Residential Agricultural Pesticide Exposures and Risks of Spontaneous Preterm Birth

Pesticides exposures are aspects of the human exposome that have not been sufficiently studied for their contribution to risk for preterm birth. We investigated risks of spontaneous preterm birth from potential residential exposures to 543 individual chemicals and 69 physicochemical groupings that were applied in the San Joaquin Valley of California during the study period, 1998–2011

Similarity percentage analysis of the relative pathogen composition between consecutive sampling events.

<p>The greatest differences in honey bee colony pathogen composition was explained by changes in abundance of a different pathogen between consecutive sampling events. The percent difference attributed to each pathogen between consecutive sampling events was calculated using a similarity percentage (SIMPER) analysis of a Bray-Curtis dissimilarity matrix. The cumulative difference of the top three pathogens in comparisons between consecutive sampling events is reported as a percentage. The difference in percent mite infestation was calculated in comparisons between consecutive sampling events. When the change in mean mite infestation was greatest, DWV accounted for the most difference in honey bee colony pathogen composition.</p

Honey bee (<i>Apis mellifera</i>) colony health and pathogen composition in migratory beekeeping operations involved in California almond pollination

<div><p>Honey bees are important pollinators of agricultural crops. Pathogens and other factors have been implicated in high annual losses of honey bee colonies in North America and some European countries. To further investigate the relationship between multiple factors, including pathogen prevalence and abundance and colony health, we monitored commercially managed migratory honey bee colonies involved in California almond pollination in 2014. At each sampling event, honey bee colony health was assessed, using colony population size as a proxy for health, and the prevalence and abundance of seven honey bee pathogens was evaluated using PCR and quantitative PCR, respectively. In this sample cohort, pathogen prevalence and abundance did not correlate with colony health, but did correlate with the date of sampling. In general, pathogen prevalence (i.e., the number of specific pathogens harbored within a colony) was lower early in the year (January—March) and was greater in the summer, with peak prevalence occurring in June. Pathogen abundance in individual honey bee colonies varied throughout the year and was strongly associated with the sampling date, and was influenced by beekeeping operation, colony health, and mite infestation level. Together, data from this and other observational cohort studies that monitor individual honey bee colonies and precisely account for sampling date (i.e., day of year) will lead to a better understanding of the influence of pathogens on colony mortality and the effects of other factors on these associations.</p></div

Commercially managed honey bee colonies were longitudinally monitored before, during, and after the 2014 almond pollination season.

<p>Honey bee colonies from two Minnesota-based commercial beekeeping operations that transport their colonies to California for almond pollination were monitored from January 2014 to January 2015. At each sampling event, colony health, using colony population size as a proxy for health, was monitored and samples of live honey bees were obtained. In a subset of samples described in the table, PCR was utilized to assess pathogen prevalence and qPCR was utilized to determine pathogen abundance.</p

The probability of honey bee colonies accumulating <i>Varroa destructor</i> mite densities above the threshold recommended for treatment is greater later in the year.

<p>Honey bee colonies have a high probability of accumulating levels of <i>Varroa destructor</i> mites that surpass the threshold for recommended treatment (i.e., 3%) by the end of the pathogen monitoring period in both beekeeping operations. Results from a generalized linear mixed effects model with a binomial family distribution indicate that by the end of the sampling period, colonies had a 99.0% chance of crossing the recommended treatment threshold. Since mite count data differed between beekeeping operations, binomial regression results were plotted independently. Mite count data obtained from honey bee samples collected from colonies rated dead (black diamonds), weak (blue triangles), average (green circle), and strong (yellow square) are shown with their unique colony identifier numbers, with the first digit identifying the pallet and the second digit identifying individual colony. A best-fit line (blue) with odds-estimates surrounded by upper and lower standard error estimates (gray) depicts the odds of a colony surpassing the recommended treatment threshold (y-axis), which increases with the day of the year (x-axis).</p

Relative pathogen composition of honey bee colonies visualized by sample event.

<p>Pathogen compositions of honey bee colonies form unique and defined clusters according to the month they were sampled (i.e., January—dark brown, March—light brown, June—yellow, August—orange, or September—red). The position of each point indicates the pathogen composition of each sample relative to all other samples (i.e. samples with more similar pathogen compositions are closer), calculated using a Bray-Curtis dissimilarity and plotted on a non-metric multidimensional scaling (NMDS) plot with an associated stress value of (0.197); the results from a permutational analysis of variance (PERMANOVA) in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0182814#pone.0182814.t001" target="_blank">Table 1</a> indicated that the sampling event explained the most amount of variance in the pathogen composition.</p

Analysis of variance of pathogen composition.

<p>The relative pathogen composition of honey bee samples, based on all pathogen abundances, is primarily explained by the date the sample was obtained (or “day of year”) and level of <i>Varroa</i> mite infestation. Results from a permutational analysis of variance (PERMANOVA) indicate that the day of year (R² = 0.61), followed by the percent mite infestation (R² = 0.01) explained the most variation in the relative pathogen composition of all honey bee samples; factors contributing significantly to the relative pathogen composition are indicated by <i>p</i>-values ≤0.05 in bold.</p

DWV abundance increased from January to September 2014.

<p>DWV abundance was greatest in honey bee samples with higher mite infestation levels, obtained later in the year (June through September). A. The natural log transformed values of the relative DWV RNA abundance as determined by qPCR (y-axis) analysis of honey bee samples obtained from colonies with varying colony health ratings (i.e., dead (black diamonds), weak (blue triangles), average (green circle), and strong (yellow square)); the date of sample collection (i.e. day of year, x-axis). The best-fit line (blue), surrounded by upper and lower standard error estimates (gray), indicates that DWV abundance increased at an exponential rate across the longitudinal monitoring period. Unique colony identifier numbers, with the first digit identifying the pallet and the second digit identifying individual colony, label each point on the graph and illustrate changes in the pathogen abundance individual colonies throughout the sampling period. B. The residual abundance of the natural log transformed values of DWV RNA abundance (y-axis) increases as the level of mite infestation (x-axis) increases.</p