ORIGINAL PAPER

The grass may not always be greener: projected reductionsin climatic suitability for exotic grasses under futureclimates in Australia

R. V. Gallagher • D. Englert Duursma •

J. O’Donnell • P. D. Wilson • P. O. Downey •

L. Hughes • M. R. Leishman

Received: 2 January 2012 / Accepted: 24 September 2012 / Published online: 6 October 2012

� Springer Science+Business Media Dordrecht 2012

Abstract Climate change presents a new challenge

for the management of invasive exotic species that

threaten both biodiversity and agricultural productiv-

ity. The invasion of exotic perennial grasses through-

out the globe is particularly problematic given their

impacts on a broad range of native plant communities

and livelihoods. As the climate continues to change,

pre-emptive long-term management strategies for

exotic grasses will become increasingly important.

Using species distribution modelling we investigated

potential changes to the location of climatically

suitable habitat for some exotic perennial grass species

currently in Australia, under a range of future climate

scenarios for the decade centred around 2050. We

focus on eleven species shortlisted or declared as the

Weeds of National Significance or Alert List species in

Australia, which have also become successful invad-

ers in other parts of the world. Our results indicate that

the extent of climatically suitable habitat available for

all of the exotic grasses modelled is projected to

decrease under climate scenarios for 2050. This

reduction is most severe for the three species of

Needle Grass (genus Nassella) that currently have

infestations in the south-east of the continent. Com-

bined with information on other aspects of establish-

ment risk (e.g. demographic rates, human-use,

propagule pressure), predictions of reduced climatic

suitability provide justification for re-assessing which

weeds are prioritised for intensive management as the

climate changes.

Keywords Alert List � Climate change �Exotic grasses �Maxent � Species distribution models �Weeds of national significance

Introduction

Anthropogenic climate change and species invasions

are two key attributes of global change. Understanding

the potential interactions between these agents is

critical for effective adaptation planning to conserve

biodiversity, and for ensuring continued agricultural

productivity in coming decades. Of particular concern

is the invasion of grassland communities by exotic

grass species that threaten both the ecological integrity

of native vegetation and the economic viability of

arable lands (D’Antonio and Vitousek 1992; Klink and

Machado 2005). The plant family Poaceae (grasses) is

Electronic supplementary material The online version ofthis article (doi:10.1007/s10530-012-0342-6) containssupplementary material, which is available to authorized users.

R. V. Gallagher (&) � D. Englert Duursma �J. O’Donnell � P. D. Wilson � L. Hughes �M. R. Leishman

Department of Biological Sciences, Macquarie

University, North Ryde, NSW 2109, Australia

e-mail: rachael.gallagher@mq.edu.au

P. O. Downey

Institute for Applied Ecology, University of Canberra,

Bruce, ACT 2601, Australia

123

Biol Invasions (2013) 15:961–975

DOI 10.1007/s10530-012-0342-6

the second largest contributor to the exotic species

pool globally (Heywood 1989) and grasses have a long

history of human-mediated translocation due to their

value as both a food source and as fodder (Barnard

1964; DeWet 1981; Mott 1986).

In Australia, a number of exotic grass species have

become serious invaders, especially in the northern

rangelands and south-east of the continent (Lonsdale

1994; Grice 2003; Prober and Thiele 2005; Grice

2006). Of the 94 invasive exotic species that threaten

rangeland biodiversity in Australia, almost one third

are grasses (28 %) (Martin et al. 2006) and collec-

tively, these species pose the greatest weed threat to

biodiversity in some parts of Australia. For example,

exotic grasses comprised 26 % (33 species) of the 127

exotic plant species formally listed as posing a threat

to biodiversity in New South Wales (Coutts-Smith and

Downey 2006). Given that a large proportion of exotic

grasses were initially introduced for agriculture and

that many are still being actively planted, their current

management poses significant challenges (Grice

2004). Regrettably, only a small proportion of intro-

duced exotic grasses have actually become important

contributors to agricultural productivity in Australia;

Lonsdale (1994) found that only 21 of the 463 species

of exotic grass introduced to northern rangelands

between 1945 and 1987 have proven useful. A number

of these grasses have subsequently escaped cultivation

to become either naturalised or invasive in the

landscape, resulting in long-term effects on native

vegetation composition and structure (Butler and

Fairfax 2003; Lenz et al. 2003; Clarke et al. 2005;

Brooks et al. 2010).

Exotic grass invasions in Australia have been

implicated in the reduction of understorey plant

diversity, changes in seed bank composition and in

shifts in the functional make-up of vegetation (Lunt

1990; Fairfax and Fensham 2000; Setterfield et al.

