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arxiv: 2505.10156 · v3 · submitted 2025-05-15 · 🧬 q-bio.PE

Population dynamics of generalist/specialist strategies in the feast-famine cycles

Rintaro Niimi , Chikara Furusawa , Yusuke Himeoka This is my paper

Pith reviewed 2026-05-22 15:22 UTC · model grok-4.3

classification 🧬 q-bio.PE
keywords generalistsspecialistsgrowth-death trade-offnutrient fluctuationsmicrobial populationspopulation dynamicsfeast-famine cyclestheoretical ecology
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The pith

The relative balance between growth and death rates determines whether generalist or specialist strategies dominate microbial populations in fluctuating nutrient environments.

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

This paper aims to show that the growth-death trade-off, combined with resource-use trade-offs, is key to determining if generalist or specialist strategies dominate in microbial populations. The model uses discrete stochastic nutrient events to simulate real fluctuations, revealing that high average growth rates with weak trade-offs promote generalists, but strong trade-offs promote specialists. A sympathetic reader cares because understanding these dynamics helps explain how microbes thrive in variable natural environments where nutrients are not constantly available. Previous studies missed this by not accounting for the higher death rates during scarcity that fast growers face.

Core claim

The authors introduce a unified mathematical model that simultaneously incorporates the resource-use trade-off and the growth-death trade-off arising from temporally fluctuating nutrients modeled as discrete stochastic events. They show that the relative balance between growth and death rates critically influences strategy dominance: high average growth rates and weak trade-offs favor generalists, while intense growth-death trade-offs promote specialists.

What carries the argument

Unified mathematical model combining resource-use trade-off with growth-death trade-off under stochastic discrete nutrient supply events.

Load-bearing premise

The growth-death trade-off intersects with the resource-use trade-off in a manner that produces a switch in strategy dominance.

What would settle it

An experiment that varies the strength of the growth-death trade-off while keeping nutrient supply stochastic and observes if specialists take over when the trade-off intensifies.

Figures

Figures reproduced from arXiv: 2505.10156 by Chikara Furusawa, Rintaro Niimi, Yusuke Himeoka.

Figure 1
Figure 1. Figure 1: Schematic illustration of the model. The central cycle depicts the environmental dynamics where the population alternately experiences the feast phase and the famine phase. Discrete nutrient supply events trigger the transition to the feast phase, whereas nutrient consumption by cells leads to resource depletion, driving the transition to the famine phase. The panels on the right illustrate the two key phy… view at source ↗
Figure 3
Figure 3. Figure 3: The figure shows that the transition between generalist- and specialist-dominant [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 2
Figure 2. Figure 2: Schematic of the population dynamics model and examples of the [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Transition between generalist and specialist dominance. [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Temporal average of the population as a function of [PITH_FULL_IMAGE:figures/full_fig_p011_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Normalized population density distribution. [PITH_FULL_IMAGE:figures/full_fig_p015_5.png] view at source ↗
read the original abstract

Microbial populations exhibit a broad spectrum of nutrient utilization strategies, ranging from strategies utilizing diverse nutrients, called "generalists," to those being highly adapted to specific nutrients, called "specialists." The mathematical conditions for the diversification of nutrient utilization strategies are central questions in theoretical ecology. Previous studies have shown that trade-offs among different resource utilization functions that cells cannot utilize broad types of substrates at near-maximum speed are crucial for the emergence of diverse strategies. However, in natural settings, nutrient availability often fluctuates over time, imposing additional trade-offs on cells. Cells that grow rapidly under nutrient-rich conditions will suffer a higher death rate under nutrient-poor conditions, creating a growth-death trade-off that intersects with the classical resource-use trade-off. Here, we introduce a unified mathematical model that simultaneously incorporates the resource-use trade-off and the growth-death trade-off. The nutrient supply was modeled as discrete stochastic events, capturing realistic temporal fluctuations. We show that the relative balance between growth and death rates critically influences the dominance of either generalist or specialist strategies. Specifically, under conditions of high average growth rates among different environments and a weak trade-off between growth and death rates, generalists prevail. In contrast, when the growth-death trade-off is intense, specialists emerge as the dominant strategy. Our findings reveal that accounting for the growth-death trade-off is crucial for understanding how microbial communities adapt and evolve in temporally varying environments.

