The Function of Prehistoric Agricultural Systems in Sāmoa: a GIS Analysis of Resilience to Flooding

The Function of Prehistoric Agricultural Systems in Sāmoa: A GIS Analysis of Resilience to Flooding

Thesis

Presented in Partial Fulfillment of the Requirements for the Degree Master of Arts in the

Graduate School of The Ohio State University

Craig Harris Shapiro

Graduate Program in Anthropology

The Ohio State University

2020

Thesis Committee

Julie S. Field, Advisor

Jeffrey H. Cohen, Committee Member

Kristen J. Gremillion, Committee Member

Copyrighted by

Craig Harris Shapiro

2020

Abstract

This thesis is focused on preliminary survey of a LiDAR dataset—digital imagery that results from airborne survey, and which reveals elevation changes in the ground surface.

It reveals the Sāmoan Islands as an entirely human-modified environment, consisting of a system of ditches and terraces that extend from the coast to the remote interior. These ditches and terraces served as a mitigation system that drained saturated soils and controlled flooding in the past, which in turn supported local agricultural production and maintained the integrity of the island’s soils and ecosystem. This project examines the similarities and differences between three study areas in eastern Atua province of ‘Upolu island. It suggests that prehistoric Sāmoans not only knew how to target specific soils for agricultural production, but that they knew that they needed to place ditches in order to maximize the agricultural output of an area.

Dedication

This document is dedicated to all those who have supported me and believed in my ability to

complete this project, especially my parents, who are my most enduring supporters.

iii

Acknowledgments

I would like to thank my advisor Dr. Julie Field for her guidance and insight during this project, as well as my Ohio State University committee members Drs. Kristen Gremillion and

Jeffrey Cohen. Special thanks must be extended to The Center for Sāmoan Studies at The

National University of Sāmoa, as they provided access to the data that allowed me to complete this project. Lastly, a loving thank you to my parents, brother, and extended family for their support throughout this endeavor.

Vita

Education:

May 2015……………………………………Bachelor of Arts Degree in Sociology and

Anthropology, Washington & Lee University,

Graduating GPA 3.414

May 2020……………………………………Pursuing Masters of Arts in Anthropology, The

Ohio State University, Current accumulative

GPA 3.870

Professional Experience and Employment:

Fall 2019 – Spring 2020……………………. Graduate Teaching Assistant, The Ohio State

University

Fall 2018 – Spring 2020……………………. University Fellow, The Ohio State University

October, 2015 – December, 2017……………United States Peace Corps Volunteer, Peace

Corps – Sāmoa

August – September, 2015………………………Contract Archaeologist/Drone Operator,

Corporacion Nacional Forestal – Te Peu, Easter

Island (Rapa Nui), Chile

July – August, 2015……………………………. Archaeological Field Technician, Washington &

Lee University

April, 2013 – May, 2015……………………….. Field Archaeologist/Drone Operator, Clark

Honors College, University of Oregon

July – August, 2013……………………………..Research Assistant, Australian National

University and The Vanuatu Cultural Centre –

Tafea Province

Educational Experiences:

April – May, 2014……………………………….ANTH 377, Field Methods in Archaeology, Washington and Lee University

May – July, 2014……………………………….. Archaeological Field School at Heraclea Sintica American Research Center in Sofia, Bulgaria

July – August, 2014……………………………..Dig in the Roman City of Sanisera and GIS applied in Archaeology, The Sanisera Archaeological Institute for International Field Schools in Menorca, Spain

Research Presentation:

Spring 2019……………………………………...Society of American Archaeology 84th Annual

Meeting

Fields of Study

Major Field: Anthropology vi

Table of Contents

Abstract ...... ii Dedication ...... iii Acknowledgments...... iv Vita ...... v List of Tables ...... ix List of Figures ...... x Chapter 1. Introduction ...... 1 Is the Past the Future in Sāmoa? ...... 2 Content of this Thesis ...... 4 Chapter 2. Background ...... 5 Pacific Island Settlement...... 5 Geography of the Sāmoan Archipelago ...... 9 Chapter 3. Materials and Methods ...... 11 Remotely Sensed Data: LiDAR ...... 11 Identification and Analysis of Archaeological Features in LiDAR Data ...... 13 Hydrological Analyses ...... 16 The Distribution of Soils in Relation to Ditching ...... 29 Chapter 4. Results ...... 31 Tafatafa ...... 32 Solosolo...... 32 Aleipata ...... 33 Soil Analysis ...... 34 Chapter 5. Discussion ...... 37 Intensity of Ditching in Relation to Soil Type ...... 37 A Dearth of Environmental Data ...... 38 vii

Possible Expansions for Further Research ...... 41 Key Differences Between Sāmoa and Other Pacific Archipelagoes ...... 44 Regional Differences in Sāmoa ...... 46 Chapter 6. Conclusion ...... 47 Bibliography ...... 50

viii

List of Tables

Table 1 Solosolo ditching on various soil types ...... 30 Table 2 Aleipata ditching on various soil types ...... 30 Table 3 Tafatafa ditching on various soil types ...... 30 Table 4 Results Table...... 32

List of Figures

Figure 1 A map of ‘Upolu and corresponding project areas from Green (2002). Red polygons indicate the boundaries of each of this project’s study areas in the eastern Atua province. Solosolo on the north coast, Tafatafa on the south coast, and Aleipata on the east coast...... 3 Figure 2 Aleipata Ditches Map. The ditches are overlaid on the LiDAR image from the Center for Sāmoan Studies at the National University of Sāmoa ...... 14 Figure 3 Solosolo Ditches Map...... 15 Figure 4 Tafatafa Ditches Map ...... 16 Figure 5 Tafatafa mosaic DEM with “no data” lines...... 18 Figure 6 Solosolo Flow Direction. 1 represents Eastward flow, 2 = SE, 4 = S, 8 = SW, 16 = W, 32 = NW, 64 = N, 68 = Null Values, 128 = NE...... 19 Figure 7 Solosolo Flow Direction Zoom ...... 20 Figure 8 Solosolo Flow Direction Zoom with Ditches ...... 21 Figure 9 Solosolo Flow Accumulation ...... 23 Figure 10 Solosolo Flow Accumulation Zoom ...... 24 Figure 11 Solosolo Flow Accumulation Zoom with Ditches ...... 25 Figure 12 Aleipata Ditches and Drained Areas ...... 26 Figure 13 Solosolo Ditches and Drained Areas ...... 27 Figure 14 Tafatafa Ditches and Drained Areas ...... 28 Figure 15 Precipitation map (Gosling et al. 2019) ...... 45

Chapter 1. Introduction

Archaeological research in Sāmoa has documented the extent and productivity of ancient agricultural systems that supported a large human population in the tenth to seventeenth centuries AD (Quintus & Clark 2016). Preliminary survey of a LiDAR dataset produced by The World Bank in 2014 has revealed the Sāmoan Islands as an entirely human-modified environment, consisting of a system of ditches and terraces that extend from the coast to the remote interior. This system was largely abandoned after

European contact in the 18th century, and the traditional knowledge pertaining to its purpose and maintenance has since been lost. It is hypothesized here that these ditches and terraces served as a mitigation system that drained saturated soils and controlled flooding in the past, which in turn supported local agricultural production and maintained the integrity of the island’s soils and ecosystem. The goal of this research is to reconnect this LiDAR dataset with its original purpose as outlined by the World Bank: to enhance the country’s resilience in the face of climate change. To that end, this research seeks to determine if and how archaeological features enhanced agricultural production, but also mitigated past extreme climatic events, particularly floods. A broader impact of this research is to indicate how these prehistoric features could be integrated into modern efforts to enhance climate-resilient food production.

