Abstract
We consider a system of two competing populations in two-dimensional heterogeneous environments. The populations are assumed to move horizontally and vertically with different probabilities, but are otherwise identical. We regard these probabilities as dispersal strategies. We show that the evolutionarily stable strategies are to move in one direction only. Our results predict that it is more beneficial for the species to choose the direction with smaller variation in the resource distribution. This finding seems to be in agreement with the classical results of Hastings (1983) and Dockery et al. (1998) for the evolution of slow dispersal, i.e. random diffusion is selected against in spatially heterogeneous environments. These conclusions also suggest that broader dispersal strategies should be considered regarding the movement in heterogeneous habitats.
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References
Altenberg L (1984) A generalization of theory on the evolution of modifier genes. PhD thesis. Stanford University
Altenberg L (2012) Resolvent positive linear operators exhibit the reduction phenomenon. Proc Nat Acad Sci U.S.A. 109(10):3705–3710. https://doi.org/10.1073/pnas.1113833109
Asher AUMUM, Ruuth RSJSJ, Wetton WBTRBTR (1995) Implicitexplicit methods for time-dependent partial differential equations. SIAM J Numer Anal 32(3):797–823. https://doi.org/10.1137/0732037
Belgacem BFF, Cosner CCC (1995) The effects of dispersal along environmental gradients on the dynamics of populations in heterogeneous environments. Can Appl Math Quart 3(4):379–397. https://doi.org/10.1137/0732037
Cantrell CRSRS, Cosner CCC (2003) Spatial ecology via reaction-diffusion equations. John Wiley and Sons Ltd., New Jersey. https://doi.org/10.1002/0470871296
Cantrell CRSRS, Cosner CCC, Lou LYY (2010) Evolution of dispersal and the ideal free distribution. Math Biosci Eng 7(1):17–36. https://doi.org/10.3934/mbe.2010.7.17
Chen CXX, Hambrock HRR, Lou LYY (2008) Evolution of conditional dispersal: a reaction-diffusion-advection model. J Math Biol 57(3):361–386. https://doi.org/10.1007/s00285-008-0166-2
Clobert CJJ, Danchin DEE, Dhondt DAAAA, Nichols NJDJD (eds) (2001) Dispersal. Oxford University Press, Oxford
Cosner CCC (2014) Reaction-diffusion-advection models for the effects and evolution of dispersal. Discrete Contin Dynam Syst A 34(5):1701–1745. https://doi.org/10.3934/dcds.2014.34.1701
Courant CRR, Hilbert HDD (1953) Methods of mathematical physics, vol I. Interscience, New York
Dickie DMM, Serrouya SRR, McNay MRSRS, Boutin BSS (2017) Faster and farther: wolf movement on linear features and implications for hunting behaviour. J Appl Ecol 54(1):253–263. https://doi.org/10.1111/1365-2664.12732
Dieckmann DUU, Law LRR (1996) The dynamical theory of coevolution: a derivation from stochastic ecological processes. J Math Biol 34(5–6):579–612. https://doi.org/10.1007/BF02409751
Diekmann DOO (2003) A beginner’s guide to adaptive dynamics. Banach Center Publ 63(1):47–86. https://doi.org/10.4064/bc63-0-2
Dockery DJJ, Hutson HVV, Michaikow MKK, Pernarowsk PMM (1998) The evolution of slow dispersal rates: a reaction diffusion model. J Math Biol 37(1):61–83. https://doi.org/10.1007/s002850050120
Fretwell FSDSD, Lucas Jr LJHLHL (1969) On territorial behavior and other factors influencing habitat selection in birds. I. Theoretical development. Acta Biotheoretica 19(1):16–36. https://doi.org/10.1007/BF01601953
Geritz GSAHSAH, Kisdi KEE, Meszena MGG, Metz MJAJJAJ (1998) Evolutionarily singular strategies and the adaptive growth and branching of the evolutionary tree. Evol Biol 12(1):35–57. https://doi.org/10.1023/A:1006554906681
