The ‘light touch’ of the Black Death in the Southern Netherlands: an urban trick?

Roosen, J.; Curtis, Daniel R.

doi:10.1111/ehr.12667

Cited by 27 publications

(15 citation statements)

References 76 publications

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“…Historical N e follows the same approximate trajectory for both populations but is consistently lower for the northern cluster, corroborating previous observations of increased homozygosity in northern Dutch populations 1 and consistent with a model of northerners representing a founder isolate from southerners (although a more complex demographic model may better explain these observations) 1,2 . The apparent absence of N e decline in 14 th -century Netherlands initially hints at the possibility that the Black Death had a weaker impact in the region than elsewhere in Europe; although this agrees with the views of some historians, it is hotly debated by others 29 . Per province, however, most N e estimates display a prominent dip at this time (Figure 4b), suggesting that merging non-randomly mating subpopulations into a countrywide group (Figure 4a) artificially inflates diversity, thus smoothing over any population crash following the Black Death.…”

Section: Discussionsupporting

confidence: 65%

Dutch population structure across space, time and GWAS design

Byrne

Rheenen

Berg

et al. 2020

Preprint

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We studied fine-grained population genetic structure and demographic change across the Netherlands using 1 genome-wide single nucleotide polymorphism data (1,626 individuals) with associated geography (1,422 2 individuals). We applied ChromoPainter/fineSTRUCTURE, identifying 40 haplotypic clusters exhibiting 3 strong north/south variation and fine-scale differentiation within provinces. Clustering is tied to country-wide 4 ancestry gradients from neighbouring lands and to locally restricted gene flow across major Dutch rivers. 5 Despite superexponential population growth, north-south structure is temporally stable, with west-east 6 differentiation more transient, potentially influenced by migrations during the middle ages. Within Dutch 7 and international data, GWAS incorporating fine-grained haplotypic covariates are less confounded than 8 standard methods. 9 The Netherlands is a densely populated country on the northwestern edge of the European continent, bounded by 10 Germany, Belgium and the North Sea. The country is divided into twelve provinces and has a complex demographic 11 history, with occupation by several Germanic peoples since the collapse of the Roman Empire, including the 12 Frisians, the Low Saxons and the Franks. Over 17 million individuals now inhabit this relatively small region 13 (41,500km 2 ), making it one of the most densely populated countries in Europe. Despite its small geographical size, 14 previous genetic studies of the people of the Netherlands have demonstrated coarse population structure that 15 correlates with its geography, as well as apparent heterogeneity in effective population sizes across provinces 1,2 . 16 These observations suggest that the demographic past of the Dutch population has left residual signatures in its 17 present regional genetic structure; however this has not been fully explained in the context of neighbouring 18 populations and thus far the use of unlinked genetic markers have limited the resolution at which this structure can 19 be described. This resolution limit also confines the extent to which the confounding effects of population structure 20 can be controlled in genomic studies of health and disease such as genome-wide association studies (GWAS). As 21 these studies continue to seek ever-rarer genetic variation with ever-increasing cohort sizes, intricate understanding 22 and fine control of population structure is becoming increasingly relevant, but increasingly challenging 3 . 23 Recent studies have showcased the power of leveraging shared haplotypes to uncover and characterise previously 24 unrecognised fine-grained genetic structure within populations, yielding novel insights into the demographic 25 composition and history of Britain and Ireland 4-7 , Finland 8 , Japan 9 , Italy 10 and Spain 11 . Haplotype sharing has also 26 revealed genetic affinities between populations, enabling inference of historical admixture events using modern 27 populations as proxies for ancestral admixing sources 12 . Furthermore, geographic informat...

