pith. sign in

arxiv: 2405.15602 · v1 · submitted 2024-05-24 · 🧮 math.DS

Far-from-equilibrium travelling pulses in sloped semi-arid environments driven by autotoxicity effects

Pith reviewed 2026-05-24 01:25 UTC · model grok-4.3

classification 🧮 math.DS
keywords Klausmeier modeltravelling pulsesautotoxicitygeometric singular perturbation theoryhomoclinic orbitssemi-arid vegetationpattern formationfast-slow systems
0
0 comments X

The pith

An extension of the Klausmeier model shows autotoxicity can produce travelling vegetation pulses on slopes.

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

The paper extends the one-dimensional Klausmeier vegetation model by adding effects from toxicity compounds released by the plants themselves. Numerical simulations first reveal pulse-shaped solutions that travel across the domain. Geometric singular perturbation theory is then applied after a scaling analysis identifies a suitable asymptotic regime, proving existence by constructing homoclinic orbits in the resulting four-dimensional travelling-wave system. The analysis extracts biological implications about how autotoxicity influences moving patterns and shows close agreement between the analytically constructed pulses and the numerical ones.

Core claim

In the extended Klausmeier model that incorporates autotoxicity, travelling pulse solutions exist and can be proven by constructing corresponding homoclinic orbits in the associated four-dimensional ordinary differential equation system. This construction is carried out via geometric singular perturbation theory once a scaling analysis places the system in an appropriate singularly perturbed regime. The resulting solutions match numerical simulations and allow extraction of biological observations on the role of autotoxicity in vegetation dynamics.

What carries the argument

Construction of homoclinic orbits in the four-dimensional travelling-wave system obtained after scaling the model equations, using geometric singular perturbation theory to handle the fast-slow decomposition.

If this is right

  • Travelling pulses persist under autotoxicity in sloped semi-arid settings.
  • Pulse speed and width are controlled by the strength of the toxicity feedback.
  • Autotoxicity can shift stationary patterns into moving ones.
  • The analytic pulses agree quantitatively with direct numerical simulations of the PDE.
  • Biological conclusions follow about how plant-produced toxins affect vegetation movement on slopes.

Where Pith is reading between the lines

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

  • The same scaling and homoclinic construction technique could be tested on other extensions of the Klausmeier model that include different feedback mechanisms.
  • Two-dimensional versions of the model might admit analogous travelling stripes whose stability could be checked numerically.
  • The role of autotoxicity in preventing or enabling pattern invasion into bare soil could be explored by varying the toxicity parameter across the existence threshold.

Load-bearing premise

The model parameters admit a scaling that places the travelling-wave system in a singularly perturbed regime where the fast-slow decomposition remains valid.

What would settle it

Numerical integration of the scaled four-dimensional travelling-wave system that fails to locate the predicted homoclinic orbits, or constructed orbits whose profiles deviate substantially from simulated pulse solutions.

Figures

Figures reproduced from arXiv: 2405.15602 by Annalisa Iuorio, Frits Veerman, Gabriele Grif\`o.

