Field-based measurement tools to distinguish clonal Typha taxa and estimate biomass: a resource for conservation and restoration

Two species of clonal Typha [T. latifolia (native) and T. angustifolia (exotic)] hybridize to form the highly invasive, heterotic (high vigor) T. x glauca in North American wetlands leading to increased primary production, litter accumulation, and biodiversity loss. Conservation of T. latifolia has become critical as invasive Typha has overwhelmed wetlands. In the field, Typha taxa identification is difficult due to subtle differences in morphology, and molecular identification is often unfeasible for managers. Furthermore, improved methods to non-destructively estimate Typha biomass is imperative to enhance ecological impact assessments. To address field-based Typha ID limitations, our study developed a predictive model from 14 Typha characters in 7 northern Michigan wetlands to accurately distinguish Typha taxa (n = 33) via linear discriminant analysis (LDA) of molecularly identified specimens. In addition, our study developed a partial least squares regression (PLS) model to predict Typha biomass from field collected measurements (n = 75). Results indicate that two field measurements [Leaf Counts, Longest Leaf] can accurately differentiate the three Typha taxa and advanced-generation hybrids. The LDA model had a 100% correct prediction rate of T. latifolia. The selected PLS biomass prediction model (sqrt[Typha Dry Mass] similar to log[Ramet Area at 30 cm] + Inflorescence Presence + Total Ramet Height + sqrt[Organic Matter Depth]) improved upon existing simple linear regression (SLR) height-to-biomass predictions. The rapid field-based Typha identification and biomass assessment tools presented in this study advance targeted management for regional conservation of T. latifolia and ecological restoration of wetlands impacted by invasive Typha taxa.