Biocoenosis Nest: A City Woven by Collective Intelligences

Biocoenosis Nest A City Woven by Collective Intelligences

Oscar Villarreal The Bartlett School of Architecture MArch Urban Design, RC 16

September 2020

Biocoenosis Nest A City Woven by Collective Intelligences

Student: Oscar Villarreal Studio Tutors: Claudia Pasquero, Filippo Nassetti History and Theory Tutor: Emmanouil Zaroukas Skills Tutors: Filippo Nassetti, Michael Brewster

The Bartlett School of Architecture B-Pro MArch Urban Design Research Cluster 16 University College London London, United Kingdom, 2020

Biocoenosis Nest_A City Woven by Collective Intelligences

bio_“life”

Koinosis_“sharing”

Nidus_”nest”

Biocoenosis /n. 1. an association of different organisms forming a closely integrated community.

Nest /n. 1. a structure or place made or chosen by a bird for laying eggs and sheltering its young.

2. a place where an insect or other animal breeds or shelters.

Biocoenosis Nest_A City Woven by Collective Intelligences

Abstract For the past 200,000 years humans have become the dominant species on the planet by gaining control over other entities. This dominance initially brought incommensurable benefits to the human species but, in the last hundreds of years, human dominance has led to a population and consumption growth that is damaging the environment and biodiversity. In order to avoid catastrophic scenarios, by-products of the Anthropocene era, a non-human centred approach is needed in order to consider other intelligences and create a response to biodiversity loss taking into account climate change.

London’s East End, having been an industrial area that triggered human over population and pollution, becomes a starting point to explore the possible catastrophic futures. Through the exploration of the current climate change scenarios, which could generate a flood in London’s East End, the project Biocoenosis Nest, instead of creating a boundary with water, embraces the water and explores the marshlands that are created by an increase in water volumes. The marshland becomes a biodiversity node that can restore the current loss by providing habitat and nutrients required by non-humans to grow. Aiming to connect the marshland properties to the biodiversity’s optimal habitat locations in the inner part of the city, the use of a non-human intelligence provides a closer approach to a non-human centred distribution. In this proposal, slime mould becomes the entity that defines the routing network that will shape the terrain. This terrain is then explored with the non-human intelligence of machine learning to create a city that combines the human and non-human entities in a more balanced coexistence, thus creating the Marshland City. In order to approach the collective coexistence, the bird nest is analysed to rethink human and non-human interaction, and with a fibrous system, the synthetic nest is generated. The architectural volumes extracted from the Marshland City with code algorithms are analysed according to the different human and non-human programs required to become multi-specie nodes. These volumes are formed combining the different programmatic typologies to form a Synthetic Fibrous Nest. The Biocoenosis Nest is woven by collective intelligences responding to the needs and requirements of multiple entities.

The Marshland City embraces a flood caused by climate change to establish controlled coexistence in a post natural scenario. The Synthetic Fibrous Nest allows humans on the inside and non-humans on the outside. This creates an association of different organisms in a non-invasive integrated community.

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Content

Abstract �������������������������������������������������������������������� 5 Chapter I ������������������������������������������������������������������ 13 Historical Development And Urbanization in 13 London’s East End13 Chapter II ���������������������������������������������������������������� 21 Human dominance over biodiversity21 Chapter III ���������������������������������������������������������������� 31 Climate change effects in the environment31 Chapter IV �������������������������������������������������������������� 47 Marshlands As A Source For Coexistance And Natural Development47 Chapter V ���������������������������������������������������������������� 59 Slime Mould as a non-human intelligence59 Chapter VI �������������������������������������������������������������� 69 Nutrient distribution network with Generative Adversarial Network69 Chapter VII �������������������������������������������������������������� 83 Fibrous synthetic nest83 Conclusion ������������������������������������������������������������ 117 References ������������������������������������������������������������ 120 List of Figures �������������������������������������������������������� 122

Figure [1] Marshland City, part of the Biocoenosis Nest

Chapter I Historical Development And Urbanization in London’s East End

Biocoenosis Biocoenosis Nest_A Nest_A City City Woven Woven by by Collective Collective Intelligences Intelligences

Green area London’s East decline End Since In the the twobeginning centuries that of the followed 1800’sthe London’s 1700’s,East London End has wasseen utterly many transformed changes to become mainly due the to the centre social of activism the world and and thethe Industrial metropolis Revolution, that we transforming know today.

