Reserve selection for conserving groundwater biodiversity

Reserve selection for conserving groundwaterbiodiversity

GEORGES MICHEL*, FLORIAN MALARD †, LOUIS DEHARVENG ‡, TIZIANA DI LORENZO § ,

BORIS SKET– AND CLAUDE DE BROYER**

*Commission Wallonne d’Etude et de Protection des Sites Souterrains, Brussels, Belgium†UMR CNRS 5023, Ecologie des Hydrosystemes Fluviaux, Universite Claude Bernard Lyon 1, Villeurbanne, France‡UMR 5202, Origine, Structure et Evolution de la Biodiversite, Museum National d’Histoire Naturelle, Paris, France§Dipartimento di Scienze Ambientali, Universita di L’Aquila, L’Aquila, Italy–Oddelek za biologijo, Biotehniska fakulteta, Univerza v Ljubljani, Ljubljana, Slovenia

**Departement des Invertebres (Carcinologie), Institut Royal des Sciences Naturelles de Belgium (IRScNB), Brussels, Belgium

SUMMARY

1. A possible conservation strategy to minimise the risk of groundwater biodiversity

loss due to human activities consists in designing a network of reserve areas at the

continental scale that collectively include most groundwater species. To this end, we

compared the efficiency of three area selection methods (species richness hotspots,

endemism hotspots and complementarity) and examined the influence of spatial

constraints (reduced extent and increased aggregation of reserve areas) on the

representation of 1059 groundwater species in six European regions.

2. Presence data from a data base elaborated as part of a European initiative on

groundwater biodiversity, the PASCALIS project, were referenced onto 4675 grid cells

(0.2 by 0.2�). Complementary performed much better than traditional selection methods

for maximising species representation in a reserve network arbitrarily limited to 10% of

all the cells containing groundwater fauna. It captured 155 more species than areas

selected on richness and 77 more species than areas selected on endemism hotspots.

3. Representing species in a specified proportion of their area of occupancy (i.e. 100%,

50% and 10% of the area of occupancy of species occurring in 1, £10, and >10 cells,

respectively) required inclusion of 46% of the cells containing groundwater fauna. The

reserve network needed to achieve this level of coverage may be too large and fragmented

to be implemented and managed in practice.

4. Reduction of the reserve areas to 10% of the landscape containing groundwater fauna

and their aggregation into a smaller number of cell clusters resulted in a more realistic

reserve network that represented 73.8% (782 species) and 59.1% (274 endemics) of the total

number of species and endemics, respectively.

5. We propose several research priorities to improve the design of effective ground-

water reserve networks in Europe: (i) devising sampling strategies that reduce

uncertainties in the placement of reserves and increase the number of alternative

reserve networks and (ii) shifting from a grid-cell selection approach to an aquifer-

selection approach that incorporates species representation targets, minimum space

requirement and also socio-economic costs related to the vulnerability of aquifers and

degree of human activity in the catchment.

Correspondence: Georges Michel, Commission Wallonne d’Etude et de Protection des Sites Souterrains, 20 Avenue Gilbert, 1050

Brussels, Belgium.

E-mail: [email protected]

Freshwater Biology (2009) 54, 861–876 doi:10.1111/j.1365-2427.2009.02192.x

� 2009 Blackwell Publishing Ltd 861

Keywords: biodiversity hotspots, complementary areas, endemism, reserve design, subterraneanecosystem

Introduction

In the face of the irretrievable loss of biodiversity

world-wide, the designation of nature reserve net-

works has become a cornerstone of most conservation

strategies (McDonnell et al., 2002). Whereas earlier

reserves were largely selected in an ad hoc fashion,

considerable progress has been made over the

last 20 years in the designation of reserve net-

works that meet well-defined ecological objectives

(Bedward, Pressey & Nicholls, 1991; Pressey et al.,

1993; Prendergast, Quinn & Lawton, 1999). This

progress owes much to the development of heuristic

algorithms that find near optimal solutions to prob-

lems such as the representation of attributes (habitats

or species) in a near minimum area (Diniz-Filho et al.,

2005), or their maximisation for a given fixed area or

cost (Pressey, Possingham & Day, 1997; Possingham,

Ball & Andelman, 2000; Fischer & Church, 2005). All

heuristic algorithms use complementarity, ‘a measure

of the extent to which an area, or set of areas,

contributes unrepresented features to an existing area

or set of areas’ (Margules & Pressey, 2000, p. 249). As

heuristics become increasingly sophisticated, they can

be used to generate more flexible conservation sce-

narios that explicitly consider configurational features

such as size, aggregation, spacing and connectivity of

individual patches (Fischer & Church, 2003; Fuller

et al., 2006; Moilanen & Wintle, 2006) and economic

costs of reserved areas (Faith et al., 2003; Stewart &

Possingham, 2005). Flexibility refers to the possibility

of identifying not one, but a range of reserve networks

that meet a particular conservation goal, this property

being particularly advantageous for solving conflict

situations (Possingham et al., 2000). The incorporation

of spatially explicit constraints in the design of reserve

networks is a product of the Single Large Or Several

Small reserves debate (SLOSS) which addresses the

significance of spatial configuration of reserves

areas in determining their conservation efficiency

(Diamond, 1975; Simberloff & Martin, 1991;

Ovaskainen, 2002; Halpern & Warner, 2003). Heuristic

algorithms have been successfully applied to design

reserve networks for the conservation of a variety of

ecosystems and organisms, including a coral reef

(Leslie et al., 2003), tropical rain forest (Chave,

Wiegand & Levin, 2002), plants (Barker, Wardlaw &

Brown, 1996), invertebrates (Pante et al., 2006), fishes

(Cook & Auster, 2005), birds (Garson, Aggarwal &

Sarkar, 2002) and mammals (Fjeldsa et al., 2004).

Although much effort has been made to protect

groundwater resources, the conservation of ground-

water biodiversity is still in its infancy (Juberthie,

1995; Danielopol et al., 2003). There is no evidence that

environmental policy for protecting aquifers used for

drinking water supply would contribute much to the

maintenance of groundwater biodiversity. Most ini-

tiatives for the conservation of groundwater fauna

consist in designating a number of threatened species

(Arntzen et al., 2008) or identifying cave systems that

harbour an unusually high number of species (i.e. the

hotspot approach; Juberthie & Juberthie-Jupeau, 1975;

Malard, Gibert & Laurent, 1997; Culver & Sket, 2000).

The majority of groundwater species on earth could

potentially be listed as endangered taxa because their

geographical range size is extremely small compared

to those of epigean species (Gibert & Deharveng,

2002). As Culver et al. (2000, p. 395) argued, ‘there are

simply too many subterranean species at risk to deal

with them one at time’. A more comprehensive

strategy for minimising the risk of groundwater

biodiversity loss due to human activities would

consist in identifying a network of protected sites

that collectively encompass the majority of ground-

water species at large spatial scales. In the past, few

attempts were made in this direction, probably

because distributional data on groundwater fauna

were too sparse to be used for simulating any realistic

conservation scenarios at spatial scales larger than

that of the region (Culver et al., 2000; Ferreira et al.,

2007). In recent years, however, distributional data

bases became available in a number of countries,

including the U.S.A. (Culver et al., 2000), Italy (Stoch,

2001), Slovenia and most Balkan countries (Sket,

Paragamian & Trontelj, 2004), France (Ferreira et al.,

2003) and the U.K. (Proudlove et al., 2003), thereby

providing unique opportunities to design groundwa-

ter reserve networks. Although groundwater species

862 G. Michel et al.

� 2009 Blackwell Publishing Ltd, Freshwater Biology, 54, 861–876

inventories are still highly incomplete, it is preferable

to design reserve areas based on our present state of

knowledge rather than letting human activities cause

further groundwater habitat damage and biodiversity

loss.

