**3. Conclusions**

**Variables Values considered Units Variables Values obtained Units** CvgEE1 0,90 (\$/MWh) CvpEE5 66.060,00 (\$/year) CvgH2 0,55 (\$/MWh) CvpH6 18.837,50 (\$/year)

*CvgEE: average annual variable cost depending on the generation of electric energy in cogeneration system.*

*CvgH: average annual variable cost depending on the generation of thermal energy in cogeneration system.*

*CvpEE: average annual variable cost of electricity in the cogeneration system, according Eq. (24).*

*CvpH: average annual variable cost of thermal energy in the cogeneration system, according Eq. (25).*

**Variables Values considered Units Variables Values obtained Units** Cbf1 75.004,50 (\$/year) Cv5 216.993,84 (\$/year)

TE3 34,25 (GWh/year) EE4 73,40 (GWh/year)

*Biotechnological Applications of Biomass*

*EE: Annual electricity generated (useful annual electric exergy).*

*Data and results estimated by Eqs. (24) and (25).*

Ct2 57.091,84 (\$/year) CvpEE3 66.060,00 (\$/year) CvpH4 18.837,50 (\$/year)

Variable cost2 216,99 (1000 \$/year)

**considered**

Total cost2 1.014,02 (1000 \$/year)

*Ct: average annual cost of transporting forest biomass, according Eq. (27).*

*CvpEE: average annual variable cost for production of electricity, according Eq. (24).*

*CvpH: average annual variable cost for production of thermal energy, according Eq. (25).*

*Cv: average annual variable cost for production of electrical and thermal energy, according Eq. (23).*

**Variables Values considered Units Variables Values obtained Units** Fixed costs1 797,03 (1000 \$/year) total cost3 1.014,02 (1000 \$/year)

107,65 (GWh/year) exergoeconomic

**Units Variables Values**

cost3

**obtained**

**Units**

106,16 (kWh/\$)

*Cbf: cost of forest biomass, according Eq. (28).*

*Data and results estimated by Eq. (23).*

*Data and results estimated by Eq. (31).*

**Variables Values**

*Data and results estimated by Eq. (32).*

*TE: Thermal useful annual exergy.*

*1*

*2*

*3*

*4*

*5*

*6*

*1*

*2*

*3*

*4*

*5*

*1*

*2*

*3*

*1*

*2*

*3*

**178**

**Table 16.**

**Table 15.**

*According to Eq. (18).*

*According to Eq. (23).*

*According to Eq. (31).*

Useful annual exergy1

*According to Eq. (18).*

*According to Eq. (31).*

*According to Eq. (32).*

**Table 14.**

**Table 13.**

The parameter used in this chapter to assess the feasibility of the proposed agroenergetic alternative is the exergoeconomic cost.

The non-conventional alternative evaluated is the use of a silvopastoral system aimed at the production of forest biomass and its energy utilization in a thermal and electric energy cogeneration system.

The economic feasibility analysis is a cost/benefit analysis, which can be done based on tariff parameters practiced in the energy market.

For that, comparative measures with the values practiced in the energy sector can be used.

Just as an example, a comparison parameter is the value practiced from 2003 onwards for the average electricity supply tariff for the Brazilian electric system for all consumption classes and geographic regions of the country [28], which is (61.40 \$/MWh), much higher than the value found in the present simulation (9.42 \$/MWh).

Therefore, this study presents indications of good viability for this energy alternative as a possibility, which can be inserted among the renewable energy options in the energy matrix of the future.


**3. Conclusions**

Food chains and food webs describe the structure of communities and their energy flows, and they present interactions between species. Recently, diverse methods have been developed for both experimental studies and theoretical/computational studies. They improve our fundamental ecological knowledge and are e ffectively used for various applications, including the monitoring and assessment of ecosystems. In particular, ecological monitoring and assessment have advanced in recent decades. Along with the progress of molecular and eDNA techniques, the process of monitoring and assessment has become rapid and accurate. A wide variety of ecological disturbances associated with temperature and salinity changes and other environmental factors are being recognized as threats to the food chain functions of freshwater and marine ecosystems.

