Diversity and Distributions, (Diversity Distrib.) (2008) 14, 78–86
Blackwell Publishing Ltd
BIODIVERSITY
RESEARCH
Biodiversity surrogate groups and
conservation priority areas:
birds of the Brazilian Cerrado
Míriam Plaza Pinto1,†, José Alexandre Felizola Diniz-Filho2*,
Luis Mauricio Bini2, Daniel Blamires3,4 and Thiago Fernando L. V. B. Rangel1,5
1
Programa de Pós-Graduação em Ecologia e
Evolução, ICB, Universidade Federal de Goiás,
Goiânia, GO, Brazil, 2Departamento de
Biologia Geral, ICB, Universidade Federal
de Goiás, Goiânia, GO, Brazil, 3Programa de
Pós-Graduação em Ciências Ambientais,
Universidade Federal de Goiás, Goiânia, GO,
Brazil, 4Unidade Universitária de Quirinópolis,
Universidade Estadual de Goiás, Avenida Brazil
Qd. 03, Lt. 01 s/n°. Conjunto Hélio Leão, 75860000 Quirinópolis, GO, Brazil, 5Department
of Ecology and Evolutionary Biology, University
of Connecticut, 75 North Eagleville Road,
Unit 3043, Storrs, CT 06269-3043, USA
*Correspondence: José Alexandre Felizola
Diniz-Filho, Departamento de Biologia Geral,
ICB, Universidade Federal de Goiás, Goiânia,
GO, Brazil.
E-mail: [email protected]
†Present address: Programa de Pós-Graduação
em Ecologia, Laboratório de Vertebrados,
Departamento de Ecologia, Universidade Federal
do Rio de Janeiro, C.P. 68020, 21941-590 Rio de
Janeiro, RJ, Brazil
ABSTRACT
The choice of surrogates of biodiversity is an important aspect in conservation biology. The quantification of the coincidence in the spatial patterns of species richness
and rarity between different groups and the vulnerability of groups are different
approaches frequently considered to accomplish this task. However, a more appropriate approach is to verify the efficiency of priority networks selected using information from one group of organisms to capture the biodiversity of other groups.
Using a deconstructive approach, the main purposes of this study were to evaluate
the performance of some orders and families of birds in the Cerrado biome (a
savanna-like biome) as surrogates of other bird groups, in a pairwise analysis, and to
investigate the characteristics of these groups that predict the efficiency in representation of other groups. We used biogeographical data on bird orders or families with
more than 10 species that occur in the Brazilian Cerrado. The best surrogate group
was the Thamnophilidae. Moreover, this group is not the most specious, favouring
further survey efforts that are necessary to verify the conservation value of areas at
suitable scales. The majority of the species from this family are dependent on forest
habitats, one of the characteristics that most influenced representativeness level,
probably due to the spatial distribution of these habitats throughout the Brazilian
Cerrado. Beta diversity patterns of the different groups also affected representativeness, and our analyses showed that the networks selected by a surrogate group will
be more effective in the representation of other groups of species if their patterns of
beta diversity (not richness) are correlated.
Keywords
Beta diversity, biodiversity, birds, efficiency, representativeness, reserve selection.
INTRODUCTION
Selection of priority areas for conservation can be defined as
an optimization problem with the purpose to represent all the
conservation targets cost-effectively (Cabeza & Moilanen,
2001). Nowadays, the optimization reserve selection problem
is frequently solved using algorithms based on the concept of
complementary (Margules et al., 1988; Howard et al., 1998;
Araújo & Williams, 2000), which is 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).
