Pacific Science, vol. 68, no. 1 July 16, 2013 (Early view) 

Climate change and weed impacts on small

island ecosystems: Lantana camara L. (Magnoliopsida: Verbenaceae) distribution in

Fiji

By: Subhashni Taylor* and Lalit Kumar Abstract Oceanic island ecosystems are particularly vulnerable to invasion by exotic species and this vulnerability may be intensified by the effects of climate change, particularly if favourable climate conditions allow exotic invaders to spread to new areas. Effective management of such species requires knowledge of their potential distribution under current and future climate. This research examines the value of species distribution modelling in invasive species management in small island ecosystems using the specific case of Lantana camara L. invasion in Fiji. A niche model of L. camara was developed using CLIMEX species distribution modelling (SDM) software. Several sources of data were used to develop the model including phenological observations and geographic distribution records. The model was used to estimate its potential distribution under historical climate. The CSIRO-Mk3.0 Global Climate Model (GCM) was used to explore the impacts of climate change on its potential future distribution. The model was run with the A1B and A2 scenarios for 2030 and 2070. Large parts of both the major islands of Fiji, Viti Levu and Vanua Levu, were shown to have high climatic suitability for L. camara. However, under future climate scenarios, the climatic suitability for L. camara in Fiji was projected to contract. The results illustrate that SDMs can play a useful role in the formulation of cost-effective invasive species management strategies and the resulting species distribution maps have broad applicability throughout the many islands of the South Pacific region. *Corresponding Author E-mail: btaylo26@une.edu.au

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Introduction

The Pacific Islands region has been designated as one of the 25 biodiversity "hot spots" for

conservation priority in the world, and the flora of the Pacific Islands has been estimated to

comprise over 3300 endemic plant species that are unique to the region (Myers et al. 2000). The

combined threats of climate change and invasive species puts them at risk of extinction (Meyer

2000, Myers et al. 2000). Invasive species have many negative impacts on the native flora and

communities of islands and the added impacts of climate change can further exacerbate such

threats, particularly if favourable climate conditions allow invasives to expand their distribution.

Therefore, it is important to protect and maintain this rich but fragile natural heritage from the

impacts of invasive species.

A review of invasive species in the Pacific region (Sherley et al. 2000) has outlined a

number of issues that are barriers to effective management of such species in the region. Some

problems that are highlighted in the report include: (1) shortage and inaccessibility of scientific

information on basic biology, including distribution, for risk assessment and effective

management; (2) inadequate quarantine and risk assessment facilities; (3) lack of awareness of

the impacts of invasive species on biodiversity; and (4) threats to biodiversity are not yet well

understood.

Biosecurity agencies require information on the potential distribution and relative

abundance of invasive species under current and future climate scenarios if they are to carry out

risk assessments and formulate effective long term management strategies (Kriticos et al. 2011a).

Species distribution models (SDMs), bioclimatic models, and ecological niche models (ENMs)

are valuable tools that can be used in such instances (Guisan and Zimmerman 2000). A species’

preferred climate is inferred from its distribution data, commonly termed the ‘environmental

envelope approach’ (Barry and Elith 2006). The major assumption behind such models is that

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climate is the primary factor defining the potential range of plants and other poikilotherms

(Woodward 1987). A range of software has been developed which can be used to model species’

current and future distributions (Peterson et al. 2011). CLIMEX (Hearne Scientific Software

2007, Sutherst et al. 2007) is one such software that has been frequently used in invasive species

distribution modelling (Poutsma et al. 2008, Sutherst and Bourne 2009, Chejara et al. 2010,

Kriticos et al. 2011a). A range of information types, such as experimental observations of a

species’ growth response to temperature and soil moisture, current distribution and seasonal

phenology data, can be used within CLIMEX to model potential distributions of species. In a

comparison of CLIMEX with two correlative modelling methods (MaxEnt and Boosted

Regression Trees), Webber et al. (2011) found that CLIMEX was better placed to project a

species’ distribution in a novel climate such as a new continent, or under a future climate

scenario. Furthermore, a review of the various climate-based packages intended for potential

species distribution assessment found that ‘CLIMEX was the most suitable climate modelling

package for undertaking Weed Risk Assessments because it can support model-fitting to a global

plant distribution, includes a climate change scenario mechanism, and provides an insight into

the plant’s ecological response to climate’ (Kriticos and Randall 2001).