Is the Past the Future in Sāmoa?

In other parts of the world (Cantú 2019), the restoration and maintenance of traditional agricultural systems has been shown to inhibit the negative impacts of increasingly severe and frequent floods. In other studies, the abandonment and subsequent degradation of prehistoric agricultural systems has moved the human- modified landscape away from a point of equilibrium, and restorations of archaeological features like chinampas, rain-fed field systems, and aqueducts are necessary to provision a growing population (Thompson 1994; Marshall et al. 2017).

This research seeks to test the hypothesis that ditches in Samoa redirected water and drained soil. These drainage systems were likely required to cultivate certain areas, while they enhanced production in all others. Additionally, this research supports a theoretical model for how prehistoric agriculture in Sāmoa was practiced, with emphasis placed on the development of complex systems that pair drainage and cultivation. This drainage network likely has a direct impact on agricultural production, which in turn facilitates changes in demography and social complexity.

This project analyzes the practical modern-day hydrological function of this agricultural system to expand understanding of prehistoric Polynesian human- environment interactions and social networks, and in so doing reveal how contemporary

Sāmoans can continue to benefit from traditional agricultural systems and thrive in such a wet ecosystem. This thesis aims to answer five questions:

1) Can GIS analysis of the LiDAR dataset be used to visually identify the extent of the prehistoric ditch and terrace system in Sāmoa?

2) How did the ditch system function with regards to hydrology and the natural flow of water in extant and adjacent stream systems?

3) What impact did the ditch system have on land use within drained spaces?

4) Can similarities and differences in soils or ditching intensity be detected between different regional land use systems? And what do these similarities and differences suggest?

5) In what ways did the ditching system ameliorate flooding and/or increase resilience to climate-related extreme weather events?

United States Peace Corps Volunteer.

Content of this Thesis

In the following chapters this thesis will analyze and evaluate the ancient ditching system of Sāmoa and its impact on flood mitigation and the creation of drained agricultural spaces. In chapter 2, the prehistory and geography of Sāmoa will be presented in order to provide a context for the study of prehistoric agricultural technology. In chapter 3, the materials and methods used to analyze archaeological, geological, and hydrological data will be presented and discussed. In chapter 4, the results will be compiled and presented by study area. In chapter 5, the discussion will outline the implications of this research and the difficulties encountered during its completion. Opportunities for expansion of this project will also be summarized.

Chapter 6 will serve as the conclusion for this thesis.

Chapter 2. Background

Pacific Island Settlement

The ability to transform tropical island jungles and recreate familiar domestic landscapes was crucial to the success of the earliest Pacific Islanders (Kirch 2000).

Sāmoans are part of a diverse group of Austronesian language speakers that spread more than half the distance around the world from the coastlines of Island Southeast Asia to

Madagascar in the Western Indian Ocean to Rapa Nui in the Eastern Pacific Ocean

(Denham 2014; Gosden 2012). This incredible expansion was made possible by the

Austronesians’ knowledge of pottery- making and sailing—allowing provisioned boats to cross seas with flora and fauna (pigs, chickens, dogs, bananas, taros, breadfruits, star fruits, etc.) to replicate their lifestyle in a virgin island destination (Allen 2003; Anderson

2002; Hunt 2007; Prebble & Wilmshurst 2009; Whistler 1991). Archaeological materials like pottery, obsidian, basalt tools, shell tools (fishhooks) and ornaments provide evidence for a distinct maritime-focused culture by 3,000 BP, a mere 2,500 years after the first Austronesian settlers left Taiwan for the Northern Philippines. At this point,

Austronesian settlers mingled with the indigenous Papuan peoples of the coasts of New

Guinea, the Bismarck Archipelago, and the Solomon Islands (archipelago) (Kennett and

Winterhalder 2008; Mithen 2012). Within this region the Austronesians adopted agricultural and food processing techniques that had existed for 30,000 years (most

5 significantly, the earth oven and root crops such as taro and yam), and a healthy number of genes (including some malaria resistant ones) from the Papuans (Green 1992; Kirch

2000). This cultural admixture became what has since been named “The Lapita Culture”, which ca. 3300 BP consisted of small populations scattered across Near Oceania

(spanning the Bismarck Archipelago to the southern end of the Solomon Islands).

Beginning ca. 3200 BP, this population expanded out into Remote Oceania to settle the islands south of the Solomon Islands, including the Santa Cruz, Torres, and Banks Island

Groups, followed by the larger archipelagos of Vanuatu, New Caledonia, and Fiji. In the span of just 400 years, they discovered and settled every archipelago as far east as Sāmoa and Tonga—bringing with them a uniquely Oceanic package of flora and fauna (Allen

2003; Anderson 2002; Athens 2009; Hunt 2007; Prebble & Wilmshurst 2009; Whistler

1991).

The introduction of food plants (taro, yams, bananas), economic plants (coconuts, ti, candlenut), and animals (chickens, pigs, and rats) was important, as the islands of

Remote Oceania all experience a significant ecological “bottleneck” effect (Burley and

Addison 2014; Kirch 2000; Mithen 2012; O’Connor & Hiscock 2014). Due to Oceanic winds and currents, which move from the east to the west, the ecological diversity of the region declines significantly the further east one travels (Kirch 2010; O’Connell et al.

2011; O’Connor & Hiscock 2014). This bottleneck becomes much more dramatic in the remote Pacific, where only a limited number of species (though, an incredible variety of birds and endemic flowering plants) were able to reach islands in the millennia before human colonization (Kirch 2000, 2010; Whistler 1991).

The cultural region of West Polynesia (WP), which includes Fiji, New Caledonia,

Wallis & Futuna, Tokelau, Niue, Tuvalu, Tonga, and Sāmoa, developed as the descendants of the Lapita settlers established permanent communities. This region served as the jumping off point for the colonization of Central and Eastern Polynesia (the area between Hawaiʻi, Rapa Nui, and Aotearoa) after approximately another 1,650 years.

