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Geographic range, population structure and conservation status of the green python (Morelia viridis), a popular snake in the captive pet trade

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Accurate knowledge of distribution and population size is required for effective conservation and management of wild species. Here we report on the first estimates of the distribution and density of the green python (Morelia viridis), an iconic rainforest species widely kept in captivity. We used climatic modelling to predict its distribution in Papua New Guinea, and both climate and vegetation mapping to predict its Australian distribution. We used mark-recapture methods to estimate the density and population structure of green pythons at Iron Range, northern Australia. Bioclimatic analyses suggested that there is extensive climatically suitable habitat in Papua New Guinea (= 200000 km(2)), but very little in Australia (similar to 300 km(2)). However, use of vegetation maps increases the predicted suitable area of occupancy in Australia to 3127 km(2), including nine regional ecosystems. Density estimates at Iron Range were 4-5 ha(-1) in the complex vine forest regional ecosystem; however, only half of these were mature adults. The large predicted area of occurrence and the high density in the one intensively studied area suggest that the species is not vulnerable to extinction in the short term. However, more studies are needed in both New Guinea and Australia, especially to quantify the impact of harvesting green pythons for the pet trade.
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Introduction
The green python (Morelia viridis Schlegel, 1872) is a small
(<1.5 m) python inhabiting a large part of New Guinea, includ-
ing some satellite islands, and Cape York Peninsula, Australia
(Barker and Barker 1994; O’Shea 1996). Although there are
records from a large geographic area, the true distribution and
estimates of population size of the green python are largely
unknown. Globally, the species is listed on Appendix 2 of the
Convention on International Trade in Endangered Species
(Inskipp and Gillett 2003), while the Australian population is
listed as ‘Rare or Insufficiently Known’ (Cogger et al. 1994).
Such conservation assessments are hampered by an inadequate
knowledge of the species’ biology. It is an iconic rainforest
species, and one of the most sought-after snake species in the
captive pet industry. This interest from the captive trade stems
from the snake’s striking colours and remarkable colour
change, with individuals hatching either bright yellow or brick
red and changing to bright green (Wilson et al. 2006b; Wilson
et al. 2007). Many are exported from Indonesia (West Papua)
each year to satisfy the captive pet trade (UNEP-WCMC
CITES trade database). Despite the high profile of green
pythons and the potential pressures on wild populations, infor-
mation for determining their conservation status is severely
lacking.
Here we use data at multiple scales to address these knowl-
edge gaps, including two complementary methods to predict
their distribution, both globally, and specifically in Australia.
Climatically suitable habitat was predicted for Australia and
Papua New Guinea, whereas in the Australian region these
results were combined with the distribution of regional eco-
system known to be used by green pythons. Density and popu-
lation structure were then estimated using data from an
intensively studied population in northern Australia. We
combine all known data and discuss the likely conservation
status of this species in the wild following IUCN criteria (IUCN
2001).
Methods
Species localities for predictive distributions
Localities in Papua New Guinea were primarily derived from
museum specimens, the locality lists in O’Shea (1996), or our
fieldwork in Papua New Guinea conducted in late 2005. In
Australia localities were primarily based on our fieldwork, with
additional localities from personal communications with local
people (see Appendices 1 and 2 for site details). Museum speci-
mens were used only if their locality could be accurately deter-
mined. Locations consisted of a latitude, longitude and
elevation. Where elevations were not recorded as primary data
they were derived from topographic maps.
Distribution prediction
BIOCLIM is part of the ANUCLIM software package (Houlder
et al. 1999) and is used to predict the bioclimatic space occupied
by an organism and to make predictions on the geographic pres-
ence or absence of that organism in a defined area. The
BIOCLIM analysis procedure and general limitations are
Australian Journal of Zoology, 2007, 55, 147–154
10.1071/ZO06078 0004-959X/07/030147© CSIRO 2007
David WilsonA,B,C and Robert HeinsohnA
AFenner School of Environment and Society, Australian National University, Canberra, ACT 0200, Australia.