In this paper, we take advantage of the distribu-

tional data assembled as part of a large coordinated

European initiative, the PASCALIS project (Protocols

for the ASsessment and Conservation of Aquatic Life

In the Subsurface; Gibert, 2001), to explore the

capacity for designing subsurface reserve systems

that adequately represent well-defined conservation

objectives at the European scale. Whereas heuristics

have long been used to design effective reserve

networks in terrestrial and marine systems, this study

is one of the first to groundwater systems and one of

few applications of complementarity algorithms

(Culver et al., 2000; Ferreira et al., 2007). The objectives

of this paper are (i) to compare the efficiency of

hotspot selection (species richness and endemism)

and complementarity in representing as many species

and endemics as possible within an area-limited

reserve network; (ii) to examine the effect of spatial

constraints (reduced extent and increased aggregation

of reserved areas) on the representation of species or a

given proportion of their area of occupancy and (iii) to

identify research avenues for implementing efficient

groundwater reserve networks at the continental

scale.

Methods

Species distribution data

Distribution data were extracted from the groundwa-

ter species database elaborated within the framework

of the European project PASCALIS (Deharveng et al.,

2009). They comprised a total of 10 183 records for

1059 obligate groundwater species (i.e. stygobiotic

species) in six countries (Belgium, France, Italy,

Portugal, Slovenia and Spain). The data were

compiled from multiple sources including: (i) pub-

lished records; (ii) existing data bases; (iii) personal

collections and (iv) extensive sampling surveys

carried out in six selected regions (Dole-Olivier et al.,

2009). Species lists, records and distribution maps

were submitted to taxonomists for correction and

validation. Presence data were referenced onto 4675

0.2 by 0.2� grid cells (400 km2) covering the land

territory of the six countries, using a geographical

information system (ARCGISARCGIS 8.2 software, Esai, Red-

lands, CA, U.S.A.).

Geographical patterns of diversity

We first investigated distribution patterns of diversity

because they ultimately control the capacity to design

reserve networks with distinct conservation objectives

and spatial constraints. The frequency distribution of

range sizes of species was determined using two

distinct measures: the area of occupancy of a species

and its extent of occurrence (Gaston, 1991). Area of

occupancy was the number of cells in which a species

occurred. The total number of cells contained within

the rectangle drawn between the most widely

separated sites occupied by a species, was used as a

measure of its extent of occurrence. We defined

endemics as species that were restricted to a single

cell. Distribution maps of species richness and

endemism were produced by counting the number

of species and endemics present in the 4675 cells of

the grid coverage. Latitudinal variation of biodiversity

was examined by counting the number of all

stygobiotic species and of the endemic, and exclusive

species in each of fifteen 1�-latitudinal bands spanning

the study area from 36 to 51�N. Total richness and

endemic species richness were plotted against the

number of distribution records per sampling band to

assess the influence of sampling heterogeneity on

geographical diversity patterns.

Comparison of selection methods

We used three methods for choosing 10% of all the

0.2 · 0.2� grid cells containing species to represent the

diversity of groundwater fauna in the study area: (i)

hotspots of species richness; (ii) hotspots of endemism

and (iii) complementarity. All of these methods have

been suggested, and in some cases applied, for

designating priority sites of groundwater species

conservation (Culver & Sket, 2000; Culver et al.,

2000; Ferreira et al., 2007). Although this upper 10%

limit was an arbitrary threshold used for comparing

methods, it followed the guidelines of Natura 2000, a

network of nature conservation sites across Europe,

which recommended that at least 10% of the

European landscape should be reserved for conserva-

tion purposes. In the present study, this upper 10%

Groundwater reserve selection 863


limit corresponded to 128 cells because only 1280 cells

out of 4675 contained at least one species. Defining

hotspots of richness and endemism [methods (i) and

(ii)] consisted of selecting the 128 grid cells richest in

total species and endemics, respectively. A random

draw was performed to select between ties (i.e. grid

cells having the same score) in the number of species

richness or endemism.

In this study, complementarity (method iii) refers to

the degree to which a cell contributes otherwise

unrepresented species to a set of cells (Vane-Wright,

Humphries & Williams, 1991). We used MARXANMARXAN

software (version 1.8.6, University of Queensland,

Australia) for selecting a network of 128 complemen-

tary cells (Possingham et al., 2000; Ball & Possingham,

2001). This optimisation package attempts to minimise

the total cost of a reserve network:

Total cost ¼X

cells

cell costs

þ BLM�X

cells

boundary costs

þX

species

SPF� penalty

þ cost threshold penalty,

where the first term is the sum of the costs of cells

included in the reserve network (all cells were

attributed a cost of 1), the second term is the cost of

the total boundary length of the network, the third

term is a penalty given to species that have not met

their representation target and the last term is a cost

applied when exceeding a desired number of cells.

The species penalty factor (SPF) is a weighting factor

which determines the relative importance for ade-

quately representing a particular species. Boundary

costs of each cell selected in the network are added up

and multiplied by the boundary length modifier

(BLM). The BLM is a constant that can be varied to

adjust the spatial aggregation of the reserve network

(Stewart & Possingham, 2005): a small BLM allows the

selection of isolated cells with high conservation value

whereas a high BLM enables clustering of cells despite

an increase in cell costs. Boundary costs equal to the

length of adjacent cells were automatically generated

using the ARCGISARCGIS extension developed by A. Cottam

(http://www.ecology.uq.edu.au). The reserve net-

work was aggregated by experimentally increasing

the BLM value from 0 (no clustering constraint) to

1000. We used complementarity to represent as many

species as possible at least once in a reserve network

of 128 cells. To this end, we applied a high penalty to

enforce a 128-cell maximum. We followed the proce-

dure described in Ardron, Lash & Haggarty (2002)

which consisted in using the summed irreplaceability

algorithm (Pressey, Johnson & Wilson, 1994) in

concert with simulated annealing (Kirkpatrick, Gelatt

& Vecchi, 1983) to select the reserve network. Then,

the swap iterative improvement algorithm was used

to check for the utility of each cell in the reserve

network (Ball & Possingham, 2000). This procedure

was performed 1000 times and the best reserve

network (i.e. the one with the best score according

to the objective function) was retained for comparison

with the hotspot networks.