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

2 major / 2 minor

Summary. The paper develops a unified mathematical model for microbial nutrient utilization strategies that integrates classical resource-use trade-offs with an additional growth-death trade-off induced by temporally fluctuating nutrient availability, modeled as discrete stochastic supply events. The central claim is that the relative balance between growth and death rates governs strategy dominance: generalists prevail under high average growth rates across environments combined with weak growth-death trade-offs, whereas intense growth-death trade-offs favor specialists.

Significance. If the reported dominance switch is robust to modeling choices, the work would meaningfully extend prior theoretical ecology results on resource-use trade-offs by incorporating realistic feast-famine dynamics and their associated mortality costs. This could help explain observed microbial diversity in variable environments. However, the abstract supplies no equations, parameter values, or verification steps, and the reader's assessment notes low soundness and potential circularity in the dependence on 'relative balance' and trade-off intensity, limiting immediate impact.

major comments (2)
  1. [Model construction (unified mathematical model)] The intersection of the growth-death trade-off with the resource-use trade-off is load-bearing for the central claim yet under-specified. The abstract states that 'the growth-death trade-off is assumed to intersect with the resource-use trade-off in a form that produces the reported dominance switch,' but provides no explicit functional form (linear, hyperbolic, or otherwise) linking maximum growth rate to famine death rate, nor any sensitivity analysis to alternative shapes or stochastic event frequencies.
  2. [Results and discussion] The reported conditions for generalist dominance ('high average growth rates among different environments and a weak trade-off') versus specialist dominance ('intense' trade-off) appear to hinge on the specific parameterization of the discrete stochastic nutrient supply process. Without reported robustness checks or explicit equations, it is unclear whether the switch is a general outcome or an artifact of fixed event magnitude/frequency choices.
minor comments (2)
  1. [Abstract] The abstract would be strengthened by including at least one key model equation or a brief statement of the trade-off functional form to allow readers to assess the claimed conditions without the full text.
  2. [Introduction] Notation for 'relative balance' and 'intensity' of trade-offs should be defined more precisely when first introduced to avoid ambiguity in interpreting the dominance results.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their detailed review and constructive suggestions. We have carefully considered the comments regarding model specification and robustness of results. We address each point below and plan to incorporate clarifications and additional analyses in the revised manuscript.

read point-by-point responses

  1. Referee: [Model construction (unified mathematical model)] The intersection of the growth-death trade-off with the resource-use trade-off is load-bearing for the central claim yet under-specified. The abstract states that 'the growth-death trade-off is assumed to intersect with the resource-use trade-off in a form that produces the reported dominance switch,' but provides no explicit functional form (linear, hyperbolic, or otherwise) linking maximum growth rate to famine death rate, nor any sensitivity analysis to alternative shapes or stochastic event frequencies.

    Authors: We agree that the abstract does not provide the explicit functional form or sensitivity analysis. In the full manuscript (Methods section), the growth-death trade-off is implemented by setting the famine death rate proportional to the maximum growth rate via an intensity parameter (linear form d = τ μ_max). We will update the abstract to state this functional form explicitly and add a new subsection with sensitivity analyses for alternative forms (e.g., hyperbolic) and a range of stochastic event frequencies and magnitudes. revision: yes

  2. Referee: [Results and discussion] The reported conditions for generalist dominance ('high average growth rates among different environments and a weak trade-off') versus specialist dominance ('intense' trade-off) appear to hinge on the specific parameterization of the discrete stochastic nutrient supply process. Without reported robustness checks or explicit equations, it is unclear whether the switch is a general outcome or an artifact of fixed event magnitude/frequency choices.

