Using fine-scale field data modelling for planning the management of invasions of Oenothera stucchii in coastal dune systems

Abstract

Invasive alien species risk assessment and adaptive management are often hindered by a lack of information for most species. This work aims at predicting the probability of successful establishment and invasion of Oenothera stucchii Soldano, a neophyte invasive species belonging to the sect. Oenothera subsect. Oenothera, in xerophilous grasslands of grey dunes. Based on fine-scale field data, we modelled O. stucchii presence/absence and abundance as a function of environmental factors, human disturbance, and attributes of the recipient community through a zero-inflated Poisson model. The invasion success of O. stucchii depended on a combination of factors which differed when considering either the patterns of occurrence (species presence/absence) or those of species abundance. While human-driven disturbance strongly influenced the probability of presence/absence of O. stucchii, patterns of abundance were mostly driven by a combination of environmental and biotic features. Attributes of the recipient community remarkably influenced both O. stucchii presence and abundance. Based on fine-scale field data, we determined the mechanisms which drive the spatial patterns of presence and abundance of O. stucchii in xerophilous grasslands and provided quantitative thresholds to identify the most susceptible areas of grey dune habitats prone to invasion, which combine human disturbance (distance from the nearest beach access), attributes of the resident community (resident vegetation cover and structure), and environmental disturbance (foredune ridge height). These results provide useful insights to be used to plan cost-effective measures to prevent O. stucchii establishment and spread in sandy coastal systems. Our model may also be applied to closely related congener species included in the subsect. Oenothera, sharing similar biological and ecological traits.