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Research Article
An integrative framework to assess the spatio-temporal impact of plant invasion on ecosystem functioning
expand article infoChristiane Werner, Christine Hellmann, André Große-Stoltenberg§|
‡ University Freiburg, Freiburg, Germany
§ Justus Liebig University Giessen, Giessen, Germany
| Center for International Development and Environmental Research, Giessen, Germany
Open Access

Abstract

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.

Key words

community structure, environmental context, functional tracer, invader-ecosystem interaction, remote sensing, spatio-temporal heterogeneity, spatio-temporal modelling

Introduction

Biological invasions of non-native species pose a large threat to biodiversity (IPBES 2019). Many invasive plant species significantly alter the biophysical and biochemical environment, thereby facilitating their own growth (Gaertner et al. 2014), with cascading effects on the structure and functioning of the invaded ecosystems. However, mechanisms of invader impact can be manifold (Ehrenfeld 2010; Sapsford et al. 2020), and challenges to quantify impact occur due to the multiplicity of invading species, context-dependencies, and interactions, as well as intraspecific trait variation (Sapsford et al. 2020). Predicting the effects of biological invasions on ecosystem functioning and services is of uttermost importance for prioritizing management and anticipating undesirable consequences of invasions (Simberloff et al. 2013; Jeschke et al. 2014; Brundu et al. 2020; Essl et al. 2020; Kumschick et al. 2020; Pyšek et al. 2020b; Ricciardi et al. 2021; Gallardo et al. 2024; Vilà et al. 2024). To date, multiple hypotheses and concepts have been developed that capture different aspects of invasion impact, habitat invasibility, and species’ invasiveness (Hobbs and Humphries 1995; Alpert et al. 2000; Whitney and Gabler 2008; Enders et al. 2020; Novoa et al. 2020; Catford et al. 2021; Cavieres 2021; Hui et al. 2023), although data to thoroughly test these are often scarce (Gioria et al. 2023).

The ecological impact of invasive species depends on direct interactions between native and invasive plants, which in turn are influenced by native and invasive species’ traits (Pyšek et al. 2012, 2020a; Sapsford et al. 2020), or trait differences between both groups (Castro-Díez et al. 2014; Lee et al. 2017; Kuebbing et al. 2018; Dyderski and Jagodziński 2019). Consequently, there is increasing awareness of the relevance of interactions and dynamics for invasion success, such as the interplay between invading species’ traits and the recipient ecosystem (Kueffer et al. 2013; Kumschick et al. 2015; Sardans et al. 2017; Novoa et al. 2020). However, new approaches are needed to quantitatively assess these interactions (Gioria et al. 2023).

Species interactions as well as plant-soil feedbacks take place on a spatially confined scale within centimetres to metres in the neighbourhood of the invader (Mitchell et al. 2006), which is further referred to as fine-scale. While the temporal aspect of such feedbacks has been documented (Yelenik and D’Antonio 2013; Gioria and Osborne 2014), studies on impacts at plant-individual scale are clearly underrepresented (Crystal-Ornelas and Lockwood 2020). The spatial arrangement and distribution of native and invasive species is a decisive factor determining whether individuals interact and, potentially, which type of interaction – i.e., competition or facilitation – dominates (Hellmann et al. 2016a, 2016b; Cavieres 2021).

In addition, there is growing recognition that the effect of the invasive species is influenced by the environmental conditions, such as microclimatic or local edaphic conditions (Sapsford et al. 2020; Catford et al. 2021). The environmental context, namely the fine-scale spatial heterogeneity of abiotic and biotic conditions, is an important but often neglected dimension (Jarić et al. 2019; Fenesi et al. 2023), and the spatio-temporal variation of a system is a major source of uncertainty of impact assessments (Probert et al. 2020). Local availability of resources, such as water, nutrients or light, can shift the competitive balance between invasive and native species (Werner et al. 2010; Soliveres et al. 2015; Alba et al. 2019; Haberstroh et al. 2021). Therefore, predicting ecological impacts of invasive species considering context-dependency is still a major challenge (Ricciardi et al. 2021).

We posit that spatio-temporal heterogeneity can represent an environmental property in itself, which can affect invasibility, or else be affected by invasion, and requires better inclusion in invasion ecology. In the past, progress was hampered by a lack of methodological approaches, but now significant progress, e.g. in remote sensing technology, allows capturing high-resolution information on environmental heterogeneity at fine scales where plant-plant interactions take place.

Hence, we advocate combining advances in various disciplines of ecophysiology, invasion ecology, remote sensing, mapping, and modelling. This will open new opportunities to characterize environmental heterogeneity and associated changes in invasive-native species interaction at high-spatiotemporal resolution to better predict invasion dynamics and impact, as outlined in the following.

Concept for integrating fine-scale environmental heterogeneity and functional changes into spatial models of invader-ecosystem interactions

Direct interactions between the invasive and native species, such as above- or belowground facilitation or competition for e.g. resources, spaces, or pollinators will determine the invaders’ successful establishment and growth (Fig. 1). These direct interactions are embedded in the environmental context, which is likewise shaping the competitive balance if conditions favour either native or invasive substitute by species. The competitive balance may even shift between competition and facilitation under changing environmental conditions or extreme climatic events (Werner et al. 2010; Grossiord 2020; Cavieres 2021; Haberstroh and Werner 2022). Inversely, some invaders can change environmental properties through positive- or negative feedback loops, thereby often promoting their own invasion success, which can ultimately result in regime shifts (Gaertner et al. 2014) and potentially magnify the impact beyond direct competition through cascading effects (Carboni et al. 2021). Currently, there exists a robust theoretical framework addressing the success of invasiveness as a result of direct and indirect interactions and the interplay between species traits, as well as on the recipient community structure and functioning and the environmental context (Gaertner et al. 2014; Novoa et al. 2020; Pyšek et al. 2020a; Hui et al. 2023; summarized in Fig. 2).

Figure 1.

Invasive species directly interact with native species by competition or facilitation e.g. for above and belowground resources, thereby changing the biotic and abiotic environment locally.

Figure 2.

Mechanisms determine plant invasion impact. Plant invasion impact results from direct and indirect interactions between invasive and native species based on the interplay between invasive species traits, structure and function of the recipient community, and spatio-temporal heterogeneity of the environment. Direct interactions between invasive and native species result from competition or facilitation, e.g. for resources, whereas indirect interactions are mediated via subsequent changes in the biotic and abiotic environment, which may favour self-reinforcing processes of the invader or stabilizing processes of the community. Examples of important factors for each category are given in the boxes.

However, detecting and tracing the impacts of invasive species in natural environments have been hindered in the past due to a lack of suitable measurements and integration methods to explicitly quantify the spatio-temporal dimensions involved. This would require not only mapping the invasive species and its spread in natural systems, but also quantifying local changes in different abiotic and biotic processes that are altered by the invader (i.e. quantifying the local impact of the invader). As the latter is a function of both the environmental conditions and the structure and functioning of the native community, both need to be quantified at high spatio-temporal resolution. For each of these aspects, the required tools are at hand, but new integrative analyses are required.

Hence, we posit a way forward (Fig. 3) on how 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) apply effective approaches for integration of spatio-temporal information from different scales, e.g. via different (artificial intelligence-based) modelling approaches, for better prediction of invasive species impact.

