land & environment court of nsw proceedings no. 10224 …

Proceedings No. 10224 of 2012 Joint Report – Ecological Issues

8 September 2012 1

LAND & ENVIRONMENT COURT of NSW

Proceedings No. 10224 of 2012

Bulga Milbrodale Progress Association

v Warkworth Mining Ltd & Minister for Planning and Infrastructure

JOINT EXPERT REPORT

ECOLOGICAL ISSUES

8 September 2012 1 PARTICIPANTS

Mr Stephen Bell – ecological expert for the Applicant, Bulga Milbrodale Progress Association Inc

Dr AnneMarie Clements – restoration ecological expert for the Second Respondent, Warkworth Mining Limited

Dr Margaret Donald – statistics expert for the Second Respondent, Warkworth Mining Limited

Dr David Robertson - ecological expert for the Second Respondent, Warkworth Mining Limited 2 SCOPE OF EXPERT CONFERENCE

The experts have discussed a variety of ecological and statistical issues related to the Appeal.

The experts for the Second Respondent have differing areas of expertise. All participated in discussions

concerning statistics, Dr Clements and Dr Robertson participated in discussions concerning the identity and

offsetting of Warkworth Sands Woodland, while Dr Robertson.

3 KEY AREAS of AGREEMENT Statistics (Mr Bell, Dr Donald, Dr Clements, Dr Robertson)

Classification and ordination can be used appropriately on ecological data and are useful in exploring patterns in

data, including vegetation data.

Classification and ordination are useful in exploring patterns in floristic data.

“Primer” methods of classification and ordination are fine to use if used appropriately.

Small sample sizes can be inappropriate to draw conclusions from. This is why it is important to use classification

and ordination in support of field data and inspections, and why their results are not accepted blindly.

Different botanists can produce differing data sets for the same community that can be attributable to observer

differences.


8 September 2012 2

Classification and ordination results cannot be used in isolation to determine whether or not an endangered

ecological community is present at any given site.

Warkworth Sands Woodland (Mr Bell, Dr Clements, Dr Robertson)

Aeolian sand deposits exist across the Northern Biodiversity Area as mapped by Dr Hazleton and referred to in

paragraph 10 and 11 in Chapter 7 of Dr Clements statement of evidence.

Identity of vegetation across the SBA and Impact area as mapped in the EA.

WSW woodlands occur on deposits of aeolian sands as defined in the Final Determination.

Central Hunter Grey Box Ironbark Woodland and Central Hunter Ironbark Spotted Gum Grey Box Forest

generally occur on Permian sediments, as defined in the Final Determination.

Hunter Lowland Redgum Forest occurs on Permian sediments as defined in the Final Determination.

Warkworth Sands deposits have a restricted distribution and consequently native vegetation that occurs on

Warkworth Sands has a restricted distribution.

In terms of Warkworth Sands Woodland all – accept Dr Hazelton’s findings about the aeolian sand deposits

namely:

Aeolian sand deposits occur in the Northern Biodiversity Area (Archerfield), Southern Biodiversity Area

(HMA2 and Springwood, and HMA 3) and the Disturbance Area. These are the sand dunes as defined

in the Final Determination for Warkworth Sands Woodland in the Sydney Basin Bioregion.

The total extent of aeolian sand >0.5 m depth in the Northern and Southern Biodiversity Areas is at

least 550 ha (plus the Lockwood (2007) unmapped sands on the Springwood property). There is

approximately 113 ha of aeolian sand >0.5 m depth on the Disturbance Area.

All – the calculated areas of aeolian sand >0.5 m depth based on the findings of Lockwood (2007) in the

Biodiversity Areas are approximately those shown in the table below:

Location Biodiversity Areas (ha) Sand > 0.5 m depth (ha) Southern Biodiversity Area HMA3 329 140 NDA1 359 minor HMA2 81 75 NDA2 122 6 HMA1 257 45 Springwood property 104 at least ½, say 50 (as estimated

by Pam Hazelton) Northern Biodiversity Area (the Archerfield property)

342 293

Total 1595 about 607

Dr Robertson and Mr Bell agree that, in general, the WSW mapped within the SBA and DA, as shown in the EA, is

relatively accurate. However, Mr Bell has not had the opportunity to validate mapping across all of the SBA and

DA. Dr Clements has not verified the mapping.

Warkworth Sands Woodland has high conservation values as per its endangered ecological community status.


8 September 2012 3

Warkworth Sands Woodland has been highly modified since European settlement, as has the vegetation of the

Hunter Valley generally.

Native vegetation on the Warkworth Sands deposits is regenerating. There are also exotic plant species present.

The time since cessation of livestock grazing has an influence on the condition of native vegetation, with the most

intact vegetation having been protected from grazing for the longest.

East of Wallaby Scrub Road, in the northern half, grazing probably ceased about 1984, west of Wallaby Scrub road

grazing ceased about 2003 and on the NBA it ceased in 2009. In the Springwood property in the SBA, grazing

ceased in February 2011.

Warkworth Sands Woodland occurs in an area of low rainfall. The Hunter Valley is in a low point of the Great

Dividing Range and so plant species typically found west of the Great Dividing Range can occur on the aeolian

sands together with coastal species. The co-occurrence of eastern and western species is a feature of the

community.

Mr Bell notes: Data collected by Mr Bell, Dr Clements and Dr Robertson for some parts of the NBA show more

species in common with Central Hunter Grey Box – Ironbark Woodland EEC and/or Central Hunter Ironbark -

Spotted Gum – Grey Box Forest EEC than they do for WSW EEC (see attached graphs in Appendix A).

Dr Clements and Dr Robertson note that such occurrences are on aeolian sands and are part of Warkworth Sands

Woodland as explained in their respective statements of evidence.

Offsets for Other Communities (Mr Bell, Dr Robertson)

The Ironbark & threatened species offset sites (Bowditch, Goulburn River, Putty and Seven Oaks) have not been

designed to offset impacts upon Warkworth Sands Woodland. Rather they have been designed to offset impacts

predicted for Central Hunter Grey Box Ironbark Woodland (CHGBIW) and Central Hunter Ironbark Spotted Gum

Grey Box Forest (CHISGGBF), and threatened fauna species including threatened birds, bats and Squirrel Glider

(as referred to in the Flora and Fauna assessment for the EA).

The Ironbark & threatened species offset sites were carefully selected as strategic regional offsets for a number of

reasons:

Offsets on the Hunter Valley floor that contain CHGBIW and CHISGGBF are limited in quantity and quality. Some offsets are available in the Southern Biodiversity Area (SBA) for CHGBIW but no CHISGGBF is available.

Ironbark & threatened species offset sites contain freehold land containing a mixture of remnant vegetation and native vegetation with regeneration potential. These lands with the proposed change of land use and management from grazing to conservation provide current and long term potential regenerating habitat for native fauna;

Ironbark & threatened species offset sites are strategically linked to national parks and nature reserves and to conservation initiatives such as the Great Eastern Ranges Initiative (GERI);

Ironbark & threatened species offset sites are located in a critical area of the state where climate change is anticipated to impact and will provide a transition zone especially along the 17km of riparian vegetation (in the Goulburn River offset), for threatened fauna and non threatened fauna to move from the central western region of the state toward the east as climate change dries the inner areas; and

Ironbark & threatened species offset sites do not contain mining tenements and are not as threatened by future growth of State significant industries as other lands considered.


8 September 2012 4

They were selected in consultation with the NSW Department of Planning and Infrastructure (DP&I), and what is

now the NSW Office of Environment and Heritage (OEH) and Commonwealth Department of Sustainability,

Environment, Water, Populations and Communities (SEWPaC) to provide offsets for Ironbark communities that

could not be found elsewhere on the valley floor, and to provide extensive areas of habitat for threatened fauna

found within the Warkworth Project area. The remote offsets were not chosen to offset Warkworth Sands

Woodland (WSW) Endangered Ecological Community (EEC).

The offsets as proposed for Ironbark & threatened species offset sites are appropriate for their stated purpose.

However, in comparison to survey effort expended on the Warkworth development site (DA) and adjacent offsets

(SBA, NBA), that completed on all other offsets is limited, but is ongoing. Consequently their appropriateness is

contingent on confirmation of habitat and community mapping.

Wildlife Corridors (Mr Bell, Dr Robertson)

The Warkworth Extension will progressively impact upon a large patch of vegetation as the open cut mine is

developed westwards. The large patch of native vegetation including WSW, CHBIW and CHSGIF, will be reduced

in size by mining. However, at no point in the future will connectivity of habitats be severed in a north south

direction. A continuity of habitats will be maintained for threatened fauna and other native fauna via the

maintenance of habitats in the SBA.

The buffer area also contains fauna habitat that contributes to the continuity of habitats for fauna at the present

date. However, that is not included in the SBA and so may not be protected in the long term.

Mining rehabilitation will occur progressively and will begin to replace the forest and woodland that will be cleared

by the open cut mining. This rehabilitation will help in the long term to restore patch size of forest and woodland

habitat, and to maintain connectivity of habitats in a north-south alignment.

Dr Robertson has examined how each threatened species and endangered ecological community will be treated by

the offset package, and specifically how the offset package was designed to provide adequate habitat areas for

each entity. A table is attached to this document that shows a detailed analysis of the way that the offset package

caters for each species and/or community that is predicted to be impacted by the mining proposal of 2011.

4 AREAS OF PARTIAL AGREEMENT (Mr Bell, Dr Clements, Dr Robertson)

The parties discussed the two scenarios concerning the likelihood of extinction of WSW that were the subject of Dr

Robertson’s Statement of Evidence, as outlined below in bold. There was partial agreement on the scenarios and

what they mean for WSW. In the section below, the background is provided in bold text and is cut directly from Dr

Robertson’s Statement of Evidence.

Note that Mr Bell has adapted a version of the scenario table for his answers to questions and this appears as

“Table 5.2” immediately following the original scenarios table.

