The absolutely necessary nutrients for plant growth are the following: N, P, K, S, Ca, Mg (macronutrients), Zn, Cu, B, Mn, Fe, Mo (micronutrients). Without one of these nutrients, plant organism can not grow normally and survive. The physiological roles of these nutrients are described in detail below.

1.5. Macronutrients

Nitrogen: It is a primary component of nucleic acids, proteins, amino acids, purines, pyrimidines and chlorophyll. Nitrogen exerts a significant effect on plant growth, as it reduces biennial bearing and increases the percentage of perfect flowers. In olive trees, lack of N leads to decreased growth, shorter length of annual shoots (<10cm), fewer leaves, reduced flowering and decreased yield [31].

Phosphorus: P is a component of high-energy substances such as ATP, ADP and AMP; it is also important for nucleic acids and phospholipids. Phosphorus affects root growth and maturation of plant tissues and participates in the metabolism of carbohydrates, lipids and proteins [31].

Potassium: K plays a crucial role in carbohydrate metabolism, in the metabolism of N and protein synthesis, in enzyme activities, in the regulation of the opening and closing of stomata, thus to the operation of photosynthesis, in the improvement of fruit quality and disease tolerance, in the activation of the enzymes peptase, catalase, pyruvic kinase e. t.c. [31,38].

Calcium: It is the element that participates in the formation and integrity of cell membranes, in the integrity and semipermeability of the plasmalemma, it increases the activity of many enzymes, it plays a crucial role in cell elongation and division, in the transfer of carbohydrates e. t.c. [31,38].

Magnesium: It is part of chlorophyll molecule, it activates the enzymes of Crebs’ cycle and it also plays a role in oil synthesis [38].

Sulphur: Sulphur plays role in the synthesis of some amino-acids, such as cysteine, cystine, methionine, as well as in proteins synthesis. It also activates some proteolytic enzymes, such as papaine, bromeline e. t.c. Finally, it is part of some vitamins’ molecule and that of gloutathione [31,38].

The methanogenic archaea, three selected methanogen families (Methanobacteriaceae, Methanosetaceae and Methanosarcinaceae) and methanotrophic bacteria belonging to groups I and II were detected using FISH (Fluorescence in situ hybridization) with 16S rRNA — targeted oligonucleotide probe labelled with indocarbocyanine dye Cy3. The prokaryotes were hybridized according to the protocol by Pernthaler et al. (2001). Briefly, the supernatants which were used also for TCN were filtered onto polycarbonate membrane filters (0.2 pm GTTP; Millipore), filters were cut into sections and placed on glass slides. For the hybridization mixtures, 2 pL of probe-working solution was added to 16 pL of hybridization buffer in a microfuge tube. Hybridization mix was added to the samples and the slides with filter sections were incubated at 46 °C for 3 hours. After incubation, the sections were transferred into preheated washing buffer (48 °C) and incubated for 15 minutes in a water bath at the same temperature. The filter sections were washed and air — dried. The DAPI staining procedure followed as previously described. Finally, the samples were mounted in a 4:1 mix of Citifluor and Vecta Shield. The methanogens and methanotrophs were counted in three replicates from each locality and the relative proportion of bacteria, archaea, methanogens and methanotrophs to the total number of DAPI stained cells was then calculated.

