Summary
This systematic review examines the current state of multimodal data integration for plant biodiversity analysis and prediction, evaluating 12 major biodiversity platforms and assessing their contributions to conservation efforts. The authors synthesise advances in biodiversity informatics, Darwin Core standardisation, Species Distribution Models, and machine learning applications, whilst identifying persistent challenges in data interoperability, bias correction, and remote sensing integration. The findings emphasise the critical need for harmonised data frameworks to strengthen predictive modelling and inform global conservation policy aligned with CBD and UN SDG 15.
UK applicability
The review's findings on data standardisation, interoperability frameworks, and biodiversity monitoring strategies are directly applicable to UK conservation policy and practice, particularly for meeting domestic and international biodiversity targets. UK-based environmental monitoring programmes and conservation bodies could benefit from adopting the harmonised data integration approaches and addressing the identified challenges in spatial-temporal bias correction.
Key measures
Biodiversity platform capabilities; data interoperability; species occurrence data; trait data; taxonomic checklists; environmental variables; remote sensing integration; spatial and temporal bias assessment
Outcomes reported
The review identifies and evaluates key open-access biodiversity data sources, integration methodologies, and computational tools (including Species Distribution Models and machine learning) for predicting changes in plant biodiversity. It assesses data quality, interoperability challenges, and spatial-temporal biases across 12 major biodiversity platforms.
Topic tags
Dig deeper with Pulse AI.
Pulse AI has read the whole catalogue. Ask about this record, its theme, or how the findings apply to UK farming and policy — every answer cites the underlying studies.