In 2011, I was asked to provide expert opinion as to the impact of the Project as described in

the Assessment Documents (EA (EMGA Mitchell McLennan 2010a), RTS (EMGA Mitchell

McLennan 2010b), PPR (EMGA Mitchell McLennan 2011)) on WSW. A summary was provided

in response to Umwelt’s peer review (2011) of the Ecological Assessment (Cumberland

Ecology 2011). In light of further research, additional site inspections for the SoE and review

of Mr Bell’s report (2012), the responses have been updated and are provided below.

In order to explain my views, the latest area estimates for WSW and Warkworth Sands

Grassland (WSG) are summarised in Table 5.1. These are based upon data in the current

Umwelt (2011) report together with revised estimates of the total amounts of WSG thought to

be present at the time of the decision to provide the 2003 approval.


8 September 2012 5

In Table 5.1, three scenarios for WSW are summarised:

Scenario 1 (version a) - 2003 development consent based on 2003 data for WSW

extent;

Scenario 1 (version b) - 2003 development consent based on 2011 data for WSW

extent; and

Scenario 2 - current proposal based on 2011 data for WSW extent.

Note that for the purposes of compiling Table 5.1, I have checked the report entitled:

Warkworth Sands Woodland - An Endangered Ecological Community Distiribution, Ecological

Significance and Conservation Status (Peake et al. 2002). The report states that out of 800 ha

of WSW estimated to remain in 2002, half was estimated to be dominated by E. crebra, E.

moluccana and Corymbia maculata. As such species are actually dominants of other

communities, half of the 800 ha was actually another forest community. Therefore at the time

of the decision to approve the previous mine extension, there was much less than 800 ha. For

this reason, the data provided in Scenario 1 (version a) uses an estimate of 400 ha of WSW

remaining as that was what was really known at the time based upon the aforementioned

estimates by Peake et al (2002). The other version of Scenario 1, that being (b) and scenario 2

are based on the present and more accurate figure of 464.8 ha, the current estimate based

upon recent survey work of WSW.


8 September 2012 6

Table Error! No text of specified style in document..1 2011 Data for WSW and WSG

Item Scenario 1a# Scenario 1b Scenario 2

Item Area

% Pre-

European Area

% Pre-

European Area

% Pre-

European

WSW Pre-European 3,076.9 3,038.0 3,038.0

WSW Remaining 400.0 13% 464.8 15.3% 464.8 15.3%

WSW to be cleared 35.6 1.2% 35.6 1.2% 103.5 3.4%

WSW left after

clearing

364.4 11.8% 429.2 14.1% 361.3 11.9%

WSW offset of

existing vegetation

71.2 2.3% 71.2 2.3% 130.1 4.3%

WSW to be re-

established

71.2 2.3% 71.2 2.3% 234.1 7.7%

WSW left following

clearing and re-

established

435.6 14.2% 500.4 16.5% 595.4 19.6%

WSW conserved

within a Walkworth

Mining Limited

(WML) offset

including re-

establishment

142.4 4.6% 142.4 4.7% 364.2 12.0%

WSW conserved

within Bulga mine

and Wambo mine

offsets

119.7 3.9% 119.7 3.9% 119.7 3.9%

WSW not in an

offset

173.5 5.6% 238.3 7.8% 111.5 3.7%

Notes: # Based on ‘Warkworth Sands Woodland –An Endangered Ecological Community Distribution, Ecological Significance and Conservation Status (Peake et al, 2002) on Hunter Botanic Gardens website, for the 800 ha remaining in 2002, approximately 50% is regarded as Eucalypt crebra/moluccan and corymbia maculat, a different community. Accordingly, the pre-European extent of 6,`53.8 ha inferred by Mr Travis Peake in the 2011 review has been halved on this basis.


8 September 2012 7

Table Error! No text of specified style in document..2 2011 Data for WSW and WSG (Version of data referred to by Mr Bell)

Item Scenario 1a# Scenario 1b Scenario 2

Item Area

% Pre-

European

%

Extant Area

% Pre-

European

%

Extant Area

% Pre-

European

%

Extant

WSW Pre-European 3,076.9 100% - 3,038.0 100% - 3,038.0 100% -

WSW Remaining 400.0 13% - 464.8 15.3% - 464.8 15.3% -

WSW to be cleared 35.6 1.2% 8.9% 35.6 1.2% 7.6% 103.5 3.4% 22.3%

WSW left after clearing 364.4 11.8% 91.1% 429.2 14.1% 92.3% 361.3 11.9% 77.8%

WSW offset of existing vegetation 71.2 2.3% 17.8% 71.2 2.3% 15.3% 130.1 4.3% 28.0%

WSW to be re-established 71.2 2.3% 17.8% 71.2 2.3% 15.3% 234.1 7.7% 50.4%

WSW left following clearing and re-established 435.6 14.2% 108.9% 500.4 16.5% 107.7% 595.4 19.6% 128.1%

WSW conserved within a Walkworth Mining Limited

(WML) offset including re-establishment

142.4 4.6% 35.6% 142.4 4.7% 30.6% 364.2 12.0% 78.4%

WSW conserved within Bulga mine and Wambo mine

offsets

119.7 3.9% 30.0% 119.7 3.9% 25.8% 119.7 3.9% 25.8%

WSW not in an offset 173.5 5.6% 43.4% 238.3 7.8% 51.3% 111.5 3.7% 24.0%


8 September 2012 8

Experts Answers to Questions:

1. What are the risks of extinction to the WSW community if the clearing currently approved under the 2003 approval (Scenario 1) occurs (not taking account of proposed regeneration)?

Dr Robertson’s opinion: WSW could in theory become extinct, via some stochastic event such as severe drought

or a sequence of other extreme events (e.g. repeated extreme bushfires). In 2003 clearing was approved to

reduce the WSW community to 364.4 ha based on 2003 data.

Extinction is unlikely and I am unaware of any such scenarios where several hundred ha of such vegetation was

largely set aside for permanent conservation. The community will be conserved within several separate properties

owned by different mining companies at Mt Thorley Warkworth, Wambo and Bulga. Such lands are proposed for

conservation and not for clearing and so it is hard to conceive of a reason why the vegetation would become

absolutely extinct.

The vegetation is in relatively small patches and is subject to edge effects. However, on current data it appears to

be in good condition. It does not appear to be suffering from major deleterious weed invasion or other comparable

threats that would make it become extinct. While such edge effects may harm the vegetation (such as by reducing

the abundance of some native species in favour of weeds), they do not make it become extinct as such.

Dr Clement’s Opinion: I find it difficult to rely on the numbers in the above table and the extent and accuracy of

800 ha of mapped Warkworth Sands Woodland appears questionable.

From the Story et al. (1963) mapping of aeolian dune sands (Warkworth Land System), there is restricted

distribution of dune sands in the central Hunter and hence native vegetation growing these sands is a rare

commodity. Story et al. (1963) mapped, at a scale of 1:250,000, 12 patches of Warkworth Land System, covering a

total of about 38 km2. Of these 12 patches, two are mapped on the land investigated in 2012. One patch was on

the 2012 Northern Biodiversity Area (the Archerfield property) and the other included the 2012 Southern

Biodiversity Area and the northern section of the Disturbance Area as well as adjoining areas offsite further to the

west. There are other mapped patches nearby (see Figure 3.1 of Unwelt 2011, or Figure 5.c-2 of Clements 2012 or

Figure 3.7 of Bell 2012).

In the nomination of the Warkworth Sands Woodland by Hunter Region Rare Plants Committee dated 29 May

2002, there is a figure titled “Location of Warkworth Sands Woodland”. The mapping pattern is similar to that of

Story et al. (1963), but excluded the patch on the Archerfield property. There is an area of about 800 ha mapped on

the figure in the nomination, consistent with the text “WSW is known only from Singleton Local Government Area,

where it currently occupies an area of approximately 800 ha”. Overlaying the boundaries of the Disturbance and

Biodiversity Areas, the more than 300 ha of aeolian dune sand on the Archerfield property was not included (Figure

1 attached).

My assessment of loss and conservation gain from the 2003 approval is based on areas of Warkworth Sands

(aeolian sands near Warkworth), not on areas supporting canopy trees on aeolian sands. The data from the four

long transects illustrate the natural regeneration of the aeolian sands with the removal of grazing pressure.

In paragraph 10 of the final determination for Warkworth Sand Woodland gazetted on 14 October 2011, it is stated

that:

Ongoing threats include open-cut coalmining, sandmining and the construction of mining infrastructure

as well as pressures from agricultural clearing, altered fire frequency, weed invasion and grazing.

Open-cut coal mining


8 September 2012 9

The 2003 Warkworth Mine approval results in the loss of about 40 ha of vegetation on the aeolian sand east of

Wallaby Scrub Road (see attached email from Dr Pamela Hazelton). Grazing on this 40 ha area probably ceased in

about 1984.

Pressures from agriculture

The “Green Offset Strategy 2002” associated with the 2003 approval was conservation of about 1650 ha, including

about 266 ha being on sand > 0.5 m depth. That is an offset ratio for Warkworth Sands Woodland of about 1: 6 (40

ha being cleared for the coal mine approval in 2003 to 266 ha in Green Offsets).

Areas Green Offset Strategy 2002 areas (ha) Sand > 0.5 m depth (ha) based on Lockwood (2007) HMA1 477 45 (west) + 27.5 (east, west of Wallaby Scrub Road) HMA2 81 75 HMA3 329 140 NDA1 644 minor NDA2 122 6 Total 1653 266 ha

The 2003 approval reduced the threat to Warkworth Sands Woodland from grazing. In 2003, all of the green offset

area was being cattle grazed and subject to weed invasion. It is likely that the 2003 Warkworth Mine approval

would remove the grazing and associated clearing threats, hence increasing the chance of natural regeneration on

266 ha of aeolian dune sand.

