Details of metabolite and enzymes in case study 1.
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More than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\\n\\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\\n\\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\\n\\nAdditionally, each book published by IntechOpen contains original content and research findings.
\\n\\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\\n\\n\\n\\n
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'
Simba Information has released its Open Access Book Publishing 2020 - 2024 report and has again identified IntechOpen as the world’s largest Open Access book publisher by title count.
\n\nSimba Information is a leading provider for market intelligence and forecasts in the media and publishing industry. The report, published every year, provides an overview and financial outlook for the global professional e-book publishing market.
\n\nIntechOpen, De Gruyter, and Frontiers are the largest OA book publishers by title count, with IntechOpen coming in at first place with 5,101 OA books published, a good 1,782 titles ahead of the nearest competitor.
\n\nSince the first Open Access Book Publishing report published in 2016, IntechOpen has held the top stop each year.
\n\n\n\nMore than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\n\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\n\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\n\nAdditionally, each book published by IntechOpen contains original content and research findings.
\n\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\n\n\n\n
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Computational systems biology is a field of biological study that combines the knowledge of science and engineering. The objective of this field is to model the behaviour of biochemical reactions through a computational approach. Within this field, the structures and complexity of biological processes can be investigated as a system [1]. Therefore, computational systems biology enables the scientist to represent the biological process as a system. This allows the biochemical process in a living cell to be manipulated as a real factory and gives a way for scientists to improve the cell production (microbial production).
Integrating the knowledge of microbial production with genomic techniques and biotechnology processes creates the ability to manipulate a living cell to act like a real cell factory, thus opening new doors for researchers seeking to improve microbial productions [2]. One example of improving the microbial production is the optimisation of a biochemical systems production. Generally, biochemical systems can be defined as a series of chemical reactions found in a microorganism cell. With the knowledge of microbial production and genomic techniques, the biochemical systems can be represented as a dynamic mathematical model such as the Michaelis-Menten type [3], the stoichiometric approach [4], flux-balance analysis [5], metabolic control analysis [6] and biochemical systems theory (BST) [7]. Among these various choices, this work uses the BST representation to model the biochemical system. An advantage of using the BST is that prior knowledge of the mechanisms for each reaction is not required in order to build equations and the mathematical models can be designed by identifying the reactants and their interconnections [7]. For that reason, a canonical form that uses an ordinary differential equation (ODE) representation is suitable for modelling biochemical systems [1].
The optimisation of the biochemical systems production is a biotechnological process that aims to improve production by fine-tuning the chemical reaction. Besides that, the total amount of chemical concentrations involved also needs to be taken into account [8, 9]. To date, many studies have been carried out to develop methods for the optimisation of the biochemical systems production. Researchers tend to use the computational methods due to the flexibility of the mathematical models allowing to reduce the required costs and time. Popular methods used are the linear programming method (Vera et al. 2010; Xu 2012) and the geometric programming method [10, 11]. These methods depend on the definitions of the decision variables and the equality and inequality constraints, which could cause a convergence problem if the definition process is not performed well [12]. In order to overcome this problem, the present study was carried out using the stochastic method. The stochastic method operates on an evolving set of candidate solutions. In the evolving process, the candidate solutions are modified by the stochastic operator to produce the next generation. Using the stochastic operator, the search direction is determined by a random method, which makes it more efficient and robust [13]. In addition, the stochastic method does not rely on the manipulation of the objective function and constraints or the initialisation of a feasible point [14]. There are many stochastic methods that can be adopted for the optimisation process, among which is the genetic algorithm (GA) that has been widely found to be the most suitable method [15, 16, 17]. The GA works by representing the chemical reaction in the biochemical systems as a chromosome. The chromosome is then evolved and modified by a crossover and mutation process, with the intention to improve the solution.
As mentioned above, this chapter uses the BST method to model biochemical systems. Within the BST, two representations are typically used, namely, the S-system and generalised mass action (GMA). This study employs the GMA representation due to its ability to represent the nonlinearity of a biochemical systems and superior performance in optimisation [10]. The GMA uses the power law function, which is an ODE to model the biochemical systems. Applying only the GA for the optimisation of biochemical systems is not sufficient as the GA only fine-tunes the chemical concentrations. Therefore, a method is needed to deal with the biochemical systems. Implementing the Newton method for the biochemical systems is a good choice because the GMA model that represents the biochemical systems can be viewed as a nonlinear equations system [8, 18, 19, 20, 21, 22]. It also has been found that the Newton method is suitable for the nonlinear equations system due to the convergence speed, simplicity and ease of use [23, 24].
Using the Newton method with the GA in optimising the biochemical systems production is a good choice because the Newton method deals with the biochemical systems, while the GA is used to fine-tune the chemical concentrations by representing the chemical concentrations into a chromosome. However, several problems do occur when dealing with large biochemical systems that contain many chemicals and has complex structures where it makes the representation of the solution become complex and difficult to evaluate. Hence, a method is needed in order to overcome these problems by simplifying the representation of the solution. Using the cooperative co-evolutionary algorithm (CCA) is a good choice because it has the ability to simplify the representation of the candidate solution by decomposing a single chromosome into multiple sub-chromosomes [17, 25, 26].
In this chapter, a hybrid method known as the advanced Newton cooperative genetic algorithm (ANCGA) that combined the Newton optimisation method; the GA and the CCA were presented. This method models biochemical systems as a system of nonlinear equations and applies the Newton method to solve the system. In the optimisation process, the GA and the CCA were used to represent the variables in a nonlinear system in order to search the best solution. The GA was used to maximise the production, while the CCA was used to minimise the total amount of chemical concentrations involved. The ANCGA that proposed in this study is the improvement of the existing method [17]. The reason of proposing the ANCGA is due to the previous algorithm that takes longer time for the optimisation process. Moreover, the performance of the previous work can be improved in terms of maximising the production and minimising the total amount of chemical concentrations involved. In order to do that, this work introduces a concept of external population. The external population was used to store the best solution found in every generation. The reason of using this concept was to avoid the best solution found in every generation from being lost during the reproduction process. The methods used in this study are presented in the following order. Firstly, the proposed method is explained in detail. Case studies of the fermentation pathway in Saccharomyces cerevisiae (S. cerevisiae) and the tryptophan (trp) of biosynthesis in Escherichia coli (E. coli) are then presented. Following that, the results are discussed, and a brief conclusion is made.
This section describes the proposed ANCGA in detail. The ANCGA is proposed in order to improve the performance of the previous method [17] in terms of computational time. In addition, the ANCGA is hope to improve the performance of the previous method [17] in maximising the production and minimising the total amount of chemical concentrations involved. Figure 1 shows the flowchart of ANCGA. The ANCGA operates by treating the biochemical systems as a system of nonlinear equations and then uses the Newton method in solving the nonlinear equations system. Then, the GA and CCA were used in the optimisation process. The detailed operation of the ANCGA is described in the following steps:
The flow chart of ANCGA.
