Journal of Applied Ecology 2011, 48, 330–339
doi: 10.1111/j.1365-2664.2010.01932.x
Cost-effectiveness of plant and animal biodiversity
indicators in tropical forest and agroforest habitats
Michael Kessler1,2*, Stefan Abrahamczyk1,2,3, Merijn Bos3,4, Damayanti Buchori5,
Dadang Dwi Putra6, S. Robbert Gradstein1, Patrick Höhn3, Jürgen Kluge1,7,
Friederike Orend1, Ramadhaniel Pitopang8, Shahabuddin Saleh8, Christian H. Schulze9,
Simone G. Sporn1, Ingolf Steffan-Dewenter3,10, Sri S. Tjitrosoedirdjo5 and Teja Tscharntke3
1
Albrecht-von-Haller-Institute of Plant Sciences, University of Göttingen, Untere Karspüle 2, D-37073 Göttingen,
Germany; 2Systematic Botany, University of Zürich, Zollikerstrasse 107, CH-8008 Zürich, Switzerland; 3Agroecology,
University of Göttingen, Grisebachstr. 6, D-37077 Göttingen, Germany; 4Louis Bolk Institute, Hoofdstraat 24, 3972 LA
Driebergen, the Netherlands; 5Faculty of Biology, Bogor Agricultural University, Jalan Padjajaran, 16144 Bogor,
Indonesia; 6Celebes Bird Club, c ⁄ o Balai Penelitian dan Pengembangan Zoologi, Puslitbang Biologi – LIPI, Jl. Raya
Bogor Jakarta Km 46, Cibinong 16911, Indonesia; 7Faculty of Geography, University of Marburg, Deutschhausstraße
10, 35032 Marburg, Germany; 8Faculty of Agriculture, Tadulako University, Palu, Indonesia; 9Department of Animal
Biodiversity, Faculty of Life Sciences, University of Vienna, Rennweg 14, A-1030 Vienna, Austria; and 10Department
of Animal Ecology I, University of Bayreuth, Universitätsstrasse 30, 95440 Bayreuth, Germany
Summary
1. Biodiversity data are needed for conservation and management of tropical habitats, but the high
diversity of these ecosystems makes comprehensive surveys prohibitively expensive and indicator
taxa reflecting the biodiversity patterns of other taxa are frequently used. Few studies have produced the necessary comprehensive data sets to assess the quality of the indicator groups, however,
and only one previous study has considered the monetary costs involved in sampling them.
2. We surveyed four plant groups (herbs, liverworts, trees, lianas) and eight animal groups (ants,
canopy and dung beetles, birds, butterflies, bees, wasps and the parasitoids of the latter two) in 15
plots of 50 · 50 m2 each, representing undisturbed rainforest and two types of cacao agroforest in
Sulawesi, Indonesia. We calculated three biodiversity measures (a and b diversity; percentage of
species indicative of habitat conditions), built simple and multiple regression models among species
groups (single groups, combinations of 2–11 groups, averaged relative diversity of all 12 groups),
and related these to three measures of survey cost (absolute costs and two approaches correcting for
different sampling intensities).
3. Determination coefficients (R2 values) of diversity patterns between single study groups were
generally low (<0Æ25), while the consideration of several study groups increased R2 values to up to
0Æ8 for combinations of four groups, and to almost 1Æ0 for combinations of 11 groups. Survey costs
varied 10-fold between study groups, but their cost-effectiveness (indicator potential versus monetary cost) varied strongly depending on the biodiversity aspect taken into account (a or b diversity,
single or multiple groups, etc.).
4. Synthesis and applications. We found that increasing the number of taxa resulted in best overall
biodiversity indication. We thus propose that the most cost-efficient approach to general tropical
biodiversity inventories is to increase taxonomic coverage by selecting taxa with the lowest survey
costs.
Key-words: agroforestry management, alpha diversity, beta diversity, biodiversity indication,
conservation biology, conservation priorities, land-use gradients, plant and insect communities,
rainforests, survey costs
*Correspondence author. E-mail: [email protected]
2010 The Authors. Journal of Applied Ecology 2010 British Ecological Society
Cost-effectiveness of biodiversity indicators 331
Introduction
Biodiversity data are of great importance for decisions on
design and location of reserves, management of natural habitats, monitoring of biodiversity changes, and the assessment
of long-term sustainability of land-use systems. However, the
field surveys needed for a full census of biodiversity of a prospected site or region exceed the budget of almost any project.
One solution is to restrict a census to groups of organisms
whose diversity patterns are believed to reflect overall biodiversity patterns or other important ecosystem parameters,
and thus function as biodiversity surrogates (Pearson 1994;
Prendergast 1997; Pharo, Beatti & Binns 1999; Rodrigues &
Brooks 2007), although the search for such biodiversity indicators has not been particularly successful (Wolters, Bengtsson & Zaitsev 2006; Billeter et al. 2008; Kessler et al. 2009).
