Soil microbial diversity is still a difficult field to study, especially due to the several limitations of techniques. Since 95-99% of organisms cannot be cultivated by culture based — methodologies, the microbial diversity of soils shall be assessed by molecular biology techniques (Elsas & Boersama, 2011).

New DNA and RNA sequencing techniques provide high resolution information, especially using depth sequencing of metagenomic samples. Most of times a high amount of the obtained sequences are related with unknown genes or unknown organisms, involving a high cost per sample. Since soils imply in most of times in high spatial variability, which means high number of samples and replicates, fingerprinting techniques are recommended prior to sequencing in order to reduce costs for the high resolution techniques.

The first study of microbial diversity in Amazon soils using molecular techniques, by means of clone library, showed a high prokaryotic diversity (Borneman & Tripplett, 1997). Analyzing 100 sequences, differences between mature forest and pasture were detected, and about 18% of sequences were related to unknown Bacteria. A decade after, analyzing 654 clones similar results were detected in other study site, in which 7% of sequences could not be classified in any bacterial phyla (Jesus et al., 2009). In both studies land use changes was an important factor, and the unknown species were surveyed showing that depth sequencing should be used to better characterize the Amazon soils.

The most popular techniques for soil microbial communities fingerprinting are DGGE and the terminal restriction fragments length polymorphism (T-RFLP), which should be complemented by sequencing information to provide an overview of the study sites. Such techniques consist in extraction of nucleic acids from the soil samples; followed by amplification by PCR, aiming to target specific microbial groups according to the primers chosen (i. e. a universal primer for 16S rRNA gene will give a general prokaryotic overview of the samples). After PCR the amplicons should be analyzed by denaturizing gel separation (DGGE) or digestion with restriction enzymes and analysis of the dye labeled fragments (T — RFLP), or DNA sequencing. In turn metagenomics techniques allow sequencing without preview amplification by PCR and other techniques to be considered (Elsas & Boersama,

2011) .

T-RFLP consists in a PCR using dye labeled primers followed by a digestion with restriction enzymes, purification and reading in a DNA sequencer. The PCR amplifies a specific gene (mainly the 16S rRNA gene for prokaryotic diversity), and the restriction enzymes fragment the PCR products according to its polymorphism. The sequencer separates the fragments by length reading them in an electrophoresis run. So the presence of distinct fragment sizes found in different soil samples allows the diversity separation among them (Jesus et al., 2009). Clone libraries consist in cloning the PCR amplicons into bacterial vectors, followed by DNA sequencing. Since the PCR from environmental samples amplify different DNA sequences of different organisms at the same time, cloning technique allows the separation of amplicons and the sequencing of individual sequences (Borneman & Tripplett, 1997). Different studies using other molecular approaches to access the diversity of Amazon soils (Table 1) are described below.

In Western Amazon a T-RFLP analysis of the bacterial communities showed how it was influenced by soil attributes correlated to land use (Jesus et al., 2009). Community structure changed with pH and nutrient concentration. By DNA sequencing, bacterial communities presented clear differences among the different sites. Pasture and one of crops presented the highest diversity. Secondary forest presented similar diversity with the community structure of the primary forest, showing that bacterial community can be restored after agricultural use of the soils. Using the automated ribosomal intergenic spacer amplification (ARISA) technique distinct microbial structures were also observed between agricultural and forest soils (Navarrete et al., 2010). Seasonal changes in the two different years of sampling and distinct band patterns were observed for fungal, bacterial and archaeal richness.

Different patterns between Terra Preta soil (Dark Earth or Anthrosols) and an adjacent soil were observed in the Southwestern Amazon using 16S rRNA gene sequencing (Kim et al.,

2007) . Acidobacteria were predominant in both sites but 25% greater species richness was

Grossman et al. (2010) studying the three same Dark Earths sites, including one additional site, "Dona Stella", and using different molecular techniques also found difference among the samples.. T-RFLP of the 16S rRNA genes provided clear distinction between the two types of soils, and the same result was observed using DGGE and 16S rRNA sequencing. While T-RFLP provided a good fingerprinting between Anthrosols and Adjacent soils, 16S rRNA sequencing provided better resolution of the changes, indicating Verrucomicrobia as an important group to the Anthrosols, Proteobacteria and Cyanobacteria for Adjacent soils; while Pseudomonas, Acidobacteria and Flexibacter were found in both sites.

Studying the "Hatahara" site, differences in bacterial communities were also observed among Amazonian Dark Earth, black carbon and an adjacent oxisol by T-RFLP (Navarrete et al., 2010). By pyrosequencing it was shown that the most predominant phyla were Proteobacteria, Acidobacteria, Actinobacteria and Verrucomicrobia. About one-third of the sequences corresponded to unclassified Bacteria. For archaeal structure comparison by T — RFLP the soil attributes were more important than the type of soil, if it was Terra Preta or adjacent soils (Taketani, 2010). DNA sequencing showed that Candidatus spp. was the most abundant genera in both types of soils. An amoA clone library showed differences among the sampled sites, but also did not show differences between Terra Preta and the adjacent soil.

