Post-fire restoration of soil microbial communities in a Quercus suber population

University of Minho Science School Eunice Conceição Lima da Costa Post-fire restoration of soil microbial communities in a Quercus suber population January of 2020 Post-fire restoration of soil microbial communities in a Quercus suber population UMinho | 2020 Eunice Costa University of Minho Science School Eunice Conceição Lima da Costa Post-fire restoration of soil microbial communities in a Quercus suber population Master thesis Master in Molecular Biology, Biotechnology and Bioentrepreneurship in Plants This work was realized under the supervision of: Doctor Maria Teresa Correia Guedes Lino Neto And Doctor Paula Cristina dos Santos Baptista January of 2020 v Direitos de autor e condições de utilização do trabalho por terceiros Este é um trabalho académico que pode ser utilizado por terceiros desde que respeitadas as regras e boas práticas internacionalmente aceites, no que concerne aos direitos de autor e direitos conexos. Assim, o presente trabalho pode ser utilizado nos termos previstos na licença abaixo indicada. Caso o utilizador necessite de permissão para poder fazer um uso do trabalho em condições não previstas no licenciamento indicado, deverá contactar o autor, através do RepositóriUM da Universidade do Minho. Atribuição-NãoComercial-SemDerivações CC BY-NC-ND https://creativecommons.org/licenses/by-nc-nd/4.0/ vi Acknowledgments “No matter were I go, I know I’m not alone, I feel my saviour there beside me. He leads me through the night, he’s always been my guide. He promised He will never leave me” – Nik Day 2020 Portanto queria agradecer à Professora Teresa Lino Neto, minha orientadora, pela paciência que teve comigo na realização deste trabalho; pelo incentivo e encorajamento que me deu, por aumentar a minha fé e pelo precioso conhecimento que me transmitiu. Agradeço também à Daniela pelo tempo dispendido comigo e por sempre se demonstrar diponivel a ensinar-me e a ajudar-me em todas as coisas que eu precisava. À Helena e à Juliana, na qualidade de amigas, que me ampararam nos momentos mais difíceis que encontrei ao longo deste trabalho experimental. Ao Rómulo, o senhor Doutor e à Helena, que sabiam identificar as minhas fraquezas, quando mais ninguém as via. Ao senhor Luis, pois fazia o impossível na aquisição de material ncesserário a este estudo. Ao resto dos meus amigos, pelo apoio e carinho que me deram até agora e finalmente, e não menos importante, quero dedidar esta tese aos meus pais e à minha tia Chica, pelo amor e confiança que depositaram e depositam em mim. Pelo seu conforto ao longo deste tempo, pelas dificuldades que passamos e pelo exemplo de persistência e de força que eles foram e sempre serão para mim. “Tenho-vos dito estas coisas para que em mim tenhais paz; no mundo teries aflições, mas tende bom animo, Eu venci o mundo” – João 16:33 vii Declaração de integridade Declaro ter atuado com integridade na elaboração do presente trabalho académico e confirmo que não recorri à prática de plágio nem a qualquer forma de utilização indevida ou falsificação de informações ou resultados em nenhuma das etapas conducente à sua elaboração. Mais declaro que conheço e que respeitei o Código de Conduta Ética da Universidade do Minho. viii Restauração pós-incêndio de comunidades microbianas do solo de Quercus suber Resumo Os incêndios florestais causam distúrbios com enorme impacto nas florestas portuguesas de sobreiro. Devido à presença de cortiça, os sobreiros ( Quercus suber L.) são reconhecidos como estando bem-adaptados à ocorrência de incêndios e a altas temperaturas. Contudo, a sustentabilidade das florestas de sobreiro também depende em grande parte das comunidades microbianas do solo, como seja a presença de fungos ectomicorrízicos ou rizobactérias. Neste trabalho, os efeitos da temperatura nas comunidades microbianas dos solos de sobreiro foram avaliados, bem como a restauração ecológica das florestas de Quercus suber após um episódio de incêndio florestal. A avaliação da taxa específica de morte microbiana foi realizada recorrendo a ensaios de temperatura (50ºC a 125ºC) efetuados com solos de sobreiro. A identificação das bactérias mais resistentes ao calor foi efetuada com auxílio a métodos de identificação molecular. Os resultados revelaram um aumento da taxa específica de morte microbiana com a temperatura, em que as comunidades bacterianas revelaram ser mais sensíveis ao calor que os fungos, provavelmente devido à produção de esporos. De acordo com este resultado, bactérias formadoras de esporos, tal como Bacillus spp foram identificadas como sendo as mais resistentes ao calor. Além disso, a análise da comunidade bacteriana por metabarcoding revelou uma elevada abundância de Firmicutes (Bacillus) em amostras de solo submetidas a 100ºC por 15 e 30 min. Estes resultados fornecem assim uma previsão do que poderá acontecer às comunidades bacterianas após um incêndio nas florestas de sobreiro. Aproveitando a ocorrência natural de um incêndio em Santa Marta das Cortiças (Braga), as comunidades microbianas presentes em solos de sobreiro foram analisadas por metabarcoding. Colheram-se amostras de solo de nove árvores, diferenciadas pelos danos causados pelo fogo (queimadas, semi-queimadas e não-queimadas). A comunidade bacteriana foi comparada ao longo do tempo e os resultados revelaram que, de 5 a 18 meses após o incêndio, existiram diferenças na composição das comunidades bacterianas. Os solos de sobreiro apresentaram sempre uma elevada abundância de bactérias gram-negativas (Proteobacteria), seguidas de Acidobacteria, Actinobacteria e Chloroflexi. A variação do microbioma entre os solos afetados é explicada pelas diferenças na composição química do solo, intensidade com que o fogo atingiu os solos de sobreiro e fatores endafoclimáticos. Palavras-chave: Comunidades Microbianas, Incêndios florestais, Quercus suber ; Solo ix Post-fire restoration of soil microbial communities in a Quercus suber L. population Abstract Wildfires cause disturbances with huge impact on Portuguese cork oak forests. Due to the production of cork, Quercus suber L. trees are recognized to be well-adapted to fires occurrence and to high temperatures. However, cork oak sustainability is also largely dependent on soil microbial communities, such as the presence of ectomycorrhizal fungi or rhizobacteria. In this work, the effects of temperature on cork oak soil microbial communities was studied, as well as the ecological restoration of Quercus suber forests after a wildfire episode. An evaluation of soil microbial specific death rate was performed using temperature assays (50ºC to 125ºC) on soils collected from cork oak trees. The most temperature resistant bacteria were identified by sequencing. Results revealed an increase of microbial death specific rate with temperature. Bacterial communities were more heat sensitive than fungi, as their survival is probably ensured by spore production. Accordingly, spore-forming bacteria such as Bacillus spp were identified as the most heat resistant bacteria. In addiction bacterial community analysis by a metabarcoding approach revealed high abundance of Firmicutes (Bacillus) on soil samples subjected at 100ºC for 15 and 30 min. Furthermore, these results provided a prediction of what could happen to bacterial communities after a wildfire in cork oak forests. Taking advantage from the natural occurrence of a natural wildfire in Santa Marta das Cortiças (Braga), cork oak soil microbial communities were surveyed using a metabarcoding approach. Soil samples from nine trees with three different types of fire damage (burnt, half-burnt and non-burnt) were collected, and bacterial community compared along time. Results revealed that from 5to 18-months post-fire, there are differences on the composition of bacterial communities. A higher incidence of gram-negative bacteria (Proteobacteria), followed by Acidobacteria, Actinobacteria and Chloroflexi were always found for all cork oak soils. The variation on microbial communities among fire-affected soils is explained by differences on soil chemical composition, fire intensity and edaphoclimatic factors. Key-words: Quercus suber ; Microbial communities, Soil, Wildfires x Index Resumo ................................................................................................................................................ viii Abstract ................................................................................................................................................. ix Table index ............................................................................................................................................xii Figure index .......................................................................................................................................... xiv Chapter 1: General introduction...............................................................................................................1 1.1. Importance and distribution of cork oak ..................................................................................2 1.2. The importance of associated forest microbial communities ....................................................5 1.3. Bibliography .........................................................................................................................11 Chapter 2: The effect of temperature increase in soil microbial communities ..........................................15 2.1. Thermal inactivation of soil microbial communities ................................................................16 2.2. Microbial thermal death described by mathematical models ..................................................17 2.3. Material and Methods ...........................................................................................................19 2.3.1. Quercus suber L. soil sampling ....................................................................................19 2.3.2. Experimental design .....................................................................................................20 2.3.3. Temperature treatments and title determination ...........................................................21 2.3.4. Determination of cell death kinetics ..............................................................................22 2.3.5. Analysis of colony biodiversity .......................................................................................22 2.3.6. Identification of the most heat resistant bacteria ...........................................................22 2.3.7. Metabarcoding analysis of temperature treated soil .......................................................24 2.3.7.1. DNA sequence analysis and quality control ...............................................................25 2.4. Results and Discussions .......................................................................................................26 2.4.1. Soil chemical analysis ..................................................................................................26 2.4.2. The effect of heat on soil microbial communities...........................................................27 2.4.3. Identification of heat resistant bacteria..........................................................................32 2.4.4. Metabarcoding analysis of temperature treated soil .......................................................34 2.5. Conclusions .........................................................................................................................43 2.6. Bibliography .........................................................................................................................44 Chapter 3: Evaluation of soil microbial communities after a wildfire ........................................................47 3.1. Microorganism identification: a metabarcoding approach ......................................................48 3.2. Material and Methods ...........................................................................................................51 3.2.1. Quercus suber L. study population ...............................................................................51 3.2.2. Experimental design .....................................................................................................54 3.2.3. Metabarcoding approach for soil microbial communities analysis ..................................54 3.2.3.1. DNA sequence analysis and quality control ...............................................................55 3.3. Results and discussion .........................................................................................................56 1 Chapter 1: General introduction 2 1.1. Importance and distribution of cork oak Cork oak ( Quercus suber L.) (Figure 1) belong to the Cerris sub-genus ( Cerris section) of Quercus genus, according to the new classification (Denk et al. , 2017). This genus is a member of the Fagaceae family, which includes other genera such as Castanea and Fagus (Toribio et al. , 2005). Cork oaks are low spreading trees with short stems and thick branches (Aronson et al. , 2009). This perennial trees are monoecious with a flowering period from April to June that can be extended until autumn (Pereira & Tomé, 2004). Cork oak flowering begins at 15 to 20 years of age, with fruit production (acorns) fluctuating over the years. For example, Quercus suber can have a high yield of acorns for 2 to 3 years and a yield, followed by a reduced yield for 10 years (Pereira & Tomé, 2004). Male flowers are pendulous, long and come from the auxiliary buds of the previous year's branches, and female flowers appear in the new under vigorous growth (Gil & Varela, 2008). The leaves of cork oaks are green, dark, and denticular, where 5 to 8 pairs of secondary veins diverge from its central vein. Leaves usually measure between 2.5 to 10 cm x 1.2 to 6.5 cm (Amorim, 2014). This Quercus species is characterized by the presence of a bark with a continuous layer of thick and grooved cork on the outside (Pereira, 2007a). Figure 1 – Cork oak ( Quercus suber) tree. 3 Cork grow is promoted during spring and early summer, being highly controlled by annual climatic variations, such as winter precipitation and spring temperatures (Pereira & Tomé, 2004). The first cork is removed at 25-30 years of age and take places every 9 years (Leal et al. , 2008).The cork is the suberous parenchyma originated by the cork meristem, which gives to this material characteristic features. This lightweight, elastic material is a good thermal and electrical insulator, acoustic and vibration absorber and has also the ability to be compressed without lateral expansion (Gil, 2007). This last trait is due to the elasticity conferred by the cells present in this material, which promote an elastic recovery due to the action of the compressed gas inside them (Gil, 2007). Cork is also a material that dissipates creep energy, has low thermal conductivity, chemical and biological stability (Gil, 2007). Due to these characteristics, cork has a high economic value (Pereira, 2007a), for the production of wine bottling stoppers (alimentary industry), paving, wall and ceiling cladding (construction industry), thermal insulation (aerospace industry), badminton shuttlecocks and baseball balls (sports), shoe soles (footwear industry) and other applications (Leal et al. , 2008; Direito, 2011) Cork removal is done manually and the first stripping, from which virgin cork is produced, is performed when the tree has a trunk perimeter of 70 cm at breast height (Pereira, 2007b). This is a value is specified by Portuguese legislation. This perimeter occurs when the tree is between 18 and 25 years old. The second stripping occurs between the ages of 27 and 35 and the remaining ones are performed when cork oak has 45 years of age (Pereira & Tomé, 2004; Costa et al. , 2010). From the third stripping, every 9 years. The cork undergoes morphological changes (Pereira & Tomé, 2004). Virgin cork is characterized by many severe fractures, due to the intense radial growth and tangential stress. In the remaining stripings, cork presents smoother fractures, derived from the decrease in stem growth stresses (Pereira, 2007b; Oliveira & Costa, 2012). The phenotypic and morphological biodiversity that exists among cork oaks is due to the hybridization that existed in the past (Pereira, 2007a). For this reason, today we can find different qualities of cork from distinct cork oaks. Cork removal contributes to an increase of trees vulnerability to external biotic and abiotic agents (Catry et al. , 2012). After stripping (Figure 2), the exposure of the living tissue causes an immediate loss of water and plant responds by closing their stomata interrupting plant nutrition (Leal et al. , 2008). Cork oaks tree start their recovering after 24-30 days after the regeneration of phloem. 4 Quercus suber is one of the most important evergreen tree of the western Mediterranean region, being a common species of oak from the Iberian Peninsula (Pereira & Tomé, 2004; Costa et al. , 2010). From Morocco and Iberian Peninsula to the western edge of the Italian Peninsula, Figure 2 – Cork oaks that were recently stripped, displaying their living tissues exposed to environmental stressors. Figure 3 – Quercus suber L. world distribution, APCOR. Portugal, north of Africa and southwestern of Spain are the areas where cork oak tree predominates, followed by France and Italy. 5 cork oak forest covers over 1.5 million hectares in Europe and 700 000 hectares in North Africa (Figure 3) (Pereira & Tomé, 2004; Aronson et al. , 2009; Mauri, 2016). Accordingly, Quercus suber is well adapted to hot and dry temperatures being considered as a very tolerant specie (Pereira, 2007a). 1.2. The importance of associated forest microbial communities Plants shelter a large diversity of microbial communities, and the dynamics of plant-microorganism interaction are known to be crucial for maintaining the ecosystem functioning (Parasuraman et al. , 2019). Indeed, microbial communities have existed together with plants for millions of years and play important roles in host growth and health (Singh et al. , 2019). For example, plant associated microorganisms can provide nutrients (including water), as well as higher resistance to abiotic and biotic factor. Soil microorganisms can also play an important role on the maintenance of forest ecosystems by producing organic and inorganic compounds through the decomposition of litter, death microbial biomass and death wood (Baldrian, 2017). Studies performed over the past decade have identified microorganisms associated with different plant hosts and specific plant organs. Plant microbiota or microbiome, comprises all active microorganisms that interact with plants (Orozco-Mosqueda et al. , 2018), also including the functional genetic pool of viruses and prokaryotes (Compant et al. , 2019; Singh et al. , 2019). Plant microbiota live in different habitats, which comprises the whole plant or their organs, such as leaves, roots, shoots, seeds and flowers (Singh et al. , 2019). Accordingly, different microbiotas could be found associated with a plant, such as the phyllosphere, endosphere, episphere, rhizosphere, rhizoplane, anthosphere, spermosphere, carposphere microbiotas (Shade et al. , 2017; Ciafardini & Zullo, 2018), soil microbiome (Al-Dagal & Fung, 1990; Levy et al. , 2018; Compant et al. , 2019). The phyllosphere comprises all the aboveground plant organs, such as leaves, stems and flowers (Remus-Emsermann & Schlechter, 2018). Several studies over the past decade found that in the phyllosphere there is a great microbial diversity, which is specific for each plant species (Lindow & Brandl, 2003; Esser et al. , 2015). Indeed, bacteria (mainly Pseudomonas syringae and Erwinia (Pantoea) spp, are predominant microorganisms on leaf surfaces (Lindow & Brandl, 2003) and are capable of reaching populations with densities greater than 104 to 105 bacteria per mm-2 leaf surface, [108 bacteria per g-1 leaf in extreme cases (Remus-Emsermann et al. , 2014)]. Furthermore, different authors revealed that phyllospheric bacteria are more prevalent in areas closer to trichomes and 6 stomata (Remus-Emsermann & Schlechter, 2018). A better knowledge on epiand endophyte fungal communities that thrive in the phyllosphere would be useful for getting a better understanding of the factors that influence fungal composition and structure, in order to predict their response to climate change (Gomes et al. , 2018). Two different habitats can be distinguished. The surface of plant tissues are inhabited by microorganisms called epiphytes (Faeth et al. , 2002; Backman & Sikora, 2008; Turner et al. , 2013). Other microorganisms (known as endophytes) are able to colonize the internal tissues of the plant (Orozco-Mosqueda et al. , 2018). The specific location of endophytes in cell tissues are not well described, although these microorganisms were found in xylem vessels, intracellular apoplast, dead or dying cells (Turner et al. , 2013). Symbiotic systemic endophytes are well known for increasing plant fitness reducing herbivory by alkaloids production and increasing the nutrient uptake in poor soils. In return the host plant gives refuge and photosynthates to the endophyte (Faeth et al. , 2002). Both endophyte and epiphyte display relationships with plants which are characterized for being neutral, beneficial or detrimental (Faeth et al. , 2002; Backman & Sikora, 2008; Turner et al. , 2013). The area of soil that contains the plant root system is called rhizosphere and is composed, not only by the plant roots, but also by rhizodeposited adhesives, peeled root cells and exudates. Rhizosphere is highly influenced by the physiological processes that occur within the plant (Clairmont et al. , 2019), being also considered as a site of high soil microbial activity, thus representing one of the most complex ecosystems (Compant et al. , 2019). Indeed, the rhizospheric soil has been described as “the most complicated biomaterial on the planet”, since comprises two components: abiotic structure and biological diversity. Associated to the root, the rhizoplane is the community of microorganisms on the root surface (microbial biofilms) (Clairmont et al. , 2019). Relationships between microbial communities in rhizosphere and rhizoplane are not yet known or have been poorly studied. The root microbiota is mainly composed by Acidobacteria , Verrucomicrobia , Bacteroidetes , Proteobacteria , Planctomycetes and Actinobacteria (Compant et al. , 2019), which are known to establish relationships with roots (Singh et al. , 2019). The community of microorganisms present in rhizosphere can provide substances that drive plant growth and development by producing phytohormones that stimulate root growth. In certain cases, microorganisms can even stimulate uncontrolled cell growth (as the case of Agrobacterium tumefaciens or Ustilago maydis ). This morphological modification may result in an increase in 7 water uptake by plants. For example, the fungi of Tricoderma sp. grows and