At broad spatial scales, complementarity analyses can be useful
to establish a first approximation of priority areas for conservation within the new framework of ‘conservation biogeography’
(Whittaker et al., 2005). However, data for species’ distributions
78
are frequently incomplete (Rondinini et al., 2006), and this problem have been recently called ‘Wallacean shortfall’ (see Tognelli,
2005; Whittaker et al., 2005; Bini et al., 2006). Good range data
are frequently available only for a few species, so that it is
common in conservation to use the information of a given group
of species or conservation focal group as surrogate of biodiversity
patterns in other (generally taxonomic defined) groups of species
(Simberloff, 1998; Caro & O’Doherty, 1999; Andelman & Fagan,
2000; Moreno et al., 2007). The choice of these surrogates may
be based on variable criteria, ranging from small groups of
flagship or umbrella species up to patterns at higher levels in the
biological hierarchy, such as species assemblages, habitat types,
and landscape features (Margules & Pressey, 2000; Andelman &
Fagan, 2000). Even though surrogate groups are frequently used,
the criteria used to select them are not clearly justified (Araújo,
1999; Caro & O’Doherty, 1999; Tognelli, 2005).
DOI: 10.1111/j.1472-4642.2007.00421.x
© 2007 The Authors
Journal compilation © 2007 Blackwell Publishing Ltd www.blackwellpublishing.com/ddi
Biodiversity surrogate groups and priority areas
The most common way to identify possible surrogate taxa is
via the quantification of the relationships among the spatial
patterns of species richness and rarity for different groups
(Howard et al., 1998; van Jaarsveld et al., 1998; Lamoreux et al.,
2006; Moreno et al., 2007). However, a high correlation may be
not a sufficient evidence to judge the effectiveness of a taxon to
indicate the relative conservation value of different areas regarding
other groups of organisms (Su et al., 2004). On the other hand, if
the groups studied have congruent patterns of complementarity,
the priority areas selected by a group (surrogate, or focal) will
result in an efficient representation of other target groups (Howard
et al., 1998). Thus, a more appropriate test is to verify to what extent
the network of priority areas selected using information for one
group captures of the diversity of other taxonomic groups (Howard
et al., 1998; Cabeza & Moilanen, 2001; Kati et al., 2004a).
In this work, data on extents of occurrence of bird species in the
Cerrado biome (recognized as a biodiversity hotspot by Myers
et al., 2000) were used to select networks of priority areas based on
the concept of complementarity. Following a ‘deconstructive’
approach (sensu Marquet et al., 2004), Cerrado birds were divided
in 29 taxonomic groups (orders, and families in the case of
Passeriformes). Biodiversity surrogates have different meanings
depending on the author consulted (Caro & O’Doherty, 1999;
Moreno et al., 2007). Here we verified how a network selected for
a given focal group represented the species of other groups (targets),
by means of an analysis of ‘cross-taxon congruence in patterns of
complementarity’ (Howard et al., 1998). In a second step, we
evaluated which characteristics of the focal groups tested can better
explain their efficiency in representation of conservation targets.
Table 1 Bird species richness in the Brazilian Cerrado according to
the group order, and within the order Passeriformes, according to
family. Classification based on Sibley & Monroe (1990). Groups that
have more than 10 species (italicized) were tested as surrogate
groups. Number of cells in the networks selected for each group is
also shown.
Order
Struthioniformes
Tinamiformes
Craciformes
Galliformes
Anseriformes
Piciformes
Galbuliformes
Trogoniformes
Coraciiformes
Cuculiformes
Psittaciformes
Apodiformes
Trochiliformes
Strigiformes
Columbiformes
Gruiformes
Ciconiiformes
Passeriformes
METHODS
Data
A grid of 181 cells with a spatial resolution of 1° (Diniz-Filho
et al., 2006; Pinto et al., 2007) was established, covering the area
corresponding to the Brazilian Cerrado (Brazil, 1999), a biome
located at the central region of this country. The ranges of 723
bird species that breed in this area (Silva, 1995) were mapped at
the grid. Distribution maps used in this study were published in
several sources (see Appendix S1 in Supplementary Material).
Next, a presence/absence matrix of the 723 species in the 181 grid cells
was constructed. Species were separated by orders and families
following the classification of Sibley & Monroe (1990) (Table 1).
