An ecological niche modelling of Upper Palaeolithic stone tool groups in the Kanto-Koshinetsu region, eastern Japan

Abstract

Ecological niche modelling (ENM) is a computer-based method to extrapolate the niche probability of a species through machine learning using (1) known location of a species' occurrence and (2) environmental factors such as temperature, precipitation and elevation. ENM is applicable to prehistoric archaeology based on the assumption that human behaviour and niche constructions were largely constrained by environmental factors. This paper applies ENM to a large-scale data of Upper Palaeolithic (UP) occupations in the Kanto-Koshinetsu region of eastern Japan in order to quantitatively assess and visualise ecological niche of UP populations to understand their behavioural strategies. Spatial niche probability was calculated with reconstructed palaeoclimatic data and landform indices for the Last Glacial Maximum (21 ka), taking into consideration four major lithic groups (trapezoids, kakusuijo-sekki, backed blades and microliths), which may reflect different strategies in resource procurement. Results indicated that niche probability was generally high in Musashino, Sagamino and Shimousa uplands in the southern Kanto region, although the observed site distribution was probably biased by intensive rescue excavations due to urban developments. The distance to the obsidian outcrops in the central highland remarkably contributed to the model. A closer look revealed two patches of high niche probability area in the Hakone mountains for the kakusuijo-sekki, backed blades and microliths. These patches were probably parts of a 'niche corridor' in association with the transportation of obsidian raw materials between the central highland and Hakone area via the Fujikawa valley.

Long-lasting effects of historical land use on the current distribution of mammals revealed by ecological and archaeological patterns

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Predictive Spatial Modelling

“…However, many other options are becoming available, including ecological niche modelling (cf. Kondo, 2015;Banks, 2017), Monte Carlo simulations (cf. Kvamme, 1997;Vanacker et al, 2001) and Bayesian statistics (cf.…”

Archaeological Predictive Modeling Using Machine Learning and Statistical Methods for Japan and China

“…As the origin of directional settlement research, Willey's work revealed that the location of human settlements is closely related to the characteristics of the natural environment. Subsequent studies by other scholars [7][8][9][10][11] further elucidated the relationship between archaeological sites and space: the locations of archaeological sites are non-random since spatial conditions limit human behaviors. As such, the realization that archaeological sites are closely related to the natural environment, coupled with the processual emphasis on quantitative methods, enabled the formulation of statistical models for predicting archaeological sites in a region.…”