BPresent address: School for Tropical and Marine Biology, James Cook University, Townsville 4811, Australia.
CCorresponding author. Email: david.wilson2@jcu.edu.au
Abstract. Accurate knowledge of distribution and population size is required for effective conservation and
management of wild species. Here we report on the first estimates of the distribution and density of the green python
(Morelia viridis), an iconic rainforest species widely kept in captivity. We used climatic modelling to predict its
distribution in Papua New Guinea, and both climate and vegetation mapping to predict its Australian distribution. We used
mark–recapture methods to estimate the density and population structure of green pythons at Iron Range, northern
Australia. Bioclimatic analyses suggested that there is extensive climatically suitable habitat in Papua New Guinea
(200000 km2), but very little in Australia (~300 km2). However, use of vegetation maps increases the predicted suitable
area of occupancy in Australia to 3127 km2, including nine regional ecosystems. Density estimates at Iron Range were
4–5 ha–1 in the complex vine forest regional ecosystem; however, only half of these were mature adults. The large predicted
area of occurrence and the high density in the one intensively studied area suggest that the species is not vulnerable to
extinction in the short term. However, more studies are needed in both New Guinea and Australia, especially to quantify
the impact of harvesting green pythons for the pet trade.
Geographic range, population structure and conservation
status of the green python (Morelia viridis), a popular
snake in the captive pet trade
www.publish.csiro.au/journals/ajz
CSIRO PUBLISHING
D. Wilson and R. Heinsohn148 Australian Journal of Zoology
explained in detail elsewhere (Nix 1986; Lindenmayer et al.
1991; Nix and Switzer 1991; Houlder et al. 1999).
We derived two predicted distributions from the locality
data, the total range of the species based on minimum and
maximum predicted bioclimatic values, and the ‘core’ distri-
bution based on the 10–90 percentile levels of the multivariate
bioclimatic profile (Lindenmayer et al. 1991; Sumner and
Dickman 1998). Core areas represent those areas that have the
greatest conservation value for a species, and may act as refugia
under altered climatic conditions (Lindenmayer et al. 1991).
Locality data from Australia and Papua New Guinea were used
independently for their own region when running BIOCLIM.
Regional ecosystem use
As no detailed land cover classifications are available for New
Guinea this technique was used only in Australia. The vegeta-
tion in Queensland has been categorised into regional ecosys-
tems comprising a vegetation community that is consistently
associated with a particular combination of landform and soil
(Sattler and Williams 1999). Locations were overlain onto the
regional ecosystem map for Queensland in ArcView 3.1 (ESRI
1999). The area of each regional ecosystem where green
pythons were recorded was taken from Neldner (1999) and
ArcView map areas (see Table 1). Habitat preferences per se
were not examined as this requires comparison on used and
unused sites, and unused sites are difficult to comprehensively
determine for this species.
Density and abundance
The density and regional ecosystem use of green pythons was
studied in the Iron Range area, in north-eastern Australia
(12°45S, 143°17E). The climate there is strongly seasonal,
with most rain falling in a distinct ‘wet’ season between
December and May (for more details see Wilson et al. 2006b).
Spotlighting transects were established in the four most
common regional ecosystems in the Iron Range area:
Complex vine forest (regional ecosystem 3.3.1),
Transitional rainforest (regional ecosystem 3.12.8),
Dune rainforest (regional ecosystem 3.2.12), and
Woodland (regional ecosystem 3.3.31).
For further details of these ecosystems see Neldner (1999).