Figure 1
Figure 1. Figure 1: Spatial profiles for surface water U (a), biomass V (b) and toxicity S (c) of a travelling pulse, obtained by numerical integration of system (4) over a domain with length L = 1000 with periodic boundary conditions. The parameter values are A = 1.2, B = 0.45, ε = 0.005, D = 4.5, and H = 1. 3 Existence of travelling pulses In this section, we apply GSPT to prove the existence of a constant speed travelling … view at source ↗
Figure 2
Figure 2. Figure 2: Slow (blue) and superslow (purple) dynamics on the critical manifolds [PITH_FULL_IMAGE:figures/full_fig_p010_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Schematic representation of the slow (blue) and superslow (purple) dynamics of the [PITH_FULL_IMAGE:figures/full_fig_p011_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Schematic representation of the fast dynamics associated to a travelling pulse [PITH_FULL_IMAGE:figures/full_fig_p012_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: The singular skeleton orbit Φ0 (31) given in Proposition 4, obtained by matching superslow (purple), slow (blue) and fast (green) orbits of the reduced problems (20), (18) and the layer problem (13). 3.4 Persistence The singular skeleton orbit Φ0 (31) provides the backbone for the main result of this paper: the existence of a homoclinic solution in system (12). The persistence of Φ0 for 0 < δ ≪ 1 is given … view at source ↗
Figure 6
Figure 6. Figure 6: Sketch of the dynamics on M(3) as described in the proof of Theorem 5. The teal set corresponds to Wu δ,µ(p1(a)) as defined in (35), the red section represents Σ as in (34), and the blue curve shows the set Γ defined in (36). The teal and blue points correspond to p1(a) and p3(a, s∗ ), respectively. The concatenation of the pair of heteroclinics determined in Lemma 2 and Lemma 3 provides a heteroclinic con… view at source ↗
Figure 7
Figure 7. Figure 7: Sketch of the dynamics described in the proof of Theorem 5 in ( [PITH_FULL_IMAGE:figures/full_fig_p016_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Bifurcation diagrams obtained by performing numerical continuation of system (4) [PITH_FULL_IMAGE:figures/full_fig_p018_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Profiles of the field variables (first column: [PITH_FULL_IMAGE:figures/full_fig_p019_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Homoclinic orbits obtained by performing numerical continuation of system (4) [PITH_FULL_IMAGE:figures/full_fig_p020_10.png] view at source ↗
read the original abstract

In this work, an extension of the 1D Klausmeier model that accounts for the toxicity compounds is considered and the occurrence of travelling stripes is investigated. Numerical simulations are firstly conducted to capture the qualitative behaviours of the pulse-type solutions and, then, geometric singular perturbation theory is used to prove the existence of such travelling pulses by constructing the corresponding homoclinic orbits in the associated 4-dimensional system. A scaling analysis on the investigated model is performed to identify the asymptotic scaling regime in which travelling pulses can be constructed. Biological observations are extracted from the analytical results and the role of autotoxicity in travelling patterns is emphasized. Finally, the analytically constructed solutions are compared with the numerical ones, leading to a good agreement that confirms the validity of the conducted analysis. Numerical investigations are also carried out in order to gain additional information on vegetation dynamics.

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

1 major / 0 minor

Summary. The manuscript extends the 1D Klausmeier vegetation model by adding an autotoxicity term and studies travelling pulse (stripe) solutions on slopes. Numerical simulations first illustrate the pulses; a scaling analysis then reduces the PDE to a 4D fast-slow ODE system in a specific asymptotic regime, after which geometric singular perturbation theory is invoked to prove existence by constructing a homoclinic orbit. Biological conclusions are drawn from the analytic pulses, and the constructed solutions are compared with the numerics, showing good agreement.

Significance. If the GSPT construction is valid, the work supplies the first rigorous existence proof for toxicity-driven travelling pulses in an extended Klausmeier system, together with explicit parameter regimes and direct numerical validation. This strengthens the mathematical foundation for pattern-formation models in semi-arid ecology and isolates the mechanistic role of autotoxicity.

major comments (1)
  1. [Scaling analysis and GSPT construction] Scaling analysis and GSPT construction: the manuscript identifies an asymptotic regime that yields a 4D system, but does not explicitly verify normal hyperbolicity of the critical manifold (eigenvalues of the fast Jacobian) or the transversality conditions required for the slow-manifold matching that assembles the homoclinic orbit. These verifications are load-bearing for the existence claim; without them the application of Fenichel theory and the subsequent geometric construction remain incomplete.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their careful reading of the manuscript and for highlighting the need for explicit verification of key technical conditions in the GSPT construction. We address the major comment below and will incorporate the necessary additions in a revised version.

read point-by-point responses
  1. Referee: Scaling analysis and GSPT construction: the manuscript identifies an asymptotic regime that yields a 4D system, but does not explicitly verify normal hyperbolicity of the critical manifold (eigenvalues of the fast Jacobian) or the transversality conditions required for the slow-manifold matching that assembles the homoclinic orbit. These verifications are load-bearing for the existence claim; without them the application of Fenichel theory and the subsequent geometric construction remain incomplete.

