American foulbrood is the most destructive brood disease of honeybees (Apis mellifera) globally. The absence of a repeatable, universal typing scheme for the causative bacterium Paenibacillus larvae has restricted our understanding of disease epidemiology. We have created the first multilocus sequence typing scheme (MLST) for P. larvae, which largely confirms the previous enterobacterial repetitive intergenic consensus (ERIC)–polymerase chain reaction (PCR)-based typing scheme's divisions while providing added resolution and improved repeatability. We have used the new scheme to determine the distribution and biogeography of 294 samples of P. larvae from across six continents. We found that of the two most epidemiologically important ERIC types, ERIC I was more diverse than ERIC II. Analysis of the fixation index (FST) by distance suggested a significant relationship between genetic and geographic distance, suggesting that population structure exists in populations of P. larvae. Interestingly, this effect was only observed within the native range of the host and was absent in areas where international trade has moved honeybees and their disease. Correspondence analysis demonstrated similar sequence type (ST) distributions between native and non-native countries and that ERIC I and II STs mainly have differing distributions. The new typing scheme facilitates epidemiological study of this costly disease of a key pollinator.

Identifying and understanding patterns in movement data are amongst the principal aims of movement ecology. By quantifying the similarity of movement trajectories, inferences can be made about diverse processes, ranging from individual specialisation to the ontogeny of foraging strategies. Movement analysis is not unique to ecology however, and methods for estimating the similarity of movement trajectories have been developed in other fields but are currently under-utilised by ecologists. Here, we introduce five commonly used measures of trajectory similarity: dynamic time warping (DTW), longest common subsequence (LCSS), edit distance for real sequences (EDR), Fréchet distance and nearest neighbour distance (NND), of which only NND is routinely used by ecologists. We investigate the performance of each of these measures by simulating movement trajectories using an Ornstein-Uhlenbeck (OU) model in which we varied the following parameters: (1) the point of attraction, (2) the strength of attraction to this point and (3) the noise or volatility added to the movement process in order to determine which measures were most responsive to such changes. In addition, we demonstrate how these measures can be applied using movement trajectories of breeding northern gannets (Morus bassanus) by performing trajectory clustering on a large ecological dataset. Simulations showed that DTW and Fréchet distance were most responsive to changes in movement parameters and were able to distinguish between all the different parameter combinations we trialled. In contrast, NND was the least sensitive measure trialled. When applied to our gannet dataset, the five similarity measures were highly correlated despite differences in their underlying calculation. Clustering of trajectories within and across individuals allowed us to easily visualise and compare patterns of space use over time across a large dataset. Trajectory clusters reflected the bearing on which birds departed the colony and highlighted the use of well-known bathymetric features. As both the volume of movement data and the need to quantify similarity amongst animal trajectories grow, the measures described here and the bridge they provide to other fields of research will become increasingly useful in ecology. Significance statement As the use of tracking technology increases, there is a need to develop analytical techniques to process such large volumes of data. One area in which this would be useful is the comparison of individual movement trajectories. In response, a variety of measures Communicated by L. Z. Garamszegi

The use of bio‐logging devices to track animal movement continues to grow as technological advances and device miniaturisation allow researchers to study animal behaviour in unprecedented detail. Balanced against the remarkable data that bio‐loggers can provide is a need to understand the impact of devices on animal behaviour and welfare. Recent meta‐analyses have demonstrated the impacts of device attachment on animal behaviour, but there is a concern about the frequency and clarity with which device effects are reported. One aspect lacking in many studies is assessment of the statistical power of tests of device effects, yet such information would assist the interpretation of results. We address this issue by providing an overview of the statistical power, as well as the Type M (magnitude) and Type S (sign) error rate, of tests of device effects within the avian tracking literature across a range of assumed effect sizes. The median power of statistical tests ranged from 9% to 65% across a range of assumed effect sizes corresponding to benchmark values for small, moderate and large effects (d = 0.2, 0.5, 0.8, respectively). Moreover, when using effect sizes derived from previous a meta‐analysis (d = 0.1), median power was only 6%. When assuming smaller effect sizes, statistical tests were characterised by high Type M and Type S error rates, suggesting that statistically significant results of device effects will tend to exaggerate the size of such effects and may estimate the sign of an effect in the wrong direction. Well‐designed tracking studies will reduce device effects to low levels and consequently issues associated with low power will be commonplace. Nevertheless, assessment of device effects remains important, particularly when embarking on novel tracking studies. We recommend that statistical tests of device effects are reported clearly and are routinely accompanied by assessment of statistical power, including Type M and Type S errors, based upon realistic external estimates of effect size. Reporting the statistical power can help avoid the pitfalls of overstating results from individual studies, shift the emphasis to accurate reporting of effect sizes and guide decisions about the ethical impacts of device attachment.

We investigated the genetic architecture of courtship song and cuticular hydrocarbon traits in two phygenetically distinct populations of Drosophila montana. To study natural variation in these two important traits, we analysed within-population crosses among individuals sampled from the wild. Hence, the genetic variation analysed should represent that available for natural and sexual selection to act upon. In contrast to previous between-population crosses in this species, no major quantitative trait loci (QTLs) were detected, perhaps because the between-population QTLs were due to fixed differences between the populations. Partitioning the trait variation to chromosomes suggested a broadly polygenic genetic architecture of within-population variation, although some chromosomes explained more variation in one population compared with the other. Studies of natural variation provide an important contrast to crosses between species or divergent lines, but our analysis highlights recent concerns that segregating variation within populations for important quantitative ecological traits may largely consist of small effect alleles, difficult to detect with studies of moderate power.