This Special Issue included empirical studies on food chains and food webs in aquatic ecosystems. They confirmed the usefulness of their methods including isotope, DNA-barcoding with gut contents, and eDNA for biological monitoring and ecosystem assessment. In further studies, however, theoretical and computational approaches including food web modelling and network analyses are expected to characterize quantitatively the interactions among species as well as ecosystem structures and dynamics through the collaborative works between experimental and computational scientists.

**Author Contributions:** Conceptualization, I.-S.K. and Y.-S.P.; writing—original draft preparation, I.-S.K. and Y.-S.P.; writing—review and editing, I.-S.K. and Y.-S.P. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the National Research Foundation of Korea (NRF) funded by the Korean government (MSIP) (grant numbers NRF-2019R1A2C1087099 and NRF-2020R1A2C1013936).

**Acknowledgments:** We would like to thank all contributors in this Special Issue and all reviewers who provided very constructive and helpful comments to evaluate and improve the manuscripts.

**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.


Preface

The book, Analytical Chemistry, deals with some of the more important areas of the title's subject matter, which we hope will enable our readers to better appreciate just some of the numerous topics of importance in Analytical Chemistry today. The topics being covered herein deal with the following topics: 1) Principal Component Analysis: The Basic Building Block of Chemometrics; 2) Mineralogy and Geochemistry of Subbituminous Coal; 3) Kinetic Methods of Analysis with Potentiometric and Spectrophotometric Detectors; 4) Analytical Chemistry - Today and Tomorrow, Biochemical and Chemical Information, Basic Standards, Analytical Properties and Challenges; 5) Analytical Method Validation in Biotechnology Today, Method Validation, ICH/FDA Requirements, and Related Topics; and 6) Peptide and Amino Acid Separations and Identification - Chromatography, Spectrometry and Other Detection Methods. These topics are up-to-date, thoroughly referenced and literature cited to the present time, and truly review the major and most important topics and areas in each of these subjects. They should find widespread acceptance and studious involvement on the part of many readers interested in such generally important areas of Analytical Chemistry today. Analytical Chemistry has come to assume a tremendously important part of many, if not most, research areas in chemistry, biochemistry, biomedicine, medical science, proteomics, peptidomics, metabolomics, protein/peptide analysis, forensic science, and innumerable other areas of current, scientific research and development in modern times. As such, it behooves all those involved in these and other research and development areas to keep themselves up-todate and current in the very latest developments in these and other areas of Analytical Chemistry.

> **Ira S. Krull** Chemistry and Chemical Biology, Northeastern University, Boston, USA

**Chapter 1**

© 2012 Cordella, licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use,

distribution, and reproduction in any medium, provided the original work is properly cited.

© 2012 Cordella, licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

**PCA: The Basic Building Block of Chemometrics**

Modern analytical instruments generate large amounts of data. An infrared (IR) spectrum may include several thousands of data points (wave number). In a GC-MS (Gas Chromatography-Mass spectrometry) analysis, it is common to obtain in a single analysis 600,000 digital values whose size amounts to about 2.5 megabytes or more. There are different methods for dealing with this huge quantity of information. The simplest one is to ignore the bulk of the available data. For example, in the case of a spectroscopic analysis, the spectrum can be reduced to maxima of intensity of some characteristic bands. In the case analysed by GC-MS, the recording is, accordingly, for a special unit of mass and not the full range of units of mass. Until recently, it was indeed impossible to fully explore a large set of data, and many potentially useful pieces of information remained unrevealed. Nowadays, the systematic use of computers makes it possible to completely process huge data collections, with a minimum loss of information. By the intensive use of chemometric tools, it becomes possible to gain a deeper insight and a more complete interpretation of this data. The main objectives of multivariate methods in analytical chemistry include data reduction, grouping and the classification of observations and the modelling of relationships that may exist between variables. The predictive aspect is also an important component of some methods of multivariate analysis. It is actually important to predict whether a new observation belongs to any pre-defined qualitative groups or else to estimate some quantitative feature such as chemical concentration. This chapter presents an essential multivariate method, namely *principal component analysis* (PCA). In order to better understand the fundamentals, we first return to the historical origins of this technique. Then, we will show - via pedagogical examples - the importance of PCA in comparison to traditional univariate data processing methods. With PCA, we move from the one-dimensional vision of a problem to its multidimensional version. Multiway extensions of PCA, PARAFAC and Tucker3 models are exposed in a second part of this chapter with brief historical and bibliographical elements. A PARAFAC example on real data is presented in order to illustrate the interest in this powerful

