![Comparison of observed and expected numbers of species according to their naturalization status and (a) life span and (b) growth form categories. The Pearson's residuals derived from the contingency table are shown. Positive values of residuals sign higher observed values than expected, negative residuals sign lower observed values than expected. The color represents the associated Pearson's residuals. [Abbreviations used: G = Grass, H = Herb, P = Palm, S = Shrub, SS = Sub-shrub, T = Tree, TW = Twiner, V = Vine].](https://www.researchgate.net/profile/Anzar-Khuroo/publication/362371194/figure/fig1/AS:11431281078782475@1660239384008/Comparison-of-observed-and-expected-numbers-of-species-according-to-their-naturalization_Q320.jpg)
![Comparison of observed and expected numbers of species according to their naturalization status and native biogeographical region. The Pearson's residuals derived from the contingency table are shown. Positive values of residuals sign higher observed values than expected, negative residuals sign lower observed values than expected. The color represents the associated Pearson's residuals. [Nativity -AFR = Africa, ATM= Asia temperate, ATR = Asia tropical, AUS = Australasia, EUR = Europe, NAM = Northern America, PAC = Pacific, SAM = Southern America].](https://www.researchgate.net/profile/Anzar-Khuroo/publication/362371194/figure/fig2/AS:11431281078772652@1660239384051/Comparison-of-observed-and-expected-numbers-of-species-according-to-their-naturalization_Q320.jpg)
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Global Ecology and Conservation 38 (2022) e02246
Available online 29 July 2022
2351-9894/© 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
Diversity, distribution and drivers of alien ora in the Indian
Himalayan region
Sajad Ahmad Wani
a
,
1
, Rameez Ahmad
a
,
*
,
1
, Ruquia Gulzar
a
, Irfan Rashid
b
,
Akhtar Hussain Malik
a
, Irfan Rashid
c
, Anzar Ahmad Khuroo
a
,
*
a
Centre for Biodiversity & Taxonomy, Department of Botany, University of Kashmir, Srinagar 190006, Jammu and Kashmir, India
b
Biological Invasion Research Laboratory, Department of Botany, University of Kashmir, Srinagar 190006, Jammu and Kashmir, India
c
Department of Geoinformatics, University of Kashmir, Srinagar 190006, Jammu and Kashmir, India
ARTICLE INFO
Keywords:
Biodiversity
Alien ora
Plant invasion
Himalaya
Naturalization
Management
ABSTRACT
The Himalaya – a global biodiversity hotspot – harbors diverse ora and fauna, but increasingly
beset with multiple threats, including biological invasion by alien species. Here, we aimed to
investigate the diversity, distribution, and drivers of alien ora in the Indian Himalayan Region
(IHR), a region spread across 12 states/union territories in India. We developed a comprehensive
checklist on alien ora of IHR based on review of 141 studies published during the years 1934 to
2022, and further disentangled the key environmental (average annual rainfall, total area, pro-
tected area, forest area, total plant richness) and socioeconomic (total population, trafc length)
drivers that better explain regional alien and naturalized plant richness. We recorded 771 alien
plant species, including 375 cultivated and 396 naturalized species. We found that the species
native to Southern America and those with perennial life span and herbaceous growth form were
most represented in the IHR. Similarly, the annual herbs native to Southern America had a higher
probability to become naturalized in the IHR. Based on the species composition of the total alien
and naturalized ora distributed across different parts of the IHR, we found evidence of distance
decay of oristic similarity. The total alien plant richness was best explained by the average
annual rainfall, while the naturalized plant richness was best predicted by total trafc length. Our
results identify the key environmental (i.e., average annual rainfall) and socioeconomic (i.e., total
trafc length) drivers that determine the diversity and distribution patterns of alien and natu-
ralized plants in the IHR. Our ndings have practical applications in developing a scientically-
informed management and policy framework to mitigate the impacts of plant invasions and to
predict the potential future plant invaders in the Himalaya. Overall, the checklist of alien ora of
the IHR represents a step forward in lling the knowledge gaps on biological invasions from the
Himalaya – a globally data-decient region.
1. Introduction
The terrestrial global biodiversity hotspots are rich repositories of diverse ora and fauna, but are currently experiencing an
* Corresponding authors.
E-mail addresses: rameezkhuroo929@gmail.com (R. Ahmad), anzarak@uok.edu.in (A.A. Khuroo).
1
First authorship equally shared.
Contents lists available at ScienceDirect
Global Ecology and Conservation
journal homepage: www.elsevier.com/locate/gecco
https://doi.org/10.1016/j.gecco.2022.e02246
Received 21 April 2022; Received in revised form 30 June 2022; Accepted 21 July 2022
Global Ecology and Conservation 38 (2022) e02246
2
extreme level of threat by a multitude of anthropogenic drivers (Myers et al., 2000, 2014; Chitale et al., 2014; Tilker et al., 2019).
Besides the rapid rate of habitat loss, the biodiversity hotspots are beset with the problem of biological invasions due to intentional
and/or unintentional introduction of alien species (Li et al., 2016; Pathak et al., 2019). For example, Early et al. (2016) reported that
about 16% of global biodiversity hotspots are highly vulnerable to invasive alien species (IAS). Historically, human-mediated activities
have introduced a large pool of species outside their native distribution ranges (van Kleunen et al., 2015; Dawson et al., 2017; Pyˇ
sek
et al., 2017; Patzelt et al., 2022), however most of the introduced species fail to establish self sustaining populations in the wild (Patzelt
et al., 2022). Among the introduced species, only a small fraction establish and maintain populations without direct human inter-
vention (referred to as naturalized species) and only a subset of naturalized species spread rapidly and exert negative ecological and
economic impacts (referred to as invasive species) (Richardson et al., 2001; Blackburn et al., 2011). Although alien species have
occupied almost all parts across the globe, the available primary data on their diversity and distribution is still uneven and biased
across continents, with highest number of species reported from North America and Europe, whereas the lowest number recorded from
temperate and tropical Asia (Dawson et al., 2017; Pyˇ
sek et al., 2017, 2018; van Kleunen et al., 2015, van Kleunen et al.,2019).