    Authors: The reported dominance switch is obtained from both analytical approximations of the stochastic process and numerical simulations across parameter regimes, as detailed in the Results. We acknowledge that additional explicit robustness checks would strengthen the claim of generality. In the revision we will add supplementary figures and text showing that the qualitative switch between generalist and specialist dominance persists when event magnitude and frequency are varied over an order of magnitude. revision: yes

Circularity Check

0 steps flagged

Model exploration of trade-off balance yields strategy dominance without definitional reduction or fitted predictions

full rationale

The paper defines a unified dynamical model that explicitly incorporates both the classical resource-use trade-off and an additional growth-death trade-off, with nutrient supply implemented as discrete stochastic events. It then varies parameters representing average growth rates and the intensity of the growth-death trade-off to observe shifts in dominance between generalist and specialist strategies. No equations or sections reduce the reported outcomes to fitted parameters by construction, nor do they rely on self-citations for uniqueness theorems or ansatzes that would make the central claim tautological. The results are generated from forward simulation or analysis of the independently specified model equations, making the derivation self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

Model rests on domain assumptions about fluctuating nutrients and the existence of a quantifiable growth-death trade-off; no independent evidence for these is supplied in the abstract.

free parameters (1)
  • growth-death trade-off intensity
    The strength of the trade-off is varied to produce the reported switch between generalist and specialist dominance.
axioms (1)
  • domain assumption Nutrient availability fluctuates over time as discrete stochastic events.
    This is invoked to capture realistic temporal fluctuations in the unified model.

pith-pipeline@v0.9.0 · 5791 in / 1234 out tokens · 42133 ms · 2026-05-22T15:22:53.098793+00:00 · methodology

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Works this paper leans on

44 extracted references · 44 canonical work pages

  1. [1]

    Inordinate Fondness Multiplied and Redistributed: The Number of Species on Earth and the New Pie of Life

    Larsen BB, Miller EC, Rhodes MK, Wiens JJ. Inordinate Fondness Multiplied and Redistributed: The Number of Species on Earth and the New Pie of Life. The Quarterly Review of Biology. 2017 Sep;92(3):229-65. doi:10.1086/693564

    work page doi:10.1086/693564 2017

  2. [2]

    Prokaryotes: The Unseen Majority

    Whitman WB, Coleman DC, Wiebe WJ. Prokaryotes: The Unseen Majority. Proceedings of the National Academy of Sciences. 1998 Jun;95(12):6578-83. doi:10.1073/pnas.95.12.6578

    work page doi:10.1073/pnas.95.12.6578 1998

  3. [3]

    Will a Large Complex System Be Stable? Nature

    May RM. Will a Large Complex System Be Stable? Nature. 1972 Aug;238(5364):413-4. doi:10.1038/238413a0

    work page doi:10.1038/238413a0 1972

  4. [4]

    The Feasibility and Stability of Large Complex Biological Networks: A Random Matrix Approach

    Stone L. The Feasibility and Stability of Large Complex Biological Networks: A Random Matrix Approach. Scientific Reports. 2018 May;8(1):8246. doi:10.1038/s41598-018-26486-2

    work page doi:10.1038/s41598-018-26486-2 2018

  5. [5]

    Mechanisms of Maintenance of Species Diversity

    Chesson P. Mechanisms of Maintenance of Species Diversity. Annual Review of Ecology and Systematics. 2000 Nov;31(1):343-66. doi:10.1146/annurev.ecolsys.31.1.343

    work page doi:10.1146/annurev.ecolsys.31.1.343 2000

  6. [6]

    Niche Partitioning Facilitates Coexistence of Closely Related Honey Bee Gut Bacteria

    Brochet S, Quinn A, Mars RA, Neuschwander N, Sauer U, Engel P. Niche Partitioning Facilitates Coexistence of Closely Related Honey Bee Gut Bacteria. eLife. 2021 Jul;10:e68583. doi:10.7554/eLife.68583

    work page doi:10.7554/elife.68583 2021

  7. [7]

    Low-Level Resource Partitioning Supports Coexistence among Functionally Redundant Bacteria during Successional Dynamics