Agricultural production technology in West Polynesia, in particular Sāmoa, is thus an especially interesting topic of study as it likely had an important role in shaping the future of food production in the rest of the Pacific (Burley and Addison 2014; Kirch 2000;

Weisler 1997, 2002). Not only is the Sāmoan archipelago completely modified by humans, but it also has a relatively low ecological diversity (even after human introductions) compared to Island Southeast Asia and New Guinea (Kirch 2000). Sāmoa and the rest of West Polynesia also served as the reservoir for the overwhelming majority of flora and fauna that were later distributed across the rest of Polynesia during colonization (Kirch 2000, 2010; Wilmshurst & Higham 2004).

Sāmoa Culture History

Evidence of the Lapita-era settlement of Sāmoa comes solely from the Mulifanua

Site on northwestern ‘Upolu island. Discovered during the dredging process for the

Mulifanua ferry berth, the site is situated in a sandy layer beneath .75 m of paleo- beachrock, which is itself beneath 1.50 m of lagoon and roughly 115 m offshore

(Dickinson & Green 1998). Mulifanua revealed the only dentate-stamped Lapita pottery found in Sāmoa—indicating the initial phase of Sāmoan colonization at an approximate

7 range of 2,880-2,750 cal BP (Petchey 2001). Due to island subsidence and sea level rise, the original village of Mulifanua was inundated and lost. Other sites like it may have existed on other shorelines in Sāmoa, but similarly they have been lost to the sea, and may also have been few in number. Efforts have been made using topography and bathymetry to recreate the Sāmoan coastline ca. 3,000 BP, in order to indicate past locations of sandy coastal flats favorable to Lapita settlement. These reconstructions have indicated that many of these types of areas were not present until approximately 2,500 BP

(Rieth et al. 2008). Sāmoa, with its steep forested slopes, may have been simply uncolonizable for roughly 350 years after the first Lapita discovery. In fact, an increase in the number of settlements across the entire archipelago is not evident until 2,300-2,000

BP (Rieth et al. 2008), when sea levels lowered, exposing coastal terraces. Thus,

Mulifanua alone is representative of the Lapita phase in Sāmoa.

After the Lapita phase ends at roughly 2,700 BP, small settlements were constructed around the Sāmoan islands and settlements were founded in the interior— most likely after 2,300 BP (Rieth et al. 2008). During this phase and until about 1,500

BP, Sāmoans expanded their settlements and continued to manufacture plainware pottery, while the cessation of pottery-making at or after 1,600 BP indicates a transition into a new phase (Clark 1996; Green 2002). The ‘Dark Age’ that followed (Rieth & Addison

2008) is a period marked by the loss of ceramics and consequent decrease of archaeological evidence for dense populations. While the evidence is minimal from

1,600 BP and later, it is clear that the population continued to grow until the arrival of

Europeans (Gosling et al. 2019). The period of 1,000 BP to European contact is the final

8 phase of Sāmoan prehistory. In this final phase, social complexity is indicated by enhanced food production strategies, modified village/settlement patterns, and monumental architecture (Quintus et al. 2015; Jennings & Holmer 1980; Clark &

Herdrich 1993). Agricultural ditching used to manage water and enhance food production was developed during this period. The ditches discussed in this thesis are subsequently referred to as “prehistoric” because the knowledge of them has been lost to time. Much of Sāmoan prehistoric culture, from the time before European contact, is still largely unknown.

Geography of the Sāmoan Archipelago

The Sāmoan archipelago is made up of 16 islands (10 in Independent Sāmoa and

6 in American Sāmoa), the largest being Savaiʻi, ‘Upolu, and Tutuila, while Savaiʻi and

‘Upolu (both in Independent Sāmoa) make up the overwhelming majority of the archipelago’s landmass. All are volcanic islands formed in the last 1 million years, and volcanic activity is still a threat in Savaiʻi (Kear & Wood 1959).

The Sāmoan climate is characterized by the interplay between the South Pacific

Convergence Zone, the Trade Winds, and the Inter-Tropical Convergence Zone (with prevailing winds from the southeast), and the temperature remains relatively constant year-round between 23 and 30 degrees Celsius (Gosling et al. 2019). The climate is tropical and frequently rainy, as the mean annual temperature is 26.6 degrees Celsius with an annual rainfall varying from 2,000 to 7,000 mm (Morrison 1991). Compared to the Hawaiian archipelago, and nearby Fiji, Sāmoa is considerably wetter and less windy.

These circumstances are perfect for the year-round cultivation of the Sāmoan staple crop, taro (Colocasia excuelenta), as it requires an average daily temperature of at least 21 degrees Celsius, grows well in sloped landscapes, and thrives in wet conditions as long as there is sufficient circulation (Carson 2006; Onwueme 1999). Taro is likely the crop that was grown in the prehistoric agricultural systems of Aleipata, Solosolo, and

Tafatafa. Each study area is gradually sloping, though not too steep to have serious problems with erosion or planting taro, and situated in an area where water runoff is possible.

Chapter 3. Materials and Methods

Remotely Sensed Data: LiDAR

In 2014, The World Bank sponsored a climate resilience program in Sāmoa seeking to strengthen community responses to climate-related disasters (Keenan 2014).

The Enhancing the Climate Resilience of Coastal Resources and Communities Project was financed through a grant from the Pilot Program for Climate Resilience (PPCR) of the Strategic Climate Fund. The Strategic Climate Fund (within the framework of the

Climate Investment Funds—a multi-donor fund supported by fourteen countries) allocated US$14.6 million for this project via the PPCR (Keenan 2014). This program was also affiliated with Australia’s Department of Foreign Affairs and Trade, NGIS

Australia, and numerous wings of the Sāmoan government (including the Ministry for

Natural Resources and the Environment (MNRE), the United Nations Development

Programme Sāmoa office, and Fugro LADS). It included a light detection and ranging

(LiDAR) survey of the entire territory, which sought to identify potential hazards as revealed by 3-dimensional terrain maps.

LiDAR, also known as 3D laser scanning, is a remote sensing and surveying method which uses a pulsed laser to measure variable distances from an airborne drone to the earth (Chase et al. 2012). LiDAR generates a point cloud (a high-resolution measurement of the surface of the ground) by sending out laser pulses and timing when those laser pulses are reflected back to a sensor on the instrument. This allows the instrument to generate a precise measurement of the ground surface even underneath

11 thick vegetation. The LiDAR dataset produced by the PPCR project revealed the surface of the Sāmoan Islands to be entirely human modified, with hundreds of archaeological features spread across its hillslopes and valleys. The LiDAR dataset’s original purpose, as stated by CRCSI, was to update digital elevation models (DEMs), so that a risk assessment could be made from inundation models of coastal areas (2014).