Reserve selection
Optimization routines based on the concept of complementarity
(Vane-Wright et al., 1991; Cabeza & Moilanen, 2001) were
applied to select networks of priority areas for each order that
contained more than 10 species in the Cerrado list (Tinamiformes, Anseriformes, Piciformes, Galbuliformes, Cuculiformes,
Psittaciformes, Trochiliformes, Strigiformes, Columbiformes,
Gruiformes, and Ciconiiformes). Networks selected for groups
with one, two, or three (until 10) species would be too small and
therefore certainly inefficient to represent other bird groups. Our
Total
Family
Tyrannidae
Thamnophilidae
Furnariidae
Formicariidae
Vireonidae
Corvidae
Muscicapidae
Sturnidae
Certhiidae
Hirundinidae
Passeridae
Fringillidae
Number
of species
1
16
8
2
11
35
18
8
8
14
32
5
36
31
18
15
59
132
54
64
6
7
4
5
2
12
7
2
114
726
Number of
cells selected
3
3
4
3
2
4
4
2
3
2
2
7
5
5
3
6
results were not affected by the exclusion of these small groups
due to the pairwise approach that we followed (see below).
For the Passeriformes, we used a higher taxonomic resolution,
and families that contained more than 10 species were used
(Tyrannidae, Thamnophilidae, Furnariidae, Certhiidae, Fringillidae).
The Passeriformes were divided into families due to the large
number of species in this order. Complementarity solutions were
also obtained for all 723 bird species in the region as well.
The simulated annealing algorithm in the SSM (Site Selection
Module) routine of the  software was used to find minimal
networks that represent each species at least once (Possingham
et al., 2000; see also Andelman et al., 1999). Two hundred runs
and 10,000,000 iterations for each run were performed. There are
multiple combination of cells that satisfy a given representation
goal. Thus, we retained the first 100 solutions of SSM with the
same smallest number of cells (‘near optimal solutions’). The
relative importance of each cell was established using the
© 2007 The Authors
Diversity and Distributions, 14, 78–86, Journal compilation © 2007 Blackwell Publishing Ltd
79
M. P. Pinto et al.
frequency in which it occurs in the alternative networks. This is
an estimate of the irreplaceability of the cell (see Meir et al.,
2004), measuring the likelihood of a given cell to ensure achievement of a set of conservation targets (Ferrier et al., 2000; Meir
et al., 2004; Diniz-Filho et al., 2006).
Measuring efficiency of bird groups
We used the first 100 best networks (i.e. with minimum number
of cells selected) established by SSM for each bird group to measure
the efficiency of those networks in representing the diversity of
other groups (those not used in the reserve selection process)
and of all birds (Howard et al., 1998; Reyers et al., 2000; Moore
et al., 2003; Kati et al., 2004b). So, each bird group is, at the same
time, a focal group (when its network is being used to evaluate
the representation of other bird groups) and a target group
(sensu Hammond, 1995). The relative number of species of each
target group that occurred in each of the 100 networks selected
based on a focal group (number of species represented in the
network/total number of species of that group) was used to evaluate
the efficiency of this group. Thus, Rij was then defined as the mean
efficiency of the network selected for the group i in representing
the species of group j. Subsequently, the mean representation
efficiency of surrogate group i (Ri) was estimated considering the
mean of Rij values calculated for the different k groups.
Characteristics of the surrogate groups
In order to evaluate the characteristics of the potential focal
groups that influenced their efficiency, the following variables
were obtained for each bird group: total richness (in Cerrado),
mean geographical range size (mean number of cells over all the
species of a given group), mean body size (body length, data
obtained from several sources, see Appendix S1 in Supplementary
Material), proportion of species dependent on open habitats,
proportion of species independent on specific habitats, and
proportion of species dependent on forest habitats (following the
classification scheme proposed by Silva, 1995). We also obtained,
for each bird group, the estimate of beta diversity β given by
[(R/αmax.) – 1]/[N – 1] (Harrison et al., 1992; Blackburn & Gaston,
1996) – being R the total number of species in the group, α the
mean number of species in each cell, and N the number of cells.
