LeRoy Rodgers | Geology Reports

USGS2019

Typha (cattail) invasion in North American wetlands: Biology, regional problems, impacts, ecosystem services, and management

Typha is an iconic wetland plant found worldwide. Hybridization and anthropogenic disturbances have resulted in large increases in Typha abundance in wetland ecosystems throughout North America at a cost to native floral and faunal biodiversity. As demonstrated by three regional case studies, Typha is capable of rapidly colonizing habitats and forming monodominant vegetation stands due to traits such as robust size, rapid growth rate, and rhizomatic expansion. Increased nutrient inputs into wetlands and altered hydrologic regimes are among the principal anthropogenic drivers of Typha invasion. Typha is associated with a wide range of negative ecological impacts to wetland and agricultural systems, but also is linked with a variety of ecosystem services such as bioremediation and provisioning of biomass, as well as an assortment of traditional cultural uses. Numerous physical, chemical, and hydrologic control methods are used to manage invasive Typha, but results are inconsistent and multiple methods and repeated treatments often are required. While this review focuses on invasive Typha in North America, the literature cited comes from research on Typha and other invasive species from around the world. As such, many of the underlying concepts in this review are relevant to invasive species in other wetland ecosystems worldwide.

Wetlands↗

USGS2019

Optimal spatial allocation of control effort to manage invasives in the face of imperfect detection and misclassification

Imperfect detection and misclassification errors are often ignored in the context of invasive species management. Here we present an approach that combines spatially explicit models and an optimization technique to design optimal search and destroy strategies based on noisy monitoring observations. We focus on two invasive plants, melaleuca ( Melaleuca quinquenervia ) and Old World climbing fern ( Lygodium microphyllum ), which continue to cause important damages to the Everglades ecosystem. We present a methodological framework that combines Hidden Markov Random Field (HMRF, initially developed for image analysis) and linear programming to optimally search for invasive species. A benefit of this approach is that it accounts for the spatial structure of the system by using a spatially explicit modeling approach (i.e. HMRF), and does not require repeated visits to model the probability of occurrence of species. We found on simulated cases that our approach can lead to substantial improvements in control efficiency when compared to state of the art model-free approaches. For example, in the case of the old world fern, simulations showed that the optimal strategy would allow managers to control up to 34% more sites than with model-free approaches that ignored misclassification and imperfect detection. For melaleuca it was possible to control up to 20% more sites. The vast increase in imagery data obtained from different sources (e.g. unmanned aerial systems, and satellite) provides great opportunities to improve management of natural resources by applying modern computational methods such as the one we present. Our approach can substantially increases the efficiency of invasive species control by accounting for imperfect detection, misclassification error and the spatial structure of the system. Our approach is applicable to other systems and problems, for example it could be applied to the control of plant pathogens, or optimal extraction of resources (e.g. minerals or biological resources).

Ecological Modelling↗