Carlos LARA-ROMERO*
Área de Biodiversidad y Conservación, Escuela Superior de
Ciencias Experimentales y T ecnología, Universidad Rey Juan Carlos, C/Tulipán s/n,
E-28933 Móstoles (Madrid), Spain. E-mail: carlos.lara@urjc.es
Emilio
VIRGÓS Área de Biodiversidad y Conservación, Escuela Superior de Ciencias
Experimentales y T ecnología, Universidad Rey Juan Carlos, C/Tulipán s/n, E-28933
Móstoles (Madrid), Spain. E-mail: emilio.virgos@urjc.es
Eloy REVILLA Department of Conservation Biology,
Estación Biológica de Doñana
CSIC, Calle
Américo Vespucio s/n, E-41092 Sevilla, Spain. E-mail: revilla@ebd.csic.es
ABSTRACT
1. Estimations of European badger population density in the UK are usually based on surveys of numbers of main setts. However, this approach cannot be used in low- density areas, such as the Mediterranean region, where no main setts can be defined.
Therefore, an alternative method is needed to estimate badger density over large areas.
2. We reviewed the existing published information to evaluate whether badger density is correlated to the density of all setts in an area (not only the main setts) and to ask whether badger density can be predicted from total sett number throughout the geographical range of the species.
3. In multiple regression analysis, badger density and the size of the study area explained 73% of the variance in sett density. Badger density had a significant positive effect on sett density, while the effect of study area size was not significant.
Therefore, total sett density can be used to obtain an estimation of relative badger density in all habitats and regions throughout the badger’s geographical range, allowing comparative research.
Keywords : abundance, density estimate, human
–wildlife interactions, large-scale surveys, wildlife research
INTRODUCTION
The European badger Meles meles is widely distributed throughout the western
Palaearctic region in a broad range of environments and ecological conditions (Del
Cerro et al. 2010). In most of Britain and Central Europe, badger populations can reach densities of up to 25 individuals per km 2 (Wilson et al. 1997), although densities of less than five individuals per km 2 are more common (Reason et al. 1993,
Wilson et al. 1997, Palphramand et al. 2007). In these high- or medium-density areas,
*Correspondence author.

badgers live in territories occupied by large social groups (Kruuk 1989) and centred in a communal burrow where most individuals sleep. These communal burrows or main setts can easily be distinguished from smaller setts in the field by their large number of entrances (generally more than five), large spoil heaps, well-worn paths connecting entrances and signs of regular use (Roper 1992, Macdonald et al. 1996).
In high-density areas, large-scale estimates of badger population density and distri- bution are based on the number of main setts detected in the field (Wilson et al.
1997, Schley et al. 2004, Newton-Cross et al. 2007).
In other regions, such as the Mediterranean, and in boreal environments, badgers live in pairs or small groups at densities of less than 0.7 adults km 2 (Broseth et al.
1997a, Kowalczyk et al. 2000, Revilla et al. 2001, Rosalino et al. 2004). In these low-density areas, badgers use small setts sporadically, and no clear main setts can be defined (Broseth et al. 1997b, Revilla et al. 2001, Remonti et al. 2006). Therefore, the estimation of badger density based on the number of main setts is not applicable in low-density areas (Revilla et al. 2001, Remonti et al. 2006). Other approaches such as intensive capture
–recapture or radiotracking programs (e.g. Kowalczyk et al. 2000,
Palphramand et al. 2007) often cannot be used due to the lack of time and resources or due to the large scale required in some monitoring programs. Therefore, an indirect technique, such as the estimation of sett density, might be the best approach considering economic and time constraints (Sadlier et al. 2004). The relevant ques- tion is whether an estimation of badger sett density (made by recording all types of setts in an area) can be used to predict badger density in both high- and low-density areas.
In this paper, we use data from the literature to analyze the relationship between sett density and badger population density in contrasting regions throughout
Europe. In particular, we evaluate whether badger density is correlated to the density of all setts in an area (not only the main setts) and whether badger density or at least relative abundance of badgers can be predicted from the number of badger setts recorded.
METHODS
We reviewed 19 studies on badger sett density, population density and study area size for 21 populations over the species’ whole distribution range (Table 1). Size of study area was needed because density and abundance estimates cannot be com- pared for areas of different sizes or of unknown size. In particular, log
10 of the population density estimates decreased linearly with the log
10 of the measured area of the study site (see Schonewald-Cox et al. 1991, Smallwood & Schonewald 1996).
Badger densities were estimated using a battery of standard methods validated for their use in estimating the population density of this species (Table 1). In radiotrack- ing studies, badger setts were located by following marked individuals, and addi- tional sett surveys were carried out to locate other setts in the area.
To evaluate the relationship between sett density and badger density, we per- formed a multiple regression analysis with sett density as the response variable and badger density and study area size as predictors. We included study area size in the multiple regressions in order to control statistically for its effect on population density estimate [following rationale from Schonewald-Cox et al. (1991) and
Smallwood and Schonewald (1996)]. Both densities and study area size were log- transformed, and we checked for normality, homogeneity of variance and linearity

