Improving Environmental DNA Sensitivity for Dreissenid Mussels by Targeting Tandem Repeat Regions of the Mitochondrial Genome
Round 1
Reviewer 1 Report
The authors present an improved methodology for utilising eDNA to detect and quantify zebra and quagga mussel populations, focused on the Laurentian Great Lakes. The methodology builds on previous work that was based upon an incomplete DNA sequencing. The manuscript is well-written and appropriately referenced, with appropriate figures and tables. There are a few minor issues which should be addressed prior to publication.
Line 18, there is an extraneous "in"
Line 62, PCR should be spelled out on first usage
Lines 300 and subsequent, Langlois et al appears as 2020 in the text but 2021 in the references.
Referencing should conform to the numeric referencing style of the journal.
Author Response
Comments and Suggestions for Authors
The authors present an improved methodology for utilising eDNA to detect and quantify zebra and quagga mussel populations, focused on the Laurentian Great Lakes. The methodology builds on previous work that was based upon an incomplete DNA sequencing. The manuscript is well-written and appropriately referenced, with appropriate figures and tables. There are a few minor issues which should be addressed prior to publication.
Comment: Line 18, there is an extraneous "in"
Response: It has been removed
Comment: Line 62, PCR should be spelled out on first usage
Response: PCR has been spelled out in the first usage
Comment: Lines 300 and subsequent, Langlois et al appears as 2020 in the text but 2021 in the references.
Response: Reference details updated
Comment: Referencing should conform to the numeric referencing style of the journal.
Response: References were modified to numeric form
Reviewer 2 Report
May 23rd, 2022
Review for ‘Improving environmental DNA sensitivity for dreissenid mussels by targeting tandem repeat regions of the mitochondrial genome’ submitted to Journal of Water.
General comments:
Overall authors did a commendable job with the writing of this submission. The manuscript is well written, well organized, clear, and communicates the significance of the work and the results. Authors have used new genomic sequencing data from zebra and quagga mussel that suggests unique tandem repeats in the mitochondrial genome of these two species may allow for 1) the design of species-specific eDNA qPCR markers and 2) the development of qPCR markers that would have increased sensitivity based on the shear number of tandem repeats that are included in the respective mitochondrial genome of each species. The authors demonstrate multiple levels of validation (direct gDNA comparison, mesocosm comparisons, and comparisons from water samples collected in Lake Erie) where an increase in sensitivity is apparent with these mitochondrial tandem repeat (mtTR) markers compared to previously published markers, 3 that target mitochondrial single copy (per genome) genes and 1 that targets a multi-copy nuclear gene. Specific comments or suggested edits are provided below.
Minor comments:
Abstract, line 18: change ‘in within’ to simply ‘within’
Abstract, line 19: edit ‘for the barcode genes’ to ‘for the mitochondrial barcode genes’
Abstract, line 20: using the word ‘cell’ here seems to be making a premature jump. It is more accurate here to replace ‘cell’ with ‘mitochondrial genome’
Abstract, line 20: replace ‘eDNA sensitivity’ with ‘eDNA assay sensitivity’
Abstract, line 22-23: was the 10-fold increase identical for both zebra and quagga mussel. If not please be specific as some of the figures suggested the sensitivity increases were more apparent with quagga mussel
Abstract, line 26: replace ‘of a complete’ to ‘the complete’
Introduction, line 34: replace ‘spread’ with ‘distributed’
Introduction, line 36: ‘benthification’ is mis-spelled
Introduction, line 49: hyphenate ‘dreissenid-related’
Table 1: Please provide Klymus et al. 2020 as a reference in the table legend for LOD/LOQ determination
Table 1: Spacing of the column headers is off as well as the primers column. Also can authors provide a more detailed description of how ‘Efficiency’ was determined/calculated?
Methods, line 185: What depth was the water where samples were taken? Do authors expect the water samples collected 30 cm below the surface would have been well mixed?
Figure 1: This might be a question more for the journal, but is there concern the colors depicted in this figure as well as some of the data comparisons would be a problem for some readers?
Methods, line 198: Can authors provide the calculation/equation for determining PCR efficiency?
Results, Figure 3: Panel labels A-D are partially cut off.
Supplemental Information: To reiterate, is there concern the colors depicted in the figures might be a problem for some readers?
