Spatial Risk Assessment of Alien Invasive Plants in China

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Spatial Risk Assessment of Alien Invasive Plants in China Fan Bai, Ryan Chisholm, Weiguo Sang, and Ming Dong Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/es400382c • Publication Date (Web): 05 Jun 2013 Downloaded from http://pubs.acs.org on June 14, 2013

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Environmental Science & Technology

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Spatial Risk Assessment of Alien Invasive Plants in China

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Fan Bai1, Ryan Chisholm2,3, Weiguo Sang1*, Ming Dong1*

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1

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Chinese Academy of Science, Beijing 100093, China

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2

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14 Science Drive 4, Singapore 117543

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3

9

of Panamá

State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Department of Biological Sciences, Faculty of Science, National University of Singapore, Smithsonian Tropical Research Institute, P.O. Box 0843-03092, Balboa, Ancón, Republic

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*Corresponding author:

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Weiguo Sang, State Key Laboratory of Vegetation and Environmental Change, Institute of

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Botany, Chinese Academy of Science, Beijing 100093, China, E-mail: [email protected];

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Phone: +86 10 62836278; FAX: +86 10 82599519.

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Ming Dong, State Key Laboratory of Vegetation and Environmental Change, Institute of

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Botany,

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[email protected]; Phone: +86 10 82594676 .

Chinese

Academy

of

Science,

Beijing

100093,

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Table: 2

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Figure:6 (figure 6 is large)

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Word Number: 6580

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1

ACS Paragon Plus Environment

China,

E-mail:


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Abstract:

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The large-scale distribution patterns of alien invasive plants (AIP) can provide key

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information and a theoretical basis for management strategies, including the prevention of

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invasions, the control and eradication of established AIPs, and the identification of areas at

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high risk of invasion. This study aims to quantify distribution patterns of AIP in China, to

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develop approaches that measure the social, economic and ecological impacts, and to

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identify areas that are at higher risk of plant invasion. Based on published literature, there

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were 384 AIPs in China, representing 233 genera from 66 families. Climatic factors were

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among the primary factors determining AIPs’ overall distribution patterns. The majority of

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AIPs were tropically distributed in China, meaning that they were mainly restricted to

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southern China. Temperate-distributed AIPs, those distributed only or predominantly in

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northern China, were fewer but had higher average rates of spread than tropically

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distributed AIPs. Average ecological and economic impact per AIP was negatively

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correlated with AIP richness, meaning that areas with relatively few AIPs nevertheless have

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some of the most detrimental ones. Our comparative evaluation showed that the risk of

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invasion differed among regions of China, with high-risk areas in southern China (Yunnan,

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Guangxi and Guangdong) and central coastal areas of eastern China (Shandong, Hebei,

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Jiangsu). In the context of climate change, areas around latitudes of 33°N, including Hebei,

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Shandong, Henan and Jiangsu, should be given more attention for the control and

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prevention of plant invasions. Predictions of high-risk areas for future invasions differed

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depending on the scale of aggregation and the evaluation index, indicating that invasive

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risk assessments should be based on multiple factors.

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INTRODUCTION

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Alien Invasive Plants (AIPs) are defined as non-native plant species that have established

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naturalized populations and subsequently spread over large geographic areas and have

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negative economic and ecological impacts1, 2. Global environmental change and economic

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development have accelerated the colonisation and spread of AIPs, many of which pose

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serious threats to crop production and native biodiversity3, 4. In China, AIPs have caused

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ecological disasters and economic losses5, 6. Many studies across the world have focused on

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the mechanisms that facilitate invasion and on the prediction of future invasions for the

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purposes of risk assessment7-9. The distribution patterns of AIPs, based on large-scale data

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analysis, can provide key information and a theoretical basis for their management and

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control10-12. In China, large-scale and comprehensive analyses of AIP distributions have

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seldom been possible, because of a shortage of information13.

