Remote sensing technology is increasingly applied to map the occurrence of invasive plant species, yet its use to map their ecological impact remains limited. Furthermore, invader-induced changes beyond the canopy, as well as the environmental context, are rarely considered. This study aimed to assess the impacts of an invasive tree on ecosystem functioning at the landscape scale using remote sensing, taking into account both spatial effects and environmental heterogeneity. Specifically, we investigated a coastal Mediterranean dune ecosystem invaded by the N-fixing tree Acacia longifolia (Andrews) Willd. ('Acacia'). Four vegetation indices were calculated as proxies of ecosystem functions, and these indices were used to compute functional diversity in terms of spectral Rao's Q for assessing impacts by Acacia based on airborne hyperspectral data. Vegetation cover and topographic indices derived from airborne LiDAR (Light Detection and Ranging) were used to account for spatial heterogeneity. For seven sites, we employed Generalized Linear Mixed Models to model the effects of environmental variables and Acacia-related variables on proxies of ecosystem functions. Significant impact of the invader was found beyond the invaded area augmenting to 50 % total impact on ecosystem functions. These spatial impacts are particularly prevalent at rather early stages of invasion (similar to 20 % invader cover at landscape level). Consequently, the impact of invaders is underestimated when spatial effects are ignored, but it is overestimated when environmental heterogeneity is neglected. Furthermore, functional diversity decreases due to invasion, though it reaches its maximum at the edges of invader stands, where Rao's Q index captures spectral effects of both the invader and the native vegetation. Thus, we highlight that both 2D and 3D remote sensing data complement each other in remote sensing-driven impact assessments. We envision that advancements in remote sensing of ecosystem structure and functioning in terms of increasing availability of high spectral, spatial and temporal data as well as enhanced methods for data analysis will facilitate tracing the context-dependent and function-specific spatial effects of invasive species especially at early stages of invasion to enable timely management.

Invasive species can alter the structure and functioning of the invaded ecosystem, but predictions of the impact of invasive species on ecosystem functioning are weak. Invasion is determined by the interplay of invasive species traits, the recipient community, and the environmental context. However, efficient approaches to assess the spatial dimension of functional changes in heterogeneous environments and altered plant-plant interactions are lacking. Based on recent technological progress, we posit a way forward to i) quantify the fine-scale heterogeneity of the environmental context, ii) map the structure and function of the invaded system, iii) trace changes induced by the invader with functional tracers, and iv) integrate the different spatio-temporal information from different scales using (artificial intelligence-based) modelling approaches to better predict invasion impacts. An animated 3-D model visualisation demonstrates how maps of functional tracers reveal spatio-temporal dynamics of invader impacts. Merging fine- to coarse-scale spatially explicit information of functional changes with remotely sensed metrics will open new avenues for detecting invader impacts on ecosystem functioning.

High impact invasive plant species, such as the N-fixing and water-spending tree Acacia longifolia, are a major threat to ecosystem functioning worldwide. For example, Acacia's impact on nutrient and water-cycling in Mediterranean dune ecosystems is well understood. However, early detection of such impacts remains challenging. Therefore, novel approaches are required to map functional indicators of high invader impact. Here, we tested in a real world context if the stable isotopes δ13C and δ15N could be such mappable indicators. First, we show that A. longifolia differs regarding its biochemical leaf traits from the native species of the same growth form particularly regarding leaf N content as well as δ13C and δ15N. This may indicate a high impact on N and water cycling, and can be retrieved from hyperspectral data. Second, the impact of the invader on N cycling was mapped joining the spatial distribution of δ15N with airborne laserscanning data. Foliar δ15N of a non-fixing, native species increased in vicinity of invasive stands indicating an uptake of N previously fixed by the invader. Finally, those impacts possibly result in an increase of productivity of the whole dune ecosystem even when invader cover is low. This increase can be mapped integrating hyperspectral imagery with LiDAR data. Thus, there is potential to retrieve functional indicators of high impact including stable isotopes using remote sensing.