    Yu XA, McLean C, Hehemann JH, Angeles-Albores D, Wu F, Muszy´ nski A, et al. Low-Level Resource Partitioning Supports Coexistence among Functionally Redundant Bacteria during Successional Dynamics. The ISME Journal. 2024 Jan;18(1):wrad013. doi:10.1093/ismejo/wrad013

    work page doi:10.1093/ismejo/wrad013 2024

  8. [8]

    Trade-Offs in Community Ecology: Linking Spatial Scales and Species Coexistence

    Kneitel JM, Chase JM. Trade-Offs in Community Ecology: Linking Spatial Scales and Species Coexistence. Ecology Letters. 2004;7(1):69-80. doi:10.1046/j.1461-0248.2003.00551.x

    work page doi:10.1046/j.1461-0248.2003.00551.x 2004

  9. [9]

    Trade-Offs Predicted by Metabolic Network Structure Give Rise to Evolutionary Specialization and Phenotypic Diversification

    Ekkers DM, Tusso S, Moreno-Gamez S, Rillo MC, Kuipers OP. Trade-Offs Predicted by Metabolic Network Structure Give Rise to Evolutionary Specialization and Phenotypic Diversification. Molecular Biology and Evolution. 2022 Jun;39(6):msac124. doi:10.1093/molbev/msac124

    work page doi:10.1093/molbev/msac124 2022

  10. [10]

    Dynamics of Lake Michigan Natural Phytoplankton Communities in Continuous Cultures Along a Si:P Loading Gradient

    Kilham SS. Dynamics of Lake Michigan Natural Phytoplankton Communities in Continuous Cultures Along a Si:P Loading Gradient. Canadian Journal of Fisheries and Aquatic Sciences. 1986 Feb;43(2):351-60. doi:10.1139/f86-045

    work page doi:10.1139/f86-045 1986

  11. [11]

    Evidence for a Three-Way Trade-off between Nitrogen and Phosphorus Competitive Abilities and Cell Size in Phytoplankton

    Edwards KF, Klausmeier CA, Litchman E. Evidence for a Three-Way Trade-off between Nitrogen and Phosphorus Competitive Abilities and Cell Size in Phytoplankton. Ecology. 2011;92(11):2085-95. arXiv:23034941

    work page 2011

  12. [12]

    Interdependence of Cell Growth and Gene Expression: Origins and Consequences

    Scott M, Gunderson CW, Mateescu EM, Zhang Z, Hwa T. Interdependence of Cell Growth and Gene Expression: Origins and Consequences. Science. 2010 Nov;330(6007):1099-102. doi:10.1126/science.1192588. May 1, 2026 23/26

    work page doi:10.1126/science.1192588 2010

  13. [13]

    Quantitative Proteomic Analysis Reveals a Simple Strategy of Global Resource Allocation in Bacteria

    Hui S, Silverman JM, Chen SS, Erickson DW, Basan M, Wang J, et al. Quantitative Proteomic Analysis Reveals a Simple Strategy of Global Resource Allocation in Bacteria. Molecular Systems Biology. 2015 Feb;11(1):784. doi:10.15252/msb.20145697

    work page doi:10.15252/msb.20145697 2015

  14. [14]

    Evolution of Diversity in Metabolic Strategies

    Caetano R, Ispolatov Y, Doebeli M. Evolution of Diversity in Metabolic Strategies. eLife. 2021 Aug;10:e67764. doi:10.7554/eLife.67764

    work page doi:10.7554/elife.67764 2021

  15. [15]

    Glycolysis/Gluconeogenesis Specialization in Microbes Is Driven by Biochemical Constraints of Flux Sensing

    Schink SJ, Christodoulou D, Mukherjee A, Athaide E, Brunner V, Fuhrer T, et al. Glycolysis/Gluconeogenesis Specialization in Microbes Is Driven by Biochemical Constraints of Flux Sensing. Molecular Systems Biology. 2022 Jan;18(1):e10704. doi:10.15252/msb.202110704

    work page doi:10.15252/msb.202110704 2022

  16. [16]

