Biodiversity and Conservation10: 271–285, 2001.© 2001Kluwer Academic Publishers. Printed in the Netherlands.

Flexibility and the use of indicator taxa in theselection of sites for nature reserves

PAUL HOPKINSON1,2,∗, JUSTIN M.J. TRAVIS3, JULIANNE EVANS4,RICHARD D. GREGORY5, MARK G. TELFER2 and PAUL H. WILLIAMS6

1NERC Centre for Population Biology, Imperial College at Silwood Park, Ascot, Berkshire SL5 7PY, UK;2Biological Records Centre, NERC Centre for Ecology and Hydrology, Monks Wood, Abbots Ripton,Huntingdon, Cambridgeshire PE17 2LS, UK;3Department of Biology, Imperial College at Silwood Park,Ascot, Berkshire SL5 7PY, UK; Current address: Climate Impacts Group, Plant Ecology, Lund University,SE-223 62 LUND, Sweden;4Royal Society for the Protection of Birds, The Lodge, Sandy, BedfordshireSG19 2DL, UK;5British Trust for Ornithology, The Nunnery, Thetford, Norfolk IP24 2PU, UK; Currentaddress: Royal Society for the Protection of Birds, The Lodge, Sandy, Bedfordshire SG19 2DL, UK;6Biogeography and Conservation Laboratory, The Natural History Museum, Cromwell Road, LondonSW7 5BD, UK;∗Author for correspondence (e-mail: paul_hopkinson@mail.com)

Received 29 November 1999; accepted in revised form 2 April 2000

Abstract. ‘Minimum’ sets of complementary areas represent all species in a region a given number oftimes. In recent years, conservation assessments have centred around the evaluation of these ‘minimum’sets. Previous research shows little overlap between ‘minimum’ sets and existing nature reserves and be-tween ‘minimum’ sets for different taxonomic groups. The latter has been used as an argument to discountthe use of indicator taxa in the selection of sites for nature reserves. However, these ‘minimum’ set analyseshave only considered a single set for each taxonomic group when there are, in fact, a large number ofequally valid ‘minimum’ sets. We present new methods for evaluating all of these alternative ‘minimum’sets. We demonstrate that if all of the sets are evaluated, significantly higher levels of overlap are foundbetween ‘minimum’ sets and nature reserves, and pairs of ‘minimum’ sets for different taxonomic groups.Furthermore, significantly higher proportions of species from non-target taxonomic groups are recordedin the ‘minimum’ sets of target groups. Our results suggest that previous conservation assessments using‘minimum’ sets may have been unduly pessimistic.

Key words: biodiversity, near-minimum sets, priorities, protected areas, selection algorithm

Introduction

Biodiversity conservation potentially requires a large number of areas to be managedor protected. In practice, budgets are limited, so efficient targeting of resources isrequired. ‘Minimum’ sets of complementary areas can aid reserve planners in thesebudgeting decisions. They are derived using reserve selection algorithms and are ap-proximations of the least number of areas required to represent all species in a regiona given number of times – maximising biodiversity preservation while minimisinginvestment costs.

272

Conservation biologists have devoted considerable effort to the development (Pres-sey et al. 1996; Ando et al. 1998; Williams 1999), comparison (Csuti et al. 1997;Pressey et al. 1997) and application (Saetersdal et al. 1993; Branch et al. 1995;Lombard 1995; Lombard et al. 1995; Mugo et al. 1995; Williams et al. 1996a) ofreserve selection algorithms and the ‘minimum’ sets which they derive. ‘Minimum’sets are more accurately described as near-minimum. True minimum sets (whichcontain fewer areas than near-minimum sets) can theoretically be found using linear-programming branch and bound algorithms (Possingham et al. 1993; Underhill 1994;Willis et al. 1996), albeit at a greater cost in computer processor time (Pressey et al.1996; Csuti et al. 1997).

Recent work has advised caution in the use of indicator taxa (Gaston 1996; Pren-dergast and Eversham 1997; Williams and Gaston 1998) in the selection of sites fornature reserves, because there is little overlap between both hotspots of species rich-ness (Prendergast et al. 1993a; Williams et al. 1996b; Howard et al. 1998; Lawtonet al. 1998) and near-minimum sets for different taxonomic groups (Williams et al.1996b; Dobson et al. 1997; van Jaarsveld et al. 1998). Howard et al. (1998) exam-ined the proportion of species from non-target taxonomic groups recorded in near-minimum sets of target groups. They found relatively high proportions of non-targetspecies recorded in these near-minimum sets. This has been proposed as a bettermethod for assessing potential indicator taxa (Williams et al. 1999).

The problem with all these near-minimum set analyses is that only a single setis considered for each taxonomic group when there are, in fact, a large number ofequally efficient sets (Figure 1). At each iteration of a selection algorithm, a final,arbitrary, tie-breaking rule is invoked to separate equally valid areas which containthe same species or combination of species (Csuti et al. 1997; Pressey et al. 1997). If adifferent area was selected by the arbitrary tie-breaking rule, then the near-minimumset derived by the selection algorithm would be different. Since this rule may beinvoked at several different iterations of the selection algorithm, an array of near-minimum sets is possible, containing different combinations, but with the same totalnumber of complementary areas.