To demonstrate how groundwater reserve networks

can be designed to protect certain species, we intro-

duced another complementarity solution in which we

gave higher conservation values to taxonomically

isolated taxa (hereafter referred to as the weighted

complementary areas). Taxonomic distinctness for a

particular species was defined as the cumulative

taxonomic distance (measured as tree length on the

Linnaean classification) between this species and all

other species in the study area (Clarke & Warwick,

1998). For each species, the following scores were

used to calculate the taxonomic distinctness index:

1 = congeneric species; 2 = species in the same family

but different genera; 3 = the same order but different

families; 4 = the same class but different orders;

5 = same phylum but different classes and 6 = differ-

ent phyla. The taxonomic distinctness index was used

as a SPF that directed the relative search effort for

taxonomically isolated taxa. To this end, the index for

all species was experimentally normalised between 1

and 50 in MARXANMARXAN.

The total number of species and endemics, the

average distinctness index of species and the propor-

tion of shared cells were used to compare the reserve

networks obtained with the different selection meth-

ods. In order to determine the effect of retaining one

network (A) rather than another (B) on species

composition, the total number of species present in

any combination of two reserve networks was decom-

posed into three components: (i) the percentage of

species shared by networks A and B; (ii) the percent-

age of species present exclusively in network A and

(iii) the percentage of species present exclusively in



network B. The sum of the three components equalled

100%. We also compared the frequency of species

representation in the reserve network by each of the

three selection methods.

Spatial constraints

We explored the effect of reduced size and increased

compactness of the reserve network on our ability to

(i) represent all species at least once and (ii) capture a

certain proportion of their area of occupancy. For this

second scenario, representation targets were defined

as a proportion of each species’ area of occupancy,

choosing for illustrative purpose targets of 100%, 50%

and 10% for species having areas of occupancy of 1

(i.e. endemic species by definition), £10 and >10 cells,

respectively. The minimum network size for achiev-

ing these two conservation goals was determined

using MARXANMARXAN.

The selection frequency of each cell in 1000 runs

performed in MARXANMARXAN, which indicates how many

times each cell was included in the final solution,

was used to assess the flexibility of the reserve

networks. Next, we examined the reduction in the

number of species that met their targets when

successively reducing the network size to 128 cells

by applying a cost-threshold penalty and aggregat-

ing these 128 cells into a smaller number of clusters

by varying the BLM. Raising the BLM to increase

compactness of the reserve network showed that

little aggregation was achieved in a reserve net-

work limited to 128 cells without reducing our

conservation targets. This situation of conflicting

constraints was irresolvable by the selection

algorithm. Thus, we experimentally set the SPFs at

values lower than 1 so that some species could be

under-represented by the algorithm, in order to

aggregate the reserve network.

Results

Geographical patterns of diversity

The distribution of geographical range sizes was

strongly skewed towards small areas of occupancy

and extents of occurrence (Fig. 1a). A total of 1059

species, 464 (44%) were endemic and 763 (72%) had

an extent of occurrence of less than 16 (i.e. 24) grid

cells at most. Only 9.4 and 12.0 % of species had an

area of occupancy respectively higher than 10 cells

and an extent of occurrence higher than 128 cells. Of

a total of 4675 cells, 1280 (27.4%) contained at least

one species (Fig. 2). A high proportion of cells

contained relatively few species: 78% of the 1280

cells had less than 5 species (Fig. 1b). The 464

endemic species were distributed over 262 cells,

70% of which contained only one endemic. A

disproportionately high number of species and

endemics occurred in five latitudinal bands extend-

ing from 42 to 47�N (Fig. 3). A total of 705 species

(67%) were exclusive to a least one of these five

latitudinal bands. The number of species and

endemics in a latitudinal band increased linearly

with the number of distributional records (species

richness: y = 0.13x, n = 16, r2 = 0.84, P < 0.001; en-

30

20

10

0

40

30

20

10

0

40

33–6417–329–165–83–421 > 64

33–6

4

65–1

28

129–

256

257–

512

513–1

024

1025

–204

8

17–3

29–1

65–

83–

421

Species richness

Endemic richness

Number of cells

Number of species

Extent of occurrence

Area of occupancy

Per

cent

age

of c

ells

Per

cent

age

of s

peci

es

(a)

(b)

Fig. 1 Frequency distribution of (a) the extent of occurrence

and area of occupancy among the 1059 species listed in the

European data base on groundwater biodiversity and (b) total

species richness and endemic species richness among the 1280

grid cells (0.2 · 0.2�) containing groundwater fauna.



demic richness: y = 0.04x, n = 16, r2 = 0.77,

P < 0.001). Despite this uneven latitudinal distribu-

tion of species and endemics, the ratio of endemic

richness to species richness calculated for each band

decreased linearly with increasing latitude

(y = )0.02x + 1.2, n = 15, r2 = 0.76, P < 0.001).

Species richness1–6

41–8820–407–19

0 300 km

2

3 4

5

1

6

Endemic richness1

10–155–92–4

50°

40°

45°

–5°(a)

(b)

0° 5° 15°10°Belgium

France

Portugal

Spain

Italy

Slovenia

Fig. 2 Patterns of total species richness

(a) and endemic species richness

(b) among 0.2 · 0.2� grid cells in southern

Europe. Circles show areas sampled

intensively during the PASCALIS survey

of groundwater biodiversity in six

European regions: (1) Wallonia;

(2) southern French Jura; (3) Cantabria;

(4) Roussillon; (5) Lessinian Mountains

and (6) Krim Massif.

0

100

200

300

400

36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 510

1000

2000

3000

4000

Latitude (°N)

Num

ber

of r

ecor

ds (

)

Num

ber

of s

peci

es (

),

end

emic

spe

cies

(

)or

spe

cies

exc

lusi

ve to

a 1

° la

titud

inal

ban

d (

)

Fig. 3 Latitudinal variation in the number

of species, endemic species, exclusive

species and distributional records across

Europe.



Comparison of reserve networks derived from three

selection methods

Selection of the most species-rich cells resulted in a

reserve network that differed markedly from those

obtained by selecting the cells richest in endemics and

most complementary (Fig. 4, Tables 1 & 2). Richness

hotspots contained 78 and 155 fewer species and 101

and 85 fewer endemics than the endemism hotspots

and complementary areas without SPF, respectively

(Table 1). The reserve networks of 128 cells based on

endemism hotspots and complementary shared only

52 and 59 cells with richness hotspots, respectively.

Endemism hotspots and complementary areas were

typically more fragmented and extended further

southwards: they comprised 87 and 95 clusters (i.e.

groups of contiguous cells and isolated cells), 51 and

43 of which occurred south of the 42� latitude,

respectively. In contrast, richness hotspots had only

44 clusters, five of which were located south of the 42�latitude. Selection of the most species-rich cells

increased the frequency of occurrences of species in

the reserve network. Fifty-six per cent of the species

occurred more than once in the richness hotspots

whereas this proportion was only 39% and 37% for

the endemism hotspots and complementary cells,

respectively (Fig. 5).

The reserve networks resulting from selection of the

cells richest in endemics and most complementary

shared 81% and 70% of species and cells, respectively

(Table 2). However, whereas complementary areas

captured 77 more species, they contained only 16

endemic species less than endemism hotspots (Ta-

ble 1). Application of SPFs for preferentially selecting

taxonomically isolated species (i.e. higher average

distinctness index in Table 1) decreased the number

of species and endemics captured by the algorithm,

although weighted complementary areas still con-

tained 39 more species than endemism hotspots.

