Costly signalling and the distribution of monumental mounds in Savai‘i and ‘Upolu, Sāmoa

Abstract

Monumental architecture has long been a focus of archaeological research. The construction of large and elaborate structures is a costly endeavour, which may seem to have little immediate benefit. Costly signalling is one explanation which can be applied to this problem by determining the contexts in which monument construction would carry a selective advantage. This study investigates stone and earthen mounds and platforms from Savai'i and 'Upolu in Sāmoa. These are among the most common type of archaeological feature in the landscape, yet targeted research explaining why these costly mounds were built is lacking. One of the primary expectations of costly signalling is that monuments will be located near areas where contested resources are abundant. To test this expectation, a semi-automated feature identification method was used to identify mounds in LiDAR data, and a model of agricultural productivity was produced to map locations where the cultivation of dryland taro should have been successful. Mounds were found to be preferentially located near areas with high agricultural potential, suggesting that costly signalling could be an explanation for mound construction in Sāmoa, where mounds served to demonstrate resource holding potential and competitive ability.

Evaluating Mask R‐CNN models to extract terracing across oceanic high islands: A case study from Sāmoa

“…While terracing and other forms of landscape modification were recorded during these initial surveys, it was generally assumed that the focus of land use was on the coast during the entirety of the cultural sequence that began by 2800-2400 calBP across the archipelago (Green, 2002). The acquisition and use of lidar data have fundamentally altered these views, at least for the Manu'a group and parts of S amoa (Glover et al, 2020;Jackmond et al, 2018;Quintus et al, 2015Quintus et al, , 2017. In Manu'a, paired pedestrian surveys and manual analysis of lidar datasets have allowed documentation of the full distribution of terracing on the island of Ofu and Olosega (Quintus, 2020).…”

“…Likewise, applications of lidar datasets to archaeological survey in S amoa have provided novel insights into the high density of occupation in some locations (Glover et al, 2020;Jackmond et al, 2018).…”

“…Recently, the use of semiautomated techniques has proven useful for such island-scale surveys (Glover et al, 2020), and the use of automated feature extraction techniques offers another mechanism to scale up these analyses even more (Freeland et al, 2016). Machine learning and, specifically, deep learning hold the potential to increase the efficiency and success of lidar analysis across Oceania.…”

“…From these analyses, it is estimated that terracing encompasses over 30% and 60% of each island's interior, respectively (Quintus, 2018). Likewise, applications of lidar datasets to archaeological survey in Sāmoa have provided novel insights into the high density of occupation in some locations (Glover et al, 2020; Jackmond et al, 2018). These results highlight how extensively these interior landscapes were used and modified.…”

Collective action problems led to the cultural transformation of Sāmoa 800 years ago

“…Little research has been conducted on the agricultural potential of the Falefa Valley. ‘Upolu-wide analyses by Schroth [ 65 ], for example, suggest that soil fertility decreases as one moves inland from the coast, but these analyses included only one sample location in the Falefa Valley [see 39 ]. More recent work by Terry et al [ 66 ] documented an extremely high sedimentation rate in the Falevao basin, approximately 4 cm/year over the last 40 years, and they attribute this to storm-derived alluvial flooding.…”