This flexibility in the choice of area added to the near-minimum set for eachselected area is often recognised (Nicholls and Margules 1993; Pressey et al. 1993;Saetersdal et al. 1993; Lombard et al. 1995; Williams et al. 1996b; Ando et al.1998) but rarely considered in analyses (Lomolino 1994; Pressey et al. 1994;Trinder-Smith et al. 1996; Williams 1999). A single path is taken through manypossible routes. One near-minimum set is arbitrarily chosen and evaluated. Here,we use new techniques to demonstrate methods that evaluate all near-minimum sets,and we compare our results with those of a single, arbitrarily chosen, near-minimumset.

273

Figure 1. Map of Great Britain showing the flexible 10 km squares in the near-minimum sets for the 37species of British dragonfly. Each near-minimum set comprises five 10 km squares. The single red 10 kmsquare is in every near-minimum set – there are no flexible squares. To complete a near-minimum set,one square of each of the other four colour groups is required. There are 7140 possible combinations of5 squares.

274

Methods

Data

To illustrate our methods, species’ presence data within the 2862 10 km× 10 kmsquares (10 km square = 100 km2) of the British National Grid were used for the fol-lowing taxonomic groups: liverworts, aquatic plants, non-marine molluscs, dragon-flies, orthopteroid insects (Orthoptera, Dermaptera and Dictyoptera), carabid beetles,butterflies and birds (breeding and wintering). The non-avian distribution data wereextracted from the Biological Records Centre database at the Institute of TerrestrialEcology, Monks Wood (Harding 1990; Harding and Sheail 1990), and the avian dis-tribution data were supplied by the British Trust for Ornithology (Lack 1986; Gibbonset al. 1993).

Selection algorithm

A progressive rarity selection algorithm [Williams (1998) based on Margules et al.(1988)] was run on the data for each taxonomic group. Rarity-based algorithms arethe most effective in determining the minimum number of sites necessary to representall species (Kershaw et al. 1994; Csuti et al. 1997). The Williams (1998) algorithmfirst selected all 10 km squares with species that were equally or more restricted thanthe representation goal – a single occurrence of each species. A simple set of ruleswere then applied iteratively until the representation goal was achieved (Table 1). The

Table 1. The set of rules applied in the reserve selection algorithm (Williams 1998). The rarest species istaken to be the one with the fewest grid-cell records.

1 Select all areas with species that have single records2 The following rules are applied repeatedly until all species are represented:

A Select areas with the greatest complementary richness in just the rarest species (ignoring less rarespecies). If there are ties then:

B Select areas among ties with the greatest complementary richness in the next-rarest species and soon. If there are persistent ties then:

C Select areas among ties with the greatest complementary richness in the next-next-rarest speciesand so on. If there are persistent ties, then:

D Select areas among ties with the greatest complementary richness in the next-next-next-rarest spe-cies and so on. If there are persistent ties, or no next- or next-next- or next-next-next-rarest species,then:

E Select areas among persistent ties with the lowest grid-cell number (first encountered). This is anarbitrary rule, used rather than random choice among ties in order to ensure repeatability in tests;other criteria, such as proximity to previously selected cells, or number of records in surroundingcells, can be added(Repeat stepsA–E until all species are represented.)

3 Determine the goal essential species for each selected area, and from these (i) identify and reject anyareas that are, in hindsight, unnecessary to represent all species, and (ii) identify fully and partly flexibleareas

4 Reorder areas by complementary richness

275

output of the selection algorithm provided (1) an arbitrarily selected near-minimumset derived using a final, tie-breaking rule (first area encountered), and (2) the equallyvalid flexible 10 km squares derived at each iteration of the selection algorithm fromwhich all possible near-minimum sets were determined. The tie-breaking rule, firstarea encountered, was used rather than random choice among ties in order to ensurerepeatability in tests.

Overlap between RSPB reserves and near-minimum sets

One hundred and twenty-four 10 km squares in the British National Grid containthe centre of an Royal Society for the Protection of Birds (RSPB) nature reserve.These squares were used to represent a national reserve network designated almostexclusively for birds. For each taxonomic group, the number of flexible 10 km squar-es derived at each iteration of the selection algorithm that overlapped with RSPBreserves were counted. The maximum overlap was the number of groups of flexible10 km squares that contained at least one square that overlapped with a reserve. Forexample, the near-minimum sets for dragonflies were each comprised of five 10 kmsquares (Figure 1). Each colour group of 10 km squares contained at least one 10 kmsquare that overlapped with an RSPB reserve (Table 2). Therefore, the maximumoverlap between RSPB reserves and near-minimum sets for dragonflies was 100%(Table 3).