Major comments:
Introduction, paragraph starting at line 75: in this reviewer’s view the authors are missing a point regarding mitochondrial genome copy number per cell. It is certainly true that if dreissenids possess tandem repeat regions in every mitochondrial genome in each cell then mitochondrial markers that target these areas will likely have a sensitivity advantage over mitochondrial markers that target single copy coding genes. The trouble with the paragraph is making comparisons to multi-copy nuclear genes since it is (at least in this paper) unknown how many mitochondrial genome copies per cell were realized from the samples collected for comparison (foot tissue of dreissenids). This comparison requires the per cell copy comparison to be more important than the per genome copy comparison, and this piece of information is unknown.
Introduction, paragraph starting at line 75: In any of these comparisons how do the authors reconcile assay sensitivity differences inherent in the markers themselves? Have the authors verified each marker compared is amplifying at peak levels/maximal efficiency? No information is provided on this point. So even though it is possible the sensitivity differences shown could certainly be the result of more target copies available for detection, it’s also possible that some unknown level of the differences in sensitivity could also be caused by differences in inherent amplification efficiency (underlying nucleotide composition of each marker). Authors should provide some explanation, particularly in the discussion, on this point.
Introduction, paragraph starting at line 94: The argument made in this paragraph is again significant in comparing mt:mt assays, but the per genome copy number argument does not apply in making comparisons between multi-copy nuclear and mitochondrial assays since the number of mitochondrial genomes per cell (or per tissue) is not described in dreissenids. This is related to a similar comment above.
Methods, line 158: EvaGreen was used so with no mention of hydrolysis probes all of these assays were performed as ‘non probe-based’ assays, correct? There is no mention of how melting curve analysis was done in the PCR methods (although there is mention of melting curve analysis in one sentence of the results). Some of the assays compared were based on previously published probe-based assays. Authors should acknowledge that the previously published assays were converted to non probe-based assays.
Discussion, line 299: In this report, this specific marker comparison would likely have benefited by using synthetic standards since this comparison would have verified the exact target DNA copy number tested per marker to reveal any underlying inherent differences in marker sensitivity rather than assuming some unknown difference in copy number between nuclear DNA and mitochondrial DNA as well as some unknown number of tandem repeats. It may be true that using synthetic DNA standards for determining LOD and LOQ is not the best empirical strategy as the authors have pointed out, but the reader is still left assuming the sensitivity differences seen in the data are the result of specific differences in target copy number. However, the authors have not provided enough compelling evidence to rule out markers that may poorly perform in general. No mention of assay optimization is provided that may maximize efficiency for each primer set. It is also true based on table 1 that simple differences in the length of each marker may contribute to differences in inherent sensitivity. The gDNA comparisons provided also make broad assumptions on the copy numbers that may or may not have been tested and as such, the inherent sensitivity of each marker is still in question even with the gDNA comparisons. The authors correctly point out the mitochondrial make-up of target species cells, tissues, individuals, and/or populations are unknown, and this underscores the need for a comparison of marker sensitivity using synthetic DNA standards.
Overall: Recommend publication with major revisions. Authors need to address points above, and would be a benefit to the paper to rule out inherent marker sensitivity differences by showing assays are running at maximal efficiency using synthetic standards and describing any differences realized. Authors have made a compelling argument for the targeting of tandem repeat regions in dreissenid mitochondrial genomes for increasing eDNA assay sensitivity, but the value in that argument should be framed as a ‘proof of concept’ development at this stage rather than any specific recommendations about marker design until more is known how these tandem repeat regions differ within tissues of individuals and across the dreissenid population as a whole.
Author Response
General comments:
Overall authors did a commendable job with the writing of this submission. The manuscript is well written, well organized, clear, and communicates the significance of the work and the results. Authors have used new genomic sequencing data from zebra and quagga mussel that suggests unique tandem repeats in the mitochondrial genome of these two species may allow for 1) the design of species-specific eDNA qPCR markers and 2) the development of qPCR markers that would have increased sensitivity based on the shear number of tandem repeats that are included in the respective mitochondrial genome of each species. The authors demonstrate multiple levels of validation (direct gDNA comparison, mesocosm comparisons, and comparisons from water samples collected in Lake Erie) where an increase in sensitivity is apparent with these mitochondrial tandem repeat (mtTR) markers compared to previously published markers, 3 that target mitochondrial single copy (per genome) genes and 1 that targets a multi-copy nuclear gene. Specific comments or suggested edits are provided below.
