No abstract

Plant-plant interactions are key processes shaping plant communities, but methods are lacking to accurately capture the spatial dimension of these processes. Isoscapes, i.e. spatially continuous observations of variations in stable isotope ratios, provide innovative methods to trace the spatial dimension of ecological processes at continental to global scales. Herein, we test the usefulness of nitrogen isoscapes (δ(15) N) for quantifying alterations in community functioning following exotic plant invasion. Nitrogen introduced by an exotic N(2) -fixing acacia could be accurately traced through the ecosystem and into the surrounding native vegetation by combining native species foliar δ(15) N with spatial information regarding plant location using geostatistical methods. The area impacted by N-addition was at least 3.5-fold greater than the physical area covered by the invader. Thus, downscaling isoscapes to the community level opens new frontiers in quantifying the spatial dimension of functional changes associated with invasion and in resolving the spatial component of within-community interactions.

Oligotrophic ecosystems, previously considered to be more resilient to invasive plants, are now recognised to be highly vulnerable to invasions. In these systems, woody legumes show belowground ecosystem engineering characteristics that enable invasion, however, the underlying processes are not well understood. Using a Portuguese primary dune ecosystem as an oligotrophic model system, belowground biomass pools, turnover rates and stoichiometry of a native (Stauracanthus spectabilis) and an invasive legume (Acacia longifolia) were compared and related to changes in the foliage of the surrounding native (Corema album) vegetation.We hypothesized that the invasive legume requires less phosphorus per unit of biomass produced and exhibits an enhanced nutrient turnover compared to the native vegetation, which could drive invasion by inducing a systemic N/P imbalance.Compared with the native legumes, A. longifolia plants had larger canopies, higher SOM levels and lower tissue P concentrations. These attributes were strongly related to legume influence as measured by increased foliar N content and less depleted d 15 N signatures in the surrounding C. album vegetation. Furthermore, higher root N concentration and increased nutrient turnover in the rhizosphere of the invader were associated with depleted foliar P in C. album.Our results emphasize that while A. longifolia itself maintains an efficient phosphorus use in biomass production, at the same time it exerts a strong impact on the N/P balance of the native system. Moreover, this study highlights the engineering of a belowground structure of roots and rhizosphere as a crucial driver for invasion, due to its central role in nutrient turnover. These findings provide new evidence that, under nutrient-limited conditions, considering co-limitation and nutrient cycling in oligotrophic systems is essential to understand the engineering character of invasive woody legumes.

Hyperspectral remote sensing is an effective tool to discriminate plant species, providing vast potential to trace plant invasions for ecological assessments. However, necessary baseline information for the use of remote sensing data is missing for many high-impact invaders. Furthermore, the identification of the suitable classification algorithms and spectral regions for successfully classifying species remains an open field of research. Here, we tested the separability of the invasive tree Acacia longifolia from adjacent exotic and native vegetation in a Natura 2000 protected Mediterranean dune ecosystem. We used continuous visible, near-infrared and short wave infrared (VNIR-SWIR) data as well as vegetation indices at the leaf and canopy level for classification, comparing five different classification algorithms. We were able to successfully distinguish A. longifolia from surrounding vegetation based on vegetation indices. At the leaf level, radial-basis function kernel Support Vector Machine (SVM) and Random Forest (RF) achieved both a high Sensitivity (SVM: 0.83, RF: 0.78) and a high Positive Predicted Value (PPV) (0.86, 0.83). At the canopy level, RF was the classifier with an optimal balance of Sensitivity (0.75) and PPV (0.75). The most relevant vegetation indices were linked to the biochemical parameters chlorophyll, water, nitrogen, and cellulose as well as vegetation cover, which is in line with biochemical and ecophysiological properties reported for A. longifolia. Our results highlight the potential to use remote sensing as a tool for an early detection of A. longifolia in Mediterranean coastal ecosystems.