Seeing like a conservationist: How in/visibilities generated by geospatial technologies are used to piece together conservation landscapes

Keywords: Protected areas; wildlife corridors; geospatial data; satellite imagery; Kenya; Tanzania
Session Type: Virtual Paper Abstract
Day: Friday
Session Start / End Time: 2/25/2022 11:20 AM (Eastern Time (US & Canada)) - 2/25/2022 12:40 PM (Eastern Time (US & Canada))
Room: Virtual 49

Abstract

Protected areas remain one of the most important spatial units of biodiversity conservation. Yet, there is growing awareness of the challenges that face disconnected and isolated populations of wildlife in ‘biogeographical islands’ of protected areas. Within these spaces, resource competition, disease transmission and inbreeding can all lower the survivability of wildlife. New global initiatives, such as 30X30, aim to solve this problem by improving connectivity between protected areas, piecing them together to create larger, intact conservation landscapes.

Geospatial technologies now play a vital role in the piecing together of conservation landscapes. For example, satellites are used to track animal movements and generate images of land cover between protected areas, providing decisionmakers with information used to map and acquire land for new wildlife corridors between protected areas. Nevertheless, there are gaps and omissions in the geospatial data used to produce connected landscapes. These include information about seasonal grazing areas for livestock, sites of cultural and spiritual significance, and other meanings and relations ascribed to land by local populations.

In this paper, we rely on research carried out in two countries that have prioritised the creation of connected conservation landscapes: Kenya and Tanzania. Specifically, we examine what is made visible and rendered invisible by the geospatial technologies being used by conservationists, including government authorities, conservation organizations, and scientific researchers. We suggest relying on geospatial data that fails to ‘see’ certain landscape attributes risks reproducing and further embedding forms of exclusion and inequality in conservation landscapes.