Using T-RFLP of bacterial 16S rRNA, distinct patterns were observed among biomes and land uses in the Southwestern Amazon (Lammel et al., 2010). Southwestern Amazon is divided in two mainly biomes, Tropical Forest and Cerrado (Brazilian Savanna). Over the last three decades these natural vegetations have been converted to pasture and agriculture. Land use was the most important factor to distinguish the bacterial communities, and it was correlated with the soil chemical changes: pH — due to liming and chemical fertility — due to fertilizers application. Pristine Tropical Forest and Cerrado formed distinct clusters, but they were more similar to each other than in relation to pasture or soybean field (Fig. 7).

In Eastern Amazon wetland soils Archaeal community was characterized by 16S rRNA gene libraries and by isolation of methanogenic Archaea (Pazinato et al., 2010). Archaeal diversity decreased with depth and the most of sequences belonging to Crenarchaeota, Methanosarcina and Metahnobacteriam genera were isolated from the sites.

These different techniques showed a high microbial diversity on Amazon soils. Fingerprinting techniques, such as T-RFLP and ARISA, were sensitive tools to detect difference in the microbial structure among the different sites and land uses. However only DNA sequencing provided a better resolution of the diversity, i. e. identify taxonomic groups and report unknown Bacteria that probably belong to new taxonomic groups. These pioneer studies showed, in general, that diversity does not decrease from pristine vegetation to agricultural uses, but the structure of microbial community as a whole is affected by land use changes. They can be restored after stopping the soil cultivation followed by secondary forest growth. The Amazon region is a "hot spot" regarding the soil microbial diversity.

The geoscience laser altimeter system (GLAS) is the only Lidar operating space-borne system. GLAS is the important part of NASA earth science enterprise carried on the ice, cloud and land elevation satellite (ICESat) from 12 January 2003 (Afzal et al., 2007). This instrument has three lasers, each of which has a 1064 nm lidar channel for surface altimetry and dense cloud heights, and a 532 nm lidar channel for the vertical distribution of clouds and aerosols (NASA, 2007). The three lasers have been operated one at a time, sequentially throughout the mission. The mission mode involved 33 day to 56 day campaign, numerous times per year, to extend the operation life. The main objective of the GLAS instrument is to measure the ice sheet elevations and changes in elevation through time. Second objective is the cloud detections and measurements, atmospheric aerosol vertical profiles, terrain elevation, vegetation cover, and sea ice thickness. The figure 1 shows the world elevation maps for 2009 ICESat elevation data (national snow and ice data center, NSIDC, available online at: http://nsidc. org/data/icesat/world_track_laser2F. html)

Nevertheless, only a small number of studies have used airborne lidar data to evaluate the DTM which was derived from satellite laser altimetry GLAS data over forested areas. GLAS which is only operating on board ICESat, records the full waveform returns, and provides a high precision elevation data with nearly global spatial coverage at a low end user cost (Fricker et al., 2005; Martin et al., 2005; Schutz et al., 2005; Magruder et al., 2007; Neuenschwander et al., 2008). Space-borne data are mainly used to model the global canopy height for evaluating carbon budget (Xing et al., 2010).

Recently, Duong et al. (2007, 2009) compared terrain and feature heights derived from the satellite (GLAS) observations with a nationwide airborne lidar dataset (the Actual Height model of the Netherlands: AHN). They found that the average differences between GLAS — and AHN-derived terrain heights are below 25 cm over bare ground and urban areas. Over forests, the differences are even smaller but with a slightly larger standard deviation of about 60 cm (Chen, 2010). Harding et al. (2001) utilized GLAS full waveform data to generate the average forest CHM, and the results presented the variations of important canopy attributes, such as height, depth, and the over-story, mid-story, and under-story forest layers. Sun et al. (2007,2008) applied GLAS waveforms to estimate the forest canopy height in the flat area in Northern China mountains, and found that the ICESat-derived forest height indices was well correlated with the field-measured maximum forest height R2 = 0.75 where R2 is the coefficient of determination.

ICESat World Elevations — Laser 2F

September 30 — October 11, 2009

A volume of 250 pl of fixed sample was centrifuged at 13000 x g for 3 minutes and the supernatant was removed. The sample was washed once by adding 1 ml PBS and centrifuged again. The sample was then divided into four tubes: a negative control containing no probe to observe autofluorescence, a negative control to observe non-specific binding events, a positive control where a universal eubacterial probe was added (Bact 338) and a sample to be hybridised by a specific AOB detection probe. The samples were serially dehydrated in successively increasing concentrations of molecular grade ethanol (60%, 80%, 100% v/ v). After adding 1 ml of the ethanol solution, the sample was vortexed and left for 3 minutes. The sample was then centrifuged at 13000 x g for 3 minutes and the supernatant was removed.