Due to logistic constraints, transect lengths varied between
regional ecosystems and were discontinuous. There were 17 km
of transect in complex vine forest, 3 km in woodland, 2.3 km in
transitional rainforest and 1 km in dune forest, with a transect
width of 30 m (15 m either side of the transect path). This width
was the furthest from the transect that an individual could be
reliably detected, as determined during two weeks of survey
work before the first field season. Transects were surveyed for
green pythons each fortnight for two consecutive wet seasons
(December 2002–April 2003 and December 2003–April 2004)
for a maximum of 21 surveys. Surveys commenced after
2000 hours and all sightings were made by hand-held spotlight
from a slow-moving (<10 km h–1) car or by foot. Green pythons
observed on transects were initially marked with a uniquely
coded passive integrated transponder tag (Gibbons and Andrews
2004), which was recorded on all subsequent survey encounters
to generate a recapture history for each individual. The location
of all sightings was recorded using a GPS (Garmin 12).
The open-population Jolly–Seber method in the program
MARK (White and Burnham 1999) was used to analyse the
recapture histories for this population. This method estimates
the population abundance at the start of the survey period for a
known area. For this model we assumed that survival was time
dependent, but the probability of recapture was constant
throughout the study.
Population demographic data were also collected from indi-
viduals during spotlight surveys. Morphological measurements
taken from recaptures of individuals were used to determine
growth rates, while the initial capture record for each individual
was used to determine the sex and age structure of the popu-
lation. Ages were determined from snout–vent lengths using the
regression equation of Wilson et al. (2006b). The movements,
home range and habitat use of individuals and of different sex
and age classes were determined using radio-tracking tech-
niques on 27 individuals that were followed for up to 451 days
over 18 months (see Wilson et al. 2006afor more details).
Results
Distribution
In Papua New Guinea BIOCLIM predicts 245535 km2of cli-
matically suitable habitat, with a ‘core’ area of 26 321 km2
Table 1. Regional ecosystems categories where green pythons were found in Australia, the dominant tree species and the extent of each ecosystem.
Regional Vegetation description Total extent in Total extent on
ecosystem protected areas (ha)ACape York (ha)A
3.2.7 Corymbia intermedia or C. clarksoniana woodland in wet coastal areas 2420 11300
3.2.12 Araucarian microphyll vine forest on coastal dunefields and beach ridges 1170 12000
3.3.1 Closed semideciduous mesophyll vine forest 12930 48850
3.3.31 Eucalyptus tetradonta ±Corymbia clarksoniana ±C. tessellaris woodland on coastal plains 9460 55000
3.5.5 Corymbia novoguinensis ±C. tessellaris woodland on northern Cape York Peninsula none 6250
3.5.13 Melaleuca viridifolia, Asteromyrtus brassii woodland on flat plains 770 8615
3.11.3 Simple evergreen notophyll vine forest on exposed metamorphic and granitic slopes 8300 79 000
3.12.3 Notophyll vine forest 3150 77600
3.12.8 Corymbia clarksoniana ±C. tessellaris open forest on coastal ranges and lowlands 2170 33400
Total for all vegetation types 40370 332015
AValues taken from Neldner (1999). Protected areas are those listed under the Nature Conservation Act 1992, and include national parks, conservation parks
and resource reserves (Neldner 1999).
Australian Journal of Zoology 149
(Fig. 1). Large core areas are predicted on the lower slopes of
the Huon Peninsula and southern portion of the central
cordillera, plus parts of the trans-Fly region. Green pythons
were predicted to be absent from the central highlands and
swamp areas of the trans-Fly and Sepik drainages. BIOCLIM
also predicts green pythons to be on New Britain, and many of
the smaller satellite islands of New Guinea. In Australia
BIOCLIM predicts there to be 292.82 km2of climatically suit-
able habitat, with a core area of 15.73 km2(Fig. 2a). This core
area is contained within the Iron Range area, with smaller frag-
ments of suitable habitat predicted further south in the
McIllwraith Range and in isolated pockets (of single grid cells)
further north along the coast. Interestingly, sites in Australia
predicted no climatically suitable habitat in Papua New Guinea
and vice versa.