In this paper, we examine the utility of bioclimatic models in invasive species

management in small island ecosystems. Limited research has been carried out in this respect,

particularly in the context of the islands of the South Pacific (however, see Christenhusz and

Toivonen 2008, Rödder and Lötters 2010). We ask the following question: Can species

distribution modelling be used to deal with some of the issues that are seen as barriers to

effective management of invasive species in the South Pacific, specifically the issues related to

lack of information on invasive species distribution?

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We answer this question by developing a model of the climate responses of the highly

noxious weed, Lantana camara L. based on its native and invasive distribution using the

CLIMEX software package. We then use this model to examine its potential current distribution

and future distribution under climate change on the island of Fiji in the South Pacific.

Materials and Methods

Study Area

The Fijian Islands archipelago is located in the Pacific Ocean between longitudes 174º E and

178º W and latitudes 12º to 22º S. It comprises approximately 330 islands of which one third are

inhabited. The archipelago covers about 1.3 million km2 of the South Pacific Ocean with the

total land area being 18,333 km2. Viti Levu and Vanua Levu are the two main islands, making up

15,985 km2 of the land area (Figure 1). The total population is just over 800,000 with most

people living on the two main islands (Fiji Islands Bureau of Statistics 2011).

Figure 1 near here

Lantana camara L.

Lantana camara L. (hereafter called Lantana) has been ranked among one of the world’s 100

worst invasive alien species (Lowe et al. 2000). Its native range is Central and northern South

America and the Caribbean (Day et al. 2003). However, it is now a major weed in many tropical

and subtropical countries with its global distribution encompassing approximately 60 countries

or island groups between 35º N and 35º S (Day et al. 2003). Lantana was introduced to the

Pacific islands after European contact and since then has rapidly naturalized and become a

noxious weed in this region (Figure 1). It has been reported on many islands such as Hawaii,

Cook Islands, Fiji, Samoa, Vanuatu, New Caledonia and Solomon Islands (Thaman 1974,

Waterhouse 1997, Day et al. 2003, Global Invasive Species Database 2012) and is considered

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among the most significant invasive taxa found on the majority of the islands in the region

(Meyer 2000). Lantana forms dense stands that exclude native seedlings by inhibiting their

germination, seedling growth and survival through competition (Sharma et al. 2005).

Furthermore, a single plant can produce up to 12 000 seeds per year which are dispersed over

long distances by birds and mammals (Stock et al. 2009). The ability to spread through layering

also makes it a successful invader (Day et al. 2003, Stock et al. 2009). These characteristics,

combined with a favourable climate, an abundance of open habitats and a relative absence of

natural competition, have allowed Lantana to become naturalized in many of the Pacific island

groups (Thaman 1974). It can potentially cause a reduction in biodiversity and increased

susceptibility to fire intrusion (Berry et al. 2011). Other potential impacts include decline in soil

fertility and an alteration of ecosystem processes (Day et al. 2003). Lantana has been classified

as a “dominant invader” and one of the most important threats to biodiversity in many Pacific

island nations due to its widespread occurrence and its ability to form dense stands thereby

severely impacting native biota (Meyer 2000).

Distribution of Lantana in Fiji

The distribution of Lantana in Fiji was ascertained from a variety of sources, including Smith

(1991), Day and Neser (2000), A. Macanawai (Principal Research Officer, Plant Protection

Section, Department of Agriculture, Koronivia Research Station, Nausori, Fiji, personal

communication) and A. Tye (Former Invasive Species Adviser, Secretariat of the Pacific

Regional Environment Programme). In Fiji, Lantana has been reported along road sides in most

provinces of Viti Levu and in some coconut plantations on Vanua Levu and Taveuni (A.