We evaluated different beta diversity indices, but only this one
was used in the multiple regression models because all β measures
were highly correlated with each other. Multiple regression models,
obtained using the  software (Spatial Analysis in Macroecology,
Rangel et al., 2006), were used with the purpose of quantifying the
relationships between these measures and the mean efficiency (Ri).
Model selection procedures (see MacNally, 2000; Burnham &
Anderson, 2002) were used to choose the most parsimonious
explanations for efficiency, based on Akaike’s Information
Criterion (AICc, second-order AIC, which is necessary for a small
sample sizes). As recommended by Burnham & Anderson (2002;
p. 71), AICc differences (∆i) were calculated over all candidate
models in the set. Models plausibility decrease with increasing ∆i
and therefore this quantity offers the level of empirical support
80
for a given model (see Burnham & Anderson, 2002; p. 70).
Finally, ∆i values were also used to compute Akaike’s weight of
each model (wi), which provides evidence that the model is actually the best explanatory model. These values of wi are usually
standardized by their sum across all models evaluated, so they are
dependent on the set of models used. An important advantage of
using model selection criteria is that they permit inferences from
more than one model, what is not possible when other traditional
methods (i.e. fit maximization and null hypothesis testing) are
used. In addition, model selection is especially valid for making
inferences from observational data, when data are collected from
complex systems (Johnson & Omland, 2004), as in our case.
Patterns of beta diversity
Jaccard’s similarity index (Legendre & Legendre, 1998) was used
to measure similarity between the cells. The complement of this
index shows how the places are different in term of species composition (Moore et al., 2003) and can also be loosely interpreted
as a complementarity measure. Then, a symmetric matrix of the
Jaccard’s coefficients (181 rows and 181 columns) was obtained
for each group. The Mantel’s r coefficient for matrix comparison
(Manly, 1997; Legendre & Legendre, 1998) was used to correlate
these Jaccard pairwise matrices and to quantify how much the
pattern of change in species composition between cells was
correlated in the different groups. The patterns of associations
among matrices (Mantel’s r) were synthesized by a dendrogram
(; Legendre & Legendre, 1998). A polygonal relationship
appears between pairwise Mantel’s correlation and efficiency, as
well as between pairwise richness correlation and efficiency (see
Results below). These relationships were tested using the 
software (Gotelli & Entsminger, 2006), with 5000 permutations.
The basic idea of the routine to test for constraint envelopes in
the ‘macroecology’ module of  is to calculate the dispersion
(σ2) among number of points counted in each quadrant of the
bivariate 2-D space and compare this dispersion with a null
distribution of this statistics constructed after data randomization.
RESULTS
Spatial variation in bird richness
The spatial pattern in richness for all the birds that occur in the
Cerrado (Fig. 1a) shows an increasing gradient from the northeast to the western region of the biome. The mean pairwise
correlation between the richness of different bird groups was
0.34. The maximum value of correlation was found between
the total bird richness and Tyrannidae richness (r = 0.97,
P < 0.0001), and the minimum value was found between
Galbuliformes and Gruiformes (r = –0.44, P < 0.0001; see
Appendix S2 in Supplementary Material).
Complementarity solutions and irreplaceability
The networks selected for each group had different number of
cells and spatial configurations (Table 1), as a consequence of the
© 2007 The Authors
Diversity and Distributions, 14, 78–86, Journal compilation © 2007 Blackwell Publishing Ltd
Biodiversity surrogate groups and priority areas
Figure 1 Spatial patterns in species richness of birds that reproduce in the Cerrado (a) and spatial patterns of irreplaceability (b) for all the birds
from Cerrado, estimated as the frequency in which cells appeared in the 100 minimum solutions by SSM.
different patterns of spatial distribution of species. The networks
selected to represent all the birds from the Cerrado contained 15
cells. Eight of them occurred in all of the 100 solutions, being
then completely irreplaceable (Fig. 1b).