In addition, each of the other mines also had offset strategies with separate offset areas.

In conclusion, the 2003 approval with the clearing of 40 ha and the green offset strategy on 266 ha of aeolian sand

(including the removal of grazing and associated clearing threats, and resultant increased chances of natural

regeneration) was unlikely to result in the extinction of the Warkworth Sands Woodland.

Mr Bell’s Opinion: Listing on the Threatened Species Conservation Act 1995 indicates that there is a risk of

extinction for WSW if current threats do not cease to operate (Paragraph 12 of Final Determination). Extinction is a

possibility through the action of a single stochastic event or a sequence of events such as the interplay between

severe drought, severe weed invasion and disease (eg phytophthora). Extant areas of WSW spread over a number

of small patches, some within and adjacent to larger patches of other vegetation, suggest that edge effects and

weed invasion will be potentially more serious than if all WSW occurred within a single large stand of vegetation. If

left unchecked, severe weed invasion (eg Lantana) may lead to restructuring of the vegetation through changes to

soil properties.

2. What are the risks of extinction to the WSW community if the clearing proposed for current approval (i.e. if Warkworth Extension is approved) occurs (not taking account of proposed regeneration)?

Dr Robertson’s opinion: If the current proposal is approved I believe that again the risks of extinction are minimal

despite the small size of the community. This is because the main remnants will be conserved in perpetuity, UNE

research will guide and monitor the restoration ecology work, there will be funding for the Recovery Plan provided

by the proponent and because the areas of WSG will be regenerated. It is also salient to consider that the

Northern BA is separated from the mine and from the Southern BA and so is an independent site. That means that

if there are bushfires or other disturbances to the Southern BA, the Northern BA is more likely to remain intact.

There would be 361.3 ha of WSW left post regeneration, most of which would be in permanent conservation. This

is not greatly dissimilar to the scenario 1b in ecological terms.


8 September 2012 10

This is a similar area to what was approved in 2003 based on the data available at that time, i.e. 364.4 ha (i.e.

scenario 1 version a). Even if the 2011 data was known at the time of the 2003 extinction, which would result in the

clearing to 429.2 ha, the areas are not greatly dissimilar in ecological terms.

Dr Clement’s Opinion: Again my assessment of loss and conservation gain is based on areas of Warkworth

Sands (aeolian sands near Warkworth), not on areas supporting canopy trees on aeolian sands. The data from the

four long transects illustrate the natural regeneration of the aeolian sands with the removal of grazing pressure.

The 2012 approval is for loss of about 113 ha of aeolian sands on the Disturbance Area and conservation offset of

about 607 ha on the Biodiversity Areas. The larger total area (than the 2003 approval) of aeolian sands on the

Biodiversity Areas is due to:

the acquisition of the 104 ha grazing property, Springwood. Dr Hazelton and I estimated at least 50 ha of

aeolian sands from the aerial photographs and onsite survey. In about 2000, permission for aeolian sand

extraction was granted on the Springwood Property to the north of HMA2 (O’Brien 1999 cited in the

nomination of the Warkworth Sands Woodland by the Hunter Region Rare Plant Committee and HLA-

Envirosciences 2000 in Technical Note No 1 by Hunter Region Botanic Gardens). The absence of sand

extraction and absence of continued grazing represented an approximately 50 ha environmental gain of

native vegetation regenerating on aeolian sands.

the acquisition of the 342 ha grazing property, Archerfield. From the sand depth data in Lockwood (2007)

and Dr Hazelton’s findings, there is about 290 ha of aeolian sands with >0.5 m depth.

Both these properties were mapped by Story et al. (1963) to include Warkworth Land System.

In conclusion, the 2012 approval results in the clearing of about 113 ha of aeolian dune sand on the Disturbance

Area and conservation offset of 607 ha of aeolian sand in the Biodiversity Areas. The 2012 approval also includes

the removal of the listed threats - sand extraction on the Springwood property, grazing and associated clearing.

The removal of threats increases the chances of survival of the community. The 2012 approval is considered to be

better conservation outcome and that it is even more unlikely to result in the extinction of the Warkworth Sands

Woodland than the 2003 approval.

Mr Bell’s Opinion: As noted above, there is always the risk of extinction to WSW through a single stochastic

event or a series of stress-inducing events. This risk will exist regardless of the scenario imposed.

3. Do the risks of extinction between the scenarios in question 1 differ from those in question 2?

Dr Robertson’s opinion: The risks of extinction will vary between scenario 1 and scenario 2. One major

difference between scenario 1 (version b) and 2 is that although scenario 2 would result in less vegetation

remaining excluding regeneration (364.4 ha versus 429 ha), the vegetation remaining would have better long term

conservation prospects as more of the proponent’s land would be permanently protected and because of the

funding of the Recovery Plan (which is not available under scenario 1). The regeneration is supported by the

University research program and the additional funding of $500,000 for research into genetics/climate change, and

the proponent’s commitment to fund the recovery plan are not available under scenario 1.

Dr Clement’s Opinion: The main difference between the 2003 and 2012 scenarios are that the consent authority

in 2003 was assessin an application with a Green Offset of lower conservation value and smaller area of aeolian

sand than that in the 2012 Biodiversity Areas.

Since the 2003 Approval, there has been:


8 September 2012 11

1. Recent acquisition of additional areas of aeolian sands in the Biodiversity Areas - two former grazing

properties acquired for Biodiversity Offsetting with about 50 ha of aeolian sand on Springwood and about 290 ha of

aeolian sand on Archerfield.

2. Degradation from grazing and associated clearing in the Green Offset areas in 2003 is expected to be

greater than that observed on the same land in 2012 due to times of grazing cessation.

3. Regeneration following rain – less regeneration in 2003 than in 2012

At the time of the 2003 approval - there was likely to be more intact Warkworth Sands Woodland on the area to be

mined than there was in the Green Offset proposed to be conserved. The vegetation of the aeolian sands prior to

the 2003 approval was being actively grazed in the Green Offset areas. On the 2003 approved mining area,

grazing probably ceased in about 1984 on the 40 ha of aeolian sand in the north half of the land, east of Wallaby

Scrub Road. Furthermore, rainfall prior to the 2003 approval was below average in 2002.

Prior to the May/June 2012 surveys in Clements SoE, there was above average rainfall in September and

December 2011, and February and March 2012. Rainfall recorded has also been above average in 2007 to 2011

with more than 20% above average rainfall recorded in 2007, 2010 and 2011. Therefore good regeneration was

expected and regeneration was observed.

4. Research by UNE, Colin Bower and other consultants - The data recorded by UNE is related to research

program. It includes:

records of time of flowering, fruiting and seed set of a large number of the species on the aeolian sands. Using

these recorded fruiting times and rainfall records from Jerrys Plains, as a Restoration Ecologist, I can now predict

the chance of the germination and establishment of the species on the aeolian sands useful for the planning of

assisted regeneration. For example, Banksia integrifolia which does not hold its seeds, has ripe fruit in summer.

Over the past 10 years germination of Banksia integrifolia was likely to have occurred during the summers of

2003/2004, 2004/2005, 2007/2008, 2008/2009, 2009/2010, 2010/2011 and 2011/2012.

soil bank testing, it was found that the native species germinating were groundlayer species, as well as exotic

species. There was largely an absence of shrub and tree species in the soil seed bank tested by UNE.

Colin Bower’s observation of dense colonisation by Allocasuarina luehmannii on overgrazed areas of aeolian sands

is insightful. .As a Restoration Ecologist, I can now use this information in determining the expected patterns of the

regenerating vegetation.

In conclusion, given the higher rainfall prior to the 2012 approval and exclusion from grazing, the vegetation on

most of the Southern Biodiversity Area (ungrazed for about 10 years) more closely resembles the area east of

Wallaby Scrub Road (probably ungrazed for approximately the last 30 years) than the vegetation observed for the

2003 Approval.

Similarly, the data from the long transects and onsite observation show the natural regeneration occurring on the

aeolian sands of the two recently acquired properties, Springwood and Archerfield.

Mr Bell’s Opinion: In Scenario 2, it is assumed that successful regeneration of WSW will occur across all of the

NBA. Under this scenario, a total of 294ha of WSW is to be established; however I do not believe that all of the

NBA is capable of supporting WSW as it is defined in the Final Determination. As a consequence, the risks to

extinction will be higher under Scenario 2 as there is a lower amount of WSW EEC present and recoverable within

the NBA, yet 103.5 ha (22% of extant) will be cleared within the Development Area. The additional restoration

research proposed under Scenario 2, together with funding for the Recovery Plan, will decrease the risk to

extinction for WSW, but under an assumption that restoration will be successful.

4. To what extent does the regeneration proposed reduce the risk of extinction if it is successful?


8 September 2012 12

Dr Robertson’s opinion: Regeneration would help secure the vegetation in several ways. First, in both scenarios

1 (version b) and 2 it would increase the total area of WSW. This would be from 429.2 ha to 500.4 ha in scenario 1

(version b) and from 361.3 ha to 595.4 ha in scenario 2. When expressed in percentage terms, scenario 2 would

result in an increase in the pre-European extent of WSW to 19.6% compared to an increase of 14.2% under

scenario 1. Additional vegetation area does help provide security against such factors as edge effects, etc. It also

buffers against major stochastic events such as severe repeated bushfires.

I believe though, that regeneration of surrounding areas of other native forest vegetation, and rehabilitation will also

play an important role in securing this vegetation. The existing forest and woodland remaining after mining will be

augmented by both regeneration and rehabilitation. Large blocks of forest and woodland will be formed including

WSW and various Box/Ironbark/Spotted Gum forest. The resultant large area will also be more stable and better

buffered against weeds and stochastic events.