    Authors: We agree that explicit verification of normal hyperbolicity and the relevant transversality conditions is required to make the application of Fenichel theory and the homoclinic construction fully rigorous. In the revised manuscript we will add a dedicated subsection that computes the eigenvalues of the fast Jacobian along the critical manifold and confirms that all eigenvalues have nonzero real parts (hence normal hyperbolicity). We will also state and verify the transversality conditions that guarantee the transverse intersection of the stable and unstable manifolds of the slow manifolds used in the matching argument. These additions will be placed immediately after the scaling analysis and before the geometric construction of the homoclinic orbit. revision: yes

Circularity Check

0 steps flagged

No circularity: GSPT existence proof is independent of inputs

full rationale

The paper extends the Klausmeier model with toxicity, performs scaling analysis to select an asymptotic regime yielding a 4D fast-slow system, and invokes geometric singular perturbation theory (external theorems on normally hyperbolic manifolds and homoclinic construction) to prove existence of travelling pulses. This chain does not reduce any claimed result to a fitted parameter, self-defined quantity, or load-bearing self-citation; the minor reference to the base Klausmeier model supplies only the starting PDE and carries no part of the existence argument. The derivation remains self-contained against external mathematical benchmarks.

Axiom & Free-Parameter Ledger

2 free parameters · 2 axioms · 0 invented entities

The proof rests on the standard assumptions of geometric singular perturbation theory (normally hyperbolic critical manifolds, transversality of stable/unstable manifolds) plus the modelling choice that toxicity acts as a local inhibitor with its own diffusion or decay rate. No new entities are postulated. A small number of nondimensional parameters are introduced by the scaling analysis and treated as free within the existence interval.

free parameters (2)
  • toxicity strength parameter
    The rate at which plants produce and are inhibited by the autotoxin is introduced as a free parameter whose value determines the regime where pulses exist.
  • scaled slope and diffusion ratios
    The scaling analysis produces nondimensional groups that must lie in specific ranges for the fast-slow decomposition to hold; these ranges are chosen to enable the homoclinic construction.
axioms (2)
  • standard math The extended reaction-diffusion system admits a normally hyperbolic slow manifold on which the reduced flow can be analyzed.
    Invoked when geometric singular perturbation theory is applied to construct the homoclinic orbit in the 4D system.
  • domain assumption The toxicity variable diffuses or decays at a rate that permits separation of time scales in the chosen asymptotic regime.
    Required for the scaling analysis that identifies the regime in which travelling pulses can be constructed.

pith-pipeline@v0.9.0 · 5679 in / 1714 out tokens · 27424 ms · 2026-05-24T01:25:13.223375+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

59 extracted references · 59 canonical work pages

  1. [1]

    The Global Land Outlook

    United Nations Convention to Combat Desertfication. The Global Land Outlook . UNCCD, Bonn, second edition, 2022. ISBN 978-92-95118-53-9. URL https://www. unccd.int/resources/global-land-outlook/global-land-outlook-2nd-edition . 21

  2. [2]

    de Soyza, W.G

    A.G. de Soyza, W.G. Whitford, J.E. Herrick, J.W. Van Zee, and K.M. Havstad. Early warning indicators of desertification: examples of tests in the Chihuahuan Desert.Journal of Arid Environments, 39(2):101–112, 1998. doi: 10.1006/jare.1998.0391

  3. [3]

    Tirabassi, J

    G. Tirabassi, J. Viebahn, V. Dakos, H.A. Dijkstra, C. Masoller, M. Rietkerk, and S.C. Dekker. Interaction network based early-warning indicators of vegetation transitions. Ecological Complexity, 19:148–157, 2014. doi: 10.1016/j.ecocom.2014.06.004

  4. [4]

    Yamagishi, N

    K. Gowda, S. Iams, and M. Silber. Signatures of human impact on self-organized veg- etation in the Horn of Africa. Scientific Reports, 8(1):3622, 2018. doi: 10.1038/s41598- 018-22075-5

