pith. sign in

arxiv: 2604.14759 · v1 · submitted 2026-04-16 · 🧮 math.DS

Beyond the Critical Depth: The Metabolic and Physical Drivers of Phytoplankton Persistence in a Changing Ocean

Pith reviewed 2026-05-10 10:17 UTC · model grok-4.3

classification 🧮 math.DS
keywords phytoplankton persistencecritical depth hypothesisdynamical systemsclimate projectionsocean regime shiftFloquet exponentmetabolic driversphysical mixing
0
0 comments X

The pith

Future ocean projections show metabolic factors taking over phytoplankton stability from physical mixing

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper builds a non-autonomous dynamical systems model to assess phytoplankton community stability across the full annual cycle rather than just bloom onset. It linearizes the equations around the extinction equilibrium to obtain an invasion growth rate given by the Floquet exponent, which yields a critical nutrient threshold for long-term persistence. Applying this to end-of-century high-emission climate output reveals a global expansion of regions where metabolic rates set stability, displacing areas historically controlled by mixing. A reader would care because the base of the marine food web and carbon cycle could reorganize as a result, especially with polar trade-offs between newly viable waters and newly barren ice-free zones.

Core claim

Linearization of the seasonally forced phytoplankton model around the zero state produces the Floquet exponent as the invasion growth rate; the resulting critical nutrient requirement serves as the bifurcation value separating extinction from uniform persistence. End-of-century SSP5-8.5 projections then identify a worldwide regime shift in which metabolic-driven regimes expand and displace historically mixing-governed regions, accompanied by a 1:4 ratio of newly viable niches to ice-free polar deserts and the North Atlantic Subpolar Gyre remaining a mixing refuge.

What carries the argument

The Floquet exponent of the linearized non-autonomous system, which functions as the invasion growth rate and sets the critical nutrient threshold for persistence under annual forcing.

If this is right

  • The thermal dominance index quantifies the transition from mixing-driven to metabolic-driven ecological control across the globe.
  • Warming lowers the critical nutrient requirement and opens previously marginal waters, but cryospheric retreat offsets this at the poles.
  • The North Atlantic Subpolar Gyre continues to be anchored by mixing dynamics against global thermalization.
  • Metabolic constraints increasingly set the long-term persistence boundary for phytoplankton communities.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Updating bloom models to track full-year stability with metabolic rates could refine predictions for fisheries and carbon export.
  • The same non-autonomous stability approach may extend to other seasonally forced marine or terrestrial populations.
  • Polar monitoring should test whether the projected net loss of viable area occurs as cryosphere retreats.

Load-bearing premise

The thermodynamic temperature dependence of biological rates stays valid in future conditions and the linearized annual periodic system without grazing or nutrient recycling fully captures invasion and persistence.

What would settle it

Field or satellite observations of whether phytoplankton persistence thresholds and geographic distributions in warming regions match the predicted lowering of the critical nutrient requirement and the expansion of metabolic regimes.

Figures

Figures reproduced from arXiv: 2604.14759 by CMM), Luis Mart\'i, Mara Freilich, Mat\'ias Neto, Nayat Sanchez-Pi, Pablo Marquet (PUC.

Figure 1
Figure 1. Figure 1: Thermal Performance Curve (TPC) for the maximum phytoplankton growth rate [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Global Map of the Critical Nutrient Requirement ( [PITH_FULL_IMAGE:figures/full_fig_p010_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Spatial distribution of the thermal dominance index, [PITH_FULL_IMAGE:figures/full_fig_p011_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: (a) Critical Nutrient Shift (∆γcrit): Changes in the persistence threshold between 2024 and 2100 under the high-emission SSP5-8.5 scenario. Green tones (∆γ < 0) indicate lower critical nutrient requirements, while purple tones (∆γ > 0) indicate greater nutrient requirements. (b) Thermal Dominance Index: Spatial distribution of the thermal dominance index DT in 2100. The widespread expansion of red tones (D… view at source ↗
Figure 5
Figure 5. Figure 5: Spatial categorization of ecological transitions based on the [PITH_FULL_IMAGE:figures/full_fig_p014_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: (a) Regional Nutrient Shift: The core of the SPG exhibits a dampened response (pale [PITH_FULL_IMAGE:figures/full_fig_p016_6.png] view at source ↗
read the original abstract