The selection of surrogate study groups is furthermore a
matter of deciding between study groups that are adequate
surrogates and those that are easy, in terms of lower costs
and time effort, to sample (Favreau et al. 2006; Wolters,
Bengtsson & Zaitsev 2006; Rodrigues & Brooks 2007).
‘High-performance indicator study groups’ are those that
fulfil both criteria (Gardner et al. 2008).
Recently, Gardner et al. (2008) presented a framework for
assessing the performance of indicator study groups using a
weighting technique to balance the costs and indicative value
of different study groups. Because costs for the survey of different study groups typically differ by about an order of magnitude due to differences in species numbers, ecology, sampling
methods, and time effort for identification (Lawton et al. 1998;
Favreau et al. 2006; Wolters, Bengtsson & Zaitsev 2006;
Rodrigues & Brooks 2007), Gardner et al. (2008) advocate the
use of standardized survey costs, in which rarefaction is used
to calculate the survey cost at a comparable level of survey
completeness for the different study groups. This approach is
intuitively appealing, but has the important potential drawback that whereas survey costs are readily standardized, the
same is not true for biodiversity indication. Accordingly,
Gardner et al. (2008) compared the standardized survey costs
with non-standardized indicator performances. A further
potential problem with their approach is that the standardization procedure relies on the accuracy of species richness estimators (Colwell & Coddington 1995) to calculate the sampling
completeness of the respective taxa. However, there is a
positive relationship between sampling completeness and the
estimated total richness and, at low levels of sampling completeness, the estimators become unreliable (Herzog, Kessler &
Cahill 2002; Walther & Moore 2005).
A more general shortcoming of many previous studies on
biodiversity indicators is that often only a single taxon is evaluated (Wolters, Bengtsson & Zaitsev 2006; Billeter et al. 2008).
Furthermore, most studies evaluate the indicator potential
only among the sampled taxa, rather than relative to total biodiversity. While it is true that a full sampling of all taxa is not
yet available from any site, it has been argued that averaging
the relative diversity patterns of a larger number of taxa may
give an indication of total biodiversity (Westphal et al. 2008).
In the present study we set out to explore solutions to the
problems outlined above. We applied the approach of Gardner
et al. (2008), but also propose an alternative to calculate the
cost-effectiveness of different groups for biodiversity indication, based on the calculation of residuals of the indicator
potential relative to absolute sampling costs, and compare this
approach to that of Gardner et al. (2008). Further, we used
multiple linear models to assess the performance of multiple
predictor species groups used in combination. Finally, we evaluated our results at the level of estimated total biodiversity.
Materials and methods
STUDY AREA AND SITE SELECTION
The study took place in an area of about 10 km2 at 850–1100 m a.s.l.
at the western border of the Lore Lindu National Park in and around
the village of Toro in the Kulawi Valley, Central Sulawesi, Indonesia.
The region has an annual average (±SE) temperature of 24Æ0
(±0Æ16) C and a mean monthly rainfall of 143Æ7 (±22Æ74) mm.
There are no clear seasonal precipitation fluctuations. The natural
vegetation of the area is submontane rainforest. For the present
study, we selected 15 plots of 50 · 50 m2 each with a minimum distance of 300 m between them. The plots were situated in three habitat
types: mature forest (4 plots), high-shade agroforests (7 plots; canopy
closure >60%), and low-shade agroforests (4 plots; canopy closure
<60%). A full description of the study area is available in Appendix
S1 in Supporting Information.
SPECIES COLLECTION AND IDENTIFICATION
Twelve species groups were chosen for the biodiversity assessment:
trees, lianas, terrestrial herbs, understorey liverworts, birds, butterflies, lower canopy ants, lower canopy beetles, dung beetles, bees,
wasps, and the parasitoids of the latter two. The organisms were sampled in each plot following a consistent sampling protocol and identified at least to morphospecies, when a specimen could not be named
reliably. Depending on the study group, plots were sampled simultaneously or consecutively in random order, so that no confounding
effects of temporal environmental variability are to be expected. Full
data on species collection and determination is available in Appendix
S1 in Supporting Information.
DATA ANALYSES
Diversity estimates
For the analyses of a-diversity we used the number of species
recorded in each plot. Because observed species richness values in
field studies are typically an underestimate of the actual number of
species occurring at a site (Colwell & Coddington 1995), we used
the species richness estimator Chao2 (Chao 1987) as implemented
in EstimateS 8 Windows (Colwell 2006) to estimate the actual total
number of species per study group across all plots. We did not,
however, correct the species numbers per plot, for two reasons.
