the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Computation of Self-recruitment in Fish Larvae using Forward- and Backward-in-Time Particle Tracking in a Lagrangian Model (SWIM-v2.0) of the Simulated Circulation of Lake Erie (AEM3D-v1.1.2)
Abstract. Accurately estimating self-recruitment (SR), the fraction of recruits in a location that originated locally, is critical for understanding population connectivity. Biophysical models have been typically applied to compute SR by releasing a certain number of larval particles from each assumed source location and tracking them forward in time. However, various strategies have been employed for releasing these larval particles: including randomly, consistently, or a number proportional to the location’s area or larval production, which causes ambiguous results. We demonstrate, using theoretical arguments and numerical simulations from Lake Whitefish (Coregonus clupeaformis) larvae in Lake Erie, that SR depends on larval production at each source location. This dependency suggests that SR may not be computed unambiguously in these models unless realistic larval production is released from all potential source locations. In contrast, parentage analysis studies typically computed SR by assessing the fraction of sampled juveniles that originate locally at a settlement location, instead of identifying larval production at all sources. Therefore, tracking larval particles backward from the settlement location is proposed as a straightforward approach for computing SR. Our findings demonstrate that SR is independent of the number of larval recruits at the settlement location, supporting the employment of backtracking models with randomly released larval particles. In this way, considerable effort and resources, that would otherwise be spent on identifying all potential sources and their larval output, in forward tracking can be saved. We believe this result will have important implications for studies on larval dispersal and recruitment in aquatic systems.
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Status: final response (author comments only)
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CEC1: 'Comment on gmd-2024-215', Juan Antonio Añel, 09 Apr 2025
Dear authors,
I have to point out a couple of minor issues regarding the replicability of your manuscript, and therefore the compliance with the policy of the journal. First, it would be good that you indicate the exact Matlab version that you have used in your work. This is important to be able of tracking back the implementation of some algorithms, routines or functions that you could have used. Also, it is necessary to be able to track the potential impact on your work of bugs that could be discovered in Matlab in the future, as has happened in the past with many others.
As you use Matlab, I understand that your code is in the M Language. I have not seen among the assets published in the Zenodo repositories such code, but a bunch of binary .mat files. I ask you to publish these files in a format that is not proprietary and binary, and can be accessed without using a proprietary software such as Matlab. Please, do it.
Additionally, if your code can be run with free software, such as GNU Octave (it would be desirable that it is), you should note it. Please, check it.
Juan A. Añel
Geosci. Model Dev. Executive Editor
Citation: https://doi.org/10.5194/gmd-2024-215-CEC1 -
AC1: 'Reply on CEC1', wei shi, 17 Apr 2025
First, it would be good that you indicate the exact Matlab version that you have used in your work. This is important to be able of tracking back the implementation of some algorithms, routines or functions that you could have used. Also, it is necessary to be able to track the potential impact on your work of bugs that could be discovered in Matlab in the future, as has happened in the past with many others.
Response: We thank the Chief Editor for the review. We used version R2024a and we have noted it in the manuscript.
As you use Matlab, I understand that your code is in the M Language. I have not seen among the assets published in the Zenodo repositories such code, but a bunch of binary .mat files. I ask you to publish these files in a format that is not proprietary and binary, and can be accessed without using a proprietary software such as Matlab. Please, do it.
Response: All the data and code (as well as a "read me.docx" file) have been published in .txt formate and are publicly available at https://zenodo.org/records/15222144, readers can access these files through the free softward notepad++.
Additionally, if your code can be run with free software, such as GNU Octave (it would be desirable that it is), you should note it. Please, check it.
Response: We have added the following to the code section of the manuscript: The SWIM-v2.0 model was implemented in MATLAB R2024a using standard functions and without reliance on specialized toolboxes. While we have not explicitly tested the code in GNU Octave, we expect that it should run in Octave without or with only minor modifications. Users are welcome to adapt the code for use with Octave or other compatible environments.
Citation: https://doi.org/10.5194/gmd-2024-215-AC1 -
CEC2: 'Reply on AC1', Juan Antonio Añel, 18 Apr 2025
Dear authors,
Many thanks for your reply, and for caring about improving the replicability of the manuscript.
Juan A. Añel
Geosci. Model Dev. Executive Editor
Citation: https://doi.org/10.5194/gmd-2024-215-CEC2
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CEC2: 'Reply on AC1', Juan Antonio Añel, 18 Apr 2025
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AC1: 'Reply on CEC1', wei shi, 17 Apr 2025
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RC1: 'Comment on gmd-2024-215', Romain Chaput, 07 May 2025
Title: Computation of self-recruitment in fish larvae using forward and backward in time tracking in a Lagrangian model (SWIM-v2.0) of the simulated circulation of Lake Erie (AEM3D-v1.1.2).
I have carefully reviewed the manuscript by Shi et al., which presents a valuable theoretical and modelling framework for estimating self-recruitment (SR) in larvae by using both forward and backward particle tracking in a biophysical model. The approach is based on a Lagrangian model. This methodology is especially used in the context of population connectivity studies, where the accurate estimation of SR is important for ecological research, conservation planning, and ecosystem management.
