Sensing animals:
Interpreting the lives of animals through digital
technologies
Ilja van de Rhoer
10530290
Research Master Thesis
Department of Media Studies
University of Amsterdam
Supervisor: Dr. Alex Gekker
Second Reader: Dr. Abe Geil
Table of Contents
1. Introduction………...3
2. Computing animals in the age of big data...…………....8
2.1 Animals rather calculative than biological...
...8
2.2 Computation, data and sensors……….………10
2.3 Moving with animals………12
2.4 Rethinking animals………...15
3. Animals reaching global scales………19
3.1 Networks, infrastructures and media ecologies………...19
3.2 The future of movement ecology……….
21
3.3 Animals and networks………
..23
3.4 Planetary computation………...
24
4. Digital conservation and a new biopolitics of life…….27
4.1
Biopolitics, or the governing of life………...
27
4.2 Biopolitics, or the enhanced governing of animal life………..
29
4.3 Biopolitics, or the automated governing of animal life………
31
5. Conclusion……….35
6. Works Cited………..39
Abstract. We live in a time of ecological crises in with enormous impact on the biodiversity of this planet. Over a million animals and plants are currently at the brink of extinction, mainly caused by human activity. At the same time we live in an information age with an unprecedented amount of digital devices that monitor, steers, enhance and change our everyday lives. In this thesis I will look at several digital devices that are used as ‘technologies of distance’ (Porter 1995) that make present and interpretable the lives of animals. My focus will be on wildlife because this is the area in which animals are experiencing the highest risk of becoming extinct. The problem that will be posed in this thesis is How are technologies of distance reconfiguring our relationships with animals in the wild. In order to find a an answer to this issue I will first explore how animals are being operationalized by digital technologies like sensors that make present the lives as animals in the form of quantified data. During the next step I will look at how digital sensors integrate animals in digital networks and media infrastructures that enables researchers to understand the dynamic interaction of animals on a global level. Finally I will look at how the use of these digital sensors and the information infrastructure of which they are part enable a new form of conservation of wildlife that is increasingly becoming automated. By the end of this thesis it will have become clear that making the life of animals present and interpretable is just as much a scientific endeavor to counter biodiversity extinction as it is about now exercises of human power on nature through the mediation of digital technologies.
Keywords: Animals, computations, sensors, biopolitics, technologies of distance, automation “We can't see everywhere. We can't see everywhere all at once”
Timothy Morton – The Ecological Thought
1. Introduction
The year is 1822, spring, in a field close to the North-German village Klütz some villagers notice something very peculiar. A stork has been walking around in an unusual manner, as the townspeople approach the animal they suddenly notice a long arrow stuck in its body (Figure 1). Unfortunately the animal wouldn’t be able to continue its journey and passed away not long after its discovery. Upon inspecting the deceased body the villagers are surprised by the material used for the arrow, instead of the normal pinewood, the object is made from wood that only grows in Central Africa (Cheshire & Uberti 2006 144). In the next approximately twenty years twenty-five further specimen were found with similar arrows stuck in their bodies, together they would form the earliest evidence of long-distance migration of birds.
Fig.1 Rostocker Pfeilstorch © Zoologische Sammlung der Universität Rostock
Nowadays stork migration can be observed continually, in real-time and without even having to leave your house. In fact not only movement but also a whole range of other parameters – from heart rate to wind pressure - are being recorded and can be viewed instantaneously and anywhere in the world (Kays et al 2015, 6). The reason for this are digital sensors, GPS, 3 and 4G phone signals, satellites and the wireless and wired networks of the internet and the web that can make the lives of animals present and interpretable anywhere and anytime. The use of sensors that are attached on or inside the bodies of animals can register biological and environmental processes and convert these into quantified data that can be used to understand and interfere into animals and their environments. The development of digital sensors to study and manage wildlife comes at a time in which two radical changes are occurring on a global order. On the one hand we are seeing the rise of what sociologist Manuel Castells calls the Information Age or Network Society (2010) in which computers, smart phones, the internet and social networks are permeating every aspect of our daily lives. On the other hand we are in the midst of what is called the Anthropocene, the geological era that is characterized by significant human impact on the earth’s ecosystems that cases rapid change in the global climate and increasing extinction of biodiversity caused by human activity (Joppa 2015).
In recent years there has been a turn in media studies towards analyzing this intersection between digital technologies and the Anthropocene called Ecomedia studies which takes as its object the relationship between non-print media and the natural environment either through cultural representations or by researching the impact media technologies have on the environment (Ziser 2016). Which is itself part of a wider movement in cultural studies called the environmental humanities an interdisciplinary research field in the humanities that studies the relationships between humans and the natural environment (Rust et al. 2016). Ecomedia studies the entanglement between the information age and the Anthropocene, i.e. between humans, media and the environment, to be able to inform, educate hopefully motivate people to take measures to counter the ongoing environmental crises.
Ecomedia studies is already an established research field in the analyses of ‘old’ media like television (Vivanco 2004; Pike 2012) and film (Ivakiv 2013; Gustafsson & Kaapa 2013) but there has also been a slowly growing interest in the study of new media and the environment. To name just a few examples Sue Thomas (2013) interrogates the prevalence of nature derived metaphors and imagery online while Kember and Zylinska (2012) examine the interlocking technical, social and biological processes of mediation. Others look at the material flows between new media and the natural environment and how new media consumes, despoils and waste natural resources (Parikka 2011; Maxwell & Miller 2012). Again others have turned to the study of communication infrastructures in order to study their impact on the environment, from material sites like data centers and mobile phone towers (Mattern 2013; Parks 2015) to global undersea cable networks (Starosielski 2015). Together these studies are helping us to understand the impacts media technologies have on nature. Ecomedia is also increasingly conducting research in field of Animal Studies an interdisciplinary research field that studies the intersection between humans and animals in which it explores the spaces that animals occupy in human social and cultural worlds and the interactions humans have with them” (DeMello 2012, 4). Like with the environment in general ecomedia has turned to the relation between media in television (Lippit 2000; Kilborn 2006), film (Ladino 2013; Pick 2013) and videogames (Molloy 2011). In regards to new media there has also been a growing interest in animals, from use of animal images to sell products and create moods on Getty Images (Kramer 2005), to the influence of insect research and representations on the development of new media, the ethical challenges that new media poses on animal lives (Zylinska 2009). In short Ecomedia is slowly expanding towards the study of animals in relation to media.