While the classical Critical Depth Hypothesis (CDH) effectively explains the onset of blooms as transient instabilities, it does not fully capture the seasonal decoupling of biological rates and the long-term persistence of phytoplankton communities in fluctuating thermal environments. To address these limitations, we introduce a parsimonious framework that leverages the theory of non-autonomous dynamical systems to diagnose the stability of phytoplankton communities throughout the entire annual cycle. By linearizing the dynamics around the extinction equilibrium, we identify the invasion growth rate -formally the Floquet exponent-and derive the critical nutrient requirement ($\gamma$crit) as a bifurcation point for uniform persistence. Using end-of-the-century projections from the GFDL-ESM4 model under a high-emission scenario (SSP5-8.5), we identify a global regime shift characterized by a widespread expansion of metabolic-driven regimes, which increasingly displace regions where stability was historically governed by physical mixing. Relevance to Life Sciences. Quantitative analysis of system stability challenges CDH by demonstrating that metabolic constraints increasingly modulates phytoplankton persistence in a changing ocean. Our results, based on high-emission projections, reveal a profound physical-biological decoupling at the poles: while warming reduces the critical nutrient requirement ($\gamma$crit) facilitating persistence in previously marginal waters, this metabolic expansion is offset at poles. A 1:4 ratio between newly viable niches and ice-free deserts suggests that cryospheric retreat does not guarantee a proportional expansion of life. In addition, we identify the North Atlantic Subpolar Gyre as a ''metabolic refuge'' where mixing dynamics still anchor the ecosystem against global thermalization. By providing a ''radiography'' of the future ocean's complexity, this methodology offers a mechanistic basis to deconstruct how the dynamic balance between environmental energy and metabolic demands may determine the functional integrity of the marine biosphere under extreme anthropogenic forcing. Mathematical Content. The temperature dependence of biological rates is modeled using a thermodynamic equation, coupling population dynamics with seasonal variations in mixed layer depth and temperature. Given the non-autonomous nature of the system under annual forcing, we characterize the stability of the extinction equilibrium through its associated invasion growth rate. This rate is analytically derived as the Floquet exponent $\lambda$P , which provides a rigorous condition for uniform persistence (Theorem 3.2). The numerical analysis of this exponent, projected onto a global scale, quantifies the relative influence of environmental drivers on the stability threshold $\gamma$crit. This allows for the definition of the thermal dominance index (DT ), a metric that identifies the geographic transition from mixing-driven to metabolic-driven ecological control.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

4 major / 3 minor

Summary. The paper claims that a non-autonomous dynamical systems framework, by linearizing phytoplankton dynamics around the extinction equilibrium under annual forcing and deriving the invasion growth rate as the Floquet exponent λ_P, yields an analytic critical nutrient requirement γ_crit as the bifurcation point for uniform persistence (Theorem 3.2). Applying this to GFDL-ESM4 projections under SSP5-8.5 identifies a global regime shift with expansion of metabolic-driven regimes (quantified via thermal dominance index D_T) displacing mixing-driven ones, a 1:4 ratio of new viable niches to ice-free deserts, and the North Atlantic Subpolar Gyre as a metabolic refuge, thereby challenging the classical Critical Depth Hypothesis with metabolic constraints increasingly dominating in a warming ocean.

Significance. If the central derivation and assumptions hold, the work provides a mathematically rigorous, mechanistic alternative to the Critical Depth Hypothesis by coupling thermodynamic temperature dependence of rates with seasonal mixed-layer dynamics and using Floquet theory to obtain a persistence threshold. The analytic derivation of λ_P and γ_crit, together with the global projection onto a regime-shift map, offers a falsifiable, parameterizable tool for assessing physical-biological decoupling at high latitudes and identifying refugia; this could inform ecosystem models if validated.

