the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 18 Dec 2023
Long-term legacy of phytoremediation on plant succession and soil microbial communities in petroleum-contaminated sub-Arctic soils
Abstract. Phytoremediation can be a cost-effective method of restoring contaminated soils using plants and associated microorganisms. Most studies follow the impacts of phytoremediation solely across the treatment period and have not explored long-term ecological effects. In 1995, a phytoremediation study was initiated near Fairbanks, Alaska, to determine how the introduction of annual grasses and/or fertilizer would influence degradation of petroleum hydrocarbons (PHCs). After one year, grass and/or fertilizer treated soils showed greater decreases in PHC concentrations compared to untreated plots. The site was then left for 15 years with no active site management. In 2011, we re-examined the site to explore the legacy of phytoremediation on contaminant disappearance, as well as plant and soil microbial ecology. We found that the recruited vegetation, along with current bulk soil microbial community structure and function were all heavily influenced by initial phytoremediation treatment. The number of diesel-degrading microorganisms (DDM) was positively correlated with increasing amounts of vegetation on the site, and inversely correlated with PHC concentrations. Even 15 years later, the initial use of fertilizer had significant effects on microbial biomass and microbial community structure activities. We conclude that phytoremediation treatment has long-term, legacy effects on the plant community, which, in turn, impacts microbial community structure, function, and continued TPH disappearance. It is therefore important to consider phytoremediation strategies that not only influence site remediation rates in the short-term, but that also prime the site for restoration of vegetation across the long-term.
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RC1: 'Comment on egusphere-2023-2097', Eglantina Lopez Echartea, 05 Feb 2024
The manuscript investigates into a reevaluation of a petroleum-contaminated site in Fairbanks, Alaska, fifteen years after the introduction of annual grasses and/or fertilizer treatments to investigate their impact on the degradation of petroleum hydrocarbons. The study meticulously examines contaminant concentrations, as well as the dynamics of plant and soil microbial ecology. This research is particularly significant given the infrequency of long-term phytoremediation studies, acknowledging the necessity for extended investigations in a field where remediation timelines often exceed those of conventional physico-chemical treatments.
The overall quality of the study is commendable, featuring an ample number of figures, tables, and well-referenced content.
I propose the following suggestions to enhance the manuscript:
- Script Accessibility: It would be beneficial if the authors could provide a link to the scripts employed for statistical and sequencing analyses on a platform such as GitHub. This transparency enhances the reproducibility of the study.
- Pyrosequencing Data Clarification: The authors are encouraged to include a statement elucidating the comparability between results obtained through pyrosequencing and Next-Generation Sequencing (NGS). A justification for the relevance of publishing pyrosequencing data in the context of modern microbiome studies would further strengthen the manuscript.
- Enhanced Figure Visualization: Figures resembling the color-coded schemes in Figures 3 and 4 would significantly enhance the clarity of the manuscript. The use of color could aid in distinguishing between different treatments, facilitating a more accessible interpretation of the data.
- Comparison of Concentrations: While the manuscript notes that crude oil and diesel concentrations remain around 500 ppm after a decade of treatment, it would be valuable to compare and discuss these findings with other bioremediation and phytoremediation studies. An exploration of whether similar concentrations are observed in other studies after a decade of treatment would contribute valuable insights to the field, as those are expected to be lower.
Citation: https://doi.org/10.5194/egusphere-2023-2097-RC1 -
RC2: 'Comment on egusphere-2023-2097', Anonymous Referee #2, 07 Feb 2024
The manuscript builds on a few prior papers published from a phytoremediation experiment initiated in 1995 in which crude oil- or diesel- contaminated soil received fertilizer, one of two plant treatments, or both to determine whether such treatments impacted the hydrocarbon degradation rate. In 2011 after 15 years without active management, the plant communities for each plot at this site were assessed, and soil samples were taken. A study describing the 2011 plant community at the species level and soil physiochemical properties (including hydrocarbon level) was published in 2013, which also included coarse data on the soil microbial community (T-RFLP analysis).
