When do young birds disperse? : Tests from studies of golden eagles in Scotland

Peer reviewedPublisher PD

Thoracic X-Strap Harness. Design and Method 2

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Laser Guide Star for Large Segmented-Aperture Space Telescopes, Part I: Implications for Terrestrial Exoplanet Detection and Observatory Stability

Precision wavefront control on future segmented-aperture space telescopes presents significant challenges, particularly in the context of high-contrast exoplanet direct imaging. We present a new wavefront control architecture that translates the ground-based artificial guide star concept to space with a laser source aboard a second spacecraft, formation flying within the telescope field-of-view. We describe the motivating problem of mirror segment motion and develop wavefront sensing requirements as a function of guide star magnitude and segment motion power spectrum. Several sample cases with different values for transmitter power, pointing jitter, and wavelength are presented to illustrate the advantages and challenges of having a non-stellar-magnitude noise limited wavefront sensor for space telescopes. These notional designs allow increased control authority, potentially relaxing spacecraft stability requirements by two orders of magnitude, and increasing terrestrial exoplanet discovery space by allowing high-contrast observations of stars of arbitrary brightness.Comment: Submitted to A

Non-territorial GPS-tagged golden eagles Aquila chrysaetos at two Scottish wind farms: Avoidance influenced by preferred habitat distribution, wind speed and blade motion status

From PLOS via Jisc Publications RouterHistory: received 2020-09-22, collection 2021, accepted 2021-06-21, epub 2021-08-05Publication status: PublishedFunder: natural researchFunder: the royal society for the protection of birdsFunder: snhFunder: roy dennis wildlife foundationFunder: ruth tingayFunder: sse renewablesFunder: scottishpower renewables; funder-id: http://dx.doi.org/10.13039/100012017Wind farms can have two broad potential adverse effects on birds via antagonistic processes: displacement from the vicinity of turbines (avoidance), or death through collision with rotating turbine blades. These effects may not be mutually exclusive. Using detailed data from 99 turbines at two wind farms in central Scotland and thousands of GPS-telemetry data from dispersing golden eagles, we tested three hypotheses. Before-and-after-operation analyses supported the hypothesis of avoidance: displacement was reduced at turbine locations in more preferred habitat and with more preferred habitat nearby. After-operation analyses (i.e. from the period when turbines were operational) showed that at higher wind speeds and in highly preferred habitat eagles were less wary of turbines with motionless blades: rejecting our second hypothesis. Our third hypothesis was supported, since at higher wind speeds eagles flew closer to operational turbines; especially–once more–turbines in more preferred habitat. After operation, eagles effectively abandoned inner turbine locations, and flight line records close to rotor blades were rare. While our study indicated that whole-wind farm functional habitat loss through avoidance was the substantial adverse impact, we make recommendations on future wind farm design to minimise collision risk further. These largely entail developers avoiding outer turbine locations which are in and surrounded by swathes of preferred habitat. Our study illustrates the insights which detailed case studies of large raptors at wind farms can bring and emphasises that the balance between avoidance and collision can have several influences

Prevalence of post-traumatic stress disorder and common mental disorders in health-care workers in England during the COVID-19 pandemic: a two-phase cross-sectional study

BACKGROUND: Previous studies on the impact of the COVID-19 pandemic on the mental health of health-care workers have relied on self-reported screening measures to estimate the point prevalence of common mental disorders. Screening measures, which are designed to be sensitive, have low positive predictive value and often overestimate prevalence. We aimed to estimate prevalence of common mental disorders and post-traumatic stress disorder (PTSD) among health-care workers in England using diagnostic interviews. METHODS: We did a two-phase, cross-sectional study comprising diagnostic interviews within a larger multisite longitudinal cohort of health-care workers (National Health Service [NHS] CHECK; n=23 462) during the COVID-19 pandemic. In the first phase, health-care workers across 18 NHS England Trusts were recruited. Baseline assessments were done using online surveys between April 24, 2020, and Jan 15, 2021. In the second phase, we selected a proportion of participants who had responded to the surveys and conducted diagnostic interviews to establish the prevalence of mental disorders. The recruitment period for the diagnostic interviews was between March 1, 2021 and Aug 27, 2021. Participants were screened with the 12-item General Health Questionnaire (GHQ-12) and assessed with the Clinical Interview Schedule-Revised (CIS-R) for common mental disorders or were screened with the 6-item Post-Traumatic Stress Disorder Checklist (PCL-6) and assessed with the Clinician Administered PTSD Scale for DSM-5 (CAPS-5) for PTSD. FINDINGS: The screening sample contained 23 462 participants: 2079 participants were excluded due to missing values on the GHQ-12 and 11 147 participants due to missing values on the PCL-6. 243 individuals participated in diagnostic interviews for common mental disorders (CIS-R; mean age 42 years [range 21-70]; 185 [76%] women and 58 [24%] men) and 94 individuals participated in diagnostic interviews for PTSD (CAPS-5; mean age 44 years [23-62]; 79 [84%] women and 15 [16%] men). 202 (83%) of 243 individuals in the common mental disorders sample and 83 (88%) of 94 individuals in the PTSD sample were White. GHQ-12 screening caseness for common mental disorders was 52·8% (95% CI 51·7-53·8). Using CIS-R diagnostic interviews, the estimated population prevalence of generalised anxiety disorder was 14·3% (10·4-19·2), population prevalence of depression was 13·7% (10·1-18·3), and combined population prevalence of generalised anxiety disorder and depression was 21·5% (16·9-26·8). PCL-6 screening caseness for PTSD was 25·4% (24·3-26·5). Using CAPS-5 diagnostic interviews, the estimated population prevalence of PTSD was 7·9% (4·0-15·1). INTERPRETATION: The prevalence estimates of common mental disorders and PTSD in health-care workers were considerably lower when assessed using diagnostic interviews compared with screening tools. 21·5% of health-care workers met the threshold for diagnosable mental disorders, and thus might benefit from clinical intervention. FUNDING: UK Medical Research Council; UCL/Wellcome; Rosetrees Trust; NHS England and Improvement; Economic and Social Research Council; National Institute for Health and Care Research (NIHR) Biomedical Research Centre at the Maudsley and King's College London (KCL); NIHR Protection Research Unit in Emergency Preparedness and Response at KCL