This thesis will continue the research on the relationship between new media and animals by asking how digital media operationalizes animals in order to both generate knowledge on them and as a tool to manage animal populations. It follows on the one hand work by philosopher and biologist Donna Haraway (1991) who asserts the intimately entanglement of animals, humans and technologies which she calls hybrids. On the other is takes as its basis the field of culturomics that looks at human behavior and cultural trends through quantitative analysis (Michel et al. 2010). In media studies there has been done a lot of work on the relationship between humans and media, from the early work of Marshall McLuhan on media as extensions of man to recent studies that look at the relationship between human consciousness and technical systems that act on our behave (Hayles 2017). Likewise there has been a lot of work on quantification in media studies whether this is quantification of the human body through biometrics and quantified selves (Rettberg 2014; Swan 2013), culture (Couldry & Hepp 2016; Mckenzie 2010) or human conduct (Yeung 2018; Cheney-Lippold 2011). In this thesis I want to extend this research to the natural world of animals to understand how animals and their environments are being quantified and animals are turned into techno-biological hybrids in the light of the enormous environmental crises that causes enormous extinctions all around the planet.
A few other research fields should be mentioned that are influencing this thesis and Ecomedia studies in general. Firstly the American-Canadian and European schools of Media Ecology. The first school emerged in the 1970’s by scholars like Marshall McLuhan, Neil Postman and Walter Ong to describe the effects media have on subjects and their
environments “A medium is a technology within which a culture grows; that is to say, it gives form to a culture’s politics, social organization, and habitual ways of thinking” (Postman 2000, 10). Ecology refers in this context refers to the ongoing interactions between humans and media. The European school of media theory emerged in the first decade of the twenty-first century by scholars like Matthew Fuller (2005) and Jussi Parikka (Goddard & Parikka 2011) to designate the dynamic interrelations between media, objects, humans and natural and cultural processes with an empathize on the material-spatial conditions of media
Other streams that have been influential for Ecomedia studies are firstly has also been recently a wider current of ideas that in various ways have been influential to rethinking the relations between new media and nature. Firstly the actor-network theory developed a.i. by philosopher Bruno Latour (2005) who notes that humans never act alone but are always part of networks that include environmental factors and technologies. Another stream is object-oriented ontology that shifts our focus from an anthropocentric viewpoint of things and towards the ecosphere of things and their relations (Harman 2005). A comparative stream is new materialism that argues that matter is imbued with a liveliness that can exhibit distributed agency (Bennet 2010), material agency (Barad 2007) and the inherent interconnectedness between matter, things and actors (Morton 2010). These thinkers question the separation between nature and humans and instead show their deep entanglement
A final research field that is highly influential for this paper is Cybernetics or the science of communication and control in the animal and the machine. Developed in the 1950’s by Norbert Wiener, cybernetics looks at humans, animals and machines as self-regulatory systems that maintain themselves through the continual regulation of informational flows. Communication operates through the flow of information within and between systems who are able to modulate themselves in response to these external or internal communications (Thacker 2010, 120). Although cybernetics as a separated research field has waned in the last decades its influence is still great, especially in new media studies. Katherine Hayles for instance reintroduces the term cybernetics to designate complex, networked, adaptive, and coevolving environments through which information and data are pervasively flowing (2010, 149). Cybernetics can be understood in these terms as complex adaptive systems or multiagent environments – made of humans, animals and machines - that evolve and adapt through informational exchanges.
This thesis will be divided into three chapters that each approaches the technological/biological configuration of digital devices in a different way. In the first chapter my focus will be on sensors as digital devices that translate the qualitative processes that the define animals and their relations to the environment into quantitative units that can be used for digital computation. I will use as a case study the biological discipline called movement ecology that studies the movement of animals through the use of tracking devices in order to understand how this conversion is enacted in actual practice. By the end of this chapter I will have shown how digital computation is influencing our understanding of animals in radical new ways and to such degrees that confusion arises in regards to the difference quantified representations of animals and the actually creatures themselves.
In the second chapter I will turn to network and infrastructure studies in order to understand how digital technologies are able to integrate radical different things, beings and processes into a single structure. The case study that I will use for this chapter is a new project
in movement ecology that has introduced the first global database on animal movement while at the same time implementing an enormous amount of sensors on animals in order to track their movement on a planetary level. On the one hand this enables scientist to think the dynamics of animals as a global entity that can be observed, understood, affected by human actions, debated in political processes, cared about, and managed. On the other hand this allows animals across radical different taxonomic domains to be united through this sensor network and central data repository which is itself part of a larger development in which computation is increasingly integrating both the cultural and natural world of animals and humans. By the end of this chapter we will see that animals in this new global media infrastructure are reimagined as one of the many ‘users’ that in some way interact with this new global structure.
In the final chapter I will look at the role of digital technologies as a means for humans to control and manage animal and environmental dynamics. Here I will first explore the emergence of a new form of management over life called biopolitics, a term developed by philosopher Michel Foucault to describe a new mode of governance of living beings on the level of the species. Then I will explore how sensors that are attached to the bodies of animals enhance conservation management of wildlife keepers who use sensors as tool to foster or disallow the circulation of animals between environments. Lastly I will look at the increasing automation of digital technologies in conservation management in which the agency and presence of wildlife keepers is steadily removed from conservation practices.
By the end of this thesis I hope to have shown the increasing role digital is playing in our relationships with animals. On the one hand the use of digital technologies will enable to make present and interpretable in new ways by manifesting them on our computers, smart phones and other digital devices. On the other hand, as computation increasing humans are removed from the natural world through the increasing mediation of digital technologies. Just like humans, animals are slowly becoming integrated in media environments in which bodies, environments and actions are increasingly quantified and digitalized while the animals themselves become bio-technical hybrids. This analysis of animals and digital technologies will serve as a contribution in Ecomedia studies and hopefully in our shared care for the biodiversity on our planet.
2. Computing animals in the age of big data
In this first chapter I will explore the use of what Theodor Porter (1996) calls ‘technologies of distance’ i.e. technologies that extend the capacity of humans to observe and record the presence of other lives. In order to make life interpretable these technologies quantify that what it makes present, making the world into an ordered reality that can be interpreted. In the coming sections I will explore how technologies of distance are making the lives of animals present through quantification. With the rise of digital technologies and big data science a whole array of new modes has been opened to investigate the lives of animals while simulations and predictive modelling allows us to reimagine animals in new ways. The technologies of distance that will be the primary object of my research are sensors that are being attached to the bodies of animals. These devices enable the capturing and convergence of the animal and their environment into quantified units that can be used for computational processing. By the attachment of sensors onto or sometimes inside the animal animals are becoming increasingly entangled with digital technologies I dub this type of entwinement between technologies and animals biosensors.
