Multiple invasion trajectories induce niche dynamics inconsistency and increase risk uncertainty of a plant invader

Our knowledge of how niche dynamic patterns respond to invasion trajectories and in�uence invasion risk prediction is elusive for the majority of notorious invaders, hindering scienti�c understanding, biosecurity planning and practice, and management implementation. We used Mikania micrantha, one of the most notorious invasive alien species in the world, to test the hypothesis that multiple invasion trajectories could induce niche dynamics inconsistency and increase risk uncertainty of invasive alien species. We compiled a robust database of M. micrantha occurrence across its native range in Central and South America and invaded ranges in China. This database was used to clarify different invaded ranges and invasion trajectories of M. micrantha in China. Principle Component Analysis of climatic variables associated with the database was used to detect its niche dynamic patterns associated with multiple invasion trajectories. Maximum Entropy algorithm was used to predict the high-risk area of M. micrantha invasion using occurrence datasets for invaded ranges where niches remained conservative, and to identify area changes with the inclusion of occurrences datasets for invaded ranges where niche shifts occurred. M. micrantha invasion occurred in three geographically distinct regions, with conservative climate niches in southern and southeastern China and climatic niche shifts in southwestern China. A high-risk area for M. micrantha invasion spanned multiple provinces and cities, and expanded considerably with the inclusion of the occurrence dataset for southwestern China. Our �ndings contribute to the theoretical understanding of invasion mechanisms and the practical optimization of biosecurity planning and implementation.


Introduction
Biological invasions are an increasingly severe problem worldwide, stemming from increasing anthropogenic disturbances and a changing climate (Bellard et al. 2013).Invasive alien species (IAS) colonize new habitats and outcompete native species, negatively affecting the environment, and human well-being and livelihoods (Pyšek et al. 2020).Colonization of new regions outside of a species native range induced by human activities is largely linked to shifts in the species realized climatic niches (Atwater et al. 2018).Lacking awareness of niche dynamics of IAS impairs niche-based predictions of invasion risk assessments (Atwater and Barney 2021).Understanding niche dynamics of IAS and resulting risk uncertainties is one of the fundamental bases in mitigating invasion impacts and preventing invasions to new environments.
The concept of niche dynamics could derive various features from the niche conservatism hypothesis (Pearman et al. 2008), in particular, whether the climatic niche requirements of IAS shift between native and invaded ranges (Atwater et al. 2018;Dellinger et al. 2016;Manzoor et al. 2020).In accordance with this hypothesis, the unifying framework for niche dynamics could be broadly decomposed into three niche patterns, including niche stability (the proportion of an exotic niche of an IAS overlapping with its native niche), niche expansion (the proportion of an exotic niche of IAS that is not occupied in its native range), and niche un lling (the proportion of the native niche of an IAS that is not yet occupied in its exotic range) ( IAS experience niche dynamics in novel environments through ecological and/or evolutionary changes, for example, release from top-down regulators and/or niche occupation in invaded ranges (Alexander and Edwards 2010), increased phenotypic plasticity associated with rapid growth and fast phenological development (Hahn et al. 2012), and/or rapid evolutionary adjustments driven by high genetic diversity (Barbosa et al. 2019).However, these mechanisms may also bring about inconsistent niche dynamic patterns in IAS in different invaded environments (Datta et al. 2019;Dinis et al. 2020;Pili et al. 2020).
Thus, the occurrence of inconsistency in niche dynamics could be closely associated with adaptive responses of IAS resulting from differences in invasion trajectories (Banerjee et al. 2019b; Barbosa et al. 2019).The invasion trajectory demonstrates a series of stages that an IAS goes through when its propagules are transported to a new area (Reise et al. 2017).Despite their importance in shaping ecological adaptabilities of IAS, invasion trajectories occurred in different ranges are poorly or not at all documented (Bellard and Jeschke 2016; Pyšek et al. 2008).Moreover, such a shortfall in scienti c knowledge makes niche dynamics inconsistency caused by multiple invasion trajectories barely elaborated.
Spatio-temporal niche dynamics relevant to IAS invasion trajectories serve as a theoretical basis for invasion risk assessment through application of ecological niche models (ENMs) (Atwater and Barney 2021; Dinis et al. 2020;Lamsal et al. 2018).ENMs are based on correlative statistical analyses of species-environment relationships, and can quantitatively de ne a species climatic niche and predict its potential distribution across space and time (Guisan et al. 2017;Guisan and Zimmermann 2000).This technical approach allows researchers and environmental managers to identify areas most susceptible to invasions, and thus to increase the e ciency in resource allocation in IAS management (McGeoch et al.

