Potential distribution of two Ambrosia species in … Qin et al...2001) for bias, and records with obvious geocoding errors were discarded. The resulting data set was then overlaid

Potential distribution of two Ambrosia speciesin China under projected climate change

Z QIN*, A DITOMMASO†, R S WU* & H Y HUANG‡*The Department of Ecology, College of Agriculture, South China Agricultural University, Guangzhou, China, †Department of Crop

and Soil Sciences, Cornell University, Ithaca, NY, USA, and ‡Department of Biology, Shaoguan University, Shaoguan, China

Received 17 February 2014

Revised version accepted 8 May 2014

Subject Editor: Jos�e Gonzalez-Andujar, CSIC, Spain

Summary

The invasion of Ambrosia artemisiifolia and Ambrosia

trifida from their native range to occupy large areas in

China has raised considerable concern. Using the max-

imum entropy (Maxent) method, we developed models

for each Ambrosia species, based on occurrence records

from both native ranges (North America) and their

invaded ranges (e.g. northern and south-western Eur-

ope) to predict the availability and distribution of suit-

able habitats for these two species in China. For each

species, we also assessed potential shifts in habitat suit-

ability for the year 2050, using three general circula-

tion models (GCMs) and two emission scenarios.

Elevation and average mean precipitation in October

contributed most to model development for both spe-

cies. Potential distribution projections under future cli-

matic change scenarios suggested an averaged

percentage of suitable area (2.21%) and habitat gain

(1.49%) in A. artemisiifolia distribution, with further

expansion to environmentally favourable locations in

south-east coastal regions, northern Taiwan and the

Beijing–Tianjin–Tangshan area in northern China.

Future predicted percentage of suitable area for A. tri-

fida was 0.03% with a very limited suitable habitat

gain of <1% although this species had the potential to

continue to spread in northern China. Our findings

suggest that management priorities should be focused

on A. artemisiifolia, whilst effective control strategies

for A. trifida may be optimised by concentrating

efforts on those relatively fewer regions of China

where the species is currently abundant.

Keywords: ragweed, climate change, distribution mod-

els, invasive plants, Maxent.

QIN Z, DITOMMASO A, WU RS & HUANG HY (2014). Potential distribution of two Ambrosia species in China under

projected climate change. Weed Research 54, 520–531.

Introduction

China has seen the introduction and subsequent estab-

lishment of up to 270 invasive alien plant species com-

prising 0.9% of the country’s vascular flora in the past

200 years (Weber et al., 2008). Of the 59 botanical

families represented, the Asteraceae contains the high-

est number of invasive species (Weber et al., 2008).

The genus Ambrosia within the Asteraceae encom-

passes at least 40 annual or short-lived perennial

species (Bassett & Crompton, 1975), with numerous

intraspecific taxa, of which Ambrosia artemisiifolia L.

(common ragweed) and Ambrosia trifida L. (giant rag-

weed) are most frequently found in China. Both

Ambrosia species are annuals native to North America

and are known invaders with high potential for

colonising new areas (Harrison et al., 2003). As the

most important of the introduced Ambrosia species,

A. artemisiifolia has spread to large parts of Europe

and some Asian countries (Chrenov�a et al., 2010;

Correspondence: A DiTommaso, Weed Ecology and Management Laboratory, Department of Crop and Soil Sciences, Cornell University, Ithaca,

NY 14853, USA. Tel: (+1) 607 254 4702; Fax: (+1) 607 255 2644; E-mail: [email protected]

© 2014 European Weed Research Society 54, 520–531

DOI: 10.1111/wre.12100

Bruun et al., 2011), colonising a broad range of habi-

tats including wastelands, roadsides, grasslands and

cultivated fields. The presence of this species has sub-

stantially altered the biodiversity, structure and func-

tion of invaded ecosystems (Sheppard et al., 2006).

Ambrosia artemisiifolia was first introduced into China

in the 1930s and has since extended its range from

northern to southern China (Wan & Wang, 1990; Qi

et al., 2011). The first A. trifida populations were

observed in northeast China around 1935. The

increased presence of A. trifida has been reported

mainly in Beijing, Hebei since the 1950s. The distribu-

tional range of this species has now expanded further

into central and northern parts of the country (Yin

et al., 2010). Ambrosia trifida is also one of the most

competitive and troublesome weeds and its biology is

similar to that of A. artemisiifolia, but A. trifida is

more frost-resistant, develops faster and seeds mature

earlier (Abul-Fatih et al., 1979; Rich, 1994). Although

A. trifida is less common than A. artemisiifolia in

China, both species were accidentally introduced into

the country and represent two of the most noxious

invasive plants (Huang et al., 2009). The major con-

cerns with these two species include crop yield losses

due to competition and human health impacts from

their highly allergenic pollen (Zeng et al., 2010). These

deleterious effects are expected to increase substantially

in coming decades, due to expanding international

trade and improved transport infrastructures that will

increase opportunities for additional introductions and

movement of these species. Importantly, global climate

change may exacerbate these effects by providing addi-

tional regions that favour the colonisation and estab-

lishment of these species.