Minor comments:
Comment: Abstract, line 18: change ‘in within’ to simply ‘within’
Response: This has been fixed
Comment: Abstract, line 19: edit ‘for the barcode genes’ to ‘for the mitochondrial barcode genes’
Response:This has been fixed
Comment: Abstract, line 20: using the word ‘cell’ here seems to be making a premature jump. It is more accurate here to replace ‘cell’ with ‘mitochondrial genome’
Response:This has been fixed
Comment: Abstract, line 20: replace ‘eDNA sensitivity’ with ‘eDNA assay sensitivity’
Response:This has been fixed
Comment: Abstract lines 22-23: was the 10-fold increase identical for both zebra and quagga mussel. If not please be specific as some of the figures suggested the sensitivity increases were more apparent with quagga mussel
Response:This has been updated to reflect the higher sensitivity found for zebra mussels.
Comment: Abstract, line 26: replace ‘of a complete’ with ‘the complete’
Response:This has been fixed
Comment: Introduction, line 34: replace ‘spread’ with ‘distributed’
Response: This has been fixed
Comment: Introduction, line 36: ‘benthification’ is misspelled
Response: This has been fixed
Comment: Introduction, line 49: hyphenate ‘dreissenid-related’
Response: This has been fixed
Comment: Table 1: Please provide Klymus et al. 2020 as a reference in the table legend for LOD/LOQ determination
Response: This has been added to the table legend
Comment: Table 1: The spacing of the column headers is off as well as the primers column. Also, can the authors provide a more detailed description of how ‘Efficiency’ was determined/calculated?
Response: Table spacing has been fixed. The calculation for PCR efficiency has been added to the Methods (see response below).
Comment: Methods, line 185: What depth was the water where samples were taken? Do authors expect the water samples collected 30 cm below the surface would have been well mixed?
Response: For the mesocosm experiments, water samples were collected after mixing the water within each mesocosm by stirring with a glove for ~3-5 seconds. Therefore, the water was well mixed before collection. We have added this information to the Methods section.
Comment: Figure 1: This might be a question more for the journal, but is there concern the colors depicted in this figure as well as some of the data comparisons would be a problem for some readers?
Response: We will refer to the editor’s opinion on figure colors. We have additionally included black and white options for each figure.
Comment: Methods, line 198: Can authors provide the calculation/equation for determining PCR efficiency?
Response: PCR efficiency was calculated following the Minimum Information for Publication of Quantitative Real-time PCR Experiments (MIQE Guidelines) (Bustin et al. 2009). This guideline suggests PCR amplification efficiency should be established using a set of calibration curves. Here, we used log-dilutions of gDNA (ranging from 10 ng to 0.00001 ng per reaction) to calibrate a standard curve for each qPCR assay. Using the calibrated standard curves for serial dilutions of gDNA, PCR efficiency was calculated as 10-1/slope - 1, where the slope was calculated from the logarithm of the initial template concentration plotted against the Cq (quantification cycle) for each dilution (see Figure 2 for examples of the standard curves). The theoretical target of 1.00 (or 100% efficiency) indicates that the amount of product doubles with each cycle.
Comment: Results, Figure 3: Panel labels A-D are partially cut off.
Response: Panel labels look good in Figure 3. The inner box is redrawn with think lines for Figure.
Comment: Supplemental Information: To reiterate, is there concern the colors depicted in the figures might be a problem for some readers?
Response: We will refer to the editor’s opinion on figure colors. We have additionally included black and white options for each figure.
Major comments:
Comment: Introduction, paragraph starting at line 75: in this reviewer’s view, the authors are missing a point regarding mitochondrial genome copy number per cell. If dreissenids possess tandem repeat regions in every mitochondrial genome in each cell then mitochondrial markers that target these areas will likely have a sensitivity advantage over mitochondrial markers that target single-copy coding genes. The trouble with the paragraph is making comparisons to multi-copy nuclear genes since it is (at least in this paper) unknown how many mitochondrial genome copies per cell were realized from the samples collected for comparison (foot tissue of dreissenids). This comparison requires the per-cell copy comparison to be more important than the per genome copy comparison, and this piece of information is unknown.