Figure 3.

Framework for integrating fine-scale environmental heterogeneity and functional changes into spatial models of invader-ecosystem interactions. Maps of biochemical and biophysical heterogeneous environments can be directly incorporated into predictive models of impact measures across different sites or stages of invasion. Essential complementary spatial data include the location of the invasive species, maps of functional tracers that reflect local changes in key functions induced by the invader, and maps of the recipient community structure and function (e.g. Hellmann et al. (2017)). Technological advances in measurement techniques, sensor networks, and remote sensing will facilitate the collection of high-resolution data on the environmental context, the invasive species, and the recipient community, thereby improving the understanding of invasion dynamics and processes, particularly at the neighbourhood scale.

Environmental context

The environmental context is defined as the biogeochemical and physical matrix, which provides the background for both native and invasive species’ biotic interactions (Fig. 3). This includes the fine-scale spatio-temporal heterogeneity in, e.g. abiotic conditions like hydrological and edaphic conditions or microclimate, as well as resource patches, e.g. after local disturbance. On the scale of centimetres to kilometres, variation in these conditions can influence plant performance and interactions, thereby creating a multi-layered mosaic of background conditions. An inherent challenge of geospatial analysis is that fine-scale or high resolution data are often collected over small areas, while for large areas only coarse-scale data are available (Millington 2021). However, research rapidly advances regarding the retrieval of two- and three-dimensional geospatial information on the environmental matrix using sensory networks (Allan et al. 2018; Lahoz-Monfort and Magrath 2021; Besson et al. 2022; Sethi et al. 2022) and remote sensing (Lausch et al. 2020; Lahoz-Monfort and Magrath 2021; Skidmore et al. 2021; Timmermans and Kissling 2023). Currently, a broad set of novel digital tools to describe the environmental context is emerging, including UAVs (Unmanned Aerial Vehicles), microsatellites (e.g. PlanetScope), active sensor systems based on LiDAR (Light Detection and Ranging) or SAR (Synthetic Aperture Radar) technology for vegetation structure analysis, and advanced versions of the established Landsat and Sentinel-2 satellites (Crowley and Cardille 2020).

Structure and function

The environmental context further shapes native species distribution patterns as well as structure and function of the community. Indeed, spatial complexity, in which invasive species interactions take place, can be a proxy for ecosystem structure and dynamics in itself (Parrott 2010). Similarly to assessing the environmental conditions, advances in remote sensing techniques allow for multiscale and multi-temporal mapping of species distribution, and landscape structure and function (Crowley and Cardille 2020), which are exceptionally valuable to describe both the environmental context and structure of the recipient community. LiDAR, SAR, and digital aerial photogrammetry hold great potential to describe ecosystem structural parameters such as vegetation height, cover, density, structural complexity, and population structure (Valbuena et al. 2020). Proofs-of-concept exist for high-resolution automated structural measurements (Calders et al. 2023), while both active and passive sensor systems can be used to map ecosystem functions (Pettorelli et al. 2018).

Moreover, spatially explicit maps of the invader are required, as impact is related to abundance in various forms (Sofaer et al. 2018; Strayer 2020; O’Loughlin et al. 2021). Access to spatial data on invasive plant distribution is improving (Fusco et al. 2023), and distribution maps of the invader can be created and updated across scales using data from UAS (Uncrewed Aerial Systems), aircrafts, and satellites (Vaz et al. 2018; Timmermans and Kissling 2023). Freely available earth observation data are extremely useful to produce such distribution maps, particularly in data poor regions (Truong et al. 2017). Novel approaches to map invasive species and characterize the species composition of the recipient community include methods of Deep Learning (DL) and other methods of machine learning. In invasion ecology, the potential use of DL is manifold (Christin et al. 2019; Hirn et al. 2022; Perry et al. 2022), but its main application is probably species identification and mapping (Christin et al. 2019; Kattenborn et al. 2021; Borowiec et al. 2022; Müllerová et al. 2023).

Functional tracers of invader impact

Ultimately, the impact of the invader, i.e. its effect on the biophysical, biochemical, and biological environment, has to be assessed. One effective way to quantify invasive species impact is the use of functional tracers, which reflect local changes in key functions at fine spatio-temporal scales. The choice of suitable tracers will depend on both the invasive species under study and invaded community properties, and should capture the processes likely altered by the invasive species, for example nitrogen for N-fixing invaders in N-poor environments or water balance for water-spending invaders in water-limited systems (Fig. 2). Stable isotopes, for example, can provide such functional or ecophysiological tracers, as they reflect changes in interactions and altered functional processes at fine spatial resolution (Cheesman and Cernusak 2016), and provide great potential to unravel mechanisms of invasions (McCue et al. 2020). Applied in a spatially explicit manner (i.e. isoscapes (Rascher et al. 2012)), they can trace local changes in plant-plant interaction and in environmental conditions (Hellmann et al. 2017). Maps can be generated for isotopic measurements of the soil environment or leaves of different native species, thereby directly mapping the invader impact on these species (Hellmann et al. 2016a, 2016b; Nielsen et al. 2016; Sena-Souza et al. 2023). For example, atmospheric N2-input by N-fixing invaders can be traced into the native vegetation by nitrogen isotopes (δ15N, example in Fig. 3). These changes can lead to cascading effects on other ecosystem functions. Besides nutrient cycling, subsequent changes, e.g. in water and/or carbon cycles, may also be involved (Le Maitre et al. 2015; Dzikiti et al. 2017). Competition for water can affect the water-use-efficiency and hydraulic regulation of native species (Haberstroh et al. 2021), with cascading effects on ecosystem water balance (Rascher et al. 2010; Caldeira et al. 2015; Le Maitre et al. 2020). Changes in native species’ carbon isotope ratio (δ13C) can resolve changes in water use efficiency in response to competitive or facilitative invader interactions (Hellmann et al. 2016a; Crous et al. 2019; Sena-Souza et al. 2023). Even shifts between facilitation and competition with increasing distance to the invader have been observed (Hellmann et al. 2016a).

Different tracers can be combined (Funk et al. 2017), such as C, N, P concentrations or other biochemical properties that may be affected by the invasion (Drenovsky et al. 2012; Hellmann et al. 2016a; Helsen et al. 2020; Sena-Souza et al. 2023) or which might reveal functional differences (Große-Stoltenberg et al. 2018b; Meira-Neto et al. 2023) along environmental gradients (Crous et al. 2019). Thereby, differences in the spatial dimension of impact can be revealed both between native and invasive species, depending on their susceptibility to these changes, and with respect to different processes involved.

Remote sensing techniques have also proven invaluable in examining functional properties of invasive species (Helsen et al. 2020; Andrew et al. 2014; Dzikiti et al. 2016; Große-Stoltenberg et al. 2018a, 2018b; Ewald et al. 2018; Hacker and Coops 2022; Große-Stoltenberg et al. 2023) as well as plant-plant interactions (Chen et al. 2022). Further, first studies show that mycorrhizal traits (Chaudhary et al. 2022), which may constrain invasion success (Pringle et al. 2009) and/or be altered after invasion (Lekberg et al. 2013), can be inferred from hyperspectral data at leaf (Jantzen et al. 2023) and canopy level (Sousa et al. 2021). Where remote sensing methods reach their limits, novel field-portable instruments and sensor networks are very promising tools to measure ecophysiological information at the individual plant level with very high temporal resolution (Tognetti et al. 2022).

