Vegetation Survey Design for Conservation: Gradsect Sampling of ...

Biological Conservation 50 (1989) 13-32

Vegetation Survey Design for Conservation: Gradsect Sampling of Forests in North-eastern New South Wales M. P. Austin & P. C. Heyligers CSIRO, Division of Wildlife and Ecology, PO Box 84,

Lyneham, ACT 2602, Australia (Received 21 February 1989; revised version received 28 February 1989; accepted 1 March 1989)

A B S TRA C T A method is described which obtains a representative sample of thefloristic variation in a forested area of c. 20 000 km 2. Using the climatic, topographic and lithological characteristics of the study area, a series of gradsects (transects incorporating significant environmental gradients) were selected to represent the environmental variability present in the area. Rules for afield sampling strategy are outlined which ensure that the widest possible range of environments are sampled with reasonable constraints on travelling time and costs. Gradsects in combination with a set of explicit sampling rules are shown to be an effective technique for obtaining a representative data set.

INTRODUCTION In countries like Australia, little is known of the detailed distribution of plants or animals and the communities they form. Decisions on conservation areas are often made on the basis of inadequate information, or require special surveys. Biological surveys are costly and the size of areas can be very large (i.e. 224000km 2 for the Nullarbor plain in Western Australia (McKenzie et al., this issue)). Burbidge (in press) provides details o f costs and further examples. With t h e advent of Environmental Impact Statements and challenges in the courts regarding potential conservation areas, biological information must be definitive, explicit and consistent. Explicit methods are required which provide a truly representative sample 13 Biol. Conserv. 0006-3207/89/$03"50 © 1989 ElsevierSciencePublishers Ltd, England. Printed in Great Britain

14

M. P. Austin, P. C. Heyligers

and make surveys more cost-effective. We present such a method recently developed in Australia, which uses detailed environmental stratification to ensure a representative sample.

SURVEY DESIGN Surveys of the vegetation of large areas are expensive and agencies responsible for conservation have few resources. Areas of conservation significance may be small and unlikely to be detected by arbitrary traverse surveys or random sampling. Even in regions with a well-known flora, surveys intended to provide data for identifying a representative reserve network require a sampling scheme which ensures that the range of vegetation communities is sampled. To avoid bias, particularly against species-poor communities, some form of explicit sampling is required. Regional surveys of biological diversity differ from the usual statistical sampling techniques in that their purpose usually requires information about distributional patterns rather than unbiased estimates of the mean abundance of individual species. Vegetation survey design usually is not given detailed consideration in ecological texts. Often only random and simple stratified sampling are mentioned (e.g. Green, 1979; Greig-Smith, 1983) and then only for studies of small areas. With numerous environmental factors influencing composition, regional vegetation is usually highly heterogeneous and requires several levels of stratification to ensure a representative sample. Accounts of regional surveys with explicit stratification are rare. Noy-Meir (1971) used a systematic survey based on seven transects, which proportionally represented ten land types, to sample 240 000 km 2 of semi-arid vegetation in southern Australia. Orloci & Stanek (1979) used a form of nested environmental stratification to sample 900km 2 of the Alaskan Highway using previously mapped ecoregions and terrain types. Austin & Basinski (1978) present a similar stratification of an area of 6000kin 2, and discuss these types of methods. Bunce and co-workers have used various forms of multivariate classification to define land classes in order to stratify areas in the British Isles for vegetation and other types of surveys (Bunce & Shaw, 1973; Bunce & Smith, 1978; Bunce & Last, 1981). Recent Australian examples include Margules & Nicholls (1987) on the mallee patches of the Eyre Peninsula and in this volume McKenzie et al. on the biological diversity of the Nullarbor Region. Where there is no existing information on vegetation, surveys should sample various combinations of environmental variables as a means of obtaining a representative sample of vegetation; Austin & Margules (1986)

Survey design .for forests

15

provide further discussion. Though stratification is essential for surveys, the practical problems of travel costs and accessibility must be incorporated into any cost-effective survey.

