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Describing and Mapping Human-Induced Vegetation Changein the Australian Landscape
Richard Thackway Æ Robert Lesslie
Received: 29 May 2007 / Accepted: 14 April 2008 / Published online: 3 June 2008
� Springer Science+Business Media, LLC 2008
Abstract Australian reporting requirements for native
vegetation require improved spatial and temporal infor-
mation on the anthropogenic effects on vegetation. This
includes better linkage of information on vegetation type
(e.g., native vegetation association), extent and change,
vegetation condition, or modification. The Vegetation
Assets, States and Transitions (VAST) framework is pre-
sented as a means for ordering vegetation by degree of
anthropogenic modification as a series of condition states,
from a residual or base-line condition through to total
removal. The VAST framework facilitates mapping and
accounting for change and trends in the status and condi-
tion of vegetation. The framework makes clear the links
between land management and vegetation condition states,
provides a mechanism for describing the consequences of
land management practices on vegetation condition, and
contributes to an understanding of resilience. VAST is a
simple communication and reporting tool designed to assist
in describing and accounting for anthropogenic modifica-
tion of vegetation. A benchmark is identified for each
vegetation association. Benchmarks are based on structure,
composition, and current regenerative capacity. This article
describes the application of the VAST framework as a
consistent national framework to translate and compile
existing mapped information on the modification of native
vegetation. We discuss the correspondence between these
compiled VAST datasets at national and regional scales
and describe their relevance for natural resource policy and
planning.
Keywords Vegetation condition � Modification states �States and transitions � Land management practices �Degradation � Landscape alteration levels � Restoration �Monitoring change � Priority setting
Introduction
Vegetation management is an important mechanism for
sustaining and securing natural resource benefits, including
water, food and fibre, carbon sequestration (Maltby and
others 1999; Abel and others 2003; Millennium Assessment
2005), soil health (McKenzie and others 2004), and biodi-
versity (Hobbs and Saunders 1993). The history of European
settlement in Australia, since first contact with indigenous
peoples in the late 1700s, has seen large areas of native
vegetation either modified, replaced, or removed to meet
changing social-ecological needs and aspirations (Walker
and others 2006). National reporting requirements for native
vegetation oblige stakeholders to monitor and report infor-
mation on extent (i.e., its coverage across the landscape),
type (i.e., associations), and condition (i.e., anthropogenic
effects on vegetation extent and type) (NLWRA 2007).
Increasing recognition of the strong linkage between
vegetation management, vegetation modification and natural
resource outcomes has generated new demands for vegeta-
tion information—including information that adequately
describes anthropogenic effects on vegetation. Information
is required in a form that meaningfully translates a wide
variety of vegetation, land management, and ecological data
into terms that can be applied to natural resource manage-
ment policy, program development, and reporting.
Our review of the literature on the uses and values of
native vegetation shows a consistent need to describe and
map the relative degree of anthropogenic modification of
R. Thackway (&) � R. Lesslie
Bureau of Rural Sciences, GPO Box 858, Canberra,
ACT 2601, Australia
e-mail: [email protected]
123
Environmental Management (2008) 42:572–590
DOI 10.1007/s00267-008-9131-5
native vegetation against either an explicit or implicit
benchmark or reference condition state. This is set to be its
condition at the time of European settlement. We discuss the
rationale for using a pre-European benchmark for measuring
vegetation condition below. At the national level, such
reference condition states have been, and continue to be,
widely recognized in national policies and programs including
the National Wilderness Inventory (Lesslie and Maslen 1995),
the National Forest Policy Strategy (Commonwealth of Aus-
tralia 1992), the Native Vegetation Management Framework
(Commonwealth of Australia 2004), the National Monitoring
and Evaluation Framework (NLWRA 2005), and for listing of
nationally threatened ecological communities (DEWHA
2006). In addition to these national requirements, numerous
state and territory vegetation programs have also defined
similar condition state benchmarks (e.g., Oliver and others
2002; Parkes and others 2003).
While there have been considerable advances in meth-
ods for describing, classifying, and mapping native
condition states based on survey, inventory, and modeling
(EMR 2006), there is no established national framework
that can guide the collection and/or compilation of infor-
mation on condition states. Having said that, Australian
researchers are nevertheless leaders in this area of applied
ecology (EMR 2006).
This article describes the application of the Vegetation
Assets, States and Transitions (VAST) framework as a
consistent national framework to translate and compile
existing mapped information on the modification of native
vegetation. We discuss the correspondence between these
compiled VAST datasets at national and regional scales
and describe their relevance for natural resource policy and
planning.
VAST classifies vegetation associations by degree of
anthropogenic modification as a series of condition states,
from a natural condition state through to total removal. For
the purposes of this framework each dataset that describes
vegetation communities must have either an explicit or
implicit benchmark or reference state for native vegetation,
which is set to be its condition at the time of European
settlement of Australia. Condition states in the VAST
framework are defined by breakpoints in vegetation com-
position, structure, and regenerative capacity resulting
from land use and land management practices in relation to
the identified benchmark condition state.
Approaches to Describing Vegetation Extent and Type
Vegetation classification entails the grouping of vegetation
characteristics according to defined criteria. Vegetation
classification systems are usually framed around extent,
structure, taxonomic composition, and functional attributes
and are tailored to address particular sectoral interests. For
example, forest industry vegetation classifications gener-
ally focus on structural and functional attributes relevant to
timber production (e.g., tree height, stem density, or age
structure). Agriculture and pastoral interests often focus on
structural, taxonomic composition, and functional attri-
butes associated with marketing selected crop types (e.g.,
tonnes per hectare, cultivars) and pasture productivity (e.g.,
palatability of species and tonnes per hectare).
Vegetation information requirements for natural
resource management (including water, salinity, carbon,
and biodiversity management) are much broader. Addi-
tional requirements include support for assessments of
multiple outcomes, trade-offs, and development scenarios
and the monitoring of change and performance reporting
(McIntyre and others 2002; Thackway and others 2006). In
this context, vegetation is conventionally described and
classified in terms of its extent, structural arrangement
(height and spacing), and floristics (taxonomic grouping)
(AUSLIG 1990; ESCAVI 2003). The National Vegetation
Information System (NVIS) is the agreed national protocol
for surveying, classifying, and mapping vegetation extent
and type (ESCAVI 2003; Hnatiuk and others 2008).
Approaches to Describing Vegetation Modification
In addition to extent, structure, taxonomic composition, and
functional attributes, information is also required about
vegetation modification, or the degree of vegetation change
measured against a putative base-line or reference condition
(Hobbs 1994; Hobbs and Norton 1996; McIntyre and Hobbs
1999; Hobbs and Hopkins 1990; Hnatiuk and others 2008;
Thackway and others 2006). This analytical approach to
assessing vegetation condition states is required in order to
understand and address patterns, levels and, effects of
management intervention on vegetation, assess natural
resource condition, and to maintain and/or restore habitat
(McIntyre and Hobbs 1999; Hobbs and Hopkins 1990;
Parkes and others 2003). It is also needed to support the
development of sustainable production systems, and inform
policy and program discussions and debate on options for
trade-offs and land management practices.
Several broad methods for assessing vegetation modifi-
cation have been developed in Australia and elsewhere as
part of various research projects, but they are applied at
different scales and have not been consistently imple-
mented across jurisdictions:
1. Analysis of vegetation resilience and regenerative
capacity in applications such as ecological restora-
tion—(e.g., Hobbs and Hopkins 1990; Tongway and
Hindley 1995; Walker and others 2006),
Environmental Management (2008) 42:572–590 573
123
2. Analysis of vegetation landscape processes such as
succession and fragmentation, (e.g., Forman and
Godron 1986; Forman 1977; McIntyre and Hobbs
1999, 2000; Lesslie 1997, 2001),
3. Scoring, ranking, and indices derived from key veg-
etation structural, floristic and/or functional attributes
associated with native vegetation condition states
(Keighery 1994; Parkes and others 2003; Newell and
others 2003; Oliver and others 2002), and
4. State and transition models (e.g., Westoby and others
1989; Phelps and Bosch 2002; Macleod and others
1993; Hobbs and Norton 1996; Yates and Hobbs 1997).
While these methods contribute important insights into the
condition states of native vegetation communities, there is
value in a conceptual framework that can integrate the
ecological principles from these approaches into an ordinal
classification.
The classical approach to the analysis of modification of
natural vegetation generally distinguishes natural cover
(native species dominant) from nonnatural (built-up areas
and agricultural cover) (Forman 1977). McIntyre and
Hobbs (2000) argue that such a one-dimensional binary
approach is too limiting, contending rather that landscapes
vary on two dimensions, i.e., destruction and modification.
While McIntyre and Hobbs (2000) suggest four condition
or habitat modification states (i.e., unmodified, modified,
highly modified, and destroyed), they do not provide
diagnostic criteria to enable the assessment and classifica-
tion of these condition states or their transitions.
Methods for describing anthropogenic modification of
vegetation should address the identification of a benchmark
or reference states and transitions from that reference states
(Hnatiuk and others 2008). Relevant issues in this context
include choice of (1) an appropriate conceptual perspective
for describing vegetation, (2) ambiguity in determining
whether effects represent a fundamental transition in state
or a change within the normal limits of persistence and
development, and (3) uncertainty regarding the place and
effects of humans in the environment (Lesslie 1997).
Values and Perspectives on Vegetation
Description of characteristics such as ‘‘condition,’’ ‘‘modi-
fication,’’ or ‘‘integrity’’ for complex natural systems such as
vegetation are often ambiguous or contradictory because
important differences in interpretation can arise as a result of
differences in conceptual perspective and the use of limited
observation sets (Levin 1992; O’Neill and others 1986).
