyura 0.1 10 Trochidae 133.8 Cyrenidae 205 Trochidae 203.8 Gastropoda 533.4 Veneridae 11.2 Pteriidae 0.1
[100]
5.3.4 FRAGMENTATION RATES
The fragmentation rates for Yindayin are plotted in Figure 5.14—these data show a strong linear trend for increasing fragmentation over time (Ordinary Least Squares, R2 = 0.8795, p =
0.0000066052). This trend sees fragmentation rates climb from a 5:1 NISP:MNI ratio in spit
11, to 23:1 by spit 1. Three possible explanations for increased fragmentation have been considered: (1) increased weathering due to low deposition rates; (2) increased trampling due to increasing site occupation; and (3) increased deposition of highly fragmentable taxa.
25.00
20.00
15.00
10.00
Fragmentation Rate Fragmentation 5.00 R² = 0.8792 0.00 1 2 3 4 5 6 7 8 9 101112 Spit
Fragmentation Rate Linear (Fragmentation Rate)
Figure 5.14: Fragmentation rates (NISP:MNI) for all taxa
Experimental research has suggested that high fragmentation rates can result from lower rates of deposition due to prolonged exposure to weathering (Hiscock, 1985: 89; Faulkner,
2010: 1946). This may be applicable to open sites; however, it would be less likely in sheltered contexts such as Yindayin. The accumulation and deposition rates, and the absence of marked fragmentation after the two long hiatus periods, does not support a relationship between increased fragmentation and lowered deposition at Yindayin.
[101]
The second explanation is that increased site occupation has led to increased trampling and therefore higher fragmentation. For this explanation to be true, clear levels of increased occupation would be required. The data on quantification, accumulation, and deposition does not consistently show the constant increase required to produce the trends illustrated in Figure 5.14. Further to this, the peaks noted for shell deposition in spits 1 and 10 do not have corresponding peak in fragmentation.
Figure 5.15: Volume corrected NISP for Neritidae and Potamididae families
Phase III Phase II Phase I 0.6
0.5
0.4
0.3
0.2
Volume Corrected NISP Corrected Volume 0.1
0 1 2 3 4 5 6 7 8 9 101112 Spit
Potamididae Neritidae
The third explanation, that the trend in increased fragmentation reflects increased deposition of highly fragmentable taxa, appears the most parsimonious. This issue has been noted for vertebrate taxa, with Cannon (2013: 398-400) demonstrating the effects of differential fragmentation rates and NISP counts between artiodactyls and lagomorphs in the Mimbres Valley, New Mexico. At Yindayin, it is the increasing deposition of the
Potamididae that is causing the increased fragmentation rates over time. Fragmentation rates are initially low due to the dominance in spit 10 of the Neritidae, a family of small bodied, robust taxa with high MNI/low NISP counts (Figure 5.15).
[102]
Phase III Phase II 80 Phase I 4.5 70 4 60 3.5 3 50 2.5 40 2 30 1.5 20 1 10 0.5 Neritidae fragmentation rate fragmentation Neritidae Potamididae fragmentation rate fragmentation Potamididae 0 0 1 2 3 4 5 6 7 8 9 101112 Spit
Potamididae Neritidae
Figure 5.16: Comparison of Neritidae and Potamididae fragmentation rates (note the different vertical scales for each taxon)
From spit 9 onwards, NISP data is dominated by the larger Potamididae family. The average fragmentation rates for the Potamididae in the Yindayin assemblage are eleven times greater than that of the Neritidae (Figure 5.15). As the Neritidae decline and the
Potamididae increase, so too does the overall assemblage fragmentation rate. The correlation between Potamididae NISP and fragmentation is statistically strong (Ordinary
Least Squares, R2 = 0.7439, p = 0.000306). As previously noted, the increased fragmentation of Potamididae may relate to its mode of processing, with shells often cooked over open coals and the apertures routinely broken during meat extraction (Bourke, 2015: 8; Currey,
1979: 307).
[103]
8
7
6
5
4 R² = 0.1083
3
Fragmentation rate Fragmentation 2
1
0 1 2 3 4 5 6 7 8 9 101112 Spit
Figure 5.17: Fragmentation rates (NISP:MNI) for all taxa excluding Potamididae
The purpose of this study is to identify if there has been an intensification of site use at
Yindayin, and as noted in the second fragmentation explanation above, increased site use can lead to increased fragmentation due to trampling. The relationship between assemblage fragmentation and site occupation intensity is obscured by the effects of Potamididae fragmentation. If Potamididae are excluded from the calculation of fragmentation, the previously strong trend is replaced by one that is statistically very weak (Ordinary Least
Squares, R2 = 0.1083, p = 0.29625) (Figure 5.17). There is a minor peak across spits 6 to 8 that relates to increased volumes of the highly fragmentable Asaphis violascens and
Pinctada Albina in combination with increased Potamididae fragmentation. Overall, fragmentation rates do not provide secure evidence for increased trampling and therefore increased intensity of site occupation.
5.3.5 COMMON, NON-DOMINANT TAXA
The results above have mostly discussed changes within the dominant taxa, however, changing abundance for common but non-dominant taxa can reveal additional information
[104] such as how taxa with similar characteristics deviate from one another or factors that may be driving minor but important changes in patch preference. Table 5.12 lists the MNI and volume corrected MNI for non-Potamididae/Neritidae6 taxa with a total MNI greater than
100. High and low abundances have been highlighted in blue and yellow respectively for spits 1–10. In terms of the volume corrected numbers, the highest abundances for 6 out of
11 taxa are recorded in spit 10. Most taxa decline in the second half of the assemblage with
6 out of 11 taxa recording their lowest abundances in spit 2.
Through understanding the characteristics of individual taxa, we can assess whether trends in abundance relate to taxa-specific issues or whether they are responding to broader environmental conditions or changes in foraging behaviour. Variations in A. violascens, A. striata, G. pectinatum, N. planospira, and reef platform species abundances will be discussed in the next section and potential explanations noted.
6 N. planospira has been included in this analysis as it has a different habitat preference to the rest of the Neritidae family.
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Table 5.12: MNI and volume corrected MNI for common, non-dominant taxa (high and low abundances have been highlighted in blue and yellow respectively)
Taxa Measure 1 2 3 4 5 6 7 8 9 10 11 12 Total Asaphis violascens MNI 8 4 7 7 34 57 86 38 23 138 9 0 411 VC MNI 0.00016 0.00008 0.00014 0.00014 0.00068 0.00114 0.00172 0.00076 0.00023 0.00138 0.00045 0 0.00688 Atactodea striata MNI 274 64 28 9 6 6 6 16 37 184 2 0 632 VC MNI 0.00548 0.00128 0.00056 0.00018 0.00012 0.00012 0.00012 0.00032 0.00037 0.00184 0.0001 0 0.01049 Polyplacophora MNI 95 39 42 43 37 19 24 45 48 177 9 0 578 VC MNI 0.0019 0.00078 0.00084 0.00086 0.00074 0.00038 0.00048 0.0009 0.00048 0.00177 0.00045 0 0.00958 Conomurex luhuanus MNI 8 6 5 7 11 19 12 24 42 93 0 0 227 VC MNI 0.00016 0.00012 0.0001 0.00014 0.00022 0.00038 0.00024 0.00048 0.00042 0.00093 0 0 0.00319 Gafrarium pectinatum MNI 1 0 1 3 6 11 14 15 52 80 3 0 186 VC MNI 0.00002 0 0.00002 0.00006 0.00012 0.00022 0.00028 0.0003 0.00052 0.0008 0.00015 0 0.00249 Lambis lambis MNI 10 3 6 9 10 17 24 11 7 22 1 0 120 VC MNI 0.0002 0.00006 0.00012 0.00018 0.0002 0.00034 0.00048 0.00022 0.00007 0.00022 0.00005 0 0.00214 Lunella cinerea MNI 14 7 7 9 23 53 80 71 77 269 9 0 619 VC MNI 0.00028 0.00014 0.00014 0.00018 0.00046 0.00106 0.0016 0.00142 0.00077 0.00269 0.00045 0 0.00919 Monodonta labio MNI 24 18 28 10 23 22 38 37 74 288 22 0 584 VC MNI 0.00048 0.00036 0.00056 0.0002 0.00046 0.00044 0.00076 0.00074 0.00074 0.00288 0.0011 0 0.00872 Nerita planospira MNI 25 4 5 10 28 8 4 10 46 210 6 1 357 VC MNI 0.0005 0.00008 0.0001 0.0002 0.00056 0.00016 0.00008 0.0002 0.00046 0.0021 0.0003 0.00005 0.00479 Pinctada albina MNI 1 0 3 6 27 87 85 43 34 94 2 0 382 VC MNI 0.00002 0 0.00006 0.00012 0.00054 0.00174 0.0017 0.00086 0.00034 0.00094 0.0001 0 0.00642 Saccostrea cucullata MNI 2 2 3 7 16 2 5 1 6 47 15 0 106 VC MNI 0.00004 0.00004 0.00006 0.00014 0.00032 0.00004 0.0001 0.00002 0.00006 0.00047 0.00075 0 0.00204
[106]
ATACTODEA STRIATA, ASAPHIS VIOLASCENS, AND GAFRARIUM PECTINATUM,
Phase III Phase II Phase I 0.006
0.005
0.004
0.003
0.002
Volume corrected MNI corrected Volume 0.001
0 1 2 3 4 5 6 7 8 9 101112 Spit
Atactodea striata Asaphis violascens Gafrarium pectinatum
Figure 5.18: Volume corrected MNI for key Intertidal sand/mud species
While these bivalves all occupy the same broad habitat (littoral sand), they display different patterns of abundance (Figure 5.18). A. striata and G. pectinatum peak in spit 10, decline in spit 9 and then continue to taper off through the upper spits. After spit 4, G. pectinatum is virtually absent while A. striata increases in spit 3 before a sharp increase in spit 1. A. violascens peaks in abundance in spit 10, declines in spit 9 and then peaks again in spit 7. In spit 6, A. violascens begins to taper off with significantly reduced MNI in the last four spits.
Despite its diminutive size, A. striata demonstrates high but infrequent abundances. Its increased abundance is likely due to its tendency to congregate in high concentrations and at depths of less than 5cm, thereby facilitating mass harvesting (Jew and Fitzpatrick, 2015:
478; Thomas, 2001: 86). The low abundance of G. pectinatum in the assemblage may be attributable to its low meat weights and therefore low return on energy investment. While it is an abundant species in some locations (see Ansari et al., 1986: 262 and ; Thomas, 2001:
[107]
85), it may have only been collected incidentally while foragers sought out other more productive bivalves.
There is a considerable size difference between A. violascens and the other two taxa.
Specimens from the central Pacific have an average meat weight of 9g, while G. pectinatum and A. striata average 1g and less than 1g respectively (Thomas, 2001: 86-87). If foragers were targeted in their harvesting, the larger size of A. violascens may have made it more attractive relative to the other two bivalves. There are two peaks for A. violascens, the first in spit 10 followed by another in spit 7. The peak in spit 10 is accompanied by peaks for several other taxa including A. striata and G. pectinatum. This peak suggests that foragers were generalised and sweeping in their foraging strategy, taking most prey on encounter.
The peak in spit 7 has no such accompaniment. It is unlikely that A. striata and G. pectinatum abundances were depressed during spit 7. Both taxa occupy similar substrates at depths above and below A. violascens, and therefore any disruption to the substrate would likely have affected all three taxa. Overpredation appears unlikely for A. violascens due to the possible hiatus in spit 9 and low abundances in spit 8. The peak in spit 7 for A. violascens may relate to its increased abundance allowing people to be more targeted in their foraging strategy. The cause of its reduction in abundance after spit 7 is unclear, however, the increased productivity of mangrove patches may have reduced the need to forage in the Intertidal sand/mud patch.
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NERITA PLANOSPIRA
0.007 Phase III Phase II Phase I
0.006
0.005
0.004
0.003
0.002
Volume corrected MNI corrected Volume 0.001
0 1 2 3 4 5 6 7 8 9 101112 Spit
Terebralia spp. Terebralia palustris Potamididae Nerita planospira
Figure 5.19: Volume corrected MNI for N. planospira relative to other prominent mangrove species
0.016 Phase III Phase II Phase I 0.014 0.012 0.01 0.008 0.006 0.004 Volume corrected MNI corrected Volume 0.002 0 1 2 3 4 5 6 7 8 9 101112 Spit
Nerita undata Nerita sp. Nerita polita Nerita planospira
Figure 5.20: Volume corrected MNI for N. planospira relative to other prominent Nerita species
Nerita planospira is the only Nerita species identified at Yindayin that is directly associated with mangrove habitats (Eichhorst, 2016: 549). N. planospira abundance does not conform to the abundance patterns of the main mangrove species (Figure 5.19), instead there is a peak in abundance in spit 10 that mirrors that seen in other Nerita species (Figure 5.20). The
[109] peak in spit 10 occurs across most patches and for the most common taxa, excluding the
Potamididae family. The presence and abundance of N. planospira suggests that mangrove habitats were available during this period making the low Potamididae abundance in spit 10 even more of an anomaly. N. planospira live on mangrove roots while the Potamididae are general found on mangrove mud, therefore differences in the species, densities, and structure of the mangroves, may be responsible for the variation between the two taxa.
CONOMUREX LUHUANUS, LAMBIS LAMBIS, AND TRIDACNA CROCEA
Phase I 0.001 Phase III Phase II 0.0009 0.0008 0.0007 0.0006 0.0005 0.0004 0.0003 0.0002 Volume corrected MNI corrected Volume 0.0001 0 1 2 3 4 5 6 7 8 9 101112 Spit
Conomurex luhuanus Lambis lambis Tectus niloticus Tridacna crocea Tridacna spp.
Figure 5.21: Volume corrected MNI for reef platform species
The abundance and characteristics of reef taxa C. luhuanus, L. lambis, and T. crocea provide information that may aid in understanding foraging within the reef platform patch (Figure
5.21). C. luhuanus has the highest MNI of the reef platform taxa. Its highest abundances occur in spits 9 and 10, after which it declines throughout Phase II and only appearing in limited quantities in Phase III. This taxon tends to aggregate in colonies, a characteristic that facilitates mass harvesting (Catterall et al., 2001: 605). Despites this, C. luhuanus is resilient to hunter-gatherer predation due to sub-adults burying themselves in sand, short
[110] maturation periods (2–3 years), and their habitats extending into sub-tidal zones (Poiner and Catterall, 1988: 191, 193). Their resilience and the potential absence of field processing
(see below) mean that alternative explanations are required for its declining abundance over time. Catterall et al. (2001: 604) have noted that C. luhuanus are susceptible to long term environmental fluctuations and therefore the decrease in abundance in the assemblage may indicate changing environmental conditions.
C. luhuanus may provide insight into the presence or absence of field processing at Yindayin.
Field processing of larger invertebrate taxa can reduce the amount of material returned to
‘home base’, meaning the dietary importance of some taxa may be underrepresented based on midden material alone. Unlike larger reef platform species (L. lambis, and Tridacna spp.),
C. luhuanus are not sizable enough to make field processing profitable. From spit 6, C. luhuanus and L. lambis have very similar densities, while by the start of Phase III, T. niloticus also has similar densities. The coalescence of this declining trend for these three reef species may suggest decreasing productivity within that patch or a shift away from the reef to a predominantly upper intertidal foraging strategy. As C. luhuanus and T. niloticus are unlikely to be field processed, the alignment of L. lambis abundances with these two taxa could suggest that they too were not subject to field processing.
Trends in L. lambis may aid in our understanding of Tridacna spp. abundance. The largest
MNI (12) for this taxon comes from the T. crocea which is the smallest of the Tridacnidae
(up to 150 mm), and therefore the least likely to have undergone field processing (Lamprell et al., 1992: 74). L. lambis is of a similar size (130-200 mm according to Dharma et al., 2005:
76) to T. crocea yet the MNI for L. lambis is ten times greater (120). In view of this, the low abundance of T. crocea may reflect actual abundances. If this is true, then the low
[111] level/absence of other Tridacna species in general may also reflect reduced reef productivity and/or preference for upper intertidal foraging.
5.3.6 SUMMARY OF QUANTIFICATION TRENDS
All three measures show low levels for Phase I in both the raw and volume corrected data.
The volume-corrected data for weight and NISP both indicate an increasing trend from spit
10 onwards. MNI presents a different picture with peaks in spits 1 and 10 bookending a flatter trend between spits 2 and 9. Of the two trends presented by these measures, MNI is more likely to be representative of the real abundances at Yindayin. The trends in NISP have been linked to increasing Potamididae deposition (and likely modes of processing) rather than increased occupation at the site and, as noted in 4.1.3, there are multiple reasons to avoid reliance on weight data.
The analysis of the contributions of different taxa shows that a relatively small number of taxa dominate the assemblage. Out of a total of 85 taxonomic categories, 10 taxa make up over 80% of the MNI, NISP, and weight counts. Two families dominate the assemblage—the
Potamididae and Neritidae. MNI data highlights the early contribution of the Neritidae and the late contribution of the Potamididae, while NISP and weight measures emphasise the high overall contribution of the Potamididae to the assemblage. Contributions by other common taxa are highest in spit 10 and mostly decline over time. Analysis of some of the common but non-dominant taxa show that a range of variables can affect their abundance and therefore explaining the general decline of this group may require a multi-faceted approach to isolate individual factors from broader processes.
The quantification measures for invertebrates provides data with which to assess both EIC1
(site intensity) and EIC2 (deposition). Volume corrected MNI does not display the increasing
[112] trend seen in the section 5.2 Site Occupation data. The intensity noted in NISP, weight, and deposition rates for Phase III is also not as apparent in MNI terms. The MNI data for spit 10 is important due to the dominance of Neritidae and significant numbers of other small, hard substrate taxa—this is significant in terms of EIC4 (foraging efficiency) and will be discussed further in 5.4 Diversity Indices.
5.4 DIVERSITY INDICES
This section discusses the three diversity indices outlined in Chapter 6 and assesses their validity in view of known deficiencies and characteristics of the assemblage that may affect their calculation. Table 5.13 displays the calculated NTAXA, Simpson 1-D and Shannon-
Weiner (H’) diversity indices. NTAXA has been used for NISP and MNI for the Simpson 1-D and Shannon-Weiner (H’) indices.
Table 5.13: Diversity Indices—Family level or higher
Index 1 2 3 4 5 6 7 8 9 10 11 12
Individuals MNI 1310 670 784 621 1012 847 959 802 1186 4761 150 5 NISP 29425 15324 17111 12026 15887 10335 11484 12969 10288 26753 749 39
NTAXA 27 20 24 21 28 27 27 27 25 36 16 9
Simpson 1-D 0.7353 0.693 0.6526 0.6779 0.6828 0.7112 0.7028 0.7466 0.6975 0.5425 0.7259 0.32
Shannon H' 1.604 1.518 1.469 1.54 1.575 1.776 1.776 1.904 1.756 1.417 1.767 0.5004
5.4.1 NTAXA—RICHNESS
NTAXA has been calculated using NISP data (Figure 5.22). NTAXA data at the family level shows that taxonomic richness peaks in spit 10, and is at its lowest in spits 11 and 12.