    The Evolution of Ecological Specialization

    Futuyma DJ, Moreno G. The Evolution of Ecological Specialization. Annual Review of Ecology and Systematics. 1988;19:207-33. arXiv:2097153

    work page 1988

  17. [17]

    Evolution of Ecological Niche Breadth

    Sexton JP, Montiel J, Shay JE, Stephens MR, Slatyer RA. Evolution of Ecological Niche Breadth. Annual Review of Ecology, Evolution, and Systematics. 2017;48(1):183-206. doi:10.1146/annurev-ecolsys-110316-023003

    work page doi:10.1146/annurev-ecolsys-110316-023003 2017

  18. [18]

    Fluctuation Induces Evolutionary Branching in a Mathematical Model of Ecosystems

    Tachikawa M. Fluctuation Induces Evolutionary Branching in a Mathematical Model of Ecosystems. PLOS ONE. 2008 Dec;3(12):e3925. doi:10.1371/journal.pone.0003925

    work page doi:10.1371/journal.pone.0003925 2008

  19. [19]

    Experimental Evolution with E

    Cooper TF, Lenski RE. Experimental Evolution with E. Coli in Diverse Resource Environments. I. Fluctuating Environments Promote Divergence of Replicate Populations. BMC Evolutionary Biology. 2010 Jan;10(1):11. doi:10.1186/1471-2148-10-11

    work page doi:10.1186/1471-2148-10-11 2010

  20. [20]

    Coexistence in a Variable Environment: Eco-evolutionary Perspectives

    Kremer CT, Klausmeier CA. Coexistence in a Variable Environment: Eco-evolutionary Perspectives. Journal of Theoretical Biology. 2013 Dec;339:14-25. doi:10.1016/j.jtbi.2013.05.005

    work page doi:10.1016/j.jtbi.2013.05.005 2013

  21. [21]

    The Experimental Evolution of Specialists, Generalists, and the Maintenance of Diversity

    Kassen R. The Experimental Evolution of Specialists, Generalists, and the Maintenance of Diversity. Journal of Evolutionary Biology. 2002;15(2):173-90. doi:10.1046/j.1420-9101.2002.00377.x

    work page doi:10.1046/j.1420-9101.2002.00377.x 2002

  22. [22]

    Tuning Environmental Timescales to Evolve and Maintain Generalists

    Sachdeva V, Husain K, Sheng J, Wang S, Murugan A. Tuning Environmental Timescales to Evolve and Maintain Generalists. Proceedings of the National Academy of Sciences. 2020 Jun;117(23):12693-9. doi:10.1073/pnas.1914586117

    work page doi:10.1073/pnas.1914586117 2020

  23. [23]

    Cycles of Famine and Feast: The Starvation and Outgrowth Strategies of a marineVibrio

    Srinivasan S, Kjelleberg S. Cycles of Famine and Feast: The Starvation and Outgrowth Strategies of a marineVibrio. Journal of Biosciences. 1998 Oct;23(4):501-11. doi:10.1007/BF02936144

    work page doi:10.1007/bf02936144 1998

  24. [24]

    Microbial Ecology of Organic Aggregates in Aquatic Ecosystems

    Simon M, Grossart HP, Schweitzer B, Ploug H. Microbial Ecology of Organic Aggregates in Aquatic Ecosystems. Aquatic Microbial Ecology. 2002 Jun;28(2):175-211. doi:10.3354/ame028175

    work page doi:10.3354/ame028175 2002

  25. [25]

    Frequency- and Amplitude-Dependent Microbial Population Dynamics during Cycles of Feast and Famine

    Merritt J, Kuehn S. Frequency- and Amplitude-Dependent Microbial Population Dynamics during Cycles of Feast and Famine. Physical Review Letters. 2018 Aug;121(9):098101. doi:10.1103/PhysRevLett.121.098101

    work page doi:10.1103/physrevlett.121.098101 2018

  26. [26]