Additionally, advances in wireless, autonomous microsensors, such as leaf wearable sensors of ecophysiological processes (Frey et al. 2023; Reimer et al. 2021), may offer novel sampling strategies. Currently, novel autonomous sensor networks are being developed (e.g. ECOSENSE, Werner et al. 2024; Allan et al. 2018; Besson et al. 2022; Tognetti et al. 2022), enabling high spatial coverage of different functional properties in heterogeneous environments with distributed sensors continuously recording at high temporal resolution. Autonomous sensing is coupled to wireless data transmittance and real-time data assimilation into large databases to streamline the information flow and enable real-time analysis. Though still in its infancy, these novel automated sensing networks may also provide valuable new insights in invasion ecology. Such standardized and automated networks of field sensors are required to validate proxies of ecosystem functioning derived from satellite data, particularly in heterogeneous ecosystems (Naethe et al. 2024). Clearly, challenges apply when sampling at such fine resolution regarding data volume, data heterogeneity, varying data quality, and timely data availability, which requires sophisticated data management and analysis (Farley et al. 2018), as well as appropriate sampling strategies. Despite all technological progress, trade-offs between resolution and extent of analysis will still apply. Nevertheless, these new technologies bear the potential to provide the high spatial coverage required in heterogeneous environments to quantify invader-ecosystem interactions and validate remote sensing data for model transfer and upscaling.

Integration: spatial modelling of functional changes and impact assessment across different stages of invasion

Integration of the information on functional changes by the invader, characteristics of the recipient community, and the environmental context from different sources and at different scales is needed to assess and predict the invader impact on ecosystem functioning along gradients of invasion in heterogeneous ecosystems (Figs 3, 4). Once the functional tracer of impact is identified, the spatio-temporal dimension of the impact is understood, and spatial layers of both ecosystem structure and invader distribution at appropriate scale are available, alterations of ecosystems in the neighbourhood of the invader can be mapped using spatial modelling approaches.

Figure 4.

Model visualisation of spatio-temporal dynamics of invader impacts based on the suggested framework. Modelled isoscapes centred around a N2-fixing invasive plant species using the functional tracer δ15N and information on the environmental matrix in a nutrient poor ecosystem based on Hellmann et al. (2017). Reddish colours indicate high-levels of atmospheric fixed nitrogen inputs (e.g. dense invader patches and flushes of N into native vegetation); yellow colours indicate lower levels of impact, while whitish colours indicate no impact and are representative for the original status before invasion. The local functional changes do not occur uniformly. Isoscapes are plotted onto high-resolution airborne LiDAR data fused with true colour imagery to illustrate the effect of LiDAR-derived vegetation structure of the recipient community and topography on invader impact in this heterogeneous ecosystem. The 3D map was created using QGIS version 3.30. An animated 3D-video of the model can be found at https://tinyurl.com/4hs23b8p.

Novel technologies clearly facilitate data sampling at multiple resolutions (see above). This enables explorative analysis of species-environment interactions at multiple scales, which is essential when spatio-temporal dimension of the effect is not known (see Holland and Yang 2016). Integrative approaches include mixed-effect models (Golicz et al. 2023), which have been applied to map invader-ecosystem interactions (Hellmann et al. 2017). To assess model transferability, area of applicability, variable selection, and methods of cross-validation need to be carefully evaluated (Ludwig et al. 2023). Recording data with high spatio-temporal resolution will inevitably lead to large data sets. Again, machine learning approaches, in particular DL, have recently gained popularity to analyze complex spatio-temporal datasets (Wikle and Zammit-Mangion 2023). Within the field of ecology, the versatility of DL is evident (Christin et al. 2019; Hirn et al. 2022; Perry et al. 2022), with its primary utility likely lying in species identification and mapping (Christin et al. 2019; Kattenborn et al. 2021; Borowiec et al. 2022; Müllerová et al. 2023). Further, methods of DL enable multi-trait retrieval across vegetation types even when data are scarce (Cherif et al. 2023). Recent progress also includes the retrieval of traits using hybrid models, which combine leaf-canopy-atmosphere radiative transfer modelling with Gaussian processes and enable upscaling of trait maps from the local to regional level, including uncertainty estimates (Estévez et al. 2022). Especially in earth system sciences, DL approaches have been used to model system states and analyze systems processes (Reichstein et al. 2019). Future progress regarding model interpretability and explainability is expected if data-driven approaches and physical models are combined (Reichstein et al. 2019), and concepts are developed for automated workflows and pipelines to study ecosystem dynamics (Besson et al. 2022). However, progress in automated data collections and analysis based on artificial intelligence is not a solution per se to answer questions in ecology. It also poses challenges, e.g. in terms of standards, protocols and workflows, data infrastructure and data quality. Thus, defining scope and scale to study ecological phenomena will still be required (de Koning et al. 2023).

In the following, we will use an example to quantify the impact of a N-fixing invader in a N-poor Mediterranean ecosystem as one efficient but not exclusive way to integrate spatio-temporal information and functional tracers for invader impact assessment (Fig. 4).

We used field-based maps of both a N-fixing invasive species and a functional tracer (δ15N), which were joined with airborne LiDAR data on topography (environmental context) and vegetation structure (recipient community) to model functional changes across sites and stages of invasion (Hellmann et al. 2017). Plotting these maps onto fused airborne LiDAR and true colour image data allows visualizing and communicating context-dependent invader-ecosystem interactions at the molecular level, which are otherwise undetectable. The nitrogen isoscapes in Fig. 4 are centred around a N-fixing invasive species and illustrate the effect of dynamic N enrichment by the invader (reddish colours) in a N-poor open dune ecosystem (indicated by the yellowish colours) in three-dimensional space. This N-fixing effect does not occur uniformly around the invader, but is shaped by vegetation structure and topography. This explains, for example, N-transfer from fixation into the non-N-fixing native vegetation (yellowish colours), and the flush of nitrogen into sparsely vegetated areas downhill from the invader, which will slowly be transformed into denser habitats.

Other integrative approaches include the combination of field-based and remotely sensed data on native and invasive species distributions, vegetation structure, Leaf Area Index, or evapotranspiration to, for example, estimate water consumption of an invader in riparian habitats (Nagler et al. 2009; Dzikiti et al. 2017). To transfer and validate approaches across landscapes, information on topography (environmental context) and species composition (recipient community) is deemed essential (Le Maitre et al. 2015).

In summary, the importance of linking ecophysiology with remote sensing data to understand invasion processes has been outlined (Niphadkar and Nagendra 2016), and integrative approaches on mapping invader-ecosystem interactions are at hand (Nagler et al. 2009; Dzikiti et al. 2016; Hellmann et al. 2017). Due to technological progress, automated systems to study ecosystem dynamics at unprecedented scales are being developed and implemented (Allan et al. 2018; Besson et al. 2022; Tognetti et al. 2022), with potential to build digital twins (de Koning et al. 2023) of plant invasion impact, i.e. dynamic virtual representations or models used for simulations and analyses. We envision that these new approaches will also lead to advances in the field of biological invasions, namely to better understand fine-scale invader-ecosystem interactions, test invasion theories, and provide robust validation data for interactions across spatio-temporal dimensions in heterogeneous ecosystems.

