Shadow system approach to a plankton model generating harmful algal bloom

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February 2017, 37(2): 829-858. doi: 10.3934/dcds.2017034

Shadow system approach to a plankton model generating harmful algal bloom

Hideo Ikeda ^1, , Masayasu Mimura ^2, and Tommaso Scotti ^2,

1.	Department of Mathematics, University of Toyama, Gofuku 3190, Toyama, 930-8555, Japan
2.	Graduate School of Advanced Mathematical Sciences, Meiji University, Nakano 4-21-1, Tokyo, 164-8525, Japan

Received April 2015 Revised December 2015 Published November 2016

Fund Project: The first author is supported in part by JSPS KAKENHI Grant No. 22244010 and 15K04995. The second author thanks for partial support by JSPS KAKENHI Grant No. 15K13462.

Spatially localized blooms of toxic plankton species have negative impacts on other organisms via the production of toxins, mechanical damage, or by other means. Such blooms are nowadays a worldwide spread environmental issue. To understand the mechanism behind this phenomenon, a two-prey (toxic and nontoxic phytoplankton)-one-predator (zooplankton) Lotka-Volterra system with diffusion has been considered in a previous paper. Numerical results suggest the occurrence of stable non-constant equilibrium solutions, that is, spatially localized blooms of the toxic prey. Such blooms appear for intermediate values of the rate of toxicity $μ$ when the ratio $D$ of the diffusion rates of the predator and the two prey is rather large. In this paper, we consider a one-dimensional limiting system (we call it a shadow system) in $(0,L)$ as $D \to \infty $ and discuss the existence and stability of non-constant equilibrium solutions with large amplitude when $μ$ is globally varied. We also show that the structure of non-constant equilibrium solutions sensitively depends on $L$ as well as $μ$.

Keywords: Harmful algal bloom, plankton model, shadow system.

Mathematics Subject Classification: Primary:35B32, 35B35, 35B36, 35K57;Secondary:35Q9.

Citation: Hideo Ikeda, Masayasu Mimura, Tommaso Scotti. Shadow system approach to a plankton model generating harmful algal bloom. Discrete and Continuous Dynamical Systems, 2017, 37 (2) : 829-858. doi: 10.3934/dcds.2017034

References:

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[2]	M. G. Crandall and P. H. Rabinowitz, Bifurcation, perturbation of simple eigenvalues, and linearized stability, Arch. Rational Mech. Anal., 52 (1973), 161-180.
[3]	I. R. Falconer and A. R. Humpage, Tumor promotion by cyanobacterial toxins, Phycologia, 35 (1996), 74-79.
[4]	R. S. Fulton Ⅲ and H. W. Paerl, Toxic and inhibitory effects of the blue-green alga Microcystis aeruginosa on herbivorous zooplankton, J. of Plankton Research, 9 (1987), 837-855.
[5]	R. S. Fulton Ⅲ and H. W. Paerl, Zooplankton feeding selectivity for unicellular and colonial Microcystis aeruginosa, Bull. of Marine Science, 43 (1988), 500-508.
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[7]	E. M. Jochimsen, Liver failure and death after exposure to microcystins at Hemo-dialysis Center in Brazil, N. Engl. J. Med., 338 (1998), 873-878.
[8]	Y. Kan-on, Global bifurcation structure of positive stationary solutions for a classical Lotka-Volterra competition model with diffusion, Japan Journal of Industrial and Applied Mathematics, 20 (2003), 285-310. doi: 10.1007/BF03167424.
[9]	W. E. A. Kardinaal, Competition for light between toxic and nontoxic strains of the farmful cyanobacterium Microcystis, Applied and Environmental Microbiology, 73 (2007), 2939-2946.
[10]	W. Lampert, Inhibitory and toxic effects of blue-green algae on Daphnia, Int. Revue ges. Hydrobiol., 66 (1981), 285-298.
[11]	W. Lampert, Further studies on the inhibitory effect of the toxic blue-green Microcystis aeruginosa on the filtering rate of zooplankton, Arch. Hydrobiol., 95 (1982), 207-220.
[12]	Y. Nishiura, Global structure of bifurcating solutions of some reaction-diffusion systems, SIMA J. Math. Anal., 13 (1982), 555-593. doi: 10.1137/0513037.
[13]	K. G. Porter and J. D. Orcutt Jr, Nutritional adequacy, manageability, and toxicity as factors that determine food quality of green and blue-green algae for Daphnia, in Evolution and Ecology of Zooplankton Communities (ed. W. C. Kerfoot), University Press of New England, Hanover, NH, USA, (1980), 268-281.
[14]	T. Scotti, M. Mimura and J. Y. Wakano, Avoiding toxic prey may promote harmful algal blooms, Ecological Complexity, 21 (2015), 157-165. doi: 10.1016/j.ecocom.2014.07.004.
[15]	A. M. Turing, The chemical basis of morphogenesis, Philos. Trans. Roy. Soc. Lond. Ser. B, 237 (1952), 37-72. doi: 10.1098/rstb.1952.0012.
[16]	Q. Wang, C. Gai and J. Yan, Qualitative analysis of a Lotka-Volterra competition system with advection, Discreat and Contin. Dyn. Syst. -Series A, 35 (2015), 1239-1284. doi: 10.3934/dcds.2015.35.1239.
[17]	L. Zhou and C. V. Pao, Asymptotic behavior of a competition-diffusion system in population dynamics, Nonlinear Analysis, 6 (1982), 1163-1184. doi: 10.1016/0362-546X(82)90028-1.