Invasive plant species can have high, self-reinforcing impacts on ecosystem structure and functioning that induce permanent changes of ecosystem properties. Therefore, early detection and timely management is required to alleviate ecosystem consequences of invasion. Integrating airborne hyperspectral imagery with LiDAR data can deliver spatially explicit information on invader occurrence and ecosystem transformations even at early stages of invasion. However, relevant "model invaders" and well-characterized ecosystems need to be identified to both increase predictive power of invasion theory and prioritize management. In addition, there is still a knowledge gap regarding sensor-based approaches that are valid in space and time to assess the impact of invasive engineers on ecosystem functioning as well as the potential to induce regime shifts. In this study, occurrence and spatio-temporal impact of the invasive N-2-fixing shrub, Acacia longifolia, was assessed in a heterogeneous, Mediterranean dune ecosystem. The invader was mapped using vegetation indices derived from airborne hyperspectral images as well as airborne LiDAR data using Random Forest classification with a Sensitivity of 0.79, a Positive Predicted Value (PPV) of 0.81, and Cohen's Kappa of 0.77. Invaded sites varied between early stages with low cover, where isolated patches were detected, to heavily infested A. longifolia thickets. Analysis of historical images showed that the invader could establish under the harsh conditions of open dune plains, possibly triggered by human interference. The recently developed Near-Infrared Vegetation Index (NIRv), which is related to Gross Primary Production (GPP), increased linearly and significantly with increasing invader cover. This indicated a GPP-related regime shift induced by the invader, changing ecosystem productivity representative of open shrublands to that of forests. Such a shift could even be identified at early stages of invasion. Thus, the NIRv index may provide an appropriate sensor-based "model metric" to assess impacts of invasive engineers. This offers the opportunity to predict and anticipate regime shifts as a basis for timely management.

Aim: The impact of invasive species may depend on dissimilarity of their functional traits relative to the native community. Therefore, comparing species traits in a multidimensional space can help to better understand invader impacts, but novel methods are needed to effectively measure multiple traits across diverse plant communities. The main aim was to assess biochemical leaf parameters based on field spectra in a whole-community approach. Our specific objectives were to assess (1) biochemical differentiation within the plant community, (2) accuracy of spectroscopic prediction models of biochemical parameters, and (3) ability to depict the multivariate differentiation using field spectroscopy. Location: Mediterranean dune ecosystems, Atlantic coast, southwest Portugal. Methods: We analysed leaf biomass of 18 species, including two invasive acacias, for C, delta C-13, N, delta N-15, lignin, fibre and tannin. Additionally, we collected leaf and canopy field spectra of each sampled plant. We used partial least squares (PLS) regression to predict biochemical parameters from field spectra. Further, we assessed the biochemical differentiation of the species using PCA based on wet chemically determined as well as spectroscopically predicted values. Results: We found high biochemical variation among species and, in particular, marked trait dissimilarity between invasive Acacia spp. and native species, primarily with respect to N content. Biochemical parameters were predicted successfully based on field spectra. Prediction accuracies were particularly high with C, delta C-13, N and tannin. A PCA of biochemical parameters showed that invasive Acacia spp. were distinct from native species of the same life form, but grouped with native dwarf shrubs. This pattern was accurately reproduced by a PCA using spectroscopically predicted values. Conclusions: Invasive Acacia spp. have different leaf traits compared to native species of similar growth form. Their trait dissimilarity likely exacerbates their impacts on the ecosystem. This trait dissimilarity in leaf biochemistry can be accurately predicted with hyperspectral whole-community models. Thus, field spectroscopy can substantially increase the spatial and temporal resolution of measurements and hence facilitate assessments of ecosystem functioning and invader impacts at ecosystem scale.

Spatial heterogeneity of ecosystems crucially influences plant performance, while in return plant feedbacks on their environment may increase heterogeneous patterns. This is of particular relevance for exotic plant invaders that transform native ecosystems, yet, approaches integrating geospatial information of environmental heterogeneity and plant-plant interaction are lacking. Here, we combined remotely sensed information of site topography and vegetation cover with a functional tracer of the N cycle, δ15N. Based on the case study of the invasion of an N2-fixing acacia in a nutrient-poor dune ecosystem, we present the first model that can successfully predict (R 2 = 0.6) small-scale spatial variation of foliar δ15N in a non-fixing native species from observed geospatial data. Thereby, the generalized additive mixed model revealed modulating effects of heterogeneous environments on invader impacts. Hence, linking remote sensing techniques with tracers of biological processes will advance our understanding of the dynamics and functioning of spatially structured heterogeneous systems from small to large spatial scales.

Environmental heterogeneity and plant-plant interactions are key factors shaping plant communities. However, the spatial dimension of plant-plant interactions has seldom been addressed in field studies. This is at least partially rooted in a lack of methods that can accurately resolve functional processes in a spatially explicit manner. Isoscapes, that is, spatially explicit representations of stable isotope data, provide a versatile means to trace functional changes on spatial scales, for example, related to N-cycling (foliar δ15N) and water use efficiency (WUEi, foliar δ13C). In a case study in a nutrient-depleted Mediterranean dune ecosystem, we analysed the spatial impact of the invasive N2-fixing Acacia longifolia on three native species of different functional types using δ15N and δ13C isoscapes and spatial autocorrelation analyses. Isoscapes revealed strong spatial patterns in δ15N and δ13C with pronounced species-specific differences, demonstrating distinct spatial ranges of plant-plant interactions. A coniferous tree and an ericaceous dwarf shrub showed significant enrichment in δ15N within a range of 5-8 m surrounding the canopy of A. longifolia, indicating input of N originating from symbiotic N2-fixation by the invader. In the dwarf shrub, which was most responsive to invader influence, enrichment in δ13C additionally demonstrated spatially explicit changes to WUEi, while a native N2-fixer was unresponsive to the presence of the invader. Furthermore, δ15N and δ13C isoscapes yielded different patterns, indicating that plant-plant interactions can have distinct spatial distributions and ranges based on the process measured. Additionally, the magnitude of the effect differed between field situations with high and low invasion pressure. This study highlights that the spatial scale must be accounted for when assessing the effects and outcome of species interactions. Functional tracers such as stable isotopes enable us to quantify spatial ranges of plant-plant interactions, providing empirical data that can help to better understand and predict complex species interactions in multifaceted natural environments.