Overlap between pairs of near-minimum sets for different taxonomic groups

To determine the maximum overlap between near-minimum sets for each pair oftaxonomic groups, a binary matrix ofn1 columns byn2 rows was derived.n1 andn2 were the numbers of 10 km squares in each near-minimum set for the two tax-onomic groups. Each cell in the matrix was a comparison between two groupsof flexible 10 km squares – one from the near-minimum sets of each of the twotaxonomic groups. Cells in the matrix contained one if there was a 10 km squarecommon to both groups of flexible squares, zero otherwise. The maximum over-lap was calculated as the maximum sum of the matrix values, with the condition

Table 2. Overlap between RSPB reserves and the flexible 10 km squares for dragonflies. The colourgroups refer to those used in Figure 1. See ‘Methods’ for details.

Number of 10 km squares

Colour groupNumber of flexible10 km squares Overlapping with reserves Not overlapping with reserves

Red 1 1 0Orange 4 1 3Green 5 2 3Light blue 17 3 14Dark blue 21 1 20

276

Table 3. Overlap between RSPB reserves and near-minimum sets. The maximum (and arbitrary) over-lap are the proportion of 10 km squares in the near-minimum set which also contain an RSPBreserve.

Taxonomic group

Number of 10 kmsquares in eachnear-minimum set

Number ofnear-minimumsets Overlap

Difference betweenmaximum andarbitrary overlaps

Liverworts 28 225,504 0.18 (0.07) 0.11Aquatic plants 26 807,923,289,600 0.31 (0.08) 0.23Non-marine molluscs 24 11,386,552,320 0.33 (0.17) 0.16Dragonflies 5 7140 1.00 (0.20) 0.80Orthopteroid insects 6 408 0.50 (0.17) 0.33Carabid beetles 43 6,062,364 0.19 (0.12) 0.07Butterflies 8 3519 0.25 (0.00) 0.25Birds (breeding) 28 29,859,840 0.32 (0.25) 0.07Birds (wintering) 25 3,029,376,000 0.44 (0.24) 0.20

that only one number was taken from each row and each column. This maximumoverlap was converted to a proportion of the maximum possible overlap. The max-imum possible overlap was defined as the number of 10 km squares in the smallerof the pair of near-minimum sets. For example, to determine the maximum over-lap between near-minimum sets for dragonflies and orthopteroid insects,n1 = 5and n2 = 6 (Table 3). The binary matrix for this example is shown in Table 4a.The maximum overlap between the near-minumum sets for this pair of taxonomicgroups is 3 – the summation of the bold figures in Table 4a. The coincidence be-tween the two groups of flexible 10 km squares represented by the 1 in the bottomleft cell of the matrix (wheren1 = 1 andn2 = 6) is not included in the summa-tion. It is excluded because only one value can be taken from each row and eachcolumn (Table 4b–d). As a proportion of the maximum possible overlap (5 – thenumber of 10 km squares in the near-minimum sets for dragonflies), the maximumoverlap between the near-minimum sets for dragonflies and orthopteroid insects is0.6 (Table 5).

Proportion of species from non-target taxonomic groups recorded in thenear-minimum sets of the different target taxonomic groups

For the 10 km squares of the near-minimum sets for each taxonomic group (the tar-get taxon), the maximum proportions of species recorded from the other taxonom-ic groups (the non-target taxa) were determined. For the dragonflies, orthopteroidinsects and butterflies, these proportions were determined by evaluating all of thenear-minimum sets. For each of the other taxonomic groups, due to the large numberof near-minimum sets (Table 3), 10,000 near-minimum sets were randomly chosen.These sets were used to estimate the maximum proportion of species from non-targettaxonomic groups recorded in the 10 km squares of the near-minimum sets for thesetarget taxonomic groups.

277

Table 4. Illustration of the method used to determine the maximum overlap between pairs of near-min-imum sets for different taxonomic groups. The example shown is between dragonflies and orthopteroidinsects. See ‘Methods’ for details.

For each analysis described above, the single, arbitrary, near-minimum set foreach taxonomic groups was also evaluated. Proportions were arcsine transformed forstatistical analysis (Sokal and Rohlf 1995).

Results

Overlap between RSPB reserves and near-minimum sets

The mean (± standard error) maximum overlap between RSPB reserves and near-minimum sets (0.39± 0.08) was more than twice the arbitrary overlap (0.14± 0.03)(t = 2.241;d.f. = 16;P < 0.05) (Figure 2a). The probability of randomly selectinga near-minimum set with the maximum possible overlap with RSPB reserves (aver-aged across all taxonomic groups) was only 0.003. The maximum overlap betweenRSPB reserves and near-minimum sets for the various taxonomic groups ranged from0.18, for liverworts, to 1.00, for dragonflies (Table 3).

Coincidence between pairs of near-minimum sets for different taxonomic groups

In 34 of the 36 pairwise comparisons of overlap between near-minimum sets fordifferent taxonomic groups, the maximum level was higher than the arbitrary level.