Furthermore, the geographical patterns of future invasions are predicted to be different from those of today, and the number of alien
and naturalized species from developing countries, particularly those supporting relatively higher proportion of local biodiversity
because of their relatively lower economic development, is expected to increase in the near future (Seebens et al., 2015, 2019).
Therefore, an inventory of the alien species at regional scale based on globally standardized criteria is a prerequisite for making global
assessment of current status and future trends of alien oras, and to investigate their drivers and consequences (Seebens et al., 2017,
2018; Omer et al., 2021a).
Owing to the scale and extent of invasive alien plants’ spread and their impact on biodiversity and ecosystem functioning, biotic
inventories on alien and/or naturalized oras have become available for many regions of the world (e.g., Vinogradova et al., 2018,
Ansong et al., 2019; Pyˇ
sek et al., 2019; Sandvik et al., 2019; Fuentes et al., 2020; Meddour et al., 2020; Bordbar and Meerts, 2021;
Brandt et al., 2021; Omer et al., 2021a, 2021b; see also Pyˇ
sek et al., 2017; van Kleunen et al., 2019 for a global overview), including
countries and/or regions supporting the global biodiversity hotspots (e.g., Bhutan - Dorjee et al., 2020; Caucasus – Akatova and
Akatov, 2019; China - Xu et al., 2012; Zhang et al., 2021; India - Khuroo et al., 2012; Inderjit et al., 2018; Nepal - Bisht et al., 2016;
Russia - Leostrin and Pergl, 2021). Similarly, research focusing on alien plant invasions in the mountains is receiving increasing
attention (Alexander et al., 2016; Pauchard et al., 2016; Pathak et al., 2019), motivated in part by concern about the possible impacts
of IAS in mountain ecosystems where the climate is currently experiencing rapid warming trends (Gallardo et al., 2017; Hamid et al.,
2020). However, the majority of these inventories on alien and naturalized plants are available from the developed world, therefore
the geographical bias in terms of lacking data from developing countries limits our capacity to assess the magnitude of biological
invasions in mountain areas at a global scale (Dawson et al., 2017; Pyˇ
sek et al., 2017, 2019; van Kleunen et al., 2019).
India, one of the fastest developing countries, having a major portion of its geographical area under four global biodiversity
hotspots including the Himalaya (Singh, 2010; Zachos and Habel, 2011), is at an increased risk of invasion by alien species (Khuroo
et al., 2012, 2021; Inderjit et al., 2018; Pathak et al., 2019). About 49% of the total area of India is predicted to be prone to invasion at
moderate to high levels, and the biodiversity hotspot regions coincide with ‘invasion hotspots’ (Adhikari et al., 2015). The mountain
landscapes of the Indian Himalaya – representing the major portion of the Himalaya biodiversity hotspot – are experiencing un-
precedented anthropogenic pressure owing to accelerated economic activities (Chitale et al., 2014; Ahmad et al., 2019a, 2019b;
Khuroo et al., 2021) that have led to both intentional and unintentional introductions, and in many cases to the spread, of IAS (Pathak
et al., 2019; Khuroo et al., 2021). Despite the severe invasion risk posed to the native biodiversity of this ecologically fragile region, the
scarcity of scientic data on diversity and distribution of alien plants, and their environmental and economic drivers warrants urgent
research attention. In the absence of such studies, it is difcult to develop a holistic management strategy and policy framework to
mitigate the adverse impacts of those alien species which spread as invasive. Therefore, it is of global importance to document the
diversity and distribution of alien ora of this region and to disentangle its environmental and socioeconomic drivers.
It is in this context that the present study aimed to address the following questions: (i) What is the total taxonomic diversity of alien
ora in the Indian Himalayan Region, and its biogeographic afliation, life-history traits, and naturalization status? (ii) How is the
alien and naturalized species pool distributed across the Indian Himalayan Region, and what is the extent of species’ compositional
similarity between different sub-regions? and (iii) What are the major environmental and socio-economic drivers that determine the
patterns of alien and naturalized plant species richness in the Indian Himalayan Region?
By addressing these research questions, our study is expected to ll the existing global knowledge gap in invasion biology by
making available an inventory of alien ora in this global biodiversity hotspot and unraveling the patterns of distribution and dis-
entangling the drivers in the study region.
2. Materials and methods
2.1. Study area
The Himalaya, spread across different countries in South Asia, occupies a prominent place in the mountain systems of the world
(Sekar and Srivastava, 2010; Khuroo et al., 2021). The majority of the area under the Himalaya falls in India. The IHR, located between
27◦−36◦N latitude and 74◦−97◦E longitude, starts from Jammu and Kashmir in the west to Arunachal Pradesh in the east covering
about 16.2% of the country’s area (Sekar, 2012; Khuroo et al., 2021). In India, the IHR is administratively divided into 12 states/union
territories (hereafter regions): Jammu and Kashmir, Himachal Pradesh, Uttarakhand, Sikkim, Arunachal Pradesh, Meghalaya,
Nagaland, Manipur, Mizoram, Tripura, and the hill regions of Assam and West Bengal (Fig. 1). In this study, Jammu and Kashmir, also
S.A. Wani et al.
Global Ecology and Conservation 38 (2022) e02246
3
includes Ladakh, as the two regions have been recently separated and the data is available prior to their separation. The IHR represents
an extended protected area network comprising of a total of ve biosphere reserves, 28 national parks, and 98 wildlife sanctuaries,
which together cover an area of 51,899 km
2
(Mathur et al., 2000; Khuroo et al., 2021). The region is endowed with an extensive range
of physiographic, climatic, and soil features. The region is considered as amongst the most eco-fragile mountainous landscapes of the
world and harbors rich oristic diversity (Samant et al., 2007), including 8000 species of angiosperms, 44 species of gymnosperms, 600
species of pteridophytes, 1736 species of bryophytes: 1159 species of lichens and 6900 species of fungi (Singh and Hajra, 1997; Khuroo
et al., 2021).
