Improving Statistical Sampling and Vegetation Monitoring

Project purpose

The purpose of these projects was to improve ways of describing the status and spatial and temporal trends of natural vegetation communities on conservation lands in central Orange County and San Diego County, California. The projects developed and tested monitoring approaches that reliably separate natural variation from trend, and they provide exemplars of how to develop a monitoring program from initial pilot studies, variance components analysis, and power analyses.

Project background

In 1991, the California Department of Fish and Wildlife established the Natural Community Conservation Planning (NCCP) program. The NCCP is a collaborative and broad ecosystem conservation approach for protecting lands throughout California. This IEMM research involves assessing and improving the monitoring of the following selected NCCP land areas in San Diego and Orange counties.

In 2004, The Nature Conservancy (TNC) Conservation Easement Working Group called for improved monitoring of conservation easements. As part of that effort, this research evaluated the precision and accuracy of different sampling designs and field protocols for monitoring vegetation communities in TNC’s Irvine Ranch open space easement areas in central Orange County from 2007 to 2010.

In addition, the Nature Reserve of Orange County was similarly investigated for this project. These analyses focused primarily on coastal sage scrub, chaparral, and grassland communities. 

In San Diego County, the Multiple Species Conservation Program (MSCP) serves as a large network of acquired and permanently protected areas. These connected landscapes have been scientifically and logistically challenging to monitor, as well as costly.

To address these challenges, this study focused primarily on evaluating the cost and accuracy of different sampling designs and field protocols for monitoring coastal sage scrub and chaparral vegetation communities in MSCP areas.

Research methods

To develop recommendations for optimizing monitoring of NCCP lands in San Diego and Orange counties, we analyzed  three major sources of variation: temporal (interannual), spatial and methodological. Spatial variation included vegetation community, site and plot levels. Methodological variation included two levels: protocol (quadrat vs. point intercept) and team.

Several suites of response variables were also analyzed, including species richness, cover of major functional groups (e.g. native shrubs, non-native forbs), and several example species from each functional group.

Research results

Given the geographic scale of this project, and the separate stakeholders involved in both Orange County and San Diego County, we combined some our high-quality data to improve our analyses for shared metrics. We then separated it out for site-specific reporting and recommendations.

Using an adaptive process, we evaluated potential monitoring strategies for precision and cost effectiveness, developed a training program for field staff, quantified variability in the system and fine-tuned the sampling strategy based on our results.

Specific methods, findings, and recommendations for the different conservation areas can be found in our project reports.

Why this research matters

Conservation easements and land acquisitions have become an important tool for protecting natural resources in the United States. For conservation organizations and government agencies alike, monitoring to detect ecological change is an important component of fulfilling the purpose of these land protections.

However, developing effective monitoring programs for conservation plans is scientifically and logistically challenging. The IEMM has been involved in numerous projects with the aim of improving vegetation monitoring methods by refining field protocols, reducing inter-observer variability, and reducing cost.

These projects address many of the fundamental questions surrounding the selection of both response designs and sampling designs and provide a foundation for long-term monitoring. They are also powerful case studies in the value of using advanced statistical techniques to develop and refine cost effective monitoring programs.

Project reports: