Alarm calls of vervet monkeys

I used observational data from natural encounters with predators and the controlled pre- sentation of aerial and terrestrial predator alarm calls to assess the hypothesis that these acoustically discernable calls trigger context- and predator-appropriate behaviour in free- ranging vervet monkeys (Chlorocebus pygerythrus). my results, from two study groups in South Africa, show that the modal natural and experimental response was not to initiate escape behaviour, either immediately or in the 10s following an alarm call, but to look at the sound source. When monkeys did take evasive action, which occurred no more fre- quently than doing nothing at all, contextually- inappropriate behaviour (i.e., behaviour that was not appropriate for evading the specific predator type) was as likely to occur as contextually-appropriate behaviour. I also found that the distance at which calls were heard was negatively correlated with the probability of some form of evasive action. I suggest that the large size of our groups, and the consequently greater mean distance at which natural calls were heard, may explain why our animals displayed low levels of active response and less predator-appropriate evasion or vigilance than expected, given previous work on this species (Seyfarth et al. 1980. Science, 210, 801-803). As the frequency and rapidity with which respondents looked towards the loudspeaker confirmed the general salience of the calls, I conclude that the broader social and ecological framework in which calls occur, rather than a simple contextually regular linkage between call types and specific predators, shapes animals responses to calls in this species.University of Lethbridge, Natural Sciences and Engineering Research Counci

Taking the aggravation out of data aggregation: A conceptual guide to dealing with statistical issues related to the pooling of individual-level observational data

Field data often include multiple observations taken from the same individual. In order to avoid pseudoreplication, it is commonplace to aggregate data, generating a mean score per individual, and then using these aggregated data in subsequent analyses. Aggregation, however, can generate problems of its own. Not only does it lead to a loss of information, it can also leave analyses vulnerable to the “ecological fallacy”: the drawing of false inferences about individual behavior on the basis of population level (“ecological”) data. It can also result in Simpson's paradox, where relationships seen at the individual level can be completely reversed when analyzed at the aggregate level. These phenomena have been documented widely in the medical and social sciences but tend to go unremarked in primatological studies that rely on observational data from the field. Here, we provide a conceptual guide that explains how and why aggregate data are vulnerable to the ecological fallacy and Simpson's paradox, illustrating these points using data on baboons. We then discuss one particular analytical approach, namely multi-level modeling, that can potentially eliminate these problems. By highlighting the issue of the ecological fallacy, and increasing awareness of how datasets are often organized into a number of different levels, we also highlight the manner in which researchers can more positively exploit the structure of their datasets, without any information loss. These analytical approaches may thus provide greater insight into behavior by permitting more thorough investigation of interactions and cross-level effects