Depending on the time of year we measured the emissions, values of Ea ranged from 430 to 925 kg year-1 for methane. Annually, approximately 0.7 tonne of methane was emitted to the atmosphere from the water level of the Sitka stream (total area ca 0.2 km2). The majority of annual methane emissions (90 %) occured in the lower 7 km of the stream (stretch IV and V) that represents only 1/5 of the total stream area. In addition, contribution of methane emissions to the total annual emissions was found to be the highest during spring-summer period (Mach et al.

er concentration [цд CH4 L-1]

Soil temperature influences root growth, thus nutrient and water uptake and, of course, biomass production. Most nutrients are absorbed with energy consumption (energetic uptake), so, low and very high soil temperatures negatively influence root growth and nutrient uptake. Furthermore, low soil temperatures induce a water deficit [32].

1.2. Air temperature

Air temperature directly influences photosynthesis, which is the most important physiological function in plants. The optimum temperature for photosynthesis depends on plant species and also on cultivar for the same species. Usually, the optimum temperature for maximum photosynthetic activity is around 25oC for most vegetative species. When temperature exceeds 35oC photosynthesis is inhibited, thus biomass production may be restrained. High temperatures are associated with a high vapor pressure deficit between leaves and the surrounding air. The same applies to fruit, where high temperatures may cause fruit drop in olive trees [31]. On the other hand, low temperatures act negatively in photosynthesis function and starch is redistributed and is accumulated in organs protected from frost, such as roots. Very low temperatures (<-12oC) damage the leaf canopy, shoot and branches of trees [31].

Gas flux across the air-water interface was determined by the floating chamber method four times during the year period in 2005 — 2006. The open-bottom floating PE chambers (5L domes with an area of 0.03 m2) were maintained on the water’s surface by a floating body (Styrene) attached to the outside. The chambers (n = 4 — 5) were allowed to float on the water’s surface for a period of 3 hours. Previous measurements confirmed that time to be quite enough to establish linear dependence of concentration change inside the chambers on time for the gas samples collected every 30 min over a 3 hour period. Due to trees on the banks, the chambers at all study sites were continuously in the shade. On each sampling occasion, ambient air samples were collected for determining the initial background concentrations. Samples of headspace gas were collected through the rubber stopper inserted at the chamber’s top, and stored in 100mL PE gas-tight syringes until analysis. Emissions were calculated as the difference between initial background and final concentration in the chamber headspace, and expressed on the 1m2 area of the surface level per day according to the formula:

where F is a gas flux in mg m-2day’1; a is a concentration of particular gas in the chamber headspace in pg L-1; cr is a concentration of particular gas in background air; V is volume of the chamber in L; t is time of incubation in hr; p is an area of chamber expressed in m2 . For each chamber, the fluxes were calculated using linear regression based on the concentration change as a function of time, regardless of the value of the coefficient of determination (cf. Duchemin et al. 1999, Silvenoinen et al. 2008).

In order to assess emissions produced from a total stream area, the stream was divided into five stretches according to the channel width, water velocity and substrate composition. For each stretch we have then chosen one representative sampling site (locality I-V) where samples of both stream and interstitial waters and sediments, respectively, were repeatedly taken. Localities were chosen in respect to their character and availability by car and measuring equipments. For calculation of whole-stream gases emissions into the atmosphere, the total stream area was derived from summing of 14 partial stretches. The area of these stretches was caculated from known lenght and mean channel width (measured by a metal measuring type). Longitudinal distance among the stretches was evaluated by using ArcGIS software and GPS coordinates that have been obtained during the field measurement and from digitalised map of the Sitka stream. The total area of the Sitka stream was estimated to be 181 380 m2 or 0.18 km2. Stretches have differed in their percentual contribution to this total area and also by their total lenght (Table 1).

The total annual methane emissions to the atmosphere from the five segments of the Sitka stream, Ea (kg yr-1) were derived from seasonal average, maximum or minimum emissions measured on every locality and extrapolated to the total area of the particular segment. The total methane emissions produced by the Sitka stream annualy were then calculated according to the following formula:

Ea = (X pi * Fi * 365) / 1 000000 (5)

where Ea is average, maximal or minimal assess of emission of methane from the total stream area in kilograms per year; pi is an area of stretch (in m2) representing given locality; Fi is average, maximal or minimal assess of the methane from a given locality expressed in mg m-2day2.