The following step is to hybridize the samples. Hybridisation buffer (HB) was prepared according to Amann et al (1990). HB was added so that the final volume including the probe will be 40 pl. Thus, for the negative control for autofluorescence, 40 pl HB is added. For a hybridisation containing only one probe (2ul), 38ul HB is added. For a hybridisation containing two probes ( 2+2 pl) 36 pl HB is added. The samples were prehybridized for 15 minutes at the hybridisation temperature. After prehybridisation, 2 pl of probe (50 ng/ pl) was added to the samples that were then incubated at the optimal hybridisation temperature for the given probe (Table 1) for at least 4 hours (or overnight).

Following hybridisation, the samples were centrifuged at 13000 x g for 3 minutes and the supernatant was removed. A volume of 0.5 ml of wash buffer was added and the sample was mixed using a pipette before being incubated for 15 minutes at the same temperature as the hybridisation step. The washing step was again repeated.

The samples were centrifuged again at 13000 x g for 3 minutes, the supernatant was removed and 1 ml of MilliQ water was added. Finally, the samples were centrifuged, the supernatant removed and the samples resuspended in 100 ul MilliQ water.

A 10 ul aliquot of the sample was added to a gelatine-coated slide with Teflon-coated wells of a known diameter (Appendix 4.1) and allowed to dry in a hybridization oven at 30oC. The sample spot on the slide was mounted in a small drop of the antifadent-Citifluor (AFI, Canterbury, UK). A cover glass was sealed carefully on the top of the slide by applying clear nail varnish to the edges to prevent movement during microscopy. The slide was then stored at -20oC in the dark and was prepared for viewing.

To provide reliable results on tree location, height, and number of detected trees the local maximum detection method is introduced by Vazirabad and Karslioglu (2009). This method determines the canopy height by applying a variable window size. The window size selection is related to the height and density of trees. High trees were easier to detect with large windows while short trees were easier to detect with small windows. The derivation of the appropriate window size to search for tree tops relies on the assumption that there is a relation between the height of trees and their crown size. In the 100*100 m test area, the correctness of single tree detection was calculated approximately 91%. The main reason for 9% error is referred to the not detected trees which are located in the corners and edges of the searched patch. To deal with this problem, the standard rectangle windows, variable size and variable shape are recommended (figure 6).

Four window sizes such as standard 3*3 m, standard 5*5 m, rotated 3*3 m (5*5 m), and rotated 5*5 m (9*9 m) are employed (each pixel represents one meter). Tree heights from CHM show that they vary between 2 m to 25 m (figure 6, right). The single tree detection method works in several steps. First generation of a tree height model is required to obtain the tree height. In this model the algorithm looks for all nonzero values and then creates a sorted list depending on the point height above ground (reducing data makes searching procedure faster). In the second step a tree height specific filtering is accomplished, by moving the window pixel by pixel over the tree height model. By changing the window size and shape repeatedly the procedure is continuing up to the end. Six reference patches are provided for counting manually the number of trees by using orthophotos. Density and height of trees are variable inside the patches. The total 7479 trees are detected in whole 1*1 km2. Tree height, dbh, and crown diameters are estimated in the whole area. All this information is adapted to the Log Transformed model for biomass estimation. Hence the total biomass which is given in kilograms for every tree in vegetation cover area is calculated as 1,966,123.3 kg.

Fig. 8. Biomass model and dbh

3. Conclusion

A comprehensive review has been done within this chapter concerning the use of Lidar for biomass estimation. As a consequence it can be said that the reasons for the underestimation of biomass in relation to the tree height need further studies. The development of large footprint Lidar systems on the spaceborne platform GLAS will allow the biomass estimations on a global scale. Spaceborne systems are restricted to record regional and detailed forest data mainly due to the ground track resolution of the system. However, since they receive data continuously, biomass estimation and carbon storage studies are possible every time which can be regarded as a great benefit. Airborne Lidar has the advantages of variable height flying systems and hence collects more precise data with respect to the shape of the terrain. Taking advantages of intensity information from Lidar data provides more information about the interpretation of the ground surface. There are several full waveform airborne Lidar operational systems. But some substantial challenges still exist such as the huge data processing and the interpretation of waveform for complex objects like trees. The fast progresses in computer technologies will help overcome such problems. On the other hand, the high point density in terrestrial systems can help to evaluate the results of other systems. Besides, it allows to model vegetation canopy characteristics particularly concerning tree species estimations in detail. From the data acquisition point of view, it is obvious that models and methods need to exploit the whole potential of the full waveform data for biomass estimation in future. The investigation on the point density in Lidar data represents that having a sufficient number of points has a large impact on the filtering results. The result of the segmentation filtering shows a high capability of adaptation in different landscapes. But it requires choosing correct segmentation parameters by considering the point density. Point spacing plays also an important role for the selection of the interpolation method with respect to the DTM, DSM, and CHM resolution. The methods for individual tree detection which are described and evaluated in the application part are performing well, but they are still under development. Hence more empirical studies are required for improving the quality of the approaches.