Dr Clement’s Opinion: None of the aeolian sand dunes support intact native vegetation (Bower 2004, Gross

2007, Clements SoE 2012). Gross describes the vegetation on the aeolian sands as varying in quality from nearly

denuded to recovering to intact.

All of the vegetation on the aeolian sands is naturally regenerating post disturbance, with the exception on and

downslope of the former orchards, and in and near the former quarries. Both the orchard and quarry impacted

areas appear to have disturbance landform and removed/transported sands which support dense exotic shrub

growth, i.e., Lantana camara. The dense growth of the exotic grass Melenis repens is restricted to and adjoining

high light environments.

Unlike classic mine rehabilitation where re-construction of landform and soil profile takes place, followed by

establishment of soil fauna and flora, the Biodiversity Areas are regenerating naturally. The works required will

involve only works which assist natural regeneration on the aeolian sand dunes, with the exceptions of former

orchards and sand extracted areas where some minor landform reconstruction may be needed.

The 2012 approval provides an opportunity to reduce the existing threats to the native vegetation regenerating on

about 606 ha of aeolian sands. This natural regeneration can be assisted by applying the findings of research data

in conjunction with a range of restoration ecology techniques.

Mr Bell’s Opinion: Successful restoration of WSW will reduce the risk of extinction, as it will result in larger areas

of WSW. However, it has not been demonstrated that successful restoration of WSW has occurred in heavily

grazed lands with high levels of exotic grass species. The scientific literature cautions against the assumption that

restoring fully functioning ecosystems is possible, and argues that such an assumption should not be used as an

offset to clearing established ecosystems (see Section 3.5 in my Expert Report).

5. What is the likelihood of the regeneration being successful if it is adequately funded to support best practice regeneration techniques to be used?

Dr Robertson’s Opinion: In my experience, projects such as the WSW research program are highly likely to be

successful. This is because there is already compelling evidence that heavily cleared sand landscapes

regenerated unaided to form the WSW at the subject site today. The Ecological Assessment (2010b) provided

compelling evidence from historic aerial photographs to show that the sands vegetation has regenerated from

essentially grassland to woodland since the 1960s as grazing was removed. Umwelt (2011) acknowledges this

and agrees with the finding.

If best practice methods are used to aid the regeneration, then this would greatly increase the probability of

successful regeneration.


8 September 2012 13

Dr Clement’s Opinion: As a restoration ecologist, the best long-term conservation outcomes are achieved by

mimicking natural systems. Planting trees with ongoing watering such as on slag heaps at Broken Hill have proved

to be both expensive and pointless with plants dying and dust blowing as soon as the watering ceases.

Restoration in challenging climatic environments (such as the low rainfall at Warkworth) requires the application of

research, a developed understanding of the environmental setting and of the ecology of the species on the sand

system.

It is not a matter of funding. It is related to applying ecological restoration to mimic the natural processes.

From analysis of the long transect data, there are patterns in the vegetation that are related to:

time since disturbance;

extent of disturbance;

early colonising species including algal slimes, lichens;

secondary species requiring active soil fungal relationships;

distribution of retained seeding trees (such as Allocasuarina luehmannii, Angophora floribunda, Banksia

integrifolia, Callitris spp. and Eucalyptus spp.) on the disturbed and partially cleared grazing land;

sand deposition history (regular crest and swales, natural dune re-workings such as on HMA2).

In ecological restoration like in assisting any natural processes, there are no certainties of achieving fixed

outcomes, but there is an increased probability of achieving enhanced species diversity, higher projected foliage

cover especially of the native canopy, subcanopy and shrub species. On the naturally regenerating aeolian sands

near Warkworth, Diuris sulphurea (Tiger Orchid) was recorded on the former grazing paddocks of Archerfield

indicating that the sands are relatively intact. Dense lichen cover was also observed on disturbed sands on HMA3.

In order to increase long-term conservation of the offset lands, the existing fragmentation and degradation

associated with the former orchards, sand extraction and tracks, needs to be addressed. This would include

reconstruction of the sand dune landform, and the use of windrows of native biomass, which can encourage

trapping of wind blown sand. Seed sowing of early colonising species can also be incorporated (see works on

exposed dunes, photographs in Appendix D of Preferred Project Report 2011).

For the Northern and Southern Biodiversity Area, I am confident that native vegetation assemblages will continue

to naturally regenerate if the existing threats continue to be minimised on the aeolian sands. The rate of the natural

regeneration is dependent on rainfall, and availability of ripe seed at the time of the rain events.

There are no reference sites of Warkworth Sands Woodland with intact native vegetation that can be used to set

the “goal” for the restoration success.

Mr Bell’s Opinion: Regeneration is more likely to be successful if it is fully funded and supports best practice

restoration techniques. Research into the best restoration techniques will aid regeneration, but it does not ensure

success in itself.

6. What are the risks to WSW that is not in an offset?

Dr Robertson’s Opinion: WSW that is not in an offset will not receive the protection of offset lands. There will be

no permanent protection against weeds, feral animals, land clearing, etc. There would also be no firm guarantee

that such vegetation wouldn’t be degraded by further human activities that may lawfully occur in some areas such

as grazing.


8 September 2012 14

Dr Clement’s Opinion: The native vegetation would continue to be threatened by existing grazing and associated

clearing land use, sand extraction and possibly the extension of coal mines. The 2012 approval provides an

opportunity to conserve and enhance the naturally regenerating native vegetation on Warkworth Sands (aeolian

sand dunes near Warkworth).

Mr Bell’s Opinion: : As I understand it, there are greater risks to WSW if they are not included in offset lands, as

management of grazing, weeds, feral animals and land clearing will not be monitored.

7. Are there benefits to the survival of the WSW to having an extra 200 hectares of WSW managed in an offset area?

Dr Robertson’s Opinion: As stated in 4 and 6 above, increased areas of WSW in protected offset areas will

greatly increase the prospects for long term survival of this plant community. The additional 200 ha would be

extremely valuable as a contribution to ensure survival of this community.

Dr Clement’s Opinion: If the 200 ha was a large unfragmented patch of native vegetation, with a low edge to area

ratio which would minimise risk of edge effects, then this would be an ideal situation for any conservation area.

However, if the additional 200 ha consisted of a 5 m wide linear strip of canopy trees with some native understorey

component, then this would be less ideal but may have some corridor benefit for the movement of bats, birds and

genetic flow of native plant species – primarily trees and shrubs. A narrow linear patch adjoining a road may be of

little value to long-term conservation, especially in terms of native diversity, due to high edge effects and high risk

of exotic species invasion.

An additional 200 ha of Warkworth Sand Woodland surrounded by and/or adjoining native vegetation would

increase the chance of long-term survival of all of the native vegetation strata and of the native vegetation of the

central Hunter. Given the restricted distribution of the aeolian sands in the central Hunter, an additional 200 ha may

be critical for the long-term survival of the native vegetation of these sand dunes.

Mr Bell’s Opinion: There are benefits to the long-term survival of WSW if 200 ha are managed in an offset area.

However, I do not believe that all of the NBA comprises WSW EEC.

8. Does the increase in WSW of 95.4 hectares from regeneration of WSG decrease the risk of extinction if it is successful?

Dr Robertson’s Opinion: As above. The WSG to be regenerated will add a significant area of WSW to the

overall area of WSW in existence. In total, if this were regenerated there would be 595.4 ha in existence and most

would be within lands proposed for conservation in perpetuity.

Dr Clement’s Opinion: All of the vegetation in the investigation area on Warkworth Sands is regenerating. None

of the vegetation is intact Warkworth Sands Woodland. The natural regeneration in these areas is being assisted

by reduction in existing threats from grazing and associated clearing, as well as above average rainfall in the past

few years, especially over the summer months. As vegetation on the aeolian sands naturally regenerates, the

diversity of native species increases. Gradually, the abundance of the dominant exotic pasture grass Melinis

repens will decrease as it is be shaded out by the growth of native shrubs and trees.

Assisting natural regeneration is crucial for the long-term survival of the ecosystem.

Mr Bell’s Opinion: If 95.4 ha of WSG can be successfully returned to WSW EEC, then a decrease in the risk to

extinction of WSW will occur. It is logical that more areas of WSW EEC existing will ensure better protection for this

vegetation community.


8 September 2012 15

9. Assuming that the regeneration of WSW proposed in both Scenarios 1 (currently approved) and 2 (proposed to be approved) are successful are the prospects of survival higher under Scenario 2?

Dr Robertson’s Opinion: I believe that the prospects for survival would be higher in Scenario 2 for the following

reasons:

There would be a bigger area of WSW (95.4 ha larger than scenario 1 (version b));

There would be additional contributions to research on climate change tolerance of the re-

established community and the preparation of a recovery plan;

Scenario 2 would increase WSW by 19.6% of the pre-European area compared with 14.2% at the

time of the 2003 approval; and

There would be a greater proportion of the vegetation proposed in long term conservation areas.

Dr Clement’s Opinion: The 2011 approval appears to be better than the 2003 approval for long-term conservation

and for natural regeneration of Warkworth Sands Woodland, especially due to the larger size of Biodiversity Areas

on aeolian sands. The areas of aeolian sands (>0.5 m sand depth from Lockwood 2007 and Dr Hazelton advice)

being conserved under the two scenarios are given on page 7.4 of my SoE, namely:

266 ha in the Green Offset Strategy associated with the 2003 approval; and

about 606 ha (assuming at least ½ of the 104 ha on Springwood) associated with the 2012 approval.

Given that natural regenerating is occurring on the offset areas of both the 2003 and 2012 approvals (due to

removal of the pre-existing threats from the grazing and associated clearing), the main differences between the

approvals is a more than doubling the size of Offset areas on aeolian sands.

Mr Bell’s Opinion: If regeneration of WSW is successful, there is a greater likelihood of survival under Scenario 2

due to commitments to restoration ecology research, management and funding towards a recovery plan.


