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Languages, history and human adaptation

Languages, history and human adaptation. Clare Holden UCL. Why languages?. Cultural or ethnic marker 6000 languages (ethno-linguistic groups) 20-200 families Population history Language trees and networks Evolution of cultural diversity Empirical yet formal approach.

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Languages, history and human adaptation

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  1. Languages, history and human adaptation Clare Holden UCL

  2. Why languages? • Cultural or ethnic marker • 6000 languages (ethno-linguistic groups) • 20-200 families • Population history • Language trees and networks • Evolution of cultural diversity • Empirical yet formal approach

  3. “Curiously parallel” Evolutionary biology Historical linguistics Charles Darwin (1859, 1871) August Schleicher (Indo-European, 1862)

  4. X Z S N Linguistic data(basic vocabulary) Multistate coding Binary coding ‘Belly’

  5. Shambala G23 Zigula G31 Ngulu G34 Kaguru G12 Gogo G11 Luguru G35 Nyakyusa M31 2 44 Yao P21 2 Digo E73 1 Giryama E72a 1 23 Hadimu G43c Pokomo E71 1 100 Sagala E74b 19 Nyamwezi F22 Sukuma F21 Sumbwa F23 Mambwe M15 Gikuyu E51 Non-Bantu Kamba E55 Caga E62 1 West Bantu Hima J13 Zinza J23 45 Haya J22H Ganda J15 Soga J16 73 Rundi J62 Rwanda J61 1 802 Hunde J51 Shi J53 East Bantu 800 Lega D25 3 Ndebele S44 A24 Swati S43 A31 A43 A32 C41 C40 Ngoni S45 18 Zulu S42 A75 B25 C36 C61a C51 C57 D32J15 J16 95 Xhosa S41 Tsonga S53 B31 C61b D37J13 J22E51 E71 100 Sotho S33 B11 J51 J53 J61 E55 Tswana S31 Lozi S34 B73 C34 C75D25 J62 J23 F21 E62E72 Venda S21 B80 C71D24 F23 E73 E74 F22 Ndau S15 100 46 H12 H16iH31 C84 G12 G31 G23 East Bantu Shona S10 D10 H32 G11 G34 G43 Nyanja N31a H16hH31 Nyasa N31 L33 G35 Cewa N31b 97 Kunda N42 M15 M31 South West Bantu Sena N44 K11 K22M42 Tumbuka N21 2 Makwa P31 1 R11 K19 K14S45 N21 Lala M52 100 L42 M54P21 N31 Lamba M54 99 Bemba M42 N31b Central Bantu S34 P31 M52N42 Kaonde L42 27 N31a Luba L33 R22 Songe D10S M64N44 Tonga M64 1 S10 Binja D24 1 S44 S15 Lwena K14 1 South West Bantu 100 Ndembu K22 1 R31 Ciokwe K11 S21 Gangela K19 S31 S53 Herero R31 Ndonga R22 Central Bantu Umbundu R11 1 Sikongo H16h S43 Sundi H16i 4 Yombe H12b 1 S33 S42 100 Yaka H31 4 Yaka Kasongo H31 2 Suku H32 S41 67 Madzing B80Mp 1 Teke B73 5 Bakoko A43b Fang A75 1 53 Duala A24 2 Puku A32 1 Kota B25 1 45 Mpongwe B11a Tsogo B31 Kela C75 100 Mongo Nkundo C61 1 Mongo C61 3 Tetela C71 1 28 63 Lele C84 1 Sakata C34 West Bantu 65 Doko C40D 48 Ngombe C41 Lingala C36 Bira D32 2 Kumu D37 2 Bubi A31 Likile C57 Mbesa C51 Ejagham 800 Tiv 802 Bantu language trees Bayesian MCMC sample Holden and Gray, in press Holden, Pagel and Meade, 2005

  6. Bantu network East Bantu West Bantu Central Bantu SW Bantu Holden and Gray, in press

  7. Ancestral statesSpread of cattle among Bantu-speakers Mace and Holden 2005

  8. Co-evolution • Testing adaptive hypotheses • Do two traits co-evolve along branches of tree? • Cultural, genetic, environmental traits • Bantu-speakers • Spread of cattle led to the loss of matrilineal descent • Indo-Europeans • Marriage payments co-evolve with monogamy/ polygyny

  9. How fast is cultural change? • Gain/ loss of trait over time, e.g. 500 yrs • Calibrate tree using archaeological dates Matri lost P=0.83 Matriliny Cattle Patriliny/mixed Cattle Matri gained P=0.01 Cattle lost P=0.81 Cattle gained P=0.03 Cattle lost P=0.18 Cattle gained P=0.19 Matri lost P=0.22 Matriliny Cattle absent Patriliny/ mixed Cattle absent Matri gained P=0.35 Holden and Mace (2003, 2005)

  10. Origin of dowry in Indo-Europeans Laura Fortunato, UCL

  11. Summary • Language trees and networks • Phylogenetic methods • Population/cultural history • Reconstructing other cultural traits on trees • Testing co-evolutionary hypotheses • Rate of cultural evolution

  12. Acknowledgements Laura Fortunato, UCL Russell Gray, University of Auckland Ruth Mace, UCL Andrew Meade, University of Reading Mark Pagel, University of Reading AHRB Centre for the Evolutionary Analysis of Culture, UCL The Wellcome Trust The Marsden Trust, New Zealand

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