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This work is supported by the Scottish Government, the Scottish Office and the DECC SEA programme. ; Technological developments over the last 20 years have meant that telemetry studies have used a variety of techniques, each with different levels of accuracy and temporal resolution. This presents a challenge when combining data from these different tracking systems to obtain larger sample sizes or to compare habitat use over time. In this study, we used a Bayesian state-space modelling approach to integrate tracking data from multiple tag types and standardise position estimates while accounting for location error. Harbour seal (Phoca vitulina) telemetry data for the Moray Firth, Scotland, were collated from three tag types: VHF, Argos satellite and GPS–GSM. Tags were deployed on 37 seals during 1989 to 2009 resulting in 37 tracks with a total of 2886 tracking days and a mean duration of 87 days per track. A state-space model was applied to all of the raw tracks to provide daily position estimates and a measure of the uncertainty for each position. We used this standardised tracking dataset to model their habitat use and preference, which was then scaled by the population size estimated from haul-out counts to give an estimate of the absolute number of harbour seals using different parts of the Moray Firth. As expected for a central place forager, harbour seals most frequently occurred in areas close to their inshore haul-out sites. However, our analyses also demonstrated consistent use of offshore foraging grounds, typically within 30 km of haul-out sites in waters < 50 m deep. The use of these statistical models to integrate and compare different datasets is especially important for assessing longer-term responses to environmental variation and anthropogenic activities, allowing management advice to be based upon datasets that integrate information from all available tracking technologies. ; Postprint ; Peer reviewed

This study was partly funded by Beatrice Offshore Wind Ltd. and Moray Offshore Wind Farm (East) Ltd. using equipment previously purchased by UK Department of Energy & Climate Change, Scottish Government, Oil and Gas UK, COWRIE and Moray Offshore Renewables Ltd. P.T. and I.G. were core funded by University of Aberdeen. A.B. was core funded by the collaboration between University of Aberdeen and Marine Scotland Science through the MarCRF PhD studentship. N.M. was core funded by Centre for Environment, Fisheries and Aquaculture Science. ; Peer reviewed ; Publisher PDF

Funded by Department of Energy and Climate Change (UK), BES, ASAB, Greenpeace, Environmental Trust, Scottish Natural Heritage, Scottish Government, Whale and Dolphin Conservation, Talisman Energy (UK) Ltd., DECC, Chevron, Natural Environment Research Council Acknowledgments Funding for this work was provided by the Department of Energy and Climate Change (UK). Photo-identification data were collected during a series of grants and contracts from the BES, ASAB, Greenpeace Environmental Trust, Scottish Natural Heritage, Scottish Government, Whale and Dolphin Conservation, Talisman Energy (UK) Ltd., DECC, Chevron, and the Natural Environment Research Council. All survey work was carried out under Scottish Natural Heritage Animal Scientific Licences. The authors have no conflict of interest to declare. We thank Mark Bravington for his helpful advice at the early stages of this work and two anonymous reviewers for their useful comments on the manuscript. ; Peer reviewed ; Publisher PDF

Funding for this work was provided by the Department of Energy and Climate Change (UK). Photo-identification data were collected during a series of grants and contracts from the BES, ASAB, Greenpeace Environmental Trust, Scottish Natural Heritage, Scottish Government, Whale and Dolphin Conservation, Talisman Energy (UK) Ltd., DECC, Chevron, and the Natural Environment Research Council. ; Accurate estimates of fecundity rate are key to population assessments and effectively direct conservation efforts. We present a new approach to estimate fecundity rate based on the probability of a female giving birth, conditional on a previous birth t years ago, from which an expected inter-birth interval (IBI) can be estimated. We use generalized linear mixed-effects models to account for individual and temporal variability and apply the approach to individual reproductive histories of bottlenose dolphins (Tursiops truncatus) from the east coast of Scotland. We estimate a fecundity rate of 0.222 (95% CI = 0.218–0.253) and an expected IBI of 4.49 yr (95% CI = 3.94–4.93 yr). We use simulated data samples to show that the approach produces estimates with a minimum bias of <3%. Simulations are also used to investigate the effect of the most common data-driven biases in the estimates of birth intervals and fecundity rate; we recommend longitudinal studies of at least 10 yr and capture probabilities of at least 0.3 when using this methodology. The approach may be modified to incorporate other parameters of interest and should be applicable to any population with comprehensive data on birth intervals. ; Publisher PDF ; Peer reviewed