In the described study, we observed that the FlhD mutants made quantitatively higher amounts of biofilms on numerous carbon sources. Interestingly, the parental strain did not form higher quantities of biofilm than the mutant on any of the tested carbon sources. These observations shed light into the ongoing controversial debate, elucidating the role of motility in biofilm formation. In certain bacterial species including Yersinia enterocolitica, the presence of motility has been shown to be beneficial for biofilm formation (Wang et al.,

2007) . Several previous studies from our lab demonstrate that the absence of motility enhances the ability of E. coli to form substantial amounts of biofilm. As one example, strains transformed with the FlhD expressing plasmid pXL27 showed diminished biofilm forming capabilities (Prufi et al., 2010). Additionally, ongoing studies carried out in the lab with E. coli O157:H7 and the E. coli K-12 strains MC1000 and AJW678 point in the same direction, exemplifying our belief that FlhD and motility are detrimental to biofilm formation for our bacterial strains and under the conditions of our experiments (Sule et al., unpublished data). As a second observation, carbon sources that supported maximal biofilm formation by either strain all fed into glycolysis eventually, and produced actetate. Although the carbon sources that promoted the highest biofilm amounts were different for the two strains, they still were in the same pathway. The previous high-throughput experiment that had pointed towards nutriition as instrumental in determining biofilm associated biomass had also postulated acetate metabolism as one of the key players in biofilm formation (Prufi et al.,

2010) . Phosphorylation of OmpR and RcsB by the activated acetate intermediate acetyl phosphate (Kenney et al., 1995) and acetylation of RcsB by acetyl-CoA (Thao et al., 2010) have been described in the past. These activated 2CSTS response regulators then affect the expression level of biofilm associated cell surface organelles, such as flagella, type I fimbriae, curli, and capsule (Ferrieres & Clarke, 2003; Francez-Charlot et al., 2003; Oshima et al., 2002; Prufi, 1998; Shin & Park, 1995) (Figure 6). The positive effect on biofilm amounts of carbon sources that lead to the production of acetate can be explained with the combined inhibitory effect of acetyl phosphate and acetyl-CoA on flagella through OmpR and RcsB and the above described disadvantage of flagella and motility during biofilm formation. We however do not state that acetate is the sole controlling mechanism as the complexity of the bacterial system cannot be explained based on a small number of signaling molecules.

The most striking observation obtained from our studies pertains to the pattern of growth and biofilm formation on sugar acids. It was observed that the FlhD mutants grew to lower optical densities on sugar acids, but formed much higher amounts of biofilm as compared to the parental strain. Previous work from the Prufi lab had shown similar defects in growth of flhD mutants on sugar acids (Prufi et al., 2003), biofilm formation was not tested in that study. The inverse effect of sugar acids on growth and biofilm amounts may have implications in the intestine. Mutants in flhD have an early disadvantage in colonization, but recover after prolonged incubation (Horne et al., 2009). They even take over the population after more than two weeks (Leatham et al., 2005). The initial lack of colonization could be explained by the inability of the flhD mutant to degrade the numerous sugar acids present in the intestine (Peekhaus & Conway, 1998). On the other hand, the ability to take over the bacterial population at a later stage may have to do with the lack of the flagellin, which is a potent cytokine inducer (McDermott et al., 2000). The here discovered ability to make an increased amount of biofilm may add to the long term survival of flhD mutants in the intestine. Bacteria deep within the biofilm will be protected from the immune system, while metabolizing very slowly and not needing much nutrition.

Among the carbon sources that were the least supportive of biofilm formation, the inability of the C5-sugars to support growth and/or biofilm formation was the most striking. Ribose supported growth by the parent strain, but yielded the lowest biofilm amount of all tested carbon sources. The flhD mutant did not even grow on ribose. According to Fabich and coworkers (Fabich et al., 2008), ribose is not among the carbon sources that the E. coli K-12 strain MG1655 utilizes when bacteria colonize the intestine. Our data are consistent with this observation. Since E. coli O157:H7 EDL933 does actually utilize ribose in the intestine, ribose utilization may constitute a mechanism by which pathogenic E. coli can find a niche in the intestine to co-exist with the commensal E. coli strains.

The inability to grow on lyxose is also consistent with previous observations, where only a mutation in the rha locus enabled the bacteria to grow on lyxose via the rhamnose pathway (Badia et al., 1991). Normally, E. coli are unable to grow on lyxose. Most interesting is the behavior of the two strains on xylose. The parent E. coli strain was unable to grow on xylose. The flhD mutant did grow, while producing moderately low amounts of biofilm. Co­utilization of glucose and xylose by E. coli strains is of upmost importance during the production of biofuels, since the fermented plant material contains both, cellulose (polymer of glucose) and hemicellulose (polymer of glucose and xylose), in addition to lignin. Much research is currently dedicated to the genetic modification of E. coli that enables the bacteria to utilize xylose more efficiently (Balderas-Hernandez et al., 2010; Hanly & Henson, 2010). It would be interesting to see whether a mixture of our parent strain and its isogenic flhD mutant would be able to co-utilize glucose and xylose, particularly since the mutant produced a moderate amount of biofilm which can also be beneficial to the production of biofuels.