2016).
Mikania micrantha Kunth (mile-a-minute weed; Asteraceae), native to Central and South America, is a fast-growing perennial vine capable of both sexual and asexual reproduction; it is now present in many Asia-Paci c countries (Day et al. 2016).Its biological and ecological traits, such as e cient reproductive capacity, and considerable allelopathic effects (Tripathi et al. 2012), allow established M. micrantha to quickly smother native vegetation, including native trees, plantation species and agricultural crops, decreasing their yield and biodiversity.The vine has caused severe damage to agricultural lands, forests, and natural environments in invaded ranges (Day et al. 2012), and is recognized as one of the top 100 worst invasive species by the International Union for Conservation of Nature (Lowe et al. 2000).China is among countries most affected by M. micrantha invasion and suffers enormous ecological and economic losses in forestry and agriculture (Zhang et al. 2019;Zhang et al. 2004).The Chinese government has listed M. micrantha as the most problematic terrestrial invasive plant nationwide (The State Forestry Administration of China 2013) and made great efforts in its prevention and control (Zhang et al. 2019).
Knowledge of niche dynamics inconsistency associated with different invasion trajectories of IAS still remains elusive.Additionally, detailed information on how niche dynamics inconsistency in uences the prediction of invasion risk is lacking for the majority of notorious invaders, restricting explicit risk assessment and feasible management, especially in invasion-prone countries such as China (Liu et al. 2019).In this study, we hypothesized that multiple invasion trajectories could induce niche dynamics inconsistency and increased risk uncertainty of invasive alien species.To test this hypothesis, we used Mikania micrantha, the most notorious invasive plant in China, as a case study and aimed to explore its: i) invaded ranges and derived invasion trajectories, ii) niche dynamic patterns associated with multiple invasion trajectories, and iii) changes in high-risk area resulting from inconsistent niche dynamic patterns.Our ndings contribute to the theoretical understanding of mechanisms responsible for IAS niche dynamics, and support IAS management in countries and regions vulnerable to biological invasions.

General analytical process
Niche dynamics (changing niches) undermine con dence in ENM-based predictions of IAS ecological niches and invasion risk assessments (Atwater and Barney 2021; Pili et al. 2020).Therefore, on the basis of understanding invaded ranges and derived invasion trajectories of IAS, we rstly considered whether niche dynamic patterns were consistent and how they changed, and then conducted risk certainty or uncertainty assessments.A owchart was developed to demonstrate the general analytical process (see Fig. 1).Insert Fig. 1 here