2005). In some regions, particularly the northern

savannas, profound changes in floristic structure due

to invasion by exotic grasses have shifted vegetation

communities into well-defined alternate states (Brooks

et al. 2010). The transition to an exotic dominated

grass layer in these regions results primarily from the

disruption of established fire regimes. Exotic grasses

produce large amounts of flammable biomass leading

to increased fuel loads that amplify the intensity and

frequency of fires (D’Antonio and Vitousek 1992;

Rossiter et al. 2003; Setterfield et al. 2010). Increased

biomass and vegetation height can result in fires that

transform vegetation communities and fires in exotic-

dominated grasslands typically burn at greater inten-

sities, and later in the season, than in native-dominated

grasslands. In addition, exotic grasses are capable of

rapidly regenerating and outcompeting native vegeta-

tion under these altered fire regimes. Thus many exotic

grasses are considered to be transformer species (sensu

Richardson et al. 2000). Given the key role that exotic

grasses play in re-structuring vegetation once estab-

lished, there is an urgent need for information on their

response to changing climate regimes to facilitate

long-term management.

Across the globe, mean land surface temperatures

have increased by 0.74 �C in the period 1906–2007 and

are projected to rise at a faster rate over the next two

decades (0.2 �C/decade) (IPCC 2007). Carbon dioxide

emissions are currently tracking a ‘worst-case sce-

nario’ (Friedlingstein et al. 2010) and as a result global

mean temperatures are projected to rise by 4 �C by

2100 (likely range 2.4–6.4 �C) (IPCC 2007). Across

Australia, warming trends are consistent with the

global average, with temperatures rising 0.9 �C since

1950, accompanied by significant regional declines in

average rainfall across the east and south-west of the

continent (CSIRO 2011). Increased frequency and/or

intensity of extreme events such as droughts have also

been predicted for much of the continent in coming

decades (IPCC 2012). Understanding how these

changes in climate may affect the potential distribu-

tional extent of exotic species is a key challenge.

Native and exotic species are already responding to

changing climate regimes by shifting their ranges to

track optimal conditions for growth and survival and

range shifts have been observed across many taxa,

primarily animals (Chen et al. 2011). Effective, long-

term management strategies for exotic species need to

incorporate reliable projections of potential species’

responses. In this context, species distribution model-

ing (SDM) has been widely applied in invasion

biology in a diverse range of studies, including

examination of the potential expansion or contraction

of exotic species’ habitat (Roura-Pascual et al. 2004;

Thuiller et al. 2005; Fitzpatrick et al. 2007; Beaumont

et al. 2009a; Bradley et al. 2009; Gallagher et al. 2010;

Webber et al. 2011), detection of target areas for the

identification of biocontrol agents (Mukherjee et al.

2011; Trethowan et al. 2011), and identification of

invasion hotspots (O’Donnell et al. 2011).

962 R. V. Gallagher et al.

123

The central aim of this study was to model the

distribution of climatically suitable habitat under

baseline and future climates for eleven exotic

perennial grasses that have become naturalised or

invasive in Australia. We used the SDM program

Maxent (Phillips et al. 2006) to ask: (1) what are the

projected distributions of climatically suitable hab-

itat for these exotic grasses in Australia under

current conditions (1950–2000 average) and how are

these projected to change by 2050?, and (2) are the

projected responses to changing climate regimes

consistent across the eleven grass species examined,

or do they vary based on their current geographic

location?

Methods

Species data

The eleven exotic grass species chosen have all been

either declared or shortlisted as Weeds of National

Significance (WoNS) in Australia (Thorp and Lynch

2000) or have been placed on the Australian Alert List

of Environmental Weeds (http://www.weeds.gov.au/

publications/guidelines/alert/index.html). Herbarium

records indicate that the species have been present in

Australia for between 24 and 210 years (Australia’s

Virtual Herbarium: http://chah.gov.au/avh/public_

query.jsp). Selected information on each species’

native and exotic range, known impacts on native

vegetation and agricultural systems, minimum resi-

dence time and reason for introduction to Australia are

provided in Table 1.

We deliberately excluded three grass species listed

on the WoNS and Alert List from this study. We

excluded the stipoid grass Nassella charruana (Alert

list species) due to a very low number of unique

georeferenced occurrence points (collection locations)

available for building distribution models globally

(n = 15 in the Global Biodiversity Information

Facility (GBIF) Data Portal; http://data.gbif.org/

occurrences/). We also excluded the semi-aquatic

grasses Hymenachne amplexicaulis (declared WoNS

species) and Spartina anglica (shortlisted WoNS

species) because water body location is likely to out-

weigh the influence of climate in predicting the loca-

tion of suitable habitat (Daehler and Strong 1996;

Houston and Duivenvoorden 2002).

