Computational protocol: Impact of Lowland Rainforest Transformation on Diversity and Composition of Soil Prokaryotic Communities in Sumatra (Indonesia)

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Protocol publication

[…] The sampling sites are located in the province Jambi of southwest Sumatra, Indonesia (Figure , map data was obtained from http://www.diva-gis.org/). The two landscapes, Harapan Rainforest Concession (H) and Bukit Duabelas (b), were selected for this study. Both landscapes harbor the typical land use systems in Sumatra, resulting from conversion of lowland rainforest to managed rubber and oil palm systems. In addition, suitable lowland rainforest sites (reference sites) were still present in both landscapes. Soil texture differed, with primarily loam Acrisol soils in Harapan and clay Acrisol soils in Bukit Duabelas. Within each landscape we analyzed four land use systems: secondary lowland rainforest, rubber agroforest (jungle rubber), rubber plantation, and oil palm plantation. The rainforest sites represent systems with low anthropogenic influence. Jungle rubber represents the next higher level of anthropogenic influenced land use systems. Jungle rubber is a traditional extensively managed agroforestry system, which is established by planting rubber trees into secondary rainforest. The systems with the highest anthropogenic impact are rubber and oil palm plantations, which are monocultures with high fertilizer usage and liming. The age of the rubber trees (Hevea brasiliensis) in jungle rubber and rubber plantation land use systems ranged from 15 to 40 and 6 to 16 years, respectively. The age of oil palm trees (Elaeis guineensis) in plantations varied between 8 and 15 years. The agricultural management for both plantation types included application of herbicides every 6 months and amendment of 100–300 kg ha −1 yr −1 inorganic NPK fertilizer in rubber plantations and 300–600 kg ha −1 yr −1 in oil palm plantations (for details, see Kotowska et al., ).Sampling of soils was carried out from November to December 2012. The four land use systems lowland rainforest (core plots BF1-BF4 and HF1-HF4), jungle rubber (core plots BJ1-BJ4 and HJ1-HJ4), rubber plantations (core plots BR1-BR4 and HR1-HR4), and oil palm plantations (core plots BO1-BO4 and HO1-HO4) were replicated four times resulting in 32 sampling sites (for georeferences, see Table ). Soil cores were recovered from three subplots of each plot, resulting in a total of 96 subplots. After removal of litter and root overlay, three soil cores (5–7 cm top soil, 10–20 g soil each) were taken with a soil corer and a shovel from each subplot at an average distance of 1.90 m to adjacent trees (random trees in rainforest). The samples were stored in sterile plastic bags. Subsequently, the soil samples were transported in cool boxes on ice packs to the laboratory in Indonesia within 12 h. The three soil samples per subplot were homogenized and coarse roots and stones (>5 mm) were removed. The composite samples were frozen and stored at a deep freezer (−40°C) until shipment to Germany. Samples were transported frozen (cool boxes and ice packs) to the German laboratory within approximately 25 h and stored there at −80°C until further use. Further information on sampling sites and experimental design are given in Barnes et al. (). [...] The resulting 16S rRNA gene sequences were processed and analyzed employing QIIME 1.8 (Caporaso et al., ). Initially, sequences shorter than 300 bp, containing unresolved nucleotides, exhibiting an average quality score lower than 25, harbor mismatches longer than 3 bp in the forward primer, or possessing homopolymers longer than 8 bp were removed with split_libraries.py. Additionally, we used cutadapt (Martin, ) with default settings for efficient reverse primer removal. Subsequently, pyrosequencing noise was removed by employing Acacia (Bragg et al., ) with default settings. Chimeric sequences were removed using UCHIME (Edgar et al., ) with Ribosomal Database Project (RDP) (Cole et al., ) as reference dataset (trainset10_082014_rmdup.fasta).Operational taxonomic unit (OTU) determination was performed at a genetic divergence of 3% (species level) with pick_open_reference_otus.py using the Silva NR SSU 119 database as reference (Quast et al., ). Taxonomic classification was performed with parallel_assign_taxonomy_blast.py against the same database. OTU tables were created using make_otu_table.py. Singletons, chloroplasts, unclassified OTUs and extrinsic domain OTUs were removed from the table by employing filter_otu_table.py. Singletons were removed to improve comparability and avoid possible inclusion of artificial sequences (Zhou et al., ). Sample comparisons were performed at the same surveying effort (bacteria 6800 and archaea 2000 sequences). Diversity estimates and rarefaction curves were generated by employing alpha_rarefaction.py. Non-metric multidimensional scaling (NMDS) and statistical tests were performed with the vegan package (Oksanen et al., ) in R (R Development Core Team, ) and based on weighted Unifrac (Lozupone et al., ) distance matrixes. Significance was determined using the envfit function of vegan package in R (Gergs and Rothhaupt, ) to fit environmental vectors and factors onto the NMDS. Significance of tested variables are indicated in brackets. Profile clustering networks were constructed based on complete and subsampled OTU tables using the QIIME script make_otu_network.py. […]

Pipeline specifications

Software tools DIVA-GIS, QIIME, cutadapt, Acacia, UCHIME, SAMtools, vegan, UniFrac
Applications Phylogenetics, 16S rRNA-seq analysis
Diseases Focal Nodular Hyperplasia
Chemicals Calcium