Presentation short abstract

Global biodiversity datasets are increasingly being used to characterize species distribution in Indigenous Peoples' Lands. Drawing on a case study in Bolivia, I show how such data-driven analyses can be affected by biases, and discuss the implications for the local communities in the studied areas.

Presentation long abstract

Emerging technologies and databanks enable easier access to biological data. However, these data are known to contain substantial biases, reflecting human patterns that go beyond technological limitations. Despite these well-known biases, several studies have used global biodiversity databases to characterize species distribution across Indigenous Peoples’ Lands.

I will demonstrate the influence of data biases on spatial biodiversity characterizations and discuss the implications for local communities. Therefore, I will present analyses of tree-species-richness distribution in Bolivia that considerably differ when using different databases. I relate these observations to spatial and taxonomic biases in the databases, and to the data’s institutional provenance.

I will discuss my findings along two lines. Firstly, the identified biases are known to relate to human processes. However, the datasets commonly do not provide clear information about the contexts of the data collection procedures. Therefore, analyses derived from these sources may unwittingly reflect and reproduce long-standing injustices.

Secondly, global biodiversity datasets are based on a Western paradigm of managing global resources, disregarding alternative understandings of human-nature relationships. Critically, with all simplifications required to merge data from different sources, the information provided by the datasets does not align with Indigenous holistic conceptions of the environment. The deep, complex knowledge systems underpinning Indigenous Peoples’ relations with nature are therefore disregarded and potentially obscured.

Through these discussions, I explore the socio-ecological and ethical implications of a broader integration of data-driven methods into conservation practice. The reflections are applicable to other evolving data-driven conservation approaches, highlighting their relevance for decision-making.