A process-functional view of vegetation, for instance,
may focus on the effect of the removal of dominant or key
species in terms of the alteration of material and energy
flows and process rates but ignore essential features of
taxonomic composition. Alternatively, a structural-
compositional focus may account for changes in taxonomic
composition, species abundance and community structure,
but fail to account for functional substitution and com-
pensation effects. Thus, the replacement of native forest
vegetation with a plantation forest of species alien to a
locality may be regarded as a major modification from a
population-community standpoint while from an ecological
function perspective, using measures of nutrient cycling or
standing biomass, such a change may be not be regarded as
a major modification.
Alternative perspectives on vegetation condition states
involving aesthetic, ethical or economic criteria may simi-
larly lead to differing conclusions concerning vegetation
condition (King 1993). For example, the condition states of
forests disturbed by fire, disease, or logging may be
unchanged from an economic and process standpoint, but be
highly changed from a biotic, aesthetic, and ethical per-
spective (Lesslie 1997; Thackway and others 2006). In view
of these issues, Thackway and others (2006) argue that it is
vital that any definition of vegetation condition include:
1. a statement of perspective and values to which the
condition applies,
2. consideration of the long-term stability of the vegeta-
tion under current management conditions,
3. key attributes of the vegetation, such as structure (e.g.,
open forest, grassland) and species of plants present,
4. key attributes of the environment (e.g., soil, water)
5. key attributes that may form the basis of particular
perspectives (e.g., social, cultural or spiritual perspec-
tives), and
6. a clearly defined or documented method of assessing the
attributes, including benchmarks and reference sites.
On this basis we propose that vegetation modification
should be described in structural-compositional terms on
the basis that, regardless of conceptual standpoint or
interpretation, vegetation change does not occur without a
change in structure and composition.
Vegetation Dynamics and Regenerative Capacity
Highly modified vegetation, such as crops and plantation
forests may be obvious and readily identifiable using
available land use and land cover information. However,
describing the modification of natural vegetation raises
difficulties akin to the problem of describing ‘‘integrity’’ or
‘‘resilience’’—concepts which involve the description of
adaptive capacity, characteristic composition, and func-
tional organization (Angermeier and Karr 1994; Frey 1975;
Holling 1973, 1996; Walker and others 2006).
For native vegetation, the notion of a ‘‘static’’ reference
condition, based on the Clementsian (1936) concept that
communities develop in a predictable fashion toward a
574 Environmental Management (2008) 42:572–590
123
specified end point or climax, is unrealistic. Even if there is
unambiguous evidence of a change in vegetation (e.g.,
change in species or change in structure), there may be
doubt as to whether this new condition represents a fun-
damental transition, or simply a shift within natural limits
of persistence or development of communities (Pickett and
White 1985; Trudgill 1977).
Consequently, we propose that a pragmatic approach to
vegetation modification should regard native vegetation
structure and composition as dynamic and that a change in
state (i.e., a sustained change in the structure and compo-
sition of native vegetation) is linked to a change in
regenerative capacity.
To assist in the process of classifying regenerative
condition states we propose four native condition states
based on change in regenerative capacity: (1) no apparent
perturbation, (2) a shift within the normal limits of per-
sistence, (3) regenerative capacity is limited or at risk, and
(4) regenerative capacity is suppressed or lost.
Human Intervention: ‘‘Natural’’ Versus Anthropogenic
Change
There are philosophical issues regarding the role of humans
in shaping vegetation characteristics and how these influ-
ences should be regarded in terms of condition states and
transitions. At one extreme is the view that any anthropo-
genic effect on vegetation is equivalent to the effect of any
species and thus should be considered ‘‘natural.’’ An
opposing view would hold all vegetation to be a human
artifact, and therefore disturbed, because of the pervasive
influence of humans in the biosphere, both in pre-techno-
logical and post-technological societies (Lesslie 1997). A
useful approach to resolving the issue of the role and effect
of humans in shaping vegetation is to identify anthropo-
genic change as change that results from the application of
artificial technology and energy subsidies (Odum 1975;
Lesslie 1997; McIntyre and others 2002). Unless such an
approach is adopted, profoundly different interpretations of
vegetation modification can occur. It follows from this
approach that vegetation conditions prevailing at the time
of European settlement of Australia should be accepted as a
notional (and where necessary assumed) reference or
benchmark state.
There remains the problem of distinguishing anthropo-
genic conditions from those occurring naturally. Even if an
effect on vegetation is clearly anthropogenic, it may not
differ significantly in degree, extent or effect to natural
disturbances such as landslides, storm damage, wildfire, or
disease. Moreover, the pervasive effects of human activity
in the biosphere and complex linkages with natural systems
and processes make it increasingly difficult to separate
anthropogenic from naturally occurring condition states. Of
global significance in this context is the impact on vegeta-
tion of changing atmospheric composition and enhanced
climate change (Climate Change Science Program and the
Subcommittee on Global Change Research 2004). In the
Australian context fire has played a major role in shaping
the structure and composition of vegetation in most Aus-
tralian landscapes since the Tertiary Period (Gill and others
1981). This includes the profound effects of Aboriginal
burning for up to 50,000 years (Jones 1969; Bowman 1998)
and additional post-settlement change through altered fire
intensity and frequency (Leigh and Noble 1981). The
deliberate or inadvertent introduction of invasive species in
concert with fire has also been responsible for major veg-
etation changes, the full effects of which are complex and
yet to be fully realized (McIntyre and others 2002).
Despite the desirability of untangling natural from
human-induced effects, in some circumstances it may not
be feasible or realistic (Hobbs and Hopkins 1990). For
native vegetation it may not be possible to distinguish
anthropogenic from natural states in terms of structure,
composition, or regenerative capacity alone.
Faced with the complexity and uncertainty of untangling
natural from human-induced effects, we propose a prag-
matic solution, the key distinguishing feature for classifying
the condition states of vegetation must be evidence of land
use, land management practices, or other active interven-
tion, such as the deliberate or inadvertent introduction of
organisms into natural vegetation, e.g., promoting exotic
pasture species in rangelands to increase productivity.
The VAST Framework
A state and transition model is used as the framework for
describing the modification states of vegetation types
because it provides (1) an appropriate gradient of change
for describing vegetation modification, (2) assistance in
classifying the effects of land management practices in
changing the state of vegetation, (3) assistance in identi-
fying what data and information are necessary to define
normal limits of change and succession, and (4) assistance
in identifying gaps and uncertainty where more data and
information are needed. State and transition models are
useful tools to assist decision-makers assess and commu-
nicate the actions and effort needed to plan realistic goals
for vegetation management, and to monitor and report
progress toward desired management outcomes.
The VAST framework explicitly distinguishes the extent
of native and non-native vegetation from the VAST con-
dition states (mapping criteria), which are defined by
diagnostic criteria. Diagnostic criteria include objective
criteria (composition and structure) with more ‘interpreta-
tive’ criteria (current regenerative capacity) (Table 1). This
Environmental Management (2008) 42:572–590 575
123
Ta
ble
1T
he
veg
etat
ion
asse
ts,
stat
esan
dtr
ansi
tio
ns
(VA
ST
)cl
assi
fica
tio
nfr
amew
ork
.In
crea
sin
gv
eget
atio
nm
od
ifica
tio
nfr
om
left
tori
gh
t
Nat
ive
veg
etat
ion
exte
nt
No
n-n
ativ
ev
eget
atio
nex
ten
t
Do
min
ant
stru
ctu
rin
gp
lan
tsp
ecie
sin
dig
eno
us
toth
elo
cali
tyan
d
spo
nta
neo
us
ino
ccu
rren
ce–
i.e.
,a
veg
etat
ion
com
mu
nit
yd
escr
ibed
usi
ng
defi
nit
ive
veg
etat
ion
typ
esre
lati
ve
toes
tim
ated
pre
17
50
stat
es
Do
min
ant
stru
ctu
rin
gp
lan
tsp
ecie
sin
dig
eno
us
toth
elo
cali
ty
bu
tcu
ltiv
ated
;al
ien
toth
elo
cali
tyan
dcu
ltiv
ated
;o
ral
ien
toth
elo
cali
tyan
dsp
on
tan
eou
s
Sta
te0
:
Nat
ura
lly
bar
e
Sta
teI:
Res
idu
al
Sta
teII
:
Mo
difi
ed
Sta
teII
I:
Tra
nsf
orm
ed
Sta
teIV
:
Rep
lace
d-a
dve
nti
veS
tate
V:
Rep
lace
d-m
an
ag
edS
tate
VI:
Rem
ov
ed
Veg
eta
tio
nco
nd
itio
nst
ate
(ma
pp
ing
crit
eria
)
Are
asw
her
en
ativ
e
veg
etat
ion
do
es
no
tn
atu
rall
y
per
sist
and
rece
ntl
yn
atu
rall
y
dis
turb
edar
eas
wh
ere
nat
ive
veg
etat
ion
has
bee
nen
tire
ly
rem
ov
ed.
