Distribution Of Invasive Plants Research Articles

Predictor importance in habitat suitability models for invasive terrestrial plants

AbstractAimDue to the socioeconomic and environmental damages caused by invasive species, predicting the distribution of invasive plants is fundamental for effectively targeting management efforts. A habitat suitability model (HSM) is a powerful tool to predict potential habitat of invasive species to help guide the early detection of invasive plants. Despite numerous studies of the predictors used in HSMs, there is little consensus about the most appropriate predictors to use in creating ecologically realistic predictions from HSMs.LocationThe contiguous United States.MethodsWe explore 220 invasive terrestrial plant species' existing HSMs constructed with consistent modelling algorithms, background generation methods, predictor resolution, and geographic extent, and calculate the relative importance of predictors for each species. We sort predictors into eight groups (topography, temperature, disturbance, atmospheric water, landscape water, substrate, biotic interaction, and radiation) and compare the importance of predictor groups by plant lifeforms and phylogenetic relatedness.ResultsHuman modification and minimum winter temperature were generally the two highest performing individual predictors across the species studied. The highest‐performing predictor groups were disturbance, temperature, and atmospheric water. Across lifeforms, there were minimal differences in the influences of predictor groups, although woody plant models exhibited the largest differences in predictor importance when compared with non‐woody plant models. Additionally, we found no significant relationship between the importance of predictor groups and phylogenetic relatedness.Main ConclusionsThis study has implications for informing predictor selection in invasive plant HSMs, leading to more reliable and accurate models of invasive terrestrial plants. Our results emphasize the need to critically select predictors included in HSMs, with special consideration to temperature and disturbance predictors, to accurately predict habitat of invasive plant for detection and response of invasive plant species. With more accurate predictions, managers will be better prepared to address invasive species and reduce their threats to landscapes.

Monitoring the spatial–temporal distribution of invasive plant in urban water using deep learning and remote sensing technology

Despite water ecosystems being capable of sustaining biodiversity and enhancing the overall resilience of the urban environment, they are highly susceptible to biological invasions. Invasive aquatic plants (IAPs) threaten the natural environment by reducing the diversity of native aquatic plants and animal communities. Detecting IAPs and mapping their distribution is crucial for the protection of urban water ecosystems. This study is the first of its kind to use high-resolution unmanned aerial vehicle (UAV) imagery and deep learning approaches to monitor the expansion of Pistia stratiotes (water lettuce). A DJI Matrice 300 RTK was utilized to capture time-series images with a resolution of 0.018 m on the Guanyin Lake, Cangshan District, Fuzhzou, China, during an outbreak and subsequent rapid growth stage of water lettuce. Three deep learning model architectures and three backbones were combined to detect water lettuce. Model performance and the ability to generalize the model were evaluated to determine the optimal model for water lettuce detection from time-series high-resolution UAV imagery. Results show that the DeepLabv3 + model with ResNet-34 achieved superior performance in detecting water lettuce from time-series imagery, yielding an average accuracy of 90.24 % (85.33 %≤F1_score ≤ 96.54 %) for water lettuce detection on five different dates. For the UAV image acquired on September 26th, the U-Net model with ResNet-18 yielded the highest accuracy (F1_score = 92.46 %), but it was not the optimal model for multi-temporal water lettuce detection on subsequent dates. The distribution of water lettuce can have large variations at different times, with an average change rate of 49.50 % every two days and the highest change rate up to 60.33 %. The study demonstrates that the combination of UAV imagery and a deep learning model can achieve excellent accuracy for water lettuce monitoring and provide a method to map IAPs in dynamic urban water systems over time.

Characterization of Chinese Tallow Invasion in the Southern United States

Chinese tallow is a non-native invasive tree expanding in range and abundance throughout the southern United States. Several biogeographical studies mapping tallow distribution and examining key underlying environmental factors relied on the U.S. Forest Service Forest Inventory and Analysis (FIA) data, representing forestlands at scales of ~2400 ha. However, given that most invasive trees, like tallow, are cosmopolitan and dynamic in nature, FIA data fails to capture the extent and severity of the invasion especially outside areas classified as forestlands. To develop tallow maps that more adequately depict its distribution at finer spatial scales and to capture observations in non-forestlands, we combined verified citizen science observations with FIA data. Further, we described spatiotemporal patterns and compared citizen science to FIA and other previously published distribution maps. From our work, although tallow is prevalent in the south, Louisiana, Texas, and Mississippi were the most invaded states. Tallow was associated with flatwoods and prairie grasslands of the Gulf Coast. Annual extreme minimum temperatures of less than −12.2 °C (10 °F) represented the northern limit of naturalized tallow populations. Tallow’s northward and inland expansion was captured in citizen science and FIA data, indicating a tallow spread rate ranging from 5 to 20 km annually over the last decade. Systematic sampling, such as FIA, and citizen science data both have their own unique pitfalls. However, the use of citizen science data can complement invasive plant distribution mapping, especially when combined with data from established systematic monitoring networks. This approach provides for a more complete understanding of invasive tree extent and spatiotemporal dynamics across large landscapes.

