2022
DOI: 10.1016/j.ecolmodel.2022.110115
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Simulations of human migration into North America are more sensitive to demography than choice of palaeoclimate model

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Cited by 2 publications
(3 citation statements)
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“…The latter included 14 previously unpublished radiocarbon dates [25]. Models were coded in R v 4.2.0 using the package paleopop [26].…”
Section: Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…The latter included 14 previously unpublished radiocarbon dates [25]. Models were coded in R v 4.2.0 using the package paleopop [26].…”
Section: Methodsmentioning
confidence: 99%
“…The population growth and expansion of Palaeolithic humans across Eurasia following the last glacial maximum (LGM, a period from 26.5 to 19 ka BP [ 38 ]) was modelled using a process-explicit climate-informed spatial genetic model (CISGeM) that accurately reconstructs global genetic patterns and arrival times of anatomically modern humans [ 39 ]. This model has previously been used to disentangle the impact of humans on megafauna over palaeo timescales [ 6 , 28 ].…”
Section: Methodsmentioning
confidence: 99%
“…Theories of range shifts, population declines and extinctions are now being tested directly using historical and palaeo-reconstructions (Fordham et al, 2021(Fordham et al, , 2022, permitting inferences of how biodiversity is likely to respond to future environmental change (Fordham et al, 2020). However, reconstructing past demographic changes at landscape scales poses unique modelling challenges, including the reliance on indirect proxies to draw inferences about range collapses and the timing and location of extinction (Dietl et al, 2015); uncertainty in reconstructions of past climates (Rutherford et al, 2005) and human-driven environmental threats (Ellis et al, 2021;Pilowsky, Manica, et al, 2022); and a lack of information on the ecological lifestyles and traits of many species (Fordham et al, 2016). Some of these issues can be addressed, at least in part, using process-explicit models, particularly if they are combined with pattern-oriented modelling (POM) techniques (Box 1).…”
Section: Introductionmentioning
confidence: 99%