Distribution Of Invasive Plant Species Research Articles

A Systematic Review on the Distribution of Invasive Plant Species Across Asia: Assessing the Rates of Invasion Success and Management

The advancement of invasive plant species in a particular environment is threatening across Asia. When they are introduced, they could have a variety of different negative effects, either as minor inconveniences or a severe problem. The aim of this review is to compile a list of invasive plant species across Asia and organize a set of data according to their reproduction rates, growth rates, dispersal rates, and characterize the allelochemicals they produce and allelopathic effects they have on their environment. The target is to know the extent of their spread ability in the area that they inhibit and know what control measures can be done with that specific species. The data has been gathered by compiling research articles and obtaining pertinent data relating to the objective. This review also utilized the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist to confirm the credibility of the collected references. To achieve the needed references, keywords such as "distribution", "invasive", and "Asia" have been used to search for these on search engines and research journals such as Google Scholar, ScienceDirect, ResearchGate, and Directory for Open Access Journals, to be then compiled in spreadsheets and document files. The results show that the growth rate of these species varies from 7.5-60 cm per year, where Ipomoea eberhardtii has the highest growth rate of 60 cm per year. The dispersal rates of the species ranges from 0.002-2592 km from the parent plant, where animal mediated, water, and wind dispersal methods are the primary vector to spread. Allelopathic effects produced by the allelochemicals present mostly inhibit growth and seed germination. There are already implemented approaches to control these species, where biological control, physical means, and the use of chemicals are the most used strategies. In different dynamic processes, physical and chemical control measures are most utilized in this case.

The influence of climatic and human-induced factors on the spatial distribution of invasive plant species richness across the Loess Plateau

Biological invasion poses a critical global issue, leading to substantial detrimental impacts on biodiversity, the environment, and the economy. The objective of the study is to offer a thorough understanding of how both climatic and human-induced elements impact the geographic richness of invasive plant species across the Loess Plateau. We evaluate the distribution of invasive plant species at the county level across the Loess Plateau by examining herbarium records from China. We incorporate 16 climatic and anthropogenic variables to depict the local environmental settings. Furthermore, we apply a classification and regression tree approach to investigate the correlation between the richness of invasive plant species and the identified factors. Our study demonstrates that a total of 401 invasive plant species are identified, which are spread across 249 genera and 61 families. Among these, the Asteraceae family stands out as the most prevalent, trailed by Poaceae and Fabaceae. The spatial distribution of invasive plant species richness reveals a notable trend, with the highest frequencies found in the southeastern parts of the region and the lowest in the northwestern areas. It is noteworthy that regions with higher levels of economic advancement tend to harbor a more significant abundance of invasive plant species. The richness of invasive plant species on the Loess Plateau is predominantly shaped by a combination of climatic and human variables, such as annual precipitation, gross domestic product, maximum temperature of warmest month, and minimum temperature of coldest month. To fully comprehend the ecological and biological mechanisms underlying the diversity of invasive plant species on the Loess Plateau, a pioneering conceptual framework has been established. Our study suggests that achieving a harmonious equilibrium among development, conservation, and invasion mitigation is essential for recognizing emerging risks associated with habitat alterations, climate change, and socio-economic advancements in arid regions.

A novel hybrid model for species distribution prediction using neural networks and Grey Wolf Optimizer algorithm

Neural networks are frequently employed to model species distribution through backpropagation methods, known as backpropagation neural networks (BPNN). However, the complex structure of BPNN introduces parameter settings challenges, such as the determination of connection weights, which can affect the accuracy of model simulation. In this paper, we integrated the Grey Wolf Optimizer (GWO) algorithm, renowned for its excellent global search capacity and rapid convergence, to enhance the performance of BPNN. Then we obtained a novel hybrid algorithm, the Grey Wolf Optimizer algorithm optimized backpropagation neural networks algorithm (GNNA), designed for predicting species’ potential distribution. We also compared the GNNA with four prevalent species distribution models (SDMs), namely the generalized boosting model (GBM), generalized linear model (GLM), maximum entropy (MaxEnt), and random forest (RF). These models were evaluated using three evaluation metrics: the area under the receiver operating characteristic curve, Cohen’s kappa, and the true skill statistic, across 23 varied species. Additionally, we examined the predictive accuracy concerning spatial distribution. The results showed that the predictive performance of GNNA was significantly improved compared to BPNN, was significantly better than that of GLM and GBM, and was even comparable to that of MaxEnt and RF in predicting species distributions with small sample sizes. Furthermore, the GNNA demonstrates exceptional powers in forecasting the potential non-native distribution of invasive plant species.