    Dynamics of Bacterial Populations under the Feast-Famine Cycles

    Himeoka Y, Mitarai N. Dynamics of Bacterial Populations under the Feast-Famine Cycles. Physical Review Research. 2020 Mar;2(1):013372. doi:10.1103/PhysRevResearch.2.013372. May 1, 2026 24/26

    work page doi:10.1103/physrevresearch.2.013372 2020

  27. [27]

    rpoS Mutations and Loss of General Stress Resistance in Escherichia Coli Populations as a Consequence of Conflict between Competing Stress Responses

    Notley-McRobb L, King T, Ferenci T. rpoS Mutations and Loss of General Stress Resistance in Escherichia Coli Populations as a Consequence of Conflict between Competing Stress Responses. Journal of Bacteriology. 2002 Feb;184(3):806-11. doi:10.1128/JB.184.3.806-811.2002

    work page doi:10.1128/jb.184.3.806-811.2002 2002

  28. [28]

    Proteases and Protein Degradation in Escherichia Coli

    Maurizi MR. Proteases and Protein Degradation in Escherichia Coli. Experientia. 1992 Feb;48(2):178-201. doi:10.1007/BF01923511

    work page doi:10.1007/bf01923511 1992

  29. [29]

    Escherichia Coli Can Survive Stress by Noisy Growth Modulation

    Patange O, Schwall C, Jones M, Villava C, Griffith DA, Phillips A, et al. Escherichia Coli Can Survive Stress by Noisy Growth Modulation. Nature Communications. 2018 Dec;9(1):5333. doi:10.1038/s41467-018-07702-z

    work page doi:10.1038/s41467-018-07702-z 2018

  30. [30]

    Slower Growth of Escherichia Coli Leads to Longer Survival in Carbon Starvation Due to a Decrease in the Maintenance Rate

    Biselli E, Schink SJ, Gerland U. Slower Growth of Escherichia Coli Leads to Longer Survival in Carbon Starvation Due to a Decrease in the Maintenance Rate. Molecular Systems Biology. 2020 Jun;16(6):e9478. doi:10.15252/msb.20209478

    work page doi:10.15252/msb.20209478 2020

  31. [31]

    Evolution Restricts the Coexistence of Specialists and Generalists: The Role of Trade-off Structure

    Egas M, Dieckmann U, Sabelis MW. Evolution Restricts the Coexistence of Specialists and Generalists: The Role of Trade-off Structure. The American Naturalist. 2004 Apr;163(4):518-31. doi:10.1086/382599

    work page doi:10.1086/382599 2004

  32. [32]

    Trait–Fitness Relationships Determine How Trade-off Shapes Affect Species Coexistence

    Ehrlich E, Becks L, Gaedke U. Trait–Fitness Relationships Determine How Trade-off Shapes Affect Species Coexistence. Ecology. 2017;98(12):3188-98. doi:10.1002/ecy.2047

    work page doi:10.1002/ecy.2047 2017

  33. [33]

    Growth Tradeoffs Produce Complex Microbial Communities on a Single Limiting Resource

    Manhart M, Shakhnovich EI. Growth Tradeoffs Produce Complex Microbial Communities on a Single Limiting Resource. Nature Communications. 2018 Aug;9(1):3214. doi:10.1038/s41467-018-05703-6

    work page doi:10.1038/s41467-018-05703-6 2018

  34. [34]

    Nutrient Levels and Trade-Offs Control Diversity in a Serial Dilution Ecosystem

    Erez A, Lopez JG, Weiner BG, Meir Y, Wingreen NS. Nutrient Levels and Trade-Offs Control Diversity in a Serial Dilution Ecosystem. eLife. 2020 Sep;9:e57790. doi:10.7554/eLife.57790

    work page doi:10.7554/elife.57790 2020

  35. [35]

    Fine-Scale Diversity of Microbial Communities Due to Satellite Niches in Boom and Bust Environments