develops in association with plant roots, contributing for their development (Singh et al. , 2019). Plant microbiomes perform functions for the plant growth and health. Thus, the understanding of interactions between plants and the microbial world, as well as the factors inherent to the formation of microbial communities, lead to a better understanding of plants as meta-organisms (Compant et al. , 2019). Also, a better knowledge of the benefits taken by microorganisms through this association are being achieved (Compant et al. , 2019). For example, a wide variety of compounds secreted by plants, such as organic acids, vitamins, sugars, among others, are currently known to be used by microbial communities as signals (Singh et al. , 2019). On the other hand, volatile substances and other compounds, are secreted by microorganisms in order to directly or indirectly activate plant immunity, control plant development or plant morphogenesis (Singh et al. , 2019). For example, cytokinins and auxins are transferred by microorganisms to areas around the roots, in order to promote the growth of the root system (Singh et al. , 2019). There are also volatile compounds produced by PGPR – Plant Growth Promoter Rhizobacteria - such as acetoin and 2,3-butanediol that are used as signalling compounds for plant growth (Singh et al. , 2019). Plant associated microbiomes are also important on promoting plant growth and on defence to biotic factors (Orozco-Mosqueda et al. , 2018). Soils have a huge microbiological diversity (Zheng et al. , 2019), which are essential for virtually all biogeochemical cycle processes (Prosser et al. , 2007), such as nitrogen, phosphorus (Sergaki et al. , 2018) and carbon cycles (Crowther et al. , 2016). Also, these microorganisms can provide protection against abiotic and biotic stress (Sergaki et al. , 2018) and regulate decomposition of organic matter (Zheng et al. , 2019), being also fundamental in mineralization and immobilization processes (PizarroTobías et al. , 2015). All of these functions are possible due to the metabolic versatility of these microbial communities (Prosser et al. , 2007). Soil microbes can resist to significant variations in soil physical parameters occurring during the day and seasons, such as temperature and moisture, which are crucial for the microbial activity (Maier et al. , 2009). Many microorganisms are also resistant to the soil texture itself, which varies from soil to soil, according to other environmental factors (Maier et al. , 2009). Furthermore, the presence of certain organisms could result in biotic stressors for others, as microbial communities from different soils are frequently in competition (Maier et al. , 2009). Indeed, certain microorganisms can segregate inhibitory substances, which may influence the emergence of new microbial communities, by inhibiting their appearance or reducing the number of existing 8 microorganisms in that community, in order to maintain a biotic balance in the soil (Maier et al. , 2009). Besides these, many factors are known to disturb forest ecosystems, such as anthropogenic factors, environmental pollution and fires (Mataix-Solera et al. , 2011; Baldrian, 2017). Accordingly, human behaviour can influence the fire frequency and changes in the vegetation patterns, which have led to environmental problems in some locations over the past five decades (Mataix-Solera et al. , 2011). Indeed, forest fires are a major cause of changes in forest ecosystems, increasing the risk of soil erosion and desertification (Granged et al. , 2011). Despite that, forest fires can provide diverse benefits to the ecosystems, such as the consumption of superfluous vegetation (organic matter) and release of nutrients into the soil (inorganic matter) (Cowan et al. , 2016). Among others, the negative impacts of forest fires comprise the heat that directly affects soil microbial communities, the alteration of the physical structure, the sudden acidification of soil, decrease of moisture and/or electrical conductivity (Pérez-Valera et al. , 2017). Forest fires generally have more negative than positive impacts on the physical, chemical and biological soil properties (MataixSolera et al. , 2011), which also depend on the intensity of the fire occurrence (Pérez-Valera et al. , 2017). Many authors have suggested definitions and indicators of forest fire severity, by the assessment of physical, chemical and ecological changes observed after fires (Cowan et al. , 2016). In addition, the intensity of forest fires can be rated based on the rate of thermal energy production and forest fire duration (Certini, 2005). This assessment should take into account, not only the impacts of above ground fire, but also the spread of heat belowground, which affects the soil forest ecosystem (Mataix-Solera et al. , 2011). Low intensity fires typically include those that are applied in controlled conditions, during specific weather conditions and which serve to reduce the amount of fuel ( e.g. dry leaves, twigs). In contrast, a severe fire is usually responsible for leaving a trail of negative effects on the soil: reducing the organic matter by combustion, the decrease of nutrients through volatilization, soil erosion, changes on aggregate stability that weakens the soil structure and changes on microbial communities (Mataix-Solera et al. , 2011). Therefore, although low severity fires do not significantly affect soils, severe burning can affect a wide range of soil properties and in some cases those impacts are irreversible (Granged et al. , 2011). Thus, soil properties may have a permanent, long-term or short-term alteration induced by fire, depending on severity and frequency of fires and post-fire weather conditions. After a wildfire episode, soils suffer important biological, chemical and physical changes (Granged et al. , 2011). Among these, changes in the chemical properties of soils are one of the 9 most important, due to the defragmentation of clay minerals and increase of inorganic compounds. Wildfires provide ashes from the burning soil, which are rich in magnesium, potassium and calcium. The amount of ash (micro and macronutrients), soil nutrients, organic matter content are all factors that contribute for pH variation in the soil after a fire (Altun et al. , 2004). High amounts of ashes are known to promote an acidification of acidic and neutral soils (basic soils are not greatly affected) (Altun et al. , 2004). As the soil pH is related to the functioning of terrestrial ecosystems, soil fertility and biogeochemical processes, the acidification of soils will affect different ecosystem functions. Therefore, some authors argue that soil pH is negatively correlated with temperature, also suggesting that this phenomenon may be caused by increased accumulation of soil organic matter and increased organic acids (Hong et al. , 2019). Depending on the fire severity, the impact on organic matter can be the volatilization of the micronutrients, carbonization or complete oxidation. The combustion of organic matter is consistent with a decrease of soil pH and increase of electrical conductivity (Granged et al. , 2011). While forest fires can cause depletion of organic material on the soil surface, nutrients as phosphorous, potassium, calcium, magnesium, among others, can change in their quantity and spatial distribution after this forest disturbance, moving to deeper areas of the soil after rainfall (Altun et al. , 2004). Furthermore, certain compounds suffer specific modifications. For example, soil organic nitrogen compounds can remain after low-intensity fires, but proteins suffer specific modifications on their peptide form. This progressive modification occurs at two stages: first some NH2 groups, together with an amino acid are removed, without changing the peptide structure; then the N-amide group is converted to heterocyclic compounds (Certini, 2005). During severe fires, organic nitrogen is converted to its inorganic forms, ammonium (NH4+) and nitrate (NO3). Ammonium is a direct product of combustion, while nitrate forms ammonium a few weeks or months after fire, as a result of nitrification reactions. Forest fires do not necessarily have the same impact on phosphorus (P), as on available soil nitrogen, because P losses through volatilization or leaching are small. However, fires result in an enrichment of available P for a short period time. Indeed, in acidic soils, orthophosphate binds to Al, Fe and Mn oxides through chemical adsorption, while in neutral or alkaline soils P binds to Ca minerals and precipitates as calcium phosphate. The time at which the positive effect of fire on P availability is short and is highly variable, depending on several factors (Certini, 2005). Forest fires also negatively influence the biological properties of soil (Ekinci, 2006). 10 The temperatures reached by forest fires tend to promote the death of living beings present in the soil and in extreme cases can completely sterilize the soil. During a wildfire episode, soil surface temperatures can reach between 200 °C to 300 °C, making nearly impossible a microbial colonization after forest fire, being the exception fungal spores that can withstand severe temperatures (Buscardo et al. , 2010; Mataix-Solera et al. , 2011). The ability of certain microbial species, communities or ecosystems to recover after a disturbance without significantly altering their structure, function or productivity is called resilience. This term can be used to assess the ability of a specific species, community or ecosystem to return to its original state after the end of the disturbance (Granged et al. , 2011). Soil microbial communities are highly dependent on the environment. Some microbial groups can benefit from those altered circumstances while other became harmed (Pérez-Valera et al. , 2017), due to the previously described factors or by the absence of water (Cowan et al. , 2016). Therefore, mechanisms such as competition between microorganisms can limit the similarity between microbial communities before and after wildfire episode (Pérez-Valera et al. , 2017). Ectomycorrhizal fungi are sensitive to forest fires because mortality of this fungi usually occurs at temperatures around 60ºC and above (Cowan et al. , 2016). Although high intensity fires affect the function of ectomycorrhizal fungal communities, leading to their total extinction (Buscardo et al. , 2010), some ectomycorrhizal fungi can produce spores able to survive at high temperatures (Cowan et al. , 2016). Such resistance structures contribute to the similarities in composition of ectomycorrhizal communities before and following forest fires (Buscardo et al. , 2010). In any case, fire is known to promote significant loss of fungal biomass, according to the abundance of mycorrhizae, even though there is a "protection" of these microorganisms that develop in deeper soils (Mataix-Solera et al. , 2011). In contrast to fungi, Smith et al. (2008) analysed the bacterial community of burnt and unburnt soil and concluded that the composition of microbial communities was significantly different in both conditions. The presence of Alphaproteobacteria and Gammaproteobacteria was characteristic of unburned soils, while Betaproteobacteria and Bacillus were the most common in burnt soils (Smith et al. , 2008). Therefore, changes in soil microbial communities can lead in many ways to soil degradation and consequently to ecosystem disturbance. For this reason, soil microbes can be important indicators of soil quality and consequently indicators of the health status of the plant (Barreiro et al. , 2010; Pizarro-Tobías et al. , 2015). 17 There are certain microbes more resistant to temperature than others (Mataix-Solera et al. , 2011). For example, some nitrifying bacteria, protozoa and fungi cannot resist temperatures higher than 70ºC. Arbuscular fungi suffer cell death at temperatures ranging from 80ºC to 90ºC, while most microbial communities do not live above 115ºC. However, spore-forming strains can maintain their communities at higher temperatures due to late spore burst (Mataix-Solera et al. , 2011). The maximum heat treatment by which microorganisms maintain their viability defines their maximal heat tolerance (Hugo, 1971). Most soil microbial communities are resistant to large thermal ranges on soil. The optimal temperature range for growth of each community and temperature resistance makes their distinction (Hugo, 1971). While psychrophilic microorganisms are characterized by preferring temperatures of 20ºC, the mesophilic prefer 20-45ºC, thermophilic (45-90ºC) and hyperthermophile (>90ºC). The differentiation between such microorganisms provides a pattern of temperature-induced mortality in their natural habitats (Hugo, 1971). The temperatures suitable for the growth of thermophilic microorganisms are consequently inhibiting the growth of mesophilic microorganisms and the optimal temperature for growth of these later microorganisms are growth inhibitors for psychrophilic microorganisms (Maier et al. , 2009). Although there is a wide variety of microorganisms in the soil, most of them are mesophilic due to the buffering effect of the soil temperature (Maier et al. , 2009). 2.2. Microbial thermal death described by mathematical models Microbiology uses mathematical modelling to describe the response of microbial populations to environmental conditions, as well as to discriminate the parameters that influence those responses (Klotz et al. , 2007; Ferrer et al. , 2009; Huang, 2014; Garre et al. , 2018). Predictive microbiology follows a few steps on data analysis. First, samples must be subjected to different environmental conditions and plotted into survival or growth curves. Then, each survival or growth curve must be analysed using a suitable primary model to determine kinetic parameters, such as growth or death rates. Finally, analysis on parameter effects such as temperature is performed using secondary models (Huang, 2017). Primary models describe changes in the number of microorganisms, or other microbial responses, as a function of time and secondary models describe how the primary models vary depending on other parameters, such as temperature, acidity, water activity, salt content, among others (Zimmeramann, 2012; Huang, 2014). But on predictive microbiology, mathematical models can also be classified as tertiary models (Ferrer et al. , 2009). Tertiary models 18 are software applications generated by primary and secondary models, usually used by nonmodellers (Ferrer et al. , 2009). Mathematical primary models can translate thermal death of microorganisms by plotting the Log10 of survival fraction vs time or plotting the percentage of survival fraction vs time (Klotz et al. , 2007; Toure et al. , 2017). These graphical representations (survival curves) are a cumulative form of the temporal distribution of lethal/deadly events on microorganisms (Klotz et al. , 2007; Toure et al. , 2017). The behaviour of microorganisms can be interpreted in different ways. The inactivation process resembles the first order of reaction. The lethal events can occur randomly over time in a population of cells that are similar in their susceptibility to the agent, but differences in sensitivity to lethal agents can occur between individual cells (Klotz et al. , 2007). For many decades, mathematical prediction of microbial death used the first order of kinetics – survival fraction vs time used to follow a linear relationship (Corradini et al. , 2007; Huang, 2014, 2019). However, the majority of environmental survival curves are not linear (Corradini et al. , 2007; Huang, 2014). According to different environmental conditions, survival curves (Figure 4) can have several shapes (Moats, 1971; Peled et al. , 1977; Casolari, 1988) and inactivation kinetics depends on the conditions applied to microbial cells (Klotz et al. , 2007). Survival curves may be linear - the microbial survival rate is proportional to the number of surviving cells over time - or nonlinear. Nonlinear survival curves are subdivided into tailed curves, shoulder-tailed curves (sigmoid), shoulder curves, and logarithmic curves. Survival shoulder curves are characterized by the appearance of a lag phase on the first minutes of heat treatment. This indicates a noninstantaneous thermal death, due to the presence of agglomerated microbial communities. Tail curves (biphasic) are usually interpreted by the presence of intrinsically more resistant microorganisms than others, normally two subpopulations where one microbial community is more most heat resistant than the other. Sigmoid curves are a combination of survival biphasic curves with survival shoulder curves. Finally, logarithmic curves express heterogenous communities of microorganisms that differ on heat sensitivity (Moats, 1971; Peleg & Cole, 1998b; Xiong et al. , 1999). Microbial rates elucidate the time required to reach a microbial event or condition (Smelt & Brul, 2014). Therefore, microbial death rates indicate the rate at which microbial inactivation occurs over time (Moats, 1971) and describes the behaviour of microbial communities during heat treatment (Smelt & Brul, 2014). Microbial death rates cannot be directly determined as they depend on the number of surviving cells over time. 19 The objective of this chapter is to evaluate the influence of temperature on soil microbial communities by evaluating the title of microbial suspensions from cork oak soil samples, to which temperatures of 50ºC to 150ºC were applied. With the help of survival graphs [(N/NO) vs time and Log (N/NO) vs time], the decrease of microbial load over time was evaluated and specific rates calculated. The decrease in biodiversity was tested by counting the bacterial morphotypes found in different samples and the most resistant bacteria to 125ºC were identified by molecular methods. In this chapter, differences on microbial communities present in soil samples subjected to heat treatment were also evaluated along time, by using a barcoding approach. 2.3. Material and Methods 2.3.1. Quercus suber L. soil sampling A cork oak tree located at University of Minho (Braga, Portugal) was chosen for evaluating the response of its soil microbial community to heat (Figure 5). The sampled cork oak was apparently healthy with no signs of defoliation or other diseases signs, being near to other healthy cork oak trees. Soil samples were collected in three dates (14th March, 28th May and 24th June of 2019). After removing the uppermost layer of soil that consisted of plant litter and other organic material, three independent soil cores (5 cm in diameter and 10 cm in depth) were collected. Soil sampling was performed under the middle of cork oak canopy, in the three tree trunk directions. In total, 9 soil cores (3 cores × 3 sampling dates) were collected, which were kept at 4ºC until processing (Figure 6). The three soil samples (cores) from each collection date were thoroughly mixed. Figure 4 – Common microbial survival curves. Linear survival curve(a); Non-linear: survival tailed curve (b); survival shoulder-tailed curve (c); survival shoulder curve (d); N/N0 - microbial cell survivors; adapted (Toure et al., 2017). a b c d Time Log (N/N0) 20 A soil sample was sent for chemical analysis performed by A2-Análises Químicas TM (Guimarães, Portugal). The chemical soil analysis included gravimetry, pH (H2O), pH (CaCl2), electrical conductivity, organic matter, organic carbon, total nitrogen; carbon: nitrogen ratio; phosphorus (P2O5), potassium (K2O), calcium (CaO), magnesium (MgO), sodium; boron, sulphur, iron and manganese. 2.3.2. Experimental design Figure 5 - Cork oak tree used on the study of temperature influence on soil microbial communities. GPS coordinates at University of Minho are 41 33'45.76"N; 8 23'40.87"O (photo taken on 14-05-2019). Figure 6 - Soil sampling method for analysis of temperature influence on soil microbial communities. A mix of three cores was created in order to form several samples for more and complemented information see Table 1. 21 For evaluating the effect of temperature on soil microbial community, soil samples from distinct collection dates were used in different temperature assays (Figure 6). For each temperature treatment, different periods were applied to allow the construction of survival curves (Table 1). Table 1 - Temperature treatments performed on cork oak soils to evaluate the influence of temperature on soil microbial communities. Temperature treatments were performed with distinct collected soils: 14th March (a); 28th May (b) and 24th June of 2019 (c). Time (min) Temperature (ºC) 50 75 80 100 125 150 a b c a b c a b c a b c a b c a b c 0 - - - - - - 15 - - - - - - 30 - - - - - - 50 - - - - - - - - - - - - - - - 60 - - - - - - - - - 80 - - - - - - - - - - - - - - - 90 - - - - - - - - - 120 - - - - - - 150 - - - - - - - - - - - - - - - 180 - - - - - - - - - - - - - - - 2.3.3. Temperature treatments and title determination Each soil mixture (2g) was subjected to different temperature treatments (Table 1), using a Memmert Model 700 incubator (Memmert GmbH, Germany) . Temperatures were certified by using a data logger Thermocouple USB EL-USB-TC-LCD from Lascar Eletronics. After temperature treatment a microbial solution was prepared by using 1g of treated soil and sterilized water (5mL for temperature treatments of 125ºC and 150ºC and 10mL for the remaining treatments). This difference was due to the predicted reduction in the number of microorganisms present in soil samples treated at 150ºC or 125ºC, in comparison to the remaining temperature treatments. After a thoroughly mixing by vertexing, serial 10-fold dilutions (1mL of final volume) were prepared, in order to enable the title determination (Figure 7). From all dilutions, 100µL were spread onto PCA (Plate Count Agar) medium ( VWR Prolabo Chemicals) for bacterial forming units count and PDA (Potato Dextrose Agar) medium ( Liofilchem srl , Italy) for fungal forming units counts. Tree technical replicates were performed for each experiment. Incubation proceeded at 30ºC for 48h before colonies counting. For determining the title as colony forming unit (CFU) per g of soil, the equation 1 was used. 22 Equation 1: 𝑇𝑖𝑡𝑙𝑒 (𝐶𝐹𝑈/𝑔 𝑜𝑓 𝑠𝑜𝑖𝑙 )=𝐶𝑜𝑙𝑜𝑛𝑖𝑒𝑠 𝑐𝑜𝑢𝑛𝑡 ∗ ( 1 10−𝑛)∗ 1 𝑖𝑛𝑜𝑐𝑢𝑙𝑎𝑡𝑒𝑑 𝑣𝑜𝑙𝑢𝑚𝑒 (𝑚𝐿)∗ 𝑓𝑜𝑙𝑑 𝑑𝑖𝑙𝑢𝑡𝑖𝑜𝑛 𝑜𝑓 𝑠𝑜𝑖𝑙 where 𝑛 corresponds to the dilution. 2.3.4. Determination of cell death kinetics The survival curves were obtained by plotting the percentage of surviving cells (N/N0 * 100), where N is the microbial title at time t and N0 is the title at t=0. Microbial survival curves were also plotted on a log-linear scale, using the log10 (N/N0) on the y-axis and time on x-axis. Graphical representations were performed using a nonfit curve One phase decay provided by GraphPad prism 7.04 software. The effect of temperature on microbial community death was evaluated by a graph representing the specific death rate as a function of temperature. 