Spatial and taxonomic biases in bat records: Drivers and conservation implications in a megadiverse country.

Biases in data availability have serious consequences on scientific inferences that can be derived. The potential consequences of these biases could be more detrimental in the less-studied megadiverse regions, often characterized by high biodiversity and serious risks of human threats, as conservation and management actions could be misdirected. Here, focusing on 134 bat species in Mexico, we analyze spatial and taxonomic biases and their drivers in occurrence data; and identify priority areas for further data collection which are currently under-sampled or at future environmental risk. We collated a comprehensive database of 26,192 presence-only bat records in Mexico to characterize taxonomic and spatial biases and relate them to species' characteristics (range size and foraging behavior). Next, we examined variables related to accessibility, species richness and security to explain the spatial patterns in occurrence records. Finally, we compared the spatial distributions of existing data and future threats to these species to highlight those regions that are likely to experience an increased level of threats but are currently under-surveyed. We found taxonomic biases, where species with wider geographical ranges and narrow-space foragers (species easily captured with traditional methods), had more occurrence data. There was a significant oversampling toward tropical regions, and the presence and number of records was positively associated with areas of high topographic heterogeneity, road density, urban, and protected areas, and negatively associated with areas which were predicted to have future increases in temperature and precipitation. Sampling efforts for Mexican bats appear to have focused disproportionately on easily captured species, tropical regions, areas of high species richness and security; leading to under-sampling in areas of high future threats. These biases could substantially influence the assessment of current status of, and future anthropogenic impacts on, this diverse species group in a tropical megadiverse country

The Archaeology of The Upper City and Adjacent Suburbs

This volume contains reports on sites excavated in the upper walled city at Lincoln and adjacent suburbs between 1972 and 1987. The project included large-scale excavations which yielded some stunning finds and revealed considerable information about several periods of the city's history. Each site is described in turn, incorporating stratigraphic, artifactual and environmental information, and the common threads are brought together in a general discussion. Structural and artifactual evidence for the post-medieval period also give a flavor of the local life-style in the 16th-18th centuries

Spatial, seasonal and climatic predictive models of Rift Valley fever disease across Africa

Understanding the emergence and subsequent spread of human infectious diseases is a critical global challenge, especially for high-impact zoonotic and vector-borne diseases. Global climate and land-use change are likely to alter host and vector distributions, but understanding the impact of these changes on the burden of infectious diseases is difficult. Here, we use a Bayesian spatial model to investigate environmental drivers of one of the most important diseases in Africa, Rift Valley fever (RVF). The model uses a hierarchical approach to determine how environmental drivers vary both spatially and seasonally, and incorporates the effects of key climatic oscillations, to produce a continental risk map of RVF in livestock (as a proxy for human RVF risk). We find RVF risk has a distinct seasonal spatial pattern influenced by climatic variation, with the majority of cases occurring in South Africa and Kenya in the first half of an El Niño year. Irrigation, rainfall and human population density were the main drivers of RVF cases, independent of seasonal, climatic or spatial variation. By accounting more subtly for the patterns in RVF data, we better determine the importance of underlying environmental drivers, and also make space- and time-sensitive predictions to better direct future surveillance resources

Patient needs in advanced Renal Cell Carcinoma: What are patients’ priorities and how well are we meeting them?

Treatment options and duration of therapy for patients with metastatic renal cell carcinoma (mRCC) have increased. Many patients now spend in excess of 2 years on active therapy. These patients’ needs, and the ability of health services to respond to them, are poorly understood. Ten patients living with mRCC for more than 2 years and treated with at least one targeted agent were selected at random from three hospitals in the United Kingdom (UK). One interviewer who was not involved in their care conducted in-depth interviews. Interview transcripts were analysed using Interpretative Phenomenological Analysis (IPA) to identify issues of greatest importance to patients, and to understand how well patients felt their needs were being addressed. Perceived delay in initial diagnosis was a major theme. Being told the truth about treatment side effects upfront was important, but was often at odds with perceived delivery. ‘Dealing with side effects’, understanding dose and its effects and not letting ‘negative thoughts get in’ were highlighted as important, but were highly personal to patients and areas where patients struggled. Concordance was observed with delivery of ‘a clear next step’ for treatment, timely access to drugs and guidance on a drug ‘holiday’. Patient experience of mRCC and its treatment requires a tailored approach. This research suggests there are key opportunities for service improvement and improved communication throughout the pathway to better meet the needs of patients, including non-clinical support to build personal resilience

Whombat: An open-source annotation tool for machine learning development in bioacoustics

1. Automated analysis of bioacoustic recordings using machine learning (ML) methods has the potential to greatly scale biodiversity monitoring efforts. The use of ML for high-stakes applications, such as conservation research, demands a data-centric approach with a focus on utilizing carefully annotated and curated evaluation and training data that is relevant and representative. Creating annotated datasets of sound recordings presents a number of challenges, such as managing large collections of recordings with associated metadata, developing flexible annotation tools that can accommodate the diverse range of vocalization profiles of different organisms, and addressing the scarcity of expert annotators. 2. We present Whombat a user-friendly, browser-based interface for managing audio recordings and annotation projects, with several visualization, exploration, and annotation tools. It enables users to quickly annotate, review, and share annotations, as well as visualize and evaluate a set of machine learning predictions on a dataset. The tool facilitates an iterative workflow where user annotations and machine learning predictions feedback to enhance model performance and annotation quality. 3. We demonstrate the flexibility of Whombat by showcasing two distinct use cases: an project aimed at enhancing automated UK bat call identification at the Bat Conservation Trust (BCT), and a collaborative effort among the USDA Forest Service and Oregon State University researchers exploring bioacoustic applications and extending automated avian classification models in the Pacific Northwest, USA. 4. Whombat is a flexible tool that can effectively address the challenges of annotation for bioacoustic research. It can be used for individual and collaborative work, hosted on a shared server or accessed remotely, or run on a personal computer without the need for coding skills.Comment: 17 pages, 2 figures, 2 tables, to be submitted to Methods in Ecology and Evolutio