Regional ecosystem preferences in Australia
In Australia green pythons were recorded from nine regional
ecosystems, totalling an area of 3127 km2(Fig. 2b, Table 1).
Most of the suitable regional ecosystems were concentrated
around the Iron and McIlwraith Ranges, with a smaller discrete
area at the Lockerbie Scrub, and a few isolated patches between
them (Fig. 2b). There were also suitable areas south of the Laura
divide, notably in the Cape Melville area, but these were
excluded from further analysis (see Discussion). Most records
were from the Iron Range area, with two records each from the
McIlwraith Range area and Lockerbie Scrub. Most records were
from complex vine forest (regional ecosystem 3.3.1). There are
488.5 km2of this regional ecosystem on Cape York Peninsula,
with 129.3 km2of this in protected areas (Neldner 1999).
Density, abundance and population structure
In total, 101 individuals were captured 147 times in complex
vine forest during the fortnightly surveys over the two wet
seasons. The total number of green pythons in the surveyed area
Distribution of green pythons
0 200 400 600 800100
Kilometres
Sepik drainage
Huon Peninsula
New Britain
Fly delta
N
Fig. 1. BIOCLIM prediction of climatically suitable areas for the green
python (Morelia viridis) in Papua New Guinea. Light grey represents total
range, while dark grey represents the predicted core range. Dots are the
sighting locations on which the prediction is based.
!
!
!
!!
!
075 22530037.5
Kilometres
Lockerbie Scrub
Iron Range
McIlwraith Range
Laura Basin
a)
N
150
!!!
0 75 150 225 30037.5
Kilometres
Lockerbie Scrub
Iron Range
McIlwraith Range
Laura Basin
b)
N
Fig. 2. Predicted distribution of the green python (Morelia viridis) in Australia. (a) BIOCLIM prediction of the total range. The core area is hidden under
the clump of sightings at Iron Range. (b) Prediction based on regional ecosystem distributions matched with known sighting localities. In both cases dots are
sighting localities on which the predictions are based.
D. Wilson and R. Heinsohn150 Australian Journal of Zoology
of this regional ecosystem was estimated at 227 ± 85 (s.e.) using
the Jolly–Seber model in MARK (White and Burnham 1999).
Given a survey area of 50 ha (0.5 km2, as defined in the
Methods), this equates to ~4–5 ha–1 in this regional ecosystem.
Seven individuals were caught in transitional rainforest, but no
recaptures were made, hence mark–recapture analysis tech-
niques could not be used to estimate abundance in this regional
ecosystem. Juveniles were found only in canopy gaps within
complex vine forest and were never found in transitional rain-
forest. No green pythons were recorded from either the wood-
land or dune rainforests, and these transects were discontinued
after 10 repeats.
On the basis of the known-age structure of this population
(Wilson et al. 2006b) these 227 individuals comprise 49 adult
females and 65 adult males, 75 immature females and 14 imma-
ture males, and 14 juvenile females and 10 juvenile males
(Fig. 3a). The age structure of this population is positively
skewed, with a mean age of 3.4 years and a maximum predicted
age of at least 13 years (Fig. 3b).
Discussion
This study is the first to estimate the density and structure of a
green python population and adds to our knowledge of this
unusual and charismatic species in the wild. On the basis of bio-
climatic data and the distribution of regional ecosystems used,
green pythons appear to have a large potential distribution in
Papua New Guinea, including inshore islands and much of the
lowlands and foothills. In Australia green pythons are restricted
to very small areas of suitable habitat on eastern Cape York
Peninsula; however, the one intensively sampled regional
ecosystem contained high densities of individuals.