This chapter continues new media research that focusses on the quantification of the human body through biometrics and other forms of quantification that turn the living body and culture into a series of processes that can be constantly extracted as data (Swan 2013). One big difference between humans and animals in this regard is that the former is often in some degree aware that data is being extracted on them through digital means and often even enact some form of ‘quantified self-reporting (Turkle 2015) through biometric devices and apps. Animals on the other hand are as far as we know of unaware that they are being quantified and that the data subtracted from their bodies is used for digital computation. This poses ethically dilemma similar to those asked in media studies concerning data extraction on humans like privacy (Nissenbaum 2010) and consent (Solove 2013). By the end of this chapter I will have investigated the increasingly hybridity (Haraway 1991) of animals and technologies and have shown how digital devices enable a form of mediation (Couldry & Hepp 2016) that enables humans to make present and interpretable the lives of animals.
2.1 Animals: rather calculative than biological
Until the end of the eighteenth century classical science comprised all living beings within a single world view; organisms were divided in hierarchically organized taxonomic scales based on common visible traits. In fact, as the philosopher Michel Foucault wrote in a somewhat provokingly tone “life itself did not even exist until then. All that existed were living beings, viewed through a grid of knowledge constituted by natural history” (2002, 127– 128). It was only during the nineteenth century with the birth of biology as a scientific discipline that living organism were starting to be studied as living beings: evolving, appetite-driven, secret, discontinuous, mendacious, inscrutable, always on the prowl (ibid., 277). But it would take until the first decades of the twentieth century until animals were starting to be studied in their own particularity instead of as variations of general biological processes. The ethologist Jacob von Uexküll introduced his idea of the Umwelt in the 1920’s to describe the infinite variety of perceptual and affective relations animals have with their
surroundings (Agamben 2004, 40-41). Although all animals exist in the same world, their experiences of time and space differ from each other based on their specific embodiments and relations with their environment. Each animal was perceived as having its own particular reference frame through which they exist and act also called an animal’s subjective experience of itself and its surroundings (Nagel 1974). At the same time research has shown that humans and nonhuman animals have a lot in common - from biological processes like seeing, hearing, grasping and digesting to qualities that we would normally only ascribe to humans like intelligence and emotions (DeMello 2012, 359 – 365). Even though humans and nonhuman animals have their own subjective relationships to their particular Umwelten, they nevertheless share the same world in which commonalities between species could create ways of mutual understanding and companionship (Haraway 2003).
Although animals and humans share the same world and have a lot in common there is a tendency that runs throughout modernity to set humans apart from other animals. One of the central qualities on which this divide is built is the unique capability of humans to have language. The ancient Greek philosopher Aristotle already distinguished humans from animals because of their ability to speak, an idea that was reaffirmed in the seventeenth century by the French philosopher René Descartes (DeMello 2012, 39). Twentieth century philosophers like Martin Heidegger and Hannah Arendt again made language the key quality that separates humans from nonhuman animals (Asdal et al. 2017, 4). Although there is now an overall consensus humans and animals differ because of the former’s use of symbolic language (DeMello 2012, 365-366).. At the same time it is readily acknowledged that animals have other complex systems of communication through sounds, smells, colors etc.
During the twentieth century two different research fields were developed to explore these animal communication systems. The first approach is called biosemiotics and looks at the relationships between semiotics and biology. Founded by Thure von Uexküll, son of Jacob von Uexküll, biosemiotics looks at how signs are communicated throughout living systems (Buchanan 2008, 30-31). By looking at biological phenomena like recognition, memory, categorization, mimicry, learning and communication and applying semiotic theories to these processes, the formation of meaning and the decoding of signs in animals could be interpreted in similar vein as humans decode meaning. In this way biosemiotics opened a way to explore commonalities of the use and interpretation of signs between humans and nonhuman animals. The second approach is called cybernetics and looks at the relation between biology and computational information processing. Developed by Norbert Wiener in the 1950’s, cybernetics looks at animals, humans and machines in parallel fashion as self-regulating information processing systems (Sharov 2010, 1051). Wiener and other cybernetic researcher emphasized a quantitative view of information over a qualitative model as was used in biosemiotics, one in which information was understood as being quantifiable, transmissible over different media, and bearing no semiotic value (i.e., having no “meaning”) (Zylinska 2009, 128). Communication within and between these systems is understood as informational exchanges that serve as input data for the system that then processes this information and generates some form of response or output data. (Sharov 2010, 1058). From a cybernetic point of view the qualitative different communication systems of various animal species, humans and machines can be brought back to a single quantitative model that can be applied to both living and non-living systems.
In the life sciences cybernetics has been influential for understanding animals and life in general as quantitative and informational processes of self-regulation. In fields like bioinformatics, nanomedicine and Biocomputing life at the genetic and cellular level is recontextualized through notions of information and code. In this scheme biology is increasingly treated as a technology consisting of code sequences that can be stored, computed, manipulated and controlled (Thacker 2010, 123). Other approaches in the life sciences look at the cybernetic exchanges between animals and their surroundings. Ecosystem ecology for instance looks at the circulation of energy and matter between animals and the environment through systems of dynamic feedback loops (Bocking 1997). While population ecology looks at the cybernetic regulation of genetic distributions in animal populations (Kingsland 1985).
One interesting discipline in the latter group of biological approaches is movement ecology, the study of animal movement distributions (see introduction and section 1.3). What make this study special is their high reliance on sensors that are attached to or inside the body of animals, which relay information about the location and the surrounding environmental conditions to databases and to biologist. This conjoining of sensor and animal results in a constant cybernetic relationship in which the qualitative experiences of animals are fed to the sensors as input data that is then being processed and reconfigured as a quantitative set of output data. In this approach animals are transformed into biosensors animal-technical entanglements that mediate their perceptual and affective experiences to databases and scientific labs. In the rest of this chapter I will explore these biosensors as a technology of distance that facilitates a new form of making present of animals through digital mediation. Through the use of computation and big data analysis these digital devices can not only make the life of animals interpretable but also allow humans to reimagine animals in a new way.