Macanawai, personal communication). It has also been reported as a garden escapee and being

widely distributed in Fiji in cultivated areas, particularly on the drier sides of the large islands

(A. Tye, personal communication) as well as in the cooler and wetter eastern and mountainous

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regions (Day and Neser 2000). On Viti Levu, Lantana was reported in Lautoka, Nadarivatu,

Yaqara, Navua, Colo-i-Suva and Lami while in Vanua Levu it was found in Labasa and

Savusavu as well as on Taveuni Island (Smith 1991). Smith’s (1991) observations were made

over 20 years ago and therefore will need to be interpreted with caution although the personal

communication data is more recent.

CLIMEX Software

A model of the potential distribution of Lantana under current and future climate scenarios was

developed using CLIMEX software (Hearne Scientific Software 2007, Sutherst et al. 2007). An

eco-physiological growth model forms the basis of this software where it is assumed that a

population experiences a favourable season with positive growth and an unfavourable season

with negative population growth. Parameters that describe a species’ response to climate can be

inferred from its geographic range and phenological observations (Sutherst et al. 2007). The user

can then apply these parameters to novel climates to project the potential range in new regions or

climate scenarios (Kriticos et al. 2011a). An annual growth index (GIA) describes the potential

for population growth during favourable climate conditions. The GIA is determined from the

temperature index (TI) and moisture index (MI) which represent the species’ temperature and

moisture requirements for growth. The probability that the population can survive unfavourable

conditions is described by four stress indices (cold, wet, hot and dry) and up to four interaction

stresses (hot–dry, hot–wet, cold–dry and cold–wet). The growth and stress indices are calculated

weekly and combined into an overall annual index of climatic suitability, the Ecoclimatic index

(EI) which is theoretically scaled from 0 to 100. An EI value of zero indicates that the species

will not be able to survive at that location, 1–10 indicate marginal habitats, 10–20 can support

substantial populations while EI values >20 are highly favourable (Sutherst et al. 2007). For a

detailed description of parameters, see Sutherst and Maywald (1985). In this study, parameters

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describing Lantana’s climatic preferences were fitted using the methodology described in

Sutherst and Maywald (1985), Chejara et al (2010) and Kriticos et al (2011a).

Meteorological Data and Climate Change Scenarios

The modelling component of this study employed the CliMond 10´ gridded climate data

(Kriticos et al. 2011b). Five variables, average minimum monthly temperature (Tmin), average

maximum monthly temperature (Tmax), average monthly precipitation (Ptotal) and relative

humidity at 09:00 h (RH09:00) and 15:00 h (RH15:00), were used to represent historical climate

(averaging period 1950–2000). The same five variables were used to depict potential future

climate in 2030 and 2070, based on the CSIRO-Mk3.0 Global Climate Model (GCM) (Gordon et

al. 2002) with the A1B and A2 SRES scenarios (IPCC 2000). The A1B scenario describes a

world with a balanced use of fossil and non-fossil resources while A2 describes a varied world

with high population growth but slow economic development and technological change. The B

family of scenarios were not assessed here because of the findings of Rahmstorf et al (2007) that

recent global temperature increases were much higher than the hottest IPCC scenarios. The

CSIRO-Mk3.0 GCM was selected for this study because it performed well compared to other

GCMs at representing basic aspects of the observed climate at a regional scale (Hennessy and

Colman 2007, Kriticos et al. 2011b).

Fitting CLIMEX Parameters

Information on the global distribution of Lantana was downloaded from the Global Biodiversity

Information Facility (GBIF). The GBIF is a database of natural history collections around the

world for various species which is available for download. A total of 4126 records were

downloaded of which only 1740 records were used in parameter fitting because many records did

not have geolocations or were repetitions and were subsequently discarded. Further data on

Lantana’s exotic distribution in South Africa (SAPIA 2006) and Asia (Biswas 1934, Jafri 1974,

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Thakur 1992, Chen and Gilbert 1994, Press et al. 2000) were used for fitting stress parameters.