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AIP distribution patterns depend on species’ traits and reflect the invasiveness of individual

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plant species14, 15. Many factors have been associated with ecosystem invasibility and the

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invasiveness of individual plant species. These include ecological factors (e.g., dispersal

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opportunity, initial population sizes, residence times, numbers of introduction attempts,

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biodiversity, geography and climate), economic activities (e.g., land use, agricultural intensity,

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the presence of forestry and fishery industries, trade intensity and road density), and social

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factors (e.g., demography and history)16, 17.

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Despite decades of research, universal determinants of invasiveness and invasibility have yet 3



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to be discovered17. Therefore, the risk assessment of invasive species and invasibility of

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invaded ecosystems must be considered jointly in multi-dimensional analyses that include

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traits of AIPs, distribution range, richness, abundance, invasion rate, the degree of invasive

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impact, and so on9, 18-21. However, most large-scale analyses of AIP distributions in China to

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date have focussed solely on AIP richness as a metric of ecosystem invasion risk 5, 22.

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Knowing which invaded areas are most at risk and which traits of invasive species

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determine invasiveness is potentially of great value to invasion ecology and management23.

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The pattern of AIP distribution in China is related to both the natural and economic

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development conditions24. Southern China has been considered the area with highest

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invasion risk in China because it has the highest AIP richness, has suffered large economic

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losses caused by AIP, has suitable climatic conditions for many invasive species and is

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undergoing an economic boom5,

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invaded by several high-impact AIPs that are continuing to expand, it has been the subject

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of relatively fewer studies26. Accordingly, investments in AIP prevention have been

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directed mainly to southern China rather than northern China. Furthermore, these

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investments have been directed towards AIPs with the largest economic impact, neglecting

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those that may have potentially large ecological impacts.

10, 25

. Meanwhile, although Northern China has been

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In view of the global acceleration of plant invasions and the limited knowledge about AIP

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in China, the identification of AIP distribution hotspots in China is a key priority. By

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collecting information on AIPs in China from published floras, internet databases and other 4


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literature, this paper builds a set of invasive indices to describe the AIP distribution patterns,

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to shed light on the determining factors and to identify high-risk areas for invasion in China.

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We hypothesised that (1) Southern China would have more AIPs and higher invasion risk

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than Northern China, consistent with previous studies5,10; (2) Climatic factors would be the

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primary determinants of AIP distributions in China, as they are across the world27and as

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was found in one previous study in China24; (3) The invasion risk assessment would be

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sensitive to the indices of invasion used, because different metrics convey different

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information about the invasion process9,21.

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METHODS

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Study area

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The study area covers the whole of China (18.05–54.55°N, 74.55–135.05°E), and employs

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four scales of regional subdivision: 28 provinces, 6 geo-districts, and North/South (Figure

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1). The species distribution data by provinces were the basic units used to estimate plant

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invasions at the province scale. The four centrally administered municipalities (Beijing,

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Tianjin, Shanghai and Chongqing), Hong Kong and Macao were merged into neighbouring

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provinces (Hebei, Jiangsu, Sichuan and Guangdong), respectively. Based on similarities in

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environmental and economic conditions, six aggregated geo-districts were defined as

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Northeast (Liaoning, Jilin, and Heilongjiang), North center (Hebei, Shanxi, and

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Neimenggu), Northwest (Shaanxi, Gansu, Qinghai, Ningxia, and Xinjiang), Southeast

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(Jiangsu, Zhejiang, Anhui, Fujian, Taiwan, Jiangxi, and Shandong), South center (Henan,

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Hubei, Hunan, Guangdong, Guangxi, and Hainan), and Southwest (Sichuan, Guizhou, 5



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Yunnan, and Xizang). A further scale of aggregation was attained by combining Northeast,

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North center and Northwest into a North region (11 provinces) and Southeast, South center

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and Southwest into a South region (17 provinces).

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Classification of distributions of AIPs

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Based on recorded presences and absences of invasive plants in provincial administrative

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units, species were classified into five distribution types (SI 1): Cosmopolitan species that

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occur in all 28 provinces (CO); Restricted southern species, that occur only in the southern

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provinces (OS); Species that occur in a higher proportion of southern than northern

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provinces (PS); Species that occur in a higher proportion of northern than southern

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provinces (PN); Restricted northern species, that occur only in the northern provinces (ON).

