Les travaux développés dans cette thèse définissent une approche pour la gestion des impacts des évolutions des profils UML sur les modèles instances. Sur la base d'organisation des diverses connaissances identifiées lors de l'analyse des évolutions d'un profil UML, nous proposons un processus automatisé PEM (Profil Evolution Method) permettant l'identification des évolutions a posteriori sous forme de différences, la reconstruction de ces différences en opérations d'évolution conformes au métamodèle UML, la classification de leur impact et, finalement, l'adaptation des modèles à la nouvelle version du profil UML. L'approche intègre également une activité de formalisation et d'utilisation de patrons d'évolution contenant toutes les informations essentielles employées à chaque étape de notre processus. Ces patrons sont employés dans notre système P²E (Papyrus Profile Evolution) pour guider le concepteur des modèles ou des profils, dans la gestion des évolutions d'un profil UML en évaluant efficacement un sous ensemble suffisant et pertinent d'éléments et de paramètres d'évolution d'un profil UML.Les principales contributions de l'approche résident dans la formalisation d'opérateurs d'évolution à partir du métamodèle UML, la proposition d'une classification des impacts des évolutions sur les modèles instances, la modélisation semi-formelle et explicite d'un catalogue de patron d'évolution contenant les solutions d'adaptation à appliquer sur les modèles instances. / This thesis proposes an approach for the management of UML profiles evolutions and their impacts on instance models. Based on the analysis of standardized UML profiles evolutions, we propose an automated process called PEM (Profile Evolution Method) allowing the evolutions identification a posteriori and their representations as differences; the reconstruction of these differences as evolution operations that are compliant with the UML metamodel; their impacts classification and, finally, the instance models adaptation towards the new UML profile version.The approach includes also an activity of formalization of previous knowledge in the form of evolution patterns. The patterns contain all the essential information used at each step in our P²E system (Papyrus Profile Evolution). They guide the models designer or the profiles designer, in the management of the UML profiles evolution by evaluating efficiently a sufficient subset with pertinent evolving elements and their evolution parameters.In this approach, the major contributions consist in the formalization of evolution operators extracted from the UML metamodel, the proposal of an evolution impacts classification, the specification of a pattern catalog that is semi-formal and explicit for the designers. Finally, the specification of adaptation solutions (to adapt the old model versions to the new UML profile version).

Sleeper populations are established populations of a non-native species whose population growth is limited by one or more abiotic or biotic conditions, such as climate change. While the northeastern US is predicted to be a hotspot for future invasions, identifying potential sleeper populations before they become invasive can inform proactive, climate-smart invasive species management. I focused on 169 introduced species that are established in one or more northeastern states. I used the Environmental Impact Classification for Alien Taxa (EICAT) framework to systematically identify and review the peer-reviewed literature for these candidate species to quantify their negative ecological and socioeconomic impacts. I identified 49 plants with ‘major’ impacts linked to the decline of multiple native species or loss of community diversity. Using high negative ecological impact, habitat suitability, and climate suitability as selection criteria, I highlight 37 species as high priority for management in the North Atlantic –Appalachian Region.