T HE EMERGENCE OF ECOSYSTEMS PATTERNS BY MEANS OF B IOGEOGRAPHIC C OMPUTATION Rodrigo Pasti Mackenzie

Collective behavior 2 a single entity representing a system

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

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

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

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. ?

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

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.

 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.

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

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 Ecosystems  Ecosystems are highly complex and dynamic environments.

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

Relations

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

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

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 :

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

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

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

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

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

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

The Blind Watchmaker Algorithm 24

The Blind Watchmaker Algorithm 25

The Blind Watchmaker Algorithm 26

Adaptive Radiation in Surfaces 27

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

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

T HE EMERGENCE OF ECOSYSTEMS PATTERNS BY MEANS OF B IOGEOGRAPHIC C OMPUTATION Rodrigo Pasti Mackenzie

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