During the industry the city’s located growth near in thepopulation Thames River and(Britannica, size, the environment T. E. , 2017)suffered (Britannica, the consequences T. E. , 2015). The of human urban made program infrastructure in the East (London End changed legacyindevelopment time with a corporation, influenced government 2020). Between strategy the to 1930’s benefit andthe thepeople 2010’s, and thethe growing city (Oakley, human development n.d.).

It wasn’t affected until the green 1950’s area when population, the Eastthus Endaffecting had to the rebuild biodiversity many in London areas after (Newbold, damages resulting 2016). Several from the decades war. The witnessed government the decline createdofnew the greenspace, including developments focused woodland, on elevating grassland, the economy water, whilst and rock, attracting especially investors after the Industrial and visitors to the Revolution new era (Nature’s of the EastDangerous End. As timeDecline passed,‘Unprecedented’, construction was 2019). With the overcrowding theindustry Thamesdeveloping River until technology at a fast state, made themassive city becomes infrastructure highly polluted. and the river became obsolete for big trading companies (London’s advances Royal Docks, n.d.). The London Docklands Development Corporation (LDDC) took control and created what we know today: Canary Wharf, London City Airport, London Arena, and many other developments (Oakley, n.d.).

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Figure [2] London’s East End neighborhood, 1903

Biocoenosis Nest_A City Woven by Collective Intelligences

Figure [3] The Salvation Army, 1902

Due In the to two the industrial centuries growth that followed and thethe growing 1700’s, population London was of immigrants utterly transformed working intothe become docklands, the centre there was of the a shortage world andofthe proper metropolis housing. thatSocial we know groups today. were During created the city’s to help growth imptove in population the living conditions and size, the of environment the working class. suffered the consequences of human made infrastructure (London legacy development corporation, 2020).

Between the 1930’s and the 2010’s, the growing human development affected the green area population, thus affecting the biodiversity in London (‘Unprecedented’, 2019). With the industry developing at a fast state, the city became highly polluted.

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Biocoenosis Nest_A City Woven by Collective Intelligences

Figure [4] Docklands saturation, 1900

With In thethe twoIndustrial centuries Revolution that followed andthe the1700’s, tradingLondon companies was utterly developing transformed fast around to become the UK, thethe centre docklands of the world and started to get the saturated metropolisand thatbigger we know ships today. hadDuring difficulties the city’s tryinggrowth to loadinand population unload and in the size, Thames the environment River. As a suffered the consequences consequence, there was a boost of human in railway made connectivity infrastructure and soon (London the docklands legacy development lost part ofcorporation, the trading success.

2020).

Between the 1930’s and the 2010’s, the growing human development affected the green area population, thus affecting the biodiversity in London (‘Unprecedented’, 2019). With the industry developing at a fast state, the city became highly polluted.

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Biocoenosis Nest_A City Woven by Collective Intelligences

Figure [5] Second World War destruction,1942

After In thethe two Second centuries World that War, followed the bombing the 1700’s, and fire London distruction was utterly in the area transformed gave risetotobecome significant the changes. centre ofThe the government world and the decided metropolis to create that we a new knowneighborhood today. During instead the city’sofgrowth rebuilding in population the damaged and size, structures. the environment This new development suffered the consequences started to create of the human history made of many infrastructure areas not(London only aslegacy a refurbishment development of old corporation, warehouses, 2020). but also Between the creation the 1930’s of new andtypologies. the 2010’s, the growing human development affected the green area population, thus affecting the biodiversity in London (‘Unprecedented’, 2019). With the industry developing at a fast state, the city became highly polluted.

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Biocoenosis Nest_A City Woven by Collective Intelligences

Figure [6] Canary Wharf

In the last two years, centuries the governement, that followed the through 1700’s, the London Docklands was utterly Development transformed to Corporation become the (LDDC), centre started of the world to intervene and the the metropolis area withthat newwe developments know today. During such asthe Canary city’s Wharf, growth London in population City airport, and size, thethe London environment Arena, suffered and many the other consequences developments of human as a proposal made infrastructure to boost the area (London as anlegacy economic development node in London corporation, balancing 2020). the Between west and the the 1930’s east. Also, andthe theLDDC 2010’s, developed the growing new housing human in development order to bring affected people theback green to the area East population, End. thus affecting the biodiversity in London (‘Unprecedented’, 2019). With the industry developing at a fast state, the city became highly polluted.