2.2. Development of checklist
We developed a checklist of alien ora in the IHR by undertaking an extensive review of scientic literature (141 research studies)
published over the last century (1934–2022) (see supporting Appendix-A). Although, the standard methodology for conducting
literature search is through online databases (e.g., Web of Science, Scopus) using specic keyword combinations and Boolean oper-
ators (O’Dea et al., 2021). However, given the aim of the present study, we used the regional and local oristic studies and new plant
reports related to IHR which are mostly not indexed in such databases and form the basis of our review. The procedure for developing
alien ora checklist was as follows: First, using the alien species list by Khuroo et al. (2012) as reference, we used the regional and state
oras of IHR to segregate those species occurring in the IHR. Next, we made targeted searches directly in the Google platform using the
combination of words related to alien species names (or its synonyms) and Indian Himalayan region or names of the states/union
territories forming the IHR to include additional species from the published papers. Finally, we also screened the references of the
related published papers to look for additional species records from the region. We included all the species in our checklist as ‘alien’,
which were reported as ‘exotic’, ‘non-indigenous’, ‘foreign’, ‘waifs’, ‘adventives’, ‘aliens’, ‘naturalized’, ‘invasive’ ‘introduced’, ‘alien
weeds’, non-native, ‘stranger’ and ‘immigrants’ from different parts of the IHR. We excluded those alien plants which occur exclusively
under the greenhouse conditions, pots, indoors, and other such articial conditions (Khuroo et al., 2012). We veried the taxonomic
validity of each species record primarily using Plants of the World online database (POWO,2022; https://powo.science.kew.org/). For
counting species in the checklist and further analysis, all the infra-specic taxa (subspecies, varieties, forma) in the checklist were
Fig. 1. Map of the study area.
S.A. Wani et al.
Global Ecology and Conservation 38 (2022) e02246
4
treated as separate taxa. Also, the family arrangement of taxa was upgraded according to the Angiosperm Phylogeny Group-IV
Classication (Angiosperm Phylogeny Group, 2016). To record the alien status for each region, available major regional and state
oras, weed oras, research papers, books, and eld guides were screened to extract data on alien plant species (supporting
Appendix-A).
2.3. Species traits: origin and life history
We assigned the native distribution range of each species using the online database of the Plants of the World online (POWO, 2022;
https://powo.science.kew.org/). For analysis purposes, we aggregated the native distribution range of each species into ‘continent’
according to the plant distribution scheme developed by Taxonomic Databases Working Group (TDWG; currently named Biodiversity
Information Standards; Brummitt, 2001). If a species’ native range falls within more than one continent, it was assigned to each of
them for analysis purposes. Also, we assigned the species life-span (annual, biennial, perennial) and growth-form (herb, grass, palm,
shrub, subshrub, tree, twiner, vine) categories based on eld observations, supplemented with authentic secondary sources, such as
e-oras (http://www.eoras.org). We adopted a standard terminology for these categories (Mabberley, 2008).
2.4. Naturalization status
We assigned the naturalization status to each alien species present in the checklist based on relevant published literature (Ap-
pendix-A). We divided the species pool in the checklist into two broad categories (i) alien species under cultivation (Cl) (i.e., including
those species with no known history or present literature or eld-based record of escape into the wild), and (ii) alien species growing in
the wild in one or more regions of IHR, what we refer in this study as naturalized (N), including both cultivation escapes as well as
accidentally introduced alien species growing in the wild without human intervention (Richardson et al., 2001; Mehraj et al., 2021).
2.5. Data analysis
We performed all the analyses in R 4.0.2 (R Core Team, 2020) using the packages cited within. We used the Chi-square test on
Pearson’s contingency tables to test whether there are signicant differences in the observed numbers of alien species among the
regions in terms of representation of species with different life-history categories and of different origins. The observed counts within
the combination of categories were compared with counts expected based on equal distribution across the categories. The deviation of
observed values from expected values within individual categories was expressed using Pearson’s residuals in R package “vcd 1.4–8”
(Meyer et al., 2020). Similarly, we also used the Chi-square test to examine whether there are signicant differences in the observed
species number with respect to species’ traits (native distribution range, life span, and growth form) and naturalization status (i.e.,
cultivated v/s naturalized). Since, the Chi-square is a signicance statistic that does not tell anything about the strength of the rela-
tionship between the studied variables, we also used Cramer’s V, the most commonly used power test to estimate the strength of the
relationship in cases where Chi-square test showed signicant results between the variables (McHugh, 2013). The Cramer’s V ranges
between 0 and 1, with values close to 0 indicating no relationship between the studied variables and close to 1 suggesting strong
relationship (Akoglu, 2018). For ease of interpretation, the values of Cramer’s V >0 suggest no relationship, >0.05 – weak rela-
tionship, >0.10 moderate relationship, >0.15 – strong relationship and >0.25 – very strong relationship between the variables
(Akoglu, 2018). We calculated the Cramer’s V statistic using the “rcompanion 2.4.1 package (Mangiaco, 2021).
To evaluate the similarity among each pair of regions in the IHR in terms of their alien and naturalized species composition, we
calculated the Sorenson’s similarity index, C
s
=2ab/a +b, where ‘a’ is the number of species found in region A; ‘b’ is the number of
species in region B and ‘ab’ is the number of species common to both regions (Magurran, 2004). The Sorenson similarity index (C
s
)
varies between 0 (completely different) and 1 (completely identical) (Magurran, 2004). Also, to test if the dissimilarity in species
composition between the regions was a function of geographical distance between them, we performed the Mantel test using the R
package “vegan 2.5–7” (Oksanen et al., 2020), wherein we analyzed the matrix of Sorenson’s dissimilarity index between regions with
a matrix of corresponding geographical distances between them. For calculating the geographical distance, we rst extracted the
centroids of the polygons (i.e., states) using the ‘feature to point’ function and next exported the geocoordinates of these centroids using
the ‘Add XY Coordinates’ function from data management tools in ArcGis v. 10.2 (https://www.esri.com/en-us/arcgis). The euclidean
method was preferred for calculating the geographical distance between each pair of regions (Boscoe et al., 2012). The Sorenson’s
dissimilarity matrix and geographical distance were calculated using the vegdist() function in “vegan 2.5–7” package. We used Monte
Carlo simulation based on 10,000 permutations to evaluate the statistical signicance of the resultant correlation (Legendre and
Vaudor, 1991).