Both native and exotic distribution data were used for model parameterization to ensure that the

complete range of environmental conditions in which lantana may occur was covered. Growth

parameters were fitted using seasonal phenology data from the southern states of Brazil (Winder,

1980, 1982). Although Winder’s study was restricted to Lantana tiliaefolia and L. glutinosa,

these data were used in parameter fitting based on the ecological similarity of these two species

to the weedy taxa of Lantana. Each parameter was iteratively adjusted until a reasonable match

was seen between the potential and known distribution of Lantana in these areas. The intention

was to get maximum EI values near known vigorous populations and to minimize EI values

outside the recorded distribution of Lantana. The parameters were checked to ensure that they

were biologically reasonable (Table 1). For a detailed explanation of the parameter-fitting

procedure and model validation, refer to Taylor et al. (2012) and Taylor and Kumar (2012). The

fitted parameters were then used to project lantana’s potential distribution in Fiji under historical

climate (averaging period 1950–2000) and climate change scenarios.

Results

Historical Climate

Figure 2 shows the projected distribution in the Pacific region for the islands that could be shown

at this scale. These were Vanuatu, New Caledonia and Solomon Islands. The presence data

shown in Figure 1 accords well with the projected distribution on these three islands in Figure 2.

The observed distribution of Lantana in Fiji is shown in Figure 3 while the potential distribution

under historical climate is shown in Figure 4. The current distribution shows a good match with

the modelled distribution. Most of Vanua Levu,Viti Levu and Taveuni are shown to be highly

suitable or suitable for Lantana.

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Future Climate

Potential distribution under future climate scenarios using the CSIRO-Mk3.0 GCM are shown in

Figures 5 and 6. A contraction in suitable climate areas was seen for this climate change model,

particularly throughout the island of Vanua Levu but also on the eastern half of Viti Levu

(Figures 5 and 6) with this trend even clearer in the 2070 scenario. The two emission scenarios

showed very similar results for both time periods.

The changes in wet stress between current climate and 2070 was also mapped (Figure 7)

as this was the only stress that appeared to be impacted by climate change. Increasing levels of

wet stress is seen on the eastern side of Viti Levu and throughout Vanua Levu.

Table 1 near here

Figures 2, 3, 4, 5, 6 and 7 near here

Discussion

The utility of species distribution modelling was examined as a tool for the purposes of

informing the formulation of long term management strategies for invasive species on small

island ecosystems. This was done using the specific case of Lantana invasion in Fiji. The results

show that under current climate, large parts of Fiji are conducive to Lantana growth and spread

(Figure 4) and this accords well with the observed distribution (Figure 3). The likelihood of

further spread of this highly noxious weed from infested to uninfested areas is possible given the

rapid increase in travel and transportation. Furthermore, habitat disturbance caused by activities

such as logging, land clearing for agriculture and introduction of large mammals such as cattle

and sheep provide further suitable habitats for Lantana invasion (Thaman 1974, Day et al. 2003).

In particular, containment efforts around the three southern provinces on Viti Levu where

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Lantana currently is not known to occur (Figure 3) and where climatic suitability is shown to be

marginal (Figure 4) may be effective in preventing spread to these uninvaded regions.

An important aspect of invasive species management is to educate the citizens and to

raise public awareness of the impacts of such species, not only on agriculture but also on native

biodiversity. The maps of potential Lantana distribution produced in this study can be a useful

tool in public awareness campaigns. Furthermore, maps of potential distribution resulting from

such modeling exercises may also be valuable for other aggressive invaders that have been

identified in many South Pacific countries such as African tulip tree (Spathodea campanulata),

guava (Psidium guajava), giant sensitive plant (Mimosa diplotricha), mile-a-minute (Mikania

micrantha) and elephant grass (Pennisetum purpureum) amongst others (Sherley et al. 2000).

They can be employed to enlist the help of local communities in the management of existing

infestations and the prevention of further invasion, particularly through transfer from invaded

areas to weed free areas. Potential distribution maps can also inform coordination and

collaboration efforts on management so that areas of high conservation status can be targeted for

weed control.