The LiDAR dataset, upon completion of The World Bank’s Enhancing the

Climate Resilience of Coastal Resources and Communities Project, was then given to

MNRE to curate. Working in collaboration with MNRE, the Center for Sāmoan Studies

(CSS) at the National University of Sāmoa (NUS) was given access to a version of the dataset. It is unclear if CSS was given access to the original .las files (an open, binary file format used specifically to store LiDAR point cloud data) by MNRE. The version of the data made available online and obtained for this research (when working in Sāmoa with CSS in November 2017) does not include .las files (standard file format for LiDAR point cloud data). Rather, it includes LiDAR files in serial vector format (SVF), (which essentially provides an image of the ground) and DEM (digital elevation model). The

DEMs are 3-dimensionally mapped bare-earth elevation source data that can be used to model hydrology or inundation of a landscape.

In order to conduct hydrological modeling with the CSS dataset, however, a few modifications needed to be made. Both the LiDAR files (SVF files) and the DEMs

(stored as TIF files— a high quality raster graphic image computer format) were generated by fixed wing drone flyovers of the island of ‘Upolu—with dozens of such files comprising a single region. The generated files consist of 1 km by 1 km sections

12 and appear as a gridded patchwork covering the entire island. In addition, these files have streaking diagonal lines of “no data” due to their original processing at The National

University of Sāmoa.

Identification and Analysis of Archaeological Features in LiDAR Data

This project began with analyzing Serial Vector Formatted (SVF) files. These files provide an image of variations in the ground surface. Pixel values are expressed using a graded color palette, and variable tones on the image represent changes in elevation relative to an object’s surroundings—i.e., a ditch looks like a thick, dark gray line cutting through a lighter gray background. Identification of such ditches was done visually, using the human eye to identify the color differences in the images. Once the ditch feature is visually identified, a polyline file was created in ArcGIS to mark its location and configuration. Using the Start Editing feature in the Editor toolbar, the polyline file was selected and then used to trace over the identified ditched areas. The ends of the ditch features were sometimes difficult to distinguish, as there were sometimes gaps in the ditches, endpoints in gullies, or they merged with another ditch.

Subsequently, the ditches were not recorded individually, but as a single expanded network of linear features.

Once the line shapefile was created for each and the entire network traced by polylines, a new column (Length) was created in their attribute tables, and then the calculate geometry option was used to ascertain the total distance of each ditching system. The process identified 39,142 m of ditches in Aleipata, 22,826 m of ditches in

Solosolo, and 10,723 m of ditches in Tafatafa (Figures 2 – 4). Afterwards, a new polygon shapefile was created for each study area in order to trace the outline of the ditched area—this was to determine the total area of each study area. A polygon was then created to outline the area of the ditches in each of Aleipata, Solosolo, and Tafatafa, and a new column (Area) was created in the polygon attribute table. Then, the calculate geometry option was used to ascertain the total area of each study area. This analysis showed the Aleipata area to be the largest study area at 3.5 km2, Solosolo at 2.9 km2, and

Tafatafa at 1.5 km2.

Figure 2 Aleipata Ditches Map. The ditches are overlaid on the LiDAR image from the Center for Sāmoan Studies at the National University of Sāmoa

Figure 3 Solosolo Ditches Map

Figure 4 Tafatafa Ditches Map

Hydrological Analyses

The next step was preparing the DEMs for hydrological analyses. The purpose of these analyses was to model how water flows across the surface of a DEM, and thus determine how the ditches operate in conjunction with the modeled surface to redirect water flow. In addition, the hydrological analyses sought to determine how the ditches

16 themselves collect water and keep surrounding areas drained. GIS models hydrological flow using the Flow Accumulation tool, but several steps were required. First, in order to do anything with the DEMs, the “no data” lines (as seen in Figure 5 showing the Tafatafa region) needed to be removed. The “no data” lines misdirected the modeled water flow in GIS—instead of modeling where the water was accumulating. Figure 5 shows the results of the Mosaic to New Raster tool for combining all the smaller TIF files into a larger image to be processed as a single unit for the region of Tafatafa. Hydrological tools only work with a single DEM, so combining the files was necessary to be able to run analyses on the entire area at one time. After this step, it was necessary to open the

Set Null tool and insert the script “Value = 0” so the null values would then have the value of 0. Then, after opening the image analysis window, the Mosaic DEM was highlighted and “add function” was selected. In this new window, Identity Function was chosen, scrolled to “insert function,” and Elevation Void Fill was selected to change the void values by calculating the average of the cells around it. It was then necessary to change the max void width to 0 and run the command—creating a DEM without the null diagonal lines cutting across it.

Figure 5 Tafatafa mosaic DEM with “no data” lines.

Using this new DEM with no null values permitted utilization of the Flow

Direction tool, which indicates the way in which water will flow from cell to cell. Figure

6 was created by inputting the Solosolo Mosaic DEM (with no null values) into the Flow

Direction tool. In order to get this final image, it took a round of taking the original flow direction image, using the Sink tool to search for sinks in the DEM, then using the Fill tool to attempt to fill those sinks, and then using the Flow Direction tool again with the

DEM from the Fill tool. After going through the repetitive Flow Direction tool, Sink tool, Fill tool, Flow Direction tool process, the Flow Direction tool eventually generates a raster with just the eight values that identify the cardinal directions, and these are the

18 various colors of the generated image. Interestingly, when zooming into the flow direction image, the areas where the prehistoric agricultural ditches are present are clearly visible. In fact, they align perfectly with the line shapefile that was derived from the

LiDAR imagery (Figure 8). This is because those ditches from the polyline shapefile are directing the water modelled in the Flow Direction tool.

Figure 6 Solosolo Flow Direction. 1 represents Eastward flow, 2 = SE, 4 = S, 8 = SW, 16 = W, 32 = NW, 64 = N, 68 = Null Values, 128 = NE.

Figure 7 Solosolo Flow Direction Zoom

Figure 8 Solosolo Flow Direction Zoom with Ditches

The next step was to run the Flow Accumulation tool in default to determine the pathway that water would make across the modeled DEM during a rainfall event. This step is essentially a test to make sure the process would work properly. After seeing that the Flow Accumulation results would resemble the shape of the ditch feature networks, a weight factor representing differences in rainfall across ‘Upolu was added to the Flow

Accumulation tool. The goal of this analysis was to see how variable amounts of rainfall were handled by the ditching networks, and to also see if network structure was related to rainfall amounts. Rainfall data were obtained from a raster that was created by the

Sāmoa Meteorology Division of the Ministry for Natural Resources and the Environment.

Rainfall amounts from the month of August were used because it is the most variable time of the year, with substantial differences between Aleipata, Solosolo, and Tafatafa.