GRADSECT SAMPLING Gradsect sampling, that is gradient-directed transect sampling of vegetation, is intended to provide a description of the full range offloristic variation in a region. This approach was first proposed by A. N. Gillison to overcome problems of inadequate representative sampling and accessibility when sampling regions, while minimising survey costs. Gradsects are deliberately selected to contain the strongest environmental gradients present in an area to optimise the amount of information gained in proportion to the time and effort spent. While the notion of sampling along gradients is probably as old as ecology itself, Gillison & Brewer (1985) were the first to show statistically that gradsects capture more information about vegetation attributes than randomly placed transects of similar length. Sampling along a gradsect maximises variation from plot to plot, and accessibility can be enhanced by choosing a locality with an adequate road network to reduce travel time. Helman (1983), and Helman & Austin (unpublished data), in a study of rainforest patches on the South Coast of New South Wales, extended the approach to incorporate levels of environmental stratification, including rocktype and geographical location within the gradsect. The opportunity to sample a large region using gradsects and to evaluate gradsect efficiency arose with a request to survey forests in the North Coast region of New South Wales. The purpose was to obtain an appropriate floristic description of the forests in order to design an appropriately stratified survey of the arboreal marsupial fauna of the forest. Braithwaite et al. (this issue) provide an example of fauna survey stratified by vegetation communities. Our intention was to carry out a two-stage sampling design based on the experience of Helman's rainforest study with (1) purposive selection of gradsects; and (2) adequate environmental stratification and replication within gradsects in order to sample the full range of environmental variability. The study area

The study area comprised the catchments of the Bellinger, Nambucca, Macleay, Hastings and Manning Rivers. These originate on the Dorrigo Plateau and New England Tablelands, and flow to the Tasman Sea through


16

LOCATION ~

~

gradsects

COIFSHa/hour

,~yare, ~

,

• Armldale

~ ~ ....'~../ " ~ PortMacquarie

dL 152°E

"~,mlng RiVer

LITHOLOGYCLASSES

/

-L 153°E

J

1

Fig. l(a). The study area: location of gradsects and rock type classes (defined in Table 1).

Survey design for forests

17

ALTITUDE CLASSES

\

\"

1

)i~5 , ( i

RAINFALL CLASSES

Fig. l(b).

The study area: altitude and rainfall classes (defined in Table 1).


18

a strongly dissected, largely forested landscape. The area contains several National Parks as well as large tracts of State Forest and has been one of the most important timber-producing areas in New South Wales since European settlement. The area is encompassed by the Dorrigo and Hastings 1:250000 map sheets. For practical purposes the limits of the study area were defined by latitude and longitude (Fig. 1). Gradsect selection A regular grid with 0-01 ° (approx. 1 km 2) spacing in both longitudinal and latitudinal directions was generated for the study area. This grid comprised 18 826 points. Previously Austin (1978) and Austin et al. (1984) have shown that rocktype, rainfall and temperature have a strong influence on the distribution of plant species. Thus, the following data were recorded for each grid point: (a) geological map unit, taken from the Dorrigo-Coffs Harbour and Hastings 1:250000 geological map sheets; (b) altitude (metres above sea level), interpolated using algorithms developed by Hutchinson (1984, 1986) from digital map data provided by the then Division of National Mapping; (c) annual means of rainfall, and of daily maximum, mean and minimum temperature, estimated according to the method of Hutchinson (1984, 1986) and Hutchinson & Bischof (1983). Maps of classes of the variables were plotted. The class intervals were chosen to produce maps suitable for visual assessment of the gradients in the variables. Temperature and altitude correlate strongly in the region; altitude was used instead of temperature because it could be easily determined during fieldwork. Taking account of access routes and with special attention to less common rocktypes, several possible positions for gradsects were established. A graph of altitude/rainfall combinations for the gridpoints falling within the gradsects was compared with the graph for the total study area. Using this graph three belt transects, 15 km wide, running from the coast to the tablelands were chosen. A distinct under-representation of low and mid altitude/high rainfall combinations was detected (Fig. 2) and remedied by adding a short gradsect in the north-eastern corner of the study area (Fig. la). There is considerable overlap between the environmental envelopes of the three major gradsects. Hence, their number could have been reduced. However, their environmental overlap provides a degree of geographical replication of similar environments. Once satisfied that the gradsects represented the study area, we developed

Survey designfor forests

19

0 0 0 0 0 0 O0 O0 O0 000 000 O0 00.. 000000 0000.0. 00000 000. .. 0 0000000 000 . . . . . . 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 . 0 0 . . . . . . 0 0 0 0 . * * 0 0 • * OOO . . . . . . . O• O* ***00•*• OOO• OOOOOOO• OO * 0 0 " 0 * * * • • * OOO• O. • OO O• * * * 0 0 0 . 00• O• • • **~*

2300

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0000• •

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O.