8 September 2012 16

5 KEY MATTERS of DISAGREEMENT Statistics (Mr Bell, Dr Donald, Dr Clements, Dr Robertson)

There remains disagreement between the parties following joint discussions.

Mr Bell

There is a place for numerical statistics in ecology, but it cannot be used solely in the determination of the presence

or otherwise of an EEC: it can be used in a supportive role to assist other methods such as soil analysis,

comparison of species lists etc. The comparison of species from various datasets to some listed Hunter EECs that I

have done (and shown in Appendix A) supports the notion that some areas are more representative of other EECs

besides WSW, and lend support to my statistical analysis.

The data analyses that I have undertaken for this case are based on limited data collection opportunities in the

field, as is typical of Court-related investigations. While I appreciate the effort that Dr Donald has put into the

additional statistical analysis of my and others data, I have not had the time to further investigate her techniques or

results. The techniques that I used are the same as those that frame our existing understanding of vegetation

patterns in the Hunter Valley and Sydney Basin, and as used in numerous publications on vegetation patterns in

the scientific literature (at least 60 Australian papers that I am aware of). These techniques are widely practiced by

NSW Government departments (over 20 major studies in the last 15 years) and are the basis behind most of the

Hunter EECs, and indeed over 40 EECs in New South Wales. None of the classifications performed by other

workers have subjected their data to the rigours suggested by Dr Donald. If my methods are wrong or

inappropriate, then I would suggest that the existing framework of our delineation and understanding of vegetation

communities for the entire Sydney Basin bioregion (and elsewhere), including a great many existing EECS, will

need to be urgently reviewed.

Dr Donald

Statisticians performing unsupervised learning always attempt to test their conclusions. This may be by fitting other

models, by choosing other settings, by bootstrapping or some other one of the myriad ways in which unsupervised

learning can be undertaken. To do so is not particularly rigourous, just good science. Model-based analyses are

generally preferred by statisticians for determining groupings as they account for errors in the data

At the very least a k-means clustering on the MDS (ordination) data should have been undertaken by Mr Bell. Had

this been done for Analysis A and Analysis B, it would have become clear to Mr Bell that the data in these analyses

support no more than one group. That is, the ordination of the Bell data supplied by Mr Bell does not support the

presence of three clusters, named “Core WSW”, “Marginal WSW” and “Non WSW”. See Appendix B.

Dr Donald Appendix B also presents a reductio-ad-absurdam in Appendix B of the position that use of upwards

agglomerative hierarchical grouping together with the SIMPROF test leads without further testing to definition of

vegetation communities. A failure by Mr Bell to understand the algorithm he uses (evidenced in Table 3.5 of Mr

Bell’s statement of evidence) , has led to a misplaced faith in it, a failure to test its conclusions and to faulty

science.


8 September 2012 17

Dr Donald carried out further analyses (see Appendix B) and found that the model-based analyses of the data

show that

1. The 20 quadrat data (Stephen Bell’s Analysis B) and 4 quadrat data (Stephen Bell’s

Analysis A) cannot be broken into more than one group; and

2. The combined data (248 quadrats from six observers) consist of probably five vegetation

groupings.

As a result of these various analyses, I would recommend multidimensional scaling followed by fitting mixtures of

normal models (Method 4 in Appendix B).

Dr Donald and Dr Clements are concerned that professional statisticians are not involved in the pattern analyses of

vegetation in the Hunter Valley and Sydney Basin and in the scientific literature. Applying techniques without

scientific rigour to form the basis behind most of the Hunter EECs, and indeed over 40 EECs in New South Wales,

as claimed by Mr Bell, is a major concern.

Dr Clements

Cluster outcomes do not define an ecological community. Preston and Adam (2004) point out that:

The definition of an ecological community adopted by the legislation requires that “an assemblage of

species” and a “particular area” be specified.

Ordination should be used in conjunction with cluster analysis. Ordination is frequently more suitable than cluster

analysis as it does not impose a rigidly hierarchical structural pattern on the data when there is none (Crisp and

Weston 1993).


8 September 2012 18

The ordination of the Bell data supplied by Mr Bell does not support the presence of three clusters, named “Core

WSW”, “Marginal WSW” and “Non WSW”.

References

Crisp M.D. and Weston P.H. (1993) Geographic and orthogenetic variation in morphology of Australian Waratahs

(Telopea: Proteaceae). Syst Biol 42(1): 49-76.

Preston, B. J. and Adam, P. (2004). Describing and listing threatened ecological communities under the

Threatened Species Conservation Act 1995 (NSW): Part 1 - the assemblage of species and the particular area.

Environmental and Planning Law Journal 21, 250-263.

Warkworth Sands Woodland (Mr Bell, Dr Clements, Dr Robertson)

Nature and extent of WSW on the Northern Biodiversity Area (NBA). The parties do not agree about the

extent of WSW mapped on the NBA. Mr Bell holds the view that the area mapped in the EA is in error and that too

large an area is mapped.

Mr Bells Opinion: Although it has been established by Dr Hazelton that Aeolian sand occurs across the NBA, I do

not believe that all of the vegetation growing on such substrate can be considered as the WSW EEC. This is

because the combination of species present within some remnant stands of vegetation supports a greater number

and proportion of species in common with the Central Hunter Grey Box – Ironbark Woodland EEC and/ or the

Central Hunter Ironbark - Spotted Gum – Grey Box Forest EEC than the WSW EEC. I am appending some graphs

to this report (Appendix A) illustrating the comparative numbers of species from various datasets with these three

EECs.

The Final Determinations for the Central Hunter Grey Box – Ironbark Woodland EEC and the Central Hunter

Ironbark - Spotted Gum – Grey Box Forest EEC both state in Paragraph 1 that each EEC ‘generally occurs on

Permian sediments in the Hunter Valley”. I understand this statement to mean that it may also occur on sediments

derived from other geological ages. Neither final determination states that either EEC cannot occur on Aeolian

sands.

Dr Clements Opinion: The data obtained from four long transects orientated at right angles to the contours on the

aeolian sands, show the relationship between depth of sand, landform and species composition. Warkworth Sands

Woodland is a listed endangered ecological community that occurs on aeolian sand deposits in the Hunter Valley.

The description in the Final Determination about distribution of Allocasuarina luehmannii and Eucalyptus crebra

being related to thin sandy veneer overlies the Permian substrate are not supported by the recorded data in the

transects. The occurrence of both of these species appears to be related to the presence of seed bearing trees

located nearby. Therefore, germination occurs during time of sufficient rain. Areas with dominance of saplings of

Eucalyptus crebra may be transitional on the naturally regenerating native vegetation on these aeolian sands. The

nitrogen-fixing Allocasuarina luehmannii on the aeolian dunes of the central Hunter appears to be a colonising

species. Longer-term studies are required to determine whether these species decrease over time.

Both species Allocasuarina luehmannii and Eucalyptus crebra are listed as characteristic species in the Final

Determination. Hence, area with these species on aeolian sands I have included in the area of regenerating

Warkworth Sands Woodland.

Dr Robertson’s Opinion: I remain of the view that the areas of WSW and WS grassland in the impact areas and

SBA that were summarised in the PPR were appropriate for impact assessment. However, this year, with the

benefit of much more data and with recent higher rainfall I have revised my estimates of WSW and WS grassland

that remain in several areas including the NBA and adjacent land, and within the HMA3. This reinforces my views


8 September 2012 19

about the appropriatness of the WSW offsets provided and about my answers to the original scenarios (Chapter 5

of my SOE).

Whether or not Ironbark-dominated vegetation is part of the WSW. Mr Bell believes that it does not constitute

a valid part of the community. Dr Clements and Dr Robertson believe that it is a valid part of the Final

Determination.

Mr Bells Opinion: As stated under the proceeding question, vegetation dominated by ironbark eucalypts share

more species with the Central Hunter Grey Box – Ironbark Woodland EEC and/ or the Central Hunter Ironbark -

Spotted Gum – Grey Box Forest EEC than with the WSW EEC. The occurrence of these areas on Aeolian sands

does not exclude the potential for these EECs to be present, as there is no mention of such in the Final

Determinations for these EECs. See appended graphs in Appendix A.

Dr Clements Opinion: See above. If native vegetation is on aeolian sand in cental Hunter near Warkworth, then it

is part of WSW.

Dr Robertson’s Opinion: I believe that the Ironbarks and Red Gums are an integral part of the WSW woodland

and that any forest or woodland on aeolian sand in the Warkworth locality is a form of WSW.

The nature and extent of regeneration that is possible for WSW in the NBA. Mr Bell believes that the extent of

WSW regeneration planned for the NBA may not occur. He further believes that the Ironbark regeneration that has

occurred on the NBA is not an example of successful regeneration of WSW. Dr Clements and Dr Robertson

believe that the area can be regenerated as evidenced by historic aerial photography and strong evidence of

regeneration at other sites such as the SBA. Dr Clements also believes that there is evidence of successful

regeneration of sands vegetation in other parts of the state.

Mr Bells Opinion: The Australian and international literature that I have reviewed in relation to restoration ecology

raises a number of concerns about the likelihood of success of restoring an EEC (see Section 3.5 in my Expert

Report). The caution raised in these papers suggests to me that we should be very confident of success prior to

clearing the remaining WSW.

Closely linked to this is defining exactly what should be regenerated – which interpretation of WSW EEC is

acceptable: that including ironbark regeneration or that excluding it. I do not consider that ironbark regeneration

accurately represents the WSW EEC. Ironbark regeneration occurring in the NBA is not an example of successful

regeneration of WSW EEC, but is more representative of regenerating Central Hunter Grey Box – Ironbark

Woodland EEC.

Dr Clements Opinion: My professional opinion is clear. If the vegetation is on aeolian sands in the central Hunter

near Warkworth, then it is of conservation value. Almost all of the area with aeolian sands surveyed in May/June

2012, supported some native species with natural regeneration occurring. The native vegetation will continue to

naturally regenerate provided that the threats are reduced. This has been well illustrated in the four transects.

