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. 2016 May 5;11(5):e0154641.
doi: 10.1371/journal.pone.0154641. eCollection 2016.

Population Dynamics of Early Human Migration in Britain

Affiliations

Population Dynamics of Early Human Migration in Britain

Mayank N Vahia et al. PLoS One. .

Abstract

Background: Early human migration is largely determined by geography and human needs. These are both deterministic parameters when small populations move into unoccupied areas where conflicts and large group dynamics are not important. The early period of human migration into the British Isles provides such a laboratory which, because of its relative geographical isolation, may allow some insights into the complex dynamics of early human migration and interaction.

Method and results: We developed a simulation code based on human affinity to habitable land, as defined by availability of water sources, altitude, and flatness of land, in choosing the path of migration. Movement of people on the British island over the prehistoric period from their initial entry points was simulated on the basis of data from the megalithic period. Topographical and hydro-shed data from satellite databases was used to define habitability, based on distance from water bodies, flatness of the terrain, and altitude above sea level. We simulated population movement based on assumptions of affinity for more habitable places, with the rate of movement tempered by existing populations. We compared results of our computer simulations with genetic data and show that our simulation can predict fairly accurately the points of contacts between different migratory paths. Such comparison also provides more detailed information about the path of peoples' movement over ~2000 years before the present era.

Conclusions: We demonstrate an accurate method to simulate prehistoric movements of people based upon current topographical satellite data. Our findings are validated by recently-available genetic data. Our method may prove useful in determining early human population dynamics even when no genetic information is available.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Habitability of a piece of land as a function of altitude, surface flatness, and the availability of water.
The altitude sensitivity is the strongest. However, since many ancient cites are known to be located well above sea level, we have kept the habitability at relatively high values up to an altitude of 2 km. Land unevenness also has significant impact on choice of habitation; it exhibits a steeper function vis-à-vis habitability. The dependence on water is also based on experiences from early cultures [5]. We assume that up to 2 km distance, water can be directly accessed (carried) while, for distance beyond that, the permeability of land to create subterranean water sources assumes more importance. We, therefore, assume that access to water becomes difficult up to 10 km and falls with a power law between 2 and 10 km. Please see the text for more details.
Fig 2
Fig 2. Habitability map of England, Wales, Scotland, and Ireland.
The population density is highest in the red regions and is sparsest in the blue regions. The map corresponds to habitability before the entry of humans.
Fig 3
Fig 3. Locations of initial population in the simulation.
The markers show the four locations from which the simulation begins.
Fig 4
Fig 4. Evolution of the population pattern in steps of 500 iterations.
Each image corresponding to roughly 500 years. The population density is highest in the red regions and is sparsest in the blue regions.
Fig 5
Fig 5. Maps of the British Isles.
a) the current British population density b) the genetic map, c) the habitability of the landmass, and d) simulated distribution in population after 2000 steps.
Fig 6
Fig 6. Comparison of the fixation index, FST, deduced by Leslie et al. [7] in their genetic studies, with the distance parameter deduced from our population dynamics simulations.
The Pearson correlation coefficient is 0.8703, indicating a strong positive correlation. The solid line is a spline fit to the data points and has a χ2 value of 0.7574.
Fig 7
Fig 7. Basic units of computation.
Each block was taken as 1 km by 1 km.

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