Representing all species at least once

A total of 316 cells (29.8% of the 1280 cells containing

groundwater fauna) were needed to achieve the goal

of representing all 1059 species at least once (Fig. 6a).

The reserve network representing all species exhibited

a low level of flexibility. Only 373 cells out of 1280

appeared in at least one of the 1000 runs of the

optimisation algorithms, among which 278 (88%)

appeared in all solutions representing 1013 species.

Of the 278 cells appearing in all runs, 262 contained

the entire distribution of one or more species and

were therefore irreplaceable. Although one-quarter of

the cells (316 cells out of 1280) were needed to capture

all species at least once, a high proportion of species

rapidly accumulated in relatively few cells (Fig. 7). Of

a total of 1059 species, 531 (50%) were represented in

only 32 cells and 74% of species were represented in

96 cells. Adding an additional cell beyond 96 cells

resulted in an increase of only two or one species. The

best solution of the optimisation algorithm when

constraining the reserve network to a maximum

extent of 128 cells comprised 79.7% (844) of the total

0 300 km

(a)

(b)

(c)

45°

45°

45°

Fig. 4 Distribution of the 128 cells selected by three different

methods to represent groundwater fauna in southern Europe:

(a) richness hotspots, (b) endemism hotspots, and

(c) complementary areas. See Tables 1 & 2 for species

representation in each network.



number of species and 67.7% (314) of the total number

of endemics (Fig. 6b). This solution with no aggrega-

tion constraint (BLM = 0) contained 95 clusters most

of which (79%) were distributed in the two southern

thirds of the study area (latitude < 45�N). The aggre-

gated solution obtained by experimentally varying the

values of BLM and SPF comprised only 37 cell

clusters, that collectively encompassed 73.8% (782)

of the total number of species and 59.1% (274) of the

total number of endemics (Fig. 6c). The aggregation

constraint resulted in the exclusion of many isolated

cells in the most southern third of the study area and

delineation of larger cell clusters along a longitudinal

Alpine axis that extended from Cantabria to the

Dinaric karst. The 10 clusters (65.6% of 128 selected

cells) located along this axis comprised 72.3% of the

total species and 72.6% of the endemics included in

the aggregated reserve network.

Representing a certain proportion of the species area

occupancy

A total of 484 cells (45.7% of the 1280 cells contain-

ing groundwater fauna) were needed to meet the

Table 2 Differences in species composition between any combination of 2 reserve networks of 128 cells selected using different

methods and representation targets

Reserve networks Features

Endemism

hotspots (B)

Complementary areas (B)

No

SPF

With

SPF

Area of

occupancy

Richness hotspots (A) Shared cell 40.6 46.1 47.7 64.8

Shared species 69.7 73.0 74.7 81.9

Exclusive species

A 10.6 4.7 5.8 2.6

B 19.7 22.2 19.4 15.6

Endemism hotspots (A) Shared cell 70.3 60.2 53.1

Shared species 81.0 76.8 75.3

Exclusive species

A 5.2 9.4 10.8

B 13.8 13.7 13.9

Complementary areas

No SPF (A) Shared cell 69.5 68.7

Shared species 85.1 86.3

Exclusive species

A 9.7 9.7

B 5.3 4.1

With SPF (A) Shared cell 60.2

Shared species 82.4

Exclusive species

A 9.4

B 8.2

SPF, species penalty factor.

Table 1 Species representation in different kinds of reserve networks limited to 128 cells

Reserve networks

No.

records

No.

species

Proportion of

species (%)

No.

endemics

Proportion of

endemics (%)

Distinctness

index

Richness hotspots 5858 689 65.1 229 49.4 2.66

Endemism hotspots 4005 767 72.4 330 71.1 2.60

Complementary areas

No SPF 4359 844 79.7 314 67.7 2.59

With SPF 4380 805 76.0 291 62.7 2.76

Area of occupancy 5163 795 75.1 275 59.3 2.59

Total cells (1280) 10183 1059 100.0 464 100.0 2.55

SPF, species penalty factor.



conservation targets of the area of occupancy for all

species (Fig. 8a). Flexibility was low because 84% of

cells included in the network appeared in all runs of

the algorithm. The constraints imposed on the max-

imum area (128 cells) and aggregation of the reserve

network resulted in a severe reduction in the propor-

tion of species for which the conservation target was

achieved. The reserve network with a maximum

extent of 128 cells still contained 795 species (75.1%)

but only 628 of them met their conservation target

(59.3% of the total number of species; Fig. 8b).

Inclusion of conservation targets as percentages of

the area of species occupancy markedly affected the

ability of the algorithm to select endemic species

(Table 1) and resulted in a spatial configuration

resembling that of richness hotspots (Fig. 4, Table 2).

Aggregation of the 128 cells resulted in a network

made of only 17 clusters, most of which fell in the 42–

47�N latitudinal band (Fig. 8c). This aggregated net-

work contained 63.4% and 49.8% of the total number

of species and endemics, respectively, and fulfilled the

conservation targets of only 53.8% of the species.

Discussion

Hotspots and complementary areas

When designing priority areas for stygobiotic species

protection, the efficiency of the selection methods

depends on the conservation targets initially chosen.

If emphasis is placed on vulnerable species with

narrow distributional ranges, then the recommended

network of reserves would focus on hotspots of

endemism. If a given proportion of the species range

needs to be included in the reserve network, then

networks ensuring the duplication of richness hot-

spots are desirable. If the criteria require the greatest

number of species overall to be included in the

network, then complementarity selection is a power-

ful approach as it pays particular attention to

between-cell diversity. Indeed, the selection of com-

plementary areas was more efficient than the selection

of hotspots of species richness and endemism in

maximising the representation of species in a reserve

0 300 km

(a)

(c)

(b)

45°

45°

45°

Fig. 6 Depiction of the best reserve networks obtained from

1000 runs of the complementarity algorithm for representing all

species at least once. (a) Solution with no spatial constraints

(representation goal fully achieved); (b) reserve network

limited to 10% of the cells containing groundwater fauna;

(c) aggregated reserve network limited to 10% of the cells

containing groundwater fauna. See text for species

representation in solutions (b) and (c).

Number of cells occupied

Per

cent

age

of s

peci

es

17–329–165–83–421

40

20

0

60 Richness hotspots

Endemism hotspots

Complementary areas

Fig. 5 Frequency of species representation in reserve networks

as determined by each of three selection methods: richness

hotspots, endemism hotspots, and complementary areas.



network limited to 10% of the 1280 cells containing

groundwater fauna. The performance of complemen-

tarity over traditional selection methods for repre-

senting species in a near minimum area or

maximising their representation in a given fixed area

was emphasised in a number of studies (Williams

et al., 1996; Kati et al., 2004; Fox & Beckley, 2005).

Araujo (1999) found that the complementarity crite-

rion performed better than richness and rarity hot-

spots for complementing current reserve areas for

vertebrates and plants in Portugal.

The heuristic algorithm, which gave explicit atten-

tion to patterns of between-cell complementarity,

selected the most species-rich cells in multiple

regions, thereby potentially facilitating maintenance

of regionally distinct assemblages at the European

scale. In contrast, richness hotspots were heavily

influenced by the concentration of many species-rich

cells in a few regions. Regions designated as hotspots

of species richness coincided with areas that had

received a disproportionately high sampling effort.