The rapid spread of Ambrosia species and the

increasing threats and damage posed by them have

stimulated research to increase our understanding of

their invasion biology that could ultimately lead to

more effective control strategies. The first essential

step is to predict and map their expected ranges of

habitat suitability and assess their potential geographic

distributions in response to factors such as climate

change. Among various modelling approaches, niche-

based species distribution models (SDMs) are currently

gaining interest and have been used to assess and iden-

tify regions with high invasion potential (e.g. Peterson,

2003). These models use known species distributions,

in conjunction with a set of environmental variables,

to generate a correlative model of the environmental

conditions that meet a species’ ecological requirements

(Peterson, 2003). This makes it possible to project

modelled niches into new regions and estimate the

geographical distribution of suitable conditions for

species’ occurrence.

In this study, we employed a widely used SDM, the

maximum entropy (Maxent) modelling approach, to

map the current distribution of A. artemisiifolia and

A. trifida in China, based on globally known occur-

rence records and related environmental variables. The

potential distribution of the two Ambrosia species in

2050 was also determined, based on projected avail-

ability of suitable habitats. Ultimately, this work

would help prioritise management efforts against these

two invasive species in China by identifying habitats

that are likely to be colonised in a changing climate.

Materials and methods

Species occurrence data collection

A total of 6299 occurrence records of A. artemisiifolia

were retrieved from the Global Biodiversity Informa-

tion Facility (GBIF; www.gbif.org). Meanwhile, occur-

rence records were collected from Chinese Virtual

Herbarium databases, the China Species Information

System and National Standard Integration of Species

Information for Teaching and Resources Sharing Plat-

form, as well as local Flora books, published scientific

research literature and field surveys. All the obtained

records were integrated and checked to ensure that

there were no duplicates. Records from the 1950s in the

integrated data set were selected to maintain temporal

correspondence between occurrence sampling and the

climate data set used (see below). The data set was

checked in the DIVA-GIS software (Hijmans et al.,

2001) for bias, and records with obvious geocoding

errors were discarded. The resulting data set was then

overlaid with a 10 km grid, and one record was ran-

domly selected from each cell, thereby 3850 (2213 and

1637 records in the native and invaded range, respec-

tively) records for A. artemisiifolia were produced.

Using the same method, 2165 records for A. trifida

(1850 and 315 records in native and invaded range,

respectively) were produced for model prediction.

Selection of environmental variables

The current climate data set (1950–2000) was obtained

from the WorldClim 1.4 database (version 1.4, http://

www.worldclim.org) and consisted of monthly total

precipitation, and monthly mean, minimum and maxi-

mum temperature, 19 derived bioclimatic variables, as

well as one topography (elevation) layer at a spatial

resolution of five arc min. Slope and aspect were

calculated using the elevation data set at the same

spatial resolution and were used as potential explana-

tory variables. Hence, a full set of 70 environmental

variable layers representing climatic (temperature,


Ambrosia distribution in China 521

precipitation) and topographic (elevation, slope, and

aspect) cover conditions was initially considered for

model building.

To reduce the high colinearity and minimise model

overfitting, pairwise correlation analyses were first per-

formed to identify redundant climatic variables for each

species. Values of temperature and precipitation vari-

ables for 70% randomly selected occurrence records

(native and invaded range) were used to calculate Pear-

son’s correlation coefficients. Variables showing a cor-

relation > 0.90 were considered redundant, whilst low-

correlated variables were employed to construct niche

models. Subsets of temperature and precipitation

redundant variables were then used separately to gen-

erate reduced non-correlated climatic variables with

high explanatory power by principal component analy-

sis (PCA). For each of the PCA factors, the variable

with the highest factor loadings (which measure the

correlations between the original variables and the fac-

tor axes) was selected (> 0.90). The resulting climatic

data set for both Ambrosia species included 12 and 14

variables for which temperature and precipitation vari-

ables were approximately equally represented. All these

variables combined with three topographic variables

were considered to be representative candidate predic-

tors and were assessed through Maxent’s jackknife

test. As a consequence, variables that contributed

< 1.0% were eliminated and the final explanatory vari-

ables obtained were then used to build Maxent models

for each Ambrosia species (Table 1).