  5. [5]

    R.M. May. Thresholds and breakpoints in ecosystems with a multiplicity of stable states. Nature, 269(5628):471–477, 1977. doi: 10.1038/269471a0

  6. [6]

    Rietkerk, F

    M. Rietkerk, F. van den Bosch, and J. van de Koppel. Site-specific properties and irreversible vegetation changes in semi-arid grazing systems. Oikos, 80:241–252, 1997. doi: 10.2307/3546592

  7. [7]

    P.M. Saco, M. Moreno-de las Heras, S. Keesstra, J. Baartman, O. Yetemen, and J.F. Rodr´ ıguez. Vegetation and soil degradation in drylands: nonlinear feedbacks and early warning signals. Current Opinion in Environmental Science & Health , 5:67–72, 2018. doi: 10.1016/j.coesh.2018.06.001

  8. [8]

    Bastiaansen, A

    R. Bastiaansen, A. Doelman, M.B. Eppinga, and M. Rietkerk. The effect of climate change on the resilience of ecosystems with adaptive spatial pattern formation. Ecology Letters, 43:414–429, 2020. doi: 10.1111/ele.13449

  9. [9]

    Rietkerk, R

    M. Rietkerk, R. Bastiaansen, S. Banerjee, J. Van De Koppel, M. Baudena, and A. Doel- man. Evasion of tipping in complex systems through spatial pattern formation. Science, 374, 2021. doi: 10.1126/science.abj0359

  10. [10]

    E. Meron. Nonlinear Physics of Ecosystems. CRC Press, Boca Raton, first edition, 2015. doi: 10.1201/b18360

  11. [11]

    Community Ecol- ogy 8(1), 103–109 (2007)

    S. Mazzoleni, G. Bonanomi, F. Giannino, M. Rietkerk, S. Dekker, and F. Zucconi. Is plant biodiversity driven by decomposition processes? an emerging new theory on plant diversity. Community Ecology, 8(1):103–109, 2007. doi: 10.1556/ComEc.8.2007.1.12

  12. [12]

    DOI 10.1111/nph.13121

    S. Mazzoleni, G. Bonanomi, G. Incerti, M.L. Chiusano, P. Termolino, A. Mingo, M. Sen- atore, F. Giannino, F. Carten` ı, M. Rietkerk, and V. Lanzotti. Inhibitory and toxic effects of extracellular self-dna in litter: a mechanism for negative plant–soil feedbacks? New Phytologist, 205(3):1195–1210, 2015. doi: 10.1111/nph.13121

  13. [13]

    Science284(5421), 1826–1828 (1999)

    C.A. Klausmeier. Regular and irregular patterns in semiarid vegetation. Science, 284: 1826–1828, 1999. doi: 10.1126/science.284.5421.1826

  14. [14]

    HilleRisLambers, M

    R. HilleRisLambers, M. Rietkerk, F. van de Bosch, H.H.T. Prins, and H. de Kroon. Vegetation pattern formation in semi-arid grazing systems. Ecology, 82(1):50, 2001. doi: 10.1890/0012-9658(2001)082[0050:VPFISA]2.0.CO;2. 22

  15. [15]

    The American Naturalist 160(4), 524–530 (2002)

    M. Rietkerk, M.C. Boerlijst, F. van Langevelde, R. HilleRisLambers, J. van de Koppel, H.H.T. Prins, and A. de Roos. Self-organisation of vegetation in arid ecosystems. The American Naturalist, 160(4):534, 2002. doi: 10.1086/342078

  16. [16]

    Journal of Mathematical Biology 51(2), 183–197 (2005)

    J.A. Sherratt. An analysis of vegetation stripe formation in semi-arid landscapes. Journal of Mathematical Biology, 51:183–197, 2005. doi: 10.1007/s00285-005-0319-5

  17. [17]

