Hoverflies of the Timon-David collection (Diptera, Syrphidae)

Recent declines in the health of honey bee colonies used for crop pollination pose a considerable threat to global food security. Foraging by honey bee workers represents the primary route of exposure to a plethora of toxins and pathogens known to affect bee health, but it remains unclear how foraging preferences impact colony-level patterns of stressor exposure. Resolving this knowledge gap is crucial for enhancing the health of honey bees and the agricultural systems that rely on them for pollination. To address this, we carried out a national-scale experiment encompassing 456 Canadian honey bee colonies to first characterize pollen foraging preferences in relation to major crops, then explore how foraging behaviour influences patterns of stressor exposure. We used a metagenetic approach to quantify honey bee dietary breadth and found that bees display distinct foraging preferences that vary substantially relative to crop type and proximity, and the breadth of foraging interactions can be used to predict the abundance and diversity of stressors a colony is exposed to. Foraging on diverse plant communities was associated with increased exposure to pathogens, while the opposite was associated with increased exposure to xenobiotics. Our work provides the first large-scale empirical evidence that pollen foraging behaviour plays an influential role in determining exposure to dichotomous stressor syndromes in honey bees.Significance StatementInsect-mediated pollination is an important ecological process that is crucial for food production. Managed honey bee colonies are one of the most important insect pollinators, but their health has been under threat from a variety of stressors. Bee workers are primarily exposed to stressors while foraging and understanding how bee foraging preferences are related to exposure risk could provide pivotal information to improve management efforts. Here, we studied honey bee foraging preferences in relation to prominent Canadian crops and across a gradient of modified environments. We found that honey bees show distinct, measurable foraging preferences and that dietary diversity is a strong predictor of the stressors that colonies are exposed to.

Honey bees are important insect pollinators that provide critical pollination services to fruit and nut crops in the US. They face challenges likely due to pressures associated with agricultural intensification related habitat loss. To better understand this, pollen preferences of foraging bees and the nutritional profile of pollen brought into hives by foraging bees in crop fields and nut orchards can provide valuable information. We trained bees to forage on bee-collected pollen from hives placed for pollination services in almond orchards, sunflower fields, or mixed species from inter-row plantings. Using bees trained to a certain kind of hive pollen, we applied a binary scoring system, to test preferences of these preconditioned foragers. We also performed metabolomic analyses of the hive pollen used for training and testing to elucidate their nutritional content. Irrespective of preconditioning, bees collected all the available choice pollen types, predominantly choosing hive-collected mixed species pollen (MSP), followed by almond orchard pollen. The hive-collected MSP was chemically diverse, richest in cholesterol, vitamins, and phytochemicals quercetin, kaempferol, coumarin, and quinine, but was not consistently high for essential amino acids and polyunsaturated fatty acids. Although diversity in chemical profiles may not directly relate to plant species diversity, our results suggest that foragers collect a variety of pollen types when available reiterating the importance of diverse floral resources.

On social media, the user generated contents, e.g., Articles and images, can be assigned with multiple labels. In this paper, we focus on the problem of performing multi-label classification on social media data, where the user generated contents are associated with multiple labels. Multi-label learning studies the problem where each object is represented by a single instance and associated with a set of labels. Current multi-label learning algorithms mainly exploit label correlations globally, by assuming that the label correlations are shared by all the examples. In real applications, however, different examples may share different label correlations. In this paper, we propose a Local Pair wise Label Correlation (LPLC) method for social media content categorization. We try to exploit the strongest local pair wise label correlations between the ground truth labels for each training example by computing the maximum conditional probabilities. If two labels have strong correlation, there will be a larger conditional probability of one label given by another. In the training stage, we find the most correlated labels for ground truth labels of each training example. In the test stage, we make prediction through maximizing the posterior probability, which is estimated with the distribution of each label in the k nearest neighbors and their most correlated local pair wise label correlations. We compare our method with six well-established multi-label learning algorithms over nine data sets from different social media data domains and scales. Comparison results with the state-of-the-arts approaches manifest competitive performances of our method.

