University of St Andrews

About: University of St Andrews is a education organization based out in St Andrews, Fife, United Kingdom. It is known for research contribution in the topics: Population & Laser. The organization has 16260 authors who have published 43364 publications receiving 1636072 citations. The organization is also known as: St Andrews University & University of St. Andrews.

Red deer stags use formants as assessment cues during intrasexual agonistic interactions.

Abstract: While vocal tract resonances or formants are key acoustic parameters that define differences between phonemes in human speech, little is known about their function in animal communication. Here, we used playback experiments to present red deer stags with re-synthesized vocalizations in which formant frequencies were systematically altered to simulate callers of different body sizes. In response to stimuli where lower formants indicated callers with longer vocal tracts, stags were more attentive, replied with more roars and extended their vocal tracts further in these replies. Our results indicate that mammals other than humans use formants in vital vocal exchanges and can adjust their own formant frequencies in relation to those that they hear.

Recognition of Objects and Their Component Parts: Responses of Single Units in the Temporal Cortex of the Macaque

Abstract: We investigated the role that different component parts play in the neural encoding of the visual appearance of one complex object in the temporal cortex. Cells responsive to the sight of the entire human body (but no to control stimuli) were tested with two subregions (head alone with the body occluded from sight and the body alone with the head occluded). Forty-two percent (22 of 53) of cells responded to the whole body and to one of the two body regions tested separately: 72% (17 of 22) responding to the head and 28% (5 of 22) to the rest of the body. Forty-two percent (22 of 53) of cells responded independently to both regions of the body when tested in isolation. The remaining cells (17%, 9 of 53) were selective for the entire body and unresponsive to component parts. The majority of cells tested (90%, 35 of 39) were selective for perspective view (e.g., some cells respond optimally to the side view of the body, others to the back view). Comparable levels of view sensitivity were found for responses to the whole body and its parts. Results indicate (1) separate neuronal analysis of body parts and (2) extensive integration of information from different parts. Contrary to influential models of object recognition (Marr and Nishihara, 1978; Biederman, 1987), the results indicate view-specific processing both for the appearance of separate object components and for integration of information across components.

Patterns and causes of species richness: a general simulation model for macroecology

Abstract: Understanding the causes of spatial variation in species richness is a major research focus of biogeography and macroecology. Gridded environmental data and species richness maps have been used in increasingly sophisticated curve-fitting analyses, but these methods have not brought us much closer to a mechanistic understanding of the patterns. During the past two decades, macroecologists have successfully addressed technical problems posed by spatial autocorrelation, intercorrelation of predictor variables and non-linearity. However, curve-fitting approaches are problematic because most theoretical models in macroecology do not make quantitative predictions, and they do not incorporate interactions among multiple forces. As an alternative, we propose a mechanistic modelling approach. We describe computer simulation models of the stochastic origin, spread, and extinction of species' geographical ranges in an environmentally heterogeneous, gridded domain and describe progress to date regarding their implementation. The output from such a general simulation model (GSM) would, at a minimum, consist of the simulated distribution of species ranges on a map, yielding the predicted number of species in each grid cell of the domain. In contrast to curve-fitting analysis, simulation modelling explicitly incorporates the processes believed to be affecting the geographical ranges of species and generates a number of quantitative predictions that can be compared to empirical patterns. We describe three of the 'control knobs' for a GSM that specify simple rules for dispersal, evolutionary origins and environmental gradients. Binary combinations of different knob settings correspond to eight distinct simulation models, five of which are already represented in the literature of macroecology. The output from such a GSM will include the predicted species richness per grid cell, the range size frequency distribution, the simulated phylogeny and simulated geographical ranges of the component species, all of which can be compared to empirical patterns. Challenges to the development of the GSM include the measurement of goodness of fit (GOF) between observed data and model predictions, as well as the estimation, optimization and interpretation of the model parameters. The simulation approach offers new insights into the origin and maintenance of species richness patterns, and may provide a common framework for investigating the effects of contemporary climate, evolutionary history and geometric constraints on global biodiversity gradients. With further development, the GSM has the potential to provide a conceptual bridge between macroecology and historical biogeography.