    Siteur, E

    K. Siteur, E. Siero, M.B. Eppinga, J.D.M. Rademacher, A. Doelman, and M. Rietk- erk. Beyond turing: The response of patterned ecosystems to environmental change. Ecological Complexity, 20:81–96, 2014. doi: 10.1016/j.ecocom.2014.09.002

  18. [18]

    Carten` ı, A

    F. Carten` ı, A. Marasco, G. Bonanomi, S. Mazzoleni, M. Rietkerk, and F. Giannino. Neg- ative plant soil feedback explaining ring formation in clonal plants.Journal of Theoretical Biology, 313:153–161, 2012. doi: 10.1016/j.jtbi.2012.08.008

  19. [19]

    van der Stelt, A

    S. van der Stelt, A. Doelman, G. Hek, and J.D.M. Rademacher. Rise and fall of periodic patterns for a generalized Klausmeier-Gray-Scott model. Journal of Nonlinear Science , 23(7):39–95, 2013. doi: 10.1007/s00332-012-9139-0

  20. [20]

    Marasco, F

    A. Marasco, F. Giannino, and A. Iuorio. Modelling competitive interactions and plant–soil feedback in vegetation dynamics. Ricerche di Matematica, 69:553—-577, 2020. doi: 10.1007/s11587-020-00497-6

  21. [21]

    Eigentler and J.A

    L. Eigentler and J.A. Sherratt. An integrodifference model for vegetation patterns in semi-arid environments with seasonality. Journal of Mathematical Biology , 81:875–904,

  22. [22]

    doi: 10.1007/s00285-020-01530-w

  23. [23]

    Iuorio, M.B

    A. Iuorio, M.B. Eppinga, M. Baudena, F. Veerman, M. Rietkerk, and F. Giannino. How does negative plant-soil feedback across lifestages affect the spatial patterning of trees? Scientific Reports, 2023. doi: 10.1038/s41598-023-44867-0

  24. [24]

    Iuorio, M

    A. Iuorio, M. Baudena, M.B. Eppinga, F. Giannino, M. Rietkerk, and F. Veerman. Travelling waves due to negative plant-soil feedbacks in a model including tree life-stages. Mathematical Biosciences, 2023. doi: 10.1016/j.mbs.2023.109128

  25. [25]

    Spiliotis, L

    K. Spiliotis, L. Russo, F. Giannino, and C. Siettos. Numerical bifurcation analysis of turing and symmetry broken patterns of a vegetation pde model. Preprint, 2023. doi: 10.48550/arXiv.2303.13248

  26. [26]

    Kealy and D.J

    B.J. Kealy and D.J. Wollkind. A nonlinear stability analysis of vegetative turing pattern formation for an interaction-diffusion plant-surface water model system in an arid flat en- vironment. Bulletin of Mathematical Biology , 74(4):803–833, 2012. doi: 10.1007/s11538- 011-9688-7

  27. [27]

    Zelnik, S

    Y. Zelnik, S. Kinast, H. Yizhaq, G. Bel, and E. Meron. Regime shifts in models of dryland vegetation. Philosophical Transactions of the Royal Society A, 371(2004):20120358, 2013. doi: 10.1098/rsta.2012.0358

  28. [28]

    G.Q. Sun, L. Li, and Z.K. Zhang. Spatial dynamics of a vegetation model in an arid flat environment. Nonlinear Dynamics, 73(5):2207–2219, 2013. doi: 10.1007/s11071-013- 0935-3. 23

  29. [29]

    Consolo and G

    G. Consolo and G. Grif` o. Eckhaus instability of stationary patterns in hyperbolic reaction-diffusion models on large finite domains. Partial Differential Equations and Applications, 3:57, 2022. doi: 10.1007/s42985-022-00193-0

  30. [30]

    Grif´ o, G

    G. Grif´ o, G. Consolo, C. Curr´ o, and G. Valenti. Rhombic and hexagonal pattern for- mation in 2D hyperbolic reaction-transport systems in the context of dryland ecology. Physica D, 449:133745, 2023. doi: 10.1016/j.physd.2023.133745

  31. [31]

