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Ancient, veteran and notable trees are ecologically important keystone organisms and have tangible connections to folklore, history and sociocultural practices. Although found worldwide, few countries have such a rich history of recording and treasuring these trees as the UK, with its extensive Royal and aristocratic land ownership, unique land management methods and long-standing interest in natural history and species record collecting. As a result, the UK has collated an extensive database of ancient, veteran and notable trees called the Ancient Tree Inventory (ATI). The ATI is the result of a successful, long-term citizen science recording project and is the most comprehensive database of ancient and other noteworthy trees to date. We present here the first review of the ATI in its entirety since its initiation in 2004, including summaries of the UK ancient, veteran and notable tree distributions, the status and condition of the trees, and key information about the recording process and maintenance of the database. Statistical analysis of components of the dataset, comprising 169,967 tree records, suggest there are significant differences in the threats, size, form and location of different types of trees, especially in relation to taxonomic identity and tree age. Our goal is to highlight the value of the ATI in the UK, to encourage the development of similar ancient tree recording projects in other countries, and to emphasise the importance to conservation of continued efforts to maintain and expand databases of this kind.

Honey is a complex sweet food stuff with well-established antimicrobial and antioxidant properties. It has been used for millennia in a variety of applications, but the most noteworthy include the treatment of surface wounds, burns and inflammation. A variety of substances in honey have been suggested as the key component to its antimicrobial potential; polyphenolic compounds, hydrogen peroxide, methylglyoxal and bee-defensin 1. These components vary greatly across honey samples due to botanical origin, geographical location and secretions from the bee. The use of medical grade honey in the treatment of surface wounds and burns has been seen to improve the healing process, reduce healing time, reduce scarring and prevent microbial contamination. Therefore, if medical grade honeys were to be included in clinical treatment, it would reduce the demand for antibiotic usage. In this review, we outline the constituents of honey and how they affect antibiotic potential in a clinical setting. By identifying the key components, we facilitate the development of an optimally antimicrobial honey by either synthetic or semisynthetic production methods.

Antimicrobial resistance is an ever-increasing global issue that has the potential to overtake cancer as the leading cause of death worldwide by 2050. With the passing of the “golden age” of antibiotic discovery, identifying alternative treatments to commonly used antimicrobials is more important than ever. Honey has been used as a topical wound treatment for millennia and more recently has been formulated into a series of medical-grade honeys for use primarily for wound and burn treatment. In this systematic review, we examined the effectiveness of differing honeys as an antimicrobial treatment against a variety of multidrug-resistant (MDR) bacterial species. We analysed 16 original research articles that included a total of 18 different types of honey against 32 different bacterial species, including numerous MDR strains. We identified that Surgihoney was the most effective honey, displaying minimum inhibitory concentrations as low as 0.1% (w/v); however, all honeys reviewed showed a high efficacy against most bacterial species analysed. Importantly, the MDR status of each bacterial strain had no impact on the susceptibility of the organism to honey. Hence, the use of honey as an antimicrobial therapy should be considered as an alternative approach for the treatment of antibiotic-resistant infections.