Characterization of Emerging Xanthomonas campestris Isolates on the Nonnative Weed Garlic Mustard (Alliaria petiolata).

Nonnative plant infestations provide unique opportunities to investigate pathogen emergence with evolutionarily recent plant introduction events. The widespread distribution of invasive plants and their proximity to genetically related crops highlights the risks of nonnative plants acting as ancillary hosts and fostering microbial recombination and pathogen selection. Garlic mustard (Alliaria petiolata) is a widespread, nonnative cruciferous weed that grows throughout North America and along the forested edges of diverse agricultural fields. The recent identification of a novel Xanthomonas campestris pv. incanae strain isolated from a diseased A. petiolata population led to the current investigation of the distribution and diversity of X. campestris isolates from naturally infected A. petiolata. A total of 14 diseased A. petiolata sites were sampled across three states, leading to the identification of diverse X. campestris pathotypes and genotypes. Pathogenicity assays and multilocus sequence analyses identified pathogenic X. c. pv. incanae and X. c. pv. barbareae strains collected from disparate A. petiolata populations. Moreover, independently collected X. c. pv. incanae strains demonstrated a broad cruciferous host range by infecting cabbage (Brassica oleracea var. capitata), garden stock (Matthiola incana), and the cover crop yellow mustard (Guillenia flavescens). This study highlights the genetic variability and host potential of natural X. campestris populations and the potential risks to Brassica crops via widespread, dense garlic mustard reservoirs.

Determining representative pseudo-absences for invasive plant distribution modeling based on geographic similarity

IntroductionThe use of pseudo-absence data constrained by environmental conditions can facilitate potential distribution predictions of invasive species. However, pseudo-absence data generated by existing methods are usually not representative because the relationship between the presence and pseudo-absence points is either simplistic or neglected. This could under or overestimate the potential distribution of invasive species.MethodsTo address this deficiency, this study proposes a new method for obtaining pseudo-absence data based on geographic similarities. First, the reliability of pseudo-absences was quantified based on the geographic similarity to the occurrence of species. Subsequently, a representative pseudo-absence reliability threshold interval was determined. Finally, different pseudo-absence acquisition methods were assessed by combining virtual species with a real invasive species.ResultsThe analysis demonstrated that the geographic similarity method can improve model accuracy and achieve a more realistic distribution compared with the traditional method of sampling for pseudo-absence data.DiscussionThis result indicates that the pseudo-absence data obtained using the geographic similarity approach were more representative. Our study provides valuable insights into improving invasive plant distribution predictions by considering the geographical relationships between species occurrences and the surrounding environments.

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.

Ecological niche modeling of interactions in a buzz-pollinated invasive weed

Plant species that are alien and invasive represent an important threat to biodiversity and ecosystem function. These plants compete with native flora for resources and pollinator services. Although most invasive plant species do not depend on pollinators for reproduction and can reproduce uniparentally, some depend on pollinator-assisted reproduction and therefore rely on recruitment of pollinators in the new habitat. Here, we studied Solanum rostratum , a high-risk invasive weed widely distributed around the world that depends on buzz-pollination. It has a floral morphology that depends on medium and large-sized buzzing bees for pollination. The main aim of this study is to model the distribution of an invasive plant, Solanum rostratum, using ecological niches based on its buzz-pollinator distribution. Thus, we hypothesized that S. rostratum is more likely to establish successfully in geographic regions where the appropriate buzz pollinators are also found. We considered the realized and potential distribution of S. rostratum and its pollinators based on occurrence data and ecological niche models combining both abiotic (climatic conditions) and biotic (potential pollinator distribution) factors using the MaxEnt algorithm. Five climatic variables related to temperature and precipitation were important in determining the realized distribution of S. rostratum, while seasonal temperature was the most important variable in determining pollinator distribution. The distributions of the bumblebees ( Bombus fraternus , B. griseocollis , and B. impatiens ) were closely related to the realized distribution of S. rostratum . However, the potential distribution of S. rostratum is wider than that of the pollinator included here, suggesting that other factors such as modifications in the reproductive system or recruitment of other buzzing and non-buzzing bees might be occurring in some invasive populations. We also found that S. rostratum has the potential to expand to unoccupied regions in South America, South Africa, Eastern Europe, and the Middle East. In conclusion, modeling of niches used by invasive species considering both climatic variables and biological interactions such as pollination would be very useful to study and predict the distribution of invasive plants which depend on pollinators to reproduce and establish in new habitats.