Future climate projections

Future climate conditions were based on precipitation

and temperature projections for the year 2050 from

three atmosphere-ocean general circulation models

[GCMs] (CCCMA: CGCM2, CSIRO: MK2 and HAD-

CM3). These three GCMs were selected because they

are most widely used and can produce very different

predictions for future rainfall and temperature (e.g.

Fuller et al., 2012). Each of these climate models pro-

jects two emission scenarios reported in the Special

Report on Emissions Scenarios (SRES) by the Inter-

governmental Panel on Climate Change (IPCC; http://

www.grida.no/climate/ipcc/emission/). Scenario B2a

emphasises more environmentally conscious and re-

gionalised solutions to economic, social and environ-

mental sustainability. Compared with B2a, scenario

A2a also emphasises regionalised solutions to eco-

nomic and social development, but it is less environ-

mentally conscious. For each Ambrosia species, six

modelled climate scenarios were performed indepen-

dently.

To determine whether predicted suitable habitats

for each Ambrosia species would expand or contract in

the future in relation to current potential habitats, we

summed the potential range loss and gain, respectively,

then calculated the percentage of predicted range

change using the methods provided by Hu et al.

(2010). The turnover rate (T) was calculated using the

equation below:

Table 1 Percentage contribution of environmental variables to the Maxent models for Ambrosia artemisiifolia and A. trifida potential

distribution in China. Variables explaining <1% of total variability are not listed. Also shown for each variable are minimum and

maximum values, means � standard deviations and median values

A. artemisiifolia A. trifida

Variables

Percent

contribution

(Min,

Max) Mean Median Variables

Percent

contribution

(Min,

Max) Mean Median

alt 38.7 5, 1891 152.3 � 265.4 57 prec10 47.7 8, 80 38.3 � 20.3 39

prec4 23.9 11, 232 110.3 � 63.8 122 alt 26.5 4, 516 122.3 � 129.4 75

prec10 6.9 8, 192 62.9 � 33.7 59.5 tmax10 6.3 119, 235 183.4 � 33.9 190

bio12 6.9 307, 2660 1249.9 � 523.3 1316 bio15 6.1 41, 147 98.6 � 29.6 99

prec9 4.7 34, 318 114.9 � 61.8 91.5 prec8 2.1 52, 242 161.0 � 41.5 159

bio15 4 12, 147 69.4 � 27.3 59.5 aspect 1.9 0, 350 176.0 � 97.5 163

prec11 2.7 4, 186 42.8 � 28.6 42 bio2 1.8 69, 125 103.3 � 19.1 111

bio9 2.6 �214, 271 61.2 � 109.8 76 tmean6 1.2 181, 263 228.6 � 22.1 237

tmean5 1.6 115, 268 209.6 � 36.2 216 prec6 1.2 64, 303 109.6 � 51.9 90

bio8 1.6 165, 286 242.5 � 26.8 246

tmin11 1.2 �167, 191 68.2 � 88.6 85

aspect 1.0 2, 354 190.9 � 107.1 183

Variable definitions: Bio2: mean diurnal range (max temp–min temp); Bio8: mean temperature of wettest quarter (°C 9 10); Bio9: mean

temperature of driest quarter (°C 9 10); Bio12: annual precipitation (mm); Bio15: precipitation seasonality; tmax2, tmax3, tmax10 rep-

resent average monthly maximum temperature (°C 9 10) in October; tmean5, tmean6 represent average monthly mean temperature

(°C 9 10) in May and June respectively; tmin11 represents average monthly minimum temperature (°C 9 10) in November; prec4,

prec6, prec8, prec9, prec10 and prec11 represent average monthly precipitation (mm) in April, June, August, September, October and

November respectively. alt: altitude (m); aspect (°): derived from altitude.


522 Z Qin et al.

T ¼ 100� ðGþ LÞðSRþ GÞ

� �ð1Þ

where, T = species turnover rate; G = species gain; L =species loss and SR = current species distribution. A

turnover rate of 0 indicates that the species assemblage

does not change, whereas a turnover rate of 100 indi-

cates that they are completely different from previous

conditions (Trisurat et al., 2011).

