In the context of the biodiversity and climate crises, reliably documenting past and current species distributions is of paramount importance for deciphering the main drivers of species occurrences and range shifts and forecasting those under various global change scenarios. For that purpose, species observation records are essential and, according to the FAIR data principles, should be shared with a broad community of researchers and other stakeholders. Various databases have been created to compile and centralize information about biodiversity in recent years, among which the Ocean Biodiversity Information System (OBIS), dedicated to the marine realm, and the Global Biodiversity Information Facility (GBIF) are the most renowned ones. Here we evaluated how 38 selected “true mangrove” species are represented in those databases and assessed the quality and reliability of the information on geographical location of those observations. While OBIS and GBIF are extremely valuable databases, they still contain erroneous information, highlighting the need for closer communication among scientific experts and database managers together with the implementation of automated validation processes (e.g., using ecosystem distribution maps) to improve the data curation and data quality assurance processes. Further, we showed clear data deficiencies in many regions, including biodiversity hotspots. Many valuable observations are either hidden in publications or in private repositories but not shared with the global community, a practice that should change for the benefit of mangrove conservation and management. We encourage mangrove researchers to be proactive in correcting those data deficiencies by systematically submitting their observations, following the FAIR data principles.
Cheung C.Y. and Helfer V. (2025) A critical appraisal of occurrence data in OBIS and GBIF databases: a case study on true mangrove species. Bulletin of Marine Science 101(3): 1449-1474. 10.5343/bms.2024.0022
@article{Cheung2025,
Title = {A critical appraisal of occurrence data in OBIS and GBIF databases: a case study on true mangrove species},
Author = {Cheung, Cheuk Yiu and Helfer, Véronique},
Editor = {},
Journal = {Bulletin of Marine Science},
Year = {2025},
Pages = {1449-1474},
Volume = {101},
Doi = {10.5343/bms.2024.0022},
Abstract = {In the context of the biodiversity and climate crises, reliably documenting past and current species distributions is of paramount importance for deciphering the main drivers of species occurrences and range shifts and forecasting those under various global change scenarios. For that purpose, species observation records are essential and, according to the FAIR data principles, should be shared with a broad community of researchers and other stakeholders. Various databases have been created to compile and centralize information about biodiversity in recent years, among which the Ocean Biodiversity Information System (OBIS), dedicated to the marine realm, and the Global Biodiversity Information Facility (GBIF) are the most renowned ones. Here we evaluated how 38 selected “true mangrove” species are represented in those databases and assessed the quality and reliability of the information on geographical location of those observations. While OBIS and GBIF are extremely valuable databases, they still contain erroneous information, highlighting the need for closer communication among scientific experts and database managers together with the implementation of automated validation processes (e.g., using ecosystem distribution maps) to improve the data curation and data quality assurance processes. Further, we showed clear data deficiencies in many regions, including biodiversity hotspots. Many valuable observations are either hidden in publications or in private repositories but not shared with the global community, a practice that should change for the benefit of mangrove conservation and management. We encourage mangrove researchers to be proactive in correcting those data deficiencies by systematically submitting their observations, following the FAIR data principles.},
}
TY - JOUR
AU - Cheung, Cheuk Yiu
AU - Helfer, Véronique
TI - A critical appraisal of occurrence data in OBIS and GBIF databases: a case study on true mangrove species
T2 - Bulletin of Marine Science
PY - 2025
SP - 1449-1474
VL - 101
DO - 10.5343/bms.2024.0022
AB - In the context of the biodiversity and climate crises, reliably documenting past and current species distributions is of paramount importance for deciphering the main drivers of species occurrences and range shifts and forecasting those under various global change scenarios. For that purpose, species observation records are essential and, according to the FAIR data principles, should be shared with a broad community of researchers and other stakeholders. Various databases have been created to compile and centralize information about biodiversity in recent years, among which the Ocean Biodiversity Information System (OBIS), dedicated to the marine realm, and the Global Biodiversity Information Facility (GBIF) are the most renowned ones. Here we evaluated how 38 selected “true mangrove” species are represented in those databases and assessed the quality and reliability of the information on geographical location of those observations. While OBIS and GBIF are extremely valuable databases, they still contain erroneous information, highlighting the need for closer communication among scientific experts and database managers together with the implementation of automated validation processes (e.g., using ecosystem distribution maps) to improve the data curation and data quality assurance processes. Further, we showed clear data deficiencies in many regions, including biodiversity hotspots. Many valuable observations are either hidden in publications or in private repositories but not shared with the global community, a practice that should change for the benefit of mangrove conservation and management. We encourage mangrove researchers to be proactive in correcting those data deficiencies by systematically submitting their observations, following the FAIR data principles.
ER -