Christophe B.Y. Cordella

http://dx.doi.org/10.5772/51429

**1. Introduction**

Additional information is available at the end of the chapter

technique for handling high dimensional data.


**4. Discussion**

The seed bank has been the subject of studies in different forest types. However, literature surveys carried out in the present study reveal that very few studies reporting on the Amazon rainforest have been published. This highlights the need to expand research on seed banks in natural and anthropized areas. The data used in this chapter account for regions close to the capitals of the states of Amazonas and Pará (**Table 1**), owing to easy access by highways, in addition to universities and research institutes with a long tradition in ecological studies.

Changing land use in the Brazilian Amazon threatens the extinction of a significant number of species and consequent loss of environmental functions and services of the largest tropical forest on the planet [35, 37, 62–64]. Despite repeated warnings and concerns of conservationists and the scientific community, deforestation continues at an accelerated rate [63, 65]. The replacement of the forest by pasture has been the main means of occupation and use of the land, as agriculture advances in the region [65–67]. The resilience of the forest and natural regeneration depends on several factors. Among them are type and intensity of the initial disorder, recurrence of disorders, topography, soil type, and the maintenance of accessible propagation sources [3, 36, 40, 68, 69]. The main mechanisms involved in the regeneration of these altered areas occur through the seed bank, dispersion of seeds from nearby areas, and vegetative regeneration, which includes surviving plants capable of sprouting from both the aerial part and the roots [3, 8, 69].

We generally do find a high density of seeds in the altered areas. Nevertheless, the type and intensity of disturbances and changes occurring around in forest areas contribute to corresponding changes in the floristic composition of the seed bank [5]. Moreover, invasive, or ruderal, species are common and cause the impoverishment of the seed bank [70–74]. Thus, understanding the effects of different types of land use on the seed bank is fundamentally essential to understand the evolution of the landscape, identify obstacles to the restoration of the forest, and, consequently, ensure the regeneration of forest environments and maintenance of environmental services [41, 64, 65, 69, 70, 74].

Our data support the results of other studies carried out in tropical regions where the density of seeds in the topsoil is highly variable [5, 10, 14, 21, 33, 34]. Seed density has increased from the old-growth forest to the altered areas (**Figure 1**). The observed variations in seed density in each class of land use (**Table 1**, **Figure 1**) reflect differences in forest typology, canopy opening, and sampling time among

**77**

*Ecology of the Seed Bank in the Amazon Rainforest DOI: http://dx.doi.org/10.5772/intechopen.94745*

in the process of ecological succession.

tive age, and rate of evolution [95].

the areas [9, 14, 19, 21]. In addition, intrinsic variations are associated with the seed

The seed bank is characterized by the occurrence and dominance of a limited number of botanical families. These families contribute markedly to common species and genera in secondary forests or the early stages of forest regeneration. Among the ten most abundant families in the seed bank, the presence of a high number of seedlings belonging to the Melastomatacea family stands out. This family has high diversity in the Neotropics, with approximately 3000 species, being composed of shrubs, lianas, herbs, epiphytes, and trees [75]. In the Amazon basin, the family is mainly composed of small tree species and shrubs, and it occurs in