Step 1—randomly generate the initial n sub-chromosomes in m sub-populations and create an empty external population. The number of sub-populations (m) must be the same to the number of variables in the nonlinear equations system. The sub-chromosomes represent the variables in the nonlinear equations system. The sub-chromosome is in the binary format.
Step 2—evaluate the sub-chromosome. The evaluation process starts when a representative from every sub-population is selected to produce a complete solution that is known as a cooperative chromosome. The selection of representatives is based on their fitness value, where the lowest values are selected first. This process is known as the sub-chromosome evaluation. The objective of this process is to minimise the total amount of chemical concentrations involved by letting representatives that have the lowest fitness values from every sub-population to be combined together.
Step 3—produce the cooperative chromosome. The cooperative chromosome is produced after all the selected representatives are combined together. The cooperative chromosome is the complete solution. The formation of the cooperative chromosome is depicted in Figure 2.
The formation of the cooperative chromosome.
Step 4—evaluate the cooperative chromosome. In this step, the cooperative chromosome is tested. The evaluation process starts with an encoding of the cooperative chromosome into variables in the nonlinear equations system. Then, the Newton method is used to solve the nonlinear equations system. In the evaluation process, a condition might occur depending on whether or not the cooperative chromosome follows the set of constraints. If the cooperative chromosome follows the constraints, then the procedure goes ahead to Step 8; if not, it goes to Step 5.
Step 5—decompose the cooperative chromosome into sub-chromosomes. After solving the nonlinear equations system using the Newton method, the variables in the nonlinear equations system are decoded back into the cooperative chromosome form. Then, the cooperative chromosome is decomposed into multiple sub-chromosomes. After that, all the sub-chromosomes are sent back to their own sub-populations in order to perform selection and reproduction.
Step 6—select a pair of sub-chromosome for the reproduction process. The selection process is based on their fitness value, where the lowest fitness value is selected first.
Step 7—produce new generations. In this step, the genetic operators of crossover and mutation are applied on the selected sub-chromosomes in order to produce new generations. This process is performed up to the last sub-chromosome. Then, the new generation is processed again, starting from Step 2.
Step 8—copy the cooperative chromosome into the external population. The process is performed by copying selected cooperative chromosome that fulfil the constraints and put the selected cooperative chromosome into external population. This process is intended to keep the best solution in every generation and prevent it from being lost in the reproduction process (Step 7). At this stage, two conditions may occur: either the maximum number of generations is reached or the maximum number of cooperative chromosomes in the external population is achieved. If these two conditions are fulfilled, the procedure jumps to Step 10; otherwise, the procedure continues to the next step. During this process, if the maximum number of cooperative chromosomes in the external population is reached before the maximum number of generations is achieved, the cooperative chromosome that has the lowest fitness value is deleted and replaced by a newly copied cooperative chromosome. However, if the maximum number of generations is reached before the maximum number of cooperative chromosomes in the external population is achieved, the procedure moves to Step 10.
Step 9—select some of the cooperative chromosomes from the external population. This process refers to the elitism of external population concept. The elitism of external population concept works where some (with y probability) of the cooperative chromosomes from the external population are selected and combined with the current sub-chromosomes. The selection process is based on their fitness value, where the cooperative chromosomes from the external population that have the highest fitness value are selected first. Then, this process goes back to Step 5.
Step 10—choose the best solution. The best solution is chosen among all the cooperative chromosomes in the external population. The selection is based on the fitness values of the cooperative chromosomes, where the cooperative chromosome with the highest fitness value is chosen.
Step 11—return to the best solution. This step decodes the selected cooperative chromosome into its real value (the variable in the nonlinear equations system) and gives the best solution set.
In this section, the effectiveness and efficiency of the ANCGA is demonstrated. The effectiveness of the proposed method refers to the ability of the ANCGA to obtain the best result, while the efficiency refers to the ability of the ANCGA to maintain its performance in producing the best result in several case studies. Two case studies were used, namely, the S. cerevisiae pathway and the E. coli pathway. In order to test the performance of the ANCGA, a Java program based on two Java libraries, namely, jMetal [27] and JAMA of the version 1.0.3, was developed. The jMetal library can be downloaded from
In this case study, the ANCGA was used to optimise ethanol production in the S. cerevisiae pathway. The GA was used to represent the chemical reactions in the S. cerevisiae pathway, which were metabolites and enzymes. Details of the metabolites and enzymes, including the initial steady-state values, are presented in Table 1. The pathway was suspended in a cell culture at p. 4.5 and had the following ODE models [28].
Metabolite/enzyme | Symbol | Initial steady-state value |
---|---|---|
Glcin | X1 | 0.0345 |
G6P | X2 | 1.0110 |
FDP | X3 | 9.1440 |
PEP | X4 | 0.0095 |
ATP | X5 | 1.1278 |
Vin | Y1 | 19.70 |
VHK | Y2 | 68.50 |
VPFK | Y3 | 31.70 |
VGAPD | Y4 | 49.90 |
VPK | Y5 | 3440.00 |
VCarb | Y6 | 14.31 |
VGro | Y7 | 203.00 |
VATPase | Y8 | 25.10 |
Details of metabolite and enzymes in case study 1.
Eq. (2) shows the fluxes at the steady-state condition.
For the total amount of chemical concentration involved, it can be formulated as follows:
In the optimisation process, the GMA model was treated as a nonlinear equations system, where all the GMA models were set to be equal to 0. This gave all the ODE models in Eq. (1) the following forms:
For the metabolite concentration constraint, the constraint was set to 20% from the steady-state value, which was in the range between 0.8 and 1.2 [8, 29]. Thus, the constraint for this case study became as follows:
Meanwhile, the enzyme concentration constraint was set in the range between 0 and 50 from the steady-state value [8, 29]. The enzyme concentration constraint can be formulated as follows:
For case study 2, the ANCGA was used to optimise the end product of this pathway, which was trp production. The complete description of this pathway was provided by Xiu and colleagues [30]. The GMA models of this pathway are given as follows:
where X1 is the mRNA concentration, X2 is the enzyme concentration and X3 is the trp concentration. The rates of all reactions in this pathway at steady state are given as follows:
The trp production in this case study is given by the reaction V34 [31]. This leads to optimisation that can be formulated as follows:
For the total amount of chemical concentrations involved, it can be formulated as follows:
Similar to case study 1, the GMA model was set to be equal to 0, thus Eq. (8) became as follows:
In this case study, the GA and CCA only represent several chemical concentrations. This was because not all chemical concentrations were being tuned [1, 10, 11]. The chemical concentrations that tuned were X1 up to X6 and X8. These chemical concentrations including their initial steady states are summarised in Table 2. For the other chemical concentrations which were X7 and X9 up to X13, fixed values were used [1, 10, 11]. Eq. (12) lists the range of these chemicals.