4. Conclusion

In summary, we developed an assay system that quantifies biofilm biomass in the presence of distinct nutrients. The assay enables the user to screen a large number of such nutrients for their effect on biofilm amounts. Examples of metabolic analysis relate back to previous literature, as well as giving raise to new hypotheses. Yielding further evidence for the previous hypothesis that acetate metabolism was important in determining biofilm amounts can serve as a positive control that the assay actually yields data of biological significance. Particularly with respect to life in the intestine and the production of biofuels, the data open new avenues of research by providing testable hypotheses. Overall, there is no limit to extensions of the assay into different bacterial species or serving the development of high — throughput data mining algorithms that will computerize the statistic/ metabolic analysis that we started in this study.

Both the radiometric and texture aspects are less efficient for area discrimination in the presence of speckle. Reducing the speckle would improve the discrimination among different land use types, and would make the usual per-pixel or textual classifiers more efficient in radar images. Ideally, this supports that the filters reduce speckle without loss of information.

In the case of homogeneous areas (e. g. agricultural areas), the filters should preserve the backscattering coefficient values (the radiometric information) and edges between the different areas. In addition for texture areas (e. g. forest), the filter should preserve the spatial variability (textual information).

Many adaptive filters that preserve the radiometric and texture information have been developed for speckle reduction. Filtering techniques generally can be grouped into multi­look processing and posterior speckle filtering techniques. Multi-look processing is applied during image formation, and this procedure averages several statistically independent looks of the same scene to reduce speckle (Porcello et al. 1976). A major disadvantage of this technique is that the resulting images suffer from a reduction of the ground resolution that is proportional to the number of looks N (Martin and Turner 1993). To overcome this disadvantage, or to further reduce speckle, many posterior speckle-filtering techniques have been developed. These techniques are based on either the spatial or the frequency domain. The Wiener filter (Walkup and Choens, 1974) and other filters with criteria of minimum mean-square error (MMSE) are examples of filtering algorithms that are based upon the frequency domain (Li 1988). The Wavelet approaches have been used to reduce speckle in SAR images, following Mallat’s (1989a, b) theoretical basis for multi-resolution analysis. Gagnon and Jouan (1997), Fukuda and Hirosawa (1998), and Simard et al. (1998) have successfully applied wavelet transformation to reduce speckle in SAR images. Gagnon and Jouan (1997) presented a Wavelet Coefficient Shrinkage (WCS) filter, which performs as well as the standard filters for low-level noise and slightly outperforms them for higher-level noise. The wavelet filter proposed by Fukuda and Hirosawa (1998) has satisfactory performance in both smoothing and edge preservation.

There are also other filters less frequently used, such as the mean filter, the median filter, the Kalman filter (Woods and Radewan 1977), the Geometric filter (Crimmins 1985), the adaptive vector linear minimum mean-squared error (LMMSE) filter (Lin and Allebach 1990), the Weighting filter (Martin and Turner 1993), the EPOS filter (Hagg and Sties 1994), the Modified K-average filter (Rao et al. 1995) and a texture-preserving filter (Aiazzi et al.

1997) .

Arbuscular mycorrhizal fungi (AMF) are also an important microbial group in soil, since they can form symbiosis with most of the plants, contributing to plant health and nutrition. AMF is beneficial to tropical plants and presents potential influence on soil processes and plant diversity, increasing the interest For studying this group this group, especially in Amazon where little is known about them (Sturmer & Siqueira, 2010).

Most of AMF studies consist on identification of its spores from soil samples. Since AMF produce spores significantly bigger than the other fungi species, it is possible to separate them from soil samples by sieve and centrifugation in a sucrose gradient. Up to now, the studies in Brazilian Amazon were made using this approach (Leal et al., 2009; Mescolotti et al. 2010; Sturmer and Siqueira, 2010).

In Southwestern Amazon an AMF study compared three land uses: native vegetation, soybean fields and pastures, in two regions: Sinop (Forest) and Campo Verde (Cerrado), both in Mato Grosso State, Brazil (Mescolotti et al., 2010). Comparing Forest with Cerrado different patterns were observed. The largest amount of spores was found in soybean fields in the Forest region, and the number of spores was the same for the three land uses in the Cerrado region. Glomus spp. was the most common specie found (Fig. 8.).

Glomus sp1 Glomus sp2 Glomus sp3

In Western Amazon different AMF patterns were observed in different land uses (Sturmer & Siqueira, 2010). A total of 61 AMF morphotypes were recovered and 30% could not be classified as known species. Acaulospora and Glomus were the most common genera identified in the sites and higher AMF richness values were found in agriculture and pasture sites, than in the pristine areas. AMF patterns were also influenced by land use in a survey using different trap cultures in the same region (Leal et al., 2009). Among all trap plants and land uses, a higher number of spores were found in pasture and young secondary forest. In total 24 AMF species were recovered. Acaulospora spp. (10 species) was the most common genera followed by Glomus spp (5 species). Both studies showed that in Amazon soils the land use change from pristine vegetation to pasture and crops did not reduce the AMF diversity and probably new AMF species were found.