Model development and evaluation

The maximum entropy method, as implemented in

Maxent (Version 3.3.3k; Princeton University, Prince-

ton, NJ, USA), was used to model the potential distri-

bution range of A. artemisiifolia and A. trifida. As a

general-purpose machine algorithm, Maxent can be

applied to presence-only data to produce habitat suit-

ability predictions as a function of corresponding envi-

ronmental variables. Higher function values indicate

more suitable conditions for the given species (Phillips

et al., 2006). Maxent has shown comparable perfor-

mance to several traditional tools that use presence/

absence data, including general linear models and gen-

eral additive models (e.g. Elith et al., 2006) and has

been widely used to model the potential geographic

distribution of several plant species (e.g. Trisurat et al.,

2009).

A Maxent model for each Ambrosia species was

built using point occurrence data from both the native

range and invaded range to encompass the most com-

prehensive estimation of the species’ ecological niche.

Training models that have used data from native and

invaded ranges have been shown not to significantly

affect model performance or result in overfitting (Bro-

ennimann & Guisan, 2008). Here, a random subsample

of points selected from both native and invaded ranges

was obtained for each Ambrosia species. Out of these

occurrence points, 75% were used for training the

model and the remaining 25% were used for testing

the model. To validate model robustness, the fivefold

cross-validation method was used, whereby the data

had been split randomly into five equal parts, with

20% of the observations as test cases and the remain-

ing subsets as training cases. This was repeated five

times, and the averaged model was built across all the

replicates to create the maps. Models were run in

hinge-features with the default convergence threshold

(10�5) and number of iterations (500). The 10th per-

centile training presence threshold (above which it is

considered that the species is present) was used, as it

was considered as a highly conservative estimate of a

species’ tolerance to each environmental variable and

can therefore provide more ecologically significant

results (Svenning et al., 2008). The logistic output for-

mat with suitability values ranging from 0 (unsuitable)

to 1 (optimal) was used (Phillips & Dud�ık, 2008).

Average, maximum, minimum, median and standard

deviations of Maxent scores were generated from the

replicated runs. A more stringent threshold of 0.5 was

used to convert Maxent estimates to binary presence/

absence maps for calculation of future changes in habi-

tat suitability for both Ambrosia species. Maxent

model predictions for both Ambrosia species was eval-

uated by calculating the area under the curve (AUC)

of the receiver operating characteristic plot using an

independent data set from the projected range in

China. As a threshold independent measure of predic-

tive accuracy based only on the ranking of locations,

AUC is normally calculated to establish a model’s abil-

ity to differentiate between presence and absence

observations (Galletti et al., 2013). It can be used to

compare presences with background points for the

assessment of presence-only modelling techniques.

Although the applicability of the AUC for the assess-

ment of presence/absence predictions has been ques-

tioned (e.g. Lobo et al., 2008), values > 85% are

suggested as a baseline for model accuracy (Pearce &

Ferrier, 2000; Foody, 2008).

Results

Model performance and environmental variable

responses

The Maxent models developed for both Ambrosia spe-

cies predicted invasion localities significantly better

than random expectations. Mean training AUC values

for A. artemisiifolia over 25 iterations projected in

China was 0.9309 (SD = 0.0006), whereas mean train-

ing AUC values for A. trifida was 0.9889 (SD =0.0010). The percentage that individual environmental

variables contributed to building the prediction model

of a species differed between the two Ambrosia species

(Table 1). There were 12 and 9 variables with contri-

butions >1% identified to be important in creating

model fit for A. artemisiifolia and A. trifida respec-

tively. Of the most important four variables (contribut-

ing > 5%), elevation (alt) and average monthly mean

precipitation in October (prec10) contributed most to

both species. Annual precipitation (bio12) and precipi-

tation seasonality (bio15) were also important variables

for A. artemisiifolia and A. trifida, respectively, with

nearly equal contribution.

Response curves of critical environmental variables

for both Ambrosia species were consistent with

their biological tolerances and ecological preferences.