Species’ collection records (latitude and longitude

coordinates) which characterise the distribution of

each species throughout its exotic and native range,

were downloaded from Australia’s Virtual Herbarium

(AVH) (http://chah.gov.au/avh/public_query.jsp) and

the Global Biodiversity Information Facility (GBIF)

(http://data.gbif.org/). All subspecies records were

pooled at the species level because of the difficulty in

resolving sub-specific taxa from online records and all

location data for known taxonomic synonyms was also

included. The number of georeferenced locality points

used to build the model for each species is provided in

Table 1. After removing duplicate records at a 5 arc

minute resolution, the average number of georefer-

enced locations compiled for each species globally

was 1324, and varied between 123 (Nassella hyalina)

and 6279 (Eragrostis curvula). Although herbarium

records may provide spatially biased location data

(e.g. close to roads, built-up areas), they offer the most

comprehensive source of georeferenced data for

modelling studies of this kind. Given that widespread

national mapping of these 11 grass species has yet to

be performed in Australia, we are unable to indepen-

dently validate the adequacy of herbarium records in

depicting the known extent of infestations.

Climate data

Eight bioclimatic variables were used to build models for

each species under baseline conditions: annual mean

temperature (�C), mean monthly temperature range (�C),

maximum temperature of the warmest month (�C),

minimum temperature of the coldest month (�C),

isothermality (100*standard deviation of monthly tem-

perature), precipitation of the driest month (mm),

precipitation of the wettest month and precipitation

seasonality (coefficient of variation of monthly rainfall).

These variables are measures of temperature and precip-

itation trends, seasonal variation, and extremes, which

influence important aspects of the thermal and water

balance of organisms, ultimately limiting various phys-

iological functions and thus species’ distributions. Base-

line climate conditions for the period 1950-2000 were

represented by the WorldClim gridded climate dataset at

a 5 arc minute resolution (see Hijmans et al. 2005; data

available at: www.worldclim.org/download.htm). This

spatial resolution was chosen because it closely corre-

sponds to the positional accuracy of species-level

observations in the AVH and GBIF (i.e. *10 km) and it

The grass may not always be greener 963

123

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964 R. V. Gallagher et al.

123

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ades

area

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tern

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C4

1,3

54

The grass may not always be greener 965

123

provides a landscape level appraisal of climate suitability

across Australia. We acknowledge that climatic micro-

sites may provide suitable habitat for species to persist at

lower spatial resolutions, but identifying these locations

was not the main aim of this study. However, climate

suitability should only be interpreted at the spatial reso-

lution at which the model was calibrated.

To represent future climate, we selected four global

climate models (GCMs) from the 23 models available

in the IPCC Fourth Assessment Report (AR4) (BCCR

BCM v. 2.0; CRNM CM3; CSIRO Mk 3.0; MIROC v.

3.2 medres; see Table 2), and downloaded gridded

projection data from the Climate Change, Agriculture

and Food Security Downscaled GCM Data Portal

(http://www.ccafs-climate.org/). The models chosen

have been shown to model climate in the Australian

region with high levels of accuracy (Suppiah et al.

2007, however see Perkins et al. 2007). We selected

model output for the A2a greenhouse gas emissions

scenario, which describes a future with continuously

increasing population size and regionally oriented

economic development. CO2 emissions are projected

to grow rapidly under the A2 scenario family due lar-

gely to land use change (Nakicenovic and Swart 2000).

Table 2 Summary details of the four Global Climate Models

(GCMs), derived from the IPCC 4th Assessment Report (IPCC

2007)

Abbreviation Full title Authors

BCCR Bjerknes Centre for

Climate Research

(BCCR), Bergen

Climate Model

(BCM) Version 2.0

Bjerknes Centre for

Climate Research,

University of

Bergen, Norway

CSIRO

Mk3.0

CSIRO Mk3.0 CSIRO Atmospheric

Research, Australia

CNRM CM3 Centre National de

Recherches

Meteorologiques

Climate Model

Version 3

Centre National de

Recherches

Meteorologiques,

Meteo France,

France

MIROC 3.2

medres

K-1 Coupled GCM

(MIROC) version

3.2 medium

resolution

Center for Climate

System Research,

University of

Tokyo; National

Institute for

Environmental

Studies; Frontier

Research Center for

Global Change

Ta

ble

1co

nti

nu

ed

Tax

on

Co

mm

on

nam

e

Nat

ive

ran

ge

Do

cum

ente

d

exo

tic

ran

ge

Imp

acts

&is

sues

Min

imu

m

resi

den

ce

tim

ein

Au

stra

lia

Intr

od

uct

ion

pat

hw

ays

inA

ust

rali

a

Ph

oto

syn

thet

ic

pat

hw

ay

Nu

mb

ero

f

geo

refe

ren

ced

ob

serv

atio

ns

Th

emed

a

qu

adri

val

vis

(Ws)