Efficiency
The networks efficiencies of the different groups in representing
the other bird groups (Rij) and all the bird diversity are shown in
Table 2. The Tyrannidae, Thamnophilidae, Furnariidae, and
Fringillidae represented on average more than 90% of all the
birds species (a ‘subgroup to group’ approach; see Table 2). The
networks selected for the Tinamiformes, Anseriformes, Galbuliformes, Cuculiformes, Strigiformes, Columbiformes, Gruiformes,
Ciconiiformes, and Certhiidae were not efficient in representing
all species of none of the bird groups. The following subgroups
were 100% efficient in representing other subgroups (in parenthesis) diversity: Piciformes (Cuculiformes and Strigiformes),
Psittaciformes (Strigiformes), Trochiliformes (Cuculiformes),
Tyrannidae (Strigiformes, Gruiformes, and Certhiidae), Thamnophilidae (Cuculiformes and Strigiformes), Furnariidae (Cuculiformes and Strigiformes), and Fringillidae (Strigiformes).
The variables more correlated with average efficiency (Ri) were
proportion of species dependent of forest habitats (r = 0.8322;
P = 0.0001), beta diversity (β, r = 0.7225; P = 0.0016), and mean
range size (r = –0.7199; P = 0.0017).
Among all the models estimated to predict the average efficiency
of the subgroups (Table 3), the three best ones according to AIC
values were those that included (i) richness and proportion of
species dependent on forest habitats (wi = 0.378); (ii) richness,
beta diversity, and proportion of species dependent on forest
habitats (wi = 0.160); and (iii) richness, mean range and propor-
tion of species dependent on forest habitats (wi = 0.111). In this
case, the wi value is the evidence power of a given model (Burnham
& Anderson, 2002), and in Table 3 only those models with wi
higher or equal to 0.01 were presented. The importance of the
different explanatory variables, estimated by the average wi of the
models in which the variable was included, is presented in
Table 4. Richness was the variable with the highest average wi.
Despite the positive non-linear relationship between richness
and mean efficiency (Fig. 2), networks selected for groups not so
rich can be as efficient as those selected for the richest group.
The highest correlation between the matrices of Jaccard’s coefficients was registered between Fringillidae and Tyrannidae
(Mantel’s r = 0.93), and the lowest between Cuculiformes and
Figure 2 Correlation between the richness and the mean efficiency
(r = 0.57; P = 0.02) of the surrogate groups along the 16 bird groups
tested as surrogate groups in Brazilian Cerrado.
© 2007 The Authors
Diversity and Distributions, 14, 78–86, Journal compilation © 2007 Blackwell Publishing Ltd
81
M. P. Pinto et al.
Table 2 Efficiency of the different surrogate groups (first column) in relation to the other groups. (A) Tinamiformes, (B) Anseriformes,
(C) Piciformes, (D) Galbuliformes, (E) Cuculiformes, (F) Psittaciformes, (G) Trochiliformes, (H) Strigiformes, (I) Columbiformes,
(J) Gruiformes, (L) Ciconiiformes, (M) Tyrannidae, (N) Thamnophilidae, (O) Furnariidae, (P) Certhiidae, and (Q) Fringillidae. Ri indicates the
average efficiency of each surrogate group over all the other groups. We must read, for example, ‘the network of the Tinamiformes (A) represent,
in average, 0.80 (80%) of the Anseriformes’ (B) diversity. Numbers in bold indicate efficiencies higher than 90%.
