Assessing applicability of the iNaturalist and GBIF citizen science platforms for investigating abnormal colouration in elasmobranchs

Technical Summary: Assessing Applicability of iNaturalist and GBIF for Investigating Abnormal Colouration in Elasmobranchs

Problem Statement
Global trends indicate increasing extinction risks for chondrichthyan species (sharks, rays, and chimeras). While overfishing impacts are well-documented, a significant knowledge gap remains regarding the prevalence, drivers, and ecological fitness implications of skin colour aberrations (e.g., hypomelanosis, hypermelanosis, xanthism, and piebaldism). Although such instances are recorded as rare, the authors posit that open-access citizen science image repositories may contain unexploited data capable of strengthening the knowledge base regarding the prevalence of these phenomena. However, uncertainty exists regarding the comparative applicability of major platforms—specifically iNaturalist and the Global Biodiversity Information Facility (GBIF)—for retrieving usable data for such research, particularly concerning licensing restrictions and data availability.

Methodology
The study utilized the smallspotted catshark (Scyliorhinus canicula) in the UK as a case study to evaluate the suitability of iNaturalist and GBIF repositories.

• Data Extraction: On April 1, 2026, target data were extracted from both databases using species-specific search terms.
• Filtering Criteria: The initial datasets were rigorously filtered to exclude images that were egg cases, had prohibitive body orientations, showed insufficient body coverage, exhibited extensive post-mortem deterioration, or possessed poor image quality.
• Comparative Analysis: The authors compared the volume of initial observations against the volume of "usable" images post-screening. They also analyzed temporal trends in image availability and investigated the overlap between the two platforms, specifically examining the impact of licensing restrictions on data transfer and usability.
• User Engagement: To address licensing confusion, the authors contacted several iNaturalist users to gauge their awareness of licensing restrictions and their willingness to have images used for research.

Key Results

• Data Volume: Initial datasets comprised 2,420 images from iNaturalist and 1,557 from GBIF. After filtering, the usable datasets were 669 images for iNaturalist and 509 for GBIF.
• Temporal Trends: Both platforms exhibited similar temporal trends, with marked increases in relevant observations in 2019 and 2022, and a downturn in 2021 attributed to global activity restrictions during the COVID-19 pandemic.
• Platform Overlap and Licensing: Despite the larger initial volume on iNaturalist, the number of usable images post-screening was broadly comparable between the two platforms. A significant finding was that 75% (48 of 64) of iNaturalist observations displaying potential skin colour aberrations were also present in the GBIF dataset.
• Licensing Confusion: The discrepancy in total image availability versus usable data was largely attributed to licensing restrictions. GBIF hosts only research-grade observations with free licensing (CC0, CC BY, or CC BY-NC), whereas iNaturalist contains observations with various licensing constraints. The authors found that many iNaturalist users were unaware of these restrictions despite available guidance, and confusion even extended to platform administrators regarding whether observations constitute intellectual property.

Key Contributions

• Validation of Data Sources: The study demonstrates that while iNaturalist generates higher initial volumes of observations, GBIF provides a comparable volume of usable images for research after filtering, primarily due to its strict adherence to free licensing.
• Identification of Barriers: The paper identifies licensing confusion as a primary barrier preventing the full utilization of citizen science data. It highlights that users often upload images without understanding the implications for scientific reuse.
• Actionable Recommendations: The authors propose three specific recommendations to enhance the research utility of citizen science datasets:
  1. Open Licensing: Users should select Creative Commons licenses (CC0, CC BY, or CC BY-NC) to ensure legal accessibility for research.
  2. Comprehensive Documentation: Users should capture multiple high-resolution images from various angles to facilitate accurate identification of species, life stage, and sex.
  3. Project Engagement: Observations should be submitted to relevant curated projects to accelerate expert verification and increase the likelihood of data integration into GBIF.

Significance and Claims
The paper claims that citizen science platforms hold immense potential for addressing research questions regarding biodiversity and ecological health, such as assessing the prevalence of skin colour aberrations. However, realizing this potential requires overcoming prohibitive factors, chiefly image licensing issues and the need for standardized recording protocols.

The authors assert that researchers planning specific image-use (e.g., for identifying injuries, strandings, or ecological health) should conduct rapid reviews of both iNaturalist and GBIF. They emphasize that GBIF offers inherent advantages regarding licensing clarity and the provision of citable Digital Object Identifiers (DOIs) for downloaded datasets, which improves transparency and research tracking.

Ultimately, the study advocates for alignment with the FAIR (Findable, Accessible, Interoperable, Reusable) Guiding Principles. It calls for a collaborative effort where platforms and users work together to ensure that the accelerating volume of biodiversity image-sharing, driven by ubiquitous smartphone use, translates into vital, legally accessible data for ecological research, conservation monitoring, and species management. The paper does not claim to have solved the licensing issue but rather highlights the confusion and proposes a pathway for users and researchers to navigate it effectively.