To evaluate the relative roles of different environmental and socio-economic drivers of alien and naturalized plant species richness
per region, we performed the Generalized Linear Model (GLM) analysis. For this purpose, we used environmental variables such as
average annual rainfall (AAR) (mm), protected area (PA) (km
2
), forest area (FA) (km
2
), total land area (TA) (km
2
), and total plant
species richness (TPR); and socio-economic variables such as total population (TP), total trafc length (TTL) (km), total road length
(TRL) (km), surfaced road length (SRL) (km) and railway length (RL) (km) as explanatory variables, while as the number of alien and
naturalized plant species were used as dependent variables. The environmental and socio-economic variables were obtained from the
secondary sources (see supporting Appendices A and B for data sources) at the spatial scale of states in line with state-wise alien and
naturalized plant richness. Before analysis, multicollinearity among the explanatory variables was tested using Pearson’s correlation
matrix (Sokal and Rohlf, 1995). We considered r =0.8 as a threshold of collinearity between two predictors (Fuentes et al., 2015).
S.A. Wani et al.
Global Ecology and Conservation 38 (2022) e02246
5
Among the highly correlated variables, the one having a direct impact on the alien species distribution was chosen based on the
recently published literature (Dar et al., 2015; Fuentes et al., 2015; Ascens˜
ao and Capinha, 2017; Inderjit et al., 2018; Rashid et al.,
2021). We used GLM with the negative binomial model, as it is appropriate for overdispersed count data characterized by conditional
variance greater than the conditional mean (McCullagh and Nelder, 1989; Rojas-Sandoval et al., 2017). At rst, we created a full model
with all the selected explanatory variables together, and then new models were generated by simplications of the full model to
evaluate explanatory variables. As a result, all models including all possible combinations of selected explanatory variables were
created for each dependent variable. Model selection was done based on the corrected Akaike Information Criterion (AICc), a
modication of the AIC with a small sample bias adjustment (Burnham and Anderson, 2002). The model with the lowest AICc value is
Fig. 2. Representative invasive alien plant species in the Indian Himalayan Region: a) Leucanthemum vulgare, b) Ageratina adenophora, c) Eichhornia
crassipes, d) Nymphaea mexicana, e) Parthenium hysterophorus, f) Lantana camara, g) Datura stramonium, h) Alternanthera philoxeroides. (Image credits:
A. A. Khuroo, R. Gulzar)
S.A. Wani et al.
Global Ecology and Conservation 38 (2022) e02246
6
considered to be most parsimonious and AICc differences between 0 and 2 suggest weak differences among the models, while dif-
ferences larger than 4 are considered to be large (Arnold, 2010). These analyses were performed using the glm.nb function in “MASS”
package (Venables and Ripley, 2002), together with the dredge function from “MuMIn 1.43.17” package (Barton, 2020) for different
model comparisons.
3. Results
3.1. Taxonomic composition
We recorded a total of 771 alien plant species in the IHR belonging to 459 genera in 112 families. The angiosperms were repre-
sented by 734 (ca. 95%) species under 445 genera in 107 families and gymnosperms by 37 (ca. 5%) species under 14 genera in ve
families (see supporting Appendix-C). The checklist of all the plant species, with their respective family, native biogeographic range,
naturalization status, and life-history traits are given in supporting Appendix-C. Of the 112 families represented in the alien ora of the
IHR, the ve species-rich families were Fabaceae (101 species), Asteraceae (72 species), Solanaceae (43 species), Myrtaceae (32
species) and Malvaceae (30), which together contribute 278 (ca. 36%) plant species in the entire alien ora of the IHR. Similarly, the
ve species-rich genera include Senna (16 species), Solanum (15 species), Eucalyptus and Ipomoea (14 species each), and Acacia (11
species), together contributing a total of 70 (ca. 9%) alien plant species in the alien ora of the IHR (Appendix-C). Some of the
representative invasive alien plant species occuring in the IHR are shown in Fig. 2.
3.2. Region-wise alien species richness
The distribution of total alien ora in the 12 regions in IHR shows considerable variation in species richness (Table 1). The highest
number of alien plant species (633) are present in Uttarakhand, followed by Assam (364 species), and Tripura (269 species), while the
lowest number (108 species) is found in Meghalaya (Table 1). Once again Uttarakhand turned out to be the most dominant region in
terms of both the cultivated and naturalized alien plant species with 319 and 314 species respectively (Table 1). In contrast, Sikkim
showed the highest value (0.0271 and 0.0195 species km
-2
) for total and naturalized alien plant species densities (i.e., species number
relative to the area of the region), while as Jammu and Kashmir exhibited the lowest values for each of the total, cultivated and
naturalized plant species densities (0.0009, 0.0002 and 0.0007 species km
-2
respectively) (Table 1).