Reaction | Initial steady-state value |
---|---|
X1 | 0.0345 |
X2 | 1.0110 |
X3 | 9.1440 |
X4 | 0.0095 |
X5 | 1.1278 |
X6 | 19.70 |
X8 | 25.10 |
Summary of reaction concentrations in case study 2.
In performing the experiments, many parameter settings were used. The list of all parameter settings used in this study is listed in Table 3, whereas Table 4 presents the parameter settings in producing the best result. The binary coding was used to represent the chemical concentrations. For the Newton method, fixed parameters were used, namely, 50 for the maximum number of iterations and 10−6 for tolerance.
Parameter | Rate |
---|---|
Number of sub-populations | Depend on the number of variables in nonlinear equations system |
Number of sub-chromosomes in sub-population | [100,110,120,130,140,150] |
Number of chromosomes in external population P | [100,110,120,130,140,150] |
Maximum number of generations | [100,110,120,130,140,150] |
Crossover rate | [0.1,0.2,0.3,0.4,0.5] |
Mutation rate | [0.1,0.2,0.3,0.4,0.5] |
Elitism rate | [0.1,0.2,0.3,0.4,0.5] |
List of all parameter settings used.
Parameter | Case study 1 | Case study 2 |
---|---|---|
Number of sub-populations | 11 | 7 |
Number of sub-chromosomes in sub-population | 150 | 140 |
Number of chromosomes in external population P | 100 | 100 |
Maximum number of generations | 150 | 130 |
Crossover rate | 0.3 | 0.4 |
Mutation rate | 0.1 | 0.1 |
Elitism rate | 0.2 | 0.2 |
Parameter settings in producing optimum solution.
The full results obtained by the ANCGA when applied on S. cerevisiae pathway are given in Table 5. At the best solution, the ANCGA was able to increase the F1 (ethanol production) up to 53.02 bigger than its initial steady-state value. For the F2 (total amount of chemical concentrations involved), the proposed method was able to reduce it to 293.5249. All metabolites and enzymes fulfilled their constraints, with all the metabolites staying in the range of 0.8–1.2, while all the enzymes were in the range of 0–50. The performance of the ANCGA was assessed by comparing the result obtained by ANCGA with other works, and the comparison results are listed in Table 6. As shown in the table, the ANCGA produced higher results as compared to other methods. In addition, to verify the results achieved by the ANCGA, an average of 100 independent runs was recorded. The results are summarised in Table 5. It shows that the average result for the metabolites and enzymes fulfilled their constraints, whereby they were in their optimum range, thus leading to the conclusion that the ANCGA is able to produce reliable results. It can be said that the ANCGA can produce higher production of ethanol as compared to the methods used in other studies.
Variables | Best solution 1 | Average |
---|---|---|
X1 | 1.1240 | 0.9951 |
X2 | 1.0322 | 1.0018 |
X3 | 0.9900 | 1.0053 |
X4 | 1.1407 | 1.1297 |
X5 | 1.0001 | 0.9831 |
Y1 | 49.8103 | 49.9793 |
Y2 | 45.3702 | 45.0767 |
Y3 | 45.3452 | 49.8103 |
Y4 | 48.5112 | 47.4064 |
Y5 | 49.4448 | 49.3426 |
Y8 | 49.7563 | 49.7876 |
F1 | 53.0200 | 52.7499 |
F2 | 293.5249 | 294.5178 |
The full result of case study 1.
Work by | F1 | F2 |
---|---|---|
Xu [11] | 52.38 | 297.664 |
Rodriguez-Acosta et al. [29] | 52.31 | 295.270 |
Previous method [17] | 52.91 | 294.800 |
ANCGA | 53.02 | 293.5249 |
Comparison with other works for case study 1.
The full results of the E. coli pathway are presented in Table 7. The ANCGA was able to improve the F1 (production of trp) to 3.9774 from its initial steady state. Meanwhile, the proposed method was able to reduce the F2 (total amount of chemical concentrations involved) to 6006.4280. All variables representing the chemical reaction followed their constraints and were in the optimum range. To assess the performance of the ANCGA, the results achieved were compared to the results of other methods, with the details of the comparison shown in Table 8. As presented in the table, the F1 of the ANCGA was higher when compared to the methods employed in other works. Similar to the previous case study, 100 experiments were conducted, and the average result was calculated in order to validate the ANCGA results. Table 7 presents the average result. From the data in Table 7, it can be concluded that the ANCGA is reliable in performing the optimisation of this pathway because the average of all the variables follows their constraints. From the observations presented in Tables 7 and 8, it can be concluded that the ANCGA is effective in optimising the trp production as well as producing reliable results.
Variables | Best solution | Average |
---|---|---|
X1 | 0.8064 | 1.0742 |
X2 | 0.8046 | 1.1085 |
X3 | 0.8000 | 0.8000 |
X4 | 0.0054 | 0.0054 |
X5 | 4.0116 | 4.4694 |
X6 | 5000 | 5000 |
X8 | 1000 | 1000 |
F1 | 3.9774 | 3.9616 |
F2 | 6006.4280 | 6007.4575 |
The full result of case study 2.
Work by | F1 | F2 |
---|---|---|
Marin-Sanguino et al. [10] | 3.062 | 6006.1412 |
Vera et al. [1] | 3.05 | 6007.1314 |
Xu [11] | 3.946 | 6007.7814 |
Previous method [17] | 3.9759 | 6006.5581 |
ANCGA | 3.9774 | 6006.4280 |
Comparison with other works for case study 2.
The external population concept used by ANCGA can be validated by comparing it with the previous method proposed in [17]. The aim of the external population concept was to reduce the computational time and the number of generations. To learn the effect of the external population concept, several experiments were conducted. To investigate the decrease in the number of generations, F1 was set to 52.5 for case study 1 and 3.90 for case study 2. After F1 was achieved, the process was stopped. This helped to investigate which method required more generations in achieving the target production. Figures 3 and 4 illustrate the comparisons of all case studies. In both figures, the maximum number of the external population was smaller as compared to the maximum number of the previous method in achieving F1. This was caused by the concept of external population that was introduced in this study. By using this concept, the best solutions found in the iteration process could be maintained and thus enabled the number of generations to be reduced. In addition, it was found that this concept tended to converge faster than the previous method. This meant that the use of the external population concept allowed faster search of the best solution. In conclusion, the external population concept had an impact in reducing the number of generations and helped in faster convergence as compared to previous methods. To determine the statistical significance between the proposed method and previous methods, the paired t-test and the Wilcoxon signed-rank test were used. The result of the statistical tests showed that all p-values were <0.05, thus confirming that the proposed method significantly improved the previous method.
