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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

Average values for all measured (A) soil environmental variables, (B) dominant 16S rRNA-determined bacterial phyla, and (C) grouped lipids ± 95% confidence intervals for all soil samples at all time points.Columns in gray indicate time points closest to peak greenness at each site, which are used for cross-site comparisons. Significant differences over time, within sites, are indicated with * (p < 0.05), ** (p < 0.01), *** (p < 0.001), as compared to the time point at peak greenness using repeated measures ANOVA. Differences between sites are not indicated here, but are described in Fig 2A.
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pone.0135352.g001: Average values for all measured (A) soil environmental variables, (B) dominant 16S rRNA-determined bacterial phyla, and (C) grouped lipids ± 95% confidence intervals for all soil samples at all time points.Columns in gray indicate time points closest to peak greenness at each site, which are used for cross-site comparisons. Significant differences over time, within sites, are indicated with * (p < 0.05), ** (p < 0.01), *** (p < 0.001), as compared to the time point at peak greenness using repeated measures ANOVA. Differences between sites are not indicated here, but are described in Fig 2A.

Mentions: From 2009–2010, 408 soil samples were collected and a subset were analyzed from four biomes that represent a broad latitudinal gradient with unique soil properties and distinct climates with different levels of intra-annual variability in temperature and precipitation, as defined by the NEON design (Fig 1). Soils were collected from taiga (NEON Domain 19; Caribou-Poker Creek, Alaska), tropical/subtropical moist broadleaf forest (NEON Domain 20; Laupahoehoe, Hawaii), tropical/sub-tropical coniferous forest (NEON Domain 3; Ordway-Swisher Biological Station, Florida) and temperate grassland/savanna/shrubland (NEON Domain 15; Onaqui-Benmore, Utah). Sampling did not require permits and did not involve endangered or protected species. For more NEON site information see http://www.neoninc.org/science-design/field-sites. Sampling dates were designated to include the beginning and end of growing seasons, as well as periods representative of annual temperature or precipitation extremes. Samples for microbial community analysis were collected within a grid measuring 160 × 320 m divided into eight 80 × 80 m cells. Soil cores (7.5 cm in diameter encompassing the 0–10 cm depth interval below the litter layer) were collected from three randomly assigned GPS coordinates within each of the eight cells. In addition, sub-sets of the three cores corresponding to each cell were combined for a composite sample representative of each cell. Cores within each grid were homogenized, sieved through 2 mm mesh, and subsamples were either air-dried or frozen at -80°C for specific downstream analyses. Subsamples of sieved soils collected from Hawaii were frozen at both -20°C and -80°C to compare the effect of storage temperature on microbial community composition.


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)

Average values for all measured (A) soil environmental variables, (B) dominant 16S rRNA-determined bacterial phyla, and (C) grouped lipids ± 95% confidence intervals for all soil samples at all time points.Columns in gray indicate time points closest to peak greenness at each site, which are used for cross-site comparisons. Significant differences over time, within sites, are indicated with * (p < 0.05), ** (p < 0.01), *** (p < 0.001), as compared to the time point at peak greenness using repeated measures ANOVA. Differences between sites are not indicated here, but are described in Fig 2A.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0135352.g001: Average values for all measured (A) soil environmental variables, (B) dominant 16S rRNA-determined bacterial phyla, and (C) grouped lipids ± 95% confidence intervals for all soil samples at all time points.Columns in gray indicate time points closest to peak greenness at each site, which are used for cross-site comparisons. Significant differences over time, within sites, are indicated with * (p < 0.05), ** (p < 0.01), *** (p < 0.001), as compared to the time point at peak greenness using repeated measures ANOVA. Differences between sites are not indicated here, but are described in Fig 2A.
Mentions: From 2009–2010, 408 soil samples were collected and a subset were analyzed from four biomes that represent a broad latitudinal gradient with unique soil properties and distinct climates with different levels of intra-annual variability in temperature and precipitation, as defined by the NEON design (Fig 1). Soils were collected from taiga (NEON Domain 19; Caribou-Poker Creek, Alaska), tropical/subtropical moist broadleaf forest (NEON Domain 20; Laupahoehoe, Hawaii), tropical/sub-tropical coniferous forest (NEON Domain 3; Ordway-Swisher Biological Station, Florida) and temperate grassland/savanna/shrubland (NEON Domain 15; Onaqui-Benmore, Utah). Sampling did not require permits and did not involve endangered or protected species. For more NEON site information see http://www.neoninc.org/science-design/field-sites. Sampling dates were designated to include the beginning and end of growing seasons, as well as periods representative of annual temperature or precipitation extremes. Samples for microbial community analysis were collected within a grid measuring 160 × 320 m divided into eight 80 × 80 m cells. Soil cores (7.5 cm in diameter encompassing the 0–10 cm depth interval below the litter layer) were collected from three randomly assigned GPS coordinates within each of the eight cells. In addition, sub-sets of the three cores corresponding to each cell were combined for a composite sample representative of each cell. Cores within each grid were homogenized, sieved through 2 mm mesh, and subsamples were either air-dried or frozen at -80°C for specific downstream analyses. Subsamples of sieved soils collected from Hawaii were frozen at both -20°C and -80°C to compare the effect of storage temperature on microbial community composition.

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