This current study expands insight into the soil microbial community present in the 2011 samples with: 1) PLFA analysis, to determine total microbial biomass and provide another broad profile of soil microbes; 2) 16S rRNA sequencing, to characterize the diversity of bacterial taxa; and 3) Estimates of the number of cultivable diesel- degrading microbes present. The plant community data that had already been described in the 2013 paper is also re-aggregated in a coarser way, by % coverage of different vegetative types (grasses, forbs, trees, or bare ground). Soil and plant metrics are linked to microbial data through various analyses.
The methods and analyses are appropriate, though the study is limited by the lack of true replication in the initial experimental design chosen in the 90s. The authors use 454 pyrosequencing which is today an outdated sequencing technology, but was more standard circa 2011 if that is when the initial 16S sequencing was done.
The long-term nature of the study is commendable, as relatively few papers track the impacts of a single management event after considerable time has passed. There are also some noteworthy patterns in the microbial data. It is striking that bacterial community composition still diverged by initial plant/fertilizer treatment 15 years after the fact, and this contributes to other literature that documents long-term effects of fertilization and/or plant cover on soil microbial community composition. Nonetheless, I feel that further exploration of the 16S data would have strengthened the study. Differential abundance analysis in particular could highlight key OTUs differing between treatments, as was done for rhizosphere and endosphere bacteria of plants harvested from this same experimental site (Papik et al 2023).
In general, the focus and clarity of the manuscript could be much improved, particularly concerning which results have been published before and which are presented here for the first time. Moreover, much of the data is presented in tables, and would be far more accessible as figures. Few treatment-to-treatment statistics are presented, and these need to be added. The conclusions are also rather sweeping and not always backed up with the data, nor are they particularly novel nor deterministic. Soil properties, plant communities, and microbial communities all co-vary from plot to plot, so only speculative conclusions can be drawn on the processes driving these patterns.
Specific suggestions for improvement:
INTRODUCTION
Line 44: Define PHC here as well and not just in the abstract
Line 51 - 54: A slightly expanded discussion of how phytoremediation is thought to work would be useful. As is, it is unclear if it is thought that the plant metabolites themselves are directly degrading hydrocarbons, or if plant metabolites are stimulating microbes which then degrade hydrocarbons? Do different plants seem better or worse at improving remediation efforts? Do different microbes? Etc
I feel that lines 139-146 should be moved to the end of the introduction to make it immediately clear that the expanded microbial data is the focal element for this paper. This could also be made clear with a table highlighting the prior studies from this site, including what was characterized in each study (soil properties, hydrocarbon levels, plant community, and/or microbial community) and how it was characterized in the past vs. now, perhaps incorporated as a panel into Figure 1.
METHODS
Line 111: Define TPH acronym
Lines 111 – 114: This may fit better moved to the results section.
Table 1: As this data is not new to this study, I think it should be supplemental. I also think it would be better presented as a % reduction from the time 0 TPH ppm value rather than raw values. Stats should also be included so the reader can clearly see where statistical differences between treatments lie.
Section 2.4: It would be useful to add a brief description of how MPN is conducted to give readers the general idea of the method without needing to look up another paper.
General note: treatment “p1” is sometimes inconsistently labelled instead as “p” (e.g. line 111, Figure S1, etc.)
RESULTS
I suggest that you begin with a section like “Prior findings on TPH, Soil properties, and plant community” combining condensed versions of lines 111-114, section 3.1, and most of section 3.2. This context is important when interpreting the microbial data and ought to be summarized briefly, but it should be clear that it has been published and discussed in greater depth before.
Lines 207 – 210: Did treatment make any difference in the % reduction of TPH?
Table S2: The soil textural data (sand, silt, and clay) that was published in Leewis et al 2013 should be provided in Table S2 as it is referenced later. Was the table cut off? Additionally, stats should be displayed so readers can clearly see significant differences between treatments.
Lines 213 – 214: Looking at the sand, silt, and clay percentages in Leewis et al 2013, it seems like sand contents are comparable in CO vs. DE soils, and actually clay content was somewhat higher in CO soils. How does this translate to CO being considered coarser in texture? I do see that CEC is lower in CO soils, but while CEC and texture are related CEC is also influenced by many other factors (type of clay, pH, etc). It was mentioned that CO came from a gravel pad – was gravel % included in the textural analysis, or lumped in with sand?
Lines 230 – 232: This ground cover % data is novel to this study, to my understanding, and should be kept separate from the “Prior findings” section. Expanded discussion of patterns in Figure 2 would be helpful.
