951 resultados para Spawning season


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One factor that is investigated as a possible clue to etiological factors in Autism Spectrum Disorders (ASD) is season of birth. Season of birth effects could be the result of temperature, toxins, dietary changes, viral infections, and cultural or social factors that change seasonally (Bolton, Pickles, Harrington, Macdonald, & Rutter, 1992). A number of studies have looked for season of birth effects in ASD with no conclusive results. The current study analyzed season of birth effects in a sample of 441 children diagnosed with ASD. Analysis was also repeated after excluding prematurely born children from the data. Level of functioning and gender effects were tested by breaking the sample into a number of sub-groups. While there were no season of birth effects in the sample of all children with ASD when compared to children without ASD in either the entire sample or the non-premature sample, there were significant differences in the season of birth of low functioning children with ASD when compared with high functioning children with ASD.

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Samuel Secunda

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Signatur des Originals: S 36/F04356

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Precipitation for 2011 was less than the longterm climate average. Early in the year, precipitation lagged behind normal, but then tracked close to the normal accumulation rate from mid-April through mid-August. After that time, precipitation amounts greatly lagged behind normal, and the year ended almost 7 in. behind the long-term average. (Figure 1). Overall, 2011 will be remembered for good moisture early, but ending the season with almost no rainfall.

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In deserts, seedling emergence occurs only after precipitation threshold has been exceeded, however, the presence of trees modifies microenvironmental conditions that might affect the effectiveness of a water pulse. In the Monte desert, Prosopis flexuosa trees generate different micro-environmental conditions that might influence grass seedlings establishment. The objective of this work was: a) to know the effective minimum water input event that triggers the emergence of native perennial grass seedlings; b) to relate this fact with the effect of the shade of P. flexuosa canopy and the seasonal temperatures. Three important forage species of the Monte were studied: Pappophorum caespitosum and Trichloris crinita, with C4, and Jarava ichu, with C3 metabolism. Each season, seeds of these species were sown in pots placed at two light conditions: shade (similar to P. flexuosa cover) and open area, and with seven irrigation treatments (0, 10, 20, 30, 40, 2*10 and 3*10 mm). J. ichu did not emerge in any of the treatments. Significant seedling emergence was registered for P. caespitosum and T. crinita in shade conditions with 40 mm irrigation treatment in summer. Since 40 mm precipitation events are infrequent in the Monte, seedling emergence for these species would be restricted to exceptional rainy years. The facilitating effect of P. flexuosa shade would be important during the hot season.

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Acoustic estimates of herring and blue whiting abundance were obtained during the surveys using the Simrad ER60 scientific echosounder. The allocation of NASC-values to herring, blue whiting and other acoustic targets were based on the composition of the trawl catches and the appearance of echo recordings. To estimate the abundance, the allocated NASC -values were averaged for ICES-squares (0.5° latitude by 1° longitude). For each statistical square, the unit area density of fish (rA) in number per square nautical mile (N*nm-2) was calculated using standard equations (Foote et al., 1987; Toresen et al., 1998). To estimate the total abundance of fish, the unit area abundance for each statistical square was multiplied by the number of square nautical miles in each statistical square and then summed for all the statistical squares within defined subareas and over the total area. Biomass estimation was calculated by multiplying abundance in numbers by the average weight of the fish in each statistical square then summing all squares within defined subareas and over the total area. The Norwegian BEAM soft-ware (Totland and Godø 2001) was used to make estimates of total biomass.

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Acoustic estimates of herring and blue whiting abundance were obtained during the surveys using the Simrad ER60 scientific echosounder. The allocation of NASC-values to herring, blue whiting and other acoustic targets were based on the composition of the trawl catches and the appearance of echo recordings. To estimate the abundance, the allocated NASC -values were averaged for ICES-squares (0.5° latitude by 1° longitude). For each statistical square, the unit area density of fish (rA) in number per square nautical mile (N*nm-2) was calculated using standard equations (Foote et al., 1987; Toresen et al., 1998). To estimate the total abundance of fish, the unit area abundance for each statistical square was multiplied by the number of square nautical miles in each statistical square and then summed for all the statistical squares within defined subareas and over the total area. Biomass estimation was calculated by multiplying abundance in numbers by the average weight of the fish in each statistical square then summing all squares within defined subareas and over the total area. The Norwegian BEAM soft-ware (Totland and Godø 2001) was used to make estimates of total biomass.

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Acoustic estimates of herring and blue whiting abundance were obtained during the surveys using the Simrad ER60 scientific echosounder. The allocation of NASC-values to herring, blue whiting and other acoustic targets were based on the composition of the trawl catches and the appearance of echo recordings. To estimate the abundance, the allocated NASC -values were averaged for ICES-squares (0.5° latitude by 1° longitude). For each statistical square, the unit area density of fish (rA) in number per square nautical mile (N*nm-2) was calculated using standard equations (Foote et al., 1987; Toresen et al., 1998). To estimate the total abundance of fish, the unit area abundance for each statistical square was multiplied by the number of square nautical miles in each statistical square and then summed for all the statistical squares within defined subareas and over the total area. Biomass estimation was calculated by multiplying abundance in numbers by the average weight of the fish in each statistical square then summing all squares within defined subareas and over the total area. The Norwegian BEAM soft-ware (Totland and Godø 2001) was used to make estimates of total biomass.

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Acoustic estimates of herring and blue whiting abundance were obtained during the surveys using the Simrad ER60 scientific echosounder. The allocation of NASC-values to herring, blue whiting and other acoustic targets were based on the composition of the trawl catches and the appearance of echo recordings. To estimate the abundance, the allocated NASC -values were averaged for ICES-squares (0.5° latitude by 1° longitude). For each statistical square, the unit area density of fish (rA) in number per square nautical mile (N*nm-2) was calculated using standard equations (Foote et al., 1987; Toresen et al., 1998). To estimate the total abundance of fish, the unit area abundance for each statistical square was multiplied by the number of square nautical miles in each statistical square and then summed for all the statistical squares within defined subareas and over the total area. Biomass estimation was calculated by multiplying abundance in numbers by the average weight of the fish in each statistical square then summing all squares within defined subareas and over the total area. The Norwegian BEAM soft-ware (Totland and Godø 2001) was used to make estimates of total biomass.

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Acoustic estimates of herring and blue whiting abundance were obtained during the surveys using the Simrad ER60 scientific echosounder. The allocation of NASC-values to herring, blue whiting and other acoustic targets were based on the composition of the trawl catches and the appearance of echo recordings. To estimate the abundance, the allocated NASC -values were averaged for ICES-squares (0.5° latitude by 1° longitude). For each statistical square, the unit area density of fish (rA) in number per square nautical mile (N*nm-2) was calculated using standard equations (Foote et al., 1987; Toresen et al., 1998). To estimate the total abundance of fish, the unit area abundance for each statistical square was multiplied by the number of square nautical miles in each statistical square and then summed for all the statistical squares within defined subareas and over the total area. Biomass estimation was calculated by multiplying abundance in numbers by the average weight of the fish in each statistical square then summing all squares within defined subareas and over the total area. The Norwegian BEAM soft-ware (Totland and Godø 2001) was used to make estimates of total biomass.