0

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0 . .

. . . .

•

*

0"**** ******

15oo

c-

1100

• m*mm*:~****,~*

700 I

I

I

0

4O0

800

I

1200

I

1600

Altitude (metres) Fig. 2. Comparison of the altitude-rainfall combinations found in the gradsects with those of the study area as a whole. For each combination representation in one of the three major gradsects (~), or in the additional high-rainfall gradsect (O), or unrepresented (.) is indicated.

rules for sampling within gradsects. The three major variables, lithological substrate, altitude and rainfall, were divided into classes• The 63 geological map units were combined into nine classes, based on dominant rock type. Classes for the other variables were chosen pragmatically to capture sufficient environmental detail. Class intervals for altitude were set at 250 m, starting from sea level; those for rainfall were set at 200 mm from 700 mm year- 1, with one class allocated to values exceeding 2100 mm year- 1 (Table 1). Each of the environmental cells was given a three-digit label representing rock type, altitude and rainfall• Each grid point was then allocated to one of these three-dimensional cells. Theoretically, this subdivision produced 9 × 7 × 8 = 504 environmental cells, but actually less than half of these cells (215) occurred in the study area, mainly due to the localised distribution of rocktypes (Fig. 1). Gradsects fully representative of the environmental variation would include observations in every cell. Forty-three of the realised cells were not represented in the gradsects. Nearly half of those (18) contained only one or two gridpoints, i.e.


20

TABLE 1

Classes of Environmental Factors

Rocktype

Class 1

2 3 4 5 6 7 8

9

Description Quaternary alluvial, paludal and estuarine deposits; mainly sands, silts and mud Quaternary quartzose beach and dune sand Basalts, tufts and minor dolerite Adamellites and other 'basic' granites 'Acid" granites Serpentinite, ultrabasic intrusives Limestones Sedimentary rocks, predominantly coarse-grained; e.g. conglomerates, greywackes, sandstones Sedimentary rocks, predominantly finegrained, e.g. slates, phyllites, mudstones, argillites

Altitude

Average annual rainfall

Class

Range

Class

Range

1 2

0 250m 251-500m

1 2

700-900mm 901 1 100ram

3

501 750m

3

1 101-1 300mm

4 5 6 7

751-1000m 1 001-1 250m 1 251 1 500m > 1 500m

4 5 6 7 8

1 301-1500mm 1 501-1 700mm ! 701 1 900mm 1 901 2 100mm > 2 100mm

were extremely rare in the study area, confirming the representative nature of the gradsects selected.

Sampling design within gradsect In theory, each grid point in each environmental cell should have an equal, or at least known, probability of being sampled to ensure a lack of bias. In practice this is impossible: access to many grid points would require days of walking and/or use of helicopters. Bias introduced by selecting accessible grid points is unavoidable. The following explicit sampling procedure was therefore adopted to ensure consistency of sampling and to provide the opportunity for others to determine the degree of bias. Bias due to accessibility was made explicit by arbitrarily restricting sampling to within 0.5 km of tracks or fire trails accessible by vehicles. This was necessary to limit salary costs and the time taken. Geographical replication was enhanced by dividing the long gradsects into segments. In each segment the gridpoint numbers per environmental cell varied considerably but only in a few cases exceeded 100, while numbers greater than 50 were virtually limited to cells containing the most common rocktype, viz. class 9--predominantly fine-grained sedimentary rocks. Table 2 gives an example of the gridpoint frequencies for one segment.


21

TABLE 2 Size and Sample Frequency of the Environmental Cells of the Middle Segment of the Southern Gradsect

Rocktype

Altitude classes

Rainfall classes 2

1

3

6

3

.

2 3 4

--.

5

.

2

.

3

--

4

4

.

.

.

.

.

.

.

.

5

8

9

---

- -

- -

2 (1)

15 (1)

2 6 (1)

---

16 (1) 1 (0)

37 (1) 34 (1)

-.

1

.

--

38 (1) 22(1)

-.

