How to interpret past human mobility patterns

celtic-europe-national-geographic

New paper (behind paywall), Interpreting Past Human Mobility Patterns: A Model, by Reiter and Frei Eur J Archaeol (2019).

Interesting excerpts (modified for clarity; emphasis mine):

Present investigations of mobility can be divided into two main groups: 1) individual mobility, and 2) group mobility.

Research approach

(…) it is arguable that, ‘the reality of a mobile existence is far more complex than the ordering principles used to describe it’ (Wendrich & Barnard, 2008: 15). It seems that the most accurate means of modelling mobility is through a thorough examination of a variety of phenomena in combination with archaeological context. Notable examples of these defining criteria include:

  1. Mobility (length of time, season);
  2. Number of journeys;
  3. Segment of the population which moved (as defined by gender, age, health, occupation, or social position);
  4. General socio-political organization;
  5. Logistics and available modes of transport.

means-identifying-individual-vs-group-mobility

In an ideal world, these five categories should be investigated via multiple samples from multiple individuals from a site, region, and culture group who represent the full gamut of ages, sexes, and social levels. Unfortunately, the fragmentary nature of the archaeological record rarely includes material suitable for covering all parameters.

A mobility model

Thirty years ago, David Anthony criticized archaeologists for their approach to migration: ‘instead of developing the needed tools, archaeologists have avoided the subject’ (Anthony, 1990: 895).

Although there are (and always will be) holes in the record, we propose a mobility model composed of four over-arching mobility patterns which we have named as follows:

NOTE. Cases explored in the paper are within brackets.

  1. Non-migratory [no mobility: The Case of Singen (Germany)];
  2. Point-to-point migratory [The Case of the Skrydstrup Woman (Denmark)];
  3. Back-and-forth [The case of Haraldskaer Woman (Denmark)];
  4. Repeated mobility, subdivided into
    • Cyclical mobility [The cases of Nieder-Mörlen (Germany) and Ötzi (Italy)]
    • Non-cyclical mobility [The cases of the medieval Silk Road, Roman York, Viking Age Trelleborg, and La Tène Bohemia]

human-mobility-model

All told, the mobility patterns identified in the present model cleave to three overarching kinds of mobility: non-mobility, single mobility/migration, and multiple movements. The causes of non-mobility and different types of mobility can be manifold.

Non-mobility may include lack of sufficient funds or surplus, social obligations, health status, age, and social standing (serf, slave, landed gentry).

Single, unidirectional movements may have been caused by marriage alliances; family movements; social, political, or economic instability; violence (enslavement, kidnapping); or health issues.

By contrast, individuals who show evidence of multiple movement were likely to have been moving because mobility formed part of their employment, beliefs (ritual), or lifestyle. Although a warrior or soldier, herder, trader, or traveller within an extensive kinship network may present very different mobility patterns, they are all unified by the fact that their chosen occupation or social group(s) exhibit some form of mobility mandate.

The causes of back-and-forth mobility are difficult to define as different reasons could spur a single to-and-from journey to a specific place of cultural, religious, or personal importance.

Repeated mobility, be it cyclical or more irregular (non-cyclical), can also be closely related to social status. For example, a peddler, small-scale trader, or migrant worker’s identity and integration (or nonintegration) into the society (or societies) with which they have contact can be defined by their transitory lifestyles. (…) both the profession and its mobile nature removed metalworkers from ‘normal’ society; in many cases, they formed a separate social category (Neipert, 2006). This could also be the case with warriors. Although contact with migratory workers or specialists was necessary for temporary collaboration, prolonged contact might involve severe social change (Neaher, 1979; Bollig, 1987).

Related

Indo-European pastoralists healthier than modern populations? Genomic health improving over time

genetic-risk

A new paper has appeared at BioRxiv, The Genomic Health Of Ancient Hominins (2017) by Berence, Cooper and Lachance.

Important results are available at: http://popgen.gatech.edu/ancient-health/.

While the study’s many limitations are obvious to the authors, they still suggest certain interesting possibilities as the most important conclusions:

  • In general, Genetic risk scores (GRS) are similar to present-day individuals
  • Genomic health seems to be improving over time
  • Pastoralists could have been healthier than older and modern populations

Some details and shortcomings of the study (most stated by them, bold is from me) include:

  • Allele selection: only some of the known autosomal disease-associated SNPs were included
  • Discovered disease-associated SNPs are known to be biased toward European diseases
  • Ancient sample selection and genomic quality: only 147 ancient genomes were included, from 449 available, with a conventional cut made at 50% of the focal 3180 disease-associated loci. These samples did not include the same loci. All this can affect whether an individual has high or low GRS (a relationship was found between GRS percentiles and sequencing coverage for ancient samples).
  • Phase 3 of the 1000 Genomes Project was used. However, many disease alleles that segregated in the past remain undiscovered – therefore, GRS for ancient individuals should be considered to be underestimated.
  • Genetic risk scores were calculated for each individual (with different sets of disease-associated loci), hence they were not comparable across individuals. So GRS were standardized as GRS percentiles, with certain assumptions, comparing them to modern individuals
  • Multiple comparisons with all data available, using multiple groups, in the small sample selected: comparisons were made between standardized GRS percentile, sample age (i.e. estimated date), mode of subsistance, and geographic location.
  • Older samples have worse coverage, especially Altai Neandertal, Ust’-Ishim, and Denisovan (which might influence results in hunter-gatherers)
  • Northern ancient individuals (using latitude values) show healthier genomes: but, most ancient individuals are from Eurasia, and samples are heterogeneous.
  • Agriculturalists show a higher genetic risk for dental/periodontal diseases than hunger-gatherers and pastoralists. However, this disease has the smallest number of risk loci (k = 40), so risk in older samples might be underestimated, and pastoralists are the more recent agriculturalist population (most used agriculture as a complementary diet), so it is only natural that selection had an impact over time in this aspect.
  • Pastoralists have the smallest sample size (19 samples) and geographic range, so conclusions about this group are still less trustworthy.
  • Genetic risk percentile ≠ Genomic health ≠ phenotypic health (not deterministic), and also disease-associated alleles in modern populations ≠ same effects in past environments.

To sum up, an interesting approach to studying genomic health with the scarce data available, but too many comparisons, with too many hypotheses being tested, which remind to a brute-force attack on data that can therefore yield statistically significant results anytime, anywhere.