Surrogate group
A
B
C
D
E
F
G
H
I
J
L
M
N
O
P
Q
Ri
A
B
C
D
E
F
G
H
I
J
L
M
N
O
P
Q
1.00
0.63
0.81
0.81
0.62
0.92
0.77
0.67
0.80
0.47
0.86
0.85
0.85
0.86
0.77
0.87
0.80
1.00
0.82
0.79
0.74
0.81
0.89
0.80
0.78
0.81
0.73
0.91
0.82
0.83
0.80
0.83
0.87
0.56
1.00
0.83
0.66
0.83
0.73
0.65
0.77
0.50
0.76
0.92
0.94
0.90
0.75
0.94
0.77
0.38
0.94
1.00
0.75
0.92
0.67
0.68
0.75
0.27
0.69
0.97
0.99
0.91
0.87
0.98
0.92
0.72
1.00
0.95
1.00
0.94
1.00
0.91
0.91
0.73
0.90
0.93
1.00
1.00
0.94
0.95
0.67
0.59
0.75
0.75
0.66
1.00
0.81
0.67
0.69
0.53
0.59
0.89
0.88
0.82
0.72
0.87
0.84
0.73
0.82
0.83
0.73
0.85
1.00
0.78
0.79
0.67
0.75
0.92
0.85
0.87
0.81
0.86
0.98
0.88
1.00
0.98
0.94
1.00
0.98
1.00
0.98
0.81
0.96
1.00
1.00
1.00
0.98
1.00
0.94
0.87
0.94
0.93
0.82
0.94
0.94
0.90
1.00
0.79
0.91
0.94
0.94
0.94
0.92
0.97
0.93
0.98
0.93
0.90
0.82
0.93
0.93
0.87
0.91
1.00
0.84
1.00
0.93
0.93
0.87
0.92
0.99
0.93
0.97
0.98
0.94
0.98
0.98
0.97
0.99
0.93
1.00
0.98
0.97
0.98
0.98
0.98
0.86
0.63
0.90
0.88
0.76
0.87
0.86
0.76
0.82
0.54
0.80
1.00
0.93
0.91
0.84
0.93
0.76
0.30
0.90
0.83
0.74
0.85
0.78
0.66
0.70
0.25
0.66
0.95
1.00
0.91
0.79
0.93
0.76
0.58
0.84
0.80
0.64
0.81
0.83
0.64
0.72
0.49
0.64
0.93
0.94
1.00
0.74
0.90
0.84
0.60
0.92
0.93
0.80
0.91
0.83
0.80
0.79
0.56
0.75
1.00
0.92
0.91
1.00
0.89
0.81
0.69
0.85
0.84
0.75
0.88
0.87
0.76
0.81
0.62
0.74
0.95
0.94
0.91
0.81
1.00
0.85
0.67
0.89
0.87
0.76
0.90
0.86
0.77
0.81
0.60
0.77
0.94
0.93
0.91
0.84
0.92
Figure 3 Correlation between the Mantel’s r and the efficiency of each bird group (a) and the correlation between richness correlation between
groups and the efficiency of each bird group (b). The first figure contains a non-random pattern according to 5000 randomizations in 
(see text for details).
Anseriformes (0.25). The average correlation was equal to 0.59
(see Appendix S3 in Supplementary Material).
The relationship between Mantel’s r and mean efficiency created
a significant envelope (Fig. 3a), with the points distributed in the
left superior part of the graph. Dispersal of points in this 2-D
space (σ2) was equal to 108 (P = 0.029 with 5000 randomizations
in ECOSIM). Then, when the similarity between cells (Jaccard)
differs between the groups compared (i.e. there is a low Mantel’s
r), the efficiency can be low or high. When the groups have similar
patterns of beta diversity (high Mantel’s r), the network selected
for one group will represent most of the diversity of the other
group. However, no relationship between richness correlation
82
(correlation between species richness of a pair of groups) and
mean efficiency appears, with σ2 = 33.3 (P = 0.249 with 5000
randomizations) (Fig. 3b). Thus, in this case, high correlations
between groups’ richness do not indicate that one will be a good
surrogate for the other.
DISCUSSION
In this study, few groups showed coincident patterns in species
richness. In any case, congruence in species richness may be an
inadequate measure of the value of these groups as surrogates
(Howard et al., 1998; Reyers et al., 2000). Measuring the ability of
© 2007 The Authors
Diversity and Distributions, 14, 78–86, Journal compilation © 2007 Blackwell Publishing Ltd
Biodiversity surrogate groups and priority areas
Table 3 Regression models evaluating mean efficiency of the surrogate groups. Only the models with wi ≥ 0.01 are included. Predictors:
(1) richness, (2) mean range, (3) mean body size, (4) meta diversity (β), (5) proportion of species dependent on open habitats, (6) proportion
of species independent of specific habitats, and (7) proportion of species dependent on forest habitats. Horizontal lines separated the different
models. Bold values indicate the highest wi values.