2.3.5. Analysis of colony biodiversity For evaluating the microbial community biodiversity after application of different temperatures, the morphological characterization of obtained colonies was performed. For this evaluation, plates containing well separated colonies were used. The criteria used to distinguish colonies were based on morphological characteristics, such as colony shape, border, colour and elevation. The number of morphotypes found in each temperature/time point was compared. 2.3.6. Identification of the most heat resistant bacteria The molecular identification of the most resistant bacteria to temperature was performed using the 16S ribosomal RNA as a barcode. DNA extractions were performed using a Quick-DNATM Figure 7 - Preparation of soil microbial suspensions and corresponding 10-fold dilutions 23 Fungal/Bacterial Miniprep Kit ( Zymo Research, USA ) following the supplier conditions. A PCR amplification was performed using the pair of primers 799F and 1492R for the 16S rRNA region in a PCR rection of 25μL of final volume (Table 2). Amplification occurred on a Bio-Rad T100 Thermal Cycler using the following programme: 3 min at 94ºC for initial denaturation, followed by 35 cycles of 30s at 94ºC for denaturation, 1min at 55ºC for annealing temperature and 30s at 72ºC for the extension. PCR was ended with a final extension of 10 min at 72ºC. PCR products were evaluated by electrophoresis on an agarose gel (1% w/w) prepared in 0.5x TAE buffer (0.01M Tris; pH 8.0; 47.5mM acetic acid; 25mM EDTA). For allowing the visualization of DNA, green safe buffer (nzytech) was added to the agarose gel (100 μL of green safe buffer /100mL gel). A mix of 8μL of DNA plus 2μL of green dye (nzytech) was loaded into the agarose gel, as well as the molecular marker ( 100 bp plus ready-to-use DNA Ladder , bioron). Electrophoresis ran at 109V for 30min. The agarose gel was observed with an UV transilluminator (VWR Genosmart) and pictures were taken with an image acquisition system (VWR Genosmart). Table 2 – PCR Components (25 μL) for the identification of the most heat resistant bacteria PCR components Amount (μL) ddH2O 19.3 Complete KCl reaction Buffer (10x) bioron 2.5 dNTPs (10mM) 0.5 Primer 799F* (10 mM) 0.5 Primer 1492R* (10 mM) 0.5 DSFTaq DNA polymerase bioron 0.2 DNA template 1 Total 25 * 799F – AACMGGATTAGATACCCKG; 1492R – GGTTACCTTGTTACGACTT; using these primers a 693bp PCR product is expected The PCR product was purified by adding isopropanol (40 μL of 75% v/v) to 10 μL of PCR product. After an incubation of 15 min at room temperature, a centrifugation of 4500 rpm for 30 min, at 4ºC, was performed. The supernatant was discarded and another centrifugation at 300 rpm for 1 min at 4°C was performed. The supernatant was again discarded and the precipitated DNA was washed by adding 150 μL of isopropanol 75% (v/v). Centrifugation was repeated at 300rpm for 7 min at 4°C and final supernatant was discarded. The obtained pellet was dried in a horizontal flow chamber for at least 30 min. DNA was resuspended in 10 μL of ultrapure sterilized H2O. Obtained samples were sent for sequencing at STABVIDA (Caparica, 24 Portugal). Sequences were blasted against NCBI (https://blast.ncbi.nlm.nih.gov/Blast.cgi). The best blast hits were considered based on higher similarity identity and e-value. 2.3.7. Metabarcoding analysis of temperature treated soil Soil samples from the second collection, subjected to 100ºC for 0, 15 and 30min, were selected for metabarcoding sequencing. Soil DNA was extracted from 1g using the DNeasy Power Soil Kit ( Qiagen, The Netherlands ) with some changes (Figure 8). Figure 8 – DNeasy Power Soil Kit protocol for extracting DNA form Cork oak soil samples. Changes to supplier’s protocol are highlighted in bold and underlined. The quantity and quality of obtained DNA were accessed using a Nanodrop spectrometer ND-1000 3.8.1 , (Thermo Fisher Scientific) using A260 nm /A280 nm and A260 nm /A230 nm values. For accurately quantify the DNA before sending to sequencing services, a fluorescent DNA quantification assay was performed with the Qubit dsDNA HS Assay Kit (Thermo Fisher Scientific, Waltham, MA, USA), using Qubit 4 fluorometer. To ensure DNA quality for Ilumina Miseq sequencing, a PCR amplification was performed using the pair of primers 16S V3V4 FW and 16S V3V4 RV for the 16S rRNA region and ITS1F and ITS2 for ITS region in a 25μL PCR reaction (Table 3). Amplification occurred on a Bio-Rad T100 Thermal Cycles using the following programme: 4 min at 94ºC for initial denaturation, followed by 25 35 cycles of 30s at 94ºC for denaturation, 30s at variable temperatures (53ºC to 55ºC) for annealing temperature and 30s at 72ºC for the extension. PCR was ended with a final extension of 10 min at 72ºC. Amplified products were separated in an agarose gel electrophoresis, as described in the previous section. Table 3 – PCR Components (25 μL) for 16S rRNA ITS region amplification, for bacteria and fungi respectively identification. PCR components Amount (μL) ddH2O 20.15 Complete NH4 reaction Buffer (10x): pH 8.8, 0.1% Tween 20, 25mM MgCl2 Bioron 2.5 dNTPs (10 mM) 0.25 Primer 16S V3V4 FW* or ITS1F** (10 mM) 0.5 Primer 16S V3V4 RV* or ITS2** (10 mM) 0.5 DSFTaq DNA polymerase Bioron (5U/ μL) 0.1 DNA template 1 Total 25 * 16S V3V4 FW – TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTA; 16S V3V4 RV – GTCTCGTGGGCTCGGAGATGTGTATAAGAGA CAGGAC; using these primers a 400 bp PCR product is expected. ** ITS1F – CTTGGTCATTTAGAGGAAGTAA; ITS2 – GCTGCGTTCTTCATCGATGC; using these primers a 230 bp PCR product is expected 2.3.7.1. DNA sequence analysis and quality control Raw reads were extracted from Illumina MiSeq®System in fastq format and quality-filtered with PRINSEQ version 0.20.4 (Schmieder & Edwards, 2011) to remove sequencing adapters, reads with less than 100 bases and trim bases with an average quality lower than Q25 in a window of 5 bases. The quality report of each library sequencing was viewed in FastQC v0.11.8 (Andrews, 2010). The sequence trimming was performed in Sickle (Joshi & Fass, 2011). Additional quality filtering and overlapping paired-end reads merge was performed using BayesHammer module of Spades 3.12.0 software (Nikolenko et al. , 2013). Using Usearch v11.0.667 , read pair were merged (Edgar & Flyvbjerg, 2015). Detection and removal of sequencing adapters and primers, as well as the clipping of limited skewing and poor quality read ends was performed by Ea-utils (fastq-mcf) v 1.04.636 (Aronesty, 2013). Sequence results organization into Operational Taxonomic Units (OTUs) at 97% of identity (which represent bacterial species) was performed using micca 1.7.0 software (Albanese et al. , 2015). At the same time, taxonomy was assigned by using the SILVA database (Silva release 132) (Quast et al. , 2013; Yilmaz et al. , 2014; Glöckner et al. , 2017), in order to classify each bacterial 26 OTU sequence and convert data into a biom format . This final analysis was achieved using Qiime 1.9.1 (Caporaso et al. , 2010). Unknown classifications, OTUs identified as chloroplasts or mitochondria were removed from OTU table, as well as those low abundance OTUs (with less than 5 reads. Analysis of α-diversity data was calculated using the Past3 software v3.26 (Hammer, 2019). Rarefaction curves were constructed using GraphPad Prism 7.04 software and diversity indices determined . Venn diagram was obtained by Excel software . β -diversity was computed in Community Analysis Package (CAP) version 6.2.4 , were Jaccard and Sorenson's similarity coefficients were determined using ANOSIM analysis (Beals, 1984). 2.4. Results and Discussions 2.4.1. Soil chemical analysis For evaluating the effect of heat on the microbial community associated to cork oak, the soil was sampled from a specific tree (Figure 5). Table 4 – Chemical analysis of studied soils taken from a Cork oak. Analysed parameter Cork oak soil Reference value Results Interpretation Granulometry Sandy Sand (78.1% Sand; 9.1% Clay; 12.8% Lime) - pH (H2O) 5.85 ± 0.01 Acid 3.4 – 7.8 pH (cacl2) 5.19 ± 0.01 Slghly acid - Electrical conductivity 112 ± 2 μS/cm Non saline - Organic matter 9.38 ± 0.01 % Very high - Organic carbon 5.44 ± 0.04 % - - Total nitrogen 0.39 ± 0.02 % In excess - Carbon: nitrogen 13.9 Normal - Phosphorus (P2O5) 156.8 ± 0.4 mg/kg High 98 – 162 mg/kg Potassium (K2O) 281.9 ± 1.3 mg/kg In excess 74 – 140 mg/kg Calcium (CaO) 1603.1 ± 0.9 mg/kg In excess 819 – 1047 mg/kg Magnesium (MgO) 270.0 ± 0.4 mg/kg In excess 100 – 205 mg/kg Sulphur 12.4 ± 0.1 mg/kg High >10 mg/kg Iron 130.6 ± 0.1 mg/kg In excess 50 – 100 mg/kg Manganese 186.1 ± 0.0 MnAI In excess 25 – 100 MnAI Boron 0.77± 0.00 mg/kg Low 0.5 – 5 mg/kg Sodium 28.7.0 ± 0.1 mg/kg - - 33 Table 7 - Identification of bacteria resistant to high temperatures, according to the sequencing of bacterial barcode 16S. The three best top-hits are displayed for each sequence (number in parenthesis reveal the number of hits for each species). Isolate Top-Hit E-value Identity percentage (%) Sample size (bp) B2 Bacillus thuringiensis (18) 2e-159 – 7e-158 100 – 99.36 312 Bacillus cereus (56) 5e-159 – 3e-157 100 – 99.36 Bacillus anthracis (3) 5e-159 – 3e-157 100 – 99.36 B3 Hyphomonas jannaschiana 7e-19 93.06 329 B4 Bacillus megaterium (36) 2e-154 – 4e-155 99.04 – 98.14 324 Bacillus aryabhattai (25) 2e-154 – 9e-157 99.04 – 98.14 Bacillus subtilis (8) 1e-155 – 3e-156 98.44 – 98.14 B5 Bacillus cereus (25) 2e-159 – 4e160 99.37 – 98.75 322 Bacillus anthracis (2) 4e-160 99.37 Bacillus thuringiensis (23) 4e-160 99.37 – 98.75 B6 Bacillus cereus (25) 3e-156 – 9e157 98.75 – 98.44 323 Bacillus anthracis (2) 3e-157 98.75 Bacillus thuringiensis (21) 3e-156 – 3e-157 98.75 – 98.44 B7 Bacillus cereus (25) 2e-163 – 9e-162 100 – 99.37 320 Bacillus anthracis (9) 2e-163 – 9e162 100 Bacillus thuringiensis (13) 2e-163 – 9e162 100 – 99.68 B8 Bacillus thuringiensis 3e-122 93.46 311 B9 Bacillus cereus (26) 1e-160 – 5e1-59 99.07 – 98.76 325 Bacillus anthracis (9) 3e-161 – 5e-159 99.07 – 98.76 Bacillus thuringiensis (12) 1e-160 – 5e-159 99.07 – 98.76 B10 Bacillus velezensis 1e-46 93.33 467 B11 Bacillus subtilis (44) 2e-163 – 9e-162 100 – 99.69 320 Bacillus amyloliquefaciens (10) 2e-163 – 9e-162 100 – 99.69 B12 Bacillus spp 4e-135 97.55 288 B13 No similarity found - - 368 B14 Bacillus spp 1e-140 95.94 355 As most samples were blasted as Bacillus , a sequence alignment was performed to verify similarities between sequences (Annex 3). A phylogenetic tree was prepared to relate sequences (Figure 13). A clear cluster was performed by the complex Bacillus thuringiensis/cereus/anthracis (B2, B5, B6, B7 and B9), which have been all described as members of the Bacillus cereus group and considered as belonging to one and the same species ((Helgason et al. , 2000)) . In addition, other detected bacteria included B. megaterium (or B. aryabhattai based on their sequence homology, (Freedman et al. , 2018)), and B. amyloliquefaciens (or its sister species B. subtilis , (Freedman et al. , 2018)). From all bacteria, B. cereus isolates were the most frequently detected, possibly revealing their higher abundance. The higher abundance of Bacillus as heat resistant bacteria could be related with their ability to form resistance spores as they are known to be sporeforming bacteria (Kort et al. , 2005). Bacillus spores are widely studied due to easy genetic manipulation (Setlow, 2006). The use of High Pressurized Carbon Dioxide (HPCD) indicated that bacterial spores can be destroyed with high temperatures (≥60ºC) (Rao et al. , 2015). Accordingly, 34 the application of moist heat (87ºC - 90ºC) on Bacillus subtilis spores resulted in spore destruction (Rao et al. , 2015). Similarly, Bacillus cereus spores can be eliminated at 70ºC using 600MPa (Evelyn & Silva, 2015). Comparing heat inactivation between cells and spores, spore destruction requires longer time periods than bacterial cells (Rao et al. , 2015). Phylogenetic tree (Figure 13) presents a unknown sequence (B10) and Bacillus subtilis (B14) as the most divergent sequences found on the studied bacterial morphotypes. Bacillus subtilis (B11) seems to be an ancestor of Bacillus megaterium (B4) and sequences obtained from B2, B5, B6, B7 and B9 morphotypes are genetically similar (wich were identified as Bacillus cereus ). These results may be explained by the often exchange of genetic material between some Bacillus species in natural environments (Helgason et al. , 2000; Donnarumma et al. , 2010). 2.4.4. Metabarcoding analysis of temperature treated soil In order to evaluate microbial communities in detail after temperature application, molecular identification of heat-treated soil communities was performed by using 16S and ITS barcoding. DNA was extracted from soil samples subjected to temperatures of 100ºC for several periods of time (Table 8) and amplified. PCR amplification reaction occurred using the primer pair for 16S V3V4 region ( V3V4FW and 16S V3V4 RV ) for bacteria identification and using the primer pair for the ITS region ( ITS1F and ITS2 ) for fungi identification (Figure 14 and Figure 15 respectively). Due to the low amounts of DNA extracted (Table 8) and funding limitations, only one soil sample was sequenced for bacterial barcode (B – 28th May) using the initial treatment times (100ºC for 0, 15 and 30 min). Figure 13 - Phylogenetic tree from sequence alignment of bacteria resistant to high temperatures 35 The results will be then evaluated and a decision regarding subsequent sequencing of other samples will be taken. As no other samples were jointly sent for fungal barcode sequencing, samples are still awaiting to be sequenced. Sequence data analysis from 16S region amplification was performed based on Pylro et al., (2014) pipeline and other authors (Soh et al. , 2013; Pylro et al. , 2014; Mataragas et al. , 2018; Sadaiappan et al. , 2020). Figure 14 – Electrophoresis of 16S V3V4 region from soil samples treated with 100ºC of temperature. DNA was extracted and used to amplify 16S V3V4 region by using the 16S V3V4FW and 16S V3V4 RV primer pair. Legend: bp – 100bp plus ready-to-use DNA Ladder Bioron; Heat treatment samples: B collection (1 – 0 min; 2 – 15 min; 3 – 30 min; 4 – 60 min; 5 – 90 min ; 6 – 120 min); A collection (7– 0 min; 8 – 15 min; 9 – 30 min; 10 – 50 min; 11 – 80 min; 12 – 120 min; C collection (13 – 0 min; 14 – 15 min; 15 – 30 min; 16 – 60 min; 17 – 90 min; 18 – 120 min; 100 bp Figure 15 – Electrophoresis of ITS region from soil samples treated with 100ºC of temperature. DNA was extracted and used to amplify ITS region by using the ITS1F and ITS2 primer pair. Legend: bp – 100bp plus ready-to-use DNA Ladder Bioron; Heat treatment samples: B collection (1 – 0 min; 2 – 15 min; 3 – 30 min; 4 – 60 min; 5 – 90 min ; 6 – 120 min); A collection (7– 0 min; 8 – 15 min; 9 – 30 min; 10 – 50 min; 11 – 80 min; 12 – 120 min; C collection (13 – 0 min; 14 – 15 min; 15 – 30 min; 16 – 60 min; 17 – 90 min; 18 – 120 min; 100 bp 36 Table 8 – Quantification of soil DNA, extracted from samples subjected to 100ºC of temperature, by nanodrop and Qubit method, for microbial communities’ analysis by metabarcoding approach Assay Samples nanodrop ng/μL Qubit ng/μL A260/280 A260/230 A (March 2019) 0 min 61.20 1.07 1.94 2.11 15 min 15.70 11.30 1.85 1.67 30 min 7.20 0.44 1.73 1.51 50 min 2.90 Out of range 1.56 0.96 80 min 3.00 1.06 1.34 0.62 120 min 2.50 1.18 0.84 0.44 B (May 2019) 0 min 75.00 28.80 1.84 2.44 15 min 19.10 8.80 1.48 1.71 30 min 5.30 0.63 1.54 1.71 60 min 2.20 0.70 1.21 0.64 90 min 2.40 1.14 1.17 0.75 120 min 5.40 0.16 1.05 0.58 C (June 2019) 0 min 57.40 43.40 1.90 1.85 15 min 16.00 6.40 1.71 1.28 30 min 4.60 0.31 1.56 0.93 60 min 7.70 0.16 1.52 0.66 90 min 4.70 0.94 1.53 0.82 120 min 13.50 0.37 1.30 0.74 A total of 207.272 raw reads were obtained from the three soil samples subjected at 100ºC for 0, 15 and 30 min (Table 9). The trimming process resulted in 206.524 reads and merged pairend reads resulted in 89.540 reads. Detection and removal of sequencing adapters and primers, and clipping of limited skewing and poor quality at read’s resulted in 64.069 sequences (Aronesty, 2013). After sequence analysis, 218 OTUs were allocated as unclassified, 3 classified as chloroplasts, 3 as mitochondria and 1 as non-identified bacteria. At the end, 634 identified OTUs were classified. Table 9 - Number of 16S rDNA V3-V4 reads of soil samples subjected to 100ºC of temperature, after merging and quality filtering, as the number of obtainied taxonomic classifications. Reads processing Taxonomic classification of OTUs Name Initial read pairs After trimming Merged reads Unclassified reads OTUs Phylum Class Order Family Genus 0 min 50.576 50.362 21.747 12.358 458 8 12 76 83 134 15 min 67.826 67.598 29.054 20.544 418 10 13 73 74 108 30 min 88.870 88.564 38.739 31.167 359 9 13 65 63 108 total 207.272 206.524 89.540 64.069 634 20 44 53 129 217 37 2.4.4.1. Bacterial community analysis Data analysis on bacterial abundance revealed differences on soil bacterial communities with time of heat exposure. Without heat treatment, the most abundant phyla were Proteobacteria (comprising 41% of total number of reads), followed by Actinobacteria (35%), Acidobacteria (7%), and Bacteroidetes (6%) (Figure 16). Several authors report the presence of Proteobacteria , Acidobacteria and Actinobacteria as being the most abundant phyla in the soil. In addition, they all report the low presence of the genus Firmicutes . Within Proteobacteria , the most abundant class was Gammaproteobacteria (21%), followed by Alphaproteobacteria (15%), and Deltaproteobacteria (5%). From Actinobacteria the most abundant class was Thermoleophilia (16%), followed by Actinobacteria (15%); and from Bacteroidetes, the most abundant was Bacteroidia (5%). The most abundant genus belongs to Gaiellales family ( Thermoleophilia , Actinobacteria ) with 6% of the total number of reads and classified as unknown Gaiellales genus. For further reference, it is notable that Firmicutes phylum (0.6%) is represented as “other” phylum. Figure 16 – Representation of bacterial taxonomic abundance obtained from sampled cork oak soil (without heat treatment) application. In this krona graph (Ondov et al., 2011), OTUs are identified until family level (97% identity). 38 After the application of heat (100ºC) for 15 min, there was a clear alteration on the bacterial community. In this sample, the most abundant phyla were Firmicutes (55%), followed by Actinobacteria (21%), Proteobacteria (15%), and Bacteroidetes (3%) (Figure 17). The most significant change was the relative increase on Firmicutes reads, almost all belonging to Bacillales order, that increased from a relative abundance of 0.6% (non-treated soils) to 55% (in heat treated soils for 15 min). In contrast, Actinobacteria and Proteobacteria decreased their abundance from 41% to 15%, and 35% to 21%, respectively. The most abundant Actinobacteria classes were Actinobacteria (11%) and Thermoleophilia (7%); and those from Proteobacteria were Gammaproteobacteria (7%) and Alphaproteobacteria (5%). The most predominant family in this sample was Bacillaceae (44%), being Bacillus spp the most predominant genus (42% of the total number of reads). After 30 min of heat (100ºC) application, the increase in Firmicutes ( Bacillales ) even higher (81% of of total number of reads), which was accompanied by a decrease on Actinobacteria (from 15% to 10%) and Proteobacteria (from 21% to 7%) phyla (Figure 18). Figure 17 – Representation of bacterial taxonomic abundance obtained from cork oak soil treated at 100ºC for 15 min. In this krona graph (Ondov et al., 2011), OTUs are identified until family level (97% identity). 39 Figure 18 – Representation of bacterial taxonomic abundance obtained from cork oak soil for 30 min of 100ºC application. OTUs identified until genus level (97 % identity). Figure 19 – Abundance and richness of bacterial phyla detected in untreated cork oak soils or treated with 100ºC for 15 and 30 min, as revealed by Illumina MiSeq 16S amplicon sequencing (A) Phylum abundance (identified at 97 %); (B) Phylum richness (identified at 97. Phylum abundance and richness higher than 5 % are represented as “Others”. 40 Within Firmicutes , the most abundant order was Bacillaceae (59%), comprising almost 55% of the total number of reads from the Bacillus genus. These changes on bacterial community with temperature are better described on bar graphs displaying the relative abundance of phyla (Figure 19A) or classes (Figure 20A). However, not only relative taxa abundance changes with temperature, but also their richness. The total number of detected genus decreased with temperature: 458 bacterial genera detected in nontreated soils, followed by 418 and 359 genera found in soils heat-treated 15 min and 30 min, respectively (Figure 21, Annex 3). Also, the relative number of Firmicutes ( Bacilli ) taxa increased with heat, while there was a decrease on Proteobacteria ( Alphaproteobacteria and Gammaproteobacteria ) (Figures 19B and 20B). These results show that the time period of heat exposure is translated into a significant modification of bacterial communities. The similarity between samples, as evaluated by Jaccard’s or Sorenson indexes, revealed that there is a decrease in similarity with heat (Table 10). Figure 20 – Bacterial class revealed by Illumina MiSeq 16S amplicon sequencing (A) Class abundance (identified at 97 %) present on cork oak soil treated with 100ºC for 0, 15 and 30 min; (B) Class richness (identified at 97 %) present on cork oak soil treated with 100ºC for 0, 15 and 30 min. Class abundance and richness higher than 5 % are represented in the colour legend. 41 Table 10 - Coefficients of similarity between bacterial communities present in different cork oak soil treated with 100ºC for 0, 15 and 30 min. The Jaccard’s index is displayed in the lower side of the table (blue) and the Sorenson’s index in the upper side (green). The displayed indexes range between 0 - 1 (0: zero similarity; 1: maximum similarity). A gradient pattern of colours was applied according to the similarity’s values (lighter colour - lower similarity value; darker colour - higher similarity value). 0 min 15 min 30 min 0 min 1 0,914 0,892 15 min 0,841 1 0,92 30 min 0,805 0,852 1 The results also reveal that there are microorganisms such as Bacillus spp are better adapted to heat situations than other microorganisms ( Proteobacteria members). For example, the presence of Proteobacteria in greater abundance in natural and recovering environments has been reported to be higher than in situations of environmental stress, such as those with increased temperature (Hernández et al. , 2020) . On the other hand, gram-positive bacteria (such as Bacillus spp ) have been reported to be more resistant to heat due to the presence of a thicker cell wall than gramnegative bacteria (eg, Burkholderia genus from Burkholderiaceae family - Proteobacteria ) (Jing et al. , 2019). In addition, the high presence of Bacillus spp on soil may interfere with the abundance of other microorganisms as this genus has the ability to produce antimicrobial proteins (Grutsh et al. , 2018). Figure 21 – Venn diagram of shared bacterial OTUs between untreated, and heat (100ºC) treated soils for 15 and 30 min. The OTUs were classified up to genus level (97 % identity) and represent total of 634 genus. OTUs number identified in each sample are in parentheses. 