Factors affecting virus prevalence in honey bees in the Pacific-Northwest, USA

Global efforts to assess honey bee health show viruses are major stressors that undermine colony performance. Identifying factors that affect virus incidence, such as management practices and landscape context, could aid in slowing virus transmission. Here we surveyed viruses in honey bees from 86 sites in the Pacific Northwest, USA, and tested effects of regional bee density, movement associated with commercial pollination, julian date, and hive management on virus prevalence. We also explored patterns of virus co-occurrence and spatial autocorrelation to identify whether local transmission was a primary driver of pathogen distribution. Our surveys found widespread prevalence of Deformed wing virus (DWV), Sacbrood virus (SBV), and Black queen cell virus (BQCV). BQCV and SBV were most prolific in commercial apiaries, while Chronic bee paralysis virus (CPBV) was more common in hobbyist apiaries than commercial apiaries. DWV was most common in urban landscapes and was best predicted by mite prevalence and julian date, while the incidence of both SBV and BQCV were best predicted by regional apiary density. We did not find evidence of additional spatial autocorrelation for any viruses, although high co-occurrence suggests parallel transmission patterns. Our results support the importance of mite management in slowing virus spread and suggest that greater bee density increases transmission. Our study provides support that viruses are widespread in honey bees and connects known mechanisms of virus transmission to the distribution of pathogens observed across the Pacific Northwest.

Asymmetrical Lightning Fire Season Expansion in the Boreal Forest of Northeast China

All-season warming is assumed to advance snowmelt and delay snow accumulation; additionally, coupled with warming-induced drought stress, all-season warming could extend both the beginning and the end of the fire season. Using fire data updated for 1968–2018, we found an asymmetrical expansion of the lightning fire season in the boreal forest of Northeast China. Lightning fires have not advanced into the early fire season (May–June) but have largely extended into the snowless late fire season (July–September) since the late 1990s (mean end Julian date delayed by 51.1 days for 1998–2018 compared with 1968–1997, p < 0.001). Despite significant warming, the Julian days of snowmelt have changed only slightly, which has prohibited the fire season from advancing into early spring. The expansion of lightning fires into July–September was associated with a warming-induced significant increase in evapotranspiration and a decrease in soil/fuel moisture.

Influence of Precise Products on the Day-Boundary Discontinuities in GNSS Carrier Phase Time Transfer

Global navigation satellite system (GNSS) precise point positioning (PPP) has been widely used for high-precision time and frequency transfer. However, the day-boundary discontinuities at the boundary epochs of adjacent days or batches are the most significant obstacle preventing PPP from continuous time transfer. The day-boundary discontinuities in station estimates and time comparisons are mainly caused by the code-pseudorange noise during the analysis of observation data in daily batches, where the absolute clock offset is determined by the average code measurements. However, some discontinuities with amplitudes even more than 0.15 ns may still appear in station clock estimates and time comparisons, although several methods had been proposed to remove such discontinuities. The residual small amplitude of the day-boundary discontinuities in some PPP station clock estimates and time comparisons through new GNSSs like Galileo seems larger, especially using precise clock products with large discontinuities. To further understand the origin of the day-boundary discontinuities, the influence of GNSS precise products on the day-boundary discontinuities in PPP station clock estimates and time comparisons is investigated in this paper. Ten whole days of Multi-GNSS Experiment (MGEX) from modified Julian date (MJD) 59028 to 59037 are used as the observation data. For a comparative analysis, the station clock estimates are compared with global positioning system (GPS) and Galileo observations through PPP and network solutions, separately. The experimental results show that the daily discontinuities in current combined GPS final and rapid clock products are less than 0.1 ns, and their influence on the origin of day-boundary discontinuities in PPP station clock estimates and time comparison are statistically negligible. However, the daily discontinuities in individual Analysis Centers (ACs) GPS products are more extensive, and their influence on the origin of the day-boundary discontinuities in GPS PPP station clock estimates cannot be ignored. The day-boundary discontinuities demonstrate random walk noise characteristics and deteriorate the station clocks’ long-term frequency stability, especially at an average time of more than one day. Although Galileo clock daily discontinuities are different from those of GPS, their influence on the day-boundary discontinuities in station clock estimates is nearly similar to the GPS PPP. The influence of daily discontinuities of Galileo clocks on PPP time comparison is similar to GPS and is not particularly critical to time comparison. However, combined and weighted MGEX products should be developed or Galileo IPPP should be used for remote comparison of high-stability clocks.

