Sexual selection arises from social interactions, and if social environments vary so too should sexual selection. For example, male–male competition often occurs either in the presence or in the absence of females, and such changes in the social environment could affect the form and strength of sexual selection. Here we examine how the presence of a female influences selection arising from male–male competition in a leaf‐footed cactus bug, Narnia femorata, which has a resource defence mating system. Males compete for territories on cacti because females lay eggs on the cactus plants. Females are not always present when this competition first occurs; however, the presence or absence of the female matters. We found that both the form and strength of selection on male traits, those traits that influenced success in intrasexual competition, depended on the social context. When a female was not present, male size and the area of the sexually dimorphic hind legs was only marginally important to winning a contest. However, males with larger overall size and leg area were more likely to win in the presence of a female. There was also positive quadratic selection on these traits when a female was present with both the largest and the smallest males winning. The implication is unexpected alternative strategies when females are present. Our results support the notion that sexual selection should be studied under all relevant social contexts.

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AimTo explore in a feasibility study whether ‘e‐cycling’ was acceptable to, and could potentially improve the health of, people with Type 2 diabetes.MethodsTwenty people with Type 2 diabetes were recruited and provided with an electric bicycle for 20 weeks. Participants completed a submaximal fitness test at baseline and follow‐up to measure predicted maximal aerobic power, and semi‐structured interviews were conducted to assess the acceptability of using an electric bicycle. Participants wore a heart rate monitor and a Global Positioning System (GPS) receiver in the first week of electric bicycle use to measure their heart‐rate during e‐cycling.ResultsEighteen participants completed the study, cycling a median (interquartile range) of 21.4 (5.5–37.7) km per week. Predicted maximal aerobic power increased by 10.9%. Heart rate during electric bicycle journeys was 74.7% of maximum, compared with 64.3% of maximum when walking. Participants used the electric bicycles for commuting, shopping and recreation, and expressed how the electric bicycle helped them to overcome barriers to active travel/cycling, such as hills. Fourteen participants purchased an electric bicycle on study completion.ConclusionsThere was evidence that e‐cycling was acceptable, could increase fitness and elicited a heart rate that may lead to improvements in cardiometabolic risk factors in this population. Electric bicycles have potential as a health‐improving intervention in people with Type 2 diabetes.

Identifying the boundaries of a social insect colony is vital for properly understanding its ecological function and evolution. Many species of ants are polydomous: colonies inhabit multiple, spatially separated, nests. Ascertaining which nests are parts of the same colony is an important consideration when studying polydomous populations. In this paper, we review the methods that are used to identify which nests are parts of the same polydomous colony and to determine the boundaries of colonies. Specifically, we define and discuss three broad categories of approach: identifying nests sharing resources, identifying nests sharing space, and identifying nests sharing genes. For each of these approaches, we review the theoretical basis, the limitations of the approach and the methods that can be used to implement it. We argue that all three broad approaches have merits and weaknesses, and provide a methodological comparison to help researchers select the tool appropriate for the biological question they are investigating.

BackgroundIncreases in physical activity through active travel have the potential to have large beneficial effects on populations, through both better health outcomes and reduced motorized traffic. However accurately identifying travel mode in large datasets is problematic. Here we provide an open source tool to quantify time spent stationary and in four travel modes(walking, cycling, train, motorised vehicle) from accelerometer measured physical activity data, combined with GPS and GIS data.MethodsThe Examining Neighbourhood Activities in Built Living Environments in London study evaluates the effect of the built environment on health behaviours, including physical activity. Participants wore accelerometers and GPS receivers on the hip for 7 days. We time-matched accelerometer and GPS, and then extracted data from the commutes of 326 adult participants, using stated commute times and modes, which were manually checked to confirm stated travel mode. This yielded examples of five travel modes: walking, cycling, motorised vehicle, train and stationary. We used this example data to train a gradient boosted tree, a form of supervised machine learning algorithm, on each data point (131,537 points), rather than on journeys. Accuracy during training was assessed using five-fold cross-validation. We also manually identified the travel behaviour of both 21 participants from ENABLE London (402,749 points), and 10 participants from a separate study (STAMP-2, 210,936 points), who were not included in the training data. We compared our predictions against this manual identification to further test accuracy and test generalisability.ResultsApplying the algorithm, we correctly identified travel mode 97.3% of the time in cross-validation (mean sensitivity 96.3%, mean active travel sensitivity 94.6%). We showed 96.0% agreement between manual identification and prediction of 21 individuals’ travel modes (mean sensitivity 92.3%, mean active travel sensitivity 84.9%) and 96.5% agreement between the STAMP-2 study and predictions (mean sensitivity 85.5%, mean active travel sensitivity 78.9%).ConclusionWe present a generalizable tool that identifies time spent stationary and time spent walking with very high precision, time spent in trains or vehicles with good precision, and time spent cycling with moderate precisionIn studies where both accelerometer and GPS data are available this tool complements analyses of physical activity, showing whether differences in PA may be explained by differences in travel mode. All code necessary to replicate, fit and predict to other datasets is provided to facilitate use by other researchers.Electronic supplementary materialThe online version of this article (10.1186/s12966-018-0724-y) contains supplementary material, which is available to authorized users.