42 Also, Bacillus genus , have “ the Quorum sensing” throughout they can govern cell density and regulate gene expression that can enhance this genus survival (Grutsh et al. , 2018). This mechanism consists in the production and release of self-inducers (signalling molecules) in order to increase cell concentration, in response to fluctuations in population density at the cellular level (Miller & Bassler, 2001; Grutsh et al. , 2018). The number of shared OTUs among cork oak samples revealed that about 34% of bacterial genera (220) are present in all cork oak samples. 2.4.4.2. Bacterial diversity Microbiome diversity is an important indicator of healthy or degraded ecosystems. A large diversity of microbes is generally associated with good ecosystem conditions (Calle, 2019). Various ecological indices can be used to assess diversity, which are subdivided into alpha and beta diversity. Alpha diversity measures the variability of species within a sample, while beta diversity accounts for differences in composition between samples (Gotelli & Coldwell, 2001). In alpha diversity, the most common measure is richness - number of different species present in a specific environment (Gotelli & Coldwell, 2011; Calle, 2019). For this reason, bacterial diversity was assessed by plotting rarefaction curves (Figure 22) and by determining different diversity parameters. Rarefaction curves revealed greater microbial diversity in non-treated soils than in heat stressed soils (100ºC). Statistical analysis ( ANOVA) proved that among the three samples, the Figure 22 – Rarefaction curves for bacterial community (number of OTUs) at 97 % identity. Treatments included 0, 15 and 30 min of heat application (100ºC). Rarefaction curves were obtained at Past3 software v3.26 (Hammer, 2019) . and plotted in GraphPad Prism 7.04 software . 49 ITS regions contain several copies in the fungal genome (Nilsson et al. , 2009; Fajarningsih, 2016; Letchuman & Cosmecuticals, 2018; Ghosh et al. , 2019) and are easily amplified (Baldwin et al. , 1995; Letchuman & Cosmecuticals, 2018). This last aspect adds value to ITS regions used on metabarcoding approaches (Baldwin et al. , 1995; Letchuman & Cosmecuticals, 2018). Figure 23 – rDNA region used in fungal identification. It is divided into SSU (18S and 5.8S) and LSU (28S). ITS regions are located between 18S subunit and 28S subunit and include 5.8S subunit. Primer for ITS region amplification are represented in black arrows Figure 24 – 16S rDNA gene used on bacteria identification. It is divided into conserved regions (blue) and hypervariable regions (red). Primer for hypervariable region amplification is also presented (green arrow), as the sequence methodologies used on metagenomic approach 50 16S rDNA genes are DNA barcodes for bacterial identification (Woo et al. , 2008; Kim & Chun, 2014), due to their presence on almost all bacteria (Janda & Abbott, 2007). The 16S (SSU) rRNA gene codes for the 30S ribosomal RNA subunit and is composed by conserved regions interspaced by 9 hypervariable regions (V1-V9) (Figure 24) (Wang & Qian, 2009; Jo et al. , 2016). 16S rRNA is used on metagenomic approach (Janda & Abbott, 2007), but its hypervariable regions (V1-V9) only provide bacterial taxonomic identification, until genus level (Beckers et al. , 2016; Jo et al. , 2016). Next Generation Sequencing (NGS) improved sequencing technologies, which led to a great advance on genomic analysis (De Sá et al. , 2018; Wilson et al. , 2019). In the past years several sequencing technologies have been used, such as 454 GS FLX, SOLiD (Sequencing by Oligonucleotide Ligation and Detection), Ion Torrent, PacBio, Ilumina HiSeq and Ilumina MiSeq (De Sá et al. , 2018). These technologies differ on the biochemistry used for sequencing and on the amount of data produced (De Sá et al. , 2018). For example, Illumina platform uses sequencing by synthesis chemistry (SBS) (Mardis, 2008; Illumina, 2017; De Sá et al. , 2018). Due to the low costs per base and the high precision base to base, Illumina platform has been considered one of the most promising for the analysis of bacterial and fungal communities (Degnan & Ochman, 2012). Illumina MiSeq technology is the best for small genomes sequencing (throughput from 0.3 to 15 Gb) and Illumina HiSeq is required for studies that need high-throughput sequencing and can produce 1Tb of data (De Sá et al. , 2018). After sequencing, data analysis is performed using bioinformatic tools (Prjibelski et al. , 2019)- In a metabarcoding approach the sequences are clustered into Operational Taxonomic Units (OTUs) (Matallana-Surget et al. , 2018). Through web services ( QUIIME – Quantitative insight into microbial ecology), each OTU is matched with a certain taxon (Caporaso et al. , 2010; Matallana-Surget et al. , 2018; Mataragas et al. , 2018). SILVA database is used for the identification of bacterial communities (Quast et al. , 2013; Yilmaz et al. , 2014; Glöckner et al. , 2017), and UNITE alpha database for fungal communities (Abarenkov et al. , 2010; Tedersoo et al. , 2018). Despite this, scientists have to be concerned about the human error on creating reference libraries for microorganism assemblage and with the decrease in the morphological description of new species(Kowalska et al. , 2019). The aim of this work is the identification and composition analysis of soil microbial communities present in cork oak stands that were damaged by a wildfire. In particular, the soil microbial community recovering will be evaluated along time. To accomplish this, soil sampling 51 was performed 5-, 12and 18-months post-fire on Santa Marta das Cortiças – Portugal. Microbial communities were evaluated by a metabarcoding approach, using 16S rDNA V23V4 for bacteria identification and ITS region for fungi identification, in Illumina MiSeq platform. Diversity analysis of obtained OTUs was then performed to compare soil microbial communities. 3.2. Material and Methods 3.2.1. Quercus suber L. study population At 15th October of 2017, the cork oak stand from Santa Maria das Cortiças (Braga, Portugal) suffered a wildfire, initiated at Guimarães which went through 1200 ha. Soils were collected from different affected bark oak trees, after 5- (21st March 2018), 12- (1st October 2018) and 18months (16thApril of 2019) after wildfire occurrence (Table 12). Soils were taken from near nine existing cork oaks, which differ on fire damages (Table 13, Figure 25) The burnt condition was related with the appearance of damage caused by the wildfire across whole tree (almost 100%). Trees were externally carbonized, and soil displayed no plant cover (26A). Half-burnt trees were those which exhibit superficial fire damage (50%; 26B). These trees displayed a damage gradation, from severe damage in the base part of the tree to a healthier crown, suggesting that the wildfire did not reach the entire tree. In these cork oak trees, there was also no plant cover. Figure 25 – Region map of the sampled cork oak forest ( Stª Marta das Cortiças , Braga) used for the study of soil microbial communities by metabarcoding approach. Photo from 06-09-2017, Google earth Pro. Sampled trees are highlighted by a red sign. 52 Table 12 – Sampled forest after 5-, 12and 18-months after wildfire damage. Description of forest characteristics at the time of soil sampling. Forest Characteristics 5 months post-fire (March 2018) • Stripped trees • No cover vegetation • Soil fulfilled with dead leaves (organic matter) and carbonized around trees 12 months post-fire (October 2018)  Tree top with leaves  Soil covered with weeds and a dense shrubbery 18 months post-fire (April 2019)  Eucalyptus plantlets growing near the affected wildfire trees  Soil showing signs of recovery (structure) Table 13 - Fire damages and GPS coordinates of each sampled cork oak tree Nº of tree State GPS Coordinates 1 Burnt N 41⁰ 31’ 1,34878’’ W 8⁰ 23’ 40,77539’’ 2 Burnt N 41⁰ 31’ 1,32707’’ W 8⁰ 23’ 39,67187’’ 3 Burnt N 41⁰ 30’ 59,81914’’ W 8⁰ 23’ 39,79708’’ 4 Burnt N 41⁰ 30’ 57,49129’’ W 8⁰ 23’ 40,34968’’ 5 Non-Burnt N 41⁰ 30’ 59,48872’’ W 8⁰ 23’ 40,77044’’ 6 Non-Burnt N 41⁰ 30’ 59,63946’’ W 8⁰ 23’ 38,60779’’ 7 Non-Burnt N 41⁰ 30’ 59,11177’’ W 8⁰ 23’ 39,00666’’ 8 Half-Burnt N 41⁰ 31’ 0,68’’ W 8⁰ 23’ 38,41931’’ 9 Half-Burnt N 41⁰ 31’ 0,83965’’ W 8⁰ 23’ 37,66376’’ 53 Although whole Santa Marta das Cortiças area was attacked by wildfire, some regions were just exposed to high temperatures, being possible to find non-burnt trees (Figure 26C). From each sampled tree, three soil cores were collected with a core cutter (10 cm in depth and 5 cm in diameter). The cores were equally distributed around the tree and taken from under the middle of tree canopy. After removing the uppermost layer of soil that consisted of plant litter and other organic material, three independent soil cores (5 cm in diameter and 10 cm in depth) were collected from the cork oak soil and kept at 4ºC until processing. In total, 81 soil cores (3 sampling times × 9 trees × 3 cores) were collected. For chemical analysis, soils were grouped according to cork oak fire damages (burnt, halfburnt and not-burnt) and sampling time (5and 18-months after fire), thus creating 6 soil samples. These samples were chemically analysed by A2-Análises químicas TM (Guimarães, Portugal). The chemical soil analysis included gravimetry, pH (H2O), pH (CaCl2), electrical conductivity, organic matter, organic carbon, total nitrogen; carbon: nitrogen ratio; phosphorus (P2O5), potassium (K2O), calcium (CaO), magnesium (MgO), sodium; boron, sulphur, iron and manganese. Figure 26 – Quercus suber tree selection according to tree damage status. a) 100% damaged cork oak; b) 50% damaged cork oak; c) cork oak without fire damage 54 3.2.2. Experimental design Soil cores from each tree were thoroughly mixed, sieved by a 45-mesh sieve and stored at -80°C until DNA extraction. For considering the same number of samples for each plant condition, soil cores were combined as depicted in Figure 27, creating 54 samples for DNA extraction (18 per sampling date). 3.2.3. Metabarcoding approach for soil microbial communities analysis Soil DNA was extracted from 1g using the DNeasy Power Soil Kit ( Qiagen, The Netherlands ) with some changes, using the optimized protocol from chapter 2. The quantity and quality of obtained DNA were accessed using a Nanodrop spectrometer ND-1000 3.8.1 , (Thermo Fisher Scientific) using A260 nm /A280 nm and A260 nm /A230 nm values. For accurately quantify the DNA, before sending to sequencing services, a fluorescent DNA quantification assay was performed with Qubit dsDNA HS Assay Kit (Thermo Fisher Scientific, Waltham, MA, USA), using Qubit 4 fluorometer . For evaluating if DNA was amplifiable, a PCR amplification was performed using the pair of primers 16S V3V4 FW and 16S V3V4 RV for the 16S rRNA region and ITS1F and ITS2 for ITS region in a PCR reaction of 25μL (Table 14). Amplification occurred on a Bio-Rad T100 Thermal Cycles using the following programme: 4 min at 94ºC for initial denaturation, followed by 35 cycles of 30s at 94ºC for denaturation, 30s at variable temperatures (53ºC to 55ºC) for annealing temperature and 30s at 72ºC for the extension. PCR was ended with a final extension of 10 min, at 72ºC. PCR products were evaluated by electrophoresis on an agarose gel (1%, w/w) prepared in 0.5x TAE buffer (0.01M Tris; pH 8.0; 47.5mM acetic acid; 25mM EDTA). For allowing the visualization of DNA, green safe buffer (nzytech) was added to the agarose gel (100 μL of green safe buffer /100mL gel). A mix of 8μL of DNA plus 2μL of green dye (nzytech) was loaded into the Figure 27 - Soil sampling method for metabarcoding approach on soil microbial communities. 55 agarose gel, as well as the molecular marker ( 100 bp plus ready-to-use DNA Ladder , bioron). Electrophoresis ran at 109V for 30min. The agarose gel was observed with an UV transilluminator (VWR Genosmart) and pictures were taken with an image acquisition system (VWR Genosmart). Table 14 - PCR Components (25 μL) for 16S rRNA ITS region amplification, for bacteria and fungi respectively identification. PCR components Amount (μL) ddH2O 20.15 Complete NH4 reaction Buffer (10x): pH 8.8, 0.1% Tween 20, 25mM MgCl2 Bioron 2.5 dNTPs (10 mM) 0.25 Primer 16S V3V4 FW* or ITS1F** (10 mM) 0.5 Primer 16S V3V4 RV* or ITS2** (10 mM) 0.5 DSFTaq DNA polymerase Bioron (5U/ μL) 0.1 DNA template 1 Total 25 * 16S V3V4 FW – TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTA; 16S V3V4 RV – GTCTCGTGGGCTCGGAGATGTGTATAAGAGA CAGGAC; using these primers a 400 bp PCR product is expected. ** ITS1F – CTTGGTCATTTAGAGGAAGTAA; ITS2 – GCTGCGTTCTTCATCGATGC; using these primers a 230 bp PCR product is expected 3.2.3.1. DNA sequence analysis and quality control Due to budjet limitations, only the bacterial community proceeded to sequencing by Illumina. From each of the total 54 soil DNA samples (3 sampling times x 3 burnt conditions x 6 soil samples), 10 μL were taken to perform DNA sequence and sent for sequencing to a service provider (Biocant; Cantanhede, Portugal). Raw reads were extracted from Illumina MiSeq®System in fastq format and quality-filtered with PRINSEQ version 0.20.4 (Schmieder & Edwards, 2011) to remove sequencing adapters, reads with less than 100 bases and trim bases with an average quality lower than Q25 in a window of 5 bases. The quality report of each library sequencing was viewed in FastQC v0.11.8 (Andrews, 2010). The sequence trimming was performed in Sickle (Joshi & Fass, 2011). Additional quality filtering and overlapping paired-end reads merge was performed using BayesHammer module of Spades 3.12.0 software (Nikolenko et al. , 2013). Using Usearch v11.0.667 , read pairs were merged (Edgar & Flyvbjerg, 2015). Detection and removal of sequencing adapters and primers, as well as the clipping of limited skewing and poor quality read ends, was performed by Ea-utils (fastqmcf) v 1.04.636 (Aronesty, 2013). Sequence results organization into Operational Taxonomic Units (OTUs) at 97% of identity was performed using micca 1.7.0 software (Albanese et al. , 2015). At the same time, taxonomy 56 was assigned by using the SILVA database (Silva release 132) (Quast et al. , 2013; Yilmaz et al. , 2014; Glöckner et al. , 2017), in order to classify each bacterial OTU sequence and convert data into a biom format . This final analysis was achieved using Qiime 2 (Caporaso et al. , 2010). Unknown classifications, OTUs identified as chloroplasts or mitochondria were removed from OTU table, as well as those low abundance OTUs (with less than 5 reads). Analysis of α-diversity data was calculated using the Past3 software v3.26 (Hammer, 2019). Rarefaction curves were constructed using GraphPad Prism 7.04 software and diversity indices determined . Venn diagram was obtained by Excel software . 3.3. Results and discussion 3.3.1. Chemical analysis of Santa Marta das Cortiças soils For evaluating cork oak soil restauration after wildfire episode, chemical analysis on sampled soils collected 5and 18-months after fire occurrence was performed (Table 15 and Table 16). Chemical analysis carried out on all soil samples revealed the presence of an acid soil on Santa Marta das Cortiças (Tables 15 and 16). As previously referred (chapter 2), acid soils are a characteristic of cork oak forests (Torres, 2008) and were also detected at Universidade do Minho sampled tree (Table 4, Chapter 2). Although fire intensity on the ground is reported to affect the soil pH values (Hamman et al. , 2007; Granged et al. , 2011; Mataix-Solera et al. , 2011), there was a significative differences on pH (H2O) values (P<0.01). The higher values of organic matter found in half-burnt tree soils (25.54%), were due to low intensity fire, that promoted the falling of the remaining leaves and twigs (Mataix-Solera et al. , 2011). And comparing unburnt and burnt soils, 5 months post-fire, there was a slight decrease on organic matter on soils that suffered wildfire occurrence (from 15.89% to 8.40%), which could be due to the conversion of organic to inorganic matter through the carbonization process. Santa Marta das Cortiças forest presented a content of organic matter (OM) lower than halfand burnt tree soils [non-burnt soil (8.4 ± 0.01%)], which may be explained by the remains of cork oaks stripping.Burnt trees did not displayed any leaves and unburnt trees still preserved their leaves. Comparing Santa Marta das Cortiças soil to Universidade do Minho cork oak sampled soil the contente of OM from halfand burnt tree soils, at 5 months post-fire, is higher than cork oak sampled soil from Universidade do Minho. But OM content obtained for non-affected cork oak soil, from Santa Marta das Cortiças revealed to be lower. The effects of fire on organic matter are described to contribute to the variation of nutrients (Granged et al. , 2011). 57 Table 15 – Chemical analysis of soils collected on March 2018, 5-months post-fire occurrence, at Santa Marta das Cortiças (Braga). Parametres Burnt Half-burnt Non-burnt Reference values Results Interpretation Results Interpretation Results Interpretation Granulometry Areno-Franca (75.7% Sand; 15.1% Clay; 9.2% Lime) Areno-Franca (Sand 80.4%; 8.2% Clay; 11.4% Lime) Areno-Franca (77.7% Sand; 11.8% Clay; 10.5% Lime) pH (H2O) 3.98 ± 0.01 Very acid 4.39 ± 0.01 Very acid 4.36 ± 0.01 Very acid 3.4 – 7.8 pH (CaCl2) 3.52 ± 0.01 Acid 3.44 ± 0.01 Acid 3.75 ± 0.01 Acid - Electrical conductivity 283 ± 2 μS/cm Non saline 345 ± 2 μS/cm Non saline 178 ± 2 μS/cm Non saline - Organic matter 15.89 ± 0.01 % Very high 25.54 ± 0.01 % Very high 8.40 ± 0.01 % Very high - Organic carbon 9.22 ± 0.04 % - 14.81 ± 0.04 % - 4.87 ± 0.04 % - - Total nitrogen 0.67 ± 0.02 % In excess 0.99 ± 0.02 % In excess 0.32 ± 0.02 % In excess - Carbon: nitrogen 13.7 Normal 15.0 High 15.1 High - Phosphorus (P2O5) 60.1 ± 0.4 mg/kg Low 31.5 ± 0.4 mg/kg Very low 105.3 ± 0.4 mg/kg High 98 – 162 mg/kg Potassium (K2O) 163.6 ± 1.3 mg/kg In excess 216.7 ± 1.3 mg/kg In excess 103.5 ± 1.3 mg/kg High 74 – 140 mg/kg Calcium (CaO) 127.6 ± 0.9 mg/kg Very low 120.7 ± 0.9 mg/kg Very low 312.4 ± 0.9 mg/kg Very low 819 – 1047 mg/kg Magnesium (MgO) 84.8 ± 0.4 mg/kg Medium 206.7 ± 0.4 mg/kg In excess 323.3 ± 0.4 mg/kg In excess 100 – 205 mg/kg Sulphur 35.8 ± 0.1 mg/kg High 30.9 ± 0.1 mg/kg High 15.4 ± 0.1 mg/kg High >10 mg/kg Iron 358.2 ± 0.1 mg/kg In excess 351.0 ± 0.1 mg/kg In excess 177.0 ± 0.1 mg/kg In excess 50 – 100 mg/kg Manganese 425.1 ± 0.0 MnAI In excess 355.4 ± 0.0 MnAI In excess 379.1 ± 0.0 MnAI In excess 25 – 100 MnAI* Boron 0.77 ± 0.00 mg/kg High 0.70 ± 0.00 mg/kg High 0.63 ± 0.00 mg/kg high 0.5 – 5 mg/kg Sodium 35.0 ± 0.1 mg/kg - 45.6 ± 0.1 mg/kg - 22.6 ± 0.1 mg/kg - - With the depletion of organic material on the soil surface, the concentration on nutrients, such as phosphorous, potassium, calcium, magnesium, among others, could be disturbed by a fire occurrence. This could occur due to subsequent rainfall, which move them to deeper soil after rainfall (Altun et al. , 2004), or through volatilization [312.4 mg/kg (non-burnt soil) to 127.6 mg/kg (burnt soil)]. In addition, the combustion of MgCO3, forms MgO + CO2. But the lower presence of Mg on burnt tree soils is explained by CO2 reabsorption through MgO molecules, forming MgCO3 at temperatures lower than 900ºC (Parraa & Lopeza, 1996; Certini, 2005; Guerrero et al. , 2005). After 18-months, the fire occurrence, some of these macronutrients increased their amounts on non-burnt tree soils (Calcium 487.5 mg/kg, Magnesium 337.2 mg/kg and sulphur 13.3 mg/kg) but decrease on burnt soils. This may by explanted by lixiviation processes occurred throughout rainfalls and radiation through soils were subjected. 58 Table 16 – Chemical analysis of soils collected on April 2019, 18-months post-fire occurrence, at Santa Marta das Cortiças (Braga). Parameters Burnt Hal-bunt Non-burnt Reference values Results Interpretation Results Interpretation Results Interpretation Granulometry Areno-Franca (Sand 80.0%; 9.6% Clay; 10.4% Lime) Areno-Franca (Sand 79.4%; 8.2% Clay; 12.4% Lime) Areno-Franca (Sand 79.3%; 11.1% Clay; 9.6% Lime) pH (H2O) 4.22 ± 0.01 Very acid 4.49 ± 0.01 Very acid 4.35 ± 0.01 Very acid 3.4 – 7.8 pH (CaCl2) 3.68 ± 0.01 Acid 3.64 ± 0.01 Acid 3.41 ± 0.01 Acid - Electrical conductivity 143 ± 2 μS/cm Non saline 109 ± 2 μS/cm Non saline 97 ± 2 μS/cm Non saline - Organic matter 14.20 ± 0.01 % Very high 26.61 ± 0.01 % Very high 16.46 ± 0.01 % Very high - Organic carbon 8.24 ± 0.04 % - 15.43 ± 0.04 % - 9.55 ± 0.04 % - - Total nitrogen 0.66 ± 0.02 % In excess 1.12 ± 0.02 % In excess 0.55 ± 0.02 % In excess - Carbon: nitrogen 12.5 Normal 13.8 Normal 17.5 High - Phosphorus (P2O5) 123.6 ± 0.4 mg/kg High 177.5 ± 0.4 mg/kg In excess 105.6 ± 0.4 mg/kg High 98 – 162 mg/kg Potassium (K2O) 113.3 ± 1.3 mg/kg High 251.3 ± 1.3 mg/kg In excess 149.3 ± 1.3 mg/kg In excess 74 – 140 mg/kg Calcium (CaO) 174.8 ± 0.9 mg/kg Very low 337.6 ± 0.9 mg/kg Very low 487.5 ± 0.9 mg/kg Low 819 – 1047 mg/kg Magnesium (MgO) 59.4 ± 0.4 mg/kg Medium 146.4 ± 0.4 mg/kg High 337.2 ± 0.4 mg/kg In excess 100 – 205 mg/kg Sulphur 25.6 ± 0.1 mg/kg High 21.8 ± 0.1 mg/kg High 13.3 ± 0.1 mg/kg High >10 mg/kg Iron 365.7 ± 0.1 mg/kg In excess 481.2 ± 0.1 mg/kg In excess 293.3 ± 0.1 mg/kg In excess 50 – 100 mg/kg Manganese 384.9 ± 0.0 MnAI In excess 376.6 ± 0.0 MnAI In excess 409.0 ± 0.0 MnAI In excess 25 – 100 MnAI Boron 0.49 ± 0.00 mg/kg low 0.53 ± 0.00 mg/kg High 0.69 ± 0.00 mg/kg high 0.5 – 5 mg/kg Sodium 29.6 ± 0.1 mg/kg - 42.1 ± 0.1 mg/kg - 41.3 ± 0.1 mg/kg - - Statistical analysis ( One-Way ANOVA ), revealed significant differences between tree soil samples, p<0.01 for soil sampling on March of 2018, and p<0.001 for April 2019. 3.3.2. Amplification of soil DNA samples As previously described, 54 soil samples were taken from Santa Marta das Cortiças in order to extract DNA (Annex 1) for evaluating soil microbial communities after a wildfire episode by molecular methods. A PCR optimization was performed to guarantee the amplification of 16S rDNA and ITS rRNA gene marker (Figure 28 e Figure 29). PCR optimization consisted on using a gradient of annealing temperatures, using the primer pairs 16S V3V4 FW and 16S V3V4 RV (for bacteria barcode); and ITS1 and ITSF2 (for fungi barcode). The electrophoresis revealed that 54ºC and 53ºC were the ideal annealing temperatures for amplifying rDNA 16S and ITS regions, respectively. 