Floral Complexity Traits as Predictors of Plant-Bee Interactions in a Mediterranean Pollination Web

Despite intensive research, predicting pairwise species associations in pollination networks remains a challenge. The morphological fit between flowers and pollinators acts as a filter that allows only some species within the network to interact. Previous studies emphasized the depth of floral tubes as a key shape trait that explains the composition of their animal visitors. Yet, additional shape-related parameters, related to the handling difficulty of flowers, may be important as well. We analyzed a dataset of 2288 visits by six bee genera to 53 flowering species in a Mediterranean plant community. We characterized the plant species by five discrete shape parameters, which potentially affect their accessibility to insects: floral shape class, tube depth, symmetry, corolla segmentation and type of reproductive unit. We then trained a random forest machine-learning model to predict visitor identities, based on the shape traits. The model’s predictor variables also included the Julian date on which each bee visit was observed and the year of observation, as proxies for within- and between-season variation in flower and bee abundance. The model attained a classification accuracy of 0.86 (AUC = 0.96). Using only shape parameters as predictors reduced its classification accuracy to 0.76 (AUC = 0.86), while using only the date and year variables resulted in a prediction accuracy of 0.69 (AUC = 0.80). Among the shape-related variables considered, flower shape class was the most important predictor of visitor identity in a logistic regression model. Our study demonstrates the power of machine-learning algorithms for understanding pollination interactions in a species-rich plant community, based on multiple features of flower morphology.

Activity patterns of insectivorous bats during a seasonal transition period from hibernation to reproduction

Bat activity is influenced by fluctuating environmental variables. It may also be influenced by energetic pressures related to pregnancy, lactation, and emergence following winter inactivity. We evaluated nightly changes in relative bat activity at Royal National Park in response to Julian date, ambient temperature, precipitation, wind speed and moon phase on a nightly scale for six weeks during spring, as insectivorous bats move out of hibernation or frequent, prolonged torpor, and into the maternal season. Interestingly, later Julian date (reflecting seasonal transition) was the sole variable that best predicted total nightly activity. In addition, we opportunistically assessed bat activity in response to a severe storm, considered a Category 1 cyclone, resulting in 96.4mm of rain in one night and wind speeds up to 94km h–1. Only one species of bat, Chalinolobus gouldii, was active during the storm, with activity restricted to the latter part of the evening when precipitation had reduced, indicating rapid resumption of activity following severe weather. The results of this research can be used as an indicator of emergence from winter inactivity and highlight activity patterns of bat species in relation to environmental variables to inform timing of monitoring programs, bat surveys, and targeted research.

Environmental effects in the formation of contemporary groups of Santa Inês lambs

The objective of this work was to study the influence of environmental effects, sex of the lamb, type of birth, year of birth, birth season, julian birth date, and lambing weight on the weights and biometric measurements of Santa Inês lambs from birth to weaning (70 days of age), and to define the best-adjusted statistical model. Data on weights, heights, body lengths, and thoracic perimeters obtained at birth and close to 28 and 70 days of age from 270 lambs were used. Three analyses were carried out considering different models; one with isolated environmental factors, one with animals grouped into contemporaries according to lamb sex, type of birth, year of birth, and season of birth (GC1), and one with animals gathered in groups of contemporaries according to lamb sex, type of birth, year of birth, season of birth, and the julian date of birth (GC2). Environmental factors influenced a large proportion of body weights and measurements from birth to weaning; the main factors were type of birth and year of birth. The GC2 evaluations presented higher determination coefficients and lower values for Akaike information criteria. However, many observations were discarded; 114, 111, and 103 compared with those rejected by the GC1 analysis, which included two, three, and four observations at birth, 28 days of age, and at weaning, respectively. This may lead to the exclusion of animals with genetic potential.