278

Table

5.O

verla

pbe

twee

npa

irsof

near

-min

imum

sets

for

diffe

rent

taxo

nom

icgr

oups

.Dat

aar

eth

epr

opor

tion

ofth

em

axim

umpo

ssib

leov

erla

p,w

hich

isde

fined

asth

enu

mbe

rof

10km

squa

res

inth

esm

alle

rof

the

pair

ofne

ar-m

inim

umse

ts(T

able

3).T

hem

axim

um(a

ndar

bitr

ary)

over

lap

are

give

nin

the

uppe

rrig

htp

ortio

nof

the

tabl

e,an

dth

edi

ffere

nce

betw

een

thes

etw

ova

lues

inth

elo

wer

left

port

ion

ofth

eta

ble.

Aqu

atic

Non

-mar

ine

Ort

hopt

eroi

dB

irds

Liv

erw

orts

plan

tsm

ollu

scs

Dra

gonfl

ies

inse

cts

Car

abid

beet

les

But

terfl

ies

Bre

edin

gW

inte

ring

Liv

erw

orts

0.15

(0.0

0)0.

13(0

.00)

0.20

(0.2

0)0.

50(0

.33)

0.18

(0.0

4)0.

25(0

.00)

0.07

(0.0

7)0.

16(0

.00)

Aqu

atic

plan

ts0.

150.

21(0

.04)

0.60

(0.0

0)0.

17(0

.00)

0.27

(0.0

8)0.

13(0

.00)

0.23

(0.0

0)0.

28(0

.00)

Non

-mar

ine

mol

lusk

s0.

130.

170.

80(0

.20)

0.33

(0.0

0)0.

21(0

.00)

0.50

(0.1

3)0.

04(0

.00)

0.33

(0.0

0)D

rago

nflie

s0.

000.

600.

600.

60(0

.00)

0.40

(0.0

0)0.

40(0

.00)

0.60

(0.2

0)0.

80(0

.00)

Ort

hopt

eroi

din

sect

s0.

170.

170.

330.

600.

17(0

.00)

0.17

(0.0

0)0.

17(0

.00)

0.33

(0.0

0)C

arab

idbe

etle

s0.

140.

190.

210.

400.

170.

13(0

.00)

0.21

(0.1

1)0.

24(0

.04)

But

terfl

ies

0.25

0.13

0.37

0.40

0.17

0.13

0.13

(0.0

0)0.

13(0

.00)

Bird

s(b

reed

ing)

0.00

0.23

0.04

0.40

0.17

0.10

0.13

0.20

(0.0

4)B

irds

(win

terin

g)0.

160.

280.

330.

800.

330.

200.

130.

16

279

Table

6.P

ropo

rtio

nof

spec

ies

from

non-

targ

etta

xono

mic

grou

psre

cord

edin

the

near

-min

imum

sets

ofth

edi

ffere

ntta

rget

taxo

nom

icgr

oups

.T

hene

ar-m

inim

um

set(

targ

et)

taxo

nom

icgr

oup

isth

eco

lum

nhe

adin

g,an

dth

e(n

on-t

arge

t)ta

xono

mic

grou

pis

the

row

title

.M

axim

um(a

ndar

bitr

ary)

prop

ortio

ns,

and

the

diffe

renc

ebe

twee

nth

ese

two

valu

es(in

italic

s),

are

give

n.

Liv

erw

orts

Aqu

atic

plan

tsN

on-m

arin

em

ollu

sks

Dra

gonfl

ies

Ort

hopt

eroi

din

sect

sC

arab

idbe

etle

sB

utte

rflie

sB

irds

(bre

edin

g)B

irds

(win

terin

g)

Liv

erw

orts

0.75

(0.6

6)0.

72(0

.55)

0.37

(0.2

5)0.

37(0

.24)

0.76

(0.7

3)0.

65(0

.57)

0.75

(0.6

5)0.

72(0

.57)

0.0

90.1

70.1

20.1

30.0

30.0

80.1

00.1

5A

quat

ic0.

79(0

.75)

0.84

(0.8

0)0.

80(0

.70)

0.73

(0.6

7)0.

86(0

.84)

0.68

(0.5

0)0.

84(0

.80)

0.83

(0.7

5)pl

ants

0.0

40.0

40.1

00.0

60.0

20.1

80.0

40.0

8N

on-m

arin

e0.

78(0

.70)

0.84

(0.7

9)0.

73(0

.60)

0.69

(0.6

0)0.

84(0

.79)

0.76

(0.6

9)0.

79(0

.73)

0.84

(0.7

2)m

ollu

sks

0.0

80.0

50.1

30.0

90.0

50.0

70.0

60.1

2D

rago

nflie

s0.

95(0

.92)

0.95

(0.8

9)0.

92(0

.84)

0.89

(0.8

4)0.

95(0

.95)

0.89

(0.7

0)0.

97(0

.92)

1.00

(0.7

8)0.0

30.0

60.0

80.0

50.0

00.1

90.0

50.2

2O

rtho

pter

oid

0.83

(0.8

0)0.