The comparison of results of elitism concept and non-elitism concept for case study 1.
The comparison of results of elitism concept and non-elitism concept for case study 2.
Meanwhile, to investigate the decrease in computational time, the maximum number of generations was not set, but F1 was set to 52.5 for case study 1 and 3.9 for case study 2. After F1 was achieved, the process was terminated. Table 9 lists the computational time results, and it was found that the ANCGA required less time as compared to the method in [17]. This situation occurred because the high-quality solutions were stored in the external population and then combined with the current solution in the optimisation process. Copying the high-quality solutions into the external population prevented them from being lost (because the optimisation process involving crossover and mutation operation could lose the high-quality solutions). By storing the high-quality solutions into the external population, it would be able to keep the best solution until the optimisation process stopped. To determine the significant improvement of the proposed method against the previous method, the paired t-test and the Wilcoxon signed-rank test were used. The p-value from both tests was <0.05. From this finding, the proposed method and the previous method were statistically different from each other, and the improvement of the proposed method could be accepted.
Method | Case study 1 | Case study 2 |
---|---|---|
ANCGA | 75.56 | 38.07 |
Previous method [17] | 80.40 | 40.45 |
The computation times obtained (in second).
Improving production has become an important issue in the optimisation of biochemical systems. Many factors need to be considered to ensure optimal production. In this work, a hybrid method for constraint optimisation of the biochemical systems production known as the ANCGA was presented. The ANCGA was developed based on a previous method [17], where the ANCGA combined the Newton method, GA and CCA. This study introduced a concept of external population. The aim of this concept was to reduce computational time. In this work, the biochemical system was modelled by a nonlinear equations system. In the optimisation process, the Newton method was employed to deal with a system of nonlinear equations. The GA and CCA were then applied to fine-tune the chemical concentration value in the nonlinear system in order to search for the best solution. During the optimisation process, the high-quality solutions were copied and stored into the external population. The purpose of this process was to avoid the loss of high-quality solutions during the optimisation process. Then, some solutions from the external population were mixed with the next generation of solutions. By doing this, the computational time and number of generations were reduced. In the present study, the proposed method was applied on two case studies, and better results were obtained as compared to the methods presented in other works. In addition, the results were validated, and they demonstrated that the constraints of all the components in the biochemical system were fulfilled. Thus, it can be concluded that the performance of the ANCGA is effective and reliable in producing the best result.
Special appreciation to Universiti Malaysia Pahang for the sponsorship of this study by approving the RDU Grant Vot No. RDU180307. Special thanks to the reviewers and editor who reviewed this manuscript.
The South Shetland Archipelago is located in the northern part of Antarctic Peninsula and is formed by 10 large islands (some reaching 100 km of lenght) and many smaller ones. The Maritime Antarctica, especially near the Antarctic Peninsula, have recorded the most significant temperature increases in the entire Southern Hemisphere, with 0.34° C per decade in the South Shetland Islands and between 1 and 1.4° C per decade (recorded since 1980) at the Rothera research station on the Antarctic Peninsula [1]. Data indicate that the marine water around the Antarctic Peninsula is up to 3° C warmer on average, contributing up to 50% of the ice melting already recorded [2].
The Antarctic Peninsula region had one of the most intense climatic warming trends over the last decades (increase of 0.56° C/decade in air temperature and 3°C in surface temperature since 1950) [3, 4, 5]. A statistically significant (at 3%) increasing trend in temperature was observed during the years 1944–1996, when the temperature increased by 1.6° C by analyzing the temperature in King George and in Deception Islands, from the South Shetland Archipelago. But in regions as the Admiralty Bay in King George Island the mean temperature was higher than 0.7° C, and the Wanda Glacier located there is retreating fast, having lost already 31% of its volume (compared to 1979) attributed to the regional warming [6].
The use of modeling proved that the ice river of the Thwaites Glacier that drains into the Amundsen Sea, in Western Antarctica, is already destabilized. The melting of these glaciers will raise the sea 1.2 meters on the planet, but the process will be very slow, probably hundreds of years [7].
Radar data proved that Pine Island Glacier retreated 31 km between 1992 and 2011, but has now reduced this speed. Until 2009 nothing was recorded about this retreat, when the melting and destabilization of glaciers suddenly began. The Larsen Glacier was one of the first to indicate a retreat (having persisted for 10,000 years), started to fall apart in 2002, collapsing in a period of 35 days and it is expected to disappear in 18 years [2].
In a reconstruction of changes in ice since the last glacial maximum, having studied at least 674 glacier data across the Antarctic Peninsula, it has been demonstrated as an environmental factor (the increase in the temperature of seawater rather than the atmosphere), was directly related to the retreat of glaciers [8]. The north–south gradient of increased retreat in the glaciers has a high correlation with ocean temperatures, since the water is cold in the Northwest and becomes progressively warmer at depths below 100 meters to the south. These waters of medium depth in the southernmost regions have been warming up since the 1990s, at the same time that the acceleration in the retreat of the glaciers began. And these waters are reaching lower and lower depths and affecting the emerged parts, as they heat up the platform. Almost all of the glaciers studied have declined since 1940.
The eastern region of the Antarctic continent, on the other hand, is slightly different from the region of the peninsula, since it is on dry land and has very thick ice. But because it is a more remote region, few scientists venture into the area and little data has been collected. However, data recently gathered from satellites and airplanes show another scenario. The Totten Glacier, for example, seems to be one of the most vulnerable, with the radar showing that there is a channel in the depths of it, which allows the entry of hot sea water that melts the ice and explains the loss of mass. This glacier can contribute to an increase of up to 3.5 meters in sea level [9].
Plant species on this continent are restricted to ice-free areas (except for microscopic algae that can grow directly on the ice) and are formations very threatened by climate change, as they do not support temperature changes very well. At the same time, plant communities are advancing in areas recently exposed by the retreat of ice and more favorable temperatures, resulting in the so-called “Antarctic greening”. Analyzing five cores at three sites over 150 years, revealed increased biological activity over the past ca. 50 years, in response to climate change, suggesting that terrestrial ecosystems will alter rapidly under future warming, resulting in a greening similar to that registered to the Arctic [10].
It is important to note that a considerable carbon reservoir exists in cryobiont algae, which form extensive colonies directly on the ice. With the increase in temperature, it is expected that 62% of the blooms of small islands (like in the South Shetland archipelago) of low altitude will disappear [11].
There are at least 3 ways in which organisms can adapt to changes in the environment: 1- they can use the margins of physiological flexibility and then support changes. 2- can change the range of biological capacity which is highly dependent on the magnitude and rate of change. This ability is linked to the organism’s reproductive capacity, but mutation rates, number of reproductive events and generation time are also linked. 3- they can migrate to have more favorable conditions. For Antarctic plants, the problems to be faced are greater to adapt, as they do not have an efficient disperser except the wind for lichen and moss spores, and must compensate locally for the differences to survive. And perhaps one of the big problems is getting nutrients. These are brought to the continent basically by animals, from their diet consisting of marine organisms [12]. A schematic of the flow of nutrients to the terrestrial environment can be seen in Figure 1.