In spite of commonly held view of streams as well-oxygenated habitats, we found both surface and interstitial water to be supersaturated with methane compared to the atmosphere at all five localities (Mach et al. in review). Availability of interstitial habitats for bacteria and archaea carrying out anaerobic processes has been confirmed by our previous (Hlavacova et al. 2005, 2006; Cupalova & Rulik 2007) and contemporary findings. During this study we found relatively well developed populations of methanogenic archaea at all localities and that all localities also showed positive methanogenic potential. Emissions of methane from water ecosystems results from complex microbial activity in the carbon cycle (production and consumption processes), which depends upon a large number of environmental parameters such as availability of carbon and terminal electron acceptors, flow velocity and turbulence, water depth. In our previous paper (Hlavacova et al. 2006), we suggested that surface water concentrations, and as a consequence methane gas emissions to the atmosphere would result from downstream transport of gases by stream water (advection in/out), and moreover, from autochthonous microbial metabolism within the hyporheic zone. If so, surface water is continually saturated by gases produced by hyporheic metabolism, leading to supersaturation of surface water and induced diffusion of these gases out of river water (volatizing). Moreover, the run-off and drainage of adjacent soils can also contribute greatly to the degree of greenhouse gas supersaturation (De Angelis & Lilley 1987, Kroeze & Seitzinger 1998, Worral & Lancaster 2005, Wilcock & Sorrell 2008). For example, CH4 in the estuarine waters may come from microbial production in water, sediment release, riverine input and inputs of methane-rich water from surrounding anoxic environments (Zhang et al. 2008b). For the European estuaries, riverine input contribute much to the estuarine CH4 due to high CH4 in the river waters and wetlands also play important roles. However, low CH4 in the Changjiang Estaury (China) may be resulted from the low CH4 in the Changjiang water together with the low net microbial production and low input from adjacent salt marshes (Zhang et al. 2008b). Dissolved methane concentrations in a surface water of Sitka stream is consistent with literature data on methane in rivers published by Middelburg et al. (2002) and Zhang et al. (2008b).

Iron: Iron plays an important role in chlorophyll synthesis, without being part of its molecule. Furthermore, it participates in the molecule of Fe-proteins catalase, cytochrome a, b, c, hyperoxidase e. t.c. In addition to that, it is found in the enzymes nitric and nitrate reductase, which are responsible for the transformation of NO3- into NH4+, as well as in nitrogenase, which is the responsible enzyme for the atmospheric N capturing [38].

Manganese: Manganese is activator of the enzymes of carbohydrates metabolism, those of Crebs’ cycle, and of some other enzymes, such as cysteine desulphydrase, glutamyl transferase e. t.c. It also plays a key-role in photosystem II of photosynthesis, and particularly in the reactions liberating O2. Finally, Mn acts as activator of some enzymes catalyzing oxidation and reduction reactions [38].

Zinc: Zn plays crucial role in tryptophane biosynthesis, which is the previous stage from IAA (auxin) synthesis (direct influence of Zn on plant growth and biomass production). IAA concentration is significantly reduced in vegetative tissues suffering from Zn deficiency. In addition to the above, Zn is part of some metal-enzymes [38].

Copper: Cu is activator of some enzymes, as well as it is part of enzymes catalyzing oxidation and reducing reactions, such as oxidase of ascorbic acid, lactase, nitrate and nitric reductase e. t.c. [38].

Boron: B plays role in the transfer of sugars along cell membranes, as well as in RNA and DNA synthesis. It also participates to cell division process, as well as to the pectine synthesis [38].

Molybdenum: It is part of the enzyme nitrogenase (capturing of atmospheric N) and nitric reductase (transformation of NO3- to NO2-). Mo also participates to the metabolism of ascorbic acid [38].

As it is clear from all the above physiological roles of nutrients, the deficiency of even one of them in the mineral nutrition of higher plants depresses their growth, thus biomass production. So, in order to achieve the maximum biomass production, apart from the optimum conditions of all the other environmental and agronomic factors influencing plant growth (temperature, soil humidity, photoperiod, light intensity), it should always be taken care of maintaining the optimum levels of all the necessary soil nutrients. This is usually achieved with the correct fertilization program of the different crops. For example, fruit trees have high demands in K, since fruit production is a K sink and reduces its levels in plant level. According to Therios (2009) [31], potassium plays an important role in olive nutrition. Thus, fruit trees should be periodically fertilized (usually K fertilizers applied during autumn, or winter, and are incorporated into the soils) with enhanced doses of potassium fertilizers (usually K2SO4). Apart from chemical fertilizers, organic amendments can be also applied under limited nutrient conditions in order to enhance plant growth. According to Hu et al. (2009) [35], stem length, shoot and root biomass, as well as crop yield of maize were all greatly increased by the application of organic amendments on a sandy loam soil. Apart from the application of chemical fertilizers, organic amendments e. t.c., another modern method to improve yields and to increase biomass is the irrigation of crops with FFC H2O, a commercial product currently utilized by the agriculture, fishery and food industries in Japan. In the study of Konkol et al. (2012) [39], radish and shirona plants irrigated with FFC H2O developed larger average leaf area by 122% and greater dry weight and stem length by 39% and 31%, respectively, compared to the plants irrigated with deionized H2O. FFC H2O offers agriculturalists a simple and effective tool for the fortification of irrigation waters with micronutrients [39].