Response curves of elevation (Fig. 1A) and October

mean precipitation (Fig. 1C) for A. artemisiifolia exhib-

ited generally similar patterns as those for A. trifida

(Fig. 2A and B), suggesting that habitats of low eleva-

tion and with high October mean precipitation had

high potential suitability for the two species, especially

A. trifida. Locations with elevations below 1000 m

were likely to be within the potential suitability for

A. artemisiifolia and A. trifida, with average highest

probability of presence being 0.52 and 0.70

respectively. October mean precipitation above

200 mm was most favourable for a stable occurrence

probability of A. trifida, whereas the occurrence proba-

bility of A. artemisiifolia increased as shown by the

elevated response of prec10. The response curves also

indicated that habitats with April mean precipitation

(prec4, see Fig. 1B) below 200 mm and annual precipi-

tation (Fig. 1D) between 1000 and 3500 mm favoured

the presence of A. artemisiifolia, whilst habitats with

mean maximum October temperatures (tmax10, see

Fig. 2C) below 18°C and a tolerance for high ranges

of precipitation seasonality (Fig. 2D) favoured the

presence of A. trifida.

Current distribution predictions for Ambrosia

species

Model predictions of the current distribution of both

Ambrosia species corresponded well with their observed

distribution in China (Figs 3 and 4). The estimated

latitude range with high occurrence probabilities of

A. artemisiifolia extended from north-eastern China in

the north to the south-eastern coastal areas and as far

south as Taiwan. Potentially suitable regions for estab-

lishment of this invasive weed were located in the bor-

dering areas of Beijing, Tianjin and Hebei, part of

northern Jiangxi, mid-eastern Anhui and adjacent

regions in Jiangsu province, as well as part of southern

Hubei stretching into north-central Hunan province.

Additional suitable regions included south-eastern

coastal areas, such as Shanghai, Zhejiang, Fujian,

Guangdong and a small part of Guangxi. The north-

ern parts of Hong Kong and perimeter locations of

Taiwan were also found to be suitable regions for

A. artemisiifolia. However, suitable habitats were not

detected in north-eastern regions of the country under

the current environmental model, despite the reported

A B

C D

Fig. 1 Response curves of environmental variables (contributing > 5%) to the Maxent model for Ambrosia artemisiifolia. The grey cen-

tre line represents the mean values derived from the cross-validation runs, while the black shading delineates the standard deviation.

Variable definitions: alt: altitude (m); prec4 and prec10 represent April and October precipitation (mm) respectively; bio12: annual

precipitation (mm).


524 Z Qin et al.

occurrence of this species in the region. In general,

2.02% of the total land area was projected to be envi-

ronmentally suitable for A. artemisiifolia.

The present climate suitability of invaded areas by

A. trifida was mainly confined to part of central Beij-

ing and the southern part contiguous to Hebei, despite

recorded occurrences in other north-eastern and east-

ern provinces, such as Heilongjiang, Liaoning. Overall,

0.03% of the total land area was projected to be envi-

ronmentally suitable for A. trifida.

Future distribution predictions

For the general circulation models and special report

emission scenarios selected, the average percentage

decline in suitable range in the year 2050 was 0.30%

for A. artemisiifolia and 0.001% for A. trifida. The

average percentage range gain for the two species was

1.49% and 0.04% respectively. The average predicted

range change for A. artemisiifolia was 1.17, about 30

times greater than for A. trifida. Hence, the average

percentage turnover was estimated to be 1.75% and

0.04% for A. artemisiifolia and A. trifida respectively

(Table 2). Except for the MK2-B2a model for A. trifi-

da, the ranges of the two invasive species were pro-

jected to increase across model scenarios.

Almost all the current suitable areas for A. artemis-

iifolia remained favourable and showed a possible

expansion under future climate scenarios (Fig. 3).

South-eastern coastal areas were predicted to experi-

ence about a 0.8% increase in suitable habitats. Both

central (including northern Jiangxi, parts of Hunan

and Hubei) and northern China (including Beijing,

Tianjin and Hebei) were projected to experience a

0.3% gain in suitable habitats under future climate sce-

narios. The predicted range gain under climate change

was 0.05% in Taiwan. In contrast, suitable habitat was

projected to decrease by 0.15% in southern China. For

A. trifida, central Beijing and the southern portions

contiguous to Hebei regions would still provide suit-

able environmental conditions for this species and

show an increase of 1.2% in area. Small portions of

several north-eastern provinces (0.87%) were also pre-

dicted to be favourable for the establishment of this

Ambrosia species by the year 2050.