--

--

- -

--

1 (0) 12 (1) 1(0)

.

--

11 (1)

30(1)

2 3 4

---

55 (2) 86 (2) 1 (0)

17 (l) 75 (2) 26 (1)

5

--

-

.

1(o)

1

5

--45 (1) 2(x)

1 (0)

2 3 4

4(x) 3 (1) .

.

--

6

.

--.

.

5

i(0)

2(x)

28 (1) 4 (x)

--

1 (0) 2 (1)

--

The plain numbers refer to the total number of gridpoints in a cell; the numbers in parentheses to the number of samples (a sample consisting of a suite of up to 5 plots), while an x indicates that sites belonging to this cell were not easily accessible and hence that no sample was obtained.

The following rules were used to determine the sampling of the environmental cells within each segment. (1) (2)

(3) (4)

If an environmental cell contained only one grid point in the segment, no sample was taken. If a cell was represented by 2, 3 or 4 points, a sample should be obtained if the site could be reached without undue demand on travelling time, e.g. e n r o u t e to other sample sites. If a cell contained 5 points or more, up to 50, one sample should be taken. If a cell contained between 50 and 100 points, two samples, and if it contained more than 100 points, 3 samples, should be procured.

22


An application of this sampling strategy is shown in Table 2 where the number in parentheses gives the number of samples obtained. At the local scale aspect and topographic position have a strong influence on vegetation composition (Austin, 1978; Austin et al., 1984), so a further stratification was imposed. Each sample was to consist of five 50 × 20m plots chosen from different topographic positions, namely a crest, an exposed (north-westerly) slope, a sheltered (southerly) slope, a slope of intermediate aspect, and a drainage line, i.e. a lower slope, a valley flat or a gully, as the case may be. These were not necessarily in a toposequence, but were located within I km 2 such that travel time was minimised. Where more than one sample was required per environmental cell, these samples were where possible geographically separate. All species of canopy trees, either present as mature trees or as saplings, were listed and the three most c o m m o n species ranked according to their abundance. The survey required definition of forest variation based on the canopy species, so only general data were collected on the understorey or ground layer, saving additional time and expense. Practical difficulties Problems arose with software development and a failure to identify the relevant environmental variables in one part of the study area. Standard computer packages are available for estimating the climatic surfaces but numerous programs were necessary to adapt these surfaces to the needs of the survey. This required the efforts of a programmer (R. Bateup) for some months. Existing climate and geology maps can be used to design such a survey without the need for software, as was done by Helman (1983). A deficiency in the sampling strategy occurred with the quaternary sediments, rocktype classes 1 and 2. Extensive clearing and the negligible relief of these coastal sediments further complicated the problem. An important environmental variable in these low relief areas appeared to be the level of the water table, a variable not considered in our pre-survey stratification. A d hoc substitution of aspect by a suite of plots based on drainage status during the survey was not successful. An additional sampling programme was devised to remedy the shortcomings in the lowland areas. The total number of plots sampled in rocktype class 1 was brought into line with the numbers sampled for other classes of similar size, classes 3, 4 and 8. The extra plots were allocated to the relevant environmental cells such that the plot totals for each cell were proportional to the number of gridpoints contained in the cell and the range of existing drainage conditions within each cell was sampled. Accessibility presented problems in the deeply dissected country along the

23


m a r g i n o f the t a b l e l a n d s . F o r t u n a t e l y , suitable d a t a collected b y officers o f the N e w S o u t h W a l e s N a t i o n a l P a r k s a n d Wildlife Service using h e l i c o p t e r s were a v a i l a b l e to us a n d were used to a u g m e n t o u r o w n samples.