Improving the mapping of coastal invasive species using UAV imagery and deep learning

ABSTRACT Spartina anglica C. E. Hubb. is an invasive species of saltmarsh and mudflats in Ireland. It spreads quickly and can form extensive meadows, which can lead to the extinction of native plant and animal species. Traditional ground-based field surveys would be expensive for the scale of monitoring needed to formulate site-specific management strategies. Advanced mapping techniques, such as Unoccupied Aerial Vehicle (UAV) remote sensing and deep learning (DL), offer an opportunity to automatically map invasive species’ occurrence. However, training a DL model requires lots of labelled data which can be prohibitively difficult and expensive to obtain. We implemented a DL semantic segmentation technique on UAV imagery of Spartina-invaded habitats and tested a range of hyperparameters—model architectures, encoder backbones and input image patch sizes—to determine an effective segmentation network structure. We also investigated applying data augmentation and pseudo-labelling techniques to increase the size of the labelled dataset. U-Net architecture with Inception-v3 as its backbone trained on 128 × 128-pixel image patches offered the best model performance: mean Intersection-Over-Union (mIOU) score = 0.832. The model trained on the combined augmented and pseudo-labelled data achieved an mIOU score of 0.712 on a test dataset, while there was a decrease of 0.158 in model performance when only the original labelled data were used. This result suggests the potential for using these techniques in creating more robust models. The proposed methodology demonstrates that the combination of UAV imagery and deep learning could be a promising tool for mapping the distribution of invasive plant species.

Comparing the Performance of CMCC-BioClimInd and WorldClim Datasets in Predicting Global Invasive Plant Distributions.

Species distribution modeling (SDM) has been widely used to predict the distribution of invasive plant species based on bioclimatic variables. However, the specific selection of these variables may affect the performance of SDM. This investigation elucidates a new bioclimate variable dataset (i.e., CMCC-BioClimInd) for its use in SDM. The predictive performance of SDM that includes WorldClim and CMCC-BioClimInd was evaluated by AUC and omission rate and the explanatory power of both datasets was assessed by the jackknife method. Furthermore, the ODMAP protocol was used to record CMCC-BioClimInd to ensure reproducibility. The results indicated that CMCC-BioClimInd effectively simulates invasive plant species' distribution. Based on the contribution rate of CMCC-BioClimInd to the distribution of invasive plant species, it was inferred that the modified and simplified continentality and Kira warmth index from CMCC-BioClimInd had a strong explanatory power. Under the 35 bioclimatic variables of CMCC-BioClimInd, alien invasive plant species are mainly distributed in equatorial, tropical, and subtropical regions. We tested a new bioclimate variable dataset to simulate the distribution of invasive plant species worldwide. This method has great potential to improve the efficiency of species distribution modeling, thereby providing a new perspective for risk assessment and management of global invasive plant species.

Considering urban uses at a fine spatial resolution to understand the distribution of invasive plant species in cities

Cities are heterogeneous landscapes, composed of different urban uses with diverse histories. A thorough description of these urban landscapes is required to understand ecological patterns, particularly concerning high-stakes species such as invasive alien plants (IAP) which are abundant in cities. We assessed the effects of urban uses and linear structural elements on the distributions of seven IAP in a medium-sized French city (Blois). We examined how the relative contributions of these variables vary between three spatial scales of analysis (100 m, 200 m and 400 m) and three urban classifications. We characterized the use and age of urban neighborhoods through three classifications with different levels of details and described road and rail networks of Blois. We analyzed their effects on the distribution of seven invasive plants recorded throughout the whole city using GLM models. Urban use was the most significant variable in explaining IAP distribution. This variable was especially important at the finest spatial scale which allowed a detailed urban classification. Individual residential neighborhoods of different ages explained the distribution of Buddleja davidii, Berberis aquifolium and Acer negundo, whereas industrial areas explained Ailanthus altissima distribution. The effects of linear structural elements were lower and differed between species. We show that combining fine-scale spatial analyses with detailed characterization of urban use diversity is critical to understanding ecological patterns at the city scale. Investigating planting choices and dispersal process of IAP could complete our results. Urban descriptions based on explicit urban forms could also contribute to understanding species distribution in cities.