For A. artemisiifolia, the B2a scenario suggested a

general higher range expansion in suitable regions and

lower percentage turnover than the A2a scenarios

(Table 2). Under both SRES scenarios, predictions of

the different models for the various future climate

scenarios used produced very different outcomes,

for which A. artemisiifolia had the largest potential

A B

C D

Fig. 2 Response curves of environmental variables (contributing > 5%) to the Maxent model for Ambrosia trifida. Line and shading col-

ours are as for Fig. 1. Bio15 represents precipitation seasonality; tmax10 represents maximum October temperature (°C 9 10).



distribution in suitable regions. The lowest habitat loss

was estimated by the HadCM3 models, whereas the

narrowest suitable distribution area with the highest

habitat loss was predicted by the CGCM2 model. The

predicted change in range and turnover percentage

exhibited similar patterns using the three models.

Differences in model outputs were most notable using

the A2a scenario. Three key regions were identified as

vulnerable to major expansion of suitable habitats for

A. artemisiifolia using the HadCM3 models under

future climate scenarios. These included the central

China region (eastern Hunan, south-east Hubei, part

of Henan and Jiangxi) to south Anhui and Jiangsu.

Suitable areas located at the junction of Beijing, Hebei

and Tianjin were predicted to increase and extend

northward to Shandong. Parts of south-eastern Guan-

gxi and Hainan would also experience gains in suitable

habitats.

Variations in model predictions were different for

A. trifida. Compared with the A2a scenario, the B2a

scenario showed a lower range expansion in suitable

regions, range gain and turnover percentage. The Had-

CM3 models predicted the largest potential distribu-

tion in suitable regions and smallest range reduction

for A. trifida under the two scenarios. Although

the CGCM2 model identified suitable habitats and

A

B

C

Fig. 3 Predicted potential distribution of Ambrosia artemisiifolia in China under current and future climate conditions. (A) The current

distribution of A. artemisiifolia with known records shown as filled diamonds. Maps from left to right show the spatial distribution of

A. artemisiifolia in 2050 as predicted by three atmosphere-ocean general circulation models: CCCMA: CGCM2, CSIRO: MK2 and

HADCM3 under SRES A2a and B2a climate change scenarios respectively. Higher Maxent values in these maps suggest a higher cli-

matic suitability for the species. (B) Maps show the corresponding spatial distribution changes for A. artemisiifolia estimated with Max-

ent models under six different scenarios. Positive values indicate increases in habitat suitability by 2050, while negative values indicate

decreases in habitat suitability. (C) Maps show percentage change in habitat suitability derived from averages of three models under

SRES A2a, B2a scenarios and all the modelled scenarios for A. artemisiifolia. Light grey shading reflects no change in the logistic proba-

bility of occurrence of suitable habitats under climate change, dark grey shading reflects a decline in suitable habitats, and black shading

reflects a gain in suitable habitats.


526 Z Qin et al.

moderate range gain under the A2a scenario, it did not

detect changes in environmentally suitable areas under

the B2a scenario. Based on HadCM3 models, current

suitable regions for A. trifida were predicted to

increase markedly by 2050, extending north-eastward

into large parts of the Provinces of Jilin and Heilongji-

ang and covering parts of eastern Neimongol. An

additional region that was predicted to experience a

substantial increase in environmentally suitable habi-

tats was located in the border areas of eastern Sichuan

and Chongqin.

Discussion

Model results and performance – A. artemisiifolia

The current spatial distribution of A. artemisiifolia fol-

lowed similar patterns as hypothesised by Sha et al.

(2000) based on biological characteristics and regional

environmental conditions (Fig. 3). We found increases

in the occurrence of suitable environmental conditions

along coastal regions under future climate projections,

especially for Zhejiang, Guangdong, Fujian and

Guangxi regions where the presence of A. artemisiifolia

has been reported (e.g. Zeng et al., 2010). Further

expansion into environmentally optimal locations was

predicted for the Beijing–Tianjin–Tangshan area in

northern China, as well as south-eastern regions

of mainland China and northern Taiwan, where A. ar-

temisiifolia is currently found. The presence of suitable

environmental conditions for A. artemisiifolia in these

regions was consistent with historical occurrence data

from field survey reports, although the potential area

of expansion differed considerably depending on the

model and emission scenario used. However, predicted

favourable conditions for the establishment of A. ar-

temisiifolia in some areas did not match well with the

presence of this species in these areas. For instance,

A

B

C

Fig. 4 Predicted potential distribution of Ambrosia trifida in China under current and future climate conditions. Map legends are as for

Fig. 3.



north-eastern regions of China including the Provinces

of Heilongjiang, Jilin and Liaoning were predicted to

be at most marginally suitable for colonisation of this

species under both current and future climate scenar-

ios; however, these are the very regions that are gener-

ally acknowledged to have experienced the first

successful introduction of A. artemisiifolia into China

and from where the species has subsequently spread

southward (Wan & Wang, 1990; Qi et al., 2011). It

seems that A. artemisiifolia populations have already

adapted to the diverse environmental conditions pres-

ent across invaded regions that span from north-east-

ern to southern China. This view is supported by the

presence of extensive and abundant populations of this

species in more southern regions that experience rela-

tively warmer and wetter climatic conditions than in

the north-eastern region where the species was origi-

nally introduced. It is likely that A. artemisiifolia will

benefit greatly from climate change, as environmentally

suitable ranges for this species are projected to increase

by 2050.

