2016
DOI: 10.1016/j.quaint.2016.02.003
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Resilience and the population history of the Kuril Islands, Northwest Pacific: A study in complex human ecodynamics

Abstract: Living in remote places can strain the adaptive capacities of human settlers. It can also protect communities from external social, political and economic forces. In this paper, we present an archaeological population history of the Kuril Islands. This string of small volcanic islands on the margins of the Northwest Pacific was occupied by maritime hunting, fishing and gathering communities from the mid-Holocene to recent centuries. We bring together (1) 380 new and previously published archaeological radiocar… Show more

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Cited by 49 publications
(38 citation statements)
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“…In a similar line of reasoning, Ben Fitzhugh et al. () suggest that extralocal social networks among foraging groups were key in promoting population resilience to local climate and environmental variability on the Kuril Islands of the Pacific Northwest.…”
Section: Environmental Management Resilience and Sustainable Futuresmentioning
confidence: 93%
“…In a similar line of reasoning, Ben Fitzhugh et al. () suggest that extralocal social networks among foraging groups were key in promoting population resilience to local climate and environmental variability on the Kuril Islands of the Pacific Northwest.…”
Section: Environmental Management Resilience and Sustainable Futuresmentioning
confidence: 93%
“…However, relative to the adjacent Coromandel mainland, it rates as a small island following the definition in Fitzhugh et al . (), with a land area of 18.7 km 2 and a coastline of 43.6 km (Figure ). Māori traditions refer to Ahuahu in waka (canoe) voyaging traditions for New Zealand, and specify it as the first landing place of the Horouta canoe that introduced kūmara to New Zealand (New Zealand Geographic Board : 52; Turei ).…”
Section: Offshore Islands and Ahuahu (Great Mercury Island)mentioning
confidence: 99%
“…The first (employed by Bandy, 2005;Collard et al, 2010;Downey et al, 2014;Zahid et al, 2016;Fitzhugh et al, 2016) is an estimator of the mean annualized growth rate, here denoted :…”
Section: Growth Rate Estimators Used In Dtfamentioning
confidence: 99%
“…As mainstream population research returns to an interest in exploring "large-scale, longterm, interdisciplinary population problems" (Coleman et al, 2015: S1), archaeological demography is uniquely positioned to provide one of the longest views on human population available (Binford, 1962: 224;Plog, 1973: 194), vastly exceeding the temporal scope even of historical demography (van de Walle, 2006). It is therefore timely that a handful of recent studies in demographic temporal frequency analysis (dTFA)archaeological research aimed at the recovery of paleopopulation abundance information from temporal frequency distributions (tfds) of archaeological deposits (e.g., sites)have included efforts to estimate paleopopulation growth rates based on tfds from study regions spanning the globe (Bandy, 2005;Rallu, 2007: 20;Collard et al, 2010;Peros et al, 2010;Kelly et al, 2013;Williams, 2013;Downey et al, 2014Downey et al, , 2016Zahid et al, 2016;Fitzhugh et al, 2016;Goldberg et al, 2016; for an early example, see Hommon, 1976:181, 217; see Supplementary Material for an extensive list of archaeological and paleontological publications featuring tfds). The papers by Collard et al (2010), Downey et al (2014Downey et al ( , 2016, and Zahid et al (2016) stand out among these for their tacitly biodemographic interrogations of such growth rate estimates - Collard et al (2010) focusing on the upper limits of biologically plausible growth and thus the implications of high growth rates for immigration inputs; Downey et al (2014Downey et al ( , 2016 focusing first on the fertility, mortality, and growth implications of Neolithization (2014) and the growth rate signals of adaptive resilience and vulnerability (2016); and Zahid et al (2016) focusing on the fertility and mortality experiences of individuals that may be inferred from particular growth rates (under the strong assumptions of Lotka"s stable population model [Lotka, 1925[Lotka, , 1939cf.…”
Section: Introductionmentioning
confidence: 99%