Schematic view of the contribution of nutrients for the terrestrial ecosystems (adapted from [12]).
Penguins are climate indicators and changes in their populations have been described over the past 50 years, mainly associated with changes in ice dynamics [13, 14]. Pygoscelis adeliae (Adélie penguin) is the most dependent on ice and the most widely distributed species, occurring throughout the continent, as it is circumpolar. P. antarctica (the Antarctic Penguin) is found almost exclusively in the Antarctic Peninsula [15] and P. papua (the Papua Penguin) occurs in both the Antarctic peninsula and sub-Antarctic islands. The latter species has been showing its Antarctic populations expanding rapidly in the last 50 years, which is being associated with the increase in temperature in the region. Changes in ice dynamics have allowed the species to move further south, while P. adeliae and P. antarctica had a decrease in their populations mainly because availability of Krill, its main food. Adélie penguin populations are decreasing throughout the Antarctic Peninsula but they have remained stable on the east side of the continent, where the influences are still not so felt. In the past this species seems to have resisted climate change better. If it continues at this rate, it is estimated that populations can be reduced by 30% by 2060 and 60% by 2099 [16].
Fewer penguins means less availability of nutrients as they are one of the main sources of guano for the continent. Therefore, changes in plant communities that depend on this input may not happen. The melting of glaciers ends up exposing areas with rocks and sediments that will allow the installation of terrestrial vegetation, but nutrients must be available specially for the nitrophilic species.
Even cryobiont algae are found in ice, because it receives a spray of nutrients from penguins existing at least 5 km away [11]. The melting of the ice allows the melting water flows to carry these nutrients to the plants that grow on its banks, especially species like Wanstorfia spp., Brachythecium spp. and Sanionia spp. [17].
The Pinnipedia also have their contribution to the terrestrial environment, especially during periods when they are on land to rest. The deposition of feces and urine helps to nitrify the ground, but trampling can be harmful. A case described for the Signy Islands exemplifies this aspect, where the population of Arctocephalus gazella (fur seal) has greatly increased in recent years, completely destroying the existing vegetation in an area of about 80 hectares [18, 19, 20, 21].
Antarctica has only two native plants forming flowers: Deschampsia antarctica Desv. (Poaceae - Figure 2) and Colobanthus quitensis Kunth. (a Caryophyllaceae - Figure 3). There are already records of other Angiosperms occurring in the region, but these have been introduced by man, such as Poa annua L., and climate change can contribute to the occurrence of more plants in the region [22]. These two plants compete for space with all other species, but because they are larger and more complex, they need a large availability of nutrients and water. Therefore, they are usually found close to sources of nitrogen, such as in the vicinity of penguin rockeries or nests of other birds. Both have a chemical arsenal to survive the conditions of the Antarctic cold, especially a reasonable concentration of sugars in their cells: there is at least ten times more sugar in vacuoles than in sugarcane, foreseeing a potential use of this source in future. This accumulation of sugar is a protection against very cold periods in Antarctica [23].
Deschampsia antarctica, the Antarctic grass. Scale = 20 cm.
Colobanthus quitensis among mosses and rock fragments.
Regarding the distribution of these phanerogams in the study area, it is possible to mention their occurrence in almost all the South Shetland Islands and areas of the Antarctic Peninsula free of ice. They can occur as small isolated tufts of a maximum of 15 cm, or forming fields of a few meters, but almost always associated with different Bryophyta and Marchantiophyta. Carpets even seem to stimulate grass development, but not its survival [24].
There are studies reporting the photoprotective effect of Deschampsia antarctica and Colobanthus quitensis extracts against UVB. The photoprotective properties have been attributed to several molecules, such as flavonoids and carotenoids, which absorb UV and act as antioxidants [25, 26].
It is possible that changes in temperature may interfere with the growth and development of populations of these species as has been shown experimentally [27, 28]. In the Argentine Islands, an increase of 25 times for D. antarctica and 5 times for C. quitensis was recorded in 30 years of observation [29]. Data collected in 2009 and historical data since the 1960s on the distribution of the two Antarctic vascular plants on Signy Island revealed that D. antarctica increased its coverage by 191% and the number of occurrence sites by 104%. C. quitensis increased its coverage by 208% and the number of occurrence sites by 35%. All due to the increase of 1.2° C in the air temperature and all the changes that this caused in the region [30].
Studying the formations of these phanerogams in the Fildes and Coppermine Peninsulas, in addition to locations in the Antarctic Peninsula in order to assess their responses to the increase in local temperatures, it was discovered that the populations of D. antarctica are expanding in the South Shetland Islands, but this expansion is not continuous in the Antarctic Peninsula, as the plants disappeared at 3 points, suggesting that there are other biotic and abiotic factors involved [31].
The fauna and flora associated with these plants is also very rich. There are bacteria, fungi and microscopic animals, many with a symbiotic or survival relationship with these plants. A high mortality of terrestrial microbial communities was detected along the South Shetland Islands. These communities are said to be dying from physiological problems and lack of nitrogen, in addition to changes in their microstructure, which seems to be associated with the rupture of the biogeochemical gradient of the microbial ecosystem. Caused by a strange but high abundance (explosion) of the associated fungi and the physical changes caused by them. All of these changes are related to the high temperatures recorded in the region. Some new diseases have been registered, especially for Antarctic grass, indicating that something is making possible the occurrence of these phytopathologies, but more studies are needed [32, 33].
There are also birds, of which at least the skuas (Catharacta spp.) and the gulls (Larus dominicanus) use these plants more frequently to make their nests. In a survey, scientists identified the seagull’s preference for Deschampsia antarctica at Cierva Point in the Antarctic Peninsula [34]. More or less availability of this raw material can affect the reproduction of these birds.
These plants can be found in reproduction, but in general they are sterile. But higher average temperatures can contribute to increasing seed maturation, germination and seedling survival, although this has not yet been proven experimentally [35, 36].
Among the species that most stand out on more consolidated areas and even on Antarctic rocks, are mosses. The group that represents the bryophytes also has some liverworts occurring, but in this text, all will be commonly called mosses. There are, therefore, Marchantiophyta, popularly called hepatics, and the representatives of the genus Marchantia are those that present the largest gametophyte (Figure 4), although small species of other genera sometimes take very large areas. Large populations have been found recently, such as the rare Hygrolembidium isophyllum in Harmony Point - Nelson Island [37]. Marchantia is thallose, reproducing basically by direct fragmentation of the thallus or by specialized structures, the propagules, formed in receptacles such as in the figure (called conceptacles). But the group most represented in species in the area are the leafy liverworts (about 22 species). They even have a relationship with other organisms, as in the case of Cephaloziella varians, which is associated with a mycorrhizal fungus Rhizoscyphus ericae (ericoid symbiosis) throughout Antarctica [38, 39, 40].