Given the restricted distribution of aeolian sands and regenerating of native species, the offset areas are of high

conservation value. Assisted regeneration in these areas (fencing, removal of cattle, possibly direct seeding) is

appropriate.

Dr Robertson’s Opinion: I am in agreement with Dr Clements that the woodland can be regenerated and that it is

regenerating at present.


8 September 2012 20

Offsets for Other Communities (Mr Bell, Dr Robertson)

There is no disagreement between the parties following joint discussions. Offset properties remote from the

Warkworth locality were selected with a view to compensation for the loss of habitat for threatened fauna and other

non-WSW EECs. Both parties agree that these offsets do not compensate for the loss of WSW EEC, and were not

intended to.

Wildlife Corridors (Mr Bell, Dr Robertson)

There is no disagreement between the parties following joint discussions. The existing corridor will be progressively

cleared and rehabilitated as mining advances, such that corridor functions for fauna are likely to be maintained.

The joint authors of this document have signed to verify that this document accurately reflects their contributing

views.

8th September 2012

David Robertson Stephen Bell

Anne Marie Clements Margaret Donald


8 September 2012 21

Appendix A Comparisons of plant species against EEC Final Determinations (contributed by Mr Bell)

The following graphs have been constructed largely following criticisms of my Expert Report where I did not adequately compare species lists present within the mine lands against those included on the Final Determination for WSW EEC. I have now done this, using my own data together with that collected and reported on by Dr Clements and Dr Robertson. In addition to WSW EEC, I have also compared species against other potential EECs that occur in the area, showing that WSW EEC is not the best or only option for determining the identity of some areas of vegetation.

Graph 1: Comparison of species included in Final Determinations for WSW, CHGBIW and

CHISGGBF EECs, using species from Cluster Group 11 of Dr Clements, and shown in Paragraph 55 of her Chapter 5. This graph shows that, using this method, Cluster Group 11 is best affiliated with WSW. I have no disagreement here.


8 September 2012 22


CHISGGBF EECs, using species from Cluster Group 12 of Dr Clements, and shown in Paragraph 6 of her Chapter 6. Again, this graph shows that, using this method, Cluster Group 12 is best affiliated with WSW, with which I agree.


8 September 2012 23


CHISGGBF EECs, using species from Cluster Group 14 of Dr Clements, and shown in Paragraph 8 of her Chapter 6. This graph shows that, using this method and the tabulated list of 16 species, Cluster Group 14 may again best be affiliated with WSW (BUT see following Graph 4 where all species recorded are compared).


8 September 2012 24


CHISGGBF EECs, using the full complement of species from Cluster Group 14 of Dr Clements (not restricted to the 16 tabulated by Dr Clements), and referred to in Paragraph 8 of her Chapter 6. This graph shows that, using this method and the full complement of species from cluster group 14, Cluster Group 14 is equally or more likely to represent CHISGGBF or CHGBIW EECs.


8 September 2012 25


CHISGGBF EECs, using species from Cluster Group 15 of Dr Clements, and shown in Paragraph 12 of her Chapter 6. This graph shows that, using this method, Cluster Group 15 is equally or more likely to represent CHGBIW EECs than WSW EEC.


8 September 2012 26

Graph 6: Dr Clements data and species in common with three Central Hunter EEC Final

Determination (FD) lists. This graph shows that there is actually a greater proportion of CHGBIW EEC species present within Dr Clements’ dataset than there is for WSW EEC, and perhaps cluster analysis using characteristic CHGBIW EEC species could have been done in addition to WSW EEC species.


8 September 2012 27

Graph 7: Data from Dr Robertson’s compiled species lists compared against four Central

Hunter EEC Final Determination (FD) lists. This graph shows that, based on the numbers of species present, vegetation Dr Robertson has defined as HLRF actually supports more species from the CHISGGBF EEC than it does from HLRF EEC; what he has defined as CHGBIW could equally also be WSW EEC; but that both his WSW EEC and WSG groupings do support marginally more species from the WSW EEC than the other alternatives. If we look at the % comparisons of species common to the final determinations (lower chart), we see that both of his WSG and CHGBIW groups support a greater proportion of CHGBIW EEC species; his HLRF group still supports a greater proportion of CHISGGBF EEC species; and his WSW group supports only marginally more WSW EEC species than CHISGGBF EEC.


8 September 2012 28

Graph 8: Data from Mr Bell’s 20 sampled quadrats compared against three Central

Hunter EEC Final Determination (FD) lists. This graph shows that, based on the numbers of species present, quadrats Q01, Q02 and Q03 (all from the NBA) support more species in common with CHGBIW EEC and CHISGGBF EEC than they do with WSW EEC. Q11 (from Callitris forest in the SBA) is also more similar to both of these EECs than it is to WSW EEC. Nearly all other quadrats support more WSW EEC species than CHGBIW EEC or CHISGGBF EEC, in keeping with my understanding of WSW EEC.


8 September 2012 29

Graph 9: Data from Mr Bell’s 20 sampled quadrats compared against three Central

Hunter EEC Final Determination (FD) lists, expressed as a % of total FD lists. As above this graph shows that, based on the percentage of species present, quadrats Q01, Q02 and Q03 (all from the NBA) support more species in common with CHGBIW EEC and CHISGGBF EEC than they do with WSW EEC. Q11 (from Callitris forest in the SBA) is also more similar to both of these EECs than it is to WSW EEC. All other quadrats support more WSW EEC species than CHGBIW EEC or CHISGGBF EEC, with the exception of Q15 and W05.


8 September 2012 30

Graph 10: Breakdown of habitat types for all 48 plant species listed in Paragraph 2 of the

WSW EEC Final Determination (FD). This graph shows that 45 of the 48 species (94%) occur in dry habitat types or are generalists not restricted to damp environments. Information sourced from Royal Botanic Gardens PlantNet web site, augmented by 20 years of experience with these species in the region. Two of the three species included in the ‘damp’ category (Melaleuca decora & Eucalyptus glaucina) were included in the list of less common species in the original nomination; only Melaleuca thymifolia occurs in damper habitats.


8 September 2012 31

Appendix B: Further Statistical Analyses by Dr Donald

AppendixB‐SupplementaryEvidence10224Donald Page1

AppendixB‐Jointexperts’statementProceedingsNo.10224of2012:AdditionalinformationfromDrMargaretDonald

1. I have analysed Stephen Bell’s floristic data for Warkworth used in his analyses A and B (Bell,

2012) .

2. I have analysed the floristic data from 248 quadrats collected by six sets of observers;

Stephen Bell, Cumberland Ecology, Anne Clements & Associates, Colin Bower, UNE, and

Andrews Neil, which are described more fully in (Clements, 2012).

My analyses lead me to the following conclusions:

1. The statistical analyses of Stephen Bell are inadequate and his conclusions with respect to

vegetation groupings cannot be sustained by statistical analyses undertaken with any sort of

rigour.

a. In particular, the use of an upward agglomerative hierarchical method in

combination with the test SIMPROF of PRIMER (Clarke & Gorley, 2006) for finding

vegetation patterns leads to unreplicable (by any other grouping method)

fragmentation of the data to the point where single quadrats are said to form

‘groups’ of differing vegetation. I cannot see how a single quadrat can be said to

form a replicable, describable vegetation pattern. This pattern of fragmentation into

many sets of groups, many of which are single quadrats, was repeated in the many

(>20) analyses I undertook on the two data sets (20 quadrat set and 248 quadrat

set). I discuss this further below.

b. When exploring data to find potential groups, statisticians will typically use many

methods to see how sensitive their conclusions might be to method choice. See for

example, (He et al., 2003) and in a vegetation context, (Crisp & Weston, 1993).

2. Model‐based analyses of the data show that

a. The 20 quadrat data (Stephen Bell’s Analysis B) and the 4 quadrat data (Stephen

Bell’s Analysis A, by implication) cannot be broken into more than one group. See

the analyses below; and

b. The combined data (248 quadrats from six observers) consist of probably five

vegetation groupings.

3. Analyses of the combined data set using the methods of PRIMER and a significance level of

0.001 for the SIMPROF test indicate the presence of considerable observer differences,

despite all observations being reduced to presence/absence data. These differences may be

ascribable to differences between botanists observing the same vegetation (‘overlooking’,

misidentification). For these types of errors in the context of vegetation surveys, see e.g.

(Archaux et al., 2009; Kercher, Frieswyk, & Zedler, 2003; Nilsson & Nilsson, 1985; Scott &

Hallam, 2003; Vittoz & Guisan, 2007). They may be attributable to differences in year or

season, or they may represent real vegetation pattern differences. However, despite these

numerous possibilities I believe that a large proportion of the groupings found (using the

SIMPROF test and hierarchical clustering) are attributable to observer differences. This

means that


a. vegetation clusterings based on the findings of a single botanist using this method

may be unreliable and unreplicable by other observers; and

b. where such groupings have not been subjected to rigorous statistical testing,

findings are almost certainly unreliable and unreplicable by other observers.

I discuss additional problems with observer differences in the context of vegetation surveys

in my earlier affidavit (Donald, 2012).

My analyses have led me to the view that use of an upwards agglomerative hierarchical

method, together with the SIMPROF test does not lead to statistically sustainable

descriptions of vegetation communities.

It is unclear how many dimensions should be used in a multidimensional scaling (ordination)

to reduce the dimensions of the data to permit multivariate normal mixtures to be fitted

(model‐based fitting). However, vegetation communities/patterns (groupings) should be

found by model‐based analyses and not by such a deterministic inflexible method as that

used by Stephen.

2.2p17“The techniques I have employed….. promote this technique for floristic descriptions of ecological

communities”

Stephen employs one technique at one setting. This is not sound statistics. This problem affects his

various conclusions based on his analyses. In particular 2.3.2 p 20, 3.5 p32, 3.2.2 p43, 3.2.2 pp44‐46.