show all references

References:

[1]	M. G. Crandall and P. H. Rabinowitz, Bifurcation from simple eigenvalues, Journal of Functional Analysis, 8 (1971), 321-340. doi: 10.1016/0022-1236(71)90015-2.
[2]	M. G. Crandall and P. H. Rabinowitz, Bifurcation, perturbation of simple eigenvalues, and linearized stability, Arch. Rational Mech. Anal., 52 (1973), 161-180.
[3]	I. R. Falconer and A. R. Humpage, Tumor promotion by cyanobacterial toxins, Phycologia, 35 (1996), 74-79.
[4]	R. S. Fulton Ⅲ and H. W. Paerl, Toxic and inhibitory effects of the blue-green alga Microcystis aeruginosa on herbivorous zooplankton, J. of Plankton Research, 9 (1987), 837-855.
[5]	R. S. Fulton Ⅲ and H. W. Paerl, Zooplankton feeding selectivity for unicellular and colonial Microcystis aeruginosa, Bull. of Marine Science, 43 (1988), 500-508.
[6]	G. E. Hutchinson, The paradox of the plankton, Am. Nat., 95 (1961), 137-145. doi: 10.1086/282171.
[7]	E. M. Jochimsen, Liver failure and death after exposure to microcystins at Hemo-dialysis Center in Brazil, N. Engl. J. Med., 338 (1998), 873-878.
[8]	Y. Kan-on, Global bifurcation structure of positive stationary solutions for a classical Lotka-Volterra competition model with diffusion, Japan Journal of Industrial and Applied Mathematics, 20 (2003), 285-310. doi: 10.1007/BF03167424.
[9]	W. E. A. Kardinaal, Competition for light between toxic and nontoxic strains of the farmful cyanobacterium Microcystis, Applied and Environmental Microbiology, 73 (2007), 2939-2946.
[10]	W. Lampert, Inhibitory and toxic effects of blue-green algae on Daphnia, Int. Revue ges. Hydrobiol., 66 (1981), 285-298.
[11]	W. Lampert, Further studies on the inhibitory effect of the toxic blue-green Microcystis aeruginosa on the filtering rate of zooplankton, Arch. Hydrobiol., 95 (1982), 207-220.
[12]	Y. Nishiura, Global structure of bifurcating solutions of some reaction-diffusion systems, SIMA J. Math. Anal., 13 (1982), 555-593. doi: 10.1137/0513037.
[13]	K. G. Porter and J. D. Orcutt Jr, Nutritional adequacy, manageability, and toxicity as factors that determine food quality of green and blue-green algae for Daphnia, in Evolution and Ecology of Zooplankton Communities (ed. W. C. Kerfoot), University Press of New England, Hanover, NH, USA, (1980), 268-281.
[14]	T. Scotti, M. Mimura and J. Y. Wakano, Avoiding toxic prey may promote harmful algal blooms, Ecological Complexity, 21 (2015), 157-165. doi: 10.1016/j.ecocom.2014.07.004.
[15]	A. M. Turing, The chemical basis of morphogenesis, Philos. Trans. Roy. Soc. Lond. Ser. B, 237 (1952), 37-72. doi: 10.1098/rstb.1952.0012.
[16]	Q. Wang, C. Gai and J. Yan, Qualitative analysis of a Lotka-Volterra competition system with advection, Discreat and Contin. Dyn. Syst. -Series A, 35 (2015), 1239-1284. doi: 10.3934/dcds.2015.35.1239.
[17]	L. Zhou and C. V. Pao, Asymptotic behavior of a competition-diffusion system in population dynamics, Nonlinear Analysis, 6 (1982), 1163-1184. doi: 10.1016/0362-546X(82)90028-1.