The strong redundancy in species composition among

the most species-rich cells limited the total number of

species and endemics included in that particular

reserve system. This is confirmed by inspection of

the world list of subterranean biodiversity hotspots

(i.e. caves and wells with more than 20 species) in

which the five richest cave systems in France comprise

in total less then 60 different species because they are

located in only two ecoregions (the Pyrenees and

Languedoc) (Culver & Sket, 2000).

Complementary areas obtained without species

penalties were almost as efficient as endemism

hotspots in selecting endemics. This is not surprising

because (i) endemism hotspots are complementary by

nature and (ii) endemic species themselves strongly

influence the cell selection process as they constitute a

high proportion of the total richness of the ground-

water fauna (44%). The possibility of preferentially

selecting taxonomically isolated taxa using SPFs

without dramatically affecting the total number of

different species included in the reserve network

illustrates the variety of conservation strategies that

can be addressed using the objective function in

0 300 km

(a)

(b)

(c)

45°

45°

45°

Fig. 8 Depiction of the best reserve networks obtained from

1000 runs of the complementarity algorithm for representing

100%, 50% and 10% of the area of occupancy of species

present in 1, £10, and >10 cells, respectively. (a) Solution with no

spatial constraints (representation goal fully achieved);

(b) reserve network limited to 10% of the cells containing

groundwater fauna; (c) aggregated reserve network limited to

10% of the cells containing groundwater fauna. See text for

species representation in solutions (b) and (c).

0

20

40

60

80

100

0 50 100 150 200 250 300Number of cells

Per

cent

age

of to

tal s

peci

es n

umbe

r

Fig. 7 Species accumulation curve for 316 cells representing all

species at least one.



MARXANMARXAN. Alternatively, lower penalty factors could

be applied to species with doubtful taxonomic status

or distribution range to ensure that they do not drive

the cell selection process (but see Ardron et al., 2002,

for a discussion on the multiplicity of SPF usage).

The choice of a particular selection method not only

affected the number of represented species but also

the spatial distribution and fragmentation of the

reserve network. Species richness hotspots formed a

few cell clusters at mid latitudes, where sampling was

most intensive, whereas the distribution of cells

richest in endemics and most complementary was

more fragmented and extended southward. This

reflects the occurrence of many cells containing

endemic species in the Iberian and Italian peninsulas.

The southward increase in the ratio of endemic

species richness to total species richness was consis-

tent with the hypothesis that many species went

extinct in northern areas during Pleistocene glacia-

tions (Howarth, 1980). However, the spatial distribu-

tion of selected cells in all reserve networks was

heavily influenced by the occurrence of a mid-latitude

ridge of high biodiversity (c. 42–47�N). This ridge

accounted for 87%, 59% and 63% of cells and 92%,

80% and 79% of species included in the richness

hotspots, endemism hotspots and complementarity

areas, respectively. Most species-rich cells within this

ridge were located along the Alps and Pyrenees in

areas of high topographic diversity which comprised

multiple groundwater habitats in both karst and

unconsolidated sediments (alluvium, colluvium, gla-

cial till and glacial outwash). Culver et al. (2006)

hypothesised that the mid-latitude ridge of high

biodiversity (42–46�N) in the European terrestrial

cave fauna reflects the distribution of regions where

surface productivity remained high over the Qua-

ternary.

Influence of spatial constraints on species representation

Representing all species at least once requires a

reserve network that spans 30% of the cells compris-

ing groundwater fauna. This solution may seem

unrealistic to implement in a world where resources

allocated for conservation are severely limited. How-

ever, over the total land area of the six studied

countries this represents less than 7% of the cells.

Moreover, conservation of stygobiotic fauna also

means conservation of clean water resources and

other subterranean ‘goods’. However, reducing the

network area to 10% of the cells containing ground-

water fauna (i.e. <3% of the study area) would allow

capturing about 80% and 68% of the total number of

species and endemics. Culver et al. (2000) showed,

using a complementarity algorithm, that 50% of

obligate cave fauna occurred in less than 1% of the

U.S. landscape, and Ferreira et al. (2007) demonstrated

that only 1.2% of the French territory was needed to

capture 60% of groundwater species. The fast accu-

mulation rate of species in a few selected cells

reflected the disproportionately high contribution of

between-region diversity to total biodiversity. Malard

et al. (2009) used additive partitioning to quantify the

distribution of groundwater diversity among three

nested spatial levels in southern Europe (i.e. aquifers,

catchments and regions) and showed that beta diver-

sity between regions contributed 70% of total ground-

water species richness. Thus, protecting about five

species-rich and complementary aquifers in each of

the biogeographical regions should facilitate conser-

vation of a large proportion of species. Gering, Crist &

Veech (2003) showed that a significant proportion of

total species richness among arboreal beetles of

eastern deciduous forest of the U.S.A. was attributed

to between-region diversity and suggested that the

most effective conservation strategy would be to

acquire and protect sites within different ecoregions.

Although this strategy holds true for preserving

groundwater fauna, it would require protecting a

comparatively higher number of sites because the

distribution range of species and the extent of ecore-

gions are typically smaller in the subsurface environ-

ment than in the surface environment (Gibert &

Deharveng, 2002; Ferreira et al., 2005).

The aggregation constraint imposed on the area-

limited network resulted in a relatively small loss in

the number of species (7%) and endemics (13%)

selected by the complementarity algorithm. This

suggests that more highly aggregated groundwater

reserves could be designed to satisfy particular

management needs without greatly affecting the total

number of protected species. A small set of aggre-

gated reserve areas could be more practical to imple-

ment and manage than a widely scattered reserve

system. The increase in reserve compactness was

achieved by aggregating nearby cells in the mid-

latitude ridge of exceptionally high species richness

while excluding isolated cells with few species in



sparsely sampled southern regions. There is a limit to

the extent of aggregation because the final number of

reserve clusters must retain reserves from each

species-rich ecoregion in order to encompass the

diversity among regions. From considerations of life-

history and dispersal patterns of surface animals,

large connected patches are often advocated for

reserve delineation (Ovaskainen, 2002; Fischer &

Church, 2003; Naidoo & Ricketts, 2006). However,

this may be contraindicated for groundwater conser-

vation strategies, as many species are narrow range

endemics with limited dispersal capacity (Stoch, 1995;

Sket, 1999; Galassi, 2001; Trontelj et al., 2009). More-

over, the demography, life history and movement

behaviour of most groundwater species are unknown,

thereby complicating determination of the optimum

size and spacing of individual reserves.

Reduced size and increased aggregation were typ-

ically more constraining when the conservation goal

was to represent a desired proportion of species area-

occupancy. Indeed, area and aggregation constraints

successively decreased to 59.3% and 53.8% of the

proportion of species that met their conservation

targets. Moreover, the achievement of conservation

targets in the area-limited network resulted in the

exclusion of a disproportionate number of endemic

species. Most studies have recognised the need for an

effective reserve network to protect a minimum

proportion (>20%) of the distribution range of species

(Ney-Nifle & Mangel, 2000; Airame et al., 2003).