Two Marchantiophyta, the thallose Marchantia berteroana (above) and the leafy Cephalozia sp. (below).
Many species of liverworts are associated with dominant species in the plant community, and this reflects an interdependence. If the dominant species are threatened, by climate change, for example, their dependents will also be [41].
Bryophyta, or mosses themselves, have so far collected 113 species, within 55 genera and 17 families [18, 42]. The mosses present two main forms of growth: the pleurocarpic, where the moss stalk is prostrate, forming continuous carpets and in general covering more extensive areas if they are available (Figures 5 and 6); and the acrocarpic form, where the mosses grow upright, forming tufts or smaller cushions (Figures 7–11). The moss species with the highest occurrence and highest biomass in all ice-free spots is the pleurocarpic Sanionia uncinata, a carpet former with curved leaves, twisted like a scythe [18].
A moss carpet moved by wind being fixed by a scientist.
Two large carpet of Sanionia uncinata associated to Warnstorfia sarmentosa in the wettest areas.
Tufts of the moss Syntrichia sp. (red circles) growing among whale bones and a carpet of Sanionia uncinata.
Polytrichastrum alpinum, one of the tallest moss (left) and Pohlia cruda (right).
Bartramia patens, with sporophyte (left) and Bryum palescens (right).
Hennediella heimii with sporophyte.
Chorisodontium acyphyllum, dull green and acrocarpic surrounded by a light green carpet of Sanionia uncinata (pleurocarpic).
Antarctic moss fields can be very old and even deeper layers of growth can be alive even though they have been buried for over a thousand years by acrocarpic development. In 2014 research showed that the moss Chorisodontium acyphyllum remained alive after remaining frozen for more than 1500 years. A 1.4-meter-thick tuft was sectioned every 20 cm (layer by layer) and placed to germinate under ideal conditions. In up to 8 weeks everyone started growing. The deepest layer was dated by radiocarbon and estimated between 1533 and 1697 years [43]. Acrocarpic and pleurocarpic mosses buried under more than 600 years by a glacier were re-exposed by the retreat of the ice and parts of the moss were able to activate again and grow normally “in vitro” [44].
The most available substrate in Antarctica is rock, but there are species that grow in soil. Andreaea, with four acrocarpic species occurring in Antarctica, for example, is exclusively saxicolous (name given to species that grow on rocks) [18].
Mosses are capable to colonize areas such as those closest to the sea and even receiving splashes of salt water waves, to the interior of the continent and including areas of recent exposition by ice retreat. The species Muelleriella crassifolia P. Dusén requires at least some contact with marine spray to develop, which is achieved in some coastal rocks. Eventually it can be found with other groups that have species with this preference, called halophytes, such as some of the lichens of the genus Verrucaria [45, 46]. The alterations in sea level will affect directly this community.
The rise in temperature in Antarctic regions has been accelerating the growth of mosses in particular since the late half of the 20th century. A study of 5000 years old population of Polytrichum strictum (in Lazarev Bay, on Alexander Island), dated by radiocarbon millimeter by millimeter, demonstrated that the population accumulated around 1.25 mm/year in the 19th and early 20th century and then increased its growth from 1955 until reaching 5 mm per year until the end of 1970, currently reducing growth to 3,5 mm/year. The authors also found that the associated amoeba population also increased considerably over the same period [47].
Studying a 1500 km gradient from Antarctic Maritime to the south of the Antarctic Peninsula (in the region of Lazarev Bay, Alexander Island) the accumulation in the banks of moss began to increase around the 1950’s, reaching peaks in the Lazarev Bay in the 1970’s (about 0.1 g of dry matter/cm2/year) and Signy Island in the 1990s (0.06 g/DM cm2/year); the most recent measurements indicate around 0.04 g of dry matter/cm2. In continental Antarctica the growth of mosses is inversely proportional to the speed of the summer wind and proportional to the number of days above 0° C and the temperature of the summer [48].
Data collected in the Windmill Islands show evidence that the endemic moss Schistidium antarctici is likely to be more susceptible to climate change than the co-occurring and cosmopolitan species such as Ceratodon purpureus and Bryum pseudotriquetrum. And this in particular due to the habitat requirements, much more associated with water in the endemic species [49]. The rapid permanent ice-melting in areas like the South Shetland can result in dryer areas and reduction of plant communities.
Antarctica was the last continent discovered and in the first botanical studies the samples revealed one main taxonomical difficulty: no fertile mosses were found. Among the Antarctic mosses only 22 are found commonly fertile [18] despite some of these species are relatively rare. Reproduction by spores is only possible with water in liquid form, since the antherozoid needs to swim to the correspondent archegonia, fertilize it to form sporophyte and then finely the spores are formed inside a capsule. Since Antarctica is known to have water mostly if form of ice and snow, and being called the biggest desert in the world, it is somewhat difficult and sometimes impossible in some areas to achieve fertilization.
There is a huge difference in precipitation from Dry Valleys at 77,8° S (50 mm) to Livingston Island at 62.6° S (80 mm) [50]. An increase in precipitation was found at Faraday Station, according to data collected from 1956 to 1992 in the Antarctic Peninsula. This increase is connected with the diminishing sea ice and the intensification of evaporation, a higher humidity of the air and more dynamic cyclonic activity, especially in the winter season [51]. All these aspects can affect directly Antarctic plants, contributing also to mosses achieve fertilization.
In tropical and temperate areas ca. 75 ± 90% of the mosses are found fertile. In the maritime Antarctic the value reduces to approximately 25 ± 33% and in continental Antarctica to only 10%. In Margerite Bay fertility of 43% was found (19 species: 17 mosses, 2 liverworts) and in Alexander Island 47% (17 species; 16 mosses, 1 liverwort). 51 sterile species were found among 111 known from Antarctica (46% with sporophyte) [18, 35].
It is interesting that mostly saxicolous mosses are found fertile (Table 1), and this is probably because the rock surface is hottest than the environment, melting the snow deposited and resulting in liquid water available more frequently than on other surfaces. As the species usually grow on cracks, the water is piped over them [52].
