Accounting for phenology in the analysis of animal movement

The analysis of animal tracking data provides important scientific understanding and discovery in ecology. Observations of animal trajectories using telemetry devices provide researchers with information about the way animals interact with their environment and each other. For many species, specific geographical features in the landscape can have a strong effect on behavior. Such features may correspond to a single point (eg, dens or kill sites), or to higher dimensional subspaces (eg, rivers or lakes). Features may be relatively static in time (eg, coastlines or home‐range centers), or may be dynamic (eg, sea ice extent or areas of high‐quality forage for herbivores). We introduce a novel model for animal movement that incorporates active selection for dynamic features in a landscape. Our approach is motivated by the study of polar bear (Ursus maritimus) movement. During the sea ice melt season, polar bears spend much of their time on sea ice above shallow, biologically productive water where they hunt seals. The changing distribution and characteristics of sea ice throughout the year mean that the location of valuable habitat is constantly shifting. We develop a model for the movement of polar bears that accounts for the effect of this important landscape feature. We introduce a two‐stage procedure for approximate Bayesian inference that allows us to analyze over 300 000 observed locations of 186 polar bears from 2012 to 2016. We use our model to estimate a spatial boundary of interest to wildlife managers that separates two subpopulations of polar bears from the Beaufort and Chukchi seas.     

Sexual selection in female perceptual space: how female túngara frogs perceive and respond to complex population variation in acoustic mating signals

Female preferences for male mating signals are often evaluated on single parameters in isolation or small suites of characters. Most signals, however, are composites of many individual parameters. In this study we quantified multivariate traits in the advertisement call of the túngara frog, Physalaemus pustulosus. We represented the calls in multidimensional scaling space and chose nine test calls to represent the range of population variation. We then tested females for phonotactic preference between calls in each pair of the nine test calls. We used statistics developed for paired comparisons in such "round robin" competitions to evaluate the null hypothesis of equal attractiveness, and to examine the degree to which females responded to calls as being different from or similar to one another in attractiveness. We then examined the attractiveness of each test call relative to all other test calls as a function of their location in multivariate acoustic space (the acoustic landscape) to visualize sexual selection on calls. Finally, we used methods from cognitive psychology to illustrate the females' perception of call attractiveness in multivariate space, and compared this perceptual landscape to the acoustic landscape of quantitative call variation. We show that correlations between individual call characters are not strong and thus there are few biomechanical constraints on their independent evolution. Most call variables differed among males, and there was high repeatability of call characters within males. Females often discriminated between pairs of calls from the population, and there were significant differences among calls in their attractiveness. Female preferences for calls were not stabilizing. The region of the acoustic landscape that was most attractive to females included the mean call but was not centered around it. The females' perceptual or preference landscape did not correlate with the call's acoustic landscape, and female perception of calls decreased rather than enhanced call differences.     

Modeling motivational deficits in mouse models of schizophrenia: behavior analysis as a guide for neuroscience

In recent years it has become possible to develop animal models of psychiatric disease in genetically modified mice. While great strides have been made in the development of genetic and neurobiological tools with which to model psychiatric disease, elucidation of neural and molecular mechanisms thought to underlie behavioral phenotypes has been hindered by an inadequate analysis of behavior. This is unfortunate given the fact that the experimental analysis of behavior has created powerful methods for isolating and describing the functional properties of behavioral mechanisms that are capable of providing deep understanding of behavioral phenotypes. A better understanding of the biological basis of normal behavior and its disturbance in psychiatric disease will require the application of these rigorous behavior analytic tools to animal models. In this review we provide an example of a merging of genetic and behavioral methods and illustrate its utility in the analysis of a mouse model of the motivational deficits in schizophrenia. The synergy between basic behavior analysis, neuroscience, and animal models of psychiatric disease has great potential for achieving a deeper understanding of behavior and its neurobiological mechanisms as well as for leading to improvements in diagnosis and treatment in clinical settings.     

The brain as a source of selection on the social niche: examples from the psychophysics of mate choice in túngara frogs

The main premise of this article is that various cognitive functions involved in signal analysis, memory, and decision making, all modulated by the animal's internal milieu, can generate selection for the forms of signals used in social interactions. Thus, just as an animal's view of its world, its Umwelt, determines how it interacts with its ecological niche, it can influence the evolution of its social niche. Thus, the brain is not only a landscape on which selection can act, but also it is a powerful source of selection on the animal's social niche.     

Spatial distribution of lead in enamel and coronal dentine of wistar rats

Lead is one of the most hazardous environmental toxins known. The assessment of lead in dental hard tissues is important in the understanding of its toxic effects on oral tissues and in estimating exposure and body burden in individuals exposed to lead from the environment. However, current information on the uptake and distribution of lead in enamel and dentine is limited. The aim of this project was to study, at high resolution, the spatial distribution of lead in enamel and coronal dentine using an experimental rat model. A dose of 40 mg/L of lead nitrate was administered to pregnant female rats during the periods of gestation and lactation through drinking water. First mandibular molar teeth were removed from their 15-d-old pups and the distribution of lead was studied using a nuclear microprobe (NMP). The distribution of lead in enamel and coronal dentine showed four distinct zones with significantly different mean lead concentrations (p<0.05). High levels of lead were observed in the superficial regions of enamel and in the dentine directly adjacent to the pulp. Additionally, the results confirmed that the NMP is capable of mapping the distribution of lead in teeth at micron resolutions with a detection limit of approx 1 microg/g.     

Insulin production and signaling in renal tubules of Drosophila is under control of tachykinin-related peptide and regulates stress resistance

The insulin-signaling pathway is evolutionarily conserved in animals and regulates growth, reproduction, metabolic homeostasis, stress resistance and life span. In Drosophila seven insulin-like peptides (DILP1-7) are known, some of which are produced in the brain, others in fat body or intestine. Here we show that DILP5 is expressed in principal cells of the renal tubules of Drosophila and affects survival at stress. Renal (Malpighian) tubules regulate water and ion homeostasis, but also play roles in immune responses and oxidative stress. We investigated the control of DILP5 signaling in the renal tubules by Drosophila tachykinin peptide (DTK) and its receptor DTKR during desiccative, nutritional and oxidative stress. The DILP5 levels in principal cells of the tubules are affected by stress and manipulations of DTKR expression in the same cells. Targeted knockdown of DTKR, DILP5 and the insulin receptor dInR in principal cells or mutation of Dilp5 resulted in increased survival at either stress, whereas over-expression of these components produced the opposite phenotype. Thus, stress seems to induce hormonal release of DTK that acts on the renal tubules to regulate DILP5 signaling. Manipulations of S6 kinase and superoxide dismutase (SOD2) in principal cells also affect survival at stress, suggesting that DILP5 acts locally on tubules, possibly in oxidative stress regulation. Our findings are the first to demonstrate DILP signaling originating in the renal tubules and that this signaling is under control of stress-induced release of peptide hormone.     

Estimating dataset size requirements for classifying DNA microarray data.

A statistical methodology for estimating dataset size requirements for classifying microarray data using learning curves is introduced. The goal is to use existing classification results to estimate dataset size requirements for future classification experiments and to evaluate the gain in accuracy and significance of classifiers built with additional data. The method is based on fitting inverse power-law models to construct empirical learning curves. It also includes a permutation test procedure to assess the statistical significance of classification performance for a given dataset size. This procedure is applied to several molecular classification problems representing a broad spectrum of levels of complexity.