Principal component analysis of pre-weaning growth traits in Santa Inês lambs

The aim of this study was to evaluate the interrelation among growth traits in Santa Inês lambs using principal component analysis. Data on 270 lambs born between 2008 and 2013 were used. The variables studied were weight at birth,adjusted weight from birth to weaning, and average daily gains at 14, 28, 52, and 70 days of age. The wither height, thoracic perimeter, and length at birth and adjusted at 28 and 70 days of age (weaning), were also used. The statistical model comprised the fixed effects of the contemporary group, the linear effect of julian date at birth, and the linear and quadratic effects of lambing weight. The first principal component explained approximately 89% and 85% of the total variation for body weight at different ages and average daily gains, respectively. The second principal component compared heavier animals or those that gained more weight at 14 days with lighter lambs or those that gained less weight at 14 days of age and explains the variation of approximately 5% and 10%, respectively, among the animals. The first principal component accounted for 77%, 91%, and 77% of the variation for weight, thoracic perimeter, wither height, and length at birth, 28 days, and 70 days of age, respectively, and compared large animals or animals with greater body volume. The second component, with values of 16%, 6%, and 14% of the total variation at the three ages, respectively, compared animals with distinct conformation to discriminate animals of different shapes and verified that the weights at the different ages and the thoracic perimeter are important sources of variability among the animals.

Autumn crane migration and climate change in the Carpathian Basin

Weekly counts of roosting cranes were conducted between 2006–2014 in all significant stopover sites of Common Cranes (Grus grus) in Hungary, as an extension of the survey programme performed in Hortobágy National Park since 1994. As a result, I detected consistently asymmetrical migration phenologies in Hortobágy, indicating a superposition of several migration waves. Cranes used nearly 60 roost sites in Hortobágy with the largest ones situated in extensive fishponds. In contrast, cranes migrating in Körös-Maros National Park started to overwinter regularly, increasingly roosting in dry wetlands. In Kiskunság cranes tended to stage for shorter periods than in Körös-Maros, with the largest flocks to be observed in Lake Fehér of Szeged fishponds and additionally in a number of alkali lakes. Cranes observed in the Borsodi Mezőség region are considered satellite flocks staging in Hortobágy, as shown by irregular fluctuations. As a probable result of climatic variability, cranes migrating in Hortobágy have shown the advancement of first arrival dates and exhibited significant, positive relations with local mean monthly temperatures. Additionally, we detected a positive relationship between the Julian date of autumn migration peak and winter mean of the North-Atlantic Oscillation, indicating later autumn arrivals during warmer periods.

A COMBINED SENSITIVITY ANALYSIS OF SEVEN POTENTIAL EVAPOTRANSPIRATION MODELS

Graphical and partial derivatives approaches were used to analyse the sensitivity of variables for the seven potential evapotranspiration models (PET). The models, which have different data requirements and structures, are Hamon, Hargreaves-Samani, Jensen-Haise, Makkink, Turc, Priestley-Taylor, and Penman. Julian date based mean imputation was used to fill the missing data. Tukey's outlier detection method was employed before estimating the PET. Partial derivative approach was conducted by combining the absolute values of the error term through a root mean square and changing to the finite difference form. According to partial derivatives analysis, Hamon is the most sensitive model followed by Penman, Priestley-Taylor, Hargreaves-Samani, Jensen-Haise, Turc, and Makkink models. Temperature is more sensitive meteorological input in Jensen-Haise and Makkink models while solar radiation is more sensitive ones in Turc and Priestley-Taylor models. Wind speed and relative humidity are the most and less sensitive ones in Penman model. Graphical analysis showed that Hamon was the most sensitive PET model with respect to the temperature while Priestley-Taylor was the one with respect to the solar radiation. Turc is the less sensitive PET model with respect to temperature and solar radiation. Overall, graphical method gives clearly comparison for sensitivity of PET. However, it does not indicate its sensitivity values compared to partial derivative approach.