Summary1. The R package SIMR allows users to calculate power for generalized linear mixed models from the LME4 package. The power calculations are based on Monte Carlo simulations. 2. It includes tools for (i) running a power analysis for a given model and design; and (ii) calculating power curves to assess trade-offs between power and sample size. 3. This paper presents a tutorial using a simple example of count data with mixed effects (with structure representative of environmental monitoring data) to guide the user along a gentle learning curve, adding only a few commands or options at a time.
Evolutionary graph theory is the study of birthdeath processes that are constrained by population structure. A principal problem in evolutionary graph theory is to obtain the probability that some initial population of mutants will fixate on a graph, and to determine how that fixation probability depends on the structure of that graph. A fluctuating mutant population on a graph can be considered as a random walk. Martingales exploit symmetry in the steps of a random walk to yield exact analytical expressions for fixation probabilities. They do not require simplifying assumptions such as large population sizes or weak selection. In this paper, we show how martingales can be used to obtain fixation probabilities for symmetric evolutionary graphs. We obtain simpler expressions for the fixation probabilities of star graphs and complete bipartite graphs than have been previously reported and show that these graphs do not amplify selection for advantageous mutations under all conditions.
BackgroundDespite the hundreds of diet apps available for use on smartphones (mobile phones), no studies have examined their use as tools for dietary assessment and tracking in sports nutrition.ObjectiveThe aim is to examine the prevalence and perceptions of using smartphone diet apps for dietary assessment and tracking among sports dietitians.MethodsA cross-sectional online survey to examine the use and perception of diet apps was developed and distributed to sports dietitians in Australia, Canada, New Zealand, the United Kingdom, and the United States (US).ResultsThe overall response rate from the 1709 sports dietitians invited to participate was 10.3% (n=180). diet apps were used by 32.4% (57/176) of sports dietitians to assess and track the dietary intake of athletes. Sports dietitians from the US were more likely to use smartphone diet apps than sports dietitians from other countries (OR=5.61, 95% CI 1.84-17.08, P=.002). Sports dietitians used 28 different diet apps, with 56% (32/57) choosing MyFitnessPal. Overall, sports dietitians held a positive perception of smartphone diet apps, with the majority of respondents viewing diet apps as “better” (25/53, 47%) or “equivalent” (22/53, 41%) when compared with traditional dietary assessment methods.ConclusionsNearly one-third of sports dietitians used mobile phone diet apps in sports nutrition practice, and viewed them as useful in helping to assess and track the dietary intake of athletes.
Enteric methane (CH4) emissions and dry-matter intake (DMI) can be accurately and precisely measured in respiration chambers (RC), whereas automated head chambers (GreenFeed; GF) and the SF6 tracer method can provide estimates of CH4 emissions from grazing cattle. In New Zealand, most dairy cattle graze pasture and, under these conditions, DMI also has to be estimated. The objective of the current study was to compare the relationship between CH4 production and DMI of New Zealand dairy cattle fed forages using the following four measurement methods: RC with measured DMI (RC); sulfur hexafluoride (SF6) with measured DMI (SF6-DMI); SF6 with DMI estimated from prediction equations or indigestible markers (SF6); GF with measured or estimated DMI (GF). Data were collected from published literature from New Zealand trials with growing and lactating dairy cattle fed forage-based diets and data were analysed using a mixed-effect model. The intercept of the linear regression between CH4 production and DMI was not significantly different from zero and was omitted from the model. However, residual variance (observed–predicted values) increased with an increasing DMI, which was addressed by log-transforming CH4 per unit of DMI and this model was used for final data analysis. The accuracy of the four methods for predicting log CH4 per unit of DMI was similar (P = 0.55), but the precision (indicated by residuals) differed (P < 0.001) among methods. The residual standard deviations for SF6, GF and SF6-DMI were 4.6, 3.4 and 2.1 times greater than the residuals for RC. Hence, all methods enabled accurate prediction of CH4 per unit of DMI, but methodology for determining both CH4 and DMI affected their precision (residuals).
The basic functional characteristics of spiking neurones are remarkably similar throughout the animal kingdom. Their core design and function features were presumably established very early in their evolutionary history. Identifying the selection pressures that drove animals to evolve spiking neurones could help us interpret their design and function today. This paper provides a quantitative argument, based on ecology, that animals evolved neurones after they started eating each other, about 550 million years ago. We consider neurones as devices that aid an animal's foraging performance, but incur an energetic cost. We introduce an idealised stochastic model ecosystem of animals and their food, and obtain an analytic expression for the probability that an animal with a neurone will fix in a neurone-less population. Analysis of the fixation probability reveals two key results. First, a neurone will never fix if an animal forages low-value food at high density, even if that neurone incurs no cost. Second, a neurone will fix with high probability if an animal is foraging high-value food at low density, even if that neurone is expensive. These observations indicate that the transition from neurone-less to neurone-armed animals can be facilitated by a transition from filter-feeding or substrate grazing to episodic feeding strategies such as animal-on-animal predation (macrophagy).
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