Fungi disperse spores to move across landscapes and spore liberation takes different patterns. Many species release spores intermittently; others release spores at specific times of day. Despite intriguing evidence of periodicity, why (and if) the timing of spore release would matter to a fungus remains an open question. Here we use state-of-the-art numerical simulations of atmospheric transport and meteorological data to follow the trajectory of many spores in the atmosphere at different times of day, seasons, and locations across North America. While individual spores follow unpredictable trajectories due to turbulence, in the aggregate patterns emerge: Statistically, spores released during the day fly for several days, whereas spores released at night return to ground within a few hours. Differences are caused by intense turbulence during the day and weak turbulence at night. The pattern is widespread but its reliability varies; for example, day/night patterns are stronger in southern regions. Results provide testable hypotheses explaining both intermittent and regular patterns of spore release as strategies to maximize spore survival in the air. Species with short-lived spores reproducing where there is strong turbulence during the day, for example in Mexico, maximize survival by releasing spores at night. Where cycles are weak, for example in Canada during fall, there is no benefit to releasing spores at the same time every day. Our data challenge the perception of fungal dispersal as risky, wasteful, and beyond control of individuals; our data suggest the timing of spore liberation may be finely tuned to maximize fitness during atmospheric transport.
Impulse-momentum theorem is a basic matter of the mechanics. A zero cost experiment can be used in the classroom, without any apparatus, in order to verify the fundamental relationship between an impulsive force and the linear momentum variation. Using various data, the use of an electronic sheet such Excel gives an 'impulse' to put in practice the 'errors-analysis' theory.
The ability to predict wind is crucial for both energy production and weather forecasting. Mechanistic models that form the basis of traditional forecasting perform poorly near the ground. In this paper, we take an alternative datadriven approach based on supervised learning. We analyze a massive dataset of wind measured from anemometers located at 10 m height in 32 locations in two central and north west regions of Italy (Abruzzo and Liguria). We train supervised learning algorithms using the past history of wind to predict its value at a future time (horizon). Using data from a single location and time horizon we compare systematically several algorithms where we vary the input/output variables, the memory of the input and the linear vs non-linear learning model. We then compare performance of the best algorithms across all locations and forecasting horizons. We find that the optimal design as well as its performance vary with the location. We demonstrate that the presence of a reproducible diurnal cycle provides a rationale to understand this variation. We conclude with a systematic comparison with state of the art algorithms and show that, when the model is accurately designed, shallow algorithms are competitive with more complex deep architectures.
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