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.

Interactions of plants with arbuscular mycorrhizal fungi (AMF) may range along a broad continuum from strong mutualism to parasitism, with mycorrhizal benefits received by the plant being determined by climatic and edaphic conditions affecting the balance between carbon costs vs. nutritional benefits. Thus, environmental conditions promoting either parasitism or mutualism can influence the mycorrhizal growth dependency (MGD) of a plant and in consequence may play an important role in plant-plant interactions. In a multifactorial field experiment we aimed at disentangling the effects of environmental and edaphic conditions, namely the availability of light, phosphorus and nitrogen, and the implications for competitive interactions between Hieracium pilosella and Corynephorus canescens for the outcome of the AMF symbiosis. Both species were planted in single, intraspecific and interspecific combinations using a target-neighbor approach with six treatments distributed along a gradient simulating conditions for the interaction between plants and AMF ranking from mutualistic to parasitic. Across all treatments we found mycorrhizal association of H. pilosella being consistently mutualistic, while pronounced parasitism was observed in C. canescens, indicating that environmental and edaphic conditions did not markedly affect the cost:benefit ratio of the mycorrhizal symbiosis in both species. Competitive interactions between both species were strongly affected by AMF, with the impact of AMF on competition being modulated by colonization. Biomass in both species was lowest when grown in interspecific competition, with colonization being increased in the less mycotrophic C. canescens, while decreased in the obligate mycotrophic H. pilosella. Although parasitism-promoting conditions negatively affected MGD in C. canescens, these effects were small as compared to growth decreases related to increased colonization levels in this species. Thus, the lack of plant control over mycorrhizal colonization was identified as a possible key factor for the outcome of competition, while environmental and edaphic conditions affecting the mutualism-parasitism continuum appeared to be of minor importance.

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 δ15N 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.

Understanding interactions between native and invasive plant species in field settings and quantifying the impact of invaders in heterogeneous native ecosystems requires resolving the spatial scale on which these processes take place. Therefore, functional tracers are needed that enable resolving the alterations induced by exotic plant invasion in contrast to natural variation in a spatially explicit way. 15N isoscapes, i.e., spatially referenced representations of stable nitrogen isotopic signatures, have recently provided such a tracer. However, different processes, e.g. water, nitrogen or carbon cycles, may be affected at different spatial scales. Thus multi-isotope studies, by using different functional tracers, can potentially return a more integrated picture of invader impact. This is particularly true when isoscapes are submitted to statistical methods suitable to find homogeneous subgroups in multivariate data such as cluster analysis. Here, we used model-based clustering of spatially explicit foliar δ15N and δ13C isoscapes together with N concentration of a native indicator species, Corema album, to map regions of influence in a Portuguese dune ecosystem invaded by the N2-fixing Acacia longifolia. Cluster analysis identified regions with pronounced alterations in N budget and water use efficiency in the native species, with a more than twofold increase in foliar N, and δ13C and δ15N enrichment of up to 2‰ and 8‰ closer to the invader, respectively. Furthermore, clusters of multiple functional tracers indicated a spatial shift from facilitation through N addition in the proximity of the invader to competition for resources other than N in close contact. Finding homogeneous subgroups in multi-isotope data by means of model-based cluster analysis provided an effective tool for detecting spatial structure in processes affecting plant physiology and performance. The proposed method can give an objective measure of the spatial extent of influence of plant-plant interactions, thus improving our understanding of spatial pattern and interactions in plant communities.

The work described in this paper is aimed at validating hyperspectral airborne reflectance data collected during the Regional Experiments For Land-atmosphere EXchanges (REFLEX) campaign. Ground reflectance data measured in a vineyard were compared with airborne reflectance data. A sampling strategy and subsequent ground data processing had to be devised so as to capture a representative spectral sample of this complex crop. A linear model between airborne and ground data was tried and statistically tested. Results reveal a sound correspondence between ground and airborne reflectance data (R2 > 0.97), validating the atmospheric correction of the latter.