(i.e
.
op
ento
pri
mar
y
succ
essi
on
)
Nat
ive
veg
etat
ion
com
mu
nit
y
stru
ctu
re,
com
po
siti
on
,an
d
reg
ener
ativ
e
cap
acit
yin
tact
–n
o
sig
nifi
can
t
per
turb
atio
nfr
om
lan
du
se/l
and
man
agem
ent
pra
ctic
e
Nat
ive
veg
etat
ion
com
mu
nit
y
stru
ctu
re,
com
po
siti
on
and
reg
ener
ativ
e
cap
acit
yin
tact
-
per
turb
edb
yla
nd
use
/lan
d
man
agem
ent
pra
ctic
e
Nat
ive
veg
etat
ion
com
mu
nit
yst
ruct
ure
,
com
po
siti
on
and
reg
ener
ativ
eca
pac
ity
sig
nifi
can
tly
alte
red
by
lan
du
se/l
and
man
agem
ent
pra
ctic
e
Nat
ive
veg
etat
ion
rep
lace
men
t–sp
ecie
s
alie
nto
the
loca
lity
and
spo
nta
neo
us
in
occ
urr
ence
Nat
ive
veg
etat
ion
rep
lace
men
tw
ith
cult
ivat
ed
veg
etat
ion
Veg
etat
ion
rem
ov
ed-
alie
nat
ion
ton
on
-
veg
etat
edla
nd
cov
er
Dia
gn
ost
iccr
iter
ia
Cu
rren
tre
gen
erat
ive
cap
acit
y(i
nte
rpre
tati
ve)
a
Co
mp
lete
rem
ov
al
of
in-s
itu
reg
en-
erat
ion
cap
acit
y
exce
pt
for
eph
emer
als
and
low
erp
lan
ts
Nat
ura
lre
gen
erat
ive
cap
acit
yu
nm
od
ified
Nat
ura
lre
gen
erat
ion
cap
acit
yp
ersi
sts
un
der
pas
tan
d/o
r
curr
ent
lan
d
man
agem
ent
pra
ctic
es
Nat
ura
lre
gen
erat
ive
cap
acit
yli
mit
ed/a
t
risk
un
der
pas
tan
d/o
r
curr
ent
lan
du
seo
r
lan
dm
anag
emen
t
pra
ctic
es.
Reh
a-
bil
itat
ion
and
rest
ora
tio
np
oss
ible
thro
ug
hm
od
ified
lan
d
man
agem
ent
pra
ctic
e
Reg
ener
atio
np
ote
nti
al
of
nat
ive
veg
etat
ion
com
mu
nit
yh
asb
een
sup
pre
ssed
and
in-s
itu
resi
lien
ceat
leas
t
sig
nifi
can
tly
dep
lete
d.
May
stil
lb
e
con
sid
erab
lep
ote
nti
al
for
rest
ora
tio
nu
sin
g
assi
sted
nat
ura
l
reg
ener
atio
n
app
roac
hes
Reg
ener
atio
np
ote
nti
al
of
nat
ive
veg
etat
ion
com
mu
nit
yli
kel
yto
be
hig
hly
dep
lete
d
by
inte
nsi
ve
lan
d
man
agem
ent.
Ver
y
lim
ited
po
ten
tial
for
rest
ora
tio
nu
sin
g
assi
sted
nat
ura
l
reg
ener
atio
n
app
roac
hes
Nil
or
min
imal
reg
ener
atio
n
po
ten
tial
.
Res
tora
tio
n
po
ten
tial
dep
end
ent
on
reco
nst
ruct
ion
app
roac
hes
Veg
etat
ion
stru
ctu
re(o
bje
ctiv
e)b
Nil
or
min
imal
Str
uct
ura
lin
teg
rity
of
nat
ive
veg
etat
ion
com
mu
nit
yis
ver
y
hig
h
Str
uct
ure
is
pre
do
min
antl
y
alte
red
bu
tin
tact
,
e.g
.,a
lay
er/s
trat
a
and
/or
gro
wth
form
san
d/o
rag
e
clas
ses
rem
ov
ed
Do
min
ant
stru
ctu
rin
g
spec
ies
of
nat
ive
veg
etat
ion
com
mu
nit
y
sig
nifi
can
tly
alte
red
,
e.g
.,a
lay
er/s
trat
a
freq
uen
tly
and
rep
eate
dly
rem
ov
ed
Do
min
ant
stru
ctu
rin
g
spec
ies
of
nat
ive
veg
etat
ion
com
mu
nit
yre
mo
ved
or
pre
do
min
antl
y
clea
red
or
extr
emel
y
deg
rad
ed
Do
min
ant
stru
ctu
rin
g
spec
ies
of
nat
ive
veg
etat
ion
com
mu
nit
y
rem
ov
ed
Veg
etat
ion
abse
nt
or
orn
amen
tal
576 Environmental Management (2008) 42:572–590
123
Ta
ble
1co
nti
nu
ed
Nat
ive
veg
etat
ion
exte
nt
No
n-n
ativ
ev
eget
atio
nex
ten
t
Do
min
ant
stru
ctu
rin
gp
lan
tsp
ecie
sin
dig
eno
us
toth
elo
cali
tyan
d
spo
nta
neo
us
ino
ccu
rren
ce–
i.e.
,a
veg
etat
ion
com
mu
nit
yd
escr
ibed
usi
ng
defi
nit
ive
veg
etat
ion
typ
esre
lati
ve
toes
tim
ated
pre
17
50
stat
es
Do
min
ant
stru
ctu
rin
gp
lan
tsp
ecie
sin
dig
eno
us
toth
elo
cali
ty
bu
tcu
ltiv
ated
;al
ien
toth
elo
cali
tyan
dcu
ltiv
ated
;o
ral
ien
toth
elo
cali
tyan
dsp
on
tan
eou
s
Sta
te0
:
Nat
ura
lly
bar
e
Sta
teI:
Res
idu
al
Sta
teII
:
Mo
difi
ed
Sta
teII
I:
Tra
nsf
orm
ed
Sta
teIV
:
Rep
lace
d-a
dve
nti
veS
tate
V:
Rep
lace
d-m
an
ag
edS
tate
VI:
Rem
ov
ed
Veg
etat
ion
com
po
siti
on
(ob
ject
ive)
b
Nil
or
min
imal
Co
mp
osi
tio
nal
inte
gri
ty
of
nat
ive
veg
etat
ion
com
mu
nit
yis
ver
y
hig
h
Co
mp
osi
tio
no
fn
ativ
e
veg
etat
ion
com
mu
nit
yis
alte
red
bu
tin
tact
Do
min
ant
stru
ctu
rin
g
spec
ies
pre
sen
t-
spec
ies
do
min
ance
sig
nifi
can
tly
alte
red
Do
min
ant
stru
ctu
rin
g
spec
ies
of
nat
ive
veg
etat
ion
com
mu
nit
yre
mo
ved
Do
min
ant
stru
ctu
rin
g
spec
ies
of
nat
ive
veg
etat
ion
com
mu
nit
y
rem
ov
ed
Veg
etat
ion
abse
nt
or
orn
amen
tal
Exa
mp
les
Bar
em
ud
;ro
ck;
riv
eran
db
each
san
d,
salt
fres
hw
ater
lak
es,
rock
slid
esan
d
lav
afl
ow
s
Old
gro
wth
fore
sts;
Nat
ive
gra
ssla
nd
sth
at
hav
en
ot
bee
ng
raze
d;
Wil
dfi
rein
nat
ive
fore
sts
and
wo
od
lan
ds
of
a
nat
ura
lfr
equ
ency
and
/or
inte
nsi
ty;
Nat
ive
veg
etat
ion
typ
es
man
aged
usi
ng
sust
ain
able
gra
zin
g
syst
ems;
Sel
ecti
ve
tim
ber
har
ves
tin
g
pra
ctic
es;
Sev
erel
y
bu
rnt
(wil
dfi
re)
nat
ive
fore
sts
and
wo
od
lan
ds
no
to
fa
nat
ura
lfr
equ
ency
and
/or
inte
nsi
ty
Inte
nsi
ve
nat
ive
fore
stry
pra
ctic
es;
Hea
vil
y
gra
zed
nat
ive
gra
ssla
nd
san
dg
rass
y
wo
od
lan
ds;
Ob
vio
us
thin
nin
go
ftr
ees
for
pas
ture
pro
du
ctio
n;
Wee
dy
nat
ive
rem
nan
tp
atch
es;
Deg
rad
edro
adsi
de
rese
rves
;D
egra
ded
coas
tal
du
ne
syst
ems;
Hea
vil
yg
raze
d
rip
aria
nv
eget
atio
n
Sev
ere
inv
asio
ns
of
intr
od
uce
dw
eed
s;
Inv
asiv
en
ativ
e
wo
od
ysp
ecie
sfo
un
d
ou
tsid
eth
eir
no
rmal
ran
ge;
Iso
late
dn
ativ
e
tree
s/sh
rub
s/g
rass
spec
ies
inth
eab
ov
e
exam
ple
s
Fo
rest
pla
nta
tio
ns;
Ho
rtic
ult
ure
;T
ree
cro
pp
ing
;O
rch
ard
s;
Rec
laim
edm
ine
site
s;
En
vir
on
men
tal
and
amen
ity
pla
nti
ng
s;
Imp
rov
edp
astu
res.
