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Soil nutrient distribution is heterogeneous in space and time, potentially altering nutrient acquisition by trees and microorganisms. Ecologists have distinguished “hot spots” (HSs) as areas with enhanced and sustained rates of nutrient fluxes relative to the surrounding soil matrix. We evaluated the spatial and temporal patterns in nutrient flux HSs in two mixed-conifer forest soils by repeatedly sampling the soil solution at the same spatial locations (horizontally and vertically) over multiple seasons and years using ion exchange resins incubated in situ. The climate of these forests is Mediterranean, with intense fall rains occurring following summers with little precipitation, and highly variable winter snowfall. Hot spots formed most often for NO₃⁻and Na⁺. Although nutrient HSs often occurred in the same spatial location multiple times, HSs persisted more often for PO₄³⁻NH₄⁺, and NO₃⁻, and were more transient for Ca²⁺, Mg²⁺, and Na⁺. Sampling year (annual precipitation ranged from 558 to 1223 mm) impacted the occurrence of HSs for most nutrients, but season was only significant for PO₄³⁻, NH₄⁺, NO₃⁻, and Na⁺, with HSs forming more often after fall rains than after spring snowmelt. The frequency of HSs significantly decreased with soil depth for all nutrients, forming most commonly immediately below the surficial organic horizon. Although HSs accounted for less than 17% of the sampling volume, they were responsible for 56–88% of PO₄³⁻, NH₄⁺, and NO₃⁻resin fluxes. Our results suggest that macronutrient HSs have a disproportional contribution to soil biogeochemical structure, with implications for vegetation nutrient acquisition strategies and biogeochemical models.

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Novel tungstenooxaziridine complex promotes the oxyamination of substituted alkenes with high stereoselectivity and stereospecificity. Mechanistic studies provide evidence for a highly selectivesyn-difunctionalization pathway.

 

Bombus vosnesenskii Radowszkowski, 1862 is one of three bumble bee species commercially available for pollination services in North America; however, little is documented about B. vosnesenskii colony life cycle or the establishment of ex situ rearing, mating, and overwintering practices. In this study, we documented nest success, colony size, and gyne production; recorded the duration of mating events; assessed overwintering survival of mated gynes; and evaluated second-generation nest success for colonies established from low- and high-elevation wild-caught B. vosnesenskii gynes. Of the 125 gynes installed, 62.4% produced brood cells (nest initiation) and 43.2% had at least 1 worker eclose (nest establishment). High-elevation B. vosnesenskii gynes had significantly higher nest initiation and establishment success than low-elevation gynes. However, low-elevation colonies were significantly larger with queens producing more gynes on average. Mating was recorded for 200 low-elevation and 37 high-elevation gynes, resulting in a mean duration of 62 and 51 min, respectively. Mated gynes were then placed into cold storage for 54 days to simulate overwintering, which resulted in 59.1% of low-elevation gynes surviving and 91.9% of high-elevation gynes surviving. For second-generation low-elevation gynes, 26.4% initiated nesting and 14.3% established nesting. Second-generation high-elevation gynes did not initiate nesting despite CO2 narcosis treatments. Overall, these results increase our understanding of B. vosnesenskii nesting, mating, and overwintering biology from 2 elevations. Furthermore, this study provides information on successful husbandry practices that can be used by researchers and conservationists to address knowledge gaps and enhance the captive rearing of bumble bees.

 

