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Data collected by autonomous sensors, including camera traps and acoustic recorders, have enormous potential to generate new scientific insights in ecology and related fields. Modern machine learning and AI classification methods are critical to analysing these often immense data streams. Accordingly, considerable effort has been dedicated to building AI models that accurately detect and classify species and events of interest in these data. These AI models, however, form only one part of a larger research framework that is needed to answer ecological questions using sensor data. We argue that a deep understanding of this research context is required to develop and apply appropriate AI models that can support scientific advances in ecology and evolution. In this manuscript, we contextualize the use of AI methods in autonomous biodiversity surveys, focusing on terrestrial bioacoustics as a case study, by discussing six sequential areas that together form a research project: hardware, field deployments, data management, detection and classification using AI and related models, statistical analysis and ecological insight. For each area, we briefly highlight several ways in which decisions made in that area can constrain, support or interact with the development and application of AI models. We conclude with several suggestions for better development and integration of AI models into ecological research, including the need for additional research at the interface of AI models and statistical analysis, the question of achieving human‐level performance with AI models and the sources of future methodological advances in AI for ecology.

Common names of species are important for communicating with the general public. In principle, these names should provide an accessible way to engage with and identify species. The common names of species have historically been labile without standard guidelines, even within a language. Currently, there is no systematic assessment of how often common names communicate identifiable and biologically relevant characteristics about species. This is a salient issue in ornithology, where common names are used more often than scientific names for species of birds in written and spoken English, even by professional researchers. To gain a better understanding of the types of terminology used in the English-language common names of bird species, a group of 85 professional ornithologists and non-professional contributors classified unique descriptors in the common names of all recognized species of birds. In the AvianLexiconAtlas database produced by this work, each species’ common name is assigned to one of ten categories associated with aspects of avian biology, ecology, or human culture. Across 10,906 species of birds, 89% have names describing the biology of the species, while the remaining 11% of species have names derived from human cultural references, human names, or local non-English languages. Species with common names based on features of avian biology are more likely to be related to each other or be from the same geographic region. The crowdsourced data collection also revealed that many common names contain specialized or historic terminology unknown to many of the data collectors, and we include these terms in a glossary and gazetteer alongside the dataset. The AvianLexiconAtlas can be used as a quantitative resource to assess the state of terminology in English-language common names of birds. Future research using the database can shed light on historical approaches to nomenclature and how people engage with species through their names.

If humans aim to sustainably coexist with wildlife, we must understand how our activity impacts the communication systems of urban animal populations. We know much about the effects of anthropogenic noise on bird song, but relatively little about how avian visual signals are affected by urbanization. Such an effect may occur if urbanization alters the food available to species with color based on carotenoids, which they must obtain from their diet. Over 3 years, we compared a comprehensive suite of visual signals in male and female Northern Cardinals (Cardinalis cardinalis) in a rural and an urban population. We predicted that urban birds would have enhanced carotenoid-based signals as they likely have access to more carotenoids from invasive plants that thrive in cities. We used reflectance spectrometry, digital image analyses. and avian visual models to quantify hue. saturation, and brightness of chest (male), underwing (female), and bill (male and female) signals. Compared to rural males, urban males had redder chest feathers in 1 year and redder bills in every year. Urban females had more saturated underwing color than rural females in every year. These color differences were sufficient to be distinguished by the avian visual system. Female bill color did not vary between sites. Interestingly, urban birds had significantly reduced mass-related body condition compared to rural birds. Thus, urban males and females can display enhanced carotenoid-based signals despite being in relatively poor condition. We suggest that urban birds have more access to carotenoids from invasive honeysuckle (Lonicera spp.) than rural birds. This color enhancement could affect the information content of the signals and the dynamics of the social and mating systems. These results stand in stark contrast to the predominant trend of decreased color in urban birds and highlight the complex and varied potential effects of urbanization on animal communication. Received 26 June 2022. Accepted 17 September 2022.

