Animal movement in dynamic landscapes : interaction between behavioural strategies and resource distributions

Most ecological and evolutionary processes are thought to critically depend on dispersal and individual movement but there is little empirical information on the movement strategies used by animals to find resources. In particular, it is unclear whether behavioural variation exists at all scales, or whether behavioural decisions are primarily made at small spatial scales and thus broad-scale patterns of movement simply reflect underlying resource distributions. We evaluated animal movement responses to variable resource distributions using the grey teal (Anas gracilis) in agricultural and desert landscapes in Australia as a model system. Birds in the two landscapes differed in the fractal dimension of their movement paths, with teal in the desert landscape moving less tortuously overall than their counterparts in the agricultural landscape. However, the most striking result was the high levels of individual variability in movement strategies, with different animals exhibiting different responses to the same resources. Teal in the agricultural basin moved with both high and low tortuosity, while teal in the desert basin primarily moved using low levels of tortuosity. These results call into question the idea that broad-scale movement patterns simply reflect underlying resource distributions, and suggest that movement responses in some animals may be behaviourally complex regardless of the spatial scale over which movement occurs.

On animal distributions in dynamic landscapes

Few landscapes are static and patterns of resource distribution can vary markedly in time and space. Patterns of movement and dispersal in response to environmental change are equally diverse and occur on a broad range of temporal scales. For patterns of movement and dispersal that vary spatially and temporally throughout the life-time of an individual, the concepts of home range and geographic range alone do not adequately describe the observed patterns of distribution of individuals, populations or species. Here we further simplify Gauthreaux (1982) classification of movement types into a simple bipartite system and link movements (or lack thereof) to a similarly simple classification of ranges. In addition, we introduce two new indices that describe the relationship of an individual's life-time movements to its distribution as described by home and geographic range. Our interest in this subject arose from endeavouring to interpret local changes in waterbird abundance in the arid interior of the Australian continent and to understand these changes in relation to patterns of resource distribution and movement. In this discussion we focus on terrestrial vertebrates capable of multiple breeding events throughout an extended life-time.

Animal visual systems and the evolution of color patterns; sensory processing illuminates signal evolution

Animal color pattern phenotypes evolve rapidly. What influences their evolution? Because color patterns are used in communication, selection for signal efficacy, relative to the intended receiver's visual system, may explain and predict the direction of evolution. We investigated this in bowerbirds, whose color patterns consist of plumage, bower structure, and ornaments and whose visual displays are presented under predictable visual conditions. We used data on avian vision, environmental conditions, color pattern properties, and an estimate of the bowerbird phylogeny to test hypotheses about evolutionary effects of visual processing. Different components of the color pattern evolve differently. Plumage sexual dimorphism increased and then decreased, while overall (plumage plus bower) visual contrast increased. The use of bowers allows relative crypsis of the bird but increased efficacy of the signal as a whole. Ornaments do not elaborate existing plumage features but instead are innovations (new color schemes) that increase signal efficacy. Isolation between species could be facilitated by plumage but not ornaments, because we observed character displacement only in plumage. Bowerbird color pattern evolution is at least partially predictable from the function of the visual system and from knowledge of different functions of different components of the color patterns. This provides clues to how more constrained visual signaling systems may evolve.

Sources of variation in fibre diameter attributes of Australian alpacas and implications for fleece evaluation and animal selection

Sources of variation in fibre diameter attributes of Australian alpacas and implications for fleece evaluation and animal selection were investigated using data collected in the years 1994–97, from 6 properties in southern Australia. Data were analysed using REML (multiple regression analysis) to determine the effect on mean fibre diameter (MFD) and coefficient of variation of MFD (CV(FD)) of age, origin (property), sex (entire male, female), breed (Huacaya, Suri), liveweight, fibre colour, individual, and interactions of these effects. The mean (n = 100) age (range) was 4.2 years (0.1–11.9), liveweight 72.0 kg (12.0–134 kg), MFD 29.1 μm (17.7–46.6 μm), CV(FD) 24.33% (15.0–36.7%).

A number of variables affected MFD and CV(FD). MFD increased to 7.5 years of age, and correlations between MFD at 1.5 and 2 years of age with the MFD at older ages were much higher than correlations at younger ages. Fibre diameter 'blowout' (increase with age) was positively correlated with the actual MFD at ages 2 years and older. There were important effects of farm, and these effects differed with year and shearing age. Suris were coarser than Huacayas with the effect reducing with increased liveweight; there was no effect of sex. Fleeces of light shade were 1 μm finer than dark fleeces. CV(FD) declined rapidly between birth and 2 years of age, reaching a minimum at about 4 years of age and then increasing; however, CV(FD) measurements on young animals were very poor predictors of CV(FD) at older ages, and the response of CV(FD) to age differed with farm and year. Suris had a higher CV(FD) than Huacayas on most properties, and MFD, liveweight, and sex did not affect CV(FD). Fleeces of dark shade had higher CV(FD) than fleeces of light shade in 2 of the years. It is concluded that there are large opportunities to improve the MFD and CV(FD) of alpaca fibre through selection and breeding. The potential benefit is greatest from reducing the MFD and CV(FD) of fibre from older alpacas, through reducing the between-animal variation in MFD and CV(FD). Sampling alpacas at ages <2 years is likely to substantially decrease selection efficiency for lifetime fibre diameter attributes.

Feature extraction for animal fiber identification

Fiber identification has been a very important task in many industries such as wool growing, textile processing, archaeology, histochernical engineering, and zoology. Over the years, animal fibers have been identified using physical and chemical approaches. Recently, objective identification of animal fibers has been developed based on the cuticular information of fibers. Effective and accurate extraction of representative features is essential to animal fiber identification and classification. In the current work, two different strategies are developed for this purpose. In the first method, explicit features are extracted using image processing. However, only implicit features are used in the second method with an unsupervised artificial neural network. It is found that the use of explicit features increases the accuracy of fiber identification but requires more effort on processing images and solid knowledge of what features are representative ones.

Identification and classification of animal fibres using artificial neural networks

It has been an important and challenging task to classify and evaluate the contents in wool blends. Quantitative characterisation of animal fibre scale patterns has attracted considerable attention, since it is the major evidence for identification and subsequent classification purpose. Although techniques such as imaging processing and linear demarcation functions have been used to identify unknown fibre type with some success, a more comprehensive approach is required to perform this task. In this paper, a new approach is presented, which employs non-linear demarcation functions by using an artificial neural network (ANN). Based on scale pattern features extracted by using image processing techniques the artificial neural network (ANN) model is to classify mohair and merino fibres. It is observed that the techniques developed in this work are very effective and have the potential to be applied to other animal fibres.