Gra

der

Gra

ss,

Hab

ana

Oat

Gra

ss

Ind

ia,

Nep

alA

rgen

tin

a,

Au

stra

lia,

Ch

ina,

Ind

on

esia

,F

iji,

Mau

riti

us,

New

Cal

edo

nia

,

Om

an,

Th

aila

nd

,

US

A,

Wes

t

Ind

ies

Dis

pla

ces

nat

ive

gra

ss

spec

ies;

po

or

qu

alit

y

fod

der

;F

orm

sd

ense

mo

no

cult

ure

sth

atre

du

ce

bio

div

ersi

tyan

din

crea

se

fire

haz

ard

Fir

st her

bar

ium

reco

rd:

19

35

Qu

een

slan

d

Del

iber

atel

y

intr

od

uce

dfo

r

pas

ture

imp

rov

emen

t.S

eed

dis

per

sed

by

win

d,

wat

eran

dm

ach

iner

y

C4

49

1

All

no

men

clat

ure

foll

ow

sth

eco

nv

enti

on

sre

cog

nis

edb

yth

eA

ust

rali

anN

atio

nal

Her

bar

ium

,C

anb

erra

Let

ters

foll

ow

ing

each

spec

ies

nam

ere

pre

sen

tth

ep

rio

rity

list

ing

cate

go

ryin

Au

stra

lia:

A=

Ale

rtL

ist

of

En

vir

on

men

tal

Wee

ds;

Wd

=d

ecla

red

Wee

do

fN

atio

nal

Sig

nifi

can

ce;

Ws

=sh

ort

list

edW

eed

of

Nat

ion

alS

ign

ifica

nce

966 R. V. Gallagher et al.

123

Modelling approach

We used the species distribution modelling tool

Maxent (v. 3.3.1) (Phillips et al. 2006), driven by the

dismo package in R x64 v.2.15.0 (v. 0.7-17). Maxent is

designed to work explicitly with presence-only occur-

rence data, such as that acquired from herbaria and has

been found to perform as well or better than a range of

other statistical modelling approaches in comparative

studies (Elith et al. 2006; Graham and Hijmans 2006).

Maxent is an additive model that estimates the

distribution of a species by assigning weights to the

predictive variables used (in this case climatic vari-

ables), such that the predicted distribution is closest to

uniform (maximum entropy) whilst conforming to the

empirical averages of the climate data at the occur-

rence locations (Elith et al. 2010a).

Maxent models under baseline climate conditions

were built with distribution data from both the native

and exotic ranges of each grass species across the

globe and projected onto future climate surfaces for

Australia. Using data from the entire range (native and

exotic) has been shown to provide a more compre-

hensive evaluation of the location of potential habitat

under baseline and future climates than using data

from the exotic range alone (Broenniamm and Guisan

2008; Beaumont et al. 2009b). Including data from the

native range (where species are more likely to be at

equilibrium with climate conditions) is important for

addressing the equilibrium assumption inherent to all

SDMs. This is particularly important for calibrating

models of the grass Piptochaetium montevidense that

has only been present in Australia since 1988 and has

only one documented infestation in this country.

All Maxent models were built using the default

settings with the following exceptions: (1) linear,

quadratic and product features were selected (hinge

and threshold features were de-selected), (2) models

were trained on five cross-validated data partitions,

and (3) background points used to calibrate models

were restricted to areas within the same Koppen-

Geiger climate classification as known occurrences for

each species (see Online Resource 1). Background

points are locations throughout the study area that

represent the range of environmental conditions while

not inferring presence or absence of a species (Elith

et al. 2010b) and several recent studies have high-

lighted the importance of restricting backgrounds

either geographically (VanDerWal et al. 2009;

Acevedo et al. 2012), climatically (Webber et al.

2011) or based on data collection patterns (Elith et al.

2010b) to improve model accuracy. We downloaded a

gridded Koppen-Geiger climate classification from

www.koeppen-geiger.vu-wien.ac.at for the period

1951–2000 at a resolution of 0.5� (Kottek et al. 2006).

Background points were extracted using the function

randomPoints in R package dismo.

For each species, any grid cell could have only one

background point, and background points could not

occur in the same cells as presence points. The ratio of

background points per area was calculated so that the

species with smallest area of Koppen-Geiger regions

(23,322,790 km2) had 10000 background points and

this ratio was used to calculating the number of

background points for the other species.

To evaluate the accuracy of the bioclimatic range

output, we used two metrics: the area under the curve

(AUC) of the receiver operating characteristic (ROC)

and the extrinsic omission rate. The AUC statistic is

threshold-independent, summarising the performance

of a model at all possible threshold values by a single

number (see Fielding and Bell 1997 for a thorough

explanation). AUC scores greater than 0.75 are

considered to provide an adequate level of discrimi-

nation (Elith et al. 2006), but AUC scores above 0.95

are desirable (Swets 1988).