major comments (4)
  1. [Theorem 3.2] Theorem 3.2: The derivation of γ_crit as the bifurcation point for uniform persistence assumes strictly periodic annual forcing, yet the SSP5-8.5 end-of-century fields from GFDL-ESM4 contain secular trends in temperature and mixed-layer depth; this violates the periodicity required for the Floquet exponent λ_P to govern long-term invasion and persistence.
  2. [Mathematical Content] Mathematical Content section (thermodynamic temperature dependence): The biological rate parameters entering λ_P are not demonstrated to be independent of the data or indices used to define D_T and the regime boundaries, creating circularity that undermines the claim that metabolic drivers are independently quantified.
  3. [Numerical analysis] Numerical analysis of the exponent and global projections: The reported regime shift, 1:4 niche-to-desert ratio, and identification of metabolic refugia rest on a single model run under one scenario with no ensemble, no sensitivity to thermodynamic parameters, and no inclusion of grazing or nutrient recycling; these omissions are load-bearing because the skeptic note indicates such processes can alter the invasion threshold even when λ_P > 0.
  4. [Linearization around the extinction equilibrium] Linearization around the extinction equilibrium: While the invasion growth rate is formally derived, the manuscript does not test whether additional limiting factors (grazing, recycling) prevent persistence in regions where λ_P indicates invasion is possible, weakening the geographic expansion claims.
minor comments (3)
  1. [Abstract] The abstract states that λ_P is analytically derived and γ_crit obtained as a bifurcation point yet provides no equations, making immediate assessment of the central claim difficult.
  2. [Definition of D_T] The thermal dominance index D_T is introduced without an explicit equation or reference to how it is computed from λ_P and γ_crit.
  3. No comparison is made to other Earth-system models or to observational time series that could validate the projected regime boundaries.

Simulated Author's Rebuttal

4 responses · 0 unresolved

We thank the referee for the constructive and detailed comments, which help clarify the assumptions and scope of our non-autonomous dynamical systems framework. We address each major comment point by point below, indicating revisions to the manuscript where they strengthen the presentation without altering the core results.

read point-by-point responses
  1. Referee: [Theorem 3.2] The derivation of γ_crit as the bifurcation point for uniform persistence assumes strictly periodic annual forcing, yet the SSP5-8.5 end-of-century fields from GFDL-ESM4 contain secular trends in temperature and mixed-layer depth; this violates the periodicity required for the Floquet exponent λ_P to govern long-term invasion and persistence.

    Authors: We acknowledge that secular trends in the SSP5-8.5 fields strictly violate the periodicity assumption required for classical Floquet theory. In the revised manuscript, we clarify that the annual cycle is extracted and treated as the dominant periodic forcing for each projected year, with secular changes viewed as slow parameter variation. This multi-timescale approximation is standard for such projections. We have added explicit discussion in Section 3.2 and the Discussion section noting this limitation and its implications for interpreting persistence over centennial scales, along with a brief robustness check using detrended forcing fields. revision: partial

  2. Referee: [Mathematical Content] The biological rate parameters entering λ_P are not demonstrated to be independent of the data or indices used to define D_T and the regime boundaries, creating circularity that undermines the claim that metabolic drivers are independently quantified.

    Authors: The thermodynamic parameters (activation energies and Q10 values) are taken from independent literature sources on phytoplankton physiology and are fixed prior to any analysis of the GFDL-ESM4 output. D_T is a derived diagnostic index computed from the relative weighting of temperature-dependent versus mixing-dependent terms in λ_P, but it does not alter or depend on the input parameter values. We have revised the Mathematical Content section to include an explicit statement of parameter independence, a table of all fixed parameters with literature citations, and a note that D_T serves only for post-hoc regime classification. revision: yes

  3. Referee: [Numerical analysis] The reported regime shift, 1:4 niche-to-desert ratio, and identification of metabolic refugia rest on a single model run under one scenario with no ensemble, no sensitivity to thermodynamic parameters, and no inclusion of grazing or nutrient recycling; these omissions are load-bearing because the skeptic note indicates such processes can alter the invasion threshold even when λ_P > 0.

    Authors: We agree that a single ESM and scenario limits robustness. The revised Methods section now justifies the selection of GFDL-ESM4 and SSP5-8.5 as a high-emission end-member. We have added a parameter sensitivity analysis (varying key thermodynamic coefficients by ±20%) in the supplement, confirming that the global regime-shift pattern and 1:4 ratio remain qualitatively unchanged. Grazing and recycling are omitted from the minimal invasion model by design; we have expanded the Discussion to cite relevant literature on their potential effects and to qualify that the reported trends indicate directional shifts in metabolic control rather than absolute persistence predictions. revision: partial

  4. Referee: [Linearization around the extinction equilibrium] While the invasion growth rate is formally derived, the manuscript does not test whether additional limiting factors (grazing, recycling) prevent persistence in regions where λ_P indicates invasion is possible, weakening the geographic expansion claims.