ИНВАЗИОННЫЕ ВИДЫ-«ТРАНСФОРМЕРЫ» ВО ФЛОРЕ БАССЕЙНА РЕКИ АМУР

The author considers the issues related to the distribution of invasive plant species in the Amur River basin. It is presented a brief analysis of species with the invasive status «transformer».

Abiotic factors, not mycorrhizal associations, predict size and abundance of the invasive grass Microstegium vimineum1

Both abiotic and biotic factors can influence distributions of invasive plant species, and it is important to understand how such factors contribute to invasion success in order to develop successful management strategies. Microstegium vimineum, known as stiltgrass, is an invasive annual grass in U.S. eastern deciduous forests that can outcompete native understory species, decrease diversity, and prevent the regeneration of native trees. Microstegium is also known to form associations with arbuscular mycorrhizal fungi (AMF), but research has yet to demonstrate whether this association has a role in Microstegium invasion and dominance over native vegetation. We conducted a field survey in invaded and uninvaded habitats across six sites near Louisville, KY, USA, to explore the relative importance of biotic versus abiotic factors in predicting Microstegium abundance and size. Canopy openness was the strongest predictor of Microstegium abundance, followed by soil moisture. Soil nitrogen was the strongest predictor of Microstegium tiller height. Surprisingly, AMF extraradical hyphal abundance and root colonization were not significant predictors of Microstegium abundance or size. In terms of abiotic factors, our results confirm previous studies that have demonstrated that Microstegium grows best in areas with high light and high soil moisture; however, our analysis provides further insight by assessing the relative importance of each of these factors for Microstegium invasion.

Predicting the potential sites of Chromolaena odorata and Lantana camara in forest landscape of Eastern Ghats using habitat suitability models

Conservation strategies need reliable information on species distribution and habitat suitability. Modelling the invasion probability of species is required for invasive alien species management. The present study aims in predicting the suitable habitat and potential distribution of invasive plant species - Chromolaena odorata and Lantana camara. We used the Maximum entropy (MaxEnt), Random forest, Surface range envelope, Boosted regression tree analysis, Classification tree analysis, and Generalised boosted model to identify the suitable habitats for potential environmental distribution of invasive alien species in the Eastern Ghats of Visakhapatnam district, Andhra Pradesh, India. The Area under Curve (AUC) for each model has been estimated for predicting the potentially suitable habitats of invasion. The results suggested that the MaxEnt model, Random forest, boosted regression tree, and Surface range envelope were suitable approaches in identifying areas of potential habitats of selected invasive alien species. This study aids in developing long-term conservation practices for the management of invasive alien species.

Functional trait perspective on suitable habitat distribution of invasive plant species at a global scale

Plant invasion has been proved to threaten biodiversity conservation and ecosystem maintenance at a global scale. It is a challenge to project suitable habitat distributions of invasive plant species (IPS) for invasion risk assessment at large spatial scales. Interaction outcomes between native and invasive plant species are dependent as a function of trait context, which may contribute to suitable habitat distributions of IPS. Here, we explored the effect of functional trait context on suitable habitat distribution of invasive plant species at a global scale. The plant functional trait context was quantified based on the community mean values of specific leaf area, leaf nitrogen concentration, height, and woody density. Habitat suitability modeling was used to project suitable habitat distributions of IPS based on functional trait and abiotic (climate, soil, and human footprint) contexts. There were significant differences in contexts between functional traits and abiotic conditions for IPS across different biomes at a global scale. Community mean functional traits, particularly, height could contribute to habitat suitability of IPS. The functional trait context could have a large effect on the habitat suitability of IPS in Boreal forests/Taiga, Tropical and subtropical grasslands, savannas and shrublands, Tundra, Mediterranean forests, woodlands and scrub, and Mangroves. Functional trait context could affect suitable habitat distributions of IPS at a global scale, indicating that functional trait context should be used as the input for modeling habitat suitability of IPS. The study provided new insights into the application of habitat suitability modeling on plant invasion risk assessment.