The two most common methods to measure the utilization of substrates by microorganisms are Biolog (Garland & Mills, 1991; Zak et al., 1994) and the respiratory response to addition of substrates, known as substrate induced respiration (SIR) (Degens & Harris, 1997; Degens et al., 2001). The authors claim that these techniques are sensitive enough to distinguish changes in the catabolic diversity that occur over short periods of time, as well as large differences that occur in the soil after a few years (Graham & Haynes, 2005). The main substrates used for SIR analysis are shown in Table 2. The diverse substrates are dissolved in 2 ml of solution for each equivalent of 1g dry soil and incubated in sealed bottles. The flow of CO2 for each sample is usually measured in an Infra-Red Gas Analyser (IRGA), after incubation of bottles for 4 hours at 25oC.

Few studies have been carried out in the Amazon region. Among these is the work of Mazzetto et al. 2008. This research evaluated the possibility to check whether there are

catabolic patterns in the Amazon soils under agricultural cultivation, native forest and pasture. A total of 60 areas were chosen distributed as: 20 native forest, 20 agricultural lands and 20 pasture sites in the regions of Mato Grosso and Rondonia, which are part of the Brazilian Amazon.

At first analyses were performed only in the native areas, which could be separated in Amazon rainforest, Cerrado and Cerradao. The low catabolic response obtained in the Cerrado soils may be linked to the frequent firing process that this biome suffers (Fig. 9). According to Arocena & Opio (2003), fire has a major impact on the physical (aggregate stability, clay content) and chemical (pH) soil properties, with significant influence on the microbial biomass. According to Hart (2005) fire alters the structure of microbial biomass, this being a selection factor in areas exposed to periodic events. Campbell et al. (2008) demonstrated in their studies that the use of carbonated substrates decreases with burning of area, suggesting a lower resistance/resilience of the microbial community. Among the substrates that can be influenced by burning of vegetation is arginine, which has a low response in Cerrado and Cerradao soils. The use of arginine in the microbial metabolism requires the presence of deaminase arginine enzyme, which is inhibited by fire.

Regarding the disturbed areas analysis were realized aiming to characterize the diversity of soil microbial biomass at these sites (Fig. 10), and to check the possible separation of the areas through multivariate statistical analysis (Fig. 11).

Soils under pasture had significant catabolic responses to amine and carbohydrate, and individually to the substrates glutamic acid, glutamine, glucose, mannose, serine and fumaric acid. In contrast soils under native vegetation had significant responses to malonic acid, malic acid and succinic acid. Soils under agriculture use did not show significant responses to any substrate examined, however they showed expressive responses to the aminoacids group, but not statistically different from the pasture soil (Fig. 10).

The canonical analysis showed that datasets related to CDP had great success in distinguishing the three land uses analyzed (Fig. 11). CV1 explained 67.5% of the variability observed, separating pastures from native areas and agriculture. Averages of native and agriculture areas were negative (-1.38 and -0.58, respectively) for CV1, while the average of pasture was positive (1.96). Asparagine, histidine and quinic acid with highly negative values were closely tied to native areas and agriculture, while glutamic acid and glucosamine had great representation in relation to pasture. CV2 explained 32.5% of the variability observed, separating native areas from agriculture and pastures. The average of native areas for the second axis was positive (1.34), while those of agriculture and pastures were negative (-1.02 and -0.32, respectively). The main substrates that provided this separation were serine and quinic acid, which showed negative values (linked to pasture and agriculture), and the tartaric acid, considered the more representative substrate related to native areas.

Among the major substrates involved, serine is documented as present in root exsudates (Bolton et al., 1992), quinic acid is a component of plant tissues (Gebre & Tchaplinski, 2002), and tartaric acid is one of main intermediary compounds of the Krebs cycle, in the basic metabolism of aerobic microorganisms (Tortora et al., 2005).

When only one ecoregion (Alto Xingu) was selected for analysis results of the CDP approach was even more significant (Fig. 12). CV1 explained 66.5% of the variability, separating native areas (-7.87 — negative score) of areas under agriculture and pasture (4.33 and 0.49 — positive scores, respectively). The main substrates involved in such axis were: succinic acid and malonic acid, with negative values. With positive values quinic acid and glucose also contributed to the separation observed. CV2 explained the remaining 33.5% of the variability, separating areas under pasture (4.84 — positive score) of native and agricultural areas (-2.04 and -2.65 — negative scores, respectively). Among the major substrates in this axis are highlighted asparagine and tartaric acid showing negative values, while lysine and pantothenic acid had positive values (Fig. 12).

Taking into account only data corresponding to the agricultural areas present in the database, we could distinguish areas under perennial crops, tillage and conventional tillage. By means of discriminant analysis the reallocation of data was performed in order to observe if datasets was homogeneous among the land uses analyzed. Data from areas under conventional tillage were relocated with 70% success, while data from conventional tillage and perennial cultivation showed higher percentage (98% and 100%, respectively). The same analysis was performed for pasture data that could be reallocated according to the following classification: typical pasture (100% success), improved pasture (95% success) and degraded pasture (91% success). This high percentage of reallocation of data shows that the microbial communities analyzed by CDP have high correlation with the use of land deployed. According to Mazzetto et al. 2008 the application of substrate induced respiration was efficient in distinguishing the land uses. The composition of microbial community revealed, through CDP approach, a close relationship with vegetation cover, regardless of climatic factors or the soil type.