Response: We agree with the reviewer that comparisons between assays that target nuclear and mitochondrial genes are difficult to interpret as the inherent number of mitochondrial genomes varies across cell types, and thus the ratio of nuclear to mitochondrial DNA may vary depending on what type of tissue is shed within the environment (for example, gamete sex cells are likely to differ in mitochondrial DNA concentration compared to adult gill cells). However, we argue it is still an important factor to consider when investigating assay sensitivity within an eDNA application. Since the onset of eDNA research for macrobial organisms, the majority of eDNA assays target a mitochondrial gene region (data summarized from Thalinger et al. 2021 within the Introduction). One reason for this is that initial eDNA studies hypothesize that mitochondrial DNA will be more concentrated within the environment because multiple mitochondrial genomes occur within a eukaryotic cell. However, this hypothesis neglects the fact that many nuclear genes appear as multiple copies throughout the nuclear genome. Thus, a multi-copy nuclear gene may occur at a higher copy number than a single-copy mitochondrial gene within a cell, and therefore be more concentrated within the environment. The few studies that have investigated comparisons between multi-copy nuclear genes vs single-copy mitochondrial genes within eDNA samples have consistently found increased sensitivity for multi-copy gene assays. These studies have looked at fish (Minamoto et al. 2017, Dysthe et al. 2018, Jo et al. 2020) and bivalves (Marshall et al. 2021). A recent review within the journal Environmental DNA (Jo et al. 2022) explains how comparisons between concentrations of nuclear and mitochondrial genes within an eDNA sample can be useful for better interpretation of eDNA detections. Furthermore, the authors state “compared to mitochondrial eDNA (mt-eDNA), multi-copy nuclear eDNA (nu-eDNA) is expected to show higher detectability and yield”. This review is cited within the Discussion and can be found in the references. Additionally, targeting multi-copy nuclear genes within molecular microbial studies has provided increased sensitivity with qPCR analysis (Braun et al. 2021, Shan et al. 2021). These previous studies, for microbial or macrobial eDNA, set the groundwork to suggest that targeting a multi-copy gene improves assay sensitivity compared to the standard “barcode” assays. Although these previous examples target nuclear gene regions, it still provides a necessary background to the reader to suggest we can improve eDNA sensitivity by targeting regions of the genome that appear as multi-copies.
Comment: Introduction, paragraph starting at line 75: In any of these comparisons how do the authors reconcile assay sensitivity differences inherent in the markers themselves? Have the authors verified each marker compared is amplifying at peak levels/maximal efficiency? No information is provided on this point. So even though it is possible the sensitivity differences shown could certainly be the result of more target copies available for detection, it’s also possible that some unknown level of the differences in sensitivity could also be caused by differences in inherent amplification efficiency (underlying nucleotide composition of each marker). Authors should provide some explanation, particularly in the discussion, on this point.
Response: Previous studies have compared eDNA sensitivity between nuclear and mitochondrial gene-targeted assays (Minamoto et al. 2017, Dysthe et al. 2018, Jo et al. 2020, Marshall et al. 2021), and Jo et al. (2022) explain how differences in concentrations between nuclear and mitochondrial gene targets can improve eDNA interpretation. In the current study, we tested all assays for PCR efficiency to ensure adequate performance across assays. This is described in the Methods (section 2.3 Assay Validation), Results (section 3.3 Assay Sensitivity – Standard Curves), and the PCR efficiencies are reported in Table 1 (see Comment 5 below for a more detailed response).
Comment: Introduction, paragraph starting at line 94: The argument made in this paragraph is again significant in comparing mt: mt assays, but the per genome copy number argument does not apply in making comparisons between multi-copy nuclear and mitochondrial assays since the number of mitochondrial genomes per cell (or per tissue) is not described in dreissenids. This is related to a similar comment above.
Response: See the above response. While we agree that comparisons between nuclear and mitochondrial gene assays are difficult, we still find value in comparing sensitivity and detectability across these assays. In this current study, we did calculate and compared qPCR efficiencies across assays (see Comment 5 below). We are confident that all assays were performing at similar efficiency and that differences in assay sensitivity within gDNA dilutions and within eDNA samples are related to the DNA copy number per genome for each assay (see Comment 5 for a detailed explanation).