The potentially important source and sinks terms for dissolved methane in the water column of the Sitka stream are shown in Figure 6. Previously calculated rates of inputs (benthic fluxes) and loss of dissolved CH4 through evasion to the atmosphere can be combined together with advection inputs and losses to yield a CH4 dynamics (budget) for any particular section of the stream.

The CH4 budget determined for the 2011 sampling period in an experimental stream section is summarized in Figure 7. Benthic fluxes were measured along a stream section 45 m long
with an area being ~ 200 m2. Positive fluxes of CH4 were found to occur at 30.9 % of the study area. Assuming that average benthic flux of methane across the sediment-water interface was 15.40 mg m-2 day-1, the benthic flux of 3081.39 mg CH4 day-1 should occur from the whole area of 200 m2. Average emission flux of CH4 across the water-air interface for all study sites was determined to be 14.47 ± 4.73 mg CH4 m-2 day-1. This value is slightly lower than the direct benthic flux of CH4 and suggests that some portion of methane released from the bottom sediments may contribute to increasing concentration of CH4 in the surface water. Average flow of the Sitka stream during time of benthic fluxes measurements was 0.351 m3s-1 (i. e. 351 L’1s-1). Therefore, we may expect that water column was enriched at least by 187.4 mg (i. e. 0.006 pg L-1) of CH4 from sediment at 45 m long section near study site IV during one day. Next study site V is located some 4 km downstream from the site IV. Average CH4 concentration difference in the stream water between these study sites was found to be 3.2 pg L-1 of CH4 indicating that CH4 supply exceeds slightly CH4 removal. Methane fluxes from the sediment would contribute to this concentration difference only by 0.6 pg L-1, thus, the immediate difference in the CH4 budget found between two studied sites IV and V indicates that there must likely be other sources of methane supply to the stream water (Fig. 7). This „missing source" seems to be relatively small (0.9 mg CH4 0.351 m-3s-1), however, net accumulation of CH4 in the stream water during 4 km section of the Sitka stream below study site IV was almost 78 g CH4 per one day.

Low atmosphere humidity speeds up transpiration by leaf surface. Increase of the rate of transpiration causes reduction of vegetative tissues water content, thus depression in the rate of growth and biomass production.

1.3. Photoperiod

Photoperiod is the duration of light in 24 hours and it is one of the most important factors influencing vegetative growth. Plant species whose vegetative growth is mostly influenced by long day conditions are Populus robusta, Ulmus Americana and Aesculus hippocastanus [30].

1.4. Light intensity

Light, together with CO2, are the two main factors influencing photosynthetic rate. By increasing light intensity up to an optimum limit the maximum photosynthetic rate, so the greatest biomass production can be achieved.

Interstitial water samples for carbon isotopic analysis of methane and carbon dioxide were collected in 2010 — 2011 through three courses at study site. Sampling was performed by set of minipiezometers placed in a depth of 20 to 60 cm randomly in a sediment. After sampling, refrigerated samples were transported (within 72 hours) in 250 mL bottles to laboratory at the Department of Plant Physiology, Faculty of Science University of South Bohemia in Ceske Budejovice, which are equipped with mass spectrometry for carbon isotopes measurements. Firstly both water samples, for methane and for carbon dioxide, were extracted to helium headspace. After relaxation time isotopic equilibrium was achieved and four subsamples of gas were determined by GasBanch (ThermoScientific) and IRMS DeltaplusXL equiped by TC/EA (ThermoFinnigan) for analysis of S13CO2. Afterwards S13CO2 of water samples were calculated from gaseous S13CO2 by fractionation factor from a linear equation (Szaran 1997):

sJ3C = — (0.0954 + 0.0027) T[°C] + (10.41 + 0.12) (6)

Stable isotope analysis of 13C/12C in gas samples was performed using preconcentration, kryoseparation of CO2 and gas chromatograph combustion of CH4 in PreCon (ThermoFinnigan) coupled to isotope ratio mass spectrometer (IRMS, Delta Plus XL, ThermoFinnigan, Brehmen, Germany). After conversion of CH4 to CO2 in the Finningan standard GC Combustion interface CO2 will be tranfered into IRMS. The obtained 13C/12C ratios (R) will be referenced to 13C/12C of standard V-PDB (Vienna-Pee-Dee Belemnite)(Rs), and expressed as S13C = (Rsample/Rstandard — 1) x 1000 in %. The standard deviation of S13C determination in standard samples is lower than 0.1% with our instrumentation. From our data, we also calculated an apparent fractionation factor ае that is defined by the measured SCHt and SCO2 (Whiticar et al. 1986):

aC = (dCO2 + 103) / (dCH4 + 103) (7)

This fractionation factor gives rough idea of magnitude of acetoclastic and hydrogenotrophic methanogenesis.

