ABC’s Bird-Smart Wind Energy Campaign: Protecting Birds from Poorly Sited Wind Energy Development

This article summarizes American Bird Conservancy’s (ABC’s) strategies and goals for achieving Bird-Smart wind energy in the United States. We describe the current and projected impact of wind energy development on birds and bats in the United States. We also discuss how bird (and bat) conservation goals could be made more compatible with wind energy development through improved science and regulation. We provide examples of poorly sited wind energy projects, existing and proposed, which call into question the efficacy of current voluntary federal permitting guidelines. We discuss the need for improved transparency and independent site-by-site pre-construction risk assessment, science-based decision-making, independent collection and reporting of post-construction bird (and bat) fatality data, and consideration of cumulative impacts

Climate change and eutrophication risk thresholds in English rivers

Climate change is expected to alter water quality in rivers, but where and when this may happen is uncertain. This report describes a study of projected response in the amount of algal plant growth (phytoplankton biomass). Increasing algal growth is one of the ecological manifestations of eutrophication in slow flowing rivers, where the water starts to resemble a green soup. Eutrophication is a process in which too much nutrient in water causes algae and higher plants to grow excessively. Eutrophication alters the quality of the water and how it can be used. Phytoplankton (suspended algae) is considered to be a useful indicator of eutrophication in standing freshwaters and can also be useful as one measure of impacts in rivers, particularly slow flowing rivers. Excess algal growth can result in blooms that eventually die off. The disruption of dissolved oxygen dynamics in the water column may, in turn, have adverse impacts on fish and macroinvertebrates. The onset and decline of algal blooms is measured by the concentration of chlorophyll (a green pigment in algae) in the water. In this context, algal bloom risk – and the risk of negative eutrophication impacts in the lower reaches of rivers – is identified through observations of threshold chlorophyll concentrations. Exceedence of a chlorophyll concentration threshold is not by itself used in the diagnosis of river eutrophication but can be used as a proxy for algal blooms for understanding and modelling risk. The future risk of eutrophication impact, including algal blooms, is affected by changes in the concentration of nutrients from altered river flow and changes in phosphorus inputs from a range of sources. An earlier study (Phase 1 of this project) demonstrated that climate change impacts on river flow would increase phosphorus concentrations by 2050 and beyond. However, climate-driven changes in river temperature regime and light, and plant responses to these, are also important in altering the future risk of excess algal growth. This report considers these aspects. The first step was to identify the variables that control eutrophication and the thresholds in these variables which determine the potential for algal blooms. Algal blooms tend to occur only in rivers with a residence time (the time water takes to travel from an upstream distance to a site) of over 4 days. Below 4 days, blooms are rare. Such long residence times in the UK tend to occur in canals, and slow flowing and shallow gradient rivers (often in their lower reaches). Using this residence time threshold of 4 days, a total of 26 sites in England on 24 different rivers with available data for analysis of trends were identified out of the 115 sites from Phase 1. Water quality data were used to identify the ranges of river flow and water temperature within which algal blooms were measured (as determined by chlorophyll concentration) for each site. Site-specific thresholds were identified from plots of variables of water quality against chlorophyll concentration. In this study, a chlorophyll threshold of 30µgl-1 indicated the onset of an algal bloom for most rivers. Thresholds ranged between 15µgl-1 and 100µgl-1 . For larger rivers, with higher chlorophyll levels (such as the Thames), the thresholds for algal blooms are higher. A phosphorus threshold of 30µgl-1 was selected for all sites, based on understanding developed through nutrient limitation experiments across a range of UK rivers in other studies. A sunlight duration threshold of 65W/m2 /day was chosen for all the sites based on a minimum of at least 3 hours of full sunshine per day over ~3 consecutive days (derived from earlier work). A bloom is likely to occur if all thresholds are met at the same time. These are called bloom risk days and they represent overall risk based on all measured variables. A spreadsheet model was developed and applied to the 26 sites. The model used daily estimates of controlling variables (phosphorus concentrations, river flow, water Climate change and eutrophication risk thresholds in English rivers v temperature and sunlight duration) from 1951 to 2098 to estimate when the derived thresholds for each variable were met and likely to cause an algal bloom. Phosphorus concentration estimates from earlier work were used under current wastewater treatment conditions and under an improved wastewater treatment scenario. Bloom risk days (when the river flow, water temperature, sunshine duration and phosphorus concentration thresholds for algal growth were all met) increased between the baseline period (1961 to 1990) and the 2050s future period (2040 to 2069). The median increase is about 8 days across all sites from about 50 in the baseline period, although the maximum increase is up to 15 days. The change in risk is variable by the 2080s (2070 to 2098), with about 50% of sites showing reduced risk relative to the baseline period, resulting in a median increase of about 4 days and a maximum of up to 16 days. Analysis of the number of threshold days for each individual driver indicates that phosphorus thresholds are met most days of the year and that phosphorus concentrations do not prevent bloom development except at one site. Phosphorus management strategies may therefore not be effective in reducing the risk of algal blooms occurring in slow flowing rivers, an observation confirmed by the fact only 3 sites showed a reduction in risk using an improved phosphorus treatment scenario. There is more variability in the number of days the other thresholds are met, resulting in a varying pattern of risk between sites and time periods. After phosphorus concentration thresholds, river flow thresholds are most frequently met. Sunlight duration and water temperature thresholds are least often met. The interaction between flow variability, water temperature and sunlight duration would appear to determine the variability that emerges by the 2080s. The role of water temperature and sunlight duration seems to be significant in both limiting the number of days all thresholds are met and in controlling the timing of attainment of all thresholds, with both thresholds tending to be exceeded later in the year than those for river flow and phosphorus concentration. With the lowest number of threshold days at the greatest number of sites, exposure to sunlight may be the most important factor in preventing algal blooms. There is considerable uncertainty in the estimation of future water temperature, which was derived from air temperature using simple regression methods. This may result in a variable estimate of bloom risk days that requires further exploration with more reliable projections of future water temperature. A better way of estimating water temperature would really help to model future water quality. These results suggest that management strategies focusing on reducing sunlight and thermal interactions (both through river shading by trees) may be particularly effective in reducing the risk of blooms on some rivers in the future. This could be explored using the spreadsheet model developed for this project. Whilst phytoplankton blooms tend to be observed in lowland reaches of English rivers, the approach applied here is independent of this, is equally applicable anywhere, and has potential for use in an approach for assessing eutrophication in slow flowing rivers. It would also be useful to identify more sites across England at which residence time thresholds are met in order to assess potential vulnerability to eutrophication