    Authors: The linearization establishes a necessary condition for invasion from low densities. We have revised the Discussion to explicitly state that λ_P > 0 identifies regions where metabolic and physical conditions permit potential persistence, but that sufficiency in the presence of grazing or recycling would require nonlinear simulations with additional compartments. This caveat is now included when interpreting the geographic expansion and refugia results, without changing the reported regime-shift diagnostics. revision: yes

Circularity Check

0 steps flagged

No circularity: Floquet derivation and regime mapping are independent of fitted inputs

full rationale

The paper derives the invasion growth rate as the Floquet exponent λP via linearization of the non-autonomous system around the extinction equilibrium, yielding γcrit as the bifurcation threshold for uniform persistence (Theorem 3.2). The thermodynamic temperature dependence is introduced as an explicit modeling assumption, not fitted to the same data used for DT or regime boundaries. The thermal dominance index DT is subsequently defined from numerical evaluation of this exponent under external GFDL-ESM4 SSP5-8.5 fields. No step equates a claimed prediction to a parameter fit by construction, invokes self-citation for a uniqueness theorem, or renames an empirical pattern as a new result. The geographic regime-shift claim follows directly from applying the independent stability analysis to projected forcing, rendering the derivation self-contained.

Axiom & Free-Parameter Ledger

1 free parameters · 2 axioms · 2 invented entities

The framework rests on standard periodic dynamical systems theory and external climate-model forcing; biological rate parameters are introduced via a thermodynamic temperature dependence whose specific values are not shown to be derived within the paper.

free parameters (1)
  • biological rate parameters in thermodynamic temperature dependence
    Temperature dependence of growth and mortality rates is modeled thermodynamically; specific parameter values are required to compute the Floquet exponent and γcrit but are not derived from first principles in the abstract.
axioms (2)
  • domain assumption The annual cycle provides periodic forcing allowing Floquet theory to characterize stability of the extinction equilibrium
    Invoked to justify the invasion growth rate as the Floquet exponent λP.
  • domain assumption Linearization around the zero-population equilibrium captures the condition for uniform persistence
    Basis for Theorem 3.2 and the bifurcation definition of γcrit.
invented entities (2)
  • Thermal dominance index (DT) no independent evidence
    purpose: Metric to identify geographic transition from mixing-driven to metabolic-driven ecological control
    Defined from numerical analysis of the Floquet exponent projected globally.
  • metabolic refuge no independent evidence
    purpose: Label for regions such as the North Atlantic Subpolar Gyre where mixing dynamics still dominate
    Identified from the regime-shift analysis of future projections.

pith-pipeline@v0.9.0 · 5799 in / 1812 out tokens · 56432 ms · 2026-05-10T10:17:06.635232+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

45 extracted references · 45 canonical work pages

  1. [1]

    T. R. Anderson , Plankton functional type modelling: running before we can walk?, Journal of Plankton Research, 27 (2005), pp. 1073–1081, https://doi.org/10.1093/plankt/fbi076

  2. [2]

    Ardyna and K

    M. Ardyna and K. R. Arrigo , Phytoplankton dynamics in a changing arctic ocean, Nature Climate Change, 10 (2020), pp. 892–903, https://doi.org/10.1038/s41558-020-0905-y

  3. [3]

    J. I. Arroyo, B. Díez, C. P. Kempes, G. B. West, and P. A. Marquet , A general theory for temperature dependence in biology, Proceedings of the National Academy of Sciences, 119 (2022), p. e2119872119, https://doi.org/10.1073/pnas.2119872119

  4. [4]

    K. G. Baker and R. J. Geider , Phytoplankton mortality in a changing thermal seascape, Global Change Biology, 27 (2021), pp. 5253–5261, https://doi.org/10.1111/gcb.15772. 26 M. A. NETO, P. A. MARQUET, M. A. FREILICH, L. MARTÍ, AND N. SANCHEZ-PI

  5. [5]

    M. J. Behrenfeld , Abandoning sverdrup’s critical depth hypothesis on phytoplankton blooms, Ecology, 91 (2010), pp. 977–989, https://doi.org/https://doi.org/10.1890/09-1207.1

  6. [6]

    J. E. Bissinger, D. J. S. Montagnes, J. harples, and D. Atkinson , Predicting marine phytoplank- ton maximum growth rates from temperature: Improving on the eppley curve using quantile regression, Limnology and Oceanography, 53 (2008), pp. 487–493, https://doi.org/10.4319/lo.2008.53.2.0487

  7. [7]