2.2 Computation, data and sensors
In this section I will introduce some terms that enable me to frame biosensors as a technology of distance, beginning with the notion computation. This term designates a process of counting or calculating. Computers and similar devices are machines that can be instructed to carry out computational operations: a computer is fed input data that is used for internal calculative processes in order to output data in another form (Berry 2011, 10). In order for a computer to compute something a phenomena has to be translated into quantifiable metrics often through the use of a system of numbers and specifically binary models, referred to as digital code which is the material through which calculative processing is enacted (ibid., 2). The input and output of these computational processes is known as data. In general data refers to descriptions or representations of phenomena into categories, measures or other quantified units that allows it to be recorded, analyzed and reorganized (Mayer-Schonberger & Cukier 2013, cf5). In computational devices data are discrete, clearly defined, clustered into datasets, have associated metadata (data about data) and can be linked to other datasets to provides insights not available from a single dataset (Kitchin 2014, 1). This last characteristic is also described as data amplification’ i.e. when data is combined it enables far greater insights by revealing associations, relationships and patterns that remain hidden if the data remains isolated (Crampton et al. 2012). Data are commonly treated as if they are neutral,
objective and pre-analytic in nature but in reality ‘raw data is an oxymoron’ (Gitelman & Jackson 2013, 2). Data are always embedded in social-technical networks through which data is generated and are always framed on multiple layers: technically, economically, ethically, temporally, spatially and philosophically (Kitchin 2014, 2). Even though data is never objective it has nevertheless enabled, through computational analysis, enormous insights in the physical, biological and social structures of our world.
When data is converted into digital form they become available for digital processing, but digital computation is dependent on operations that specify the sequence of calculations to be performed on data, in other words algorithms. These procedures entail a series of simple operations that a computer can execute to accomplish a given task, they are “encoded procedures for transforming input data into a desired output, based on specified calculations” (Gillespie, 2014, 167). Algorithms can be used in many different ways; they can select which information is most relevant, map and suggest preferences, manage our online interactions and provide a means to know what there is to know and how to know it (ibid.). Increasingly algorithms have the capacity to respond and adapt to external inputs; to learn rapidly and to recursively base new outputs upon their ‘experiences’ (Parisi 2015). One of the central critiques on algorithms is that although they emit a sense of neutrality they are nevertheless programmed by humans who built specific presumptions into the algorithm. (Gillespie 2014, 170). Therefor it remains important to stay critical towards the supposed neutrality of algorithms especially in an era in which they have become the key logic that drives the computational flows of information.
Together data and algorithms form the building blocks for a new era in digital computation big data. In the last decades digital computation technologies have enabled the production of massive quantities of data about people, things and the relations between them. Although quantity is an important aspect of big data it is not what characterizes this new paradigm “[Big data] is less about data that is big than it is about a capacity to search, aggregate, and cross-reference large data sets (Crawford & boyd 2012, 663). In general big data can be defined as a cultural, technological and scholarly phenomenon that builds on maximizing computation power and algorithmic accuracy to gather, analyze, link and compare large data sets (ibid.). Big data enables the identification and visualization of patterns that couldn’t be derived from smaller data sets that uses less computing power. This new mode of analysis is being hailed as the holy grail of behavioral knowledge and seen as an important tool for offering insights into everything, from climate change to cancer research (Van Dijck 2014, 199).
The input for digital computers is recorded and collated by all kinds of technical devices that turn the ways we live, speak, act and think into quantifiable data that then becomes amenable for computation and processing (Berry 2011, 2). The central technology that is used to make the lives of animals in the wild ready for digital computation is sensors. Media scholar Jennifer Gabrys describes them as “exchangers between earthly processes, modified electric cosmos, human and nonhuman individuals (2016, 13). In general they are technologies that are programmed to track processes like air quality or traffic levels and translate them into data that can be relayed to computers and other computational devices. Sensors are able to make entities, things and processes present and interpretable through measuring and collecting data on real-time events “Sensors make it possible to listen in on a
planet that has always been “talking to us,” but which we can only now begin to hear” (IBM, “The Internet of Things”).
In this section I have introduced a series of terms that will enable me to frame and understand sensors and other digital devices as technologies of distance that can make animals present and interpretable. In the next section I will introduce the biological discipline called movement ecology as a case study that heavily relies on both the use of sensors and big data. In will use this section to explore how sensors collect and relay data on animals and while doing so influences how we understand animals.
2.3 Moving with animals
In this section I will introduce my case study. In general movement ecology can be described as the biological discipline that studies the movement of animals, how animals move and what causes animals to move (Nathan et al. 2008). Movement ecology aims to identify the properties that enable or constrain movement of animals, like physiology (does it have legs, wings, fins), climatological and elemental conditions (dry desserts or icy tundra), diet, metabolism and life-cycle and which senses animals use to locate themselves and to navigate within their environments (tracking scent, orientating by the sun of the earth’s magnetic fields or echolocation) (Hodgetts & Lorimer 2018, 5). Because so many factors influence the movement of animals movement ecology is a highly interdisciplinary field that integrates research of various fields in the life sciences, from environmental studies to botany (Benson 2016, 143).
The scale of research in movement ecology ranges from local research on red and grey squirrel interaction on the British isle to world spanning migration patterns of wading birds or the movement patterns of southern elephant seals (Hodgetts 2017). The general aim of movement ecologists it to find answers on topics from animal physiology, behavior, interaction and land use to wider concerns like environmental change, animal-human disease distribution and the prediction of natural disasters like volcanic eruptions or seismic activity (Benson 2016). For some movement ecologist movement is taken to be such an important explanatory quality that it is seen as the defining characteristic of animals and sometimes even life itself (Kays et al 2015, 1; Nathan et al. 2008, 19052). Overall movement ecology aims to enable and increase our understanding of the dynamics of our planet on biological, physical and cultural scales through the tracking of animals.
“Location is everything” (Cheshire & Uberti 2016, 144). Although the primary object in movement ecology is movement it has to be first operationalized as a sequence of discrete “steps and stops” taken by an organism within a Cartesian space in order for it to be studied (Benson 2016, 137). The product that results from this conversion is a geospatial set of coordinates that are referred to as ‘tracks’, by clustering these coordinates together various types of patterns are formed that are referred as ‘movement phases’ that denotes the animal’s changing goals like feeding, mating, or avoiding predators and lifetime tracks that represent an animals complete movement track from birth to death (fig.2). In order for an animal to be tracked it has to fir certain criteria like being recognizable, detectable and distinctive in order to produce movement paths or tracks that can be recorded and analyzed in productive ways (Verma et al., 2015: 657). These criteria divide animals between those that can be tracked by
movement ecologist and those – at least until new technologies are developed – that can’t be tracked
Fig.2 Visualization of a movement path at increasing spatiotemporal scales. © Nathan et al (2008).