3.2.2,p51,Table3.5“Statistically significant groups of Core WSW and Marginal WSW support the same groups evident in

Analysis B, reinforcing that result in a regional context.

This remark demonstrates Stephen’s lack of understanding of the tool he is using. Regardless of how

many new quadrats one introduces into the analysis, the pairwise dissimilarities between sites

already discussed remain unchanged under the addition of more quadrats. The algorithm proceeds

by pairing sites which are closest together and then by pairing groups of sites based on their average

similarity. Dissimilarities between pairs of sites are unchanged, hence the same sets of pairs and

then groups will pair up. This process is only interrupted and changed if one of the new sites is

closer to one of the old sites or old groupings than a previous group or site. Hence, as the

dendrograms of Figures 3.12, 3.13, 3.14, and 3.16 show, much of each previous dendrogram is

preserved. What dictates the joining up process, is the choice of species to be used (here all natives),

the choice of whether or not to analyse the data in terms of cover or to base the dissimilarity

measure on presence/absence alone, and finally on the choice of dissimilarity measure. Once these

decisions have been made only one dendrogram is possible, and SIMPROF compares just one pair of

groups.

3.2.2p52,Table3.6Table 3.6 tabulates Stephen Bell’s groupings of his data into three groups (Core WSW, Marginal

WSW and non WSW) as being supported by four different analyses. As discussed above these form

essentially the one analysis. In particular, the hierarchies tested for, and shown, are not

independent from analysis to analysis.


The job of a statistician is to distinguish signal from noise. Part of that job is also to look for sources of noise/error in measurement. If we consider a plant taxonomist/ecologist/botanist in the field, trying to list all the plant species in a 0.04 ha plot and trying to assess the cover for each species, we see that differences between observers can arise from:

1. misidentification of a species; 2. failing to notice a species (‘overlooking’ a species); 3. seeing insufficient of the plant to allow identification to species level and identifying only to

genus (e.g., Conyza sp.); 4. one observer using a scale of 1‐5 for cover, while another uses a scale of 1‐6 (for example); 5. when using the same 5 point scale, one observer might assess cover at 3, another at 4; Thus,

there are a series of potential inter‐observer errors between taxonomists assessing cover on the same plot.

This means that if we wish to use cluster analysis to identify vegetation assemblages with a view to identifying unique communities which may be observed by others and which are not ephemeral and do not change by season or by year, we need to be conscious of these sources of error, and to consider deeply which of the species seen should be used in the cluster analysis and how. In particular, how should we use plants identified only to genus?

1. Plant species identified to genus only should not be used, since no future observer can replicate this without coarsening the analysis to genus in this instance.

2. Including species observed in very few quadrats, typically adds to error. See the discussion below where the analysis shows considerable inter‐observer error between very competent plant taxonomists.

Unsupervised learning (cluster analysis) in statistics has many methods, all of which differ in both approach and in the results that they deliver. A statistician undertaking unsupervised learning will typically use several methods and techniques to see how robust his conclusions are to method choice. See e.g., (Crisp & Weston, 1993) and in another related context (small n, large p) (He, et al., 2003). This use of many methods to substantiate one’s conclusions has not been undertaken by Stephen Bell and this is part of the reason his statistics are flawed.

DataThe data discussed includes two data sets: Stephen Bell’s 20 quadrat Warkworth data set of his

analysis B; and the 248 quadrat dataset from six observers. Both datasets include observations

covering differing years and seasons.

DatatransformationsI used Stephen Bell’s 20 quadrat data as given, since I wished to replicate his findings. The analysis

reported here uses 118 native species (or plant species identified to genus). The Braun‐Blanquet

cover measures are not transformed to presence/absence, and as in Stephen’s analysis, I use the

Bray‐Curtis dissimilarity measure.

I also used the standard dimension reduction technique of multidimensional scaling (ordination),

which transforms the paired dissimilarities between sites into a lower dimensional space, and in this

case, a lower dimensional space of three dimensions.

The combined dataset (248 quadrats) was collected sometimes with a cover measure and

sometimes as presence/absence data. For a meaningful analysis, this implies that all data must be


reduced to presence/absence data prior to analysis. The Jaccard measure of dissimilarity is the

measure usually employed by ecologists when looking for groupings with presence/absence data.

Hence, this was the dissimilarity measure used for analysis of these data, and this was used in the

multidimensional scaling.

Mr Tony Rodd suggested that there was likely to be some disagreement between botanists as to

when, precisely, they encountered Eucalyptus blakelyii, E. tereticornis or an intergrade of the two.

Hence, observations of this species were analysed as being instances of E. tereticornis. All instances

of taxa being recorded to genus level only, were not used.

MethodsI used various publicly available R software packages for the various analyses and analysed the

datasets using

1. The upward agglomerative hierarchical method used by Stephen, with average linking

between clusters, together with the SIMPROF test of PRIMER (Clarke & Gorley, 2006). This is

implemented in R in the ‘clustsig’ package of (Whitaker & Christman, 2010).

2. In concert with upward agglomerative hierarchical clustering, I used the bootstrap

techniques of (Suzuki, 2012) and implemented in his R‐package ‘pvclust’. Bootstrapping is a

statistical method where one forms repeated random samples of the data, which have the

same sample size as the original data, and then perform the same analysis on the 1000 or so

bootstrapped resamples. It is hoped that this perturbation of the data will result in more

robust conclusions.

3. The Dirichlet stick‐breaking method for finding the number of groupings and the groups.

See e.g., (B.Dunson, 2010; D. Dahl, 2006; D. B. Dahl, 2004; Duan, Guindani, & Gelfand, 2007;

Dunson, 2010; Dunson, Xue, & Carin, 2008). This was programmed in R modifying code

supplied by David Dunson.

4. Fitting various k‐means clustering algorithms with differing assumptions about the nature of

each group’s multivariate variance. Here, one fits several normal distributions to the data

and chooses how many groups there are on the basis of some statistical criterion (in this

case, the BIC, or Bayesian Information Criterion). The methods are well described by many

writers, including (Hastie, Tibshirani, & Friedman, 2001). The R package used was ‘mclust’

(Fraley & Raftery, 2003; Fraley, Raftery, Murphy, & Scrucca, 2012).

5. A model‐based method using the principal components of the multidimensionally scaled

data and then transforming the axes to display more effectively the clusters found. The R

packages used were ‘cluster’ (Maechler, Rousseeuw, Struyf, & Hubert, 2012) and ‘fpc’

(Hennig, 2012).

These last three methods used the three dimensions from the multi‐dimensional scaling and looked

for groupings across the three dimensional space. (These MDS spaces differ depending on the

different species used in finding the pairwise dissimilarities between sites.)

In the pursuit of reproducible clustering using the methods advocated and used by Stephen, I used

many different species sets in analysing the 248 quadrat dataset. I had hoped that by

1. using species recorded on at least 30 quadrats; or

2. using species found by all observers (29 native species common to all observers), or


3. confining myself to shrubs and trees,

I might find a more satisfactory set of groupings and a consistent set of groupings. However, this

was not to be. The analyses I report for the 248 quadrat dataset are those using 147 native species

(found on at least 10 sites), and 29 species (those native species common to all observers).

Results

StephenBell’s20quadratdatasetMethod 1. Using Method 1, for 20 quadrats we find 3 groups at SIMPROF<0.001 and the Jaccard

dissimilarity after reducing the data to presence/absence, groups are: [Q01, Q02], [Q15, Q03, Q14]

and a further group containing the remaining sites.

Using the same analysis method, with Bray‐Curtis and SIMPROF set to <0.001 we find two groups:

[Q01, Q02] with another group consisting of the remaining sites

Method 2. Bootstrapping the upwards agglomerative model with Bray‐Curtis gave [Q01, Q02] as a

group, with the remaining sites unable to be formed into any consistent group. Bootstrapping the

same model treating the data as binary gave no groupings, i.e., the data could not be separated into

more than one group.

Method 3. Using the stick‐breaking method of fitting different numbers of normal models at the

same time (a Monte Carlo method) to the multidimensional scaled data, gave a probability of 84%

for there being just one group, and a probability of 15% for there being two groups. (A possible 6

groups were fitted but there were no instances of groupings with more than 2 components in the

10000 iterations after burn‐in).

Method 4. Fitting various k‐means models to the three dimensional (MDS) space found from the

Bray‐Curtis dissimilarity, and testing groupings consisting of up to 10 groups, gave an unequivocal

decision for one group, using the BIC criterion.

Method 5.This method, too, returned the unequivocal answer that the data consist of one group

only.

The248QuadratdataMethod 1. With the significance level for SIMPROF set at 0.001, and the paired dissimilarities

between sites based on 147 species, the algorithm returned 32 groups with many groups consisting

of a single quadrat. Changing the set of species used to the 29 common species gave 17 groups,

with many of them again being single quadrats only.

Method 2. The bootstrap applied to the upwards agglomerative fit returned just one group with a

couple of sites left outside, when used with the dissimilarities based on 147 native species, but 20

groups when used with dissimilarities based on the 29 common species.

Method 3. The Dirichlet stick‐breaking method found an approximate probability of 80% for 3

groups, followed by a probability of 14% for 4 groups, when applied to scaling from the 147 species.

The story was quite different for the 29 species MDS space, with the probability for two groups

being highest at approximately 91%, and with three groups being chosen for less than 10% of the

MCMC iterations.


Method 4. The methods of (Fraley, et al., 2012), which fit sets of k‐means, decide via the Bayesian

Information Criterion on a model with 5 groups, each with a different group mean, and differing

variances in the three dimensions for the 147 species space, and for three groups when using the

three dimensional space based on the 29 species dissimilarities.

Method 5. Clustering using principal components on the MDS space gives a decision for three

groups. This was true for the 147 species 3‐D space and for the 29 species 3‐D space.