Richness is relatively consistent between spits 5 and 9, however there is a more uneven pattern from spit 4 onwards that is concurrent with the dominance of the Potamididae family.
[113]
40 Phase III Phase II Phase I
35
30
25
20 NTAXA 15
10
5
0 1 2 3 4 5 6 7 8 9 101112 Spit
Figure 5.22: NTAXA for taxa aggregated to family level or higher—NISP
Measures of richness such as NTAXA are sensitive to sample size. To test whether the sample is representative, species accumulation curves have been plotted using accumulated
NTAXA and NISP counts for each spit (Figure 5.23). The generated curve demonstrates asymptotic tendencies and therefore the derived total NTAXA is a fair representation of the total population.
[114]
45 40 35 30 25 20 NTAXA 15 10 5 0 0 20000 40000 60000 80000 100000 120000 140000 160000 180000 Cumulative NISP
Figure 5.23: Species accumulation curve for groups aggregated to family or higher—NISP
A nestedness matrix has also been generated to further asses the representativeness of the sample—the summary data and matrix are shown in Table B-1 and Table B-2 in APPENDIX A
. The overall nestedness temperature value is 16.63°, indicating that there is a high degree of nestedness for the assemblage. This implies that the samples are all drawn from the same community. It also indicates that the most common taxa are prevalent throughout the assemblage.
5.4.2 DIVERSITY—SIMPSON 1-D AND SHANNON H’
Figure 5.24 plots the Simpson 1-D and Shannon H’ indices for MNI aggregated to family level or higher. These index values move in parallel with initial low values in spits 10 and 12, a high in spit 8, followed by a gradual and muted decline until spit 3. Between spits 1 and 9, the Simpson index moves within a narrow band centred between 0.65 and 0.75. For the same period the Shannon index has a range of 1.41 to 1.78. For both sets of data, the decline in spit 10 is driven by the peak in the Neritidae identified previously while the gradual decline from spit 8 correlates with the increase in Potamididae quantities.
[115]
This Simpson index suggests that the assemblage has moderate to low diversity across most spits and very little variation after spit 9. While the Shannon index provides an idea of the direction of change in diversity, the numerical values do not intuitively convey the extent to which diversity is changing. Magurran (1988: 35) has recognised that most values for this index tend to fall between 1.5 and 3 and that large values are only achieved when there are large numbers of species in the sample. The Shannon index for spits 1–5, 10, and 12 are below or close to 1.5, suggesting low levels of diversity at these times. In combination, these two indices suggest that there is low to moderate diversity throughout the entire sequence at Yindayin.
Phase III Phase II Phase I 2 1 1.8 0.9 1.6 0.8 1.4 0.7 D 1.2 0.6 1 0.5 0.8 0.4 Shannon H' Shannon 0.6 0.3 1- Simpson 0.4 0.2 0.2 0.1 0 0 1 2 3 4 5 6 7 8 9 101112 Spit
MNI - Shannon H' MNI - Simpson 1-D
Figure 5.24: Diversity—Simpson 1-D Index vs Shannon H’
5.4.3 SUMMARY OF INDICES
All three indices suggest there was moderate and stable diversity across most of the occupation at Yindayin. This stability is related to the foragers focussing on a core group of taxa throughout the assemblage, albeit in changing proportions. NTAXA data has shown that after spit 10, richness is stable and that the assemblage is representative of the population
[116] from which it was drawn. The nestedness analysis re-iterated the representativeness of the assemblage and the presence and dominance of a core group of taxa. This dominance is also apparent from the Simpson and Shannon indices and explains the relative stability in diversity over time. The Simpson index indicates that after an initial reduction in diversity in spits 10 and 12 there is moderate diversity across the rest of occupation. The Shannon H’ index displays a similar trend, however, at a lower diversity range.
Diversity information relates directly to the analysis of EIC3 (diversity). According to the
Prey-choice model, increased diversity can indicate economic intensification in the
Boserupian sense. The increase in diet breadth can be seen as a decrease in foraging efficiency as more effort is required to capture less productive prey. The diversity data for spit 10 indicates a broadening of diet (in terms of species richness) but also a narrowing of focus onto a smaller group of taxa. In spit 10, foragers are taking a wide range of taxa but also pursuing the Neritidae in greater numbers which has suppressed the diversity indices.
In terms of EIC4, the broadening of diet implies decreasing foraging efficiency, however the dominance of a small group of taxa suggests a narrowing of foraging preference. This pattern requires further consideration.
[117]
5.5 PATCH PREFERENCES
This section collates the quantification data according to the habitat/patch preferences for each taxon to assess changes in patch preference over time. Figure 5.25 and Figure 5.26 illustrate the volume corrected MNI for the 7 patch types for each spit—the terrestrial patches identified in Table 4.1: Description of Patches for Yindayin Taxa have been omitted from this analysis. Figure 5.27 displays the percentage composition of each of the patches by spit with high and moderate abundance patches at the top and low abundance patches at the bottom. Percentages of MNI by patch are listed in Table 5.14: Percentage of MNI by patch and spit (dominant patches highlighted in yellow) below.
Table 5.14: Percentage of MNI by patch and spit (dominant patches highlighted in yellow)
Spit Intertidal Intertidal Mangrove Mangrove Mangrove Reef flat Supralittoral -hard -sand/mud -Landward -Mid zone -Seaward -hard 1 17% 22% 39% 2% 0% 3% 17% 2 16% 10% 43% 1% 0% 4% 25% 3 18% 6% 49% 1% 0% 2% 24% 4 15% 5% 44% 1% 1% 5% 30% 5 16% 9% 36% 1% 0% 3% 36% 6 21% 20% 11% 2% 1% 4% 40% 7 23% 21% 6% 2% 0% 4% 45% 8 27% 15% 14% 1% 0% 5% 38% 9 25% 13% 16% 0% 0% 4% 41% 10 27% 11% 7% 0% 0% 3% 52% 11 42% 12% 5% 1% 1% 1% 39% 12 20% 0% 40% 0% 0% 0% 40%
[118]
0.025
0.02
0.015
0.01 Volume Corrected MNI Corrected Volume
0.005
0 1 2 3 4 5 6 7 8 9 10 11 12 Spit
Intertidal - hard Intertidal - soft/firm Mangrove - Landward Supralittoral - hard
Figure 5.25: Volume corrected MNI for high and moderate abundance patches
[119]
0.0014
0.0012
0.001
0.0008
0.0006 Volume Corrected MNI Corrected Volume
0.0004
0.0002
0 1 2 3 4 5 6 7 8 9 10 11 12 Spit
Mangrove - Mid zone Mangrove - Seaward Reef flat
Figure 5.26: Volume corrected MNI for low abundance patches
[120]
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
1
2
3
4
5
6
7
8
9
10
11
12
Intertidal - hard Intertidal - soft/firm Mangrove - Landward Mangrove - Mid zone Mangrove - Seaward Reef flat Supralittoral - hard
Figure 5.27: Percentage of MNI by patch
[121]
5.5.1 PHASE I
Volume corrected MNI data for Phase I (Figure 5.25 and Figure 5.26) shows an overall preference for hard substrates in both the supralittoral and intertidal zones. Contributions from other patches are limited, however all patches are represented in spit 11, while only supralittoral-hard (40%), intertidal-hard (20%) and landward mangrove (40%) patches are found in spit 12.
5.5.2 PHASE II
Phase II begins with a substantial preference for supralittoral hard substrates and to a lesser extent, intertidal hard substrates. Figure 5.25 shows that spit 10 has the highest levels of volume corrected MNI for supralittoral hard, intertidal hard, and reef flat patches, while
MNI for the Intertidal-soft/firm patch is only exceeded by spit 1. While mangrove patches are only lightly used at this point, it is still notable that MNI for landward mangrove in spit
10 is not exceeded until spit 5. Despite the broad range of patches being used, diversity indices for this time are lower than at any other point in Phase II or III. The decrease in diversity is due to the concerted exploitation of Neritidae species from supralittoral and intertidal hard substrates. If the Neritidae family is excluded from the calculations, diversity becomes very high (Simpson 1-D = 0.8845, Shannon H’ = 2.342). Foraging in spit 10 appears to be generalised, with taxa from most patches being exploited in high numbers.
The main change in patch preference within Phase II is the transition from hard substrate taxa to mangrove taxa. The dominance of hard substrate patches gradually declines after spit 10 with the rising use of mangrove patches drawing level in spit 5 and then surpassing them in all later spits. While the mid zone and seaward mangrove patches remain low throughout the assemblage, the landward mangrove species (T. palustris, Terebralia spp.
[122] and Potamididae) more than triples their relative contribution between spits 5 and 6, and proportionally continues to increase for the rest of Phase II (see Figure 5.25).
Volumes of taxa from the Intertidal-soft/firm patch (G. pectinatum, A. violascens, and A. striata) are relatively low during Phase II with relative importance peaking in spits 6 and 7 before falling away at the end of Phase II.
The abundance of reef flat taxa implies it was a very minor patch, contributing only 2–5% of the material throughout Phase II. Concerns about field processing of large taxa from this patch have been raised by (Codding et al., 2014a: 242). While the true extent of field processing is difficult to judge, comparisons between non-field processed taxa and likely candidates for processing suggest that its effects may not be substantial—refer to section
5.3.5 for this analysis.
5.5.3 PHASE III
The modern dates obtained for spits 1 and 2 mean that only general observations of change between these two spits and spit 3 is possible. Overall, the trends in Phase III tend to follow on from those in Phase II. The Mangrove-landward patch is dominant throughout while the supralittoral hard patch continues to decline. Of note is the substantial rise in intertidal sand/mud taxa after spit 3 attributable to increased A. striata numbers. The Intertidal-hard patch remains stable while the remaining patches continue to be relatively minor contributors.
[123]
5.5.4 MEAT WEIGHT
Table 5.15: Total meat weight (g) for selected patches lists the calculated meat weights for the three largest patches across all spits—for this exercise, all mangrove patches have been aggregated. Meat weights have been calculated by multiplying MNI values by published meat weight data (see Table A-13 in APPENDIX A). Data for rare taxa was not available, however, 96.1% of all hard substrate and 97.9% of mangrove MNI are accounted for in this analysis. While data for the Reef platform and Intertidal-sand/mud patches has been omitted, the data included in this analysis still accounts for over 90% of the total MNI for
Yindayin. Of the omitted patches, only the Intertidal-mud sand patch data may be significant due to the contribution of taxa from this patch in spits 6 to 8 — the depressed figures for these spits is likely to be the product of this omission.
Table 5.15: Total meat weight (g) for selected patches
Phase III Phase II Phase I Spit 1 2 3 4 5 6 7 8 9 10 11 12 Total Supralittoral-hard 259 203 222 220 439 408 512 365 580 2944 70 2 6223 Intertidal-hard 297 135 161 127 179 236 297 318 421 1768 70 1 4010 Mangrove 2321 1328 1757 1223 1568 437 279 471 721 797 16 6 10922 Volume Corrected 0.05754 0.03331 0.04278 0.03138 0.04372 0.02163 0.02178 0.02307 0.01722 0.05508 0.00781 0.00047 Meat Weight (all)
[124]
100% 0.07 90% 0.06 80% 70% 0.05 60% 0.04 50% 40% 0.03 30% 0.02 Percentage/Patch 20% 0.01
10% (g/cm3) weight meat total VC 0% 0.00 1 2 3 4 5 6 7 8 9 101112 Spit
Supralittoral - hard Intertidal - hard Mangrove VC Total Meat Weight
Figure 5.28: Proportional contribution to meat weight by selected patches
The data shows that there is an even total split in meat weight between mangrove species and hard substrate patches. Figure 5.28 illustrates the initial high contribution of the hard substrate patches and the swift transition to mangrove dominance from spit 5 onwards. The peak in other measures seen in spit 10 is still significant in this analysis—the volume corrected total meat weights for spit 10 are higher than at any other period. While the exclusion of the reef and Intertidal-sand/mud patches may have slightly skewed the overall figures, this result is still significant and is not exceeded until spit 1. While the data presented here is coarse, it provides an estimate of the relative importance of these patches, in terms of subsistence, that may be hidden when the raw MNI data is viewed.
5.5.5 CHANGING PREFERENCES AND ECONOMIC INTENSIFICATION
The patch data displays a change in dominance with the Mangrove-landward patch overtaking the two hard substrate patches around 2,000–2,300 BP. This was not simply the inclusion of an additional patch—there is a real proportional increase in mangrove patch taxa with a concurrent reduction in supralittoral and intertidal hard substrate taxa. The
[125] patch choice model links preferences for patches to the average return rate of that patch relative to others. A decrease in foraging in one patch suggests that there has been a change in the return rates for one or both patches. The likely causes for this situation are:
1. over-exploitation of hard substrate species has led to the depression of populations and decreased encounter rates; and/or 2. socially mediated changes have led to changes in food preferences; and/or 3. changing environmental conditions have led to decreased hard substrate taxa abundance; and/or 4. changing environmental conditions have led to increased mangrove taxa abundance (sensu Codding et al., 2010; Harris and Weisler, 2017: 201).
Based on the characteristics of the dominant hard substrate taxa, Neritidae, the first proposition is unlikely. As examples from the Torres Straits, Australia, the central Pacific, and Caribbean demonstrate, this family is resilient to human predation due to its high maturation rate (Codding et al., 2014a: 243; Giovas, 2009: 4024). The second proposition is difficult to test in the archaeological record. Ethnographic data may provide some understanding of the process for changes in food preference (for an example see Bourke,
2015), however, linking that to events at Yindayin would still be speculative and only appropriate if the data does not conform to one of the other explanations. The last two propositions are potential explanations that will be explored in section 5.6 Environment and
Climate data.
The change in patch preference illustrated above does not signify economic intensification in the Boserupian sense. For decreasing efficiency to be identified, a pattern of increased exploitation of lesser ranked patches is required. Accurately assessing the ranking of patches is difficult as we cannot be sure of the relative productivity of each during different periods. As is noted in the next section, the climate and environment of this area are known
[126] to have fluctuated over the entire period of occupation and therefore the productivity of patches would vary as well. At a very coarse level, easily accessed patches such as the supralittoral and Intertidal-hard habitats would have had lower search and handling costs and the potential for a high ranking. The high abundance of taxa noted in the MNI data for these patches would have offset their small size. The Mangrove-landward patch would also have been attractive due to the size of Terebralia spp. relative to the small taxa available elsewhere and its greater accessibility relative to the more seaward mangrove patches. The
Intertidal-soft/firm patch may have been less attractive due to the reduced visibility of prey and the effort required to dig prey out of the substrate. The size of taxa in the reef flat patch and the presence of other reef taxa such as fish and turtle would have made this patch attractive, however, high inundation may have offset this to some extent—the issue of field processing also complicates the assessment of ranking for this patch.
5.6 ENVIRONMENT AND CLIMATE DATA
For economic intensification to be present, environmental and climate factors must first be assessed to see if they provide a more parsimonious explanation for the patterns observed.
This section presents the environmental and climate data for Yindayin during the Holocene.
The prevalent conditions during occupation will be highlighted and their implications will be discussed. The collated data for Yindayin is displayed in Table 5.16 alongside the date ranges for occupation.
[127]
Table 5.16: Comparison of climate/environmental variation with occupation at Yindayin (grey/orange = amelioration/fluctuation or increasing instability)
Phase III Phase II Phase I Spit 1 2 3 Hiatus 4 5 6 7 8 9 10 Hiatus 11 12 Median Date 66 51 179 2083 2312 2073 2384 2505 2825 2938 6286 N/A cal BP Falling Oscillation Sea levels1 ca. 2,000 Stable Oscillation 1m ca. 4,500– High Stand BP 4,800 BP Cessation Modern ca. 4,800– Reef 5,500 BP Stable? Instability? Peak vertical accretion formation2 Flinders atoll ca. 4,100 cal BP Effective Wet phase ca. 40–220 cal BP Decreasing EP Increasing EP Precipitation3 Temperature4 1-2°C hotter Increasing ENSO Peak Low frequency and Frequency & amp. ca. Increasing frequency and amplitude of ENSO events amplitude amplitude5 1,200 BP PCB Extreme ca. 1930, ca. 70 BP ca. 2525 BP Cyclones6 1965 BP
Data sources:
1. (Lewis et al., 2008: 78-79; Lewis et al., 2013) 2. (Smithers et al., 2006: 180) 3. (Burrows et al., 2014: 1713-1714; Rowland et al., 2015: 157; Shulmeister, 1999: 82) 4. (Shulmeister, 1999: 82) 5. (Carré et al., 2014: 1045; Gagan et al., 2004; Leonard et al., 2016: 1248; Moy et al., 2002: 164; Rowland et al., 2015: 157) 6. (Nott and Hayne, 2001: 509)
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5.6.1 SEA LEVEL VARIATION
Sea level data has been sourced from Lewis et al. (2008) and Lewis et al. (2013). These data show the mid-late Holocene highstand peaking at 1.3–1.5m between 6,770 and 5,750 cal BP
(Lewis et al., 2013: 126). It is notable that the earliest occupation date for Yindayin falls within the highstand range. Two sea level oscillations of approximately 1m have been identified between 4,800–4,500 and 3,000–2,700 BP (Lewis et al., 2008: 79; Lewis et al.,
2013: 126). The second of these oscillations straddles spits 8 (2,505 cal BP), 9 (2,827 cal BP) and 10 (2,939 cal BP). Oscillation of sea levels during this period may have disrupted habitats on Yindayin and may explain the potential hiatus identified for spit 9. Lewis et al.
(2013: 126) estimate that the decline of sea levels to modern levels occurred rapidly after
2,000 BP. The decline in sea levels is contemporaneous with the abandonment witnessed at the end of Phase II and the occupation of the chenier plains of Princess Charlotte Bay
(Beaton, 1985: 9).
5.6.2 REEF FORMATION
Most fringing reefs in the GBR region were initiated after the postglacial marine transgression, an average of 7,100 years before present (Smithers et al., 2006: 180). Vertical reef accretion across the GBR was most intense between 8,000 and 5,000 BP, particularly for fringing reefs (Smithers et al., 2006: 180). Rising sea levels, higher sea-level temperatures, as well as other ameliorating environmental conditions, encouraged coral growth and reef accretion between initialisation and 5,500 BP (Smithers et al., 2006: 180-
181). Across the GBR, there is a cessation in reef progradation between 5,500–4,800 BP, most likely due to the stabilisation of sea levels and the resulting exhaustion of space into
[129] which reefs could vertically and horizontally expand (Kennedy and Woodroffe, 2002: 255;
Smithers et al., 2006: 181).