65 The same conclusions are taken for bacterial classes changes, found on Santa Marta das Cortiças soils, either affected or not affected by fire. The most abundant classes in the cork oak soil not affected by fire (5 months after fire) were Alphaproteobacteria (25.59%), Gammaproteobacteria (7.24%) and Deltaproteobacteria (2.07%), belonging to the phylum Proteobacteria (Figure 31A, Annex 7). From Actinobacteria , the most abundant classes were Actinobacteria (16.63%) and Thermoleophilia (5.79%); while from Acidobacteria , the Acidobacteria class was the prevalent (28.76%). After 12 months of fire occurrence, (Figure 31C, Annex 10), the abundances of these classes changed, with a decrease on Alphaproteobacteria and Acidobacteria , (24.16% and 22.28% respectively), and an increase in Actinobacteria , Gammaproteobacteria , Thermoleophilia and Deltaproteobacteria (25.21%, 22.28%, 9.62% and 1.27%, respectively). However, 18 months post-fire, (Figure 31E, Annex 13), communities again suffer variations in their abundance. In this case, Acidobacteria and Deltaproteobacteria , the classes that increase their abundance in cork oak soil were not affected by fire (26.24% and 2.45%). Fifty percent of damage to cork oak trees created disturbances in the microbial phyla present in soils, but also in the classes, through March 2018 (Figure 31A, Annex 8), October 2018 (Figure 31C, Annex 11) and April 2019 (Figure 31E, Annex 14). Some bacterial classes abundances increased as Actinobacteria [20.20% (March 2018); 32.31% (October 2018) and 24.46% (April 2019)], Gammaproteobacteria [11.22% (March 2018); 21.59% (October 2018) and 26.31% (April 2019)], and Ktedonobacteria [3.65% (March 2018); 4.19% (October 2018) and 2.07% (April 2019)]. Others classes decreased their abundance when compared to non-affected cork oak soils, such as Alphaproteobacteria [18.14% (March 2018); 17.31% (October 2018) and 20.78% (April 2019)] and Thermoleophilia [2.41% (March 2018); 3.01% (October 2018) and 1.94% (April 2019)]. Small variations on microbial classes throughout time on half-burnt tree soils were detected along time. Also, Bacilli class ( Firmicutes phylum) was identified in small amounts (<2%), higher than non-affected cork oak soils, at half-burnt tree soils collected on March and October (5and 12 months post-fire in Santa Marta das Cortiças ). But in April 2019, 18 months post-fire, Bacilli ’s abundance was lower when compared with non-affected cork oak soils (<1%). Regarding to burnt cork oak tree soils, the highest abundant classes of bacteria in March 2018 (Figure 31A, Annex 9) when compared to non-burnt cork oak soils, were Acidobacteria (22.07%), Actinobacteria (18.67%), Alphaproteobacteria (18.44%), Gammaproteobacteria (17.27%), Ktedonobacteria (4.82%) and Bacilli (3.12%). But Thermoleophilia (3.23%) and Acidimicrobiia (1.81%) were present in lower abundances when compared to non-burnt cork oak 66 soils. After 12 months from fire occurrence, the abundances of these classes changed, displauing a higher abundance of Actinobacteria , Bacilli , Gammaproteobacteria and Ktedonobacteria (35.58%; 1.56%; 15.39% and 4.23% respectively; Figure 31C, Annex 12). Other classes presented decreased values of abundance when compared to non-burnt cork oak soils. However, 18 months after fire, communities suffer small variations in their abundance (Figure 31E, Annex 15). In this case, Acidimicrobiia (2.68%), Actinobacteria (21%), Gammaproteobacteria (16.14%) and Ktedonobacteria (5.97%), were the classes that had their abundance higher than in cork oak soils not affected by fire. In ecological successions, the abundance of Proteobacteria and Acidobacteria are greater than other phyla (Zhang et al. , 2016). From Proteobacteria , forest fire increases Betaproteobacteria and decreases deltaand Alphaproteobacteria (Xiang et al. , 2014). But phyla abundance (e.g. Actinobacteria and Proteobacteria ) is influenced by abiotic factors such as precipitation, soil texture and pH, which occurred post-fire (Zeng et al. , 2016). Nevertheless, bacterial sensitiveness to those factors, plus fire severity and organic matter content post-fire, determinates microbial community recovery speed in ecosystem (Bach et al. , 2010; Banning et al. , 2011; Zeng et al. , 2016). Bacterial abundance can also be influenced by the appearance of invasive or exogenous vegetation ( Eucalyptus spp - Table 12). These habitats contribute for microbial communities variations throughout ecological successions (Bach et al. , 2010; Zeng et al. , 2016), explaining the variations detected on Santa Marta das Cortiças soils either affected or not affected by fire. Plant cover after wildfire can promote the growth of some bacteria and inhibit the growth / appearance of others, through soil chemical modifications (Zhang et al. , 2018). Indeed, pH is reported to influence Proteobacteria, Actinobacteria and Chloroflexi phyla, where a high pH values is know to be a limiting factor (Xiang et al. , 2014; Zeng et al. , 2016). Nevertheless, pH do noy have a direct influence on Acidobacteria growth (Xiang et al. , 2014; Zeng et al. , 2016). Proteobacteria prefer soils rich in nutrients, while Acidobacteria phyla thrive on poorer soils (Zhang et al. , 2016). Soils with high concentrations of clay, can better protect microbial communities from heat and pH fluctuations (Bach et al. , 2010). Soil chemical composition also influences the microbial communities at the restoration of the ecosystem after a wildfire (Bach et al. , 2010; Xiang et al. , 2014). 67 Bacterial responses when subjected to abiotic stress factors (wildfires) can be different among phyla and class (Xiang et al. , 2014). Fire severity controls the responses (resilience) of bacterial communities in the face of these disorders (Allison & Martiny, 2009; Baho et al. , 2012; BentoGonçalves et al. , 2012; Holden et al. , 2016). This may explain the higher abundance, of soil bacterial communities, of half-burnt tree soils when compared with burnt tree soils. The dynamics of bacterial communities follow a pattern in the development of ecosystems. This pattern can be predicted and contextualized if, the various edaphoclimatic factors were considered (Banning et al. , 2011). These changes on relative abundance phyla are better described on bar graphs displaying the relative abundance of phyla (Figure 30A, C and E) or classes (Figure 31A, C and E). Figure 32 – Venn diagram of shared bacterial OTUs 5 months post-fire (A); 12 months post-fire (B) and 18 months post-fire (C). And bacterial OTUs shared between non-burnt cork oak soil (D); half-burnt cork oak soil (E) and between burnt cork oak soils (F) is also provided. The OTUs were classified up to genus level (97 % identity) and represent total of 3354 genus. OTUs number identified in each sample are in parentheses. 68 The number of OTUs shared between the different types of trees depending on time period is variable (Annex 17). At 5 months post-fire from the 3.354 OTUs identified, 319 belonged only to non-burnt tree soils, 222 belonged to burnt tree soils and 116 to half-burnt tree soils (Figure 32A). Seven months later (12 months after fire), the number of OTUs exclusive to each type of soil, increases to 339, 149 and 349 respectively (Figure 32B). Throughout recovery time, the structure of the bacterial communities becomes similar (Banning et al. , 2011; Xiang et al. , 2014). However, 18 months after fire occurrence, the number of shared OTUs increases, promoting a decrease on exclusive OTUs to 246, 194 and 114 respectively (Figure 32C). However, these values are higher than those found for cork oak soils 5 months after the forest fire. Therefore, it was possible to evaluate the number of OTUs unique to each type of tree (Figure 32D, E and F). 3.3.4.1. Bacterial diversity Bacterial diversity was assessed by plotting rarefaction curves (Figure 33) and also by determining different diversity parameters. Rarefaction curves revealed greater microbial diversity after 18 months and lowest 12 months after fire occurrence. Cork oak soils provided from non-burnt trees are the ones with higher diversity, followed by burnt trees soils and half-burnt soils. As a plateau was reached in all curves, we can infer that the analysed bacterial communities are well represented. Richness ( S ) was estimated by the number of different OTUs observed in the sample. However, this parameter may underestimate the real richness of a certain environment as few species may not be detected (Gotelli & Coldwell, 2011; Calle, 2019). Therefore, different diversity indices were used for depicting diversity of microbial communities, such as Simpson (1-D) , Shannon (H’) , or Fisher_alpha (Table 18). Both the rarefaction curves and diversity parameters indicated that diversity on cork oak (non-burnt tree soils) studied forest is higher 18 months postfire, followed by 5and then 12-months post-fire. Due to an increase of environmental temperatures on April 2019 (Spring). Also, a decrease in microbial diversity is observed for 50% damaged cork oak soils. Although it increases with fire intensity (burnt cork oak soils). Furthermore, the homogeneity of communities, as evaluated by Equitability index (J) , decreases during the first 12 months post-fire, indicating a high dominance of few species within the community in relation to others, but increases after 18 months. 69 Figure 33 – Rarefaction curves for bacterial community (number of OTUs) at 97 % identity. Samples came from A – 5 months post-fire; B – 12 months post-fire and 18 months post-fire (C). Rarefaction curves were obtained at Past3 software v3.26 (Hammer, 2019) and plotted in GraphPad Prism 7.04 70 Table 18 - Diversity parameters for cork oak soil samples harvested 5-, 12and 18-months post-fire at Santa Marta das Cortiças. S – OTUs number. Time postfire Tree damage S Simpson (1-D) Shannon (H’) Equitability index (J) Fisher_alpha 5 months (March 2018) Non-burnt 2514 0.9907 5.882 0.7512 455.5 Half-burnt 2151 0.9863 5.513 0.7184 365.7 Burnt 2486 0.9922 5.886 0.7528 430.1 12 months (October 2018) Non-burnt 1930 0.9863 5.567 0.7359 348.2 Half-burnt 1994 0.9904 5.548 0.7302 342.7 Burnt 1989 0.9899 5.639 0.7424 346.3 18 months (April 2019) Non-burnt 2564 0.9905 5.895 0.751 454.1 Half-burnt 2092 0.9909 5.683 0.7433 361.4 Burnt 2746 0.9921 5.862 0.7404 463.3 3.4. Conclusions The results revealed that during the experiment there were not great changes on the bacterial communities present on cork oak soils that were not affected by the fire. In addition, slight variations for halfand burnt tree soils were found. These variations are due to the ecological succession occurring on the soil of Santa Marta das Cortiças . This succession is characterized, in this case, by the restoration of soil microbial communities. However, among the most abundant phyla in the soils of unburned trees were Proteobacteria, Acidobacteria and Actinobacteria . Over time, these phyla revealed seasonal changes. For example, lower environmental temperatures (during the winter up to March) increased Actinobacteria abundance on soil samples not affected by wildfire. But higher environmental temperatures increased the abundance of Acidobacteria (Spring). These results indicate that bacterial communities are influenced by edaphoclimatic factors. Half-burnt cork oak soils had a greater OTU abundance than burnt oak soils throughout recovery time. This variation may also be explained by soil chemical composition, present on burnt cork oak soils. Such soils displayed a high content of phosphorus, which is a limiting factor for bacterial ( Proteobacteria and Actinobacteria ) expansion (Zeng et al. , 2016). Soil acidity also influences the presence of some bacterial communities on soils ( Acidobacteria ). 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Catena 161: 85–95. 74 Chapter 4: Final conclusions and Future perspectives 81 Annex 4 – Quantification of soil DNA extracted from soil samples from Stª Marta das Cortiças by nanodrop and Qubit method, for microbial communities’ analysis by metabarcoding approach Tree damage 5 months 12 months 18 months Sample nanodrop Qubit A260 nm/A280 nm A260 nm/A230 nm Sample nanodrop Qubit A260 nm/A280 nm A260 nm/A230 nm Sample nanodrop Qubit A260 nm/A280 nm A260 nm/A230 nm ng / μL ng / μL ng / μL Non-burnt A6S1F1 58.20 31.20 1.67 1.37 A6S1F3 13.60 8.60 1.54 1.04 A6S1F5 5.40 2.26 1.38 0.68 A6S23F1 4.80 1.37 1.45 0.62 A6S23F3 23.80 17.60 1.59 1.12 A6S23F5 49.80 41.60 1.86 1.89 A7S1F1 98.40 39.60 1.51 1.16 A7S1F3 62.40 54.40 1.80 1.65 A7S1F5 94.90 90.20 1.85 1.96 A7S23F1 53.60 34.20 1.75 1.59 A7S23F3 56.90 42.00 1.72 1.46 A7S23F5 76.90 61.20 1.82 1.96 A8S1F1 49.50 36.80 1.67 1.65 A8S1F3 52.60 37.00 1.64 1.55 A8S1F5 67.90 56.20 1.83 1.86 A8S23F1 46.30 36.00 1.76 1.76 A8S23F3 54.60 60.80 1.82 1.84 A8S23F5 60.50 52.40 1.82 1.75 Half-burnt A9S1F1 127.40 4.80 1.33 0.90 A9S1F3 24.30 12.90 1.58 1.00 A9S1F5 99.90 27.20 1.47 1.06 A9S2F1 49.50 18.10 1.60 1.26 A9S2F3 19.40 10.00 1.59 0.97 A9S2F5 113.20 18.70 1.45 0.96 A9S3F1 67.60 4.32 1.36 0.86 A9S3F3 38.40 24.40 1.71 1.25 A9S3F5 51.50 18.50 1.56 1.12 A10S1F1 153.20 43.20 1.43 0.99 A10S1F3 14.40 10.10 1.60 1.07 A10S1F5 28.80 18.20 1.66 1.38 A10S2F1 107.70 61.20 1.63 1.29 A10S2F3 11.20 6.88 1.67 0.95 A10S2F5 14.20 7.46 1.79 1.14 A10S3F1 117.10 27.00 1.45 1.01 A10S3F3 28.10 18.60 1.56 1.21 A10S3F5 24.90 12.70 1.82 1.14 Burnt A1S123F1 46.60 33.80 1.79 1.80 A1S123F3 22.60 18.30 1.61 1.13 A1S123F5 25.60 6.40 1.74 1.33 A2S123F1 45.50 16.00 1.37 0.83 A2S123F3 21.50 2.94 1.41 0.94 A2S123F5 48.20 11.30 1.46 0.89 A4S1F1 176.70 93.80 1.54 1.29 A4S1F3 53.70 49.60 1.84 2.21 A4S1F5 94.20 78.80 1.86 1.89 A4S23F1 107.50 69.40 1.70 1.64 A4S23F3 68.70 69.40 1.83 1.97 A4S23F5 55.80 480.00 1.83 2.01 A5S1F1 15.30 8.30 1.55 0.93 A5S1F3 14.50 10.70 1.80 1.04 A5S1F5 21.40 13.20 1.09 1.75 A5S23F1 45.50 32.80 1.71 1.56 A5S23F3 25.70 18.80 1.70 1.35 A5S23F5 26.90 18.60 1.75 1.62 82 Annex 5 - Electrophoresis of 16S V3V4 region from soil samples of Stª Marta das Cortiças. DNA was extracted and used to amplify 16S V3V4 region by using the 16S V3V4FW and 16S V3V4 RV primer pair. (A, B) PCR performed on non-diluted DNA samples; (C, D, E) PCR performed on 24x DNA diluted samples; bp – 100bp plus ready-to-use DNA Ladder Bioron. 83 Annex 6 - Electrophoresis of ITS region from soil samples of Stª Marta das Cortiças. DNA was extracted and used to amplify ITS region by using the ITSF1 and ITS2 primer pair. (A, B, C) PCR performed on non-diluted DNA samples; (D, F, E) PCR performed on 24x DNA diluted samples; bp – 100bp plus ready-to-use DNA Ladder Bioron 84 Annex 7 - Representation of bacterial taxonomic abundance, 5 months post-fire (March 2018) occurrence on Santa Marta das Cortiças of a non-burnt cork oaks soil. OTUs are identified until genus level (97 % identity). Annex 8 - Representation of bacterial taxonomic abundance, 5 months post-fire (March 2018) occurrence on Santa Marta das Cortiças of a half-burnt cork oaks soil. OTUs are identified until genus level (97 % identity). 85 Annex 9 - Representation of bacterial taxonomic abundance, 5 months post-fire (March 2018) occurrence on Santa Marta das Cortiças of burnt cork oaks soil. OTUs are identified until genus level (97 % identity). Annex 10 - Representation of bacterial taxonomic abundance, 12 months post-fire (October 2018) occurrence on Santa Marta das Cortiças of non-burnt cork oaks soil. OTUs are identified until genus level (97 % identity). 86 Annex 11 - Representation of bacterial taxonomic abundance, 12 months post-fire (October 2018) occurrence on Santa Marta das Cortiças of a half-burnt cork oaks soil. OTUs are identified until genus level (97 % identity). Annex 12 - Representation of bacterial taxonomic abundance, 12 months post-fire (October 2018) occurrence on Santa Marta das Cortiças of burnt cork oaks soil. OTUs are identified until genus level (97 % identity). 87 Annex 13 - Representation of bacterial taxonomic abundance, 18 months post-fire (April 2019) occurrence on Santa Marta das Cortiças of non-burnt cork oaks soil. OTUs are identified until genus level (97 % identity). Annex 14 - Representation of bacterial taxonomic abundance, 18 months post-fire (April 2019) occurrence on Santa Marta das Cortiças of hal-burnt cork oaks soil. OTUs are identified until genus level (97 % identity). 88 Annex 15 - Representation of bacterial taxonomic abundance, 18 months post-fire (April 2019) occurrence on Santa Marta das Cortiças of burnt cork oaks soil. OTUs are identified until genus level (97 % identity). Annex 16 – Bacterial OTUs identified by Illumina MiSeq sequencing of 16S amplicon. Bacterial sequences were classified till genus level with an identity of 97%; 0, 15 and 30 min of temperature (100ºC) application. Phylum Class Genus 0 min 15 min 30 min Acidobacteria Acidobacteriia Acidipila 2 13 1 0 Acidobacteria Acidobacteriia Bryobacter 13 10 4 1 Acidobacteria Acidobacteriia Bryobacter 7 6 0 1 Acidobacteria Acidobacteriia Candidatus Koribacter 1 9 3 3 Acidobacteria Acidobacteriia Candidatus Solibacter 10 8 1 0 Acidobacteria Acidobacteriia Candidatus Solibacter 12 6 0 0 Acidobacteria Acidobacteriia Candidatus Solibacter 2 17 5 3 Acidobacteria Acidobacteriia Candidatus Solibacter 4 7 1 1 Acidobacteria Acidobacteriia Candidatus Solibacter 48 7 0 4 Acidobacteria Acidobacteriia Candidatus Solibacter 51 17 7 2 Acidobacteria Acidobacteriia Candidatus Solibacter 53 6 0 0 Acidobacteria Acidobacteriia Candidatus Solibacter 7 84 37 8 Acidobacteria Acidobacteriia Candidatus Solibacter 8 17 17 4 Acidobacteria Acidobacteriia Granulicella 1 20 14 3 Acidobacteria Acidobacteriia uncultured Acidobacteriaceae (Subgroup 1) 1 9 1 2 Acidobacteria Acidobacteriia uncultured Acidobacteriaceae (Subgroup 1) 12 9 5 1 Acidobacteria Acidobacteriia uncultured Acidobacteriaceae (Subgroup 1) 2 5 0 1 Acidobacteria Acidobacteriia uncultured Acidobacteriaceae (Subgroup 1) 3 7 2 1 89 Annex 16 (Continuation) Phylum Class Genus 0 min 15 min 30 min Acidobacteria Acidobacteriia uncultured Acidobacteriaceae bacterium 28 11 2 4 Acidobacteria Acidobacteriia uncultured Acidobacteriaceae bacterium 31 32 9 6 Acidobacteria Acidobacteriia uncultured Acidobacteriaceae bacterium 35 18 2 0 Acidobacteria Acidobacteriia uncultured Acidobacteriaceae bacterium 36 8 10 1 Acidobacteria Acidobacteriia uncultured Acidobacteriaceae bacterium 37 3 2 1 Acidobacteria Acidobacteriia uncultured Acidobacteriaceae bacterium 54 8 1 0 Acidobacteria Acidobacteriia uncultured Acidobacteriaceae bacterium 80 33 3 4 Acidobacteria Acidobacteriia uncultured Acidobacteriaceae bacterium 81 7 1 2 Acidobacteria Acidobacteriia uncultured Acidobacteriaceae bacterium 88 1 4 1 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 1 53 12 3 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 13 17 17 4 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 18 21 2 3 Acidobacteria Acidobacteriia unknown Subgroup 2 bacterium 12 4 1 2 Acidobacteria Acidobacteriia unknown Subgroup 2 bacterium 20 2 5 1 Acidobacteria Acidobacteriia unknown Subgroup 2 bacterium 3 20 14 13 Acidobacteria Acidobacteriia unknown Subgroup 2 bacterium 8 87 63 20 Acidobacteria Blastocatellia (Subgroup 4) RB41 1 19 3 3 Acidobacteria Blastocatellia (Subgroup 4) RB41 2 9 0 2 Acidobacteria Blastocatellia (Subgroup 4) RB41 4 6 0 0 Acidobacteria Holophagae unknown Subgroup 7 bacterium 5 5 3 1 Acidobacteria Subgroup 11 unknown Subgroup 11 bacterium 1 6 0 0 Acidobacteria Subgroup 11 unknown Subgroup 11 bacterium 2 7 0 1 Acidobacteria Subgroup 22 unknown Subgroup 22 bacterium 1 10 10 8 Acidobacteria Subgroup 22 unknown Subgroup 22 bacterium 2 9 4 8 Acidobacteria Subgroup 5 unknown Subgroup 5 bacterium 1 5 5 2 90 Annex 16 (Continuation) Phylum Class Genus 0 min 15 min 30 min Acidobacteria Subgroup 5 unknown Subgroup 5 bacterium 3 36 37 22 Acidobacteria Subgroup 5 unknown Subgroup 5 bacterium 4 13 6 6 Acidobacteria Subgroup 5 unknown Subgroup 5 bacterium 7 7 2 2 Acidobacteria Subgroup 5 unknown Subgroup 5 bacterium 8 25 15 6 Acidobacteria Subgroup 6 unknown Subgroup 6 bacterium 1 3 4 0 Acidobacteria Subgroup 6 unknown Subgroup 6 bacterium 10 7 6 2 Acidobacteria Subgroup 6 unknown Subgroup 6 bacterium 11 5 7 3 Acidobacteria Subgroup 6 unknown Subgroup 6 bacterium 12 7 9 3 Acidobacteria Subgroup 6 unknown Subgroup 6 bacterium 3 1 6 2 Acidobacteria Subgroup 6 unknown Subgroup 6 bacterium 4 4 4 2 Acidobacteria Subgroup 6 unknown Subgroup 6 bacterium 5 4 13 7 Acidobacteria Subgroup 6 unknown Subgroup 6 bacterium 6 7 4 1 Acidobacteria Subgroup 6 unknown Subgroup 6 bacterium 9 15 7 5 Acidobacteria Thermoanaerobaculia Subgroup 10 4 6 4 2 Actinobacteria Acidimicrobiia CL500-29 marine group 1 19 33 23 Actinobacteria Acidimicrobiia CL500-29 marine group 2 3 7 7 Actinobacteria Acidimicrobiia CL500-29 marine group 3 4 10 7 Actinobacteria Acidimicrobiia CL500-29 marine group 4 8 1 3 Actinobacteria Acidimicrobiia CL500-29 marine group 5 1 4 4 Actinobacteria Acidimicrobiia Iamia 1 8 18 10 Actinobacteria Acidimicrobiia Iamia 2 16 17 9 Actinobacteria Acidimicrobiia Iamia 3 8 3 6 Actinobacteria Acidimicrobiia Iamia 4 8 5 2 Actinobacteria Acidimicrobiia Ilumatobacter 1 3 11 9 Actinobacteria Acidimicrobiia Ilumatobacter 2 7 8 6 Actinobacteria Acidimicrobiia IMCC26207 7 4 6 Actinobacteria Acidimicrobiia unknown Acidimicrobiaceae bacterium 3 5 7 Actinobacteria Acidimicrobiia unknown Ilumatobacteraceae bacterium 2 19 4 8 Actinobacteria Acidimicrobiia unknown Ilumatobacteraceae bacterium 3 17 12 15 Actinobacteria Acidimicrobiia unknown Ilumatobacteraceae bacterium 4 15 12 6 Actinobacteria Acidimicrobiia unknown IMCC26256 bacterium 12 14 20 4 Actinobacteria Acidimicrobiia unknown IMCC26256 bacterium 16 19 15 10 Actinobacteria Acidimicrobiia unknown IMCC26256 bacterium 2 11 14 4 Actinobacteria Acidimicrobiia unknown IMCC26256 bacterium 4 46 35 25 97 Annex 16 (Continuation) Phylum Class Genus 0 min 15 min 30 min Bacteroidetes Bacteroidia unknown Chitinophagaceae bacterium 19 6 7 6 Bacteroidetes Bacteroidia unknown Microscillaceae bacterium 1 34 52 42 Bacteroidetes Bacteroidia unknown Microscillaceae bacterium 11 1 5 1 Bacteroidetes Bacteroidia unknown Microscillaceae bacterium 12 9 8 18 Bacteroidetes Bacteroidia unknown Microscillaceae bacterium 14 2 5 2 Bacteroidetes Bacteroidia unknown Microscillaceae bacterium 17 7 1 2 Bacteroidetes Bacteroidia unknown Microscillaceae bacterium 2 40 36 23 Bacteroidetes Bacteroidia unknown Microscillaceae bacterium 3 19 17 21 Bacteroidetes Bacteroidia unknown Microscillaceae bacterium 4 9 18 22 Bacteroidetes Bacteroidia unknown Microscillaceae bacterium 5 3 16 11 Bacteroidetes Bacteroidia unknown Microscillaceae bacterium 6 10 10 11 Bacteroidetes Bacteroidia unknown Microscillaceae bacterium 7 9 11 6 Bacteroidetes Bacteroidia unknown Microscillaceae bacterium 8 1 7 4 Bacteroidetes Bacteroidia unknown Microscillaceae bacterium 9 8 6 4 Bacteroidetes Bacteroidia unknown Saprospiraceae bacterium 12 8 9 Bacteroidetes Bacteroidia unknwon AKYH767 bacterium 1 2 3 1 Bacteroidetes Bacteroidia unknwon AKYH767 bacterium 2 11 4 0 Bacteroidetes Bacteroidia unknwon AKYH767 bacterium 5 10 3 1 Bacteroidetes Bacteroidia unknwon env.OPS 17 bacterium 12 6 0 0 Bacteroidetes Bacteroidia unknwon env.OPS 17 bacterium 14 6 1 0 Bacteroidetes Bacteroidia unknwon env.OPS 17 bacterium 16 2 4 1 Bacteroidetes Bacteroidia unknwon env.OPS 17 bacterium 17 3 4 5 Bacteroidetes Bacteroidia unknwon env.OPS 17 bacterium 25 1 4 5 Bacteroidetes Bacteroidia unknwon env.OPS 17 bacterium 9 18 13 11 Bacteroidetes Bacteroidia unknwon KD3-93 bacterium 2 2 2 2 Bacteroidetes Bacteroidia unknwon NS11-12 marine group bacterium 3 15 10 6 Bacteroidetes Ignavibacteria unknwon BSV26 bacterium 18 8 2 Bacteroidetes Ignavibacteria unknwon OPB56 bacterium 2 2 3 3 98 Annex 16 (Continuation) Phylum Class Genus 0 min 15 min 30 min Chlamydiae Chlamydiae Candidatus Protochlamydia 1 0 5 1 Chlamydiae Chlamydiae Candidatus Rhabdochlamydia 10 2 5 1 Chlamydiae Chlamydiae Neochlamydia 2 3 2 2 Chlamydiae Chlamydiae unknown Parachlamydiaceae bacterium 1 1 4 3 Chlamydiae Chlamydiae unknown Parachlamydiaceae bacterium 7 2 6 0 Chlamydiae Chlamydiae unknown