However, the question arises whether this strategy,

which tends to underrepresent endemic species,

should be applied to groundwater systems although

the distribution range of most groundwater species is

so poorly documented. Ferreira et al. (2007) showed

that the range size of groundwater species collected in

France markedly increased over the last 100 years as

distributional data accumulated. Conversely, recent

genetic studies showed that the apparently wide

distribution range of several groundwater species

was an artefact generated by our ignorance of cryptic

diversity (Goricki & Trontelj, 2006; Lefebure et al.,

2006, 2007; Trontelj et al., 2009).

Research needs for improving groundwater reserve

design

We identified three major research gaps that severely

limit the implementation of an effective groundwater

reserve network at the European scale. First, sampling

heterogeneity results in uncertainties in the placement

of individual reserves. The strong positive correlation

(r = 0.92) between the number of species and distri-

bution records suggests that there is a risk to misdirect

conservation actions towards a few highly sampled

regions. Analyses of changes that have occurred over

the last 60 years in apparent patterns of stygobiotic

richness hotspots in France (Ferreira et al., 2007),

Slovenia (Culver et al., 2004) and the U.S.A. (Culver

et al., 2000) yielded disparate findings, ranging from

little to marked variation in the location of hotspots in

response to increased sampling effort. Nevertheless,

efforts should be made to focus faunal inventories in

weakly sampled regions of southern Europe, thereby

increasing chances of identifying new complementary

hotspots. This sampling strategy need not markedly

increase the proportion of the landscape allocated for

groundwater biodiversity conservation but it could

increase the fragmentation of the reserve network if a

greater number of hotspots required protection.

Secondly, the lack of flexibility to design ground-

water reserve networks severely limits our ability to

circumvent conflicts between biodiversity conserva-

tion and economic activity by proposing alternative

scenarios with similar species coverage. Flexibility is a

key feature of reserve networks that often determines

their successful implementation (Rodrigues, Cerdeira

& Gaston, 2000). Although the narrow distribution

range of most groundwater species inevitably affects

flexibility, incomplete sampling in most regions also

severely reduces flexibility in the placement of indi-

vidual reserves. An adequate strategy would thus be

to probe for alternative richness hotspots by sampling

additional areas in species-rich regions that have been

sampled in a heterogeneous way. For example,

increased sampling along the southern alluvial corri-

dor of the Rhone River, France, could result in a

number of aquifers with similar species assemblages

(Dole-Olivier et al., 1994; Gibert et al., 2000).

Thirdly, the establishment of a groundwater reserve

network is ultimately dependent upon socio-economic

factors. These factors can either be assessed as post hoc

filters of groundwater reserves selected according to

biological criteria or, preferably, they can be integrated

earlier as costs in the objective function (Faith et al.,

2003; Stewart & Possingham, 2005; Yang et al., 2005).

However, consideration of socio-economic factors

would necessarily impose that the grid cell approach



be replaced by an approach focusing on aquifer

selection (Di Lorenzo et al., 2004). Indeed, the two

features that need to be considered for evaluating the

implementation cost of a reserve, namely groundwa-

ter vulnerability and extent of human activity, can

only be assessed at the aquifer scale. Groundwater

vulnerability, which refers to the sensitivity of ground

water to human activities, depends on intrinsic prop-

erties of aquifers such as soil cover, thickness of the

vadose zone and sediment permeability. Human

activities resulting in the loss (by groundwater extrac-

tion) or degradation (by diffuse and point-source

pollution) of groundwater habitats likewise have to be

mapped at the scale of the surface catchment that

contributes to the aquifer recharge.

Conservation measures would be more constraining

and costly when the interaction between human

activities and aquifer vulnerability results in a high

risk of biodiversity loss. Yet, even the least expensive

conservation measures would be difficult to imple-

ment because there is almost no specific policy frame-

work nor legislative means at the European, national or

local scale to protect groundwater fauna (Danielopol

et al., 2004). The distribution of groundwater biodiver-

sity reserves does not necessarily fit that of aquifers

used for drinking water supply which are currently

managed for conservation through a number of regu-

lations (COST, 1995; European Commission, 2006).

With recent advances in the knowledge of groundwa-

ter biodiversity (Deharveng et al., 2009), lack of specific

legislation to protect groundwater ecosystems emerges

as a major constraint for the successful conservation of

groundwater fauna (Stoch, 2001).

Acknowledgments

We are indebted to the numerous scientists who were

involved in the assembly of the European groundwa-

ter species database. We thank three anonymous

reviewers for comments that improved an earlier

version of this manuscript. This work was supported

by the European project PASCALIS (Protocols for the

ASsessment an Conservation of Aquatic Life In the

Subsurface, No EVK2-CT-2001-00121).

References

Airame S., Dugan J., Laferty K., Leslie H., Mc Ardle D. &

Warner R. (2003) Applying ecological criteria to

marine reserve design: a case study from the California

Channel islands. Ecological Applications, 13, S170–S184.

Araujo M. (1999) Distribution patterns of biodiversity

and the design of a representative reserve network in

Portugal. Diversity and Distributions, 5, 151–163.

Ardron J.A., Lash J. & Haggarty D. (2002) Modelling a

Network of Marine Protected Areas for the Central Coast of

British Columbia. Version 3.1. Living Oceans Society,

Sointula, BC.

Arntzen J.W., Denoel M., Miaud C., Andreone F., Vogrin

M., Edgar P., Isailovic J.C., Ajtic R. & Corti C. (2008)

Proteus anguinus. IUCN Red List of Threatened Species,

IUCN. Available at: http://www.iucnredlist.org.

Ball I.R. & Possingham H.P. (2000) MARXAN (V1.8.2):

Marine Reserve Design Using Spatially Explicit Annealing, a

Manual. Available at: http://www.ecology.uq.edu.au.

Ball I.R. & Possingham H.P. (2001) The design of marine

protected areas: adapting terrestrial techniques. In:

Proceedings of the International Congress on Modelling and

Simulation, Natural Systems (Vol. 2) (Eds F. Ghassemi,

P. Whetton, R. Little & M. Littleboy), pp. 769–774. The

Modelling and Simulation Society of Australia and

New Zealand Inc., Canberra.

Barker P., Wardlaw T. & Brown M. (1996) Selection and

design of Phytophthora management areas for the

conservation of threatened flora in Tasmania. Biological

Conservation, 76, 187–193.

Bedward M., Pressey R.L. & Nicholls A.O. (1991) Score

and score classes for evaluation criteria: a comparison

based on the cost of reserving all natural features.

Biological Conservation, 56, 281–294.

Chave J., Wiegand K. & Levin S. (2002) Spatial and

biological aspects of reserve design. Environmental

Modeling and Assessment, 7, 115–122.

Clarke K.R. & Warwick R.M. (1998) A taxonomic

distinctness index and its statistical properties. Journal

of Applied Ecology, 35, 523–531.

Cook R. & Auster P. (2005) Use of simulated annealing

for indentifying essential fish habitat in a multispecies

context. Conservation Biology, 19, 876–886.

COST (1995) Hydrogeological Aspects of Groundwater Pro-

tection in Karstic Areas. Final Report. European Com-

mission, Directorate-General XII Science, Research and

Development, Brussels.