86(0

.69)

0.83

(0.7

4)0.

91(0

.66)

0.89

(0.8

6)0.

74(0

.69)

0.83

(0.7

1)0.

89(0

.83)

inse

cts

0.0

30.1

70.0

90.2

50.0

30.0

50.1

20.0

6C

arab

id0.

62(0

.54)

0.65

(0.5

5)0.

72(0

.62)

0.53

(0.4

1)0.

51(0

.42)

0.54

(0.4

2)0.

67(0

.63)

0.70

(0.5

9)be

etle

s0.0

80.1

00.1

00.1

20.0

90.1

20.0

40.1

1B

utte

rflie

s0.

88(0

.84)

0.89

(0.8

4)0.

91(0

.86)

0.86

(0.7

4)0.

77(0

.72)

0.93

(0.9

1)0.

88(0

.81)

0.91

(0.7

7)0.0

40.0

50.0

50.1

20.0

50.0

20.0

70.1

4B

irds

0.82

(0.7

9)0.

81(0

.75)

0.78

(0.7

5)0.

67(0

.62)

0.64

(0.5

7)0.

86(0

.86)

0.66

(0.5

7)0.

85(0

.79)

(bre

edin

g)0.0

30.0

60.0

30.0

50.0

70.0

00.0

90.0

6B

irds

0.83

(0.8

1)0.

83(0

.78)

0.86

(0.7

7)0.

80(0

.58)

0.81

(0.7

7)0.

89(0

.88)

0.73

(0.6

9)0.

88(0

.86)

(win

terin

g)0.0

20.0

50.0

90.2

20.0

40.0

10.0

40.0

2

280

Across all taxonomic groups, the mean maximum overlap (0.29±0.03) was over sev-en times greater than the mean arbitrary overlap (0.04±0.01) (t = 9.392;d.f. = 70;P < 0.001) (Figure 2b). The maximum overlap varied from 0.80 between non-marinemolluscs and dragonflies, to 0.04 between non-marine molluscs and breeding birds(Table 5).

Proportion of species from non-target taxonomic groups recorded in thenear-minimum sets of the different target taxonomic groups

The mean of the maximum proportions of species from non-target taxonomic groupsrecorded in the near-minimum sets of a target group (0.79± 0.02) was significantlygreater than the mean for the arbitrary near-minimum sets (0.71± 0.02) (t = 3.678;d.f. = 142;P < 0.001) (Figure 2c). Maximum proportions ranged from 1.00, alldragonflies recorded in the near-minimum sets of wintering birds, to 0.37, for non-marine molluscs recorded in the near-minimum sets of dragonflies and liverwortsrecorded in the near-minimum sets of orthopteroid insects (Table 6).

Discussion

The purpose of this study was to illustrate potential shortcomings in previous con-servation assessments by comparing the methods used in those studies with our newtechniques. Both methods were applied to national distribution data for selected tax-onomic groups in Great Britain. Location information for RSPB nature reserves wasalso used.

Some previous studies have discounted the use of indicator taxa in the selection ofsites for nature reserves due to poor overlap between near-minimum sets derived fordifferent taxonomic groups, and between near-minimum sets and hotspots of speciesrichness and rarity (Lombard 1995; Dobson et al. 1997; Pimm and Lawton 1998; vanJaarsveld et al. 1998). Based on our results (Figure 2), this low level of overlap is nosurprise as only a single near-minimum set for each taxonomic group was evaluated.More recently, it has been suggested that the assessment of indicator taxa using near-minimum sets should focus on the proportion of species from non-target taxonomicgroups recorded in the near-minimum set of a target taxonomic group (Balmford1998; Howard et al. 1998; Williams et al. 1999). Our results support this methodof conservation assessment, but emphasise the need to consider the flexibility in thechoice of areas which comprise the near-minimum sets for each taxonomic group.This flexibility is reduced in regions of high local endemism (Lombard et al. 1999).

The quality and accuracy of near-minimum set, and other large-scale, analysesare limited by the data upon which they are based. Great Britain has the world’smost extensive, high quality, national distribution data mapped at a 10 km square

281

Figure 2. Comparisons between the means (± standard error bars) of (a) the arbitrary and maximum over-lap between near-minimum sets and RSPB reserves, (b) the arbitrary and maximum overlap between pairsof near-minimum sets for different taxonomic groups, and (c) the arbitrary and maximum proportions ofspecies from each non-target taxonomic group recorded in the near-minimum sets of the different targettaxonomic groups.

resolution (Harding and Sheail 1990). These data are periodically published in dis-tribution atlases for different taxonomic groups (e.g. Heath et al. 1984; Merritt et al.1996; Haes and Harding 1997; Preston and Croft 1997). For non-avian taxonomicgroups, the distribution of species are inferred by mapping the accumulated recordscollected by volunteer recorders over periods of 20 or more years. The accuracy ofdistribution maps derived using data collected in this way is hard to ensure (Bullock1991; Rich and Woodruff 1992; Prendergast et al. 1993b; Freitag et al. 1998), but forthe taxonomic groups used in the present study, this problem is minimised due to thecomprehensive geographical coverage of the submitted records (Prendergast 1994).This has been achieved by a unique and impressive collation of data from volunteers.It is an assumption of these analyses that the species’ distributions are accurate. Theideal datasets for all large-scale studies would contain distribution data at finer res-olutions. With the exception of birds, coverage at finer resolutions throughout GreatBritain is still some years away. For birds, species’ distribution data are collected overshort (3–4 year) temporal periods during surveys organised by the British Trust forOrnithology (Lack 1986; Gibbons et al. 1993) – a huge research effort made possiblefor birds because of the large numbers of volunteer recorders who participate in suchschemes.

