Patterns, Processes And Models Of Microbial Recovery In A Chronosequence Following Reforestation Of Reclaimed Mine Soils
| dc.contributor.author | Sun, Shan | en |
| dc.contributor.committeechair | Badgley, Brian D. | en |
| dc.contributor.committeemember | Strahm, Brian D. | en |
| dc.contributor.committeemember | Strickland, Michael | en |
| dc.contributor.committeemember | Zipper, Carl E. | en |
| dc.contributor.committeemember | Li, Song | en |
| dc.contributor.department | Crop and Soil Environmental Sciences | en |
| dc.date.accessioned | 2019-02-23T07:00:31Z | en |
| dc.date.available | 2019-02-23T07:00:31Z | en |
| dc.date.issued | 2017-08-31 | en |
| dc.description.abstract | Soil microbial communities mediate important ecological processes and play essential roles in biogeochemical cycling. Ecosystem disturbances such as surface mining significantly alter soil microbial communities, which could lead to changes or impairment of ecosystem functions. Reforestation procedures were designed to accelerate the reestablishment of plant community and the recovery of the forest ecosystem after reclamation. However, the microbial recovery during reforestation has not been well studied even though this information is essential for evaluating ecosystem restoration success. In addition, the similar starting conditions of mining sites of different ages facilitate a chronosequence approach for studying decades-long microbial community change, which could help generalize theories about ecosystem succession. In this study, the recovery of microbial communities in a chronosequence of reclaimed mine sites spanning 30 years post reforestation along with unmined reference sites was analyzed using next-generation sequencing to characterize soil-microbial abundance, richness, taxonomic composition, interaction patterns and functional genes. Generally, microbial succession followed a trajectory along the chronosequence age, with communities becoming more similar to reference sites with increasing age. However, two major branches of soil microbiota, bacteria and fungi, showed some contrasting dynamics during ecosystem recovery, which are likely related to the difference in their growth rates, tolerance to environmental change and relationships with plants. For example, bacterial communities displayed more intra-annual variability and more complex co-occurrence networks than did fungi. A transition from copiotrophs to oligotrophs during succession, suggested by taxonomic composition shifts, indicated that the nutrient availability is one important factor driving microbial succession. This theory was also supported by metagenomic analysis of the functional genes. For example, the increased abundance of genes involved in virulence, defense and stress response along ages indicated increased competition between microorganisms, which is likely related to a decrease of available nutrients. Metagenomic analysis also revealed that lower relative abundances of methanotrophs and methane monooxygenase at previously-mined sites compared with unmined sites, which supports previous observations that ecological function of methane sink provided by many forest soils has not recovered after 30 years. Because of the difficulty identifying in situ functional mechanisms that link soil microorganisms with environmental change, modeling can be a valuable tool to infer those relationships of microbial communities. However, the extremely high richness of soil microbial communities can result in extremely complicated models that are difficult to interpret. Furthermore, uncertainty about the coherence of ecological function at high microbial taxonomic levels, grouping operational taxonomic units (OTUs) based on phylogenetic linkages can mask trends and relationships of some important OTUs. To investigate other ways to simplify soil microbiome data for modeling, I used co-occurrence patterns of bacterial OTUs to construct functional groups. The resulting groups performed better at characterizing age-related microbial community dynamics and predicted community structures and environmental factors with lower error. | en |
| dc.description.abstractgeneral | Disturbances to ecosystems are known to largely impact important ecological functions such as soil carbon loss, decreased nutrient retention and increased greenhouse gas emission. As a result, surface mining, which totally removes the topsoil and original vegetation, has severe negative influences on forest ecosystem function. Reforestation is performed on reclaimed mined sites to accelerate the return of forest vegetation and ecosystem functions. Although considerable research has shown that the plant community can be well developed after 30 years, little is known on whether ecosystem functions are also recovered during a similar time period. As direct mediators of many ecological processes in the environment, soil microorganisms are important for understanding the restoration progress of ecosystems. They could also provide early indications of restoration progress compared to plants. Historically, most soil microorganisms have been difficult to study because they are highly diverse and the majority cannot be cultured in lab, making it difficult to understand changes in the total soil microbiota. However, technological advances such as DNA sequencing have made it feasible to study soil microorganisms in detail. In this work, we studied soil microbial communities from reclaimed mined sites ranging from 5 to 30 years post-reforestation. We found that overall the microbial community was recovering from the disturbances of surface mining, but many differences from unmined soils still remain after 30 years, such as the unrecovered function as methane sink. Two major groups of soil microorganisms, bacteria and fungi, showed different characteristics during recovery, which are likely due to differences between the two groups with regard to growth rates, tolerance to environmental change and relationships with plants. Mathematical modeling is a useful tool for simulating changes and impacts on microbial communities under different conditions, given that actual interactions between microorganisms and their environment can be difficult to measure. However, the high complexity of soil microbial communities becomes an obstacle for modeling that needs to be addressed by simplifying data describing soil microbial community. One approach is grouping organisms based on their natural evolutionary relationships, but this can mask the trends of some microorganisms since all organisms in these groups do not always respond the same to environmental change. Here we used a method of grouping microorganisms based on their co-occurrence patterns, which resulted in better predictions of changes in community structure and environmental factors when applied in modeling. | en |
| dc.description.degree | PHD | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:12536 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/87754 | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | soil microbiota | en |
| dc.subject | reforested mined soils | en |
| dc.subject | chronosequence | en |
| dc.subject | community structure | en |
| dc.subject | metagenome | en |
| dc.subject | co-occurrence group | en |
| dc.title | Patterns, Processes And Models Of Microbial Recovery In A Chronosequence Following Reforestation Of Reclaimed Mine Soils | en |
| dc.type | Dissertation | en |
| thesis.degree.discipline | Crop and Soil Environmental Sciences | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | doctoral | en |
| thesis.degree.name | PHD | en |