Complete draft of replies to Reviewer #1

R/sim_dist.R

@@ -37,8 +37,8 @@
 #'                    length as ages
 #' @param phi_year    Defines autocorrelation through years. Can be one value or a vector of the same
 #'                    length as years
-#' @param group_ages  Make space-age-year variance equal across these ages
-#' @param group_years Make space-age-year variance equal across these years
+#' @param group_ages  Make space-age-year noise equal across these ages
+#' @param group_years Make space-age-year noise equal across these years
 #' @param model       String indicating either "exponential" or "matern" as the correlation function
 #'
 #' @export

analysis/paper/rebuttal_letter.Rmd

@@ -30,14 +30,9 @@ A1C 5X1, Canada
 E-mail: [email protected]  
 Phone: (709) 772-2067  
 
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@@ -49,16 +44,11 @@ I found the paper very interesting and the package of great potential value and
 
 1. Verify some of the equations and model description used in the model. I think that some of them lack description to fully understand what was done and few others are misleading, even incorrect. The detail can be found below but in general, they are linked to the problem of bias correction for log-normal distribution, re-scaling”, moving from abundance at age to abundance at length.  
 
-*Please see our specific responses to lines X, X, and X.*
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 2. Add additional explanation to justify the choices. E.g. why population dynamics is modeled independently of the spatial distribution. Is this reasonable, limitation, etc.  
 
-*TODO*
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 3. Few suggestions (add-ons, renaming) to increase the generality of the paper (without requiring too much work)
-Please see our specific responses to...  
 
-*TODO*
+*We hope we have addressed each of these main comments by 1) clarifying several equations, 2) adding more details and justifications, and 3) modifying naming conventions and clarifying extensibility. See replies below for more details.*
 
 Detailed comments:  
 
@@ -109,39 +99,39 @@ I have a few comments on this section:
 
 3. L161. I think it would be good to say upfront that there are two ways of defining the spatial structure. Using the function in the package or user defined.
 
-*TODO*
+*Given a suggestion from Reviewer #2, we have restructured the paper to have two core sections: "Model structure" and "Using SimSurvey". We have included a blanket statement under the "Model structure" section noting that users can circumvent specific components of the framework.*
 
 4. L161: I think another possibility is to generate a random field by using the package “Randomfields” for example. This way you can generate a map where “depth” is patchily distributed (maybe more like an island type case study)  
 
-*TODO*  
+*Good idea! We actually pursued this idea in an earlier iteration of `make_grid`, however, we abandoned the option because it was difficult to automate; the random field created random problems with the depth-stratification (e.g. one cell strata).*
 
 5. L163-164: please add more description about the division or strata. How can we set it up and what do you specifically mean by division and strata? Is one nested within the other, or not necessarily? Your examples are based on Atlantic Ocean and people in other regions might not be familiar with how these divisions are created.  
 
-*TODO*
+*We have added more detail on the structure of the divisions and strata.*
 
 6. L163: Why only focus on “depth-based strata”? I think it would be good to allow the user to choose their own stratification approach. It could be depth based as you did (which happens most often in surveys) but it could technically be any other thing (user supplied). This allows more flexibility.  
 
-*TODO* 
+*See reply to point 2. above.* 
 
 7. L167: the equation is misleading and I am not sure it is right based on what is written in the text. You mentioned later on L178: that you “re-scaled” so that the total number of fish in a specific year and age across space is equal to the single number from the population dynamics model. If so, the re-scaling should be done in the identify (natural) scale, not in log scale. In log scale, even if ,, sums to zero across space, the sum in the identity scale won’t match. This is often refer to as “bias correction” for the log normal models. And you should ideally show how the rescaling was done in terms of the equations too.  
 
-*TODO* 
+*Right, the equation presented was not an accurate reflection of the calculations. We have revised the equation to explicitly show how the values were normalized to sum to 1.* 
 
 8. L167: this “depth preference” function is very simplistic and gives only very “smooth” symmetric distribution. More often, fish have a skewed depth preference: often right-skewed.  
 
-*TODO*
+*True. Some users may find this parameterization insufficient for their species and we hope they will implement their own closure to use in the `sim_distribution` function to better simulate the effect. In addition to our blanket statement under the "Model structure" section, we have added a more specific statement under the "Using SimSurvey" section stating that alternate formulations can be used by supplying alternate closures to the core functions.*
 
 9. L173-174: I think it might be worth adding, in simpler terms, the meaning of the spatial smoothing and scaling parameters.
 
-*TODO*
+*We have prefaced that sentence with "The rate at which point-to-point spatial correlation decays with distance is controlled by...".*
 
 10. L178: it is another question of scaling. How did you exactly do the scaling? In the identity scale? By dividing my the sum of the effects? I am asking this because depending on how you did the rescaling, your correlation structure in space and age might have been affected and is not the same as the one specified in L172. Did you verify that?
 
-*TODO*
+*See reply to point 7. above*
 
 Table 4. “group_ages”. Ok but how is the variance controlled for the other age classes?  
 
-*TODO*
+*"Variance" was a poor word choice. We have replaced it with "noise" as it is the simulated noise that we fix across multiple age groups.*
 
 L189: “user supplied”. This is a good feature. However, I think it is important to mention here that user have to make sure that they use the correct projection method to ensure that each grid is of the same dimension.  
 
@@ -153,39 +143,39 @@ L216: reference to figure 3?
 
 L221: there is not “group_years” argument in Table 4.
 
-*TODO*  
+*We have added it to the table.*  
 
 L237: “this function”. I think it would be better to replace “this function” with “sim_distribution” as you do not mention the word “sim_distribution” in the sentence above this.
 
