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talk · Friday 25 September · 11:10–⁠11:25 · SAL C

Reporting structure and sampling-bias correction in citizen-science bird data across Asian cities

Yeshan Qiu · From Standards to Implementation: Connecting Observation Data in Asia to GBIF Infrastructure

Recording time 3:16:58–3:33:47Open on Vimeo ↗

Summaries and key moments are machine-produced. Check the recording for the speaker’s exact words.

The short versionAbundant citizen-science records do not guarantee inference support; reporting structure can distort richness hotspots and urban-biodiversity relationships unless it is diagnosed and controlled.

Overview

What this was about

Yeshan Qiu, from the user side, examined how citizen-science reporting structure shapes what GBIF bird data can support in urban biodiversity analysis. Using about 2.6 million cleaned 2024 GBIF bird records across 13 Asian cities on 2 km grids, she characterised spatial support, environmental representation, inventory/observer support and effects on ecological inference. Reporting was uneven across and within cities, concentrated in accessible places (dense roads, near parks and water) and increasingly compressed in larger cities; standardising to 10 reporting events per grid shrank the apparent richness gap between high- and low-reporting grids from 2.9x to 1.6x and changed urban pattern-biodiversity relationships (road density effect from about -0.2 to -0.4).

Why it matters. Cross-city urban ecology increasingly relies on GBIF and eBird data; this work shows when conclusions are safe and argues for richer sampling-event metadata and fitness-for-use frameworks.

Key ideas

In the room

  • Observed occurrence patterns combine the ecological process and an observation/reporting process driven by where people go and report.
  • Framework of four gaps: spatial reporting support, environmental representation, inventory and observer support, and effect on ecological inference.
  • Data: GBIF bird occurrences from 2024 across 13 Asian cities, about 2.6 million records after cleaning, about 2 million within 2 km analysis grids; predictors cover accessibility, built form, vegetation, water and city-level descriptors.
  • Hong Kong, Mumbai, Seoul and Singapore cover nearly the entire city; Beijing, Dhaka and Shanghai are thin in space and time; within-city structure varies (diffuse, single core, multiple hotspots).
  • Sampled grids have denser roads and buildings and are closer to parks; this selectivity strengthens with city size, compressing reporting into a smaller footprint.
  • Parks are associated with more records, more observers and broader temporal coverage.
  • Standardising to 10 reporting events per grid reduced the apparent richness advantage of high-reporting grids from about 2.9x to about 1.6x.
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Transcript

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Programme abstract

Reporting structure and sampling-bias correction in citizen-science bird data across Asian cities

Yeshan Qiu

Citizen-science bird records are widely reused in urban biodiversity research through GBIF. Their value depends not only on record volume, but also on whether sampling effort, observer identity, checklist type, and observation locations are documented. Because these reporting features vary among cities, common bias-adjustment methods, including complete-checklist filtering, effort or observer covariates, occupancy models, spatial thinning, and accessibility-related corrections, may not be equally feasible.

We examine how reporting structure affects the diagnosis and correction of sampling bias in urban bird data. Using GBIF-mediated bird occurrence records from 2021 across 14 cities in Southeast, East, and South Asia, we compare record volume, checklist availability, observer participation, temporal continuity, and spatial concentration. We ask whether these reporting differences correspond to spatial bias patterns, and how biodiversity estimates and biodiversity-environment relationships change under different bias-adjustment choices.

For each city, we summarise reporting structure using record and checklist counts, unique-observer counts, observer dominance, temporal continuity, and spatial concentration. We explore spatial bias by modelling observation intensity against variables representing accessibility, repeated-site use, observer activity, and environmental-gradient coverage. We then compare biodiversity models fitted with effort covariates, observation-process proxies, and combined approaches.

The study identifies where standard correction strategies are feasible, where proxy-based approaches are needed, and where data should be limited to descriptive use. It links citizen-science data reuse to reporting standards and fitness-for-use discussions.

Submitted to the conference programme before the talk, so the talk itself may differ.