Distribution
Overall, the BIOCLIM analysis indicated that green pythons are
potentially widely distributed in Papua New Guinea, but
restricted to small areas of far northern Australia. BIOCLIM
was chosen because of its simplicity of use and the requirement
of presence-only data, rather than the presence or absence
required by more detailed models (Elith et al. 2006). It is also a
well established approach that has been used previously to
predict the potential distributions for a variety of plant and
animal species in Australia (Nix 1986; Lindenmayer et al. 1991,
1996; Olsen and Doran 2002). Climatic conditions in areas
where green pythons were predicted to occur are characterised
by hot and wet summers with cooler winters, typically corre-
sponding to areas of rainforest.
In Papua New Guinea BIOCLIM predicts both a large core
and total area of suitable habitat (Fig. 1). The distribution of
green pythons could not be predicted in West Papua (Indonesia),
as we could find no accurate climate models for this area,
although we believe that this area would contain extensive suit-
able habitat due to the similar climate and landforms between
Papua New Guinea and West Papua. Large areas of the central
highlands were excluded, as were the higher altitudes on the
Huon Peninsula and south along the central cordillera, presum-
ably due to low minimum temperatures. Portions of the Fly delta
(in the south-west), which are covered in low alluvial plains and
flats, and the Sepik drainage (in the north), which is dominated
by lowland freshwater swamps (Paijmans 1976), are also
excluded from the predicted habitat. BIOCLIM predicts sub-
stantial climatically suitable habitat on New Britain, and on
islands between New Britain and the mainland (Fig. 1), despite
there being no records from any of these islands. These islands
have never been connected to mainland New Guinea (Mayr and
Diamond 2001), and green pythons have apparently not been
able to colonise these islands. However, their presence on other
oceanic islands such as Biak, off the north coast of Irian Jaya,
shows that colonisation over water can occur. In contrast, the
islands at the south-eastern tip of New Guinea were previously
connected to the mainland (Mayr and Diamond 2001) and do
contain green pythons (O’Shea 1996), as predicted by the
BIOCLIM analyses.
The climatic analysis did not show a clear demarcation
between the northern and southern watersheds in Papua New
Guinea (Fig. 1). This is in contrast to the findings of Rawlings
and Donnellan (2003), who found clear genetic differences
between the northern and southern populations.
0
10
20
30
40
50
60
70
80
Juvenile Immature Adult
Age class
Number of individuals
a)
0
10
20
30
40
50
60
70
12345678910111213>13
Age (years)
Number of individuals
b)
Fig. 3. Demographic composition of the green python (Morelia viridis)
population in the survey area. Total numbers are based on the population
estimate from a Jolly–Seber model in MARK, while the proportion in each
category is based on the size distribution of all captures during fieldwork
(Wilson et al. 2006b). Size class distributions (a) of females (black
columns) and males (grey columns); age distributions of all individuals (b).
Age categories listed are the upper bound of each range.
Australian Journal of Zoology 151
Australian distribution
In Australia, both bioclimatic analysis and the distribution of
regional ecosystems where green pythons were found were used
to predict their potential distribution. Climatic modelling for the
Cape York Peninsula area is poor due to the low number of data
points (H. Nix, pers. comm. 2006), suggesting that, rather than
using the regional ecosystems to further decrease the area pre-
dicted by climatic analysis (Meyer and Thuiller 2006), the two
methods should be used in a complementary fashion. The area
of regional ecosystems in Australia where green pythons have
been found is considerably larger than the climatically suitable
area as predicted by BIOCLIM (3127 km2compared with
293 km2). Given this disparity, which one is closer to the true
extent of occurrence of the green python? The distribution of
location records in Australia may reflect the true distribution
and density of green pythons, but may also represent the easiest
access points into suitable habitat. Iron Range, where most
records occur, is a popular area with both amateur naturalists
and scientists due to the diversity of animal species that occur
there (Kikkawa et al. 1981). In comparison, the McIlwraith
Range is relatively remote and comparatively difficult to access.