Although Great Britain has a large volume of high quality species’ distributiondata, there are some potential problems which must be borne in mind when inter-preting the results of near-minimum set, and other large-scale, analyses. First, howdoes the resolution of the grid cells used in recording species’ distributions compareto the actual (or potential) size of reserves? In Great Britain, few nature reserves

282

are within an order of magnitude of the size of a 10 km square (for RSPB reserves,mean (± standard error) area = 6.3± 1.3 km2). This means that some of the speciesrecorded within coarse grid cells may not be present on actual reserves (Lombardet al. 1995; Mugo et al. 1995). The second problem is the static nature of the data.National distribution data, collected over several decades, may mask potential shiftsin species’ ranges which can occur as environmental conditions change (Barkham andMacGuire 1990). The only study to date that has addressed this problem is Virolainenet al. (1999) who used two datasets on vascular plants of boreal lakes in Finland fromsurveys conducted 63 years apart. Near-minimum sets which represented 100% ofspecies in 1933–1934, contained less than 84% of species in 1996. Both the temporaland spatial resolution of the datasets used in this study are continually improving, andrefined assessments will become possible in the future. Furthermore, when designingreserve networks many other factors including species’ abundance, threats to persis-tence, habitat requirements, and ecological processes are important considerations(Cowling et al. 1999; Lombard et al. 1999).

The purpose of this paper is not to make practical recommendations aboutreserve acquisition, but to illustrate a method that will be useful as more refineddata become available. Near-minimum set analyses are valuable tools in providinga first step towards selecting priority areas for conservation. Areas selected at coarseresolutions can be further evaluated at finer resolutions and the methods reappliedif/when additional data are available. Furthermore, since practical conservation mustcompete with other land uses, the flexibility which techniques such as those pre-sented here offer can provide a series of equally valid alternative sites for potentialreserve acquisition. The quantitative nature of near-minimum set analyses can alsoprovide focus, direction and, perhaps most importantly, accountability for conserva-tion efforts (Scott et al. 1993; Williams et al. 1996b). These and other quantitativemethods are now being used in practical conservation decisions in both Australia(Pressey 1998) and South Africa (Lombard et al. 1997; A.S. van Jaarsveld, personalcommunication).

Although the techniques presented here are applied to 10 km square data for GreatBritain, the methods are applicable to conservation assessments at any geographicscale or grid resolution, since flexibility in the choice of areas which comprise near-minimum sets has been recognised across a wide range of geographical scales andgrid resolutions (Nicholls and Margules 1993; Pressey et al. 1993, 1994; Saetersdalet al. 1993; Lomolino 1994; Lombard et al. 1995; Williams et al. 1996b; Ando et al.1998; Williams 1999). Our results show that erroneous conclusions may have beendrawn where flexibility has not been considered in previous near-minimum set an-alyses. The real situation may, in fact, be more positive than hitherto supposed. Wetherefore suggest that the techniques presented here are used in future near-minimumset analyses.

283

Acknowledgements

PH was supported by a NERC CASE Studentship with the Biological Records Cen-tre, ITE Monks Wood. JMJT was supported by a NERC studentship. We thank thevolunteers of the various national recording schemes for collection of the data, andMalcolm Ausden, Tim Blackburn, John Lawton, John Prendergast, Iain Williams andtwo anonymous referees for useful comments on various drafts of the manuscript.

References

Ando A, Camm J, Polasky S and Solow A (1998) Species distributions, land values, and efficient conser-vation. Science 279: 2126–2128

Balmford A (1998) On hotspots and the use of indicators for reserve selection. Trends in Ecology andEvolution 13: 409

Barkham J and MacGuire F (1990) The heat is on. Natural World Winter 1990: 24–28Branch WR, Benn GA and Lombard AT (1995) The tortoises (Testudinidae) and terrapins (Pelomedusidae)

of southern Africa: their diversity, distribution and conservation. South African Journal of Zoology 30:91–102

Bullock JA (1991) The distribution of a taxon is that of its students and the diversity of a site is a matter ofserendipity? Antenna 15: 6–7

Cowling RM, Pressey RL, Lombard AT, Desmet PG and Ellis AG (1999) From representation to persis-tence: requirements for a sustainable system of conservation areas in the species-rich mediterranean-climate desert of southern Africa. Diversity and Distributions 5: 1–21