The Melastomataceae family is an important component of the seed bank of the Amazon rainforest [7, 19, 60], as well as other forest types in the Neotropical region [9, 23, 79, 80]. Its high abundance can likely be attributed to the number of small seeds produced per individual [18, 81], longevity [82], and photoblastic seeds, favoring the recruitment of seedlings in environments with greater luminosity [83–86]. The Melastomataceae family is composed of pioneer species that require high to low light, as well as species tolerant to shading [86–88]. Given the great importance of this family to the seed bank, more detailed studies need to be performed in order to better understand the spectrum and functionality of this group

Urticaceae seedlings consist of *Cecropia*, *Coussapoa*, and *Pourouma* configured as an important component in forest types with low land-use intensity, such as oldgrowth forests, forest fragments with different historical changes, and forests with logging of wood species (**Figure 2**). The pioneer species of *Cecropia* stand out for colonizing secondary areas that have suffered low impact disturbances, those are

In the present study, seedlings of tree species predominated in the seed bank, except for agricultural areas where herbs predominated (**Figure 3**). A decrease in tree seedlings and an increase in herbs can already be observed in secondary forests. Herbs increased density with intensity of disturbance, with low density in old-growth forests (**Figure 3**). In general, the forest seed bank is dominated by trees (49% on average), while cultivated areas and secondary forests are dominated by herbs (75% on average) [14]. The high density of herbs in secondary forests and forest fragments results from the occurrence of anthropized areas around these areas [5, 14, 22, 29, 33]. The importance of shrubs and small trees is little studied in successional processes in tropical forests. Most studies focus on changes in the structure and floristic composition of the woody layer [89–94], but such studies exclude many groups that occur in high density in the seed bank, groups which can play a relevant role in the mechanisms of ecological succession. These groups also respond to different time scales in biological attributes, such as lifetime, reproduc-

Secondary forests in the Amazon may result from the abandonment of areas previously used for different purposes, such as shifting agriculture, pastures, and mining [63, 65, 96, 97], which rarely originate from natural disorders [46].  These areas usually have a seed bank with high density and composition mainly consisting of locally produced herb seeds [41, 65, 69]. This seed bank is very similar to that with established vegetation cover [39–44], which is not seen in old-growth forests [14, 98, 99]. Among the ten most abundant families in the seed bank, common herbs from high-impact degraded areas, such as Poaceae, Rubiaceae, Asteraceae, and Cyperaceae predominate. However, seedlings of typical families from the seed bank of old-growth forests do occur (**Figure 2**). Floristic composition

bank [20, 54], as well as methodological differences [15, 52, 56].

high abundance and diversity in the forest understory [76–78].

more important in the succession processes of these areas [39–43].

#### *Ecology of the Seed Bank in the Amazon Rainforest DOI: http://dx.doi.org/10.5772/intechopen.94745*

the areas [9, 14, 19, 21]. In addition, intrinsic variations are associated with the seed bank [20, 54], as well as methodological differences [15, 52, 56].

The seed bank is characterized by the occurrence and dominance of a limited number of botanical families. These families contribute markedly to common species and genera in secondary forests or the early stages of forest regeneration. Among the ten most abundant families in the seed bank, the presence of a high number of seedlings belonging to the Melastomatacea family stands out. This family has high diversity in the Neotropics, with approximately 3000 species, being composed of shrubs, lianas, herbs, epiphytes, and trees [75]. In the Amazon basin, the family is mainly composed of small tree species and shrubs, and it occurs in high abundance and diversity in the forest understory [76–78].

The Melastomataceae family is an important component of the seed bank of the Amazon rainforest [7, 19, 60], as well as other forest types in the Neotropical region [9, 23, 79, 80]. Its high abundance can likely be attributed to the number of small seeds produced per individual [18, 81], longevity [82], and photoblastic seeds, favoring the recruitment of seedlings in environments with greater luminosity [83–86]. The Melastomataceae family is composed of pioneer species that require high to low light, as well as species tolerant to shading [86–88]. Given the great importance of this family to the seed bank, more detailed studies need to be performed in order to better understand the spectrum and functionality of this group in the process of ecological succession.