Areas of suitable regional ecosystems south of the Laura Basin
were excluded from ‘area of occurrence’ estimates as no green
pythons have been recorded from this area, and the Laura Basin
is an effective barrier restricting the southward spread of rain-
forest species (Lavarack and Godwin 1987). Interestingly,
BIOCLIM predicted no substantial areas of climatically suitable
habitat in Australia where green pythons had not been previ-
ously recorded (Fig. 2a). Climatically suitable habitat exists in a
few isolated locations between Iron Range and the northern tip
of Cape York Peninsula; however, these do not appear to contain
suitable vegetation (based on regional ecosystem mapping) and
have never been surveyed for green pythons. Even the
Lockerbie Scrub, where there were two green python records,
has little climatically suitable habitat (Fig. 2a).
This study highlights the markedly different conclusions that
may be drawn using different distribution prediction methods
(Loiselle et al. 2003). There are biases in both methods and
neither option should be viewed as more accurate. Both models
should be used as indicators of predicted distribution, and be
considered in conjunction with other traits that may limit distri-
bution . Importantly, both methods predict the Iron Range area
to be core habitat for green pythons in Australia, highlighting
the conservation significance of this area (Mackay and Nix
2001). The Iron–McIlwraith Range area forms the largest
remaining area of lowland tropical rainforest in Australia, and is
distinct from more southerly rainforests in its flora and fauna.
Both floristically and faunally, this area is more related to the
Melanesian lowland rainforests of New Guinea, with which it
shares a large number of genera and species (Kikkawa et al.
1981; Webb and Tracey 1981; Crisp et al. 2001).
Density and abundance
Our estimate of 4–5 ha–1 in complex vine forest is well within
the range of densities reported, both for snake species world-
wide and most other published studies on tropical snake species
(Parker and Plummer 1987; Brown and Shine 2002). The
density of green pythons was actually greater than expected.
Individuals were rarely encountered during fieldwork, with new
individuals encountered only every 2–3 h (authors’ unpub. data).
However, in support of our estimates, although we caught only
101 individuals during surveys, in total we caught 207 individ-
uals in the study area during all research, similar to the 227 indi-
viduals predicted in the same area using the Jolly–Seber model.
Radio-tracking shows that individuals do not always come to the
ground at night to hunt (Wilson 2007), and this behaviour may
give the impression of fewer individuals than suggested by the
density estimates. The age structure of the Iron Range popu-
lation appears to be healthy, with significant numbers of juve-
niles and a high proportion of recently mature adults.
There have been two other well studied Australian pythons,
the carpet python (M. spilota imbricata), which had a density of
~0.5 ha–1 in temperate south-western Australia (Pearson et al.
2005), and the water python (Liasis fuscus), which had
encounter rates of ~0.5 h–1 (Brown and Shine 2002), similar to
the encounter rate for green pythons (authors’ unpub. data). By
comparison, Schulte (1988) estimated the density of the
emerald tree boa (C. caninus) to be 0.004 ha–1 in the Peruvian
Amazon. This discrepancy is particularly noteworthy as green
pythons and emerald tree boas show strikingly convergent evo-
lution in many aspects of their ecology. Both species are tropi-
cal, arboreal specialists and show ontogenetic colour change
from yellow or red juveniles to green adults at ~55 cm (Stafford
and Henderson 1996; Wilson et al. 2006b).
Our density estimates suggest that there are substantial
numbers of green pythons in complex vine forest at Iron Range,
and this area represents the single largest known population in
Australia. Adult green pythons were also observed in areas of
transitional rainforest, but juveniles were never recorded in this
regional ecosystem. This suggests that transitional rainforest
can be recolonised, but may not be suitable for breeding in.
Green pythons were opportunistically recorded in a further three
regional ecosystems in the Iron Range area; however, density
estimates are not available for these areas. These ecosystems lie
primarily within the protected area of the Iron Range National
Park.
