Summary1. We present a new R package, diveRsity, for the calculation of various diversity statistics, including common diversity partitioning statistics (h, G ST ) and population differentiation statistics (D Jost , G 0 ST , v 2 test for population heterogeneity), among others. The package calculates these estimators along with their respective bootstrapped confidence intervals for loci, sample population pairwise and global levels. Various plotting tools are also provided for a visual evaluation of estimated values, allowing users to critically assess the validity and significance of statistical tests from a biological perspective.2. diveRsity has a set of unique features, which facilitate the use of an informed framework for assessing the validity of the use of traditional F-statistics for the inference of demography, with reference to specific marker types, particularly focusing on highly polymorphic microsatellite loci. However, the package can be readily used for other co-dominant marker types (e.g. allozymes, SNPs).3. Detailed examples of usage and descriptions of package capabilities are provided. The examples demonstrate useful strategies for the exploration of data and interpretation of results generated by diveRsity. Additional online resources for the package are also described, including a GUI web app version intended for those with more limited experience using R for statistical analysis.

The purpose of the study was to quantify the influence of selected motor unit properties and patterns of activity on amplitude cancellation in the simulated surface electromyogram (EMG). The study involved computer simulations of a motor unit population with physiologically defined recruitment and rate coding characteristics that activated muscle fibers whose potentials were recorded on the skin over the muscle. Amplitude cancellation was quantified as the percent difference in signal amplitude when motor unit potentials were summed before and after rectification. The simulations involved varying the level of activation for the motor unit population, the recording configuration, the upper limit of motor unit recruitment, peak discharge rates, the amount of motor unit synchronization, muscle fiber length, the thickness of the subcutaneous tissue, and the motor unit properties that change with advancing age. The results confirmed a previous experimental report (Day SJ and Hulliger M, J Neurophysiol 86: 2144-2158, 2001) that amplitude cancellation in the surface EMG can reach 62% at maximal activation. A decrease in the range of amplitudes of the motor unit potentials, as can occur during fatiguing contractions, increased amplitude cancellation up to approximately 85%. Differences in the amount of amplitude cancellation were observed across all simulated conditions, and resulted in substantial changes in the absolute magnitude of the EMG signal. The most profound factors influencing amplitude cancellation were the number of active motor units and the duration of the action potentials. The effects of amplitude cancellation were minimal (<5%) when the EMG amplitude was normalized to maximal values, with the exception of variations in peak discharge rate and recruitment range, which resulted in differences up to 17% in the normalized EMG signal across conditions. These results indicate the amount of amplitude cancellation that can occur in various experimental conditions and its influence on absolute and relative measures of EMG amplitude.

Age-related changes in the basic functional unit of the neuromuscular system, the motor unit, and its neural inputs have a profound effect on motor function, especially among the expanding number of old (older than ∼60 yr) and very old (older than ∼80 yr) adults. This review presents evidence that age-related changes in motor unit morphology and properties lead to impaired motor performance that includes 1) reduced maximal strength and power, slower contractile velocity, and increased fatigability; and 2) increased variability during and between motor tasks, including decreased force steadiness and increased variability of contraction velocity and torque over repeat contractions. The age-related increase in variability of motor performance with aging appears to involve reduced and more variable synaptic inputs that drive motor neuron activation, fewer and larger motor units, less stable neuromuscular junctions, lower and more variable motor unit action potential discharge rates, and smaller and slower skeletal muscle fibers that coexpress different myosin heavy chain isoforms in the muscle of older adults. Physical activity may modify motor unit properties and function in old men and women, although the effects on variability of motor performance are largely unknown. Many studies are of cross-sectional design, so there is a tremendous opportunity to perform high-impact and longitudinal studies along the continuum of aging that determine 1) the influence and cause of the increased variability with aging on functional performance tasks, and 2) whether lifestyle factors such as physical exercise can minimize this age-related variability in motor performance in the rapidly expanding numbers of very old adults.

Understanding the population structure and patterns of gene flow within species is of fundamental importance to the study of evolution. In the fields of population and evolutionary genetics, measures of genetic differentiation are commonly used to gather this information. One potential caveat is that these measures assume gene flow to be symmetric. However, asymmetric gene flow is common in nature, especially in systems driven by physical processes such as wind or water currents. As information about levels of asymmetric gene flow among populations is essential for the correct interpretation of the distribution of contemporary genetic diversity within species, this should not be overlooked. To obtain information on asymmetric migration patterns from genetic data, complex models based on maximum‐likelihood or Bayesian approaches generally need to be employed, often at great computational cost. Here, a new simpler and more efficient approach for understanding gene flow patterns is presented. This approach allows the estimation of directional components of genetic divergence between pairs of populations at low computational effort, using any of the classical or modern measures of genetic differentiation. These directional measures of genetic differentiation can further be used to calculate directional relative migration and to detect asymmetries in gene flow patterns. This can be done in a user‐friendly web application called divMigrate‐online introduced in this study. Using simulated data sets with known gene flow regimes, we demonstrate that the method is capable of resolving complex migration patterns under a range of study designs.

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