Data collection and organization
We initially compiled a robust database of occurrence data for M. micrantha spanning the native range in Central and South America and the invaded range in China.Native occurrences of M. micrantha were obtained from the Global Biodiversity Information Facility (GBIF; https://www.gbif.org/).It has been argued that nearly one-fth of contributing datasets in GBIF might be biased by rasterized coordinates (Zizka et al. 2019), suggesting that the altitude information of GBIF records in mountainous regions could be potentially problematic.Therefore, we retrieved altitude information from the Shuttle Radar Topography Mission (SRTM) database V4 (http://srtm.csi.cgiar.org)using the geographical coordinates of GBIF records, and compared the information with that provided in the GBIF database.The occurrence records of M. micrantha with an altitude error of more than 300 m were removed and a total of 1934 records were retained for the follow-up analysis (Fig. 2a).We further complemented the database with eld data collected during our eld surveys in southwestern China (n = 292, Fig. 2b).Given the potential for a geographic bias in the global databases (Meyer et al. 2016) and limited coverage of eld surveys, we also collated location records from published literature by searching major English and Chinese bibliographic databases.Extensive literature searches were performed in (1) Web of Science (https://www.webofscience.com/wos/alldb/basic-search) using combinations of the following keywords: "Mikania micrantha" OR "Mikania" in addition to AND "China" or AND "Chinese", and (2) China National Knowledge Infrastructure (https://www.cnki.net/)using the combination of the following keywords: "Mikania micrantha" OR "Mikania".These searches were limited to articles published between 2000 and 2020 to avoid an earlier misidenti cation of M. micrantha (Kong et al. 2000a;Kong et al. 2000b).All returned articles were carefully reviewed and only conclusive information on occurrence locations was extracted.Baidu Map™ (http://api.map.baidu.com/lbsapi/getpoint/index.html) was used to georeference occurrence location lacking geographic coordinates.A total of 2648 occurrence records of M. micrantha in China were obtained, and the number of data points for invasive distribution was more than enough for the prediction of ecological niche (Banerjee et al. 2017;Hill et al. 2017).Additionally, information on the year of occurrence record was collected for the clari cation of invaded ranges and derived invasion trajectories of M. micrantha in China.

Analysis of niche dynamic patterns
Niche dynamic patterns of M. micrantha were analyzed by applying the analytical framework suggested by Broennimann et al. (2012).A kernel density smoothing method was used for analyses in a given environmental space which was de ned by the six climatic variables.A Principal Component Analyses (PCA) was conducted to estimate niche overlap for the entire environmental space (PCA-env) of M. micrantha native and invaded (China) ranges using the 'ecospat' package in R language (Di Cola et al.

2017).
To maintain the same environmental space for all pairwise niche comparisons, we performed the PCAenv on the pooled available climatic conditions of M. micrantha native and invaded (China) ranges.Predicted scores from the rst two PCA-env axes were used to de ne a two-dimensional environmental space.The environmental space was then divided into 100 × 100 grid cells of equal size with each cell corresponding to a unique set of environmental conditions represented by a linear combination of the six bioclimatic variables (Broennimann et al. 2012).
A kernel density function was used to convert occurrence points of M. micrantha to smoothed densities of occurrences.Similarly, 10,000 points were randomly generated within the native and invaded (China) ranges, and then were used to estimate the smoothed density of available environments.Niche overlaps between native and invaded ranges and between two invaded ranges were calculated using Schoener's D index (Schoener 1970) which varies between 0 and 1 indicating no overlap and complete overlap, respectively.A bootstrap resampling approach with 100 iterations was used for estimating 95% con dence interval of Schoener's D.
One-tailed niche equivalency and niche similarity tests were conducted to detect evidence of niche dynamic patterns.Niche equivalency test was conducted to verify the null hypothesis that niche overlap between native and invaded (China) ranges of M. micrantha remains consistent when randomly reallocating occurrence data between two compared ranges.If the observed value of niche overlap is signi cantly lower than the randomly simulated distribution (p-value < 0.05), the null hypothesis of niche equivalency is rejected and existence of niche dynamic pattern is accepted.Niche similarity test was conducted to verify the null hypothesis that the niche overlap between native and invaded (China) ranges of M. micrantha is explained by regional similarity in tested climatic conditions of two compared ranges.We set the overlap alternative parameter as "higher", the niche dynamic hypotheses to "lower", "higher" and "lower" respectively for expansion, stability, and un lling, and the rand type as "2".If the observed value of niche overlap is signi cantly lower than the simulated distribution of niche overlap (p-value < 0.05), the null hypothesis of niche similarity is rejected and existence of niche dynamic pattern is accepted.
The potential centroid shift of available niches between native and invaded (China) ranges was identi ed by diagramming niche overlap in a two-dimensional climatic space.Thus, we identi ed three indices of niche dynamic patterns including niche stability, niche expansion, and niche un lling (Broennimann et