The peak reef accretion period overlaps with the Phase I of occupation. It is likely that during this Phase I, reef formation within the Flinders Group had not stabilised. A microatoll between Flinders and Stanley Islands has been dated to 5660+/-205 BP (4,100 cal BP7) suggesting that local sea levels and reef formation continued to fluctuate well after
Phase I—the 4,500–4,800 BP oscillation in sea level supports this idea (Chappell, 1982: 407;
Hopley et al., 2007: 78; Lewis et al., 2008: 79; Lewis et al., 2013: 126). The limited amount of reef species identified during Phase I may be due to the instability of local fringing reefs.
5.6.3 EFFECTIVE PRECIPITATION AND TEMPERATURE
Studies by Shulmeister and Lees (1995) and Shulmeister (1999) have shown that ameliorating climate, as represented by increasing effective precipitation and temperature, gradually increased from the early Holocene. Amelioration peaked between 5,500 BP and
3,700 BP with precipitation and temperature higher than modern levels (200–1,000mm and
1–2°C respectively) (Shulmeister, 1999: 82). The period between 7,000 BP and 3,700 BP is estimated to have been approximately 50% wetter than today (Shulmeister and Lees, 1995:
12). Effective precipitation declined markedly after 3,700 BP and climate in general become increasingly variable due to the initiation of the ENSO dominated circulation system in
Northern Australia (Shulmeister, 1999: 82; Shulmeister and Lees, 1995: 14-15). Humification
7 Calibrated with OxCal 4.3 using Marine 13 calibration curve and ∆R of 12±10 [130] data from the Atherton tablelands indicate that condition in north Queensland during Phase
III were wetter than average (Burrows et al., 2014: 1713-1714).
Phase I of occupation of Yindayin corresponds to a period of high precipitation, while Phase
II sits within an increasingly arid period. Increasing aridity may have forced foragers to seek out new areas due to reduced productivity and may explain the timing of the initiation of
Phase II. Increasing aridity over time may also have made mangroves more accessible which may account for its increasing importance as a resource patch (Turney and Hobbs, 2006:
1747).
5.6.4 FLUCTUATIONS IN THE EL-NINO SOUTHERN OSCILLATION (ENSO)
ENSO events are low in concentration prior to 7,000 BP but increase over time, becoming more common after 4,000 BP. Peak frequency occurs around 1,200 BP with peak amplitude between 2,000–1,000 BP (Moy et al., 2002: 164). A study by Leonard et al. (2016: 1248) on corals from the GBR shows less frequent and less intense ENSO patterns between 5,200 to
4,300 BP and cooler sea surface temperatures relative to the previous millennia and modern levels.
The higher occurrence and increased amplitude of ENSO events after long periods of stability may have disrupted biota and forced foragers to change behaviours to account for more variable conditions. Occupation during Phase II falls in a period of increasing variability in ENSO patterns and occupation and abandonment of Stanley Island may be a response to changing resource patterns on the mainland.
[131]
5.6.5 EXTREME CYCLONIC EVENTS
Data from Nott and Hayne (2001) demonstrates that extreme-cyclonic activity that has played a part in the ecological development of the Princess Charlotte Bay area. Their dating of storm ridges created by Category 5 cyclones (Figure 5.29) shows that storms that occurred at ca. 70, 1930, 1965, and 2,525 cal BP align or overlap with occupation at Yindayin
(Nott and Hayne, 2001: Figure 1).
END10 9 10 END09 8 END08 END07 END06 7 6 END05 END04 5 3 4 END03 END01 SR12 1 SR11 SR10 SR9 SR8 SR7 SR6 SR5 Storm Ridge dates Yindayin Dates Yindayin Ridge dates Storm SR4 SR3 SR2 SR1
0 500 1000 1500 2000 2500 3000 Dates cal BP
Figure 5.29: Comparison of dating between extreme cyclonic events and dating of Yindayin occupation (spit numbers in callout boxes)
The occurrence of a cyclone at 2,525 cal BP falls within the timeframes established or spit 8.
There is no marked decrease in productivity for this spit, suggesting that there was little disruption to reef or mangrove habitats on Yindayin or that recovery was swift. If the
[132] potential hiatus between spits 8 and 9 occurred, then re-occupation of spit 8 may relate to disruption caused by the cyclone to other areas within Princess Charlotte Bay—this would require further investigation of mainland sites. The two cyclones that occur at the end of
Phase II may provide explanation for the hiatus or at least provide additional impetus to abandon foraging in the area.
5.6.6 SUMMARY OF ENVIRONMENT AND CLIMATE DATA
The environmental data provides the context for changes that occurred at Yindayin. The key environmental and climate variations that may have affected occupation at Yindayin are:
Phase I occurs during the sea level highstand. Sea level oscillation between 3,000 BP and 2,700 BP is centred on spit 9 and seas begin falling just after the end of Phase II; Reef formation is linked to sea levels which continue to fluctuate and may have been unstable during Phase I and the start of Phase II; Effective precipitation and temperature were high during Phase I and declined over the length of Phase II. Fluctuations in ENSO increased over time, with Phase II facing increasingly variable climate as a result. Extreme cyclonic activity has been noted just prior to spit 8 and again just after the end of Phase II.
The link between these events and the data in this chapter will be discussed further in
Chapter 6.
5.7 OTHER CULTURAL MATERIALS
Only a cursory examination of the stone material was conducted during the primary sort, however, knapped material was rare. The material found was mostly debitage or unworked flakes, with a single core found in spit 10. Most of lithic material is limited to spits 10–12
[133] with only 3 flakes identified beyond this period. Other materials include a single bored
Monetaria annulus (cowrie) that was found in spit 6 and a piece of a clay pipe from spit 1.
5.8 SUMMARY
This chapter outlined the results generated through the application of the methods outlined in Chapter 4. These results show that the midden material from Yindayin displays three distinct phases dating back to 6,286 cal BP. Occupation during Phase I was fleeting, while occupation during Phase II was steady after an intense initial occupation and possible abandonment. Foraging in Phase II underwent a transition, with initial preferences shown for taxa from hard substrates to a more mangrove dominated strategy. The most recent period, Phase III, saw a continuation of that foraging strategy, with high abundances of mangrove taxa being deposited. The diversity of taxa deposited at Yindayin remained low to moderate throughout Phases II and III and reflected the dominance of the hard substrate and mangrove patches. The effects of climate and accompanying environmental change fluctuated over the periods that Yindayin was occupied. Environmental conditions throughout occupation were variable with sea levels fluctuations, increasing aridity, and a general unpredictability in weather patterns as ENSO frequency and amplitude increased.
The following chapter will discuss these results, beginning with an evaluation of the economic intensification criteria set out in Chapter 2. This is followed by a synthesis of the economic intensification results, the environmental and climate data, and Beaton’s chronology of Princess Charlotte Bay, to produce a model of occupation at Yindayin. The future directions for research in the region are presented followed by conclusions about this research and how they relate to broader questions in coastal archaeology.
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Chapter 6 DISCUSSION AND CONCLUSIONS
6.1 INTRODUCTION
The results in Chapter 5 have identified trends and anomalies in the types, quantities, and diversity of invertebrates being consumed throughout occupation at Yindayin. This chapter will first assess these results against the economic intensification criteria outlined in Chapter
3 (section 3.5). Environmental and climate information will then be contrasted with the data to evaluate the economic structure and occupation patterns at Yindayin, particularly where the data cannot be explained with reference to economic intensification. Information from
Beaton’s study of Princess Charlotte Bay will also be considered to assess if changes on the mainland align with economic and occupation changes at Yindayin. The future directions for research in the region will be presented, followed by conclusions about the findings and how they relate to broader questions in coastal archaeology.
6.2 TESTING THE ECONOMIC INTENSIFICATION CRITERIA
For this study, economic intensification has been defined as a sustained increase in the production/procurement of economic resources that is accompanied by a decrease in foraging efficiency (Butler and Campbell, 2004: 336). The results from Chapter 5 allow us to evaluate patterns of occupation and the structure of the foraging economy at Yindayin against the criteria set out in Chapter 2 (section 2.5).
The following section outlines the assessment of the criteria. There are no quantitative thresholds for the criteria, therefore assessments are based on identifying the spits that appear to be outliers to the overall trends. Each criterion will build on previous ones, with only the spits that meet all four criteria being considered as candidates for economic
[135] intensification. The results for each criterion will be discussed and spits that meet the criteria will be highlighted.
1. Evidence for increased intensity of site occupation and deposition of cultural material (site occupation).
Accumulation rates (section 5.2.2) and deposition rates (section 5.2.3) have been used to assess the intensity of site occupation and deposition. Accumulation rates display two definite breaks in occupation after ca. 6,286 cal BP and ca. 2,073 cal BP, plus an additional reduction that may indicate a third break between ca. 2,825 cal BP and ca. 2,505 cal BP.
Accumulation is at its highest in spits 1 and 10. Deposition rates of all excavated components except stone, peak in spit 1, while spit 10 is notable due to high levels of shell, stone, and bone deposition relative to other spits in Phases I and II. Spit 5 shows notable peaks in shell, bone, and charcoal, but it is unclear what effect the possible pit/hearth noted between spits 4 and 6 (see section 5.2.4) has had on the density of material in this section of the deposit.
The data indicates that there is an increased intensity of site accumulation and deposition of materials in spits 1 and 10. No other spits exhibit occupation or deposition characteristics that suggest increased activity. There is no clear unidirectional trend for increasing intensity at the site, however, there are two pulses in spits 1 and 10 that may satisfy this criterion, with a possible third pulse in spit 5.
2. Evidence of increased quantities of subsistence material being deposited.
Invertebrate quantification (section 5.3.1) and meat weight yields (section 5.5.4) have been used to assess for increased quantities of subsistence material.
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Over the period of occupation at Yindayin there is no obvious increasing unidirectional trend in invertebrate material (MNI) being deposited. There are two definite peaks in volume corrected invertebrate MNI, the first and most substantial in spit 10, and a second smaller increase in spit 1. Spit 10 is dominated by small bodied taxa from hard substrates while Spit
1 is dominated by the larger mangrove dwelling Potamididae species. The comparison of meat weight values for spits 1 and 10 suggest that they provided similar meat yields (see section 5.5.4). There is also a minor peak for spit 5 but again, some clarity has been lost due to the potential for mixing of material with spits 4 and 6.
The data indicates that there is an increased discard of subsistence remains in spits 1 and 10 and a general stability in all other spits. While there is no unidirectional trend, the peak in discard in spits 1 and 10 suggest that the criterion may have been met for these two spits.
The results for criterion 1 and 2 suggest that in spits 1 and 10 there was an increase in the amount of subsistence and occupational material being deposited that may represent increasing economic intensity. These signals can be produced by standardisation; therefore, diversification must also be assessed.
3. Evidence for increased diversity in both taxa and environmental patches being exploited.
Richness and diversity indices (section 5.4), and patch preference data (section 5.5) has been used to assess this criterion. From spit 9 until the end of Phase II, diversity values across the midden fall within in a relatively constrained range (Simpsons 1-D 0.6779–
0.7466). The diversity and richness values from spit 10 present deviations that require further explanation. The pattern witnessed for spit 10 suggests a decrease in diversity yet an increase in richness. While decreased diversity suggests specialisation, the increase in
[137] richness implies diversification and therefore a decrease in foraging efficiency. Earle (1980:
20) has stated “Other things being equal, a rise in population density should result in two simultaneous processes—an intensification of existing strategies and diversification into new strategies”. The first part of the statement “Other things being equal” is important here as oscillating sea levels between 2,700–3,000 cal BP and the onset of increased ENSO fluctuations around 3,000 BP are likely to have affected foraging strategies. A generalised foraging strategy that combines low diversity and wide diet-breadth spreads foraging risk and therefore may be employed during periods of resource unpredictability—as this was a reoccupation, some of the unpredictability may relate to the initial unfamiliarity of foragers with resource patterns in the area (Binford, 2001: 402). Foragers during spit 10 were mostly taking small bodied, hard substrate prey that are resilient to sea level change, but also offsetting the risk of resource procurement failure by foraging across all patches. This pattern does not suggest that process of economic intensification was present during spit
10.
The data indicates an increasing trend in diversity for Phase III. The Simpson 1-D index increases from a relatively low score of 0.6526 in spit 3, to 0.693 in spit 2, and peaks at
0.7353 in spit 1—this is the second highest diversity result recorded at Yindayin. This increase in diversity is directly related to the growing inclusion of A. striata in the assemblage (see section 5.3.5 for details). In spit 3 two families, Potamididae and Neritidae, dominate the assemblage, providing 80.5% of the total MNI. The Mesodesmatidae (A. striata) rise from 3.6% of total MNI to 9.5% and 20.9% in spits 2 and 3 respectively. The increased contribution of the Mesodesmatidae is offset by an equivalent decrease in
Potamididae and Neritidae, resulting in an increase in diversity. The data on richness is
[138] relatively steady after spit 10 but undergoes a period of fluctuation from spit 4 onwards— this period coincides with the dominance of mangrove taxa. During the mangrove period,
NTAXA for spits 1 to 4 averaged 23 compared to the average of 28.3 for spits 5 to 10.
Richness in Phase III fluctuates across spits 1 to 3, starting at 24, declining to 20, and then peaking at 27 in spit 1. When viewed against the average richness results for the mangrove period, spit 1 represents a significant increase in richness.
Section 2.3.2 identified the following patch related signals as possible indicators of economic intensification:
a decrease in the dominance of previously productive patches, an increase in the evenness of patch contributions, an increase in the use of more marginal or difficult to access patches.
While all patches are exploited in all spits during Phase II, there is a change in patch dominance that occurs around spit 5. Spits 6 to 10 MNI data shows a clear preference for taxa from the two hard substrate patches. By spit 5, MNI for the Mangrove-landward taxa almost equal to the two hard substrate patches and surpasses them from spit 4 onwards.
This change in patch ranking likely reflects an increase in abundance and density of the
Potamididae family that increased the productivity of the Mangrove-landward patch. Across
Phase III there an increase in the use of the intertidal sand/ mud patch, with the proportion of total MNI for this patch increasing from 6% in spit 3 to 22% in spit 1 (see Table 5.14). As noted above this change is almost solely represented by the increasing numbers of A. striata.
While spit 10 met criterion 1 and 2, it fails criterion 3. As already noted, the generalised foraging strategy employed here is likely to represent a response to environmental
[139] unpredictability (Binford, 2001: 402). The data for spit 1 suggests an increasing overall diversity and therefore that it meets this criterion. Diversity and richness data increase over
Phase III and peak in spit 1 while at the same time there is an increase in the contribution of lesser ranked patches.
4. Evidence for a decline in foraging efficiency.
Richness and diversity indices (section 5.4), and patch preference data (section 5.5) have been used to assess this criterion in conjunction with an assessment of relative abundance and contribution to the diet (section 5.3.1 MNI Count and section 5.5.4 Meat weight).
In simple terms, foraging efficiency refers to how much food is gathered per person, per unit of time (Kelly, 2013: 46). A decrease in foraging efficiency implies that people are obtaining less food relative to their effort. As we cannot directly observe the effort of past foragers or know how many foragers there were, we must rely on identifying increases in diversity that include less efficient resources (Braje and Erlandson, 2009: 271). The stability in diversity values noted in criterion 3 for Phase II implies that there is little change in foraging efficiency. While there is an increase in diversity between spits 9 and 10, it is not accompanied by an increase in overall abundance. In addition, the transition from hard substrate to mangrove dominance may indicate an increase in foraging efficiency at the end of Phase II, as meat weight calculations suggest that fewer mangrove species are required to supply the same amount of meat as the smaller hard substrate taxa. While mangroves are marginally more difficult to traverse than rocky substrates, the difference in search costs are unlikely to outweigh the increase in energy returns. The transition from hard substrate to mangrove exploitation makes sense from a prey choice perspective as it would increase foraging efficiency.
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The greatest potential for decreased foraging efficiency can be found in Phase III. Diversity in this phase increases between the start and end of the phase due mostly to the increased contribution of A. striata. Based purely on meat weight values A. striata at <1g would be a lower ranked taxon than the Potamididae species. It has been noted previously that A. striata are easy to harvest and aggregate in high numbers (Jew and Fitzpatrick, 2015: 478;
Thomas, 2001: 86). If abundances of A. striata were high enough to allow mass harvesting, its inclusion in the diet could actually represent an increase in foraging efficiency and therefore the reorganisation of foraging to incorporate a highly productive small prey. This issue cannot be resolved with the data that we have but future work to assess the biology and fecundity of A. striata may aid in understanding whether its inclusion represents increased abundance relating to favourable environmental conditions or diversification in the face of resource pressure.
Overall there is a decrease in foraging efficiency in spit 10, however this decrease represents environmental instability rather than economic intensification. At the end of Phase II, foraging efficiency increases as the larger Potamididae species supplant small hard substrate taxa. The dominance of Potamididae continues into Phase III, however, diversity indices, richness, and patch preferences point to a broadening of diet that may represent decreasing foraging efficiency
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6.2.1 ASSESSMENT OF ECONOMIC INTENSIFICATION
As the above assessments have shown, spit 1 is the only period where the criteria for economic intensification may have been met. Phase III, and in particular spit 1, demonstrates the following characteristics:
high relative levels of site occupation in terms of accumulation and deposition rates; high relative quantities of invertebrate material; increasing diversity and richness in the taxa being exploited after a period of more focused foraging; a possible decreasing trend in foraging efficiency.
The combination of these four observations suggest there was a narrow subsistence focus at the beginning of Phase III with high relative abundances of the dominant taxa being deposited. The abundances of the dominant invertebrate resources increase in spit 1, and additional, less efficient resources are added to this group. The combination of high abundance of dominant taxa with the addition of less efficient taxa suggests that foragers would have produced less food relative to the amount of effort, and therefore foraging efficiency would have been decreasing. Over this same period there is an increase in accumulation and deposition rates, potentially indicating longer periods of occupation, however, the recent age of these deposits suggests that increased preservation may be a factor. Climate and environmental pressure may have been reduced during Phase III, with humification data from the Atherton Tablelands suggesting this was a period of above average rainfall (Burrows et al., 2014: 1713-1714).
The above analysis demonstrates that economic intensification may have only occurred near the very end of occupation at Yindayin. As already noted, environmental and climate
[142] change may have played a greater part in influencing the intertidal foraging behaviour at
Yindayin—this is explored in the following section.
6.3 A MODEL FOR CHANGE AT YINDAYIN
The following section is an explanatory model that describes occupation at Yindayin. The model synthesises the information relating to the economic intensification assessment, the environmental and climatic data, and Beaton’s chronology of Princess Charlotte Bay. The key data for this model is illustrated in Table 6.1.
