Measuring succession: methods for establishing long-term vegetation monitoring sites

Abstract

Successional stages are often characterized by dominant plant species (species with the highest cover) and their effect on the structure and function of an area through time. However, the plant species determining the ecosystem properties and plant community dynamics may not be the dominant, especially when it is exotic. Understanding plant community dynamics in ecosystems that are uncharacterized and/or affected by invasive plant species requires a data-driven approach and proper placement of monitoring plots. To generate robust datasets on vegetation change through time, monitoring plot placement must consider the scale of ecological variation for both vegetation and soils and plots would ideally be replicated within similar ecological site types to quantify the consistency of successional transitions. We characterized soil and vegetation across and within seven floodplains affected by Russian olive (Elaeagnus angustifolia L.) along the Yellowstone River in southeastern Montana, USA. Using modern Classification and Regression Trees (CART) and multivariate net differentiation, we identified five distinct plant community types, or classes, characterized by their tertiary woody plant cover, not the primary species, Russian olive. Our findings indicate that Russian olive communities differ across space, and these riparian areas can be classified into distinct plant community types. Characterizing plant community types via this analytical approach should allow practitioners to modify management decisions and forecast succession within relevant plant communities.