Detection of changes in high-risk area
High-risk area of M. micrantha invasion was predicted using Maximum Entropy (MaxEnt) (Phillips et al. 2006), a machine-learning algorithm that is widely used in ecology, biogeography, and evolution for modeling species niches and distributions (Elith et  Based on the assessment results of niche conservatism of M. micrantha, we developed two prediction models, one excluding and one including the occurrence datasets of niche shift ranges.We randomly selected 10,000 points in 0.5-degrees buffer areas around occurrences as the background samples for both models.When selecting model parameters, we used models with regularization multiplier (RM) values ranging from 0.5 to 6 (with increments of 0.5) with six feature class (FC) combinations (L, LQ, H, LQH, LQHP, with L = linear, Q = quadratic, H = hinge, and P = product).Based on the lowest Δ AICc score, the FC = LQHP and RM = 5.5 were used in the niche shift ranges for the "including" model, and the FC = LQH and RM = 0.5 were used in the niche shift ranges for the "excluding" model.Based on these parameters, suitable habitat distributions of M. micrantha was constructed.The average test AUC values for the "including" model and "excluding" model were 0.83 and 0.84, and the OR10 were all close to their expected value of 0.1.All analyses were conducted using the Package Wallace in R (Kass et al. 2018).
To detect the change in high-risk area of M. micrantha invasion between the "excluding" and "including" scenarios, we de ned predictive maps with two classes of habitat suitability (< 0.5 = low and ≥ 0.5 = high) using the reclass tool in ESRI ArcGIS 10.2 platform, and accepted the high-suitability range as the highrisk area.Then, we calculated the change in high-risk area of M. micrantha invasion using the SDMtoolbox (Brown et al. 2017).

Invaded ranges and derived invasion trajectories
Three invaded ranges of M. micrantha were identi ed on the basis of spatial and temporal patterns of species occurrences (Fig. 2b).The invaded range in southwestern China (IRSWC) contained 292 occurrence records since the 1980s (220 from eld surveys and 72 from literature surveys).The occurrences were distributed across two provinces, with the majority in Yunnan (291).The invaded range in southern China (IRSC) contained 318 occurrence records since the 1880s (253 from literature surveys and 65 from GBIF).The occurrences were distributed across four provinces and cities, with the majority in Guangdong (147) and Hong Kong (136).The invaded range in southeastern China (IRSEC) contained 2648 occurrence records since the 1986 (all from GBIF) which all were located in Taiwan.
The invaded ranges of M. micrantha were geographically distinct and featured with different invasion histories, indicating that there were multiple invasion trajectories occurred in China (Fig. 2b).The IRSWC was located in the inland mountainous region neighboring the Yunnan Plateau; the IRSC involved a mainland coastal plain and an offshore hilly island; and the IRSEC was characterized as a maritime island with hilly and mountainous landscapes.M. micrantha occurrence in the IRSWC was rstly reported near the border with Myanmar in 1983 (Du et al. 2006)

Niche dynamic patterns associated with multiple invasion trajectories
The majorities of the IRSC and IRSEC occurrences remained within the climatic range of native occurrences, while a considerable proportion of the IRSWC occurrences appeared outside of that range (Fig. 3a).The rst two PCA axes explained 74% of the variance in the set of six bioclimatic variables.The minimal temperature of the coldest month (Bio6) and the temperature annual range (Bio7) were the most important variables in the rst and second principle components, respectively (Fig. 3b).
Insert Fig. 3 here Our analysis indicated multiple niche overlap, equivalency, and similarity patterns of M. micrantha between native and invaded (China) ranges.The overlap values between the native range and the IRSWC and between the native range and the IRSC were lower than that between the native range and the IRSEC; the equivalencies between the native range and the IRSWC and between the native range and the IRSC were non-signi cant while that between the native range and the IRSEC was signi cant; the similarities between the native range and the IRSWC and between the native range and the IRSEC were nonsigni cant while that between the native range and the IRSC was signi cant (Table 1).Also, our analysis indicated multiple niche overlap, equivalency, and similarity patterns of M. micrantha between invaded (China) ranges.The overlap values between the IRSC and the IRSWC and between the IRSEC and the IRSWC were lower than that between the IRSEC and the IRSC; the equivalencies between the IRSC and the IRSWC and between the IRSEC and the IRSWC were non-signi cant while that between the IRSEC and the IRSC was signi cant; the similarities between the IRSC and the IRSWC and between the IRSEC and the IRSWC were non-signi cant while that between the IRSEC and the IRSC was signi cant (Table 1).Our analysis indicated multiple niche expansion, stability and un lling patterns of M. micrantha between native and invaded (China) ranges.The expansion value between the native range and the IRSWC was higher than those between the native range and the IRSC and between the native range and the IRSEC; the stability value between the native range and the IRSWC was lower than those between the native range and the IRSC and between the native range and the IRSEC; the un lling value between the native range and the IRSWC was higher than those between the native range and the IRSC and between the native range and the IRSEC (Table 1; Fig. 4).Also, our analysis indicated multiple niche expansion, stability and un lling patterns of M. micrantha between invaded (China) ranges.The expansion values between the IRSC range and the IRSWC and between the IRSEC and the IRSWC were higher than that between the IRSEC and the IRSC; the stability values between the IRSC and the IRSWC and between the IRSEC and the IRSWC were lower than that between the IRSEC and the IRSC; the un lling values between the IRSC and the IRSWC and between the IRSEC and the IRSWC were higher than that between the IRSEC and the IRSC (Table 1; Fig. 4).
Insert Fig. 4 here