Gyllenberg GMM, Kisdi KEE, Weigang WHCHC (2016) On the evolution of patchtype dependent immigration. J Theor Biol 395:115–125. https://doi.org/10.1016/j.jtbi.2016.01.042
Hastings HAA (1983) Can spatial variation alone lead to selection for dispersal? Theor Popul Biol 24(3):244–251. https://doi.org/10.1016/0040-5809(83)90027-8
He HXX, Ni NW-MW-M (2016) Global dynamics of the Lotka-Volterra competitiondiffusion system: diffusion and spatial heterogeneity I. Commun Pure Appl Math 69(5):981–1014. https://doi.org/10.1002/cpa.21596
Hecht HFF (2012) New development in FreeFem++. J. Numer. Math. 20(3–4):251–265. https://doi.org/10.1515/jnum-2012-0013
Hess HPP (1991) Periodic-parabolic boundary value problems and positivity, vol 247. Pitman research notes in mathematics series. Longman Scientific and Technical, Harlow
Hillen HTT, Painter PKJKJ (2013) Transport and anisotropic diffusion models for movement in oriented habitats. In: Lewis M, Maini P, Petrovskii S (eds) Dispersal, individual movement and spatial ecology. Springer, Berlin, Heidelberg, pp 177–222. https://doi.org/10.1007/978-3-642-35497-7_7
Hutson HVV, Michaikow MKK, Poláčik PPP (2001) The evolution of dispersal rates in a heterogeneous time-periodic environment. J Math Biol 43(6):501–533. https://doi.org/10.1007/s002850100106
Kisdi KEE, Utz UMM, Gyllenberg GMM (2012) Evolution of condition-dependent dispersal. In: Clobert J, Baguette M, Benton TG, Bullock JM (eds) Dispersal and spatial evolutionary ecology. Oxford University Press, Oxford, pp 139–151. https://doi.org/10.1093/acprof:oso/9780199608898.003.0011
Lam LKYKY, Lou LYY (2014a) Evolution of dispersal: evolutionarily stable strategies in spatial models. J Math Biol 68(6):851–877. https://doi.org/10.1007/s00285-013-0650-1
Lam LKYKY, Lou LYY (2014b) Evolutionarily stable and convergent stable strategies in reaction-diffusion models for conditional dispersal. Bull Math Biol 76(6):261–291. https://doi.org/10.1007/s11538-013-9901-y
McKenzie MHWHW, Merrill MEHEH, Spiteri SRJRJ, Lewis LMAMA (2012) How linear features alter predator movement and the functional response. Interface Focus 2(2):205–216. https://doi.org/10.1098/rsfs.2011.0086
Ni NW-MW-M (2011) The mathematics of diffusion, vol 82. CBMS-NSF regional conference series in applied mathematics. SIAM, Philadelphia
Potapov PAA, Schlaegel SUU, Lewis LMAMA (2014) Evolutionarily stable diffusive dispersal. Discrete Contin Dynam Syst B 19(10):3319–3340. https://doi.org/10.1007/s002850100106
Slover SNN (2019) “The evolution of dispersal in two-dimensional habitats”. MA thesis. Ohio State University
Smith SHLHL (1995) Monotone dynamical systems: an introduction to the theory of competitive and cooperative systems, vol 41. Mathematical surveys and monographs. American Mathematical Society, Providence
Acknowledgements
We thank Professor Yoshikazu Giga for the helpful discussions which motivated the study of anisotropic diffusion. We sincerely thank the referees for their suggestions which helped to improve the manuscript. We are also very grateful to Maxime Chupin for his assistance with the post-processing of the numerical simulations.
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Appendix
Appendix
1.1 Some remarks on solutions to a wave equation
In the proofs of Lemmas 4.3 and 4.4, the following result, which seems to be of self interest, plays an important role in eliminating the degeneracy of the function F and in establishing the strict concavity of the function \(\Lambda (p,q)\) with respect to q.
Lemma 8.1
Let W in \(C^2(\Omega )\cap C^1(\overline{\Omega })\) be a solution to the system
Then, the function W is constant.
