Texts of Kolima dialect of Yukaghir

<p>Clinical chemistry data of monkeys fed on diets containing GM rice or non-GM rice.</p

Properties of annual plant communities subjected to N addition

In October 2008, sixty 8×8m plots were randomly placed across an experimental area of 5,400 m2 in the center of the Gurbantunggut desert (44.876 N, 87.823 E), with an average separation distance of about 10 m. The plots had similar plant community composition and structure before the N treatments. From year 2008 to 2011, five N concentrations plus one control (without N) were randomly applied on the plots, totaling 10 replicates of each of the six concentrations. The rates of six N treatments were 0, 0.5, 1, 3, 6 and 24 g N m-2 a-1 (hereafter denoted as N0, N0.5, N1, N3, N6 and N24, respectively). The N treatments were applied in two equal pulses per year in March after snow thaw and October before snowfall every year, coinciding reasonably with a fall-spring pulses associated with rainy seasons, and with fertilization (and thus deposition) pulses in agricultural areas of the region. Each applied N treatment consisted of 2:1 molar ratio of NH4+: NO3- (as NH4NO3 and NH4Cl), in 3 L of water per plot (about 0.037 mm of rainfall equivalent) applied using a spray. Controls received an equivalent amount of water only. The first treatment began in October 2008 and were repeated every year after that to simulate long-term effects of N deposition. In each plot, we established one 1× 1 m permanent quadrat for the investigation of community composition and structure. The richness (number of species per plot) and density (number of individuals per plot) were measured in mid-spring (April), late spring (May – June), and summer (July - August) in each year after N treatments. These seasons were chosen because various life forms of plants reach their peak biomass during these periods. We calculated the evenness index J’ based on the number of individuals per species (Tuomisto, 2012). Peak aboveground biomass provides a good estimate of annual aboveground production in communities dominated by annual plants (Sala et al., 1988). We measured production three times each year, concurrent with community measures. We selected one 0.5×0.5 m quadrat (far from the permanent quadrat for community investigation) in each 8×8 m plot for the measurement of aboveground and belowground biomass. All plants from the quadrat were collected using spades. We separated shoot and root portions for each species in the lab (after washing adherent sand from roots using tap water), and measured the biomass after drying at 70 oC for 24 hours in the oven to obtain consistent weight. The aboveground biomass was the total shoot biomass of all annual plants (including ephemeroid plants). The belowground biomass was the total root biomass of all annual plants excluding ephemeroid plants. The ephemeroid plants were excluded because their roots were cloned together (two or more ramets fused together) and exist for several years. The root biomass of ephemeroids was significantly higher than other annual plants and significantly differed among quadrants, even before the start of the experiment. In order to use community structure as a variable in some analyses, data reduction was necessary. As a data reduction tool, we used nonmetric multidimensional scaling of total above and belowground biomass by species, based on the Bray-Curtis distance measure (McCune and Grace, 2002). Prior to ordination, we omitted any species that were present in fewer than 3 samples to reduce noise and omitted any samples that lacked any plant biomass because empty samples are incompatible with this distance measure. After these modifications, we performed a general relativization, rescaling the abundance of all species within a sample such that they summed to 1. We obtained a two-axes ordination and rotated it so that Axis 1 correlated with season, the apparent strongest driver of composition. We saved axis scores for each sample for use in our structural equation model

MOVE framework.

Extreme precipitation usually cause grievous losses&casualties, which varies greatly under different scenarios. This paper took Henan province as an example, it innovatively constructed three different extreme precipitation scenarios and built indicators system of social vulnerability from exposure, sensitivity and resilience based on MOVE framework. Social Vulnerability Indexs(SoVI) were then calculated by mathematical models under three different reoccurrence intervals. The results show that SoVI was low in the west and high in the north. High SoVI areas expanded to the middle and south as recurrence intervals increased. SoVI in each area of Henan province increased along with the recurrence intervals at different growth rates. The larger the recurrence interval was, the faster the SoVI increased. The results indicate SoVI is greatly affected by disaster levels, which need to be incorporated into social vulnerability. This study provides not only a new thought for social vulnerability assessment, but also a reference for the policymakers to formulate related risk management policies.</div

Indicators system of social vulnerability to extreme precipitation in Henan province.

Indicators system of social vulnerability to extreme precipitation in Henan province.</p

Trend of the balance between Nitrogen fixation (acetylene reduction) and AO rates, as the net difference for each BSC type an origin, according to the legend inserted.

<p>Columns show the mean of n = 40 determinations (differences between 8 ARA value and 5 AO value), and error bars depict standard errors. Different lowercase letters indicated significant differences (p < 0.05) for comparisons among temperatures within a single BSC type.</p

The resistance index under extreme precipitation in Henan province.

(Drawn by the authors themselves, the numbers in the bar are the resistance index in each area).</p

Evolution trend of SoVI to extreme precipitation in Henan province under three recurrence intervals.

(Drawn by the authors themselves, the numbers of areas with different SoVI under three recurrence intervals are counted from Fig 7).</p

Variation trend of three extreme precipitation scenarios in each area of Henan province.

Variation trend of three extreme precipitation scenarios in each area of Henan province.</p

Spatial distribution of EI under three extreme precipitation scenarios in Henan province.

(The author used ARCGIS10.2 to draw. The base map is based on the standard map GS(2017)1268 publicly provided by the Ministry of Natural Resources of P.R.C).</p

Spatial distribution of SoVI to extreme precipitation in Henan province under three recurrence intervals.

(The author used ARCGIS 10.2 to draw. The base map is based on the standard map GS(2017)1268 publicly provided by the Ministry of Natural Resources of P.R.C).</p