Analytical Decision Framework for Modeling and Assessing Marine Meiofaunal Biodiversity Change

Abstract Marine meiofauna are small but ecologically important organisms that support sediment processes, nutrient cycling, benthic food webs, and environmental assessment. Their short generation times, high abundance, close association with sediment conditions, and sensitivity to pollution, oxygen stress, organic enrichment, habitat alteration, and physical disturbance make them valuable indicators for biodiversity monitoring, environmental impact assessment, restoration evaluation, and ecosystem management. As meiofaunal research has expanded, the challenge has shifted from describing biodiversity patterns to choosing analytical and modeling workflows that support appropriate ecological inference, environmental assessment, and management interpretation. Studies based on abundance counts, molecular sequences, image-derived measurements, environmental gradients, or predictive models do not answer the same questions. When methods are selected without considering the research objective, available data, sampling design, assumptions, diagnostics, uncertainty, and validation needs, conclusions may be incomplete or misleading. Here, we synthesize analytical approaches used in marine meiofaunal biodiversity studies and develop a question-driven decision-support framework for analytical and modeling method selection. The framework links research and assessment objectives to data structures, assumptions, diagnostics, validation needs, uncertainty, and interpretation limits. Established approaches remain essential for biodiversity description, community comparison, disturbance assessment, and environmental-response analysis, whereas automated imaging, machine learning, explainable artificial intelligence (AI), causal reasoning, and network analysis add value only when matched to appropriate data and validation designs. This review provides practical guidance for building transparent, reproducible, and management-relevant workflows for meiofaunal biodiversity assessment in an era of complex ecological data and AI-supported analysis.