As highlighted by Totola & Chaer (2002) and San Miguel et al. (2007), the importance of functional and catabolic diversity lies in the fact that only based on changes in the genetic diversity it is not possible infer whether some functions of soil were lost or not. The physiological profile of microbial community allows accessing the metabolic capacity of the microbial biomass as a whole, through tests realized with specific carbon sources defined in the laboratory.

2. Conclusion

Soil microbial diversity is still a difficult field to study, since 95-99% of organisms cannot be cultivated by culturing methodologies. The most popular techniques for soil microbial communities fingerprinting are DGGE and T-RFLP, which should be complemented by sequencing information to provide an overview of the study areas, especially those with high spatial variability that requires the collection of a high number of samples and replicates. New DNA and RNA sequencing provide high resolution information especially using depth sequencing of metagenomic samples.

Using DGGE, T-RFLP and other approaches, it has been clear that land use changes influenced significantly the diversity and structure of microbial communities in the Amazonian soils. Data available of DNA sequencing provided a high resolution view pointing changes of specific microbial groups and also the high quantities of unknown microorganisms. Catabolic diversity profile was efficient in distinguishing the land uses. The composition of microbial community revealed, through CDP approach, a close relationship with vegetation cover, regardless of climatic factors or the soil type.

Land use changes modify the genetic structure of microbial communities in the Amazonian soils, but they do not reduce the diversity in the areas affected by deforestation and conversion for pasture and crops, in comparison with the native areas. Also many new species are to be discovered in such areas.

An extensive test of laser profiler was performed at the Stuttgart University (1990) where Differential Global Positioning System (DGPS) and Inertial Measurement Unit (IMU) was integrated in the laser system for the first time to provide precise positioning and orientation (attitude) of the airborne platform. Soon after that, the scanning mechanism was designed by Optech company (Canada — ALTM system)

Laser profiler was developed in the forestry research by NASA’s Goddard space flight center (GSFC) on the basis of Riegl laser rangefinder with 20 ns wide laser pulse and repetition rate of 2 kHz. There are three main commercial suppliers of airborne laser scanning systems, Optech International Inc., Leica Geosystem, and Riegl which are producing the data for the forest inventory and biomass estimation researches.

Generally, other companies completed their systems which utilize these three laser scanner instruments. Besides these commercial systems, a number of other systems built by US government research agencies are offered for scientific research purposes, like NASA, ATM, RASCAL, SLICER, Laser Vegetation Imaging Sensor (LVIS), and ScaLARS. LVIS has been developed by NASA for the topography mapping, elevation and the forest growing on it. A special design of scanning system such as the full waveform is required for the scanning of vegetation covered regions to capture the reflected pulse in different returns. This scanner has been used in USA (California, eastern states), Central America (Costa Rica and Panama). It was also applied in Amazonian forests of Brazil to generate direct measurements of canopy height and relatively aboveground biomass map. (Shan and Toth, 2009)

The distribution of hybridized cells was subsequently visualised by means of a Leica TCS SP2 UV confocal laser scanning microscope (CLSM) equipped with Leica DMRXA microscope. Images were captured and processed using LCS V2.5.1040-1 software. For observation x 60 Na 1.32 lenses were applied.

The CLSM was run in the following mode: single channel for Fluorescene and double channel for Carbocyanine-5. Fluorescene was detected using excitation at 488 nm and a long pass emission filter in the range of 500-530 nm. Cy5 was detected using excitation at 633 nm and a long pass emission filter of 650-680 nm. The artificial colours green and red were assigned to the monochrome images acquired in the fluorescene and Cy5 channels respectively. The LCS software actively mixed colours so that a cell emitting red and green (the AOB) would appear yellow. For each sample, only 5 fields of view were randomly recorded in view of the time and budget available for the process.

1.1.2 Enumeration technique

An Excel spreadsheet constructed by Coskunur (2000) was used to carry out the calculation based on Equation 1 below:

where

K = average number of microcolonies in one ml of sample

A1 = area of sample spot (the area can be calculated from the diameter of the sample spot, [n(D/2)2])

A2 = area of one field view

N = average number of ammonia oxidizer microcolonies/field of view V = volume of sample applied Vo = original volume of sample

ODF = other dilution factors not considered above may be required (e. g. volume of sample spun down). Where no ODF, default value = 1 The spreadsheet was designed for the quantification of AOB population in wastewater treatment plants following FISH and quantification typically using CLSM produced images. It requires that the user inputs data concerning the number of AOB microcolonies, the shortest and longest diameter of the microcolonies, area measurements of the fields of view and sample spots and dilution factors used in FISH. The spreadsheet returns the average number of microcolonies and geometric mean diameter. This data sheet can also be used to calculate the concentration of AOB in mg/l, the % AOB in terms of total bacterial population (measured by volatile suspended solids, VSS), following an empirically determined conversion factor, in terms of total cell numbers.