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Table 6.1: Comparison of data with dates from Yindayin (yellow/blue shading = below/above average, grey/orange = amelioration/fluctuation or increasing instability)
Yindayin spits and dates Phase III Phase II Phase I Spit 1 2 3 4 5 6 7 8 9 10 11 12 Hiatus Hiatus Median Date 66 51 179 2083 2312 2073 2384 2505 2825 2938 6286 N/A cal BP Climate/Environment Data Falling ca. Oscillation Sea levels 2,000 Stable Oscillation 4,500–4,800 High Stand BP BP Modern Cessation Reef formation Stable? Instability? 4,800–5,500 Peak vertical accretion BP Effective Wet phase ca. 40–220 cal BP Decreasing EP Increasing EP Precipitation Temperature 1–2°C hotter Increasing temperature ENSO Peak Modern Low frequency and Frequency & amp. ca. Increasing frequency and amplitude of ENSO events amplitude amplitude 1,200 BP ca. 1930, PCB Extreme ca. 2525 cal ca 70 1965 cal Cyclones BP BP Accumulation/Deposition data Age Depth Steep Declining Steep Steep Hiatus? Steep N/A Curve Hiatus Hiatus VC total 0.52116 0.32464 0.35656 0.27887 0.38099 0.25994 0.23372 0.28034 0.1318 0.35393 0.25906 0.13128 material Invertebrate quantification data Total MNI 1310 670 784 621 1012 847 959 802 1186 4761 150 5 VC Total MNI 0.0262 0.0134 0.0157 0.0124 0.0202 0.0169 0.0192 0.016 0.0119 0.0476 0.0075 0.0003 NTAXA 27 20 24 21 28 27 27 27 25 36 16 9 Simpson 1-D 0.7353 0.693 0.6526 0.6779 0.6828 0.7112 0.7028 0.7466 0.6975 0.5425 0.7259 0.32 Meat weight 0.05754 0.03331 0.04278 Hiatus 0.03138 0.04372 0.02163 0.02178 0.02307 0.01722 0.05508 Hiatus 0.00391 0.00023 Dominant Potamididae Potamididae Potamididae Potamididae Neritidae Neritidae Neritidae Neritidae Neritidae Neritidae Neritidae Neritidae Family (MNI) Dominant Mangrove- Mangrove- Mangrove- Mangrove- Supralittoral Supralittoral Supralittoral Supralittoral Supralittoral Supralittoral Supralittoral Supralittoral Patch (MNI) landward landward landward landward Beaton’s Data Occupation of chenier plains starts ca. 2000 BP, Yindayin Walaemini rockshelter ca. 5,556 cal BP PCB Dates Mound building peaks ca. 1,000 BP, ca. 2,547 cal Chenier plain formation begins ca. 4,000 BP Mound building/chenier formation ends ca. BP Alkaline Hill ca. 3,846 cal BP 5–600 BP
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6.3.1 PHASE I: CA. 6,286 CAL BP TO 3,000 BP
There is limited data for this period. What we do know is that there were three sites occupied in the region during this period, all of which exploited invertebrates from the intertidal zone. Based on the amount of material deposited at Yindayin, occupation during
Phase I was brief. During this phase, sea levels were still rising, the fringing reef around
Stanley Island was in flux, and it was likely that mangrove habitats were unpredictable.
Motile, small taxa from rocky intertidal areas that were able to adapt to changing sea levels dominate the assemblage during this period (McNiven et al., 2014: 31). It is difficult to speculate on what brought foragers to Yindayin during this period, especially as increasing rainfall and temperature at the time may have meant increased productivity on the mainland. The reasons for abandonment of Yindayin are equally as unclear as there is very little in the dataset to inform us on this. The next oldest site in Princess Charlotte Bay is the
Walaemini rockshelter dated to 5,557 cal BP which lies along the stretch of coastline nearest to the Flinders Group (Beaton, 1985: 7). Intertidal foraging at Walaemini was dominated in its earliest levels by the mud flat species T. granosa, supplemented by other mud flat, mangrove, and rocky shore species (Beaton, 1985: 7). The next signs of occupation do not occur until 3846 cal BP at Alkaline Hill. Here too the earliest layers show people primarily exploiting T. granosa but also Terebralia spp. and T. telescopium.
6.3.2 PHASE II: CA. 2,938 CAL BP TO CA. 2,073 CAL BP
The reoccupation of Yindayin at ca. 2,938 cal BP is the next sign of occupation in the region.
The timing for this reoccupation is contemporaneous with the establishment of sites in the
Whitsunday Islands (Hill Inlet rockshelter, Nara Inlet Art Site 1), the Northumberland Group
(St Bees Island, Curlew Island, Spur Bay Midden), and in Shoalwater Bay (High Peak Island).
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It also coincides with the changes in occupation observed in the Whitsundays Islands (Nara
Inlet 1, Border Island), Shoalwater Bay (Otterbourne Island), and the Keppel Islands (Mazie
Bay). The initial occupation at may have been related to the increased variability in ENSO events. Decreasing productivity and unpredictability on the mainland may have pushed people to forage wider, leading them to again exploit the island habitats of the Flinders
Group (Rowland et al., 2015: 158).
The people who reoccupied Yindayin at the start of Phase II were generalised foragers who exploited all available patches. Like the previous phase, foragers harvested the bulk of their shellfish from the rocky intertidal, again, most likely due to the ability of these small taxa to adapt to changing sea levels. Harvests of small gastropods like N. undata and N. polita reached numbers that are were not repeated at Yindayin. Additionally, bivalves were dug in large numbers from the mud and sand of intertidal areas, while clusters of
C. luhuanus were taken in record numbers from the reef flat. Mangrove patches were exploited, with high levels of N. planospira taken, however Potamididae numbers were low.
This suggests that mangrove trees favoured by N. planospira were present, but the mud flat zones preferred by Potamididae may not have been fully formed at the start of Phase II.
Foraging theory suggests that the high richness in spit 10 represents decreasing foraging efficiency however the decrease in diversity suggests the opposite (Nagaoka, 2001: 107).
Binford (2001: 402) has argued that diversification can occur during time of resource unpredictability as foragers mitigate foraging risk. Resource unpredictability would have been high during spit 10, primarily due to disruptions to the environment but also due to the forager’s initial unfamiliarity with the islands resource patches after a period of abandonment. Spit 10 is also the only spit where concentrations of anthropogenic lithic
[146] material have been found. Barker (2004: 147) has interpreted the reduction in lithic material at Nara Inlet 1 as part of the transition from generalised coastal foragers to marine-adapted foragers that began around 3,000 BP. While the timing also fits for Yindayin, Barker’s assessment at the Whitsunday’s was also premised on an increase in wooden, bone, and shell artefacts linked to marine procurement, something that is absent from Yindayin (2004:
145-146). The near absence of lithics in later spits may relate to the use of Geloina coaxans shell as a tool, something that has been noted by Beaton (1985: 6) and tested by Harris et al.
(2017).
The foraging strategy during Spit 9 is a continuation of that seen in spit 10 but at a reduced rate. Accumulation rates and deposition rates for most materials decline, and while patch preferences remain relatively stable, abundances drop to the lowest levels seen in Phase II.
A sea level oscillation of 1m has been noted between 2,700 BP and 3,000 BP, potentially disturbing the productivity of coastal habitats in the area. With the sea level oscillation disrupting mangrove and reef patches, foragers would have increasingly relied on taking large numbers of the small bodied, hard substrate taxa. Mannino and Thomas (2002: 457) have noted that shellfish taxa on rocky shores in the intertidal are the most likely group to face sustained predation and subsequent depression. Mass harvesting may have caused a depression of these taxa, however, further analysis, as summarised by Mannino and Thomas
(2002: 458), would be required to test this idea in full. Conversely, Codding et al. (2014a:
243-244) have suggested that despite its dominance in many modern Western Pacific middens near permanent settlements, Nerita spp. populations have not been diminished—a similar observation has been made in the Caribbean (Giovas et al., 2013a: 4024). What is clear is that taxa across most patches during spit 10 are heavily exploited relative to other
[147] periods. If, by the end of spit 9, all patches were sufficiently depressed by a combination of foraging and sea level oscillation, then foragers at Yindayin may have been forced to abandon it for more productive locations in Princess Charlotte Bay.
After approximately 300 years, reoccupation begins again with spit 8 dated to ca. 2,505 cal
BP. Beaton’s basal date of 2,547 cal BP from his 1980 excavation correlates with reoccupation during spit 8. The sterility of layers below Beaton’s date suggest that this was a new occupation event, supporting the idea for a reoccupation at this time. The impetus for reoccupation of Yindayin is unclear, however a category 5 cyclone is known to have struck the Princess Charlotte Bay around ca. 2,525 cal BP. If coastal habitats in the area were destabilised at this time, it may have caused coastal foragers to seek out new areas. The foraging strategy in spit 8 continues the focus on hard substrates. This strategy continues in spits 6 and 7, however there is increased use of the intertidal sand/mud patch indicated by increased A. violascens abundance—this trend does not continue after spit 6.
During spit 5 (ca. 2312 cal BP) there is a significant change in patch preference towards mangrove exploitation. The previous exploitation of small bodied taxa suggests that they were the most abundant invertebrate resource in the area until this point. A preference for abundant small bodied invertebrates has been noted in the north Pacific when larger invertebrates were unavailable (Braje and Erlandson, 2009: 282). If they were accessible and available in high enough abundances, the larger Potamididae species would have been a higher ranked resource than the smaller hard substrate taxa. It is likely that the transition to mangrove foraging relates to the increasing relative abundance and increasing accessibility of Potamididae species. Climate data indicates that there were increasing El-Nino events throughout Phase II that produced longer and more acute periods of aridity. Increasing
[148] aridity may have caused a reduction in hard substrate taxa, although, information on the response to increased aridity of taxa like Nerita spp. is not clear (Giovas et al., 2013b: 4034).
Potamididae like T. palustris feed in the shade of mangroves and are resistant to desiccation, therefore it is likely they were less affected by the increasing aridity during
Phase II (Raw et al., 2017: 264). As noted previously, Turney and Hobbs (2006: 1747) have suggested that the onset of ENSO activity may have made mangrove areas more accessible thereby increasing encounter rates with Potamididae species.
The reasons for abandonment of Yindayin after spit 4 may be related to the increasing reliance on mangrove taxa that began in spit 5. It is around this period that sea levels undergo a rapid decline (Lewis et al., 2013: 126). This decline may have destabilised mangrove environments and decreased productivity to the point where Yindayin was no longer viable. Two Category 5 cyclones are also known to have struck Princess Charlotte Bay at ca. 1,930 cal BP and ca. 1,965 cal BP, again, potentially disrupting biota across the area.
The abandonment of Yindayin also correlates with the start of occupation of the chenier plains in Princess Charlotte Bay (Beaton, 1985: 9). The increasing abundance of T. granosa on the mud flats of Bathurst Head combined with declining resources at Yindayin may have drawn people back to the mainland.
6.3.3 PHASE III: CA. 2000 BP TO PRESENT
Dating from Yindayin shows that it was abandoned for 1,800 years. During this hiatus, the people of Princess Charlotte Bay focused their foraging on the chenier plains south of
Bathurst Head, depositing large shell mounds dominated by T. granosa. Mound building peaks around 1,000 BP but by 500–600 BP the chenier formation ceases, and with the loss of the T. granosa beds, mound building is also abandoned (Beaton, 1985: 11-12). While the
[149] peak in mound building is concurrent with intensification of occupation of other islands along the Queensland coast, the reoccupation of Yindayin is much later, fitting more with the collapse of the T. granosa beds. Haberle and David (2004: 177) have argued that the large-scale exploitation of T. granosa on the chenier plain represented a new specialised subsistence strategy that allowed increases to human populations in the area that were previously restricted by climate and environmental conditions. It is possible that this pattern of intense intertidal exploitation was moved to the mangrove economy of the islands once
T. granosa beds had collapsed on the mainland.
Reoccupation of Yindayin around ca. 179 BP sees a resumption of the foraging patterns seen in Phase II, with mangrove resources dominant but supplemented with small taxa from hard substrates. Of note is the speed and volume of invertebrate deposition that occurs at this time. While spits 1 and 2 are treated as separate entities throughout this study, dating suggests they are contemporaneous, therefore, observations of the increased intensity of occupation in spit 1 may be slightly overstated. The analysis of economic intensification suggests that this Phase underwent population driven resource stress, with increased signs of occupation, increasing quantities of invertebrate materials, and increasing diversity that suggests the failure of specialisation to provide enough resources to deal with populations in the area. While the balance of evidence appears to support this conclusion, it must be acknowledged that the increased presence of material in Phase III may be biased by the young age of this part of the midden and the greater likelihood of preservation. Hale and
Tindale (1933-1934: 77-78) noted during their investigation of the area in the 1930s that only 25 Walmbaria people inhabited the Flinders Group and Bathurst Head at the time, however, two generations previous there were 30–40 people in the islands and 30 on the
[150] mainland. They also noted that more people occupied the islands due to the greater availability of coastal and reef resources (Hale and Tindale, 1933-1934: 77-78).
6.4 FUTURE DIRECTIONS
There were a number of issues that could not be addressed in this study but may generate further information about the people of Yindayin.
Firstly, an assessment of over-exploitation would provide additional data for understanding the intensity of intertidal foraging. An analysis of size classes for key taxa would allow us to assess chronological changes in size that identify periods of high predation and feed into assessments of over-exploitation as per the criteria set out by Mannino and Thomas (2002:
458); (Harris and Weisler, 2018). In addition, an assessment of size may allow us to verify periods of initial occupation or reoccupation as, all other things being equal, initial size classes should be larger than those later in the assemblage (see Lentfer et al., 2013: 152 for an example of this). A study of inter-species relationships, that is, how exploitation of one taxon affects complimentary or competitive species, may also assist with identifying predation pressure.
Palynological studies such as that conducted by Proske and Haberle (2012) at the nearby
Lizard Island would benefit our understanding of the chronology of changes in mangrove production within the Flinders group. This data would allow us to test the hypothesis that early reliance on hard substrate taxa was due to environmental factors affecting the stability of mangrove environments and to assess the impact of storm damage.
Both the vertebrate and lithic material from Yindayin were not assessed during this study.
These materials should be a priority for future assessments due to their ability to add
[151] further clarity to this study. An assessment of the contribution of vertebrate taxa to the diet would provide a much broader understanding of foraging practices. An assessment of vertebrate, particularly fish, may help elucidate issues relating to seasonality and therefore mobility (Pike-Tay and Cosgrove, 2002). It is recommended that a more systematic analysis of the stone material from spits 10, 11, and 12 be conducted as these spits appear to contain the most lithic artefactual material. An assessment of the source for lithic material from spits 10–12 through X-ray fluorescence may provide information on whether the stone was endemic to the island or imported (although see Frahm and Doonan, 2013: for current issues with XRF use).
Additional molluscan analysis may provide data not yet considered for Yindayin. A more comprehensive assessment of meat weights may provide more accurate assessments of the calorific value and therefore the relative rankings of the invertebrates excavated at Yindayin
(see Barker, 2004: for examples of how meat weights have been used; Codding et al.,
2014a). Additionally, an analysis of shellfish nutrients may tell us more about the relative rankings of invertebrates that may not be apparent from body size alone (Hockett and
Haws, 2009). Through isotope analysis of molluscs, the season of death and therefore seasonality data may also provide data relating to mobility at Yindayin (Andrus, 2011).
Finally, further investigation and dating of sites within the Flinders Group may provide greater context for the position of Yindayin within the broader history of Princess Charlotte
Bay. Additionally, more targeted work could be conducted to address particular problems such as the sampling of open sites near to reef patches to assess field processing of reef species (Bird et al., 2002: 461).
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6.5 CONCLUSIONS
The intent of this thesis was to re-evaluate foraging behaviour at Yindayin through the analysis of the invertebrate materials. Over 162,000 individual invertebrate specimens weighing over one hundred kilograms were identified and quantified to produce subsistence data for Yindayin. A Boserupian economic intensification model was developed to test the
Yindayin data for the presence of population driven resource stress—this evaluation has shown that population pressure only becomes visible during the last 200 years. For the rest of the occupation sequence at Yindayin, environmental and climate data provided a better match for changes in foraging behaviour. The previous chronology of Princess Charlotte Bay has helped to elucidate the patterns of occupation, abandonment, and reoccupation of
Yindayin, and in turn, the Yindayin observations have filled gaps in the chronology of
Princess Charlotte Bay.
The Yindayin data does not support simple, unidirectional models of development. Patterns in subsistence exemplify non-directional foraging behaviour, with pulses of significant change and abandonment, embedded within periods of relative stability. It does not appear that population pressure was the driver for the patterns of subsistence until the very end of occupation. This is not to say that demographic pressures did not play a part elsewhere in the region or did not play a part in movements to and from the islands—additional data would be required to assess these ideas. The mid to late-Holocene presents as a period of increasing environmental fluctuation with spans of stability punctuated by pulses of rapid change—subsistence at Yindayin also reflects these patterns. Environmental change creates subsistence problems that foragers must respond to, and therefore the alignment of
[153] environmental data with changes in subsistence behaviour is unlikely to be just a coincidence (Cashdan cited in Haberle and David, 2004: 177).
The pattern in occupation at Yindayin, in many respects mirrors activity in the central and southern GBR. The timing for Phase I is not surprising considering the age of Walaemini rockshelter, however, only sites in the Whitsunday Islands that were still connected to the mainland when they were first occupied have earlier dates than Yindayin. The reoccupation of Yindayin in Phase II coincides with a raft of site establishments and increasing site use elsewhere in the GBR. Increasing environmental unpredictability and demographic restructuring have been suggested as explanations elsewhere in the GBR (Rowland et al.,
2015: 158)—this may also explain the reoccupation of Yindayin during the early part of
Phase II. The abandonment of Yindayin after ca. 2,083 is perhaps anomalous relative to the rest of the GBR, however it may reflect a movement to the increasingly productive chenier plain on the mainland. The final, intensive period of occupation during Phase III represents the first sign of resource stress that may have been related to growing populations. This event is unusual as other studies in the GBR have not noted the presence of intensification this late in the Holocene.
While some have argued that invertebrates were a low value resource to foragers, they should be a high value resource for archaeologists. This research demonstrates how theory regarding economic intensification can be linked to the archaeological record through the study of invertebrate materials. The high preservation and ubiquity of invertebrate material at coastal sites makes it one of the most common tools for understanding coastal occupation. Despite only representing a subset of foraging options available to the inhabitants of Yindayin, invertebrate data has shown how changes in foraging preference
[154] can be used to understand responses to population driven and environmental resource pressure. The data has also shown how complex foraging behaviour can be with similar patterns (e.g. spits 1 and 10) being produced by different drivers. What this tells us is that assuming a single cause to be responsible for late-Holocene intensification is perilous—the interactions between population, environment, and climate are complex and must be assessed across the entire history of a site. Similarly, these assessments must also be conducted on a region–by–region basis.
The use of economic intensification in a descriptive sense has stripped it of the explanatory power that the Boserupian model provides and has led to some of the problems with understanding the root causes for economic intensification (Morgan, 2015: 199-200). If we first understand how increased production has been generated (i.e. via increasing or decreasing efficiency) then we have a better chance of identifying those root causes.
