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  1. Abstract

    Pollinators are an essential component of terrestrial food webs and agricultural systems but are threatened by insufficient access to floral resources. Managed honey bees, as generalist foragers that hoard nectar as honey, can act as bioindicators of floral resources available to pollinators in a given landscape through their accumulation of honey. Honey yields across the United States have decreased appreciably since the 1990s, concurrent with shifts in climate, land-use, and large-scale pesticide application. While many factors can affect honey accumulation, this suggests that anthropogenic stressors may be having large-scale impacts on the floral resources that pollinators depend on for their nutrition. We used hierarchical partitioning on five decades of state-level data to parse the most important environmental factors and likely mechanisms associated with spatial and temporal variation in honey yields across the US. Climatic conditions and soil productivity were among the most important variables for estimating honey yields, with states in warm or cool regions with productive soils having the highest honey yields per colony. These findings suggest that foundational factors constrain pollinator habitat suitability and define ecoregions of low or high honey production. The most important temporally varying factors were change in herbicide use, land use (i.e. increase in intensive agriculture and reduction in land conservation programs that support pollinators) and annual weather anomalies. This study provides insights into the interplay between broad abiotic conditions and fine temporal variation on habitat suitability for honey bees and other pollinators. Our results also provide a baseline for investigating how these factors influence floral resource availability, which is essential to developing strategies for resilient plant–pollinator communities in the face of global change.

     
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  2. Abstract

    Drought is often thought to reduce ecosystem photosynthesis. However, theory suggests there is potential for increased photosynthesis during meteorological drought, especially in energy-limited ecosystems. Here, we examine the response of photosynthesis (gross primary productivity, GPP) to meteorological drought across the water-energy limitation spectrum. We find a consistent increase in eddy covariance GPP during spring drought in energy-limited ecosystems (83% of the energy-limited sites). Half of spring GPP sensitivity to precipitation was predicted solely from the wetness index (R2 = 0.47,p < 0.001), with weaker relationships in summer and fall. Our results suggest GPP increases during spring drought for 55% of vegetated Northern Hemisphere lands ( >30° N). We then compare these results to terrestrial biosphere model outputs and remote sensing products. In contrast to trends detected in eddy covariance data, model mean GPP always declined under spring precipitation deficits after controlling for air temperature and light availability. While remote sensing products captured the observed negative spring GPP sensitivity in energy-limited ecosystems, terrestrial biosphere models proved insufficiently sensitive to spring precipitation deficits.

     
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  3. Changes in phenology in response to ongoing climate change have been observed in numerous taxa around the world. Differing rates of phenological shifts across trophic levels have led to concerns that ecological interactions may become increasingly decoupled in time, with potential negative consequences for populations. Despite widespread evidence of phenological change and a broad body of supporting theory, large-scale multitaxa evidence for demographic consequences of phenological asynchrony remains elusive. Using data from a continental-scale bird-banding program, we assess the impact of phenological dynamics on avian breeding productivity in 41 species of migratory and resident North American birds breeding in and around forested areas. We find strong evidence for a phenological optimum where breeding productivity decreases in years with both particularly early or late phenology and when breeding occurs early or late relative to local vegetation phenology. Moreover, we demonstrate that landbird breeding phenology did not keep pace with shifts in the timing of vegetation green-up over a recent 18-y period, even though avian breeding phenology has tracked green-up with greater sensitivity than arrival for migratory species. Species whose breeding phenology more closely tracked green-up tend to migrate shorter distances (or are resident over the entire year) and breed earlier in the season. These results showcase the broadest-scale evidence yet of the demographic impacts of phenological change. Future climate change–associated phenological shifts will likely result in a decrease in breeding productivity for most species, given that bird breeding phenology is failing to keep pace with climate change. 
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    Free, publicly-accessible full text available July 11, 2024
  4. Biologists routinely fit novel and complex statistical models to push the limits of our understanding. Examples include, but are not limited to, flexible Bayesian approaches (e.g. BUGS, stan), frequentist and likelihood‐based approaches (e.g. packageslme4) and machine learning methods.

    These software and programs afford the user greater control and flexibility in tailoring complex hierarchical models. However, this level of control and flexibility places a higher degree of responsibility on the user to evaluate the robustness of their statistical inference. To determine how often biologists are running model diagnostics on hierarchical models, we reviewed 50 recently published papers in 2021 in the journalNature Ecology & Evolution, and we found that the majority of published papers didnotreport any validation of their hierarchical models, making it difficult for the reader to assess the robustness of their inference. This lack of reporting likely stems from a lack of standardized guidance for best practices and standard methods.

    Here, we provide a guide to understanding and validating complex models using data simulations. To determine how often biologists use data simulation techniques, we also reviewed 50 recently published papers in 2021 in the journalMethods Ecology & Evolution. We found that 78% of the papers that proposed a new estimation technique, package or model used simulations or generated data in some capacity (18 of 23 papers); but very few of those papers (5 of 23 papers) included either a demonstration that the code could recover realistic estimates for a dataset with known parameters or a demonstration of the statistical properties of the approach. To distil the variety of simulations techniques and their uses, we provide a taxonomy of simulation studies based on the intended inference. We also encourage authors to include a basic validation study whenever novel statistical models are used, which in general, is easy to implement.

    Simulating data helps a researcher gain a deeper understanding of the models and their assumptions and establish the reliability of their estimation approaches. Wider adoption of data simulations by biologists can improve statistical inference, reliability and open science practices.