Movement ecology has made extensive use of technologies throughout its history to make animal movement present and interpretable as tracks. Most ecologists attach or implement a sensor onto or inside the animals’ body that relays information to Argos or GPS satellite systems. These sensors can collect a whole range of processes on both the animal and the environment in which it is moving like temperature, humidity, light levels, acceleration, location, body temperature, heart rate, orientation, altitude, and pressure (Gabrys 2016, 81). With the increasing affordability, miniaturization, computing power and storage capacity of sensors it has become relatively easy to collect high detailed data on animal movement and relay the data across longer distances (Benson 2016, 141). Most sensors are now able to continuously and in (near) realtime collect data on animal movement providing an integrated view of animal movement and its surrounding environment (Kays 2015, 1).
Because of these improvements in sensor devices and in digital technologies in general movement ecology can bring study of animals in the realm of big data. It has now made possible to collect an unprecedented amount of data on animal movement so much so that comparable advantages are necessary to integrate, process and analyze in order to ‘stay afloat in the sensor data deluge’ (Porter et al 2012). Through the use of algorithmic sorting and analysis real-time predictive models can be modeled that integrate habitat preferences, movement abilities, sensory capacities, and animal memories into movement forecasts. While data amplification allows new insights on animal behavior by connecting sequential movement steps to form larger patterns (Kays et al 2015, 4).
With the increasing use of sensors and other digital technologies a recurrent concern is that these devices affect the activity and performance of animals and can have harmful consequences for the wellbeing of the organism. Research has shown that most large animals like elephants or elks seem to care little about these technologies that are attached to their bodies while they do have effect on the activity of smaller organisms like rodents or waterfowl (Vandenabeele et al. 2014). Reducing the negative impacts of sensors and trackers
on animals is a high priority in movement ecology both in regards to animal welfare as well as to ensure that the collected data accurately reflects animal behavior. In order to reduce the effects on animals movement ecology research is normally regulated by institutional committees that maintain high standards of animal care, which help drive constant methodological refinements to reduce the risks to animal (Kays et al 2015, 3).
Fig.3 a common starling with a sensor attached on its body. © ICARUS
Animals are being deployed with sensors and other types of tracking devices that transforms them into computational data sampling platforms that continuously collect data about their internal physiology and external environmental encounters (Marshall 2008, 4-5). Although data gathering on animals enables researchers to understand and protect animals it is also reminiscent of Michel Serres description of humans as parasites. Through tracking humans and animals are positioned in an eater-eaten relationship like those of parasites and their hosts “the eater is utterly dependent on (exists in a "parasitic" relation to) foodstuff. We eat only at the expense (on the tab) of another who is our host” (Serres quoted in Bennet 2010, 134). Sensors can in this scheme conceived as digital parasites that extract data through technological infrastructures from the biological sphere into the digital realm. Recently questions were raised concerning this continuous and immersive collection of data on animals. Anat Pick for instance wonders whether animals have a right for privacy which she defines as “the recognition of another’s separate existence at the moment of its impending infringement” (2015). Although animals might not be aware that they are rendered permanently visible, enabling animals some form of privacy would create moments of separated existence without human interference which should be a right for every living being. The animal rights philosopher Peter Singer argues that we need to treat animals on basis of equality: the ethical treatment of others should not depend on intelligence, moral capacity, physical strength or similar matters. Rather the amount of consideration to satisfy the needs and interests of others requires us to extend equality to the consideration of animals as well (Singer 1976, 5).
Movement ecologist aims to answer these concerns by only conducting research when the expected beneficial effects far outweighs the potential consequences for animals and the results have at the same time the most informative value as possible (ICARUS 2019). In other words movement ecology approaches data collection on animals through a cost-benefits lens and thereby minimizing the impact on animals while maximizing the possible amount of data being collected. In many cases this approach seems justified because most research that is being conducted on animals is done to preserve the biosphere and is therefore directly or
indirectly beneficial for these animals. But it is often hard to decide until which point research
on animals is done for the improvement of animal lives and when research is done for other reasons that don’t involve the welfare of animals.
Even with this in mind movement ecologist Roland Kays and Martin Wikelsi have announced “A golden age of animal tracking has begun which is expected to bring in the upcoming years an unprecedented level of exciting discoveries” (Kays et al. 2015, 1). As movement ecology is entering the age of big data with ongoing improvements in both data collection and processing the hopes are that our understanding of animals getting more comprehensive and complete. Sensors are technologies of distance that can make the lives of animals present and interpretable while at the same time transforming our understanding of what animals are. In the next sections I will explore in what animals are made present through digital technologies and what consequences this has for our understanding of animals.
2.4 Rethinking animals
Folklorist Boria Sax (2013) has stated that every real animal is in some way imaginary. What he means with this statement is that our understanding of animals is in some way constructed through trough artistic, mystic, legendary and literary traditions. Margo DeMello affirms the idea and notes that biology has its own forms of tradition that classify animals in particular ways that partly a matter of choice and open for revision (2012, 44-61). In this section I will explore how animals are being reimagined through digital technologies that make animals present in very different ways then we are used to.
The continuous, real-time collection of data on animals through biosensors is an incredible important development in movement ecology and the life sciences in general. So much so that efforts to mobilize, integrate and visualize data on animals are valued as contributions to discovery in their own right (Leonelli 2018, 1-2). Instead of movement ecologist going in the field to study the behavior of animals, track their movement and to make samples of their environments, they are now increasing replaced by biosensors that relay data to scientists in their lab which use this data for algorithmic analysis.
There have been predictions that big data is rendering traditional models of scientific methods obsolete by replacing hypothesis-testing with algorithmic pattern identification (Anderson 2008). Various authors on movement ecology note that increasing research that is focused on underlying environmental and ecological causes shifts towards the identification of recurring surface patterns (Benson 2016, 138; Kays et al 2015, 1). One advantage of using pattern identification as research method is that it enables the search for commonalities in surface patterns of motion across widely disparate domains. In this model the continent-spanning migrations of birds can be visualized in the same framework as the foraging flight of
a mosquito and it even allows comparison of an even broader class of bodies in motion beyond the living/non-living divide (Benson 2016, 141). In this way all animal movement can be mapped as a series of bodies in motion in a Cartesian space regardless of the nature of that body.
