Beyond spatial bias: understanding the colonial legacies and contemporary social forces shaping biodiversity data

Abstract With over 2 billion observations of species from across the earth, the Global Biodiversity Information Facility (GBIF) has become an influential repository of information for not only ecologists, but also conservation scientists and policy makers. Yet biodiversity data reveal more than species distributions. They reflect colonial histories, economic inequities, and cultural differences. This article argues for moving beyond ideas of ‘spatial bias’ in conservation science to highlight the colonial legacies and contemporary social forces that shape biodiversity data. Drawing on interviews with the GBIF staff and data publishers as well as critical mapping of GBIF data patterns, we visualize and analyze how and why colonial patterns persist and shift in this global biodiversity data. To explain diverse trajectories of postcolonial production of data, we identify three social forces–colonial legacies of collecting, infrastructures of international development, and contemporary data cultures–that structure biodiversity observation distributions and data ownership and availability. Together, these findings demonstrate how, even in the age of open access, colonial and postcolonial practices and politics continue to shape what we know about the natural world.

Authors

Institutions

Publication Details

Journal
Environmental Research Letters
Published
2025-05-09
DOI
https://doi.org/10.1088/1748-9326/add6b6
Citations
4
Primary Topic
Conservation, Biodiversity, and Resource Management
Type
article
Field-Weighted Citation Impact
4.42
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Beyond spatial bias: understanding the colonial legacies and contemporary social forces shaping biodiversity data

Hilary Oliva Faxon, Melissa Chapman
4 citations
Environmental Research Letters
Conservation, Biodiversity, and Resource Management
4.42
article

Beyond spatial bias: understanding the colonial legacies and contemporary social forces shaping biodiversity data

Hilary Oliva Faxon, Melissa Chapman
article en
4 citations

Abstract

Abstract With over 2 billion observations of species from across the earth, the Global Biodiversity Information Facility (GBIF) has become an influential repository of information for not only ecologists, but also conservation scientists and policy makers. Yet biodiversity data reveal more than species distributions. They reflect colonial histories, economic inequities, and cultural differences. This article argues for moving beyond ideas of ‘spatial bias’ in conservation science to highlight the colonial legacies and contemporary social forces that shape biodiversity data. Drawing on interviews with the GBIF staff and data publishers as well as critical mapping of GBIF data patterns, we visualize and analyze how and why colonial patterns persist and shift in this global biodiversity data. To explain diverse trajectories of postcolonial production of data, we identify three social forces–colonial legacies of collecting, infrastructures of international development, and contemporary data cultures–that structure biodiversity observation distributions and data ownership and availability. Together, these findings demonstrate how, even in the age of open access, colonial and postcolonial practices and politics continue to shape what we know about the natural world.

Environmental Research LettersVol. 20(6)
ETH Zurich (CH), University of Montana (US)
Openalex Percentile: Top 6%
Conservation, Biodiversity, and Resource Management
4.42
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.