PONE-D-21-06875

Driving the blue fleet: Temporal variability and drivers behind bluebottle Physalia physalis beachings off Sydney, Australia.

PLOS ONE

Dear Dr. Bourg,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Before this ms can be published, substantial changes need to be implemented to augment the analyses and strengthen the main lessons of the paper.

The introduction needs to be streamlined. For instance, the second paragraph (lines 17 to 46) is very lengthy and could be split into smaller focused sections.   The next two paragraphs (lines 47 to 94) could also be streamlined, and some of the material could be moved to the discussion, where it would be placed in context of the project’s findings.

As stated by one reviewer, the authors have not performed the proper statistical analysis to sustain their conclusions. Even though several papers dealing with the analytical approach required to understand environmental forcing are cited, the authors have only used person correlations in their analysis. The authors have to improve this section (by including a whole Data Analysis section in the methods) and undertake a more comprehensive analysis of the data at hand.to determine the influence of these factors (and potentially their interactions) on the beachings (and the summer stings).  The current piece-meal approach, where a single variable is considered at a time need to be augmented and strengthened. 

To facilitate the understanding of the patterns, I would also suggest focusing on the two sites with year-long data and removing the third site (rocky shore with only summer-time data).   Limiting the analysis to the two sites with year-long data (Clovelly and Maroubra) provides a more comprehensive and comparable perspective.  The ms already explains that this site is inherently different: “Note that Coogee has a small rocky outcrop (known as the Wedding Cake Island) 740 m from the beach, which limits wave action on the beach. Clovelly beach is more South-oriented and is at the end of a narrow bay, hence more protected than the two other beaches (Fig 1).”

Additionally, the Physalia physalis datasets need to be analyzed in a more quantitative fashion.  In particular, I would suggest the following analyses:

* Number of beachings:

Compare the number of beaching observations versus the number of survey days from a beach to beach.  There are 38 and 132 beaching reports for Clovelly and Maroubra respectively, even though the two beaches were surveyed on 94% and 93% of the days. Is this difference significant?  Is there an overall higher beaching rate in Maroubra?  Despite the data gaps, I would suggest you perform a cross-correlation to quantify how well the beachings data at the two beaches cross-correlate with each other. 

* Number of stings: 

I would suggest focusing this analysis on the same two beaches used in the beachings analysis, and discarding the data from Coogee.  Despite the data gaps, I would suggest you perform a cross-correlation to quantify how well the beachings data at the two beaches cross-correlate with each other. 

You state that “More than 10 stings have been reported 6, 9 and 10% of all patrolled days for Clovelly, Coogee and Maroubra”. 

Why did you not consider days where less than 10 stings have been recorded?  You could use values above and below this threshold as two separate categories (low and high), or you could take the log10-transform of the data.

How was this threshold number selected?  Seems like anomalous events should be determined on a beach-basis, not using the same threshold across all beaches.   I would suggest you provide a data summary of the number of stings reported per day, and then attempt to model these distributions to figure out “outlier days” for each beach. 

* Number of beachings VS Number of stings:

It would be very useful to investigate whether these two datasets are correlated.  Using the summer-period only, when stings are reported, can you perform a correlation for each beach, to see if there are more stings on days with more beachings.  This would be a very informative analysis.

* Wind Data: 

Can you please define the wind sectors and provide some summaries of wind speed / direction for the different seasons?  The ms currently states “predominant winds in this area are north-easterly, westerly and southerly, as shown on the windrose in Fig 1.”

The analyses of beachings per wind direction also need to involve statistical tests, using either chi-square tests or logistic regressions.  Reporting mere proportions is not enough.  You need to provide a sense of the variability (SD for the proportions) and the associated p values.  

* Ocean Currents:

Can you please report how well the near-surface and the integrated currents correlate with each other?  And report how well they match the wind speeds?   Currently, the ms states: “Here, we used daily averages at the shallowest bins (11 m and 12 m, respectively) and the depth integrated flow”.

* Seasonality:

The proportion of beachings needs to be statistically related to the different seasons.  This could be done with a chi-square test or using a logistic regression model, with the response variable of presence / absence of beachings.  The latter approach would be better, because it would allow you to assess the influence of other variables at once, including inter-annual variability.  Currently, the ms merely reports the %s of summer / winter days with beachings, and a metric of variability (SD for the proportions) is needed  Moreover, these proportions need to be compared statistically, using p values and measures of effect size (like the odds ratio).

* Lags and Multiple Temporal Scales:

While the paper mentions a “zero” lag and provides results at daily and weekly time scales, it is unclear how many lags were tested and how the weekly data were averaged and analyzed.  I would suggest you provide a summary table, showing what analyses were done, listing the lags that were attempted and the different temporal scales that were considered.