3.3. Species traits: origin and life history
The majority of the alien plant species in the IHR have native range of Southern America (319; ca. 28%), followed by Northern
America (272; 24%), Asia-temperate (165; ca. 15%), Africa (142; 13%), Asia-tropical and Europe (83; 7% each), Australasia (53, 5%)
and Pacic islands (8; 1%) (Supporting Appendix-D Table S1). At the individual region scale, Southern America again turned out to be
the major source of species pool to the alien ora of all the regions in the IHR (Table S1). However, the source regions of alien plants
differed signicantly between the individual regions (
χ
2
=172.53, df =77, p<0.001). More specically, there was a higher over-
representation of species from Australasia for Uttarakhand, from Asia-temperate and Europe for Himachal Pradesh and Jammu and
Kashmir, and from Southern America for Darjeeling (West Bengal), but highly under-represented were the species from Asia-temperate
for Darjeeling (West Bengal), from Asia-tropical for Himachal Pradesh and Jammu and Kashmir, from Australasia for Jammu and
Kashmir, and form Southern America for Uttarakhand (Supporting Appendix-D Fig. S1). However, the Cramer’s V statistic showed a
weak relationship between the source region of alien plants and their destination (Cramer’s V =0.08, 95% condence intervals
(hereafter CI): 0.08–0.10).
The alien ora of the IHR consists of 575 (ca. 75%) perennials, 187 (ca. 24%) annuals, and nine (ca. 1%) biennials (Supporting
Appendix-D Table S2). Also, perennial was the dominant life span category in all the individual regions in IHR (Table S2). Furthermore,
Table 1
Status of total alien, cultivated, and naturalized ora for each state/union territory in the Indian Himalayan Region. The numbers are shown along
with densities (km
-2
) in brackets for each category.
State/ Union territory Alien Cultivated Naturalized
Assam (AS) 364 (0.0047) 140 (0.0018) 224 (0.0028)
Arunachal Pradesh (AU) 186 (0.0023) 49 (0.0006) 137 (0.0017)
Himachal Pradesh (HP) 249 (0.0045) 78 (0.0014) 171 (0.0030)
Jammu and Kashmir (JK) 198 (0.0009) 49 (0.0002) 149 (0.0007)
Meghalaya (MG) 108 (0.0049) 28 (0.0012) 80 (0.0036)
Manipur (MN) 138 (0.0062) 48 (0.0022) 90 (0.0040)
Mizoram (MZ) 121 (0.0057) 39 (0.0019) 82 (0.0039)
Nagaland (NG) 126 (0.0076) 33 (0.0020) 93 (0.0056)
Sikkim (SK) 192 (0.0271) 54 (0.0077) 138 (0.0195)
Tripura (TR) 269 (0.0257) 108 (0.0103) 161 (0.0154)
Uttarakhand (UK) 633 (0.0119) 319 (0.0060) 314 (0.0059)
West Bengal (Darjeeling) (WB) 203 (0.0023) 51 (0.0006) 152 (0.0018)
Indian Himalayan Region (total) 771 375 396
S.A. Wani et al.
Global Ecology and Conservation 38 (2022) e02246
7
statistically these life span categories were not equally represented within the individual regions (
χ
2
=70.85, df =22, p<0.001).
Annuals were highly over-represented in Jammu and Kashmir, but highly under-represented in Uttarakhand (Supporting Appendix-D
Fig. S2). Also, highly over-represented were the perennials from Uttarakhand but under-represented from Jammu and Kashmir
(Fig. S2). Furthermore, the Cramer’s V statistic showed a moderate relationship between the life span and individual regions (Cramer’s
V =0.11, CI =0.10–0.15).
In terms of growth form, the majority of alien plant species in the IHR were herbs (282, ca. 37%) followed by trees (218; 28%),
shrubs (137; 18%), vines (48; 6%), subshrubs (31; 4%), grasses (29, 4%), palms (15, 2%) and twiners (11; 1%) (Supporting Appendix-D
Table S3). Herb was also the dominant growth form category in all the regions in the IHR (Table S3). Furthermore, these growth form
categories were not equally represented within each region (
χ
2
=158.31, df =77, p<0.001). Highly over-represented were the herbs
from Jammu and Kashmir, and palms and trees from Uttarakhand, while as highly under-represented were herbs from Uttarakhand,
shrubs from Jammu and Kashmir, and trees from Darjeeling (West Bengal) (Supporting Appendix-D Fig. S3). However, a weak
relationship was found between the growth form and individual regions (Cramer’s V =0.09, CI =0.10–0.12).
3.4. Naturalization status
Of the total 771 alien plant species present in the IHR, 375 (ca. 48%) species are known to be still under cultivation, while 396
(52%) species are growing in the wild as naturalized species. Further, the naturalization status categories were not equally distributed
within the life span (
χ
2
=61.37, df =2, p<0.001) and growth form categories (
χ
2
=171.48, df =7, p<0.001). For life span, annuals
have a higher probability to become naturalized (over-represented among the naturalized species but under-represented among the
cultivated ones), while perennials show the opposite trend (Fig. 3a). Similarly, according to the growth form, herbs have the higher
probability to become naturalized (over-represented among the naturalized species but under-represented among the cultivated ones),
while trees have the lowest probability to become naturalized (under-represented among the naturalized species but over-represented
among the cultivated ones) (Fig. 3b). Moreover, the Cramer’s V statistic showed a very strong relationship for naturalization status
with both life span (Cramer’s V =0.28, CI =0.23–0.35) and growth form categories (Cramer’s V =0.47, CI =0.42–0.53).
Furthermore, the categories of naturalization status were not equally distributed across the species’ origins (
χ
2
=49.60, df =7,
Fig. 3. Comparison of observed and expected numbers of species according to their naturalization status and (a) life span and (b) growth form
categories. The Pearson’s residuals derived from the contingency table are shown. Positive values of residuals sign higher observed values than
expected, negative residuals sign lower observed values than expected. The color represents the associated Pearson’s residuals. [Abbreviations used:
G =Grass, H =Herb, P =Palm, S =Shrub, SS =Sub-shrub, T =Tree, TW =Twiner, V =Vine].
S.A. Wani et al.
Global Ecology and Conservation 38 (2022) e02246
8
p<0.001). Species from Europe, Northern America, and Southern America had the highest probability of being naturalized; this
category was over-represented in the naturalized species, while cultivated species from these continents were under-represented
(Fig. 4).