Economic intensification in practice results in people working harder to provide food for themselves and dependents. It is usually the product of a decline in more profitable ventures (i.e. larger, easier to catch and process prey) which forces people to supplement their diet with other smaller bodied resources such as invertebrates. The causes for the decline in high value resources can be the result of over-exploitation (Mannino and Thomas,
2002), declining habitat conditions (Broughton et al., 2008), or social factors such as circumscription reducing access to prey (Whitaker and Byrd, 2014). This supplementation means collecting increasing numbers of productive, reliable, smaller bodied prey which usually entails increased handling costs. A reliance on small prey tends to reduce mobility— small prey such as plant and molluscan resources tend to collect in fixed locations while increased handling costs (i.e. time) means more time spent at processing locations near to
[155] the resources (Binford, 2001: 402). Reduced mobility and reliable resources lead to increased populations and an increased need to reduce handling costs to increase output, a process that has been seen to lead to complex foraging and social behaviour (Codding and
Bird, 2015: 13). The work here provides a means to identify the signals for identifying population based economic intensification in coastal locations. While the relationship of invertebrates with the environment is complex, it is capable of elucidation through cooperation with biologists, climate scientists, and others of relevant scientific skill—if we can understand climate/environment–invertebrate relationships then we can isolate which effects are caused by human interactions (Morgan, 2015: 195). If this is possible then we will be better able to understand the relationship between intertidal resources, population growth, and growing complexity within hunter-gatherer societies.
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APPENDIX A ABUNDANCE DATA
Table A-1: Proportion of shell from 5mm identified and indeterminant
Spit Identified Shell (g) % Identified Shell Indeterminant Shell (g) % Indeterminant Shell Total
1 20385.3 98% 485.2 2% 20870.5 2 13386.1 97% 357.3 3% 13743.4 3 14594.8 98% 344.1 2% 14938.9 4 10408.2 98% 246.3 2% 10654.5 5 12609.2 96% 526.3 4% 13135.5 6 6374.7 94% 433.7 6% 6808.4 7 6745.3 94% 450.3 6% 7195.6 8 9027.6 96% 416 4% 9443.6 9 10067.6 97% 336.8 3% 10404.4 10 18045.1 96% 834 4% 18879.1 11 550.1 97% 14.6 3% 564.7 12 18.1 93% 1.3 7% 19.4 Total 122212.1 96% 4445.9 4% 126658.0
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Table A-2: Spit weight (g) for invertebrates by lowest taxonomical level
Taxa 1 2 3 4 5 6 7 8 9 10 11 12 Grand Total Terebralia spp. 9405.8 5005.2 5849.3 3835.7 4106.3 1268 1058.7 3184 2320 1690 27 7.7 37757.7 Terebralia palustris 4373.5 4958.8 5326.6 3516.5 4155.8 589.7 422 1532.3 2196.4 952.5 7.5 0 28031.6 Lambis lambis 1205.4 685.6 725.3 645.6 912.3 1477.9 1818.2 754.7 724 1946.4 13.1 0.4 10908.9 Bivalvia 783.9 450 413.3 400.6 456.5 241.4 251.2 275.7 211.4 533.4 9.4 0.7 4027.5 Conomurex luhuanus 165.8 127 78.9 125.5 198.3 240.6 201.4 320.9 756.4 1476.3 0 0 3691.1 Polyplacophora 587.5 243.7 235.5 235.2 202.7 140.6 163 254.2 318.9 1082.2 39.2 0 3502.7 Telescopium telescopium 561.5 327.5 301.4 244.7 263.4 285.4 347.9 213 103.7 761.3 7.4 0 3417.2 Potamididae 925.4 350.8 480.6 373.4 385.2 156.2 115.7 238.9 217.8 121.6 9.2 1.2 3376 Nerita undata 106.8 132 209.1 146.3 215.3 159.8 225.2 159.2 269.7 1630 28.3 0 3281.7 Asaphis violascens 72.4 21.8 34 49.3 172.4 429.7 648.4 287.5 225.2 1103.8 53.8 2 3100.3 Tridacna spp. 429.2 324.9 197.9 80.1 164.6 95.8 55 382.2 326.2 234 0.2 1.1 2291.2 Lunella cinerea 24.2 16.8 14.4 24.7 43.8 159.9 203 213.6 357.7 1176.8 28.6 0.6 2264.1 Gafrarium pectinatum 9.1 2.9 5.8 9.3 50.8 66.9 98.2 202.3 804.7 712 10.8 0 1972.8 Nerita polita 72.5 39.1 34.5 29.5 74.5 123.6 163 125.3 188.7 998.7 9.2 0.6 1859.2 Monodonta labio 88.8 51.6 78.7 33.3 45.1 88.8 132.8 123.9 203.8 785.8 44.7 0 1677.3 Saccostrea cucullata 87.2 33.4 47.2 112.8 211 34.1 25.9 52.8 85.1 542.8 65.9 0 1298.2 Pinctada albina 11.8 0.1 3.2 10.8 31.2 197.6 214 127.3 170.5 472.6 3.2 0.1 1242.4 Nerita spp. 88.1 53 61.1 43.4 120.2 128 113.2 71.8 103.4 417.1 37.6 2.5 1239.4 Atactodea striata 605.1 127.9 68.2 13 9.7 14.9 4.5 21.7 48.5 266.9 4 0 1184.4 Anadara trapezia 70.9 24.5 46.9 49.7 281.8 52.5 21.3 2.4 125.4 115.8 2.9 0 794.1 Tectus niloticus 197 184.2 79.7 160.4 83.7 0.8 0.4 1.5 0 4.1 9.6 0.3 721.7 Geloina coaxans 63.6 11.3 53.8 9.2 22.5 63.2 201.6 205 28.7 41.9 6 0 706.8 Bivalvia 32.8 15.1 23.3 53.8 58.4 27.5 47.8 29.3 43.6 126.6 3.1 0.1 461.4 Strombidae 63.4 20.4 15.3 16.9 33.3 39.3 74.8 74.3 25.2 92.4 1 0 456.3 Tridacna crocea 0 37.2 99.9 0 3.2 42.6 43.4 40.1 25.5 89 0 0 380.9 Terebralia sulcata 30.1 35.1 1 54.1 84.4 46.6 27.7 14.7 11.2 31.3 2.9 0 339.1 Nerita costata 140.4 41.2 50.5 39.8 48.1 8.2 3.5 0.7 0.6 3.6 0 0 336.6 Nerita planospira 18.2 4.8 2.5 12 30.1 6.5 5 10.3 27.3 152.2 3.2 0.7 272.8 Anadara spp. 17.9 16.4 1.8 25.8 19.1 6.7 4.6 18.2 51.7 73.4 7.2 0 242.8 Hippopus hippopus 0 7 5.9 2.3 0 124.9 0.7 5.5 45.3 50.2 0 0 241.8 Brachyura 31.4 16 17.6 6.6 6.3 5.8 11.9 16.4 23.1 43.1 3.5 0.1 181.8 Echinoidea 16.4 13.8 8 18.3 11.9 11.9 16.4 17.3 0.7 2.7 0 0 117.4 Periglypta spp. 3 0.5 0.2 2.7 16.2 18.5 9.1 25.4 1 39.9 0.4 0 116.9 Tridacna maxima 46.6 0 0 0 20.6 0 0 0 0 9.2 0 0 76.4 Nerita albicilla 0 1 0 0 3.5 1.1 2.2 6.3 0.6 16 1.4 0 32.1 Tridacna squamosa 2.8 0 0 0 4.1 0 0 0 16.6 3.5 0 0 27 Reishia biturbercularis 10.3 0 6.1 3.2 6.2 0 0 0 0 0 0 0 25.8 Menathais tuberosa 0 0 0 21.3 0 0 0 0 0 0 0 0 21.3 Planaxis sulcatus 0.8 1.5 3 0 1.3 6.4 0 2.4 2.3 3.5 0 0 21.2 Thylacodes roussaei 7 0 0 0.2 2 3.2 1.8 1.5 0.7 1.1 0 0 17.5 Rapaninae 1.1 0 8.6 0 0.9 1.7 0 5 0 0 0 0 17.3 Trochidae 6.4 2.5 0 1.5 1.1 0.2 0.5 0.2 0 0 0 0 12.4 Peristernia australiensis 0 0 0 0 0 0 4.6 2 0 5.3 0 0 11.9 Clypeomorus bifasciata 1.7 0 0 0 0.1 0.5 0.8 4.4 0.5 2 0 0 10 Cardita aviculina 0 0 0 0 0.5 2.6 1.6 0.5 3.1 1.2 0 0 9.5 Haliotidae 0 0 0 0 0 0 0 0 0 8.8 0 0 8.8 [167]
Taxa 1 2 3 4 5 6 7 8 9 10 11 12 Grand Total Volutidae 6.2 0 0 0 0 0 0 0 0 0 0 0 6.2 Tegillarca granosa 3 0 2 0 0 0 0 0 0 0 0 0 5 Pyrazus ebeninus 4.5 0 0 0 0 0 0 0 0 0 0 0 4.5 Archaeobalanidae 1.4 0 0.9 0 0.1 0.5 0 0 0 1.2 0 0 4.1 Ostreidae 0 1.1 0 0 0.4 0 0 0 0.7 1.2 0 0 3.4 Cypraeidae 0 0 0 0 0 0 0.5 1.9 0 1 0 0 3.4 Charopidae c.f.. 0.5 0.2 0.4 0.2 0.2 0.4 0 0 0 0.6 0 0 2.5 Cronia aurantiaca 0 0 0 0 0 2.5 0 0 0 0 0 0 2.5 Euchelus Atratus 0 0 0 0 0 0.2 0.4 0 0 1.6 0 0 2.2 Camaenidae c.f.. 0 0 0 0 0 0 0 0 0.3 1.9 0 0 2.2 Canarium labiatum 0.3 0 0 0.2 0 0 0 0 0 1.6 0 0 2.1 Cerithium 0 0.2 0 0 0 0.1 0.5 0.2 0 0.9 0 0 1.9 Thais spp. 1.8 0 0 0 0 0 0 0 0 0 0 0 1.8 Brachidontes sculptus 0.3 0 0.9 0 0.1 0 0 0.1 0 0.2 0 0 1.6 Chama isaacooki 0 0 0 0 0 0 0 0 0 1.5 0 0 1.5 Siphonariidae 0 0 0 0 0.2 0.9 0.2 0 0.1 0.1 0 0 1.5 Littoraria undulata 0 0 1.3 0 0 0 0 0 0 0 0 0 1.3 Pyrene flava 0 0 0 0 0 0.3 0.6 0 0 0.4 0 0 1.3 Turbinidae 0 0 0 0 0.1 0.2 0.6 0 0 0.3 0 0 1.2 Sigmurethra 0 0 0 0.3 0.1 0 0 0 0.1 0.7 0 0 1.2 Pictocolumbella ocellata 0 0 0 0 0 0 0 0.3 0.8 0 0 0 1.1 Quidnipagus palatam 0.3 0 0.2 0 0.6 0 0 0 0 0 0 0 1.1 Pterygia crenulata 0 0 0 0 0 0 0.7 0 0 0 0 0 0.7 Umbonium 0 0 0 0 0 0 0.3 0 0 0.4 0 0 0.7 Cerithiidae 0.7 0 0 0 0 0 0 0 0 0 0 0 0.7 Cerithium traillii 0 0 0 0 0 0 0 0 0 0.6 0 0 0.6 Mytilidae sp. 0 0 0 0 0 0 0 0 0 0.6 0 0 0.6 Astele speciosa 0 0 0 0 0 0 0 0 0 0.5 0 0 0.5 Chamidae sp. 0 0 0 0 0 0 0 0 0.4 0 0 0 0.4 Zeacumantus diemenensis 0.2 0 0 0 0 0 0 0 0 0.1 0 0 0.3 Mitra variabilis 0 0 0 0 0 0 0 0.3 0 0 0 0 0.3 Melampus fasciatus 0 0 0 0 0 0 0 0 0 0.3 0 0 0.3 Pyramidella maculosa 0 0 0 0 0 0 0 0 0 0.3 0 0 0.3 Septifer sp. 0.3 0 0 0 0 0 0 0 0 0 0 0 0.3 Natica sp. 0 0 0 0 0 0 0.3 0 0 0 0 0 0.3 Angaria c.f.. 0 0 0 0 0 0 0.2 0 0 0 0 0 0.2 Pyrene c.f.. 0 0 0 0 0 0 0 0 0 0.2 0 0 0.2 Isognomon sp. 0 0 0 0 0.1 0 0 0 0 0 0 0 0.1 Planaxidae c.f. 0 0 0 0 0 0 0 0.1 0 0 0 0 0.1 Grand Total 20385.3 13386.1 14594.8 10408.2 12560.3 6374.7 6745.3 9027.6 10067.6 17837.2 440.3 18.1 121845.5
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Table A-3: Spit weight (g) for invertebrates aggregated to family level or higher
Taxa 1 2 3 4 5 6 7 8 9 10 11 12 Grand Total Potamididae 15296.3 10677.4 11958.9 8024.4 8995.1 2345.9 1972 5182.9 4849.1 3556.7 54 8.9 72921.6 Strombidae 1434.9 833 819.5 788.2 1143.9 1757.8 2094.4 1149.9 1505.6 3516.7 14.1 0.4 15058.4 Neritidae 426 271.1 357.7 271 491.7 427.2 512.1 373.6 590.3 3217.6 79.7 3.8 7021.8 Bivalvia 783.9 450 413.3 400.6 456.5 241.4 251.2 275.7 211.4 533.4 9.4 0.7 4027.5 Polyplacophora 587.5 243.7 235.5 235.2 202.7 140.6 163 254.2 318.9 1082.2 39.2 0 3502.7 Psammobiidae 72.4 21.8 34 49.3 172.4 429.7 648.4 287.5 225.2 1103.8 53.8 2 3100.3 Cardiidae 478.6 369.1 303.7 82.4 192.5 263.3 99.1 427.8 413.6 385.9 0.2 1.1 3017.3 Turbinidae 24.2 16.8 14.4 24.7 43.9 160.1 203.6 213.6 357.7 1177.1 28.6 0.6 2265.3 Veneridae 12.1 3.4 6 12 67 85.4 107.3 227.7 805.7 751.9 11.2 0 2089.7 Trochidae 95.2 54.1 78.7 34.8 46.2 89 133.8 124.1 203.8 786.7 44.7 0 1691.1 Ostreidae 87.2 34.5 47.2 112.8 211.4 34.1 25.9 52.8 85.8 544 65.9 0 1301.6 Pteriidae 11.8 0.1 3.2 10.8 31.3 197.6 214 127.3 170.5 472.6 3.2 0.1 1242.5 Mesodesmatidae 605.1 127.9 68.2 13 9.7 14.9 4.5 21.7 48.5 266.9 4 0 1184.4 Arcidae 91.8 40.9 50.7 75.5 300.9 59.2 25.9 20.6 177.1 189.2 10.1 0 1041.9 Tegulidae 197 184.2 79.7 160.4 83.7 0.8 0.4 1.5 0 4.1 9.6 0.3 721.7 Cyrenidae 63.6 11.3 53.8 9.2 22.5 63.2 201.6 205 28.7 41.9 6 0 706.8 Bivalvia 32.8 15.1 23.3 53.8 58.4 27.5 47.8 29.3 43.6 126.6 3.1 0.1 461.4 Brachyura 31.4 16 17.6 6.6 6.3 5.8 11.9 16.4 23.1 43.1 3.5 0.1 181.8 Echinoidea 16.4 13.8 8 18.3 11.9 11.9 16.4 17.3 0.7 2.7 0 0 117.4 Muricidae 13.2 0 14.7 24.5 7.1 4.2 0 5 0 0 0 0 68.7 Planaxidae 0.8 1.5 3 0 1.3 6.4 0 2.5 2.3 3.5 0 0 21.3 Vermetidae 7 0 0 0.2 2 3.2 1.8 1.5 0.7 1.1 0 0 17.5 Cerithiidae 2.4 0.2 0 0 0.1 0.6 1.3 4.6 0.5 3.5 0 0 13.2 Fasciolariidae 0 0 0 0 0 0 4.6 2 0 5.3 0 0 11.9 Carditidae 0 0 0 0 0.5 2.6 1.6 0.5 3.1 1.2 0 0 9.5 Haliotidae 0 0 0 0 0 0 0 0 0 8.8 0 0 8.8 Volutidae 6.2 0 0 0 0 0 0 0 0 0 0 0 6.2 Batilliariidae 4.7 0 0 0 0 0 0 0 0 0.1 0 0 4.8 Archaeobalanidae 1.4 0 0.9 0 0.1 0.5 0 0 0 1.2 0 0 4.1 Cypraeidae 0 0 0 0 0 0 0.5 1.9 0 1 0 0 3.4 Columbellidae 0 0 0 0 0 0.3 0.6 0.3 0.8 0.6 0 0 2.6 Mytilidae 0.6 0 0.9 0 0.1 0 0 0.1 0 0.8 0 0 2.5 Charopidae 0.5 0.2 0.4 0.2 0.2 0.4 0 0 0 0.6 0 0 2.5 Chilodontaidae 0 0 0 0 0 0.2 0.4 0 0 1.6 0 0 2.2 Camaenidae 0 0 0 0 0 0 0 0 0.3 1.9 0 0 2.2 Chamidae 0 0 0 0 0 0 0 0 0.4 1.5 0 0 1.9 Siphonariidae 0 0 0 0 0.2 0.9 0.2 0 0.1 0.1 0 0 1.5 Littorinidae 0 0 1.3 0 0 0 0 0 0 0 0 0 1.3 Sigmurethra 0 0 0 0.3 0.1 0 0 0 0.1 0.7 0 0 1.2 Tellinidae 0.3 0 0.2 0 0.6 0 0 0 0 0 0 0 1.1 Mitridae 0 0 0 0 0 0 0.7 0.3 0 0 0 0 1 Naticidae 0 0 0 0 0 0 0.3 0 0 0 0 0 0.3 Ellobiidae 0 0 0 0 0 0 0 0 0 0.3 0 0 0.3 Pyramidellidae 0 0 0 0 0 0 0 0 0 0.3 0 0 0.3 Grand Total 20385.3 13386.1 14594.8 10408.2 12560.3 6374.7 6745.3 9027.6 10067.6 17837.2 440.3 18.1 121845.5
[169]
Table A-4: MNI of lowest taxonomic level