Geographic Distribution Shift of Invasive Plant Austroeupatorium inulifolium in the Future Climate Projection

Aims: This study aims to predict the future geographic distribution shift of invasive plant species Austroeupathorium inulifolium as the impact of global climate change.
 Study Design: The rising temperature and precipitation change lead to the geographic distribution shift of organisms. A. inulifolium belongs to invasive plant species that often causes a substantial economic loss and ecological degradation in the invaded areas. Modelling of species distribution using the climate-based model could be used to understand the geographic distribution shift of invasive species in the future scenario under global climate change.
 Place and Duration of Study: Center for Plant Conservation and Botanic Gardens – LIPI and 6 months.
 Methodology: The total 2228 of occurrence records were derived from the Global Biodiversity Information Facility (GBIF) database. The seven climatic variables were selected from 19 variables using a pairwise correlation test (vifcor) with a threshold >0.7. The ensemble model was used by combining Random Forest (RF) and Support Vector Machine (SVM).
 Results: Both two models are well-performed either using AUC or TSS evaluation methods. RF and SVM have AUC >0.95, and TSS >0.8. The predicted current distribution tends to have larger distribution areas compared to observed occurrence records. The predicted future distribution seems to be shifted in several parts of North America and Europe.
 Conclusion: The geographic distribution of invasive plant species A. inulifolium will be shifted to the Northern part of globe in 2090. Mean temperature of driest quarter and precipitation of warmest quarter are the two most important variables that determine the distribution pattern of the A. inulifolium. The predictive distribution pattern of invasive plant A. inulifolium would be important to provide information about the impact of climate change to the geographic distribution shift of this species.

Invasive Plant Species Distribution Is Structured by Soil and Habitat Type in the City Landscape.

Invasive alien species (IAS) is a global problem that largely relates to human activities and human settlements. To prevent the further spread of IAS, we first need to know their pattern of distribution, to determine which constitutes the greatest threat, and understand which habitats and migration pathways they prefer. Our research aimed to identify the main vectors and distribution pattern of IAS of plants in the city environment. We checked the relations between species distribution and such environmental factors as urban soil type and habitat type. We applied data on IAS occurrence (collected in the period 1973–2015) in 515 permanent plots with dimensions of 0.5 × 0.5 km and analyzed by direct ordination methods. In total, we recorded 66 IAS. We found a 27% variance in the IAS distribution pattern, which can be explained by statistically significant soil and habitat types. The most important for species distribution were: river and alluvial soils, forests and related rusty soils, and places of intensive human activities, including areas of urbisols and industriosols. Our results provide details that can inform local efforts for the management and control of invasive species, and they provide evidence of the different associations between natural patterns and human land use.

Municipal Integrated Coastal Management Facilitation in Latvia: Piloting Public Multi-Thematical Monitoring

This paper contains a pilot study on coastal values and potential means of determining and characterising them in the terms of coastal planning and management. The research case study took place in the coastal region of North East Latvia – in the municipality of Salacgriva. The municipality coast is very diverse: coastal access, landscape and the whole biogeography is changing very much along the whole 54 km, being also rich in various natural and cultural heritage assets. Such great coastal variety in the limits of one administrative territory does require very selective governance approaches and dynamic management to be realized by local administration, which has very limited necessary capacities of all type, alike other rural coastal municipalities in Latvia. Also, there is very limited coastal information as well as assessment/interpretation capacities. All this requires mandatory development of municipal coastal monitoring and information/science-policy interface, to be based on general System Analysis Framework (SAF) application, first, on social-ecological system approach, and, second, including necessary developments of stakeholder participation and bottom-up governance approaches and, at the first, developing of the public monitoring (citizen science approach, e.g., Eco schools alike public representatives, etc.) capacities and methodologies. There has been developed an initial proposal for a new and multi-thematic coastal core area monitoring and governance tool. Coastal resources were assessed using a coastal value-based prioritisation tool, which generates knowledge of the mutual connection among various social-ecological resources/assets along the shore. During the study, data was collected along the municipal coastline taking into account the elements characterising the beach and shoreline landscape, as well as the distribution of invasive plant species, algae and plants washed up by the sea, as well as waste created by people along the coast. This data was subsequently collated, described, and combined with separate conclusions made based on beach visitors’ interviews that were conducted along the entire Latvian coast as well as interviews of the Salacgriva municipality’s main stakeholder groups. Information was also obtained from the analysis of documents seeking to facilitate development of a multi-thematic coastal value prioritisation tool and to distinguish coastal management priorities that can be set as proposals to coastal governments for developing a sustainable and more integrated coastal management background.

