Global species richness estimates have not converged

Area of Science:

• Ecology and Biodiversity Science
• Conservation Biology
• Statistical Ecology

Background:

• Estimates of global species richness have been debated for over six decades.
• Current estimations suffer from significant uncertainty and logical inconsistencies.
• Lack of consensus hinders effective conservation strategies and biodiversity assessments.

Purpose of the Study:

• To analyze the persistent uncertainty in global species richness estimates.
• To identify factors contributing to the lack of convergence in these estimates.
• To propose adaptive learning methods for accelerating estimation convergence.

Main Methods:

• Review and meta-analysis of existing global species richness estimation studies.
• Statistical evaluation of uncertainty quantification in ecological models.
• Simulation of adaptive learning frameworks for ecological data assimilation.

Main Results:

• Demonstrated failure of global species richness estimates to converge after more than sixty years.
• Identified significant and persistent uncertainty across various estimation approaches.
• Highlighted logical inconsistencies within and between different species richness datasets.
• Showcased the potential of adaptive learning to improve estimation accuracy.

Conclusions:

• Current global species richness estimates are unreliable due to persistent uncertainty and inconsistencies.
• Adaptive learning methods, prioritizing uncertainty estimation, can accelerate convergence.
• Future research should focus on integrating adaptive learning to refine biodiversity assessments and inform conservation efforts.