This new method of pattern recognition is further used as basis for predictive models for animal distributions. The data relayed through biosensors serves as the basis for animal modelling but because the information that they gathered often lacked contextual data the models that were simplified and decontextualized in order for the patterns to appear more realistic (Benson 2014, 39). As a result these models are very efficient in predicting animal movement but lack data to explain the evolutionary and ecological reasons that cause movement to occur and by abstracting away too much the simulations can make highly erroneous predictions. Because the increasing use of the models on animals there emerges a tendency to take the real animals to be just as simple as the simulations that are used to model them. This equation between animals and their models is reminiscent of the philosopher Jean Baudrillard’s idea of simulation. In modernity ‘the real’ is increasingly layered with signs and symbolic meaning that represents reality. At some point we become so reliant on these models that the simulation seems to precede reality; now it is the simulation that determines reality instead of the other way around (Baudrillard 1981, 1-2). The increasing use of simulations in movement ecology entails a danger that a scientific research strategy doesn’t become confused with an ontology concerning how animals actually are. In other words they can imitate nature but not explain it.
The use of digital sensors on animals bridges the distance between simulation and reality
even more. Sensors are not only collecting data but, through machine learning and adaptive
algorithms, are able to adapt their systems to the behavior of the animals they are attached to adjusting for instance their sampling frequency to the creatures movement behavior. These sensors develop a cybernetic relation with the animal in which the behavior of the animal functions as the input on which basis the sensor regulates its monitoring strategies. In this scheme the sensor is perceived as a proxy for the animal, through sensors in which tracked animals begin to resemble the operations of computational devices that constantly generate, collect and communicate data about themselves and their surroundings. Thus for instance a change in flight path of a bird is visualized as a calculative decision based on an analysis of physiological and environmental cues. Through sensors animals are reconfigured as cybernetic systems in which movement patterns becomes a series of calculation based adaptations.
With the continuous improvement of sensors in areas like spatial accuracy, temporal resolution and frequency of data sampling the consensus in movement ecology is that an equally increasingly process-relevant representation of animals can be generated (Kays et al 2015, 5). Although the uses of sensors causes a loss in quality because the biological processes that run through animals are being measured and quantified an increase in sampling
rates and spatial-temporal data would be able to compensate for this loss “With short enough
sampling intervals, the difference between discrete and continuous measurement may become irrelevant, inasmuch as all phenomena of interest to the biologist will take place at frequencies higher than those which can be effectively characterized given the sampling rate” (Benson 2016, 145). But this viewpoint has been criticized because it seems pointless to just increase
data collection rates if we don’t understand exactly what we are researching. Movement ecologists first need to draw attention to the subjective relationship between animals and their environments before building computational data collection systems and models\ (Mitchell & Powell 2012).
Even when a thorough and accurate representation of animal behavior can be generated through data collection there is still a problem of reductionism. In the beginning of this chapter I mentioned Thomas Nagel and his idea of animal consciousness in order to emphasize that animals have their own perceptual and affective experiences to themselves, others and their surroundings. By using sensors and other trackers on vastly diverse types of animals, their various embodiments can be captured as a single mode of informational processes and translated into standardized data quantities. On the one hand this allows sensors and the variables they measure to become generalizable across species. On the other hand the unique lifeworld of animals are transformed into generalizable forms of perception and experience that are both made look universal and totalizing (Haraway 1991, 181). George Canguilhem has describes this as a ‘universal milieu’ in which the perceptive experiences of organism are configured into the universal reality of science which establishes a version of the real that disqualifies all others (2008, 119-120). The universal milieu stands in direct opposition to the unique Umwelt’s of animals that von Uexküll has introduced. Through sensors and other digital technologies the singular and unique experiences of animals are converted into a single framework of quantified data.
In this chapter I have sought to understand how technologies of distance like digital enabled sensors allow humans to make the lives of animals present and interpretable. Through the influence of cybernetics both animals and machines were seen as self-regulating systems that operate through internal computational processing. With the rise of digital technologies animals can be increasingly described in quantitative units that are used for big data analysis that enables high-detailed representations of animals. In this section I have discussed a few consequences of using data-driven science and computational technologies for our understanding of animals. Pattern recognition and predictive models enabled researchers to find commonalities between moving bodies and to build simulations of animal movement. Two risks involved in using these methods are the generating of reductionist accounts of animal behavior by abstracting away contextual and evolutionarily factors and ontologizing the simulation by equating them with the actual animal. By equipping animals with sensors, they are becoming increasing equated with computational sensors, communicating their sensory experiences as informational exchanges
In the context of media studies and especially Ecomedia this chapter explored how digital enabled media mediates between humans and animals By attaching sensors on or inside the bodies of animals which allows for the subtraction of data ecologist continue the quantification of human life and culture into the field of nature. Just like biometric devices, and geolocation software inside smartphones and other wearables encode individuals and populations as data-centric spatial relations and systems (Barreneche 2012) so do movement ecologist reconfigure animal movement as a series of patterns on a virtual Cartesian grid. It further expands ecomedia research on the interlocking processes of digital objects and biological entities (Kember & Zylinska 2012) the increasingly hybridity between living
beings and technology. In the next chapter we will see how animals how further becoming entangled with digital technologies.