Taxa 1 2 3 4 5 6 7 8 9 10 11 12 Grand Total Nerita undata 86 103 126 114 211 189 246 149 240 1416 30 0 2910 Nerita spp. 130 66 59 69 155 151 181 155 243 1037 28 2 2276 Terebralia spp. 301 145 192 130 152 49 32 59 64 39 0 1 1164 Nerita polita 44 18 23 11 31 71 60 55 86 500 7 1 907 Terebralia palustris 90 91 126 88 132 8 5 31 69 65 1 0 706 Atactodea striata 274 64 28 9 6 6 6 16 37 184 2 0 632 Lunella cinerea 14 7 7 9 23 53 80 71 77 269 9 0 619 Monodonta labio 24 18 28 10 23 22 38 37 74 288 22 0 584 Polyplacophora 95 39 42 43 37 19 24 45 48 177 9 0 578 Asaphis violascens 8 4 7 7 34 57 86 38 23 138 9 0 411 Pinctada albina 1 0 3 6 27 87 85 43 34 94 2 0 382 Nerita planospira 25 4 5 10 28 8 4 10 46 210 6 1 357 Potamididae 96 50 61 46 51 24 9 6 11 0 0 0 354 Conomurex luhuanus 8 6 5 7 11 19 12 24 42 93 0 0 227 Gafrarium pectinatum 1 0 1 3 6 11 14 15 52 80 3 0 186 Nerita costata 43 18 29 13 20 3 2 1 1 1 0 0 131 Lambis lambis 10 3 6 9 10 17 24 11 7 22 1 0 120 Telescopium telescopium 22 8 10 5 6 14 15 6 4 17 1 0 108 Saccostrea cucullata 2 2 3 7 16 2 5 1 6 47 15 0 106 Anadara trapezia 4 1 2 3 10 3 1 1 7 12 1 0 45 Terebralia sulcata 2 1 0 4 5 12 4 3 3 8 1 0 43 Tectus niloticus 13 9 1 5 2 0 0 0 0 1 1 0 32 Nerita albicilla 0 2 0 0 2 2 1 2 1 11 1 0 22 Clypeomorus bifasciata 3 0 0 0 1 1 3 2 1 7 0 0 18 Charopidae c.f.. 3 1 3 3 1 2 0 0 0 3 0 0 16 Planaxis sulcatus 1 1 2 0 1 3 0 3 2 3 0 0 16 Geloina coaxans 0 0 1 0 0 2 4 5 1 0 0 0 13 Tridacna crocea 0 2 3 0 0 1 1 1 2 2 0 0 12 Echinoidea 0 3 2 2 3 0 1 0 0 0 0 0 11 Peristernia australiensis 0 0 0 0 0 0 3 2 0 6 0 0 11 Cardita aviculina 0 0 0 0 1 4 2 1 1 1 0 0 10 Tridacna spp. 2 1 0 5 0 0 0 0 1 0 0 0 9 Siphonariidae 0 0 0 0 2 3 1 0 2 1 0 0 9 Cerithium 0 1 0 0 0 0 2 2 0 3 0 0 8 Cerithium traillii 0 0 0 0 0 0 0 0 0 6 0 0 6 Trochidae 1 2 0 1 1 0 0 1 0 0 0 0 6 Turbinidae 0 0 0 0 1 0 2 0 0 2 0 0 5 Pyrene flava 0 0 0 0 0 1 2 0 0 1 0 0 4 Brachidontes sculptus 0 0 2 0 1 0 0 0 0 1 0 0 4 Reishia biturbercularis 1 0 1 1 1 0 0 0 0 0 0 0 4 Littoraria undulata 0 0 4 0 0 0 0 0 0 0 0 0 4 Rapaninae 1 0 1 0 0 0 0 1 0 0 0 0 3 Periglypta spp. 0 0 0 0 0 0 0 1 0 2 0 0 3 Euchelus Atratus 0 0 0 0 0 1 1 0 0 1 0 0 3 Cypraeidae 0 0 0 0 0 0 0 1 0 1 0 0 2 Haliotidae 0 0 0 0 0 0 0 0 0 2 0 0 2 [170]
Taxa 1 2 3 4 5 6 7 8 9 10 11 12 Grand Total Tegillarca granosa 1 0 1 0 0 0 0 0 0 0 0 0 2 Canarium labiatum 1 0 0 0 0 0 0 0 0 1 0 0 2 Zeacumantus diemenensis 1 0 0 0 0 0 0 0 0 1 0 0 2 Astele speciosa 0 0 0 0 0 0 0 0 0 2 0 0 2 Pictocolumbella ocellata 0 0 0 0 0 0 0 1 1 0 0 0 2 Menathais tuberosa 0 0 0 1 0 0 0 0 0 0 0 0 1 Angaria c.f.. 0 0 0 0 0 0 1 0 0 0 0 0 1 Strombidae 0 0 0 0 0 0 0 0 0 1 0 0 1 Pterygia crenulata 0 0 0 0 0 0 1 0 0 0 0 0 1 Hippopus hippopus 0 0 0 0 0 1 0 0 0 0 0 0 1 Pyramidella maculosa 0 0 0 0 0 0 0 0 0 1 0 0 1 Planaxidae c.f.. 0 0 0 0 0 0 0 1 0 0 0 0 1 Pyrene c.f.. 0 0 0 0 0 0 0 0 0 1 0 0 1 Chama isaacooki 0 0 0 0 0 0 0 0 0 1 0 0 1 Natica sp. 0 0 0 0 0 0 1 0 0 0 0 0 1 Ostreidae 0 0 0 0 0 0 0 0 0 1 0 0 1 Isognomon sp. 0 0 0 0 1 0 0 0 0 0 0 0 1 Melampus fasciatus 0 0 0 0 0 0 0 0 0 1 0 0 1 Anadara spp. 0 0 0 0 0 0 0 0 0 0 1 0 1 Mitra variabilis 0 0 0 0 0 0 0 1 0 0 0 0 1 Septifer sp. 1 0 0 0 0 0 0 0 0 0 0 0 1 Cronia aurantiaca 0 0 0 0 0 1 0 0 0 0 0 0 1 Cerithiidae 1 0 0 0 0 0 0 0 0 0 0 0 1 Grand Total 1310 670 784 621 1012 847 959 802 1186 4761 150 5 13107
[171]
Table A-5: MNI aggregated to family or higher
Taxa 1 2 3 4 5 6 7 8 9 10 11 12 Grand Totals Neritidae 328 211 242 217 447 424 494 372 617 3175 72 4 6603 Potamididae 511 295 389 273 346 107 65 105 151 129 3 1 2375 Mesodesmatidae 274 64 28 9 6 6 6 16 37 184 2 0 632 Turbinidae 14 7 7 9 24 53 82 71 77 271 9 0 624 Trochidae 25 20 28 11 24 22 39 38 74 290 22 0 593 Polyplacophora 95 39 42 43 37 19 24 45 48 177 9 0 578 Psammobiidae 8 4 7 7 34 57 86 38 23 138 9 0 411 Pteriidae 1 0 3 6 28 87 85 43 34 94 2 0 383 Strombidae 19 9 11 16 21 36 36 35 49 117 1 0 350 Veneridae 1 0 1 3 6 11 14 16 52 82 3 0 189 Ostreidae 2 2 3 7 16 2 5 1 6 48 15 0 107 Arcidae 5 1 3 3 10 3 1 1 7 12 2 0 48 Cerithiidae 4 1 0 0 1 1 5 4 1 16 0 0 33 Tegulidae 13 9 1 5 2 0 0 0 0 1 1 0 32 Cardiidae 2 3 3 5 0 2 1 1 3 2 0 0 22 Planaxidae 1 1 2 0 1 3 0 4 2 3 0 0 17 Charopidae 3 1 3 3 1 2 0 0 0 3 0 0 16 Cyrenidae 0 0 1 0 0 2 4 5 1 0 0 0 13 Fasciolariidae 0 0 0 0 0 0 3 2 0 6 0 0 11 Echinoidea 0 3 2 2 3 0 1 0 0 0 0 0 11 Carditidae 0 0 0 0 1 4 2 1 1 1 0 0 10 Siphonariidae 0 0 0 0 2 3 1 0 2 1 0 0 9 Muricidae 2 0 2 2 1 1 0 1 0 0 0 0 9 Columbellidae 0 0 0 0 0 1 2 1 1 2 0 0 7 Mytilidae 1 0 2 0 1 0 0 0 0 1 0 0 5 Littorinidae 0 0 4 0 0 0 0 0 0 0 0 0 4 Chilodontaidae 0 0 0 0 0 1 1 0 0 1 0 0 3 Cypraeidae 0 0 0 0 0 0 0 1 0 1 0 0 2 Mitridae 0 0 0 0 0 0 1 1 0 0 0 0 2 Batilliariidae 1 0 0 0 0 0 0 0 0 1 0 0 2 Haliotidae 0 0 0 0 0 0 0 0 0 2 0 0 2 Ellobiidae 0 0 0 0 0 0 0 0 0 1 0 0 1 Chamidae 0 0 0 0 0 0 0 0 0 1 0 0 1 Naticidae 0 0 0 0 0 0 1 0 0 0 0 0 1 Pyramidellidae 0 0 0 0 0 0 0 0 0 1 0 0 1 Grand Total 1310 670 784 621 1012 847 959 802 1186 4761 150 5 13107
[172]
Table A-6: NISP of lowest taxonomic level
Taxa 1 2 3 4 5 6 7 8 9 10 11 12 Grand Total Terebralia spp. 19469 10362 11643 7787 9152 2848 2379 5989 3637 2378 36 6 75686 Potamididae 2927 1263 1557 1038 1128 416 407 800 625 296 11 3 10471 Nerita undata 290 276 421 358 492 415 650 466 747 4640 47 0 8802 Asaphis violascens 265 68 106 184 646 1505 2059 771 404 2079 90 6 8183 Lambis lambis 1056 434 391 366 812 1177 1306 610 310 1384 23 1 7870 Polyplacophora 973 429 421 394 349 264 348 522 575 1941 80 0 6296 Nerita spp. 385 272 305 227 598 667 620 344 514 2154 128 8 6222 Pinctada albina 15 1 12 23 201 1015 1144 741 672 2221 7 1 6053 Terebralia palustris 1100 808 876 655 762 104 78 258 371 157 2 0 5171 Nerita polita 158 80 88 70 174 299 396 359 468 2677 31 1 4801 Monodonta labio 181 127 282 95 143 215 356 344 445 1930 124 0 4242 Telescopium telescopium 444 228 214 147 402 358 462 226 138 798 11 0 3428 Lunella cinerea 43 37 41 44 126 294 364 347 397 1356 45 2 3096 Atactodea striata 809 158 103 20 16 19 13 37 97 401 6 0 1679 Echinoidea 216 177 103 176 131 133 210 223 16 46 0 0 1431 Gafrarium pectinatum 11 4 7 10 68 100 130 161 265 502 19 0 1277 Tridacna spp. 212 134 105 39 81 68 42 151 89 112 1 1 1035 Conomurex luhuanus 67 24 35 38 61 91 107 162 108 273 0 0 966 Brachyura 134 131 97 66 31 21 39 90 87 223 4 1 924 Nerita planospira 69 9 15 35 81 30 22 42 112 481 10 1 907 Strombidae 86 19 11 30 63 57 107 133 53 182 3 0 744 Saccostrea cucullata 21 16 24 38 78 18 9 23 55 260 41 0 583 Nerita costata 198 82 104 59 75 17 4 2 1 2 0 0 544 Tectus niloticus 172 137 103 52 50 6 2 4 0 2 3 8 539 Geloina coaxans 47 11 10 14 25 71 139 89 15 29 4 0 454 Anadara spp. 21 11 3 28 24 7 10 27 40 84 12 0 267 Terebralia sulcata 7 3 3 8 20 27 19 5 4 8 1 0 105 Periglypta spp. 5 1 1 3 21 24 16 8 2 10 2 0 93 Anadara trapezia 10 2 5 6 20 3 1 2 9 19 1 0 78 Hippopus hippopus 0 4 2 2 0 21 1 5 12 8 0 0 55 Thylacodes roussaei 3 0 0 1 6 16 14 5 3 2 0 0 50 Nerita albicilla 0 3 0 0 2 2 2 4 1 15 1 0 30 Tridacna crocea 0 5 4 0 3 3 2 1 4 6 0 0 28 Clypeomorus bifasciata 3 0 0 0 1 2 3 3 1 7 0 0 20 Trochidae 2 4 0 4 4 1 1 1 0 0 0 0 17 Charopidae c.f.. 3 1 3 3 1 2 0 0 0 3 0 0 16 Planaxis sulcatus 1 1 2 0 1 3 0 3 2 3 0 0 16 Cardita aviculina 0 0 0 0 1 4 4 1 1 1 0 0 12 Peristernia australiensis 0 0 0 0 0 0 3 2 0 6 0 0 11 Ostreidae 0 1 0 0 1 0 0 0 1 7 0 0 10 Siphonariidae 0 0 0 0 2 3 2 0 2 1 0 0 10 Sigmurethra 0 0 0 3 3 0 0 0 1 2 0 0 9 Cerithium 0 1 0 0 0 1 2 2 0 3 0 0 9 Brachidontes sculptus 2 0 4 0 1 0 0 1 0 1 0 0 9 Camaenidae c.f.. 0 0 0 0 0 0 0 0 1 7 0 0 8 Turbinidae 0 0 0 0 1 1 2 0 0 2 0 0 6 Cerithium traillii 0 0 0 0 0 0 0 0 0 6 0 0 6 Rapaninae 1 0 1 0 1 1 0 1 0 0 0 0 5 Tridacna maxima 2 0 0 0 1 0 0 0 0 2 0 0 5 Tridacna squamosa 1 0 0 0 2 0 0 0 1 1 0 0 5 Littoraria undulata 0 0 4 0 0 0 0 0 0 0 0 0 4 Reishia biturbercularis 1 0 1 1 1 0 0 0 0 0 0 0 4 Cypraeidae 0 0 0 0 0 0 2 1 0 1 0 0 4 Pyrene flava 0 0 0 0 0 1 2 0 0 1 0 0 4 Euchelus Atratus 0 0 0 0 0 1 1 0 0 1 0 0 3 Canarium labiatum 1 0 0 1 0 0 0 0 0 1 0 0 3 Quidnipagus palatam 1 0 1 0 1 0 0 0 0 0 0 0 3 Volutidae 2 0 0 0 0 0 0 0 0 0 0 0 2 Tegillarca granosa 1 0 1 0 0 0 0 0 0 0 0 0 2 Archaeobalanidae 0 0 0 0 0 0 0 0 0 2 0 0 2 Haliotidae 0 0 0 0 0 0 0 0 0 2 0 0 2 [173]
Taxa 1 2 3 4 5 6 7 8 9 10 11 12 Grand Total Umbonium 0 0 0 0 0 0 1 0 0 1 0 0 2 Cerithiidae 2 0 0 0 0 0 0 0 0 0 0 0 2 Zeacumantus diemenensis 1 0 0 0 0 0 0 0 0 1 0 0 2 Pictocolumbella ocellata 0 0 0 0 0 0 0 1 1 0 0 0 2 Astele speciosa 0 0 0 0 0 0 0 0 0 2 0 0 2 Natica sp. 0 0 0 0 0 0 1 0 0 0 0 0 1 Pyramidella maculosa 0 0 0 0 0 0 0 0 0 1 0 0 1 Chamidae sp. 0 0 0 0 0 0 0 0 1 0 0 0 1 Isognomon sp. 0 0 0 0 1 0 0 0 0 0 0 0 1 Pyrazus ebeninus 1 0 0 0 0 0 0 0 0 0 0 0 1 Planaxidae c.f.. 0 0 0 0 0 0 0 1 0 0 0 0 1 Chama isaacooki 0 0 0 0 0 0 0 0 0 1 0 0 1 Pyrene c.f.. 0 0 0 0 0 0 0 0 0 1 0 0 1 Pterygia crenulata 0 0 0 0 0 0 1 0 0 0 0 0 1 Cronia aurantiaca 0 0 0 0 0 1 0 0 0 0 0 0 1 Mitra variabilis 0 0 0 0 0 0 0 1 0 0 0 0 1 Angaria c.f.. 0 0 0 0 0 0 1 0 0 0 0 0 1 Septifer sp. 1 0 0 0 0 0 0 0 0 0 0 0 1 Thais spp. 1 0 0 0 0 0 0 0 0 0 0 0 1 Mytilidae sp. 0 0 0 0 0 0 0 0 0 1 0 0 1 Melampus fasciatus 0 0 0 0 0 0 0 0 0 1 0 0 1 Menathais tuberosa 0 0 0 1 0 0 0 0 0 0 0 0 1 Grand Total 29421 15324 17109 12026 15864 10332 11484 12969 10288 26745 743 39 162344
[174]
Table A-7: NISP aggregated to family or higher
Taxa 1 2 3 4 5 6 7 8 9 10 11 12 Grand Total Potamididae 23947 12664 14293 9635 11464 3753 3345 7278 4775 3637 61 9 94861 Neritidae 1100 722 933 749 1422 1430 1694 1217 1843 9969 217 10 21306 Strombidae 1210 477 437 435 936 1325 1520 905 471 1840 26 1 9583 Psammobiidae 265 68 106 184 646 1505 2059 771 404 2079 90 6 8183 Polyplacophora 973 429 421 394 349 264 348 522 575 1941 80 0 6296 Pteriidae 15 1 12 23 202 1015 1144 741 672 2221 7 1 6054 Trochidae 183 131 282 99 147 216 359 345 445 1933 124 0 4264 Turbinidae 43 37 41 44 127 295 366 347 397 1358 45 2 3102 Mesodesmatidae 809 158 103 20 16 19 13 37 97 401 6 0 1679 Echinoidea 216 177 103 176 131 133 210 223 16 46 0 0 1431 Veneridae 16 5 8 13 89 124 146 169 267 512 21 0 1370 Cardiidae 215 143 111 41 87 92 45 157 106 129 1 1 1128 Brachyura 134 131 97 66 31 21 39 90 87 223 4 1 924 Ostreidae 21 17 24 38 79 18 9 23 56 267 41 0 593 Tegulidae 172 137 103 52 50 6 2 4 0 2 3 8 539 Cyrenidae 47 11 10 14 25 71 139 89 15 29 4 0 454 Arcidae 32 13 9 34 44 10 11 29 49 103 13 0 347 Vermetidae 3 0 0 1 6 16 14 5 3 2 0 0 50 Cerithiidae 5 1 0 0 1 3 5 5 1 16 0 0 37 Planaxidae 1 1 2 0 1 3 0 4 2 3 0 0 17 Charopidae 3 1 3 3 1 2 0 0 0 3 0 0 16 Carditidae 0 0 0 0 1 4 4 1 1 1 0 0 12 Muricidae 3 0 2 2 2 2 0 1 0 0 0 0 12 Fasciolariidae 0 0 0 0 0 0 3 2 0 6 0 0 11 Mytilidae 3 0 4 0 1 0 0 1 0 2 0 0 11 Siphonariidae 0 0 0 0 2 3 2 0 2 1 0 0 10 Sigmurethra 0 0 0 3 3 0 0 0 1 2 0 0 9 Camaenidae 0 0 0 0 0 0 0 0 1 7 0 0 8 Columbellidae 0 0 0 0 0 1 2 1 1 2 0 0 7 Cypraeidae 0 0 0 0 0 0 2 1 0 1 0 0 4 Littorinidae 0 0 4 0 0 0 0 0 0 0 0 0 4 Chilodontaidae 0 0 0 0 0 1 1 0 0 1 0 0 3 Batilliariidae 2 0 0 0 0 0 0 0 0 1 0 0 3 Tellinidae 1 0 1 0 1 0 0 0 0 0 0 0 3 Mitridae 0 0 0 0 0 0 1 1 0 0 0 0 2 Archaeobalanidae 0 0 0 0 0 0 0 0 0 2 0 0 2 Volutidae 2 0 0 0 0 0 0 0 0 0 0 0 2 Haliotidae 0 0 0 0 0 0 0 0 0 2 0 0 2 Chamidae 0 0 0 0 0 0 0 0 1 1 0 0 2 Pyramidellidae 0 0 0 0 0 0 0 0 0 1 0 0 1 Ellobiidae 0 0 0 0 0 0 0 0 0 1 0 0 1 Naticidae 0 0 0 0 0 0 1 0 0 0 0 0 1 Grand Total 29421 15324 17109 12026 15864 10332 11484 12969 10288 26745 743 39 162344
[175]
Table A-8: Volume Corrected MNI—Top 10 taxa aggregated to the lowest possible taxonomic level