An empirical investigation of climate and land-use effects on water quantity and quality in two urbanising catchments in the southern United Kingdom

Using historical data of climate, land-use, hydrology and water quality from four catchments located in the south of England, this study identifies the impact of climate and land-use change on selected water quantity and water quality indicators. The study utilises a paired catchment approach, with two catchments that have experienced a high degree of urbanisation over the past five decades and two nearby, hydrologically similar, but undeveloped catchments. Multivariate regression models were used to assess the influence of rainfall and urbanisation on runoff (annual and seasonal), dissolved oxygen levels and temperature. Results indicate: (i) no trend in annual or seasonal rainfall totals, (ii) upward trend in runoff totals in the two urban catchments but not in the rural catchments, (iii) upward trend in dissolved oxygen and temperature in the urban catchments, but not in the rural catchments, and (iv) changes in temperature and dissolved oxygen in the urban catchments are not driven by climatic variables

Qualitative impact assessment of land management interventions on ecosystem services (“QEIA”). Report-3 theme-4: water

This project assessed the impacts of 741 potential land management actions, suitable for agricultural land in England, on the Farming & Countryside Programme’s Environmental Objectives (and therefore Environment Act targets and climate commitments) through 53 relevant environmental and cultural service indicators. The project used a combination of expert opinion and rapid evidence reviews, which included 1000+ pages of evidence in 10 separate reports with reference to over 2400 published studies, and an Integrated Assessment comprising expert-derived qualitative impact scores. The project has ensured that ELM schemes are evidence-based, offer good value for money, and contribute to SoS priorities for farming

The Accuracy of Confidence Intervals for Field Normalised Indicators

This is an accepted manuscript of an article published by Elsevier in Journal of Informetrics on 07/04/2017, available online: https://doi.org/10.1016/j.joi.2017.03.004 The accepted version of the publication may differ from the final published version.When comparing the average citation impact of research groups, universities and countries, field normalisation reduces the influence of discipline and time. Confidence intervals for these indicators can help with attempts to infer whether differences between sets of publications are due to chance factors. Although both bootstrapping and formulae have been proposed for these, their accuracy is unknown. In response, this article uses simulated data to systematically compare the accuracy of confidence limits in the simplest possible case, a single field and year. The results suggest that the MNLCS (Mean Normalised Log-transformed Citation Score) confidence interval formula is conservative for large groups but almost always safe, whereas bootstrap MNLCS confidence intervals tend to be accurate but can be unsafe for smaller world or group sample sizes. In contrast, bootstrap MNCS (Mean Normalised Citation Score) confidence intervals can be very unsafe, although their accuracy increases with sample sizes