First, a previous analysis of the data has shown that betweentaxon correlations using estimated values were similar to those
obtained with the raw data (Kessler et al. 2009). Second, some of
the analyses conducted here can only be made with raw data (e.g.
the indicator group analysis) and for consistency it seems best to
use the same data throughout. Our approach assumes that sampling
2010 The Authors. Journal of Applied Ecology 2010 British Ecological Society, Journal of Applied Ecology, 48, 330–339
332 M. Kessler et al.
Biodiversity indication of individual groups
We used three measures of biodiversity indication, two based on the
relationship of diversity patterns between study groups (a- and
b-diversity) and one assessing the degree to which the species of a
given study group can be used to evaluate habitat condition.
The relationship of diversity values between study groups was
calculated using linear regression analyses (a-diversity) and Mantel
analyses (b-diversity). Because our aim was to assess to what degree a
group can be used to predict the biodiversity patterns of another
group, we calculated determination coefficients (R2-values), while
maintaining the original sign of the regression values to allow us to
indicate negative relationships (Kessler et al. 2009). Mantel analyses
are correlation tests between matrices consisting of pair-wise similarities or differences in a selected characteristic of samples or study sites
(Legendre & Legendre 1998). In the first step of the analysis, pair-wise
correlations were calculated between plot-wise similarity matrices of
each study group pair, separately for each of the two diversity measures (a- and b-diversity). For each study group, the 11 correlation
values were then averaged. Mantel analyses were conducted with
PCOrd 4Æ5 (McCune & Mefford 1999), applying 9999 randomization
runs.
To assess the degree of habitat fidelity of the species of a given study
group, we used the indicator species analysis of Dufrene & Legendre
(1997) as implemented in PCOrd 4Æ5, applying 999 randomization
runs to test for significance. This analysis combines information on
the concentration of species abundance and the faithfulness of occurrence of a species in a particular habitat. Indicator values range from
zero (no indication) to 100 (perfect indication). For our analysis, we
calculated the percentage of species of a given study group that have
significant indicator values. To assess whether the percentage
of species with significant indicator values depended on sampling
completeness, we calculated a linear Spearman correlation between
both parameters. Our intention here was not to differentiate between
the three habitat types, which were visually easily distinguishable, but
rather to quantify the degree of habitat specificity of the species of
different study groups, following the reasoning that groups with
higher habitat fidelity are better indicators of habitat conditions.
Biodiversity indication for combinations of groups
To assess the indicator potential of combinations of predictor taxa
within multiple linear models, we created a list of all possible combinations of two to 11 taxa and calculated adjusted R2 values for each
model, as well as mean adjusted R2 values of all models of each combination. The creation of linear models of all possible combinations
of predictor species groups was automated using R (R Development
Core Team 2008). The routine (set of command lines) to implement
in R is given Appendix S2 in Supporting Information. For the calculation of adjusted R2 values for the b-diversity dataset, we used the
R-function multRegress.R constructed by Legendre (2005), which
computes a multiple regression and tests the coefficient of determination (R2) by permutation.
Assessment of sampling costs
Sampling costs refer to two main resources: monetary costs and time
effort. Monetary costs included all collecting materials that could be
used only once or a few times, and could therefore not be shared
between study group surveyors. We also included site costs, as they
are typical for field surveys (e.g. transport to field sites, overnight
fees), but excluded transport costs from home country to country of
survey. Costs of non-perishable equipment were not included in the
analysis, following Gardner et al. (2008). Additional costs (‘capital’
and ‘hidden’ costs) were also excluded, since they differ for each
specific study and may distort comparisons of survey costs. These
additional costs are defined as not directly related to the diversity survey (e.g. building and maintaining reference collections for vouchers,
costs for project planning, data analyses and reporting). Time effort
was the largest contributor to the overall costs. In order to make direct
comparisons between study groups possible, we standardized the
salary requirements for five different groups of workers: 100 Euro ⁄ day for MSc students, PhD students and postdoctoral researchers
and 20 Euro ⁄ day for field assistants and other workers. Time effort
comprised the total effort needed in field and laboratory (identification), based on 5 person-days per week with 8-h working days.
Based on the above values, we used two measures of costs. First,
we used the absolute survey costs of the sampling. This measure may
be misleading because sampling intensity differed between the study
groups. For this reason, we also calculated the standardized survey
costs following Gardner et al. (2008). In this approach, individualbased rarefaction curves were constructed for each study group, followed by a recalibration of the y-axis so as to represent the proportion
of the estimated total number of species, based on the species richness
estimator Chao2 (Chao 1987) as implemented in EstimateS 8 Windows (Colwell 2006). Then, survey cost was calculated per species and
the x-axis recalibrated to represent survey costs. Finally, the survey
cost of each study group was rarefied to equate to the point at which
the sample representation is equivalent to that of the least effectively
sampled study group (Fig. 1). A problem arises when standardized
100
Dung beetles
Birds
Sample representation (% of total richness)
completeness is consistent across habitat types for a given study
group.