Spit 1 Spit 2 Spit 3 Spit 4 Spit 5 Spit 6 Rank Taxa VC MNI Taxa VC MNI Taxa VC MNI Taxa VC MNI Taxa VC MNI Taxa VC MNI 1 Terebralia spp. 0.00602 Terebralia spp. 0.0029 Terebralia spp. 0.00384 Terebralia spp. 0.0026 Nerita undata 0.00422 Nerita undata 0.00378 Terebralia 2 Atactodea striata 0.00548 Nerita undata 0.00206 0.00252 Nerita undata 0.00228 Nerita spp. 0.0031 Nerita spp. 0.00302 palustris Terebralia Terebralia 3 Nerita spp. 0.0026 0.00182 Nerita undata 0.00252 0.00176 Terebralia spp. 0.00304 Pinctada albina 0.00174 palustris palustris Terebralia 4 Potamididae 0.00192 Nerita spp. 0.00132 Potamididae 0.00122 Nerita spp. 0.00138 0.00264 Nerita polita 0.00142 palustris Asaphis 5 Polyplacophora 0.0019 Atactodea striata 0.00128 Nerita spp. 0.00118 Potamididae 0.00092 Potamididae 0.00102 0.00114 violascens Terebralia 6 0.0018 Potamididae 0.001 Polyplacophora 0.00084 Polyplacophora 0.00086 Polyplacophora 0.00074 Lunella cinerea 0.00106 palustris Asaphis 7 Nerita undata 0.00172 Polyplacophora 0.00078 Nerita costata 0.00058 Nerita costata 0.00026 0.00068 Terebralia spp. 0.00098 violascens 8 Nerita polita 0.00088 Monodonta labio 0.00036 Atactodea striata 0.00056 Nerita polita 0.00022 Nerita polita 0.00062 Potamididae 0.00048 9 Nerita costata 0.00086 Nerita polita 0.00036 Monodonta labio 0.00056 Monodonta labio 0.0002 Nerita planospira 0.00056 Monodonta labio 0.00044 10 Nerita planospira 0.0005 Nerita costata 0.00036 Nerita polita 0.00046 Nerita planospira 0.0002 Pinctada albina 0.00054 Polyplacophora 0.00038
Spit 7 Spit 8 Spit 9 Spit 10 Spit 11 Spit 12 Rank Taxa VC MNI Taxa VC MNI Taxa VC MNI Taxa VC MNI Taxa VC MNI Taxa VC MNI 1 Nerita undata 0.00492 Nerita spp. 0.0031 Nerita spp. 0.00243 Nerita undata 0.01416 Nerita undata 0.0015 Nerita spp. 0.0001 2 Nerita spp. 0.00362 Nerita undata 0.00298 Nerita undata 0.0024 Nerita spp. 0.01037 Nerita spp. 0.0014 Nerita polita 0.00005 Asaphis 3 0.00172 Lunella cinerea 0.00142 Nerita polita 0.00086 Nerita polita 0.005 Monodonta labio 0.0011 Nerita planospira 0.00005 violascens Saccostrea 4 Pinctada albina 0.0017 Terebralia spp. 0.00118 Lunella cinerea 0.00077 Monodonta labio 0.00288 0.00075 Terebralia spp. 0.00005 cucullata Asaphis 5 Lunella cinerea 0.0016 Nerita polita 0.0011 Monodonta labio 0.00074 Lunella cinerea 0.00269 0.00045 violascens Terebralia 6 Nerita polita 0.0012 Polyplacophora 0.0009 0.00069 Nerita planospira 0.0021 Polyplacophora 0.00045 palustris 7 Monodonta labio 0.00076 Pinctada albina 0.00086 Terebralia spp. 0.00064 Atactodea striata 0.00184 Lunella cinerea 0.00045 Asaphis Gafrarium 8 Terebralia spp. 0.00064 0.00076 0.00052 Polyplacophora 0.00177 Nerita polita 0.00035 violascens pectinatum Asaphis 9 Lambis lambis 0.00048 Monodonta labio 0.00074 Polyplacophora 0.00048 0.00138 Nerita planospira 0.0003 violascens Terebralia Gafrarium 10 Polyplacophora 0.00048 0.00062 Nerita planospira 0.00046 Pinctada albina 0.00094 0.00015 palustris pectinatum
[176]
Table A-9: Volume Corrected NISP—Top 10 taxa aggregated to the lowest possible taxonomic level
Spit 1 Spit 2 Spit 3 Spit 4 Spit 5 Spit 6 Rank Taxa VC NISP Taxa VC NISP Taxa VC NISP Taxa VC NISP Taxa VC NISP Taxa VC NISP 1 Terebralia spp. 0.38938 Terebralia spp. 0.20724 Terebralia spp. 0.23286 Terebralia spp. 0.15574 Terebralia spp. 0.18304 Terebralia spp. 0.05696 Asaphis 2 Potamididae 0.05854 Potamididae 0.02526 Potamididae 0.03114 Potamididae 0.02076 Potamididae 0.02256 0.0301 violascens Terebralia Terebralia Terebralia Terebralia 3 0.022 0.01616 0.01752 0.0131 Lambis lambis 0.01624 Lambis lambis 0.02354 palustris palustris palustris palustris Terebralia 4 Lambis lambis 0.02112 Lambis lambis 0.00868 Polyplacophora 0.00842 Polyplacophora 0.00788 0.01524 Pinctada albina 0.0203 palustris Asaphis 5 Polyplacophora 0.01946 Polyplacophora 0.00858 Nerita undata 0.00842 Lambis lambis 0.00732 0.01292 Nerita spp. 0.01334 violascens 6 Atactodea striata 0.01618 Nerita undata 0.00552 Lambis lambis 0.00782 Nerita undata 0.00716 Nerita spp. 0.01196 Potamididae 0.00832 Telescopium 7 0.00888 Nerita spp. 0.00544 Nerita spp. 0.0061 Nerita spp. 0.00454 Nerita undata 0.00984 Nerita undata 0.0083 telescopium Telescopium Asaphis Telescopium Telescopium 8 Nerita spp. 0.0077 0.00456 Monodonta labio 0.00564 0.00368 0.00804 0.00716 telescopium violascens telescopium telescopium Telescopium 9 Nerita undata 0.0058 Echinoidea 0.00354 0.00428 Echinoidea 0.00352 Polyplacophora 0.00698 Nerita polita 0.00598 telescopium Asaphis Asaphis Telescopium 10 0.0053 Atactodea striata 0.00316 0.00212 0.00294 Pinctada albina 0.00402 Lunella cinerea 0.00588 violascens violascens telescopium
Spit 7 Spit 8 Spit 9 Spit 10 Spit 11 Spit 12 Rank Taxa VC NISP Taxa VC NISP Taxa VC NISP Taxa VC NISP Taxa VC NISP Taxa VC NISP 1 Terebralia spp. 0.04758 Terebralia spp. 0.11978 Terebralia spp. 0.03637 Nerita undata 0.0464 Nerita spp. 0.0064 Nerita spp. 0.0004 Asaphis 2 0.04118 Potamididae 0.016 Nerita undata 0.00747 Nerita polita 0.02677 Monodonta labio 0.0062 Tectus niloticus 0.0004 violascens Asaphis Asaphis Asaphis 3 Lambis lambis 0.02612 0.01542 Pinctada albina 0.00672 Terebralia spp. 0.02378 0.0045 0.0003 violascens violascens violascens 4 Pinctada albina 0.02288 Pinctada albina 0.01482 Potamididae 0.00625 Pinctada albina 0.02221 Polyplacophora 0.004 Terebralia spp. 0.0003 5 Nerita undata 0.013 Lambis lambis 0.0122 Polyplacophora 0.00575 Nerita spp. 0.02154 Nerita undata 0.00235 Potamididae 0.00015 Asaphis 6 Nerita spp. 0.0124 Polyplacophora 0.01044 Nerita spp. 0.00514 0.02079 Lunella cinerea 0.00225 Lunella cinerea 0.0001 violascens Telescopium Saccostrea 7 0.00924 Nerita undata 0.00932 Nerita polita 0.00468 Polyplacophora 0.01941 0.00205 Lambis lambis 0.00005 telescopium cucullata 8 Potamididae 0.00814 Nerita polita 0.00718 Monodonta labio 0.00445 Monodonta labio 0.0193 Terebralia spp. 0.0018 Pinctada albina 0.00005 Asaphis 9 Nerita polita 0.00792 Lunella cinerea 0.00694 0.00404 Lambis lambis 0.01384 Nerita polita 0.00155 Nerita polita 0.00005 violascens 10 Lunella cinerea 0.00728 Monodonta labio 0.00688 Lunella cinerea 0.00397 Lunella cinerea 0.01356 Lambis lambis 0.00115 Brachyura 0.00005
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Table A-10: Volume Corrected Weight—Top 10 taxa aggregated to the lowest possible taxonomic level
Spit 1 Spit 2 Spit 3 Spit 4 Spit 5 Spit 6 Rank Taxa VC Weight Taxa VC Weight Taxa VC Weight Taxa VC Weight Taxa VC Weight Taxa VC Weight Terebralia 1 Terebralia spp. 0.188116 Terebralia spp. 0.100104 Terebralia spp. 0.116986 Terebralia spp. 0.076714 0.083116 Lambis lambis 0.029558 palustris Terebralia Terebralia Terebralia Terebralia 2 0.08747 0.099176 0.106532 0.07033 Terebralia spp. 0.082126 Terebralia spp. 0.02536 palustris palustris palustris palustris Terebralia 3 Lambis lambis 0.024108 Lambis lambis 0.013712 Lambis lambis 0.014506 Lambis lambis 0.012912 Lambis lambis 0.018246 0.011794 palustris Asaphis 4 Potamididae 0.018508 Bivalvia 0.009 Potamididae 0.009612 Bivalvia 0.008012 Bivalvia 0.00913 0.008594 violascens Telescopium 5 Bivalvia 0.015678 Potamididae 0.007016 Bivalvia 0.008266 Potamididae 0.007468 Potamididae 0.007704 0.005708 telescopium Telescopium Telescopium Telescopium 6 Atactodea striata 0.012102 0.00655 0.006028 0.004894 A. trapezia 0.005636 Bivalvia 0.004828 telescopium telescopium telescopium Telescopium 7 Polyplacophora 0.01175 Tridacna spp. 0.006498 Polyplacophora 0.00471 Polyplacophora 0.004704 0.005268 C. luhuanus 0.004812 telescopium Telescopium 8 0.01123 Polyplacophora 0.004874 Nerita undata 0.004182 Tectus niloticus 0.003208 Nerita undata 0.004306 Pinctada albina 0.003952 telescopium Saccostrea 9 Tridacna spp. 0.008584 Tectus niloticus 0.003684 Tridacna spp. 0.003958 Nerita undata 0.002926 0.00422 Lunella cinerea 0.003198 cucullata 10 Tectus niloticus 0.00394 Nerita undata 0.00264 T. crocea 0.001998 C. luhuanus 0.00251 Polyplacophora 0.004054 Nerita undata 0.003196
Spit 7 Spit 8 Spit 9 Spit 10 Spit 11 Spit 12 Rank Taxa VC Weight Taxa VC Weight Taxa VC Weight Taxa VC Weight Taxa VC Weight Taxa VC Weight Saccostrea 1 Lambis lambis 0.036364 Terebralia spp. 0.06368 Terebralia spp. 0.0232 Lambis lambis 0.019464 0.003295 Terebralia spp. 0.000385 cucullata Terebralia Terebralia Asaphis 2 Terebralia spp. 0.021174 0.030646 0.021964 Terebralia spp. 0.0169 0.00269 Nerita sp. 0.000125 palustris palustris violascens Asaphis Gafrarium Asaphis 3 0.012968 Lambis lambis 0.015094 0.008047 Nerita undata 0.0163 Monodonta labio 0.002235 0.0001 violascens pectinatum violascens Terebralia 4 0.00844 Tridacna spp. 0.007644 C. luhuanus 0.007564 C. luhuanus 0.014763 Polyplacophora 0.00196 Potamididae 0.00006 palustris Telescopium 5 0.006958 C. luhuanus 0.006418 Lambis lambis 0.00724 Lunella cinerea 0.011768 Nerita spp. 0.00188 Tridacna spp. 0.000055 telescopium Asaphis Asaphis 6 Bivalvia 0.005024 0.00575 Lunella cinerea 0.003577 0.011038 Lunella cinerea 0.00143 Gastropoda 0.000035 violascens violascens 7 Nerita undata 0.004504 Bivalvia 0.005514 Tridacna spp. 0.003262 Polyplacophora 0.010822 Nerita undata 0.001415 Nerita planospira 0.000035 8 Pinctada albina 0.00428 Polyplacophora 0.005084 Polyplacophora 0.003189 Nerita polita 0.009987 Terebralia spp. 0.00135 Lunella cinerea 0.00003 Terebralia 9 Lunella cinerea 0.00406 Potamididae 0.004778 Nerita undata 0.002697 0.009525 Lambis lambis 0.000655 Nerita polita 0.00003 palustris Asaphis Gafrarium 10 G.expansa 0.004032 Lunella cinerea 0.004272 0.002252 Monodonta labio 533.4 0.00054 Lambis lambis 0.00002 violascens pectinatum [178]
Table A-11: Foraging patch percentages—Volume corrected MNI
Spit Intertidal-hard Intertidal-sand/mud Mangrove-Landward Mangrove-Mid zone Mangrove-Seaward Reef flat Supralittoral-hard 1 17.3% 22.4% 39.3% 1.7% 0.2% 2.7% 16.5% 2 15.7% 10.5% 43.3% 1.2% 0.1% 3.9% 25.3% 3 17.9% 5.6% 49.3% 1.3% 0.0% 2.2% 23.7%
4 15.4% 4.5% 44.3% 0.8% 0.6% 4.7% 29.6% 5 15.5% 8.5% 35.9% 0.6% 0.5% 2.7% 36.2% 6 21.4% 20.0% 10.8% 1.7% 1.4% 4.5% 40.2% 7 22.5% 21.0% 5.6% 1.6% 0.4% 4.4% 44.5% 8 27.2% 14.8% 13.8% 0.7% 0.4% 5.1% 37.9% 9 25.1% 13.1% 16.1% 0.3% 0.3% 4.4% 40.7% 10 27.4% 11.1% 6.6% 0.4% 0.2% 2.7% 51.6%
11 42.0% 12.0% 4.7% 0.7% 0.7% 1.3% 38.7% 12 20.0% 0.0% 40.0% 0.0% 0.0% 0.0% 40.0%
Total 21.9% 13.5% 23.1% 1.0% 0.4% 3.4% 36.8%
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Table A-12: Foraging patch percentages—Volume Corrected NISP
Spit Intertidal-hard Intertidal-sand/mud Mangrove-Landward Mangrove-Mid zone Mangrove-Seaward Reef flat Supralittoral-hard 1 5.4% 4.4% 80.3% 1.5% 0.0% 6.2% 2.3% 2 5.1% 2.5% 81.3% 1.5% 0.0% 6.1% 3.6% 3 5.7% 2.0% 82.4% 1.3% 0.0% 4.4% 4.2%
4 5.8% 2.8% 79.3% 1.2% 0.1% 5.9% 4.9% 5 6.0% 6.5% 70.3% 2.5% 0.1% 7.7% 6.9% 6 10.9% 26.1% 33.6% 3.5% 0.3% 15.2% 10.5% 7 12.9% 29.8% 26.3% 4.0% 0.2% 15.7% 11.1% 8 12.4% 14.2% 55.3% 1.7% 0.0% 10.0% 6.2% 9 18.9% 15.4% 46.3% 1.3% 0.0% 5.8% 12.3% 10 30.7% 20.8% 12.5% 3.0% 0.0% 7.6% 25.4%
11 43.0% 18.8% 8.4% 1.5% 0.1% 4.8% 23.4% 12 7.7% 20.5% 25.6% 0.0% 0.0% 25.6% 20.5%
Total 12.1% 11.5% 50.7% 8.6% 0.1% 7.9% 9.3%
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Table A-13: Meat weight values for selected taxa
Source Taxa Applied to… Patch Meat Weight (g) Source N.undata All Nerita spp. Supralittoral-Hard 1.2 (Barker, 2004: 88) L. cinerea L. cinerea Intertidal-Hard 1.57 (Barker, 2004: 88) M. labio M. labio Intertidal-hard 1.36 (Barker, 2004: 88) S. cucullata S. cucullata Intertidal-hard 4.82 (Barker, 2004: 88) Polyplacophora Polyplacophora Intertidal-hard 2 Estimate T. palustris All Potamididae Mangrove 4.5 (Smith, 2013: 170) A.violascens A.violascens Intertidal-sand/mud 9 (Thomas, 2001: 86-87) A. striata A. striata Intertidal-sand/mud 1 (Thomas, 2001: 86-87) G.pectinatum G.pectinatum Intertidal-sand/mud 1 (Thomas, 2001: 86-87)
Only a conservative estimate of meat weight was possible for Polyplacophora due to issues of identification. During the identification and quantification, it was observed that most Polyplacophora were no larger than 3cm in size. The Yindayin Polyplacophora has a similar size range to A.striata, however,
Polyplacophora have a high meat to shell ratio (Codding et al., 2014a: 244). A figure of 2 grams per MNI, twice that of A. striata, has been used.
