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In ecology, the true causal structure for a given problem is often not known, and several plausible models and thus model predictions exist. It has been claimed that using weighted averages of these models can reduce prediction error, as well as better reflect model selection uncertainty. These claims, however, are often demonstrated by isolated examples. Analysts must better understand under which conditions model averaging can improve predictions and their uncertainty estimates. Moreover, a large range of different model averaging methods exists, raising the question of how they differ in their behaviour and performance. Here, we review the mathematical foundations of model averaging along with the diversity of approaches available. We explain that the error in model‐averaged predictions depends on each model's predictive bias and variance, as well as the covariance in predictions between models, and uncertainty about model weights. We show that model averaging is particularly useful if the predictive error of contributing model predictions is dominated by variance, and if the covariance between models is low. For noisy data, which predominate in ecology, these conditions will often be met. Many different methods to derive averaging weights exist, from Bayesian over information‐theoretical to cross‐validation optimized and resampling approaches. A general recommendation is difficult, because the performance of methods is often context dependent. Importantly, estimating weights creates some additional uncertainty. As a result, estimated model weights may not always outperform arbitrary fixed weights, such as equal weights for all models. When averaging a set of models with many inadequate models, however, estimating model weights will typically be superior to equal weights. We also investigate the quality of the confidence intervals calculated for model‐averaged predictions, showing that they differ greatly in behaviour and seldom manage to achieve nominal coverage. Our overall recommendations stress the importance of non‐parametric methods such as cross‐validation for a reliable uncertainty quantification of model‐averaged predictions.

Within protected areas, biodiversity loss is often a consequence of illegal resource use. Understanding the patterns and extent of illegal activities is therefore essential for effective law enforcement and prevention of biodiversity declines. We used extensive data, commonly collected by ranger patrols in many protected areas, and Bayesian hierarchical models to identify drivers, trends, and distribution of multiple illegal activities within the Queen Elizabeth Conservation Area (QECA), Uganda. Encroachment (e.g., by pastoralists with cattle) and poaching of noncommercial animals (e.g., snaring bushmeat) were the most prevalent illegal activities within the QECA. Illegal activities occurred in different areas of the QECA. Poaching of noncommercial animals was most widely distributed within the national park. Overall, ecological covariates, although significant, were not useful predictors for occurrence of illegal activities. Instead, the location of illegal activities in previous years was more important. There were significant increases in encroachment and noncommercial plant harvesting (nontimber products) during the study period (1999–2012). We also found significant spatiotemporal variation in the occurrence of all activities. Our results show the need to explicitly model ranger patrol effort to reduce biases from existing uncorrected or capture per unit effort analyses. Prioritization of ranger patrol strategies is needed to target illegal activities; these strategies are determined by protected area managers, and therefore changes at a site-level can be implemented quickly. These strategies should also be informed by the location of past occurrences of illegal activity: the most useful predictor of future events. However, because spatial and temporal changes in illegal activities occurred, regular patrols throughout the protected area, even in areas of low occurrence, are also required. Dentro de las áreas protegidas, la pérdida de la biodiversidad es comúnmente una consecuencia del uso ilegal de los recursos. Por esto, entender los patrones y la extensión de las actividades ilegales es esencial para la aplicación efectiva de la ley y para la prevención de la declinación de la biodiversidad. Usamos datos extensivos, recolectados en su mayoría por patrullas de guardabosques en muchas delas áreas protegidas, y modelos de jerarquía bayesiana para identificar a los conductores, las tendencias y la distribución de múltiples actividades ilegales dentro del Área de Conservación Reina Isabel (ACRI), Uganda. La intrusión (p. ej.: por pastores con ganado) y la caza furtiva de animales no comerciales (p. ej.: la captura por la carne de animales silvestres) fueron las actividades ilegales más prominentes en las diferentes áreas del ACRI. La caza furtiva de animales no comerciales se distribuyó con mayor amplitud dentro del parque nacional. En general, las covariantes ecológicas, aunque significantes, no fueron pronosticadores útiles para la aparición de las actividades ilegales; en su lugar, la ubicación de las actividades ilegales en los años previos fue más importante. Hubo incrementos significativos en la intrusión y la cosecha no comercial de plantas (productos no maderables) durante el periodo de estudio (1999-2012). También encontramos variaciones espacio-temporales en la aparición de todas las actividades. Nuestros resultados muestran la necesidad de modelar explícitamente el esfuerzo de patrullaje de los guardabosques para reducir los sesgos de los análisis existentes de captura por unidad o los que no tienen correcciones. Se necesita de la priorización de las estrategias de patrullaje de los guardabosques para atacar a las actividades ilegales; estas estrategias son determinadas por los administradores de las áreas protegidas y por eso los cambios a nivel de sitio pueden implementarse rápidamente. Estas estrategias también deben informarse con la ubicación de las apariciones pasadas de la actividad ilegal: el pronosticador más útil de eventos futuros. Sin embargo, también se requiere de patrullaje regular en toda el área protegida, incluso en zonas de aparición baja, ya que ocurrieron cambios espaciales y temporales en la actividad ilegal.