* Multi-variate Analyses:

These environmental factors are likely cross-correlated:  wind speed / direction, currents, water temperature.  I would ask the authors to explore these cross-correlations and to provide a supplementary table where these results are summarized.  If there are significant cross-correlations, I would urge the authors to use partial correlations to explore the influence of the drivers, after accounting for other cross-correlated variables. 

Moreover, it would be useful to know whether these environmental drivers differed seasonally and from year-to-year (within seasons).  This would provide the readers with a broader oceanographic background of the study area and the potential drivers. 

Finally, I would also suggest you summarize the weather (wind / current) and water temperature conditions measured during periods of unusually high and unusually low beaching (and stringing) periods.  This would provide a complementary perspective to the previous modeling approach, which would give readers a more in-depth understanding of the drivers of unusual “events”.

Please submit your revised manuscript by Jun 18 2021 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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We look forward to receiving your revised manuscript.

Kind regards,

David Hyrenbach, Ph.D.

Academic Editor

PLOS ONE

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Reviewer #1: Dear Author,

You have made a great work into collecting and preparing a several-year dataset of stranded Physalia physalis colonies in three beaches in Australia. However, because they represent just three specific geographical points with their owns costals dynamics (in sence of oceanography and meterology), I recomend you to look forward a journal with emphasis in local studies.

Moreover, you haven't perform the proper statistical analysis to sustain your conclusions. Even you have cited very good papers dealing with the analytical approach required to understand environmental forcing on straned Physalia colonies, you have used just person correlations in just some statements. You must to improve this section (by including a whole Data Analysis section in methods, for instance).

The remaining of the manuscript is based on this (non proved) correlation and forcing and by this make it impossible to approve.

The relationship with the stings again is lacking of the proper statistical approach and must be reformulated.

Reviewer #2: SOME COMMENTS ABOUT THE PAPER:

After reading the article, I write down some recommendations and some thoughts that could help the authors to tweak some comments made throughout the article.

The authors use some apostrophes in grammatical structures where their use at the scientific article level may not be necessary or these structures can be rewritten. It is recommended to review its use with a native speaker, such as in:

…P.physalis's morphology

…P. physalis's course

Line 67:

For validation, these models were compared to massive beaching events that occurred in summer 2010 off the Basque coast (France) and the Mediterranean Basin.

The Basque coast is in Spain, in the autonomous community of the Basque Country. This community ends at the border between Spain and France. In the summer of 2010, the presence of Physalia occurred at several beaches along the coast. But on some important beaches, such as La Concha Beach (in the city of Donostia-San Sebastián), small fishing boats were transformed into cleaning boats and left the beach area to meet the Portuguese man-of-war and collect them before their arrival along the beach.

Line 194:

The few number of sightings in winter as well as P. physalis supposed lifecycle could be explained by a collective death in winter.

For me, a low or no number of winter sightings in the coastal area does not mean that there are a large number of deaths in the open sea. It is to be expected that there are always Portuguese man-of-war of different ages drifting for months in the great oceanic gyres (using the wind as main driver) with a peak of reproduction that could occur at the end of summer-beginning of autumn. On the European coast of the North Atlantic Ocean, it is typical that during the winter (not only in the months of summer) there is also a notable presence of small Physalia (3-5 cm long float, 3-4 months old) that due to the very strong southerly winds of successive storms (favourable to dragging towards the Bay of Biscay) have caused the appearance of Physalia on the coast to be anticipated. Therefore, the highest mortality possibly occurs when these organisms reach the dry beach, where they no longer leave and end up dying. These organisms do not appear to die from the severe winter conditions, at least in the North Atlantic Ocean. These conditions can make it possible for them to reach the coast at a time other than summer. It is for this reason that it is important to monitor the beaches outside of the time that the beaches are patrolled and during the lifetime of the organisms.

Line 348:

[11] similarly suggest that wind is a dominant driver of P. physalis transport, but propose wind to be more relevant offshore and ocean circulation becoming the main driver in nearshore areas.

[11] suggests that the wind is the most relevant mechanism both off and on the coast for this peculiar organism. The very superficial ocean circulation (considering this as the one that exists in the first 5 centimetres of the water column, where Physalia lives) in the great gyres of ocean circulation is greatly influenced by the wind, as shown by very low-weight drift buoys floating on the surface. The data of these buoys shows that the surface ocean circulation is far from following the Ekman theory (that is, generating a surface current at 45 degrees from the wind). It is for this reason that possibly the best solution to explain the drift of Physalia is to use the wind, because also the wind is the generator of local waves and the circulation at the upper centimetres of the water column.

