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Key Edaphic Properties Largely Explain Temporal and Geographic Variation in Soil Microbial Communities across Four Biomes.

Docherty KM, Borton HM, Espinosa N, Gebhardt M, Gil-Loaiza J, Gutknecht JL, Maes PW, Mott BM, Parnell JJ, Purdy G, Rodrigues PA, Stanish LF, Walser ON, Gallery RE - PLoS ONE (2015)

Bottom Line: Quantifying the seasonal and long-term temporal extent of genetic and functional variation of soil microorganisms in response to biotic and abiotic changes within and across ecosystems will inform our understanding of the effect of climate change on these processes.To address the technical issue of the response of soil microbial communities to sample storage temperature, we compared 16S-based community structure in soils stored at -80°C and -20°C and found no significant differences in community composition based on storage temperature.Training in data analysis and interpretation of large datasets in university classrooms through project-based learning improves the learning experience for students and enables their use of these significant resources throughout their careers.

View Article: PubMed Central - PubMed

Affiliation: Department of Biological Sciences, Western Michigan University, Kalamazoo, Michigan, United States of America.

ABSTRACT
Soil microbial communities play a critical role in nutrient transformation and storage in all ecosystems. Quantifying the seasonal and long-term temporal extent of genetic and functional variation of soil microorganisms in response to biotic and abiotic changes within and across ecosystems will inform our understanding of the effect of climate change on these processes. We examined spatial and seasonal variation in microbial communities based on 16S rRNA gene sequencing and phospholipid fatty acid (PLFA) composition across four biomes: a tropical broadleaf forest (Hawaii), taiga (Alaska), semiarid grassland-shrubland (Utah), and a subtropical coniferous forest (Florida). In this study, we used a team-based instructional approach leveraging the iPlant Collaborative to examine publicly available National Ecological Observatory Network (NEON) 16S gene and PLFA measurements that quantify microbial diversity, composition, and growth. Both profiling techniques revealed that microbial communities grouped strongly by ecosystem and were predominately influenced by three edaphic factors: pH, soil water content, and cation exchange capacity. Temporal variability of microbial communities differed by profiling technique; 16S-based community measurements showed significant temporal variability only in the subtropical coniferous forest communities, specifically through changes within subgroups of Acidobacteria. Conversely, PLFA-based community measurements showed seasonal shifts in taiga and tropical broadleaf forest systems. These differences may be due to the premise that 16S-based measurements are predominantly influenced by large shifts in the abiotic soil environment, while PLFA-based analyses reflect the metabolically active fraction of the microbial community, which is more sensitive to local disturbances and biotic interactions. To address the technical issue of the response of soil microbial communities to sample storage temperature, we compared 16S-based community structure in soils stored at -80°C and -20°C and found no significant differences in community composition based on storage temperature. Free, open access datasets and data sharing platforms are powerful tools for integrating research and teaching in undergraduate and graduate student classrooms. They are a valuable resource for fostering interdisciplinary collaborations, testing ecological theory, model development and validation, and generating novel hypotheses. Training in data analysis and interpretation of large datasets in university classrooms through project-based learning improves the learning experience for students and enables their use of these significant resources throughout their careers.

No MeSH data available.


Related in: MedlinePlus

Changes in 16S rRNA-based community composition in Florida soils over time.(A) Relative abundances of all taxa classified at the phylum level and unclassified taxa over time. (B) NMDS ordination of all order-level taxa classified within the phylum Proteobacteria, which did not vary over time (ANOSIM R = 0.03968, p = 0.148). (C) NMDS ordination of all order-level taxa classified within the phylum Actinobacteria, which did not vary over time (ANOSIM R = 0.04861, p = 0.171). (D) NMDS ordination of all order level taxa classified within the phylum Acidobacteria, which varied significantly over time (ANOSIM R = 0.2057, p = 0.003).
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pone.0135352.g004: Changes in 16S rRNA-based community composition in Florida soils over time.(A) Relative abundances of all taxa classified at the phylum level and unclassified taxa over time. (B) NMDS ordination of all order-level taxa classified within the phylum Proteobacteria, which did not vary over time (ANOSIM R = 0.03968, p = 0.148). (C) NMDS ordination of all order-level taxa classified within the phylum Actinobacteria, which did not vary over time (ANOSIM R = 0.04861, p = 0.171). (D) NMDS ordination of all order level taxa classified within the phylum Acidobacteria, which varied significantly over time (ANOSIM R = 0.2057, p = 0.003).