RESULTS AND DISCUSSION

How representative was the sample? A f t e r i n c o r p o r a t i n g the a d d i t i o n a l d a t a the g r a d s e c t a p p r o a c h was e v a l u a t e d . T h e results s h o w n o o b v i o u s s h o r t c o m i n g s a n d c o n t a i n m u c h n e w i n f o r m a t i o n , p a r t i c u l a r l y o n the little s t u d i e d s c l e r o p h y l l f o r e s t rainforest transitional communities. A n explicit bias w a s i n c o r p o r a t e d to e n s u r e t h a t the s a m p l e c o n t a i n e d p r o p o r t i o n a l l y m o r e p l o t s f r o m the less c o m m o n r o c k t y p e s . T h i s w a s d o n e w h e n selecting the g r a d s e c t s a n d a g a i n w h e n selecting the n u m b e r o f suites o f p l o t s to be s a m p l e d in a n e n v i r o n m e n t a l cell. In T a b l e 3 the less f r e q u e n t r o c k t y p e s h a v e a higher p e r c e n t a g e f r e q u e n c y in the g r a d s e c t s a n d a g a i n in

TABLE 3 Sampling Results for the Rocktype Classes Roektype

Numbers and relative .[?equencies of grid interseetion points In stud)' area

1 2 3 4 5 6 7 8 9

Samples a obtained for each class

In gradseets

Number

%

Number

%

Number

%

1 392 147 1 090 712 539 114 85 3482 11 265

7.39 0.78 5.79 3-78 2.86 0-61 0.45 18-50 59-84

286 29 423 422 148 37 32 911 3 602

4.86 0.49 7.18 7.16 2.51 0-63 0'54 15.47 61 16

14b 3 23 22 9 2 4 38 124

5-86 1"26 9"62 9'20 3.77 0-86 1'67 15.91 51.89

a A sample consists of a suite of at most 5 plots to represent the different topographical aspects at a site. b Due to difficulties in sampling as explained in the text, this is not the actual number of samples for this class, but has been derived from the number of plots divided by 4, i.e. the average number of plots per sample for the area as a whole, in order not to distort the figures for the relative frequencies.


24

TABLE

Sample Distribution

4

for Rocktype

C l a s s 3: B a s a l t s , e t c . O v e r

the Altitude-Rainfall

Rainfall class

Classes

Altitude class 1

2

a 1

3

--

b

--

--

--

--

b

3

c

--

--

d

--

--

a

--

--

b

c d 4

5

6

7

8

4

0

1

0

1

--

47

101

48

--

--

13

14

2

--

--

1

1

1

--

3

5

1

4

125

94

--

0

9

40

--

--

0

0

1

0

0

5

--

a

138

--

2

3

18

47

--

b

76

--

2

1

5

27

--

c

3

--

0

0

0

1

d

15

--

0

0

0

5

a

115

20

3

4

26

36

b

75

19

3

0

0

14

2

c

4

1

1

0

0

1

0

d

23

-2

5

1

0

0

5

0

a

3

90

30

22

22

2

b

3

90

5

0

4

2

0

2

1

0

1

0

1

0

c

--

d

--

a

--

b

7

1

12

33

--

--

--

3

8

--

--

1

c

--

1

1

--

--

0

d

--

--

1

3

--

a

--

--

b

.

0 --

.

.

.

.

.

0

.

.

.

.

1

.

.

1

.

of grid intersection

t points

in the study

o f p o i n t s i n t h e g r a d s e c t s ; c, n u m b e r

d, n u m b e r

of plots.

number

environmental

area;

of samples;

cells.

of grid intersection

points

with rocktype

1090. Number

o f t h e s e p o i n t s in g r a d s e c t s : 4 2 3 .

Number

of samples (suites) for rocktype

Total number

0 3

.

b, n u m b e r

--, Non-existent

3

-.

d

Total

--

--

. . . .

c

a, Number

7

30

--

a 2

6

7

--

d

5

0

--

c

4

3: 23.

o f p l o t s i n t h e s e s a m p l e s : 83.

3:

TABLE

5

Sample Distribution for Rocktype Class 9: Predominantly F i n e - G r a i n e d S e d i m e n t a r y Rocks, Over the A l t i t u d e Rainfall Classes

Rainfall class

Altitude class

a l

2

3

4

5

6

7

8

b

c d a b c d a b c d a b c d a b c d a b c d a b c d

1

2

3

91 18 2 3 190 4 0 0 1427 219 7 34 2217 937 18 88 346 99 4 20 154 78 4" 23" 55 27 1 5

124 43 3 8 485 153 4 14 513 142 4 20 756 220 6 30 359 188 6 30 85 15 1 1 25 3 0 0

43 26 1 4 447 158 5 17 536 142 3 15 380 139 4 20 253 130 5 25 128 56 6 22 55 33 2 7