Both methanogenic archaea and aerobic methanotrophs were found at all localities along the longitudinal stream profile. The proportion of these groups to the DAPI-stained cells was quite consistent and varied only slightly but a higher proportion to the DAPI-stained cells in deeper sediment layer 25-50 cm was observed. On average 23,4 % of DAPI-stained cells were detected by FISH with a probe for methanogens while type I methanotrophs reached ~ 21,4 % and type II methanotrophs 11,9 %, respectively. All three groups also revealed non­significant higher proportion to the TCN in deeper sediment layer; the abundance of methanogens and methanotrophs remained almost unchanged with increasing sediment depth. The average abundance of methanogens (0,88 ± 0,28 and 1,07 ± 0,23 x 106 cells mL-1 in the upper and deeper layer, respectively) and type II methanotrophs (0,44 ± 0,14 x 106 cells mL-1 and 0,56± 0,1 x 106 cells mL-1) increased slightly with the sediment depth, while type I methanotrophs revealed average abundance 0,98 ± 0,23 x 106 cells mL-1 in the deeper layer being lower compared to abundance 1,07± 0,28 x 106 cells mL-1 found in upper sediment layer (Buriankova et al. 2012). Very recently, however, using the FISH method we found that abundance of methanogens belonging to three selected families reached their maximum in the sediment depth of 20-30 cm and had closely reflected vertical distribution of acetate concentrations. Species of family Methanobacteriaceae grow only with hydrogen, formate and alcohols (except methanol), Methanosarcinaceae can grow with all methanogenic substrates except formate, and members of Methanosaetaceae grow ecxlusively with acetate as energy source. All three families also showed similar proportion to the DAPI stained cells, ranging in average (depth 10-50 cm) from 9.9% (Methanosarcinaceae) to 12.3% (Methanobacteriaceae) (Fig. 8).

Limited nutrient availability influences negatively biomass production. Nitrogen deficiency strongly depresses vegetation flush. According to Boussadia et al. (2010) [8], total biomass of two olive cultivars (‘Meski’ and ‘Koroneiki’) was strongly reduced (mainly caused by a decrease in leaf dry weight) under severe N deprivation, while in an out-door pot-culture experiment with castor bean plants (Ricinus communis L.), conducted by Reddy and Matcha (2010) [9], it was found that among the plant components, leaf dry weight had the greatest decrease; furthermore, root/shoot ratio increased under N deficiency [9]. Phosphorus deficiency caused reduced biomass, photosynthetic activity and nitrogen fixing ability in mungbean (Vigna aconitifolia) and mashbean (Vigna radiata) [33]. Under P deficiency conditions, genotypic variation in biomass production is evident; according to Pang et al. (2010) [34], who studied in a glasshouse experiment the response of ten perennial herbaceous legume species, found that under low P conditions several legumes produced more biomass than lucerne. Nutrient deficiency may cause physiological and metabolism abnormalities in plants, which may lead to deficiency symptoms. There are two categories of symptoms: i) General symptoms, such as limited growth and inability of reproduction (flowering and fruit setting), caused by the deficiency of many necessary macro — or micro­nutrients, and ii) typical, characteristic, deficiency symptoms, such as chlorosis, i. e. yellowing (due to Fe deficiency). In both cases biomass production is depressed. In the study of Msilini et al. (2009) [10], bicarbonate treated plants of Arabidopsis thaliana suffered from Fe deficiency displayed significantly lower biomass, leaf number and leaf surface, as compared to control plants, and showed slight yellowing of their younger leaves. Under limited nutrient availability, arbuscular mycorrhiza fungi (AMF) may favor nutrient uptake and thus enhance biomass production. Hu et al. (2009) [35] refer that AMF inoculation of maize plants was likely more efficient in extremely P-limited soils. Generally, root colonization by AMF influences positively plant growth under N, P, or micronutrient deficiency conditions [36].