The extrinsic omission rate is a threshold-depen-

dent measure that assesses the proportion of test

localities falling outside the projected suitable area. As

Maxent provides gridded output as continuous prob-

ability fields, we converted grids to a binary classifi-

cation (0 = climatically unsuitable, 1 = climatically

suitable) using a threshold value that maximized the

test sensitivity and specificity on the receiver operat-

ing curve. We chose this threshold value because it

provided a suitable balance between commission

(false positive) and omission (false negative) error

rates for the modelled output and presented the most

conservative estimate of the known distribution of the

eleven species across Australia in a preliminary

mapping exercise (data not shown). A one-tailed

binomial test of omission was used to test whether the

model made predictions that were better than random,

based on the proportional area predicted suitable at our

chosen threshold (Anderson et al. 2002).

Under future climates, areas classified as climati-

cally suitable across all four GCM projections were

determined by summing the four thresholded raster

The grass may not always be greener 967

123

maps using the R package raster. Variation between

GCM projections was assessed by overlaying each of

the four maps of climatically suitable habitat across

Australia. Areas with a greater number of models in

agreement are depicted as darker in Fig. 1 (column 3).

Model caveats and uncertainties

There are a number of limitations inherent to distri-

bution modelling that need to be considered when

interpreting the maps they produce. For instance, it is

essential that mapped areas are interpreted as regions

of potentially suitable habitat—rather than predicted

species presence—and should not be used to extrap-

olate beyond the bounds of the data used to calibrate

the model. In this study, we used multivariate envi-

ronmental similarity surfaces (‘MESS’ maps sensu

Elith et al. 2010b) to identify regions where Maxent

was extrapolating beyond the bounds of the data used

in model training when projecting onto future climate

scenarios (see Online Resource 2). MESS maps report

the similarity of a point described by a set of

environmental variables, in this case climate variables,

to the distribution of these variables within a selection

of reference points. We calibrated MESS maps using

reference points based on climate data at the presence

Fig. 1 The extent of bioclimatically suitable habitat for eleven

invasive exotic grasses in Australia under baseline (1950-2000)

and future (2050) climate conditions. Models were built in

Maxent under current conditions and projected onto future

climate scenarios generated from four global circulation models

based on the A2a greenhouse gas emissions scenario. Maps of

future climate scenarios depict both the consensus projected

areas of across all four GCMs (second column) and the variation

in projections across the four GCMs (third column). Darker

shades in column 3 indicate greater agreement across GCM

projections. Maps of the current infestations for each species in

Australia are derived from records held in the Australian Virtual

Herbarium

968 R. V. Gallagher et al.

123

and background point locations used in each grass

model, and compared these to the distribution of

climate data in the four GCM projections for

Australia. It is important to note that scenarios of

future climate derived from GCMs carry with them a

degree of uncertainty that may be unspecified to end-

users. An inability to validate GCM performance

against actual future climates mean that these projec-

tions should be treated as hypotheses of future

climates and the use of multiple scenarios is favour-

able in order to assess areas of consensus across

different GCM projections.

Whilst Maxent performs well with presence-only

data, other modelling approaches (e.g. boosted regres-

sion trees, artificial neural networks, generalised linear

models) may result in different predicted distributions

and that ‘ensemble’ methods may capture this uncer-

tainty (Thuiller 2003; Stohlgren et al. 2010).

Although species distribution modelling techniques

provide useful pre-emptive information about the

extent of suitable habitat, they provide no information

about whether a species will be able to ‘realise’ the

modelled niche. This is dependent upon factors which

are not routinely included in models, such as dispersal

capacity, propagule pressure, introduction history,

genetic founder effects, species interactions (e.g.

competition, facilitation), lag phases and stochastic

spatial or temporal events.

Results

Model accuracy

On average, Maxent models for the eleven species

achieved a high level of accuracy in both threshold-

independent and threshold-dependent measures

(Table 3). Therefore, we are confident that the mod-

elled extent of climatically suitable habitat accurately

reflects relationships between the occurrences of these

species with climate at the 5 arc minute resolution.

AUC values were well within the accepted range of

high performing models for all species (Swets 1988;

Elith et al. 2006). Four maps for each species are

presented in Fig. 1: (1) the projected extent of

climatically suitable habitat across Australia under

baseline conditions, (2) the consensus projections of

climatic suitability for 2050 across the four GCMs, (3)

the variation in projections across the four GCMs, and

(4) the location of known infestations in Australia.