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Section: Discussionsupporting

confidence: 65%

Dutch population structure across space, time and GWAS design

Byrne

Rheenen

Berg

et al. 2020

Preprint

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“…The pattern was different during the two-year mortality crisis of 1438-1439 [6,29,30], which could correspond to a recurring epidemic or reflect a succession of two different diseases. In 1438, an epidemic suggestive of influenza raged through Europe [42], though in Dijon the spatial characteristics of mortality were compatible with a water-borne disease [29].…”

Section: Discussionmentioning

confidence: 99%

“…In Dijon, the death rate of the 2,149 registered heads of household was 15.1%, which was comparable to that of one of the last great European plagues [22]. The second epidemic developed over two years [6,29,30], in 1438 (with a death rate of 5.5% among the 1,951 registered heads of household) and in 1439 (with a death rate of 10.4% among the 1,814 registered heads of household), resulting in a cumulated death toll comparable to that of 1400. This epidemic occurred after several years of devastating and generalized starvation [31] and climate variability [32].…”

Section: Years Of Epidemicmentioning

confidence: 97%

Mortality and demographic recovery in early post-black death epidemics: Role of recent emigrants in medieval Dijon

Galanaud

Giraudoux

et al. 2020

PLoS ONE

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Objective and methods We analyze the influence of population movement on susceptibility to death and resilience during two epidemics occurring in Dijon soon after the Black Death. Using a specific program designed to propose links between entries in annual tax registers, we define tentative heads of household, the elapsed time since their first registration and their ties with other persons within the city. Results During the 1400 epidemic heads of household who were registered for 1-3 years die in large numbers, whereas during years without epidemics, their death rate is lower than that of heads of household who were registered longer. Recent registration is an epidemic vulnerability factor only in association with a low taxation status, which, when isolated, does not influence mortality. A lack of familial ties within Dijon is another vulnerability factor among the recently registered. This suggests that poor, recent emigrants are more affected by epidemic mortality. In contrast, the mortality of recently registered heads of household is indistinct during a later epidemic occurring after several years of major famine that may have selected the more resistant emigrants and/or excluded the more miserable of them from our analysis. In contrast to the first one, this second epidemic is followed by rapid demographic recovery. This latter recovery is fully explained by the contribution of poor, newly registered heads of household without ties in Dijon. Conclusion Our results outline the interaction between population movement and low socioeconomic status on death susceptibility in historical plagues and show that poor recent emigrants may also be key players in the resilience of the population after an epidemic.

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“…The gaps in spatial coverage are immediately visible when taking into account data for the Low Countries, indicated in the inset. When contrasted with an appendix of locations reporting plague outbreaks in the Low Countries just for the period 1349–1500 (part b) (Roosen & Curtis, ), the extent of the spatial gap for this region becomes apparent—and this appendix is far from exhaustive…”

Section: Content Of Historical Datasetsmentioning

confidence: 92%

Climate and society in long‐term perspective: Opportunities and pitfalls in the use of historical datasets

Bavel

Curtis

Hannaford

et al. 2019

WIREs Climate Change

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Recent advances in paleoclimatology and the growing digital availability of large historical datasets on human activity have created new opportunities to investigate long‐term interactions between climate and society. However, noncritical use of historical datasets can create pitfalls, resulting in misleading findings that may become entrenched as accepted knowledge. We demonstrate pitfalls in the content, use and interpretation of historical datasets in research into climate and society interaction through a systematic review of recent studies on the link between climate and (a) conflict incidence, (b) plague outbreaks and (c) agricultural productivity changes. We propose three sets of interventions to overcome these pitfalls, which involve a more critical and multidisciplinary collection and construction of historical datasets, increased specificity and transparency about uncertainty or biases, and replacing inductive with deductive approaches to causality. This will improve the validity and robustness of interpretations on the long‐term relationship between climate and society.This article is categorized under: Climate, History, Society, Culture > Disciplinary Perspectives

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The ‘light touch’ of the Black Death in the Southern Netherlands: an urban trick?

Cited by 27 publications

References 76 publications

Dutch population structure across space, time and GWAS design

Dutch population structure across space, time and GWAS design

Mortality and demographic recovery in early post-black death epidemics: Role of recent emigrants in medieval Dijon

Climate and society in long‐term perspective: Opportunities and pitfalls in the use of historical datasets

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