If humans aim to sustainably coexist with wildlife, we must understand how our activity impacts the communication systems of urban animal populations. We know much about the effects of anthropogenic noise on bird song, but relatively little about how avian visual signals are affected by urbanization. One way such an effect may occur if urbanization alters the food available to species with color based on carotenoids, which they must obtain from their diet. Over three years, we compared a comprehensive suite of visual signals in male and female Northern Cardinals (Cardinalis cardinals) in a rural and an urban population. We predicted that urban birds would have enhanced carotenoid-based signals as they likely have access to more carotenoids from invasive plants, especially honeysuckle (Lonicera spp.), that thrive in cities. We used reflectance spectrometry, digital image analyses, and avian visual models to quantify hue, saturation, and brightness of chest (male), underwing (female), and bill (male and female) signals. Compared to rural males, urban males had redder chest feathers in one year and redder bills in every year. Urban females had more saturated underwing color than rural females in every year. These color differences were sufficient to be distinguished by the avian visual system. Urbanization did not affect female bill color. Interestingly, urban birds had significantly reduced mass-related body condition compared to rural birds. These results show that both male and female urban birds can display enhanced carotenoid-based signals despite being in relatively poor condition. The consequences of this color enhancement are unknown, but it could affect the information content of the signals and the dynamics of the social and mating systems. These results stand in stark contrast to the predominant trend in birds of decreased color in urban areas and highlight the complex and varied potential effects of urbanization on animal communication. Competing Interest Statement The authors have declared no competing interest. Footnotes * Incorporated comments from anonymous reviewers at Ornithological Applications in preparation for submission to a new journal (Wilson Journal of Ornithology).

Common names of species are important for communicating with the general public. In principle, these names should provide an accessible way to engage with and identify species. The common names of species have historically been labile without standard guidelines, even within a language. Currently, there is no systematic assessment of how often common names communicate identifiable and biologically relevant characteristics about species. This is a particular issue in ornithology, where common names are used more often than scientific names for species of birds in written and spoken English, even by professional researchers. To gain a better understanding of the types of terminology used in the common names of bird species and their frequency of use, we used a crowdsourcing approach and recruited 85 professional ornithologists and non-professional participants to classify unique descriptors in the English-language common names of all recognized bird species from around the world. Each species' common name was assigned to one of ten categories associated with aspects of avian biology, ecology, or human culture. Across 10,906 species of birds, 89% have names describing the biology of the species, while the remaining 11% of species have names derived from human cultural references or local non-English languages. Species with common names based on features of avian biology are more likely to be related to each other or be from the same geographic region. The crowdsourced data collection also revealed that many common names contained specialized or historic terminology unknown to many of the data collectors, and this was documented in a glossary and gazetteer alongside the dataset. As the first comprehensive assessment of the state of terminology in English-language common names of birds, the AvianLexiconAtlas database sheds light on historical approaches to nomenclature and provides insight into how the general public currently engages with species through their names.Competing Interest StatementThe authors have declared no competing interest.Footnotes* The title, abstract, introduction, and conclusion have been updated to present the new dataset, glossary, and gazetteer produced during this research as a research database, now called the AvianLexiconAtlas. Researchers from across academic fields can use the resources in this database to study the history of English-language bird names from a quantitative perspective. In the text we clarify the utility of this database as a resource, but have not changed any of the methods, analyses, figures, or supplemental files.* https://github.com/ajshultz/AvianLexiconAtlas

Common Sense Oncology (CSO) prioritises treatments providing meaningful benefits for people with cancer. Here, we describe CSO principles aimed at improving the design, analysis, and reporting of randomised, controlled, phase 3 clinical trials evaluating cancer treatments. These principles include: (1) control treatment should be the best current standard of care; (2) the preferred primary endpoint is overall survival or a validated surrogate; (3) an absolute measure of benefit should be provided, such as the difference between groups in median overall survival times or the proportion of surviving patients at a prespecified time; (4) health-related quality of life should be at least a secondary endpoint; (5) toxicity should be described objectively without subjective language diminishing its importance; (6) trials should be designed to show or rule out clinically meaningful differences in outcomes, rather than a statistically significant difference alone; (7) censoring should be detailed, and sensitivity analyses done to determine its possible effects; (8) experimental treatments known to improve overall survival at later disease stages should be offered and funded for patients progressing in the control group; and (9) reports of trials should include a lay-language summary. We include checklists to guide compliance with these principles. By encouraging adherence, CSO aims to ensure that clinical trials yield results that are scientifically robust and meaningful to patients.