DiscussionThe analyses for Stephen’s data indicate no statistical support for anything other than one grouping.

Model‐based analyses (analyses 3‐5) are generally preferred by statisticians for determining

groupings.

The series of analyses for the 248 sites (quadrats) indicates the robustness and appropriateness of

using all possible species when using model‐based approaches to clustering (analyses 3‐5). The

result from Method 5 shows that considerable information may have been lost in dropping to the

shared species.

The bootstrapping does not manage to overcome the faults and problems with upwards

agglomerative hierarchical grouping. Results for these analyses are inconsistent.

The upwards agglomerative hierarchical modelling for the 248 site data was extremely interesting.

The paired dissimilarities based on 147 species gave the following groups:

Table 1 Sizes of and numbers of groups

Number of members in group 1 2 3 4 5 6 7 8 9 12 14 15 16 18 21 22

Number of groups 4 5 3 3 1 1 2 1 2 2 1 2 1 2 1 1

What is remarkable about this (and all other tables from the other 248 site analyses using Method 1

(used by Stephen) is both the number of groups, and how small so many of them are. What sort of

vegetation community is it, that has but one instance in a set of 248 sites? Fragmentation into

ridiculously small groups was not improved by moving to the 29 species common to all observers.

However, the problem of grouping by observer largely disappeared.

It is also difficult to believe that there are 32 vegetation communities within the small area being

surveyed.

I believe the SIMPROF test is picking up on differences in the patterning of presence/absence

amongst the 147 attributes, between the sets of groups being tested. But one quadrat is not an

instance of a vegetation community. This extreme fragmentation into groups, which cannot be

supported by any model‐based analysis, indicates that this technique is not appropriate for

attempting to define vegetation communities.

The major problem of the upwards agglomerative method is that the method is entirely

deterministic, with the groupings determined prior to all analysis, by the paired distances. When

SIMPROF decides whether or not a significant cut can be made, the two groups it chooses between

have been determined from the outset.


Table 2 Clusters found by the technique of Method 1 where group membership is at least 10 (32 group cluster solution from 147 species dissimilarities).

ClusterID ACA AndrewsNeil Bell Bower Cumberland UNE

4 14 1

7 16

10 1 21

11 5 2 7

19 1 11

22 1 3 7 1

23 18

29 20 1

30 4 1 9

32 5 10 2

Notice that for a large number of these groups, group membership is particular to almost just one

set of observers (clusters with IDs 4, 7, 10, 19, 23, 29). Just 4 clusters of the 32 found share group

membership with other observers (either equally or across more than 2 observers).

In other words, it seems more likely that this analysis has grouped most sets of sites by observer and

not by vegetation communities.

A graph showing species richness at each site (Figure 1) shows that UNE quadrats have a far greater species richness than those of other observers. This may be accounted for by two mechanisms: the UNE quadrats were revisited several times within 2010. The particular area surveyed is likely to differ from visit to visit, thereby adding potentially to species richness. The other mechanism may be that the same species has been identified differently on subsequent visits by different observers, giving rise to the apparent presence of two or more differing species. However, for most of the other observer teams, we can only conclude that the addition of so many species not seen/identified or misidentified by others is sufficient to induce what appear to be differences in vegetation groupings. This strong grouping by observers indicates differences between observers rather than differences in vegetation patterns. Of the over 600 different species identifications made by the six sets of observers at these Warkworth sites, only 37 species and 29 native species were common to all observers. It appears likely one set of observers consistently name a species as A while another set calls it B or simply fails to notice it. Clearly, misidentification and ‘overlooking’ can add up to differences between sites that are significant under the SIMPROF test. (Differences in scoring cover are irrelevant, since with much of the data being reported as presence/absence, all data must be reduced to presence/absence and the Jaccard dissimilarity measure used.) The kinds of groupings from upward agglomerative clustering lead me to believe that the methods used by Stephen Bell are useful for exploring data but should not be used for defining vegetation communities.


Figure 1 Species richness at each of the 248 sites. Sites are sorted by company name. Thus, ACA sites are at the left, UNE sites at the right, with sites from other observers in between.


Table 3 List of the 29 native species common to all sets of observers

Acacia amblygona Glycine clandestina

Acacia implexa Glycine tabacina

Allocasuarina luehmannii Hibbertia linearis

Angophora floribunda Hovea linearis

Aristida vagans Imperata cylindrica

Banksia integrifolia Lomandra multiflora

Breynia oblongifolia Melaleuca thymifolia

Calotis cuneifolia Microlaena stipoides

Cheilanthes sieberi Persoonia linearis

Chrysocephalum apiculatum Pimelea linifolia

Cymbopogon refractus Pteridium esculentum

Cynodon dactylon Solanum prinophyllum

Einadia hastata Spartothamnella juncea

Eucalyptus crebra Wahlenbergia gracilis

Eucalyptus tereticornis

Figure 2 Output from Method 5 for the 248 sites 147 species analysis. Here the 3 dimensions of the MDS have been reduced to 2 principal components which explain 66.67% of the variation. Within this two dimensional space, 3 groupings have been found. These are graphed in the left panel against the principal components. The right panel shows the same data on transformed axes chosen to show clearly the groupings found.


Figure 3 Method 2 for 248 site 147 species analysis. Bootstrapping the hierarchical agglomerative model. In this case, the algorithm returns one group alone with some sites unable to be consistently allocated to a group. (I see this as evidence that bootstrapping on a deterministic algorithm fails to give satisfactory results. Model‐based analyses return 3 or more groups.)


Figure 4: Method 4 for 248 site 147 species analysis. Here choosing from a possible 9 component mixture, the choice made is model VII with 5 components (groups), because this model has the highest BIC value of those considered. The different models EII, VVI etc, differ in the ways in which their variances are modelled.


Figure 5. Method 4 for 248 site 147 species analysis: Cluster groupings from the chosen five component VII model.


Figure 6 Method 2: Stephen Bell’s 20 quadrat data. Bootstrapping on the upwards agglomerative hierarchical method gives one stable group [Q01, Q02} at the 95% level. The Bray‐Curtis dissimilarity measure was not available in this package. The Canberra dissimilarity measure was used.


Figure 7 Method 4. Stephen Bell’s 20 quadrat data: Here multivariate mixtures of means models have been fitted to the 3 dimensions from the multidimensional scaling of 118 species. The model chosen has the maximum BIC value: Model EII with one component only, i.e., one group only.


Figure 8 The 20 quadrat data. Multi‐dimensional scaling (ordination) used in analyses 3‐5. Quadrats Q01 & Q02 (found to form a group in Method 2) are shown as red crosses in black circles. The cluster found by Method 2 cannot be sustained. The data are too variable to permit more than one group.


Having undertaken these analyses, some comments need to be made about the differences in the

numbers of groups found by the various methods.

Method 1 has already been extensively commented on and its deficiencies outlined.

Method 2, which again has the distances fixed prior to grouping, suffers from the same rigidity and

difficulties. For example, using Stephen’s data, if I change the distance measure to one based on

presence/absence I get no consistent groups at all. And for the 248 site 147 species distances I find

one group with this method. When I use paired distances based on the 29 species, this method

gives 20 groups. Such varied answers which depend on such trivial differences in the settings under

which the algorithm is run, mean that despite its promise, bootstrapping upwards agglomerative

clusterings is less useful than I had hoped.

Method 3 gave results that differed slightly from those of Method 4, despite using precisely the

same data. This method is a Bayesian method and may be sensitive to the priors used for the

number of components. Insufficient time was spent looking at this model’s possible sensitivity to

the priors, nor was the chain tested to verify that the MCMC simulation had reached stationarity.

Further work needs to be done to determine reasons why the results differ from those of Method 4.

Method 5 gave answers identical to those of Method 4 for the 20 quadrat data and for the scaling of

the 248 site data based on the 29 common species. However, in choosing the two most important

principal components from the 147 species MDS, important information in the third dimension

would seem to have been lost, leading to the conclusion of 3 groups rather than the 5 found by

Method 4.

Model‐based analyses of the data show that

1. The 20 quadrat data (Stephen Bell’s Analysis B) and 4 quadrat data (Stephen Bell’s

Analysis A) cannot be broken into more than one group; and

2. The combined data (248 quadrats from six observers) consist of probably five vegetation

groupings.

As a result of these various analyses, I would recommend multidimensional scaling followed by

fitting mixtures of normal models (Method 4). This method is robust to the noise of inter‐observer

error, and if one feels that 3 dimensions for the MDS are too few, this can be replaced by

multidimensional scaling in 4 or 5 dimensions.


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8 September 2012 32

Appendix C: Additional Map of WSW referred to by Dr Clements

Figure 1Study Area of the Warkworth Extension Area

on the Hunter Region Botanic Gardens Technical Note 1Warkworth Sands Woodland Map

Northern Biodiversity Area (Archerfield)

Southern Biodiversity Areas

0 1.83kilometres

3.66

Wallaby Scrub Road

Proposed Disturbance Area

HMA 3HMA 3HMA 3HMA 3HMA 3HMA 3HMA 3HMA 3HMA 3

Remainder of SBARemainder of SBARemainder of SBARemainder of SBARemainder of SBARemainder of SBARemainder of SBARemainder of SBARemainder of SBA

SpringwoodSpringwoodSpringwoodSpringwoodSpringwoodSpringwoodSpringwoodSpringwoodSpringwood

Legend

HMA 2HMA 2HMA 2HMA 2HMA 2HMA 2HMA 2HMA 2HMA 2

Buffer AreaBuffer AreaBuffer AreaBuffer AreaBuffer AreaBuffer AreaBuffer AreaBuffer AreaBuffer Area

NDA 2NDA 2NDA 2NDA 2NDA 2NDA 2NDA 2NDA 2NDA 2

Mapped Warkworth Sands Woodland