In addition, the August rainfall pattern is a good representative for island-wide rainfall means. First, polygons were created and coded with different values representing each region’s different amounts of rainfall. The next step was to define the projection of that rainfall polygon/dataset as WGS 1984, and then project the image onto the existing

Sāmoa LiDAR dataset. Using the Polygon to Raster conversion tool, the Sāmoa August rainfall map was converted to a raster (rainraster1). To do so, the cell size was set to .5 to match the DEM, the output extent of the raster was set to match the extent of the rainfall polygon (matching the coordinates), and the flow direction was set to snap to the raster

(in the environments processing extent). Lastly, the analysis created and reclassified rainraster1 to be used as the weight for the flow accumulation tool. The values consequently represent average monthly rainfall (from midpoints) in millimeters and are incorporated into the raster as the weight for the Flow Accumulation tool.

The result, after setting break values in a classified symbology to .01%, .02%,

.035%, .05%, .065%, .08%, .1%, and 100%, make the flow accumulation visible in

Figure 9—this delineates the deepness of the hue, which signifies how much water is being channeled. However, the result from the weighted Flow Accumulation turned out to look the same as the image produced by the unweighted Flow Accumulation. This is because while the three study areas have different amounts of August rainfall, the rainfall is simply a constant amount falling on each site. So, since there is no variation in the amount of rainfall over a particular site, each study area calculates having a constant and

22 even amount of rainfall. The amount of rainfall wasn’t significantly different enough to show any difference in how the water could flow, but the length, bifurcation, and depth of the ditch systems are possibly tied to the amount of rainfall—this leaves room for future study, as the size and shape of the networks can be measured.

Figure 9 Solosolo Flow Accumulation

While Figure 9 may seem like an indiscriminate array of red lines from a distance, when zoomed in the ditching network is clearly visible (Figure 10).

Figure 10 Solosolo Flow Accumulation Zoom

In Figure 10, points where the flow is redirected perpendicularly due to the presence of the agricultural ditches can be seen. The polyline shapefile for the ditches is overlaid onto the Flow Accumulation image to compare, and when the lines in the Flow

Accumulation image make sudden turns (i.e., perpendicular/90 degrees) they align with the lines from the ditch shapefile. In Figure 11, one can also see how the presence of the ditches, in addition to the natural flow across the landscape, creates drained areas. The drained areas are subsequently the open spaces between the lines of the Flow

Accumulation image and the polyline shapefile. Polygons were generated to calculate the drained areas in each study area. In Figures 12 – 14, the drained spaces, as shown by the polygons, are shown for the study areas of Aleipata, Solosolo, and Tafatafa. 24

Figure 11 Solosolo Flow Accumulation Zoom with Ditches

Figure 12 Aleipata Ditches and Drained Areas

Figure 13 Solosolo Ditches and Drained Areas

Figure 14 Tafatafa Ditches and Drained Areas

Nearly all the areas identified as drained abut at least one ditch of their agricultural system. This suggests that as the water flow is directed away from the drained area, the ditches are joined/configured in a way that systematically redirects the water. Once the polygon shapefile was created for each and the entire drainage area network traced by polygons, a new column (Area) was created in their attribute tables, and then the calculate geometry option was used to ascertain the total area of each.

Aleipata has a total drained area of 373,773 m2, Solosolo has a total drained area of

305,521 m2, and Tafatafa has a total drained area of 143,067 m2.

The directionality of the inland agricultural ditches’ water flow crosscuts the directionality of natural water flow across the landscape. This can be seen in the

28 perpendicular nature of the ditches to the natural water flow shown in the Flow

Accumulation image. In many cases, this crosscutting is what creates the many drained areas in each site. To calculate the drained area per square km, the drained area (m2) was divided by the total area (km2). This yielded 95,387 m2/km2 for Tafatafa, 105,352 m2/km2 for Solosolo, and 106,792 m2/km2 for Aleipata. When converting km2 to

1,000,000m2, the drainage area per square kilometer can then be calculated into the percent coverage area: 9.5% for Tafatafa, 10.5% for Solosolo, and 10.7% for Aleipata.

The Distribution of Soils in Relation to Ditching

Lastly, an analysis was undertaken of the distribution of soils in relation to the ditching present in the study areas. This analysis employed polygon shapefile soil data provided by CSS. To complete these analyses, the ditching line shapefiles for Solosolo,

Aleipata, and Tafatafa needed to have a projection matching the data frame. Since the data frame was set to WGS 1984 UTM Zone 2S, and as the input of the LiDAR files set the data frame projection, the Define Projection tool was used to convert each line shapefile to the corresponding projection. Then, it was necessary to confirm the soil map polygon was also in the WGS 1984 UTM Zone 2S projection. Because it was not in the data frame’s established projection already, the Project tool was used to redefine the soil map polygon’s projection. Next, the Dissolve tool was used on each shapefile. For the ditching shapefiles, this tool ensured that the data table will be a summary—allowing for the integration of the entire series of ditches as a system rather than an amalgamation of smaller ditches. For the soil map polygon shapefile, the Dissolve tool combines each

29 class of “soil order” into single non-contiguous polygons. Once these steps were completed, analyses could be run to determine the total length of each ditching system within each soil type as well as the percentage of ditching within each soil type (Tables 1-

3). This calculation was completed using the Tabulate Intersection tool—using the polygon shapefile as the “zone” and each ditching line shapefile (separately) as the

“class.” Importantly, each file was saved with the postscript “.dbf” so it could be read as text by other programs. Subsequently, the created files ending in .dbf were entered into the Table to Excel (conversion) tool to create Tables 1 – 3.

Table 1 Solosolo ditching on various soil types

OID SOIL_ORDER LENGTH PERCENTAGE 0 Andisols 10988.76599 48.30 1 Inceptisols 11595.66516 50.97 2 Oxisols 167.1049367 0.73

Table 2 Aleipata ditching on various soil types

OID SOIL_ORDER LENGTH PERCENTAGE 0 Andisols 1556.261997 4.06 1 Entisols 685.2607953 1.79 2 Inceptisols 27780.74627 72.45 3 Oxisols 7032.156762 18.34 4 Unkn 1289.010213 3.36

Table 3 Tafatafa ditching on various soil types

OID SOIL_ORDER LENGTH PERCENTAGE 0 Andisols 126.5298336 1.18 1 Histosols 2810.732195 26.21 2 Inceptisols 7755.185073 72.32 3 Oxisols 31.38988641 0.29

Chapter 4. Results

The three study areas vary in size, but ultimately the amount of arable space that is enhanced and defined by drainage are very similar. This chapter will examine the ditching system of each study area identified using LiDAR, as well as the hydrology of those study areas calculated using GIS, to juxtapose the size and configuration of drained areas in addition to the comparative length and intensity (measured as the meters of ditches divided by the square meters of the study area) of the ditching itself. Also, this section will discuss the results of soil analyses and the role soils (in tandem with rainfall) may have played in the need to drain agricultural land in prehistory.