Changes in high-risk area resulting from inconsistent niche dynamic patterns
Based on risk prediction using occurrence datasets in the native and invaded (China) ranges but excluding the IRSWC, areas at high-risk for M. micrantha invasion (habitat suitability ≥ 0.5) were found in the coastal and island regions in southern and southeastern China and the mountainous region in southwestern China.The at-risk provinces and cities included Fujian, Taiwan, Guangdong, Hongkong, Macao, Guangxi, Hainan, Yunnan and Tibet (Fig. 5a).High-risk areas were considerably expanded after including the IRSWC.In addition to the seven at-risk provinces and cities above, Guizhou and Sichuan provinces were included (Fig. 5b).Additionally, the prediction of high-risk area of M. micrantha with the IRSWC resulted in a 47% increase in the high-risk area over that predicted using the IRSC and IRSEC occurrence datasets only.The expansion of high-risk area mainly occurred in southern Fujian, western Guangxi, southern Guizhou, southern Yunnan and central Sichuan (Fig. 5c).
Insert Fig. 5  The invasion of M. micrantha in the IRSWC (e.g., Yunnan) received much less scienti c attention (Zhang et al. 2004).In fact, Yunnan is a globally recognized hotspot for alien species (Dawson et al. 2017) and acts as an invasion corridor between China and South and Southeast Asia (Liu et al. 2019).Paying more attention to this region is greatly needed for a comprehensive understanding of invasion trajectories and their driving mechanisms.Additionally, even though research lags behind in the southeastern region (Taiwan), the georeferenced record of M. micrantha in this area is highly abundant (2648 occurrence records).These samples partially support the view that hotspots of established alien species richness are predominantly islands (Dawson et al. 2017).Also, increased scienti c collaboration on IAS management between Mainland China and Taiwan is strongly needed.

Mechanisms of niche inconsistency in M. micrantha
Our results showed that M. micrantha has shifted its climatic niche in China by occupying a novel climate space in the IRSWC which was not available in its native range (Table 1; Fig. 4a).Signi cant shifts in the climatic niche of M. micrantha were also observed in India (Banerjee et al. 2017), but the authors tested the niche conservatism of M. micrantha without considering the geographic differences in invaded ranges (Banerjee et al. 2017), making the predicted distribution of IAS in a geographically diverse country less accurate (Atwater and Barney 2021).While our study helps build the scienti c basis for in-depth research in invasion-prone countries such as China, more studies on niche shifts need be conducted in many hotspots of alien species (Liu et al. 2019) given the understanding of niche dynamics is lacking for the majority of aggressive invaders.Additionally, inconsistencies in climatic niches between native and invaded ranges of IAS are attributed to either evolved environmental tolerances (Müller-Schärer et al. 2004) or release from dispersal barriers or biotic constraints (Mitchell et al. 2006).In our study, the release mechanism serves as a more feasible explanation due to the constrained evolutionary adaptation acquired by M. micrantha within a short residence time in China (Chiang et al. 2002;Du et al. 2006;Wang et al. 2003).
To the best of our knowledge, this is the rst time to detect inconsistent niche patterns of M. micrantha between invaded (China) ranges, supporting our hypothesis that multiple invasion trajectories could induce niche dynamics inconsistency of M. micrantha.Our results showed that the climatic niches of M. micrantha in the IRSC and IRSEC were alike but different from that in the IRSWC (Table 1; Fig. 4).
Similarly, a recent study reveals that the plant invader Ageratina adenophora (Crofton weed) occupied different, non-consistent realized climatic niches in different invaded ranges (Datta et

