Abstract:

Background: Amid the accelerating global biodiversity crisis driven by human activities, precise monitoring and assessment of species distributions and population dynamics have become urgent priorities for conservation. Traditional survey methods often suffer from imperfect detection, leading to biased estimates of occupancy and hindering effective management decisions. The advent of big data offers opportunities for integrating diverse sources, yet challenges in handling heterogeneity and observational biases persist. Hierarchical occupancy models, by explicitly separating ecological processes (true occupancy) from observation processes (detection probability), provide a robust statistical framework to obtain unbiased inferences and have emerged as a powerful tool in biodiversity monitoring. 

Main Content: This paper reviews the theoretical foundations of hierarchical occupancy models, including the classic single-season framework and key extensions such as multi-season (dynamic) models for quantifying colonization, extinction, and temporal trends, as well as multispecies (community) models that harness interspecific correlations to enhance inferential precision. We highlight their core advantages: correcting for false negatives through explicit detection probability estimation, flexibly integrating heterogeneous multi-source data, and generating interpretable, auditable ecological indicators for biodiversity assessment. However, practical applications face several challenges, including data quality and heterogeneity issues, violations of key assumptions (e.g., independence of observations, population closure within seasons, absence of false positives), potential constraints on the biological interpretability of parameters, high computational demands for complex models and large datasets, and difficulties in communicating results to non-specialists and policymakers. Corresponding mitigation strategies are discussed, such as standardized data preprocessing, rigorous assumption validation, interdisciplinary collaboration, algorithmic optimization, and enhanced science-policy translation. 

Conclusion: Hierarchical occupancy models significantly advance the scientific rigor and reliability of biodiversity monitoring and evaluation by addressing imperfect detection and enabling integrative analyses. Moving forward, continued methodological innovations, fusion with emerging data types and technologies, deeper cross-disciplinary integration, and efforts toward standardization and broader application will further strengthen their role in supporting evidence-based conservation in the face of ongoing global change.

Key words: hierarchical occupancy model, biodiversity monitoring, imperfect detection, population dynamics, systematic conservation planning, multi-source data integration