The overall impact of climate change upon the distribution of Lantana in Fiji will be a

decrease in its range (Figures 5 and 6). The climate change modelling indicates that all of Vanua

Levu and western Viti Levu will remain climatically suitable for Lantana invasion until 2030.

However, climatic suitability decreases in 2070 (Figure 6) with only the west coast of Viti Levu

and the island of Taveuni remaining at risk of Lantana invasion. The major reason for this

decrease in suitable climate is due to increasing wet stress (Figure 7) caused by increased soil

moisture levels. Wet stress accumulates if the soil moisture level exceeds the wet stress threshold

(SMWS). This threshold value is set at greater than or equal to the limiting high soil moisture

value (SM3). Although Lantana is often found in tropical areas receiving 3000mm of rainfall per

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year, it can successfully survive such high levels of rainfall only if the soil is well drained (Day

et al. 2003) since it has a tendency to rot in boggy or hydromorphic soils (Hamilton 1919,

Thaman 1974). The modelling results show that climate change has little impact on the

distribution of the other stresses i. e. cold, heat and dry stresses. Furthermore, the long term

reduction in climatic suitability in 2070 is a useful finding because it identifies areas that would

benefit from a concerted effort of weed control measures in the short term.

The results from this study have broader implications for plant invasions in the South

Pacific region, given the increasing concern about the threats posed by plant invaders to the

survival of the remaining native plants and habitats in island ecosystems (Mueller-Dombois and

Fosberg 1998, Denslow and Johnson 2006, Denslow et al. 2009). The collection of data on the

distribution of aggressive invaders under current and future climates is vital to the formulation of

prevention, education and management plans. Furthermore,valuable information on present and

future invasion risks that is gained from maps such as Figures 2 to 7 may encourage land

managers to consider the future implications of present-day management decisions. This is

particularly pertinent given that many invaders (for example, Lantana) were originally

introduced into new areas as garden ornamentals or pasture species which then turned out to be

major threats to native and agricultural systems.

Distribution maps such as the ones presented in this study can offer opportunities for the

formulation of cost-effective alternatives such as: (1) targeted elimination of outlying

populations of noxious weeds based on perceived future threats; (2) categorization of areas for

appropriate types of weed management as the distribution maps help to identify areas that need

to be prioritized for eradication and areas where containment would be more cost-effective; (3)

development of early-warning and response systems for potential invaders; (4) development of

strategic control initiatives, such as containment lines, designed to stop the proliferation of a

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weed into uninfested areas; (5) formulation of long term plans to monitor possible incursions into

areas containing environmental assets of high value; and (6) making informed decisions about

the provision of resources for weed control, particularly in areas where climatic suitability is

expected to decrease in the future. Such modeling exercises are especially valuable in identifying

regions currently at risk and in the future from weed species that may not be present, or recently

arrived with limited distributions such as ‘sleeper’ weeds which may be currently present as

benign populations. Distribution maps for such species can be used to undertake a program of

surveillance and local eradication of possible sleeper populations.

Model Limitations and Future Research

CLIMEX is based on the response of a species to climate. Non-climatic factors such as dispersal

potential and biotic interactions are not explicitly included in the modelling process. Although

niche modelling is underpinned by Hutchinson’s (Hutchinson 1957) fundamental and realized

niche concepts, there are conflicting views on what the models actually represent (Araújo and

Guisan 2006). Some researchers consider that niche models provide an estimate to the species’

fundamental niche (Soberon and Peterson 2005) while others think that the models present a

‘‘spatial representation of the realized niche’’ (Guisan and Zimmerman 2000, Pearson and

Dawson 2003). Therefore, in assessing the risk of invasion, it is important to remember that there

may be discrepancies between the realized and fundamental niche of the invasive species being

modelled. Other factors such as competition and lack of dispersal opportunities may lead to an

underestimation of the threat posed by the invader in some areas (Rödder and Lötters 2010). The

issue of realized and fundamental niches is particularly relevant in the case of invaders where

biological control agents have been released although this is of concern mainly if the control

agents have established successfully and provided some measure of control. In the case of

Lantana, 36 species of biological control agents have been released in 32 countries (Julien and

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Griffiths 1998) but it remains a serious problem in some parts of the world including Australia

and parts of the South Pacific (Day and Neser 2000). Consequently, the release of biocontrol

agents does not appear to have influenced the distribution and/or abundance of Lantana in these

areas. However, climate change will have implications for any new biocontrol agents that may be

released in the future since their distributions are also likely to change with climate change

(Kriticos et al. 2009). Hence, establishment of ongoing monitoring of current and future

biological control programs not only for Lantana but other invaders as well will be crucial so that

changes may be discovered in a timely manner and appropriate action taken.