Mentions: We investigated the underlying factors driving changes in soil microbial community composition over time in the 16S-based communities from Florida soils and in the lipid-based communities from Alaska and Hawaii soils. In Florida, none of the relative proportions of the top phyla identified using 16S sequencing changed significantly over time (Fig 4A). We reclassified all sequences within each of the dominant phyla and examined shifts in within-phyla community composition at the order level. Within Proteobacteria and Actinobacteria, there were no shifts in order-level community composition over time (Fig 4B and 4C). However, within Acidobacteria, the order-level communities in Florida soils shifted significantly over time (Fig 4D). We tested whether any of the measured environmental variables explained the dissimilarity within Acidobacteria in Florida soils using a permanova; no variables were significant, though CEC explained 11.6% of the dissimilarity (p = 0.059). In Alaskan soils, all categorized lipids remained proportionally the same over time (Fig 1), with the exception of SF, which decreased significantly from June to August (F 1,13 = 11.235, p = 0.005). Nonspecific lipids, or those that were not diagnostic for a particular bacterial or fungal group, increased over time (F1,13 = 7.609, p = 0.016). In Hawaiian soils, all the categorized and nonspecific lipids remained at the same proportion over time (Fig 1), with only the lipids diagnostic for GN increasing incrementally between October and February (F 3,23 = 3.49, p = 0.032). These results suggest that, while 16S and lipid-based approaches provide similar results when comparing broad differences among dissimilar communities (i.e., across sites), they provide distinctly different information when examining how highly similar communities (i.e., within sites) vary over time.


Key Edaphic Properties Largely Explain Temporal and Geographic Variation in Soil Microbial Communities across Four Biomes.

Docherty KM, Borton HM, Espinosa N, Gebhardt M, Gil-Loaiza J, Gutknecht JL, Maes PW, Mott BM, Parnell JJ, Purdy G, Rodrigues PA, Stanish LF, Walser ON, Gallery RE - PLoS ONE (2015)

Changes in 16S rRNA-based community composition in Florida soils over time.(A) Relative abundances of all taxa classified at the phylum level and unclassified taxa over time. (B) NMDS ordination of all order-level taxa classified within the phylum Proteobacteria, which did not vary over time (ANOSIM R = 0.03968, p = 0.148). (C) NMDS ordination of all order-level taxa classified within the phylum Actinobacteria, which did not vary over time (ANOSIM R = 0.04861, p = 0.171). (D) NMDS ordination of all order level taxa classified within the phylum Acidobacteria, which varied significantly over time (ANOSIM R = 0.2057, p = 0.003).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4633200&req=5