(in
clu
des
hea
vy
thin
nin
go
ftr
ees
for
pas
ture
);C
rop
pin
g;
Iso
late
dn
ativ
e
tree
s/sh
rub
s/g
rass
spec
ies
inth
eab
ov
e
exam
ple
s
Wat
erim
po
un
dm
ents
;
Urb
anan
din
du
stri
al
lan
dsc
apes
;q
uar
ries
and
min
es;
Tra
nsp
ort
infr
astr
uct
ure
;sa
lt
scal
ded
area
s
aT
he
lin
kb
etw
een
lan
dm
anag
emen
tp
ract
ices
and
reg
ener
ativ
eca
pac
ity
isan
area
of
acti
ve
rese
arch
.S
cien
cep
rov
ides
insi
gh
tsin
toth
isre
lati
on
ship
,b
ut
asy
etis
gen
eral
lyu
nab
leto
pro
vid
e
exp
lici
tm
easu
res
for
cali
bra
tio
n,
mo
del
ing
,an
dm
app
ing
.T
his
rela
tio
nsh
ipis
bet
ter
un
der
sto
od
ata
site
than
atth
ela
nd
scap
esc
ale
bM
easu
rin
gd
evia
tio
nfr
om
ab
ench
mar
kst
ate
for
veg
etat
ion
stru
ctu
rean
dco
mp
osi
tio
nin
vo
lves
the
use
of
ob
ject
ive
and
mea
sura
ble
tech
niq
ues
for
each
veg
etat
ion
typ
eo
ras
soci
atio
n(H
nat
iuk
and
oth
ers
20
08
)
Environmental Management (2008) 42:572–590 577
123
clarification is a refinement of the VAST framework pre-
sented by Thackway and Lesslie (2006).
Input datasets comprising information that informs the
requisite diagnostic criteria, including explicit or implicit
benchmarks for each vegetation type (i.e., association), can
be reclassified into VAST condition states. To assist pro-
spective users in applying the VAST framework we
provide the following seven guiding principles:
1. Contemporary patterns of vegetation in highly modi-
fied Australian landscapes comprise native, non-
native, and non-vegetated areas (states 0–VI in
Table 1). The seven broad condition states encompass
all vegetation types (native, non-native, and non-
vegetated areas) found across the landscape. Depend-
ing on requirements, additional condition substates can
be added within each of the seven main states.
2. Natural non-vegetated states and substates are bare
areas. In the context of the NVIS framework, naturally
non-vegetated ‘‘definitive vegetation types’’ (Hnatiuk
and others 2008) could be included in state 0 (e.g., salt
lakes, sand, mud flats, and rock).
3. Condition assessments can be reported at different
points in time for the same area using structural,
compositional, and functional attributes. To enable
such comparisons to be made, it is necessary to collect
and compare the same diagnostic attributes to assess
changes in the condition state of particular vegetation
associations and their extent in different parts of the
landscape.
4. Native vegetation refers to those condition states and
substates that can be defined and mapped where the
regeneration of species/communities and ecosystems is
not predominately prevented or excluded by land
management practices. Because native vegetation can
be identified by characteristics of its structure and
composition (Hnatiuk and others 2008), it provides a
distinctive, but not exclusive, set of attributes that can
be surveyed and mapped or monitored.
5. Non-native vegetation includes those condition states
and substates where the vegetative cover is predom-
inately non-native and regeneration of the native
vegetation is repeatedly suppressed or prevented by
land management practices. Such areas include VAST
V (e.g., crops, plantations, and improved pasture) and
VAST VI (areas where the vegetation has been
removed, e.g., water reservoirs, urban areas, salt
crusted areas, and tilled bare soil).
6. In the context of point 3 above, where condition states
can be defined and mapped across the whole land-
scape, management actions can be used to facilitate
transitions between condition states: (a) management
actions can ‘‘transition’’ a condition state from VAST
state I to a state III or even a state VI; (b) depending on
the value system and perspective of the land manager,
a manager with sufficient resources and knowledge
about ecological restoration can ‘‘transition’’ a condi-
tion state from VAST state III to a state I. As noted by
McIntyre and Hobbs (2000), land managers should be
strategic and aim for least cost solutions when
planning the restoration of vegetation associations,
e.g., differentiate those sites where the regenerative
capacity can be reinstated from those areas where the
regenerative capacity has been lost; and (c) noting in
the short to medium term (e.g., 10–50 years and longer
for more complex vegetation communities) it is not
possible to ‘‘transition’’ a non-native condition state
(i.e., states IV–VI) back to a native condition state.
Where stakeholders plan to restore areas that were
formally non-native vegetation types with native
species, the structure, composition and function and
the regenerative capacity of the ‘‘reconstructed native
vegetation’’ will, in the short to medium term, be
discernable as a revegetated type. For the purposes of
reporting, such revegetated areas should be denoted as
VAST state V.
7. Datasets that are eligible for translation and /or
interpretation into the VAST framework must have
implicit or explicit benchmarks (Thackway and Lesslie
2006) for each vegetation association.
In addition to the above seven guiding principles, we
provide four assumptions which underlie any application of
the VAST framework that aims to create a condition state
dataset. These assumptions have been developed through
extensive consultation with conservation biologists and
field ecologists:
1. Under natural environmental conditions (i.e., absence
of anthropogenic disturbances), the structure, compo-
sition, and function (including the regenerative
capacity) of vegetation is a response to environmental
gradients (Whittaker 1967).
2. In managed native vegetation types, the regenerative
capacity of native vegetation, can to a large extent be
measured /observed and interpreted to be the result of
previous and current land use and land management
practices.
3. The effects of managing vegetation can be observed
and interpreted as condition states at a range of scales.
Condition state datasets can be derived using a range
of methods, including inventory, mapping, and mod-
eling. For example, appropriate input datasets can be
reclassified and /or remapped into VAST condition
states provided the diagnostic attributes are inherent or
can be inferred or interpreted in a vegetation condition
dataset. The reliability of condition state datasets can
578 Environmental Management (2008) 42:572–590
123
be demonstrated and documented to assist prospective
users of this information.
4. Within a condition state, management interventions
that aim to restore ecological processes must be based
on sound ecological research and what is practical and
feasible in the field (Society for Ecological Restoration
International Science and Policy Working Group
2004). Restoration projects require information on the
extent and duration of past disturbances and their effect
on biophysical processes, cultural conditions that have
shaped the landscape, species availability, and species
performance and assembly rules (Hobbs and Norton
1996; Lockwood 1997; Thackway and Lesslie 2006).
National and Regional Case Studies
To illustrate application of the VAST framework at
national and regional scales we describe the development
of a national scale VAST dataset and discuss relationships
between it and three different regional scale VAST data-
sets; Shoalhaven region New South Wales, north west
Victoria, and Northern Territory. These case studies show
that the VAST national and regional scale datasets are
different in the three regional case studies because of dif-
ferent input datasets. These differences highlight the need
to understand issues of accuracy and precision as well as
levels of detail associated with the scale of mapping or
modeling and the need for consistency between the attri-
butes used to derive the mapped condition state datasets.
National Case Study
An interim national dataset on vegetation condition states
was derived from several readily accessible sources of
mapped information (Lesslie and others 2008). An expert
model involving an implicit pre-European benchmark
vegetation condition for each vegetation association along
with knowledge of the effects of land use and land man-
agement practices upon the integrity of the native
vegetation associations was used to classify each 1-km grid
cell. The national dataset comprises VAST condition states
0, I-III, V, and VI (Fig. 1 and Table 2). The dataset covers
the land area of Australia (768 million hectares) and
comprises information collected between 1995 and 2003.
Key inputs used to derive the national dataset include
the Biophysical Naturalness layer within the Australian
Land Disturbance Database (ALDD) held by the Australian
Government Department of the Environment, Water, Her-
itage, and the Arts (cf Lesslie and Maslen 1995), a national
land use dataset prepared for the National Land and Water
Resources Audit (Stewart and others 2001), a variety of
catchment scale land use datasets produced through the
Australian Collaborative Land Use Mapping Program
(Lesslie and others 2006), and MODIS satellite imagery
(Loveland and Belward 1997). GIS methods were used to
overlay input datasets and the VAST states in each dataset
were averaged to derive a synthetic VAST condition state
for each grid cell.
This view of vegetation condition states highlights the
characteristic regional patterns within each of Australia’s
states and territories (Table 2). The national pattern is
characterized by:
1. Very large areas of residual and modified (VAST I and
II) vegetation in central and northern Australia’s
rangelands (refer to Table 2; Western Australia,
Northern Territory, South Australia, Queensland, and
New South Wales),
2. Large areas of residual and modified (VAST I and II)
vegetation in temperate areas less suitable for agricul-
tural production, mainly mountainous forested
locations (refer to Table 2; Australian Capital Terri-
tory, New South Wales, Tasmania, Victoria, and
Western Australia),
3. Replaced (VAST V) vegetation, mainly cropping and
improved pasture, with remnant VAST II and III
(modified and transformed) vegetation in fertile, better
watered regions (refer to Table 2; Queensland, New
South Wales, Victoria, Western Australia, South
Australia, and Tasmania),
4. Extensive modified and transformed (VAST II and III)
vegetation from livestock grazing in arid and semi-arid
rangelands (the key controls being the presence of
palatable vegetation and proximity to water). Refer to
Table 2; Queensland, New South Wales, South Aus-
tralia, Western Australia, and Northern Territory), and
5. At a national level relatively small areas of removal
(VAST VI) located on the coastal margin associated
with human settlement (urban areas and water reser-
voirs). Refer to Table 2; New South Wales, Victoria,
Western Australia, Queensland, and South Australia.
Regional Case Studies
In this section the application of the VAST framework is
illustrated using three regional case studies using regional
scale datasets: Shoalhaven region, New South Wales, north
western Victoria, and the Northern Territory. In addition,
we also compare these results with the national scale
VAST dataset for the same study areas.
Shoalhaven Region, New South Wales
Figure 2a shows condition states for Shoalhaven region,
New South Wales at the national scale (Fig. 2a(i)) and the
Environmental Management (2008) 42:572–590 579
123
Fig
.1
Inte
rim
VA
ST
clas
sifi
cati
on
for
Au
stra
lia
(20
05
)
580 Environmental Management (2008) 42:572–590
123
regional scale (Fig. 2a(ii)). The Shoalhaven region area
covers 983,000 hectares and comprises information com-
piled from 2000–2004 from vegetation site surveys and
mapped at 1:100,000 scale (Tindall and others 2004). The
dataset, also known as NSW Native Vegetation Mapping
Program Series 4 or P5MA (Priority 5 Mapping Area), is
part of the Native Vegetation Mapping Program (NVMP)
(Sivertsen and Smith 2001).
