Alternative study designs and nonparametric statistical methods for adaptive management studies of invasive plants

• Published in: Invasive plant science and management 2024-09, Vol.17 (3), p.157-171
• Main Authors: McNair, James N., Frobish, Daniel, Rice, Emma K., Thum, Ryan A.
• Format: Article
• Language: English
• Subjects: Adaptive management; Agricultural land; Aquatic habitats; Aquatic plants; Environmental restoration; Field tests; Habitats; Herbicides; Invasive plants; Management methods; Nonparametric statistics; Project management; Review; Statistical analysis; Statistical methods; Statistics; Technical information; Variance analysis
• ISSN: 1939-7291; 1939-747X
• DOI: 10.1017/inp.2024.17

Adaptive management studies of invasive plants on non-agricultural lands typically employ an empirical approach based on designed field experiments that permit rigorous statistical analysis of results to quantify outcomes and assess the efficacy of management practices. When habitat restoration is the primary goal of a project, traditional plot-based study designs (e.g., the randomized complete-block design) are sometimes infeasible (this is often true in aquatic habitats) or inappropriate (e.g., when the goal is to assess effects of management practices on survival or resprouting of individual plants, such as trees or shrubs). Moreover, the assumptions of distribution-specific parametric statistical methods such as ANOVA often cannot be convincingly verified or are clearly untenable when properly assessed. For these reasons, it is worthwhile to be aware of alternative study designs that do not employ plots as experimental units and nonparametric statistical methods that require only weak distributional assumptions. The purpose of this paper is to review several of these alternative study designs and nonparametric statistical methods that we have found useful in our own studies of invasive aquatic and terrestrial plants. We motivate each statistical method by a research question it is well suited to answer, provide corresponding references to the statistical literature, and identify at least one R function that implements the method. In the Supplementary Material, we present additional technical information about the statistical methods, numerical examples with data, and a set of complete R programs to illustrate application of the statistical methods.