SPECIES | ON ROCK | ON FINE SEDIMENTS | FERTILITY |
---|---|---|---|
Andreaea regularis | X | Frequent | |
Andreaea gainii | X | Frequent | |
Schistidium cupulare | X | Rare | |
Schistidium amblyophyllum | X | Frequent | |
Schistidium deceptionensis | X | Rare | |
Schistidium leptoneuron | X | Rare | |
Schistidium antarctici | X | X | Frequent |
Schistidium hialinae | X | Frequent | |
Schistidium urnulaceum | X | Frequent | |
Schistidium steerei | Not truly saxicolous | X | Frequent |
Schistidium andinum | X | X | Frequent |
Schistidium praemorsum | X | Rare | |
Schistidium rivulare | X | X near water | Frequent |
Schistidium lewis-smithii | X | X | Rare |
Hymenoloma grimmiaceum | X | Frequent | |
Hymenoloma crispulum | X | Frequent | |
Hymenoloma antarcticum | X | Frequent |
List of saxicolous/soil growing mosses frequently found fertile in Antarctica.
There is also some preference for the availability of nutrients, especially the presence or proximity to nesting points or with the presence of birds or mammals. These species are called ornithocoprophylous or nitrophilous and often growing on slopes bathed in the excrement of the animals that occur above. Species such as Synchitria magellanica and Henediella heimii (Figure 10), among others, have this preference. With the reduction of penguin populations, already mentioned above, the unavailability of nutrients will affect these species.
Another group is represented by species that do not support high levels of nitrogen and therefore occur away from places where birds or mammals occur. They are called ornithocoprophobic or nitrophobic. Examples are Pohlia cruda and Bartramia patens (Figure 9). These classifications can be used for lichens as well and both mosses and lichens can grow associated in these places.
Mosses can be useful for Antarctic biodiversity, serving as food, as material for making nests or as a resting place for fauna. As food, they are used for this purpose mainly by arthropods, who are permanent residents of the South Pole, as there is no way out of there in winter. In this context, several other groups of microscopic beings are also inserted, with nematodes or even the smaller rotifers. Another important aspect is the associated microalgae communities.
There is also important associations with large animals, such as birds, which use plants to make their nests. The most used material can be moss (Figure 12), there may be mixtures with lichens in different proportions or even with flowering plants, but in some cases lichens (Figure 14) and phanerogams may predominate. There are, of course, birds that use other materials, such as rocks, in the case of giant petrels and penguins (Figure 13), mud with algae as is the case of Phalacrocorax atriceps, etc. [53].
Skua nest build using the moss Polytrichastrum alpinum (above) and another using Sanionia uncinata (below).
Giant petrel nest (Macronectes giganteus) build using rock fragments.
Larus dominicanus (kelp gull) nest build with mosses and the lichen Usnea.
Lichens are the most representative land group in Antarctica, despite they are not truly plants. They are formed by the symbiosis between a fungus plus an alga (most), a fungus plus a bacterium (case of Leptogium puberulum, as for example) or a fungus plus an alga and a bacterium (case of Placopsis contortuplicata). There are even lichenized mushrooms such as in Lichenomphalia spp. In the relationship, the photobiont provides the carbon source to the fungus, which can be polybasic alcohol (if it is green algae) or glucose (cyanobacteria). The fungus protects the algae from radiation and desiccation. The fungus still manages to reproduce in most cases through sexually formed spores or conidia (asexual), to fragments of the thallus or soredia. The algae reproduction is inhibited or suppressed [54].
To grow like a lichen, the spore needs to find the compatible algae that is rare in nature and lichenize. About 17,500 species of lichenized fungi and about 200 species of associated algae (100 green and 100 cyanobacteria) have been described. In this way, all of these fungi use algae in common and even different algae are used by the same species, in most cases even to adapt better to certain environments [55].
There are approximately between 386 to 427 species of lichens cited for Antarctica [55, 56] numbers that implies the most biodiverse group among terrestrials. In Antarctica in addition to the climate, limiting factors for lichens are the availability of substrate, which in most cases are rocks (in saxicolous species) or mosses (when species are muscicolous) and the presence of a source of nutrients, which can be originating from resting places or breeding animals, as already mentioned in the topic about mosses, above. These species are also starting competition with introduced ones which are being more and more frequent due climatic change.
In natural environments on the planet a succession is expected to occur. But these environments generally have trees. How is the succession of species in a mainly cryptogamic community like in Antarctica?
Perhaps one of the most ignored formation in Antarctica is that of the lichen/moss association. Mosses colonize an environment first and, to be replaced, must be annihilated. Who does that? If not an animal, mostly a set of lichens. If we look at the work already done with phytosociology in Antarctica, we see that lichens have figured as one of the most important when considering the ecological significance index [17].
Figure 15 illustrates how different species are associated with a lichen which in this case is fruticose: Sphaerophorus globosus, which forms groups up to 10 cm in height and is generally parasitic on mosses (muscicolous). In this 20 x 30 square in the figure, there are associated eight other species, of which 3 are mosses and 5 are other lichens, demonstrating how the lichen community settles on mosses and needs them to develop, even if it results in its death. In succession, it is to be expected, therefore, that lichens from the vegetation damage or kill a previously installed moss and then gradually disappear, also due to the lack of a host.
Biodiversity surrounding the fruticose lichen Spaerophorus globosus.
In this community the mosses are at a disadvantage, as they are being attacked by various parasites of the lichen group. These parasites do not even care about the moss species, but it looks like the Chorisodontium acyphyllum moss is surviving well and unscathed. This is also noticed in other parts and perhaps indicates that this moss ends up taking the place of the other parasitized and previously killed. This may show a stage of plant succession in Antarctica, which is still poorly studied.
Lichens can also occur on rock fragments and in Figure 16 there is a schematic drawing of the cover of round rocks, very common in uplifted areas. There are 17 species of lichens and one of moss occurring on the fragments. At some distance and to the unsuspecting it seems that the rocks have no vegetation, but it has adapted very well to this surface. Vegetation-free areas are in most cases rocks turned over by an animal or researcher who passed the site. So even small areas can group a considerable richness and the Antarctica is very sensitive to any disturbance, imagine the effects of climatic change.
Biodiversity in a 20 x 20 cm square of rounded rocks in Henequin point, King George Island, with 18 species.
Prasiola is the macroscopic alga that occurs in terrestrial environments in the Antarctic region with greater frequency. Only two species were being cited for the area: P. crispa (nitrophilous) and P. calophylla (nitrophobous) (Putzke & Pereira, 2013). Studying the molecular phylogeny of these algae in Antarctica, the presence of P. crispa was confirmed, that P. calophylla is different from the same species mentioned for Europe, changing its name to P. glacialis and that Prasiola antarctica is an independent species, morphologically identical P. crispa [57].
These species are among the largest primary producers in Antarctica and studies have shown that P. crispa is very resistant to desiccation and hypersaline conditions [58, 59, 60, 61].
In general, nitrophilic species occur near or inside bird colonies and nitrophobic in areas in contact with them. Often, some shallow pools of water have groups of Prasiola that prevent the growth of the surrounding mosses, demonstrating that they are somewhat allelopathic.