 *We have made the suggested change.*
 
 L158-251: In general, I think re-organising this section using sub-section headers could be useful. Just to guide the readers  
 
-*This is an excellent suggestion and we have inserted headers where appropriate.*
+*This is an excellent suggestion. By following a suggestion by Reviewer #2 to re-organize the paper into two core sections we have added more headers to help guide the readers.*
 
 L254: you say that sampling is stratified random but SRS is also an option based on Table5. Please correct.  
 
-*TODO*  
+*We have made the suggested change.*  
 
 L257: what does this mean? Does this control the number of set but how is this calculated?
 
-*TODO*  
+*We have clarified how number of sets per strata is calculated.*  
 
 L257: I do not see how you control for the total number of set in the survey? How do you control it?
 
-*TODO*  
+*We have clarified how number of sets per strata is calculated.*  
 
 L261: I think you should mention here that you can also force the sample size (as seen on table 5). Moreover, in table 5, it would be good to set-up a “ages_min” for the minimum number of ages to sample […]” so that it gives the ability to fix the sample size if needed by writing the same value for “ages_min” and “ages_cap”.
 
-*TODO*  
+*We are not sure what the reviewer would like to have implemented here. Is the suggestion to impose a minimum number of ages to collect across all length groups?*  
 
 L261: How are you making sure that the number of sampled fish for that specific cell, age and year won’t be above the total number of fish in that cell, age, and year? The probability value could be close to one and if you fish in a few a time, then you are at risk. Especially because your population dynamics model is not spatially explicit and is completely independent of the distribution function itself i.e. you can technically fish out all the fish in an area but it will be populated back the year after the way you implemented in this study… Maybe you need to put a condition (or just a note) for general users to make sure that this probability value is much below 1?
 
-*TODO*  
+*The sampling is implemented such that the number of fish sampled in a cell cannot exceed the number of fish in a cell because the population is split across sets in cases where more than one set is conducted in a cell. We have added this missing detail to our manuscript. We also added a note that the survey is assumed to have no impact on the population from one year to the next.*  
 
 L267: I recommend to clarify something here. 1. Depending on the number of fish caught? What do you mean? What is the rule you used? 2. The way you coded, sample by age is first decided, then the corresponding length is calculated, then age-subsample is determined. In reality, length sample is taken in the field, then age sub-samples are taken. While similar, I do not think it always equal. Especially, when you start including some correlation structure in the sampling. By the way, did you consider including some correlation structure in the sampling process to make more realistic?
 
-*TODO*  
+*Honestly, we do not recall what we mean by "depending on the number of fish caught". Perhaps we added those words to cover off cases where no fish are caught. Whatever the case, we have removed the statement to minimize confusion. We have also clarified the sub-sampling sequence. Finally, we have yet to consider including correlation structure in the sampling process as we went about imposing correlation via the spatial correlation of age groups (i.e. age-specific clustering tends to result in sets with high intraclass correlation). We are open to learning more about other processes that may contribute to correlated samples.*  
 
 
 L275 Table 1 on should be table 5
@@ -198,23 +188,23 @@ L275: Table5: “age_sammpling” should be “age_sampling”
 
 L275: “min_sets” you have not described it yet and what is it? You have sample from all cells? If no, this is not realistic.
 
-*TODO*  
+*We have clarified the meaning and utility of the `min_sets` argument (i.e. a small strata may be allocated only one set under a low set density scenario; this argument overrides the allocation and imposes the `min_sets` if it is greater than the allocation).*  
 
 L279: Table5 not Table1?
 
-*We have changed the page numbers accordingly.*
+*We have changed the numbers accordingly.*
 
 L285-286: Could you be more specific on how custom closures can be supplied and where?
 
-*TODO*  
+*We have included an example that ought to clarify how a custom closure can be supplied.*  
 
 L306: how are these catchability corrected abundance matrices calculated? It is important to write this information somewhere (or write “please refer to the section “Stratified analysis” for further information on the calculation of abundance indices”) or something alike and Appendix S3.
 
-*We have adopted the Reviewers suggestion and referred the reader to the section on Stratified analysis.*
+*We have clarified how this was calculated.*
 
 L336: I think it would be good to say that other methods exist and people can use it in this package (maybe)?
 
-*TODO*  
+*Good point, however, we think this is covered by referencing a paper that describes a geostatistical R package and we also note that other options can be used under the "Research opportunities" section.*  
 
 L421: color gradient. Even though it is obvious it might be good to say green to purple gradient.
 
@@ -230,7 +220,12 @@ L452: say that the color ramps from yellow to purple
 
 S1 appendix: missing figure in S1
 
-*TODO*  
+*We have included the figure*  
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 Reviewer #2:  
 
@@ -241,7 +236,7 @@ This manuscript describes an R package called SimSurvey. The package includes a
 The title announces a package for optimizing survey designs. As far as I can see the package does not allow survey design optimization, neither in terms of defining survey strata nor in terms of number of stations per stratum. The strata are defined by the user. The only option available for the number of stations is proportional to stratum surface; the user sets the minimum number of stations taken in the smallest stratum. It would be useful to be able to specify the total number of stations and test different allocation schemes, such as proportional to surface area (implemented), equal number per stratum, Neyman allocation (accounting for surface area and abundance variability), etc.
 Please consider revising the title (e.g. “compare” and instead of “optimize)” and spell out the available sampling design options.  
 
-*We agree with the Reviewer and retitled the ms "`SimSurvey`: an `R` package to compare the design and analysis of fisheries surveys by simulating spatially-correlated fish stocks".*
+*We agree with the Reviewer and retitled the ms "`SimSurvey`: an `R` package for comparing the design and analysis of fisheries surveys by simulating spatially-correlated fish stocks".*
 
 2. Manuscript structure