MESS maps comparing GCM projection surfaces for

Australia, to climate conditions across the points used

to calibrate models (presence and background points)

indicate that, in general, Maxent models are not

extrapolating into novel climates beyond the bounds

of the training data (Online Resource 2 for maps).

Projections of climatically suitable habitat

under baseline climate (1950–2000)

The models of present day climatic habitat indicate

that seven of the eleven species have formed popula-

tions at, or near, the limits of their suitable habi-

tat under baseline climate conditions for Australia

(Cortaderia selloana, Eragrostis curvula, Pennisetum

polystachion, Sporobolus africanus, S. natalensis, S.

pyramidalis, Themeda quadrivalvis; Fig. 1). Some of

these species (E. curvula, S. natalensis, T. quadrival-

vis) have also formed populations outside the areas

predicted as climatically suitable by models based on

the global niche of the species. The remaining four

grass species examined are yet to occupy the full

complement of climatically suitable habitat. In some

cases (e.g. Nassella hyalina, P. montevidense), the

projected area of climatically suitable habitat based on

models built from global occurrence records is sub-

stantially greater than the current occupied range

(Fig. 1). Colonisation of these areas of climate suit-

ability may be limited by the frequency of introduc-

tion, propagule pressure, soil properties, biotic

processes, or the dispersal capacity of individual

species. For instance, areas in the far south-west

corner of the continent that provide suitable climate

for the three Needle Grass species (genus Nassella)

may remain un-colonised if the Nullarbor Plain

provides an effective dispersal barrier from known

infestations in the east of the continent (see Fig. 1c–e).

Similarly, although areas of Tasmania are projected to

provide suitable habitat for N. hyalina, Bass Strait is

likely to provide an effective dispersal barrier for the

movement of this species without human assistance

(Fig. 1c).

African Lovegrass (E. curvula) had the largest area

of climatically suitable habitat in Australia of any

species modelled. Suitable climatic habitat for this

species was projected to occur across 2, 996, 816 km2,

or 39 % of Australia, with its range extending across

Victoria and New South Wales, and into south-east

The grass may not always be greener 969

123

Queensland and the southern parts of Western

Australia, South Australia, and the Northern Terri-

tory (Fig. 1b). Herbarium records indicate that this

species also has the most widespread realised distri-

bution in Australia, occupying 0.70 % of the continent

(Table 3) and an estimated 2.2 % of the continent

based on collated survey data (Sinden et al. 2004). By

comparison, Serrated Tussock (N. trichotoma) which

has been declared a Weed of National Significance and

targeted for monitoring and containment has an area of

climatically suitable habitat in Australia under current

conditions that is less than half of that of E. curvula

(1,304,833 km2, or 17 % of Australia) (Fig. 1e;

Table 3).

Overall, minimum temperature of the coldest

month was the most important variable for defining

species’ distributions in the seven grasses that have a

predominantly temperate distribution, explaining

Table 3 Summary statistics of model performance (AUC,

binomial probability), % of Australia currently occupied by the

exotic grass populations, percentage of the Australian continent

projected as suitable under baseline and future climates using

Maxent and % of known infestations falling outside climati-

cally suitable habitat by decade 2050

Species Geographic

region

occupied in

Australia

%

Australia

occupieda

Model performance Baseline climate

(1950–2000)

Future climate (2050)

Mean AUC

and SD

Binomial

probability

Modelled

area

(km2)

% of

continent

suitable

%

change

relative

to

baseline

% of

Australia

suitable

% of

known

Australian

infestations

outside

suitable

habitat in

2050

Cortaderiaselloana

Subtropical

and

temperate

south

0.08 0.96 (0.006) 0 1515130 20 -68 6 31

Eragrostiscurvula

Subtropical

and

temperate

south

0.70 0.93 (0.004) 0 2996816 39 -71 11 18

Nassellahyalina

Temperate

south-east

0.03 0.95 (0.016) 0 2208615 29 -78 6 18

Nassellaneesiana

Temperate

south-east

0.12 0.96 (0.013) 0 1108083 14 -85 2 76

Nassellatrichotoma

Temperate

south-east

0.18 0.97 (0.011) 0 1304833 17 -84 3 50

Pennisetumpolystachion

Tropical north 0.02 0.87 (0.013) 0 602469 8 -46 4 0

Piptochaetiummontevidense

Temperate

south-east

0.00 0.96 (0.013) 0 2154693 28 -69 9 31

Sporobolusafricanus

Tropical north

& temperate

south

0.60 0.96 (0.006) 0 1477527 19 -72 5 20

Sporobolusnatalensis

Tropical &

subtropical

east

0.08 0.93 (0.021) 0 949024 12 -71 4 39

Sporoboluspyramidalis

Tropical &

subtropical

east

0.20 0.84 (0.013) 0 1046848 14 -62 5 15

Themedaquadrivalvis

Tropical north 0.24 0.95 (0.013) 0 1303847 17 -74 4 42

a Based on the number of 50 9 50 grid cells occupied by herbarium records for each species

