Seaweed growth is often limited by light.Artificial light supply has been well studied in terrestrial agriculture, however, much less is known about its effect in seaweed aquaculture.In this study, the effects of four artificial light sources (white, red, green, and blue LEDs light) on a brown alga Sargassum fusiforme and a green alga Ulva pertusa were investigated.Seaweed growth, accumulation of photosynthetic pigments (chlorophyll a and carotenoid), and soluble protein were evaluated.White LED light was the optimal supplementary light when cultivating Ulva pertusa and Sargassum fusiforme, because it promoted seaweed growth while maintaining protein production.Meanwhile, red LED was unfavored in the cultivation of S. fusiforme, as it affected the seaweed growth and has a lower residual energy ratio underneath the water.LEDs would be a promising supplementary light source for seaweed cultivation.
There is substantial evidence of significant variations in performance of momentum-based strategies on international equity markets. This article reviews the evidence of such phenomenon with a sample of about 20,000 stocks from 18 developed countries during 1973-2001. We explore several explanations to such phenomenon. Specifically, we investigate if institutional and cultural differences across countries explain the cross-country variations in momentum returns. We also examine whether style investing is a potential source of price momentum. ABSTRACT This paper examines the intraday return behavior of the Shanghai Stock market with five-minute Shanghai Stock Exchange Composite Index (SHCI). Analysis of Variance and nonparametric tests are used to test the existence of significant time-of-the-day variation and day-end effect. Some intraday seasonal patterns are found. SHCI does not follow a Random Walk at a microstructure level according to the variance ratio test, indicating the returns are predictable to some degree. Several volatility models are applied to identify this predictability. ABSTRACT Antitrust cases in the United States involving predatory pricing must now consider the plausibility of the predator recouping its losses in addition to the need to show evidence of below cost pricing. A model is developed to estimate a dominant firm's investment in predation during the predatory period and its resulting net monopoly gains during the recoupment period. A breakeven recoupment time-period is then calculated indicating the minimum years of monopoly gains needed to justify the predatory strategy. Where market growth is strong, the length of the breakeven recoupment period can be quite short but is highly sensitive to the number of predatory years required to eliminate the fringe sector and deter reentry.
Tropical forests contribute a major sink for anthropogenic carbon emissions essential to slowing down the buildup of atmospheric CO 2 and buffering climate change impacts. However, the response of tropical forests to more frequent weather extremes and long-recovery disturbances like fires remains uncertain. Analyses of field data and ecological theory raise concerns about the possibility of the Amazon crossing a tipping point leading to catastrophic tropical forest loss. In contrast, climate models consistently project an enhanced tropical sink. Here, we show a heterogeneous response of Amazonian carbon stocks in GFDL-ESM4.1, an Earth System Model (ESM) featuring dynamic disturbances and height-structured tree–grass competition. Enhanced productivity due to CO 2 fertilization promotes increases in forest biomass that, under low emission scenarios, last until the end of the century. Under high emissions, positive trends reverse after 2060, when simulated fires prompt forest loss that results in a 40% decline in tropical forest biomass by 2100. Projected fires occur under dry conditions associated with El Niño Southern Oscillation and the Atlantic Multidecadal Oscillation, a response observed under current climate conditions, but exacerbated by an overall decline in precipitation. Following the initial disturbance, grassland dominance promotes recurrent fires and tree competitive exclusion, which prevents forest recovery. EC-Earth3-Veg, an ESM with a dynamic vegetation model of similar complexity, projected comparable wildfire forest loss under high emissions but faster postfire recovery rates. Our results reveal the importance of complex nonlinear responses to assessing climate change impacts and the urgent need to research postfire recovery and its representation in ESMs.
Abstract Africa contains some of the most vulnerable ecosystems to fires. Successful seasonal prediction of fire activity over these fire-prone regions remains a challenge and relies heavily on in-depth understanding of various driving mechanisms underlying fire evolution. Here, we assess the seasonal environmental drivers and predictability of African fire using the analytical framework of Stepwise Generalized Equilibrium Feedback Assessment (SGEFA) and machine learning techniques (MLTs). The impacts of sea-surface temperature, soil moisture, and leaf area index are quantified and found to dominate the fire seasonal variability by regulating regional burning condition and fuel supply. Compared with previously-identified atmospheric and socioeconomic predictors, these slowly evolving oceanic and terrestrial predictors are further identified to determine the seasonal predictability of fire activity in Africa. Our combined SGEFA-MLT approach achieves skillful prediction of African fire one month in advance and can be generalized to provide seasonal estimates of regional and global fire risk.
Phytoplankton movement patterns and swimming behavior are important and basic topics in aquatic biology. Heavy tail distribution exists in diverse taxa and shows theoretical advantages in environments. The fat tails in the movement patterns and swimming behavior of phytoplankton in response to the food supply were studied. The log-normal distribution was used for fitting the probability density values of the movement data of Oxyrrhis marina. Results showed that obvious fat tails exist in the movement patterns of O. marina without and with positive stimulations of food supply. The algal cells tended to show a more chaotic and disorderly movement, with shorter and neat steps after adding the food source. At the same time, the randomness of turning rate, path curvature and swimming speed increased in O. marina cells with food supply. Generally, the responses of phytoplankton movement were stronger when supplied with direct prey cells rather than the cell-free filtrate. The scale-free random movements are considered to benefit the adaption of the entire phytoplankton population to varied environmental conditions. Inferentially, the movement pattern of O. marina should also have the characteristics of long-range dependence, local self-similarity and a system of fractional order.
Abstract Motivated by the ongoing debates about the relative contribution of specific North African dust sources to the transatlantic dust transport to the Amazon Basin, the current study integrates a suite of satellite observations into a novel trajectory analysis framework to investigate dust transport from the leading two North African dust sources, namely, the Bodélé depression and El Djouf. In particular, this approach provides observation‐constrained quantification of the dust's dry and wet deposition along its transport pathways and is validated against multiple satellite observations. The current large ensemble trajectory simulations identify favorable transport pathways from the El Djouf across the Atlantic Ocean with respect to seasonal rain belts. The limited potential for long‐range transport of dust from the Bodélé depression is attributed to the currently identified extensive near‐source dust removal primarily by dry and wet deposition during boreal winter and summer, respectively.
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