The REFLEX 2012 campaign was initiated as part of a training course on the organization of an airborne campaign to support advancement of the understanding of land-atmosphere interaction processes. This article describes the campaign, its objectives and observations, remote as well as in situ. The observations took place at the experimental Las Tiesas farm in an agricultural area in the south of Spain. During the period of ten days, measurements were made to capture the main processes controlling the local and regional land-atmosphere exchanges. Apart from multi-temporal, multi-directional and multi-spatial space-borne and airborne observations, measurements of the local meteorology, energy fluxes, soil temperature profiles, soil moisture profiles, surface temperature, canopy structure as well as leaf-level measurements were carried out. Additional thermo-dynamical monitoring took place at selected sites. After presenting the different types of measurements, some examples are given to illustrate the potential of the observations made.

Abstract To date, the implications of the predicted greater intra‐annual variability and extremes in precipitation on ecosystem functioning have received little attention. This study presents results on leaf‐level physiological responses of five species covering the functional groups grasses, forbs, and legumes in the understorey of a Mediterranean oak woodland, with increasing precipitation variability, without altering total annual precipitation inputs. Although extending the dry period between precipitation events from 3 to 6 weeks led to increased soil moisture deficit, overall treatment effects on photosynthetic performance were not observed in the studied species. This resilience to prolonged water stress was explained by different physiological and morphological strategies to withstand periods below the wilting point, that is, isohydric behavior in Agrostis, Rumex, and Tuberaria, leaf succulence in Rumex, and taproots in Tolpis. In addition, quick recovery upon irrigation events and species‐specific adaptations of water‐use efficiency with longer dry periods and larger precipitation events contributed to the observed resilience in productivity of the annual plant community. Although none of the species exhibited a change in cover with increasing precipitation variability, leaf physiology of the legume Ornithopus exhibited signs of sensitivity to moisture deficit, which may have implications for the agricultural practice of seeding legume‐rich mixtures in Mediterranean grassland‐type systems. This highlights the need for long‐term precipitation manipulation experiments to capture possible directional changes in species composition and seed bank development, which can subsequently affect ecosystem state and functioning.

Linking remote sensing methodology to stable isotope ecology provides a promising approach to study ecological processes from small to large spatial scales. Here, we show that δ(15)N can be detected in fresh leaf reflectance spectra of field samples along a spatial gradient of increasing nitrogen input from an N2-fixing invasive species. However, in field data it is unclear whether δ(15)N directly influences leaf reflectance spectra or if the relationship is based on covariation between δ(15)N and foliar nitrogen content or other leaf properties. Using a (15)N-labeling approach, we experimentally varied δ(15)N independently of any other leaf properties in three plant species across different leaf developmental and physiological states. δ(15)N could successfully be modeled by means of partial least squares (PLSs) regressions, using leaf reflectance spectra as predictor variables. PLS models explained 53-73% of the variation in δ(15)N within species. Several wavelength regions important for predicting δ(15)N were consistent across species and could furthermore be related to known absorption features of N-containing molecular bonds. By eliminating covariation with other leaf properties as an explanation for the relationship between reflectance and δ(15)N, our results demonstrate that (15)N itself has an inherent effect on leaf reflectance spectra. Thus, our study substantiates the use of spectroscopic measurements to retrieve isotopic signatures for ecological studies and encourages future development. Furthermore, our results highlight the great potential of optical measurements for up-scaling isotope ecology to larger spatial scales.

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.

Nitrogen fixing plant species are among the most invasive species worldwide. However, field studies directly estimating the differential effect of native versus invasive exotic N-2-fixing plants on plant communities are lacking. The exotic leguminous shrub Acacia longifolia invades coastal dunes across Portugal often co-existing with the native N-2-fixer Stauracanthus spectabilis. Moreover, this co-existence with native species is possible due to a relatively low cover of Acacia species particularly in the south of Portugal where drought is intense. In this study we compare the impact of two different legume species (native and exotic) on the nitrogen status of a protected Mediterranean dune system. We document how presence of the exotic invader A. longifolia impacted community structure, soil properties and the foliar nitrogen concentrations and growth rates of native species. A. longifolia invaded areas had decreased biodiversity, fewer seedlings and altered soil properties (e.g., increased soil organic matter, NO3- and NH4+). A. longifolia presence was also associated with significant increases in foliar nitrogen content and delta N-15 of the endemic shrub Corema album while presence of the native legume Stauracanthus spectabilis had no discernible impact on C. album. Furthermore, a strong correlation between increased foliar nitrogen and enhanced growth rates in C. album indicate a facilitative effect of A. longifolia on this native shrub. We posit that the combination of nitrogen fixation, a rapid growth rate and production of a thick litter layer result in a very substantial impact of the invader in invaded ecosystems. (C) 2010 Elsevier Masson SAS. All rights reserved.