Bees
80
Trees
Herbs
Liverworts
Parasitoids
60
Ants
Wasps
Lianas
40
Butterflies
Canopy beetles
20
0
0
5000
10 000
15 000
20 000
25 000
Absolute survey cost (Euro)
Fig. 1. Rarefaction curves of the 12 study groups relative to absolute
survey cost. The peak of the rarefaction curve for each study group is
determined by the sampling completeness of that group. Standardized survey costs were derived from the point at which the rarefaction
curve reaches the sampling completeness of the least completely
sampled study group (beetles: 29%).
2010 The Authors. Journal of Applied Ecology 2010 British Ecological Society, Journal of Applied Ecology, 48, 330–339
Cost-effectiveness of biodiversity indicators 333
survey costs are related to regression values as measures of biodiversity indication values (see Discussion), as the latter are not based on
rarefied data. Because this approach may thus overestimate the
relative cost-effectiveness of study groups for which standardization
results in a high loss of information, we here introduce a third
measure, residual survey costs, which corrects the regression values
relative to the survey costs. This approach is based on the assumption
that an increase of monetary input leads to a continuous increase of
regression values. This relationship is not linear because (i) at low
levels of input, a rapid increase of output can be achieved which subsequently decelerates at higher levels of input, and (ii) the regression
value reaches its maximum at 1Æ0. We therefore assumed a logarithmic relation of the form a + b * ln(costs) and calculated the residual
regression values by measuring the positive and negative deviations
of the observed regression values from the best fitting logarithmic
model curve by iteratively adjusting the coefficients a and b. Positive
residuals indicate that a given taxon has a higher regression value at a
given cost level than would be expected based on the average costindication relationship of all taxa, negative residuals the opposite.
These residuals were then plotted against absolute survey costs to
assess which groups provide the highest relative indicator value at a
given cost level.
To compare the cost-effectiveness of biodiversity indication by
different study groups, we calculated regression coefficients and plotted our three indicator measures (regression relationships of a- and
b-diversity; percentages of indicator species) against the cost measures for visual comparison.
Results
In total, we recorded 863 species, with total species richness per
study group ranging from 9 cavity-nesting bee species to 198
canopy beetle species (Table 1).
Determination coefficients (R2-values) of a-diversity per site
between the different study groups ranged from an average of
R2 = –0Æ19 for trees to R2 = 0Æ15 for bees (Table 1). A similar
analysis for correspondence of patterns of b-diversity between
study groups based on Mantel analyses obtained values ranging from R2 = )0Æ03 for understorey liverworts to R2 = 0Æ46
for wasps.
Absolute survey costs varied among study groups due to
differences in numbers of sampled individuals and time
required for handling and identification, from 4830 Euro for
birds to 22,690 Euro for wasps (Fig. 1, Table 2). The proportion of salary costs was between 82 and 96% for most groups,
except for dung beetles, herbs, and lianas, where it dropped
below 80% due to relatively low sampling effort in the field.
Rarefaction curves varied between taxa, with many groups
reaching the cutoff value of 29% sampling completeness with
less than 20% of the total sampling intensity (Fig. 1). Accordingly, standardized survey costs varied even more strongly
between study groups than absolute costs, from 300 Euro for
birds to 16,960 Euro for beetles, the least completely sampled
study group (Tables 1 and 2).