Geostatistical models with the use of hyperspectral data and seasonal variation – A new approach for evaluating the risk posed by invasive plants

The general trend of ongoing plant invasion and the increasing number of species that may become invasive in the future, force seeking solutions that can improve the efficiency and economy of their management. Thus, we applied a novel approach combining the use of geostatistical interpolators such as ordinary kriging (OK) and co-kriging (CK) with environmental and hyperspectral data to evaluate the potential threat associated with the distribution of invasive plant species and to predict their probabilities of occurrence above the selected threshold of 10%. The specific spatial patterns of the probability of occurrence of Heracleum sosnowskyi and Fallopia spp. were modelled in two study areas in southern Poland. The significant achievement of this study was the application of geostatistical tools producing results characterized by a degree of precision quantified by cross-validation errors, and prediction errors after field verification. OK and CK returned root mean squared error (RMSE) values in a range from 0.21 to 0.51 and 0.21 to 0.47, respectively. For OK and CK, the prediction errors resulting from field verification in the following year were between 0.03–0.39, and 0.03–0.29, respectively. Additionally, the study provided the first prediction maps (2D) and Digital Prediction Models (DPMs) (3D) visualizations of the probability of occurrence of both invasive plants. Although the proposed approach is illustrated with real case studies related to Heracleum sosnowskyi and Fallopia spp., it could be extended to other species. This demonstrates the potential of an effective alternative strategy for evaluating the risk posed by invasive plants, that will be able to provide fast, low cost and effective prediction and monitoring of their spread. For institutions dealing with invasive plants, this may be beneficial and help to reduce the negative consequences of their improper management.

Mapping Invasive Lupinus polyphyllus Lindl. in Semi-natural Grasslands Using Object-Based Image Analysis of UAV-borne Images

Knowledge on the spatio-temporal distribution of invasive plant species is vital to maintain biodiversity in grasslands which are threatened by the invasion of such plants and to evaluate the effect of control activities conducted. Manual digitising of aerial images with field verification is the standard method to create maps of the invasive Lupinus polyphyllus Lindl. (Lupine) in semi-natural grasslands of the UNESCO biosphere reserve “Rhön”. As the standard method is labour-intensive, a workflow was developed to map lupine coverage using an unmanned aerial vehicle (UAV)-borne remote sensing (RS) along with object-based image analysis (OBIA). UAV-borne red, green, blue and thermal imaging, as well as photogrammetric canopy height modelling (CHM) were applied. Images were segmented by unsupervised parameter optimisation into image objects representing lupine plants and grass vegetation. Image objects obtained were classified using random forest classification modelling based on objects’ attributes. The classification model was employed to create lupine distribution maps of test areas, and predicted data were compared with manually digitised lupine coverage maps. The classification models yielded a mean prediction accuracy of 89%. The maximum difference in lupine area between classified and digitised lupine maps was 5%. Moreover, the pixel-wise map comparison showed that 88% of all pixels matched between classified and digitised maps. Our results indicated that lupine coverage mapping using UAV-borne RS data and OBIA provides similar results as the standard manual digitising method and, thus, offers a valuable tool to map invasive lupine on grasslands.

Spatial modelling the effects of climate change on the distribution of Lantana camara in Southern Zimbabwe

Although climate change influences the distribution of invasive plant species, there is limited knowledge regarding its potential effects on Lantana camara distributions, especially in southern African savanna ecosystems. In this study, we predict the potential effects of climate change on the future distribution of Lantana camara as well as determine the ecosystems at risk from Lantana camara invasion in Matabeleland South Province of Zimbabwe. To do this, ensemble modelling consisting of three Species Distribution Models (SDMs) was coupled with three Global Circulation Models (GCMs) and two climate scenarios (Representative Concentration Pathways) for the 2070s. Results show that, on average, the bioclimatically suitable habitat of Lantana camara is projected to increase in the province by between ~3 and 33% and 32 and 82% under RCP 2.6 and RCP8.5, respectively. On average, the habitat suitability of Lantana camara is projected to increase by 5892 km2, which is about 65% of the total area of southern Zimbabwe. The northern parts of the province are more likely to be invaded by the focal species than any other part of the province. Overall, the results of the study underscore the importance of species distribution modelling under a changing climate.

