Determining vegetation metric robustness to environmental and methodological variables

Land managers need reliable metrics for assessing the quality of restorations and natural areas and prioritizing management and conservation efforts. However, it can be difficult to select metrics that are robust to sampling methods and natural environmental differences among sites, while still providing relevant information regarding ecosystem changes or stressors. We collected herbaceous-layer vegetation data in wetlands and grasslands in four regions of the USA (the Midwest, subtropical Florida, arid southwest, and coastal New England) to determine if commonly used vegetation metrics (species richness, mean coefficient of conservatism [mean C], Floristic Quality Index [FQI], abundance-weighted mean C, and percent non-native species cover) were robust to environmental and methodological variables (region, site, observer, season, and year), and to determine adequate sample sizes for each metric. We constructed linear mixed effects models to determine the influence of these environmental and methodological variables on vegetation metrics and used metric accumulation curves to determine the effect of sample size on metric values. Species richness and FQI varied among regions, and year and observer effects were also highly supported in our models. Mean C was the metric most robust to sampling variables and stabilized at less sampling effort compared to other metrics. Assessment of mean C requires sampling a small number of quadrats (e.g. 20), but assessment of species richness or FQI requires more intensive sampling, particularly in species-rich sites. Based on our analysis, we recommend caution be used when comparing metric values among sites sampled in different regions, different years, or by different observers.