Using Long-Term Volunteer Records to Examine Dormouse (Muscardinusavellanarius) Nestbox Selection.

Within ecology, there are unanswered questions about species-habitat interactions, which could potentially be resolved by a pragmatic analysis of a long-term volunteer-collected dataset. Here, we analysed 18 years of volunteer-collected data from a UK dormouse nestbox monitoring programme to determine the influence of habitat variables on nestbox choice by common dormice (Muscardinusavellanarius). We measured a range of habitat variables in a coppiced woodland in Gloucestershire, UK, and analysed these in relation to dormouse nestbox occupancy records (by dormice, other small mammals, and birds) collected by volunteers. While some characteristics of the woodland had changed over 18 years, simple transformation of the data and interpretation of the results indicated that the dataset was informative. Using stepwise regressions, multiple environmental and ecological factors were found to determine nestbox selection. Distance from the edge of the wood was the most influential (this did not change over 18 years), with boxes in the woodland interior being selected preferentially. There was a significant negative relationship with the presence of ferns (indicative of damp shady conditions). The presence of oak (a long-lived species), and the clumped structural complexity of the canopy were also important factors in the final model. There was no evidence of competition between dormice and birds or other mammals. The results provide greater understanding of artificial dormouse nest-site requirements and indicate that, in terms of habitat selection, long-term volunteer-collected datasets contribute usefully to understanding the requirements of species with an important conservation status

Regression with Empirical Variable Selection: Description of a New Method and Application to Ecological Datasets

Despite recent papers on problems associated with full-model and stepwise regression, their use is still common throughout ecological and environmental disciplines. Alternative approaches, including generating multiple models and comparing them post-hoc using techniques such as Akaike's Information Criterion (AIC), are becoming more popular. However, these are problematic when there are numerous independent variables and interpretation is often difficult when competing models contain many different variables and combinations of variables. Here, we detail a new approach, REVS (Regression with Empirical Variable Selection), which uses all-subsets regression to quantify empirical support for every independent variable. A series of models is created; the first containing the variable with most empirical support, the second containing the first variable and the next most-supported, and so on. The comparatively small number of resultant models (n = the number of predictor variables) means that post-hoc comparison is comparatively quick and easy. When tested on a real dataset – habitat and offspring quality in the great tit (Parus major) – the optimal REVS model explained more variance (higher R2), was more parsimonious (lower AIC), and had greater significance (lower P values), than full, stepwise or all-subsets models; it also had higher predictive accuracy based on split-sample validation. Testing REVS on ten further datasets suggested that this is typical, with R2 values being higher than full or stepwise models (mean improvement = 31% and 7%, respectively). Results are ecologically intuitive as even when there are several competing models, they share a set of “core” variables and differ only in presence/absence of one or two additional variables. We conclude that REVS is useful for analysing complex datasets, including those in ecology and environmental disciplines

Frustration wave order in iron(II) oxide spinels

Frustrated magnetic materials provide a great laboratory to study the interplay between classical order and quantum fluctuations. The authors study the frustrated magnetic ground states of two Fe spinel oxides showing that the frustration is a fluctuating characteristic that manifests itself as a “frustration wave

Eu-Social Science: The Role of Internet Social Networks in the Collection of Bee Biodiversity Data

Background Monitoring change in species diversity, community composition and phenology is vital to assess the impacts of anthropogenic activity and natural change. However, monitoring by trained scientists is time consuming and expensive. Methodology/Principal Findings Using social networks, we assess whether it is possible to obtain accurate data on bee distribution across the UK from photographic records submitted by untrained members of the public, and if these data are in sufficient quantity for ecological studies. We used Flickr and Facebook as social networks and Flickr for the storage of photographs and associated data on date, time and location linked to them. Within six weeks, the number of pictures uploaded to the Flickr BeeID group exceeded 200. Geographic coverage was excellent; the distribution of photographs covered most of the British Isles, from the south coast of England to the Highlands of Scotland. However, only 59% of photographs were properly uploaded according to instructions, with vital information such as ‘tags’ or location information missing from the remainder. Nevertheless, this incorporation of information on location of photographs was much higher than general usage on Flickr (∼13%), indicating the need for dedicated projects to collect spatial ecological data. Furthermore, we found identification of bees is not possible from all photographs, especially those excluding lower abdomen detail. This suggests that giving details regarding specific anatomical features to include on photographs would be useful to maximise success. Conclusions/Significance The study demonstrates the power of social network sites to generate public interest in a project and details the advantages of using a group within an existing popular social network site over a traditional (specifically-designed) web-based or paper-based submission process. Some advantages include the ability to network with other individuals or groups with similar interests, and thus increasing the size of the dataset and participation in the project

Microbiome assembly of avian eggshells and their potential as transgenerational carriers of maternal microbiota