Proof
By the strict convexity assumption on the domain, the components \(\nu _x\) and \(\nu _y\) of the outward normal vector \(\nu \) are non-zero on the boundary \(\partial \Omega \), except possibly over a set of measure zero. Hence, \(W_x\) and \(W_y\) both vanish almost everywhere on \(\partial \Omega \). Since W belongs to \(C^1(\overline{\Omega })\), the gradient \(\nabla W\) vanishes on \(\partial \Omega \).
Set \(\eta =x+y\), \(\zeta =x-y\) and \(Z(\eta ,\zeta ):=W(x,y)\). The function Z then satisfies
where \(\Omega '\) is the image of \(\Omega \) under the map \((x,y)\mapsto (\eta ,\zeta )\). It follows from the first relation that \(Z(\eta ,\zeta )=f(\eta )+g(\zeta )\) for some functions f and g, and the second one then implies that both f and g have to be constant functions. As a consequence, Z is a constant function, and so is W. \(\square \)
It is possible to construct domains such that problem (8.1) admits non-constant solutions, if we allow Lipschitz domains with flat parts on their boundaries.
Example 1
Consider \(\Omega =(0, 1)\times (0, 1)\) and let f be an even and 2-periodic function in \(\mathbb {R}\). Set \(W(x,y)=f(x+y)+f(x-y)\), which then clearly satisfies problem (8.1), and is a positive non-constant function if f is taken positive and non-constant.
On the other hand, the type of domain given in the above example seems to be non-generic, as illustrated by the following result.
Lemma 8.2
Suppose that \(\Omega =(0, L_1)\times (0, L_2)\) for some positive numbers \(L_1\) and \(L_2\). If \(L_1/L_2\) is not a rational number, then problem (8.1) has only constant solutions.
Proof
For any W satisfying problem (8.1), we have \(W(x,y)=f(x+y)+f(x-y)\) for some scalar function f and then \(W_x=f'(x+y)+f'(x-y)\). Since \(W_x(0, y)=0\), we have \(f'(y)=-f'(-y)\), i.e. \(f'\) is an odd function. Since \(W_x(L_1, y)=0\), we have \(f'(y+L_1)=-f'(L_1-y)=f'(y-L_1)\), i.e. \(f'\) is \(2L_1\)-periodic.
Similarly, one has \(W_y=f'(x+y)-f'(x-y)\). Note that \(W_y(x,0)=0\) automatically holds. By \(W_y(x,L_2)=0\), we have \(f'(x+L_2)=f'(x-L_2)\), that is \(f'\) is also \(2L_2\)-periodic. Hence, if \(L_1/L_2\) is not rational, then \(f'\) must be a constant function. Since \(f'\) is an odd function, then \(f'=0\), that is W is a constant function. \(\square \)
1.2 A remark about a possible degeneracy induced by the domain \(\Omega \)
Throughout the paper, we have assumed that \(\Omega \) is a strictly convex domain. We now comment on this point, showing with a very basic example that a domain with flat parts on its boundary may lead to a degeneracy of the function F.
Consider \(\Omega =(0, 1)\times (0, 1)\) and let f and D be given as in Example 1. Setting
it is easy to check that, for each \(\theta \) in [0, 1], W also solves (2.3), that is \(N_\theta \equiv W\) for each \(\theta \) in [0, 1]. Since the function W is non-constant, the free growth rate a is also non-constant. Furthermore, for each value of p and q in [0, 1], problem (2.5) has a continuum of positive steady states of the form \((U, V)=(sW, (1-s)W)\), with s in (0, 1). Moreover, in this case, \(F\equiv 0\) in [0, 1], even though the function a is not constant. Indeed, since f is even, we have \(W(x,y)=W(y,x)\), which implies that \(F\equiv 0\) in [0, 1].
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Bouin, E., Legendre, G., Lou, Y. et al. Evolution of anisotropic diffusion in two-dimensional heterogeneous environments. J. Math. Biol. 82, 36 (2021). https://doi.org/10.1007/s00285-021-01579-1
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DOI: https://doi.org/10.1007/s00285-021-01579-1