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Chapter II Human dominance over biodiversity

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Green area decline In the two centuries that followed the 1700’s, London was utterly transformed to become the centre of the world and the metropolis that we know today.

During the city’s growth in population and size, the environment suffered the consequences of human made infrastructure (London legacy development corporation, 2020). Between the 1930’s and the 2010’s, the growing human development affected the green area population, thus affecting the biodiversity in London (Newbold, 2016). Several decades witnessed the decline of the greenspace, including woodland, grassland, water, and rock, especially after the Industrial Revolution (Nature’s Dangerous Decline ‘Unprecedented’, 2019). With the the industry industry developing developing at at aafast faststate, rate, the the city city becomes became highly polluted.

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Figure [7] Stratford green area loss

Biodiversity Loss 0%

Figure [8] Biodiversity loss map

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Human Invasion Side Effects With the current human dominance over other entities, the Anthropocene era had a negative effect on biodiversity, a concerning issue in our future.

The human invasive coexistence with the non-human is having side effects such as COVID-19. Since humans started to invade biodiversity’s habitat, the boundary between entities was lost. When the human destroys the nonhuman habitat, the non-human is forced to interact in the human infrastructure such as pigeon masses in buildings and plazas or rats in the underground or houses. By not having a controlled environment, the non-human either chaotically blends with the human or goes extint due to the lack of a suitable habitat.

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Figure [9] London’s common pests

Figure [10] Human and non-human invasive coexistence

Chapter III Climate change effects in the environment

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Ozone layer deplation The emissions of carbon dioxide, methane, and nitrous oxides generated by human development produced the greenhouse effect (Wuebbles, 2020).

Pollution of chlorine and bromine generated by the human industry has caused a gradual thinning in the ozone layer (Witze, 2020). This issue has caused the increase in the world’s temperature and, with a more direct exposure of UV rays, humans and non-humans are affected.

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Figure [11] Ozone layer Arctic hole

2020

2040 Average -20 C°

Figure [12] Rising temperatures in the Arctic atmosphere projection

2030

2050

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Flooding extremes The increase in the world’s temperature generates three different climatic extremes (Denchak, 2019). The first effect is higher evaporation that produces higher moisture in the atmosphere generating intense and longlasting precipitation, 0.6 percent more rain per year. The second extreme is the melting of the ice layer of the Earth. Seven hundred and fifty billion tons of ice melt every year (Howard, 2019). Finally, the third extreme produced by the increase of temperature is a drop in the atmospheric pressure that with a generation of atmospheric rivers, produces tropical or extratropical cyclones that generate storm surges at the coasts. All of these extremes together result in a rise of the sea level, around 0.04 centimeters per year. After one hundred years a four centimeter sea level rise is enough to generate floods around the world (Seneviratne, 2012).

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Figure [13] High Tide Enters Home in Mumbai

Figure [14] Diagram of the climate change extremes

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London Flooding With the climatic extreme projections of the future, London is expected to suffer from a flooding that surpasses the current Thames Barrier (Environment Agency, 2019). The current barriers can hold the increase in tide, but with the climatic extremes projections, all water volumes increase. These catastrophic scenario not only elevates the Thames River water level, but also every adjacent river, valley, or lake creating a new water volume. Figure [16] shows a timeline of the projected flooding and Figure [17] shows the new water volume in London’s East End with the Thames River as a scale reference.