4

5

40 103 16 0 1 0 5 0 408 462 136 65 7 3 30 14 404 152 72 36 5 2 14 7 325 207 125 99 4 5 20 20 129 47 51 15 3" 1 16" 5 96 26 57 5 3 0 11 0 46 -43 -2 4

6

7

--

--

--

--

--42 2 0 0 41 2 0 0 7 2 0 0 4 3 0 0 3 2 0 0 --

----3 0 0 0

--1 0 0 0 ----

a

24

--

--

23

3

b

16

--

--

22

3

--

--

---

---

0 0

---

---

c d

1 4

1 3

--

a, N u m b e r o f grid intersection points in the study area; b, n u m b e r o f points in the gradsects; c, n u m b e r o f samples; d, n u m b e r o f plots. - - , Non-existent environmental cells. " Includes contributed observations with 6 plots per sample. Total n u m b e r o f grid intersection points with rocktype 9: 11 265. N u m b e r o f these points in gradsects: 3602. N u m b e r o f samples (suites) for rocktype 9: 124. Total n u m b e r o f plots in these samples: 539.

26


the samples than in the study area as a whole. Rocktype class 1--alluvial and other quaternary deposits--is the only class that may still be under-sampled. Tables 4 and 5 show the sample distribution for two rocktype classes. When comparing the number of samples taken in each environmental cell with the number of grid points which represent this cell in the gradsects, note that the latter total is for the 10 gradsect segments, but that the sample size was determined by the number of grid points per cell in each segment separately. Consider, for example, cell 913 (rocktype 9, altitude class 1 and rainfall class 3) in Table 5. The 219 points belonging to this cell are spread over seven segments, of which one has only one grid point, five have between 5 and 50 points, and the last has 57 points. Hence a total of seven (0, 5 x 1 and 1 × 2) samples for cell 913 were obtained, giving a geographical spread over 6 gradsect segments. Comparison of the two upper figures in each class in Tables 4 and 5 shows that the gradsects succeeded in capturing a great deal of the variability present in the study area, though more successfully for the more widespread rocktypes. The lower two figures, relating to number of samples taken and number of plots enumerated, demonstrate that the survey method has enabled us to sample the variability comprehensively. Figure 3 supports this conclusion. The more frequent environmental cells are sampled proportional to frequency while rare environmental combinations are sampled independently of frequency. Figure 4 shows the number of species recorded as the number of plots increased. The original survey, with 870 plots, recorded 251 species. The additional plots (39) to compensate for inadequate stratification and sampling of the coastal environments added 10 species, while additional data from C. Bale (76 plots) and the New South Wales National Parks and Wildlife Service (40 plots) contributed only a further 2 species. The distinct steps (Fig. 4) in species number at 400 and 525 plots represent the sampling of new environmental combinations, due to switching the survey to another segment or gradsect. The curve is clearly approaching an asymptote. Use of survey results

The data matrix from the survey consists of 263 species by 1025 plots. A costeffective summary is achieved using multivariate computer classification. The strategy adopted used the Bray-Curtis coefficient as the similarity measure and flexible sorting with fl = -0.25 as the classification procedure using the PATN software package (Belbin, 1988). The dendrogram is shown in Fig. 5 down to the 20-group level. The major groups (Fig. 5) provide a level of classification equivalent to the suballiances used by Beadle (1981). The dendrogram provides an explicit, detailed classification of the floristic variation in the region.


27

STUDY AREA

¢~

40 30 2C

E oO .,Q

•

E z

• •

•

•

•

Ol0O

~o • eo

~

•

n v

I

I~0 c ' IO0 Number of grid intersection points

1000

GRADSECTS 40 30 20 E

1£

• -... O0

E

•

•

•

• •

00

O0

z 1

qm

1

oo

10

•

•

100

1000

Number of grid intersection points o No samples

Fig. 3. Comparison of number of samples obtained for the various altitude-rainfall 'cells', with the size of these 'cells' as represented in the gradsects and in the study area as a whole.

The explicit survey design allows detailed evaluation of this classification. If communities of species not identified by the classification are found, comparison of their environment and location will allow recognition of inadequate sampling, and evaluation of the relative significance of the 'new' communities. A major need of conservation management is knowledge of species distribution patterns. Inevitably, this means predicting to unsampled parts of a region as explained for example by Margules & Stein in this issue. Sampling the full range of environments ensures that predictive models derived from survey data can be used for interpolation rather than

28

M. P. Austin, P. C. Heyligers 300 eeeo"

,°,'°

...("'i

o Q 200 O.

