1. T HE EMERGENCE OF ECOSYSTEMS PATTERNS BY MEANS OF B IOGEOGRAPHIC C OMPUTATION Rodrigo Pasti rodrigo.pasti@cti.gov.br rodrigo.pasti@gmail.com Mackenzie

2. Collective behavior 2 a single entity representing a system

3. Collective behavior 3 (1) Self-organization (2) Emergence (3) Spatio-temporal variation Organisms

4. 1. N ATURAL C OMPUTING AND E COSYSTEMS C OMPUTING 2. B IOGEOGRAPHIC C OMPUTATION AND E COSYSTEMS P ATTERNS 3. E COSYSTEMS : F ROM T HEORY TO A PPLICATION 4 T HE EMERGENCE OF ECOSYSTEMS PATTERNS BY MEANS OF B IOGEOGRAPHIC C OMPUTATION

5. The computation of nature  Nature can be investigated, described and applied by means the fundamentals of Natural Computing..  Informational meaning: nature is composed of natural information system processors.  The nature is the object of study for Natural Computing, but in what sense it is possible to define the nature? Physical, Chemical, Biological… 5

6. The computation of nature Sacarose Álcool CO 2 6 Zymomonas mobilis. Source: Wikipedia.. Hello World!! Seth Lloyd (2006): Information is a measure of order, organization, a universal measure applicable to any structure, any system. ?

7. Ecosystems 7 scaleinteractions emergence molecules cell cellsorgan organsorganism organismssociety low high atoms molecule......

8. Ecosystems computing  Information processors: organisms and environmental changes. 8  Ecosystems= habitats + organisms  Information is all that exists in an ecosystem and can be changed in space and time.  Several organisms processing information: information processing in societies of organisms.

9.  Unification of research fronts in ecosystems: science of Biogeography. Understanding ecosystems  Ecology, population biology, systematics, evolutionary biology, earth sciences, etc...  Largely composed of theories.  Problem: space, time and observation. 9  To understand the ecosystems computing is necessary to investigate ecosystems.  Observe or make use of scientific publications.

10. Biogeographic Computation  Founded on Biogeography and Natural Computing.  Objective: to investigate the ecosystems computing in societies of organisms.  The quest:  Understanding ecosystems computing.  Understanding the ecosystems.  Parallel between ecosystems and artificial systems. 10

11. 1. N ATURAL C OMPUTING AND E COSYSTEMS C OMPUTING 2. B IOGEOGRAPHIC C OMPUTATION AND E COSYSTEMS P ATTERNS 3. E COSYSTEMS : F ROM T HEORY TO A PPLICATION 11 T HE EMERGENCE OF ECOSYSTEMS PATTERNS BY MEANS OF B IOGEOGRAPHIC C OMPUTATION

12. 12 Ecosystems  Ecosystems are highly complex and dynamic environments.

13. .... P ROCESSES R ELATIONS E LEMENTS E COSYSTEM Defining a computational ecosystem 13 Ecologicals (  ) Macroevolutionary (M) Microevolutionary (  ) Geographical (  ) ijij igig htht hvhv

14. Relations

15. Information processing in ecosystems  Biogeographic Processes:  Ecological. Acts on any individual. It can include the decision making of the individual.  Geographical. Alter the environment through the action of abiotic components.  Microevolutionary. Acts in the mechanisms related to the evolution on a single individual or individuals involved in reproduction process.  Macroevolutionary. Emergent evolutionary processes that require diversity of individuals and habitats, and large time scales. 15

16. Information processing in ecosystems Reproduction Mutation? DNA YesNo Mutation 16 Reproduction process Possible effect: mutation that alters drastically the phenotype

17. Information processing in ecosystems 17 t 2 = t 1 +  t 1 t 1 = 0 t 3 = t 2 +  t 2 t 4 = t 3 +  t 3 t5t5 t6t6 t7t7 t8t8 t9t9 DD h1h1 h2h2 h3h3 RR M SS DD M PS Initial conditions t 1 :

18. 1. N ATURAL C OMPUTING AND E COSYSTEMS C OMPUTING 2. B IOGEOGRAPHIC C OMPUTATION AND E COSYSTEMS P ATTERNS 3. E COSYSTEMS : F ROM T HEORY TO A PPLICATION 18 T HE EMERGENCE OF ECOSYSTEMS PATTERNS BY MEANS OF B IOGEOGRAPHIC C OMPUTATION

19. Evolutionary Algorithm Reproduction Mutation? DNA YesNo Mutation 19 Reproduction process Possible effect: mutation that alters drastically the phenotype

20. Evolutionary Algorithm  Repeat indefinitely:  Reproduction with genetic inheritance.  Variation.  Natural selection. 20

21. Evolutionary Algorithm n = 100; p = 20% n = 200 ; p = 50% n = 1000 ; p = 90% n: nr. of individuals p: ratio of “greens” Adaptive success: maximize n tempo 21

22. Evolutionary Algorithm 22 AA ij1ij1 Where i j1 and i j2 are atributes of individuals ij2ij2

23. The Blind Watchmaker Algorithm  Evolution is able to create complex patterns in nature.  How?  Random variation combined with non- random cumulative selection!  The Blind Watchmaker – Richard Dawkings  Darwin's Dangerous Idea – Daniel C. Dennett 23

24. The Blind Watchmaker Algorithm 24

25. The Blind Watchmaker Algorithm 25

26. The Blind Watchmaker Algorithm 26

27. Adaptive Radiation in Surfaces 27

28. Adaptive Radiation in Surfaces 28 t 2 = t 1 +  t 1 t 1 = 0 t 3 = t 2 +  t 2 t 4 = t 3 +  t 3 t5t5 t6t6 t7t7 t8t8 t9t9 DD h1h1 h2h2 h3h3 RR M SS DD M PS Initial condition t 1 :

29. 29 Adaptive Radiation in Surfaces Nr of species Shannon entropy generations nr. of individual

30. Final remark  We need artificial systems that have properties of self-adaptation, self-organization, learning, diversity, adaptation on demand from the environment, automatic and intelligent decision making, and all this varying in space and time! 30

31. T HE EMERGENCE OF ECOSYSTEMS PATTERNS BY MEANS OF B IOGEOGRAPHIC C OMPUTATION Rodrigo Pasti rodrigo.pasti@cti.gov.br rodrigo.pasti@gmail.com Mackenzie

32. 32 But nature is always more subtle, more intricate, more elegant than what we are able to imagine – Carl Edward Sagan