Nucleic acids were extracted from 0,3 g of sieved sediment with a Power Soil DNA isolation kit (MoBio, Carlsbad, USA) according to the manufacturer’s instructions. 16S rRNA gene fragments (~350 bp) were amplified by PCR using primer pair specific for methanogens. Primer sequences are as follows, 0357 F-GC 5′-CCC TAC GGG GCG CAG CAG-3′ (GC clamp at 5′-end CGC CCG CCG CGC GCG GCG GGCGGG GCG GGG GCA CGG GGG G) and 0691 R 5′- GGA TTA CAR GAT TTC AC -3′ (Watanabe et al. 2004). PCR amplification was carried out in 50 pL reaction mixture contained within 0.2 mL, thin walled micro-tubes. Amplification was performed in a TC-XP thermal cycler (Bioer Technology, Hangzhou, China). The reaction mixture contained 5 pL of 10 * PCR amplification buffer, 200 pM of each dNTP, 0,8 pM of each primer, 8 pL of template DNA and 5.0 U of FastStart Taq DNA polymerase (Polymerase dNTPack; Roche, Germany). The initial enzyme activation and DNA denaturation were performed for 6 min at 95°C, followed by 35 cycles of 1 min at 95°C, 1 min at 55°C and 2 min at 69°C and a final extension at 69°C for 8 min (protocol by Watanabe et al. 2004). PCR products were visualised by electrophoresis in ethidium bromide stained, 1.5% (w/v) agarose gel.

DGGE was performed with an INGENYphorU System (Ingeny, Netherlands). PCR products were loaded onto a 7% (w/v) polyacrylamide gel (acrylamide: bisacrylamide, 37.5:1). The polyacrylamide gels were made of 0.05% (v/v) TEMED (N, N,N, N-tetramethylenediamine),

0. 06% (w/v) ammonium persulfate, 7 M (w/v) urea and 40 % (v/v) formamide. Denaturing gradients ranged from 45 to 60%. Electrophoresis was performed in 1*TAE buffer (40 mM Tris, 1 mM acetic acid, 1 mM EDTA, pH 7.45) and run initially at 110V for 10 min at 60°C, afterwatds for 16 h at 85 V. After electrophoresis, the gels were stained for 60 min with SYBR Green I nucleic acid gel stain (1:10 000 dilution) (Lonza, Rockland USA) DGGE gel was then photographed under UV transilluminator (Molecular Dynamics). Images were arranged by Image analysis (NIS Elements, Czech Republic). A binary matrix was created from the gel image by scoring of the presence or absence of each bend and then the cluster tree was constructed (programme GEL2k; Svein Norland, Dept. Of Biology, University of Bergen).

A knowledge of the stable carbon isotopic ratio of methane S13C-CH4 in natural systems can be useful in studies of the mechanisms and pathways of CH4 cycling (Sansone et al. 1997). Values of carbon isotope signature of methane (S13C-CH4) indicate biogenic nature of the methane, being usually in the range -27 % up to -100 % (Conrad 2004; Michener & Lajtha 2007). Whiticar et al. (1986) demonstrated that methane in freshwater sediments is isotopically distinguished by being relatively enriched in 13C (S13C = -65 to -50%) in contrast to marine sediments (-110 to -60%). Accordingly, the two precursors of methane, namely acetate and CO2/H2, yield methane with markedly different S13C values; methane from acetate is relatively enriched in 13C. Average minimum in the carbon isotopic composition of CH4 (-61.4 %) occurred deeper in sediments (60 cm) while average maximum in S13C-CH4 occured in the lower sediment depth of 30 cm. Enrichment of 13C in CH4 probably reflects aerobic CH4 oxidation because oxidation would result in residual CH4 with S13C-CH4 values less negative than the source CH4 (Barker & Fritz 1981; Chanton et al. 2004). However, this effect has been observed only at the study site IV.