Summary Background Paroxysmal supraventricular tachycardia (SVT) in premenopausal women is often judged to be related to anxiety, and may be associated with the menstrual cycle. The aim of this study was to determine whether a cyclical variation of episodes of SVT exists and to correlate such variation with cyclical variation in plasma ovarian hormones. Methods 26 women (mean age 36 [SD 8]) years; with paroxysmal SVT were screened; those with regular menses who experienced at least three episodes of paroxysmal SVT in two consecutive 48-hour ambulatory ECG recordings were included. 13 patients (aged 32 [6] years) met these criteria. Patients underwent 48-hour ambulatory ECG monitoring and determination of plasma concentrations of oestradiol-17β and progesterone on day 7, 14, 21, and 28 of their menstrual cycle. Findings An increase in the number and duration of episodes of paroxysmal SVT was observed on day 28 as compared to day 7 of the menstrual cycle. A significant positive correlation was found between plasma progesterone and number of episodes and duration of SVT (5·6 [2·2] ng/mL; r=0·83, p=0·0004; and r=0·82, p=0·0005), while a significant inverse correlation was found between plasma oestradiol-17β and number of episodes and duration of SVT (155 [22] pg/mL; r=-0·89, p<0·0001; and r=-0·81, p=0·0007). Interpretation Women with paroxysmal SVT and normal menses exhibit a cyclical variation in the occurrence of the arrhythmia with their menstrual cycle. There is a close correlation between the episodes of paroxysmal SVT and the plasma concentrations of ovarian hormones. These data suggest that changes in plasma levels of ovarian hormones (and their interaction) may be of importance in determining episodes of arrhythmia in such patients. The mechanisms of these effects are unknown.

The appropriate management of visitors to nature reserves is an important conservation concern. In this thesis I briefly review the current literature describing the effects of disturbance on wildlife, concentrating mainly on birds (Chapter 1). Recent literature has provided worrying critiques of the practical and theoretical bases upon which management practice is based. Here, I address a number of questions that seek to clarify the impacts of human disturbance on birds. I started by asking whether behavioural measures of disturbance are accurate indices of the negative effects of disturbance. Through an experimental test of a theoretical model, I showed that animals that respond most to disturbance may in fact be those individuals that face the lowest cost associated with such disturbance (Chapter 2). Turnstones Arenaria interpres provided with extra food over three days showed stronger behavioural responses to a standardised disturbance stimulus than those without extra food. Behavioural measures are therefore not always a good index of disturbance effects. Consequently, the conclusions of some of the studies reviewed in Chapter 1 must be considered doubtful. Although some large declines in breeding success of some species are caused by human disturbance, such effects are obvious, simple to remedy and appear to be rather rare. If human disturbance is a general concern it is therefore necessary to assess whether human disturbance has impacts on species that are not obviously sensitive. To investigate this, I modelled the impact of human disturbance on the nesting success of kittiwakes Rissa tridactyla and guillemots Uria aalge (Chapter 3). By directly measuring a range of nest site parameters as well as those parameters involving human disturbance, I was able to improve the power of the analysis to detect disturbance effects over those of previous, less detailed, studies.

Background Early adolescence is a period of dynamic neurobiological change. Converging lines of research suggest that regular physical activity (PA) and improved aerobic fitness have the potential to stimulate positive brain changes, improve cognitive function and boost academic attainment in this age group, but high-quality studies are needed to substantiate these findings. The primary aim of the Fit to Study trial is to investigate whether short infusions of vigorous PA (VPA) delivered during secondary school physical education (PE) can improve attainment in maths, as described in a protocol published by NatCen Social Research. The present protocol concerns the trial’s secondary outcome measures, which are variables thought to moderate or mediate the relationship between PA and attainment, including the effect of the intervention on cardiorespiratory fitness, cognitive performance, mental health and brain structure and function. Method The Fit to Study project is a cluster-randomised controlled trial that includes Year 8 pupils (aged 12–13) from secondary state schools in South/Mid-England. Schools were randomised into an intervention condition in which PE teachers delivered an additional 10 min of VPA per PE lesson for one academic year, or a ‘PE as usual’ control condition. Intervention and control groups were stratified according to whether schools were single-sex or co-educational. Assessments take place at baseline (end of Year 7, aged 11–12) and after 12 months (Year 8). Secondary outcomes are cardiorespiratory fitness, objective PA during PE, cognitive performance and mental health. The study also includes exploratory measures of daytime sleepiness, attitudes towards daily PA and PE enjoyment. A sub-set of pupils from a sub-set of schools will also take part in a brain imaging sub-study, which is embedded in the trial. Discussion The Fit to Study trial could advance our understanding of the complex relationships between PA and aerobic fitness, the brain, cognitive performance, mental health and academic attainment during adolescence. Further, it will add to our understanding of whether school PE is an effective setting to increase VPA and fitness, which could inform future PA interventions and education policy. Trial registration ClinicalTrials.gov, NCT03286725 . Retrospectively registered on 18 September 2017. ClinicalTrials.gov, NCT03593863 . Retrospectively registered on 19 July 2018.