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The OS and PS were further classified as tropically distributed species; ON and PN were

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classified as temperate-distributed species.

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Data sources

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A list of alien invasive higher plants in China (SI 1) was compiled from five sources (SI 2).

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The distribution record of invasive plant presence and absence in provincial administrative

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units and each AIP’s life form, origin, and first recorded year of presence were taken from

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Flora of China28, Chinese Virtual Herbarium (CVH) (http://www.cvh.org.cn), Catalogue of

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Life: Higher Plants in China (CNPC) (http://www.cnpc.ac.cn) and other literature published

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before April 20115, 10, 29-34. The number of native higher plant species per province was also

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obtained from CNPC as an indicator of total provincial species richness.

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Environmental variables were collected from the official Chinese government website (http:

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//www.cma.gov.cn): mean annual temperature (MAT) (℃), mean annual precipitation

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(MAP) (mm), mean temperature of the warmest month (MTWM) (℃), growing degree

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days on a 0º basis (GDD0) ( ℃ ), photosynthetically active radiation (PAR), mean

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precipitation in June, July and August (PJJA) (mm), climatic moisture index (MI), aridity

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index (α) and forest cover (FC). Indices of economic and social activity, were obtained

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from the official website (http://www.stats.gov.cn) of the National Bureau of Statistics of

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China: gross domestic product (GDP), population density, total value of imports (log

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transformed), inbound tourist arrivals (log transformed), total length of railway mileage

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(RM), total length of inland waterway mileage, total length of substandard highway

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mileage, and cargo moved per year.

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Invasive indices

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Four indices of spatial distribution patterns were used. Both invasive species richness and

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relative invasive species richness (i.e., invasive species richness as a proportion of total

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species richness) are metrics of levels of biological invasion (invasion level)9. Absolute

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invasive species richness represents the number of invasive species and invasive species

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richness relative to total species richness represents the proportion of invasive species.

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To assess the economic and ecological impacts of individual AIPs, we constructed two

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indices: an “Impact” index that encompasses social, economic and ecological impacts of an

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invasive species, and a “Spread” index that reflects the rate of spread of a species across

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China. The impact index for a species was calculated by summing the number of categories

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of source material in which it was mentioned (1 to 4; see SI 3 for details). The spread index

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was measured as the rate of expansion of a species across geo-districts over time

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(converted to a score from 1 to 4; see SI 3 for details).

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All the provincial invasive indices were mapped using ArcGIS9.3 (ESRI, Redlands, CA,

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USA). Each index was discretised onto a rank scale from 1 to 4, such that each rank

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represented an equal interval of the index.

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Data analysis

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We simplified the classification of growth forms of AIPs into herbaceous and woody and

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compared the prevalence of these two different growth forms across distribution types (i.e.,

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CO, OS, PS, ON, PN) using a chi-squared test (R version 2.13.2). We compared the

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prevalence of species with different impact and spread indices across geo-districts (i.e., NE,

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NC, NW, SE, SC, SW), again using chi-squared tests.

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From the full list of environmental and economic explanatory variables under “Data sources”

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we selected two environmental variables (MAT and MAP) and two economic variables (GDP

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and RM) that explained the largest proportion of the variance (using a Principal Components

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Analysis (PCA); see SI 4 for details). These four explanatory variables were used in all

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subsequent analyses. We investigated the relationship between our two invasion indices and

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the environmental and economic variables across provinces with stepwise linear regression

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analysis (SPSS 13.0). We also ran a Canonical Correspondence Analysis (CCA; Canoco for

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Windows 4.5)35 to identify relationships between AIP distribution types and the

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environmental and economic variables. For the CCA, we calculated mean values of the four 8


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explanatory variables for each species by taking averages across the provinces in which the

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species occurred.