Methanogenic potential (MP) was found to be significantly higher in the upper sediment layer compared to that from deeper sediment layer. Generally, average MP varied between 0.74-158.6 pM CH4 mL-1 WW hour-1 with the highest values found at site IV. Average methanotrophic activity (MA) varied between 0.02- 31.3 nM CH4 mL-1 WW hour-1 and the highest values were found to be at the downstream localities while sediment from sites located upstream showed much lower or even negative activity. Similar to MP, values of MA were significantly higher in sediments from upper layers compared to those from deeper layers (e. g. Figs. 3c, 3d).

World population is expected to increase from 6.0 billion in 1999 to 8.5 billion by 2025. Such an increase in population will intensify pressure on the world’s natural resource base (land, water, and air) to achieve higher food production. Increased food production could be achieved by expanding the land area under crops and by increasing yields per unit area through intensive farming. Chemical fertilizers are one of the expensive inputs used by farmers to achieve desired crop yields [40]. However, during the last years, the prices of fertilizers have been considerably increased. Furthermore, soil degradation and pollution, as well as underground water pollution, are serious consequences provoked by the exaggerate use of fertilizers during last decades. These two aspects are responsible for the global concern to reduce the use of fertilizers. The best way to do that is by selecting and growing nutrient use efficient genotypes. According to Khoshgoftarmanesh (2009) [41], cultivation and breeding of micronutrient-efficient genotypes in combination with proper agronomic management practices appear as the most sustainable and cost-effective solution for alleviating food-chain micronutrient deficiency.

Nutrient use efficient genotypes are those having the ability to produce high yields under conditions of limited nutrient availability. According to Chapin and Van Cleve (1991) [11] and Gourley et al. (1994) [42], as nutrient utilization efficiency (NUE) is defined the amount of biomass produced per unit of nutrient absorbed. Nutrient efficiency ratio (NER) was suggested by Gerloff and Gabelman (1983) [43] to differentiate genotypes into efficient and inefficient nutrient utilizers, i. e. NER=(Units of Yields, kgs)/(Unit of elements in tissue, kg), while Agronomic efficiency (AE) is expressed as the additional amount of economic yield per unit nutrient applied, i. e. AE=(Yield F, kg-Yield C, kg)/(quantity of nutrient applied, kg), where F applies for plants receiving fertilizer and C for plants receiving no fertilizer.

Many researchers found significant differences concerning nutrient utilization efficiency among genotypes (cultivars) of the same plant species [1,12,13,40,44-46] Biomass (shoot and root dry matter production) was used as an indicator in order to assess Zn efficient Chinese maize genotypes, grown for 30 days in a greenhouse pot experiment under Zn limiting conditions [1]. NUE is based on: a) uptake efficiency, b) incorporation efficiency and c) utilization efficiency [40]. The uptake efficiency is the ability of a genotype to absorb nutrients from the soil; however, the great ability to absorb nutrients does not necessarily mean that this genotype is nutrient use efficient. According to Jiang and Ireland (2005) [45], and Jiang (2006) [46], Mn efficient wheat cultivars own this ability to a better internal utilization of Mn, rather than to a higher plant Mn accumulation. We also found in our experiments that, despite the fact that the olive cultivar ‘Kothreiki’ absorbed and accumulated significantly greater quantity of Mn and Fe in three soil types, compared to ‘Koroneiki’, the second one was more Mn and Fe-efficient due to its better internal utilization efficiency of Mn and Fe (greater transport of these micronutrients from root to shoots) [12] (Tables 1 and 2). Aziz et al. (2011a) [47] refer that under P deficiency conditions, P content of young leaves in Brassica cultivars increased by two folds, indicating remobilization of this nutrient from older leaves and shoot. However, differences in P remobilization among Brassica cultivars could not explain the differences in P utilization. Phosphorus efficient wheat genotypes with greater root biomass, higher P uptake potential in shoots and absorption rate of P were generally more tolerant to P deficiency in the growth medium [6]. According to Yang et al. (2011) [48], on average, the K efficient cotton cultivars produced 59% more potential economic yield (dry weight of all reproductive organs) under field conditions even with available soil K at obviously deficient level (60 mg/kg).