Heat wave event facilitates defensive responses in invasive C3 plant Ambrosia artemisiifolia L. under elevated CO2 concentration to the detriment of Ophraella communa.

To predict and mitigate the effects of climate change on communities and ecosystems, the joint effects of extreme climatic events on species interactions need to be understood. Using the common ragweed (Ambrosia artemisiifolia L.)—leaf beetle (Ophraella communa) system, we investigated the effects of heat wave and elevated CO2 on common ragweed growth, secondary metabolism, and the consequent impacts on the beetle. The results showed that elevated CO2 and heat wave facilitated A. artemisiifolia growth; further, A. artemisiifolia accumulated large amounts of defensive secondary metabolites. Being fed on A. artemisiifolia grown under elevated CO2 and heat wave conditions resulted in the poor performance of O. communa (high mortality, long development period, and low reproduction). Overall, under elevated CO2, heat wave improved the defensive ability of A. artemisiifolia against herbivores. On the other hand, enhanced adaptability to climatic changes may aggravate invasive plant distribution, posing a challenge to the control of invasive plants in the future.

Distribution of invasive plants and their association with wild ungulates in Barandabhar Corridor Forest, Nepal

Abstract Invasive and alien plant species (IAPS) are considered as major threats to native biodiversity because IAPS alter ecosystem structure and their functions. We assessed the association of four major IAPS (Mikania micrantha, Chromolaena odorata, Lantana camara, and Parthenium hysterophorus) and the abundance of wild ungulates in Barandabhar Corridor Forest (BCF), Chitwan, Nepal. We collected data on the presence of wild ungulates in IAPS invaded habitats through direct observation and sign surveys. Our study showed that the cover of M. micrantha was significantly high in Sal forest (Prominence value PV = 73.23) followed by riverine forest (PV = 40.5) and grassland (PV = 37.7) whereas P. hysterophorus was high in grasslands (PV = 22.9). Similarly, C. odorata was significantly high in Sal forest (PV =141.6%), and L. camara was high in mixed forest (PV = 22.6). It was found that there was a significant negative association of IAPS (p = 0.002) with wild ungulates. The abundances of deer and wild pigs were more in the buffer zone than in the non-buffer zone. The abundance of deer decreased with increasing cover of C. odorata, M. micrantha, and P. hysterophorus (p = 0.002). Similarly, the abundance of wild pigs decreased with increasing cover of M. micrantha and L. camara. IAPS were not uniformly distributed in different habitats and abundances of wild ungulates were less in IAPS invaded habitats. Hence, it is important to initiate management plans to control IAPS spread to avoid their negative impacts on wild ungulate population such as deer and wild pigs.

Biotic predictors improve species distribution models for invasive plants in Western U.S. Forests at high but not low spatial resolutions

Invasions by non-native plants threaten forest health and sustainability. The ability to predict areas at greatest risk to invasion is essential for informing both monitoring and management of invasive species. Species distribution models (SDMs) are often used to identify environmental correlates of a species’ occurrence and predict geographic areas that may be suitable for its presence and are commonly constructed using solely abiotic predictors. However, mounting evidence implies that not including biotic predictors in SDMs may yield less accurate models at some resolutions typical of landscape-scale models, although this possibility has rarely been evaluated in invasive plants. In this study, we determined whether including descriptors of the biotic environment improved the accuracy of SDMs built at five decreasing spatial resolutions for infestations of five common invasive plants in forests of California, Oregon, and Washington, USA and described environmental correlates of each species’ presence.Predictors of occurrence often echoed those identified in previous studies of the focal species, indicating that our models accurately identified important environmental drivers of occurrence. Including biotic predictors in the SDMs consistently improved model accuracy only at the highest resolution we examined, which may be due to the spatial scale at which biotic interactions primarily act to constrain species’ distributions, the particular biotic predictors we used in our models, or correlations between attributes of the abiotic and biotic environment. This finding suggests that, while the practice of building SDMs using abiotic predictors alone may generally yield models whose accuracy does not differ substantially from those that also include biotic predictors, the effects of biotic interactions on the distribution of invasive plants in forests may be detectable at larger scales than previously thought.