Implications for IAS management
Our identi ed high-risk areas for M. micrantha invasions using the occurrence datasets in the IRSC and IRSEC where niches remained conservative, and a considerable expansion of high-risk areas after involving the occurrence dataset in the IRSWC where niche shifts occurred (Fig. 5c).Such a change in high-risk areas indicates that the niche shift of M. micrantha could cast uncertainty on its ENM-based risk assessment due to the colonization of novel climatic conditions in the IRSWC.These ndings agree with the study of Atwater et al. (2021) who showed that niche shifts greatly matter when modeling ecological niches of introduced species because they could signi cantly reduce the transferability of native-and introduced-range ENMs.Additionally, our results suggest the existence of a geographic separation of ranges and ongoing population expansion of M. micrantha in China, while Yang et al. (2017) reported genetic differentiation of M. micrantha populations in the IRSWC and IRSC.These ndings together highlight the need for monitoring areas of potential population interchange (e.g.Guangxi; see Fig. 5), where niche shifts may occur due to interspeci c hybridization followed by rapid selection of genotypes adapted to novel climates (Mukherjee et al. 2012).
We developed a risk map for M. micrantha in China by projecting its ecological niche.Similar geographic patterns have also been found by several authors who used small-sized occurrence datasets and conducted coarse-scale predictions (Zhang et al. 2011;Zhou et al. 2012).In comparison, we made a higher-accuracy risk prediction for M. micrantha using a full occurrence dataset for China, and improved the risk assessment of M. micrantha invasion by identifying a wider high-risk area.However, extensive eld surveys are greatly required for larger occurrence datasets of M. micrantha in high-risk and less-surveyed regions, such as southern and western Guangxi, southern and western Yunnan, and southeastern Tibet (Fig. 5c).Additionally, our ndings indicate that the IRSWC plays an important role in the risk assessment of M. micrantha in China and it should be prioritized for IAS management (

Conclusions
Alien species could invade novel environments based on different regional trajectories, and multiple invasion trajectories could induce inconsistency in niche dynamic patterns and increase uncertainty in niche-based risk predictions.This study contributes to the theoretical understanding of IAS invasion mechanisms and the practical optimization of biosecurity planning and implementation.Assessment of IAS risks will bene t from including different invasion trajectories and induced niche dynamic patterns to improve accuracy in risk predictions; further studies should focus on multiple IAS to draw more general conclusions.

Declarations Figures
An analytical process for the exploration of invasion trajectories, niche dynamics inconsistency and risk uncertainty of invasive alien species Guisan et al. 2014; Petitpierre et al. 2012).
here Bioclimatic predictors derived from primary climatic data highlight climate conditions best relevant to physiological limits of species (O'Donnell and Ignizio 2012), and hence are useful for understanding and projecting ecological niches of IAS (Datta et al. 2019; Dinis et al. 2020).Based on the climatic requirements of M. micrantha (Banerjee et al. 2017), we extracted six bioclimatic variables at 2.5-minute spatial resolution from the WorldClim V2.1 (http://www.worldclim.org)(Fick and Hijmans 2017) to quantify environmental characteristics of M. micrantha locations.The selected variables included Annual Mean Temperature (bio1), Min Temperature of Coldest Month (bio6), Temperature Annual Range (bio7), Annual Precipitation (bio12), Precipitation of Wettest Quarter (bio16), and Precipitation of Driest Quarter (bio17).
, and its infestations had spread over western Yunnan during the next decades (Mo 2011; Mo et al. 2007; Wang 2013; Yang and Shao 2010; Zhao et al. 2012).The earliest M. micrantha occurrence in the IRSC was recorded in Hong Kong in 1884, and the invasion had reached a wide range in Guangdong by the early 2000s (Wang et al. 2003).M. micrantha in the IRSEC was rstly introduced in 1986 (Chiang et al. 2002), and was considered as a major threat to the native ora in Taiwan (Hwang et al. 2003).