Culver D.C. & Sket B. (2000) Hotspots of subterranean

biodiversity in caves and wells. Journal of Cave and

Karst Studies, 62, 11–17.

Culver D.C., Master L.L., Christman M.C. & Hobbs H.H.

III (2000) Obligate cave fauna of the 48 contiguous

United States. Conservation Biology, 14, 386–401.

Culver D.C., Christman M.C., Sket B. & Trontelj P. (2004)

Sampling adequacy in an extreme environment:



species richness patterns in Slovenian caves. Biodiver-

sity and Conservation, 13, 1209–1229.

Culver D.C., Deharveng L., Bedos A., Lewis J.J., Madden

M., Reddell J.R., Sket B., Trontelj P. & White D. (2006)

The mid-latitude biodiversity ridge in terrestrial cave

fauna. Ecography, 29, 120–128.

Danielopol D.L., Griebler C., Gunatilaka A. &

Notenboom J. (2003) Present state and future prospects

for groundwater ecosystems. Environmental Conserva-

tion, 30, 104–130.

Danielopol D.L., Gibert J., Griebler C., Gunatilaka A.,

Hahn H.J., Messana G., Notenboom J. & Sket B. (2004)

Incorporating ecological perspectives in European

groundwater management policy. Environmental Con-

servation, 31, 185–189.

Deharveng L., Stoch F., Gibert J. et al. (2009) Groundwa-

ter biodiversity in Europe. Freshwater Biology, 54, 709–

726.

Di Lorenzo T., Stoch F., Fiasca B., Gattone E., De

Laurentiis P., Ranalli F. & Galassi D.M.P. (2004)

Environmental quality of the groundwater in the

Lessinian Massif (Italy): signposts for sustainability.

In: Proceedings of an International Symposium on World

Subterranean Biodiversity (Ed. J. Gibert), pp. 115–124.

Villeurbanne, France, 8–10 December 2004. University

of Lyon, Lyon.

Diamond J.M. (1975) The island dilemma: lessons of

modern biogeographic studies for the design of natural

preserves. Biological Conservation, 7, 129–146.

Diniz-Filho J.A.F., Bini L.M., Bastos R.P., Vieira C.M. &

Vieira L.C.G. (2005) Priority areas for anuran conser-

vation using biogeographical data: a comparison of

greedy, rarity, and simulated annealing algorithms to

define reserve networks in Cerrado. Brazilian Journal of

Biology, 65, 251–261.

Dole-Olivier M.-J., Marmonier P., Creuze des Chatelliers

M. & Martin D. (1994) Interstitial fauna associated with

the alluvial floodplains of the Rhone River (France). In:

Groundwater Ecology (Eds J. Gibert, D. Danielopol & J.A.

Stanford), pp. 313–346. Academic Press, San Diego, CA.

Dole-Olivier M.-J., Castellarini F., Coineau N., Galassi

D.M.P., Martin P., Mori N., Valdecasas A. & Gibert J.

(2009) Towards an optimal sampling strategy to assess

groundwater biodiversity: comparison across six Euro-

pean regions. Freshwater Biology, 54, 777–796.

European Commission (2006) Directive on the protection

of groundwater against pollution and deterioration

(2006 ⁄ 118 ⁄EC). Official Journal of the European Union, L

372, 19–31.

Faith D.P., Carter G., Cassis G., Ferrier S. & Wilkie L.

(2003) Complementarity, biodiversity viability analysis

and policy-based algorithms for conservation. Envi-

ronmental Science and Policy, 6, 311–328.

Ferreira D., Dole-Olivier M.-J., Malard F., Deharveng L.

& Gibert J. (2003) Faune aquatique souterraine de

France: base de donnees et elements de biogeographie.

Karstologia, 42, 15–22.

Ferreira D., Malard F., Dole-Olivier M.-J. & Gibert J.

(2005) Hierarchical patterns of obligate groundwater

biodiversity in France. In: Proceedings of an International

Symposium on World Subterranean Biodiversity (Ed. J.

Gibert), pp. 75–78. Villeurbanne, France, 8–10 Decem-

ber 2004. University of Lyon, Lyon.

Ferreira D., Malard F., Dole-Olivier M.-J. & Gibert J.

(2007) Obligate groundwater fauna of France: species

richness patterns and conservation implications. Bio-

diversity and Conservation, 16, 567–596.

Fischer D.T. & Church R.L. (2003) Clustering and

compactness in reserve site selection: an extension of

the biodiversity management area selection model.

Forest Science, 49, 555–565.

Fischer D.T. & Church R.L. (2005) The SITES reserve

selection system: a critical review. Environmental Mod-

eling and Assessment, 10, 215–228.

Fjeldsa J., De Klerk H.M., Blyth S. & Burgess N.D. (2004)

Where are the major gaps in the reserve network for

Africa’s mammals? Oryx, 38, 17–25.

Fox N. & Beckley L. (2005) Priority areas for conservation

of Western Australian coastal fishes: a comparison of

hotspot, biogeographical and complementarity ap-

proaches. Biological Conservation, 125, 399–410.

Fuller T., Munguıa M., Mayfield M., Sanchez-Corderdo

V. & Sarkar S. (2006) Incorporating connectivity into

conservation planning: a multi-criteria case study from

Central Mexico. Biological Conservation, 133, 131–142.

Galassi D.M.P. (2001) Groundwater copepods: diversity

patterns over ecological and evolutionary scales. Hyd-

robiologia, 453 ⁄454, 227–253.

Garson J., Aggarwal A. & Sarkar S. (2002) Birds as

surrogates for biodiversity: an analysis of a data set

from southern Quebec. Journal of Biosciences, 27, 347–360.

Gaston K.J. (1991) How large is a species geographic

range? Oikos, 61, 434–438.

Gering J.C., Crist T.O. & Veech J.A. (2003) Additive

partitioning of species diversity across multiple spatial

scales: implications for regional conservation of biodi-

versity. Conservation Biology, 17, 488–499.

Gibert J. (2001) Protocols for the assessment and conser-

vation of aquatic life in the subsurface (PASCALIS): a

European project. In: Mapping Subterranean Biodiversity

(Eds D.C. Culver, L. Deharveng, J. Gibert & I.D.

Sasowsky), pp. 19–21. Special Publication 6, Karst

Waters Institute, Charles Town, West Virginia.

Gibert J. & Deharveng L. (2002) Subterranean ecosys-

tems: a truncated functional biodiversity. BioScience,

52, 473–481.



Gibert J., Malard F., Turquin M.-J. & Laurent R. (2000)

Karst ecosystems in the Rhone River basin. In: Subter-

ranean Ecosystems. Ecosystems of the World 30 (Eds

H. Wilkens, D.C. Culver & W.F. Humphreys), pp.

533–558. Elsevier, Amsterdam.

Goricki S. & Trontelj P. (2006) Structure and evolution

of the mitochondrial control region and flanking

sequences in the European cave salamander Proteus

anguinus. Gene, 378, 31–41.

Halpern B. & Warner R.R. (2003) Matching marine

reserve design to reserve objectives. Proceedings of the

Royal Society of London, Series B, 270, 1871–1878.

Howarth F.G. (1980) The zoogeography of specialized

cave animals: a bioclimatic model. Evolution, 34, 394–

406.