Global high-throughput genotyping of organellar genomes reveals insights into the origin and spread of invasive starry stonewort (Nitellopsis obtusa)

Aquatic invasive species are damaging to native ecosystems. Preventing their spread and achieving comprehensive control measures requires an understanding of the genetic structure of an invasive population. Organellar genomes (plastid and mitochondrial) are useful for population level analyses of invasive plant distributions. In this study we generate complete organellar reference genomes using PacBio sequencing, then use these reference sequences for SNP calling of high-throughput, multiplexed, Illumina based organellar sequencing of fresh and historical samples from across the native and invasive range of Nitellopsis obtusa (Desv. in Loisel.) J.Groves, an invasive macroalgae. The data generated by the analytical pipeline we develop indicate introduction to North America from Western Europe. A single nucleotide transversion in the plastid genome separates a group of five samples from Michigan and Wisconsin that either resulted from introductions of two closely related genotypes or a mutation that has arisen in the invasive range. This transversion will serve as a useful tool to understand how Nitellopsis obtusa moves across the landscape. The methods and analyses described here are broadly applicable to invasive and native plant and algae species, and allow efficient genotyping of variable quality samples, including 100-year-old herbarium specimens, to determine population structure and geographic distributions.

Assessment of the current distribution, dispersal trends and impacts of invasive species in Bana - Nui Chua Nature Reserve, Vietnam

Invasive species threaten the biodiversity and the function of ecosystems. Drone image, satellite images, and image analysis software were used to create the map of invasive distribution and the potential spreading of invasive plants. 13 most invasive plants were identified with 11 species listed as invasive species in Southeast Asia and 5 of them in the 100 world’s invasive species by IUCN. Three species Merremia boisiana (Gagn.) van Ooststr., Ipomoea eberhardtii Gagn, and Mimosa pigra were identified as the species with high-ranking impacts on biodiversity and ecosystem biodiversity in Ba Na - Nui Chua Nature Reserve (BNNR). Ipomoea eberhardtii Gagn shows the highest spreading rate at 0.65 ± 0.06 ha/month, followed by Merremia boisiana (Gagn.) van Ooststr) and Mimosa pigra at 0.12 ± 0.01 ha/month and 0.01 ± 0.001 ha/month respectively. Fresh biomass of Ipomoea eberhardtii Gagn; Merremia boisiana (Gagn.); Mimosa pigra and Sphagnetola trilobata (L.) Pruski in BNNR are 15.67; 14.9; 8.1 and 6.8 ton/ha. The database of invasive plant distribution and potential spreading will be used to monitor strategies and invasive weeds management in BNNR.

Climate based model in determining the distribution pattern of Cecropia peltata L across global landscape

Climate change becomes a major threat to the global biodiversity. It alters the ecological niche of species, even small change in temperature could have a significant impact to the distribution pattern of biodiversity. Cecropia peltata is an invasive species with wide range geographic distribution. The aim of this study is to understand the impact of climate change to the current and future distribution of invasive plant C. peltata. The Support Vector Machine (SVM) and Boosted Regression Tress (BRT) algorithm of machine learning were used to predict the current and future distribution. The occurrence records of C. peltata was obtained from Global Biodiversity Information Facility (GBIF). There were 2691 occurrence records in GBIF database. The global climatic variables with the resolution 2.5 km were used as predictors of model. VIF was used to select the multicollinearity among those variables using threshold of 0.7. The CIMP5 of Global Circulation Model (GCM) was used to understand the impact of climate change to the distribution of the plant. The future projection on year 2070 with the worst climate scenario RCP 8.5 was used on these predictive models. The SVM and BRT models were actually relevant to be used as predictive models with AUC >0.90 and categorised as excellent predictive models. The future distribution pattern was likely to be shifted compared to the current distribution prediction. The output of this study as predictive current and future distribution maps would be useful to provide an information about the potential area where the species might be invading based on the training data (observation data). Furthermore, the prediction of future distribution would be necessary to understand how the climate change literally affects the range of distribution of the invasive plant species.