Line 358:

In addition, there was a high frequency of beaching events in spring recorded during weeks that were dominated by south-westerly winds, as can be observed during September and May in Fig 5. This result is surprising since beachings would not be expected when wind is coming from land, if wind were the only driving variable.

To study the arrival of these organisms, it would be necessary to analyse not only the winds of the days prior to arrival, but also the evolution of winds throughout the life of these organisms, which could be from a few months to a year (more or less), depending on the size of the organism. Prevailing southwesterly winds could probably bring many Physalia located in the open sea below Australia. And winds from the northeast, east or southeast (in the days prior to arrival), even if they were of short duration, could cause these organisms to end up in the study beaches. Therefore, it is highly recommended to analyse the annual evolution of the wind in a very large area (several degrees in longitude and latitude) around the study area. Surely these organisms have been able to travel more than 10,000 kilometres on their journey to reach the beach.

Line 371:

Observations of ocean currents closer to the surface and of higher resolution (e.g. coastal High-Frequency RADAR) may be necessary to expose any dependence of beaching events on these variables.

The fundamental problem with using high-frequency radar observations to explain caravel drifts is that they provide information on currents at 1-3 meters above the surface. This information is quite different from that existing in the same ocean-atmosphere interface, that is, in the first centimeters of the water column. So to speak, the Portuguese caravel is a very light balloon (a caravel of 10 centimeters of float can weigh around 25 grams) that has tentacles that act as an anchor so that it does not fly. So it seems unlikely that trying to explain their drift with currents below 5-10 centimeters from the sea surface will not do much.

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Attachments

Attachment

Submitted filename: PONE-D-21-06875_reviewer.doc

PONE-D-21-06875R1Driving the blue fleet: Temporal variability and drivers behind bluebottle Physalia physalis beachings off Sydney, Australia.PLOS ONE

Dear Dr. Bourg,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

The authors have made substantial changes to the manuscript, but there are many pending issues that need to ne addressed, before this manuscript can be published.    

The main issues that need to be addresses are:

1. The paper describes analyses, but does not provide the necessary details to interpret the results

Line 111:“We explored whether sting numbers were dependent on numbers of beachgoers, but found no clear correlation between the two” Can you please explain how this was done and what were the results?

Line 121:“The daily match between these two datasets needs more investigation”. These statements also need more detailed reporting of the analyses. It is not possible to assess what was done and what is the correspondence between “stings” and “beachings”.

2. There are several untested assumptions for the statistical tests. 

Line 169:

NOTE:

The binary response data are not normal - but binomial.

The rip currents are rank data, not numerical.

3. Some of the discussion of the results compare proportions or discuss similarities, without actually performing the statistical analyses.   For instance:  Line 192:Did you attempt crosscorrelations between the two sites? This would be a much better way to assess covariability.

Table 3 and Table 4:  Can you please compare the observed proportions versus the expected proportions?  How frequently are these wind conditions observed, will influence whether these results are significant or not.

Line 198:  “Indeed, between 2016 and 2020, 50% and 46% of strandings occurred during the three months of summer in Maroubra and Coogee respectively. In Maroubra, spring is (after summer) the second season with most beaching events (30% of beachings), whereas in Coogee, beaching events are more numerous in autumn (25%) than spring” 

Can you please perform tests to determine if seasons matter: statistically speaking?  Merely mentioning the proportions of events is not enough to determine whether these proportions are significantly different from what we would expect.  Did you define the seasons equally, so each one accounts for 25% pf the time?  This would et the expected proportions.  But we need a way to assess if these proportions are significantly different from the observed proportions.

4. The writing needs to be organized:  much material needs to be moved:

• From Methods to Introduction (see notes in the pdf)
• From Results to Discussion (see notes in pdf)

5. The writing needs to be improved substantially to streamline the text, clarify the writing and fix some grammar and typos (like the persistent use of “data” in singular).

6. Finally, Page 3 – Figure Caption 1:  Can you please credit the images.

You used images from “Satellite image of the different beaches (From The Gateway to Astronaut Photography of Earth”.  Is this a free creative commons product? 

Can you provide a reference?    I found the site, but there is no information on the use of these images:  https://eol.jsc.nasa.gov/SearchPhotos/

Please submit your revised manuscript by Jan 02 2022 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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Kind regards,

David Hyrenbach, Ph.D.

Academic Editor

PLOS ONE

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Submitted filename: PONE-D-21-06875_R1_Editor_Revisions.pdf

Driving the blue fleet: Temporal variability and drivers behind bluebottle Physalia physalis beachings off Sydney, Australia.

PONE-D-21-06875R2

Dear Dr. Bourg,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.  Thank you for addressing all the requested changes and revisions.

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