Abstract
The relative importance of deterministic and stochastic processes driving patterns of human settlement remains controversial. A main reason for this is that disentangling the drivers of distributions and geographic clustering at different spatial scales is not straightforward and powerful analytical toolboxes able to deal with this type of data are largely deficient. Here we use a multivariate statistical framework originally developed in community ecology, to infer the relative importance of spatial and environmental drivers of human settlement. Using Moran’s eigenvector maps and a dataset of spatial variation in a set of relevant environmental variables we applied a variation partitioning procedure based on redundancy analysis models to assess the relative importance of spatial and environmental processes explaining settlement patterns. We applied this method on an archaeological dataset covering a 15 km2 area in SW Turkey spanning a time period of 8000 years from the Late Neolithic/Early Chalcolithic up to the Byzantine period. Variation partitioning revealed both significant unique and commonly explained effects of environmental and spatial variables. Land cover and water availability were the dominant environmental determinants of human settlement throughout the study period, supporting the theory of the presence of farming communities. Spatial clustering was mainly restricted to small spatial scales. Significant spatial clustering independent of environmental gradients was also detected which can be indicative of expansion into unsuitable areas or an unexpected absence in suitable areas which could be caused by dispersal limitation. Integrating historic settlement patterns as additional predictor variables resulted in more explained variation reflecting temporal autocorrelation in settlement locations.
Highlights
Spatial correlation is a fundamental attribute of the organization of biological systems [1,2] ranging from the growth of spatially discrete bacterial colonies in petri dishes over the patchy structure of plant and animal populations up to the spatial distribution of human settlements in the landscape [3]
Environments are typically heterogeneous at different spatial scales ranging from subtle differences in the physico-chemical environment of individual organisms up to large scale variation in habitat structure across landscapes driven by historic geomorphological processes and broad environmental gradients such as climate and productivity
The recent development of advanced spatial descriptors (PCNM - principal coordinates of neighboring matrices; MEM – Moran’s eigenvector maps [21,22,23]), provides important new opportunities since these are more powerful at detecting spatial variation and allow to identify the scales at which spatial clustering occurs
Summary
Spatial correlation (i.e. a geographic dependency of observations) is a fundamental attribute of the organization of biological systems [1,2] ranging from the growth of spatially discrete bacterial colonies in petri dishes over the patchy structure of plant and animal populations up to the spatial distribution of human settlements in the landscape [3]. Species sometimes expand into unsuitable areas where they would normally go extinct but manage to persist as a result of continuous arrival of new migrants from sources (source sink dynamics) [9,10] Examples of this in human societies include, for instance, villages or a city such as Ancient Rome that cannot sustain themselves and would perish if resources and people were not continuously brought in from other sources through exchange or trade. The method is able to distinguish whether spatial distribution patterns are the result of spatially clustered environmental conditions or environment independent processes such as source sink dynamics or dispersal limitation [26] While this method has been extensively used in ecology, its potential use in other disciplines such as the social sciences remains largely unexplored
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