Are animal personality traits linked to life-history productivity?

Animal personality traits such as boldness, activity and aggressiveness have been described for many animal species. However, why some individuals are consistently bolder or more active than others, for example, is currently obscure. Given that life-history tradeoffs are common and known to promote inter-individual differences in behavior, we suggest that consistent individual differences in animal personality traits can be favored when those traits contribute to consistent individual differences in productivity (growth and/or fecundity). A survey of empirical studies indicates that boldness, activity and/or aggressiveness are positively related to food intake rates, productivity and other life-history traits in a wide range of taxa. Our conceptual framework sets the stage for a closer look at relationships between personality traits and life-history traits in animals.

Leptin resistance in a polygenic, hyperleptinemic animal model of obesity and NIDDM : Psammomys obesus

OBJECTIVE: To investigate the effects of leptin administration to Psammomys obesus, a polygenic animal model of obesity and type 2 diabetes mellitus.

DESIGN: Longitudinal intervention study utilising three separate leptin treatment protocols lasting 7-14 d.

MEASUREMENTS: Body weight and food intake were measured daily, body fat and muscle content were estimated by carcass analysis on completion of the study. Blood glucose, plasma insulin, leptin, triglycerides and cholesterol were measured at baseline and twice each week during the study.

RESULTS: Relatively high doses of leptin were required to significantly reduce food intake and body fat content in lean Psammomys obesus, but had no discernible effect on their obese littermates.

CONCLUSION: As a species, Psammomys obesus appear to be relatively insensitive to the effects of leptin administration, compared with other rodents. Obese Psammomys obesus are leptin resistant relative to their lean littermates.

Energy metabolism and animal personality

In this paper we show how animal personality could explain some of the large inter-individual variation in resting metabolic rate (MR) and explore methodological and functional linkages between personality and energetics. Personality will introduce variability in resting MR measures because individuals consistently differ in their stress response, exploration or activity levels, all of which influence MR measurements made with respirometry and the doubly-labelled water technique. Physiologists try to exclude these behavioural influences from resting MR measurements, but animal personality research indicates that these attempts are unlikely to be successful. For example, because reactive animals “freeze” when submitted to a stress, their MR could be classified as “resting” because of immobility when in fact they are highly stressed with an elevated MR. More importantly, recent research demonstrating that behavioural responses to novel and highly artificial stimuli are correlated with both behaviour and fitness under more natural circumstances calls into question the wisdom of excluding these behavioural influences on MR measurements. The reason that intra-specific variation in resting MR are so weakly correlated with daily energy expenditure (DEE) and fitness, may be that the latter two measures fully incorporate personality while the former partially excludes its influence. Because activity, exploration, boldness and aggressiveness are energetically costly, personality and metabolism should be correlated and physiological constraints may underlie behavioural syndromes. We show how physiological ecologists can better examine behavioural linkages between personality and metabolism, as required to better understand the physiological correlates of personality and the evolutionary consequences of metabolic variability.

N-dimensional animal energetic niches clarify behavioural options in a variable marine environment

Animals respond to environmental variation by exhibiting a number of different behaviours and/or rates of activity, which result in corresponding variation in energy expenditure. Successful animals generally maximize efficiency or rate of energy gain through foraging. Quantification of all features that modulate energy expenditure can theoretically be modelled as an animal energetic niche or power envelope; with total power being represented by the vertical axis and n-dimensional horizontal axes representing extents of processes that affect energy expenditure. Such an energetic niche could be used to assess the energetic consequences of animals adopting particular behaviours under various environmental conditions. This value of this approach was tested by constructing a simple mechanistic energetics model based on data collected from recording devices deployed on 41 free-living Magellanic penguins (Spheniscus magellanicus), foraging from four different colonies in Argentina and consequently catching four different types of prey. Energy expenditure was calculated as a function of total distance swum underwater (horizontal axis 1) and maximum depth reached (horizontal axis 2). The resultant power envelope was invariant, irrespective of colony location, but penguins from the different colonies tended to use different areas of the envelope. The different colony solutions appeared to represent particular behavioural options for exploiting the available prey and demonstrate how penguins respond to environmental circumstance (prey distribution), the energetic consequences that this has for them, and how this affects the balance of energy acquisition through foraging and expenditure strategy.

Measurement error causes scale-dependent threshold erosion of biological signals in animal movement data

Recent advances in telemetry technology have created a wealth of tracking data available for many animal species moving over spatial scales from tens of meters to tens of thousands of kilometers. Increasingly, such data sets are being used for quantitative movement analyses aimed at extracting fundamental biological signals such as optimal searching behavior and scale-dependent foraging decisions. We show here that the location error inherent in various tracking technologies reduces the ability to detect patterns of behavior within movements. Our analyses endeavored to set out a series of initial ground rules for ecologists to help ensure that sampling noise is not misinterpreted as a real biological signal. We simulated animal movement tracks using specialized random walks known as Lévy flights at three spatial scales of investigation: 100-km, 10-km, and 1-km maximum daily step lengths. The locations generated in the simulations were then blurred using known error distributions associated with commonly applied tracking methods: the Global Positioning System (GPS), Argos polar-orbiting satellites, and light-level geolocation. Deviations from the idealized Lévy flight pattern were assessed for each track after incrementing levels of location error were applied at each spatial scale, with additional assessments of the effect of error on scale-dependent movement patterns measured using fractal mean dimension and first-passage time (FPT) analyses. The accuracy of parameter estimation (Lévy μ, fractal mean D, and variance in FPT) declined precipitously at threshold errors relative to each spatial scale. At 100-km maximum daily step lengths, error standard deviations of ≥10 km seriously eroded the biological patterns evident in the simulated tracks, with analogous thresholds at the 10-km and 1-km scales (error SD ≥ 1.3 km and 0.07 km, respectively). Temporal subsampling of the simulated tracks maintained some elements of the biological signals depending on error level and spatial scale. Failure to account for large errors relative to the scale of movement can produce substantial biases in the interpretation of movement patterns. This study provides researchers with a framework for understanding the limitations of their data and identifies how temporal subsampling can help to reduce the influence of spatial error on their conclusions.