Conxita Royo and Dolors Villegas

IRTA (Institute for Food and Agricultural Research and Technology), Generalitat of Catalonia Centre, UdL-IRTA

Spain

1. Introduction

Small-grain cereals are the food crops that are most widely grown and consumed in the world. Wheat and rice jointly supply more than 55% of total calories for human nutrition, occupying about 59% of the total arable land in the world (225 and 156 million ha, respectively). Global production is around 682 million metric tons for wheat and 650 million metric tons for rice (FAOSTAT, 2008). Wheat is a very widely adapted crop, grown in a range of environmental conditions from temperate to warm, and from humid to dry and cold environments. Demand for wheat and rice will grow faster in the next few decades, and yield increases will be required to feed a growing world population. Because land is limited and environmental and economical concerns constrain the intensification of such crops, yield increases will have to come primarily from breeding efforts aimed at releasing new varieties that provide higher productivity per unit area.

The most integrative plant traits responsible for grain yield increases in small-grain cereals are the total biomass produced by the crop and the proportion of the biomass allocated to grains, the so-called harvest index (Van den Boogaard et al., 1996). The product of these traits provides a framework for expressing the grain yield in physiological terms and for contextualizing past yield gains in small-grain cereals, particularly wheat and barley. Retrospective studies conducted with wheat frequently associate increases in yield with increases in partitioning of biomass to the grain, with small or negligible increases (Austin et al., 1980, 1989; Royo et al., 2007; Sayre et al., 1997; Siddique et al; 1989; Waddington et al.,

1986) , or even significant decreases (Alvaro et al., 2008a) in total biomass production. Increases in biomass have been reported in spring wheat (Reynolds et al., 1999; 2001), winter bread wheat (Shearman et al., 2005), and durum wheat (Pfeiffer et al., 2000; Wadington et al., 1987).

Since harvest index has a theoretical maximum estimated to be 0.60 (Austin, 1980), increases in grain yield of more than 20 percent cannot be expected through increasing the harvest index above the maximum levels reached currently by some wheat genotypes (Reynolds et al., 1999; Richards, 2000; Shearman et al., 2005). It is therefore generally believed that future improvements in grain yield through breeding will have to be reached by selecting genotypes with higher biomass capacity, while maintaining the high partitioning rate of photosynthetic products (Austin et al., 1980; Hay, 1995).

Total dry matter is mainly determined by two processes: i) the interception of incident solar irradiance by the canopy, which depends on the photosynthetic area of the canopy; and ii)

the conversion of the intercepted radiant energy to potential chemical energy, which relies on the overall photosynthetic efficiency of the crop (Hay & Walker, 1989). The relationship between above-ground biomass and yield has been demonstrated empirically in wheat. Positive associations (R2=0.56, P<0.05) have been reported between biomass at maturity and yield in durum wheat (Waddington et al., 1987), and between biomass at anthesis and yield in bread wheat (Reynolds et al., 2005; Shearman et al., 2005; Singh et al., 1998; Tanno et al., 1985; Turner, 1997; Van der Boogaard et al., 1996), durum wheat (Royo et al., 2005), barley (Ramos et al., 1985) and rice (Turner, 1982). In a study conducted in Mediterranean conditions with 25 durum wheat cultivars, Villegas et al. (2001) found a strong association (R2=0.75, P<0.001) of the biomass accumulated from the first node detectable stage with anthesis and yield. Vegetative growth before anthesis becomes particularly important when stresses during grain filling such as those caused by rising temperatures and falling moisture supply —usually occurring after anthesis in Mediterranean environments— limit the crop photosynthesis, forcing yield to depend greatly on the remobilization to the grain of pre-anthesis assimilates accumulated in leaves and stems (Alvaro et al., 2008b; Palta et al., 1994; Papakosta and Gagianas, 1991; Shepherd et al., 1987). The contribution of pre-anthesis assimilates to wheat grain yield and the efficiency of dry matter translocation to the filling grains seem to have increased in the last century as a consequence of breeding (Austin et al., 1980; Alvaro et al., 2008a, b).

Biomass assessment is thus essential not only for studies monitoring crop growth, but also in cereal breeding programs as a complementary selection tool (Araus et al., 2009). Tracking changes in biomass may also be a way to detect and quantify the effect of stresses on the crop, since stress may accelerate the senescence of leaves, affecting leaf expansion (Royo et al., 2004) and plant growth (Villegas et al., 2001).

Biomass assessment in breeding programs, in which hundreds of lines have to be screened for various agronomical traits in a short time every crop season, is not viable by destructive sampling because it is a time-and labor-intensive undertaking, it is subject to sampling errors, and samplings reduce the final area available for determining final grain yield on small research plots (Whan et al., 1991). Originally used in remote sensing of vegetation from aircraft and satellites, remote sensing techniques are becoming a very useful tool for assessing many agrophysiological traits (Araus et al., 2002). The measurement of the spectra reflected by crop canopies has been largely proposed as a quick, cheap, reliable and non­invasive method for estimating plant aboveground biomass production in small-grain cereals, at both crop level (Aparicio et al., 2000, 2002; Elliot & Regan, 1993; R. C.G. Smith et al., 1993) and individual plant level (Alvaro et al., 2007).