As stated in the methods section, GIS analysis of the LiDAR dataset was successfully used to visually identify the extent of the prehistoric ditch and terrace system in Sāmoa. These ditching systems function to redirect the natural flow of water towards extant and adjacent stream systems, often by perpendicularly crosscutting the natural flow of water across the landscape. Subsequently, the ditch system has had a lasting impact on local land use, as it creates drained spaces of preferential soil for use as arable land. While there are many similarities between each of the Aleipata, Solosolo, and

Tafatafa ditching systems, there are also some differences. These data are tabulated for comparison in Table 4.

Table 4 Results Table

Meters August Ditch of Intensity Drained Drained Arable Study Rainfall Length ditches of Area Area Space per Area (m2) Description (mm) (m) per km2 Ditching (m2) per km2 km2

Tafatafa Mostly inland bush and 1,500,000 plantations 350 10,723 7,148 0.007 143,067 95,387 9.54% Mostly Solosolo residential, some inland 2,900,000 plantations 250 22,826 7,871 0.008 305,521 105,352 10.54% Roughly half Aleipata residential, half inland 3,500,000 plantations 300 39,142 11,183 0.011 373,773 106,792 10.68%

*Samusu Inland (within plantation Aleipata) (cattle grazing 170,000 and coconuts) 300 4,929 28,994 0.029 67,959 513,980 51.40%

Tafatafa

Tafatafa has the smallest area, fewest ditches, and shortest total length of ditches.

Tafatafa, however, still resembles Solosolo and Aleipata in the percentage of drained areas. Being mostly inland bush and plantations, Tafatafa utilizes its deep natural ravines in concert with its ditching system to assist in channeling rainwater—of which it has the most (350 mm in August) when compared to the other study areas. Subsequently, the

143,067 m2 of drained area in Tafatafa over a 1.5 km2 study area translates to an arable space of 9.54% of the total.

Solosolo

Solosolo’s environment differs from Aleipata and Tafatafa in the sense that it has the least amount of rainfall (250 mm in August), yet two major rivers which abut the agricultural area on the east and west sides. Additionally, as Solosolo is one of the

32 largest villages on ‘Upolu, the land is more densely inhabited. Furthermore, the inland plantation areas are becoming increasingly residential as families are forced to abandon their homes along the coast (being lost to an invading ocean) and move to their plantations further up on the mountain. This inhabited landscape was crisscrossed with ditches running perpendicular to the two rivers, having drained and directed the surface water to them and created higher yield agricultural land. The 305,521 m2 of drained area in Solosolo over a 2.9 km2 study area translates to an arable space of 10.54% of the total.

Aleipata

Aleipata is a mix of residential area and bush/plantation, although it is less densely inhabited than Solosolo. The eastern half of the study area bears a strong resemblance to Solosolo in the organization of the ditching. The western half, however, exhibits more similarity to Tafatafa—being more covered in bush and having gullies which can collect redirected rainwater. The one exception is the inland Samusu area—a small patch of plantation flanked by converging ravines. The Samusu area is completely modified with just over half of the area being drained by the ditching system. Aleipata also receives less rainfall than Tafatafa, but more than Solosolo. Consequently, more effort was needed to create Aleipata’s ditching system in order to reach an agricultural coverage area consistent with Tafatafa and Solosolo. So, Aleipata has 11,183 meters of ditches per km2 within the total study area, while Tafatafa and Solosolo have 7,148 m and

7,871 m respectively. The major difference with the length of ditching is the need to create main canals (east-west) which collect rainwater from smaller perpendicular canals

(north-south)—Tafatafa and Solosolo utilize their landscapes’ rivers and ravines in ways in which Aleipata cannot. Still, the 373,773 m2 of drained area in Tafatafa over a 3.5 km2 study area translates to an arable space of 10.68%.

Soil Analysis

Regardless of the idiosyncrasies in each study area, each of Tafatafa, Solosolo, and Aleipata yielded similar rates of drained areas/arable space per square kilometer.

This suggests similar land use patterns for precontact Sāmoan communities in eastern

‘Upolu. Although there remain areas within the greater study areas (like Samusu) that contain comprehensive ditching and drainage, the frequency of ditching for each study area is roughly the same. A major similarity between the three areas is the prevalence of the inceptisol soil type. Sāmoan inceptisols are classified as humitropepts (Morrison,

1991)—soils characterized by the richness of their organic matter. The prevalence of this soil type in the ditched systems suggests it is being targeted for agricultural production.

Future investigations may reveal that it is an easier soil to manipulate, more nutrient-rich, and/or more resistant to erosion. Fifty-one percent of ditches in Solosolo, 72% of ditches in Aleipata, and 72% of ditches in Tafatafa were constructed on inceptisols (Tables 1-3).

Secondary soil types still factor significantly in each study area’s soil profile. A total of

48% of ditches in Solosolo were constructed on andisols, a young but very fertile soil formed in volcanic ash, and 18% of ditches in Aleipata were constructed on oxisols, specifically acroperox (Morrison, 1990), an older and highly weathered low-fertility tropical soil. A total of 26% of ditches in Tafatafa were constructed on histosols.

Histosols are particularly interesting, as they are composed of organic matter that are saturated year-round—meaning that they need to be drained to be agriculturally productive (Martin et al. 1997; Meindl 2000). Subsequently, Tafatafa is nearly 100% prime arable land due to the ditching system and Solosolo is one of the most agriculturally productive areas of the island due to its combination of andisols and inceptisols. This suggests that prehistoric Sāmoans not only knew how to find the best agricultural soils, but that they knew that they needed to place ditches in order to maximize the agricultural output of that area. Such a process may have been developed via trial-and-error, but ultimately served to ameliorate flooding and increase resilience to climate-related extreme weather events.

The intensity of ditching (measured as the meters of ditches divided by the square meters of the study area) in Aleipata (.011) is higher than that of Solosolo (.008) and

Tafatafa (.007). This could be due to the lower percentage of inceptisol soils (72%) in the study area and the higher percentage of denser oxisol soils (18%), thus requiring more ditches to better drain the area. Solosolo’s combination of andisols and inceptisols

(constituting 99% of the study area) and Tafatafa’s combination of histosols and inceptisols (constituting 98% of the study area) likely required less modification for agricultural production because of the nutrient-rich characteristics of each soil type and their permeability. Aleipata, having just 72% inceptisols and 4% andisols, while still very productive as an agricultural landscape, is comparatively less desirable than the other two study areas. Prehistoric Sāmoans of Aleipata were thus compensating for the

18% of oxisol soils with increased ditching. The greater amount of ditching is likely a mechanism to increase the percentage of arable land. Consequently, even though the intensity of ditching in Aleipata is highest, the percentage of arable space is still similar across the three study areas.