Concluding remarks

Spatio-temporal patterns and variation of plant-plant interactions in heterogeneous environments deserve better integration in invasion research. Here we advocate the use of functional tracers for integrating fine-scale interactions between the invasive species, the recipient community, and the environmental context into spatial models to assess context-dependency of invader impact, namely the interplay of direct and indirect invasive-native species interactions. We advocate drawing on the large toolbox of recent methods, which when combined, can open new doors for mapping and predicting changes in ecosystem functioning and for assessing and disentangling the influence of spatio-temporal heterogeneity on invader impacts. By explicitly emphasizing the spatio-temporal variation of plant-plant interactions in invasion ecology, we anticipate major advances for understanding of invasion history, patterns of spread, impact assessment, and prediction of future invasions.

Acknowledgements

Funding was provided by the DFG (WE 2681/8-1, WE 2681/10-1), EUFAR (DeInVader, EUFAR11-06), DAAD (AGS). We thank NERC’s Airborne Research Facility and Data Analysis Node for conducting the airborne survey.

Additional information

Conflict of interest

The authors have declared that no competing interests exist.

Ethical statement

No ethical statement was reported.

Funding

No funding was reported.

Author contributions

All authors developed the framework. CH and CW wrote the first draft, which was revised by CW and AGS. AGS implemented the video animation.

Author ORCIDs

Christiane Werner https://orcid.org/0000-0002-7676-9057

André Große-Stoltenberg https://orcid.org/0000-0001-6075-5497

Data availability

All of the data that support the findings of this study are available in the main text or Supplementary Information.

