Estimating negative binomial parameters from occurrence data with detection times

The negative binomial distribution is a common model for the analysis of count data in biology and ecology. In many applications, we may not observe the complete frequency count in a quadrat but only that a species occurred in the quadrat. If only occurrence data are available then the two parameters of the negative binomial distribution, the aggregation index and the mean, are not identifiable. This can be overcome by data augmentation or through modeling the dependence between quadrat occupancies. Here, we propose to record the (first) detection time while collecting occurrence data in a quadrat. We show that under what we call proportionate sampling, where the time to survey a region is proportional to the area of the region, that both negative binomial parameters are estimable. When the mean parameter is larger than two, our proposed approach is more efficient than the data augmentation method developed by Solow and Smith ( 2010 , Am. Nat. 176 , 96–98), and in general is cheaper to conduct. We also investigate the effect of misidentification when collecting negative binomially distributed data, and conclude that, in general, the effect can be simply adjusted for provided that the mean and variance of misidentification probabilities are known. The results are demonstrated in a simulation study and illustrated in several real examples.     

Energy intake of shift workers compared to fixed day workers: A systematic review and meta-analysis

Shift work is an established risk factor for a number of chronic conditions associated with excess energy intake including obesity, cardiovascular disease and type 2 diabetes. This systematic review investigated whether the 24 h energy intake of shift workers differs to that of fixed day workers. Included articles compared energy intake of shift workers (shift included midnight) with fixed day workers. There were 10 367 day workers and 4726 shift workers from 12 studies included in the qualitative analysis and meta-analyses. The standardised mean difference (95% CI) in energy intake between shift and day workers was ?0.04 (?0.11, 0.03); I² = 54%. Qualitative results on macronutrient intakes were conflicting. Reported energy intakes were not different between day workers and shift workers, suggesting that other factors such as circadian misalignment, meal timing, food choice and diurnal variation of energy metabolism at night may be responsible for the increased rates of obesity observed in shift workers. Guidance on health and well-being is required for this at-risk population group.     

The effects of posture on blood pressure and heart rate in the three-toed sloth, Bradypus tridactylus

1. In unanesthetized, minimally restrained three-toed sloths, Bradypus tridactylus, the mean arterial pressure was 125/85 mmHg and the heart rate was 83.6 beats/min. There was no significant difference between these parameters whether the animal was erect or supine in the experimental chair. 2. Animals without any restraint had a mean blood pressure of 133/87 mmHg and a heart rate of 78.1 beats/min. There was no significant difference between these parameters whether the animal was seated or suspended from a horizontal bar and there was no significant difference between the minimally restrained and the unrestrained animals. 3. Tilting from the erect to the supine position produced large increases in blood pressure parameters, 37% in systolic and 21% in diastolic, reaching the maximum effect in a mean time of 38 sec. Tilting from the supine to the erect position caused even greater increases in pressure, 43% and 38% respectively, and reaching the maximum also in a mean time of 38 sec. 4. Tilting initially increased the heart rate, in going from erect to supine by 21% in 16 sec and from supine to erect by 23% in 20 sec. 5. In going from erect to supine there was a reflex bradycardia later, 15% below control level with a maximum at 40 sec, and to a lesser degree in going from supine to erect, 9% with maximum at 50 sec.     

Cormack–Jolly–Seber model with environmental covariates: A P‐spline approach

In capture–recapture models, survival and capture probabilities can be modelled as functions of time‐varying covariates, such as temperature or rainfall. The Cormack–Jolly–Seber (CJS) model allows for flexible modelling of these covariates; however, the functional relationship may not be linear. We extend the CJS model by semi‐parametrically modelling capture and survival probabilities using a frequentist approach via P‐splines techniques. We investigate the performance of the estimators by conducting simulation studies. We also apply and compare these models with known semi‐parametric Bayesian approaches on simulated and real data sets.