970 R. V. Gallagher et al.

123

28–48 % of overall variation. The distributions of

two of the remaining tropically distributed species

(T. quadrivalivs and S. natalensis) were defined by

maximum temperature of the warmest month. For

Mission Grass (Pennisetum polystachion) and Giant

Rat’s Tail Grass (S. pyramidalis), isothermality was

the largest contributing variable. Isothermality is the

coefficient of variation which summarises the daily

and yearly oscillation in temperature and reaches a

peak in the north of the Australian continent. Values of

isothermality in this region typically exceed 50,

indicating that diurnal temperature range is more than

half the yearly temperature range.

Changes in climatically suitable habitat by 2050

All the exotic grasses examined showed reductions in

the extent of climatically suitable areas across Aus-

tralia under climate scenario for 2050 (Fig 1; Table 3).

This pattern is most pronounced in the consensus

projections across the four GCM’s (second column in

Fig. 1) however there is some variability between the

four CGMs (third column in Fig. 1). In the consensus

projection surfaces, suitable habitat is reduced for all

species by more than two-thirds (71 %) on average,

with the most severe reductions projected for the three

Nassella species. These three species (N. hyalina,

N. neesiana, N. trichotoma) currently occupy regions

in the south-east corner of Australia. By 2050, the

climatically suitable habitat of these grasses is

projected to shift further south, reducing the extent

of suitable habitat by 78, 85 and 84 % respectively.

A large proportion of established infestations in the

northern part of the exotic range of these three grasses

are projected to no longer occur in climatically

suitable habitat by the decade 2050 (N. hyalina =

18 %; N. neesiana = 76 %; N. trichotoma = 50 %;

Table 3). Across the remaining eight species, on

average, 25 % of infestations (range = 0–42 %) that

occur in habitat projected as climatically suitable

under current conditions, will fall outside the region of

projected climatically suitability under consensus

GCM forecasts for 2050 (Table 3). Suitable climate

space for the four species with known infestations in

tropical areas of Australia contracts towards the coast,

indicating that climatic conditions in inland areas may

become less suitable by 2050.

Consensus projections of climatically suitable

habitat in 2050 for each species were based on areas

of agreement across the four future GCM surfaces, but

there was some variation among the response surfaces

derived from the four models. For example, all GCMs

except CSIRO Mk. 3.0 projected areas of inland

Australia will be climatically suitable in 2050 for

African Lovegrass (E. curvula) and Giant Rat’s Tail

Grass (S. pyramidalis). Although all GCMs were

selected because they have been shown to perform

well in the Australian region, the CSIRO model was

developed specifically for Australian conditions and

may provide a more robust estimate of future climate

conditions (Perkins et al. 2007). The CSIRO model

was among the better models for reproducing observed

historical mean annual rainfall and daily rainfall

distribution (Vaze et al. 2011). However, other studies

have assigned this model a mid-range ranking for its

ability to model precipitation patterns across all of

Australia (Perkins et al. 2007).

Discussion

There has been considerable speculation that problems

associated with invasive exotic plant species will

generally worsen as a result of climate change (e.g.

Hilbert et al. 2007; Hellman et al. 2008). However, a

range of potential responses have been reported

amongst studies that have projected future distribu-

tions of weeds, including both increased areas of

habitat suitability (Pattison and Mack 2008;

Kleinbauer et al. 2010; Scott et al. 2008; Lemke

et al. 2011) and reduced areas of habitat suitability

(e.g. Bradley et al. 2009; Beaumont et al. 2009a). All

the exotic grass species examined in this study were

projected to undergo reductions in climatically suit-

able habitat by 2050, by as much as 85 % relative to

baseline climate conditions. Reductions in climatic

suitability of this magnitude were also identified in a

previous multi-species study examining the potential

effect of climate change on exotic grass distributions

in South Africa (Parker-Allie et al. 2009). This study

found that the extent of climatically suitable habitat

for 26 of the 29 invasive exotic grasses modelled may

contract under 2050 climate conditions. In addition,

studies examining the effect of climate change on

individual exotic grass species commonly report

reductions in the projected extent of climatically

suitable habitat under future climates (Bourdot et al.

2010; Watt et al. 2011).

The grass may not always be greener 971

123

The output of SDMs can provide practical infor-

mation for guiding invasive species management at all

stages along the ‘invasion continuum’ (i.e. establish-

ment, naturalisation, invasion; Richardson et al.

