Using life strategies to explore the vulnerability of ecosystem services to invasion by alien plants

Invasive plants can have different effects of ecosystem functioning and on the provision of ecosystem services, from strongly deleterious impacts to positive effects. The nature and intensity of such effects will depend on the service and ecosystem being considered, but also on features of life strategies of invaders that influence their invasiveness as well as their influence of key processes of receiving ecosystems. To address the combined effect of these various factors we developed a robust and efficient methodological framework that allows to identify areas of possible conflict between ecosystem services and alien invasive plants, considering interactions between landscape invasibility and species invasiveness. Our framework combines the statistical robustness of multi-model inference, efficient techniques to map ecosystem services, and life strategies as a functional link between invasion, functional changes and potential provision of services by invaded ecosystems. The framework was applied to a test region in Portugal, for which we could successfully predict the current patterns of plant invasion, of ecosystem service provision, and finally of probable conflict (expressing concern for negative impacts, and value for positive impacts on services) between alien species richness (total and per plant life strategy) and the potential provision of selected services. Potential conflicts were identified for all combinations of plant strategy and ecosystem service, with an emphasis for those concerning conflicts with carbon sequestration, water regulation and wood production. Lower levels of conflict were obtained between invasive plant strategies and the habitat for biodiversity supporting service. The added value of the proposed framework in the context of landscape management and planning is discussed in perspective of anticipation of conflicts, mitigation of negative impacts, and potentiation of positive effects of plant invasions on ecosystems and their services.

Multi-scale support vector algorithms for hot spot detection and modelling

The algorithmic approach to data modelling has developed rapidly these last years, in particular methods based on data mining and machine learning have been used in a growing number of applications. These methods follow a data-driven methodology, aiming at providing the best possible generalization and predictive abilities instead of concentrating on the properties of the data model. One of the most successful groups of such methods is known as Support Vector algorithms. Following the fruitful developments in applying Support Vector algorithms to spatial data, this paper introduces a new extension of the traditional support vector regression (SVR) algorithm. This extension allows for the simultaneous modelling of environmental data at several spatial scales. The joint influence of environmental processes presenting different patterns at different scales is here learned automatically from data, providing the optimum mixture of short and large-scale models. The method is adaptive to the spatial scale of the data. With this advantage, it can provide efficient means to model local anomalies that may typically arise in situations at an early phase of an environmental emergency. However, the proposed approach still requires some prior knowledge on the possible existence of such short-scale patterns. This is a possible limitation of the method for its implementation in early warning systems. The purpose of this paper is to present the multi-scale SVR model and to illustrate its use with an application to the mapping of Cs137 activity given the measurements taken in the region of Briansk following the Chernobyl accident.