3.5. Similarity in species composition of regional alien and naturalized oras
The total alien plant species similarity among the different regions in IHR as measured by Sorenson index varied from 0.25 to 0.73
(mean =0.44, SD =0.10) (Fig. 5a). Assam and Tripura are the most similar regions in terms of total alien ora, while Meghalaya and
Uttarakhand are the most dissimilar (Fig. 5a). In terms of naturalized alien plant species, the Sorensen’s similarity values varied
between 0.30 and 0.73 (mean =0.49, SD =0.10) (Fig. 5b). Here, Assam and Tripura, and Arunachal Pradesh and Sikkim are the most
similar states, while Mizoram and West Bengal (Darjeeling) showed the most dissimilarity (Fig. 5b). Statistically, the results of the
Mantel test showed a signicant correlation between the Sorensen’s dissimilarity values for both the total alien and naturalized plant
species composition and the corresponding geographical distance among the regions in IHR (r=0.34 and 0.31 respectively; p<0.05).
3.6. Drivers of alien and naturalized plant richness
The Pearson’s correlation analysis showed that total trafc length (TTL) was highly correlated with each of the total road lengths
(TRL), railway length (RL), surfaced road length (SRL), and total population (TP) (r >0.80), while as total area (TA) was highly
correlated with the protected area (PA) (r =0.92) (Supporting Appendix-D Fig. S4). Thus, from each pair of highly correlated vari-
ables, we selected TTL instead of TRL, RL, SRL, and TP and, TA instead of PA for further analysis. Based on the GLM statistic using alien
plant species richness as the dependent variable, the model showed that including all the selected explanatory variables explained 77%
of the variation in the data. However, the AICc scores for determining the most parsimonious model suggested that among all the
models obtained (Supporting Appendix-D Table S4), a single model (ΔAICc <2) including average annual rainfall turned out to be the
best predictor of the alien plant richness in each region of the IHR (AICc =151.57; AIC
weight
=0.37) (Table 2). The other ve best
models (ΔAICc <4) included a combination of average annual rainfall and total trafc length (AICc =154.51; AIC
weight
=0.08), total
trafc length (AICc =154.52; AIC
weight
=0.08), forest area (AICc =155.01; AIC
weight
=0.07), total area (AICc =155.02; AIC
weight
=
0.07) and total plant richness (AICc =155.23; AIC
weight
=0.06) (Table 2). However, these ve models were less well supported than
the most parsimonious (best-t) model (Table 2). Similarly, using the naturalized plant species richness as the dependent variable, the
results of GLM revealed that including all the explanatory variables in the model explained about 72% of the total variation in the data.
The AICc scores again suggested that among all the models obtained (Supporting Appendix-D Table S5), only a single model (ΔAICc <
2) including total trafc length was the best predictor of the naturalized plant richness in each region of the IHR (AICc =136.27;
AIC
weight
=0.36) (Table 2). The other ve best models (ΔAICc <4) included average annual rainfall (AICc =138.76; AIC
weight
=0.10),
total area (AICc =139.14; AIC
weight
=0.08), a combination of average annual rainfall and total trafc length (AICc =139.23; AIC
weight
=0.08), forest area (AICc =139.60; AIC
weight
=0.07) and total plant richness (AICc =139.73; AIC
weight
=0.06) (Table 2). However,
these ve models were once again less well supported than the best-t model (Table 2). Overall, these results indicate that the alien
Fig. 4. Comparison of observed and expected numbers of species according to their naturalization status and native biogeographical region. The
Pearson’s residuals derived from the contingency table are shown. Positive values of residuals sign higher observed values than expected, negative
residuals sign lower observed values than expected. The color represents the associated Pearson’s residuals. [Nativity - AFR =Africa, ATM=Asia
temperate, ATR =Asia tropical, AUS =Australasia, EUR =Europe, NAM =Northern America, PAC =Pacic, SAM =Southern America].
S.A. Wani et al.
Global Ecology and Conservation 38 (2022) e02246
9
plant richness increases with the optimal climatic conditions especially average annual rainfall, while the total trafc length being the
prime indicator of economic activities plays a key role in facilitating the establishment and naturalization of alien plant species in the
IHR.
4. Discussion
In the present study, a total of 771 alien plant species were recorded from the IHR, contributing about 48% to the total alien ora of
India (Khuroo et al., 2012). Also, a total of 396 naturalized plant species occur in the region, which together contributes ca. 84% of the
total naturalized ora of India (Inderjit et al., 2018). Such a higher number of alien and naturalized plant species present in the region
can be due to availability of diverse and heterogeneous landscapes, which provides multiple openings for the introduction and spread
of alien species in these regions (Bellard et al., 2014; Adhikari et al., 2015; Rojas-Sandoval et al., 2017; Khuroo et al., 2021). Also, the
rapidly increasing population and accelerating developmental activities experienced by the region as a result of reduced forest cover,
agricultural expansion, and urbanization and more recently due to increased tourism activities favor the introduction, establishment,
and spread of alien species (Jha and Bawa, 2006; Pathak et al., 2019; Khuroo et al., 2021). Moreover, the studied region has a long
history of serving as a trading route across Central Asia with eastward extension to the Far-east and westwards to the Middle East which
facilitated the easy colonization and establishment of introduced ora in the region.
The alien ora of Indian Himalaya followed the pattern of few winners at the cost of many losers, with only ve families
contributing ca. 36% of the total alien ora. This result is in accordance with the alien ora of India, wherein the rst ten families
contributed about 42% to the total alien ora (Khuroo et al., 2012). The predominant families found in this study are also among the
major contributors, although not exactly in the same order, to alien or naturalized oras of other Asian (Jiang et al., 2011; Uludag
et al., 2017; Patzelt et al., 2022) African (Ansong et al., 2019; Omer et al., 2021a) and European countries (Sandvik et al., 2019;
Leostrin and Pergl, 2021). The possible explanation for this can be that these families are known to be globally species-rich families and
hence harbor a higher proportion of alien species (Lambdon et al., 2008; Khuroo et al., 2012, 2021). Another plausible reason for this
can be the presence of key life-history traits including cosmopolitan nature and weedy tendency, and the relatively higher proportion
of agricultural or ornamental plants in these families, which in turn corresponds to a signicantly higher proportion of alien plant
species in such families (Medvecka et al., 2012; Mehraj et al., 2018; Khuroo et al., 2021).