The New South Wales Department of Environment and
Climate Change (DECC) (formerly NSW Department of
Natural Resources and NSW Department of Environment and
Conservation) used an expert model combining site-
based information, an implicit pre-European benchmark
vegetation condition for each vegetation association along
with the relative position of each mapping unit in the catch-
ment and land use and land management practices to derive
VAST states 0, I–III, V and VI (Thackway and Lesslie 2006).
DECC also worked with the original vegetation surveyors,
the mapping team, and regional experts to validate and
improve the reliability of the final vegetation condition states.
A comparison between the national and regional data-
sets shows obvious differences in the distribution of types
of VAST states found in the study area. Figure 2a(i) and
(ii) show differences in the areas and the relative propor-
tions of VAST condition states are shown in Fig. 3a. The
national level information has the same number of VAST
condition states as the regional dataset. The national scale
dataset depicts the vegetation of the study area as com-
prising VAST I (353,000 hectares or 36.0%), VAST II
(266,000 hectares or 27.0%), VAST III (47,000 hectares or
4.8%) VAST V (310,000 hectares or 31.6%), and VAST
VI. (5000 hectares or 0.5%) of the study area. In contrast,
the regional scale dataset depicts the same area as having
42.9% or 422,000 hectares mapped as VAST I, VAST II
(35,000 hectares or 3.6%), VAST III (234,000 hectares or
23.8%), VAST V (236,000 hectares or 24.0%), and VAST
VI (39,000 hectares or 4.0%) of the study area.
North Western Victoria
Figure 2b shows condition states for north western Victo-
ria at the national scale (Fig. 2b(i)) and the regional scale
(Fig. 2b(ii)). The dataset covers an area of approximately 9
million hectares and comprises information collected
between 1999 and 2003 (Thackway and Lesslie 2005).
Information on vegetation condition states was collected at
sites from a number of sources (Newell and others 2006)
using the ‘‘habitat hectares’’ approach (Parkes and others
2003). The ‘‘habitat hectare’’ score comprises 10 separateTa
ble
2A
com
par
iso
nb
etw
een
the
area
and
rela
tiv
ep
rop
ort
ion
of
VA
ST
con
dit
ion
stat
esfo
rA
ust
rali
a’s
stat
esan
dte
rrit
ori
esd
eriv
edfr
om
the
nat
ion
alsc
ale
VA
ST
dat
aset
Sta
te/T
erri
tory
VA
ST
con
dit
ion
stat
es(a
rea
inh
ecta
res)
Bar
eV
AS
T0
Res
idu
alV
AS
TI
Mo
difi
edV
AS
TII
Tra
nsf
orm
edV
AS
TII
IR
epla
ced
VA
ST
VR
emo
ved
VA
ST
VI
To
tal
area
(Hec
tare
s)
Au
stra
lian
Cap
ital
Ter
rito
ry0
(0.0
)1
11
,00
0(4
6.3
)1
8,0
00
(7.4
)8
,00
0(3
.2)
91
,00
0(3
7.9
)1
3,0
00
(5.3
)2
41
,00
0
New
So
uth
Wal
es2
41
,00
0(0
.3)
8,7
77
,00
0(1
0.9
)2
5,1
20
,00
0(3
1.3
)1
2,6
07
,00
0(1
5.7
)3
3,1
85
,00
0(4
1.4
)2
30
,00
0(0
.3)
80
,16
0,0
00
No
rth
ern
Ter
rito
ry9
48
,00
0(0
.7)
84
,74
1,0
00
(62
.9)
35
,64
8,0
00
(26
.5)
12
,88
6,0
00
(9.6
)3
86
,00
0(0
.3)
12
,00
0(\
0.0
)1
34
,62
1,0
00
Qu
een
slan
d7
60
,00
0(0
.4)
41
,17
0,0
00
(23
.8)
58
,14
7,0
00
(33
.7)
38
,65
8,0
00
(22
.4)
33
,85
0,0
00
(19
.6)
13
7,0
00
(0.1
)1
72
,72
2,0
00
So
uth
Au
stra
lia
5,7
87
,00
0(5
.9)
42
,52
3,0
00
(43
.2)
27
,12
7,0
00
(27
.6)
11
,10
8,0
00
(11
.3)
11
,79
6,0
00
(12
.0)
59
,00
0(0
.1)
98
,40
0,0
00
Tas
man
ia4
8,0
00
(0.7
)4
,16
1,0
00
(61
.4)
60
7,0
00
(9.0
)4
10
,00
0(6
.1)
1,4
41
,00
0(2
1.3
)1
13
,00
0(1
.7)
6,7
80
,00
0
Vic
tori
a3
5,0
00
(0.2
)2
,67
0,0
00
(11
.7)
4,8
15
,00
0(2
1.2
)6
57
,00
0(2
.9)
14
,35
4,0
00
(63
.1)
22
8,0
00
(1.0
)2
2,7
59
,00
0
Wes
tern
Au
stra
lia
3,9
63
,00
0(1
.6)
15
2,7
61
,00
0(6
0.5
)5
2,7
72
,00
0(2
0.9
)2
9,3
30
,00
0(1
1.6
)1
3,5
63
,00
0(5
.4)
16
1,0
00
(0.1
)2
52
,55
0,0
00
To
tal
(Hec
tare
s)7
68
,23
3,0
00
Fig. 2 Thumbnail images comparing VAST condition states in three
regional study areas of Shoalhaven region: New South Wales (a),
north western Victoria (b), and Northern Territory (c) for national and
regional scale datasets, respectively
c
Environmental Management (2008) 42:572–590 581
123
582 Environmental Management (2008) 42:572–590
123
components (7 ‘‘site condition’’ and 3 ‘‘landscape context’’
components), which are scored in relation to an explicit
pre-European benchmark for each vegetation type present
at a site. The Department of Sustainability and Environ-
ment (DSE) used a Neural Network model to spatially
extend the site-based condition data to whole region
(Newell and others 2006). The model used 13 mapped (i.e.,
GIS and remote-sensed data) variables including land
use, vegetation type, geology, climate, and tree density to
map the final regional ‘‘habitat hectare’’ scores. The north
western Victoria VAST dataset uses a 30-meter square grid
cell. The scale of the corresponding dataset on vegetation
types (i.e., ecological vegetation classes) is 1:25,000 scale
(NLWRA 2001).
The five condition states for the regional scale native
vegetation dataset from DSE were reclassified into three
classes (i.e., VAST states I, II, and III), which comprises
75 units of the ‘‘habitat hectare’’ 100-unit score range.
Information required to complete the remaining condition
states, i.e., naturally bare areas (VAST 0) and non-native
vegetation (VAST IV, V, and VI) were obtained from the
DSE’s land use and vegetation type datasets.
A comparison between the national and regional data-
sets show a similar distribution of VAST condition states
found in the study area Fig. 2b(i and ii), as well as areas
and relative proportions for each VAST condition states
Fig. 3b. The regional dataset defines 3,051,000 hectares, or
33.4%, native vegetation (comprising VAST I, II, and III)
compared to 2,630,000 hectares, or 28.8%, (comprising
VAST I, II, and III). The regional dataset shows majority of
the vegetation is VAST V with 6,076,000 hectares, or
66.6%, and the national dataset shows VAST V as having
6,439,000 hectares, or 70.5%, of the study area (Fig. 3b).
Northern Territory
Figure 2c shows condition states for the Northern Territory
at the national scale (Fig. 2c(i) and the regional scale
(Fig. 2c(ii)). The Northern Territory covers an area of
approximately 134,620,000 million hectares and the data-
base comprises information collected between 1995 and
2005 (Thackway and others 2005). The map of vegetation
types for the Northern Territory is compiled from various
scales of mapping; however, the dominant scale is
1;1,000,000 (NLWRA 2002). The Northern Territory
VAST dataset uses a 1000-meter square grid cell.
The developers of the regional scale VAST dataset used
a heuristic model involving an implicit pre-European
benchmark vegetation condition for each vegetation asso-
ciation along with land use and land management practices
information to allocate VAST states 0, I–III, V, and VI.
The primary drivers of vegetation condition state changes
were land use (scale 1:1,000,000), fire frequency 1997–
2003 (scale 1:250,000), and a surrogate of grazing intensity
(distribution of domestic stock based on distance from
water points). Each dataset was classified separately
involving reclassifying attribute class intervals into
appropriate VAST states, e.g., a grid cell of native vege-
tation with a fire frequency of 7 burns in 7 years was
classified as VAST III. All three input datasets were
overlain and the VAST states in each dataset were aver-
aged to derive a synthetic VAST condition state for each
grid cell (Thackway and others 2005).