References

  • Alba C, Fahey C, Flory SL (2019) Global change stressors alter resources and shift plant interactions from facilitation to competition over time. Ecology 100(12): e02859. https://doi.org/10.1002/ecy.2859
  • Allan BM, Nimmo DG, Ierodiaconou D, VanDerWal J, Koh LP, Ritchie EG (2018) Futurecasting ecological research: The rise of technoecology. Ecosphere 9(5): 02163. https://doi.org/10.1002/ecs2.2163
  • Alpert P, Bone E, Holzapfel C (2000) Invasiveness, invasibility and the role of environmental stress in the spread of non-native plants. Perspectives in Plant Ecology, Evolution and Systematics 3(1): 52–66. https://doi.org/10.1078/1433-8319-00004
  • Andrew ME, Wulder MA, Nelson TA (2014) Potential contributions of remote sensing to ecosystem service assessments. Progress in Physical Geography 38(3): 328–353. https://doi.org/10.1177/0309133314528942
  • Besson M, Alison J, Bjerge K, Gorochowski TE, Høye TT, Jucker T, Mann HMR, Clements CF (2022) Towards the fully automated monitoring of ecological communities. Ecology Letters 25(12): 2753–2775. https://doi.org/10.1111/ele.14123
  • Borowiec ML, Dikow RB, Frandsen PB, McKeeken A, Valentini G, White AE (2022) Deep learning as a tool for ecology and evolution. Methods in Ecology and Evolution 13(8): 1640–1660. https://doi.org/10.1111/2041-210X.13901
  • Brundu G, Pauchard A, Pyšek P, Pergl J, Bindewald AM, Brunori A, Canavan S, Campagnaro T, Celesti-Grapow L, Dechoum M de S, Dufour-Dror J-M, Essl F, Flory SL, Genovesi P, Guarino F, Guangzhe L, Hulme PE, Jäger H, Kettle CJ, Krumm F, Langdon B, Lapin K, Lozano V, Le Roux JJ, Novoa A, Nuñez MA, Porté AJ, Silva JS, Schaffner U, Sitzia T, Tanner R, Tshidada N, Vítková M, Westergren M, Wilson JRU, Richardson DM (2020) Global guidelines for the sustainable use of non-native trees to prevent tree invasions and mitigate their negative impacts. NeoBiota 61: 65–116. https://doi.org/10.3897/neobiota.61.58380
  • Caldeira MC, Lecomte X, David TS, Pinto JG, Bugalho MN, Werner C (2015) Synergy of extreme drought and shrub invasion reduce ecosystem functioning and resilience in water-limited climates. Scientific Reports 5(1): 15110. https://doi.org/10.1038/srep15110
  • Calders K, Brede B, Newnham G, Culvenor D, Armston J, Bartholomeus H, Griebel A, Hayward J, Junttila S, Lau A, Levick S, Morrone R, Origo N, Pfeifer M, Verbesselt J, Herold M (2023) StrucNet: A global network for automated vegetation structure monitoring. Remote Sensing in Ecology and Conservation 9(5): 587–598. https://doi.org/10.1002/rse2.333
  • Carboni M, Livingstone SW, Isaac ME, Cadotte MW (2021) Invasion drives plant diversity loss through competition and ecosystem modification. Journal of Ecology 109(10): 3587–3601. https://doi.org/10.1111/1365-2745.13739
  • Castro-Díez P, Godoy O, Alonso A, Gallardo A, Saldaña A (2014) What explains variation in the impacts of exotic plant invasions on the nitrogen cycle? A meta-analysis. Ecology Letters 17: 1–12. https://doi.org/10.1111/ele.12197
  • Chaudhary VB, Holland EP, Charman-Anderson S, Guzman A, Bell-Dereske L, Cheeke TE, Corrales A, Duchicela J, Egan C, Gupta MM, Hannula SE, Hestrin R, Hoosein S, Kumar A, Mhretu G, Neuenkamp L, Soti P, Xie Y, Helgason T (2022) What are mycorrhizal traits? Trends in Ecology & Evolution 3(7): 573–581. https://doi.org/10.1016/j.tree.2022.04.003
  • Chen BJW, Teng SN, Zheng G, Cui L, Li S, Staal A, Eitel JUH, Crowther TW, Berdugo M, Mo L, Ma H, Bialic-Murphy L, Zohner CM, Maynard DS, Averill C, Zhang J, He Q, Evers JB, Anten NPR, Yizhaq H, Stavi I, Argaman E, Basson U, Xu Z, Zhang M-J, Niu K, Liu Q-X, Xu C (2022) Inferring plant–plant interactions using remote sensing. Journal of Ecology 110(10): 2268–2287. https://doi.org/10.1111/1365-2745.13980
  • Cherif E, Feilhauer H, Berger K, Dao PD, Ewald M, Hank TB, He Y, Kovach KR, Lu B, Townsend PA, Kattenborn T (2023) From spectra to plant functional traits: Transferable multi-trait models from heterogeneous and sparse data. Remote Sensing of Environment 292: 113580. https://doi.org/10.1016/j.rse.2023.113580
  • Crous CJ, Drake DC, Jacobsen AL, Pratt RB, Jacobs SM, Esler KJ (2019) Foliar nitrogen dynamics of an invasive legume compared to native non-legumes in fynbos riparian zones varying in water availability. Water S.A. 45(1 January): 103–109. https://doi.org/10.4314/wsa.v45i1.12
  • de Koning K, Broekhuijsen J, Kühn I, Ovaskainen O, Taubert F, Endresen D, Schigel D, Grimm V (2023) Digital twins: Dynamic model-data fusion for ecology. Trends in Ecology & Evolution 38(10): 916–926. https://doi.org/10.1016/j.tree.2023.04.010
  • Drenovsky RE, Grewell BJ, D’Antonio CM, Funk JL, James JJ, Molinari N, Parker IM, Richards CL (2012) A functional trait perspective on plant invasion. Annals of Botany 110(1): 141–153. https://doi.org/10.1093/aob/mcs100
  • Dyderski MK, Jagodziński AM (2019) Functional traits of acquisitive invasive woody species differ from conservative invasive and native species. NeoBiota 41: 91–113. https://doi.org/10.3897/neobiota.41.31908
  • Dzikiti S, Gush MB, Le Maitre DC, Maherry A, Jovanovic NZ, Ramoelo A, Cho MA (2016) Quantifying potential water savings from clearing invasive alien Eucalyptus camaldulensis using in situ and high resolution remote sensing data in the Berg River Catchment, Western Cape, South Africa. Forest Ecology and Management 361: 69–80. https://doi.org/10.1016/j.foreco.2015.11.009
  • Dzikiti S, Ntshidi Z, Le Maitre DC, Bugan RDH, Mazvimavi D, Schachtschneider K, Jovanovic NZ, Pienaar HH (2017) Assessing water use by Prosopis invasions and Vachellia karroo trees: Implications for groundwater recovery following alien plant removal in an arid catchment in South Africa. Forest Ecology and Management 398: 153–163. https://doi.org/10.1016/j.foreco.2017.05.009
  • Enders M, Havemann F, Ruland F, Bernard-Verdier M, Catford JA, Gómez-Aparicio L, Haider S, Heger T, Kueffer C, Kühn I, Meyerson LA, Musseau C, Novoa A, Ricciardi A, Sagouis A, Schittko C, Strayer DL, Vilà M, Essl F, Hulme PE, van Kleunen M, Kumschick S, Lockwood JL, Mabey AL, McGeoch MA, Palma E, Pyšek P, Saul W-C, Yannelli FA, Jeschke JM (2020) A conceptual map of invasion biology: Integrating hypotheses into a consensus network. Global Ecology and Biogeography 29(6): 978–991. https://doi.org/10.1111/geb.13082
  • Essl F, Latombe G, Lenzner B, Pagad S, Seebens H, Smith K, Wilson JRU, Genovesi P (2020) The Convention on Biological Diversity (CBD)’s Post-2020 target on invasive alien species – what should it include and how should it be monitored? NeoBiota 62: 99–121. https://doi.org/10.3897/neobiota.62.53972
  • Estévez J, Salinero-Delgado M, Berger K, Pipia L, Rivera-Caicedo JP, Wocher M, Reyes-Muñoz P, Tagliabue G, Boschetti M, Verrelst J (2022) Gaussian processes retrieval of crop traits in Google Earth Engine based on Sentinel-2 top-of-atmosphere data. Remote Sensing of Environment 273: 112958. https://doi.org/10.1016/j.rse.2022.112958
  • Ewald M, Skowronek S, Aerts R, Dolos K, Lenoir J, Nicolas M, Warrie J, Hattab T, Feilhauer H, Honnay O, Garzón-López CX, Decocq G, Van De Kerchove R, Somers B, Rocchini D, Schmidtlein S (2018) Analyzing remotely sensed structural and chemical canopy traits of a forest invaded by Prunus serotina over multiple spatial scales. Biological Invasions 20(8): 2257–2271. https://doi.org/10.1007/s10530-018-1700-9