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Figure [15] London flooding 1928

+0.00M +0°C 2020

+0.47M +0.2°C 2030

+0.94M +0.4°C 2

+2.35M +1°C 2070

+2.82M +1.2°C 2080

+3.29M +1.4°C 2

+4.70M +2°C 2120

+5.17M +2.2°C 2130

+5.64M +2.4°C 2

+7.05M +3°C 2170

+7.52M +3.2°C 2180

+7.99M +3.4°C 2

Figure [16] London flooding timeline

2040

+1.41M +0.6°C 2050

+1.88M +0.8°C 2060

2090

+3.76M +1.6°C 2100

+4.23M +1.8°C 2110

2140

+6.11M +2.6°C 2150

+6.58M +2.8°C 2160

2190

+8.46M +3.6°C 2200

+8.93M +4°C 2220

Figure [17] London’s East End projected flooding

Chapter IV Marshlands As A Source For Coexistance And Natural Development

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Marshland formation The marshland and wetlands are a natural formation at the edges of water bodies. The marshland creates a relationship between water and soil and passes the nutrients from one to another. This transition helps micro organisms develop in both surfaces, eventually leading to vegetation growth which attracts animal species.

The marshlands host a great variety of entities, from micro organisms that move nutrients underground, to big predators or birds that live in the outer parts of the marshlands. Different species interact in the same space and benefit from each other creating a balanced coexistance.

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Figure [18] Marshlands in the UK

Figure [19] Diagram of a Marshland section

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Biodiversity habitat locations As the marshland starts to develop, it works as an independent habitat hosting multiple entities. In the inner part of the city, the benefits of the marshland are not yet present and non beneficial urbanism conditions continue to exist. In order to bring the marshlands’ benefits to the inside part of the city, current biodiversity existence was explored in order to find the proximity relationship between animal habitat location points and green area in the projected flooding scenario.

With this exploration the high density spots were extracted marking them as the most suitable areas for habitation and reproduction based on natural behaviour patterns. By mapping these densities, the interaction of human and non human can be better understood noticing current separations and invasions. These densities were defined as the optimal habitat locations.

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Figure [20] Current bird location points and green area proximity map

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Biocoenosis Nest_A City Woven by Collective Intelligences

Figure [21] Current bird location points, green area and water volumes proximity map

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Figure [22] Water volume generated by the projected flooding

The new water volume serves as a starting point to mark the marshland natural formation. From that point, the biodiversity proximity was mapped in order to evaluate the new behaviours.

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Figure [23] Biodiversity loccation points and green area after the flooding stage 1

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Figure [24] Biodiversity loccation points and green area after the flooding stage 2

Once the densities based on the proximity between biodiversity, green area, and the new water volume are mapped, the higher spots are marked as the optimal habitat locations due to the behaviour patterns nonhumans have in the current scenario. By having a combination of the current non-human activity and the new water volume, new interaction is projected taking into consideration the optimal habitat locations.

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Chapter V Slime Mould as a non-human intelligence

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Slime Mould’s bio computation Slime mould can be considered a form of non-human intelligence due to its property of finding optimal paths and its intelligence connecting food sources. The inner-city optimal habitat locations were used as the slime mould’s food source and the marshlands as the initial input. Therefore, the optimal locations found in the density map were used as an input for experimenting with the slime mould’s bio computer. This unicellular entity creates a protoplasmic network between points that can be defined as the nutrient distribution network because it transports marshland properties to optimal habitat locations in the inner part of the city.

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Figure [25] Physarum Polycephalum

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Figure [26] Slime Mould recording

Figure [27] Physarum Polycephalum

In order to test and explore the slime mould’s bio computer, an enclosed environment was designed to record and analyze its behaviour and interaction with the environement, the food source (oat flake), and the base (agar).

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Figure [28] Physarum Polycephalum

Multiple experiments such as Figures [27,28,29] were recorded testing the behaviour through time, analyzing the possible results obtained when testing the urban scale inputs. By having these projections, the test with the optimal habitat locations obtained from the proximity maps was planned and designed according to a scale that will optimize the experiment, both in time and quality of the slime mould’s growth.

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Figure [29] Slime Mould experiments

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Figure [30] Slime Mould connecting marshlands to the optimal habitat locations

During the first stage of the urban scale experiment, the main nutrient source nodes in the proximity maps representing the marshlands were used to obtain an initial response of both direction and speed of the slime mould’s growth.

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Figure [31] Slime Mould’s nutrient distribution network

This experiment connects the marshland to the optimal habitat locations in the inner part of the city, transporting the nutrient properties for biodiversity growth found in the marshland.

