Download presentation

Presentation is loading. Please wait.

Published byKaty Breed Modified over 2 years ago

1
Ecological Constraints Among Hunter-Gatherer Societies: An initial foray into Dow-Eff Modelling using Binford’s Hunter- Gatherer Data Amber L. Johnson Truman State University ajohnson@truman.edu

2
Binford 2001 339 cases, global distribution Approximately 200 variables measuring aspects of subsistence, mobility, group size, social organization Another 400 variables measuring aspects of the environment Used to develop regression equations using environment to project HG properties

3
Binford’s 339 Hunter-Gatherer Cases

4
Binford’s 142 Case Sample

5
Hunter- gatherer subsistence specialty by latitude and population density

6
Binford (2001) Projection Strategy Pairs of regression equations averaged for final projection TO PROJECTR2 for 339R2 for 142Avg R2 GATHERING.873.911.892 FISHING.827.803.815 HUNTING.798.785.792 DENSITY.763.729.746

7
TO PROJECT Binford Avg R2 Dow-Eff R2 Binford N Vars Used 339/142 Dow-Eff N Vars Used GATHERING.892.91514/611 FISHING.815.82911/49 HUNTING.792.80511/79 DENSITY.746.81810/811 Binford Models Compared to Dow-Eff Models

8
DV = Log10 (Gathering) Binford Model 2001 [R2=.892] Binford Model 2001 [R2=.892] CVTEMP LCVTEMP LCOKLM LBAR5 LWACCESS PERWLTG PERWRET RUNGRC SNOWAC WRET ELEV CMAT TEMP LPTOAE LPTORUN LSNOWAC SUCSTAB2 CVTEMP LCVTEMP LCOKLM LBAR5 LWACCESS PERWLTG PERWRET RUNGRC SNOWAC WRET ELEV CMAT TEMP LPTOAE LPTORUN LSNOWAC SUCSTAB2 Dow-Eff Model 2014 [R2=.915] Dow-Eff Model 2014 [R2=.915] CVTEMP LCVTEMP LCOKLM LBAR5 LWACCESS PERWLTG PERWRET RUNGRC SNOWAC WRET LATI CVTEMP LCVTEMP LCOKLM LBAR5 LWACCESS PERWLTG PERWRET RUNGRC SNOWAC WRET LATI

9
DV = Fishing Binford Model 2001 [R2=.815] Binford Model 2001 [R2=.815] SDTEMP LCOKLM GATHERING DEFPER LDEFPER LSNOWAC RRCORR2 RUNGRC WRET LGATHER LPTORUN PTORUN SDTEMP LCOKLM GATHERING DEFPER LDEFPER LSNOWAC RRCORR2 RUNGRC WRET LGATHER LPTORUN PTORUN Dow-Eff Model 2014 [R2=.829] Dow-Eff Model 2014 [R2=.829] SDTEMP LCOKLM GATHERING DEFPER LDEFPER LSNOWAC RRCORR2 RUNGRC WRET SDTEMP LCOKLM GATHERING DEFPER LDEFPER LSNOWAC RRCORR2 RUNGRC WRET

10
DV = Hunting Binford Model 2001 [R2=.792] Binford Model 2001 [R2=.792] TRANGE FISHING LWATRGRC MEDSTAB PGROW WATD WATRGRC WRET LEXPREY LMEANELV GROWC RLOW LWACCESS PTOAE PERWLTG RRCORR2 TRANGE FISHING LWATRGRC MEDSTAB PGROW WATD WATRGRC WRET LEXPREY LMEANELV GROWC RLOW LWACCESS PTOAE PERWLTG RRCORR2 Dow-Eff Model 2014 [R2=.805] Dow-Eff Model 2014 [R2=.805] TRANGE FISHING LWATRGRC MEDSTAB PGROW WATD WATRGRC WRET LATI TRANGE FISHING LWATRGRC MEDSTAB PGROW WATD WATRGRC WRET LATI

11
DV = Log10 (Density) Binford Model 2001 [R2=.746] Binford Model 2001 [R2=.746] HIRX LCVTEMP TEMP LCOKLM FISHING GATHERING LBIO5 LSSTAB2 MEDSTAB PERWRET RLOW BAR5 WACCESS LDEFPER LRUNOFF HIRX LCVTEMP TEMP LCOKLM FISHING GATHERING LBIO5 LSSTAB2 MEDSTAB PERWRET RLOW BAR5 WACCESS LDEFPER LRUNOFF Dow-Eff Model 2014 [R2=.818] Dow-Eff Model 2014 [R2=.818] HIRX LCVTEMP TEMP LCOKLM FISHING GATHERING LBIO5 LSSTAB2 MEDSTAB PERWRET LATI HIRX LCVTEMP TEMP LCOKLM FISHING GATHERING LBIO5 LSSTAB2 MEDSTAB PERWRET LATI

12
CONCLUSIONS Hunter-gatherer subsistence and population density are heavily conditioned by ecology Dow-Eff models perform better than standard regression models [measured by R2] using the same variables New tools present opportunities to learn more

13
Acknowledgments For rapid problem solving… Doug White (kept me from crashing right out of the garage) Anthon Eff (made sure I had all the variables I needed in the LRB file) Lukasz Lacinski (got the Galaxy web interface working smoothly with LRB) For laying the foundation… Lewis Binford

Similar presentations

OK

Chapter 2 – Simple Linear Regression - How. Here is a perfect scenario of what we want reality to look like for simple linear regression. Our two variables.

Chapter 2 – Simple Linear Regression - How. Here is a perfect scenario of what we want reality to look like for simple linear regression. Our two variables.

© 2017 SlidePlayer.com Inc.

All rights reserved.

Ads by Google