    Boyce and B

    D. Boyce and B. Worm , Patterns and ecological implications of historical marine phytoplankton change, Marine Ecology Progress Series, 534 (2015), pp. 251–272, https://doi.org/10.3354/meps11411

  8. [8]

    T. D. Brock, Calculating solar radiation for ecological studies, Ecological Modelling, 14 (1981), pp. 1–19, https://doi.org/10.1016/0304-3800(81)90011-9

  9. [9]

    C. Chen, R. Beardsley, and P. J. Franks , A 3-d prognostic numerical model study of the georges bank ecosystem. part i: physical model, Deep Sea Research Part II: Topical Studies in Oceanography, 48 (2001), pp. 419–456, https://doi.org/10.1016/S0967-0645(00)00124-7

  10. [10]

    M. Chen, M. F an, X. Yuan, and H. Zhu , Effect of seasonal changing temperature on the growth of phytoplankton, Mathematical Biosciences & Engineering, 14 (2017), pp. 1091–1117, https://doi.org/ 10.3934/mbe.2017057

  11. [11]

    Chesson, Mechanisms of maintenance of species diversity, Annual Review of Ecology, Evolution and Systematics, 31 (2000), pp

    P. Chesson, Mechanisms of maintenance of species diversity, Annual Review of Ecology, Evolution and Systematics, 31 (2000), pp. 343–366, https://doi.org/10.1146/annurev.ecolsys.31.1.343

  12. [12]

    R. W. Eppley , Temperature and phytoplankton growth in the sea, Fishery Bulletin, 70 (1972), https: //spo.nmfs.noaa.gov/content/temperature-and-phytoplankton-growth-sea

  13. [13]

    G. T. Ev ans and J. S. Parslow, A model of annual plankton cycles, Biological Oceanography, 3 (1985), pp. 327–347, https://doi.org/10.1080/01965581.1985.10749478

  14. [14]

    P. G. F alkowski, T. Fenchel, and E. F. Delong , The microbial engines that drive earth’s biogeo- chemical cycles, Science, 320 (2008), pp. 1034–1039, https://doi.org/https://doi.org/10.1126/science. 1153213

  15. [15]

    Franks, Npz models of plankton dynamics: Their construction, coupling to physics, and application, Oceanography, 58 (2002), p

    P. Franks, Npz models of plankton dynamics: Their construction, coupling to physics, and application, Oceanography, 58 (2002), p. 379–387, https://doi.org/10.1023/A:1015874028196

  16. [16]

    Freilich, A

    M. Freilich, A. Mignot, G. Flierl, and R. Ferrari , Grazing behavior and winter phytoplankton accumulation, Biogeosciences, 18 (2021), pp. 5595–5607, https://doi.org/10.5194/bg-18-5595-2021

  17. [17]

    J. A. Fuhrman, J. A. Cram, and D. M. Needham , Marine microbial community dynamics and their ecological interpretation, Nature Reviews Microbiology, 13 (2015), pp. 133–146

  18. [18]

    W. W. Gregg,Assimilation of seawifs ocean chlorophyll data into a three-dimensional global ocean model, Journal of Marine Systems, 69 (2008), pp. 205–225, https://doi.org/10.1016/j.jmarsys.2006.02.015

  19. [19]

    Hale, Ordinary Differential Equations, Dover Books on Mathematics Series, Dover Publications, 2009

    J. Hale, Ordinary Differential Equations, Dover Books on Mathematics Series, Dover Publications, 2009

  20. [20]

    R. F. Heneghan, E. Galbraith, J. L. Blanchard, C. Harrison, N. Barrier, et al. , Dis- entangling diverse responses to climate change among global marine ecosystem models, Progress in Oceanography, 198 (2021), p. 102659, https://doi.org/10.1016/j.pocean.2021.102659

  21. [21]

    S. A. Henson, R. Sanders, E. Madsen, P. J. Morris, F. Le Moigne, and G. D. Quartly , A reduced estimate of the strength of the ocean’s biological carbon pump, Geophysical Research Letters, 38 (2011), https://doi.org/10.1029/2011GL046735

  22. [22]

    Z. Hong, D. Long, K. Shan, J.-M. Zhang, R. I. Wool w ay, M. Liu, M. E. Mann, and H. F ang , Declining ocean greenness and phytoplankton blooms in low to mid-latitudes under a warming climate, Science Advances, 11 (2025), p. eadx4857, https://doi.org/10.1126/sciadv.adx4857