Operative notes are frequently mined for surgical concepts in clinical care, research, quality improvement, and billing, often requiring hours of manual extraction. These notes are typically analyzed at the document level to determine the presence or absence of specific procedures or findings (eg, whether a hand-sewn anastomosis was performed or contamination occurred). Extracting several binary classification labels simultaneously is a multilabel classification problem. Traditional natural language processing approaches-bag-of-words (BoW) and term frequency-inverse document frequency (tf-idf) with linear classifiers-have been used previously for this task but are now being augmented or replaced by large language models (LLMs). However, few studies have examined their utility in surgery. We developed and evaluated LLMs for the purpose of expediting data extraction from surgical notes. A total of 388 exploratory laparotomy notes from a single institution were annotated for 21 concepts related to intraoperative findings, intraoperative techniques, and closure techniques. Annotation consistency was measured using the Cohen κ statistic. Data were preprocessed to include only the description of the procedure. We compared the evolution of document classification technologies from BoW and tf-idf to encoder-only (Clinical-Longformer) and decoder-only (Llama 3) transformer models. Multilabel classification performance was evaluated with 5-fold cross-validation with F1-score and hamming loss (HL). We experimented with and without context. Errors were assessed by manual review. Code and implementation instructions may be found on GitHub. The prevalence of labels ranged from 0.05 (colostomy, ileostomy, active bleed from named vessel) to 0.50 (running fascial closure). Llama 3.3 was the overall best-performing model (micro F1-score 0.88, 5-fold range: 0.88-0.89; HL 0.11, 5-fold range: 0.11-0.12). The BoW model (micro F1-score 0.68, 5-fold range: 0.64-0.71; HL 0.14, 5-fold range: 0.13-0.16) and Clinical-Longformer (micro F1-score 0.73, 5-fold range: 0.70-0.74; HL 0.11, 5-fold range: 0.10-0.12) had overall similar performance, with tf-idf models trailing (micro F1-score 0.57, 5-fold range: 0.55-0.59; HL 0.27, 5-fold range: 0.25-0.29). F1-scores varied across concepts in the Llama model, ranging from 0.30 (5-fold range: 0.23-0.39) for class III contamination to 0.92 (5-fold range: 0.98-0.84) for bowel resection. Context enhanced Llama's performance, adding an average of 0.16 improvement to the F1-scores. Error analysis demonstrated semantic nuances and edge cases within operative notes, particularly when patients had references to prior operations in their operative notes or simultaneous operations with other surgical services. Off-the-shelf autoregressive LLMs outperformed fined-tuned, encoder-only transformers and traditional natural language processing techniques in classifying operative notes. Multilabel classification with LLMs may streamline retrospective reviews in surgery, though further refinements are required prior to reliable use in research and quality improvement.

Passive acoustic monitoring using Autonomous Recording Units (ARUs) is becoming a significant research tool for collecting large amounts of ecological data. Northern bobwhite Colinus virginianus is an economically important game bird whose declining populations are of conservation concern, so efforts to monitor bobwhite abundance using ARUs are being intensified. Yet, manual processing of ARU data is time consuming and often expensive, so developing automatic call detection methods is a key step in acoustic monitoring. We present here the first single species convolutional neural network (CNN) developed purely for automatic bobwhite covey call identification and classification. We demonstrate the value of meaningful data augmentation by including non‐target calls and background noise into our training dataset, as well as evaluating alternative CNN score thresholds and model extrapolation performance. We trained our CNN on 6,682 manually labeled covey calls across three groups of sites within the southeastern USA. Precision and AUC from both CNN classification and individual call detection was high (0.80–0.99), and our model showed strong extrapolation ability across site groups. However, extrapolation performance significantly decreased for sites that were more dissimilar to the training data set if our meaningful data augmentation process was omitted. Our CNN detected significantly more covey calls than manual labeling using Raven Pro software, and processing time was greatly reduced: a single one hour wav file can be now analyzed by the CNN in roughly eight seconds. We also demonstrate using a simple case study that extremely high variability in estimates of bobwhite site occupancy and detection are obtained depending on the method of acoustic data processing (manual versus CNN). Our results suggest that our CNN provides robust and time‐saving analysis of bobwhite covey call acoustic data and can be applied to future research and monitoring projects with high confidence in the performance of the model. We present a convolutional neural network purely for the detection of Northern Bobwhite covey calls. Our model performs very well compared to manual detection methods and shows strong extrapolation and precision ability, as well as significantly reduced processing times.