Comment: Methods, line 158: EvaGreen was used so with no mention of hydrolysis probes all of these assays were performed as ‘non-probe-base assays, correct? There is no mention of how melting curve analysis was done in the PCR methods (although there is mention of melting curve analysis in one sentence of the results). Some of the assays compared were based on previously published probe-based assays. Authors should acknowledge that the previously published assays were converted to non-probe-based assays.
Response: The reviewer is correct that one of the assays used in this study was adopted from a previous study that used hydrolysis probes (Gingera et al. 2017 – 16S assay), but was adapted for simplicity with EvaGreen dye analysis here. The other three assays tested here were developed as non-probe-based assays (Peñarrubia et al. 2016, Blackman et al. 2020). We have included this information in the Methods (lines 206-209). Melt curve analysis was completed on an Applied Biosystems QunatStudio Flex 6 Real-Time PCR System after each qPCR run for each assay. Melt curves were inspected for a single peak corresponding to the same temperature as that from positive control samples included in each run. We have added this information into the Methods (lines 165-167).
Comment: Discussion, line 299: In this report, this specific marker comparison would likely have benefited by using synthetic standards since this comparison would have verified the exact target DNA copy number tested per marker to reveal any underlying inherent differences in marker sensitivity rather than assuming some unknown difference in copy number between nuclear DNA and mitochondrial DNA as well as some unknown number of tandem repeats. It may be true that using synthetic DNA standards for determining LOD and LOQ is not the best empirical strategy as the authors have pointed out, but the reader is still left assuming the sensitivity differences seen in the data are the result of specific differences in target copy number. However, the authors have not provided enough compelling evidence to rule out markers that may poorly perform in general. No mention of assay optimization is provided that may maximize efficiency for each primer set. It is also true based on table 1 that simple differences in the length of each marker may contribute to differences in inherent sensitivity. The gDNA comparisons provided also make broad assumptions on the copy numbers that may or may not have been tested and as such, the inherent sensitivity of each marker is still in question even with the gDNA comparisons. The authors correctly point out the mitochondrial make-up of target species cells, tissues, individuals, and/or populations is unknown, and this underscores the need for a comparison of marker sensitivity using synthetic DNA standards.
Response: In this research, we were specifically interested in investigating the efficiency and sensitivity of these assays within a real-world eDNA application. We hypothesized that these newly developed assays would display increased sensitivity within an eDNA sample because a cell shed within the water will have a higher copy of these tandem repeat regions compared to the commonly targeted “barcode” gene regions. We thus chose to perform standard curves across log-dilutions of genomic DNA derived from foot tissue for both species, rather than using the typically used synthetic standards. We did this because the gDNA samples provide a better representation of the concentrations of copies expected within a cell and therefore within eDNA samples. A synthetic standard will not provide a true representation of comparisons between assay sensitivity within an environmental setting, because the synthetic standards do not account for differences in copy numbers per genome. For example, a qPCR targeting a multi-copy gene for the anthrax pathogen Bacillus anthracis was found to have a similar LOD to established single-copy assays using synthetic standards, yet the multi-copy gene significantly improved detection by lowering the Cq threshold by >two cycles from environmental samples (Braun et al. 2021). This example demonstrates that a standard curve generated from gDNA provides a better comparison of sensitivity when comparing assays that target regions of DNA that differ in their copy numbers per genome.
- Furthermore, we can calculate the same performance metrics for a qPCR assay using a standard curve generated from log dilutions of gDNA to those that are calculated from synthetic DNA. Using the calculated slopes from each standard curve (see Figure 2), we can calculate PCR efficiencies for each assay used in this study. The calculated PCR efficiencies are included in Table 1, and all fall in line with suggested PCR efficiencies for eDNA qPCR analysis (Klymus et al. 2020, Langlois et al. 2021, Thalinger et al. 2021).
- We also compared the differences in Cq value between the COI and mtTR assays across each of the log dilutions within the standard curve. We found that the decrease in Cq value found with the mtTR assay was consistent across all log dilutions. This information is included in the Results: The ZM mtTR assay displayed a shift of 7.05±0.62 Cq values (Figure 2A), while the QM mtTR assay displayed a shift of 4.17±0.25 Cq values (Figure 2B). Because the shift in Cq value was consistent across the dilution series, it demonstrates that the assays provided consistent results across all the gDNA concentrations, and thus PCR efficiency was consistent across assays.
