Mechanical Properties of Plant Underground Storage Organs and Implications for Dietary Models of Early Hominins

The diet of early human ancestors has received renewed theoretical interest since the discovery of elevated d13C values in the enamel of Australopithecus africanus and Paranthropus robustus. As a result, the hominin diet is hypothesized to have included C4 grass or the tissues of animals which themselves consumed C4 grass. On mechanical grounds, such a diet is incompatible with the dental morphology and dental microwear of early hominins. Most inferences, particularly for Paranthropus, favor a diet of hard or mechanically resistant foods. This discrepancy has invigorated the longstanding hypothesis that hominins consumed plant underground storage organs (USOs). Plant USOs are attractive candidate foods because many bulbous grasses and cormous sedges use C4 photosynthesis. Yet mechanical data for USOs—or any putative hominin food—are scarcely known. To fill this empirical void we measured the mechanical properties of USOs from 98 plant species from across sub-Saharan Africa. We found that rhizomes were the most resistant to deformation and fracture, followed by tubers, corms, and bulbs. An important result of this study is that corms exhibited low toughness values (mean = 265.0 J m-2) and relatively high Young’s modulus values (mean = 4.9 MPa). This combination of properties fits many descriptions of the hominin diet as consisting of hard-brittle objects. When compared to corms, bulbs are tougher (mean = 325.0 J m-2) and less stiff (mean = 2.5 MPa). Again, this combination of traits resembles dietary inferences, especially for Australopithecus, which is predicted to have consumed soft-tough foods. Lastly, we observed the roasting behavior of Hadza hunter-gatherers and measured the effects of roasting on the toughness on undomesticated tubers. Our results support assumptions that roasting lessens the work of mastication, and, by inference, the cost of digestion. Together these findings provide the first mechanical basis for discussing the adaptive advantages of roasting tubers and the plausibility of USOs in the diet of early hominins.

Gauss-Seidel Estimation of Generalized Linear Mixed Models with Application to Poisson Modeling of Spatially Varying Disease Rates

Generalized linear mixed models (GLMMs) provide an elegant framework for the analysis of correlated data. Due to the non-closed form of the likelihood, GLMMs are often fit by computational procedures like penalized quasi-likelihood (PQL). Special cases of these models are generalized linear models (GLMs), which are often fit using algorithms like iterative weighted least squares (IWLS). High computational costs and memory space constraints often make it difficult to apply these iterative procedures to data sets with very large number of cases. This paper proposes a computationally efficient strategy based on the Gauss-Seidel algorithm that iteratively fits sub-models of the GLMM to subsetted versions of the data. Additional gains in efficiency are achieved for Poisson models, commonly used in disease mapping problems, because of their special collapsibility property which allows data reduction through summaries. Convergence of the proposed iterative procedure is guaranteed for canonical link functions. The strategy is applied to investigate the relationship between ischemic heart disease, socioeconomic status and age/gender category in New South Wales, Australia, based on outcome data consisting of approximately 33 million records. A simulation study demonstrates the algorithm's reliability in analyzing a data set with 12 million records for a (non-collapsible) logistic regression model.