For measuring of microbial parameters, formaldehyde fixed samples (2 % final conc.) were first mildly sonicated for 30 seconds at the 15 % power (sonotroda MS 73, Sonopuls HD2200, Sonorex, Germany), followed by incubation for 3 hours under mild agitation with 10 mL of detergent mixture (Tween 20 0.5%, vol/vol, tetrasodium pyrophosphate 0.1 M and distilled water) and density centrifugation (Santos Furtado & Casper 2000, Amalfitano & Fazi 2008). For density centrifugaton, the non-ionic medium Nycodenz (1.31 g mL-1; Axis- Shield, Oslo, Norway) was used at 4600 G for 60 minutes (Rotofix 32A, Hettich, Germany). After the preparation processes, a 1 mL of Nycodenz was placed underneath 2 ml of treated slurry using a syringe needle (Fazi et al. 2005). 1 ml of supernatant was then taken for subsequent analysis.

1.2. Total cell numbers (TCN)

The supernatant was filtered onto membrane filters (0.2 pm GTTP; Millipore Germany), stained for 10 minutes in cold and in the dark with DAPI solution (1 mg/ ml; wt/ vol; Sigma, Germany) and gently rinsed in distilled water and 80 % ethanol. Filters were air-dried and fixed in immersion oil. Stained cells were enumerated on an epifluorescence microscope (Olympus BX 60) equipped with a camera (Olympus DP 12) and image analysis software (NIS Elements; Laboratory Imaging, Prague, Czech Republic). At least 200 cells within at least 20 microscopic fields were counted in three replicates from each locality. TCN was expressed as bacterial numbers per 1 mL of wet sediments.

Methanogenic communities associated with hyporheic sediments at two different depths (0­25 cm and 25-50 cm) along the longitudinal stream profile were compared based on the DGGE patterns. As shown in Fig. 9, the DGGE patterns varied highly among study localities (Fig. 9A), irrespective of the depth (Fig. 9B). However, presence of the bands in all samples indicates that methanogens may occur up to 50 cm of the sediment depth. The number of DGGE bands of the methanogenic archaeal communities was compared either among localites or among different sediment depths. A total of 22 different bands were observed in the DGGE image ranging from 4 (locality II) to 16 (locality IV) in the samples (Fig. 9A).

The number of DGGE bands also ranged from 2 to 10 for the samples from upper layer (0-25 cm) and from 2 to 11 for the samples from deeper layer (25-50 cm), respectively (Fig. 9B). We found no clear trend in the number of DGGE bands with increasing depth (Fig. 9B). Locality IV appears to be the richest in number of DGGE bands. We suppose that this might be due to most favorable conditions prevailing for the methanogens life as indicated by a relatively low grain median size, lower dissolved oxygen concentration or higher concentration of the ferrous iron compared to other localities (cf. Table 2).

The methanogenic community diversity in hyporheic sediment of Sitka stream was also analysed by PCR amplification, cloning and sequencing of methyl coenzyme M reductase (mcrA) gene. A total of 60 mcrA gene sequences revealed 26 different mcrA gene clones.

Most of the clones showed low affiliation with known species (< 97% nucleotide identity) and probably represented genes of novel methanogenic archeal genera/species, but all of them were closely related to uncultured methanogens from environmental samples (> 97% similarity) retrieved from BLAST. The 25 clones were clustered to four groups and were confirmed to be affiliated to Methanosarcinales, Methanomicrobiales and Methanobacteriales orders and other unclassified methanogens. The members of all three orders and novel methanogenic cluster were detected to occur in a whole bottom sediment irrespective of a depth, nevertheless, the richness of methanogenic archaea in the sediment was slightly higher in the upper sediment layer 0-25 cm (15 clones) than in the deeper sediment layer 25­50 cm (11 clones)(Buriankova et al. in review). The clones affiliated with Methanomicrobiales predominated in the deeper layer while Methanosarcinales clones dominated in the upper sediment layer. This prevalence of Methanosarcinales in the upper sediment layer was also confirmed by our FISH analyses as has been mentioned above.

4. Discussion