Figure 2: For visualization, it would be useful to add a panel with stacked bar charts for each treatment type showing the breakdown of vegetation types. The current panels should also be kept as they are useful for understanding statistical differences.
Line 250: Define PHC acronym
Figure S1: I feel this should be a main text figure, as it is the most direct summary of one of the three new pieces of data being presented. Figures S1 and S2 could be combined into a two-panel main text figure, for instance. Stats should be displayed for both as in Figure 2 so the reader can clearly see differences between treatments.
Line 258: “weak inverse relationship” – this was not statistically significant for either CO or DE soils.
Lines 260 to 265: Pearson and p-values should be displayed in the appropriate panel in Figure 3.
Line 262: “number of plants on the site” – what specifically does this mean? The number of individual plants counted? The number of different plant species identified? % plant cover vs. bare ground? Why is a graph representing this not included in Figure 3?
Line 264: Why did you only investigate the relationship between DDM and tree cover, not grass, forb, or bare cover as well? Were any of those relationships significant?
Table S4: This would be better displayed as boxplots for total biomass and potentially with stacked bar charts for the remaining columns, normalized to percentage of total PLFA for that sample so that differences in overall bacteria:fungi:protozoa ratios can be easily visualized. This could yield more patterns for discussion in the results. Again, stats should be included to aid comparison of treatments.
Line 276: In some cases, but for instance diesel c1 vs. diesel p2f is probably not significant. Stats are needed to back up this statement.
Line 278: “The same held true for all other individual PLFA biomarkers” – unclear what you mean. There are more fungi, more actinobacteria, etc in fertilized vs. unfertilized? That is unsurprising given the differences in total PFLA amounts. Ratios of relative abundance of these different groups would be more interesting to discuss.
Line 281-282: Not sure the data fully support this statement. E.g. crude oil c1 has high microbial biomass. Clearly displayed stats in Figure S2 would help clarify.
Lines 299 – 307 and Figure 4A-B: Because the PFLA community composition data is so coarse already (only 6 different types of microbes, unless for this ordination you separately considered all of the different individual biomarkers listed in Table S1 rather than aggregating them?), I don’t think doing an ordination or vector fitting adds anything that wouldn’t already be captured in a figure version of Table S4.
Line 218-319: “significant influence of soil type” – It may be useful to add a panel displaying an NMDS of all samples in this study together, perhaps with crude oil and diesel differently colored, to show separation of communities based on soil type, in addition to panels C and D.
Table S5: This would be better displayed as boxplots with stats between treatments clearly labelled. Could be combined as panels with Figures S1 and S2 into one figure.
Figure S3 discussion: is there any functional significance to these families?
Differential abundance analyses of the 16S data would contribute by identifying specific OTUs associated with particular treatments.
DISCUSSION:
Lines 356 – 357: “influenced the continuing contaminant disappearance” – the stats to back up this statement are not included in Table 1.
Lines 387 – 389: The stats to back up this statement are not included in Table S5.
Lines 399 – 400: “the percent coverage of vegetation rather than individual plant species” – Did you incorporate the species level plant data to try to explain the 16S or biomass data? If not, I would leave this comparison out. Percent cover mattered, but without an analysis of individual plant species impact on bacterial diversity we can’t say whether this mattered less.
Line 408 - 409: “number of culturable microorganisms able to degrade TPH was most strongly associated with the plant communities present” – this data was not fully displayed. There was only one positive association with tree cover.
Line 411 - 412: These findings were generally not significant.
Line 418 – 420: Native vs. non-native plants were not a focus of this study – was this a finding from 2013?
Line 432 – 434: Initial planting of annual vs. perennial plants was not the focus of this study – both plants used were annuals and had also disappeared by the 2011 check in.
In general I think the discussion should be re-focused to the microbial data. Do any of the same bacterial taxa from the heatmap or a future differential abundance analysis show up in other phytoremediation studies? Are the microbes identified as important in short term phytoremediation studies still present here? How do microbial diversity levels or biomass levels compare with other phytoremediation studies? For other long-term phytoremediation studies even if they didn't cover all of the different data covered here, are there parallels to be drawn? There are several threads that could be discussed to help contextualize the data.
Citation: https://doi.org/10.5194/egusphere-2023-2097-RC2
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