Csuti B, Polasky S, Williams PH, Pressey RL, Camm JD, Kershaw M, Kiester AR, Downs B, HamiltonR, Huso M and Sahr K (1997) A comparison of reserve selection algorithms using data on terrestrialvertebrates in Oregon. Biological Conservation 80: 83–97

Dobson AP, Rodriguez JP, Roberts WM and Wilcove DS (1997) Geographic distribution of endangeredspecies in the United States. Science 275: 550–553

Freitag S, Hobson C, Biggs HC and van Jaarsveld AS (1998) Testing for potential survey bias: the effectsof roads, urban areas and nature reserves on a southern African mammal data set. Animal Conservation1: 119–127

Gaston KJ (1996) Spatial covariance in the species richness of higher taxa. In: Hochberg M, ClobertME and Barbault R (eds) The Genesis and Maintenance of Biological Diversity, pp 221–242. OxfordUniversity Press, Oxford

Gibbons DW, Reid JB and Chapman RA (1993) The new atlas of breeding birds in Britain and Ireland:1988–1991. T and AD Poyser, London

Haes ECM and Harding PT (1997) Atlas of Grasshoppers, Crickets and Allied Insects in Britain andIreland. HMSO, London

Harding PT (1990) National species distribution surveys. In: Goldsmith B (ed) Monitoring for Conserva-tion and Ecology, pp 133–154. Chapman & Hall, London

Harding PT and Sheail J (1990) The biological records centre: a pioneer in data gathering and retrieval.In: Harding PT (ed) Biological Recording of Changes in British Wildlife, pp 5–20. HMSO, London

Heath J, Pollard E and Thomas JA (1984) Atlas of Butterflies in Britain and Ireland. Viking, Harmonds-worth

Howard PC, Viskanic P, Davenport TRB, Kigenyi FW, Baltzer M, Dickinson CJ, Lwanga JS, MatthewsRA and Balmford A (1998) Complementarity and the use of indicator groups for reserve selection inUganda. Nature 394: 472–475

Kershaw M, Williams PH and Mace GC (1994) Conservation of Afrotropical antelopes: consequences andefficiency of using different site selection methods and diversity criteria. Biodiversity and Conservation3: 354–372

284

Lack P (1986) The Atlas of Wintering Birds in Britain and Ireland. T and AD Poyser, LondonLawton JH, Bignell DE, Bolton B, Bloemers GF, Eggleton P, Hammond PM, Hodda M, Holt RD, Larsen

TB, Mawdsley NA, Stork NE, Srivastava DS and Watt AD (1998) Biodiversity inventories, indicatortaxa and effects of habitat modification in tropical forest. Nature 391: 72–76

Lombard AT (1995) The problems with multi-species conservation: do hotspots, ideal reserves and existingreserves coincide? South African Journal of Zoology 30: 145–163

Lombard AT, Cowling RM, Pressey RL and Mustart PJ (1997) Reserve selection in a species-rich andfragmented landscape on the Agulhas Plain, South Africa. Conservation Biology 11: 1101–1116

Lombard AT, Hilton-Taylor C, Rebelo AG, Pressey RL and Cowling RM (1999) Reserve selection in theSucculent Karoo, South Africa: coping with high compositional turnover. Plant Ecology 142: 35–55

Lombard AT, Nicholls AO and August PV (1995) Where should nature reserves be located in South Africa?A snake’s perspective. Conservation Biology 9: 363–372

Lomolino MV (1994) An evaluation of alternative strategies for building networks of nature reserves.Biological Conservation 69: 243–249

Margules CR, Nicholls AO and Pressey RL (1988) Selecting networks of reserves to maximise biologicaldiversity. Biological Conservation 43: 63–76

Merritt R, Moore NW and Eversham BC (1996) Atlas of the Dragonflies of Britain and Ireland. HMSO,London

Mugo DN, Lombard AT, Bronner GN, Gelderblom CM and Benn GA (1995) Distribution and protection ofendemic or threatened rodents, lagomorphs and macrosceledids in South Africa. South African Journalof Zoology 30: 115–126

Nicholls AO and Margules CR (1993) An upgraded reserve selection algorithm. Biological Conservation64: 165–169

Pimm SL and Lawton JH (1998) Planning for biodiversity. Science 279: 2068–2069Possingham H, Day J, Goldfinch M and Salzborn F (1993) The mathematics of designing a network of

protected areas for conservation. In: Sutton DJ, Pearce CEM and Cousins EA (eds) Decision Sciences:Tools for Today, pp 536–545. University of Adelaide

Prendergast JR (1994) Biodiversity hotspots in Britain. PhD Thesis, Imperial College, University ofLondon

Prendergast JR and Eversham BC (1997) Species richness covariance in higher taxa: empirical tests of thebiodiversity indicator concept. Ecography 20: 210–216