BACKGROUND AND PURPOSE: Posttraumatic stress disorder (PTSD) is a heterogeneous condition associated with a range of brain imaging abnormalities. Early life stress (ELS) contributes to this heterogeneity, but we do not know how a history of ELS influences traditionally defined brain signatures of PTSD. Here, we used a novel machine learning method – evolving partitions to improve classification (EPIC) – to identify shared and unique structural neuroimaging markers of ELS and PTSD in 97 combat-exposed military veterans. METHODS: We used EPIC with repeated cross-validation (CV) to determine how combinations of cortical thickness, surface area, and subcortical brain volumes could contribute to classification of PTSD (n = 40) versus controls (n = 57), and classification of ELS within the PTSD (ELS(+) n = 16; ELS(−) n = 24) and control groups (ELS(+) n = 16; ELS(−) n = 41). Additional inputs included intracranial volume, age, sex, adult trauma, and depression. RESULTS: On average, EPIC classified PTSD with 69% accuracy (SD = 5%), and ELS with 64% accuracy in the PTSD group (SD = 10%), and 62% accuracy in controls (SD = 6%). EPIC selected unique sets of individual features that classified each group with 75–85% accuracy in post hoc analyses; combinations of regions marginally improved classification from the individual atlas-defined brain regions. Across analyses, surface area in the right posterior cingulate was the only variable that was repeatedly selected as an important feature for classification of PTSD and ELS. CONCLUSIONS: EPIC revealed unique patterns of features that distinguished PTSD and ELS in this sample of combat-exposed military veterans, which may represent distinct biotypes of stress-related neuropathology.

In an attempt to increase power to detect genetic associations with brain phenotypes derived from human neuroimaging data, we recently conducted a large-scale, genome-wide association meta-analysis of hippocampal, brain, and intracranial volume through the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) consortium. Here, we present a freely available online interactive tool, EnigmaVis, which makes it easy to visualize the association results generated by the consortium alongside allele frequency, genes, and functional annotations. EnigmaVis runs natively within the web browser, and generates plots that show the level of association between brain phenotypes at user-specified genomic positions. Uniquely, EnigmaVis is dynamic; users can interact with elements on the plot in real time. This software will be useful when exploring the effect on brain structure of particular genetic variants influencing neuropsychiatric illness and cognitive function. Future projects of the consortium and updates to EnigmaVis will also be displayed on the site. EnigmaVis is freely available online at http://enigma.loni.ucla.edu/enigma-vis/

AbstractIn this prospective study of mental health, we examine the influence of three interrelated traits — perceived stress, rumination, and daytime sleepiness — and their association with symptoms of anxiety and depression in early adolescence. Given the known associations between these traits, an important objective is to determine the extent to which they may independently predict anxiety/depression symptoms. Twin pairs from the Queensland Twin Adolescent Brain (QTAB) project were assessed on two occasions (N= 211 pairs aged 9−14 years at baseline and 152 pairs aged 10−16 years at follow-up). Linear regression models and quantitative genetic modeling were used to analyze the data. Prospectively, perceived stress, rumination, and daytime sleepiness accounted for 8−11% of the variation in later anxiety/depression; familial influences contributed strongly to these associations. However, only perceived stress significantly predicted change in anxiety/depression, accounting for 3% of variance at follow-up after adjusting for anxiety/depression at baseline, although it did not do so independently of rumination and daytime sleepiness. Bidirectional effects were found between all traits over time. These findings suggest an underlying architecture that is shared, to some degree, by all traits, while the literature points to hypothalamic–pituitary–adrenal (HPA) axis and/or circadian systems as potential sources of overlapping influence and possible avenues for intervention.

The collection of visual and acoustic data was funded by the UK Department of Energy & Climate Change, the Scottish Government, Collaborative Offshore Wind Research into the Environment (COWRIE) and Oil & Gas UK. Digital aerial surveys were funded by Moray Offshore Renewables Ltd and additional funding for analysis of the combined data sets was provided by Marine Scotland. Collaboration between the University of Aberdeen and Marine Scotland was supported by MarCRF. ; 1. Robust estimates of the density or abundance of cetaceans are required to support a wide range of ecological studies and inform management decisions. Considerable effort has been put into the development of line-transect sampling techniques to obtain estimates of absolute density from aerial- and boat-based visual surveys. Surveys of cetaceans using acoustic loggers or digital cameras provide alternative methods to estimate relative density that have the potential to reduce cost and provide a verifiable record of all detections. However, the ability of these methods to provide reliable estimates of relative density has yet to be established. 2. These methodologies were compared by conducting aerial visual line-transect surveys (n = 10 days) and digital video strip-transect surveys (n = 4 days) in the Moray Firth, Scotland. Simultaneous acoustic data were collected from moored echolocation detectors (C-PODs) at 58 locations across the study site. Density surface modelling (DSM) of visual survey data was used to estimate spatial variation in relative harbour porpoise density on a 4 × 4 km grid. DSM was also performed on the digital survey data, and the resulting model output compared to that from visual survey data. Estimates of relative density from visual surveys around acoustic monitoring sites were compared with several metrics previously used to characterise variation in acoustic detections of echolocation clicks. 3. There was a strong correlation between estimates of relative density from visual surveys and digital video surveys (Spearman's ρ = 0·85). A correction to account for animals missed on the transect line [previously calculated for visual aerial surveys of harbour porpoise in the North Sea was used to convert relative density from the visual surveys to absolute density. This allowed calculation of the first estimate of a proxy for detection probability in digital video surveys, suggesting that 61% (CV = 0·53) of harbour porpoises were detected. There was also a strong correlation between acoustic detections and density with Spearman's ρ = 0·73 for detection positive hours. 4. These results provide confidence in the emerging use of digital video and acoustic surveys for studying the density of small cetaceans and their responses to environmental and anthropogenic change. ; Publisher PDF ; Peer reviewed