Levins (1969) and Hanski (1982a,b) presented dynamic models that describe the fraction of population sites occupied by species. Levins' model predicts a unimodal distribution of species at sites, whereas Hanski's model yields a bimodal distribution: a mode of widespread "core" species, and a mode of rare "satellite" species. The existence of this dichotomy is the core-satellite hypothesis. Hanski's model also predicts a positive correlation between the fraction of sites occupied by a species (distribution) and the average population size within occupied sites (abundance). Levins' model generates no such correlation between distribution and abundance. We find that a large set of quadrat data on tallgrass prairie plants matches some of the predictions of Hanski's model: for plants of seven different soil series, distribution and abundance are positively correlated, and the distribution of species at sites is bimodal. The bimodality is obscured if distributions from the seven soil series are aggregated. Two earlier hypotheses have been proposed to explain the core-satellite dichotomy. Williams (1964) showed that the degree of bimodality in species distributions depends on the number of quadrats examined: specifically, the smaller the sample, the larger the size of the core mode, and this is true even for randomly assembled communities of species. For our data, the size of the core mode correlates negatively with sample size, as Williams predicted. However, a computer simulation showed that the observed core modes were significantly larger than would be expected by chance. Thus, Williams' sampling hypothesis does not entirely account for the bimodal distribution of prairie plant species. Raunkiaer (1934, see "law of distribution of frequencies") argued that the core mode represented those species best adapted to a particular habitat. He also noted that when quadrats of dissimilar habitat were combined, the bimodality disappeared. The distributions of tallgrass prairie plants, however, do not conform to Raunkiaer's explanation: core species tend to be widespread across all soil series. If Raunkiaer's hypothesis were correct, each species would be widespread on only a few soil series, and each soil series would support a different assemblage of core species. Although our analyses confirm the existence of the core-satellite dichotomy, they do not provide an explicit test of Hanski's model. The essence of the models by Hanski and Levins is that the distribution of each species is dynamic and fluctuates owing to stochastic variation in immigration and local extinction. The strongest test of Hanski's model would be to demonstrate that the distribution of each species varied through time and that the composition of the core and satellite modes was not constant. Long-term data about the distribution of species in a community are needed for such comparisons.

Seasonality and season of birth of modern and late Neolithic sheep from south-eastern France using tooth enamel δ 18O analysis

Geochemical studies of geological samples require the precise determination of their major and trace element contents and, when measured, of their isotopic compositions. It is now commonly accepted that the accuracy and precision of geochemical analyses are best estimated by the concomitant analysis of international reference materials run as unknown samples. Although the composition of a wide selection of basalts is relatively well constrained, this is far from being the case for sedimentary materials. We present here a comprehensive set of major and trace element data as well as Nd, Hf, Sr and Pb isotopic compositions for thirteen commonly used international reference materials – eight magmatic rocks (BHVO‐2, BR, BE‐N, BR 24, AGV‐1, BIR‐1, UB‐N, RGM‐1) and five sediments (JLk‐1, JSd‐1, JSd‐2, JSd‐3, LKSD‐1). We determined the concentrations of over forty elements in the magmatic rocks together with Sr, Nd, Hf and Pb isotopic compositions. Our trace element results were both accurate (difference ≤ 3%) and precise (reproducibility at 1s ≤ 3%) and the isotopic results were very similar to other published values. In contrast, we observed a significant chemical and isotopic variability in the sedimentary materials, which we attribute to mineral heterogeneities in the powders. Despite the limitation imposed by this heterogeneity, our work presents a complete set of data determined with a precision not yet achieved in the literature for sedimentary material. We also provide the first Nd, Hf and Pb isotopic measurements for the five sediments, which are commonly used by the geochemical community. Our study of both basalt and sediment reference materials represents a comprehensive and self‐consistent set of geochemical data and can therefore be considered as a reference database for the community.

The genus Clausina is one of the important medicinal components of Flora of Assam. In Assam, only three species of Clausina are reported i.e., C. lansium, C. excavata and C. heptaphylla, and all of these species are restricted only a few pockets of forest of Assam. Thus, it is important to conserve this species using appropriate research. The present study focuses on the distribution, population size estimation and to find out the predicted distributional locations of genus Clausina by Maximum Entropy (Maxent) modeling for the purposes of conservation. The model is based on geospatial data sets of past and current distribution of Clausina species, climatic data and topographic attributes. The Maxent model was used to identify the current potential habitat of Clausina species and to predict the suitable habitat of the genus in the future. In our present investigation, the population size for all the species of Clausina was found to be very poor i.e., mean density, frequency of occurrence and abundance in relation to other associated species was 0.165, 12.593 and 1.576 for C. lansium; 0.544, 26.296 and 2.230 for C. excavata and 0.381, 34.44 and 11.166 for C. heptaphylla. So the present investigation is to find out the population status and distributional locations of genus Clausina for reintroduction in Assam.

Sample digestion is a critical stage in the process of chemical analysis of geological materials by ICP‐MS. We present a new HF/HNO3 procedure to dissolve silicate rock samples using a high pressure asher system. The formation of insoluble AlF3 was the major obstacle in achieving full recoveries. This was overcome by setting an appropriate digestion temperature and adding Mg to the samples before digestion. Sodium peroxide sintering was also investigated and the inclusion of a heating step to the alkaline sinter solution improved the recoveries of thirteen elements other than the lanthanides. The results of these procedures were compared with data sets generated by common acid decomposition techniques. Forty‐one trace elements were determined using an ICP‐QMS equipped with a collision cell. Under optimum conditions of gas flow and kinetic energy discrimination, polyatomic interferences were eliminated or attenuated. The measurement bias obtained for eight reference materials (BCR‐2, BHVO‐2, BIR‐1, BRP‐1, OU‐6, GSP‐2, GSR‐1 and RGM‐1) and intermediate precision (RSD) were generally better than ± 5%. The expanded measurement uncertainties estimated for two certified reference materials were mostly between 7 and 15%. New data sets for the reference materials are provided, including constituents with previously unavailable values and also for the USGS candidate reference material G‐3.