Dynein is the primary minus-end-directed microtubule motor protein. To achieve activation, dynein binds to the dynactin complex and an adaptor to form the "activated dynein complex." The protein Lis1 aids activation by binding to dynein and promoting its association with dynactin and the adaptor. Ndel1 and its paralog Nde1 are dynein- and Lis1-binding proteins that help control dynein localization within the cell. Cell-based assays suggest that Ndel1-Nde1 also work with Lis1 to promote dynein activation, although the underlying mechanism is unclear. Using purified proteins and quantitative binding assays, here we found that the C-terminal region of Ndel1 contributes to dynein binding and negatively regulates binding to Lis1. Using single-molecule imaging and protein biochemistry, we observed that Ndel1 inhibits dynein activation in two distinct ways. First, Ndel1 disfavors the formation of the activated dynein complex. We found that phosphomimetic mutations in the C-terminal domain of Ndel1 increase its ability to inhibit dynein-dynactin-adaptor complex formation. Second, we observed that Ndel1 interacts with dynein and Lis1 simultaneously and sequesters Lis1 away from its dynein-binding site. In doing this, Ndel1 prevents Lis1-mediated dynein activation. Together, our work suggests that in vitro, Ndel1 is a negative regulator of dynein activation, which contrasts with cellular studies where Ndel1 promotes dynein activity. To reconcile our findings with previous work, we posit that Ndel1 functions to scaffold dynein and Lis1 together while keeping dynein in an inhibited state. We speculate that Ndel1 release can be triggered in cellular settings to allow for timed dynein activation. 

Interactions between water and carbon dynamics underlie drought-related tree mortality. While whole-tree water relations have been shown to play a key role in the response to and recovery from drought, the role of nonstructural carbohydrates (NSC) and how their storage and allocation changes surrounding drought events deserves further attention and is critical for understanding tree survival. Here, we quantified in situ NSC responses of temperate forest trees to the 2016 drought in the northeastern United States. Sugar and starch concentrations were measured in the stemwood of five tree species from 2014 to 2019, which allowed us to monitor NSCs in relation to climatic conditions before, during, and after the natural drought. We found that immediately following the drought, measured stemwood NSC concentrations decreased. However, NSC concentrations rebounded quickly within three years. Notably, trees allocated proportionally more to starch than to sugars following the 2016 drought. In winter 2017, starch comprised 45% of total stemwood stores, whereas starch made up only 1–2% in other years. Further, we modeled and assessed the climatic drivers of total NSC concentrations in the stem. Variation in total NSC concentrations was significantly predicted by the previous year’s temperature, precipitation, and standardized precipitation-evapotranspiration index (SPEI), with stemwood concentrations decreasing following hotter, drier periods and increasing following cooler, wetter periods. Overall, our work provides insight into the climatic drivers of NSC storage and highlights the important role that a tree’s carbon economy may play in its response and recovery to environmental stress.

 

Abstract Background West Nile virus (WNV) is the leading cause of mosquito-borne illness in the continental USA. WNV occurrence has high spatiotemporal variation, and current approaches to targeted control of the virus are limited, making forecasting a public health priority. However, little research has been done to compare strengths and weaknesses of WNV disease forecasting approaches on the national scale. We used forecasts submitted to the 2020 WNV Forecasting Challenge, an open challenge organized by the Centers for Disease Control and Prevention, to assess the status of WNV neuroinvasive disease (WNND) prediction and identify avenues for improvement. Methods We performed a multi-model comparative assessment of probabilistic forecasts submitted by 15 teams for annual WNND cases in US counties for 2020 and assessed forecast accuracy, calibration, and discriminatory power. In the evaluation, we included forecasts produced by comparison models of varying complexity as benchmarks of forecast performance. We also used regression analysis to identify modeling approaches and contextual factors that were associated with forecast skill. Results Simple models based on historical WNND cases generally scored better than more complex models and combined higher discriminatory power with better calibration of uncertainty. Forecast skill improved across updated forecast submissions submitted during the 2020 season. Among models using additional data, inclusion of climate or human demographic data was associated with higher skill, while inclusion of mosquito or land use data was associated with lower skill. We also identified population size, extreme minimum winter temperature, and interannual variation in WNND cases as county-level characteristics associated with variation in forecast skill. Conclusions Historical WNND cases were strong predictors of future cases with minimal increase in skill achieved by models that included other factors. Although opportunities might exist to specifically improve predictions for areas with large populations and low or high winter temperatures, areas with high case-count variability are intrinsically more difficult to predict. Also, the prediction of outbreaks, which are outliers relative to typical case numbers, remains difficult. Further improvements to prediction could be obtained with improved calibration of forecast uncertainty and access to real-time data streams (e.g. current weather and preliminary human cases). Graphical Abstract