Investigating the role of prior and observation error correlations in improving a model forecast of forest carbon balance using Four Dimensional Variational data assimilation

Efforts to implement variational data assimilation routines with functional ecology models and land surface models have been limited, with sequential and Markov chain Monte Carlo data assimilation methods being prevalent. When data assimilation has been used with models of carbon balance, prior or “background” errors (in the initial state and parameter values) and observation errors have largely been treated as independent and uncorrelated. Correlations between background errors have long been known to be a key aspect of data assimilation in numerical weather prediction. More recently, it has been shown that accounting for correlated observation errors in the assimilation algorithm can considerably improve data assimilation results and forecasts. In this paper we implement a Four-Dimensional Variational data assimilation (4D-Var) scheme with a simple model of forest carbon balance, for joint parameter and state estimation and assimilate daily observations of Net Ecosystem CO2 Exchange (NEE) taken at the Alice Holt forest CO2 flux site in Hampshire, UK. We then investigate the effect of specifying correlations between parameter and state variables in background error statistics and the effect of specifying correlations in time between observation errors. The idea of including these correlations in time is new and has not been previously explored in carbon balance model data assimilation. In data assimilation, background and observation error statistics are often described by the background error covariance matrix and the observation error covariance matrix. We outline novel methods for creating correlated versions of these matrices, using a set of previously postulated dynamical constraints to include correlations in the background error statistics and a Gaussian correlation function to include time correlations in the observation error statistics. The methods used in this paper will allow the inclusion of time correlations between many different observation types in the assimilation algorithm, meaning that previously neglected information can be accounted for. In our experiments we assimilate a single year of NEE observations and then run a forecast for the next 14 years. We compare the results using our new correlated background and observation error covariance matrices and those using diagonal covariance matrices. We find that using the new correlated matrices reduces the root mean square error in the 14 year forecast of daily NEE by 44% decreasing from 4.22 gCm−2 day−1 to 2.38 gCm−2 day−

Energetic synchrony throughout the non-breeding season in common guillemots from four colonies

The non‐breeding season presents significant energetic challenges to birds that breed in temperate or polar regions, with clear implications for population dynamics. In seabirds, the environmental conditions at non‐breeding sites drive food availability and the energetic cost of regulatory processes, resulting in variation in diet, behaviour and energetics; however, very few studies have attempted to understand if and how these aspects vary between populations. We investigated whether non‐breeding location influenced diet, behaviour and energetics in the common guillemot Uria aalge. We studied guillemots from four UK breeding colonies, two located on the west coast of Scotland and two on the east. We quantified non‐breeding distribution, foraging behaviour and activity budgets of 39 individuals from July to March, using geolocation–immersion loggers and time‐depth recorders, and used feather stable isotope signatures to infer diet during the post‐breeding moult. We calculated energy expenditure and investigated whether the peak (an indicator of the potential vulnerability to marine threats) varied between colonies. Individuals were spatially segregated according to the coastline they breed on, with west coast guillemots distributed off the west coast of the UK and east coast guillemots distributed off the east coast. Diet and behaviour were more similar in guillemots that shared a breeding coastline than those that did not, as west coast guillemots foraged at a lower trophic level, spent less time diving and engaged in more pelagic foraging than east coast guillemots. However, energy expenditure was remarkably similar between colonies, peaking during late February/early March, indicating that, during our study period, there was high synchrony between colonies in the timing of potential vulnerability to marine threats. Therefore, any anthropogenic changes that result in decreased food availability or increased energy expenditure during late winter may have greater impacts on energy balance, with consequences for population dynamics.</jats:p

Interspecific variation in non-breeding aggregation: a multi-colony tracking study of two sympatric seabirds

Migration is a widespread strategy for escaping unfavourable conditions during winter, but the extent to which populations that segregate during the breeding season aggregate during the non-breeding season is poorly understood. Low non-breeding season aggregation may be associated with higher likelihood of overlap with threats, but with fewer populations affected, whereas high aggregation may result in a lower probability of exposure to threats, but higher overall severity. We investigated non-breeding distributions and extent of population aggregation in 2 sympatrically breeding auks. We deployed geolocation-immersion loggers on common guillemots Uria aalge and razorbills Alca torda at 11 colonies around the northern UK and tracked their movements across 2 non-breeding seasons (2017-18 and 2018-19). Using 290 guillemot and 135 razorbill tracks, we mapped population distributions of each species and compared population aggregation during key periods of the non-breeding season (post-breeding moult and mid-winter), observing clear interspecific differences. Razorbills were largely distributed in the North Sea, whereas guillemot distributions were spread throughout Scottish coastal waters and the North, Norwegian and Barents Seas. We found high levels of aggregation in razorbills and a strong tendency for colony-specific distributions in guillemots. Therefore, razorbills are predicted to have a lower likelihood of exposure to marine threats, but more severe potential impact due to the larger number of colonies affected. This interspecific difference may result in divergent population trajectories, despite the species sharing protection at their breeding sites. We highlight the importance of taking whole-year distributions into account in spatial planning to adequately protect migratory species.</jats:p