  23. [23]

    Reviewing the ecosystem services, societal goods, and benefits of marine protected areas

    C. Hor v at, K. Bisson, S. Seabrook, A. Cristi, and L. C. Matthes , Evidence of phytoplankton blooms under Antarctic sea ice, Frontiers in Marine Science, 9 (2022), https://doi.org/10.3389/fmars. 2022.942799

  24. [24]

    J. G. John, C. Blanton, C. McHugh, A. Radhakrishnan, K. Rand, H. V ahlenkamp, C. Wil- son, N. T. Zadeh, J. P. Dunne, R. Dussin, L. W. Horowitz, J. P. Krasting, P. Lin, S. Malyshev, V. Naik, J. Ploshay, E. Shevliakov a, L. Sil vers, C. Stock, M. Winton, and Y. Zeng , Noaa-gfdl gfdl-esm4 model output prepared for cmip6 scenariomip ssp585, 2018, https://doi....

  25. [25]

    P. Keil, T. Mauritsen, J. Jungclaus, C. Hedemann, D. Olonscheck, and R. Ghosh , Multiple drivers of the north atlantic warming hole, Nature Climate Change, 10 (2020), pp. 667–671, https: //doi.org/10.1038/s41558-020-0819-8

  26. [26]

    C. A. Klausmeier, Floquet theory: a useful tool for understanding nonequilibrium dynamics, Theoretical BEYOND THE CRITICAL DEPTH 27 Ecology, 1 (2008), pp. 153–161, https://doi.org/10.1007/s12080-008-0016-2

  27. [27]

    A. I. Krinos, S. K. Shapiro, W. Li, S. T. Haley, S. T. Dyhrman, S. Dutkiewicz, M. J. Follows, and H. Alexander , Intraspecific Diversity in Thermal Performance Determines Phyto- plankton Ecological Niche, Ecology Letters, 28 (2025), p. e70055, https://doi.org/10.1111/ele.70055

  28. [28]

    Kwiatkowski, O

    L. Kwiatkowski, O. Torres, L. Bopp, O. Aumont, M. Chamberlain, J. R. Christian, J. P. Dunne, M. Gehlen, T. Ilyina, J. G. John, A. Lenton, H. Li, N. S. Lovenduski, J. C. Orr, J. Palmieri, Y. Santana-F alcón, J. Schwinger, R. Séférian, C. A. Stock, A. Tagliabue, Y. Takano, J. Tjiputra, K. Toyama, H. Tsujino, M. W atanabe, A. Yamamoto, A. Yool, and T. Ziehn ...

  29. [29]

    Litchman, K

    E. Litchman, K. F. Edw ards, C. A. Klausmeier, and M. K. Thomas , Phytoplankton niches, traits and eco-evolutionary responses to global environmental change, Marine Ecology Progress Series, 470 (2012), pp. 235–248, https://doi.org/10.3354/meps09912

  30. [30]

    1996, ARA&A, 34, 645, doi:10.1146/annurev

    E. Litchman and C. A. Klausmeier , Trait-based community ecology of phytoplankton, Annual Review of Ecology, Evolution, and Systematics, 39 (2008), pp. 615–639, https://doi.org/10.1146/annurev. ecolsys.39.110707.173549

  31. [31]

    A. P. Martin, A. B. Dominguez, C. A. Baker, C. M. J. Baumas, K. M. Bisson, E. Ca v an, M. Freilich, E. Galbraith, M. Galí, S. Henson, K. F. Kv ale, C. Lemmen, J. Y. Luo, H. McMonagle, F. d. M. Viríssimo, K. O. Möller, C. Richon, I. Suresh, J. D. Wilson, M. S. Woodstock, and A. Yool , When to add a new process to a model – and when not: A marine biogeochem...

  32. [32]

    McClish and S

    S. McClish and S. M. Bushinsky , Majority of Southern Ocean Seasonal Sea Ice Zone Bloom Net Community Production Precedes Total Ice Retreat, Geophysical Research Letters, 50 (2023), p. e2023GL103459, https://doi.org/10.1029/2023GL103459

  33. [33]

    Meredith, M

    M. Meredith, M. Sommerkorn, S. Cassotta, C. Derksen, A. Ekaykin, A. Hollowed, G. Kofinas, A. Mackintosh, J. Melbourne-Thomas, M. M. C. Muelbert, G. Ottersen, H. Pritchard, and E. A. G. Schuur , Polar regions, in IPCC Special Report on the Ocean and Cryosphere in a Changing Climate, H.-O. Pörtner, D. C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. P...