Invasive plants in Brazil: climate change effects and detection of suitable areas within conservation units

Invasive exotic plants may compromise the survival, growth, and reproduction of native species and are among the leading causes of worldwide biodiversity losses. Climate changes—which will affect species distribution—may even amplify the problems caused by invasive species. Here, we used ecological niche models to evaluate the current and future distribution of 108 invasive plants in the entire Brazilian territory and the country's conservation unit facilities (CUFs). Overall, our results did not indicate a significant change in the potential distribution of invasive plants between the current and future climate scenarios, although we expect that 67.5% of the species will decrease its range in Brazil in the future. The proportion of the plants' invasive range inside conservation units varied from 1 to 12%, and results suggest that this would not increase or decrease in the future. Taken together, our results do not indicate that climate change will amplify the effects of existing invasive plants—although it may facilitate the invasion of other species. Both current and future scenarios suggest high suitability for invasive plants in the southern, southern, southeastern, and eastern coast of Brazil, comprising the Caatinga, Cerrado, and Mata Atlântica Brazilian biomes, the most populated areas of the country. We advise that conservation unit managers and authorities within these regions should continuously monitor such invasive plants to take early responses to avoid their establishment.

Long‐term trends indicate that invasive plants are pervasive and increasing in eastern national parks

While invasive plant distributions are relatively well known in the eastern United States, temporal changes in species distributions and interactions among species have received little attention. Managers are therefore left to make management decisions without knowing which species pose the greatest threats based on their ability to spread, persist and outcompete other invasive species. To fill this gap, we used the U.S. National Park Service's Inventory and Monitoring Program data collected from over 1,400 permanent forest plots spanning 12yr and covering 39 eastern national parks to analyze invasive plant trends. We analyzed trends in abundance at multiple scales, including plot frequency, quadrat frequency, and average quadrat cover. We examined trends overall, by functional group, and by species. We detected considerably more increasing than decreasing trends in invasive plant abundance. In fact, 80% of the parks in our study had at least one significant increasing trend in invasive abundance over time. Where detected, significant negative trends tended to be herbaceous or graminoid species. However, these declines were often countered by roughly equivalent increases in invasive shrubs over the same time period, and we only detected overall declines in invasive abundance in two parks in our study. Present in over 30% of plots and responsible for the steepest and greatest number of significant increases, Japanese stiltgrass (Microstegium vimineum) was the most aggressive invader in our study and is a high management priority. Invasive shrubs, especially Japanese barberry (Berberis thunbergii), Japanese honeysuckle (Lonicera japonica), multiflora rose (Rosa multiflora), and wineberry (Rubus phoenicolasius), also increased across multiple parks, and sometimes at the expense of Japanese stiltgrass. Given the added risks to human health from tick-borne diseases, invasive shrubs are a high management priority. While these findings provide critical information to managers for species prioritization, they also demonstrate the incredible management challenge that invasive plants pose in protected areas, particularly since we documented few overall declines in invasive abundance. As parks work to overcome deferred maintenance of infrastructure, our findings suggest that deferred management of natural resources, particularly invasive species, requires similar attention and long-term commitment to reverse these widespread increasing invasive trends.

Disease in Invasive Plant Populations.

Non-native invasive plants can establish in natural areas, where they can be ecologically damaging and costly to manage. Like cultivated plants, invasive plants can experience a relatively disease-free period upon introduction and accumulate pathogens over time. Diseases of invasive plant populations are infrequently studied compared to diseases of agriculture, forestry, and even native plant populations. We evaluated similarities and differences in the processes that are likely to affect pathogen accumulation and disease in invasive plants compared to cultivated plants, which are the dominant focus of the field of plant pathology. Invasive plants experience more genetic, biotic, and abiotic variation across space and over time than cultivated plants, which is expected to stabilize the ecological and evolutionary dynamics of interactions with pathogens and possibly weaken the efficacy of infectious disease in their control. Although disease is expected to be context dependent, the widespread distribution of invasive plants makes them important pathogen reservoirs. Research on invasive plant diseases can both protect crops and help manage invasive plant populations.