Simkaniaceae bacterium 4 0 4 10 Chlamydiae Chlamydiae unknwon cvE6 bacterium 4 0 7 0 Chlamydiae Chlamydiae unknwon cvE6 bacterium 9 1 7 2 Chloroflexi AD3 unknown AD3 bacterium 2 13 13 10 Chloroflexi Chloroflexia unknown JG30-KF-CM45 bacterium 1 1 8 6 Chloroflexi Chloroflexia unknown Roseiflexaceae bacterium 1 17 53 24 Chloroflexi Chloroflexia unknown Roseiflexaceae bacterium 3 10 60 17 Chloroflexi Chloroflexia unknown Roseiflexaceae bacterium 4 7 9 4 Chloroflexi Chloroflexia unknown Roseiflexaceae bacterium 5 2 3 2 Chloroflexi Chloroflexia unknown Roseiflexaceae bacterium 6 2 5 0 Chloroflexi Chloroflexia unknown Roseiflexaceae bacterium 7 2 5 1 Chloroflexi Chloroflexia unknown Roseiflexaceae bacterium 9 0 6 1 Chloroflexi Dehalococcoidia unknown S085 bacterium 4 8 1 0 Chloroflexi KD4-96 unknown KD4-96 bacterium 1 26 61 29 Chloroflexi KD4-96 unknown KD4-96 bacterium 2 3 4 3 Chloroflexi Ktedonobacteria unknown JG30-KF-AS9 bacterium 13 4 17 1 Chloroflexi Ktedonobacteria unknown JG30-KF-AS9 bacterium 3 5 31 15 Chloroflexi Ktedonobacteria unknown JG30-KF-AS9 bacterium 4 12 32 7 Chloroflexi TK10 unknown TK10 bacterium 11 14 2 1 Chloroflexi TK10 unknown TK10 bacterium 15 3 3 5 Chloroflexi TK10 unknown TK10 bacterium 27 26 13 5 Chloroflexi TK10 unknown TK10 bacterium 28 12 5 5 Chloroflexi TK10 unknown TK10 bacterium 30 4 4 5 Chloroflexi TK10 unknown TK10 bacterium 5 9 3 2 99 Annex 16 (Continuation) Phylum Class Genus 0 min 15 min 30 min Chloroflexi TK10 unknown TK10 bacterium 8 12 4 1 Dependentiae Babeliae unknown Vermiphilaceae bacterium 3 5 4 0 Elusimicrobia Elusimicrobia unknown Lineage IV bacterium 2 1 5 1 Elusimicrobia Lineage IIa unknown Lineage lla bacterium 1 8 3 3 Elusimicrobia Lineage IIa unknown Lineage lla bacterium 2 1 2 3 Elusimicrobia Lineage IIa unknown Lineage lla bacterium 4 5 3 0 Entotheonellaeota Entotheonellia unknown Entotheonellaceae bacterium 18 6 5 FCPU426 unknown FCPU426 unknown FCPU426 bacterium 1 1 2 3 Fibrobacteres Fibrobacteria unknown Fibrobacteraceae bacterium 1 5 7 2 Fibrobacteres Fibrobacteria unknown Fibrobacteraceae bacterium 2 3 8 8 Fibrobacteres Fibrobacteria unknown Fibrobacteraceae bacterium 4 3 3 5 Firmicutes Bacilli Ammoniphilus 3 19 151 Firmicutes Bacilli Aneurinibacillus 1 0 3 11 Firmicutes Bacilli Bacillus 1 7 4834 13085 Firmicutes Bacilli Bacillus 10 0 27 75 Firmicutes Bacilli Bacillus 11 0 10 9 Firmicutes Bacilli Bacillus 12 4 4 19 Firmicutes Bacilli Bacillus 13 0 2 17 Firmicutes Bacilli Bacillus 15 0 5 9 Firmicutes Bacilli Bacillus 17 0 8 3 Firmicutes Bacilli Bacillus 2 9 2243 1590 Firmicutes Bacilli Bacillus 3 5 50 192 Firmicutes Bacilli Bacillus 4 1 329 190 Firmicutes Bacilli Bacillus 5 3 427 1088 Firmicutes Bacilli Bacillus 6 6 12 91 Firmicutes Bacilli Bacillus 7 3 149 607 Firmicutes Bacilli Bacillus 8 1 10 93 Firmicutes Bacilli Bacillus 9 1 9 58 Firmicutes Bacilli Brevibacillus 0 0 10 Firmicutes Bacilli Cohnella 1 1 3 30 Firmicutes Bacilli Cohnella 2 0 2 8 Firmicutes Bacilli Cohnella 4 0 3 8 Firmicutes Bacilli Cohnella 6 0 4 11 Firmicutes Bacilli Domibacillus 2 1 23 Firmicutes Bacilli Lysinibacillus 5 61 110 Firmicutes Bacilli Paenibacillus 1 0 287 451 Firmicutes Bacilli Paenibacillus 10 2 59 32 Firmicutes Bacilli Paenibacillus 11 0 47 67 Firmicutes Bacilli Paenibacillus 12 0 32 33 Firmicutes Bacilli Paenibacillus 14 2 71 118 Firmicutes Bacilli Paenibacillus 17 0 14 13 Firmicutes Bacilli Paenibacillus 18 3 17 56 Firmicutes Bacilli Paenibacillus 20 0 18 5 100 Annex 16 (Continuation) Phylum Class Genus 0 min 15 min 30 min Firmicutes Bacilli Paenibacillus 22 0 3 21 Firmicutes Bacilli Paenibacillus 23 0 15 16 Firmicutes Bacilli Paenibacillus 25 0 0 11 Firmicutes Bacilli Paenibacillus 26 0 2 14 Firmicutes Bacilli Paenibacillus 27 1 2 6 Firmicutes Bacilli Paenibacillus 28 1 1 16 Firmicutes Bacilli Paenibacillus 29 0 22 28 Firmicutes Bacilli Paenibacillus 30 0 5 21 Firmicutes Bacilli Paenibacillus 31 0 1 10 Firmicutes Bacilli Paenibacillus 34 0 1 6 Firmicutes Bacilli Paenibacillus 35 0 5 4 Firmicutes Bacilli Paenibacillus 36 0 0 11 Firmicutes Bacilli Paenibacillus 4 1 106 197 Firmicutes Bacilli Paenibacillus 43 1 4 1 Firmicutes Bacilli Paenibacillus 45 0 2 8 Firmicutes Bacilli Paenibacillus 48 1 0 5 Firmicutes Bacilli Paenibacillus 49 0 3 8 Firmicutes Bacilli Paenibacillus 5 0 0 11 Firmicutes Bacilli Paenibacillus 50 0 1 6 Firmicutes Bacilli Paenibacillus 54 1 0 7 Firmicutes Bacilli Paenibacillus 56 0 2 8 Firmicutes Bacilli Paenibacillus 58 0 5 12 Firmicutes Bacilli Paenibacillus 59 0 2 9 Firmicutes Bacilli Paenibacillus 6 1 41 104 Firmicutes Bacilli Paenibacillus 7 2 38 89 Firmicutes Bacilli Paenibacillus 9 1 30 50 Firmicutes Bacilli Rummeliibacillus 0 33 131 Firmicutes Bacilli Shimazuella 1 0 24 42 Firmicutes Bacilli Shimazuella 2 0 4 50 Firmicutes Bacilli Solibacillus 2 1 6 Firmicutes Bacilli Sporosarcina 1 40 207 1303 Firmicutes Bacilli Sporosarcina 2 2 6 71 Firmicutes Bacilli Sporosarcina 3 0 3 6 Firmicutes Bacilli Sporosarcina 4 2 4 18 Firmicutes Bacilli Thermoactinomyces 1 2 11 Firmicutes Bacilli Tumebacillus 1 3 590 2675 Firmicutes Bacilli Tumebacillus 2 0 234 113 Firmicutes Bacilli Tumebacillus 5 0 79 41 Firmicutes Bacilli Tumebacillus 6 0 1 12 Firmicutes Bacilli unknown Bacillaceae bacterium 1 0 61 164 Firmicutes Bacilli unknown Bacillaceae bacterium 2 0 2 12 Firmicutes Bacilli unknown Paenibacillaceae bacterium 1 0 0 11 Firmicutes Bacilli unknown Sporolactobacillaceae bacterium 0 2 7 Firmicutes Clostridia Clostridium sensu stricto 1 1 1 0 20 Firmicutes Clostridia Clostridium sensu stricto 1 2 1 6 14 Firmicutes Clostridia Clostridium sensu stricto 1 3 0 1 6 Firmicutes Clostridia Clostridium sensu stricto 12 0 0 6 101 Annex 16 (Continuation) Phylum Class Genus 0 min 15 min 30 min Firmicutes Clostridia Clostridium sensu stricto 2 0 0 6 Firmicutes Clostridia Paeniclostridium 2 24 86 Firmicutes Clostridia Terrisporobacter 0 1 5 Firmicutes Clostridia unknown Clostridiaceae 1 bacterium 0 0 17 Firmicutes Clostridia unknown Peptostreptococcaceae bacterium 0 1 23 Gemmatimonadetes Gemmatimonadetes Gemmatimonas 10 6 7 2 Gemmatimonadetes Gemmatimonadetes Gemmatimonas 12 21 18 6 Gemmatimonadetes Gemmatimonadetes Gemmatimonas 14 2 3 1 Gemmatimonadetes Gemmatimonadetes Gemmatimonas 16 13 15 8 Gemmatimonadetes Gemmatimonadetes Gemmatimonas 17 10 7 3 Gemmatimonadetes Gemmatimonadetes Gemmatimonas 2 5 7 5 Gemmatimonadetes Gemmatimonadetes Gemmatimonas 21 7 4 6 Gemmatimonadetes Gemmatimonadetes Gemmatimonas 25 6 4 0 Gemmatimonadetes Gemmatimonadetes Gemmatimonas 26 12 3 5 Gemmatimonadetes Gemmatimonadetes Gemmatimonas 33 4 2 0 Gemmatimonadetes Gemmatimonadetes Gemmatimonas 34 1 5 1 Gemmatimonadetes Gemmatimonadetes Gemmatimonas 41 1 3 2 Gemmatimonadetes Gemmatimonadetes Gemmatimonas 42 7 0 0 Gemmatimonadetes Gemmatimonadetes Gemmatimonas 7 4 1 3 Gemmatimonadetes Gemmatimonadetes unknown Gemmatimonadaceae bacterium 10 9 4 0 Gemmatimonadetes Gemmatimonadetes unknown Gemmatimonadaceae bacterium 16 2 7 1 Gemmatimonadetes Gemmatimonadetes unknown Gemmatimonadaceae bacterium 18 7 1 1 Gemmatimonadetes Gemmatimonadetes unknown Gemmatimonadaceae bacterium 19 5 1 0 Gemmatimonadetes Gemmatimonadetes unknown Gemmatimonadaceae bacterium 4 38 40 15 Gemmatimonadetes Gemmatimonadetes unknown Gemmatimonadaceae bacterium 5 6 8 2 Gemmatimonadetes Gemmatimonadetes unknown Gemmatimonadaceae bacterium 6 10 35 30 Gemmatimonadetes Gemmatimonadetes unknown Gemmatimonadaceae bacterium 7 44 21 13 Gemmatimonadetes Gemmatimonadetes unknown Gemmatimonadaceae bacterium 9 13 21 7 Latescibacteria Latescibacteria unknown Latescibacteraceae bacterium 6 3 4 Nitrospirae Nitrospira Nitrospira 1 23 10 12 Patescibacteria Parcubacteria Candidatus Yanofskybacteria bacterium 2 3 6 1 102 Annex 16 (Continuation) Phylum Class Genus 0 min 15 min 30 min Patescibacteria Parcubacteria unknown Candidatus Adlerbacteria bacterium 13 0 2 4 Patescibacteria Parcubacteria unknown Parcubacteria bacterium 13 0 6 0 Patescibacteria Parcubacteria unknownunknown Candidatus Kaiserbacteria bacterium 18 0 13 1 Patescibacteria Parcubacteria unknownunknown Candidatus Kaiserbacteria bacterium 27 5 8 7 Patescibacteria Parcubacteria unknownunknown Candidatus Kaiserbacteria bacterium 29 0 6 3 Patescibacteria Saccharimonadia nknown Saccharimonadales bacterium 4 3 8 4 Patescibacteria Saccharimonadia nknown Saccharimonadales bacterium 6 7 4 1 Patescibacteria Saccharimonadia nknown Saccharimonadales bacterium 7 3 6 2 Patescibacteria Saccharimonadia nknown Saccharimonadales bacterium 89 3 5 2 Patescibacteria Saccharimonadia nknown Saccharimonadales bacterium 96 3 13 3 Planctomycetes BD7-11 unknown BD7-11 bacterium 1 4 2 Planctomycetes OM190 unknown OM190 bacterium 1 18 12 10 Planctomycetes OM190 unknown OM190 bacterium 2 5 5 2 Planctomycetes OM190 unknown OM190 bacterium 3 16 3 3 Planctomycetes OM190 unknown OM190 bacterium 4 7 4 0 Planctomycetes OM190 unknown OM190 bacterium 5 3 4 0 Planctomycetes Phycisphaerae SM1A02 1 11 29 3 Planctomycetes Phycisphaerae SM1A02 2 20 9 4 Planctomycetes Phycisphaerae SM1A02 3 7 16 6 Planctomycetes Phycisphaerae SM1A02 4 3 2 3 Planctomycetes Phycisphaerae SM1A02 5 6 0 2 Planctomycetes Phycisphaerae SM1A02 6 1 3 4 Planctomycetes Phycisphaerae SM1A02 7 6 2 2 Planctomycetes Phycisphaerae SM1A02 8 2 5 2 Planctomycetes Phycisphaerae SM1A02 9 3 3 0 Planctomycetes Phycisphaerae unknown WD2101 soil group bacterium 25 5 2 1 Planctomycetes Pla4 lineage unknown Pla4 lineage bacterium 3 40 29 12 Planctomycetes Planctomycetacia Pir4 lineage 1 19 33 46 Planctomycetes Planctomycetacia Pir4 lineage 2 2 4 3 Planctomycetes Planctomycetacia Pirellula 1 6 2 1 Planctomycetes Planctomycetacia Pirellula 2 14 2 3 103 Annex 16 (Continuation) Phylum Class Genus 0 min 15 min 30 min Planctomycetes Planctomycetacia Schlesneria 1 10 1 3 Planctomycetes Planctomycetacia unknown Gemmataceae baterium 101 3 3 1 Planctomycetes Planctomycetacia unknown Pirellulaceae bacterium 1 9 5 1 Planctomycetes Planctomycetacia unknown Pirellulaceae bacterium 18 4 2 2 Planctomycetes Planctomycetacia unknown Pirellulaceae bacterium 21 2 4 4 Planctomycetes Planctomycetacia unknown Pirellulaceae bacterium 8 9 6 2 Planctomycetes vadinHA49 unknown vadinHA49 bacterium 16 4 4 2 Planctomycetes vadinHA49 unknown vadinHA49 bacterium 17 3 3 0 Planctomycetes vadinHA49 unknown vadinHA49 bacterium 19 2 2 3 Planctomycetes vadinHA49 unknown vadinHA49 bacterium 4 7 0 0 Proteobacteria Alphaproteobacteria Afipia 1 5 0 5 Proteobacteria Alphaproteobacteria AllorhizobiumNeorhizobiumPararhizobium-Rhizobium 2 12 9 7 Proteobacteria Alphaproteobacteria Amaricoccus 1 5 2 6 Proteobacteria Alphaproteobacteria Amaricoccus 2 3 4 2 Proteobacteria Alphaproteobacteria Bauldia 1 18 3 5 Proteobacteria Alphaproteobacteria Bradyrhizobium 1 333 168 180 Proteobacteria Alphaproteobacteria Bradyrhizobium 2 82 34 42 Proteobacteria Alphaproteobacteria Candidatus Alysiosphaera 1 4 3 4 Proteobacteria Alphaproteobacteria Candidatus Alysiosphaera 2 3 2 4 Proteobacteria Alphaproteobacteria Candidatus Alysiosphaera 3 2 1 4 Proteobacteria Alphaproteobacteria Caulobacter 1 8 5 4 Proteobacteria Alphaproteobacteria Caulobacter 2 5 2 3 Proteobacteria Alphaproteobacteria Devosia 1 16 14 8 Proteobacteria Alphaproteobacteria Devosia 2 2 6 4 Proteobacteria Alphaproteobacteria Devosia 3 2 5 0 Proteobacteria Alphaproteobacteria Dongia 1 54 17 34 Proteobacteria Alphaproteobacteria Ellin6055 1 19 12 9 Proteobacteria Alphaproteobacteria Hirschia 1 3 9 3 Proteobacteria Alphaproteobacteria Hyphomicrobium 1 12 8 9 Proteobacteria Alphaproteobacteria Mesorhizobium 1 4 2 1 Proteobacteria Alphaproteobacteria Mesorhizobium 2 29 12 10 Proteobacteria Alphaproteobacteria Mesorhizobium 3 7 3 3 Proteobacteria Alphaproteobacteria Microvirga 3 7 7 7 Proteobacteria Alphaproteobacteria Microvirga 6 3 0 3 Proteobacteria Alphaproteobacteria Nordella 1 4 2 3 Proteobacteria Alphaproteobacteria Novosphingobium 3 4 4 1 Proteobacteria Alphaproteobacteria Pedomicrobium 1 51 26 16 Proteobacteria Alphaproteobacteria Pedomicrobium 3 17 4 9 Proteobacteria Alphaproteobacteria Phenylobacterium 1 34 18 8 Proteobacteria Alphaproteobacteria Pseudolabrys 3 32 24 4 Proteobacteria Alphaproteobacteria Pseudolabrys 4 9 0 2 Proteobacteria Alphaproteobacteria Reyranella 1 28 11 11 Proteobacteria Alphaproteobacteria Reyranella 2 6 1 0 104 Annex 16 (Continuation) Phylum Class Genus 0 min 15 min 30 min Proteobacteria Alphaproteobacteria Reyranella 3 38 8 7 Proteobacteria Alphaproteobacteria Rhodomicrobium 7 14 9 Proteobacteria Alphaproteobacteria Roseiarcus 1 8 1 1 Proteobacteria Alphaproteobacteria Skermanella 2 1 3 3 Proteobacteria Alphaproteobacteria Sphingomonas 1 50 11 0 Proteobacteria Alphaproteobacteria Sphingomonas 10 4 3 1 Proteobacteria Alphaproteobacteria Sphingomonas 11 1 1 5 Proteobacteria Alphaproteobacteria Sphingomonas 13 7 0 3 Proteobacteria Alphaproteobacteria Sphingomonas 2 12 2 2 Proteobacteria Alphaproteobacteria Sphingomonas 3 19 7 1 Proteobacteria Alphaproteobacteria Sphingomonas 4 6 3 3 Proteobacteria Alphaproteobacteria SWB02 1 3 3 0 Proteobacteria Alphaproteobacteria unknown A0839 bacterium 2 8 8 2 Proteobacteria Alphaproteobacteria unknown Acetobacteraceae bacterium 2 7 0 1 Proteobacteria Alphaproteobacteria unknown Acetobacteraceae bacterium 3 6 1 1 Proteobacteria Alphaproteobacteria unknown Acetobacteraceae bacterium 4 5 1 1 Proteobacteria Alphaproteobacteria unknown Alphaproteobacteria bacterium 10 5 0 1 Proteobacteria Alphaproteobacteria unknown Alphaproteobacteria bacterium 18 3 3 2 Proteobacteria Alphaproteobacteria unknown Alphaproteobacteria bacterium 4 16 10 6 Proteobacteria Alphaproteobacteria unknown Alphaproteobacteria bacterium 8 44 5 4 Proteobacteria Alphaproteobacteria unknown Caulobacteraceae bacterium 1 8 7 1 Proteobacteria Alphaproteobacteria unknown Caulobacteraceae bacterium 17 2 3 2 Proteobacteria Alphaproteobacteria unknown Caulobacteraceae bacterium 3 4 3 0 Proteobacteria Alphaproteobacteria unknown Caulobacteraceae bacterium 5 4 4 0 Proteobacteria Alphaproteobacteria unknown Caulobacteraceae bacterium 6 7 5 9 Proteobacteria Alphaproteobacteria unknown Caulobacteraceae bacterium 7 7 0 0 Proteobacteria Alphaproteobacteria unknown Caulobacteraceae bacterium 9 11 6 2 Proteobacteria Alphaproteobacteria unknown Elsterales bacterium 15 3 3 3 105 Annex 16 (Continuation) Phylum Class Genus 0 min 15 min 30 min Proteobacteria Alphaproteobacteria unknown Elsterales bacterium 29 4 6 2 Proteobacteria Alphaproteobacteria unknown Elsterales bacterium 42 3 1 4 Proteobacteria Alphaproteobacteria unknown Elsterales bacterium 60 9 0 3 Proteobacteria Alphaproteobacteria unknown Elsterales bacterium 87 8 4 5 Proteobacteria Alphaproteobacteria unknown Elsterales bacterium 95 13 0 3 Proteobacteria Alphaproteobacteria unknown Geminicoccaceae bacterium 6 0 0 Proteobacteria Alphaproteobacteria unknown KF-JG30-B3 bacterium 1 2 1 3 Proteobacteria Alphaproteobacteria unknown KF-JG30-B3 bacterium 3 6 4 1 Proteobacteria Alphaproteobacteria unknown Methyloligellaceae bacterium 1 68 58 55 Proteobacteria Alphaproteobacteria unknown Methyloligellaceae bacterium 2 12 14 7 Proteobacteria Alphaproteobacteria unknown Micropepsaceae bacterium 1 52 51 34 Proteobacteria Alphaproteobacteria unknown Micropepsaceae bacterium 10 4 5 2 Proteobacteria Alphaproteobacteria unknown Micropepsaceae bacterium 12 4 2 0 Proteobacteria Alphaproteobacteria unknown Micropepsaceae bacterium 24 2 4 2 Proteobacteria Alphaproteobacteria unknown Micropepsaceae bacterium 3 31 20 7 Proteobacteria Alphaproteobacteria unknown Micropepsaceae bacterium 4 7 7 1 Proteobacteria Alphaproteobacteria unknown Rhizobiales bacterium 1 9 7 4 Proteobacteria Alphaproteobacteria unknown Rhizobiales bacterium 2 8 2 4 Proteobacteria Alphaproteobacteria unknown Rhodospirillaceae bacterium 1 6 1 1 Proteobacteria Alphaproteobacteria unknown SM2D12 bacterium 21 2 4 1 Proteobacteria Alphaproteobacteria unknown Sphingomonadaceae bacterium 18 12 13 Proteobacteria Alphaproteobacteria unknown Xanthobacteraceae bacterium 1 5 3 6 Proteobacteria Alphaproteobacteria unknown Xanthobacteraceae bacterium 10 3 19 6 Proteobacteria Alphaproteobacteria unknown Xanthobacteraceae bacterium 12 4 1 1 106 Annex 16 (Continuation) Phylum Class Genus 0 min 15 min 30 min Proteobacteria Alphaproteobacteria unknown Xanthobacteraceae bacterium 15 11 31 9 Proteobacteria Alphaproteobacteria unknown Xanthobacteraceae bacterium 16 4 4 1 Proteobacteria Alphaproteobacteria unknown Xanthobacteraceae bacterium 18 4 0 3 Proteobacteria Alphaproteobacteria unknown Xanthobacteraceae bacterium 2 43 22 12 Proteobacteria Alphaproteobacteria unknown Xanthobacteraceae bacterium 27 8 6 5 Proteobacteria Alphaproteobacteria unknown Xanthobacteraceae bacterium 28 52 40 32 Proteobacteria Alphaproteobacteria unknown Xanthobacteraceae bacterium 29 2 3 3 Proteobacteria Alphaproteobacteria unknown Xanthobacteraceae bacterium 38 19 7 8 Proteobacteria Alphaproteobacteria unknown Xanthobacteraceae bacterium 4 98 120 98 Proteobacteria Alphaproteobacteria unknown Xanthobacteraceae bacterium 5 49 28 14 Proteobacteria Alphaproteobacteria unknown Xanthobacteraceae bacterium 6 17 19 5 Proteobacteria Alphaproteobacteria unknown Xanthobacteraceae bacterium 8 4 1 1 Proteobacteria Alphaproteobacteria unknown Xanthobacteraceae bacterium 9 24 12 20 Proteobacteria Deltaproteobacteria Anaeromyxobacter 20 3 2 2 Proteobacteria Deltaproteobacteria Anaeromyxobacter 4 26 13 9 Proteobacteria Deltaproteobacteria Anaeromyxobacter 5 9 3 12 Proteobacteria Deltaproteobacteria Anaeromyxobacter 9 2 6 4 Proteobacteria Deltaproteobacteria Bdellovibrio 28 2 6 1 Proteobacteria Deltaproteobacteria Bdellovibrio 8 3 3 1 Proteobacteria Deltaproteobacteria Haliangium 1 119 81 54 Proteobacteria Deltaproteobacteria Haliangium 12 5 3 5 Proteobacteria Deltaproteobacteria Haliangium 16 2 3 1 Proteobacteria Deltaproteobacteria Haliangium 2 14 8 7 Proteobacteria Deltaproteobacteria Haliangium 3 13 1 1 Proteobacteria Deltaproteobacteria Haliangium 4 8 3 4 Proteobacteria Deltaproteobacteria Haliangium 42 6 8 2 Proteobacteria Deltaproteobacteria Haliangium 5 14 10 15 Proteobacteria Deltaproteobacteria Haliangium 6 6 0 1 Proteobacteria Deltaproteobacteria Haliangium 7 3 2 3 Proteobacteria Deltaproteobacteria Haliangium 9 9 8 5 Proteobacteria Deltaproteobacteria Minicystis 2 5 9 113 Annex 17 (Continuation) Taxonomic classification 5 months (March 2018) 12 months (October 2018) 18 months April 2019) Plylum Class Genus Burnt Halfburnt Nonburnt Burnt Halfburnt Nonburnt Burnt Halfburnt Nonburnt Acidobacteria Acidobacteriia unknown Acidobacteriace ae bacterium 30 23 13 0 2 5 0 1 13 5 Acidobacteria Acidobacteriia unknown Acidobacteriace ae bacterium 31 6 1 14 1 0 2 4 1 17 Acidobacteria Acidobacteriia unknown Acidobacteriace ae bacterium 32 1 9 5 1 8 4 2 48 3 Acidobacteria Acidobacteriia unknown Acidobacteriace ae bacterium 33 10 8 2 0 0 1 0 4 7 Acidobacteria Acidobacteriia unknown Acidobacteriace ae bacterium 34 9 37 22 1 1 21 1 1 18 Acidobacteria Acidobacteriia unknown Acidobacteriace ae bacterium 35 6 21 6 9 35 9 16 18 2 Acidobacteria Acidobacteriia unknown Acidobacteriace ae bacterium 4 7 16 6 0 0 2 1 0 1 Acidobacteria Acidobacteriia unknown Acidobacteriace ae bacterium 7 289 410 847 77 36 373 303 102 486 Acidobacteria Acidobacteriia unknown Acidobacteriace ae bacterium 8 444 415 346 101 34 214 181 3 178 Acidobacteria Acidobacteriia unknown Acidobacteriace ae bacterium 9 67 96 152 39 27 38 24 122 118 Acidobacteria Acidobacteriia Candidatus Koribacter 1 356 550 417 238 159 330 241 73 379 Acidobacteria Acidobacteriia Candidatus Koribacter 10 12 11 4 1 3 2 6 1 7 Acidobacteria Acidobacteriia Candidatus Koribacter 11 0 0 23 0 0 10 4 13 26 Acidobacteria Acidobacteriia Candidatus Koribacter 2 165 501 213 61 150 120 211 71 227 Acidobacteria Acidobacteriia Candidatus Koribacter 3 13 21 223 6 10 125 54 84 319 Acidobacteria Acidobacteriia Candidatus Koribacter 4 0 0 65 0 0 9 0 3 34 Acidobacteria Acidobacteriia Candidatus Koribacter 5 12 23 2 7 37 1 11 6 1 Acidobacteria Acidobacteriia Candidatus Koribacter 6 6 4 0 3 1 1 4 0 3 Acidobacteria Acidobacteriia Candidatus Koribacter 7 146 169 99 53 55 44 70 7 70 Acidobacteria Acidobacteriia Candidatus Koribacter 8 16 20 19 5 1 11 13 3 10 Acidobacteria Acidobacteriia Candidatus Koribacter 9 1 0 3 2 0 5 2 0 2 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 10 1 0 18 0 0 9 0 0 47 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 11 4 0 29 1 0 9 0 0 17 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 12 0 46 0 0 0 0 0 8 0 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 14 7 11 8 6 5 1 13 0 4 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 15 28 7 17 18 6 7 34 5 19 114 Annex 17 (Continuation) Taxonomic classification 5 months (March 2018) 12 months (October 2018) 18 months April 2019) Plylum Class Genus Burnt Halfburnt Nonburnt Burnt Halfburnt Nonburnt Burnt Halfburnt Nonburnt Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 16 143 84 69 44 18 25 205 0 19 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 17 9 9 21 5 2 13 12 0 24 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 18 5 7 39 3 1 34 11 0 27 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 19 0 0 3 0 0 1 0 0 7 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 2 115 369 162 120 34 63 398 5 79 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 20 2 2 2 1 0 1 1 0 1 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 22 0 0 1 0 1 0 1 0 5 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 24 9 4 11 4 1 9 10 1 10 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 26 1 0 0 0 0 4 0 0 2 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 27 0 0 4 0 0 0 0 0 2 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 28 1634 2367 2273 383 201 634 1087 122 1311 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 29 684 36 189 236 74 25 3263 286 57 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 3 301 253 340 59 11 139 190 7 478 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 30 830 992 587 338 65 413 787 79 795 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 31 452 840 796 184 438 561 206 90 585 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 32 561 428 639 167 20 332 393 20 500 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 33 265 86 335 208 4 94 648 7 161 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 34 217 201 542 65 13 423 207 17 905 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 35 63 0 954 27 1 314 24 3 574 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 36 19 8 410 16 18 74 165 57 370 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 37 191 6 58 171 3 24 175 13 40 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 38 294 298 146 54 0 55 328 5 128 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 39 34 24 94 5 1 92 28 5 135 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 4 258 237 165 151 40 84 129 24 113 115 Annex 17 (Continuation) Taxonomic classification 5 months (March 2018) 12 months (October 2018) 18 months April 2019) Plylum Class Genus Burnt Halfburnt Nonburnt Burnt Halfburnt Nonburnt Burnt Halfburnt Nonburnt Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 40 140 148 121 16 6 62 90 7 87 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 41 28 24 55 10 1 18 23 2 74 