Comparison of the area of VAST condition states in the Shoalhaven region between regional and national datasets
0
50
100
150
200
250
300
350
400
450
0 III
VAST Condition States
Are
a ('0
00 h
a)
Regional
National
Comparison of the area of VAST condition states in north west Victoria between regional and national datasets
0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
Vast Condition States
Are
a ('0
00 h
a)
RegionalNational
Comparison of the area of VAST condition states in NT between regional and national datasets
0
10,000
20,000
30,000
40,000
50,000
60,000
70,000
80,000
90,000
VAST Condition States
Are
a ('0
00 h
a)
Regional
National
III V VI
0 III III V VI
0 III III V VI
(a)
(b)
(c)
Fig. 3 A histogram of the area of each VAST condition states in
three regional study areas of Shoalhaven region: New South Wales
(a), north western Victoria (b), and Northern Territory (c) for regional
and national scale datasets, respectively
Environmental Management (2008) 42:572–590 583
123
A comparison of the national and regional datasets
showed obvious differences in the distribution (Fig. 2c
(i and ii)), as well as differences in the areas of VAST
condition states (Fig. 3c). The national level information
shows a greater area of naturally bare areas (VAST 0)
948,000 hectares, or 0.7%, compared to only 135,000
hectares or 0.1% of the regional dataset. There are marked
differences between the area and proportions of all three
VAST condition states for native vegetation (VAST I, II,
and III) in the national and regional scale datasets (Fig. 3c).
For example, VAST I at the national scale recorded
84,741,000 hectares, or 63.0%, and the regional scale
43,899,000 hectares, or 32.7%; VAST II at the national
scale recorded 35,648,000 hectares, or 26.5%, and the
regional scale 66,594,000 hectares, or 49.5%; and VAST
III at the national scale recorded 12,886,000 hectares, or
9.6%, and the regional scale 22,982,000 hectares, or
17.1%. There was no obvious difference between the area
of VAST V in the national and regional scale datasets.
However, there was an obvious difference between the area
of VAST VI between the national and regional scale
datasets (i.e., 12,000 hectares, or 0.0%, and 407,000 hect-
ares, or 0.3%, respectively [Fig. 3c]).
Discussion
Publicly funded programs associated with natural resource
management and the sustainable use and management of
native vegetation require information on the status and
condition of native vegetation. This information is needed
at both national and regional scales to identify priorities, to
set targets for investment, and to monitor and evaluate
progress to agreed outcomes.
In this article we have demonstrated the flexibility of the
VAST framework in translating and compiling disparate
vegetation condition state datasets derived from different
scales (national and regional) and in comparing the resul-
tant datasets. In all four VAST datasets, that is the national
scale Australia-wide dataset and the three regional scale
case studies (Shoalhaven region New South Wales, north
western Victoria, and the Northern Territory) we show
that, provided the input datasets for VAST condition states
I–III contain information on how each map unit has been
modified by land use and land management practices rel-
ative to a VAST I benchmark, native vegetation condition
state datasets can be derived. Depending on the require-
ments for assessing, monitoring, and reporting, non-
vegetated (VAST state 0, i.e., naturally bare areas, and
VAST VI, i.e., removed, as described in Table 1) and non-
native cover types (VAST states IV and V, such as crops
and plantation forests as described in Table 1), many of
these attributes can be inferred and translated from land
cover and land use mapping datasets. Given that many non-
vegetated and non-native cover types have obvious spectral
signatures, such land cover types can readily be detected
and mapped using remotely sensed satellite imagery with
adequate ground-based sampling.
Accuracy and Precision
The process of translating input datasets into condition
states using the VAST framework has been evaluated
in each case by stakeholders with expert knowledge of
vegetation condition in the relevant study areas. The
qualitative review suggested that VAST mapping broadly
corresponds with condition state/s expected in the field,
given the accuracy and currency of input data used to
derive the condition states.
Comparisons between the national and the three regional
case studies in Shoalhaven region, New South Wales, north
western Victoria, and the Northern Territory highlighted
differences between the national and regional scale data-
sets. One such example was the obvious difference
observed in the national and regional scale datasets in the
Northern Territory for VAST VI (i.e., water impound-
ments, urban and industrial landscapes, quarries and mines,
transport infrastructure, and bare areas caused by land
management practices). The Northern Territory national
and regional scale datasets recorded 12,000 hectares, or
0.0%, and 407,000 hectares, or 0.3% (Fig. 2c(i and ii)) and
Fig. 3c) respectively. These differences can be attributed to
the currency of land cover datasets used to define VAST
VI, i.e., removed, where the currency of the national
dataset was 1995 and that of the regional scale dataset was
2005. Based on this comparison it can be surmised that,
over the 10 years from 1995 to 2005, approximately
396,000 hectares has been converted to VAST VI where
the vegetation had been removed.
Level of Detail
The VAST datasets presented and described in this article
have been developed from different sources and at different
scales using different approaches. The Shoalhaven region
study area in New South Wales is a good example. The
national dataset showed the predominance of Residual,
Replaced-managed, and Modified vegetation (i.e., VAST I,
II, and V, 36.0%, 27.0%, and 31.6%, respectively) and
correspondingly few grid cells with Transformed native
vegetation (4.8%). The largest areas of least modified
native vegetation (i.e., VAST I and II) are confined to
upper slopes of steeper hills and shallow stony and rocky
sandstone plateaus. In addition, there is also some associ-
ation with land use, e.g., National Parks and protected
water catchments or sheds tend to be classified as VAST I.
584 Environmental Management (2008) 42:572–590
123
The national dataset used relatively coarse 1-km grid cells
and each grid was scored using the best available heuristic
knowledge of historic and current forestry, grazing, and
cropping land uses and land management practices. Con-
dition states in this dataset were derived by inferring the
effect that land uses and land management practices have
on modifying the structure, composition, and regenerative
capacity of the native vegetation.
In contrast, the regional scale dataset recorded a similar
total area of native vegetation (i.e., VAST I, II, and III,
70.3% compared to the national 67.8%), however, the
regional level dataset depicted much finer scale patterns of
Fig. 4 A map showing the landscape alteration levels for the Shoalhaven region, New South Wales. The % levels are based on the McIntyre and
Hobbs (1999) conceptual model and correspond to (or are estimates of ) the remaining amount of native vegetation cover (i.e., the sum of VAST
I, II, and III)
Environmental Management (2008) 42:572–590 585
123
native vegetation. The regional dataset mapped the spatial
extent and the types of native vegetation using a combi-
nation of detailed color aerial photography (approximately
1:25,000 scale) and spatial modeling of floristic assem-
blages using abiotic environmental variables using a grid
of 25-m cell resolution. Many of the patches defined as
native vegetation were ground-truthed, where assessments
were made of their condition states by scoring observed
disturbances at sites and at the level of the patch. The
primary difference between the national and regional scale
datasets relates to the method used to map the extent and
types of the native vegetation and to ascribe condition
scores to map units. The national dataset underrepresented
several VAST states including VAST 0, III, and VI in the
Shoalhaven region. These differences can be explained by
the currency of the input datasets and the scale of mapping.
The national representation of condition states using the
VAST framework is of value strategically; however, at this
scale, the national VAST dataset is generally of limited
worth and should be limited to broad continental assess-
ments of condition states. This is because of the coarseness
of the spatial data and temporal variability in the accuracy
and precision of input data.
Access to VAST condition datasets coupled with spatial
analysis makes it possible to routinely describe and map
the four landscape alteration levels of McIntyre and Hobbs
(1999), i.e., Intact, Fragmented, Variegated, and Relictual.
The McIntyre and Hobbs model is widely known and
understood and can assist conservation biologists and nat-
ural resource managers address the full spectrum of human
impacts observed across agricultural and fragmented
landscapes. The habitat modification states of McIntyre and
Hobbs (1999) correspond to the VAST condition states as
follows: Unmodified (VAST 0 and I), Highly modified
(VAST II), Modified and retained (VAST III), and
Destroyed (VAST IV, V, and VI). We use VAST condition
state datasets, such as the Shoalhaven region (Fig. 2a(ii))
as the input VAST dataset (100-m 9 100-m raster) to
derive landscape alteration levels (Fig. 4). The distribution
and percent area of each landscape alteration level is shown
in Figs. 4 and 5, respectively. These were derived using the
proportions (i.e., % area) of each condition state measured
in a 500-m moving window radius using FRAGSTATS
software (Mutendeudzi and Thackway 2008).
Together the two datasets, namely the VAST condition
states—regional scale (Fig. 2a(ii)) and landscape alteration
levels (Fig. 4) help conservation biologists and natural
resource managers assess vegetation management, invest-
ment options, and set work priorities at both the regional
and national levels.
Maps and statistics of condition states and landscape
alteration levels provide insights into the spatial patterns
and processes that facilitate transitions between condition
states. McIntyre and Hobbs (1999) discuss how the context
of a patch of native vegetation (i.e., its type, extent, and
condition) will influence its likely trajectory in a spatial
context. For example, targeting those remnant patches of
native vegetation which are large in size, relatively well
connected to other large patches, and are least modified can
reduce the cost of ongoing restoration and vegetation
management. Such an approach has been proposed by
Terry and others (2006) in planning the restoration and
management of the European Green Belt project. Equally,
this information can be used to highlight those areas where
there could be resource conflict in regard to sustainable
production (i.e., resource access and security).
At the national level VAST condition states and landscape
alteration levels could be used to better target land managers
to achieve change in land management practices and bring
about improvements in vegetation condition state/s within
Fig. 5 A histogram of the
landscape alteration levels for
the Shoalhaven region, New
South Wales. The % levels are
based on the conceptual model
of McIntyre and Hobbs (1999)
and correspond to (or are
estimates of) the remaining
amount of native vegetation
cover (i.e., the sum of VAST I,
II, and III)
586 Environmental Management (2008) 42:572–590
123
the context of nominated landscape alteration levels. For
example, there is an increasing reliance in the National
Natural Resource Management Monitoring and Evaluation
Framework (NRMMC 2002) on demonstrating how pro-
gram investments in improving vegetation condition states
are meeting natural resource condition objectives.