Chapter 5. Discussion

Intensity of Ditching in Relation to Soil Type

The intensity of ditching (measured as the meters of ditches divided by the square meters of the study area) in Aleipata (.011) is higher than that of Solosolo (.008) and

This leads to questions related to competition, ditch organization, and community investment. Whether or not there was dispute over what plots were farmed by whom is

37 difficult to say, but based on modern village politick and the association between chiefly titles and land it’s probably fair to assume there were points of contention. The layout of the ditching systems and the community investment necessary to maintain their efficacy, though, can tell a lot about how the villages were organized—i.e., there is in evidence of rational choice making in how they are farming these plots.

Each system has at least one major stem flowing in the same direction as the natural flow of water across the landscape, while the branches emanating from this stem via lateral ditching are numerous. Perhaps the branches represent inputs of individual farmers or families, while the stem represents the organization of labor via the chiefly system. While families maintain their own vein, it is ultimately dependent on the chiefs to organize labor and make sure the system works as a whole. This facilitates both the increased importance on having a chiefly system to organize such labor, but also a framework for population structure and growth. The importance of ditching to Sāmoan prehistory could thus be tantamount to population expansion in the final phase of Sāmoan prehistory, as well as providing legitimacy to an increasingly powerful and complex chiefly system.

A Dearth of Environmental Data

An interesting and potentially important avenue to explore would be the analysis of different Sāmoan soils and their qualities—the range of soils, as well as their ages, characteristics, and distributions. This would create a more comprehensive understanding of the Sāmoan landscape and allow a greater understanding of the need and impact of

38 drainage systems. At the current time, much of the available literature on ‘Upolu’s geography is decades old, and the specific types of soil (beyond their soil order) are still largely undefined. Soil tests and sediment analysis would also be helpful for creating a picture of how the soil has changed over time and determining to what degree flooding, sedimentation, and erosion impacted agricultural systems in the past. The Sāmoan

Ministry of Natural Resources and the Environment (MNRE) has supposedly experimented with coring—but that process has yet to yield any results (at least via academic publications). Additionally, any data related to the underlying geology of the island—i.e., subsidence and/or geostatic pushback, prehistoric volcanic activity, and coastal transformation/reef creation or destruction—would be required to construct a more accurate picture of the landscape, and thus provide context for the geologic context and trajectory of the development of the agricultural system.

Vegetation data would similarly permit landscape recreation. Knowing the prevalence of certain crops (perhaps through residue, phytolith, or ancient DNA analysis) and how they impact the landscape would help to understand the dynamics of agricultural production and flood dynamics in the past (Perruchini et al. 2018; Watling et al. 2015;

Piperno et al. 2009; Roos et al. 2016; Shillito 2011; Allaby et al. 2014, 2015; Brown et al.

2014; Deguilloux et al. 2012). Additionally, paleobotanical and biogeochemical analyses of stable isotopes and trace elements provide an abundance of information related to paleodiet/food resources (Fischer et al. 2007; Schoeninger 2014; Hedman 2006;

Macarewicz and Sealy 2015; Burton 2008). There is, however, a recent study published about lake cores taken from central ‘Upolu. The researchers conducted a paleoecological

39 analysis which revealed microscopic and macroscopic remains and used these to reconstruct regional and local fire histories as well as ecosystem/local vegetation change

(Gosling et al. 2019). This study suggests that initial populations from 2,900–2,700 cal

BP were small in addition to discussing the “opening up” of the landscape over time.

“Opening up” refers to the gradual removal of the tree canopy to allow for farming and habitation, which expanded inland from the coast so that by the Sāmoan ‘Dark Age’

(1500-1000 cal BP) the interior of the island was largely settled. This serves as a possible explanation for the lack of archaeological evidence found along the coast of

‘Upolu during that time period, as village locations may have moved inland. In addition, this study suggests vegetation change on ‘Upolu was different than smaller islands that experienced more extensive burning and swifter inland colonization shortly following initial settlement.

Currently, because the crops prehistoric Sāmoans were growing in these agricultural areas are still unknown, this research is based on the assumption that ditching helps with productivity. The requirements to grow dryland taro, giant taro, wet taro, green bananas, fa’ipula (ripe yellow bananas), soa’a (orange bananas), breadfruit trees, and/or various yams were perhaps variable dependent on the type of soil and amount of rainfall in a particular area. Without further analyses, it is unknown whether these crops were preferentially grown in more eroded oxisols or less eroded inceptisols and andisols.

The permeability of the soil may have been key to the placement of certain crops in certain soil areas, as particular crops may need to have been grown in ditched nutrient- rich areas, while others did not. Still, without this crop-specific evidence, strong

40 statements cannot be made as to the effectiveness of ditches making the land more productive for certain crops. Further data collection related to soil types, however, will allow determinations to be made about the natural productivity of the land.

Possible Expansions for Further Research

Future study would benefit from the comparison of the stratigraphic profiles of the ditched agricultural systems to uncultivated plots (i.e., a control). This control would allow for the comparison of soil formation processes in agriculturally produced soil horizons, versus the natural soil deposition. Due to the large number of factors (i.e., soil type, rainfall, crop type, rate of ditching, etc.) a hypothetical control that could compare all three study areas is difficult to imagine. To develop such a control, an area on ‘Upolu would need to be located where ditching or any other form of landscape modification was absent and agricultural practices never took place.

Due to each study area being studied separately, the differences between the assigned rainfall weights is not apparent in ArcGIS, but rainfall still likely had a significant impact on the soils—both agriculturally modified soils and untouched soils— that formed over time. Rainfall likely effected soil quality, as soils that were too wet could not be farmed. Drainage was ultimately required for both the minimum and maximum rainfall areas in eastern ‘Upolu, as even the minimum amount of rainfall on such a wet island is still too much for agricultural production on the soils that occur naturally due to geology and age. The method used also did not allow a comparison of the differences in water flow or accumulation in the system

Another potential problem with the ditches was the method by which they were identified on the LiDAR. Ultimately, however, this may be resolved by eventually acquiring the .las files from MNRE, which have a higher resolution than the DEM files used in this study. With the current dataset it is difficult to see if the ditches are connected at certain points or not, and often the endpoints of a ditch can be challenging to distinguish. Thus, the ditches were recorded as interconnected systems (even though some of the connections require further investigation) rather than as a collection of independent but connected ditches. This means that the average ditch length was unable to be determined. However, this generates the question: What is a ditch, anyway? A single ditch could be straight, L-shaped, a grid, or just a section that was completed at a specific point in time. What looks like a single ditch—such as a straight line—could actually be three ditches connected to each other but built over a span of several hundred years.

Examining the construction sequences of ditches could reveal evidence of a growing, increasingly powerful chiefly system. Through excavation, research could identify the intersections of ditches, which are the key to determining the chronology of the ditch network’s construction. If it were shown that shorter sections were created earlier while longer sections were constructed later, this could be evidence for the shift from individual or family level cultivation to community investment. Community activities within a chiefly system would likely be directed similarly to the way it is undertaken today—by the matai (chiefs) providing tasks for the ‘aumaga (untitled men) to accomplish. A higher level of social complexity enables the construction of numerous

42 monumental projects through the organization of labor, and the chronology of these ditching systems could reveal how and when this social system evolved over time.