Figure 5 Predicted
Figure 5 al. 2011; Radosavljevic and Anderson 2014).MaxEnt is based on presence-only occurrence records and associated environmental covariates, and is wellknown for its satisfying performance for a variety of taxa (Atwater and Barney 2021; Chucholl 2017; Jiang et al. 2019; Kumar et al. 2016).

Table 1
Pairwise niche overlap (Schoener's D), equivalency, similarity, and niche dynamic patterns of Mikania micrantha between the native and invaded (China) ranges and between the invaded (China) ranges.P values of < 0.05 and < 0.01 were considered statistically signi cant and highly statistically signi cant, respectively.The IRSWC, IRSC, and IRSEC indicate invaded ranges in southwestern, southern, and southeastern China, respectively.
Banerjee et al. (2019a)western China in 1983(Du et al. 2006).This bias has been largely neglected, hindering the comprehensive understanding of the invasion history of M. micrantha in China.For instance,Banerjee et al. (2019a)presented the invasion pathways of Mikania micrantha in China without including the introduction history in the IRSWC.
were biased by focusing on the southern coastal region (Kong et al. 2000a; Lian et al. 2014; Zhang et al. 2004), since new evidences revealed other areas of invasions such as the southwestern mountainous region (Du et al. 2006; Zhang et al. 2019) and the southeastern island region (Chiang et al. 2002; Maja et al. 2008).Previous research highlights that M. micrantha in mainland China was rstly introduced from Hong Kong in 1984 (Kong et al. 2000a; Wang et al. 2003).In fact, the rst specimen was collected on the China- (Yang et al. 20172017tency of niche dynamics of M. micrantha in China could be related to different biotic and abiotic interactions resulted from multiple invasion trajectories(Atwater et al. 2018).Additionally, M. micrantha had a higher value of niche un lling in the IRSWC than those in the IRSC and IRSEC; this corresponds to the view ofAtwater et al. (2018)that shorter introduction time or wider ranges mean larger values of niche un lling given it takes time for introduced species to colonize all suitable niches(Atwater et al. 2018).M. micrantha is capable of rapid adaptation to a new environment via admixture and founder events(Yang et al. 2017).Such un lling niche pattern could be explained by dispersal limitation in mountainous environments, but it could also occur as colonizing populations slowly recover genetic diversity that was lost due to founder effects(Lee 2002).Traits de ning invasive success are context-speci c (Higgins and Richardson 2014).Western Yunnan is situated in the southern part of the Eastern Himalayan Syntaxis and exhibits a complex mountainous terrain.The IAS that are tolerant of a broader range of environmental conditions in mountainous ranges (Higgins and Richardson 2014) tends to facilitate the genotypic and phenotypic diversity(Forsman 2014), explaining the mechanism of niche shift in M. micrantha in the IRSWC.By contrast, the niche conservatism in M. micrantha in the IRSC and IRSEC is possibly associated with regional less-diverse topographic and climatic conditions.Additionally, previous research has shown a shift in climatic niche of M. micrantha in India(Banerjee et al. 2017) and similar genetic compositions of M. micrantha in northeastern India and western China(Yang et al. 2017).Both researches suggest a signi cant role of the India-Myanmar-China corridor in biological invasions under a transnational context.We recommended further studies that focus on niche dynamic patterns of M. micrantha in Myanmar to ll the research gap concerning IAS in that country.
(Lu et al. 2018017;Liu et al. 2019), even though the IRSC and IRSEC are regarded as prevention-focused regions to mainland China(Lu et al. 2018).