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RESULTS

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Patterns of AIPs

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There were 384 higher plant species recorded as alien invasive in China. These represented

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233 genera and 66 families (SI 1). The invasive plants mainly consisted of members of the

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families Asteraceae, Poaceae, Fabaceae and Brassicaceae, and genera Amaranthus in

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Amaranthaceae, Euphorbia in Euphorbiaceae, and Solanum in Solanaceae (SI 1). Both herb

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(327 species) and woody (52 species) AIPs were more likely to have southern than northern

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distributions, but herbaceous species were more likely to be cosmopolitan species

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(chi-square test, df = 4, χ2 = 20.50, p < 0.001) (Figure 2).

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The percentage composition of AIP impact indices was very similar across the six

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geo-districts (chi-square test, χ2 =3.1605, df = 15, p = 0.9994) (Figure 3A). In every district,

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fewer than ten species had the highest impact index (IM=4). In contrast, the spread indices

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were significantly skewed towards higher values in northern China (chi-square test, χ2

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=53.8344, df = 15, p < 0.001) (Figure 3B), indicating that AIP in the north (especially the

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NE and NW geo-districts) spread faster and wider than those in the south.

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Environmental parameters and patterns of AIPs

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At a national level, invasive species richness was significantly positively related to total 9



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(invasive plus native) plant richness across provinces (R2=0.542, p=0.004) (Figure 4). Similar

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qualitative patterns were observed within most regions.

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The CCA of all the AIPs by environmental and economic factors (Figure 5) and stepwise

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linear regression analyses (Table 1) both suggested that climatic factors are primary

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determinants of the overall distribution patterns of AIPs. In the CCA ordination diagram

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(Figure 5), the first axis (Axis 1) explained 60.3% of the variation in AIP distribution types.

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This axis chiefly represented a climatic gradient (factor loadings on MAT and MAP were

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-0.87 and -0.92). The second axis explained a further 17.2% of the plants’ distribution types,

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and mainly represented an economic development gradient (factor loadings on GDP and

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railway mileage were -0.85 and -0.40). The average score value (mean) and variation

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(standard deviation, σ) of temperate-distributed species (mean=0.5422, σ=0.6332) on Axis 2,

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especially PN (mean=0.9840, σ=0.8585) were both greater than those of tropically distributed

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species (mean=0.2904, σ=0.3409).

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Stepwise linear regression analyses (Table 1) showed that the various invasion indices for

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each province were better predicted by climatic variables (MAT and MAP) than by economic

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variables, although GDP was a significant predictor of absolute and relative AIP richness, and

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railway mileage was a significant predictor of both the total and average impact scores.

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The invasive indices distribution pattern in China

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AIP richness was highest in southern China (Yunnan, Guangxi and Guangdong)

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6A), while relative richness tended to be higher in eastern China, especially in Hebei,

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Shandong and Jiangsu (Figure 6B). The spatial patterns of total impact and total spread

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closely tracked the spatial pattern of AIP richness (Figure 6C-D).

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varied little across the country (Figure 6E). Average spread index was higher in the North

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region than in the South region, and there was a hotspot on the eastern boundary between

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North and South, including the provinces Liaoning, Shandong, Henan, Shanxi, Shaanxi and

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Ningxia (Figure 6F).

(Figure

Average impact index

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DISCUSSION

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Distribution of alien invasive plants in China

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Overall, the highest richness of invasive species in China was found in the south and in the

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Hebei Province area, while the highest relative richness was found in central coastal areas of

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eastern China (Figure 6A and B). Invasive species richness was positively correlated with

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local species richness (Figure 4). The majority of AIP in China have tropical distributions

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(OS and PS) and originate from the Americas (Figure 2, SI 1). Other regional characteristics

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that predicted invasive species richness were mean annual temperature and GDP (Figure 5,

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Table 1). These overall distribution patterns and ecosystem invasibility may be explained by a

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combination of biogeographic, environmental and economic factors36.

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Globally, alien species richness tends to be positively related to native species richness10, 33, 37,

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are both larger at lower latitudes) and environmental factors41-44 (the conditions that allow

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large numbers of native species to persist also allow large numbers of alien species to persist).