Green pythons were twice recorded from the McIlwraith
Range to the south of Iron Range (Christian 1997; K. McDonald,
pers. comm. 2002) and these two areas may be connected by
gallery rainforest (Legge et al. 2004). Although individual daily
movements are small, the fact that they are constantly active and
males have a roaming strategy (Wilson et al. 2006a) suggests
that the Iron Range and McIlwraith populations would be con-
nected wherever habitat corridors exist. There is a smaller area of
suitable regional ecosystem further north at the Lockerbie Scrub
where green pythons have twice been recorded (Waldren 1996;
S. Templeton, pers. comm. 2004). At most, this area might
contain a few hundred individuals and the Lockerbie Scrub may
be too small to support a green python population that is viable
in the long term.
Although the density of green pythons in the complex vine
forest seems high, we hesitate to extrapolate our estimates to the
entire possible range of green pythons. It would be problematic
to extrapolate the density estimates from the one regional
ecosystem at Iron Range to the McIlwraith Range for several
reasons. First, there have been only two reports of green pythons
from the McIlwraith Range in the last 150 years (authors’
Distribution of green pythons
D. Wilson and R. Heinsohn152 Australian Journal of Zoology
unpub. data) and this makes any extrapolation tenuous. Second,
although a density estimate is available for one of the regional
ecosystems that occurs in the McIlwraith Range, actual densi-
ties may vary between the two areas, as has been shown in other
snake species (Parker and Plummer 1987; Henderson 2002).
Third, there are substantial areas of regional ecosystems where,
although green pythons have been recorded, density estimates
are not available. Ideally, surveys should be carried out in all
regional ecosystems where green pythons are known to occur,
both at Iron Range and in the McIlwraith Range region to esti-
mate densities in these potentially important ecosystems.
Conservation status
Our data suggest that green pythons do not satisfy the IUCN cri-
teria (Table 2) for being listed as vulnerable, either globally or
within Australia. We must stress that important information is
still lacking, especially for the potentially larger New Guinea
population and, as such, there is a large element of uncertainty
in this conclusion. Although our bioclimatic analysis demon-
strated large areas of suitable habitat in Papua New Guinea, and
similarly large areas are probably also available in western New
Guinea, there are currently no data available covering the extent
to which local people hunt them for food. Further, although they
are known to be taken for the international pet trade in West
Papua, numbers taken and population densities for different
regions are not currently available.
In Australia we have a more detailed knowledge of their
distribution and population size. Green pythons do not appear to
be vulnerable under any of the IUCN criteria (Table 2); however,
further work needs to be undertaken to refine our assessment.
Long-term information on population trends is lacking, as are
density estimates from the other eight regional ecosystems
where green pythons were recorded. The population structure at
Iron Range also shows that mature individuals comprise less
than 50% of the total population, reducing the effective popu-
lation size. Surveys are needed in both the McIlwraith Range
and at Lockerbie Scrub to determine the density and distribution
of green pythons in these areas. Hence we conclude that there
are insufficient grounds to list green pythons as threatened,
either globally or in Australia, but reiterate the need for further
study, especially in New Guinea.
Acknowledgements
We thank all the people who helped search for specimens in the field,
especially K. and A. Goetze, S. Legge, S. Murphy, B. Robinson, E. Sobey
and K. Wilson. K. Nissen and J. Stein helped to generate maps. This research
was funded by the Australian Geographic Society, National Geographic
Society and the Hermon Slade Foundation. Appropriate permits were issued
by the Queensland Environmental Protection Agency (WITK00337502)
and the Australian National University (C.RE.24.02 and 27.02).
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Table 2. Assessment of the world and Australian populations of the green python (Morelia viridis) against the summarised IUCN Vulnerable
criteria (IUCN 2001)
IUCN Criteria World Australia
A. Has there been, or is there projected to be, a population decrease of more than 30% in 10 years? Not known Not known
B. Is the population extent of occurrence 20000 km2or the area of occupancy 2000 km2?NoNo
C. Is the population estimated at 10000 mature individuals? No No
D. Is the population size 1000 individuals or with a very restricted area, such that it is quickly capable of No No
becoming endangered?