The microbiome is essential for development, health and homeostasis throughout an animal's life. Yet, the origins and transmission processes governing animal microbiomes remain elusive for non-human vertebrates, oviparous vertebrates in particular. Eggs may function as transgenerational carriers of the maternal microbiome, warranting characterisation of egg microbiome assembly. Here, we investigated maternal and environmental contributions to avian eggshell microbiota in wild passerine birds: woodlark Lullula arborea and skylark Alauda arvensis. Using 16S rRNA gene sequencing, we demonstrated in both lark species, at the population and within-nest levels, that bacterial communities of freshly laid eggs were distinct from the female cloacal microbiome. Instead, soil-borne bacteria appeared to thrive on freshly laid eggs, and eggshell microbiota composition strongly resembled maternal skin, body feather and nest material communities, sources in direct contact with laid eggs. Finally, phylogenetic structure analysis and microbial source tracking underscored species sorting from directly contacting sources rather than in vivo-transferred symbionts. The female-egg-nest system allowed an integrative assessment of avian egg microbiome assembly, revealing mixed modes of symbiont acquisition not previously documented for vertebrate eggs. Our findings illuminated egg microbiome origins, which suggested a limited potential of eggshells for transgenerational transmission, encouraging further investigation of eggshell microbiome functions in vertebrates

Mode of Effective Connectivity within a Putative Neural Network Differentiates Moral Cognitions Related to Care and Justice Ethics

BACKGROUND: Moral sensitivity refers to the interpretive awareness of moral conflict and can be justice or care oriented. Justice ethics is associated primarily with human rights and the application of moral rules, whereas care ethics is related to human needs and a situational approach involving social emotions. Among the core brain regions involved in moral issue processing are: medial prefrontal cortex, anterior (ACC) and posterior (PCC) cingulate cortex, posterior superior temporal sulcus (pSTS), insula and amygdala. This study sought to inform the long standing debate of whether care and justice moral ethics represent one or two different forms of cognition. METHODOLOGY/PRINCIPAL FINDINGS: Model-free and model-based connectivity analysis were used to identify functional neural networks underlying care and justice ethics for a moral sensitivity task. In addition to modest differences in patterns of associated neural activity, distinct modes of functional and effective connectivity were observed for moral sensitivity for care and justice issues that were modulated by individual variation in moral ability. CONCLUSIONS/SIGNIFICANCE: These results support a neurobiological differentiation between care and justice ethics and suggest that human moral behavior reflects the outcome of integrating opposing rule-based, self-other perspectives, and emotional responses

Habitat correlates of Eurasian woodcock Scolopax rusticola abundance in a declining resident population

In Europe, woodland bird populations have been declining since at least the 1970s, and in Britain, around one third of woodland bird species have undergone declines over this period. Habitat change has been highlighted as a possible cause, but for some species clear evidence of this is lacking owing to an incomplete knowledge of the species’ habitat requirements. Here, we analyse national data to explain the variation in abundance of a declining woodland bird, the Eurasian Woodcock. A nationwide, species-specific survey of breeding Woodcock was conducted in 2003 and 2013 at 807 and 823 randomly selected 1-km squares respectively. The counts were compared with a range of landscape-scale habitat variables as well as local habitat measures recorded by surveyors, using generalised linear mixed models. Habitat variables were measured at a variety of spatial scales using ring buffers, although our analyses show that strong collinearity between scales hinders interpretation. At large landscape scales, breeding Woodcock abundance was correlated with total woodland area and the way this interacted with woodland type. Woodcock were more abundant in woods containing a more heterogeneous mix of woodland habitat types and in woods further from urban areas. On a smaller spatial scale, Woodcock were less likely to be found at sites dominated by beech Fagus spp. and more likely to occur in woods containing birch Betula spp. The Woodcock’s association with large, heterogeneous woods and the apparent attractiveness of certain woodland types present the most relevant topics for future research into the role of habitat change in long-term declines

Measurement of the Positive Muon Anomalous Magnetic Moment to 0.20 ppm

We present a new measurement of the positive muon magnetic anomaly, a_{μ}≡(g_{μ}-2)/2, from the Fermilab Muon g-2 Experiment using data collected in 2019 and 2020. We have analyzed more than 4 times the number of positrons from muon decay than in our previous result from 2018 data. The systematic error is reduced by more than a factor of 2 due to better running conditions, a more stable beam, and improved knowledge of the magnetic field weighted by the muon distribution, ω[over ˜]_{p}^{'}, and of the anomalous precession frequency corrected for beam dynamics effects, ω_{a}. From the ratio ω_{a}/ω[over ˜]_{p}^{'}, together with precisely determined external parameters, we determine a_{μ}=116 592 057(25)×10^{-11} (0.21 ppm). Combining this result with our previous result from the 2018 data, we obtain a_{μ}(FNAL)=116 592 055(24)×10^{-11} (0.20 ppm). The new experimental world average is a_{μ}(exp)=116 592 059(22)×10^{-11} (0.19 ppm), which represents a factor of 2 improvement in precision