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In an exception to this general trend, alien species richness is usually seen to drop sharply in

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tropical regions of the world, and this has been attributed to the complexity of biotic

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interactions in high-diversity systems acting as a barrier to invasion. Our plant data from

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China are consistent with the general global patterns, insofar as there is an overall positive

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relationship between AIP richness and native richness (Figure 4) but with low relative AIP

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richness in the tropics (Figure 6B). However, our result that absolute AIP richness is actually

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highest in the tropics in China stands in contrast to data from most of the rest of the world37.

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Further studies are needed to investigate this pattern, which, as an exception to the general

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rule of lower alien richness in the tropics, may shed light on the mechanisms underlying

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global biogeographical patterns of alien invasions.

and this is usually attributed to biogeographical factors39, 40 (native and alien species pools

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Economic factors associated with human activity can also drive distribution patterns of alien

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invasive plants6, 45. While AIP distributions between regions in China were differentiated

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mainly by climatic variables, distributions within regions were differentiated more by

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economic variables. In addition, economic variables appeared to have a stronger influence in

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temperate than in tropical regions, suggesting that human activities have had a greater impact

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in temperate regions (Figure 5). A caveat here is that there is a possible observation bias

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resulting from greater research intensity, and hence higher detection rates of alien plant 12


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species, in economically developed areas (e.g., in the vicinity of Hebei province). Although

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we cannot rule the possibility of an observation bias, our results are consistent with

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global-scale data showing that invasive species richness is strongly positively associated with

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international trade intensity46. Our observations from China reinforce the notion that invasive

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species should receive particular attention in regions with booming economies33.

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Climatic factors were found to be the primary factors impacting invasive plants’ distribution

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patterns. In coming decades, global climate change will interact with biological invasions by

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allowing new species to invade and by modifying the ecological and economic impacts of

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existing invasive species47. Recent climate-vegetation models at a national scale in China

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have indicated that forest ecosystems are moving northwards while alpine ecosystems are

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shrinking48. These trends are consistent with observations in high northern latitudes across

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the world49. In China, the responses to climate change of ecosystems at latitudes higher than

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33°N are particular uncertain50. Because these latitudes coincide with the relative richness

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and average spread index hotspot (Figure 6B, F) that includes the provinces Hebei, Liaoning,

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Shandong, Jiangsu, Henan, Shanxi, Shaanxi and Ningxia, this region should receive

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particular attention in invasive plant control and risk assessment.

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The identification of areas at high risk of biological invasion can provide the basis for the

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development of management strategies and policies. Based on our analysis of provincial

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species richness, relative richness, spread and impact index, we suggest that the highest

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invasion-risk areas are southern China (Yunnan, Guangxi and Guangdong) and the central 13



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coastal areas of eastern China (Shandong, Hebei, Jiangsu).

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Links between AIP impacts and rates of spread

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The term “invasive” commonly refers to two different but related aspects of AIPs19: the first

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is the environmental or socio-economic impact that is the chief concern of policy-makers and

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managers51; the second is the rate at which a species establishes itself and spreads in a new

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environment. The latter measure of invasiveness is generally used by conservationists and

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scientists1. The impact index used in this paper is based on both the socio-economic and

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environmental impacts of a species. Similar indices or species classifications based on the

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same information, have been used widely in AIP prevention and management at large scales

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by Chinese scientists and government agencies10, 52, 53. By contrast, indices similar to the

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spread index used in this paper, based only on natural factors and measuring a species’

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potential to rapidly colonize in a large area, have been applied in studies involving biological

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mechanisms of invasions or invasion risk assessments54, but have seldom been applied to

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large-scale invasive ecology studies in China.

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Generally, the rate of spread of an alien plant should be correlated with its impact18. Most

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species do not spread widely, nor do they cause substantial environmental or economic

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damage to a local region. But many studies similar to this one show that individual species’

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impacts are not strongly correlated with rate of spread (SI 1)

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invasive species (impact index = 3 or 4) represented only a minority of the total number of

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AIP and there were no obvious differences in the prevalence of such species across 14


19

. The most notorious

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geo-districts (Figure 3A). The seven species with the maximum value of impact index were

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not widely distributed and did not all spread rapidly (Table 2). For example, Mikania

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micrantha is a serious weed worldwide: it not only disturbs the growth and development of

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trees and crops, but also reduces the density of wild native herbaceous plants through

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allelopathic effects55. Since it first invaded China, however, its distribution has remained

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confined to three provinces: Guangdong, Guangxi and Yunan. Thus, taking account of

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potential invasive risk at the national scale, the overall harm caused by Mikania micrantha

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may be less than that of widely distribution weeds with the same degree of environmental

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and economic impact, such as Ambrosia artemisiifolia, Ambrosia trifida and Lolium

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temulentum. Therefore, AIP risk assessments should consider both the socio-economic

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effects and the biological attributes of species on scales relevant to research and

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management56.

