Lewis G. Halsey

1. Time and energy are key currencies in animal ecology, and judicious management of these is a primary focus for natural selection. At present, however, there are only two main methods for estimation of rate of energy expenditure in the field, heart rate and doubly labelled water, both of which have been used with success; but both also have their limitations. 2. The deployment of data loggers that measure acceleration is emerging as a powerful tool for quantifying the behaviour of free-living animals. Given that animal movement requires the use of energy, the accelerometry technique potentially has application in the quantification of rate of energy expenditure during activity. 3. In the present study, we test the hypothesis that acceleration can serve as a proxy for rate of energy expenditure in free-living animals. We measured rate of energy expenditure as rates of O2 consumption (VO2) and CO2 production (VCO2) in great cormorants (Phalacrocorax carbo) at rest and during pedestrian exercise. VO2 and VCO2 were then related to overall dynamic body acceleration (ODBA) measured with an externally attached three-axis accelerometer. 4. Both VO2 and VCO2 were significantly positively associated with ODBA in great cormorants. This suggests that accelerometric measurements of ODBA can be used to estimate VO2 and VCO2 and, with some additional assumptions regarding metabolic substrate use and the energy equivalence of O2 and CO2, that ODBA can be used to estimate the activity specific rate of energy expenditure of free-living cormorants. 5. To verify that the approach identifies expected trends in from situations with variable power requirements, we measured ODBA in free-living imperial cormorants (Phalacrocorax atriceps) during foraging trips. We compared ODBA during return and outward foraging flights, when birds are expected to be laden and not laden with captured fish, respectively. We also examined changes in ODBA during the descent phase of diving, when power requirements are predicted to decrease with depth due to changes in buoyancy associated with compression of plumage and respiratory air. 6. In free-living imperial cormorants, ODBA, and hence estimated VO2, was higher during the return flight of a foraging bout, and decreased with depth during the descent phase of a dive, supporting the use of accelerometry for the determination of activity-specific rate of energy expenditure.

An animal's behaviour is a response to its environment and physiological condition, and as such, gives vital clues as to its well-being, which is highly relevant in conservation issues. Behaviour can generally be typified by body motion and body posture, parameters that are both measurable using animal-attached accelerometers. Interpretation of acceleration data, however, can be complex, as the static (indicative of posture) and dynamic (motion) components are derived from the total acceleration values, which should ideally be recorded in all 3-dimensional axes. The principles of triaxial accelerometry are summarised and discussed in terms of the commonalities that arise in patterns of acceleration across species that vary in body pattern, life-history strategy, and the medium they inhabit. Using tri-axial acceleration data from deployments on captive and free-living animals (n = 12 species), behaviours were identified that varied in complexity, from the rhythmic patterns of locomotion, to feeding, and more variable patterns including those relating to social interactions. These data can be combined with positional information to qualify patterns of area-use and map the distribution of target behaviours. The range and distribution of behaviour may also provide insight into the transmission of disease. In this way, the measurement of tri-axial acceleration can provide insight into individual and population level processes, which may ultimately influence the effectiveness of conservation practice.

Dynamic body acceleration (DBA) has been used as a proxy for energy expenditure in logger-equipped animals, with researchers summing the acceleration (overall dynamic body acceleration--ODBA) from the three orthogonal axes of devices. The vector of the dynamic body acceleration (VeDBA) may be a better proxy so this study compared ODBA and VeDBA as proxies for rate of oxygen consumption using humans and 6 other species. Twenty-one humans on a treadmill ran at different speeds while equipped with two loggers, one in a straight orientation and the other skewed, while rate of oxygen consumption (VO2) was recorded. Similar data were obtained from animals but using only one (straight) logger. In humans, both ODBA and VeDBA were good proxies for VO2 with all r(2) values exceeding 0.88, although ODBA accounted for slightly but significantly more of the variation in VO2 than did VeDBA (P<0.03). There were no significant differences between ODBA and VeDBA in terms of the change in VO2 estimated by the acceleration data in a simulated situation of the logger being mounted straight but then becoming skewed (P = 0.744). In the animal study, ODBA and VeDBA were again good proxies for VO2 with all r(2) values exceeding 0.70 although, again, ODBA accounted for slightly, but significantly, more of the variation in VO2 than did VeDBA (P<0.03). The simultaneous contraction of muscles, inserted variously for limb stability, may produce muscle oxygen use that at least partially equates with summing components to derive DBA. Thus, a vectorial summation to derive DBA cannot be assumed to be the more 'correct' calculation. However, although within the limitations of our simple study, ODBA appears a marginally better proxy for VO2. In the unusual situation where researchers are unable to guarantee at least reasonably consistent device orientation, they should use VeDBA as a proxy for VO2.

The p-value has long been the figurehead of statistical analysis in biology, but its position is under threat. p is now widely recognized as providing quite limited information about our data, and as being easily misinterpreted. Many biologists are aware of p's frailties, but less clear about how they might change the way they analyse their data in response. This article highlights and summarizes four broad statistical approaches that augment or replace the p-value, and that are relatively straightforward to apply. First, you can augment your p-value with information about how confident you are in it, how likely it is that you will get a similar p-value in a replicate study, or the probability that a statistically significant finding is in fact a false positive. Second, you can enhance the information provided by frequentist statistics with a focus on effect sizes and a quantified confidence that those effect sizes are accurate. Third, you can augment or substitute p-values with the Bayes factor to inform on the relative levels of evidence for the null and alternative hypotheses; this approach is particularly appropriate for studies where you wish to keep collecting data until clear evidence for or against your hypothesis has accrued. Finally, specifically where you are using multiple variables to predict an outcome through model building, Akaike information criteria can take the place of the p-value, providing quantified information on what model is best. Hopefully, this quick-and-easy guide to some simple yet powerful statistical options will support biologists in adopting new approaches where they feel that the p-value alone is not doing their data justice.