Comparison of the mean R2-values of a- and b-diversity of
each study group to absolute survey costs showed no clear
visual patterns (Fig. 2) and there were no significant relationships between the parameters (Spearman rank correlations,
rs-values between 0Æ13 and 0Æ25, P > 0Æ44 in all cases). Study
groups with the lowest sampling costs (birds, butterflies) by
default had the highest cost-effectiveness. Plotting the mean
R2-values of a- and b-diversity of each study group against
standardized survey costs resulted in different patterns, with
Table 1. Biodiversity indication values for 12 study groups along a gradient of land-use intensification in Sulawesi
Mean
pair-wise
indication
(R2-values)
Group
Recorded
number
Number of of
individuals species
Estimated
number
Sampling
of species complete(Chao 2) ness (%)
a
b
Residuals
of mean
pair-wise
indication
(R2-values)
a
b
Indicator species (%)
Highshade
Mature agroforests forests
Lowshade
agroforests
Indication
of average
diversity
of all 12
groups
(R2-values)
a
b
Herbs
11,120
Lianas
351
Understorey liverworts
704
Trees
1,416
163
35
37
185
261
76
58
248
62Æ5
46Æ1
63Æ8
74Æ6
0Æ02 0Æ37 0Æ02 0Æ02 1
0Æ05 0Æ26 0Æ05 )0Æ09 9
)0Æ05 )0Æ03 )0Æ05 )0Æ27 0
)0Æ19 0Æ34 )0Æ20 )0Æ23 10
1
0
0
1
3
0
0
1
0Æ09
0Æ08
)0Æ06
0Æ09
0Æ64
0Æ40
0Æ01
0Æ54
Ants
Bees
Birds
Butterflies
Canopy Beetles
Dung Beetles
Parasitoids
Wasps
44
9
87
38
198
25
18
24
78
11
108
78
679
29
30
27
56Æ4
81Æ8
80Æ6
48Æ7
29Æ2
86Æ2
60Æ0
88Æ9
0Æ08
0Æ15
)0Æ15
)0Æ03
0Æ08
)0Æ06
0Æ06
0Æ07
0Æ16 0Æ08 )0Æ05 5
0Æ19 0Æ15 )0Æ04 11
0Æ34 )0Æ15 0Æ26 16
0Æ24 )0Æ03 0Æ13 3
0Æ37 0Æ08 0Æ12 1
0Æ27 )0Æ06 )0Æ23 24
0Æ42 0Æ06 0Æ12 0
0Æ46 0Æ07 0Æ12 0
0
0
1
0
2
0
6
4
0
0
5
11
2
4
6
21
0Æ16
0Æ63
)0Æ01
)0Æ06
0Æ27
)0Æ07
0Æ25
0Æ30
0Æ20
0Æ30
0Æ36
0Æ30
0Æ54
0Æ18
0Æ66
0Æ50
3,153
439
931
680
613
928
666
8,575
2010 The Authors. Journal of Applied Ecology 2010 British Ecological Society, Journal of Applied Ecology, 48, 330–339
334 M. Kessler et al.
Table 2. Survey costs for 12 study groups along a gradient of land-use intensification in Sulawesi
Field assistant
(days)
Postdoc (days)
Group
fw
hd
o
fw
hd
Herbs
Lianas
Understorey liverworts
Trees
Ants
Bees
Birds
Butterflies
Canopy beetles
Dung beetles
Parasitoids
Wasps
20
20
42
52
22
76
32
40
22
53
77
78
40
40
98
70
60
24
2
30
60
40
47
90
10
10
5
20
75
20
20
42
104
66
168
32
40
66
53
170
172
40
40
3
60
75
10
o
12
35
20
18
32
50
Materials
(Euro)
Extra
Costs
(Euro)
Total
expend
(Euro)
Standardized
survey costs
(Euro)
200
200
310
250
300
250
150
250
300
250
250
250
2200
2200
840
3600
440
1520
640
800
440
3000
1540
1560
10600
10600
16550
21330
17760
15370
4830
8850
19460
15010
17950
22690
730
1050
3900
3500
2750
1100
300
1650
16960
550
2250
10500
Survey time is given in days for field work (fw), handling and determination (hd), and other (o; databank preparation, etc.); Materials
are non-durable items for field work and collecting material; extra costs are generalized costs for transport and overnight fees at 100
Euro per week (only field work), based on a 5-day working week and 8-hour working day, except for field assistants.
R2-value
α
0·6
0·4
0·4
0·2
0·2
BE
AN
LIPA
0·0 HE
BT
DB LW
0·0
–0·2
–0·2
BI
CB
TR
5000 10 000 15 000 20 000 25 000 30 000
0·0
(c)
β
–0·4
0
5000
10 000
15 000
0·6
0·4
0·4
0·2
0·2
0·0
0·0
–0·2
–0·2
20 000
0
β
0·4
0·2
0·0
–0·2
(d)
(e)
–0·4
(f)
–0·4
5000 10 000 15 000 20 000 25 000 30 000
Absolute costs (Euro)
5000 10 000 15 000 20 000 25 000 30 000
0·6
β
Residual R2 - value
0·6
0
0·2
–0·2
–0·4
0
0·4
(b)
(a)
–0·4
R2-value
WA
α
0·6
Residual R2 - value
α
0·6
–0·4
0
5000
10 000
15 000
Standardized costs (Euro)
20 000
0
5000 10 000 15 000 20 000 25 000 30 000
Absolute costs (Euro)
Fig. 2. Relationship of mean pair-wise determination coefficients (R2-values) relative to the other 11 study groups, for different diversity measures
(a–c: a diversity; d–f: b diversity), plotted against three measures of survey costs (a, d: absolute survey costs; b, e: costs standardized after Gardner
et al. 2008; c, f: residual R2-values from logarithmic models). Abbreviations for study groups: AN: Ants; BE: Bees; BI: Birds; BT: Butterflies;
CB: Canopy Beetles; DB: Dung Beetles; HE: Herbs; LI: Lianas; LW: Understorey liverworts; PA: Parasitoids; TR: Trees; WA: Wasps.
the majority of study groups having relatively low sampling
costs (<3000 Euro) but widely diverging R2-values. Again,
there were no significant relationships (Spearman rank correlations, rs-values between 0Æ28 and 0Æ30, P > 0Æ35 in both cases).