Table 1. Densities of setts and individual badgers in European badger populations, study areas, density estimation methods and literature sources
Country, location
Sett density
(setts km 2
)
Czech Republic, Buen Forest 0.18
England, Bristol
England, Wytham Wood
England, East Sussex
England, Essex
England, Dalby Forest
9.60*
6.48
Finland, Virolahti
Ireland, Offaly
Ireland, Katesbridge
Ireland, Castleward
Ireland, Glenwhirry the Netherlands, Utrecht
Norway, Malvik
Poland, BPF
Poland, Rogow
Portugal, Grandola
Spain, Coto del Rey
Spain, Doñana
Spain, Montserrat
Switzerland, Jura Mountains 0.60
Switzerland, BCT 0.80
1.25
0.66
9.01
0.52
3.50
1.48
14.29
4.31
0.44†
0.88
0.27*
0.11
0.47
2.84
0.44
3.76
Badger density
(animals km 2
)
1.60
11.90
0.72
0.19†
0.50*
0.16
0.31
0.42
0.45
5.90†
8.40
2.88
0.26
4.90
0.24
3.20
0.67
0.36
1.98
0.95
1.80
Size of study area (km
2
) Density estimation methods
12.00
8.00
10.80
200.00
41.00
16.8
54.00
16.00
5.07
3.08
8.76
90.00
60.00
290.00
89.20
66.00
42.00
25.00
5.05
30.00
26.00
Source
Visual census Pelikan and Vackar (1978)
Capture, radiotracking and visual census Cresswell and Harris (1988)
Capture, radiotracking and visual census Kruuk (1978), Kruuk and Parish (1982)
Mean adult group
Visual census size‡ Macdonald et al. (1996)
Skinner and Skinner (1988)
Visual census, capture, dung pit index Palphramand et al. (2007)
Capture with direct enumeration
Capture
–recapture
Visual census
Visual census
Capture with direct enumeration
Capture with direct enumeration
Capture with direct enumeration
Capture with direct enumeration
Kauhala et al. (2006)
O’Corry-Crowe et al. (1996)
Feore and Montgomery (1999)
Feore and Montgomery (1999)
Feore and Montgomery (1999)
Van Apeldoorn et al. (2006)
Broseth et al. (1997a, b)
Kowalczyk et al. (2000)
Visual census Goszczyñski and Skoczyñska (1996)
Capture, radiotracking and visual census Rosalino et al. (2004)
Capture, radiotracking
Capture –recapture
Revilla et al. (2001)
Rodriguez et al. (1996)
Capture, radiotracking and visual census Rafart (2005)
Capture, radiotracking and visual census Do Linh San et al. (2007a)
Capture, radiotracking and visual census Do Linh San et al. (2007b)
BPF, Bialowieza Primeval Forest; BCT, Saint-Blaise-Cressier-Thielle.
*Estimation based on published information.
†Mean of data from several years of study.
‡Real mean adult group size estimates from parts of the same study area.

Sett density and badger abundance 81
3
2
1
0
-1
-2
-3
-3 -2 -1 0 1 2 3
−2
Fig. 1. Relationship between sett density km 2
(log setts km 2
) and badger density km 2
(log badgers km 2 ) recorded in the 21 study areas throughout Europe shown in Table 1.
through standard residual analysis. We also checked the influence and deviation of individual data points on the fitted linear regression by means of standardized residuals and Cook’s distances. Data points with standardized residuals larger than three standard deviations from the mean were considered outliers.
Cook’s distance should be similar between the different data points, and a point is considered influential when its Cook’s distance is greater than one (Chatterjee &
Hadi 2006).
RESULTS
The log-transformed densities of all setts and badgers were correlated ( r = 0.84,
P < 0.001; Fig. 1). In the multiple regression analysis, badger density and size of study area explained 73.2% of the variance for the response variable ( F
2,18
= 24.53, r = 0.86,
P < 0.001). Badger density had a significant positive effect on sett density, while the effect of study area size was not significant (Table 2). None of the data points had a large residual.
DISCUSSION
The multiple regression analysis indicated that there is a strong relationship between total sett density and badger density even when size of the study area was con- sidered. These results suggest that badger sett density can be used as a surrogate of

82 C. Lara-Romero, E. V irgós and E. Revilla
Variable
Intercept
Badger density
[log(animal km 2 )]
Study area size
[log(km 2 )]
Estimate
0.968
0.756
0.225
SE
0.611
0.167
t
1.59
4.516
P
0.130
0.001
0.180
1.254
0.225
Table 2. Estimates of the regression parameters, their standard error (SE) and statistical significances ( t and P ) badger density throughout the species’ distribution range. Reliable methods for estimating abundance are fundamental to the conservation, research and manage- ment of carnivore populations, as they provide key data to make decisions in con- servation programs (Wilson & Delahay 2001, Barea-
Azcón et al. 2007). Density estimates derived from direct methods (e.g. capture –mark–recapture, radiotracking) are likely to be the preferred choice (Wilson & Delahay 2001, Sadlier et al. 2004).
However, these techniques are generally unsuitable for estimating badger density over large areas because live trapping is expensive, labour intensive and invasive
(Wilson & Delahay 2001, Sadlier et al. 2004, BareaAzcón et al. 2007). Indirect methods that rely on counts of signs of badger presence (e.g. latrines or setts) may provide a good approach when the aim is to estimate relative abundance in different habitats or at different times (Wilson & Delahay 2001, Sadlier et al. 2004, Barea-
Azcón et al. 2007).
In conclusion, recording all setts may provide relative estimates of abundance in all habitats and regions throughout the badger’s geographical range, allowing com- parative research. This would not be possible if only main sett density was used because this technique cannot be applied in low badger density areas. Thus, we highlight the usefulness of this surveying technique and the importance of new studies on the relationship between sett density and badger density to provide more accurate estimates of badger population density.
ACKNOWLEDGEMENTS
This work was financed by Projects CGL2005-07681 and CGL2009-07301 and a For- mación Personal Investigador (FPI) grant (Spanish Ministry of Science) to Carlos
Lara-Romero.
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Submitted 6 February 2011; returned for revision 7 March 2011; revision accepted 28 April 2011
Editor: KH