Obtaining rigorous baseline density estimates of species of conservation interest is key when assisting landowners to achieve management goals on private lands. Northern bobwhite (Colinus virginianus) populations are declining throughout their range and despite being the focus of numerous private land conservation initiatives, baseline density estimates in privately owned pine forests are lacking. We sought to address this knowledge gap across the Southeastern United States by sampling 105 privately owned pine stands throughout 2018 to 2020 using observer point count and autonomous recording unit (ARU) sampling data. Using Bayesian hierarchical models, we investigated the influence of stand management (brush management or applied fire) on bobwhite density, as well as four landscape‐scale environmental variables. These included percentage cover of forest, herbaceous, agricultural or burnt land area across six different spatial scales ranging from 500‐m to 10‐km around each pine stand. Baseline density on sites with no management was estimated to be 2.24 coveys per 100 ha (1.00–5.03, 95% BCI), with little impact of applying brush management, but a trend for a positive effect of fire management (0.19, −0.01 to 0.38 95% BCI). This impact of fire was seen at both the stand‐scale, correlated with an increase in acreage of applied prescribed burn management, and across the greater landscape area, correlated with cover of burnt area within a 2‐km buffer around each site. There were also strong positive influences of herbaceous vegetation and a strong negative influence of forest cover on bobwhite density. Practical implication: our sampling efforts fill an important information gap regarding densities throughout private lands in the Southeastern United States. Our study also highlights the necessity of landscape scale planning for Northern Bobwhite conservation initiatives because the efficacy of conservation practices (i.e. prescribed fire and brush management) could be altered by the landscape surrounding the treated forest stand. Obtaining rigorous baseline density estimates of Northern Bobwhite is key when assisting landowners to achieve management goals on private lands. Using integrated Bayesian hierarchical models with point count and ARU data, we found a trend for a positive effect of fire management on quail abundance at both the stand‐scale and across the greater landscape area. Our sampling efforts fill an important information gap regarding quail densities throughout private lands in the southeastern United States, and our study highlights the necessity of landscape scale planning for Northern Bobwhite conservation initiatives.

Large, citizen-science species databases are powerful resources for predictive species distribution modeling (SDM), yet they are often subject to sampling bias. Many methods have been proposed to correct for this, but there exists little consensus as to which is most effective, not least because the true value of model predictions is hard to evaluate without extensive independent field sampling. We present here a nationwide, independent field validation of distribution models of ancient and veteran trees, a group of organisms of high conservation importance, built using a large and internationally unique citizen-science database: the Ancient Tree Inventory (ATI). This validation exercise presents an opportunity to test the performance of different methods of correcting for sampling bias, in the search for the best possible prediction of ancient and veteran tree distributions in England. We fitted a variety of distribution models of ancient and veteran tree records in England in relation to environmental predictors and applied different bias correction methods, including spatial filtering, background manipulation, the use of bias files, and, finally, zero-inflated (ZI) regression models, a new method with great potential to investigate and remove sampling bias in species data. We then collected new independent field data through systematic surveys of 52 randomly selected 1-km² grid squares across England to obtain abundance estimates of ancient and veteran trees. Calibration of the distribution models against the field data suggests that there are around eight to 10 times as many ancient and veteran trees present in England than the records currently suggest, with estimates ranging from 1.7 to 2.1 million trees compared to the 200,000 currently recorded in the ATI. The most successful bias correction method was systematic sampling of occurrence records, although the ZI models also performed well, significantly predicting field observations and highlighting both likely causes of undersampling and areas of the country in which many unrecorded trees are likely to be found. Our findings provide the first robust nationwide estimate of ancient and veteran tree abundance and demonstrate the enormous potential for distribution modeling based on citizen-science data combined with independent field validation to inform conservation planning.