  34. [34]

    J. J. Polovina, J. P. Dunne, P. A. Woodworth, and E. A. Howell , Projected expansion of the subtropical biome and contraction of the temperate and polar biomes in the north pacific under global warming, ICES Journal of Marine Science, 68 (2011), pp. 986–995, https://doi.org/10.1093/icesjms/ fsq198

  35. [35]

    Pulsifer and E

    J. Pulsifer and E. La ws , Temperature dependence of freshwater phytoplankton growth rates and zoo- plankton grazing rates, Water, 13 (2021), https://doi.org/10.3390/w13111591

  36. [36]

    Rahmstorf, Is the atlantic overturning circulation approaching a tipping point?, Oceanography, 37 (2024), pp

    S. Rahmstorf, Is the atlantic overturning circulation approaching a tipping point?, Oceanography, 37 (2024), pp. 16–29, https://www.jstor.org/stable/27333920

  37. [37]

    Exceptional twentieth-century slowdown in Atlantic Ocean overturning circulation,

    S. Rahmstorf, J. E. Box, G. Feulner, M. E. Mann, A. Robinson, S. Rutherford, and E. J. Schaffernicht, Exceptional twentieth-century slowdown in atlantic ocean overturning circulation, Nature Climate Change, 5 (2015), pp. 475–480, https://doi.org/10.1038/nclimate2554

  38. [38]

    S. Roy, D. S. Broomhead, T. Platt, S. Sathyendranath, and S. Cia v atta , Sequential variations of phytoplankton growth and mortality in an npz model: A remote-sensing-based assessment, Journal of Marine Systems, 92 (2012), pp. 16–29, https://doi.org/10.1016/j.jmarsys.2011.10.001

  39. [39]

    Séférian, S

    R. Séférian, S. Berthet, A. Yool, J. Palmiéri, L. Bopp, A. Tagliabue, L. Kwiatkowski, O. Aumont, J. R. Christian, J. P. Dunne, M. Gehlen, T. Ilyina, J. John, H. Li, M. C. Long, J. Y. Luo, H. Nakano, A. Romanou, J. Schwinger, C. A. Stock, Y. Santana- F alcón, Y. Takano, J. Tjiputra, H. Tsujino, M. W atanabe, T. Wu, F. Wu, and A. Ya- mamoto, Tracking Improv...

  40. [40]

    H. U. Sverdrup, On conditions for the vernal blooming of phytoplankton, Journal du Conseil, 18 (1953), 28 M. A. NETO, P. A. MARQUET, M. A. FREILICH, L. MARTÍ, AND N. SANCHEZ-PI pp. 287–295, https://doi.org/10.1093/icesjms/18.3.287

  41. [41]

    J. R. Taylor and R. Ferrari , Shutdown of turbulent convection as a new criterion for the onset of spring phytoplankton blooms, Limnology and Oceanography, 56 (2011), pp. 2293–2307, https://doi. org/10.4319/lo.2011.56.6.2293

  42. [42]

    N. G. W al worth, E. J. Zakem, J. P. Dunne, S. Collins, and N. M. Levine , Microbial evolutionary strategies in a dynamic ocean, Proceedings of the National Academy of Sciences, 117 (2020), pp. 5943– 5948, https://doi.org/10.1073/pnas.1919332117

  43. [43]

    W ang and X.-Q

    W. W ang and X.-Q. Zhao , Threshold dynamics for compartmental epidemic models in periodic environments, Journal of Dynamics and Differential Equations, 20 (2008), pp. 699–717, https: //doi.org/10.1007/s10884-008-9111-8

  44. [44]

    Q. Zhao, S. Liu, and X. Niu , Effect of water temperature on the dynamic behavior of phyto- plankton–zooplankton model, Applied Mathematics and Computation, 378 (2020), p. 125211, https: //doi.org/10.1016/j.amc.2020.125211

  45. [45]

    Zhao, Dynamical Systems in Population Biology, CMS Books in Mathematics, Springer-Verlag, 2003, https://doi.org/10.1007/978-0-387-21761-1

    X.-Q. Zhao, Dynamical Systems in Population Biology, CMS Books in Mathematics, Springer-Verlag, 2003, https://doi.org/10.1007/978-0-387-21761-1