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Chapter VI Nutrient distribution network with Generative Adversarial Network

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Machine Learning In order to translate the slime mould’s nutrient distribution network into an urban proposal, the results were processed with a Generative Adversarial Network. By training a neural network, the non-human bio-computer is approached without the human biased perspective. The network was trained with different inputs such as slime mould and eroded terrains seen in Figure [32], and then trained with eroded terrains and a London dataset shown in Figure [33].

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Figure [32] Inputs for a GAN Neural Network training

Figure [33] Training of a Neural Network with GAN test 5.0

Figure [34] Training of a Neural Network with GAN test 8.0

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Flood timeline After training the neural network, around 40,000 images where used to test the East part of London with the slime mould’s eroded terrain to reimagine the flooding scenario and the city’s future development. As shown in Figure [36], the flood is used as a starting point to grow the marshland environment that can be distributed along London to create a new environment shown in Figure [37]: The Marshland City.

This formation that changes with time and height recreates a marshland typology combined with the human infrastructure. This results in a new type of interaction where, by providing the non-human the necessary resources to be self-sustained, the breach to the human infrastructure is reduced. This post natural city shows a 31% growth of green area but maintains a delimited boundary between entities. The marshland distribution within the city helps to repopulate the biodiversity loss by bringing the nutrients that microorganisms require for growth.

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Figure [35] London aerial view 2020

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Initial flood

Marshland formation in the edges

Marshland formation following slime mould’s network

Human infrastructure development from nodes

Figure [36] Marshland City timeline

Marshland distribution following slime mould’s network

Marshland distribution following slime mould’s network

Human infrastructure development

Marshland City

Figure [37]MArshland City

Chapter VII Fibrous synthetic nest

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Synthetic bird nest In order to develop the architecture scale through a non-human approach, the bird nest was explored. A bird nest proves to be a habitat where diferent species interact in the same space with controlled boundaries that balance the coexistence. Having explored the urban scale through the Marshland City, each volume at the edge of the water can be considered a synthetic nest that will host multiple non-human entities coexisting with the human.

To find a balance in this interaction, referencing these volumes to a nest provide us with different parameters that can be recreated through a fibrous stilt system.

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Figure [38] Sociable weaver nest

Biocoenosis Nest_A City Woven by Collective Intelligences

Figure [39] Sociable weaver nest section diagram

The Sociable Weaver nest is used as the main bird nest reference because of the colony formations inside the nest and the neighboring species living in the top part of the nest, the leaves of the tree, the log, or the shadow.

These bird colonies that can host around 100 to 500 birds in the same nest were transposed to a colony of humans, finding a way to organize the human on the inside and the non human on the outside.

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Figure [40] Synthetic fibrous nest with shortest walk

To explore the nest formation, the shortest walk algorithm was used to create the fibrous stilt system that will be used to shape the synthetic nests in a process similar to the one birds use to allocate materials in their nests.

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Figure [41] Bird nest location parameters

A set of parameters for the nest location were extracted and mapped to be used as a reference for the synthetic nest.

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Figure [42] Combined parameters with a fibrous system

Combining multiple parameters such as weather, species type, size, and anti predator location, a conceptual approach with the fibrous system was generated to create a first attempt of a synthetic nest. This exploration will then be applied into a generic volume (Figure [43]) to analyze the fibrous behaviour and the different patterns obtained to envision a synthetic nest in a three dimensional perspective such as Figure [44].

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S_0.5 D_20 C_3 G_x1

S_0.6 D_20 C_3 G_x1

S_0.5 D_40 C_3 G_x1

S_0.6 D_40 C_3 G_x1

S_0.5 D_40 C_3 G_x0.5

S_0.6 D_40 C_3 G_x0.5

S_0.5 D_40 C_3 G_x0.5+x1

S_0.6 D_40 C_3 G_x0.5+x1

Figure [43] Synthetic Nest volumetric exploration catalogue

S_0.7 D_20 C_3 G_x1

S_0.7 D_40 C_3 G_x1

S_0.7 D_40 C_3 G_x0.5

S_0.7 D_40 C_3 G_x0.5+x1

Figure [44] Synthetic nest volumetric exploration

Biocoenosis Nest_A City Woven by Collective Intelligences

Figure [45] Marshland city human infrastructure

The human infrastructure is extracted from the Marshland City to create a computational separation between the human and the non-human.