Juberthie C. (1995) Underground Habitats and their Protec-

tion. Nature and Environment, 72. Council of Europe,

Strasbourg.

Juberthie C. & Juberthie-Jupeau L. (1975) La reserve

biologique du laboratoire souterrain du CNRS a Sauve

(Gard). Annales de Speleologie, 30, 539–551.

Kati V., Devillers P., Dufrene M., Legakis A., Vokou D. &

Lebrun P. (2004) Hotspots, complementarity or repre-

sentativeness? Designing optimal small-scale reserves

for biodiversity conservation. Biological Conservation,

120, 471–480.

Kirkpatrick S., Gelatt C.D. Jr & Vecchi M.P. (1983)

Optimization by simulated annealing. Science, 220,

671–680.

Lefebure T., Douady C.J., Gouy M., Trontelj P., Briolay J.

& Gibert J. (2006) Phylogeography of a subterranean

amphipod reveals cryptic diversity and dynamic evo-

lution in extreme environments. Molecular Ecology, 15,

1797–1806.

Lefebure T., Douady C.J., Malard F. & Gibert J. (2007)

Testing dispersal and cryptic diversity in a widely

distributed groundwater amphipod (Niphargus rheno-

rhodanensis). Molecular Phylogenetics and Evolution, 42,

676–686.

Leslie H., Ruckelshaus M., Ball I., Andelman S. &

Possingham H. (2003) Using sitting algorithms in the

design of marine reserve networks. Ecological Applica-

tions, 13, 185–198.

Malard F., Gibert J. & Laurent R. (1997) L’aquifere de la

source du Lez: un reservoir d’eau et de biodiversite.

Karstologia, 30, 49–54.

Malard F., Boutin C., Camacho A.I., Ferreira D., Michel

G., Sket B. & Stoch F. (2009) Diversity patterns of

stygobiotic crustaceans across multiple spatial scales in

Europe. Freshwater Biology, 54, 756–776.

Margules C.R. & Pressey R.L. (2000) Systematic conser-

vation planning. Nature, 405, 243–253.

McDonnell M., Possingham H., Ball I. & Cousins E.

(2002) Mathematical methods for spatially cohesive

reserve design. Environmental Modeling and Assessment,

7, 107–114.

Moilanen A. & Wintle B.A. (2006) Uncertainty analysis

favours selection of spatially aggregated reserve struc-

tures. Biological Conservation, 129, 427–434.

Naidoo R. & Ricketts T.H. (2006) Mapping the economic

costs and benefits of conservation. PLoS Biology, 4,

2153–2164.

Ney-Nifle M. & Mangel M. (2000) Habitat loss and

changes in the species–area relationship. Conservation

Biology, 14, 893–898.

Ovaskainen O. (2002) Long-term persistence of species

and the SLOSS problem. Journal of Theoretical Biology,

218, 419–433.

Pante E., Adjeroud M., Dustan P., Penin L. & Schrimm

M. (2006) Spatial patterns of benthic invertebrate

assemblages within atoll lagoons: importance of hab-

itat heterogeneity and considerations for marine pro-

tected area design in French Polynesia. Aquatic Living

Resources, 19, 207–217.

Possingham H., Ball I. & Andelman S. (2000) Mathemat-

ical methods for identifying representative reserve

networks. In: Quantitative Methods for Conservation

Biology (Eds S. Ferson & M. Burgman), pp. 291–305.

Springer-Verlag, New York.

Prendergast J.R., Quinn R.M. & Lawton J.H. (1999) The

gaps between theory and practice in selecting nature

reserves. Conservation Biology, 13, 484–492.

Pressey R.L., Humphries C.J., Margules C.R., Vane-

Wright R.I. & Williams P.H. (1993) Beyond opportun-

ism: key principles for systematic reserve selection.

Trends in Ecology and Evolution, 8, 124–128.

Pressey R.L., Johnson I.R. & Wilson P.D. (1994) Shades of

irreplaceability towards a measure of the contribution

of sites to a reservation goal. Biodiversity and Conserva-

tion, 3, 242–262.

Pressey R.L., Possingham H.P. & Day J.R. (1997) Effec-

tiveness of alternative heuristic algorithms for identi-

fying indicative minimum requirements for

conservation reserves. Biological Conservation, 80, 207–

219.

Proudlove G.S., Wood P.J., Harding P.T., Horne D.J.,

Gledhill T. & Knight L.R.F.D. (2003) A review of the

status and distribution of the subterranean aquatic

Crustacea of Britain and Ireland. Cave and Karst Science,

30, 53–74.

Rodrigues A., Cerdeira O. & Gaston K. (2000) Flexibility,

efficiency and accountability: adapting reserve selec-

tion algorithms to more complex conservation prob-

lems. Ecography, 23, 565–574.



Simberloff D. & Martin J.-L. (1991) Nestedness of insular

avifaunas: simple summary statistics masking complex

species patterns. Ornis Fennica, 68, 178–192.

Sket B. (1999) The nature of biodiversity in hypogean

waters and how it is endangered. Biodiversity and

Conservation, 8, 1319–1338.

Sket B., Paragamian K. & Trontelj P. (2004) A census of the

obligate subterranean fauna in the Balkan Peninsula.

In: Balkan Biodiversity. Pattern and Process in Europe’s

Biodiversity Hotspot (Eds H.I. Griffiths, B. Krystufek &

J.M. Reed ), pp. 309–322. Kluwer, Dordrecht.

Stewart R.R. & Possingham H.P. (2005) Efficiency, costs

and trade-offs in marine reserve system design. Envi-

ronmental Modeling and Assessment, 10, 203–213.

Stoch F. (1995) The ecological and historical determinants

of crustacean diversity in groundwaters, or: why are

there so many species? Memoires de Biospeleologie, 22,

139–160.

Stoch F. (2001) Mapping subterranean biodiversity:

structure of the database, mapping software (CKMAP),

and a report of status for Italy. In: Mapping Sub-

terranean Biodiversity (Eds D.C. Culver, L. Deharveng,

J. Gibert & I.D. Sasowsky), pp. 29–35. Special Publica-

tion 6, Karst Waters Institute, Charles Town, West

Virginia.

Trontelj P., Douady C.J., Fiser C., Gibert J., Goricki S.,

Lefebure T., Sket B. & Zaksek V. (2009) A molecular

test for cryptic diversity in ground water: how large

are the ranges of macro-stygobionts? Freshwater Biol-

ogy, 54, 727–744.

Vane-Wright R.I., Humphries C.J. & Williams P.H. (1991)

What to protect? Systematics and the agony of choice

Biological Conservation, 55, 235–254.

Williams P., Gibbons D., Margules C., Rebelo A.,

Humphries C. & Pressey R. (1996) A comparison of

richness hotspots, rarity hotspots, and complementary

areas for conserving diversity of British birds. Conser-

vation Biology, 10, 155–174.

Yang W., Khanna M., Farnsworth R. & Ona H. (2005) Is

geographical targeting cost-effective? The case of the

conservation reserve enhancement program in Illinois.

Review of Agricultural Economics, 27, 70–88.

(Manuscript accepted 29 January 2009)



Reserve selection for conserving groundwater biodiversity

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