In this case, taxa with low survey costs and high R2-values had
the highest cost-effectiveness. For a-diversity this included
ants, bees, herbs, lianas and parasitoids, and for b-diversity
birds, dung beetles, herbs, parasitoids and wasps. Finally,
evaluating the residuals of the R2-values of a-diversity, there
was no relationship between the residuals and absolute sampling costs (Spearman rank correlation, rs = 0Æ21, P = 0Æ52),
with ants, bees and canopy beetles having the highest positive
2010 The Authors. Journal of Applied Ecology 2010 British Ecological Society, Journal of Applied Ecology, 48, 330–339
Cost-effectiveness of biodiversity indicators 335
40
40
30
30
20
20
10
10
40
DB
LIAN
TR
BE
PA
BI
HE
BT
WA
CB
LW
0
0
significant indicator values also showed a tendency towards a
negative relationship with absolute sampling cost (Spearman
rank correlation, rs = )0Æ31, P = 0Æ32), with highest positive
residuals for birds, butterflies and wasps.
In the multiple linear models of all combinations of two to
11 predictor species groups, mean R2-values increased with the
number of indicator taxa included, from 0Æ18 for two taxa to
0Æ72 for 11 taxa (Fig. 4). Within categories of a certain number
of study groups, R2-values tended to increase with increased
survey costs for combinations of two to five study groups, but
decreased when more taxa where included. On the other hand,
the variance of the R2-values was higher for a than for b diversity, so that even with 11 study groups single R2-values for
specific combinations of study groups were close to zero (see
Figs S1 and S2 in Supporting Information).
Considering the determination coefficients of the individual
study groups against the averaged diversity patterns of all 12
study taxa, the results were qualitatively similar to those of the
pair-wise comparisons (Fig. 5): There was no clear relationship
between R2-values and survey costs, and the most cost-efficient
groups were the same as in the pair-wise comparisons. In
contrast, the results of the multiple regression models of
combinations of taxa against the averaged diversity patterns
differed conspicuously from those of the multiple regressions
with individual groups as dependent variables (Fig. 6). For
Residual
% of indicator species
% Of indicator species
residuals. As to b-diversity, there was no relationship of the
residuals with increasing absolute sampling costs (Spearman
rank correlation, rs = 0Æ04, P = 0Æ91), and highest positive
residuals were obtained for birds, canopy beetles, herbs,
parasitoids and wasps.
The percentage of significant indicator species ranged from
0% in understorey liverworts to 28% in dung beetles (Table 1).
These values were positively correlated to the sampling completeness of the respective study groups (Spearman rank correlation, rs = 0Æ68, P = 0Æ015). Comparing the three habitat
types, mature forests and low-shade agroforests had conspicuously higher percentages of indicator species (6Æ7 and 4Æ4%,
respectively) than high-shade agroforests (1Æ3%), but this
difference was not significant (two-tailed, one-way anova,
F2,33 = 2Æ68, P = 0Æ084). Relating the percentage of indicator
species to absolute survey costs did not result in any clear overall pattern (Spearman rank correlation, rs = )0Æ07, P = 0Æ83;
Fig. 3). Birds and butterflies had the most cost-efficient combination of mean R2-values and low survey costs, whereas dung
beetles had even higher R2-values but also higher costs. When
survey costs were standardized, there was a tendency towards
lower percentages at higher survey costs, but this was not
significant (Spearman rank correlation, rs = )0Æ47,
P = 0Æ13). In this situation, dung beetles had the highest costeffectiveness. The residuals of the percentage of species with
–10
–10
20
10
0
–10
(a)
(b)
–20
(c)
–20
0
30
5000 10 00015 00020 00025 00030 000
–20
0
5000 10 00015 00020 00025 00030 000
Absolute costs (Euro)
0
Standardized costs (Euro)
5000 10 00015 00020 00025 00030 000
Absolute costs (Euro)
Fig. 3. Relationship of three measures of survey costs for each study group (a, d: absolute survey costs; b, e: costs standardized after Gardner
et al. 2008; c, f: residual R2-values from logarithmic models) plotted against the percentage of indicator species. Abbreviations for study groups:
AN: Ants; BE: Bees; BI: Birds; BT: Butterflies; CB: Canopy Beetles; DB: Dung Beetles; HE: Herbs; LI: Lianas; LW: Liverworts; PA: Parasitoids;
TR: Trees; WA: Wasps.