For future research, this project may also enable development of a geospatial model to test agricultural resilience in volcanic Pacific Islands. Creating a predictive model for how the comprehensive ditching system functions during increasingly larger and more frequent tropical storms can predict the direction of rainwater during increasingly extreme climatic events. Having a model to test how an agricultural ditch and terrace system can reroute potentially hazardous volumes of waterflow and prevent potential landslides can be applied to other mountainous islands at risk of cyclones. Such a model would be especially expedient for numerous Pacific Island nations facing increasingly powerful storms as a result of recent climatic changes. The parameters modeled in this research will be applicable to other current environmental issues where

LiDAR datasets are available—i.e., examining the loss of mangroves along flood-prone areas of the southeastern coast of the United States. With a dataset and the proper inputs, modeling can determine the change in efficacy of any flood management system within a flood-prone landscape. Independent Sāmoa could, in this case, be the seminal case study for the expanding use of LiDAR and how it can continue to help international organizations like The World Bank promote climate resilience in ways previously unforeseen.

Key Differences Between Sāmoa and Other Pacific Archipelagoes

Sāmoa is also unique in the prevalence of ditching as an agricultural technique.

Why did Sāmoans need drainage when farmers on other Pacific Islands needed flooding

(Kirch 1994)? Why is there a lack of irrigated cultivation in Sāmoa, which some consider to be the Polynesian homeland, while later settled Eastern Polynesian archipelagoes are renowned for their complex and comprehensive irrigation systems?

The answer may lie in weather patterns, the directionality of storms, and the size and shape of the Sāmoan Islands. First, Sāmoa gets significantly more rain than many other

Pacific archipelagoes (approximately 3000 mm per year), especially when compared to those in Eastern Polynesia, such as Hawaiʻi (1000 mm per year) (Figure 15). Sāmoa also gets many storms from the east, but the elongated shape of ‘Upolu, its orientation along an east-west direction, and its relatively low elevation (1100 meters) hinders the island’s ability to stop the procession of tropical storms. In contrast, Hawaiʻi—the main island of the Hawaiian Islands—is much larger and has a maximum elevation of 10,000 meters. It can generate a clear windward and leeward distinction, which is enhanced by its lower rainfall. Sāmoa is perpetually drenched by comparison—there is more water, and therefore a need to drain rather than irrigate thirsty crops.

Figure 15 Precipitation map (Gosling et al. 2019) Sāmoa’s problem, unlike that of its neighbors, is that it is simply too wet for agriculture in some areas. This is notable in the significant percentage of histosol soils found in the Tafatafa study area. Histosols are soils that are periodically flooded and waterlogged and are consequently better at retaining organic matter (Martin et al. 1997).

Histosols, however, need to be drained in order to be useful for agriculture (Meindl

2000), as the dry taro so commonly consumed in Sāmoa cannot be grown in a stagnant swamp. As tropical storms continue to increase in frequency and magnitude due to climate change, preventing the waterlogging of such soil is key to Sāmoan agricultural success. Therefore, it is critical to promote the practice of traditional agriculture so that contemporary Sāmoans can continue to thrive in an increasingly wet island ecosystem.

Regional Differences in Sāmoa

The differences between the three study areas identified in this thesis are speculative until ground surveys can be completed. However, differences are present.

The Solosolo area is framed by two permanent rivers, creating a dense area of agricultural land in between. Tafatafa’s ditching network seems to work in tandem with the geography of the landscape to channel the water away from agricultural and perhaps formerly residential land—this may be necessary considering the relatively high amount of rainfall it receives compared to Aleipata and Solosolo. Aleipata utilizes the natural flow of the landscape to a lesser degree, but the ditches cut the natural flow perpendicularly and redirect water to natural channels. Solosolo’s network works similarly, inhibiting the natural downslope flow of water to create drier patches of land.

Remarkably, the remnants of these prehistoric agricultural systems still serve their function today, and their maintenance or even expansion could ultimately benefit the

Sāmoan people as they face heavier rainfall in the years to come.

Chapter 6. Conclusion

In the inland portions of eastern ‘Upolu Island lie the remains of networks of prehistoric agricultural ditches that still channel rainwater—alleviating the effects of flooding at lower elevations. Using the LiDAR dataset for Sāmoa and ArcGIS, this thesis compared the configuration of ditching systems in three study areas: Aleipata, Solosolo, and Tafatafa, in order to draw future comparisons to regions of Sāmoa where similar ditching is absent. Aleipata is unique in that it receives a great volume of rainfall yet lacks a major river, while Tafatafa lies in the wettest region of the country, and Solosolo is relatively dry and directly north of Tafatafa on the northern coast of ‘Upolu. The directionality of the inland agricultural ditches’ water flow crosscuts the directionality of natural water flow across the landscape. This signifies their construction as a functional endeavor to limit water flow to specific areas and subsequently drain corresponding spaces.

This project analyzes the practical modern-day hydrological functions of these agricultural systems to expand understanding of prehistoric Polynesian human- environment interactions and social networks, and in so doing reveals how contemporary

Sāmoans can continue to practice traditional agriculture and thrive in an island ecosystem that faces significant threats from climate change, including increasingly powerful storms, sea level rise, and the loss of coral reefs. Rediscovery of the Sāmoan inland

47 archaeological landscape employs the concept of ecological theory within the framework of archaeology, and can establish a new understanding of Sāmoan prehistory that can be articulated with models of emergent social complexity and human environment interaction—showing that Sāmoan landscape modification was focused heavily on water control. This thesis hypothesizes that water control helped with agricultural production through drainage or limiting sheet flow/erosion, rather than irrigation.

This research has revealed how prehistoric Sāmoa was inhabited, and how the landscape was entirely modified by their presence, with investment in the construction of water management systems. Sāmoans (like their Lapita ancestors) had traditional ecological knowledge that was both detailed and flexible, and they employed different technologies in different island areas and situations depending upon local conditions.

More broadly, this research helps to build models for estimation of agricultural yield and potentially pre-contact population estimates, and provides proof for an increasingly powerful chiefly system and indication of communal or organized labor.

Among Pacific Island nations, this research could inform policy as to the importance of maintaining or expanding traditional agricultural systems in comparable environments, especially where populations are increasing, forested island interiors are being converted to farmland or rangeland, and rainwater runoff is limited or overabundant. As island populations grow and the inhabited area spreads into more marginally productive agricultural lands, a risk management system must be in place to mitigate climatic disasters. Thus, Sāmoa’s prehistoric ditching system could serve as a

48 model not only for contemporary Sāmoans to mitigate the effects of extreme climatic events, but for a myriad of island communities around the world.

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