Predictors
R2
Intercept
Standardized beta
Beta
P
AICc
∆
wi
7
0.692
0.676
0.832
0.321
0.000
−85.652
4.180
0.047
1
2
0.727
0.960
0.463
−0.643
0.001
−0.002
0.008
0.000
−83.904
5.928
0.020
1
7
0.811
0.656
0.360
0.729
0.001
0.289
0.014
0.000
−89.832
0.000
0.378
2
7
0.705
0.752
−0.178
0.693
0.000
0.275
0.475
0.014
−82.672
7.160
0.011
3
7
0.736
0.767
−0.275
0.652
−0.002
0.258
0.170
0.005
−84.442
5.390
0.026
4
7
0.751
0.664
0.332
0.624
23.198
0.247
0.081
0.004
−85.956
3.876
0.054
1
2
5
0.793
0.949
0.441
−0.453
−0.323
0.001
−0.001
−0.141
0.007
0.019
0.077
−83.959
5.873
0.020
1
2
7
0.833
0.755
0.375
−0.235
0.540
0.001
−0.001
0.214
0.011
0.242
0.019
−87.386
2.446
0.111
1
3
7
0.821
0.702
0.323
−0.136
0.650
0.000
−0.001
0.258
0.036
0.447
0.002
−86.277
3.555
0.064
1
4
7
0.840
0.650
0.308
0.226
0.602
0.000
15.794
0.239
0.032
0.170
0.002
−88.106
1.726
0.160
3
4
7
0.781
0.731
−0.199
0.281
0.526
−0.001
19.664
0.208
0.306
0.145
0.021
−83.058
6.774
0.013
1
3
4
7
0.844
0.683
0.287
−0.095
0.209
0.556
0.000
0.000
14.629
0.220
0.051
0.581
0.224
0.009
−83.217
6.615
0.014
1
2
4
7
0.842
0.693
0.327
−0.097
0.173
0.554
0.000
0.000
12.068
0.219
0.041
0.713
0.437
0.011
−82.979
6.853
0.012
a network selected by using data from a group (potentially surrogate) in representing the biodiversity in other taxa may be a more
appropriate measure of the efficiency of that group as a surrogate
(Howard et al., 1998; Reyers et al., 2000; Kati et al., 2004b). Thus,
the networks selected by some bird groups (Tyrannidae,
Thamnophilidae, Furnariidae, and Fringillidae) were very efficient
for the representation of other groups or for the representation
all the birds considered. These groups encompass species with
high proportion of species dependent on forest habitats and with
high beta diversity. On the other hand, the networks of the
groups with large ranges were less efficient.
Probably, the identification of the characteristics that predicts
a good surrogate group was the main message of this study.
Following this line of reasoning, among all the models, the one
that considered richness and proportion of species dependent on
forest habitats had the highest Akaike weight. A model that
includes richness is important due to the larger number of cells
that are needed to represent all the species of the richer groups
(Grenyer et al., 2006). Consequently, networks with more areas
will better represent bird groups’ diversity than smaller networks
(see Fig. 2). The proportion of species dependent on forest
habitats was an important predictor of surrogacy, although
explanation for this result is not straightforward.
Another model with relatively high Akaike weight incorporated richness, beta diversity, and the proportion of species
dependent on forest habitats as explanatory variables. Beta
© 2007 The Authors
Diversity and Distributions, 14, 78–86, Journal compilation © 2007 Blackwell Publishing Ltd
83
M. P. Pinto et al.
Table 4 Measure of the importance of different variables
(summing the wi values of all the models that include the variable).
Number of models where the variable was included is indicated in
the third column. Predictors: (1) richness, (2) mean range, (3) mean
body size, (4) beta diversity (β), (5) proportion of species dependent
on open habitats, (6) proportion of species independent of specific
habitats, and (7) proportion of habitat dependent on forest habitats.