3. Animals reaching global scales
In the first chapter I looked at biosensors as technologies of distance that make present the lives of animal through digital computation devices in the form of datasets. Biosensors gave new form to animals as digital simulations while at the same time transforming our understanding of what animals are. In section 1.2 I mentioned the notion of data amplification (Crampton et al. 2012) to describe the phenomena that when datasets are linked and combined they enable far greater insights then when they are studied separately. In this chapter I will look at how digital technologies like sensors enable what I call animal amplification i.e. the linking of animals and datasets of animals through biosensors and other digital technologies that generates different knowledge then when animals are studied separately. In order to understand how animals are being amplified I turn to network and infrastructure studies which will allow me to understand how disparate entities can be reconfigured into a single system. In this chapter I continue research in new media on networks (Galloway & Thacker 2007; Mejias 2010), connectivity (Van Dijck 2013) and infrastructures (Star & Ruhleder 1996) These research areas explore how people, things and processes are assembled through information structures with enormous scope and complexity that allows for the circulation of data and other flows on massive scales. It further extends research on the environmental impacts of information infrastructures (Parks & Starosielski 2015) and the use of sensors as form of reimagining animals as technological enabled networks (Gabrys 2016). By the end of this chapter I have to have shown how animals are becoming integrated in global information infrastructures
3.1 Networks, Infrastructures and Media Ecologies
I will begin this chapter with the introduction of a few terms that will help elucidate the rest of this chapter, the first being Networks. Commonly used as a structural metaphor to describe relationships between entities or as dynamic systems of exchange, transformation and dissemination (Hayles 2016, 33). Networks have been used to interpret a whole range of phenomena, from communication technologies like the internet, the world wide web or social media platforms to disciplines like biology (genetics, neuroscience, ecology), mathematics (topology, graph theory) and cultural theory (language, market systems) (Galloway 2010, 283). In general networks can be understood as systems of interconnectivity that hold their parts in constant relation to each other. Networks are more than just an aggregation of parts but attain new forms of complexity through various levels of interconnectivity (ibid.). In graph theory a network is divided into nodes, edges and hubs: entities, links between entities and entities with many links connected to them. Normally three types of networks are identified, centralized and decentralized networks that have a single or a number of hubs with many edges and distributed networks in which all nodes have an equal amount of edges between them (see fig.4).
The second term I want to introduce integrates qualities of networks into robust systems called infrastructures. As the prefix ‘infra’ already implies, infrastructures are structures that underlie or support something more salient. They can be defined as widely shared socio-technical systems that are reliable, standardized, and widely accessible (Plantin et al. 2016, 2).
Although the term originally denoted permanent military structures, they now designate a wide range of structures like roads, tunnels electrical grids, sewers, telecommunication networks and bridges to name a few examples (Mattern 2013, 3; Parks 2015, 255). Their main characteristic is that they channel flows - of objects, people, services and data - and connect people and institutions across large scales of spaces and time. Infrastructures reside in the background of everyday life as ordinary or unremarkable, sacrificing their own visibility in the act of making something else appear (Peters 2015, 34). Although we fundamentally depend on infrastructures as the connective tissue and the circulatory systems of modernity we mainly notice them when they break down or fail (Edwards 2010, 8).
Fig.4 Centralized, decentralized, and distributed networks. © Baran (1964).
Infrastructures often start as centrally designed and controlled systems but over time parts of the structure are modified or extended and competing infrastructures arise that have their own set of standards and protocols (Plantin et al. 2016, 3). The consequence is that a series of heterogeneous local infrastructures emerge that are incompatible to each other’s systems making them incapable of channeling flows between these structures. At some point a need arises to link these different systems together into networks - dynamic systems of interconnectivity - by introducing gateways that enable interoperability between these heterogeneous infrastructures (Edwards 2010, 10-11). Through the introduction of gateways infrastructures can reach enormous scales. While at the same time, because they integrate many heterogeneous systems, infrastructures can rarely be designed, controlled or standardized in a top-down approach. Rather most infrastructures operates as decentralized or distributed networks, complex ecologies (Star & Ruhleder 1996) whose components must continually adapt to ongoing changes within the infrastructure.
Infrastructures enabled the circulation of various types of flows, people, commodities, energy etc. and with the emergence of the internet and the World Wide Web also digital media. A special term is developed to describe the distribution of data and other media content throughout distributed networks of computers and other digital technology enabled devices, namely Media infrastructures (Parks & Starosielski 2015, 1). These structures are situated sociotechnical systems that are designed and configured to support the distribution of digital media (ibid., 4). In Media infrastructures research focusses on the distribution of
digital media content and data, the technical properties and the social, political and cultural implications of these types of infrastructures. With over 7.4 billion people using the internet and the World Wide Web1 media infrastructures are now one of the most expansive networks in the world.
The final term I want to expand on in this section is the European interpretation of media ecology as already introduced in the introduction. Media ecology is influenced by the psychoanalyst Felix Guattari’s notion of the three ecologies “ecology should not be limited to the physical systems studied by environmental science but ought to include (at least) two other levels, namely a social ecology of social relations and a mental ecology of subjectivity” (Goddard & Parikka 2011, 8). Guattari describes three different ecological types or levels that are both interacting and interdependent to each other, media ecologists add a fourth ecology of technology and digital media. By adding this fourth layer media ecologist are able to investigate the dynamic processes that circulate between all ecologies, the materiality and immateriality of media object and their dependence and influence on physical, social and mental phenomena (Parikka 2012)
In this section I have introduced a few key terms that will allow me to understand the further entanglement between technologies and living beings, digital sensors and animals. In the next sections I will approach animals as they become integrated in networks and infrastructures that cross biological and technological domains. This allows for a new mode of understanding how animals interact with each other and their environment and enables ecologist to think the global dynamics of animal movements as an interconnected whole that can be observed, understood and affected by human actions. In order to elucidate how animals are integrated into networks and media infrastructures the next section will discusses a case study from movement ecology that enables a new mode of interconnection between animals on a global scale,
2.3 The future of movement ecology
In the past decade the Max Planck institute for Ornithology located at Radolfzell Germany, has been working on a project that gathers movement ecologists from all over the world together. The first phase of this project was begun in 2007 with the introduction of the Movebank database, the first global centralized data repository for movement ecology in the world. Movebank is a free online database of animal tracking data that helps researchers to manage, share, protect, analyze and archive their data (Wikelski & Kays 2019). The Movebank database uses a tool called Environmental Data Automated Track Annotation System (Env-DATA) that is able to link data on animal movement with information from global environmental datasets like weather models and satellite imagery. By aggragating data from many different investigators, studies, and species Movebank can generate high-detailed information on animal movement in relation to environmental conditions of animals and environmental processes all over the planet (ibid.).
The second phase is led by a different research project from the Max Planck institute called the International Cooperation for Animal Research Using Space (ICARUS 2019). In
1 https://www.itu.int/en/ITU-D/Statistics/Pages/facts/default.aspx
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the last years ICARUS has been building a remote sensing platform for earth observation and global monitoring of and with animals (ICARUS 2019). In August 2018 the project managed to install this receiver station on the International Space Station (ISS) thereby ushering in a new era for movement ecology research. Satellites have been used to track animal movement for decades; especially the Argos satellite has been mobilized primarily to track the movement of animals (Benson 2010). But a continuous problem with these older satellite models was that they could only receive data from very large sensor which are too heavy and big for most animals to carry (ICARUS 2019). The new ICARUS system on the other hand is able to receive data from sensors that are many times smaller than its predecessors and is able to transmit configuration commands to sensors on the ground.