    Curr` o, G

    C. Curr` o, G. Grif` o, and G. Valenti. Turing patterns in hyperbolic reaction-transport vegetation models with cross-diffusion. Chaos, Solitons & Fractals , 176:114152, 2023. doi: 10.1016/j.chaos.2023.114152

  32. [32]

    Consolo, C

    G. Consolo, C. Curr` o, G. Grif` o, and G. Valenti. Oscillatory periodic pattern dynamics in hyperbolic reaction-advection-diffusion models. Physical Review E, 105:034206, 2022. doi: 10.1103/PhysRevE.105.034206

  33. [33]

    Consolo, G

    G. Consolo, G. Grif` o, and G. Valenti. Dryland vegetation pattern dynamics driven by inertial effects and secondary seed dispersal. Ecological Modelling, 474:110171, 2022. doi: 10.1016/j.ecolmodel.2022.110171

  34. [34]

    G. Grif` o. Vegetation patterns in the hyperbolic Klausmeier model with secondary seed dispersal. Mathematics, 11:1084, 2023. doi: 10.3390/math11051084

  35. [35]

    Thompson and G

    S. Thompson and G. Katul. Secondary seed dispersal and its role in landscape organi- zation. Geophysical Research Letters, 36(2):L02402, 2009. doi: 10.1029/2008GL036044

  36. [36]

    Thompson, S

    S. Thompson, S. Assouline, L. Chen, A. Trahktenbrot, T. Svoray, and G. Katul. Sec- ondary dispersal driven by overland flow in drylands: Review and mechanistic model development. Movement Ecology, 2:7, 2014. doi: 10.1186/2051-3933-2-7

  37. [37]

    Marasco, A

    A. Marasco, A. Iuorio, F. Carten` ı, G. Bonanomi, F. Giannino, and S Maz- zoleni. Water limitation and negative plant-soil feedback explain vegetation patterns along rainfall gradient. Procedia Environmental Sciences , 19:139–147, 2013. doi: 10.1016/j.proenv.2013.06.016

  38. [38]

    Marasco, A

    A. Marasco, A. Iuorio, F. Carten` ı, G. Bonanomi, D.M. Tartakovsky, S. Mazzoleni, and F. Giannino. Vegetation pattern formation due to interactions between water availability and toxicity in plant-soil feedback. Bulletin of Mathematical Biology, 76:2866–2883, 2014. doi: 10.1007/s11538-014-0036-6

  39. [39]

    Iuorio and F

    A. Iuorio and F. Veerman. The influence of autotoxicity on the dynamics of vegetation spots. Physica D, 427:133015, 2021. doi: 10.1016/j.physd.2021.133015

  40. [40]

    Consolo, G

    G. Consolo, G. Grif` o, and G. Valenti. Modeling vegetation patterning on sloped terrains: The role of toxic compounds. Physica D , 459:134020, 2024. doi: 10.1016/j.physd.2023.134020

  41. [41]

    Figueroa

    R. Bastiaansen, P. Carter, and A. Doelman. Stable planar vegetation stripe patterns on sloped terrain in dryland ecosystems. Nonlinearity, 32(8):2759, 2019. doi: 10.1088/1361- 6544/ab1767. 24

  42. [42]

    Eigentler

    L. Eigentler. Species coexistence in resource-limited patterned ecosystems is facilitated by the interplay of spatial self-organisation and intraspecific competition. Oikos, 130: 609–623, 2021. doi: 10.1111/oik.07880

  43. [43]

    Consolo and G

    G. Consolo and G. Grif` o. Turing vegetation patterns in flat arid environments with finite soil carrying capacity. Ricerche di Matematica, 2023. doi: 10.1007/s11587-023-00783-z

  44. [44]

    Byrnes, P

    E. Byrnes, P. Carter, A. Doelman, and L. Liu. Large amplitude radially symmetric spots and gaps in a dryland ecosystem model. Preprint, 2023. doi: 10.48550/arXiv.2208.13167

  45. [45]