2000). SDMs can be used to identify at risk regions

prior to introduction as part of a weed risk assessment,

or to pinpoint areas of decreasing climatic suitability

under future climates where targeted eradication may

become increasingly feasible. For instance, the results

presented in this study indicate that changing climate

may reduce the suitability of conditions at the northern

range boundary of the three Needle Grass (Nassella)

species and African Lovegrass (E. curvula). Concen-

trating control and eradication efforts in this portion of

the current realised range may increase the probability

of for long-term successful management. In addition,

independent validation of the effect, if any, of the

projected reductions in climatically suitable habitat on

the actual distribution of the 11 grass species in

coming decades will provide information about the

utility of climate modelling in determining establish-

ment risk.

It is important to note that while the bioclimatic

ranges of the exotic grasses in this study are projected

to reduce in size by 2050, four of these species

(N. hyalina, N. neesiana, N. trichotoma, P. montevi-

dense) are yet to occupy their entire potential climatic

niche projected under baseline conditions. These

species still pose an acute threat in regions where

climatic suitability remains stable between baseline

and future conditions, and where invasive populations

capable of providing seed sources have already

become established. Managing invasive populations

of exotic grasses throughout these regions will be

challenging, given both the scale of infestations and

the need to coordinate responses among different

stakeholders who may have competing interests. For

example, some exotic grasses, such as Buffel Grass

(Cenchrus ciliaris) are actively planted for pasture

improvement and control options may be limited when

these species invade native grasslands, particularly on

private property (Marshall et al. 2010).

A shortfall of current determinations of weed

threats, such as the Australian Weed Risk Assessment

(WRA) system, is that they fail to take into account the

potential responses of exotic species to climate change

(Downey et al. 2010a), reducing the potential efficacy

of management prioritisations. However, many of

these systems use distribution attributes to select

priorities (e.g. Downey et al. 2010b), which can

subsequently be modified to account for climate

change predictions (Downey et al. 2010a). Here, we

provide consistent evidence that the exotic grass

species currently listed as Weeds of National Signif-

icance or as Alert List in Australia may not experience

an expansion in climatically suitable habitat by 2050.

Although factors other than climate contribute to the

spread of exotic species (e.g. propagule pressure,

human usage, physical disturbance), incorporating

modelled projections of climatically suitable habitat

into prioritisation systems would provide a useful

indicator of future threats, and help identify which

regions may become hotspots for invasion under

future climates (O’Donnell et al. 2011). In addition,

the predicted distributions under climate change of the

native species at risk from specific alien plants could

be matched to those for alien plants to determine

future impacts, because the degree of distributional

overlap is a critical factor in determining the threat

(Downey et al. 2010c).

Correlative models (e.g. Maxent, Boosted Regres-

sion Trees, Artificial Neural Networks) have been

widely used for projecting future climatic habitats for

species, primarily because they make use of widely

available occurrence data and open-source software.

By contrast, more mechanistic models—which use

ecophysiological data about species’ tolerances to

climate derived from experimentation or correlative

information from the species range to build models

(e.g. CLIMEX)—are less commonly used due to their

reliance on empirical data for parameterisation (Morin

and Lechowicz 2008). Explicit comparisons of the two

approaches (correlative and mechanistic) indicate

broad similarity in their projections (Robertson et al.

2003; Kearney et al. 2010; Elith et al. 2010b; however

see Webber et al. 2011 for an exploration of drivers of

divergent results). We also found broad agreement in

the output of our correlative models with previously

published projections of exotic grass species derived

from mechanistic models calibrated on inferred

climatic requirements from the species range. For

instance, the southward contraction of climatically

suitable habitat for Chilean Needle Grass (N. neesi-

ana) under future climates projected by Maxent in this

study was consistent with the output of an ecophys-

iological model of habitat suitability for this species

(Bourdot et al. 2010). Similarly, the extent of suitable

habitat we projected for Grader Grass (T. quadrivalvis)

972 R. V. Gallagher et al.

123

using Maxent was broadly similar to projections from

a CLIMEX model (Keir and Vogler 2006). This

convergence in the output derived from fundamentally

different techniques reduces the uncertainty associ-

ated with model choice when predicting potential

species’ responses to climate change.

By mapping the extent of climatically suitable

habitat this study offers a preliminary understanding

of the potential effects of changing climate on exotic

grass distributions in Australia and across the globe.

We show that all 11 grass species examined may have

smaller regions of climatically suitable habitat under a

range of future climates in Australia. If these potential

reductions in habitat suitability translate into actual

contractions in species’ geographic ranges in coming

decades, a critical opportunity for restoration goals to

be achieved may be provided.

Acknowledgments This work was supported by an Australian

Research Council Linkage grant (LP077658) in collaboration

with the NSW Department of Environment and Climate Change

(now the NSW Office of Environment and Heritage).

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