The possible causes for the differential nutrient utilization efficiency among genotypes and/or species may be one, or combination of more than one, of the following: a) genetic reasons (genotypic ability to absorb and utilize efficiently, or inefficiently, soil nutrients), b) mycorrhiza colonization of the root system, c) differential root exudation of organic compounds favorizing nutrient uptake, d) different properties of rhizosphere, e) other reasons. According to Cakmak (2002) [49], integration of plant nutrition research with plant genetics and molecular biology is indispensable in developing plant genotypes with high genetic ability to adapt to nutrient deficient and toxic soil conditions and to allocate more micronutrients into edible plant products. According to Aziz et al. (2011b) [50], Brassica cultivars with high biomass and high P contents, such as ‘Rainbow’ and ‘Poorbi Raya’, at low available P conditions would be used in further screening experiments to improve P efficiency in Brassica. More specifically, a number of genes have been isolated and cloned, which are involved in root exudation of nutrient-mobilizing organic compounds [51,52]. Successful attempts have been made in the past 5 years to develop transgenic plants that produce and release large amounts of organic acids, which are considered to be key compounds involved in the adaptive mechanisms used by plants to tolerate P-deficient soil conditions [53-55]. However, differential root exudation ability in nature exists among different plant species. According to Maruyama et al. (2005) [56], who made a comparison of iron availability in leaves of barley and rice, the difference in the Fe acquisition ability between these two species was affected by the differential mugineic acid secretion. Chatzistathis et al. (2009) [12] refer that, maybe, a similar mechanism was responsible for the differential micronutrient uptake and accumulation between the Greek olive cultivars ‘Koroneiki’ and ‘Kothreiki’. According to the same authors, differential reduction of Fe3+ to Fe2+, or acidification capacity of root apoplast (which associates with the increase of Fe3+-chelate reductase and H-ATPase activities) among three Greek olive cultivars should not be excluded from possible causes for the significant differences observed concerning Fe uptake [14]. Mycorrhiza root colonization may be another responsible factor for the differential micronutrient utilization efficiency among genotypes. According to Citernesi et al. (1998) [57], arbuscular mycorrhiza fungi (AMF) influenced root morphology of Italian olive cultivars, thus nutrient uptake and accumulation, as well as plant growth. In our study with olive cultivars ‘Koroneiki’, ‘Kothreiki’ and ‘Chondrolia Chalkidikis’, we found significant differences concerning root colonization by AMF (that varied from 45% to 73%), together with great differences in uptake and utilization efficiency of Mn, Fe and Zn among them (particularly, 1.5 to 10.5 times greater amount of Mn, Fe and Zn accumulated by ‘Kothreiki’, compared to the other two cultivars, but the differences in plant growth parameters between the three cultivars were not impressive; this is why the micronutrient utilization efficiency by ‘Kothreiki’ was significantly lower, compared to that of the other two ones). Finally, the different properties of rhizosphere among genotypes may be another important factor influencing nutrient uptake and utilization efficiency, and of course biomass production. According to Rengel (2001) [58], who made a review on genotypic differences in micronutrient use efficiency of many crops, micronutrient-efficient genotypes were capable of increasing soil available micronutrient pools through changing the chemical and microbiological properties of the rhizosphere, as well as by growing thinner and longer roots and by having more efficient uptake and transport mechanisms.

Table 1. Distribution (%) of the total per plant quantity of Mn, Fe and Zn in the three vegetative tissues (root, stem and leaves) of the olive cultivars ‘Koroneiki’ and ‘Kothreiki’, when each one was grown in three soils (from parent material Marl, Gneiss schist. and Peridotite) with different physicochemical properties (Chatzistathis et al., 2009).

Fragments of the methanogen DNA (~470 bp) were amplified by PCR using mcrA gene specific primers. Primer sequences for mcrA gene are as follows, mcrA F 5′- GGTGGTGTACGGATTCACACAAGTACTGCATACAGC-3′,mcrA R 5′-

TTCATTGCAGTAGTTATGGAGTAGTT-3′. PCR amplification was carried out in 50 pl reaction mixture contained within 0.2 mL thin walled micro-tubes. Amplification was performed in a TC-XP thermal cycler (Bioer Technology, Hangzhou, China). The reaction mixture contained 5 pL of 10 x PCR amplification buffer, 200 pM of each dNTP, 0.8 pM of each primer, 2 pL of template DNA and 2.5 U of FastStart Taq DNA polymerase (Polymerase dNTPack; Roche, Mannheim, Germany). The initial enzyme activation and DNA denaturation were performed for 6 min at 95°C, followed by 5 cycles of 30s at 95°C, 30s at 55°C and 30s at 72°C, and the temperature ramp rate between the annealing and extension segment was set to 0.1°C/s because of the degeneracy of the primers. After this, the ramp rate was set to 1°C/s, and 30 cycles were performed with the following conditions: 30 s at 95°C, 30 s at 55°C, 30s at 72°C and a final extension at 72°C for 8 min. PCR products were visualised by electrophoresis in ethidium bromide stained, 1.5% (w/v) agarose gel.