Variable
Importance
Number of models
1
2
3
4
5
6
7
0.80
0.21
0.14
0.27
0.04
0.02
0.93
19
19
18
18
8
7
18
diversity is a measure that reveals spatial changes in species composition (Blackburn & Gaston, 1996). This measure is directly
related to the complementarity concept. A high beta diversity
value indicates cells with different species compositions and high
complementarity values (Moore et al., 2003), and larger
networks are necessary to represent all the species of the surrogate
group. The last model with a relatively high Akaike weight is the
one that incorporates richness, mean range, and proportion of
species dependent on forest habitats. Surrogate groups formed by
species with small ranges and with low coincidence of their ranges
(Ryti, 1992; Williams et al., 2000, 2006; Tognelli, 2005) will have
larger networks with high irreplaceable values. In general, these
characteristics are related to each other because groups with high
beta diversity are also composed by species with restricted ranges
that are not spatially coincident. Ensuring reservation of the
regions harbouring these species will also capture diversity in
species with widely distributed and with overlapping ranges.
Beyond the positive relationship found between richness
and efficiency, high levels of efficiency were found not only for
very rich surrogate groups (Fig. 2). The efficiency of the Thamnophilidae, with 54 species, was as high (0.9261) as the one of the
Tyrannidae (0.9433), which contains 132 species. Thus, the
Thamnophilidae is tentatively suggested here as the best surrogate group to represent all other Cerrado bird groups, since
inventory efforts for this group will be smaller than other groups
with higher species richness, and most of the species of this
group are dependent on forest habitats (83%), an important
characteristic of surrogate groups as well.
A high similarity in the patterns of spatial turnover in species
composition was found only for some groups (e.g. Fringillidae
and Tyrannidae). Different patterns in beta diversity of groups
affect negatively the efficiency in the representation (Sætersdal
et al., 2003). The relationship found between the similarity in the
pattern of beta diversity between groups and the efficiency of
the surrogate groups (Fig. 3a) corroborates this affirmation. On the
other hand, a group richness may be highly correlated with other
group richness, but the first group is not necessarily a good
surrogate for the second one, as demonstrated in the relationship
84
between richness correlation between groups and efficiency
(Fig. 3b). We found in this study that reserve networks of one
surrogate group will be more efficient in representing species of
other groups if their spatial patterns of beta diversity are highly
correlated.
Thus, our analyses showed that richness correlations should
be used cautiously when the purpose is to identify surrogate
groups and that, in this case, spatial patterns of beta diversity are
a more important attribute. Efficiency in representation was
probably overestimated because we used range data (Rondinini
et al., 2006). Thamnophilidae was the best surrogate group in
our evaluation. Survey efforts that are necessary to verify the
conservation value of areas at suitable scales should be concentrated in surrogate groups. But surrogate groups can be constituted not just of similar taxonomic species, but also of species
that share important characteristics that explain efficiency.
ACKNOWLEDGEMENTS
We thank José Ferrer-Paris and two anonymous reviewers for
many comments that improved the manuscript. MPP/DB and
JAFDF/LMB are supported by doctoral and researcher CNPq
fellowships, respectively. TFLVBR is supported by a CAPES/
Fulbright fellowship. This project is part of a PRONEX program
of CNPq and SECTEC-GO (proc. 23234156).
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SUPPLEMENTARY MATERIAL
The following supplementary material is available for this article:
Appendix S1 Bibliographic sources used to generate the distribution maps and body length information.
Appendix S2 Pearson’s correlation coefficients among species
richness.
Appendix S3 Dendrogram from an  clustering upon the
Mantel’s r estimated between the Jaccard matrices of the different
groups.
This material is available as part of the online article from:
http://www.blackwell-synergy.com/doi/abs/10.1111/j.14724642.2007.00421.x
(This link will take you to the article abstract).
Please note: Blackwell Publishing is not responsible for the content or functionality of any supplementary materials supplied by
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directed to the corresponding author for the article.
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