The ICARUS project has also developed a new type of ‘micro-sensors’ that is able to transmit data to the receiver station eight hundred kilometers up in the air. These micro-sensors are equipped with Geolocators, accelerometers, magnetometers, along with temperature, pressure and humidity sensors that can collect high-detailed data on the animal being tracked. But the truly revolutionary characteristic of these sensors are their size. In order for the attachment of a sensor to an animal, a sensor can’t weigh more than five percent of the animal’s bodyweight otherwise the sensor can affect the behavior and changes of survival of the animal. ICARUS has managed to reduce the size from sixty five gram to five gram and strives to lower its weight further in the coming years (fig.5). Because of this reduction in size many animals can now for the first be tracked remotely enabling a whole new level of insight into the global movement patterns of animals. But even with this new micro-sensors almost seventy percent of all bird, sixty five percent of mammal and almost all amphibians and insect species still can’t be tracked (ibid.).
Fig.5. A Micro-sensors developed by the ICARUS project that weigh a mere five grams. © MPI f. Ornithology/ MaxCine
At the moment over thousand animals are equipped with these micro-sensors but the project hopes to eventually increase this number to over hundred thousand (Ibid.). Through the implementation of these micro-sensors ICARUS can gather information off animals living
everywhere on the planet, from elephants in Africa to fish in the Antarctic. The collected data from these sensors is then send to the remote sensing platform which relays it to the Movebank database. Together these technologies enable a dynamic flow of data from micro-sensors spread all over the world, all the way up to a satellite into space and finally stored in a virtual database ICARUS and the Mobebank database allow for a whole media ecology that crosses the biological worlds of animals, the technical level of sensors, computers and the internet and the social ecology of scientists, labs and the discourses and tools that they are using. As we will see in the next section this new media ecology is reconfiguring both the circulation and interactions between animals as well as our own understanding of these creatures.
3.3 Animals and Networks
Movebank and ICARUS are equipped with thousands of sensors, a remote platform station and a central database that are forming a new type of infrastructure for movement ecology. Between these digital systems runs a continuous flow of data that relays data to the database or send software updates to the sensors while at the same time the archived data can be accessed online and distributed over the web. The Max Planck institute has developed a particular type of infrastructure that I categorize under what I have earlier called a media infrastructure. Through the constant circulation of data from animals to the virtual realm of the web and then to researchers in lab a whole media ecology runs through this infrastructure that crosses biological, technical and social levels. In the following section I will look at the reconfiguration of animals in this media infrastructure that crosses all ecological levels and will show how through this system animals are being amplified in a new way.
The philosopher of ecology Timothy Morton has in the last year’s introduced two concepts that describe the ecological condition of our planet. The first term is called the mesh it designates the radical interconnectedness of all living and non-living things, consisting of infinite connections and infinitesimal differences. In the mesh everything is in constant relation to each other in continual feedback loops (Morton 2010, 30). The second term is hyperobject that refers to things that are immensely distributed in time and place, that happen during many decades, centuries or longer and all over earth (Morton 2010, 130). Hyperobjects like climate change, the biosphere and global warming are so vastly spread that that they transcend spatiotemporal specificity. Animals are deeply entangled with the ecological processes that run throughout the planet and being intrinsically connected to the global ecosystem that lacks spatial-temporal specificity.
From a media ecological perspective the mesh can’t be separated from the social, mental and technical ecologies that equally show deep levels of interconnection. Just like animals constantly exchange signs with each other in continual interactions so do sensors in networks ‘talk’ to each other to coordinate data sequencing, processing and transferal according to distinct algorithmic commands.. Media scholar José van Dijck has described these doubling of exchange in the context of social media platforms through the concept of connectivity. Actors connect with each other online through the sharing of content, communication with others and building communities. At the same time algorithms digitally code and formalize these relationships to build online human networks that make present the relationships with
each other (Van Dijck 2013, 16). From the perspective of connectivity the dynamic interactions of animals are formalized and coded as sequential relations which can then be communicated to other biosensors and the Movebank database and used for further computational analysis.
Biosensors reconfigure animals as nodes in networks that are at the same time highly distributed and centralized. They are distributed because all biosensors have a comparably equal amount of edges that connect them with other biosensors but at the same time they are part of a centralized network time because all biosensors relay their data equally to the remote sensing platform and from there to the Movebank database in which all nodes stand in direct relation to these two hubs. The network that ICARUS is building includes an enormous variety of species – from megafauna like elephants and rhinoceros to songbirds, fish and insect - each with their own particular sensing capabilities through which they experience their environment. Together they form “A massive multi-monitoring program would allow a quorum sensing of our planet, using a variety of species to tap into the diversity of senses that have evolved in different animal groups” (Kays et al 2015, 7). As animal-networks these biosensors can reveal interactions among and between species and with their environments through the various ways animals experience their environment. What was once a hyperobject that transcends spatiotemporal specificity can now made present and allow us to think globally.
Media and infrastructure scholar Paul Edwards has introduced the term Global Knowledge Infrastructure (GKI) to describe this making present of ecological processes on a global level. He defines these structures as sociotechnical system that collects data, models physical processes, tests theories, and ultimately generate a widely shared understanding of the planet as a whole (2010, 8). GKI aims at building well-functioning knowledge systems that can generate, share, and maintain specific knowledge about the natural world. One of the great merits of GKI is that it enables us to think globally: seeing the world as a knowable entity – a single, interconnected whole in which local forces can be understood as elements of a planetary order and global changes as being relevant to the tiny scales of local populations (ibid, 9). The ungraspable hyperobject becomes an entity that can be observed, understood, affected by human actions, debated in political processes, cared about, and managed
Movebank and ICARUS are building a new type of global knowledge infrastructure that gathers the dynamic web of animals interacting with each other and with their environments into a framework in which they can be thought globally. Through distributed sensor networks a variety of senses across taxonomic scales can be made present and conjoined through wireless systems into the centralized hub of the Movebank database. Through computation the planet becomes materialized as an object of management and programmability where natural events are seen to become analogous to computational processes. From there scientists can analyze the global biodiversity in real-time through the ongoing capturing of sensors.
3.4 Planetary computation
The media ecologist Neil Postman once wrote “[H]uman beings live in two different kinds of environments. One is the natural environment and consists of things like air, trees, rivers, and caterpillars. The other is the media environment, which consists of language, numbers,