Summary Background The built environment can affect health behaviours, but longitudinal evidence is limited. We aimed to examine the effect of moving into East Village, the former London 2012 Olympic and Paralympic Games Athletes' Village that was repurposed on active design principles, on adult physical activity and adiposity. Methods In this cohort study, we recruited adults seeking new accommodation in East Village and compared physical activity and built environment measures with these data in control participants who had not moved to East Village. At baseline and after 2 years, we objectively measured physical activity with accelerometry and adiposity with body-mass index and bioimpedance, and we assessed objective measures of and participants' perceptions of change in their built environment. We examined the change in physical activity and adiposity between the East Village and control groups, after adjusting for sex, age group, ethnicity, housing tenure, and household (as a random effect). Findings We recruited participants for baseline assessment between Jan 24, 2013, and Jan 7, 2016, and we followed up the cohort after 2 years, between Feb 24, 2015, and Oct 24, 2017. At baseline, 1819 households (one adult per household) consented to initial contact by the study team. 1278 adults (16 years and older) from 1006 (55%) households participated at baseline; of these participants, 877 (69%) adults from 710 (71%) households were assessed after 2 years, of whom 441 (50%) participants from 343 (48%) households had moved to East Village. We found no effect associated with moving to East Village on daily steps, the time spent doing moderate-to-vigorous physical activity (either in total or in 10-min bouts or more), daily sedentary time, body-mass index, or fat mass percentage between participants who had moved to East Village and those in the control group, despite sizeable improvements in walkability and neighbourhood perceptions of crime and quality among the East Village group relative to their original neighbourhood at baseline. Interpretation Despite large improvements in neighbourhood perceptions and walkability, we found no clear evidence that moving to East Village was associated with increased physical activity. Improving the built environment on its own might be insufficient to increase physical activity. Funding National Institute for Health Research and National Prevention Research Initiative.

Eusociality is one of the most complex forms of social organization, characterized by cooperative and reproductive units termed colonies. Altruistic behavior of workers within colonies is explained by inclusive fitness, with indirect fitness benefits accrued by helping kin. Members of a social insect colony are expected to be more closely related to one another than they are to other conspecifics. In many social insects, the colony can extend to multiple socially connected but spatially separate nests (polydomy). Social connections, such as trails between nests, promote cooperation and resource exchange, and we predict that workers from socially connected nests will have higher internest relatedness than those from socially unconnected, and noncooperating, nests. We measure social connections, resource exchange, and internest genetic relatedness in the polydomous wood ant Formica lugubris to test whether (1) socially connected but spatially separate nests cooperate, and (2) high internest relatedness is the underlying driver of this cooperation. Our results show that socially connected nests exhibit movement of workers and resources, which suggests they do cooperate, whereas unconnected nests do not. However, we find no difference in internest genetic relatedness between socially connected and unconnected nest pairs, both show high kinship. Our results suggest that neighboring pairs of connected nests show a social and cooperative distinction, but no genetic distinction. We hypothesize that the loss of a social connection may initiate ecological divergence within colonies. Genetic divergence between neighboring nests may build up only later, as a consequence rather than a cause of colony separation.

ObjectivesThe neighbourhood environment is increasingly shown to be an important correlate of health. We assessed associations between housing tenure, neighbourhood perceptions, sociodemographic factors and levels of physical activity (PA) and adiposity among adults seeking housing in East Village (formerly London 2012 Olympic/Paralympic Games Athletes’ Village).SettingCross-sectional analysis of adults seeking social, intermediate and market-rent housing in East Village.Participants1278 participants took part in the study (58% female). Complete data on adiposity (body mass index (BMI) and fat mass %) were available for 1240 participants (97%); of these, a subset of 1107 participants (89%) met the inclusion criteria for analyses of accelerometer-based measurements of PA. We examined associations between housing sector sought, neighbourhood perceptions (covariates) and PA and adiposity (dependent variables) adjusted for household clustering, sex, age group, ethnic group and limiting long-standing illness.ResultsParticipants seeking social housing had the fewest daily steps (8304, 95% CI 7959 to 8648) and highest BMI (26.0 kg/m2, 95% CI 25.5kg/m2 to 26.5 kg/m2) compared with those seeking intermediate (daily steps 9417, 95% CI 9106 to 9731; BMI 24.8 kg/m2, 95% CI 24.4 kg/m2 to 25.2 kg/m2) or market-rent housing (daily steps 9313, 95% CI 8858 to 9768; BMI 24.6 kg/m2, 95% CI 24.0 kg/m2 to 25.2 kg/m2). Those seeking social housing had lower levels of PA (by 19%–42%) at weekends versus weekdays, compared with other housing groups. Positive perceptions of neighbourhood quality were associated with higher steps and lower BMI, with differences between social and intermediate groups reduced by ~10% following adjustment, equivalent to a reduction of 111 for steps and 0.5 kg/m2 for BMI.ConclusionsThe social housing group undertook less PA than other housing sectors, with weekend PA offering the greatest scope for increasing PA and tackling adiposity in this group. Perceptions of neighbourhood quality were associated with PA and adiposity and reduced differences in steps and BMI between housing sectors. Interventions to encourage PA at weekends and improve neighbourhood quality, especially among the most disadvantaged, may provide scope to reduce inequalities in health behaviour.