870

525

"6

°° 400

..Q

e, e

E 23

Z

......'"'/L

100

0 0

I

I

I

300

600

900

N u m b e r of plots

Fig. 4.

Number of species recorded for number of plots sampled.

extrapolation. Nicholls provides an example for one species in this survey area (Eucalyptus radiata) in a later paper in this issue. Comparison of predictive modelling of species distributions from this designed survey and from a data base compiled from previous variously designed surveys for another region (Austin et al., 1984) suggests there are considerable advantages to be gained by using a sample from a designed survey (A. O. Nicholls, pers. comm.). Characteristic Species or Community E.pilularis E.saligna/E.pilularis E.intermedia E.acmenoides E.siderophloia Lophostemon/E.saligna/rainforest spp. E.grandis/rare communities Rainforest - Subtrop./Warm temp. Ceratopetalum/Nothofagus rainforest Melaleuca quinquenervia E.viminalis E.caliginosa E.dalrympleana/E.r adiat a E.pauciflora E.laevopinea. E.teretieornis • E.punctata • E.andrewsii/Allocasuarina E.andr ewsii/E.cam er onii

-7 "7 ---'7 --I

I I 5

10

ll5

Similarity • Potential faunal value.

Fig. 5. Dendrogramof vegetation groups defined by their major speciesor communities.

Survey design.for forests

29

The 'groups' recognised in the dendrogram (Fig. 5) define a possible sampling strategy for the proposed fauna survey. To establish predictive models of the occurrence of the animals, it will be necessary to survey a stratified sample from these groups or 'suballiances' (cf. Braithwaite et al., this issue). It is already clear from knowledge of the existing relationships that one group of tree communities is likely to be of particular significance for conservation of arboreal marsupials, namely the E. laevopinea, E. tereticornis and E. punctata 'communities' (L. W. Braithwaite, pers. comm., Fig. 5).

S U M M A R Y AND CONCLUSION The survey was designed in seven stages: (1) (2) (3) (4) (5)

(6)

(7)

Statement of the important environmental variables likely to influence the vegetation. Recognition of the variables best suited to determining position and direction of gradsects. Choice of available data and technology for defining the environmental cells and implementing the gradsect selection. Environmental stratification within gradsects including segmentation to allow geographical replication of environmental cells. Determining whether a third level of sampling is needed to take account of important environmental variables operating at a different scale, e.g. aspect and water-table depth. The degree of effort (time and expense) to be allocated to sampling the rarest environmental cells and/or to increased replication within the commonest cells. The need for follow-up surveys to compensate for failure to stratify on an important environmental variable.

The approach used here has required software and other computer technology but in principle it can be applied with nothing more than maps, pencil and paper. Once the decisions described above are made, the design is explicit, can be carried out in a consistent manner and is repeatable by other workers. The design encompasses four principles: purposive selection of gradsects, adequate environmental stratification, replication of samples where practical and randomisation of samples where practical. Effective survey design with explicit sampling needs to incorporate such features even if the particulars of the approach described here are not adopted. In countries where the vegetation is well known, surveys are often not

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explicitly designed to sample the full range of variation. In countries with poorly known vegetation, such explicit surveys are essential for conservation. A recent Workshop on 'Cost-effective Survey Methods for Nature Conservation' edited by Margules & Austin (in press) was devoted to a review of biological survey methods. The costs of surveys were examined through a series of case studies, e.g. Burbidge (in press), from which it is clear that costs are often grossly underestimated. The use of ecological theory and pattern analysis methods to increase the effectiveness of surveys was also examined. As the demand for explicit information on conservation criteria increases for use in land allocation and management, the need for improved survey methods will become increasingly apparent.

A C K N O W L E D G E M ENTS Former and present technical assistants of the CSIRO Division of Wildlife and Ecology have had the onerous task to translate ideas into printouts and we are grateful for their contribution. Ms R. Bateup deserves our special thanks for her dedication, as she carried the initial brunt of the programming. Ms C. Helman and Mr P. Gilmour undertook the field survey and have been instrumental in demonstrating the workability of the field sampling programme; their contribution to the study is gratefully acknowledged. Dr C. Bale, Armidale College of TAFE, and the NSW National Parks and Wildlife Service deserve our thanks for making data available. Thanks also to G. Caughley, D. Spratt, C. R. Margules and N. McKenzie for comment on the manuscript.