Is Medical Research Informing Professional Practice More Highly Cited? Evidence from AHFS DI Essentials in Drugs.com

This is an accepted manuscript of an article published by Springer in Scientometrics on 21/02/2017, available online: https://doi.org/10.1007/s11192-017-2292-3 The accepted version of the publication may differ from the final published version.Citation-based indicators are often used to help evaluate the impact of published medical studies, even though the research has the ultimate goal of improving human wellbeing. One direct way of influencing health outcomes is by guiding physicians and other medical professionals about which drugs to prescribe. A high profile source of this guidance is the AHFS DI Essentials product of the American Society of Health-System Pharmacists, which gives systematic information for drug prescribers. AHFS DI Essentials documents, which are also indexed by Drugs.com, include references to academic studies and the referenced work is therefore helping patients by guiding drug prescribing. This article extracts AHFS DI Essentials documents from Drugs.com and assesses whether articles referenced in these information sheets have their value recognised by higher Scopus citation counts. A comparison of mean log-transformed citation counts between articles that are and are not referenced in AHFS DI Essentials shows that AHFS DI Essentials references are more highly cited than average for the publishing journal. This suggests that medical research influencing drug prescribing is more cited than average

Multi-modality machine learning predicting Parkinson's disease

Personalized medicine promises individualized disease prediction and treatment. The convergence of machine learning (ML) and available multimodal data is key moving forward. We build upon previous work to deliver multimodal predictions of Parkinson's disease (PD) risk and systematically develop a model using GenoML, an automated ML package, to make improved multi-omic predictions of PD, validated in an external cohort. We investigated top features, constructed hypothesis-free disease-relevant networks, and investigated drug-gene interactions. We performed automated ML on multimodal data from the Parkinson's progression marker initiative (PPMI). After selecting the best performing algorithm, all PPMI data was used to tune the selected model. The model was validated in the Parkinson's Disease Biomarker Program (PDBP) dataset. Our initial model showed an area under the curve (AUC) of 89.72% for the diagnosis of PD. The tuned model was then tested for validation on external data (PDBP, AUC 85.03%). Optimizing thresholds for classification increased the diagnosis prediction accuracy and other metrics. Finally, networks were built to identify gene communities specific to PD. Combining data modalities outperforms the single biomarker paradigm. UPSIT and PRS contributed most to the predictive power of the model, but the accuracy of these are supplemented by many smaller effect transcripts and risk SNPs. Our model is best suited to identifying large groups of individuals to monitor within a health registry or biobank to prioritize for further testing. This approach allows complex predictive models to be reproducible and accessible to the community, with the package, code, and results publicly available

CfA4: Light Curves for 94 Type Ia Supernovae

We present multi-band optical photometry of 94 spectroscopically-confirmed Type Ia supernovae (SN Ia) in the redshift range 0.0055 to 0.073, obtained between 2006 and 2011. There are a total of 5522 light curve points. We show that our natural system SN photometry has a precision of roughly 0.03 mag or better in BVr'i', 0.06 mag in u', and 0.07 mag in U for points brighter than 17.5 mag and estimate that it has a systematic uncertainty of 0.014, 0.010, 0.012, 0.014, 0.046, and 0.073 mag in BVr'i'u'U, respectively. Comparisons of our standard system photometry with published SN Ia light curves and comparison stars reveal mean agreement across samples in the range of ~0.00-0.03 mag. We discuss the recent measurements of our telescope-plus-detector throughput by direct monochromatic illumination by Cramer et al (in prep.). This technique measures the whole optical path through the telescope, auxiliary optics, filters, and detector under the same conditions used to make SN measurements. Extremely well-characterized natural-system passbands (both in wavelength and over time) are crucial for the next generation of SN Ia photometry to reach the 0.01 mag accuracy level. The current sample of low-z SN Ia is now sufficiently large to remove most of the statistical sampling error from the dark energy error budget. But pursuing the dark-energy systematic errors by determining highly-accurate detector passbands, combining optical and near-infrared (NIR) photometry and spectra, using the nearby sample to illuminate the population properties of SN Ia, and measuring the local departures from the Hubble flow will benefit from larger, carefully measured nearby samples.Comment: 43 page