Prendergast JR, Quinn RM, Lawton JH, Eversham BC and Gibbons DW (1993a) Rare species, the coinci-dence of diversity hotspots and conservation strategies. Nature 365: 335–337

Prendergast JR, Wood SN, Lawton JH and Eversham BC (1993b) Correcting for variation in recordingeffort in analyses of diversity hotspots. Biodiversity Letters 1: 39–53

Pressey RL (1998) Algorithms, politics and timber: an example of the role of science in a public, politicalnegotiation process over new conservation areas in production forests. In: Wills R and Hobbs R (eds)Ecology for Everyone: Communicating Ecology to Scientists, the Public and the Politicians, pp 73–87.Surrey Beatty and Sons, Sydney

Pressey RL, Humphries CJ, Margules CR, Vane-Wright RI and Williams PH (1993) Beyond opportunism:key principles for systematic reserve selection. Trends in Ecology and Evolution 8: 124–128

Pressey RL, Johnson IR and Wilson PD (1994) Shades of irreplaceability: towards a measure of the con-tribution of sites to a reservation goal. Biodiversity and Conservation 3: 242–262

Pressey RL, Possingham HP and Day JR (1997) Effectiveness of alternative heuristic algorithms for identi-fying indicative minimum requirements for conservation reserves. Biological Conservation 80: 207–219

Pressey RL, Possingham HP and Margules CR (1996) Optimality in reserve selection algorithms: whendoes it matter and how much? Biological Conservation 76: 259–267

Preston CD and Croft JM (1997) Aquatic Plants in Britain and Ireland. Harley Books, ColchesterRich TCG and Woodruff ER (1992) Recording bias in botanical surveys. Watsonia 19: 73–95Saetersdal M, Line JM and Birks HJB (1993) How to maximize biological diversity in nature reserve

selection: vascular plants and breeding birds in deciduous woodlands, western Norway. Biological Con-servation 66: 131–138

285

Scott JM, Davis F, Csuti B, Noss R, Butterfield B, Groves C, Anderson H, Caicco S, D’Erchia F, EdwardsTC, Ulliman J and Wright RG (1993) Gap Analysis: a geographic approach to protection of biologicaldiversity. Wildlife Monographs 123: 1–41

Sokal RR and Rohlf FJ (1995) Biometry: the Principles and Practice of Statistics in Biological Research,3rd edn. WH Freeman and Company, New York

Trinder-Smith TH, Lombard AT and Picker MD (1996) Reserve scenarios for the Cape Peninsula: high-,middle- and low-road options for conserving the remaining biodiversity. Biodiversity and Conservation5: 649–669

Underhill LG (1994) Optimal and suboptimal reserve selection algorithms. Biological Conservation 70:85–87

van Jaarsveld AS, Freitag S, Chown SL, Muller C, Koch S, Hull H, Bellamy C, Krüger M, Endrödy-Younga S, Mansell MW and Scholtz CH (1998) Biodiversity assessment and conservation strategies.Science 279: 2106–2108

Virolainen KM, Virola T, Suhonen J, Kuitunen M, Lammi A and Siikamäki P (1999) Selecting networks ofnature reserves: methods do affect the long-term outcome. Proceedings of the Royal Society of LondonSeries B-Biological Sciences 266: 1141–1146

Williams P, Burgess N and Rahbek C (1999) Assessing large ‘flagship’ species for representing the di-versity of sub-Saharan mammals, using hotspots of total richness, hotspots of endemism, and hotspotsof complementary richness. In: Entwistle A and Dunstone N (eds) Has the Panda Had its Day? FuturePriorities for the Conservation of Mammalian Biodiversity. Cambridge University Press, Cambridge

Williams P, Gibbons D, Margules C, Rebelo A, Humphries C and Pressey R (1996a) A comparison ofrichness hotspots, rarity hotspots, and complementary areas for conserving diversity of British birds.Conservation Biology 10: 155–174

Williams PH (1998) WORLDMAP 4 WINDOWS: software and help document 4.19 (http://www.nhm.ac.uk/science/projects/worldmap/)

Williams PH (1999) Key sites for conservation: area-selection methods for biodiversity. In: Mace GM,Balmford A and Ginsberg JR (eds) Conservation in a Changing World: Integrating Processes into Prior-ities for Action, pp 211–249. Cambridge University Press, Cambridge

Williams PH and Gaston KJ (1998) Biodiversity indicators: graphical techniques, smoothing and searchingfor what makes relationships work. Ecography 21: 551–560

Williams PH, Prance GT, Humphries CJ and Edwards KS (1996b) Promise and problems in applyingquantitative complementary areas for representing the diversity of some neotropical plants (familiesDichapetalaceae, Lecythidaceae, Caryocaraceae, Chrysobalanaceae and Proteaceae). Biological Journalof the Linnean Society 58: 125–157

Willis CK, Lombard AT, Cowling RM, Heydenrych BJ and Burgers CJ (1996) Reserve systems for lime-stone endemic flora of the Cape lowland fynbos: iterative versus linear programming. Biological Con-servation 77: 53–62