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Instabilidades de Turing e Competição Aparente em Ambientes Fragmentados Marcus A.M. de Aguiar Lucas Fernandes IFGW - Unicamp.

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Presentation on theme: "Instabilidades de Turing e Competição Aparente em Ambientes Fragmentados Marcus A.M. de Aguiar Lucas Fernandes IFGW - Unicamp."— Presentation transcript:

1 Instabilidades de Turing e Competição Aparente em Ambientes Fragmentados Marcus A.M. de Aguiar Lucas Fernandes IFGW - Unicamp

2 Vegetation in arid ecosystems Padrões de densidade em regiões homogêneas

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4 Wolfram’s Cellular Automata

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6 After 250 iterations

7 Complex Patterns can emerge out of simple interactions between neighboring cells on a homogeneous environment Cellular automata provide examples where direct interactions occur

8 Another Mechanism: Reaction-Diffusion Systems and Turing Patterns

9 Summary 1.Turing Patterns in Homogeneous Environments 2.Turing Patterns in Networks of Fragments 3.Turing Patterns and Apparent Competition in Networks

10 1. Turing Patterns in Homogeneous Environments

11 Reaction Populations of Preys (P) and Predators (Q) Lotka-Volterra Model  and  = constant coefficients

12 The Mimura-Murray model On a diffusive prey-predator model which exhibits patchiness M. Mimura and J.D. Murray J. Ther. Biol. (1978) 75,

13 Understanding the equation for the preys The growth rate per capta is interspecies competition Allee effect logistic growth decrease because of predators P

14 Understanding the equation for the predators The growth rate per capta is mortality rate + interspecies competition logistic growth increase because of preys Q

15 Add a new ingredient: space P(x,t) = population of preys at spatial position x at time t Q(x,t) = population of predators at spatial position x at time t Add a new interaction : migration The populations at x change by sending a fraction of its individuals to neighboring sites and receiving a fraction of individuals from the same sites. Diffusion

16 The math of diffusion kk-1k+1 P(k) – P(k-1) = 2 P(k-1) = 5 P(k) = 7 P(k+1) = 8 P(k+1) – P(k) = 1 A gradient in P(k) is not enough: diffusion requires a gradient of the gradient back

17 A uniform gradient is not enough kk-1k+1 P(k) – P(k-1) = 2 P(k-1) = 5 P(k) = 7 P(k+1) = 9 P(k+1) – P(k) = 2

18 The new equations describing the dynamics of Preys (P) and Predators (Q) become local interactions diffusion diffusion coefficients Partial Differential Equations

19 Can we understand these equations? Are there simple solutions? 1 - Look for solutions that are uniform in space, i.e., situations where the populations are the same at all points in space. In this case there is no diffusion!

20 2 - Look for solutions that are constant in time:

21 Simplified equations can be solved:

22 Two solutions: 1 – extinction Q = P = 0 2 – coexistence Q 0 = 10 P 0 = 5. Are they stable?

23 Patterns of preys and predators emerging on a homogeneous environment. Preys distributed on patches. Predators everywhere, but with larger populations where the preys live. J. Theor. Biol u=P=prey v=Q=pred

24 2. Turing Patterns in Networks of Fragments

25 Predator-Prey systems on a Network: Two main difficulties: 1 – describe diffusion in the network 2 – do the stability analysis

26 Describe the network: 1 - labels the nodes from 1 to N in order of decreasing number of connections. 2 – Define the N x N adjancency matrix A ij = 1 if nodes i and j are connected A ij = 0 if nodes i and j are not connected 3 – k i = number of connections of node i

27 Examples:

28 Model diffusion: Diffusion matrix Diffusion of u at node i where u i is the population at node i

29 Example 1: For node 2: diffusion

30 Example 2: For node 1:

31 New Notation: u = prey = P v = predator = Q For zero diffusion we are back to the same equations, for which there is a homogeneous solution: each community has the same number of preys and predators. We find u i = u 0 = 5 and v i = v 0 = 10 for all nodes uv predator prey each node is a fragment, a local community

32 Movies turn on diffusion: 

33 Hysteresis

34 3. Turing Patterns and Apparent Competition in Networks u x v y predatorspreys 

35 Results Dynamics for first prey, u Dynamics for second prey, x Dynamics for u-x Parameters:  =0.12  =20.0  =0.05 f=0.5 BA network with N=1000

36 Effect of Coupling

37 Three nested predator-prey pairs in each node uv xy predatorspreys wz    Typical patterns: sites with v-u and z-w and low values of y-x sites with y-x and low values of v-u and z-w few sites with all species in equal proportions

38 Four predator-prey pairs in each node uv xy predatorspreys wz    Typical patterns: sites with v-u and z-w and low values of y-x and r-s sites with y-x and r-s and low values of v-u and z-w few sites with all species in equal proportions rs  

39 Conclusions on a homogeneous environment, density patterns can be generated dynamically, independent of intrinsic differences. on a fragmented environment with identical patches, abundance distributions can be different: there will be two types of patches: with high abundance and with low abundance.

40 if more pairs of antagonistic species interact in each patch, strong effects of apparent competition can also be dinamically generated. There will be four types of patches: - high v and u with low y and x - high y and x with low v and u - low v, u, y and x. - high v, u, y and x.


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