In a previous post, I made the observation that Digital Conservation tends to speak to conservation practitioners and technologists. Digital Conservation was an early multi-disciplinary approach to human-nature relations shaped by digital technologies. Unlike Media Ecologies, its concerns appear to have grown out of much more practical technical and technological changes happening in the field. Digital Conservation was conceptually shaped by and spoke to circles of practitioners seeking to better survey, track, surveil and understand animal behaviour and human-animal encounters using new technologies, for the ends of conservation practice.
Digital Conservation may thus be understood as the naming and cohering of a contemporary subset of ecology, itself an applied science. Where Digital Conservation was welcoming toward social science perspectives – not without teething problems of finding balance and establishing common/foundational languages and styles – the concept was and remains geared toward the applied and practical, and grounded in the empirical, technical innards of fieldwork. The term Conservation also presents an interesting choice, delineating a focus on biodiversity concerns over more general environment-related issues, and unfortunately the skew toward charismatic/obvious fauna is replicated here as with other human-nature initiatives and thinking.
In thinking about what the applied aspects of the Digital Anthropocene might cover, there would be an obvious need to engage with Digital Conservation literature and its precedents. The latter includes fairly extensive, more technical literature describing on-ground projects. These initiatives include, in a somewhat linear order, e.g. websites, tracking devices, camera traps/nestcams, apps, drones, mapping technologies, natural language generation and machine learning, image identification technologies and AI, among others. A ‘screenshot’ of some earlier technological interventions might be found in this paper: August, T. et al. 2015. Emerging technologies for biological recording. Biological Journal of the Linnean Society 115: 731–749.
An important aside emerging from this paper (but not only, see also e.g. Silvertown et al 2013) is with regards to citizen science: It seems to me that citizen science projects experienced a small resurgence for the short-ish period of time bracketing the publication year. Many of these projects were led by organisations or institutions, with wider publics contributing to a set agenda and design. I would contend that this citizen science “revival” (at least in the form described) has more or less flattened out not least due to challenges around logistics and reliability. There remains plenty of interesting thinking to be done about the ideological incompatibilities in the application of ecological sciences in relation to the (non)inclusion of and non-collaboration with publics in science. I am thinking here for example about the language and ideas of technological sensors and humans as sensors; the narrow role ‘citizens’ were/are expected to take on within these projects; and newer collaborative and community-led science-based initiatives. Which leads me to think about what Shapin and Schaffer discuss in Leviathan and the air-pump and Latour’s We have never been modern.
Some more interesting and newer technically-led iterations at the forefront of environmental technologies: Unseen Empire (that claims to be the largest camera trapping initiative undertaken on a species), WildLabs, and Microsoft’s AI for Earth (which seems to be an ambitiously expanded version of an earlier iteration that targeted mainly biodiversity conservation under the rubric of technologies for conservation, if memory serves me right). A conceptually slightly different but I think important project is Hounds of Actaeon, a project that seeks to expose wildlife trade on social media. Digitally-facilitated wildlife trade is an urgent and highly disturbing practice that no doubt calls for new thinking and technological initiatives. Suffice to say, technological field projects (and attendant publications) are now fairly ubiquitous, necessary in many parts and at the very least, well-intentioned.
To conceptualise the Digital Anthropocene meaningfully (also toward building a coherent syllabus), my sense is that the work falling under this ‘applied’ category that we pick to engage with must fulfil certain criteria. The work has to go beyond the purely technical, purely natural or purely human, to take instead a holistic, critical and ideally relational view of the human-technology-nature triad. It needs to grapple with implications of/for use, for example in terms of technological fix thinking, techno-political participation or human-nature relationships, and built on a respectful understanding of all human and non-human actors within the assemblage. It should also beyond just the field of biodiversity conservation, to include the growing suit of ‘environmental technologies’; though this phrase creates much definitional trickiness of its own. Nonetheless, on these criteria, some of the readings that come to mind include:
web/nest-cams: Chambers, Charlotte C. L. 2007. ‘Well it’s remote, I suppose, innit?’ The relational politics of bird-watching through the CCTV lens. Scottish Geographical Journal 123: 122–134.
tracking technologies: Benson, Etienne. 2010. Wired Wilderness: Technologies of Tracking and the Making of Modern Wildlife. Baltimore, MD: Johns Hopkins University Press.
a more recent and nifty post on the afterlives of wildlife tracking devices: Palmer, Alexandra. 2021. The afterlives of wildlife tracking devices. Digital Ecologies, 6 July 2021.
drones: Sandbrook, Chris. 2015. The social implications of using drones for biodiversity conservation. Ambio 44(Suppl 4): 636–647.
citizen sensing: Gabrys, Jennifer, Pritchard, Helen, and Barratt, Benjamin. 2016. Just good enough data: Figuring data citizenships through air pollution sensing and data stories. Big Data and Society 3:2.
an excellent overview: Adams, William M. 2017. Geographies of conservation II: Technology, surveillance and conservation by algorithm. Progress in Human Geography 43: 2.
Where I have not yet come across papers of, e.g. machine learning or AI for conservation, that meet the working conceptual demands I have proposed, recommendations are welcome.