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Links between species invasiveness and the overall level of invasion

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Invasiveness is determined by AIPs’ traits and adaptability to different environments.

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Species with broader tolerance of environmental conditions have the potential to spread

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further and cause more extensive environmental and economic harm57. We hypothesize that

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drought tolerance may be a key trait of AIP that have a large-scale distribution in China. In

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the north of China, where mean annual precipitation is relatively low (Figure 5, Table 1),

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invasive species are mostly drought-tolerant and the majority of them could potentially

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expand into southern China (Figure 2, Figure 6 F). In contrast, the tropically distributed

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AIP tend to be drought-intolerant and are restricted to the south of China. Range expansion 15



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seems to be a typical trait of species in response to climate change and resource availability

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in low-resource environments58, 59. Following the recent trend of increasing drought-prone

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areas across China60, the potential threat from drought-tolerant invasive species that are

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currently restricted to temperate China should be given particular attention.

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Community invasibility refers to characteristics of invaded communities that facilitate the

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invasion of alien species61. Invasibility, in contrast to invasiveness, has rarely been quantified

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because doing so requires that complex factors like invasion level, species invasiveness and

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propagule pressure be controlled. Among these factors, invasion level reflects the degree to

336

which local environmental conditions and biotic interactions can accommodate new species9,

337

23

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of invasion level were used in this study but were correlated differently with environmental

339

and economic factors and therefore convey independent information about the invasion

340

process9,

341

richness was correlated with economic factors (Table 1). The high AIP richness in southern

342

China means that these regions require management and prevention strategies that are more

343

diverse and comprehensive than in other regions (Figure 6A); the high relative AIP richness

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in the central coastal areas of eastern China suggests that these ecosystems are more

345

vulnerable to plant invasion and that greater management resources need to be mustered here,

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albeit against fewer species (Figure 6B).

and is best measured by invasive species richness or relative richness6.

62

These two metrics

: richness was correlated more with environmental factors whereas relative

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The negative relationship of average alien plant spread rate to alien plant richness across 16


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regions in China (Figure 6A) has two potential explanations. The first is that in a diverse

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community it is harder for any single species to become abundant because of the complexity

351

of biotic interactions64, 65. On this view, invasive species in southern China are restricted in

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their distributions by competitors, pathogens and herbivores, whereas invasive species in

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northern China face fewer such constraints. The second explanation is simply that if one

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considers all of the propagules of potential invasive species into a given region the species

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richness of the pool is necessarily inversely related to the average number of propagules per

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species63, 64. In any case, information on both the total alien richness and individual alien

357

invasive species’ impacts should be included in invasion risk assessments.

358 359

ACKNOWLEDGEMENTS

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This study was supported by the grants the ‘Public Environment Benefits Programme’ of

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Ministry of Environment Protection (201209027-2), the ‘111 Program’ of the Bureau of

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China Foreign Experts and Ministry of Education (2008-B08044), National Basic Research

363

Program of China the Chinese (973 Program 2009CB119200) and the Academy of Science

364

and their Fellowship for International Scientists Programme (2011T2S18). Many critical

365

comments on the manuscript were received in the 2012 Smithsonian CTFS/SIGEO and

366

CForBio Workshop in Seattle (US NSF grant DEB-1046113). We greatly appreciate the many

367

useful criticisms and comments that the reviewers have provided.

