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Updated: Jun 17, 2026

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Monitoring Spatial Segregation in Surface Colonizing Microbial Populations
Published on: October 29, 2016
Anthropogenic accessibility and observer inequality dictate spatial biodiversity patterns in Malaysia
Saman Ghasemian Sorboni1, Sharareh Pourebrahim2,3, Jit Ern Chen4
1Department of Land, Environment, Agriculture and Forestry (TeSAF), University of Padova, Padova, Italy.
Scientific Reports
|June 15, 2026
Summary
Community science data for biodiversity monitoring is biased by human behavior and taxonomy. Addressing these biases is crucial for accurate macroecological inference and effective conservation strategies.
Area of Science:
- Ecology
- Conservation Biology
- Data Science
Background:
- Global biodiversity monitoring increasingly uses open-access community science data.
- Opportunistic data collection introduces complex biases that can distort macroecological inference.
Purpose of the Study:
- To disentangle how human behavior, infrastructure, and taxonomy shape perceived biodiversity patterns in Malaysia.
- To quantify observer inequality and taxonomic disproportionality in community science data.
- To map spatial inventory completeness and identify under-sampled regions and taxa.
Main Methods:
- Analysis of 336,042 research-grade iNaturalist records.
- Estimation of species richness using Chao2.
- Application of Zero-Inflated Negative Binomial Generalized Linear Mixed Models (GLMMs) and effort-corrected Generalized Additive Models (GAMs).
Main Results:
- Extreme observer inequality (Gini = 0.854) with data concentrated in urban centers.
- A three-way interaction showed "Power Users" access remote areas, while casual observers stay near roads.
- Significant taxonomic bias favoring Aves and Reptilia, under-sampling Insecta and Fungi.
- Perceived biodiversity deficits in rugged terrains are artifacts of human inaccessibility, not ecological absences.
Conclusions:
- Community science data biases (observer behavior, taxonomy, accessibility) significantly impact macroecological inference.
- Effort-corrected models reveal that topography and elevation effects are often artifacts of sampling effort.
- Targeted state funding and structured monitoring are essential to complement opportunistic data and address geographic and taxonomic shortfalls for conservation.
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