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APPENDIX B NESTEDNESS
Table B-1: Nestedness Measures
Metric Index Z-Score RN Mean St.Dev. Min Max Nested? NODF 67.375 6.64 0.255 53.679 2.063 48.61 58.46 Yes (p<0.001) NODF_row 66.957 6.502 0.248 53.655 2.046 48.648 58.372 Yes (p<0.001) NODF_col 82.228 8.897 0.508 54.512 3.115 45.168 61.576 Yes (p<0.001) T 16.632 -7.92 -0.62 43.728 3.421 36.207 50.857 Yes (p<0.001) BR 60.0 -10.592 -0.569 139.3 7.487 124.0 155.0 Yes (p<0.001)
Table B-2: Nestedness matrix for taxa identified to the lowest taxonomic level
Repacked Spits Taxa 10 8 7 5 1 6 3 9 4 2 11 12 Nerita planospira 1 1 1 1 1 1 1 1 1 1 1 1 Nerita polita 1 1 1 1 1 1 1 1 1 1 1 1 Nerita spp. 1 1 1 1 1 1 1 1 1 1 1 1 Anadara trapezia 1 1 1 1 1 1 1 1 1 1 1 0 Saccostrea cucullata 1 1 1 1 1 1 1 1 1 1 1 0 Telescopium telescopium 1 1 1 1 1 1 1 1 1 1 1 0 Lambis lambis 1 1 1 1 1 1 1 1 1 1 1 0 Asaphis violascens 1 1 1 1 1 1 1 1 1 1 1 0 Polyplacophora 1 1 1 1 1 1 1 1 1 1 1 0 Monodonta labio 1 1 1 1 1 1 1 1 1 1 1 0 Lunella cinerea 1 1 1 1 1 1 1 1 1 1 1 0 Atactodea striata 1 1 1 1 1 1 1 1 1 1 1 0 Terebralia palustris 1 1 1 1 1 1 1 1 1 1 1 0 Terebralia spp. 1 1 1 1 1 1 1 1 1 1 0 1 Nerita undata 1 1 1 1 1 1 1 1 1 1 1 0 Terebralia. sulcata 1 1 1 1 1 1 0 1 1 1 1 0 Nerita costata 1 1 1 1 1 1 1 1 1 1 0 0 Gafrarium pectinatum 1 1 1 1 1 1 1 1 1 0 1 0 Conomurex luhuanus 1 1 1 1 1 1 1 1 1 1 0 0 Pinctada albina 1 1 1 1 1 1 1 1 1 0 1 0 Potamididae 0 1 1 1 1 1 1 1 1 1 0 0 Planaxis sulcatus 1 1 0 1 1 1 1 1 0 1 0 0 Nerita albicilla 1 1 1 1 0 1 0 1 0 1 1 0 Tridacna crocea 1 1 1 0 0 1 1 1 0 1 0 0 Charopidae c.f.. 1 0 0 1 1 1 1 0 1 1 0 0 Clypeomorus bifasciata 1 1 1 1 1 1 0 1 0 0 0 0 Tectus niloticus 1 0 0 1 1 0 1 0 1 1 1 0 Cardita aviculina 1 1 1 1 0 1 0 1 0 0 0 0 Trochidae 0 1 0 1 1 0 0 0 1 1 0 0 Siphonariidae 1 0 1 1 0 1 0 1 0 0 0 0 [182]
Echinoidea 0 0 1 1 0 0 1 0 1 1 0 0 Geloina coaxans 0 1 1 0 0 1 1 1 0 0 0 0 Reishia biturbercularis 0 0 0 1 1 0 1 0 1 0 0 0 Cerithium 1 1 1 0 0 0 0 0 0 1 0 0 Tridacna spp. 0 0 0 0 1 0 0 1 1 1 0 0 Euchelus Atratus 1 0 1 0 0 1 0 0 0 0 0 0 Rapaninae 0 1 0 0 1 0 1 0 0 0 0 0 Brachidontes sculptus 1 0 0 1 0 0 1 0 0 0 0 0 Pyrene. flava 1 0 1 0 0 1 0 0 0 0 0 0 Turbinidae 1 0 1 1 0 0 0 0 0 0 0 0 Peristernia australiensis 1 1 1 0 0 0 0 0 0 0 0 0 Pictocolumbella ocellata 0 1 0 0 0 0 0 1 0 0 0 0 Zeacumantus diemenensis 1 0 0 0 1 0 0 0 0 0 0 0 Canarium labiatum 1 0 0 0 1 0 0 0 0 0 0 0 Tegillarca granosa 0 0 0 0 1 0 1 0 0 0 0 0 Cypraeidae 1 1 0 0 0 0 0 0 0 0 0 0 Periglypta spp. 1 1 0 0 0 0 0 0 0 0 0 0 Cerithiidae 0 0 0 0 1 0 0 0 0 0 0 0 Cronia aurantiaca 0 0 0 0 0 1 0 0 0 0 0 0 Septifer sp. 0 0 0 0 1 0 0 0 0 0 0 0 Mitra variabilis 0 1 0 0 0 0 0 0 0 0 0 0 Anadara spp. 0 0 0 0 0 0 0 0 0 0 1 0 Melampus fasciatus 1 0 0 0 0 0 0 0 0 0 0 0 Isognomon sp. 0 0 0 1 0 0 0 0 0 0 0 0 Ostreidae 1 0 0 0 0 0 0 0 0 0 0 0 Natica sp. 0 0 1 0 0 0 0 0 0 0 0 0 Chama isaacooki 1 0 0 0 0 0 0 0 0 0 0 0 Pyrene c.f.. 1 0 0 0 0 0 0 0 0 0 0 0 Planaxidae c.f.. 0 1 0 0 0 0 0 0 0 0 0 0 Pyramidella maculosa 1 0 0 0 0 0 0 0 0 0 0 0 Hippopus hippopus 0 0 0 0 0 1 0 0 0 0 0 0 Pterygia crenulata 0 0 1 0 0 0 0 0 0 0 0 0 Strombidae 1 0 0 0 0 0 0 0 0 0 0 0 Angaria c.f.. 0 0 1 0 0 0 0 0 0 0 0 0 Menathais tuberosa 0 0 0 0 0 0 0 0 1 0 0 0 Astele speciosa 1 0 0 0 0 0 0 0 0 0 0 0 Haliotidae 1 0 0 0 0 0 0 0 0 0 0 0 L.undulata 0 0 0 0 0 0 1 0 0 0 0 0 Cerithium traillii 1 0 0 0 0 0 0 0 0 0 0 0
[183]
APPENDIX C 3MM DATA
Table C-1: Comparative Data for 3mm and 5mm samples from spits 2 and 6
Spit 2 MNI Spit 6 MNI Index 3mm 5mm 3mm 5mm NTaxa 20 28 22 33 MNI count 41 670 82 847 NISP count 3846 15324 2481 10332 Weight (g) 399 13386 281.2 6375 Simpson_1-D 0.8685 0.8805 0.7704 0.8852 Shannon_H 2.508 2.432 2.074 2.566
Table C-2: Rank comparison between taxa in 3mm and 5mm mesh from spits 2 and 6
Rank Spit 2, 3mm 3mm Spit 2, 5mm Spit 6, 3mm 3mm Spit 6, 5mm MNI MNI 1 Nerita spp. 12 Terebralia spp. Nerita spp. 34 Nerita undata 2 Potamididae 6 Nerita undata Nerita undata 17 Nerita spp. 3 N. polita 4 T. palustris Pinctada albina 8 Pinctada albina 4 Terebralia spp. 3 Nerita spp. Nerita polita 3 Nerita polita 5 Polyplacophora 1 A. striata Lunella cinerea 3 Asaphis violascens 6 Gafrarium pectinatum 1 Potamididae Periglypta spp. 1 Lunella cinerea 7 Tegulidae 1 Polyplacophora Telescopium telescopium 1 Terebralia spp. 8 Lambis lambis 1 M. labio Strombidae 1 Potamididae 9 Atactodea striata 1 Nerita polita Conomurex luhuanus 1 Monodonta labio 10 L. lambis 1 Nerita costata Terebralia spp. 1 Polyplacophora
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Table C-3: Species/Family ranking 3mm vs 5mm—Spit 2
NISP MNI WEIGHT RANK 3mm 5mm 3mm 5mm 3mm 5mm
1 Terebralia sp. Terebralia sp. Nerita spp. Terebralia sp. Terebralia sp. Terebralia sp. 2 Potamididae Potamididae Potamididae Nerita undata Potamididae Terebralia palustris 3 Echinoidea Terebralia palustris Nerita polita Terebralia palustris Nerita spp. Lambis lambis 4 Nerita spp. Lambis lambis Terebralia sp. Atactodea striata Echinoidea Potamididae 5 Brachyura Polyplacophora Tegulidae Nerita spp. Polyplacophora Telescopium telescopium 6 Monodonta labio Nerita undata Pinctada albina Potamididae Brachyura Tridacna spp. 7 Polyplacophora Nerita spp. Nerita costata Polyplacophora Monodonta labio Polyplacophora 8 Tegulidae Telescopium telescopium Gafrarium pectinatum Monodonta labio Tegulidae Tectus niloticus 9 Asaphis violascens Echinoidea Monodonta labio Nerita polita Lambis lambis Nerita undata 10 Lambis lambis Atactodea striata Lambis lambis Nerita costata Atactodea striata Atactodea striata 11 Atactodea striata Tectus niloticus Atactodea striata Telescopium telescopium Asaphis violascens Conomurex luhuanus 12 Telescopium telescopium Tridacna spp. Telescopium telescopium Tectus niloticus Telescopium telescopium Nerita spp. 13 Nerita costata Brachyura Strombidae Conomurex luhuanus Nerita costata Monodonta labio 14 Lunella cinerea Monodonta labio Lunella cinerea Lunella cinerea Lunella cinerea Nerita costata 15 Nerita polita Nerita costata Asaphis violascens Asaphis violascens Strombidae Nerita polita 16 Strombidae Nerita polita Archaeobalanidae Tridacna crocea Nerita polita Tridacna crocea 17 Gafrarium pectinatum Asaphis violascens Littoraria undulata Nerita planospira Pinctada albina Terebralia sulcata 18 Pinctada albina Lunella cinerea Echinoidea Echinoidea Gafrarium pectinatum Saccostrea cucullata 19 Archaeobalanidae Conomurex luhuanus Brachyura Saccostrea cucullata Archaeobalanidae Anadara trapezia 20 Littoraria undulata Strombidae Polyplacophora Lambis lambis Littoraria undulata Asaphis violascens
OTHER 5MM (21) Saccostrea cucullata (22) Arcidae (21) Nerita albicilla (22) Terebralia sulcata (21) Strombidae (22) Lunella cinerea (23) Geloina coaxans (24) Nerita planospira (23) Anadara trapezia (24) Planaxis sulcatus (23) Arcidae (24) Brachyura (25) Tridacna crocea (26) Hippopus hippopus (25) Tridacna sp (25) Echinoidea (26) Geloina coaxans (27) Gafrarium pectinatum (28) Nerita albicilla (27) Hippopus hippopus (28) Nerita planospira (29) Terebralia sulcata (30) Anadara trapezia (29) Gafrarium pectinatum (31) Pinctada albina (32) Periglypta spp. (30) Planaxis sulcatus (31) Nerita albicilla (33) Planaxis sulcatus (32) Periglypta spp. (33) Pinctada albina
[185]
Table C-4: Species/Family ranking 3mm vs 5mm—Spit 6
NISP MNI WEIGHT RANK 3mm 5mm 3mm 5mm 3mm 5mm
1 Terebralia spp. Lambis lambis Nerita spp. Nerita undata Terebralia spp. Lambis lambis 2 Asaphis violascens Terebralia spp. Nerita undata Nerita spp. Nerita spp. Terebralia spp. 3 Nerita spp. Terebralia palustris Pinctada albina Pinctada albina Asaphis violascens Terebralia palustris 4 Echinoidea Asaphis violascens Lunella cinerea Nerita polita Potamididae Asaphis violascens 5 Potamididae Telescopium telescopium Nerita polita Asaphis violascens Lambis lambis Telescopium telescopium 6 Monodonta labio Conomurex luhuanus Monodonta labio Lunella cinerea Lunella cinerea Conomurex luhuanus 7 Lambis lambis Pinctada albina Periglypta spp. Terebralia spp. Echinoidea Pinctada albina 8 Lunella cinerea Lunella cinerea Tegulidae Monodonta labio Monodonta labio Lunella cinerea 9 Pinctada albina Nerita undata Terebralia spp. Potamididae Pinctada albina Nerita undata 10 Gafrarium pectinatum Potamididae Brachyura Polyplacophora Nerita polita Potamididae 11 Polyplacophora Polyplacophora Potamididae Lambis lambis Gafrarium pectinatum Polyplacophora 12 Nerita polita Nerita spp. Lambis lambis Conomurex luhuanus Polyplacophora Nerita spp. 13 Brachyura Hippopus hippopus Asaphis violascens Telescopium telescopium Telescopium telescopium Hippopus hippopus 14 Telescopium telescopium Nerita polita Telescopium telescopium Nerita planospira Nerita undata Nerita polita 15 Nerita undata Tridacna spp. Strombidae Gafrarium pectinatum Brachyura Tridacna spp. 16 Tegulidae Monodonta labio Gafrarium pectinatum Terebralia sulcata Conomurex luhuanus Monodonta labio 17 Conomurex luhuanus Gafrarium pectinatum Atactodea striata Terebralia palustris Tegulidae Gafrarium pectinatum 18 Periglypta spp. Geloina coaxans Archaeobalanidae Atactodea striata Periglypta spp. Geloina coaxans 19 Archaeobalanidae Anadara trapezia Conomurex luhuanus Cardita aviculina Strombidae Anadara trapezia 20 Strombidae Terebralia sulcata Echinoidea Nerita costata Archaeobalanidae Terebralia sulcata 21 Arcidae Tridacna crocea Arcidae Planaxis sulcatus Atactodea striata Tridacna crocea 22 Atactodea striata Strombidae Polyplacophora Saccostrea cucullata Arcidae Strombidae OTHER 5MM (23) Saccostrea cucullata (24) Periglypta spp. (23) Siphonariidae (24) Anadara trapezia (23)Saccostrea cucullata (24) Periglypta spp. (25) Atactodea striata (26) Echinoidea (25) Geloina coaxans (26) Nerita albicilla (25) Atactodea striata (26) Echinoidea (27) Nerita costata (28) Arcidae (27) Thais spp. (28) Tridacna crocea (27) Nerita costata (28) Arcidae (29) Nerita planospira (30) Planaxis sulcatus (29) Clypeomorus bifasciata (30) Euchelus Atratus (29) Nerita planospira (30) Planaxis sulcatus (31) Brachyura (32) Thylacodes roussaei (31) Hippopus hippopus (31) Brachyura (32) Thylacodes roussaei (33) Cardita aviculina (34) Thais spp. (33) Cardita aviculina (34) Thais spp. (35) Nerita albicilla (36) Siphonariidae (35) Nerita albicilla (36) Siphonariidae (37) Tectus niloticus (38) Clypeomorus bifasciata (37) Tectus niloticus (38) Clypeomorus bifasciata (39) Archaeobalanidae (40) Euchelus Atratus (39) Archaeobalanidae (40) Euchelus Atratus (41) Cerithiidae (41) Cerithiidae [186]
APPENDIX D PATCH PREFERENCE DATA
Table D-1: Patch preferences for individual taxa
Taxa Family or Class Zone Saccostrea cucullata Ostreidae Intertidal-hard Archaeobalanidae Archaeobalanidae Intertidal-hard Chama isaacooki Chamidae Intertidal-hard Polyplacophora Polyplacophora Intertidal-hard Cronia aurantiaca Muricidae Intertidal-hard Euchelus Atratus Chilodontaidae Intertidal-hard Haliotidae Haliotidae Intertidal-hard Littoraria undulata Littorinidae Intertidal-hard Lunella cinerea Turbinidae Intertidal-hard Menathais tuberosa Muricidae Intertidal-hard Monodonta labio Trochidae Intertidal-hard Nerita albicilla Neritidae Intertidal-hard Nerita costata Neritidae Intertidal-hard Nerita polita Neritidae Intertidal-hard Pictocolumbella ocellata Columbellidae Intertidal-hard Planaxidae c.f. Planaxidae Intertidal-hard Planaxis sulcatus Planaxidae Intertidal-hard Pyrene c.f. Columbellidae Intertidal-hard Pyrene flava Columbellidae Intertidal-hard Rapaninae Muricidae Intertidal-hard Reishia biturbercularis Muricidae Intertidal-hard Septifer sp. Mytilidae Intertidal-hard Siphonariidae Siphonariidae Intertidal-hard Thais spp. Muricidae Intertidal-hard Turbinidae Turbinidae Intertidal-hard Brachyura Brachyura Intertidal-sand/mud Pinctada albina Pteriidae Intertidal-sand/mud Anadara trapezia Arcidae Intertidal-sand/mud Anadara spp. Arcidae Intertidal-sand/mud Asaphis violascens Psammobiidae Intertidal-sand/mud Atactodea striata Mesodesmatidae Intertidal-sand/mud Brachidontes sculptus Mytilidae Intertidal-sand/mud Cardita aviculina Carditidae Intertidal-sand/mud Cerithium Cerithiidae Intertidal-sand/mud Cerithium traillii Cerithiidae Intertidal-sand/mud Chamidae sp. Chamidae Intertidal-sand/mud Clypeomorus bifasciata Cerithiidae Intertidal-sand/mud
[187]
Gafrarium pectinatum Veneridae Intertidal-sand/mud Periglypta spp. Veneridae Intertidal-sand/mud Pterygia crenulata Mitridae Intertidal-sand/mud Pyramidella maculosa Pyramidellidae Intertidal-sand/mud Quidnipagus palatam Tellinidae Intertidal-sand/mud Tegillarca granosa Arcidae Intertidal-sand/mud Umbonium Trochidae Intertidal-sand/mud Natica sp. Naticidae Intertidal-sand/mud Zeacumantus diemenensis Batilliariidae Intertidal-sand/mud Volutidae Volutidae Intertidal-sand/mud Geloina coaxans Cyrenidae Mangrove-Landward Potamididae Potamididae Mangrove-Landward Terebralia palustris Potamididae Mangrove-Landward Terebralia spp. Potamididae Mangrove-Landward Cerithiidae Cerithiidae Mangrove-Landward Melampus fasciatus Ellobiidae Mangrove-Landward Nerita planospira Neritidae Mangrove-Landward Pyrazus ebeninus Batilliariidae Mangrove-Landward Telescopium telescopium Potamididae Mangrove-Mid zone Terebralia sulcata Potamididae Mangrove-Seaward Hippopus hippopus Cardiidae Reef flat Lambis lambis Strombidae Reef flat Tridacna crocea Cardiidae Reef flat Tridacna maxima Cardiidae Reef flat Tridacna spp. Cardiidae Reef flat Tridacna squamosa Cardiidae Reef flat Echinoidea Echinoidea Reef flat Tectus niloticus Tegulidae Reef flat Angaria c.f.. Trochidae Reef flat Astele speciosa Trochidae Reef flat Canarium labiatum Strombidae Reef flat Conomurex luhuanus Strombidae Reef flat Cypraeidae Cypraeidae Reef flat Mitra variabilis Mitridae Reef flat Peristernia australiensis Fasciolariidae Reef flat Thylacodes roussaei Vermetidae Reef flat Strombidae Strombidae Reef flat Trochidae Trochidae Reef flat Trochidae Trochidae Reef flat Nerita spp. Neritidae Supralittoral-hard Nerita undata Neritidae Supralittoral-hard Camaenidae c.f.. Camaenidae Terrestrial Charopidae c.f.. Charopidae Terrestrial
[188]
Sigmurethra Sigmurethra Terrestrial Isognomon sp. Pteriidae Varied Bivalvia Bivalvia Varied Gastropoda Gastropoda Varied Mytilidae sp. Mytilidae Varied Ostreidae Ostreidae Varied
[189]