α
Fig. 4. Relationship of R2-values of the
a- (left) and b-diversity (right) of combinations of 2 to 11 study groups relative to other,
single study groups, plotted against survey
costs. Grey circles indicate the mean values
for a number of combined groups, the lines
the trend of the relationship of R2-values relative to survey costs. Note that for a given
cost level, trend lines for higher numbers of
combined groups generally lie above those
for combinations with fewer taxa. Graphs
showing all single values are provided in
Fig. S1 in Supporting Information.
R2 (adjusted)
1·0
0·8
0·8
0·6
0·6
0·4
0·4
0·2
0·2
0·0
0·0
(a)
0
β
1·0
50 000
100 000
150 000
Costs (Euro)
200 000
(b)
0
50 000
100 000
150 000
Costs (Euro)
2010 The Authors. Journal of Applied Ecology 2010 British Ecological Society, Journal of Applied Ecology, 48, 330–339
200 000
336 M. Kessler et al.
R2 - value
0·6
0·6
0·4
0·4
BE
WA CB
–0·2
–0·2
0·2
0·0
(c)
(b)
–0·4
10 000
20 000
30 000
0·8
40 000
β
0·6
10 000
20 000
30 000
0·8
0·4
0·2
0·2
0·0
0·0
40 000
0
–0·2
20 000
30 000
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30 000
40 000
β
0·4
0·2
0·0
–0·2
(f)
–0·4
10 000
20 000
0·6
(e)
(d)
–0·4
10 000
0·8
β
0·6
0·4
–0·2
–0·4
0
Residual R2 - value
–0·4
0
0·4
–0·2
(a)
0
α
0·8
0·6
PA
AN
HE
LITR
0·0 BI
BT
LW
DB
0·2
0·2
0·0
R2 - value
α
0·8
Residual R2 - value
α
0·8
–0·4
0
Absolute costs (Euro)
10 000
20 000
30 000
40 000
0
10 000
20 000
30 000
40 000
Absolute costs (Euro)
Standardized costs (Euro)
Fig. 5. Relationship of three measures of survey costs for each study group (a, d: absolute survey costs; b, e: costs standardized after Gardner
et al. 2008; c, f: residual R2-values from logarithmic models) plotted against the R2-values relative to the mean biodiversity pattern of all 12 study
groups, for different diversity measures (a–c: a diversity; d–f: b diversity). Abbreviations for study groups: AN: Ants; BE: Bees; BI: Birds;
BT: Butterflies; CB: Canopy Beetles; DB: Dung Beetles; HE: Herbs; LI: Lianas; LW: Understory liverworts; PA: Parasitoids; TR: Trees;
WA: Wasps.
α
R2 (adjusted)
1·0
0·8
0·8
0·6
0·6
0·4
0·4
0·2
0·2
0·0
0·0
(a)
0
β
1·0
50 000
100 000
150 000
Costs (Euro)
200 000
(b)
0
50 000
100 000
150 000
Costs (Euro)
both a and b diversity, R2-values reached 0Æ8 when 5–7 groups
were included and approximated 1Æ0 for 11 taxa.
Discussion
This is one of very few studies that quantitatively evaluate the
cost-effectiveness of different potential biodiversity indicator
groups along a tropical land-use gradient, and only the second
to consider standardized sampling costs (Gardner et al. 2008).
At the most basic level, we obtained different results when considering different aspects of biodiversity indication (a–diversity, b-diversity, indication of habitat condition), and different
measures of survey costs. These differences have important
200 000
Fig. 6. Relationship of R2-values of the
a- (left) and b-diversity (right) of combinations of 1 to 11 study groups relative to the
averaged diversity patterns of all study
groups plotted against survey costs. Grey circles indicate the mean values for a number of
combined groups, the lines the trend of the
relationship of R2-values relative to survey
costs. Graphs showing all single values are
provided in Fig. S2 in Supporting Information.
practical implications and, as developed in detail below, lead
us to conclude that the best approach for generalized tropical
biodiversity inventories might simply be to include as many
study groups as logistically and financially feasible.
Evaluation of indicator potentials
Overall, the indicator potential (measured as determination
coefficients) of single study groups was low for all four biodiversity aspects that we considered. For a-diversity, on average not more than 4% (R2 = 0Æ04) of the variability of species
richness of one study group could be predicted by another
group. Indication of b-diversity was only slightly higher, with
2010 The Authors. Journal of Applied Ecology 2010 British Ecological Society, Journal of Applied Ecology, 48, 330–339
Cost-effectiveness of biodiversity indicators 337
R2-values of up to 0Æ05. The idiosyncrasy of diversity patterns
of different taxa is a well known phenomenon, both at the local
scale, where habitats in a given area are compared (e.g. Lawton
et al. 1998; Schulze et al. 2004; Barlow et al. 2007; Kessler
et al. 2009), and at the regional scale (e.g. Prendergast 1997;
Duque et al. 2005; Tuomisto & Ruokolainen 2005). This
remains one of the main challenges in successfully applying
biodiversity indication (Wolters et al. 2006; Rodrigues &
Brooks 2007; Billeter et al. 2008).