    D.J. Tongway. Banded Vegetation Patterning in Arid and Semiarid Environments . Springer, New York, first edition, 2001. doi: 10.1007/978-1-4613-0207-0

  46. [46]

    Dunkerley

    D. Dunkerley. Banded vegetation in some australian semi-arid landscapes: 20 years of field observations to support the development and evaluation of numerical models of vegetation pattern evolution. Desert, 23(2):165–187, 2018. URL https://jdesert.ut. ac.ir/article_69115.html

  47. [47]

    Carter and A

    P. Carter and A. Doelman. Traveling stripes in the Klausmeier model of vegetation pattern formation. SIAM Journal of Applied Mathematics , 78:3213–3237, 2018. doi: 10.1137/18M1196996

  48. [48]

    Sewalt and A

    L. Sewalt and A. Doelman. Spatially periodic multipulse patterns in a generalized Klaus- meier–Gray–Scott model. SIAM Journal on Applied Dynamical Systems , 16(2):1113– 1163, 2017. doi: 10.1137/16M1078756

  49. [49]

    J.D. Murray. Mathematical Biology II: Spatial Models and Biomedical Applications . Springer, Berlin, third edition, 2003. doi: 10.1007/b98869

  50. [50]

    R. Hoyle. Pattern formation. An introduction to methods . Cambridge University Press, New York, first edition, 2007. doi: 10.1017/CBO9780511616051

  51. [51]

    A. Doelman. Pattern formation in reaction-diffusion systems - an explicit approach. In M.A. Peletier, R.A. van Santen, and E. Siteur, editors, Complexity Science, an Introduc- tion, chapter 4, pages 129–182. World Scientific, 2018. doi: 10.1142/9789813239609 0004

  52. [52]

    Matlab version: 9.14.0 (r2023a), 2023

    The MathWorks Inc. Matlab version: 9.14.0 (r2023a), 2023. URL https://www. mathworks.com

  53. [53]

    Veerman and A

    F. Veerman and A. Doelman. Pulses in a Gierer-Meinhardt equation with a slow nonlin- earity. SIAM Journal on Applied Dynamical Systems, 12(1):28–60, 2013. ISSN 1536-0040. doi: 10.1137/120878574. URL http://dx.doi.org/10.1137/120878574

  54. [54]

    Doelman and F

    A. Doelman and F. Veerman. An explicit theory for pulses in two component, singularly perturbed, reaction-diffusion equations. Journal of Dynamics and Differential Equations , 27(3):555–595, 2015. ISSN 1040-7294. doi: 10.1007/s10884-013-9325-2. URL http: //dx.doi.org/10.1007/s10884-013-9325-2

  55. [55]

    Doelman, R.A

    A. Doelman, R.A. Gardner, and T.J. Kaper. Large stable pulse solutions in reaction- diffusion equations. Indiana University Mathematics Journal , 50(1):443–507, 2001. ISSN 0022-2518. doi: 10.1512/iumj.2001.50.1873. URL http://dx.doi.org/10.1512/iumj. 2001.50.1873. [link]. 25

  56. [56]

    E.J. Doedel. AUTO: A program for the automatic bifurcation analysis of autonomous systems. Congressus Numererantium, 30(265-284):25–93, 1981

  57. [57]

    Nonlinear programming in complex space: Sufficient conditions and duality

    N. Fenichel. Geometric singular perturbation theory for ordinary differential equa- tions. J. Differential Equations , 31(1):53–98, 1979. ISSN 0022-0396. doi: 10.1016/0022- 0396(79)90152-9

  58. [58]

    Jones, T.J

    C.K.R.T. Jones, T.J. Kaper, and N. Kopell. Tracking invariant manifolds up to exponentially small errors. SIAM J. Math. Anal. , 27(2):558–577, 1996. doi: 10.1137/s003614109325966x

  59. [59]

    C. Kuehn. Multiple Time Scale Dynamics . Springer International Publishing, 2015. doi: 10.1007/978-3-319-12316-5. URL https://doi.org/10.1007%2F978-3-319-12316-5 . 26