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Figure [46] Marshland city human infrastructure extraction

The extracted data is used to create a grid that is explored with a 3D GAN extrusion based on code algorithms.

These extruded volumes are used as a three dimensional grid to be tested with the shortest walk obtaining the fibrous system.

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Figure [47] Marshland City fibrous extrution

The fibers are generated over the extracted geometry to convert the Marshland City into a volumetric model.

The Marshland City is now translated to a fibrous system.

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Figure [48] Marshland extracted materials

Two components of the marshland such as bacteria and algae were taken to create the two main materials in the fibers. Bacteria and cement were used for the human based on its capabilites of regenerating and self repairing. Algae and cement were used for the exterior non-human program. These allow micro organisms and vegetation to grow within the fibers and generate a suitable environment for biodiversity. As seen in Figures [49,50,51], different grids were used depending on the acting entity to develop multiple programs.

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Figure [49] Biodiversity interaction based on grid resolution

Figure [50] Human synthetic nest programatic diagrams

Figure [51] Non-human synthetic nest programatic diagrams

Figure [52] Marshland City Fibrous Synthetic Nest

Figure [53] Human and non-human elevated programatic combination

Figure [54] Human and non-human elevated programatic combination

Figure [55] Human and non-human ground programatic combination

Biocoenosis Nest_A City Woven by Collective Intelligences

Figure [56] Synthetic nest volume

The 3D GAN extracted volumes are formed with a combination of programs. This allows multiple activities without an invasive connection.

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Figure [57] Synthetic nest volume

Different volumes were explored to obtain different fibrous behaviors and different programatic combinations and densities. As shown in Figure [55], each part of the volume is configured for its user and put together to form the Synthetic Nest.

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Figure [58] Synthetic Nest volume programatic diagram

Figure [59] Synthetic nest volume cluster

Figure [60] Marshland City Fibrous Synthetic Nest

Biocoenosis Nest_A City Woven by Collective Intelligences

Biocoenosis Nest_A City Woven by Collective Intelligences

Conclusion Approaching the urban catastrophic scenarios with a non-human perspective helps us understand factors that can be uncomputable for the human intelligence. Using non-human intelligences as a collective inteligent apparatus results in a post-natural development that prioritizes other entities, which in the end, benefits every existing entity.

With the Marshland City and the Fibrous Synthetic Nest, biodiversity can find its properties to grow, at the same time, different entities coexist with a boundary that can reduce human and non human interaction issues such as COVID-19. The Marshland City embraces a flood caused by climate change to establish controlled coexistence in a post natural scenario, the Fibrous Synthetic Nest allows human on the inside and non-human on the outside‌ thus creating the biocoenosis nest.

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Video To view the video presentation follow the link or scan the QR code.

https://vimeo.com/466011301

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Figure [28] Author’s own, 2020 Figure [29] Author’s own, 2020 Figure [30] Henderson, Eileen. Retreived from https://www.geograph.org.uk/photo/192171 Figure [31] Author’s own, 2020 Figure [32] Author’s own, 2020 Figure [33] Author’s own, 2020 Figure [34] Author’s own, 2020 Figure [35] Author’s own, 2020 Figure [36] Author’s own, 2020 Figure [37] Author’s own, 2020 Figure [38] San Diego Zoo. Retreived from https://animals.sandiegozoo.org/animals/sociable-weaver Figure [39] Author’s own, 2020 Figure [40] Author’s own, 2020 Figure [41] Author’s own, 2020 Figure [42] Author’s own, 2020 Figure [43 Author’s own, 2020 Figure [44] Author’s own, 2020 Figure [45] Author’s own, 2020 Figure [46] Author’s own, 2020 Figure [47] Author’s own, 2020 Figure [48] Author’s own, 2020 Figure [49] Google Images various sources.

Figure [50] Author’s own, 2020 Figure [51] Author’s own, 2020 Figure [52] Author’s own, 2020 Figure [53] Author’s own, 2020 Figure [54] Author’s own, 2020 Figure [55] Author’s own, 2020 Figure [56] Author’s own, 2020 Figure [57] Author’s own, 2020 Figure [58] Author’s own, 2020 Figure [59] Author’s own, 2020 Figure [60] Author’s own, 2020 127

Biocoenosis Nest A City Woven by Collective Intelligences