the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 10 Oct 2024
Chlorophyll shading reduces zooplankton diel migration depth in a high-resolution physical biogeochemical model
Abstract. Zooplankton diel vertical migration (DVM) is critical to ocean ecosystem dynamics and biogeochemical cycles, by supplying food and injecting carbon to the mesopelagic ocean (200–800 m). The deeper the zooplankton migrate, the longer the carbon is sequestered away from the atmosphere and the deeper the ecosystems they feed. Sparse observations show variations in migration depths over a wide range of temporal and spatial scales. A major challenge, however, is to understand the biological and physical mechanisms controlling this variability, which is critical to assess impacts on ecosystem and carbon dynamics. Here, we introduce a migrating zooplankton model for medium and large zooplankton that explicitly resolves diel migration trajectories and biogeochemical fluxes. This model is integrated into the MOM6-COBALTv2 ocean physical-biogeochemical model, and applied in an idealized high-resolution (9.4 km) configuration of the North Atlantic. The model skillfully reproduces observed North Atlantic migrating zooplankton biomass and DVM patterns. Evaluation of the mechanisms controlling zooplankton migration depth reveals that chlorophyll shading reduces by 60 meters zooplankton migration depth in the subpolar gyre compared with the subtropical gyre, with pronounced seasonal variations linked to the spring bloom. Fine-scale spatial effects (<100 km) linked to eddy and frontal dynamics can either offset or reinforce the large-scale effect by up to 100 meters. This could imply that for phytoplankton-rich regions and filaments, which represent a major source of exportable carbon for migrating zooplankton, their high-chlorophyll content contributes to reducing zooplankton migration depth and carbon sequestration time.
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RC1: 'Comment on egusphere-2024-3058', Anonymous Referee #1, 16 Nov 2024
General comments
Scientific significance
Poupon and co-workers use a migrating zooplankton model to resolve diel migration trajectories and biogeochemical fluxes. Their model successfully reproduces observed migration patterns. They also evaluate the mechanisms controlling migration depth. The study offers a valuable new perspective on the mechanisms driving this migration. Using an idealized double-gyre model that reproduces North Atlantic biophysical dynamics, coupled with biogeochemical processes and integrated zooplankton migration, the authors explore the mechanisms modulating migration and its variation across seasons, "biomes", and spatial scales. This study provides a realistic approach to modeling migration trajectories and the underlying processes, as the authors account for two zooplankton sizes, physiology, and interactions with the environment, all of which are critical factors. The authors demonstrate that chlorophyll shading is a dominant factor shaping migration depth at various temporal and spatial scales. Overall, the study is detailed and robust.
Scientific quality: yes, excellent.
Presentation quality: The manuscript is clearly written and well-structured. The number of figures, conceptual diagrams, and tables is appropriate, and they are of high quality. The supplemental material is warranted and adds value. The conclusions are well-supported and justified.
I am very positive towards the study, as the findings are important.
Specific comments:
- A key review study in the field that I think deserves mention is Nandara et al 2021, as it reviews sampling, observation and tracking simulation methods, while also emphasizing the importance of integrative approaches.
Kanchana, et al. "Two hundred years of zooplankton vertical migration research." Biological Reviews 96.4 (2021): 1547-1589.
- The model considers two zooplankton sizes, both of which fall within the mesozooplankton size fraction, with copepods being key representatives. However, could the model be applied to or used with microzooplankton ? I imagine the complexity of including both groups lies in the differences in egestion and assimilation of various elements, as well as other factors.
- N:P=1:16 did you try varying this across different biomes?
- I am curious about the difference in the dates for the datasets: migrating zooplankton data were collected between 2007 and 2019, while MODIS data span from 2002 to 2023. Given that 2023 was a particularly warm year, did including or excluding this year make a difference? Or was cleary not affecting the seasonal variations?
Detailed points:
Line 161: Nitrate and phosphate values?
There is some inconsistent formatting in the reference section.
Citation: https://doi.org/10.5194/egusphere-2024-3058-RC1 -
RC2: 'Comment on egusphere-2024-3058', Anonymous Referee #2, 10 Jan 2025
I sincerely apologize to the Authors and Editors for the delay in completing my review, and I appreciate your patience and understanding.
The manuscript by Poupon and coauthors presents the implementation of a model of zooplankton diel vertical migration (DVM) embedded within a realistic ocean biogeochemical model, run in a idealized two-gyre configuration at eddy resolution. They show that the model, despite the idealizations, captures major patterns of DVM, including migrating biomass, and depths and timing of migrations, based on simple behavioral rules. They also show that most of the variability in migration depth is caused by changes in light attenuation in the water column, which in turn are directly controlled by the distribution of chlorophyll. This variability dominates over the seasonal cycle, and from basin scales to the scales of eddies and fronts. The exception are regions of strong wintertime convective mixing, where transport by the physical circulation is vigorous enough to alter the migration depth significantly.
This is a cleverly designed, stimulating, and very well written study. It address a topic of growing interest in the ocean biogeochemistry and modeling community, detailing the development of a state-of-the-art model that can be deployed in both idealized and realistic configurations to address a variety of problems, ranging from ecological questions, to carbon export and the biological pump. The Authors also present novel scientific findings, such as the role of chlorophyll and physical mixing in altering DVM patterns at seasonal and sub seasonal scales. Thus, I am very supportive of publication, and only have few minor suggestions for the Authors to consider in revision.
