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
“Curiously parallel”
August Schleicher (Indo-European, 1862)
Charles Darwin (1859, 1871)
Evolutionary biology
Historical linguistics
Species
Genetic units of transmission
Languages
Linguistic elements, e.g. words
Reproduction Learning, often parent-offspring
Horizontal transmission
Borrowing
Hybridisation Creoles
Linguistic data(basic vocabulary)
Ashes Belly Bird
Xhosa 1 1 1
Zulu 1 1 2
Sotho 1 2 3
Ndau 1 3 3
v1 v2 v3
Xhosa 1 0 0
Zulu 1 0 0
Sotho 0 1 0
Ndau 0 0 1
Binary coding ‘Belly’Multistate coding
X Z S N
Bantu language trees– Bayesian MCMC sample
800802
A31 A43
A75 B25 C36 C61a C51 C57 D32 J15 J16
B11B73 C34 C75 D25 J62 J23 F21 E62E72
H12 H16iH31 C84H32H16hH31
A32 C41 C40
R11 K19 K14 S45 N21
R31
R22
K11
S34 P31
B31 C61b D37 J13 J22 E51 E71 J51 J53 J61 E55
B80 C71 D24 F23 E73 E74F22
L42 M54 P21
L33D10
M15 M31 K22 M42
S33 S42
S31 S53
S43
S41
S21
N31aM64 N44S44 S15
S10
M52 N42
N31
A24
G12 G31 G23 G11 G34 G43
G35
N31b
West Bantu
East Bantu
South West Bantu
Central Bantu
Non-Bantu
Shambala G23Zigula G31Ngulu G34Kaguru G12Gogo G11Luguru G35Nyakyusa M31 2Yao P21 2Digo E73 1Giryama E72a 1Hadimu G43cPokomo E71 1Sagala E74bNyamwezi F22Sukuma F21Sumbwa F23Mambwe M15Gikuyu E51Kamba E55Caga E62 1Hima J13Zinza J23Haya J22HGanda J15Soga J16Rundi J62Rwanda J61 1Hunde J51Shi J53Lega D25 3Ndebele S44Swati S43Ngoni S45Zulu S42Xhosa S41Tsonga S53Sotho S33Tswana S31Lozi S34Venda S21Ndau S15Shona S10Nyanja N31aNyasa N31Cewa N31bKunda N42Sena N44Tumbuka N21 2Makwa P31 1Lala M52Lamba M54Bemba M42Kaonde L42Luba L33Songe D10STonga M64 1Binja D24 1Lwena K14 1Ndembu K22 1Ciokwe K11Gangela K19Herero R31Ndonga R22Umbundu R11 1Sikongo H16hSundi H16i 4Yombe H12b 1Yaka H31 4Yaka Kasongo H31 2Suku H32Madzing B80Mp 1Teke B73 5Bakoko A43bFang A75 1Duala A24 2Puku A32 1Kota B25 1Mpongwe B11aTsogo B31Kela C75Mongo Nkundo C61 1Mongo C61 3Tetela C71 1Lele C84 1Sakata C34Doko C40DNgombe C41Lingala C36Bira D32 2Kumu D37 2Bubi A31Likile C57Mbesa C51Ejagham 800Tiv 802
West Bantu
South West Bantu
Central Bantu
East Bantu
73
45
19
23
44
95100
97
18
46
27
100
99
53
67
100
1006328
4865
45
100
100
100
Holden and Gray, in pressHolden, Pagel and Meade, 2005
Bantu network
Holden and Gray, in press
East Bantu
Central Bantu
SW Bantu
West Bantu
Ancestral states
Spread of cattle among Bantu-
speakers
Ejagham Tiv
Mpongwe Kota
Duala Fang
Likile Mbesa
Lingala Teke
Yombe Sikongo
Sundi Yaka Kasongo
Suku Yaka
Ngombe Bira Kumu
Gangela Ciokwe
Lwena Ndonga
Herero Umbundu
Lele Sakata
Nkundo Kela
Binja Lega Tonga
Bemba Lala
Lamba Kaonde
Luba Songe
Rundi Rwanda
Ganda Soga
Hima Zinza
Caga Gikuyu
Giryama Nyamwezi Sumbwa
Nyakyusa Yao
Mambwe Tumbuka
Sena Kunda
Cewa Nyanja
Shona
Venda Lozi
Sotho Tswana Tsonga
Ndebele Swati
Xhosa Ngoni
Zulu
Ancestral states (nodes)
= Cattle
= No cattle
= Ambiguou s state
Blue text = Cattle
Purple text = No cattle
Ethnographic populations (tips of tree)
Adoption of cattle (2)
Adoption of cattle (1)
Loss of cattle
Possible route for gain of cattle by Cewa
Mace and Holden 2005
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
MatrilinyCattle
MatrilinyCattle absent
Patriliny/ mixedCattle absent
Matri lostP=0.83
Matri gainedP=0.01
Cattle gainedP=0.19
Cattle lost
P=0.81
Matri lostP=0.22
Matri gainedP=0.35
Cattle gainedP=0.03
Cattlelost
P=0.18
Holden and Mace (2003, 2005)
Patriliny/mixedCattle
How fast is cultural change?– Gain/ loss of trait over time, e.g. 500 yrs– Calibrate tree using archaeological dates
Origin of dowry in Indo-Europeans
Laura Fortunato, UCL
Summary
• Language trees and networks– Phylogenetic methods– Population/cultural history
• Reconstructing other cultural traits on trees– Testing co-evolutionary hypotheses– Rate of cultural evolution
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