Background: The literature alludes to several studies highlighting challenges with human proxy as decision makers such as emotional burden, physician barriers, decisional conflict, accuracy, and overconfidence. However, only a small subset reported on a proxy's congruency. This study expanded on a proof-of-concept that artificial intelligence (AI) can act as a proxy decision maker with value preferences and considered its ethical implications. Aim: To compare the congruency of AI as a proxy decision maker with human proxies on end-of-life treatment decisions. Methods: Utilizing LLaMa3, an AI Large Language Model as a proxy decision tool, we recruited 15 adults and their legal decision makers as dyads to complete a value and end-of-life preference surveys for a comparison analysis. We measured the participants' overall composite value scores and collected their end-of-life preferences to use in the AI congruence evaluation. Congruency percentage was taken over three clinical hypothetical scenarios and compared between the participant with either the human or AI proxy. Results: The mean congruency percentage between the participant and human proxy was 44.4% (95% CI: 23.6-65.3), n = 12. Fifty percent of dyads had one or no matching responses across the three scenarios and 16% had perfectly matched responses. After the model's adjustment for prompt engineering and parameter fine-tuning, the congruency with AI and value inputs was 72.2% (95% CI: 67.4-77.0) with 67.0% matched responses. The model performed the same as the human proxy without value preferences with congruency of 45.3% (95% CI: 36.2-54.4). Discussion: The AI model had a 28% higher congruency as a proxy decision-maker for end-of-life treatment decisions after the inclusion of value preferences. This approach has a promising utility as a supplemental tool for human decision-making and can protect self -determination if values are pre-recorded in the event of decisional incapacity.

The context and cause of adaptive radiations have been widely described and explored but why rapid evolutionary diversification does not occur in related evolutionary lineages has yet to be understood. The standard answer is that evolutionary diversification is provoked by ecological opportunity and that some lineages do not encounter the opportunity. Three‐spined sticklebacks on the Scottish island of North Uist show enormous diversification, which seems to be associated with the diversity of aquatic habitats. Sticklebacks on the neighboring island of South Uist have not been reported to show the same level of evolutionary diversity, despite levels of environmental variation that we might expect to be similar to North Uist. In this study, we compared patterns of morphological and environmental diversity on North and South Uist. Ancestral anadromous sticklebacks from both islands exhibited similar morphology including size and bony “armor.” Resident sticklebacks showed significant variation in armor traits in relation to pH of water. However, North Uist sticklebacks exhibited greater diversity of morphological traits than South Uist and this was associated with greater diversity in pH of the waters of lochs on North Uist. Highly acidic and highly alkaline freshwater habitats are missing, or uncommon, on South Uist. Thus, pH appears to act as a causal factor driving the evolutionary diversification of stickleback in local adaptation in North and South Uist. This is consistent with diversification being more associated with ecological constraint than ecological opportunity. Evolutionary diversification is often thought to arise from “ecological opportunity:” A reduction in competition for resources following the invasion of a new habitat. Here, we show that diversification may be provoked by ecological constraint: Organisms invading habitats, in this case, acid freshwater, may be free from competition but have poor resource availability.

Ancient, veteran and notable trees are ecologically important keystone organisms and have tangible connections to folklore, history and sociocultural practices. Although found worldwide, few countries have such a rich history of recording and treasuring these trees as the UK, which has resulted in the formation over the past 15 years of a large, comprehensive database of ancient and other noteworthy trees, the Ancient Tree Inventory (ATI). Although the ATI contains over 200,000 recorded trees, there are still thought to be many more that are undiscovered across the UK, and information about their status, condition and distribution is lacking. The primary aim of this thesis is to use the ATI to gain novel and detailed insights into the true distribution of ancient and veteran trees across the UK, important predictors of their presence, and key habitat types in which they are found. The ATI suffers many of the problems of large species databases, including sampling bias, which is a major focus of this thesis. To address this problem, sampling bias is first identified and quantified, and then established and novel bias correction methods are employed to improve predictions of ancient and veteran tree distributions. By combining mathematical models at various scales, from specific habitats to the whole of England, with additional independent data from desk and field surveys, robust accurate distribution maps of ancient and other noteworthy trees are produced and verified. The models suggest that woodpasture is a particularly important habitat for ancient and veteran trees, and that their distributions are highly influenced by historical features of the environment and human factors. A key result emerging from multiple chapters of this thesis is the potentially large number of undiscovered ancient and veteran trees predicted across England: diverse alternative models produced similar and impressive total estimates of around two million trees. These results can be used to inform the conservation and protection of ancient trees, and highlight the need for more targeted surveying, tree planting and implementation of policy measures to ensure their persistence and survival into the future.