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J Hum Evol. 2015 Jan;78:1-11. doi: 10.1016/j.jhevol.2014.11.001. Epub 2014 Dec 2.

Analytical framework for reconstructing heterogeneous environmental variables from mammal community structure.

Author information

1
Department of Archaeology and Natural History, School of History, Culture and Languages, ANU College of Asia and the Pacific, The Australian National University, ACT 0200, Australia; Research Centre in Evolutionary Anthropology and Palaeoecology, School of Natural Sciences and Psychology, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, UK. Electronic address: julien.louys@anu.edu.au.
2
Research Centre in Evolutionary Anthropology and Palaeoecology, School of Natural Sciences and Psychology, Liverpool John Moores University, Byrom Street, Liverpool L3 3AF, UK.
3
Department of Anthropology, Durham University, Queen's Campus, Stockton, University Boulevard, Thornaby, Stockton-on-Tees TS17 6BH, UK.
4
Research Laboratory for Archaeology and the History of Art, School of Archaeology, University of Oxford, Dyson Perrins Building, South Parks Road, Oxford OX1 3QY, UK.

Erratum in

  • J Hum Evol. 2015 Apr;81:88-9.

Abstract

We test the performance of two models that use mammalian communities to reconstruct multivariate palaeoenvironments. While both models exploit the correlation between mammal communities (defined in terms of functional groups) and arboreal heterogeneity, the first uses a multiple multivariate regression of community structure and arboreal heterogeneity, while the second uses a linear regression of the principal components of each ecospace. The success of these methods means the palaeoenvironment of a particular locality can be reconstructed in terms of the proportions of heavy, moderate, light, and absent tree canopy cover. The linear regression is less biased, and more precisely and accurately reconstructs heavy tree canopy cover than the multiple multivariate model. However, the multiple multivariate model performs better than the linear regression for all other canopy cover categories. Both models consistently perform better than randomly generated reconstructions. We apply both models to the palaeocommunity of the Upper Laetolil Beds, Tanzania. Our reconstructions indicate that there was very little heavy tree cover at this site (likely less than 10%), with the palaeo-landscape instead comprising a mixture of light and absent tree cover. These reconstructions help resolve the previous conflicting palaeoecological reconstructions made for this site.

KEYWORDS:

Faunal community; Laeotoli; Palaeoecology; Palaeoenvironment; Vegetation heterogeneity

PMID:
25480104
DOI:
10.1016/j.jhevol.2014.11.001
[Indexed for MEDLINE]

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