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 42 26 40 70 11 0 17 21 2 24 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 43 73 41 109 18 2 59 22 2 51 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 44 22 5 179 4 1 143 36 4 328 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 45 27 0 18 0 0 31 4 0 15 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 46 15 13 43 7 0 15 20 0 47 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 47 1 1 15 3 1 6 2 3 45 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 48 4 0 2 0 0 39 6 0 28 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 49 25 0 2 1 0 12 1 0 7 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 5 82 189 56 11 0 8 103 2 23 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 50 9 10 9 2 0 9 2 1 29 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 51 12 0 2 19 0 0 4 0 0 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 52 6 2 10 5 0 17 9 4 54 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 53 2 0 21 0 0 3 0 0 9 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 54 3 2 25 2 1 16 6 1 20 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 55 9 5 7 0 0 2 33 0 5 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 56 6 6 13 2 1 6 1 0 12 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 57 2 0 13 4 0 2 4 0 5 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 58 1 0 26 1 0 9 1 2 28 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 59 42 3 47 81 0 14 127 0 8 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 6 437 422 573 300 128 168 943 91 368 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 60 44 26 77 10 4 36 36 0 33 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 61 0 0 10 0 0 2 4 5 4 116 Annex 17 (Continuation) Taxonomic classification 5 months (March 2018) 12 months (October 2018) 18 months April 2019) Plylum Class Genus Burnt Halfburnt Nonburnt Burnt Halfburnt Nonburnt Burnt Halfburnt Nonburnt Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 62 2 0 5 2 0 1 0 0 3 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 63 1 3 14 0 0 8 3 1 11 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 64 3 9 7 0 0 12 1 0 5 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 65 0 0 2 0 0 8 0 0 0 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 66 6 1 6 1 0 0 5 0 0 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 67 2 4 2 0 0 1 1 0 1 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 68 0 0 4 0 0 4 1 0 1 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 69 4 0 2 2 0 0 12 0 0 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 7 32 1 39 10 0 21 24 0 68 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 70 0 0 1 0 0 9 0 0 7 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 71 0 0 0 0 0 4 0 0 2 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 74 1 1 2 1 0 3 0 0 4 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 76 557 464 572 142 86 310 135 36 259 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 77 123 191 129 3 1 103 23 13 403 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 78 549 362 832 271 41 296 460 32 532 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 79 88 0 11 14 0 62 16 0 12 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 8 4 11 56 0 0 24 6 1 173 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 80 0 0 21 0 0 6 0 0 16 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 81 1 0 53 0 0 5 0 1 7 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 82 10 3 7 3 0 4 1 0 2 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 83 3 1 12 0 0 2 1 0 8 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 84 111 56 104 33 24 79 14 6 95 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 85 4 0 0 0 0 0 8 0 0 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 86 60 8 35 13 0 21 46 3 24 117 Annex 17 (Continuation) Taxonomic classification 5 months (March 2018) 12 months (October 2018) 18 months April 2019) Plylum Class Genus Burnt Halfburnt Nonburnt Burnt Halfburnt Nonburnt Burnt Halfburnt Nonburnt Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 87 17 4 24 4 1 1 7 0 2 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 88 17 31 33 6 1 13 14 0 19 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 89 0 1 11 0 0 5 4 0 4 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 9 1 24 19 1 0 2 4 48 16 Acidobacteria Acidobacteriia unknown Acidobacteriales bacterium 1 2627 1673 4115 996 241 2685 1940 83 3711 Acidobacteria Acidobacteriia unknown Acidobacteriia Subgroup 12 bacterium 14 6 8 1 0 11 11 0 20 Acidobacteria Acidobacteriia unknown Acidobacteriia Subgroup 13 bacterium 1 73 64 18 19 10 15 52 0 25 Acidobacteria Acidobacteriia unknown Acidobacteriia Subgroup 13 bacterium 10 8 2 4 0 0 3 0 0 0 Acidobacteria Acidobacteriia unknown Acidobacteriia Subgroup 13 bacterium 2 28 55 22 4 1 4 18 6 43 Acidobacteria Acidobacteriia unknown Acidobacteriia Subgroup 13 bacterium 3 23 22 14 3 2 14 15 1 26 Acidobacteria Acidobacteriia unknown Acidobacteriia Subgroup 13 bacterium 4 10 15 1 2 1 2 7 3 3 Acidobacteria Acidobacteriia unknown Acidobacteriia Subgroup 13 bacterium 5 4 21 5 0 0 3 9 0 5 Acidobacteria Acidobacteriia unknown Acidobacteriia Subgroup 13 bacterium 6 1 0 4 0 0 0 0 0 1 Acidobacteria Acidobacteriia unknown Acidobacteriia Subgroup 13 bacterium 7 2 0 1 2 0 0 1 0 0 Acidobacteria Acidobacteriia unknown Acidobacteriia Subgroup 13 bacterium 8 1 0 3 1 0 2 0 0 0 Acidobacteria Acidobacteriia unknown Acidobacteriia Subgroup 2 bacterium 1 29 37 90 2 7 41 31 9 236 Acidobacteria Acidobacteriia unknown Acidobacteriia Subgroup 2 bacterium 10 197 258 263 17 20 201 204 168 427 Acidobacteria Acidobacteriia unknown Acidobacteriia Subgroup 2 bacterium 11 211 34 91 85 2 24 1109 1 11 Acidobacteria Acidobacteriia unknown Acidobacteriia Subgroup 2 bacterium 12 39 82 31 13 1 20 36 8 83 Acidobacteria Acidobacteriia unknown Acidobacteriia Subgroup 2 bacterium 13 57 51 14 6 0 16 27 0 24 118 Annex 17 (Continuation) Taxonomic classification 5 months (March 2018) 12 months (October 2018) 18 months April 2019) Plylum Class Genus Burnt Halfburnt Nonburnt Burnt Halfburnt Nonburnt Burnt Halfburnt Nonburnt Acidobacteria Acidobacteriia unknown Acidobacteriia Subgroup 2 bacterium 14 34 56 35 10 1 24 30 6 66 Acidobacteria Acidobacteriia unknown Acidobacteriia Subgroup 2 bacterium 15 41 23 26 7 2 17 51 1 32 Acidobacteria Acidobacteriia unknown Acidobacteriia Subgroup 2 bacterium 16 5 30 19 6 6 20 14 1 21 Acidobacteria Acidobacteriia unknown Acidobacteriia Subgroup 2 bacterium 17 15 0 8 3 0 16 11 0 10 Acidobacteria Acidobacteriia unknown Acidobacteriia Subgroup 2 bacterium 18 205 156 308 48 2 96 281 3 125 Acidobacteria Acidobacteriia unknown Acidobacteriia Subgroup 2 bacterium 19 34 2 1 3 0 0 8 0 0 Acidobacteria Acidobacteriia unknown Acidobacteriia Subgroup 2 bacterium 2 8 0 0 2 0 7 0 0 0 Acidobacteria Acidobacteriia unknown Acidobacteriia Subgroup 2 bacterium 20 10 49 20 0 0 19 9 0 46 Acidobacteria Acidobacteriia unknown Acidobacteriia Subgroup 2 bacterium 21 2 17 2 4 0 2 3 2 1 Acidobacteria Acidobacteriia unknown Acidobacteriia Subgroup 2 bacterium 22 17 35 41 4 0 27 19 0 35 Acidobacteria Acidobacteriia unknown Acidobacteriia Subgroup 2 bacterium 23 1 12 4 0 0 0 1 0 0 Acidobacteria Acidobacteriia unknown Acidobacteriia Subgroup 2 bacterium 24 12 0 0 7 0 2 0 0 1 Acidobacteria Acidobacteriia unknown Acidobacteriia Subgroup 2 bacterium 25 99 179 108 10 7 93 90 3 101 Acidobacteria Acidobacteriia unknown Acidobacteriia Subgroup 2 bacterium 26 12 19 5 5 0 7 11 2 9 Acidobacteria Acidobacteriia unknown Acidobacteriia Subgroup 2 bacterium 27 13 14 95 8 1 19 36 1 73 Acidobacteria Acidobacteriia unknown Acidobacteriia Subgroup 2 bacterium 28 10 9 17 4 1 14 18 2 18 Acidobacteria Acidobacteriia unknown Acidobacteriia Subgroup 2 bacterium 29 2 0 1 0 1 1 0 0 4 Acidobacteria Acidobacteriia unknown Acidobacteriia Subgroup 2 bacterium 3 2631 4177 1119 847 1426 918 4844 480 2183 119 Annex 17 (Continuation) Taxonomic classification 5 months (March 2018) 12 months (October 2018) 18 months April 2019) Plylum Class Genus Burnt Halfburnt Nonburnt Burnt Halfburnt Nonburnt Burnt Halfburnt Nonburnt Acidobacteria Acidobacteriia unknown Acidobacteriia Subgroup 2 bacterium 30 5 14 4 3 2 14 3 2 4 Acidobacteria Acidobacteriia unknown Acidobacteriia Subgroup 2 bacterium 31 0 14 0 2 1 0 0 0 2 Acidobacteria Acidobacteriia unknown Acidobacteriia Subgroup 2 bacterium 32 3 0 0 0 0 0 26 0 0 Acidobacteria Acidobacteriia unknown Acidobacteriia Subgroup 2 bacterium 33 0 2 0 3 0 1 1 0 0 Acidobacteria Acidobacteriia unknown Acidobacteriia Subgroup 2 bacterium 35 3 3 1 1 0 1 4 0 1 Acidobacteria Acidobacteriia unknown Acidobacteriia Subgroup 2 bacterium 37 0 1 1 3 0 0 2 0 2 Acidobacteria Acidobacteriia unknown Acidobacteriia Subgroup 2 bacterium 38 0 0 3 0 0 1 0 0 2 Acidobacteria Acidobacteriia unknown Acidobacteriia Subgroup 2 bacterium 39 54 200 64 5 2 31 31 0 82 Acidobacteria Acidobacteriia unknown Acidobacteriia Subgroup 2 bacterium 4 1757 7063 1315 754 2333 730 2225 450 2151 Acidobacteria Acidobacteriia unknown Acidobacteriia Subgroup 2 bacterium 40 45 21 5 12 2 9 26 0 11 Acidobacteria Acidobacteriia unknown Acidobacteriia Subgroup 2 bacterium 41 8 1 0 0 0 0 2 0 0 Acidobacteria Acidobacteriia unknown Acidobacteriia Subgroup 2 bacterium 5 1675 1322 689 116 197 312 1206 61 813 Acidobacteria Acidobacteriia unknown Acidobacteriia Subgroup 2 bacterium 6 1159 951 704 221 12 714 810 22 720 Acidobacteria Acidobacteriia unknown Acidobacteriia Subgroup 2 bacterium 7 778 372 506 224 149 276 966 119 360 Acidobacteria Acidobacteriia unknown Acidobacteriia Subgroup 2 bacterium 8 234 825 431 73 451 181 160 42 377 Acidobacteria Acidobacteriia unknown Acidobacteriia Subgroup 2 bacterium 9 160 276 125 33 13 111 173 8 222 Acidobacteria Acidobacteriia Bryobacter 1 4 0 8 17 1 3 4 2 4 Acidobacteria Acidobacteriia Bryobacter 10 11 10 6 21 1 5 10 0 3 Acidobacteria Acidobacteriia Bryobacter 11 0 0 0 0 0 4 0 7 1 Acidobacteria Acidobacteriia Bryobacter 12 4 0 2 0 0 0 1 0 3 Acidobacteria Acidobacteriia Bryobacter 13 0 0 0 3 0 4 4 1 8 Acidobacteria Acidobacteriia Bryobacter 15 3 2 0 1 0 1 0 0 0 Acidobacteria Acidobacteriia Bryobacter 16 1 0 4 0 0 0 1 0 0 Acidobacteria Acidobacteriia Bryobacter 17 21 8 15 2 0 23 12 0 18 Acidobacteria Acidobacteriia Bryobacter 19 385 524 425 303 179 386 372 35 437 Acidobacteria Acidobacteriia Bryobacter 2 0 0 6 2 2 3 1 0 5 120 Annex 17 (Continuation) Taxonomic classification 5 months (March 2018) 12 months (October 2018) 18 months April 2019) Plylum Class Genus Burnt Halfburnt Nonburnt Burnt Halfburnt Nonburnt Burnt Halfburnt Nonburnt Acidobacteria Acidobacteriia Bryobacter 20 64 77 142 74 132 89 104 93 58 Acidobacteria Acidobacteriia Bryobacter 21 31 165 22 73 145 4 30 122 6 Acidobacteria Acidobacteriia Bryobacter 22 55 69 149 28 6 55 40 24 108 Acidobacteria Acidobacteriia Bryobacter 23 46 12 170 8 0 75 28 3 117 Acidobacteria Acidobacteriia Bryobacter 24 39 1 0 106 71 1 177 75 2 Acidobacteria Acidobacteriia Bryobacter 25 9 53 98 16 9 48 35 25 78 Acidobacteria Acidobacteriia Bryobacter 26 14 26 40 28 29 39 32 49 38 Acidobacteria Acidobacteriia Bryobacter 27 43 25 56 8 0 53 20 1 42 Acidobacteria Acidobacteriia Bryobacter 28 34 8 41 31 2 50 34 7 29 Acidobacteria Acidobacteriia Bryobacter 29 20 21 45 15 3 27 23 17 55 Acidobacteria Acidobacteriia Bryobacter 3 124 194 325 43 5 188 93 9 255 Acidobacteria Acidobacteriia Bryobacter 30 2 0 23 50 45 8 30 68 16 Acidobacteria Acidobacteriia Bryobacter 31 3 4 13 13 1 10 6 5 29 Acidobacteria Acidobacteriia Bryobacter 32 7 46 17 3 0 15 8 1 20 Acidobacteria Acidobacteriia Bryobacter 33 23 5 9 11 2 14 11 0 9 Acidobacteria Acidobacteriia Bryobacter 34 13 32 25 2 2 16 4 11 16 Acidobacteria Acidobacteriia Bryobacter 35 8 5 19 4 2 19 6 2 22 Acidobacteria Acidobacteriia Bryobacter 36 1 0 15 0 0 19 0 0 19 Acidobacteria Acidobacteriia Bryobacter 37 3 0 14 0 2 11 8 6 31 Acidobacteria Acidobacteriia Bryobacter 38 0 4 3 0 2 10 7 9 27 Acidobacteria Acidobacteriia Bryobacter 39 0 0 12 15 5 3 11 7 4 Acidobacteria Acidobacteriia Bryobacter 4 98 185 118 54 116 88 143 81 127 Acidobacteria Acidobacteriia Bryobacter 40 5 11 27 0 1 14 2 4 26 Acidobacteria Acidobacteriia Bryobacter 41 7 8 8 1 0 14 1 0 6 Acidobacteria Acidobacteriia Bryobacter 42 1 0 0 11 29 0 6 10 3 Acidobacteria Acidobacteriia Bryobacter 43 20 10 10 18 0 11 2 0 3 Acidobacteria Acidobacteriia Bryobacter 44 2 1 8 6 12 4 2 13 6 Acidobacteria Acidobacteriia Bryobacter 45 1 1 0 8 10 1 7 13 0 Acidobacteria Acidobacteriia Bryobacter 46 12 3 6 1 0 8 4 0 7 Acidobacteria Acidobacteriia Bryobacter 47 0 0 3 3 4 19 2 1 12 Acidobacteria Acidobacteriia Bryobacter 48 0 15 19 0 4 4 1 3 5 Acidobacteria Acidobacteriia Bryobacter 49 0 12 7 0 1 5 4 9 1 Acidobacteria Acidobacteriia Bryobacter 5 98 111 80 53 38 56 68 12 73 Acidobacteria Acidobacteriia Bryobacter 50 7 3 5 2 2 3 1 3 5 Acidobacteria Acidobacteriia Bryobacter 51 0 0 2 6 8 5 3 5 7 Acidobacteria Acidobacteriia Bryobacter 52 1 0 7 0 7 3 8 39 17 Acidobacteria Acidobacteriia Bryobacter 53 6 8 5 0 0 4 3 0 8 Acidobacteria Acidobacteriia Bryobacter 54 3 0 10 4 0 7 4 0 7 Acidobacteria Acidobacteriia Bryobacter 55 0 0 1 2 5 5 1 4 4 Acidobacteria Acidobacteriia Bryobacter 56 1 0 18 1 0 2 0 0 3 Acidobacteria Acidobacteriia Bryobacter 57 0 4 10 0 2 4 1 6 16 Acidobacteria Acidobacteriia Bryobacter 58 4 7 29 6 5 19 8 8 16 Acidobacteria Acidobacteriia Bryobacter 59 14 0 0 0 0 0 0 0 0 Acidobacteria Acidobacteriia Bryobacter 6 73 32 131 57 19 54 88 11 50 Acidobacteria Acidobacteriia Bryobacter 60 9 1 5 1 0 1 2 0 3 Acidobacteria Acidobacteriia Bryobacter 61 5 5 14 4 1 2 4 4 4 Acidobacteria Acidobacteriia Bryobacter 62 1 0 0 12 6 0 0 1 0 Acidobacteria Acidobacteriia Bryobacter 63 0 0 0 6 5 0 1 6 0 Acidobacteria Acidobacteriia Bryobacter 64 2 0 5 1 0 3 1 0 2 Acidobacteria Acidobacteriia Bryobacter 65 0 0 4 2 0 2 2 0 3 Acidobacteria Acidobacteriia Bryobacter 66 9 9 4 5 0 4 5 1 1 Acidobacteria Acidobacteriia Bryobacter 67 10 8 8 6 1 2 13 0 3 Acidobacteria Acidobacteriia Bryobacter 68 30 40 37 6 4 31 22 0 67 Acidobacteria Acidobacteriia Bryobacter 69 12 8 20 5 1 21 10 20 40 Acidobacteria Acidobacteriia Bryobacter 7 12 22 33 6 4 8 24 0 21 Acidobacteria Acidobacteriia Bryobacter 70 0 0 1 0 0 1 0 0 6 Acidobacteria Acidobacteriia Bryobacter 73 0 0 2 0 1 11 2 1 6 Acidobacteria Acidobacteriia Bryobacter 74 0 0 1 0 0 4 0 0 1 Acidobacteria Acidobacteriia Bryobacter 75 0 0 0 1 0 0 1 5 0 Acidobacteria Acidobacteriia Bryobacter 76 0 0 0 2 2 0 1 4 0 Acidobacteria Acidobacteriia Bryobacter 78 6 0 4 2 0 11 6 3 8 Acidobacteria Acidobacteriia Bryobacter 8 39 28 21 13 0 10 16 0 17 Acidobacteria Acidobacteriia Bryobacter 80 0 1 1 1 1 5 3 1 2 Acidobacteria Acidobacteriia Bryobacter 81 0 1 3 0 0 4 0 0 4 Acidobacteria Acidobacteriia Bryobacter 84 20 44 69 5 56 14 6 13 7 Acidobacteria Acidobacteriia Bryobacter 85 9 25 67 5 24 58 20 14 87 Acidobacteria Acidobacteriia Bryobacter 86 6 0 0 44 24 0 2 2 0 Acidobacteria Acidobacteriia Bryobacter 87 10 7 8 3 1 6 3 0 7 Acidobacteria Acidobacteriia Bryobacter 88 9 0 3 4 0 7 3 0 6 Acidobacteria Acidobacteriia Bryobacter 89 0 0 11 1 0 12 3 1 5 Acidobacteria Acidobacteriia Bryobacter 9 4 1 23 2 2 7 4 16 21 121 Annex 17 (Continuation) Taxonomic classification 5 months (March 2018) 12 months (October 2018) 18 months April 2019) Plylum Class Genus Burnt Halfburnt Nonburnt Burnt Halfburnt Nonburnt Burnt Halfburnt Nonburnt Acidobacteria Acidobacteriia Bryobacter 90 0 0 2 0 7 3 0 1 3 Acidobacteria Acidobacteriia Bryobacter 91 4 0 3 1 1 8 3 11 2 Acidobacteria Acidobacteriia Bryobacter 92 12 6 18 2 0 4 8 0 8 Acidobacteria Acidobacteriia Bryobacter 93 2 0 0 0 4 1 1 1 0 Acidobacteria Acidobacteriia Bryobacter 94 2 0 3 0 0 0 1 0 1 Acidobacteria Acidobacteriia Bryobacter 96 3 1 1 0 0 1 0 0 3 Acidobacteria Acidobacteriia Candidatus Solibacter 1 19 48 66 5 1 56 20 0 46 Acidobacteria Acidobacteriia Candidatus Solibacter 10 213 33 15 78 100 3 116 167 15 Acidobacteria Acidobacteriia Candidatus Solibacter 11 0 1 1 2 6 5 15 58 3 Acidobacteria Acidobacteriia Candidatus Solibacter 12 11 19 28 2 0 13 1 5 14 Acidobacteria Acidobacteriia Candidatus Solibacter 13 4 24 3 3 0 12 6 1 9 Acidobacteria Acidobacteriia Candidatus Solibacter 14 2 2 14 0 1 10 4 0 17 Acidobacteria Acidobacteriia Candidatus Solibacter 15 6 1 2 0 0 3 5 0 5 Acidobacteria Acidobacteriia Candidatus Solibacter 16 1 10 6 1 6 16 10 61 14 Acidobacteria Acidobacteriia Candidatus Solibacter 18 436 92 72 507 599 99 588 607 130 Acidobacteria Acidobacteriia Candidatus Solibacter 19 90 188 158 55 77 81 195 272 154 Acidobacteria Acidobacteriia Candidatus Solibacter 2 2 5 12 1 0 7 12 1 3 Acidobacteria Acidobacteriia Candidatus Solibacter 20 109 62 118 55 22 121 59 11 119 Acidobacteria Acidobacteriia Candidatus Solibacter 21 51 50 68 20 5 73 56 34 140 Acidobacteria Acidobacteriia Candidatus Solibacter 22 34 2 22 10 7 60 12 92 54 Acidobacteria Acidobacteriia Candidatus Solibacter 23 95 29 51 39 9 88 40 2 65 Acidobacteria Acidobacteriia Candidatus Solibacter 24 12 4 30 29 0 38 58 0 38 Acidobacteria Acidobacteriia Candidatus Solibacter 25 38 20 15 15 2 16 28 1 15 Acidobacteria Acidobacteriia Candidatus Solibacter 26 33 61 60 10 2 39 33 7 63 Acidobacteria Acidobacteriia Candidatus Solibacter 27 8 23 24 12 1 11 13 0 6 Acidobacteria Acidobacteriia Candidatus Solibacter 28 6 35 29 3 1 7 11 4 12 Acidobacteria Acidobacteriia Candidatus Solibacter 29 1 0 3 1 20 5 6 16 6 Acidobacteria Acidobacteriia Candidatus Solibacter 3 2 2 2 1 0 5 2 0 3 Acidobacteria Acidobacteriia Candidatus Solibacter 30 8 12 13 2 0 10 8 1 18 Acidobacteria Acidobacteriia Candidatus Solibacter 31 0 7 6 0 2 3 0 2 15 Acidobacteria Acidobacteriia Candidatus Solibacter 32 7 12 3 8 6 2 0 1 0 Acidobacteria Acidobacteriia Candidatus Solibacter 33 0 18 13 1 1 2 2 1 1 Acidobacteria Acidobacteriia Candidatus Solibacter 34 59 39 44 18 2 50 37 0 42 Acidobacteria Acidobacteriia Candidatus Solibacter 35 44 17 39 33 2 41 57 0 30 Acidobacteria Acidobacteriia Candidatus Solibacter 36 0 0 0 0 1 0 0 9 0 Acidobacteria Acidobacteriia Candidatus Solibacter 37 0 1 11 3 0 5 1 0 10 Acidobacteria Acidobacteriia Candidatus Solibacter 38 0 0 8 0 0 3 0 0 3 Acidobacteria Acidobacteriia Candidatus Solibacter 39 0 3 0 1 1 0 0 0 2 Acidobacteria Acidobacteriia Candidatus Solibacter 4 432 176 183 398 14 191 373 9 203 Acidobacteria Acidobacteriia Candidatus Solibacter 40 0 0 7 0 0 2 0 0 1 122 Annex 17 (Continuation) Taxonomic classification 5 months (March 2018) 12 months (October 2018) 18 months April 2019) Plylum Class Genus Burnt Halfburnt Nonburnt Burnt Halfburnt Nonburnt Burnt Halfburnt Nonburnt Acidobacteria Acidobacteriia Candidatus Solibacter 41 10 9 4 3 2 12 3 1 16 Acidobacteria Acidobacteriia Candidatus Solibacter 42 0 1 5 0 0 1 0 0 0 Acidobacteria Acidobacteriia Candidatus Solibacter 43 139 305 191 63 56 217 134 15 257 Acidobacteria Acidobacteriia Candidatus Solibacter 44 90 151 109 59 49 123 138 67 187 Acidobacteria Acidobacteriia Candidatus Solibacter 45 51 32 41 83 59 27 102 69 29 Acidobacteria Acidobacteriia Candidatus Solibacter 46 69 24 40 11 1 33 49 0 26 Acidobacteria Acidobacteriia Candidatus Solibacter 47 50 63 84 13 11 87 48 6 113 Acidobacteria Acidobacteriia Candidatus Solibacter 48 30 31 21 17 1 38 29 1 37 Acidobacteria Acidobacteriia Candidatus Solibacter 49 13 4 27 11 1 18 27 0 44 Acidobacteria Acidobacteriia Candidatus Solibacter 5 159 378 358 78 119 258 257 177 406 Acidobacteria Acidobacteriia Candidatus Solibacter 50 13 29 23 4 0 44 14 0 57 Acidobacteria Acidobacteriia Candidatus Solibacter 51 8 20 33 12 0 40 9 0 57 Acidobacteria Acidobacteriia Candidatus Solibacter 52 4 4 34 11 1 15 25 18 47 Acidobacteria Acidobacteriia Candidatus Solibacter 53 22 14 21 11 0 10 23 0 26 Acidobacteria Acidobacteriia Candidatus Solibacter 54 2 5 4 1 1 4 0 7 10 Acidobacteria Acidobacteriia Candidatus Solibacter 55 23 14 14 14 5 28 25 3 28 Acidobacteria Acidobacteriia Candidatus Solibacter 56 17 34 63 20 18 42 42 36 58 Acidobacteria Acidobacteriia Candidatus Solibacter 57 1 9 10 0 2 7 2 6 12 Acidobacteria Acidobacteriia Candidatus Solibacter 58 2 2 8 4 0 18 2 1 13 Acidobacteria Acidobacteriia Candidatus Solibacter 59 87 110 98 83 14 104 61 19 58 Acidobacteria Acidobacteriia Candidatus Solibacter 6 91 90 117 77 109 131 52 59 126 Acidobacteria Acidobacteriia Candidatus Solibacter 60 3 1 9 3 0 8 3 0 10 Acidobacteria Acidobacteriia Candidatus Solibacter 62 7 2 0 1 2 0 0 0 0 Acidobacteria Acidobacteriia Candidatus Solibacter 63 0 0 0 0 3 12 0 1 6 Acidobacteria Acidobacteriia Candidatus Solibacter 65 0 4 0 0 2 0 0 2 0 Acidobacteria Acidobacteriia Candidatus Solibacter 66 0 0 1 0 1 2 1 0 2 Acidobacteria Acidobacteriia Candidatus Solibacter 7 5 2 6 37 72 17 76 54 8 Acidobacteria Acidobacteriia Candidatus Solibacter 8 23 1 13 72 42 5 21 26 8 Acidobacteria Acidobacteriia Candidatus Solibacter 9 10 5 18 7 19 25 15 27 16 Acidobacteria Acidobacteriia Paludibaculum 7 0 0 14 21 0 0 3 0 Acidobacteria Blastocatellia (Subgroup 4) unknown 11-24 bacterium 1 0 0 0 1 0 4 0 0 1 Acidobacteria Blastocatellia (Subgroup 4) unknown 11-24 bacterium 2 0 0 0 1 3 2 0 2 0 Acidobacteria Blastocatellia (Subgroup 4) JGI 0001001H03 0 0 0 0 5 0 0 6 2 Acidobacteria Blastocatellia (Subgroup 4) RB41 3 1 5 0 0 0 0 0 0 Acidobacteria Holophagae unknown Holophagae Subgroup 7 bacterium 1 0 0 0 5 0 0 1 2 0 129 Annex 17 (Continuation) Taxonomic classification 5 months (March 2018) 12 months (October 2018) 18 months April 2019) Plylum Class Genus Burnt Halfburnt Nonburnt Burnt Halfburnt Nonburnt Burnt Halfburnt Nonburnt Actinobacteria Actinobacteria Acidothermus 7 170 231 141 159 51 444 96 71 377 Actinobacteria Actinobacteria Acidothermus 8 29 462 49 18 14 96 31 5 189 Actinobacteria Actinobacteria Acidothermus 9 136 267 253 54 31 276 49 51 163 Actinobacteria Actinobacteria Cryptosporangi um 2 0 5 0 0 2 1 1 11 Actinobacteria Actinobacteria Fodinicola 0 0 1 1 3 0 1 0 0 Actinobacteria Actinobacteria unknown Cryptosporangia ceae bacterium 0 0 2 4 0 1 1 0 0 Actinobacteria Actinobacteria Jatrophihabitan s 1 17 1 4 165 221 26 268 185 15 Actinobacteria Actinobacteria Jatrophihabitan s 10 2 6 36 61 71 91 44 57 63 Actinobacteria Actinobacteria Jatrophihabitan s 11 1 1 8 13 17 7 11 28 7 Actinobacteria Actinobacteria Jatrophihabitan s 12 0 0 0 9 7 8 4 3 6 Actinobacteria Actinobacteria Jatrophihabitan s 13 0 0 0 0 0 1 0 1 4 Actinobacteria Actinobacteria Jatrophihabitan s 2 10 15 50 97 62 141 139 74 85 Actinobacteria Actinobacteria Jatrophihabitan s 3 1 7 57 66 42 233 21 23 103 Actinobacteria Actinobacteria Jatrophihabitan s 4 0 8 4 25 66 17 55 62 20 Actinobacteria Actinobacteria Jatrophihabitan s 6 11 1 24 179 268 37 319 165 31 Actinobacteria Actinobacteria Jatrophihabitan s 7 2 5 125 52 61 321 46 30 279 Actinobacteria Actinobacteria Jatrophihabitan s 8 6 1 37 231 81 313 65 63 61 Actinobacteria Actinobacteria Jatrophihabitan s 9 6 7 15 15 33 29 29 62 59 Actinobacteria Actinobacteria Blastococcus 1 26 2 42 628 525 237 533 439 95 Actinobacteria Actinobacteria Blastococcus 2 96 3 40 899 881 133 877 378 97 Actinobacteria Actinobacteria Blastococcus 3 2 0 0 35 9 1 4 4 0 Actinobacteria Actinobacteria Modestobacter 1 2 2 3 44 39 21 16 4 3 Actinobacteria Actinobacteria Modestobacter 2 0 0 2 1 1 3 0 0 3 Actinobacteria Actinobacteria Modestobacter 3 19 0 11 