- Additionally, for comparisons between the COI and the mtTR assays, we directly quantified the concentration of gDNA within tank mesocosms and environmental water samples from Lake Erie. We then compared the quantified concentration of gDNA between COI and mtTR assays for both species. Comparisons across assays provided a strongly correlated estimate of DNA concentration, which included samples across a wide range of DNA concentrations (Figures 3 B&D, Figures 4 B&D). These strong correlations future suggest that PCR efficiency is not having an impact on the Cq value or quantification of DNA across these assays.
We can calculate the same metrics as those that are generated from synthetic DNA (e.g., PCR efficiency, LOD, and LOQ). Our data suggest that PCR efficiency was adequate for all assays and that the COI and mtTR assays provided similar quantification of DNA across mesocosm and environmental samples. Thus, PCR efficiency was not an important factor in establishing differences in sensitivity across these assays. We do not believe that performing standard curves across synthetic DNA dilutions would provide any added benefit to this study, as synthetic standards do not take into account the inherent differences in the number of copies per target within a genome.
Comment: Overall: Recommend publication with major revisions. Authors need to address points above, and would be a benefit to the paper to rule out inherent marker sensitivity differences by showing assays are running at maximal efficiency using synthetic standards and describing any differences realized. Authors have made a compelling argument for the targeting of tandem repeat regions in dreissenid mitochondrial genomes for increasing eDNA assay sensitivity, but the value in that argument should be framed as a ‘proof of concept’ development at this stage rather than any specific recommendations about marker design until more is known how these tandem repeat regions differ within tissues of individuals and across the dreissenid population as a whole.
Response: We agree with the reviewer that this work is a “proof of concept”, as many questions remain regarding these unique mitochondrial tandem repeat regions. Within the Discussion, we have highlighted that this is a proof of concept study within Lines 284-286 & 369-371. Here we detail how these tandem repeat regions were only recently discovered within the zebra mussel and the quagga mussel, and how the research community has many aspects still to investigate. For example, only a single individual has been sequenced for both species, and thus it is not clear how these repeat regions differ across individuals or across populations. Furthermore, it has been suggested that the number of repeats can differ between cells within a single individual, which would provide less quantification capabilities compared to a standard mitochondrial barcoding gene. Lastly, we assume these tandem repeat regions are also present within other Dreissena taxa, but there are no available mitochondrial genomic sequences for species other than zebra and quagga mussel. While the DNA sequence within the repeat regions is vastly different between zebra and quagga mussel, we do not know if it is different from more closely related sister taxa, thus we cannot guarantee cross-amplification from locations with co-occurring congenerics. However, most invaded habitats only contain one or both of the species tested here, which limits the concern for cross-amplification. Below are locations within the Discussion in which we address the above concerns:
- “It is not known the purpose of these mtTR regions (Calcino et al. 2020), or if they degrade at similar rates to coding regions within the mt-genome.” (Lines 317-319)
- Unlike single-copy genes, it is unknown how much variation occurs in the number of mtTR repeats within individuals, between individuals, and between populations. Currently, only one mt-genome has been sequenced for either ZM or QM, thus it is not possible to estimate population and spatial variation within this non-coding region. It is hypothesized that the mt-genome displays some levels of heteroplasmy, whereas the number of repeats can differ between individuals and even between cells within an individual (Calcino et al. 2020). Therefore, while both mtTR assays displayed increasing DNA concentration with mussel abundance across the mesocosms, the unknown level of heteroplasmy may result in misleading abundance estimates when quantifying eDNA with these mtTR assays. Additionally, no mt-genomes have been sequenced for other closely related Dreissena taxa, and thus it is not known if these mtTR assays will cross-amplify with other sister taxa in co-occurring habitats. However, these assays show clear specificity against the two tested species, with a clear distinction between ZM and QM mtTR regions. Continued investigations into the mt-genome structure across geographical populations and between dreissenid taxa will improve the evaluation and interpretation of quantified eDNA from mtTR assays in the future. (Lines 352-367)
Round 2
Reviewer 2 Report
Authors have done an adequate job addressing initial concerns. Other than a few minor comments/suggested edits (listed below), acceptable in present form.
The two comments below even though authors stated they were fixed, it does not appear those edits were reflected in the revised manuscript.
Comment: Introduction, line 36: ‘benthification’ is misspelled
Response: This has been fixed
Comment: Introduction, line 49: hyphenate ‘dreissenid-related’
Response: This has been fixed