Location‐only and use‐availability data: analysis methods converge

Quality of electron probe X-ray microanalysis determinations obtained from laboratory reference materials of the coppery alloys and basaltic glasses

The total content of arsenic and of its inorganic (As(III) and As(V)) and organic (monomethylarsonic acid, MMAA, and dimethylarsinic acid, DMAA) species were determined in a set of 21 alcoholic and alcohol-free beer samples using the technique of Hydride Generation Atomic Absorption Spectrometry. For total arsenic analysis, beer samples were dried and then microwave digested with nitric acid in polytetrafluoroethylene containers. For the speciation analysis, beers were previously subjected to ion exchange chromatography to elute the mentioned inorganic and organic arsenical species. Both microwave digestion and chromatographic separation methods were validated from certified reference materials and prepared standard solutions, respectively. The results obtained are presented in terms of the distribution and occurrence of arsenical species in the samples. The As levels of the beer samples were in the range of 1.5-12.4μg/l. The influence of the production process for the alcoholfree beers in the speciation of arsenic is discussed. In alcoholic beers MMAA was the most abundant species, and for non-alcoholic beers inorganic As(III) was similar to the organic species. An estimated intake of total As of 0.47μg/person/day and 11.4μg/person/day was obtained for average consumers and for heavy drinkers, respectively.

Ice cover on the Great Lakes plays an important role in regional climate, supports tourism and recreation, and provides ecological habitat. As the climate warms, ice cover in the Great Lakes is expected to decline, which in turn will create more lake effect precipitation, reduce ice cover for recreation, and alter habitat for aquatic species. While it is important to understand the historical ice patterns to better understand past distributions of aquatic species and improve the accuracy of forecasts for future ice cover on the lakes, Great Lakes ice cover data prior to 1973 is scarce, due to the limited routine satellite observations. We used weather station data around the Great Lakes to compile daily air temperature, calculate cumulative freezing degree-days and net melting degree-days from 1897-2023, and develop raster layers estimating ice duration and variability spatially during the historical period from 1897-1960.

The date palm (Phoenix dactylifera L.) is an iconic crop of hot and arid regions of North Africa, the Middle East and up to northwestern India. It is a member of the genus Phoenix that constitutes a monophyletic group within the Coryphoideae subfamily, in the palm family. The genus Phoenix is composed of around 14 species whose native distribution is across the South of Europe, throughout Africa, the Arabian Peninsula and South Asia. Species boundaries and relationships are poorly understood owing to the morphological similarities between the species and their ability to hybridise. In this chapter, we review the past and present distribution of Phoenix species and aspects of their reproductive biology and genome dynamics. We then summarise the current state of knowledge of the taxonomy and phylogeny of Phoenix and we outline the main challenges that have so far limited our understanding of the genus’ systematics and evolution. We conclude with a roadmap to address these challenges by combining the extraordinary resource of specimens stored in herbarium collections and the power of high throughput DNA sequencing technologies.KeywordsDate palmEvolution Phoenix PhylogenyTaxonomy

Leaf macrofossils of Ilex protocornuta sp. nov. (Aquifoliaceae) from the Late Miocene of East China: Implications for palaeoecology

The aim of this work was to predict the worldwide distribution of two pest species-Ceratitis capitata (Wiedemann), the Mediterranean fruit fly, and Lymantria dispar (L.), the gypsy moth-based on climatic factors. The distribution patterns of insect pests have most often been investigated using classical statistical models or ecoclimatic assessment models such as CLIMEX. In this study, we used an artificial neural network, the multilayer perceptron, trained using the backpropagation algorithm, to model the distribution of each species. The data matrix used to model the distribution of each species was divided into three data sets to (1) develop and train the model, (2) validate the model and prevent over-fitting, and (3) test each model on novel data. The percentage of correct predictions of the global distribution of each species was high for Mediterranean fruit fly for the three data sets giving 95.8, 81.5, and 80.6% correct predictions, respectively, and 96.8, 84.3, and 81.5 for the gypsy moth. Kappa statistics used to test the level of significance of the results were highly significant (in all cases P < 0.0001). A sensitivity analysis applied to each model based on the calculation of the derivatives of each of a large number of input variables showed that the variables that contributed most to explaining the distribution of C. capitata were annual average temperature and annual potential evapotranspiration. For L. dispar, the average minimum temperature and minimum daylength range were the main explanatory variables. The ANN models and methods developed in this study offer powerful additional predictive approaches in invasive species research.