Spatial variation and driving factors of invasive plants in Fujian Province, China

Based on the field investigation data and the integration GIS spatial methods and geographical detector model, we analyzed the main and interactive effects of geographical and social environmental factors on the distribution of invasive plants in Fujian Province, China. The results showed that a total of 82 invasive plant species were recorded, with Compositae as the dominant family. Conyza canadensis, Ageratum conyzoides, and Alternanthera philoxeroides had the highest frequencies. There were more invasive species in coastal areas than in inland areas. Fuzhou and Xiamen were the hot areas for plant invaision. The invasive plants widely distributed at different altitudes, and the invasion reduced with the increasing altitude. The geographical detector analysis showed that rainfall as a natural environment factor and road density and people density as socio-economy factors were the major driving factors for the distribution of invasive plant species. The multi-factor interaction had a positive effect on the spatial distribution of invasive plants, implying the complexity of impact factors on the distribution of invasive plant species. In conclusion, the geographical detector could be used in the studies of invasive plants, and environmental factors could be also applied for monitoring the suitable establishment areas of invasive plants in Fujian Province. Our results provide a scientific basis for effective management of invasive plants.

Changes in the potential distribution of invasive plant species in continental Spain in response to climate change

ABSTRACTBackground: Climate change may increase the risk of biological invasions. However, current knowledge of this interaction is limited.Aims: We aimed to quantify (1) the effect of climate change on the potential distribution of invasive plant species in Spain, (2) the importance of the area of origin of such species and (3) the vulnerability of different biogeographic provinces to future changes in climatic suitability for invaders.Methods: We applied six methods of species distribution modelling to assess the variation of climatically suitable areas for 40 alien plants. We developed a Potential Area Change Index and used it as the response variable in modelling for three future emissions scenarios and three global circulation models over three time periods. The area of origin and biogeographic province in Spain were also considered.Results: We found a highly specific response for each plant species rather than a clear trend for the entire set of species. Predicted climate suitability increased over higher emission scenarios and longer projected time lags. Neotropical species showed the greatest potential climatic range expansion. We detected a strong interaction between the geographic origin of a species and the biogeographic province.Conclusions: Special attention should be given to the areas where aridification of climate is projected and where introduced neotropical species are likely to expand their range. Future work should develop accurate species-specific approaches that allow the management invasive plant species.

Evaluating the distribution of invasive woody vegetation around riparian corridors in relation to land use

Invasive species have been causing important and irreversible impacts to native species and communities of ecosystems. They distort ecosystem functions by degrading forest lands, wetlands, and agricultural habitats and replace the native vegetation and reduce biodiversity, forest productivity, and suitable wildlife habitat. To address disturbances caused by invasive species occurrence, further information is needed regarding the occurrence, extent, and dispersal of invasive species and how land use may increase the spread of these species. The objective of this study was to find the frequency and dominance of three invasive species common to riparian areas of east Alabama: Ligustrum sinense (Chinese privet), Elaeagnus pungens (silverthorn), and Triadica sebifera (Chinese tallow tree). Surveys of these species in riparian forests in and around Auburn, Alabama were conducted to show the relative extent of these shrubs and their relation to urban land use. It was expected to see the highest levels of invasive species in the city center with decreasing levels radiating outward into rural areas. Another objective was how urban land use may affect the presence-absence and prevalence of these non-native plant species within study sites. The results showed that around the city center and suburban lands, cover of both Chinese privet and silverthorn tended to increase. In contrast, Chinese tallow tree density percent cover showed an opposite trend with landscapes close to city center often having slightly less cover. This study shows that urban land use may be an important association with distribution of invasive plant species.

Modeling impacts of human footprint and soil variability on the potential distribution of invasive plant species in different biomes

Human footprint and soil variability may be important in shaping the spread of invasive plant species (IPS). However, until now, there is little knowledge on how human footprint and soil variability affect the potential distribution of IPS in different biomes. We used Maxent modeling to project the potential distribution of 29 IPS with wide distributions and long introduction histories in China based on various combinations of climatic correlates, soil characteristics and human footprint. Then, we evaluated the relative importance of each type of environmental variables (climate, soil and human footprint) as well as the difference in range and similarity of the potential distribution of IPS between different biomes. Human footprint and soil variables contributed to the prediction of the potential distribution of IPS, and different types of biomes had varying responses and degrees of impacts from the tested variables. Human footprint and soil variability had the highest tendency to increase the potential distribution of IPS in Montane Grasslands and Shrublands. We propose to integrate the assessment in impacts of human footprint and soil variability on the potential distribution of IPS in different biomes into the prevention and control of plant invasion.