The manuscript is clearly written, well-structured, and provides a strong demonstration of the methodology through a case study in Lake Erie. Notably, the authors show how this approach can be linked with genetic studies to validate or complement connectivity estimates, which significantly broadens the applicability of their method.
The study is timely and offers a potentially general tool for assessing self-recruitment, larval dispersal and connectivity in aquatic systems. Overall, I support the publication of this manuscript after minor revisions. My main concern relates to the number of particles used in the simulations and how this might influence the robustness of SR estimates.
Minor Comments:
- The manuscript would benefit from a more detailed discussion on whether the number of released particles is sufficient to saturate the system, especially given the stochasticity introduced in the simulations. A paragraph in the Discussion section addressing how particle number affects SR estimates, and the potential biases introduced by under-sampling, would strengthen the paper. Ideally, some justification or sensitivity analysis could be added or referenced.
- It would be valuable to include a short discussion on how larval mortality during dispersal and settlement could influence SR estimates. Furthermore, if the model outputs or the analytical framework could allow for the inference of mortality (e.g., when both larval production and SR are known), this should be briefly discussed.
Specific Comments:
- Lines 15–17: The sentence beginning by “However, various strategies have been employed…” is somewhat confusing. Consider rephrasing for clarity.
- Line 231: The number of larvae released should be explicitly stated in the main text, along with a brief rationale for varying numbers across different release locations.
- Line 232: Mention that the tracking duration corresponds to the pelagic larval duration (PLD) of the target species.
- Line 235: Please clarify why forward tracking is conducted from four regions while backtracking is performed from only two. Does this reflect known settlement areas or observed recruitment patterns in Lake Erie?
- Table 1: Define LR_FA clearly in the caption. Also, include a brief explanation of whether the particle release numbers are sufficient for system saturation.
- Table 2: Add the term SRij to the caption.
- Table 3: The methodology used to derive the larval numbers in this table is not immediately clear. Are these values inferred from combined forward and backward simulations? Please clarify this in the caption with a brief methodological summary.
- Line 339: The phrase “different larval particle release strategies” needs clarification. Does this refer to spatial distribution, timing, number of particles, or something else?
This is a strong and valuable contribution, and with the suggested clarifications and additions, I believe the manuscript will be of high interest to the modelling and marine connectivity communities.
Citation: https://doi.org/10.5194/gmd-2024-215-RC1 - AC2: 'Reply on RC1', wei shi, 15 May 2025
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RC2: 'Comment on gmd-2024-215', Anonymous Referee #2, 14 May 2025
I got very excited by this work reporting the possibility to simulate self-recruitment (SR) without the need of information on larval production, thereby solving a long-time reported issue for comparing models and data of larval connectivity: specifically, biophysical models are appropriate tools to simulate local retention (LR) whereas field studies estimate SR.
Unfortunately, I believe that the proposed method implicitly assumes spatially homogeneous larval production and is therefore no more appropriate for simulating SR than the usual method. The assumption of homogeneous larval production is here implicit, because of the use of backward-in-time tracking, as oppposed to being explicit when using forward-in-time tracking. Indeed, imagine that a given zone is particularly suitable for larval production in the reality. In the model there is no reason why more particles would be advected back to that zone "hydrodynamically". To represent this "biological" reality of enhanced larval production the number of particles advected back to the given zone should be weighted by larval production from that zone in order to calculate SR correctly. If not, then homogeneous larval production is implicitly assumed.
It is true that the quantity simulated using back-tracking will not depend on the number of particles released in a settlement area, as long as this number is high enough to obtain a statistically meaningful value. However that simulated quantity will be comparable to SR as assessed in the field only if larval production is spatially homogeneous. In conclusion I sadly do not see how the proposed method based on back-tracking is an improvement from the one based on forward-tracking. Back-tracking is an approach that may be more efficient to use computationally than forward-tracking when focusing on particles origin rather than destination but both approaches should give the same results.
lines 40-48 I found this paragraph confusing as it looks like the authors decided to define local retention (LR) and theoretical local retention (TLR) oppositely to what was previously used (Hogan et al. 2012, Burgess et al. 2014, Lett et al. 2015).49: Parentage analysis and/or larval tagging is widely used to estimate SR, not the other quantities, to my knowledge.
69-72: True, but as explained above I believe this issue remains when using back-tracking.
87-91: Same here.
103-4: It has already been shown that LR and TLR are independent on larval production (Burgess et al. 2014, Lett et al. 2015) and there is no need of simulations to prove this.
Equ. (1) this quantity is defined as LR in Lett et al. (2015), why change?
Equ. (2) this quantity is defined as RLR in Lett et al. (2015), why change?
167-8 This is true, however as explained above that simulated quantity will be SR as assessed in the field only if larval production is spatially homogeneous.
291-2 I don't understand why SR values estimated from back-tracking and forward-tracking would be different. I don't agree that one is "correct" and the other "erroneous". They should be similar as both are based on the same assumption of homogeneous larval production, as explained above. There may be numerical reasons for the reported differences, or other technical reasons that could be explored.
Citation: https://doi.org/10.5194/gmd-2024-215-RC2
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