368

Supporting Information

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The List of alien invasive plants in China (SI 1), Literature sources for the list of alien

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invasive plants in China (SI 2), The evaluated methods of AIP impact and spread indices (SI 17



371

3) and PCA ordination diagram of environment variables and economic variables (SI 4) are

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available free of charge via the Internet at http://pubs.acs.org/ in the online version of the

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paper. The authors are solely responsible for the content and functionality of these materials.

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Queries should be directed to the corresponding author.

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376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409

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(57) Anderson, B.; Akcakaya, H. R.; Araujo, M. B.; Fordham, D. A.; Martinez-Meyer, E.; Thuiller, W.; Brook, B. W., Dynamics of range margins for metapopulations under climate change. P. Roy. Soc. B-Biol. Sci. 2009, 276, (1661), 1415-1420. (58) Engelkes, T.; Morriën, E.; Verhoeven, K. J. F.; Bezemer, T. M.; Biere, A.; Harvey, J. A.; McIntyre, L. M.; Tamis, W. L. M.; Van der Putten, W. H., Successful range-expanding plants experience less above-ground and below-ground enemy impact. Nature 2008, 456(7224), 946-948. (59) Funk, J. L.; Vitousek, P. M., Resource-use efficiency and plant invasion in low-resource systems. Nature 2007, 446(7139), 1079-1081. (60) Zou, X. K.; Ren, G. Y.; ZHANG, Q., Droughts variations in China based on a compound index of meteorological drought. Clim. Environ. Res. 2010, 15(4), 371-378. (61) Belote, R. T.; Jones, R. H.; Hood, S. M.; Wender, B. W., Diversity-invasibility across an experimental disturbance gradient in Appalachian forests. Ecology 2008, 89(1), 183-192. (62) Thompson, K.; Petchey, O. L.; Askew, A. P.; Dunnett, N. P.; Beckerman, A. P.; Willis, A. J., Little evidence for limiting similarity in a long-term study of a roadside plant community. J. Ecol. 2010, 98(2), 480-487. (63) Colautti, R. I.; Grigorovich, I. A.; MacIsaac, H. J., Propagule pressure: A null model for biological invasions. Biol Invasions 2006, 8, (5), 1023-1037. (64) Trueman, M.; Atkinson, R.; Guezou, A.; Wurm, P., Residence time and human-mediated propagule pressure at work in the alien flora of Galapagos. Biol. Invasions 2010, 12(12), 3949-3960.

517

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Page 22 of 32

TABLES

519 520

Table 1 Results of stepwise linear regressions (adjusted standardized coefficients; β) of

521

Chinese provinces’ invasion indices on economic and environmental variables.

EV

Richness

R_rich

T_IM

T_IS

A_IM

A_IS

MAT

0.675**

-

0.573**

0.769**

0.621**

-

MAP

-

-

-

-

-

-0.782**

GDP

0.263*

0.579**

-

-

-

-

RM

-

-

0.396**

-

0.369*

-

85.272 + Final equation

4.587MAT + 0.001GDP R2=0.609**

522 523 524 525 526 527 528

0.028 + -7

8.806×10 GDP 2

R =0.309**

157.226 + 8.555MAT + 0.003 RM R2=0.639**

257.660 + 9.088MAT 2

R =0.591**

1.671 + 0.004MAT + 0.96 RM R2=0.336**

** Correlation is significant at the 0.01 level (2-tailed). * Correlation is significant at the 0.05 level (2-tailed). EV: environment variables; Richness: Invasive species richness; R_rich: Relative invasive species richness; T_IM: Provincial total value of impact index; T_IS: Provincial total value of spread index; A_IM: Provincial weighted average of impact index; A_IS: Provincial weighted average of spread index; MAT: mean annual temperature; MAP: mean annual precipitation; GDP: gross national product; RM: Railway mileage.

529

22


2.978 0.0003MAP R2=0.597**

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530

Table 2 Alien invasive plants with highest scores on the impact index†.

531

532

†

†

†

Scientific name

Impact index

Spread index

Ambrosia artemisiifolia L. Ambrosia trifida L. Cenchrus echinatus Linnaeus Eichhornia crassipes (Martius) Solms Eupatorium odoratum L. Lolium temulentum Linnaeus Mikania micrantha Kunth

4 4 4 4 4 4 4

3 4 2 3 2 4 1

For definitions of the impact and spread indices, see text.

