Phytoplankton Diversity and Co-Dependency in a Stratified Oligotrophic Ecosystem in the South Adriatic Sea

Round 1

Reviewer 1 Report

SPECIFIC COMMENTS:

The paper a potentially interesting paper and I believe I have some comments that may make the paper a bit more quantitative and focused on probabilistic patterns with precise assessment of the linkage between biodiversity and environment and their complexity to define systemic risk for the ecosystems considered. I am mentioning this because the paper is based on correlation and linear assumptions that make really hard to define (1) systemic variability and (2) changes that account for spatial and or temporal long-range dependencies. I do like the ecological inventory but I feel like there is an untapped potential. Some of these aspects may be done in this paper or in future endeavour.  

 

 

GENERAL COMMENTS: 

(1) The ecological-environmental processes you investigate are largely non-linear. Non-linear models such as Convergent Cross Mapping, OIF (see Galbraith & Convertino 2021 for instance), or PCMCI (see Runge et al. 2019), Empirical Dynamical Modeling or other Machine Learning models, can account for variable non-linear interactions even without considering time delays (that are however quite important for example for your ecological dynamics, i.e. Phytoplankton and Environmental dynamics, where you are lucky to have 3D+1 dimensions). I believe this aspect is important and at least you should discuss that other spatial factors may exert a strong non-linear variability into the outputs/predictands. It would be particularly important to develop a joint indicator that fuses together salient indicators (causal predictors) that are critically important for the predicted ecological patterns and make that normalized across ecosystems to perform a comparison among those. This is also useful to define salient ecological (predictive) networks.  

 

(2) To address the model/analysis Uncertainty-Sensitivity-Relevancy coupling, global sensitivity and uncertainty analysis (GSUA) should be done to identify key determinants of model/analysis indicator variability. See Pianosi et al. (2016) for an extensive discussion about this topic and how data should be used for GSUA using a simple variance-based approach, or by using network-based models as in (1). It is essentially looking into how much variability in contained in inputs for the variability of outputs (or event better into the co-predictability versus causality based on probability distribution functions, i.e. pdfs). This is to define salient eco-environmental triggers that may be suitable for ecosystems monitoring and management.

 

(3) How indicators/predicted variables change over space/time is critical to understand site-/time-specific and universal (ecosystem invariant) shifts. Thus, indicator distributions (i.e. Phytoplankton) can be analyzed as a function of the environmental variables (or predictors) considering joint probability distribution functions (pdfs), or average value and variance and looking into indicator variability as a function of predictor gradients (env factors). Stability of ecological patterns over predictor gradients is important to quantify because that can define potential stable states over which the predicted variable is not changing, tipping points as well other potential unobserved states (e.g. how Phytoplankton is likely stabe in its community composition considering all configurations with their probability and transitions). This analysis may also be a byproduct of GSUA considering the most important predictors. 

 

 

RECOMMENDATION:

Overall I suggest that the paper can be accepted after Moderate Revisions (I wanted to give Moderate Revision but that seems not possible so I suggest ''Major''). I believe that some quantification and/or discussion above the above topics (especially the linearity/causality aspect) is needed: some others are extra elements, nonetheless important, that can be discussed a bit in the paper as well as in future efforts. In any event it is a very interesting datasets and I hope this will be made available. 

 

 

 

 

REFERENCES:

The Eco-Evo Mandala: Simplifying Bacterioplankton Complexity into Ecohealth Signatures

E Galbraith, M Convertino

Entropy 23 (11), 1471, (2021)

 

Pianosi et al. (2016)

Sensitivity analysis of environmental models: A systematic review with practical workflow

Environmental Modelling & Software

Volume 79, May 2016, Pages 214-232

 

Runge et al. (2019)

Detecting and quantifying causal associations in large nonlinear time series datasets

https://www.science.org/doi/10.1126/sciadv.aau4996

 

Packages for GSUA 

- https://www.safetoolbox.info/info-and-documentation/ 

 

Author Response

We thank for the informative and useful review on statistical analysis and acknowledge the comments. It has helped us a lot to critically review our data and analyses. Due to reviewers comments we did conclude that the spearman correlation method is not a good fit for non-linear relationships between our community and environmental data. Therefore, we decided to omit it (Table 3B) from the paper.

Convergent cross mapping (CCM) analysis was done using package ‘’rEDM‘’ to assess the causal relationships between environmental variables (temperature, Chl a, and nutrients)  and community (micro-, nano-, and picophytoplankton, and heterotophic bacteria).

Cross-map skill scores obtained by CCM analysis (Table 1) indicate that pico-fraction of community (PPEs, Synechococcus, Prochlorococcus and heterotrophic bacteria) exhibited stronger relationship to nutrients in comparison to other groups (Table 1a). Direction of the causality was positive, indicating the increase of nutrients and temperature in the water layer may had a direct impact on the higher picophytoplankton and bacteria abundances, specifically NO₃ and SiO₄ on Synechococcus and Prochlorococcus, and NO₂ on PPEs and Synechococcus (Table 1a). Temperature may have positive causal influence on heterotrophic bacteria (HB) (Table 1a). Cross-map skill scores for opposite direction (Table 1b) indicate that abundance increase of nano-, and picophytoplankton may be an indicator of higher temperature in the water layer, as well as higher NO₃ concentrations. Increase in all fractions of phytoplankton can be causally connected to the increase of SiO₄ concentrations. However, since the statistical significance of cross-map skill scores was not high enough (at least we were not confident enough), therefore the results should not be interpreted with high certainty and have decided not to include the analyses in the msc. On the other hand, we do have CCA in the msc showing good correlation of environmental variables and plankton community that we believe is sufficient for our data support.

Please see in attachment the the significance of the cross-map skill scores. Since the analysis was done for the first time, we can provide R script with the code and data used to run CCM analysis, and any further advice on the analysis is welcomed.

Comments (2) and (3) are also acknowledged, however GSUA was not implemented on our data due to lack of time to comprehend the new methods we did not use before in our analysis. However, we will for sure consider learning more about it and implement it correctly in the future data analysis.

Author Response File: Author Response.pdf

Reviewer 2 Report

I read the manuscript with interest, despite the fact that, in my opinion, it is excessively verbose. I found a few typos, but they did not obscure the message. I won't comment on the quality of the genetic research as it is not my area of expertise. I have included the rest of my comments or identified imperfections and doubts in the attached file

Comments for author File: Comments.pdf

Author Response

We thank the reviewer for their valuable comments on the manuscript and have helped us to further improve the manuscript. Our responses are given in a point-by point manner in attached file and changes in the manuscript are shown by track-changes mode in Word.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

I would have appreciated much more analysis into the structure of biodiversity vs. just reporting diversity but ok, for everything else the paper and revisions are ok. Can be accepted for publication.