During the present study, Uttarakhand was the dominant Indian Himalayan state both in terms of the number of total alien and
naturalized plant species. The probable reasons for this can be (i) more economic development (e.g., trafc length including widely
developed macadamized roads, abandoned non-macadomized roads, and railway length, and higher population density. Infact, the
locally abandoned roads that are interacting with protected areas such as forests play a major role in the introduction and naturali-
zation of some species in these pristine ecosystems (Grzedzicka, 2022) (ii) wide elevational gradient starting from the great Indian
Gangetic Plains to the highest mountain peaks in the Himalaya (Khuroo et al., 2021).
The Indian Himalaya had a majority representation of alien plant species from Southern America (28%). Again, Southern America
turned out to be the major source region to the alien ora of all the individual Himalayan regions. This trend is following several other
local and regional studies (Negi and Hajra, 2007; Weber et al., 2008a; Jiang et al., 2011; Khuroo et al., 2012; Jaryan et al., 2012;
Fig. 5. Plot of Sorensen similarity indices between the Indian Himalayan states/union territories based on (a) total alien and (b) naturalized plant
species composition. For the full form of abbreviations refer to Table 1.
S.A. Wani et al.
Global Ecology and Conservation 38 (2022) e02246
10
Table 2
Coefcients estimated from the Generalized Linear Models for all the possible combinations of the selected explanatory variables using alien and naturalized plant species richness per state/union territory
as the dependent variables. Coefcients were estimated with negative binomial models. Models are arranged by AICc values. M all are the models including all the selected variables while M1, M2, M3,
M4, M5 and M6 are the six best-t models based on the AICc values.
Average annual rainfall (mm) Forest area (km
2
) Total plant species richness Total Area (km
2
) Total trafc length (km) AICc Δ AICc AIC
weight
Models for alien plant richness
M1 -0.00027 151.57 0 0.37
M2 -0.00043 2.17e-06 154.51 2.94 0.08
M3 1.07e-06 154.52 2.95 0.08
M4 5.29e-06 155.01 3.44 0.07
M5 1.36e-06 155.02 3.45 0.07
M6 3.87e-06 155.23 3.67 0.06
M all -0.00069 1.09e-05 -5.39e-06 7.98e-05 3.06e-06 179.06 27.49 3.93e-07
Models for naturalized plant richness
M1 1.20e-06 136.27 0 0.36
M2 -0.00017 138.76 2.49 0.10
M3 1.94e-06 139.14 2.87 0.08
M4 -0.0003 1.92e-06 139.23 2.96 0.08
M5 4.83e-06 139.60 3.33 0.07
M6 3.73e-05 139.73 3.46 0.06
M all -0.00049 7.79e-06 -3.15e-06 7.00e-05 2.36e-06 164.85 28.58 2.21e-07
S.A. Wani et al.
Global Ecology and Conservation 38 (2022) e02246
11
Uludag et al., 2017; Inderjit et al., 2018). The probable reason for such a higher representation of species from Southern America can
be that these species nd the best habitats in the tropical climates of mainland India. Once established in the low-altitude areas, these
species move upwards along the mountains and become adapted to the mountainous landscapes of the Indian Himalaya as well
(Khuroo et al., 2021).
The alien ora of Indian Himalaya, as well as the individual states, was dominated by herbaceous growth form. The high proportion
of herbaceous taxa in alien floras has been reported by several other studies also (Singh et al., 2010; Jiang et al., 2011; Rojas-Sandoval
et al., 2017; Inderjit et al., 2018). The probable explanation for this can be the presence of traits like greater dispersal potential and the
ability to withstand harsh environmental conditions which can aid in a relatively higher proportion of herbs in the alien oras (Pyˇ
sek
and Hulme, 2005; Khuroo et al., 2021). Another possible explanation can be the higher level of anthropogenic disturbance witnessed in
these eco-fragile mountainous regions (Adhikari et al., 2015). Such disturbed conditions could result in the successful establishment
and spread of rapidly colonizing ruderal plant species, like herbs (Inderjit et al., 2018).
During the present study, perennials turned out to be the dominant life form in the alien ora of Indian Himalaya as well as the
individual regions. This predominance of perennials in the alien oras has been shown by several other research studies conducted
both at the regional (Singh et al., 2010; Jaryan et al., 2012; Mehraj et al., 2018) as well as the broader spatial scales (Lambdon et al.,
2008; Weber et al., 2008a). This can be attributed to the fact that perennials by employing the vegetative propagation and clonal
strategies in their life cycle, dominate the alien oras (Milbau and Stout, 2008; Khuroo et al., 2021).
In the present study, the majority of the alien species (48%) were under cultivation. This result is in agreement with the alien ora
of entire India, where 51% of species were found to be under cultivation (Khuroo et al., 2012). To the best of our knowledge, most of
the studies conducted so far have focussed only on the naturalized component of alien ora (e.g., Jiang et al., 2011; Inderjit et al., 2018;
Pyˇ
sek et al., 2017, but see Pergl et al., 2016; Patzelt et al., 2022). Interestingly, our study includes the cultivated pool of alien plant
species in addition to the wild occurring alien ora. The cultivated pool of alien ora is as equally important as the naturalized pool
because it acts as the future source of potentially invasive species (Khuroo et al., 2021). Several case studies exist in which the species
were initially under cultivation, but with time have escaped into the wild and grow at different stages along a naturalization-invasion
continuum (Khuroo et al., 2012, 2021; Gulzar et al., 2022).