At the local and regional scale VAST condition states
and landscape alteration level datasets could be used to
select local projects that aim to restore native vegetation
associations. However it should be noted that the condition
of the herbaceous layer is harder to describe and map into
particular condition states than the overstorey. The key to
restoring the functionality of the ground layer vegetation
requires on-ground evidence of local land use histories
within particular vegetation association is e.g. the appli-
cation of fertilisers and the loss of top soil. This local level
information may, in turn, give clues as to what land man-
agement practices, within condition states, might be used
to facilitate transitions between condition states. This
approach could be used, for example, to improve the
selection of landholders to be targeted in developing the
Box-Gum Stewardship Program (Australian Government
2007) and in reporting the performance of restoration
projects relative to regional environmental targets.
Where decision makers need access to information about
the modification states of native vegetation, greatest versa-
tility in decision making can be achieved using the VAST
framework where users of VAST datasets have sound
working knowledge of how land management practices can
be used to transition or change one condition state to another
for a vegetation type over time by influencing the vegetation
type’s structure, composition and regenerative capacity.
Depending on the values and ecosystem goods and services
desired from an area, decision-makers can model expected
changes in the structure of a vegetation type (e.g., cover/
density, basal area, number of layers or strata, growth
forms), its composition (e.g., dominant structuring species),
and its regenerative capacity (e.g., growth stages/age clas-
ses, weeds, viability of propagules, and vegetative
reproductive material) by changing land management
practices, i.e., treatments that modify the diagnostic attri-
butes which in turn effect the mapping of vegetation
condition states. We suggest that three parameters may be
used to describe and map the effect of a land management
practice/s on the VAST diagnostic attributes for a vegetation
type (i.e., an association); its frequency (e.g., monthly/
yearly), its seasonality (e.g., spring), and its intensity of
effect on the life form (e.g., hand cutting trees versus
mechanized tree harvesting, hand pruning versus bulldozer
chaining of trees). As an example, Thackway and others
(2007) illustrated the application of VAST as a tool for
reporting changes and trends in vegetation over a period
1980 to 2004 for a 250 hectares property in southern New
South Wales. That example utilized detailed records of
on-ground land management practices and corresponding
patterns of vegetation structure observed in a range of geo-
graphic positions and in large format aerial photography.
Observed states and transitions in the vegetation over the
24 years were interpreted relative to a VAST I benchmark.
In terms of broad-scale mapping of condition states, the
challenges of making the framework operational are con-
siderable. While alterations to the structural nature of the
overstorey can be detected using remotely sensed images,
the condition of the herbaceous layer is harder to perceive,
yet it holds the key to vegetation functionality in many
cases. While such vegetation associations present particular
difficulties, we consider that these issues can be largely
overcome by using specialized methods for field surveying
supported by spatial modeling and accuracy assessments.
McIntyre and Lavoral (2007) give specific examples and
general principles in relation to states and transitions
resulting from land use and effects on nutrients, soil dis-
turbance, and leaf traits in relation to regeneration.
Conclusion
The VAST framework is a classification that may be used
to guide the development of condition state datasets using a
range of methods and data types. The framework enables
inferences to be made about vegetation composition,
structure, and regenerative capacity, relative to an undis-
turbed benchmark.
The VAST framework provides a simple communica-
tion and reporting tool designed to assist in describing and
accounting for human-induced modification of vegetation
and arresting or reversing it to achieve desired outcomes.
The scheme presented here provides a broad classification
of vegetation modification as a basis on which strategic
planning and management decisions can be made. McIn-
tyre and others (2002) and DEWHA (2006) identified the
need for more informed decisions about the level of
intervention that is needed to ensure long-term mainte-
nance of a vegetation type. In this article we have argued
that more informed decisions can be made by using
information derived from condition state mapping for
native vegetation. Such information can be combined with
the extent of fragmentation of Australia’s native vegeta-
tion, along with who owns it and how it is managed, where
in the landscape a patch is located, the size and connec-
tivity of patches, and the modification states within the
patches.
The VAST framework can help describe and account for
changes in the status and condition of vegetation, make
explicit the links between land management and vegetation
modification, provide a mechanism for describing the
Environmental Management (2008) 42:572–590 587
123
consequences of land management on vegetation, and
contribute to the analysis of ecosystems services (including
trade-offs) provided by vegetation.
While attributes have been developed for describing and
mapping Australia’s vegetation extent and types through
the National Vegetation Information System (NLWRA
2007), there is a need for an agreed national framework to
make explicit the links between land management practices
and vegetation modification. VAST has the potential to
provide a consistent national framework for monitoring
and reporting vegetation condition states at a range of
scales. VAST datasets, like the national and regional scale
datasets discussed here, have the potential to describe the
response of vegetation to changes in land use and land
management practices, to be used for describing and
mapping vegetation changes, and for monitoring progress
toward vegetation targets. Of course, the VAST framework
could be scaled-up and adapted internationally, at the
country level, for reporting on the performance of progress
toward targets such as those described in the Millennium
Ecosystem Assessment. In addition, given the flexibility of
the VAST framework, there is also potential to use it as a
state and transition model for predicting the likely future
vegetation condition states. Such information can be used
by policy and program investors to influence or procure
desired changes in land management practices as a basis
for achieving multiple ecosystem service outcomes.
Acknowledgments The development of the VAST framework was
funded by the Natural Heritage Trust and the Bureau of Rural Sci-
ences. Many researchers and policy specialists across Australia have
contributed to the ideas in this article, including the Australian
Government Department of Agriculture, Fisheries and Forestry; the
Australian Government Department of the Environment, Water,
Heritage and the Arts; CSIRO Sustainable Ecosystems; Victorian
Government Department of Sustainability and Environment; Northern
Territory Department of Natural Resources, Environment and the
Arts; and the New South Wales Department of Environment and
Climate Change. Ian Frakes and Mijo Gavran assembled components
of the figures and tables. Adam Gerrand, Tracey Lutton, Lucy Ran-
dall, and Graham Yapp provided comments on an earlier draft.
References
Abel N, Cork S, Gorddard R, Langridge J, Langston A, Plant R,
Proctor W, Ryan P, Shelton D, Walker B, Yialeloglou M (2003)
Natural values: exploring options for enhancing ecosystem
services in the Goulburn Broken catchment. CSIRO, Canberra
Angermeier PL, Karr JR (1994) Biological integrity versus biological
diversity as policy directives. Bioscience 44:690–697
AUSLIG (Australian Soil and Land Information Group) (1990) Atlas
of Australian Resources. Volume 6 Vegetation, AUSMAP,
Department of Administrative Services, Canberra
Australian Government (2007) Box-Gum Grassy Woodlands Envi-
ronmental Stewardship Programme. htm http://www.nrm.gov.au/
Stewardship/pubs/boxgum-factsheet.pdf. Accessed 18 December
2007
Bowman DMJS (1998) Tansley review No. 101. The impact of
aboriginal landscape burning on the Australian biota. New
Phytologist 140:385–410
Clements FE (1936) Nature and structure of the climax. Journal of
Ecology 24:252–284
Climate Change Science Program and the Subcommittee on Global
Change Research (2004) Our changing planet: the U.S. Climate
Change Science Program for fiscal years 2004 and 2005. A
supplement to the president’s budgets for fiscal years 2004 and
2005. Washington
Commonwealth of Australia (1992) National forest policy statement:
a new focus for Australia’s forests. Australian Government
Publishing Service, Canberra, p 52
Commonwealth of Australia (2004) An interim approach to the native
vegetation condition indicator http://www.nrm.gov.au/monitoring/
indicators/vegetation-condition/index.html. Accessed 26 April 2006
DEWHA (Australian Government Department of Environment,
Water, Heritage and the Arts (2006) Advice to the minister on
white box–yellow box–Blakely’s red gum grassy woodlands and
derived native grasslands. http://www.environment.gov.au/
biodiversity/threatened/communities/pubs/box-gum.pdf. Acces-
sed 20 December 2007
EMR (Ecological Management and Restoration) (2006) Special Issue:
native vegetation condition mapping (Vol 7, Suppl 1, S1-S80)
http://www.blackwell-synergy.com/loi/emr
ESCAVI (Executive Steering Committee for Australian Vegetation
Information) (2003) Australian vegetation attribute manual,
Version 6.0. Department of Environment and Heritage, Canberra.
http://www.deh.gov.au/erin/nvis/avam. Accessed 26 April 2006
Forman RTT, Godron M (1986) Landscape ecology. John Wiley and
Sons, New York
Forman RTT (1977) Land mosaics—the ecology of landscapes and
regions. University Press, Cambridge
Frey D (1975) Biological integrity of water: an historical perspective.
In: Ballentine RK, Guarraia LJ (eds) The integrity of water.
Environmental Protection Agency, Washington, DC, pp 127–139
Gill AM, Groves RH, Noble IR (eds) (1981) Fire and the Australian
biota. Australian Academy of Science, Canberra
Hnatiuk R, Thackway R, Walker J (2008) Vegetation. In National
Committee for Soil and Terrain (eds) Australian soil and land
survey: field handbook, 3rd edn. CSIRO Publishing, Melbourne
Hobbs RJ, Hopkins AJM (1990) From frontier to fragments:
European impact on Australia’s vegetation. In Saunders DA,
Hopkins AJM, How RA (eds) Australian ecosystems: 200 years
of utilization, degradation and reconstruction. Proceedings of the
ecological society of Australia 16:93–114
Hobbs RJ, Norton DA (1996) Towards a conceptual framework for
restoration ecology. Restoration Ecology 4:93–110
Hobbs RJ, Saunders DA (eds) (1993) Regenerating fragmented
landscapes. Towards sustainable production and conservation.