Model results and performance – A. trifida

The predicted current distribution of A. trifida was

consistent with the findings of Huang et al. (2006) in

which the central temperate zone was ranked among

the suitable zones for colonisation. Findings from sev-

eral studies (e.g. Guo et al., 2004) suggest that A. trifi-

da may experience reductions in suitable habitats in

China, thereby limiting any large scale expansion in

future years. Our results revealed a limited suitable

habitat gain of no > 1% for A. trifida, although this

species has the potential to continue to spread in

northern China after more than 100 years of being

present in the region. Results from the CGCM2 model,

especially under the B2a scenario, suggest that A. trifi-

da may attain its range limit by 2050, highlighting the

potential unfavourable effects of climate change for

this species.

The rate and magnitude of future distribution shifts

due to climate change can vary considerably depending

on the time scale used (e.g. Rebelo et al., 2010). As we

did not assess possible variations in land area occupied

by A. trifida across different time scales in the future,

the question of whether or when favourable environ-

mental conditions for this noxious weed could disap-

pear in China remains to be explored. Recent work

has shown that during its expansion in China, A. trifi-

da has evolved several ecophysiological traits that may

aid its adaptation to a changing climate, including high

seed production and effective dispersal (Zeng et al.,

2010), phenotypic plasticity (Xue et al., 2010; Wang

et al., 2012) and genetic variability (Sha et al., 2000).Table

2Predictedchanges

inhabitatsuitabilityforA.artem

isiifoliaandA.trifidaby2050in

ChinabasedonestimationsofMaxentmodelsunder

SRESA2andB2clim

ate

changescenarios

Species

Changesofclim

atica

llysu

itable

areas*

A2asc

enario

B2asc

enario

Sce

nario

Average†

CGCM2

model

HADCM3

model

MK2

model

A2a

Average

CGCM2

model

HADCM3

model

MK2

model

B2a

Average

A.artemisiifolia

Predictedpercentagesu

itable

area

3.779

6.416

1.515

2.843

3.856

5.095

2.540

3.232

2.207

Habitatloss

(%)

0.770

0.083

0.907

0.544

0.429

0.297

0.438

0.240

0.302

Habitatgain

(%)

2.664

4.544

0.471

1.440

2.346

3.454

1.033

1.539

1.486

Predictedrangech

ange(%

)1.821

4.422

�0.433

0.889

1.901

3.131

0.590

1.287

1.174

Turnover

3.220

4.390

1.360

1.939

2.689

3.596

1.444

1.737

1.747

A.trifida

Predictedpercentagesu

itable

area

0.359

4.993

0.191

0.236

01.731

0.011

0.014

0.029

Habitatloss

(%)

0.001

0.004

0.001

0.001

00

0.025

0.018

0.001

Habitatgain

(%)

0.330

4.969

0.162

0.207

01.702

0.007

0.003

0.041

Predictedrangech

ange(%

)0.325

4.922

0.160

0.205

01.687

�0.018

0.039

0.039

Turnover

0.326

4.700

0.161

0.206

01.659

0.031

0.042

0.042

*Calculationofareabasedongridsquaresidentified

as≥50%

suitable.

†Calculationofareabasedonensemble

SRESA2aandSRESB2ascenario.


528 Z Qin et al.

These possible population-level changes add some

uncertainty to the potential range of A. trifida (Cle-

ments & Ditommaso, 2011; Clements & DiTommaso,

2012). It is possible that A. trifida may persist even

when a substantial portion of suitable habitats are lost

in a changing climate.

