Successful adaptive management relies on a flexible and self-correcting feedback loop supported by high-quality data from an ecological monitoring program. However, the high degree of variation in natural systems makes effective and efficient monitoring both difficult and expensive. We demonstrate two complimentary quantitative methods that can be used to strike a balance between statistical power and cost during the development phase of a long-term vegetation community monitoring program. We evaluated several monitoring strategies using variance decomposition and power analysis to ensure precise, high-quality data relevant to management concerns is collected while balancing costs with statistical power.
Results/Conclusions
We present the variance decomposition of cover estimates for three plant functional groups as well as species richness in three upland vegetation communities common in southern California. The variance decomposition shows that spatial variation is pronounced across response variables and vegetation communities. The power analyses quantify the cost savings that are realized if the monitoring program is designed to control for spatial variability. The power analyses also demonstrate how required effort depends on vegetation community, response variable, and field protocols. We present broader implications by integrating these tools into a process diagram for the development of long-term monitoring programs. Although every system and monitoring program is unique, this study provides a clear example of successful program development, and provides universal quantitative solutions.