  • Farley SS, Dawson A, Goring SJ, Williams JW (2018) Situating ecology as a big-data science: Current advances, challenges, and solutions. Bioscience 68(8): 563–576. https://doi.org/10.1093/biosci/biy068
  • Fenesi A, Botta‐Dukát Z, Miholcsa Z, Szigeti V, Molnár C, Sándor D, Szabó A, Kuhn T, Kovács‐Hostyánszki A (2023) No consistencies in abundance‐impact relationships across herbaceous invasive species and ecological impact metrics. Journal of Ecology 111(5): 967–1165. https://doi.org/10.1111/1365-2745.14085
  • Frey J, Holter P, Kinzinger L, Schindler Z, Morhart C, Kolbe S, Werner C, Seifert T (2023) Detailed mapping of below canopy surface temperatures in forests reveals new perspectives on microclimatic processes. Agricultural and Forest Meteorology 341: 109656. https://doi.org/10.1016/j.agrformet.2023.109656
  • Funk JL, Larson JE, Ames GM, Butterfield BJ, Cavender‐Bares J, Firn J, Laughlin DC, Sutton-Grier AE, Williams L, Wright J (2017) Revisiting the Holy Grail: Using plant functional traits to understand ecological processes. Biological Reviews of the Cambridge Philosophical Society 92(2): 1156–1173. https://doi.org/10.1111/brv.12275
  • Fusco EJ, Beaury EM, Bradley BA, Cox M, Jarnevich CS, Mahood AL, Nagy RC, Nietupski T, Halofsky JE (2023) The invasive plant data landscape: A synthesis of spatial data and applications for research and management in the United States. Landscape Ecology 38(12): 3825–3843. https://doi.org/10.1007/s10980-023-01623-z
  • Gaertner M, Biggs R, Te Beest M, Hui C, Molofsky J, Richardson DM (2014) Invasive plants as drivers of regime shifts: Identifying high-priority invaders that alter feedback relationships. Diversity & Distributions 20(7): 733–744. https://doi.org/10.1111/ddi.12182
  • Gallardo B, Bacher S, Barbosa AM, Gallien L, González-Moreno P, Martínez-Bolea V, Sorte C, Vimercati G, Vilà M (2024) Risks posed by invasive species to the provision of ecosystem services in Europe. Nature Communications 15(1): 2631. https://doi.org/10.1038/s41467-024-46818-3
  • Golicz K, Piepho HP, Minarsch EML, Niether W, Große-Stoltenberg A, Oldeland J, Breuer L, Gattinger A, Jacobs S (2023) Highlighting the potential of multilevel statistical models for analysis of individual agroforestry systems. Agroforestry Systems 97(8): 1481–1489. https://doi.org/10.1007/s10457-023-00871-x
  • Große-Stoltenberg A, Hellmann C, Thiele J, Werner C, Oldeland J (2018a) Early detection of GPP-related regime shifts after plant invasion by integrating imaging spectroscopy with airborne LiDAR. Remote Sensing of Environment 209: 780–792. https://doi.org/10.1016/j.rse.2018.02.038
  • Große-Stoltenberg A, Hellmann C, Thiele J, Oldeland J, Werner C (2018b) Invasive acacias differ from native dune species in the hyperspectral/biochemical trait space. Journal of Vegetation Science 29: 325–335. https://doi.org/10.1111/jvs.12608
  • Große-Stoltenberg A, Lizarazo I, Brundu G, Paiva Gonçalves V, Prado Osco L, Masemola C, Müllerová J, Werner C, Kotze I, Oldeland J (2023) Remote sensing of invasive wattles: state of the art and future perspectives. In: Richardson DM, Le Roux JJ, Marchante E (Eds) Wattles – Australian Acacia species around the world. CABI, UK, Wallingford, 474–496. https://doi.org/10.1079/9781800622197.0029
  • Grossiord C (2020) Having the right neighbors: How tree species diversity modulates drought impacts on forests. The New Phytologist 228(1): 42–49. https://doi.org/10.1111/nph.15667
  • Haberstroh S, Caldeira MC, Lobo-do-Vale R, Martins JI, Moemken J, Pinto JG, Werner C (2021) Nonlinear plant–plant interactions modulate impact of extreme drought and recovery on a Mediterranean ecosystem. The New Phytologist 231(5): 1784–1797. https://doi.org/10.1111/nph.17522
  • Hellmann C, Werner C, Oldeland J (2016a) A spatially explicit dual-isotope approach to map regions of plant-plant interaction after exotic plant invasion. PLoS ONE 11: e0159403. https://doi.org/10.1371/journal.pone.0159403
  • Hellmann C, Rascher KG, Oldeland J, Werner C (2016b) Isoscapes resolve species-specific spatial patterns in plant–plant interactions in an invaded Mediterranean dune ecosystem. Tree Physiology 36(12): 1460–1470. https://doi.org/10.1093/treephys/tpw075
  • Hellmann C, Große-Stoltenberg A, Thiele J, Oldeland J, Werner C (2017) Heterogeneous environments shape invader impacts: Integrating environmental, structural and functional effects by isoscapes and remote sensing. Scientific Reports 7(1): 4118. https://doi.org/10.1038/s41598-017-04480-4
  • Helsen K, Cleemput EV, Bassi L, Somers B, Honnay O (2020) Optical traits perform equally well as directly-measured functional traits in explaining the impact of an invasive plant on litter decomposition. Journal of Ecology 108(5): 2000–2011. https://doi.org/10.1111/1365-2745.13389
  • Hirn J, García JE, Montesinos‐Navarro A, Sánchez‐Martín R, Sanz V, Verdú M (2022) A deep Generative Artificial Intelligence system to predict species coexistence patterns. Methods in Ecology and Evolution 13(5): 1052–1061. https://doi.org/10.1111/2041-210X.13827
  • Hui C, Pyšek P, Richardson DM (2023) Disentangling the relationships among abundance, invasiveness and invasibility in trait space. npj. Biodiversity (Nepean) 2(1): 13. https://doi.org/10.1038/s44185-023-00019-1
  • IPBES (2019) Global assessment report on biodiversity and ecosystem services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. IPBES secretariat, Bonn, 1148 pp. https://doi.org/10.5281/zenodo.3831673
  • Jantzen JR, Laliberté E, Carteron A, Beauchamp-Rioux R, Blanchard F, Crofts AL, Girard A, Hacker PW, Pardo J, Schweiger AK, Demers-Thibeault S, Coops NC, Kalacska M, Vellend M, Bruneau A (2023) Evolutionary history explains foliar spectral differences between arbuscular and ectomycorrhizal plant species. The New Phytologist 238(6): 2651–2667. https://doi.org/10.1111/nph.18902
  • Jarić I, Heger T, Castro Monzon F, Jeschke JM, Kowarik I, McConkey KR, Pyšek P, Sagouis A, Essl F (2019) Crypticity in biological invasions. Trends in Ecology & Evolution 34(4): 291–302. https://doi.org/10.1016/j.tree.2018.12.008
  • Jeschke JM, Bacher S, Blackburn TM, Dick JTA, Essl F, Evans T, Gaertner M, Hulme PE, Kühn I, Mrugała A, Pergl J, Pyšek P, Rabitsch W, Ricciardi A, Richardson DM, Sendek A, Vilà M, Winter M, Kumschick S (2014) Defining the impact of non-native species. Conservation Biology 28(5): 1188–1194. https://doi.org/10.1111/cobi.12299
  • Kattenborn T, Leitloff J, Schiefer F, Hinz S (2021) Review on Convolutional Neural Networks (CNN) in vegetation remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing 173: 24–49. https://doi.org/10.1016/j.isprsjprs.2020.12.010
  • Kuebbing SE, Maynard DS, Bradford MA (2018) Linking functional diversity and ecosystem processes: A framework for using functional diversity metrics to predict the ecosystem impact of functionally unique species. Journal of Ecology 106: 687–698. https://doi.org/10.1111/1365-2745.12835
  • Kueffer C, Pyšek P, Richardson DM (2013) Integrative invasion science: Model systems, multi-site studies, focused meta-analysis and invasion syndromes. The New Phytologist 200(3): 615–633. https://doi.org/10.1111/nph.12415