Pola Sebaran Tumbuhan Invasif dikawasan Taman Nasional Bukit Sulap Kota Lubuklinggau

The purpose of this study was to determine the types of invasive plants that exist and to find out the pattern of invasive plant distribution. Research results obtained from 8 plots found 35 species from 25 invasive plant families, random invasive plant distribution patterns, namely in Phyllanthus urinaria (Meniran), Graptophyllum pictum L (Purple Leaf), Nephrolepis biserrata (Sword Spikes), Elais guneensis J (Sawit ), Baccaureamotleyana (Rambe), Cassia Alata (Chinese Ketepeng), Acacia auricuformis (acacia), Alpinia maccensis (galangal forest), Paraserianthes falcataria (Sengon), Clibadium surinamense (Clibadium), Gynura divaricata (Leaf of god), Urena lobal (Pulutan lobus) ), Piper aduncum (Sirihan), Setaria sphacelata (Setaria grass), Asplenium nidus (Bird's nest nails), Averhoa carambola.L (Star fruit), Artocarpus heterophyllus (Jackfruit). Conclusion, the highest level of spread pattern of invasive plants is Calamus rotang (Rattan), Chromolaena odorata (left leaf nyuh), Diplazium esculentum s (Fern), Talipariti tiliaceum (Waru) of -0.14. The lowest uniform pattern of invasive plant spread is Cenchrus purpureus (elephant grass) at -5.86.
 Keywords: sulap Hill, invasive, distribution pattern, Plants

INVASIVE PLANT SPECIES IN THE DISTURBED FOREST OF BATUKAHU NATURE RESERVE, BALI, INDONESIA

Patterns of invasive plant distribution and their underlying mechanisms are complex and vary with spatial scale. Within the mountainous tropical ecosystems of Bali Island, a local scale pattern of invasive plants is still poorly understood. This paper aimed to detect and investigate the presence of invasive species and to evaluate their relative abundance linked to forest site conditions along with an elevation range on Mount Pohen, Batukahu Nature Reserve, Bali, Indonesia. To identify the importance of environmental disturbances on species invasion, the disturbance-environmental factors and the species-environmental relationship were also measured and examined. Using the stratified random sample, 78 vegetation plots of 2 m x 2 m size were established in four forest sites. Ten invasive plant species belonging to ten genera and five families were identified. Of these invasive species, 40% were herbs, while shrubs and grasses comprised 30%, respectively. Austroeupatorium inulaefolium has the highest frequency (45% of plots); followed by Ageratina riparia, and Brachiaria reptans (40% of plots, respectively), Melastoma malabathricum (37%) and Calliandra calothyrsus (27%). Austroeupatorium inulaefolium was the most abundant invader, followed by Ageratina riparia, and the remaining invasive species were Pennisetum purpureum, Calliandra calothyrsus, Imperata cylindrica, Brachiaria reptans, Melastoma malabathricum, Lantana camara, Bidens pilosa and Blumea lacera, respectively. The distribution of invasive plants was strongly linked to the disturbance level of their respective habitat. The largest numbers invasive plants were present in burnt sites close to the forest edges with direct anthropogenic influence, while the undisturbed forest was the least invaded site. Further, the most invasive species mainly occurred at low elevations up to 1600 m a.s.l. and were rarely found at higher elevations. However, few invasive species such as Austroeupatorium inulaefolium and Melastoma malabathricum were able to colonize the highest altitude (2035 m a.s.l.), and to a lesser degree, Ageratina riparia and Brachiaria reptans were also distributed at high altitudes (1950 m a.s.l. and 1972 m a.s.l. respectively). This study provides a fine-scale analysis of invasive species distribution which will serve as a basis for conservation purposes, especially for strategic planning regarding the detection and management of invasive alien plants.

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.

Identifying habitats and corridors of an invasive plant, Ageratina altissima, in an urban forest

The spread of invasive plants in urban forests is of great concern to land managers and ecologists. Here, we analyse the distribution pattern and potential occurrence of a problematic invasive plant, Ageratina altissima, on Mt. Umyeon, in the Seoul metropolitan area of South Korea, using multiple species distribution modelling with geographic information system layers and a time series of Landsat 8 satellite imagery data. Also, we identify this species’ potential dispersal corridors using a network-based landscape lattice approach. All five species distribution models performed well with a range of test area under the receiver operating characteristic curve values of 0.843–0.899 with an average of 0.876. Distance from trails had the greatest effect on its occurrence probability, and generally exerted a negative influence within ca. 20 m of trails, indicating that urban forest trails facilitate the spread of invasive plants. Of the remote sensing-based predictors used, 7 August 2016 tasselled cap brightness index was an important variable having a negative effect on the presence of A. altissima. Elevation generally had a positive effect on its presence, although it was less important than tasselled cap brightness index. We explicitly detected the distribution pattern of high invasion risk corridors, mostly located on or around trails, which follow the central ridgeline of the study area. The results and approaches employed here can be useful for understanding, monitoring, and predicting invasive plant distribution and spread for the management of urban natural resources and biodiversity conservation.