How wild bearded capuchin monkeys select stones and nuts to minimize the number of strikes per nut cracked

Wild bearded capuchin monkeys, Cebus libidinosus, use stone tools to crack palm nuts to obtain the kernel. In five experiments, we gave 10 monkeys from one wild group of bearded capuchins a choice of two nuts differing in resistance and size and/or two manufactured stones of the same shape, volume and composition but different mass. Monkeys consistently selected the nut that was easier to crack and the heavier stone. When choosing between two stones differing in mass by a ratio of 1.3:1, monkeys frequently touched the stones or tapped them with their fingers or with a nut. They showed these behaviours more frequently before making their first selection of a stone than afterward. These results suggest that capuchins discriminate between nuts and between stones, selecting materials that allow them to crack nuts with fewer strikes, and generate exploratory behaviours to discriminate stones of varying mass. In the final experiment, humans effectively discriminated the mass of stones using the same tapping and handling behaviours as capuchins. Capuchins explore objects in ways that allow them to perceive invariant properties (e.g. mass) of objects, enabling selection of objects for specific uses. We predict that species that use tools will generate behaviours that reveal invariant properties of objects such as mass; species that do not use tools are less likely to explore objects in this way. The precision with which individuals can judge invariant properties may differ considerably, and this also should predict prevalence of tool use across species. (C) 2010 The Association for the Study of Animal Behaviour. Published by Elsevier Ltd. All rights reserved.

On estimating the basic reproduction number in distinct stages of a contagious disease spreading

In epidemiology, the basic reproduction number R-0 is usually defined as the average number of new infections caused by a single infective individual introduced into a completely susceptible population. According to this definition. R-0 is related to the initial stage of the spreading of a contagious disease. However, from epidemiological models based on ordinary differential equations (ODE), R-0 is commonly derived from a linear stability analysis and interpreted as a bifurcation parameter: typically, when R-0 >1, the contagious disease tends to persist in the population because the endemic stationary solution is asymptotically stable: when R-0 <1, the corresponding pathogen tends to naturally disappear because the disease-free stationary solution is asymptotically stable. Here we intend to answer the following question: Do these two different approaches for calculating R-0 give the same numerical values? In other words, is the number of secondary infections caused by a unique sick individual equal to the threshold obtained from stability analysis of steady states of ODE? For finding the answer, we use a susceptibleinfective-recovered (SIR) model described in terms of ODE and also in terms of a probabilistic cellular automaton (PCA), where each individual (corresponding to a cell of the PCA lattice) is connected to others by a random network favoring local contacts. The values of R-0 obtained from both approaches are compared, showing good agreement. (C) 2012 Elsevier B.V. All rights reserved.