Endometriosis is a risk factor for epithelial ovarian cancer; however, whether this risk extends to all invasive histological subtypes or borderline tumours is not clear. We undertook an international collaborative study to assess the association between endometriosis and histological subtypes of ovarian cancer. Data from 13 ovarian cancer case–control studies, which were part of the Ovarian Cancer Association Consortium, were pooled and logistic regression analyses were undertaken to assess the association between self-reported endometriosis and risk of ovarian cancer. Analyses of invasive cases were done with respect to histological subtypes, grade, and stage, and analyses of borderline tumours by histological subtype. Age, ethnic origin, study site, parity, and duration of oral contraceptive use were included in all analytical models. 13 226 controls and 7911 women with invasive ovarian cancer were included in this analysis. 818 and 738, respectively, reported a history of endometriosis. 1907 women with borderline ovarian cancer were also included in the analysis, and 168 of these reported a history of endometriosis. Self-reported endometriosis was associated with a significantly increased risk of clear-cell (136 [20·2%] of 674 cases vs 818 [6·2%] of 13 226 controls, odds ratio 3·05, 95% CI 2·43–3·84, p<0·0001), low-grade serous (31 [9·2%] of 336 cases, 2·11, 1·39–3·20, p<0·0001), and endometrioid invasive ovarian cancers (169 [13·9%] of 1220 cases, 2·04, 1·67–2·48, p<0·0001). No association was noted between endometriosis and risk of mucinous (31 [6·0%] of 516 cases, 1·02, 0·69–1·50, p=0·93) or high-grade serous invasive ovarian cancer (261 [7·1%] of 3659 cases, 1·13, 0·97–1·32, p=0·13), or borderline tumours of either subtype (serous 103 [9·0%] of 1140 cases, 1·20, 0·95–1·52, p=0·12, and mucinous 65 [8·5%] of 767 cases, 1·12, 0·84–1·48, p=0·45). Clinicians should be aware of the increased risk of specific subtypes of ovarian cancer in women with endometriosis. Future efforts should focus on understanding the mechanisms that might lead to malignant transformation of endometriosis so as to help identify subsets of women at increased risk of ovarian cancer. Ovarian Cancer Research Fund, National Institutes of Health, California Cancer Research Program, California Department of Health Services, Lon V Smith Foundation, European Community's Seventh Framework Programme, German Federal Ministry of Education and Research of Germany, Programme of Clinical Biomedical Research, German Cancer Research Centre, Eve Appeal, Oak Foundation, UK National Institute of Health Research, National Health and Medical Research Council of Australia, US Army Medical Research and Materiel Command, Cancer Council Tasmania, Cancer Foundation of Western Australia, Mermaid 1, Danish Cancer Society, and Roswell Park Alliance Foundation.

BackgroundIntraoperative skills assessment is time-consuming and subjective; an efficient and objective computer vision-based approach for feedback is desired. In this work, we aim to design and validate an interpretable automated method to evaluate technical proficiency using colorectal robotic surgery videos with artificial intelligence.Methods92 curated clips of peritoneal closure were characterized by both board-certified surgeons and a computer vision AI algorithm to compare the measures of surgical skill. For human ratings, six surgeons graded clips according to the GEARS assessment tool; for AI assessment, deep learning computer vision algorithms for surgical tool detection and tracking were developed and implemented.ResultsFor the GEARS category of efficiency, we observe a positive correlation between human expert ratings of technical efficiency and AI-determined total tool movement (r = − 0.72). Additionally, we show that more proficient surgeons perform closure with significantly less tool movement compared to less proficient surgeons (p < 0.001). For the GEARS category of bimanual dexterity, a positive correlation between expert ratings of bimanual dexterity and the AI model’s calculated measure of bimanual movement based on simultaneous tool movement (r = 0.48) was also observed. On average, we also find that higher skill clips have significantly more simultaneous movement in both hands compared to lower skill clips (p < 0.001).ConclusionsIn this study, measurements of technical proficiency extracted from AI algorithms are shown to correlate with those given by expert surgeons. Although we target measurements of efficiency and bimanual dexterity, this work suggests that artificial intelligence through computer vision holds promise for efficiently standardizing grading of surgical technique, which may help in surgical skills training.

Maize genetic diversity has been used to understand the molecular basis of phenotypic variation and to improve agricultural efficiency and sustainability. We crossed 25 diverse inbred maize lines to the B73 reference line, capturing a total of 136,000 recombination events. Variation for recombination frequencies was observed among families, influenced by local (cis) genetic variation. We identified evidence for numerous minor single-locus effects but little two-locus linkage disequilibrium or segregation distortion, which indicated a limited role for genes with large effects and epistatic interactions on fitness. We observed excess residual heterozygosity in pericentromeric regions, which suggested that selection in inbred lines has been less efficient in these regions because of reduced recombination frequency. This implies that pericentromeric regions may contribute disproportionally to heterosis.