In 2011 Clarkson and Hiscock (2011) presented several regression models for flakes with different platform types used to estimate original flake mass based on platform surface area and external...Show moreIn 2011 Clarkson and Hiscock (2011) presented several regression models for flakes with different platform types used to estimate original flake mass based on platform surface area and external platform angle in order to measure reduction intensity on lithic tools. In addition to subsampling and adding external platform surface area, Clarkson and Hiscock increased the accuracy of the regression models by using a 3D laser scanner to measure platform surface area. Most previous studies multiplied platform width and thickness as an estimate of platform surface area. In this thesis, the regression models created by Clarkson and Hiscock were tested on an archaeological sample from Colmont-Ponderosa, a Middle Palaeolithic site in Limburg, the Netherlands. Instead of a 3D laser scanner, photogrammetry was used to create 3D models. It was found that Clarkson and Hiscock’s models are not applicable on the Colmont-Ponderosa sample. New models were created using the same procedure as Clarkson and Hiscock. In addition to platform type subgroups, flake shape subgroups were made. Creating subsamples based on platform type did not influence the correlation between mass and platform surface area. Subsampling based on flake shape resulted in slightly increased correlation, probably because broader flakes have a higher mass to platform surface area than other flakes. Even though a positive linear correlation between external platform angle and mass was found, this variable was not very influential on the final regression models. It was concluded that the newly created regression models are much better at predicting original flake mass for the Colmont-Ponderosa sample. Mass predictions of individual flakes are still not very accurate, which might result in faulty results when introducing new data.Show less
Research master thesis | Archaeology (research) (MA/MSc)
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HomininSpace is an agent based modelling and simulation environment for moving hominin groups through a large scale geographical landscape. Changing carrying capacity in a reconstructed...Show moreHomininSpace is an agent based modelling and simulation environment for moving hominin groups through a large scale geographical landscape. Changing carrying capacity in a reconstructed paleoclimate is the ultimate driving force behind dispersal in HomininSpace. Changing temperatures and precipitation levels influence the carrying capacity of the landscape, and are assumed to be the most influential parameter in the mobility of ancient hominins in the underlying model. This research combines for the first time an environmental reconstruction driven by the results from isotopic measurements with a year by year demographic model for Neandertal groups moving through North-west Europe. The Neandertals utilize the energy levels from the environment in the form of the meat from large herbivores. The aim is to assess conceptual models underlying the behavior of Middle Pleistocene hominins in fluctuating climatic conditions, including severe stress-inducing environments. The research contributes to understanding past hominin behaviors regarding mobility strategy, dispersal and occupation history within changing environments. Two major types of behavior driving movement were identified and are implemented in the simulations: a dynamic mobility and a static mobility. Dynamic mobility can be best described as hominins following their preferred habitat. Static mobility is an implementation of the source and sink model, where populations stay in the same area and suffer from local extinction when the climate deteriorates and are replenished from remote source locations when conditions improve. Simulations were run from 131 ky BP to 50 ky BP. For 14.948 grid cells (148 x 101) in each of 81.000 timesteps climatic parameters are reconstructed, including elevation, temperature (yearly average, warmest and coldest month values) and precipitation levels. From these values a (grid-based) environment is reconstructed through which groups of hominins move, driven by the inferred abundance of large herbivores, representing the energy levels stored in the local environment. For each simulation different parameters can be set through the user interface implementing different models and hypotheses about hominin behavior. Output of the simulation processes include density maps of hominin presence, density maps identifying areas where hominins died and statistical information on hominin groups including sizes, composition, foraging ranges, resource deficiencies, and ages. Simulations can be started, paused and restarted at any point in time. Results can further include a log file with the key characteristics of the simulation, debug information at a desired level, screen dumps in different formats and a playable movie from snapshots at indicated intervals. Movement patterns of the simulated hominins are matched against archaeological dating information on Neandertal material taken from the literature. This data is collected in a comprehensive database which includes site name and GPS location, material dated, date assigned including accuracy and dating method, reference to the literature, and a confidence level. The archaeological data are included as Checkpoints in Space and Time of which 75 individual sites are included. Simulation results are summarized in key values allowing assessment of the level of agreement between model and archaeology on different aspects.Show less