The present study also showed that the higher tendency to become naturalized was observed in the species from Europe and the
Americas. This trend has been reported by several other researchers as well (Huang et al., 2009, 2010; Jiang et al., 2011). The probable
explanation for higher naturalization success of species from the Americas can be that most of the species from the American continents
have been reported to exhibit a strong allelopathic effect on the native species and thus exhibit strong competitive ability (Huang et al.,
2009). Another possible reason can be the climate matching between the region of origin and destination which favors the spread of
alien species and ultimately aids in their naturalization process. Similarly, herbaceous growth form has the highest tendency to become
naturalized while trees have the lower probability. This may again be attributed to species invasiveness traits possessed by the herbs
like shorter life cycle, greater dispersal potential, ability to tolerate harsh conditions, phenotypic plasticity to adapt to the changing
environment, and successful establishment in the disturbed habitats (Jiang et al., 2011; Khuroo et al., 2021).
The similarity among the 12 Indian Himalayan regions in terms of both numbers of alien and naturalized plant species showed
considerable variation. The possible explanation for such a variation in species composition may be the preferential human-mediated
introduction of alien species which is biased for different anthropogenic activities. Further, the differences in the altitudinal range and
topography of the studied Himalayan regions could be one of the key drivers that determine the composition and distribution patterns
of vegetation in eco-fragile mountainous areas (Mokarram and Sathyamoorthy, 2015; Malik and Nautiyal, 2016). More interestingly,
for both alien and naturalized plant composition, the dissimilarity between individual states was a function of distance as proved by
the Mental test, thereby providing empirical evidence for the existence of distance decay of similarity relationship.
The study identified that alien plant richness is best predicted by the average annual rainfall, while as the naturalized plant richness
is best predicted by total trafc length. The key role of average annual rainfall in governing both the total alien and naturalized plant
richness patterns has been conrmed by several other studies (Fuentes et al., 2015; Inderjit et al., 2018). This can be explained by the
fact that the alien plant species can be successful only in those new habitats where the local climatic conditions are favorable for their
growth, survival and establishment (Fuentes et al., 2015; Inderjit et al., 2018). Broadly speaking, the harsh environments with low
moisture may be less vulnerable to the growth and establishment of alien species, because only a few species are adapted to such harsh
environments (Zafferman et al., 2015). Further, the significant role of the total trafc length in shaping the patterns of naturalized
plant species in the Indian Himalayan regions is in line with the findings of several studies which have reported that transportation
networks act as conduits for the introduction and spread of alien species (Meunier and Lavoie, 2012; Dar et al., 2015; Ascens˜
ao and
Capinha, 2017; Rashid et al., 2021). Trafc length, being the backbone of the country’s economic development favors the introduction
and establishment of alien plant species and facilitates their spread and naturalization process (Meunier and Lavoie, 2012; Fuentes
et al., 2015; Rojas-Sandoval et al., 2017), as evidenced from the positive relationship between the countries leading in the road
networks and the corresponding number of alien species they harbor (Weber et al., 2008b; Christen and Matlack, 2009; Sharma et al.,
2010). Besides being a measure of economic development, the construction of road networks results in the disturbance of natural areas,
thus accelerating biological invasions (Reaser et al., 2007; Kueffer et al., 2010). Furthermore, vehicles act as an important vector for
plant dispersal, and road networks associated with higher trafc densities result in high propagule pressure of naturalized alien species
(Von der Lippe and Kowarik, 2008; Ansong and Pickering, 2013).
5. Conclusions and future research direction
The present study is novel in being the rst comprehensive compilation and analysis of alien ora in the IHR, including the
S.A. Wani et al.
Global Ecology and Conservation 38 (2022) e02246
12
cultivated species pool which was neglected completely in the previous study (Inderjit et al., 2018). We provide a detailed taxonomic
inventory of the alien ora along with their native biogeographical range, life-history traits, and naturalization status. It also pinpoints
the existing knowledge gaps in the geographical distribution of alien plant species by providing trends in diversity and regional
distribution in this Himalaya biodiversity hotspot. Finally, it also provides an empirical assessment of key environmental, and so-
cioeconomic correlates of the alien and naturalized plant richness in the individual Himalayan regions. Hopefully, the research insights
from the present study nds practical applications in developing the scientically-informed management and policy framework to
mitigate the impacts of invasive species and to predict the potential future invaders in the Himalaya – a globally data-decient region.
CRediT authorship contribution statement
Sajad Ahmad Wani: Data curation; Formal analysis; Software; Validation; Visualization; Writing - original draft, revision. Rameez
Ahmad: Conceptualization; Data curation; Formal analysis; Methodology; Software; Validation; Visualization; Writing - original draft
& editing, revision. Ruquia Gulzar: Data curation; Validation, revision. Irfan Rashid (Botany): Supervision; Writing – review &
editing. Akhtar Hussain Malik: Data curation; Validation. Irfan Rashid (Geoinformatics): Supervision; Writing – review & editing.
Anzar Ahmad Khuroo: Conceptualization; Funding acquisition; Investigation; Methodology; Project administration; Resources; Su-
pervision; Writing - original draft & editing, revision.
Declaration of Competing Interest
No conict of interest from the authors.
Data availability
Data will be made available on request.
Acknowledgments
Anzar Ahmad Khuroo acknowledges the nancial support by the Department of Biotechnology (DBT), Ministry of Science and
Technology, Government of India, under grant order No. BT/PR29607/FCB/125/17/2018. Sajad Ahmad Wani and Ruquia Gulzar
acknowledge the University Grants Commission (UGC), the Government of India for providing the fellowship. We are highly thankful
to Petr Pyˇ
sek and Jan Pergl, Institute of Botany, Czech Academy of Sciences, Czech Republic for providing constructive comments on
the previous versions of the manuscript. We are thankful to two anonymous reviewers for their useful comments which has improved
the quality of the manuscript.
Appendix A. Supporting information
Supplementary data associated with this article can be found in the online version at doi:10.1016/j.gecco.2022.e02246.
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