Figure 1. Bifurcation curves of $E_4$ in the $(D, \mu)$-plane ($L=30$) (a) and the $(L, \mu)$-plane ($D=2500$) (b), where $a=0.95$, $b=1.2$, $K=2.9$ and $R=0.43$. The curve $n$ corresponds to the $n$-mode bifurcations, where the zero solution of the linearized problem of (3) with (4) around $E_4$ destabilizes under the $n$th eigenmode $\cos(n\pi x/L)$ perturbation

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Figure 2. Global structure of equilibrium solutions of (3) with (4) when µ is varied, where L = 30, D = 2500. The other parameters are the same as the ones in Figure 1. Solid (resp. dashed) lines represent stable (resp. unstable) equilibrium solutions of (3) with (4). The right figure is a magnification of the left one where µ is close to µ_c1

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Figure 3. 1-mode equilibrium solutions $(\bar{P}_1^+(x), \bar{X}_1^+(x), \bar{Z}_1^+(x))$ of (3) with (4) for (a) $\mu=0.15$, (b) $\mu=0.5$ and (c) $\mu=3.1$. The other parameters are the same as the ones in Figure 1 and $D=2500$. Here $\bar{P}_1^+, \bar{X}_1^+$ and $\bar{Z}_1^+$ are drawn in blue, green and red colors, respectively

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Figure 4. Dependency of $D$ on the global structures of equilibrium solutions of the system (3) with (4) when $L=30$. (a) $D=800$, (b) $D=1500$, (c) $D=2500$, (d) $D=5000$, (e) $D=10000$ and (e$'$) is a magnification of (e) around $\mu=\mu_{c1}$. The other parameters are the same as the ones in Figure 1. Solid (resp. dashed) lines represent stable (resp. unstable) equilibrium solutions of (3) with (4)

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Figure 5. Three different structures of the nullclines of (14) with $d=d(\mu)=1/(1+\mu)$. (a-1) $P$-monostability, (a-2) $X$-monostability, (b) bistability and (c) coexistence. The red and white circles stand for stable and unstable equilibrium solutions of (14), respectively

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Figure 6. Schematic global structure of the constant and non-constant equilibrium solutions $(\bar{P}(\xi), \bar{X}(\xi))$ and $ (\bar{P}^{\pm}_n(x;\xi, \varepsilon), \bar{X}^{\pm}_n(x;\xi, \varepsilon)) \ (n=1, 2, \cdots)$ of (14) when $\varepsilon$ is varied

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Figure 7. Bifurcation curves of $\varepsilon = Q_0(\xi)/(n \pi)^2$ for $\xi \in I$. Here $\xi_n^l(\varepsilon)$ and $\xi_n^r(\varepsilon)$ are bifurcation values of $\xi$ for suitably given $\varepsilon$

$(n=1, 2, \cdots)$

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Figure 8. Schematic global structure of $1$-mode and $2$-mode equilibrium solutions of (14) which bifurcate from $(\bar{P}(\xi), \bar{X}(\xi))$ when $\xi$ is varied. Here the vertical and horizontal axes are $\bar{X}(0;\xi, \varepsilon)$ and $\xi \in I$, respectively

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Figure 9. Dependency of $H^{+}_n(\xi;\varepsilon)$ on $\varepsilon$. (a) $\xi_n^r(\varepsilon) \leqq \bar{\xi}^*$ and (b) $\xi_n^r(\varepsilon) > \bar{\xi}^*$

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Figure 10. The functional forms of $H_1^+(\xi;1/L^2, \mu)$ for (a) $\mu=0.1, \ (\alpha, \beta) = (0.404069, 0.668669)$, (b) $\mu=0.2, \ (\alpha, \beta) = (0.264145, 0.531191)$, (c) $\mu=1, \ (\alpha, \beta) = (0.104722, 0.2785)$, (d) $\mu=6, \ (\alpha, \beta) = (0.0636, 0.184446)$, (e) $\mu=12, \ (\alpha, \beta) = (0.0593018, 0.173621)$ and (f) $\mu=13, \ (\alpha, \beta) = (0.0589696, 0.172776)$, where other parameters are fixed as $a=0.95$, $b=1.2$, $K=2.9$, $R=0.43$ and $L=30$. The horizontal (resp. vertical) axes are $\xi$ (resp. $H^+_1(\xi;1/L^2, \mu)$)