Purified PCR amplicons (PCR purification kit; Qiagen, Venlo, Netherlands) were ligated into TOPO TA cloning vectors and transformed into chemically competent Escherichia coli TOP10F’ cells according to the manufacturer’s instructions (Invitrogen, Carlsbad, USA). Positive colonies were screened by PCR amplification with the primer set and PCR conditions described above. Plasmids were extracted using UltraClean 6 Minute Plasmid Prep Kit (MoBio, Carlsbad, USA), and nucleotide sequences of cloned genes were determined by sequencing with M13 primers in Macrogen company (Seoul, Korea). Raw sequences obtained after sequencing were BLAST analysed to search for the sequence identity between other methanogen sequences available in the GenBank database. Then these sequences were aligned by using CLUSTAL W in order to remove any similar sequences. The most appropriate substitution model for maximum likelihood analysis was identified by Bayesian Information Criterion implemented in MEGA 5.05 software. The

phylogenetic tree was constructed by the maximum likelihood method (Kimura 2- parameter model). The tree topology was statistically evaluated by 1000 bootstrap replicates (maximum likelihood) and 2000 bootstrap replicates (neighbour joining).

3. Results

Our working hypotesis suggested that along with the longitudinal profile of a stream, slope and flow conditions also change together with corresponding settling velocity, sediment composition and organic matter content. Thus, according to this prediction, sediment with prevalence of fine-grained particles containg higher amount of organic matter should dominate at the downstream stretches. Moreover, due to prevalence of anoxic environment, production of methane and its emissions was expected to be also higher here compared to that from upstream stretches. Based on our findings, it seems that this presumption is valid for the methane. In addition, we found higher methane concentrations in both the surface and interstitial water at the uppermost locality I compared to lower situated locality II. Similar situation with high methane concentration in the upstream part with subsequent decline further downstream was also reported from USA by Lilley et al. (1996). Dissimilarity of this first stretch is apparent in a comparison with the next, downstream laying stretch (locality II), represented by profile with steep valley and high slope. Generally, there were found very low methane concentrations either in surface or interstitial water and fluxes of emissions to atmosphere were also very low.

Flux rates of gaseous emissions into atmosphere depend on partial pressure of particular gas in the atmosphere and its concentration in a water, water temperature and further on the water depth and flow velocity. Thus, maximum peak of emissions may be expected during summer period and in well torrential stretch of the river. Silvennoinen et al. (2008), for example, found that the most upstream river site, surrounded by forests and drained peatlands, released significant amounts of CO2 and CH4. The downstream river sites surrounded by agricultural soils released significant amounts of N2O whereas the CO2 and CH4 concentrations were low compared to the upstream site. When consider seasonal distribution of methane emissions, it is clear, in concordance with above mentioned presumption, that majority of methane emissions was relesed during a warm period of the year (81%). Effect of temperature on methane production was also observed in southeastern USA where the most methane reased to the atmosphere during warm months (Pulliam 1993). In addition, close correlation between methane emissions and temperature was reported also from south part of Baltic Sea; the temperature has been found to be a key factor driving methane emissions (Heyer & Berger 2000).

These findings also indicate that we should be very carefull in making any generalization in total emissions estimation for any given stream or river. Even though some predictions can be made based on gas concentrations measured in the surface or interstitial water, results may be very different. From this point, noteworthy was locality IV; enormous concentrations of a methane found in the deep interstitial water were caused probably by very fine, clayed sediment containing high amount of organic carbon, as well as high DOC concentrations. Supersaturation led also to the enrichment of the surface water with methane — such places may be considered as very important methane sources for surface stream and, consequently source of emissons to the atmosphere.