  • Kumschick S, Gaertner M, Vilà M, Essl F, Jeschke JM, Pyšek P, Ricciardi A, Bacher S, Blackburn TM, Dick JTA, Evans T, Hulme PE, Kühn I, Mrugała A, Pergl J, Rabitsch W, Richardson DM, Sendek A, Winter M (2015) Ecological impacts of alien species: Quantification, scope, caveats, and recommendations. Bioscience 65(1): 55–63. https://doi.org/10.1093/biosci/biu193
  • Kumschick S, Bacher S, Bertolino S, Blackburn TM, Evans T, Roy HE, Smith K (2020) Appropriate uses of EICAT protocol, data and classifications. NeoBiota 62: 193–212. https://doi.org/10.3897/neobiota.62.51574
  • Lahoz-Monfort JJ, Magrath MJ (2021) A comprehensive overview of technologies for species and habitat monitoring and conservation. Bioscience 71(10): 1038–1062. https://doi.org/10.1093/biosci/biab073
  • Lausch A, Heurich M, Magdon P, Rocchini D, Schulz K, Bumberger J, King DJ (2020) A range of earth observation techniques for assessing plant diversity. In: Cavender-Bares J, Gamon JA, Townsend PA (Eds) Remote sensing of plant biodiversity. Springer, Cham, Cham, 309–348. https://doi.org/10.1007/978-3-030-33157-3_13
  • Le Maitre DC, Gush MB, Dzikiti S (2015) Impacts of invading alien plant species on water flows at stand and catchment scales. AoB Plants 7: plv043. https://doi.org/10.1093/aobpla/plv043
  • Le Maitre DC, Blignaut JN, Clulow A, Dzikiti S, Everson CS, Görgens AHM, Gush MB (2020) Impacts of plant invasions on terrestrial water flows in South Africa. In: van Wilgen BW, Measey J, Richardson DM, Wilson JR, Zengeya TA (Eds) Biological Invasions in South Africa. Springer, Cham, Cham, 431–457. https://doi.org/10.1007/978-3-030-32394-3_15
  • Lee MR, Bernhardt ES, Bodegom PM, Cornelissen JHC, Kattge J, Laughlin DC, Niinemets Ü, Peñuelas J, Reich PB, Yguel B (2017) Invasive species’ leaf traits and dissimilarity from natives shape their impact on nitrogen cycling: A meta-analysis. The New Phytologist 213(1): 128–139. https://doi.org/10.1111/nph.14115
  • Lekberg Y, Gibbons SM, Rosendahl S, Ramsey PW (2013) Severe plant invasions can increase mycorrhizal fungal abundance and diversity. The ISME Journal 7(7): 1424–1433. https://doi.org/10.1038/ismej.2013.41
  • Ludwig M, Moreno‐Martinez A, Hölzel N, Pebesma E, Meyer H (2023) Assessing and improving the transferability of current global spatial prediction models. Global Ecology and Biogeography 32(3): 356–368. https://doi.org/10.1111/geb.13635
  • McCue MD, Javal M, Clusella‐Trullas S, Roux JJL, Jackson MC, Ellis AG, Richardson DM, Valentine AJ, Terblanche JS (2020) Using stable isotope analysis to answer fundamental questions in invasion ecology: Progress and prospects. Methods in Ecology and Evolution 11(2): 196–214. https://doi.org/10.1111/2041-210X.13327
  • Meira-Neto JAA, Silva N, Villa PM, Silva MC, Tolentino GS, Buttschardt T, Ulm F, Máguas C (2023) Metabolic groups of plants in neotropical hyperseasonal savannas threatened by Australian Acacia invasion. Wetlands Ecology and Management 31(3): 401–417. https://doi.org/10.1007/s11273-023-09924-5
  • Millington JD (2021) Scale and hierarchy in landscape ecology. In: Francis RA, Millington JDA, Perry GLW, Minor ES (Eds) The Routledge Handbook of Landscape Ecology. Routledge, London, 49–66. https://doi.org/10.4324/9780429399480-4
  • Mitchell CE, Agrawal AA, Bever JD, Gilbert GS, Hufbauer RA, Klironomos JN, Maron JL, Morris WF, Parker IM, Power AG, Seabloom EW, Torchin ME, Vázquez DP (2006) Biotic interactions and plant invasions. Ecology Letters 9(6): 726–740. https://doi.org/10.1111/j.1461-0248.2006.00908.x
  • Müllerová J, Brundu G, Große-Stoltenberg A, Kattenborn T, Richardson DM (2023) Pattern to process, research to practice: Remote sensing of plant invasions. Biological Invasions 25(12): 3651–3676. https://doi.org/10.1007/s10530-023-03150-z
  • Naethe P, De Sanctis A, Burkart A, Campbell PK, Colombo R, Di Mauro B, Damm A, El-Madany T, Fava F, Gamon JA, Huemmrich KF, Migliavacca M, Paul-Limoges E, Rascher U, Rossini M, Schüttemeyer D, Tagliabue G, Zhang Y, Julitta T (2024) Towards a standardized, ground-based network of hyperspectral measurements: Combining time series from autonomous field spectrometers with Sentinel-2. Remote Sensing of Environment 303: 114013. https://doi.org/10.1016/j.rse.2024.114013
  • Nagler PL, Glenn EP, Hinojosa-Huerta O (2009) Synthesis of ground and remote sensing data for monitoring ecosystem functions in the Colorado River Delta, Mexico. Remote Sensing of Environment 113: 1473–1485. https://doi.org/10.1016/j.rse.2008.06.018
  • Nielsen JA, Frew RD, Whigham PA, Callaway RM, Dickinson KJM (2016) Thyme travels: 15N isoscapes of Thymus vulgaris L. invasion in lightly grazed pastoral communities. Austral Ecology 41(1): 28–39. https://doi.org/10.1111/aec.12284
  • Novoa A, Richardson DM, Pyšek P, Meyerson LA, Bacher S, Canavan S, Catford JA, Čuda J, Essl F, Foxcroft LC, Genovesi P, Hirsch H, Hui C, Jackson MC, Kueffer C, Le Roux JJ, Measey J, Mohanty NP, Moodley D, Müller-Schärer H, Packer JG, Pergl J, Robinson TB, Saul W-C, Shackleton RT, Visser V, Weyl OLF, Yannelli FA, Wilson JRU (2020) Invasion syndromes: A systematic approach for predicting biological invasions and facilitating effective management. Biological Invasions 22(5): 1801–1820. https://doi.org/10.1007/s10530-020-02220-w
  • Pettorelli N, Schulte to Bühne H, Tulloch A, Dubois G, Macinnis-Ng C, Queirós AM, Keith DA, Wegmann M, Schrodt F, Stellmes M, Sonnenschein R, Geller GN, Roy S, Somers B, Murray N, Bland L, Geijzendorffer I, Kerr JT, Broszeit S, Leitão PJ, Duncan C, El Serafy G, He KS, Blanchard JL, Lucas R, Mairota P, Webb TJ, Nicholson E (2018) Satellite remote sensing of ecosystem functions: Opportunities, challenges and way forward. Remote Sensing in Ecology and Conservation 4(2): 71–93. https://doi.org/10.1002/rse2.59
  • Probert AF, Volery L, Kumschick S, Vimercati G, Bacher S (2020) Understanding uncertainty in the Impact Classification for Alien Taxa (ICAT) assessments. NeoBiota 62: 387–405. https://doi.org/10.3897/neobiota.62.52010
  • Pyšek P, Jarošík V, Hulme PE, Pergl J, Hejda M, Schaffner U, Vilà M (2012) A global assessment of invasive plant impacts on resident species, communities and ecosystems: The interaction of impact measures, invading species’ traits and environment. Global Change Biology 18(5): 1725–1737. https://doi.org/10.1111/j.1365-2486.2011.02636.x
  • Pyšek P, Bacher S, Kühn I, Novoa A, Catford JA, Hulme P, Pergl J, Richardson DM, Wilson JRU, Blackburn TM (2020a) MAcroecological Framework for Invasive Aliens (MAFIA): Disentangling large-scale context dependence in biological invasions. NeoBiota 62: 407–461. https://doi.org/10.3897/neobiota.62.52787
  • Pyšek P, Hulme PE, Simberloff D, Bacher S, Blackburn TM, Carlton JT, Dawson W, Essl F, Foxcroft LC, Genovesi P, Jeschke JM, Kühn I, Liebhold AM, Mandrak NE, Meyerson LA, Pauchard A, Pergl J, Roy HE, Seebens H, van Kleunen M, Vilà M, Wingfield MJ, Richardson DM (2020b) Scientists’ warning on invasive alien species. Biological Reviews of the Cambridge Philosophical Society 95(6): 1511–1534. https://doi.org/10.1111/brv.12627