In several places it can be seen that the alga is lichenized, forming a different, more blackened and dotted thallus. It is the association with the fungus Mastodia tesselata, whose relationship is still controversial, as some authors believe it is parasitism and not a symbiosis. The lichen appears close to Verrucaria, a lichen with marine affinities [62].
In some cases, during the collections it can be seen that part of the algae stem is green and part is associated with the fungus and is already blackened, showing that the association may not be complete. Further studies are needed to elucidate what is the relationship between these two very different organisms.
P. crispa produces secondary metabolites with high toxicity and insecticidal power, and some studies on the subject have already been published [63, 64].
Birds can contribute to the long-distance dispersion of spores and seeds. In the first case, they can carry diaspores of fungi, mosses and pteridophytes transcontinentally (the latter group does not yet occur in Antarctica). Seeds in general can be carried via the digestive tract even. Some first evidences of dispersion of microscopic bryophyte spores have been published, where the case studied presents a transequatorial dispersion, with species carrying diaspores from one pole to the other or at least from the southern part of South America to the North Pole. Algae cells, fragments of moss leaves, elatheria and fungal spores have been found [65]. With the temperature registered due to climate changes, it is expected that new mosses may occur from introductions with the participation of birds in Antarctica.
Many birds carry these structures passively, as they can land on the fields, brush against them, use plants from these groups as material for their nests and even ingest material when transporting the food that is taken to the nests, or when feeding on carcasses arranged on plant communities (Figures 12–14). As any fragments of mosses may be sufficient to germinate and form new plants, many species in Antarctica today may have arrived there using transport in the bodies of birds and many more can be introduced in the future.
Feces can also introduce botanical material, as long as it is possible for structures to survive mechanical crushing and chemical bombardment of the digestive tract. The fact that they are eliminated with feces guarantees at least an initial supply of nutrients for their development and, since they are very small plants, the supply deposited once, may even be available for some years.
Despite this, the wind is the most important disperser of Antarctic mosses and lichens, since their main reproductive structures are spores or thallus fragments.
Antarctica, despite having the largest number of superlatives, as it is considered the coldest (−89.2° C), the driest (average annual precipitation not exceeding 100 mm), the highest (average height 2300 m), the windiest (wind speed can reach 327 Km/h), is the most unknown and the most preserved continent. However, it is isolated from the rest of the world due to its geography and ocean currents. From this isolation the populations are very different, facilitating the study of biological models, whose data can help in the explanation of global biological problems. Global climate change has been a feature in polar regions and continues to be. When discussing climate change on Earth, references are always made to glaciations and ice records [66].
The environmental superlatives of Antarctica, which determine extreme abiotic conditions for the biota, led to the evolution of fragile and unique communities, which are mainly characterized have high specialization and adaptation to environmental conditions, in addition to being very sensitive to environmental impacts of anthropic origin or caused by natural phenomena.
The climatic phenomena that occur in Antarctica are the basis for describing the climate of the Southern Hemisphere, and what happens in many countries is in part also a reflection of the phenomena that occur in the South Pole. In Antarctica the so-called “fronts” are frequent, numerous and of constant formation, these are mostly ephemeral, but many reach the southern areas of South America. In addition, the Antarctic ice is considered as a climatic archive. Air bubbles found in glacial ice can identify the composition of air from past eras. Snow samples can currently demonstrate the types of gases and particles that existed in atmospheric air many years ago. This means that through isotopes, it is possible to evaluate the activity of the sun in several eras, in addition to the biological activity, obtained by the analysis of molecules of organic origin [66].
Pollution was believed to be almost exclusively a product of the Industrial Age, but ice samples demonstrated lead pollution, dating from the Roman Empire period. The snow when deposited carries with it the characteristics of the chemical composition of the atmosphere at the moment it was formed, deposited on the continent’s surface air bubbles, salts, dust, volcanic ash, pollutants, among others. As snow does not melt on glaciers, the layers are deposited and compacted, keeping the record of climatic phenomena that occur over time preserved.
One of the global changes that can affect the Antarctic ecosystems is the so-called “hole in the Ozone layer”, which is located at the south pole, because it brings together the coldest regions of the planet and for having a very localized circulation of air masses. This despite being on Antarctica also reaches the southern tip of South America. This phenomenon contributes to the increase in ultraviolet radiation (UV-B), which, because it is mutagenic, contributes to the genetic alteration of species. Since this radiation is very intense in Antarctica, great mutation rates are expected, but it was observed that this does not occur, since these species have mechanisms that prevent DNA damage by the formation of secondary metabolites, at least in plants, whose photoprotector effects were experimentally proven [25, 26].
The retreat of the glaciers and the reduction of snow fields expand and the consequent exposure of new habitats for colonization, and the increase in the populations of plants, has been documented. Small changes in the physiology of the Antarctic organisms can affect their life histories, with indirect effects on the dynamics of the ecosystem and trophic chains. These subtle effects can be more easily detected due to the simplicity of polar ecosystems [25].
The use of plant communities in Antarctic ice-free areas to assess climatic changes consequences can be justified by facts such as:
These have a small number of species when compared to periglacial and subtropical regions, since among the species mentioned and described so far there are: two species of Magnoliophyta, Deschampsia antarctica Desv. (Poaceae) and Colobanthus quitensis (Kunth.) Bart. (Caryophyllaceae), approximately 360 species of lichens [55]. Bryophytes comprise approximately 113 species of moss and 22 species of liverworts [18].
As biodiversity is small, populations are very numerous, facilitating their delimitation and the identification of interspecific relationships.
The presence of soil is an important factor, since there are species of mosses such as, for example, Sanionia uncinata (Hedw.) Loeske and Chorisodontium aciplhyllum (Hook. F et Wils.) Broth. that grow in areas where rock fragments occur, as the soil is formed these populations are replaced by other moss species such as, for example, Polytrichum juniperinum Hedw. and Polytrichastrum alpinum (Hedw.) G.L.Smith, often associated with Deschampsia antarctica and Colobanthus quitensis [45].
Most of the species that grow in these areas, evolved under extreme environmental conditions and under intense stress, making them very well adapted to such environmental conditions.
Antarctica is still a continent with an insignificant anthropic impact, so the changes that occur in communities are the result of environmental changes arising from natural phenomena. This fact is important, since environmental variables that are selected can be evaluated based on natural phenomena.
The importance of studying plant species that grow in ice-free areas in Antarctica are strongly related to the environment, so it constitutes a potential source for assessing global changes. It is expected that climate change will have a major impact on Antarctic land biota. Studies suggest that the increase in temperature and greater availability of water can extend periods favorable to growth, increase the rates of development and reduce the duration of the life cycle, which can alter the distribution of species [5].
We acknowledge the Brazilian Antarctic Program and the CAPES and CNPq for financial support and logistic facilities to field work in Antarctica.
The authors declare no conflict of interest.
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