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Figure 11. The functional forms of $H_1^+(\xi;1/L^2, \mu)$ for (a) $\mu=0.12, \ (\alpha, \beta) =(0.353533, 0.638713)$, (b) $\mu=0.1275, \ (\alpha, \beta) =(0.340841, 0.626635)$, (c) $\mu=0.1281, \ (\alpha, \beta) =(0.339872, 0.625696)$, (d) $\mu=0.13, \ (\alpha, \beta) =(0.336846, 0.622744)$, (e) $\mu=17, \ (\alpha, \beta) =(0.0565952, 0.171585)$ and (f) $\mu=18, \ (\alpha, \beta) =(0.0564297, 0.171148)$, where other parameters are fixed as $a=0.95$, $b=1.2$, $K=2.9$, $R=0.43$ and $L=35$. The horizontal (resp. vertical) axes are $\xi$ (resp. $H^+_1(\xi;1/L^2, \mu)$)

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Figure 12. Bifurcation diagram of the shadow system (9) with (10) when $\mu$ is a free parameter. Other parameters are fixed at $a=0.95$, $b=1.2$, $K=2.9$, $R=0.43$ and $L=30$. Solid (resp. dashed) lines represent stable (resp. unstable) equilibrium solutions of (9) with (10)

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Figure 13. Relations between $\varepsilon$ and $\mu$ with respect to the bifurcation curves of $n$-mode equilibrium solutions $(n=1, 2, 3)$ of (9) and (10) with $\varepsilon= 1/L^2$, where $a=0.95$, $b=1.2$, $K=2.9$, $R=0.43$, and $L=30$. (a) $\mu \in (\mu_c, 20.0)$, (b) $\mu \in (\mu_c, 2.0)$, which is a magnification of (a), where $\mu_c = 0.11\cdots$, $\mu_{c1}=0.13\cdots$ and $\mu_{c2}=12.51\cdots$

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Figure 14. Bifurcation diagram of the shadow system (9) with (10) when $\mu$ is a free parameter. Other parameters are fixed at $a=0.95$, $b=1.2$, $K=2.9$, $R=0.43$ and $L=35$. Solid (resp. dashed) lines represent stable (resp. unstable) equilibrium solutions of (9) with (10). In the right corner, it is shown a magnification around the primary bifurcation point $(n=1)$

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Figure 15. The functional forms of $H_1^+(\xi;1/L^2, \mu)$ for (a) $\mu=0.1, \ (\alpha, \beta) = (0.375538, 0.683272)$, (b) $\mu=0.11, \ (\alpha, \beta) = (0.355264, 0.66457)$, (c) $\mu=0.12, \ (\alpha, \beta) = (0.337375, 0.647172)$, (d) $\mu=0.13, \ (\alpha, \beta) = (0.321474, 0.630946)$, (e) $\mu=50, \ (\alpha, \beta) = (0.0521753, 0.168414)$ and (f) $\mu=58, \ (\alpha, \beta) = (0.0520414, 0.168035)$, where other parameters are fixed at $a=0.95$, $b=1.2$, $K=2.9$, $R=0.43$ and $L=60$. The horizontal (resp. vertical) axes are $\xi$ (resp. $H^+_1(\xi;1/L^2, \mu)$)

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Figure 16. Bifurcation diagram of the shadow system (9) with (10) when $\mu$ is a free parameter. Other parameters are fixed at $a=0.95$, $b=1.2$, $K=2.9$, $R=0.43$ and $L=60$. Solid (resp. dashed) lines represent stable (resp. unstable) equilibrium solutions of (9) with (10). In the right corner, it is shown a magnification around the primary bifurcation point ($n=1$)

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Figure 17. The graphs of $L$ and ${\mathcal K}_2={\mathcal K}_2(1)$ as a function of $\mu$, where $a=0.95$, $b=1.2$, $K=2.9$ and $R=0.43$. (a) $\mu \in (\mu_c, 60.0)$, (b) $\mu \in (\mu_c, 20.0)$, (c) $\mu \in (\mu_c, 0.2)$. (b) and (c) are the magnification of (a), where $\mu_c = 0.11\cdots, \mu_{c1}^*=0.12\cdots, \mu_{c1}=0.13\cdots, \mu_{c2}^*=55.57\cdots$ and $\mu_{c2}=12.51\cdots$

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Figure 18. The bifurcation curve of $\varepsilon = \varepsilon^1_0(\xi) = Q_0(\xi)/\pi^2$ for $\xi \in I$. For given $\bar{\xi}^*$, $\varepsilon^1_0(\bar{\xi}^*)$ is determined

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Figure 19. Dependency of $\varepsilon$ on $H^{+}_1(\xi;\varepsilon)$. (ⅰ) $\varepsilon > \varepsilon^1_0(\bar{\xi}^*)$, (ⅱ) $\varepsilon = \varepsilon^1_0(\bar{\xi}^*)$ and (ⅲ) $\varepsilon < \varepsilon^1_0(\bar{\xi}^*)$. These show that $\varepsilon^1_0(\bar{\xi}^*)$ is the bifurcation point of the 1-mode equilibrium solutions

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