There are many techniques to deal with uncertainty when modeling data. However, there are many forms of uncertainty that cannot be dealt with mathematically that have to be taken into account when designing a biodiversity monitoring system. Some of these can be minimized by careful planning and quality control, but others have to be investigated during monitoring, and the scale and methods adjusted when necessary to meet objectives. Sources of uncertainty include uncertainty about stakeholders, who will monitor, what to sample, where to sample, causal relationships, species identifications, detectability, distributions, relationships with remote sensing, biotic concordance, complementarity, validity of stratification, and data quality and management. Failure to take into account any of these sources of uncertainty about how the data will be used can make monitoring nothing more than monitoring for the sake of monitoring, and I make recommendations as to how to reduce uncertainties. Some form of standardization is necessary, despite the multiple sources of uncertainty, and experience from RAPELD and other monitoring schemes indicates that spatial standardization is viable and helps reduce many sources of uncertainty.