     
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  5. Abstract

    Opsins, light-sensitive G protein-coupled receptors, have been identified in corals but their properties are largely unknown. Here, we identified six opsin genes (acropsins 1–6) from a coral speciesAcropora millepora, including three novel opsins (acropsins 4–6), and successfully characterized the properties of four out of the six acropsins. Acropsins 1 and 6 exhibited light-dependent cAMP increases in cultured cells, suggesting that the acropsins could light-dependently activate Gs-type G protein like the box jellyfish opsin from the same opsin group. Spectral sensitivity curves having the maximum sensitivities at ~ 472 nm and ~ 476 nm were estimated for acropsins 1 and 6, respectively, based on the light wavelength-dependent cAMP increases in these opsins-expressing cells (heterologous action spectroscopy). Acropsin 2 belonging to the same group as acropsins 1 and 6 did not induce light-dependent cAMP or Ca2+changes. We then successfully estimated the acropsin 2 spectral sensitivity curve having its maximum value at ~ 471 nm with its chimera mutant which possessed the third cytoplasmic loop of the Gs-coupled jellyfish opsin. Acropsin 4 categorized as another group light-dependently induced intracellular Ca2+increases but not cAMP changes. Our results uncovered that theAcroporacoral possesses multiple opsins coupling two distinct cascades, cyclic nucleotide and Ca2+signaling light-dependently.

     
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  6. Atmospheric electrical discharges are now known to generate unexpectedly large amounts of the atmosphere’s primary oxidant, hydroxyl (OH), in thunderstorm anvils, where electrical discharges are caused by atmospheric charge separation. The question is “Do other electrical discharges also generate large amounts of oxidants?” In this paper, we demonstrate that corona formed on grounded metal objects under thunderstorms produce extreme amounts of OH, hydroperoxyl (HO 2 ), and ozone (O 3 ). Hundreds of parts per trillion to parts per billion of OH and HO 2 were measured during seven thunderstorms that passed over the rooftop site during an air quality study in Houston, TX in summer 2006. A combination of analysis of these field results and laboratory experiments shows that these extreme oxidant amounts were generated by corona on the inlet of the OH-measuring instrument and that corona are easier to generate on lightning rods than on the inlet. In the laboratory, increasing the electric field increased OH, HO 2 , and O 3 , with 14 times more O 3 generated than OH and HO 2 , which were equal. Calculations show that corona on lightning rods can annually generate OH that is 10–100 times ambient amounts within centimeters of the lightning rod and on high-voltage electrical power lines can generate OH that is 500 times ambient a meter away from the corona. Contrary to current thinking, previously unrecognized corona-generated OH, not corona-generated UV radiation, mostly likely initiates premature degradation of high-voltage polymer insulators. 
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  7. Abstract Recent years have seen growing appreciation that rapidly intensifying flash droughts are significant climate hazards with major economic and ecological impacts. This has motivated efforts to inventory, monitor, and forecast flash drought events. Here we consider the question of whether the term “flash drought” comprises multiple distinct classes of event, which would imply that understanding and forecasting flash droughts might require more than one framework. To do this, we first extend and evaluate a soil moisture volatility–based flash drought definition that we introduced in previous work and use it to inventory the onset dates and severity of flash droughts across the contiguous United States (CONUS) for the period 1979–2018. Using this inventory, we examine meteorological and land surface conditions associated with flash drought onset and recovery. These same meteorological and land surface conditions are then used to classify the flash droughts based on precursor conditions that may represent predictable drivers of the event. We find that distinct classes of flash drought can be diagnosed in the event inventory. Specifically, we describe three classes of flash drought: “dry and demanding” events for which antecedent evaporative demand is high and soil moisture is low, “evaporative” events with more modest antecedent evaporative demand and soil moisture anomalies, but positive antecedent evaporative anomalies, and “stealth” flash droughts, which are different from the other two classes in that precursor meteorological anomalies are modest relative to the other classes. The three classes exhibit somewhat different geographic and seasonal distributions. We conclude that soil moisture flash droughts are indeed a composite of distinct types of rapidly intensifying droughts, and that flash drought analyses and forecasts would benefit from approaches that recognize the existence of multiple phenomenological pathways. 
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  8. Over the last several decades, Emerging Scholars Programs (ESPs) have incorporated active learning strategies and challenging problems into collegiate mathematics, resulting in students, underrepresented minority (URM) students in particular, earning at least half of a letter grade higher than other students in Calculus. In 2009, West Virginia University (WVU) adapted ESP models for use in Calculus I in an effort to support the success and retention of URM STEM students by embedding group and inquiry-based learning into a designated section of Calculus I. Seats in the class were reserved for URM and first-generation students. We anticipated that supporting students in courses in the calculus sequence, including Calculus I, would support URM Calculus I students in building learning communities and serve as a mechanism to provide a strong foundation for long-term retention. In this study we analyze the success of students that have progressed through our ESP Calculus courses and compare them to their non-ESP counterparts. Results show that ESP URM students succeed in the Calculus sequence at substantially higher rates than URM students in non-ESP sections of Calculus courses in the sequence (81% of URM students pass ESP Calculus I while only 50% of URM students pass non-ESP Calculus I). In addition, ESP URM and ESP non-URM (first-generation but not URM) students succeed at similar levels in the ESP Calculus sequence of courses (81% of URM students and 82% of non-URM students pass ESP Calculus I). Finally, ESP URM students’ one-year retention rates are similar to those of ESP non-URM students and significantly higher than those of URM students in non-ESP sections of Calculus (92% of ESP URM Calculus I students were retained after one year, while only 83% of URM non-ESP Calculus I students were retained). These results suggest that ESP is ideally suited for retaining and graduating URM STEM majors, helping them overcome obstacles and barriers in STEM, and increasing diversity, equity, and inclusion in Calculus. 
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