pone.0135352.g004: Changes in 16S rRNA-based community composition in Florida soils over time.(A) Relative abundances of all taxa classified at the phylum level and unclassified taxa over time. (B) NMDS ordination of all order-level taxa classified within the phylum Proteobacteria, which did not vary over time (ANOSIM R = 0.03968, p = 0.148). (C) NMDS ordination of all order-level taxa classified within the phylum Actinobacteria, which did not vary over time (ANOSIM R = 0.04861, p = 0.171). (D) NMDS ordination of all order level taxa classified within the phylum Acidobacteria, which varied significantly over time (ANOSIM R = 0.2057, p = 0.003).
Mentions: We investigated the underlying factors driving changes in soil microbial community composition over time in the 16S-based communities from Florida soils and in the lipid-based communities from Alaska and Hawaii soils. In Florida, none of the relative proportions of the top phyla identified using 16S sequencing changed significantly over time (Fig 4A). We reclassified all sequences within each of the dominant phyla and examined shifts in within-phyla community composition at the order level. Within Proteobacteria and Actinobacteria, there were no shifts in order-level community composition over time (Fig 4B and 4C). However, within Acidobacteria, the order-level communities in Florida soils shifted significantly over time (Fig 4D). We tested whether any of the measured environmental variables explained the dissimilarity within Acidobacteria in Florida soils using a permanova; no variables were significant, though CEC explained 11.6% of the dissimilarity (p = 0.059). In Alaskan soils, all categorized lipids remained proportionally the same over time (Fig 1), with the exception of SF, which decreased significantly from June to August (F 1,13 = 11.235, p = 0.005). Nonspecific lipids, or those that were not diagnostic for a particular bacterial or fungal group, increased over time (F1,13 = 7.609, p = 0.016). In Hawaiian soils, all the categorized and nonspecific lipids remained at the same proportion over time (Fig 1), with only the lipids diagnostic for GN increasing incrementally between October and February (F 3,23 = 3.49, p = 0.032). These results suggest that, while 16S and lipid-based approaches provide similar results when comparing broad differences among dissimilar communities (i.e., across sites), they provide distinctly different information when examining how highly similar communities (i.e., within sites) vary over time.

Bottom Line: Quantifying the seasonal and long-term temporal extent of genetic and functional variation of soil microorganisms in response to biotic and abiotic changes within and across ecosystems will inform our understanding of the effect of climate change on these processes.To address the technical issue of the response of soil microbial communities to sample storage temperature, we compared 16S-based community structure in soils stored at -80°C and -20°C and found no significant differences in community composition based on storage temperature.Training in data analysis and interpretation of large datasets in university classrooms through project-based learning improves the learning experience for students and enables their use of these significant resources throughout their careers.

View Article: PubMed Central - PubMed

Affiliation: Department of Biological Sciences, Western Michigan University, Kalamazoo, Michigan, United States of America.

ABSTRACT
Soil microbial communities play a critical role in nutrient transformation and storage in all ecosystems. Quantifying the seasonal and long-term temporal extent of genetic and functional variation of soil microorganisms in response to biotic and abiotic changes within and across ecosystems will inform our understanding of the effect of climate change on these processes. We examined spatial and seasonal variation in microbial communities based on 16S rRNA gene sequencing and phospholipid fatty acid (PLFA) composition across four biomes: a tropical broadleaf forest (Hawaii), taiga (Alaska), semiarid grassland-shrubland (Utah), and a subtropical coniferous forest (Florida). In this study, we used a team-based instructional approach leveraging the iPlant Collaborative to examine publicly available National Ecological Observatory Network (NEON) 16S gene and PLFA measurements that quantify microbial diversity, composition, and growth. Both profiling techniques revealed that microbial communities grouped strongly by ecosystem and were predominately influenced by three edaphic factors: pH, soil water content, and cation exchange capacity. Temporal variability of microbial communities differed by profiling technique; 16S-based community measurements showed significant temporal variability only in the subtropical coniferous forest communities, specifically through changes within subgroups of Acidobacteria. Conversely, PLFA-based community measurements showed seasonal shifts in taiga and tropical broadleaf forest systems. These differences may be due to the premise that 16S-based measurements are predominantly influenced by large shifts in the abiotic soil environment, while PLFA-based analyses reflect the metabolically active fraction of the microbial community, which is more sensitive to local disturbances and biotic interactions. To address the technical issue of the response of soil microbial communities to sample storage temperature, we compared 16S-based community structure in soils stored at -80°C and -20°C and found no significant differences in community composition based on storage temperature. Free, open access datasets and data sharing platforms are powerful tools for integrating research and teaching in undergraduate and graduate student classrooms. They are a valuable resource for fostering interdisciplinary collaborations, testing ecological theory, model development and validation, and generating novel hypotheses. Training in data analysis and interpretation of large datasets in university classrooms through project-based learning improves the learning experience for students and enables their use of these significant resources throughout their careers.

No MeSH data available.


Related in: MedlinePlus