23



533

FIGURE LEGENDS

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Figure 1: Administrative areas and geographic zones in China. NE: Northeast (1:

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Heilongjiang, 2: Jilin, 3: Liaoning); NC: North center (4: Hebei, 5: Neimenggu, 6:

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Shanxi); NW: Northwest (7: Shaanxi, 8: Ningxia, 9: Gansu, 10: Qinghai, 11:

537

Xinjiang); SE: Southeast (12: Shandong, 13: Anhui, 14: Jiangsu, 15: Jiangxi, 16:

538

Zhejiang, 17: Fujian, 18: Taiwan); SC: South center (19: Henan, 20: Hubei, 21:

539

Hunan, 22: Guangdong, 23: Guangxi, 24: Hainan); SW: Southwest (25: Guizhou, 26:

540

Sichuan, 27: Yunnan, 28: Xizang).

541 542

Figure 2: Distribution types of alien invasive plant (AIP) in China (CO:

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Cosmopolitan species; OS: Species distributed only in southern provinces; PS:

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Species distributed predominantly in southern provinces; PN: Species distributed

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predominantly in northern provinces; ON: Species distributed only in northern

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provinces). Woody AIP tend to be more southerly distributed than herbs (p < 0.001;

547

chi-square (χ2) test).

548 549

Figure 3: Proportions of alien invasive plants with each impact score ( A) and spread

550

score ( B) across geo-districts in China. Differences in the distribution of impact or

551

spread scores across six geo-districts’ were tested with chi-square (χ2) tests.

552 553

NE：Northeast; NC: North center; NW: Northwest; SE: Southeast; SC: South center;

554

SW: Southwest.

24


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555 556

Figure 4: Alien invasive plant richness versus total species richness across Chinese

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provinces.

558 559

SE: Southeast; SC: Southcenter; SW: Southwest; NE: Northeast; NC: Northcenter;

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NW: Northwest.

561 562

Figure 5: Canonical Correspondence Analysis ordination diagram of alien invasive

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plant species presence-absence data in China showing correlations with

564

environmental and economic variables. Each point represents a single species (but

565

note that all cosmopolitan (CO) species have the same provincial distribution and

566

thus map to a single point).

567

MAT: mean annual temperature; MAP: mean annual precipitation; GDP: gross

568

national product; RM: railway mileage.

569

OS: Species distributed only in southern provinces; PS: Species distributed

570

predominantly in southern provinces; PN: Species distributed predominantly in

571

northern provinces; ON: Species distributed only in northern provinces

572 573

Figure 6: Distributions of aggregate invasion indices by provinces across China

574

A: Invasive species richness; B: Relative invasive species richness; C: Provincial

575

total value of impact index (T_IM); D: Provincial total value of spread index (T_IS);

576

E: Provincial weighted average of impact index (A_IM); F: Provincial weighted

25



577

average of spread index (A_IS); 1: Heilongjiang; 2: Jilin; 3: Liaoning; 4: Hebei; 5:

578

Neimenggu; 6: Shanxi; 7: Shaanxi; 8: Ningxia; 9: Gansu; 10: Qinghai; 11: Xinjiang;

579

12: Shandong; 13: Anhui; 14: Jiangsu; 15: Jiangxi; 16: Zhejiang; 17: Fujian; 18:

580

Taiwan; 19: Henan; 20: Hubei; 21: Hunan; 22: Guangdong; 23: Guangxi; 24: Hainan;

581

25: Guizhou; 26: Sichuan; 27: Yunnan; 28: Xizang.

582 583

FIGURE 1

584

26


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585 586

FIGURE 2

587 588 589

27



590 591

FIGURE 3

592

28


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593 594 595

FIGURE 4

596

29



597 598

FIGURE 5

599

30


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600 601

FIGURE 6

602

31



Does alien invasive richness correlate with areas of the highest invasive risk in China? 450x306mm (100 x 100 DPI)


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Spatial Risk Assessment of Alien Invasive Plants in China

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