538 572 6 213 187 8 Actinobacteria Actinobacteria unknown Geodermatophil aceae bacterium 26 5 53 238 145 230 375 72 96 Actinobacteria Actinobacteria Nakamurella 1 2 0 9 11 3 9 4 2 19 Actinobacteria Actinobacteria Nakamurella 3 0 0 39 25 8 72 4 6 70 Actinobacteria Actinobacteria Nakamurella 4 2 2 8 15 36 47 13 30 15 Actinobacteria Actinobacteria hgcI clade 0 0 1 9 304 0 23 29 0 Actinobacteria Actinobacteria unknown Sporichthyacea e bacterium 1 2 0 0 230 3 0 54 6 0 Actinobacteria Actinobacteria unknown Sporichthyacea e bacterium 2 3 6 2 35 26 18 12 23 8 130 Annex 17 (Continuation) Taxonomic classification 5 months (March 2018) 12 months (October 2018) 18 months April 2019) Plylum Class Genus Burnt Halfburnt Nonburnt Burnt Halfburnt Nonburnt Burnt Halfburnt Nonburnt Actinobacteria Actinobacteria unknown Sporichthyacea e bacterium 5 12 1 10 31 10 12 19 21 21 Actinobacteria Actinobacteria unknown Frankiales bacterium 1 0 0 1 21 39 4 13 11 22 Actinobacteria Actinobacteria unknown Frankiales bacterium 10 0 0 0 8 7 1 3 3 0 Actinobacteria Actinobacteria unknown Frankiales bacterium 11 55 0 0 931 1059 0 4007 1828 17 Actinobacteria Actinobacteria unknown Frankiales bacterium 12 2 0 0 11 2 4 9 3 8 Actinobacteria Actinobacteria unknown Frankiales bacterium 13 0 0 0 1 9 0 0 1 1 Actinobacteria Actinobacteria unknown Frankiales bacterium 14 0 0 4 1 4 2 1 1 5 Actinobacteria Actinobacteria unknown Frankiales bacterium 16 0 0 0 0 2 0 3 2 0 Actinobacteria Actinobacteria unknown Frankiales bacterium 2 4 0 0 0 0 0 0 0 3 Actinobacteria Actinobacteria unknown Frankiales bacterium 3 121 19 50 2065 1165 56 340 239 54 Actinobacteria Actinobacteria unknown Frankiales bacterium 4 2 0 15 27 10 52 18 18 33 Actinobacteria Actinobacteria unknown Frankiales bacterium 5 0 0 0 31 7 1 2 0 0 Actinobacteria Actinobacteria unknown Frankiales bacterium 6 0 2 35 41 5 37 34 47 73 Actinobacteria Actinobacteria unknown Frankiales bacterium 7 2 1 20 7 18 12 2 18 31 Actinobacteria Actinobacteria unknown Frankiales bacterium 8 10 1 10 11 0 3 5 0 3 Actinobacteria Actinobacteria unknown Frankiales bacterium 9 5 9 2 2 1 9 3 1 11 Actinobacteria Actinobacteria Kineosporia 0 0 0 1 2 2 3 0 3 Actinobacteria Actinobacteria unknown kineosporiaceae bacterium 1 1 1 13 96 100 38 44 51 60 Actinobacteria Actinobacteria unknown kineosporiaceae bacterium 2 0 0 2 1 1 2 0 2 13 Actinobacteria Actinobacteria Georgenia 7 0 1 0 0 1 0 1 0 Actinobacteria Actinobacteria Cellulomonas 1 0 0 4 1 1 2 1 0 0 Actinobacteria Actinobacteria unknown Dermatophilace ae bacterium 1 0 3 30 48 8 16 21 30 Actinobacteria Actinobacteria Lapillicoccus 19 0 310 72 166 178 270 100 258 Actinobacteria Actinobacteria Phycicoccus 3 0 1 67 9 4 17 6 0 Actinobacteria Actinobacteria Conyzicola 21 4 0 4 12 1 0 5 2 Actinobacteria Actinobacteria Curtobacterium 69 144 2 24 75 2 29 5 0 Actinobacteria Actinobacteria Frigoribacteriu m 8 2 0 34 84 0 23 15 1 Actinobacteria Actinobacteria Gryllotalpicola 0 0 0 0 9 0 0 10 0 Actinobacteria Actinobacteria Gryllotalpicola 3 58 6 72 101 13 66 46 0 Actinobacteria Actinobacteria Leifsonia 1 13 13 32 260 380 117 272 241 78 Actinobacteria Actinobacteria Leifsonia 2 552 115 8 425 731 40 997 544 27 Actinobacteria Actinobacteria Leifsonia 3 26 5 0 41 33 3 32 11 1 Actinobacteria Actinobacteria Leifsonia 4 22 9 2 18 31 15 52 22 1 Actinobacteria Actinobacteria Lysinimonas 3 0 1 52 150 0 90 3 0 131 Annex 17 (Continuation) Taxonomic classification 5 months (March 2018) 12 months (October 2018) 18 months April 2019) Plylum Class Genus Burnt Halfburnt Nonburnt Burnt Halfburnt Nonburnt Burnt Halfburnt Nonburnt Actinobacteria Actinobacteria Microbacterium 3 4 0 0 1 9 0 0 2 1 Actinobacteria Actinobacteria Parafrigoribacte rium 1 2 4 1 7 3 1 0 14 6 Actinobacteria Actinobacteria unknown Microbacteriace ae bacterium 1 219 148 44 178 158 27 213 315 80 Actinobacteria Actinobacteria unknown Microbacteriace ae bacterium 2 1 0 0 54 41 13 13 33 20 Actinobacteria Actinobacteria unknown Microbacteriace ae bacterium 3 42 10 31 285 478 167 182 394 99 Actinobacteria Actinobacteria unknown Microbacteriace ae bacterium 4 0 1 4 0 0 21 1 0 3 Actinobacteria Actinobacteria unknown Microbacteriace ae bacterium 5 53 11 1 19 21 0 0 6 1 Actinobacteria Actinobacteria Arthrobacter 2 0 0 0 0 0 1 3 0 Actinobacteria Actinobacteria Arthrobacter 909 221 32 764 2365 79 1269 1135 86 Actinobacteria Actinobacteria Glutamicibacter 0 0 0 13 17 1 0 1 0 Actinobacteria Actinobacteria Paeniglutamicib acter 3 0 0 0 1 0 2 0 0 Actinobacteria Actinobacteria Pseudarthrobac ter 1 65 1 0 172 1475 4 51 441 10 Actinobacteria Actinobacteria Pseudarthrobac ter 2 212 18 110 768 1115 36 407 397 470 Actinobacteria Actinobacteria Pseudarthrobac ter 4 1 0 0 10 13 1 4 3 3 Actinobacteria Actinobacteria Sinomonas 2 1 14 7 2 11 26 0 10 Actinobacteria Actinobacteria unknown Micrococcaceae bacterium 1 138 4 13 1703 1149 11 175 392 21 Actinobacteria Actinobacteria unknown Micrococcaceae bacterium 2 98 1 1 252 622 8 76 97 4 Actinobacteria Actinobacteria unknown Micrococcales bacterium 1 0 0 13 16 8 8 5 4 9 Actinobacteria Actinobacteria unknown Micrococcales bacterium 2 0 0 0 2 0 0 0 7 4 Actinobacteria Actinobacteria Actinoplanes 0 0 1 3 1 4 2 2 6 Actinobacteria Actinobacteria Actinoplanes 1 0 9 1 2 7 2 0 25 Actinobacteria Actinobacteria Dactylosporangi um 0 0 1 5 0 0 0 1 5 Actinobacteria Actinobacteria Luedemannella 46 0 7 9 1 0 2 2 1 Actinobacteria Actinobacteria Micromonospor a 1 5 0 2 1 0 1 1 0 1 Actinobacteria Actinobacteria Micromonospor a 2 23 0 0 20 0 0 127 0 0 Actinobacteria Actinobacteria unknown Micromonospor aceae bacterium 1 0 0 2 0 0 5 0 0 5 Actinobacteria Actinobacteria unknown Micromonospor aceae bacterium 3 7 2 32 50 18 37 26 20 97 Actinobacteria Actinobacteria unknown Micromonospor aceae bacterium 4 0 0 12 24 4 9 6 6 31 132 Annex 17 (Continuation) Taxonomic classification 5 months (March 2018) 12 months (October 2018) 18 months April 2019) Plylum Class Genus Burnt Halfburnt Nonburnt Burnt Halfburnt Nonburnt Burnt Halfburnt Nonburnt Actinobacteria Actinobacteria unknown Micromonospor aceae bacterium 5 0 0 3 1 1 8 0 0 12 Actinobacteria Actinobacteria Aeromicrobium 1 0 0 11 1 0 0 0 0 Actinobacteria Actinobacteria Kribbella 1 0 0 4 5 17 0 0 8 0 Actinobacteria Actinobacteria Marmoricola 1 1 1 1 522 961 24 438 281 10 Actinobacteria Actinobacteria Marmoricola 2 0 1 0 48 24 32 52 15 6 Actinobacteria Actinobacteria Marmoricola 4 0 0 1 44 58 24 11 13 6 Actinobacteria Actinobacteria Nocardioides 1 0 0 3 4 6 9 3 1 13 Actinobacteria Actinobacteria Nocardioides 10 0 0 1 4 1 4 0 0 0 Actinobacteria Actinobacteria Nocardioides 12 1 0 0 4 0 1 1 0 0 Actinobacteria Actinobacteria Nocardioides 13 0 0 0 5 2 2 3 0 1 Actinobacteria Actinobacteria Nocardioides 14 0 0 1 0 0 4 1 0 4 Actinobacteria Actinobacteria Nocardioides 15 0 0 0 0 0 4 0 0 2 Actinobacteria Actinobacteria Nocardioides 16 0 1 0 1 10 0 1 3 0 Actinobacteria Actinobacteria Nocardioides 18 3 2 0 31 199 3 21 84 5 Actinobacteria Actinobacteria Nocardioides 19 3 0 0 24 2 0 1 1 1 Actinobacteria Actinobacteria Nocardioides 2 0 0 15 15 12 166 7 1 31 Actinobacteria Actinobacteria Nocardioides 21 1 0 0 23 7 1 1 0 0 Actinobacteria Actinobacteria Nocardioides 23 0 0 0 3 2 0 0 1 0 Actinobacteria Actinobacteria Nocardioides 25 1 0 4 6 0 0 0 0 2 Actinobacteria Actinobacteria Nocardioides 26 0 0 0 4 3 0 0 0 0 Actinobacteria Actinobacteria Nocardioides 3 0 0 1 44 10 12 4 1 11 Actinobacteria Actinobacteria Nocardioides 4 0 0 7 0 7 13 0 3 28 Actinobacteria Actinobacteria Nocardioides 5 0 0 0 4 0 8 0 1 3 Actinobacteria Actinobacteria Nocardioides 6 0 0 0 53 14 0 1 2 0 Actinobacteria Actinobacteria Nocardioides 7 0 0 0 42 0 4 4 0 1 Actinobacteria Actinobacteria Nocardioides 8 0 0 5 5 1 28 2 2 12 Actinobacteria Actinobacteria Cutibacterium 16 17 14 2 5 2 1 0 3 Actinobacteria Actinobacteria Friedmanniella 0 0 2 3 0 8 2 2 2 Actinobacteria Actinobacteria Microlunatus 1 0 8 3 0 6 7 1 8 Actinobacteria Actinobacteria Actinomycetosp ora 0 0 34 24 47 104 6 6 75 Actinobacteria Actinobacteria Actinophytocola 3 6 2 3 38 4 2 21 2 2 Actinobacteria Actinobacteria Amycolatopsis 1 1 29 3 54 513 6 69 223 12 Actinobacteria Actinobacteria Amycolatopsis 2 0 0 0 14 0 0 0 0 0 Actinobacteria Actinobacteria Crossiella 2 26 0 8 0 0 1 0 3 Actinobacteria Actinobacteria Crossiella 57 5 91 24 6 108 84 18 170 Actinobacteria Actinobacteria Longimycelium 1 9 24 91 6 13 65 9 6 131 Actinobacteria Actinobacteria Longimycelium 2 11 5 122 6 12 11 4 10 18 Actinobacteria Actinobacteria Longimycelium 3 15 9 63 62 34 62 93 46 87 Actinobacteria Actinobacteria Pseudonocardia 1 443 93 28 1926 1805 62 1187 1432 81 Actinobacteria Actinobacteria Pseudonocardia 10 9 0 8 8 0 18 5 2 14 Actinobacteria Actinobacteria Pseudonocardia 2 3 3 135 35 20 150 15 30 208 Actinobacteria Actinobacteria Pseudonocardia 3 0 2 1 11 8 9 4 14 15 Actinobacteria Actinobacteria Pseudonocardia 4 0 0 1 1 0 10 1 0 13 133 Annex 17 (Continuation) Taxonomic classification 5 months (March 2018) 12 months (October 2018) 18 months April 2019) Plylum Class Genus Burnt Halfburnt Nonburnt Burnt Halfburnt Nonburnt Burnt Halfburnt Nonburnt Actinobacteria Actinobacteria Pseudonocardia 5 0 0 0 6 1 0 2 2 0 Actinobacteria Actinobacteria Pseudonocardia 7 29 33 48 135 82 52 399 689 45 Actinobacteria Actinobacteria Pseudonocardia 8 3 0 0 22 12 0 4 4 0 Actinobacteria Actinobacteria Pseudonocardia 9 1 0 1 0 0 11 2 0 6 Actinobacteria Actinobacteria unknown Pseudonocardia ceae bacterium 1 2 3 34 13 14 17 42 12 31 Actinobacteria Actinobacteria Kitasatospora 1 14 6 14 28 0 6 7 0 2 Actinobacteria Actinobacteria Kitasatospora 2 9 1 19 0 0 0 0 0 2 Actinobacteria Actinobacteria Kitasatospora 3 4 1 1 1 0 0 0 0 11 Actinobacteria Actinobacteria Streptacidiphilu s 1 98 14 38 74 97 20 20 16 18 Actinobacteria Actinobacteria Streptacidiphilu s 2 8 3 68 39 20 62 24 79 72 Actinobacteria Actinobacteria Streptacidiphilu s 3 12 3 62 5 4 11 10 24 176 Actinobacteria Actinobacteria Streptacidiphilu s 4 2 1 1 1 4 3 2 1 3 Actinobacteria Actinobacteria Streptomyces 1 2 1 7 1 0 32 3 0 8 Actinobacteria Actinobacteria Streptomyces 10 19 0 0 1 0 1 0 0 0 Actinobacteria Actinobacteria Streptomyces 11 1 2 1 4 3 0 2 0 0 Actinobacteria Actinobacteria Streptomyces 13 1 0 1 0 0 1 0 0 3 Actinobacteria Actinobacteria Streptomyces 14 1 0 1 0 0 6 0 0 3 Actinobacteria Actinobacteria Streptomyces 15 0 0 0 0 0 0 0 0 14 Actinobacteria Actinobacteria Streptomyces 16 3 5 0 0 0 0 0 0 0 Actinobacteria Actinobacteria Streptomyces 17 2 0 0 2 0 4 0 0 2 Actinobacteria Actinobacteria Streptomyces 19 7 1 1 0 0 1 0 0 1 Actinobacteria Actinobacteria Streptomyces 2 2 0 1 0 0 16 0 0 6 Actinobacteria Actinobacteria Streptomyces 27 1 0 1 0 0 2 1 0 5 Actinobacteria Actinobacteria Streptomyces 3 1756 4505 47 371 1095 58 285 276 71 Actinobacteria Actinobacteria Streptomyces 4 159 927 27 42 338 45 121 191 30 Actinobacteria Actinobacteria Streptomyces 5 12 6 37 23 27 73 18 3 85 Actinobacteria Actinobacteria Streptomyces 6 4 0 8 30 49 27 6 27 5 Actinobacteria Actinobacteria Streptomyces 7 177 0 1 5 0 0 0 0 0 Actinobacteria Actinobacteria Streptomyces 8 56 1 1 24 0 0 12 0 2 Actinobacteria Actinobacteria Streptomyces 9 4 0 0 1 0 49 0 0 0 Actinobacteria Actinobacteria Microbispora 1 3 0 0 0 1 2 2 2 0 Actinobacteria Actinobacteria Streptosporangi um 3 18 1 1 1 0 3 0 1 0 Actinobacteria Actinobacteria Motilibacter 0 0 0 2 1 7 0 1 1 Actinobacteria Actinobacteria Actinoallomurus 1 239 92 9 484 4 9 895 15 19 Actinobacteria Actinobacteria Actinomadura 1 3 1 5 9 0 5 9 0 1 Actinobacteria Actinobacteria Actinomadura 2 12 0 1 3 0 0 8 1 0 Actinobacteria Actinobacteria Actinomadura 3 0 1 1 1 0 2 0 1 0 134 Annex 17 (Continuation) Taxonomic classification 5 months (March 2018) 12 months (October 2018) 18 months April 2019) Plylum Class Genus Burnt Halfburnt Nonburnt Burnt Halfburnt Nonburnt Burnt Halfburnt Nonburnt Actinobacteria Actinobacteria unknown Thermomonosp oraceae bacterium 2 32 0 0 0 0 2 1 0 0 Actinobacteria Actinobacteria unkown Actinobacteria bacterium 2 0 0 0 1 5 0 14 0 0 Actinobacteria MB-A2-108 unknown MBA2-110 bacterium 0 0 2 0 0 3 0 0 2 Actinobacteria Thermoleophilia Gaiella 1 0 0 111 246 213 100 74 87 89 Actinobacteria Thermoleophilia Gaiella 2 0 0 0 5 2 2 0 0 2 Actinobacteria Thermoleophilia Gaiella 5 0 0 15 0 0 4 0 1 17 Actinobacteria Thermoleophilia unknown Gaillales bacterium 1 4 2 229 477 232 255 113 142 239 Actinobacteria Thermoleophilia unknown Gaillales bacterium 10 30 9 106 15 1 88 6 1 124 Actinobacteria Thermoleophilia unknown Gaillales bacterium 11 0 0 6 0 0 0 0 0 2 Actinobacteria Thermoleophilia unknown Gaillales bacterium 12 4 0 93 9 4 49 3 1 104 Actinobacteria Thermoleophilia unknown Gaillales bacterium 13 0 0 5 0 0 2 0 0 1 Actinobacteria Thermoleophilia unknown Gaillales bacterium 16 8 0 43 11 0 29 3 1 22 Actinobacteria Thermoleophilia unknown Gaillales bacterium 18 2 1 3 1 1 22 3 4 13 Actinobacteria Thermoleophilia unknown Gaillales bacterium 19 0 0 1 1 0 4 1 0 1 Actinobacteria Thermoleophilia unknown Gaillales bacterium 2 35 1 482 13 0 183 7 1 295 Actinobacteria Thermoleophilia unknown Gaillales bacterium 20 20 0 6 2 0 8 2 0 4 Actinobacteria Thermoleophilia unknown Gaillales bacterium 21 1 0 1 0 0 13 1 0 21 Actinobacteria Thermoleophilia unknown Gaillales bacterium 22 1 1 9 0 0 4 0 0 10 Actinobacteria Thermoleophilia unknown Gaillales bacterium 23 2 0 1 0 0 14 0 0 12 Actinobacteria Thermoleophilia unknown Gaillales bacterium 25 0 0 0 0 0 15 0 0 7 Actinobacteria Thermoleophilia unknown Gaillales bacterium 28 1 1 6 0 0 6 0 0 12 Actinobacteria Thermoleophilia unknown Gaillales bacterium 29 2 0 2 0 0 12 0 0 6 Actinobacteria Thermoleophilia unknown Gaillales bacterium 3 3 1 100 5 4 80 7 13 166 Actinobacteria Thermoleophilia unknown Gaillales bacterium 31 0 0 0 3 2 1 6 1 1 Actinobacteria Thermoleophilia unknown Gaillales bacterium 32 0 0 5 2 3 0 2 1 0 Actinobacteria Thermoleophilia unknown Gaillales bacterium 39 0 0 7 0 0 1 0 0 0 135 Annex 17 (Continuation) Taxonomic classification 5 months (March 2018) 12 months (October 2018) 18 months April 2019) Plylum Class Genus Burnt Halfburnt Nonburnt Burnt Halfburnt Nonburnt Burnt Halfburnt Nonburnt Actinobacteria Thermoleophilia unknown Gaillales bacterium 4 0 0 4 0 0 1 1 0 3 Actinobacteria Thermoleophilia unknown Gaillales bacterium 43 1 0 33 10 1 39 15 2 46 Actinobacteria Thermoleophilia unknown Gaillales bacterium 5 1 0 13 0 3 45 1 1 67 Actinobacteria Thermoleophilia unknown Gaillales bacterium 50 1 0 5 1 0 1 0 0 2 Actinobacteria Thermoleophilia unknown Gaillales bacterium 51 0 0 0 1 0 2 0 0 3 Actinobacteria Thermoleophilia unknown Gaillales bacterium 52 2 0 0 2 2 6 4 0 2 Actinobacteria Thermoleophilia unknown Gaillales bacterium 57 118 126 280 78 16 224 97 30 296 Actinobacteria Thermoleophilia unknown Gaillales bacterium 58 26 9 95 7 0 45 18 3 64 Actinobacteria Thermoleophilia unknown Gaillales bacterium 59 1 0 1 99 75 1 8 7 0 Actinobacteria Thermoleophilia unknown Gaillales bacterium 6 0 0 21 40 16 21 15 8 20 Actinobacteria Thermoleophilia unknown Gaillales bacterium 60 24 4 49 6 4 44 19 7 107 Actinobacteria Thermoleophilia unknown Gaillales bacterium 62 3 0 13 1 1 9 0 0 12 Actinobacteria Thermoleophilia unknown Gaillales bacterium 63 1 0 17 3 0 14 3 0 6 Actinobacteria Thermoleophilia unknown Gaillales bacterium 7 2 0 9 25 23 43 19 28 48 Actinobacteria Thermoleophilia unknown Gaillales bacterium 8 14 1 29 2 0 21 1 0 34 Actinobacteria Thermoleophilia unknown Gaillales bacterium 9 0 0 41 0 0 38 0 0 20 Actinobacteria Thermoleophilia unknown 67-14 bacterium 1 0 0 1 2 0 2 0 0 1 Actinobacteria Thermoleophilia unknown 67-14 bacterium 2 0 0 4 0 0 2 0 0 1 Actinobacteria Thermoleophilia unknown 67-14 bacterium 3 0 0 2 1 0 2 1 0 3 Actinobacteria Thermoleophilia unknown 67-14 bacterium 4 1 1 4 1 0 4 0 0 3 Actinobacteria Thermoleophilia unknown 67-14 bacterium 5 0 0 4 5 0 6 0 0 2 Actinobacteria Thermoleophilia unknown 67-14 bacterium 6 0 0 3 18 2 2 1 0 3 Actinobacteria Thermoleophilia unknown 67-14 bacterium 7 7 0 6 6 0 9 1 0 4 Actinobacteria Thermoleophilia unknown 67-14 bacterium 8 0 0 0 18 62 0 15 11 0 Actinobacteria Thermoleophilia unknown 67-14 bacterium 9 12 2 44 26 3 45 4 2 31 Actinobacteria Thermoleophilia unknown 67-14 bacterium 10 30 10 22 46 6 50 9 0 41 Actinobacteria Thermoleophilia unknown 67-14 bacterium 11 1 1 42 36 28 79 11 21 62 Actinobacteria Thermoleophilia unknown 67-14 bacterium 12 0 0 0 19 4 0 0 0 0 Actinobacteria Thermoleophilia unknown 67-14 bacterium 13 0 0 0 17 4 0 1 0 0 136 Annex 17 (Continuation) Taxonomic classification 5 months (March 2018) 12 months (October 2018) 18 months April 2019) Plylum Class Genus Burnt Halfburnt Nonburnt Burnt Halfburnt Nonburnt Burnt Halfburnt Nonburnt Actinobacteria Thermoleophilia unknown 67-14 bacterium 14 61 68 35 26 11 86 29 8 39 Actinobacteria Thermoleophilia unknown 67-14 bacterium 15 43 39 76 127 57 138 144 52 67 Actinobacteria Thermoleophilia unknown 67-14 bacterium 16 117 64 174 134 100 163 97 33 144 Actinobacteria Thermoleophilia Conexibacter 1 4 1 4 54 27 22 10 2 20 Actinobacteria Thermoleophilia Conexibacter 10 81 105 131 132 72 165 101 44 53 Actinobacteria Thermoleophilia Conexibacter 11 0 5 3 2 0 7 0 0 3 Actinobacteria Thermoleophilia Conexibacter 12 2 1 2 2 0 1 2 0 4 Actinobacteria Thermoleophilia Conexibacter 13 6 9 1 3 1 2 2 1 1 Actinobacteria Thermoleophilia Conexibacter 2 39 38 45 68 18 92 39 42 98 Actinobacteria Thermoleophilia Conexibacter 3 1 0 29 114 34 48 14 5 14 Actinobacteria Thermoleophilia Conexibacter 4 5 4 5 26 5 26 12 2 7 Actinobacteria Thermoleophilia Conexibacter 5 0 0 0 13 3 0 0 0 0 Actinobacteria Thermoleophilia Conexibacter 6 1 0 0 7 0 5 3 1 5 Actinobacteria Thermoleophilia Conexibacter 7 7 2 14 10 5 27 10 2 3 Actinobacteria Thermoleophilia Conexibacter 8 0 0 0 4 4 0 2 3 0 Actinobacteria Thermoleophilia Conexibacter 9 2387 1402 2709 2985 1237 4415 2604 741 2811 Actinobacteria Thermoleophilia Solirubrobacter 1 516 530 586 1037 796 707 2404 752 609 Actinobacteria Thermoleophilia Solirubrobacter 2 142 89 156 109 53 131 159 24 155 Actinobacteria Thermoleophilia Solirubrobacter 3 5 1 6 3 1 11 8 2 4 Actinobacteria Thermoleophilia Solirubrobacter 4 2 0 1 5 0 0 2 0 2 Actinobacteria Thermoleophilia Solirubrobacter 5 2 0 0 103 43 3 9 14 5 Actinobacteria Thermoleophilia Solirubrobacter 6 2 0 0 32 18 18 6 3 2 Actinobacteria Thermoleophilia Solirubrobacter 7 0 0 0 7 1 1 0 1 0 Actinobacteria Thermoleophilia Solirubrobacter 8 0 2 6 0 5 20 1 16 1 Actinobacteria Thermoleophilia unknown Solirubrobacter aceae bacterium 1 7 10 19 8 3 20 6 0 16 Actinobacteria Thermoleophilia unknown Solirubrobacter aceae bacterium 2 142 171 186 141 137 240 135 100 274 Actinobacteria Thermoleophilia unknown Solirubrobacter aceae bacterium 3 263 320 69 201 27 140 52 10 141 Actinobacteria Thermoleophilia unknown Solirubrobacter aceae bacterium 4 111 42 140 121 13 167 157 15 86 Actinobacteria Thermoleophilia unknown Solirubrobacter aceae bacterium 5 0 0 0 33 7 0 2 0 0 Actinobacteria Thermoleophilia unknown Solirubrobacter aceae bacterium 6 147 54 148 64 30 145 109 13 152 Actinobacteria Thermoleophilia unknown Solirubrobacter aceae bacterium 7 0 0 0 1 20 5 1 0 1 Actinobacteria Thermoleophilia unknown Solirubrobacter aceae bacterium 8 40 20 38 33 20 64 51 23 47 Armatimonadetes Armatimonadia unknown Armatimonadal es bacterium 1 25 163 20 10 11 7 53 97 51 137 Annex 17 (Continuation) Taxonomic classification 5 months (March 2018) 12 months (October 2018) 18 months April 2019) Plylum Class Genus Burnt Halfburnt Nonburnt Burnt Halfburnt Nonburnt Burnt Halfburnt Nonburnt Armatimonadetes Armatimonadia unknown Armatimonadal es bacterium 10 0 0 2 1 1 2 15 3 3 Armatimonadetes Armatimonadia unknown Armatimonadal es bacterium 11 1 5 1 0 3 8 17 1 2 Armatimonadetes Armatimonadia unknown Armatimonadal es bacterium 12 0 0 2 1 0 1 3 2 1 Armatimonadetes Armatimonadia unknown Armatimonadal es bacterium 13 1 1 1 0 0 0 2 2 1 Armatimonadetes Armatimonadia unknown Armatimonadal es bacterium 14 0 0 0 0 0 0 0 6 0 Armatimonadetes Armatimonadia unknown Armatimonadal es bacterium 15 2 4 2 2 0 3 4 8 9 Armatimonadetes Armatimonadia unknown Armatimonadal es bacterium 16 0 0 0 0 2 0 2 2 0 Armatimonadetes Armatimonadia unknown Armatimonadal es bacterium 17 3 0 1 0 0 0 3 0 1 Armatimonadetes Armatimonadia unknown Armatimonadal es bacterium 18 0 0 2 0 0 0 1 0 3 Armatimonadetes Armatimonadia unknown Armatimonadal es bacterium 2 40 88 31 14 17 28 81 43 87 Armatimonadetes Armatimonadia unknown Armatimonadal es bacterium 20 0 0 0 0 0 3 2 0 1 Armatimonadetes Armatimonadia unknown Armatimonadal es bacterium 21 0 1 0 1 0 3 0 0 2 Armatimonadetes Armatimonadia unknown Armatimonadal es bacterium 22 0 0 3 5 35 8 42 47 10 Armatimonadetes Armatimonadia unknown Armatimonadal es bacterium 23 7 32 15 1 1 6 4 1 23 Armatimonadetes Armatimonadia unknown Armatimonadal es bacterium 24 0 12 0 3 7 1 1 23 0 Armatimonadetes Armatimonadia unknown Armatimonadal es bacterium 25 24 3 9 2 4 5 9 12 4 Armatimonadetes Armatimonadia unknown Armatimonadal es bacterium 26 5 3 2 0 5 0 4 18 4 Armatimonadetes Armatimonadia unknown Armatimonadal es bacterium 27 2 3 3 1 1 11 3 0 1 Armatimonadetes Armatimonadia unknown Armatimonadal es bacterium 3 6 58 18 44 16 9 11 58 15 138 Annex 17 (Continuation) Taxonomic classification 5 months (March 2018) 12 months (October 2018) 18 months April 2019) Plylum Class Genus Burnt Halfburnt Nonburnt Burnt Halfburnt Nonburnt Burnt Halfburnt Nonburnt Armatimonadetes Armatimonadia unknown Armatimonadal es bacterium 4 4 2 11 12 18 16 38 24 6 Armatimonadetes Armatimonadia unknown Armatimonadal es bacterium 5 4 4 5 2 7 2 19 6 9 Armatimonadetes Armatimonadia unknown Armatimonadal es bacterium 6 5 5 3 3 3 0 2 8 8 Armatimonadetes Armatimonadia unknown Armatimonadal es bacterium 7 0 0 0 0 1 0 9 10 0 Armatimonadetes Armatimonadia unknown Armatimonadal es bacterium 8 0 0 0 0 1 12 1 1 3 Armatimonadetes Armatimonadia unknown Armatimonadal es bacterium 9 1 0 0 6 8 0 2 2 1 Armatimonadetes Chthonomonadetes Chthonomonas 1 4 16 0 0 0 0 2 0 1 Armatimonadetes Chthonomonadetes Chthonomonas 2 1 3 1 0 1 0 2 12 3 Armatimonadetes Chthonomonadetes Chthonomonas 3 1 4 1 0 1 1 2 0 4 Armatimonadetes Chthonomonadetes Chthonomonas 6 0 0 0 0 2 0 2 2 0 Armatimonadetes Chthonomonadetes unknown Chthonomonad ales bacterium 1 0 0 0 0 0 0 0 11 0 Armatimonadetes Chthonomonadetes unknown Chthonomonad ales bacterium 2 6 0 0 0 0 1 0 0 0 Armatimonadetes Chthonomonadetes unknown Chthonomonad ales bacterium 3 1 1 1 0 0 0 4 0 0 Armatimonadetes Fimbriimonadia unknown Fimbriimonadac eae bacterium 1 7 42 14 9 5 23 60 90 26 Armatimonadetes Fimbriimonadia unknown Fimbriimonadac eae bacterium 10 0 0 0 2 1 5 16 3 2 Armatimonadetes Fimbriimonadia unknown Fimbriimonadac eae bacterium 11 0 0 0 0 0 1 0 0 8 Armatimonadetes Fimbriimonadia unknown Fimbriimonadac eae bacterium 12 1 3 8 0 3 6 11 15 2 Armatimonadetes Fimbriimonadia unknown Fimbriimonadac eae bacterium 13 1 8 0 0 0 1 1 0 3 Armatimonadetes Fimbriimonadia unknown Fimbriimonadac eae bacterium 14 2 2 3 0 3 0 2 0 6 Armatimonadetes Fimbriimonadia unknown Fimbriimonadac eae bacterium 17 33 63 51 11 4 51 47 36 58 Armatimonadetes Fimbriimonadia unknown Fimbriimonadac eae bacterium 18 17 68 13 1 0 10 26 6 38 Armatimonadetes Fimbriimonadia unknown Fimbriimonadac eae bacterium 19 0 9 2 0 0 1 1 7 10