Urticaceae seedlings consist of *Cecropia*, *Coussapoa*, and *Pourouma* configured as an important component in forest types with low land-use intensity, such as oldgrowth forests, forest fragments with different historical changes, and forests with logging of wood species (**Figure 2**). The pioneer species of *Cecropia* stand out for colonizing secondary areas that have suffered low impact disturbances, those are more important in the succession processes of these areas [39–43].

In the present study, seedlings of tree species predominated in the seed bank, except for agricultural areas where herbs predominated (**Figure 3**). A decrease in tree seedlings and an increase in herbs can already be observed in secondary forests. Herbs increased density with intensity of disturbance, with low density in old-growth forests (**Figure 3**). In general, the forest seed bank is dominated by trees (49% on average), while cultivated areas and secondary forests are dominated by herbs (75% on average) [14]. The high density of herbs in secondary forests and forest fragments results from the occurrence of anthropized areas around these areas [5, 14, 22, 29, 33]. The importance of shrubs and small trees is little studied in successional processes in tropical forests. Most studies focus on changes in the structure and floristic composition of the woody layer [89–94], but such studies exclude many groups that occur in high density in the seed bank, groups which can play a relevant role in the mechanisms of ecological succession. These groups also respond to different time scales in biological attributes, such as lifetime, reproductive age, and rate of evolution [95].

Secondary forests in the Amazon may result from the abandonment of areas previously used for different purposes, such as shifting agriculture, pastures, and mining [63, 65, 96, 97], which rarely originate from natural disorders [46].  These areas usually have a seed bank with high density and composition mainly consisting of locally produced herb seeds [41, 65, 69]. This seed bank is very similar to that with established vegetation cover [39–44], which is not seen in old-growth forests [14, 98, 99]. Among the ten most abundant families in the seed bank, common herbs from high-impact degraded areas, such as Poaceae, Rubiaceae, Asteraceae, and Cyperaceae predominate. However, seedlings of typical families from the seed bank of old-growth forests do occur (**Figure 2**). Floristic composition

and seed density in agricultural areas suggest the need to use forest restoration techniques after abandonment, to facilitate and accelerate the return of the forest.

Forest fragments are stretches of forest inserted in a matrix of different types of land uses, typically of anthropic origin [38, 100–102]. The areas used in this study encompass a variety of forest fragments, requiring a more detailed analysis of the characteristics of each. Increase in seed density and changes in the floristic composition of the seed bank intensify in small fragments inserted in a matrix composed of pastures, as well as recurrence of disturbances in these forest fragments [5, 24, 34, 74, 100]. While large forest fragments over 100 ha have a density and floristic composition more similar to the seed bank of old-growth forests, the seed bank also contains species typical of anthropized areas [7, 19, 102, 103].

In the areas of forests with logging of wood species, we can find a mosaic of altered and unaltered areas [45, 57, 104–110] with marked differences between open canopy areas and those that suffered little or no impact [104, 107]. Thus, a greater number of seeds are found in the soil in the centers of exploration clearings and tractor trails [45, 57]. Later, with regeneration, seed density declines and approaches pre-exploratory conditions [45]. The density, as well as life forms, of these areas is closer to that of old-growth forests (**Figures 1** and **3**). For the three most abundant families in the seed bank, floristic composition is very similar to that of old-growth forests. On the other hand, in other families, such as Cyperaceae, Rubiaceae, and Piperaceae, we see higher density of seedlings characteristic of open areas (**Figure 2**).

The seed bank is directly related to forest resilience which contributes to a large number of regenerants, including species of ecological groups not present in the arboreal stratum of old-growth forests. This means that dramatic changes in the seed bank owing to the use and management of soil will, in turn, promote changes in floristic composition and density in a manner that favors the introduction of species not commonly found in the seed bank of old-growth forests. Ultimately, these conditions cause the impoverishment of the seed bank and consequent loss of its functionality. In extreme cases where total suppression of the seed bank has occurred, its absence completely alters the successional trajectory [39–44]. The seed bank is essential for resilience, forest regeneration, and forest diversity; therefore, any changes in its functionality compromise the diversity, regeneration, and maintenance of tropical forests.