    Fridman Y, Wang Z, Maslov S, Goyal A. Fine-Scale Diversity of Microbial Communities Due to Satellite Niches in Boom and Bust Environments. PLOS Computational Biology. 2022 Dec;18(12):e1010244. doi:10.1371/journal.pcbi.1010244

    work page doi:10.1371/journal.pcbi.1010244 2022

  36. [36]

    Diauxic Lags Explain Unexpected Coexistence in Multi-resource Environments

    Bloxham B, Lee H, Gore J. Diauxic Lags Explain Unexpected Coexistence in Multi-resource Environments. Molecular Systems Biology. 2022 May;18(5):MSB202110630. doi:10.15252/msb.202110630

    work page doi:10.15252/msb.202110630 2022

  37. [37]

    Biodiversity Is Enhanced by Sequential Resource Utilization and Environmental Fluctuations via Emergent Temporal Niches

    Bloxham B, Lee H, Gore J. Biodiversity Is Enhanced by Sequential Resource Utilization and Environmental Fluctuations via Emergent Temporal Niches. PLOS Computational Biology. 2024 May;20(5):e1012049. doi:10.1371/journal.pcbi.1012049

    work page doi:10.1371/journal.pcbi.1012049 2024

  38. [38]

    NUTRIENT CYCLING IN MOIST TROPICAL FOREST

    Vitousek PM, Jr RLS. NUTRIENT CYCLING IN MOIST TROPICAL FOREST. Annual Review of Ecology, Evolution, and Systematics. 1986 Nov;17(Volume 17,):137-67. doi:10.1146/annurev.es.17.110186.001033

    work page doi:10.1146/annurev.es.17.110186.001033 1986

  39. [39]

    Levins R, Evolution in Changing Environments: Some Theoretical Explorations, Princeton University Press; 1968

    work page 1968

  40. [40]

    Thermodynamic Constraints Shape the Structure of Carbon Fixation Pathways

    Bar-Even A, Flamholz A, Noor E, Milo R. Thermodynamic Constraints Shape the Structure of Carbon Fixation Pathways. Biochimica et Biophysica Acta (BBA) - Bioenergetics. 2012 Sep;1817(9):1646-59. doi:10.1016/j.bbabio.2012.05.002. May 1, 2026 25/26

    work page doi:10.1016/j.bbabio.2012.05.002 2012

  41. [41]

    Source–Sink Dynamics Shape the Evolution of Antibiotic Resistance and Its Pleiotropic Fitness Cost

    Perron GG, Gonzalez A, Buckling A. Source–Sink Dynamics Shape the Evolution of Antibiotic Resistance and Its Pleiotropic Fitness Cost. Proceedings of the Royal Society B: Biological Sciences. 2007 Sep;274(1623):2351-6. doi:10.1098/rspb.2007.0640

    work page doi:10.1098/rspb.2007.0640 2007

  42. [42]

    Sources and Sinks: A Stochastic Model of Evolution in Heterogeneous Environments

    Hermsen R, Hwa T. Sources and Sinks: A Stochastic Model of Evolution in Heterogeneous Environments. Physical Review Letters. 2010 Dec;105(24):248104. doi:10.1103/PhysRevLett.105.248104

    work page doi:10.1103/physrevlett.105.248104 2010

  43. [43]

    Generalist Species Drive Microbial Dispersion and Evolution

    Sriswasdi S, Yang Cc, Iwasaki W. Generalist Species Drive Microbial Dispersion and Evolution. Nature Communications. 2017 Oct;8(1):1162. doi:10.1038/s41467-017-01265-1

    work page doi:10.1038/s41467-017-01265-1 2017

  44. [44]

    A Sink Host Allows a Specialist Herbivore to Persist in a Seasonal Source

    Laska A, Magalh˜ aes S, Lewandowski M, Puchalska E, Karpicka-Ignatowska K, Radwa´ nska A, et al. A Sink Host Allows a Specialist Herbivore to Persist in a Seasonal Source. Proceedings of the Royal Society B: Biological Sciences;288(1958):20211604. doi:10.1098/rspb.2021.1604. May 1, 2026 26/26

    work page doi:10.1098/rspb.2021.1604 1958