Influence of environmental variables

Outcomes from the multiclimate change scenarios in

this study suggest that both Ambrosia species can

adapt to a wider range of climate conditions and sur-

vive well under future alterations in climate. As typical

annual herbaceous invaders, these two Ambrosia spe-

cies are not physiologically active throughout the year

in all regions of China where they occur. Therefore,

seasonal variability in climatic inputs (e.g. temperature

and precipitation), as well as monthly time-step vari-

ables may be more important in predicting their distri-

butions than using annual mean climatic values as was

performed (Table 1). Establishment of A. artemisiifolia

was associated with dry springs and hot, moist sum-

mers, followed by a continually damp autumn, while

low temperatures and inadequate water supply may

delay growth and development of this species. In con-

trast to A. artemisiifolia, the range of climates in which

A. trifida occurs in China excluded regions experienc-

ing long hot and relatively wet conditions. Upper lim-

its of the October maximum temperature (tmax10)

may be correlated with the chilling requirement of

A. trifida and may correspond to the southern or low-

elevation range limits for this species in China. Based

on these data, it can be hypothesised that a combina-

tion of increasing temperatures and drought, especially

in autumn, will negatively impact the growth and sur-

vival of A. trifida, thereby limiting its abundance and

spread.

Possible limitations

The potential habitat suitability shifts of A. artemisiifo-

lia and A. trifida by 2050 were predicted using Maxent

models and a range of projected climate change scenar-

ios. However, using only climatic and topographic

parameters as explanatory variables for these scenarios

may result in large uncertainties and difficulties in

interpreting model projections. For example, the two

Ambrosia species are often observed growing at eleva-

tions of <1000 m above sea level (Feng & Zhu, 2010)

with A. trifida generally restricted to much lower eleva-

tion regions in the eastern part of the country (Zeng

et al., 2010). As one of the critical parameters for

Maxent models, elevation can influence establishment

of each Ambrosia species through its correlation with

temperature and precipitation, although having no

direct biological effect on them (Austin, 2002). How-

ever, this variable, as well as the other two topographic

variables derived, was assumed to remain the same in

the future. Therefore, projected distribution maps may

reflect the coarse shift of range with changes in suitable

environments and that the generally predictable

responses of the two Ambrosia species to shifts in

elevation remain unclear.

Edaphic factors such as soil texture and moisture

are known to significantly affect abundance and dis-

persal of invasive plant species (Pajevi�c et al., 2010).

Our projected changes in environmental suitability of

Ambrosia species may over-predict future suitable habi-

tat gains or losses because of limited availability or

incomplete data for these important parameters. More-

over, socio-economic (i.e. changes in land use patterns,

urbanisation and demographic changes) and biological

factors (i.e. interspecific competition, predation, evolu-

tion of invasive ability and agronomic practices) can

also influence the geographic spread of the two inva-

sive Ambrosia species (Stratonovitch et al., 2012).

Given the ever increasing impact of human activities,

rapid land use changes, complexities of climate vari-

ability and extreme weather events in China, anthropo-

genic-driven alterations in the landscape may lead to

substantial differences in the predicted and realised dis-

tributions of these two Ambrosia species.

Conclusions

We used a modelling method to identify regions of

China most environmentally suitable for the establish-

ment and possible spread of two non-native Ambrosia

species. We showed that A. artemisiifolia is likely to

experience an increase in suitable habitats in future

years, while the availability of favourable habitats is

likely to remain stable or decline for A. trifida. These

contrasting species-specific results are important for

informing long-term strategic management plans that

lower the adverse effects of these most troublesome

Ambrosia species in China. Based on current and

future climate change projections in this study, man-

agement efforts and resources in China should be

focused on A. artemisiifolia, as this species is predicted

to substantially increase its current range. In turn,

management costs for A. trifida may be optimised by

focusing efforts on those relatively fewer regions of

China where the species is currently abundant. Early

detection and rapid response (EDRR) programmes for

this species can also be initiated in regions predicted to

provide suitable habitats for its establishment in

response to climate change. For further study, we

recommend the use of several currently available



methodologies for projecting species distribution

ranges and as a way to better assess uncertainty in cli-

mate projections. Equally important is the need for

research that provides more robust data on plant spe-

cies’ responses to climate change.

Acknowledgements

This research was supported by the Doctoral Fund of the

Ministry of Education of China (No. 20124404110009)

and the National Natural Science Foundation of

China (No. 31300371). We thank Stephen Smith and

Brian Belcher for their help with GIS-related tasks.

We are particularly grateful to Stephen DeGloria for

his valuable suggestions and help. Anonymous review-

ers are also acknowledged for providing helpful com-

ments on earlier versions of the manuscript.

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Potential distribution of two Ambrosia species in … Qin et al...2001) for bias, and records with obvious geocoding errors were discarded. The resulting data set was then overlaid