My main suggestion is to move Section 2.3 from the Methods to the Results. This Section is basically detailing results from the model and validating it against a variety of observation, which does not properly belongs to the Methods. It could easily form the beginning of Section 3, Results, and lead into the current Section 3.1. This would lead to a smoother flow for the reader.
Specific comments:
Line 101: Model configuration. Since MOM6 is based on a generalized vertical coordinate, it may be worth to clarify how the vertical coordinate is handled in this configuration.
Forcings (Section 2.1.2). The foreigns used to set up the two-gyre configuration seem appropriate. It may be helpful to clarify whether the light forcing is temporally synchronous across the whole basin on daily timescales, or if the timing of daily cycle changes differs going east to west (as in the real world).
Similarly, hourly light forcing is probably accurate for the first-order questions investigated by the Authors, although it may miss or misrepresent “crepuscular” periods, i.e., the periods of light before sunrise and after sunset, which generally last less than 1 hour, during which there is enough light to influence the behavior of migrating organisms. This could be noted.
Section 2.2, Observations. It may be valuable to note that observations of DVM biomass are particularly difficult and observational results are likely method- and community- dependent, with different approaches observing different migrating organisms / communities. E.g., acoustics, from which many metrics of DVM behavior are obtained, are biased towards strongly sound-scattering organisms (e.g., gas-bladdered fish); net samples target specific sizes, with under-sampling of organisms smaller than the net mesh, and of large size classes because of net avoidance. The lidar-based approach by Behrenfeld et al., 2019 is very promising, and makes for an excellent addition to Fig. 2, but it is also highly uncertain and potentially biased by issues in both the lidar and net sample data that are used to generate it. That said, the broad agreement of model, lidar-based, and net-based observations (at BATS), across meridional gradients, as shown in Fig. 2, is very encouraging.
Section 2.4.1, Migration Model. I appreciate the detail of the parameterization description, and the improvements to previous formulations, such as the smooth swimming speed function, and the redistribution of migrating organisms following the prey at night. I was curious to hear whether you tried to re-distribute organisms based on non-migrating zooplankton, though I imagine that would provide nearly identical results. I also suggest adding some detail to this section, as I was slightly confused by how the transition between a vertical velocity that targets the optimal isolume during the day and the redistribution based on the excess zooplankton at night is handled. I imagine that at night, when the isolume is absent, the only velocity applied is that based on zooplankton excess, which would drive upward migrations until migrating zooplankton match the prey distribution. If there is a way to amend or expand equation (1) to include all cases, including the nighttime case, that could help clarifying this point. The redistribution method is clever, but I also wonder how biologically consistent it is, since in the real world organisms respond to a combination of proximate cues and intrinsic behaviors, without a complete knowledge of the prey distribution that only a model can provide.
To my recollection, Bianchi et al., 2012 included a biological diffusion term, mimicking random vertical movement, which prevented organisms from accumulating into a single model layer, spreading them more smoothly in the water column. It is not clear if a similar term is needed, and if it is included here. Fig. 3 shows that the migrating layers are vertically spread in a fairly realistic way. I wonder if this is an effect of numerical diffusion for vertical movement (is an implicit method used?), and if this could lead to resolution-dependent results, e.g., if time step or vertical layering were to change significantly.
Section 2.4.3. Likewise, the development of a physiological zooplankton model with internal pools is impressive, and may prove essential for simulating effects of DVM on material transport in the water column (i.e., active transport). The formulation appears to be sound, although a few aspects could be clarified. For example, it may be useful to clarify equation 7 to show that it is applied to determine growth as a function of kresp*Nmetab - respiration, and that the resulting “growth” term can be either a growth (positive) or a loss (negative) term. Furthermore, it may be worth stating more clearly that this complex physiological parameterization is only applied to migrating zooplankton, and that, in the absence of migrations, it should produce similar results as for non-migrating zooplankton (i.e., growth and mortality rates would be comparable).
Section 2.5.1: this is a really clever way of decomposing the effects of different DVM drivers. One question I had is whether the circulation component is mostly caused by vertical mixing (during deep convection), or if it includes horizontal transport components.
The match between observed and modeled DVM depth in Figure 6 is impressive. I’m not surprised by the large model variability in the sub polar sector, which may be underestimated by sparse observations in such a variable region.
Section 3.2 contains many novel and scientifically interesting results, in particular on the role of chlorophyll and circulation in driving variability in migration depth, and makes for a stimulating read. I like how the effects of different drivers are summarized in Fig. 7 and 8.
Line 432: you may be missing “studies” after “prior”, or something analogous.
Line 455: this is an interesting point about diapause; I imagine that your ecological model could be in fact used in future work to explore tradeoffs between diapause vs. wintertime activity.
Figures are excellent. The color schemes of most figures are appropriate, but some use “jet” like palettes that are not optimal for visually impaired readers; I suggest the Authors revise them to adopt perceptually uniform palettes.
Citation: https://doi.org/10.5194/egusphere-2024-3058-RC2
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