  • Rascher KG, Máguas C, Werner C (2010) On the use of phloem sap δ13C as an indicator of canopy carbon discrimination. Tree Physiology 30(12): 1499–1514. https://doi.org/10.1093/treephys/tpq092
  • Reichstein M, Camps-Valls G, Stevens B, Jung M, Denzler J, Carvalhais N (2019) Deep learning and process understanding for data-driven Earth system science. Nature 566(7743): 195–204. https://doi.org/10.1038/s41586-019-0912-1
  • Reimer J, Stöcklin S, Comella LM, Woias P, Werner C, Reindl L, Rupitsch SJ (2021) An autonomous and wireless pulse-amplitude modulated chlorophyll fluorometer. tm-Technisches Messen 88: 773–784. https://doi.org/10.1515/teme-2021-0104
  • Ricciardi A, Iacarella JC, Aldridge DC, Blackburn TM, Carlton JT, Catford JA, Dick JTA, Hulme PE, Jeschke JM, Liebhold AM, Lockwood JL, MacIsaac HJ, Meyerson LA, Pyšek P, Richardson DM, Ruiz GM, Simberloff D, Vilà M, Wardle DA (2021) Four priority areas to advance invasion science in the face of rapid environmental change. Environmental Reviews 29(2): 119–141. https://doi.org/10.1139/er-2020-0088
  • Sapsford SJ, Brandt AJ, Davis KT, Peralta G, Dickie IA, Gibson RD II, Green JL, Hulme PE, Nuñez MA, Orwin KH, Pauchard A, Wardle DA, Peltzer DA (2020) Towards a framework for understanding the context dependence of impacts of non-native tree species. Functional Ecology 34(5): 944–955. https://doi.org/10.1111/1365-2435.13544
  • Sardans J, Bartrons M, Margalef O, Gargallo-Garriga A, Janssens IA, Ciais P, Obersteiner M, Sigurdsson BD, Chen HYH, Peñuelas J (2017) Plant invasion is associated with higher plant-soil nutrient concentrations in nutrient-poor environments. Global Change Biology 23(3): 1282–1291. https://doi.org/10.1111/gcb.13384
  • Sena-Souza JP, Rodovalho NL, Andrade A, Pinto JRR, Nardoto GB (2023) Mapping the effects of Melinis minutiflora invasion on soil nitrogen dynamics in the Brazilian savanna: A dual-isotope approach. Pedobiologia 96: 150863. https://doi.org/10.1016/j.pedobi.2023.150863
  • Sethi SS, Kovac M, Wiesemüller F, Miriyev A, Boutry CM (2022) Biodegradable sensors are ready to transform autonomous ecological monitoring. Nature Ecology & Evolution 6(9): 1245–1247. https://doi.org/10.1038/s41559-022-01824-w
  • Simberloff D, Martin J-L, Genovesi P, Maris V, Wardle DA, Aronson J, Courchamp F, Galil B, García-Berthou E, Pascal M, Pyšek P, Sousa R, Tabacchi E, Vilà M (2013) Impacts of biological invasions: What’s what and the way forward. Trends in Ecology & Evolution 28(1): 58–66. https://doi.org/10.1016/j.tree.2012.07.013
  • Skidmore AK, Coops NC, Neinavaz E, Ali A, Schaepman ME, Paganini M, Kissling WD, Vihervaara P, Darvishzadeh R, Feilhauer H, Fernandez M, Fernández N, Gorelick N, Geijzendorffer I, Heiden U, Heurich M, Hobern D, Holzwarth S, Muller-Karger FE, Van De Kerchove R, Lausch A, Leitão PJ, Lock MC, Mücher CA, O’Connor B, Rocchini D, Roeoesli C, Turner W, Vis JK, Wang T, Wegmann M, Wingate V (2021) Priority list of biodiversity metrics to observe from space. Nature Ecology & Evolution 5(7): 896–906. https://doi.org/10.1038/s41559-021-01451-x
  • Soliveres S, Smit C, Maestre FT (2015) Moving forward on facilitation research: response to changing environments and effects on the diversity, functioning and evolution of plant communities: Facilitation, community dynamics and functioning. Biological Reviews of the Cambridge Philosophical Society 90(1): 297–313. https://doi.org/10.1111/brv.12110
  • Sousa D, Fisher JB, Galvan FR, Pavlick RP, Cordell S, Gioambelluca TW, Giardina CP, Gilbert GS, Imran-Narahari F, Litton CM, Lutz JA, North MP, Orwig DA, Osterag R, Sack L, Phillips RP (2021) Tree canopies reflect mycorrhizal composition. Geophysical Research Letters 48: e2021GL092764. https://doi.org/10.1029/2021GL092764
  • Timmermans J, Kissling WD (2023) Advancing terrestrial biodiversity monitoring with satellite remote sensing in the context of the Kunming-Montreal global biodiversity framework. Ecological Indicators 154: 110773. https://doi.org/10.1016/j.ecolind.2023.110773
  • Tognetti R, Valentini R, Marchesini LB, Gianelle D, Panzacchi P, Marshall JD (2022) Continuous Monitoring of Tree Responses to Climate Change for Smart Forestry: A Cybernetic Web of Trees. In: Tognetti R, Smith M, Panzacchi P (Eds) Climate-Smart Forestry in Mountain Regions. Managing Forest Ecosystems. Springer, Cham, 361–398. https://doi.org/10.1007/978-3-030-80767-2_10
  • Truong TTA, Hardy GESJ, Andrew ME (2017) Contemporary remotely sensed data products refine invasive plants risk mapping in data poor regions. Frontiers in Plant Science 8: 770. https://doi.org/10.3389/fpls.2017.00770
  • Valbuena R, O’Connor B, Zellweger F, Simonson W, Vihervaara P, Maltamo M, Silva CA, Almeida DRA, Danks F, Morsdorf F, Chirici G, Lucas R, Coomes DA, Coops NC (2020) Standardizing ecosystem morphological traits from 3D information sources. Trends in Ecology & Evolution 35(8): 656–667. https://doi.org/10.1016/j.tree.2020.03.006
  • Vaz AS, Alcaraz-Segura D, Campos JC, Vicente JR, Honrado JP (2018) Managing plant invasions through the lens of remote sensing: A review of progress and the way forward. The Science of the Total Environment 642: 1328–1339. https://doi.org/10.1016/j.scitotenv.2018.06.134
  • Werner C, Zumkier U, Beyschlag W, Máguas C (2010) High competitiveness of a resource demanding invasive acacia under low resource supply. Plant Ecology 206(1): 83–96. https://doi.org/10.1007/s11258-009-9625-0
  • Werner C, Wallrabe U, Christen A, Comella L, Dormann C, Göritz A, Grote R, Haberstroh S, Jouda M, Kiese R, Koch B, Korvink J, Kreuzwieser J, Lang F, Müller J, Prucker O, Reiterer A, Rühe J, Rupitsch S, Schack-Kirchner H, Schmitt K, Stobbe N, Weiler M, Woias P, Wöllenstein J (2024) ECOSENSE - Multi-scale quantification and modelling of spatio-temporal dynamics of ecosystem processes by smart autonomous sensor networks. Research Ideas and Outcomes 10: e129357. https://doi.org/10.3897/rio.10.e129357
  • Whitney KD, Gabler CA (2008) Rapid evolution in introduced species, ‘invasive traits’ and recipient communities: Challenges for predicting invasive potential. Diversity & Distributions 14(4): 569–580. https://doi.org/10.1111/j.1472-4642.2008.00473.x

Supplementary materials

Supplementary material 1 

Description of the animated video

Christiane Werner, Christine Hellmann, André Große-Stoltenberg

Data type: docx

This dataset is made available under the Open Database License (http://opendatacommons.org/licenses/odbl/1.0/). The Open Database License (ODbL) is a license agreement intended to allow users to freely share, modify, and use this Dataset while maintaining this same freedom for others, provided that the original source and author(s) are credited.
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Supplementary material 2 

Animated video

Christiane Werner, Christine Hellmann, André Große-Stoltenberg

Data type: mp4

This dataset is made available under the Open Database License (http://opendatacommons.org/licenses/odbl/1.0/). The Open Database License (ODbL) is a license agreement intended to allow users to freely share, modify, and use this Dataset while maintaining this same freedom for others, provided that the original source and author(s) are credited.
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