E. Does quantitative analysis show the probability of extinction in the wild is at least 10% within 100 years? Not undertaken
Does the species qualify for Vulnerable status? No No
Australian Journal of Zoology 153
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40–43. doi:10.1098/rsbl.2006.0574
Manuscript received 12 September 2006, accepted 15 May 2007
Distribution of green pythons
D. Wilson and R. Heinsohn154 Australian Journal of Zoology
http://www.publish.csiro.au/journals/ajz
Appendix 1. Details of locations in Papua New Guinea used in the
BIOCLIM prediction of the distribution of the green python (Morelia
viridis) in Papua New Guinea
Location Latitude Longitude Elevation (m)
Abam 8.95 143.183 50
Aitape 3.13333 142.333 0
Aiyurafu 6.56667 145.333 1968
Aramia River 7.93333 143.367 1
Baiyer 5.53333 144.15 1170
Biniguni 9.66667 149.283 198
Bulolo 7.2 146.65 794
Chimbu River 6.05 144.967 871
Dede 8.3 142.883 1
Derongo 5.41667 141.1 314
Fergusson Island 9.55 150.667 0
Finschafen 6.56667 147.85 0
Garaina 7.88333 147.15 699
Goroka 6.06667 145.383 1524
Kainantu 6.28333 145.867 1553
Kapuma 7.58333 144.967 1
Karimui 6.5 144.85 983
Kebil 6.2 145.033 1840
Kerema 7.96667 145.75 1
Kunini 9.08333 143 0
Kwima 6.13333 144.967 1541
Lae 6.73333 146.983 1
Lake Murray 6.81667 141.383 59
Lufa 6.31667 145.317 1621
Mafulu 8.51667 147.033 1500
Maiwara 10.35 150.35 0
Mt Lamington 8.91667 148.167 1679
Nivi 6.2 145.333 1646
Nondugl 5.86667 144.767 1702
Normanby Island 10 151.167 0
Okapa 6.53333 145.617 1814
Omati 7.73333 144.183 0
Popondetta 8.76667 148.25 156
Simbai 5.28333 144.517 2009
Sinaeada 10.3167 150.317 48
Sturt Island 8.16667 142.25 0
Telefomin 5.13333 141.617 1240
Urapmin 5.15 141.5 1808
Waghi 5.83333 144.633 0
Wau 7.33333 146.717 1200
Woitape 8.55 147.283 1850
Wombon 5.63333 141.1 191
Wonenara 6.8 145.883 1559
Zim 8.78333 143.1 91
Appendix 2. Details of locations in Australia used in the BIOCLIM
prediction of the distribution of the green python (Morelia viridis)
in Australia
Location Latitude Longitude Elevation (m)
Chili Beach 1 12.6299 143.422 5
Chili Beach 2 12.629 143.425 5
Chili Beach 3 12.6293 143.427 5
Iron Range 1 12.741 143.285 20
Iron Range 2 12.7437 143.283 50
Iron Range 3 12.7104 143.293 80
Iron Range 4 12.7541 143.288 50
Iron Range 5 12.7143 143.319 70
Iron Range 6 12.7096 143.297 80
Iron Range 7 12.7644 143.287 92
Iron Range 8 12.78 143.309 104
Iron Range 9 12.7769 143.282 116
Iron Range 10 12.7061 143.297 57
Iron Range 11 12.6987 143.3 128
Iron Range 12 12.6993 143.303 140
Iron Range 13 12.7458 143.232 152
Iron Range 14 12.7136 143.3 164
Iron Range 15 12.7459 143.23 103
Lockerbie 1 142.58 10.78 5
Lockerbie 2 142.46 10.79 80
Peach Creek 1 13.7367 143.339 530
Peach Creek 2 13.7372 143.339 550
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