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
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.
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
- 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
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.
Referee Report
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)
- [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.
- [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.
- [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.
- [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)
- [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.
- [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.
- 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
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
-
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
-
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
-
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
-
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
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
free parameters (1)
- biological rate parameters in thermodynamic temperature dependence
axioms (2)
- domain assumption The annual cycle provides periodic forcing allowing Floquet theory to characterize stability of the extinction equilibrium
- domain assumption Linearization around the zero-population equilibrium captures the condition for uniform persistence
invented entities (2)
-
Thermal dominance index (DT)
no independent evidence
-
metabolic refuge
no independent evidence
Reference graph
Works this paper leans on
-
[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]
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]
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]
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]
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]
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]
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]
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]
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]
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]
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]
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
work page 1972
-
[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]
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]
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]
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]
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
work page 2015
-
[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]
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
work page 2009
-
[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]
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]
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]
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]
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]
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]
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]
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]
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]
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]
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]
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]
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]
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]
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]
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]
S. Rahmstorf, Is the atlantic overturning circulation approaching a tipping point?, Oceanography, 37 (2024), pp. 16–29, https://www.jstor.org/stable/27333920
-
[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]
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]
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]
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]
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]
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]
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]
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]
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
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.