Invasive alien plants have impacted southern U.S. forests. This study focuses on quantifying current distribution, rate of dispersals, and economic losses caused by four major invaders — Chinese tallow (Triadica sebifera (L.) Small), Chinese Privet (Ligustrum sinense Lour.), European Privet (Ligustrum vulgare L.), and Japanese Honeysuckle (Lonicera japonica Thunb.). Using data from USDA Forest Service’s Forest Inventory and Analysis (FIA), National Oceanic and Atmospheric Administration (NOAA), and USGS Land Use and Land Cover (LULC), I first used logistic regressions to build occupation models for these four species. Second, I used VB.NET© (Microsoft, 2003) and Environment System Research Institute (ESRI) Arc View® 9.2 Geographical Information System (GIS) to model spatially explicit dispersal dynamics of these species. Finally, I evaluated economic losses associated with these invasions. The results of occupation models showed that landscape features, climatic conditions, forest and site conditions, forest management activities and disturbances, and forest ownership have statistically significant impacts on current distributions of all four species. The results of dispersal models showed that invasions of Chinese tallow, Chinese and European privets, and Japanese honeysuckle will be likely to appear on approximately 6.65 million acres of forest land in East Texas and Louisiana, 3.81 million acres in Mississippi and Alabama, and 12.55 million acres in Mississippi and Alabama, respectively, during the next 20 years. The estimated timber losses resulting from the invasion with no control for the next 20 years at 5% discount rate is 518 million dollars for Chinese tallow, 2.72 billion dollars for Chinese and European privets, and 11.05 billion dollars for Japanese honeysuckle. From an economic point of view, it would be most cost effective to execute high intensity control for these invasive species. Based on these findings, strategies for monitoring and mitigating these invasive species are proposed.

Among tools that are used to fill knowledge gaps on natural systems, ecological modelling has been widely applied during the last two decades. Ecological models are simple representations of a complex reality. They allow to highlight environmental drivers of species ecological niche and better understand species responses to environmental changes. However, applying models to Southern Ocean benthic organisms raises several methodological challenges. Species presence datasets are often aggregated in time and space nearby research stations or along main sailing routes. Data are often limited in number to correctly describe species occupied space and physiology. Finally, environmental datasets are not precise enough to accurately represent the complexity of marine habitats. Can we thus generate performant and accurate models at the scale of the Southern Ocean ?What are the limits of such approaches ?How could we improve methods to build more relevant models ?In this PhD thesis, three different model categories have been studied and their performance evaluated. (1) Mechanistic physiological models (Dynamic Energy Budget models, DEB) simulate how the abiotic environment influences individual metabolism and represent the species fundamental niche. (2) Species distribution models (SDMs) predict species distribution probability by studying the relationship between species presences and the environment. They represent the species realised niche. (3) Dispersal lagrangian models predict the drift of propagules in water masses. Results show that physiological models can be developed for marine Southern Ocean species to simulate the metabolic variations in link with the environment and predict population dynamics. However, more data are necessary to highlight detailed physiological contrasts between populations and to accurately evaluate models. Results obtained for SDMs suggest that models generated at the scale of the Southern Ocean and future simulations are not relevant, given the lack of data available to characterise species occupied space, the lack of precision and accuracy of future climate scenarios and the impossibility to evaluate models. Moreover, model extrapolate on a large proportion of the projected area. Adding information on species physiological limits (observations, results from experiments, physiological model outputs) was shown to reduce extrapolation and to improve the capacity of models to estimate the species realised niche. Spatial aggregation of occurrence data, which influenced model predictions and evaluation was also succefully corrected. Finally, dispersal models showed an interesting potential to highlight the role of geographic barriers or conversely of spatial connectivity and also the link between species distribution, physiology and phylogeny history. This PhD thesis provides several methodological advice, annoted codes and tutorials to help implement future modelling works applied to Southern Ocean marine species. / Doctorat en Sciences / info:eu-repo/semantics/nonPublished

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