Springer-Verlag, New York
Hobbs RJ (1994) Can revegetation assist in the conservation of
biodiversity in agricultural areas? Pacific Conservation Biology
1:29–38
Holling CS (1973) Resilience and stability of ecological systems.
Annual Review of Ecology and Systematics 4:1–23
Holling CS (1996) Surprise for science, resilience for ecosystems, and
incentives for people. Ecological Applications, 6:733–735
Jones R (1969) Fire-stick farming. Australian Natural History 16:
224–228
Keighery BJ (1994) Bushland plant survey: a guide to plant
community survey for the community. Wildflower Society of
Western Australia (Inc.), Nedlands
King AW (1993) Considerations of scale and hierarchy. In: Woodley
S, Kay J, Francis G (eds) Ecological integrity and the manage-
ment of ecosystems. St. Lucie Press, Ottawa, pp 19–46
588 Environmental Management (2008) 42:572–590
123
Leigh JH, Noble JC (1981) The role of fire in the management of
rangelands in Australia. In: Gill AM, Groves RH, Noble IR (eds)
Fire and the Australian Biota. Australian Academy of Science,
Canberra, pp 471–495
Lesslie RG (1997) A spatial analysis of human interference in
terrestrial environments at landscape scales. unpubl. PhD thesis.
Australian National University
Lesslie RG (2001) Landscape classification and class relatedness:
assessing native cover loss in far south-east Australia. Biodi-
versity and Conservation 10(3):427–442
Lesslie R, Barson M, Smith J (2006) Land use information for
integrated natural resources management—a coordinated
national mapping program for Australia. Journal of Land Use
Science 1(1):45–62
Lesslie R, Maslen M (1995) National wilderness inventory Australia:
handbook of procedures, content, and usage, 2nd edn. Australian
Heritage Commission, Canberra
Lesslie R, Thackway R, Smith J (2008) A national-level vegetation
assets, states and transitions (VAST) dataset for Australia. BRS
Technical Report, Bureau of Rural Sciences, Canberra
Levin SA (1992) The problem of pattern and scale in ecology.
Ecology 73:1943–1967
Lockwood JL (1997) An alternative to succession, assembly rules
offer guide to restoration efforts. Restoration Ecology 15:45–50
Loveland TR, Belward AS (1997) The IGBP-DIS Global 1km Land
cover dataset, DIScover first results. International Journal of
Remote Sensing 18:3289–3295
Macleod ND, Brown JR, Noble JC (1993) Ecological and economic
considerations for the management of shrub encroachment in
Australian rangelands. 10th Australian 14th Asian-Pacific Weeds
Conference. Weed Society of Queensland, Brisbane
Maltby E, Holdgate M, Acreman MC, Weir A (1999) Ecosystem
management: questions for science and society. Royal Holloway
Institute for Environmental Research, University of London, Egham
McIntyre S, Hobbs RJ (1999) A framework for conceptualising
human impacts on landscapes and its relevance to management
and research models. Conservation Biology 13:1282–92
McIntyre S, Hobbs RJ (2000) Human impacts on landscapes: matrix
condition and management priorities. In: Craig JL, Mitchell N,
Saunders DAA (eds) Nature conservation 5: nature conservation
in production environments: managing the matrix. Surrey Beatty
and Sons, Chipping Norton, pp 301–307
McIntyre S, Lavoral S (2007) A conceptual model of land use effects
on the structure and function of herbaceous vegetation. Agri-
culture, Ecosystems and Environment 199:11–21
McIntyre S, McIvor JG, Heard KM (eds) (2002) Managing and
conserving grassy woodlands. CSIRO Publishing, Melbourne
McKenzie N, Jacquier D, Isbell R, Bell K (2004) Australian soils
and landscapes: an illustrated compendium. CSIRO Publishing,
Melbourne
Millennium Ecosystem Assessment (2005) Ecosystems and human
well-being: biodiversity synthesis. World Resources Institute,
Washington, DC. http://www.millenniumassessment.org/en/
products.aspx. Accessed 26 April 2006
Mutendeudzi M, Thackway R (2008) A method for deriving maps of
landscape alteration levels from vegetation condition state data-
sets. BRS Technical Report, Bureau of Rural Sciences, Canberra
Newell GR, White MD, Griffioen (2006) Modelling the condition of
native vegetation in northern Victoria—a report to the northern
Victorian catchment management authorities. Arthur Rylah
Institute for Environmental Research, Victorian Department of
Sustainability and Environment and Acromap Pty Ltd., Heidel-
berg, Victoria
NLWRA (National Land, Water Resources Audit) (2007) Native
vegetation—status of information for reporting against indicators
under the national natural resource management monitoring and
evaluation framework. National Land and Water Resources
Audit, Canberra, p 31
NLWRA (National Land, Water Resources Audit) (2002) Native
vegetation assessment 2001. National Land and Water Resources
Audit, Canberra
NLWRA (National Land, Water Resources Audit) (2005) Status of
natural resource information: to underpin the national natural
resource management monitoring and evaluation framework.
May 2005. National Land and Water Resources Audit,
Canberra
NRMMC (Natural Resource Management Ministerial Council)
(2002) National natural resource management monitoring and
evaluation framework. Natural Resource Management Ministe-
rial Council. Australian Government, Canberra
Odum EP (1975) Ecology. Holt, Rinehart and Winston, New York
Oliver I, Smith PL, Lunt I, Parkes D (2002) Pre-1750 vegetation,
naturalness and vegetation condition: what are the implications
for biodiversity conservation. Ecological Management & Resto-
ration 3:176–178
O’Neill RV, DeAngelis DL, Waide JB, Allen TFH (1986) A
hierarchical concept of ecosystems. Princeton University Press,
Princeton
Parkes, DG, Newell, Cheal D (2003) Assessing the quality of native
vegetation: the ‘habitat hectares’ approach. Ecological Manage-
ment and Restoration 4(Supp. l):29–38
Phelps DG, Bosch OJH (2002) A quantitative state and transition
model for the Mitchell grasslands of central western Queensland.
Rangelands Journal 24(2):242–67
Pickett STA, White PS (eds) (1985) The ecology of natural
disturbance and patch dynamics. Academic Press, New York
Sivertsen DP, Smith PL (eds) (2001) Guidelines for mapping native
vegetation: version 2.1—guidelines and appendices. Unpub-
lished report. New South Wales Department of Land and Water
Conservation, Sydney
Society for Ecological Restoration International Science, Policy
Working Group (2004) The SER international primer on
ecological restoration. Society for Ecological Restoration Inter-
national. Tucson, USA
Stewart JB, Smart RV, Barry SC, Veitch SM (2001) 1996/97 Land
Use of Australia. Final report for project BRR5, National land
and water resources audit. Bureau of Rural Sciences, Australian
Government, Canberra
Terry A, Ullrich K, Riecken U (2006) The green belt of Europe: from
vision to reality. IUCN, Gland, Switzerland and Cambridge, UK
Thackway R, Lesslie R (2005) Vegetation assets, states and
transitions (VAST): accounting for vegetation condition in the
Australian landscape. BRS Technical Report, Bureau of Rural
Sciences, Canberra
Thackway R, Lesslie R (2006) Reporting vegetation condition using
the Vegetation assets, states, and transitions (VAST) framework.
Ecological Management and Restoration 7(Suppl 1):53–62
Thackway R, Frakes I, Lesslie R (2007) Reporting trends in
vegetation assets, states and transitions at the farm level—a
southern tablelands case study. In: Veg futures, conference in the
field, 19–23 March 2006 held in Albury-Wodonga. Greening
Australia, Canberra. http://www.live.greeningaustralia.org.au/vegfutures/pages/page124.asp
Thackway R, Lesslie R, Brocklehurst P (2005) Accounting for
vegetation condition in the Australian landscape: an approach in
the Northern Territory using VAST criteria and GIS. http://
www.nt.gov.au/nreta/natres/natveg/vegmapping/pdf/vast.pdf
Accessed 3 May 2008
Thackway R, Sonntag S, Keenan R (2006) Measuring ecosystem
health and natural resource productivity—vegetation condition
as an indicator of sustainable, productive ecosystems. Science
for Decision Makers. Bureau of Rural Sciences, Canberra, ACT
Environmental Management (2008) 42:572–590 589
123
Tindall D, Pennay C, Tozer MG, Turner K, Keith DA (2004) Native
vegetation map report series No. 4. Araluen, Batemans Bay,
Braidwood, Burragorang, Goulburn, Jervis Bay, Katoomba,
Kiama, Moss Vale, Penrith, Port Hacking, Sydney, Taralga,
Ulladulla, Wollongong. NSW Department of Environment and
Conservation and NSW Department of Infrastructure, Planning
and Natural Resources, Sydney
Tongway DL, Hindley N (1995) Manual for soil condition assessment
of tropical grasslands. CSIRO Wildlife and Ecology, Canberra
Trudgill ST (1977) Soil and vegetation systems contemporary
problems in ecology. Clarendon Press, Oxford
Walker BH, Anderies JM, Kinzig AP, Ryan P (eds) (2006) Exploring
resilience in social-ecological systems. Comparative studies and
theory development. CSIRO Publishing, Collingwood, Victoria
Westoby M, Walker BH, Noy-Meir I (1989) Opportunistic manage-
ment for rangelands not at equilibrium. Journal of Range
Management 42:266–274
Whittaker R H (1967) Gradient analysis of vegetation. Biological
Review 42:207–264
Yates CJ, Hobbs RJ (1997) Woodland restoration in the Western
Australian wheatbelt: a conceptual framework using a state and
transition model. Restoration Ecology 5:28–35
590 Environmental Management (2008) 42:572–590
123
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