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Belbin, L. (1988). PATN: Pattern Analysis Package Reference Manual. CSIRO Division of Wildlife and Ecology, Canberra, p. 320. Braithwaite, L. W., Austin, M. P., Clayton, M., Turner, J. & Nicholls, A. O. (1989). On predicting the presence of birds in Euealyptus forest types. Biol. Conserv., 50, 33-50. Bunce, R. G. H. & Shaw, M. W. (1973). A standardized procedure for ecological survey. J. Environ. Manage., 1, 239 58. Bunce, R. G. H. & Smith, R. S. (1978). An ecological survey of Cumbria. Cumbria Count)' Council and Lake District Special Planning Board Working Paper, No. 4. Bunce, R. G. H. & Last, F. T. (1981). How to characterise the habitats of Scotland. Annual Report of the Edinburgh Centre of Rural Economy, Edinburgh. Burbidge, A. A. (in press). Cost constraints on surveys for nature conservation. In Cost-effective Survey Methods for Nature Conservation, ed. C. R. Margules & M. P. Austin. CSIRO Division of Wildlife and Ecology with New South Wales National Parks and Wildlife Service, Canberra. Gillison, A. N. & Brewer, K. R. W. (1985). The use of gradient directed transects or gradsects in natural resource surveys. J. Environ. Manage., 20, 103-27. Green, R. H. (1979). Sampling Design and Statistical Methods for Environmental Biologists. John Wiley, New York. Greig-Smith, P. (1983). Quantitative Plant Ecology, 3rd edn. Blackwell Scientific Publications, London. Helman, C. (1983). Inventory analysis of southern New South Wales rainforest vegetation. MSc thesis, University of New England. Hutchinson, M. F. (1984). A summary of some surface fitting and contouring programs for noisy data. CSIRO Division of Mathematics and Statistics Consulting-Report, 84/6 CSIRO, Canberra. Hutchinson, M. F. (1986). Fitting surfaces to terrain data. In CSIRO Division o.1 Water and Land Resources Research Report 1983-1985. CSIRO, Canberra, pp. 63~,. Hutchinson, M. F. & Bischof, R. J. (1983). A new methodfor estimating the spatial distribution of mean seasonal and annual rainfall, applied to the Hunter Valley, New South Wales. Aust. Meteorol. Mag., 31, 179 84. Margules, C. R. & Austin, M. P. (in press) (eds.). Cost-effective Survey Methods for Nature Conservation. Proceedings of a CONCOM/AEC Workshop. CSIRO Division of Wildlife and Ecology with New South Wales National Parks and Wildlife Service, Canberra. Margules, C. R. & Nicholls, A. O. (1987). Assessing the conservation value of remnant habitat 'islands': Mallee patches on the western Eyre Peninsula, South Australia. In Nature Conservation: The Role of Remnants of Native Vegetation, ed. D. A. Saunders, G. W. Arnold, A. A. Burbidge & A. J. M. Hopkins. Surrey Beatty in association with CSIRO and CALM, Sydney, pp. 89-102. Margules, C. R. & Stein, J. L. (1989). Patterns in the distributions of species and the selection of nature reserves: An example from Eucalyptus forests in southeastern New South Wales. Biol. Conserv., 50, 219 38. McKenzie, N. L., Belbin, L., Margules, C. R. & Keighery, G. J. (1989). Selecting representative reserve systems in remote areas: A case study in the Nullarbor Region, Australia. BioL Conserv., 50, 239 61. Nicholls, A. O. (1989). How to make biological surveys go further with generalised linear models. Biol. Conserv., 50, 51 75.

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Noy-Meir, I. (1971). Multivariate analysis of the semi-arid vegetation in southeastern Australia: Nodal ordination by components analysis. Proc. EcoL Soc. Aust., 6, 159-93. Orloci, L., & Stanek, W. (1979). Vegetation survey of the Alaska Highway, Yukon Territory: Types and gradients. Vegetatio, 41, 1 56.