Sampling bias obscures biodiversity patterns, reveals data gaps in priority conservation areas: a call for improved documentation

Abstract

Where and how species are sampled can shape biodiversity knowledge, spatial patterns, and data-driven conservation. In megadiverse regions of the Global South, sampling remains uneven, and available data often lack synthesis needed to assess spatial heterogeneity in recorded biodiversity for conservation planning. This limitation is also evident in areas designated for biodiversity conservation. We examine this in the Philippines, where long-term research effort and policy development have supported expansion of conservation-relevant areas (hereafter "Philippine conservation-relevant areas" or PCAs). Using over a century of digitally accessible species occurrence records compiled and manually curated, we analyzed the country-wide spatial distribution of observed amphibian and squamate reptile species richness. We further tested whether observed richness across PCAs is associated with area, topographic relief, and sampling effort, and whether these relationships differ between protected and conserved areas (with management or governance schemes) and key biodiversity areas (unmanaged but priority areas for conservation). Results show strong spatial heterogeneity in occurrence records, with preserved specimens comprising 92% of all records and strongly influencing observed richness patterns. Citizen-science data tend to reinforce already well-sampled regions, whereas literature-derived records provide additional coverage in comparatively undersampled areas. PCAs show variable representation in the dataset, with observed richness and sampling effort generally increasing with area. However, this richness-area relationship weakens with increasing topographic relief. Differences in predictor-richness relationships between PCA types are also evident, with stronger area and sampling effects in protected and conserved areas and a relatively stronger elevational effect in key biodiversity areas. Notably, several high-richness areas occur outside formally designated protected areas. These results highlight uneven biodiversity data coverage and suggest that observed richness patterns may reflect both sampling structure and spatial variation. We emphasize integrating multiple biodiversity data sources and identifying undersampled regions to improve future biodiversity assessments.