A further cause of uncertainty in model projections is related to uncertainty in future

global greenhouse gas emission patterns (Kriticos et al. 2006). As such, the models based on

future climate scenarios provide an indication of the direction and magnitude of change that may

be expected in the future.

Although there have been rapid advances in the development of gridded datasets for

global climate normals (Hijmans et al. 2005, Mitchell and Jones 2005), these grids may be

inappropriate for small study regions (Mitchell and Jones 2005). Many of the small island states

of the South Pacific have small land areas with a limited number of meteorological stations.

Thus, there is a need for fine resolution climatic datasets developed specifically for these

countries as the currently available data are unlikely to reflect the climatic variation that can

occur over small distances on these mountainous islands. However, in the absence of such

datasets, this study has utilized the best available data to carry out the climate change modelling.

Furthermore, there is very limited spatial data available on endangered species and communities.

Such datasets would enhance the utility of species distribution models by identifying areas where

invaders and endangered species co-occur.

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Acknowledgements

The National Climate Change Adaptation Research Facility (NCCARF) kindly provided a grant

that allowed the principal author to purchase the CLIMEX software as well as attend a training

workshop for CLIMEX. This work benefitted from a travel grant by the National Climate

Change Adaptation Research Facility (NCCARF) to S.T.

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Table 1. The CLIMEX parameter values that were used for Lantana camara L; taken from

Taylor et al. (2012).

Parameter Mnemonic Values

Limiting low temperature DV0 10ºC

Lower optimal temperature DV1 25ºC

Upper optimal temperature DV2 30ºC

Limiting high temperature DV3 33ºC

Limiting low soil moisture SM0 0.1

Lower optimal soil moisture SM1 0.5

Upper optimal soil moisture SM2 1.2

Limiting high soil moisture SM3 1.6

Cold stress temperature threshold TTCS 5ºC

Cold stress temperature rate THCS -0.004 /week

Minimum degree-day cold stress threshold DTCS 15ºC days

Degree-day cold stress rate DHCS -0.0022 /week

Heat stress temperature threshold TTHS 33ºC

Heat stress temperature rate THHS 0.001 /week

Dry stress threshold SMDS 0.1

Dry stress rate HDS -0.01 /week

Wet stress threshold SMWS 1.6

Wet stress rate HWS 0.01 /week

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FIGURE 1. Island nations of the South Pacific Ocean where Lantana is present (P) and is one of

the top ten weeds (marked with an asterisk). Presence and importance data is adapted from

Thaman (1974), Holm et al. (1991) and Waterhouse (1997) (Inset shows the major islands in

Fiji).

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FIGURE 2. The climate (EI) for Lantana in Vanuatu, New Caledonia and Solomon Islands based

on CLIMEX under historical climate (averaging period 1950-2000).

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FIGURE 3. The current observed distribution of Lantana in Fiji.

19

FIGURE 4. The climate (EI) for Lantana in Fiji based on CLIMEX under historical climate

(averaging period 1950-2000).

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FIGURE 5. The climate (EI) for Lantana in 2030 projected using CLIMEX under the CSIRO-

Mk3.0 GCM running the SRES A1B and A2 scenarios.

FIGURE 6. The climate (EI) for Lantana in 2070 projected using CLIMEX under the CSIRO-

Mk3.0 GCM running the SRES A1B and A2 scenarios.

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FIGURE 7. The changes in wet stress from historical climate to 2070 projected using the SRES

A1B and A2 scenarios.

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