Focusing on the capability of species to discriminate
between habitats, i.e. on their utility as indicators of habitat
condition, the values were also low. On average, only 12% of
the species of a given study group were significantly associated
with a given habitat type, although such low values may actually provide important information (McGeogh, van Rensburg
& Botes 2002). The degree to which a study group included
significant indicator species was significantly correlated with
the sampling completeness of the group. This relationship has
a simple statistical background: the probability of recovering
significant associations of species with habitats is a direct function of the number of records of those species (Dufrene &
Legendre 1997). More fully sampled study groups simply have
more repeatedly sampled species for which the statistical
analysis can recover significant habitat relations. Within this
statistically determined relationship, there appears to be some
variation that may have a biological background. Thus, while
no study group had a high percentage of indicator species at
low sampling completeness, the reverse situation was found for
several study groups. In particular, ants, herbs and understorey
liverworts all had low percentages of indicator species (<5%)
despite levels of sampling completeness above 50%, suggesting
that species of these taxa may overall have low indicator
potential.
The use of several indicator groups in combination raised
the regression values relative to both the diversity of other single study groups and to that of all 12 study groups combined.
Even more strikingly, mean R2-values for higher numbers of
groups were consistently better at the same costs than those for
lower numbers of taxa (Figs 4 and 6). In these analyses, in
order to assess the indicator potential of the individual taxa
relative to the overall biodiversity, we averaged the diversity
patterns of all study groups to obtain generalized patterns.
This approach is potentially statistically debatable because the
groups that are used as indicators are also among the 12
groups used to calculate the averaged diversity pattern. However, in the absence of full biodiversity surveys, averaging all
available groups has been advocated as the best alternative
(Westphal et al. 2008). Furthermore, this approach reflects the
real life situation, where any study group is indeed part of overall diversity. Using this approach, we found that the indicator
potential for ‘overall’ biodiversity is, on average, higher than
for individual groups, and that it increases strongly with a
higher number of study groups. We conclude that the indicator
potential for biodiversity surveys across a tropical land-use
gradient such as ours is limited with single taxa and that surveys should aim to include as many different taxa as financially
and logistically possible.
Evaluation of survey costs and
cost-effectiveness
In our study, absolute survey costs for different study groups
varied by almost an order of magnitude, from 4830 Euro for
birds to 22 690 Euro for wasps. These differences are partly
related to the species richness of the taxa and the difficulty of
sampling and identifying them, but they also are related to the
fact that the taxa were sampled to different levels of completeness. We therefore applied two measures which relativize
regression values versus costs: the standardized survey cost as
calculated by Gardner et al. (2008) and the residual regression
values introduced here. Both of these approaches have potential drawbacks. In particular, while survey costs are readily
standardized, the same is not true for the biodiversity indication. The reason is that the calculation of indicator values with
rarefied data requires the selection of individuals that are
retained for the analysis, and unless there is a chronological
account of the sampled individuals, there is no straightforward
way to conduct this selection. For example, in our study dung
beetles had the highest percentage of significant indicator
species (28%), but were at the same time the group with the
seventh-highest absolute survey cost (15 010 Euro). Here,
standardization following Gardner et al. (2008) reduced the
sampling cost by about 96Æ3% (Table 2). If we had also eliminated over 96% of all sampled individuals, then both the total
number of sampled species and the percentage of significant
indicator species would certainly also have been considerably
reduced. This standardization approach is therefore misleading, because it suggests that taxa with low standardized
sampling costs have high cost-effectiveness, when in fact their
high percentage of indicator species can only be achieved as a
result of high absolute sampling costs. The use of residual
information contents avoids the above problems but, at least
in our case, the results of the analysis are qualitatively similar
to those using the absolute costs, suggesting that the only
limited correction of different sampling intensities was possible.
Selection of indicator taxa
Depending on which indicator parameters and cost measurements were applied, different study groups emerged as the
most cost-efficient groups in the various analyses. This, along
with the low overall R2-values obtained for single taxa, and the
shifting group combinations in multiple group selections,
should caution against a strong reliance on a few selected indicator taxa. Despite this caveat, some of our study groups came
up more often as high quality indicator groups than others. In
our study, if we scan over the results of the different analyses,
birds, butterflies and wasps most often showed high R2-values,
while canopy beetles, dung beetles and parasitoids did so less
frequently, and ants, bees, herbs, lianas, understorey liverworts
and trees least often. While these results are non-independent
and study-specific, there is a certain degree of concordance
with previous studies. For example, Gardner et al. (2008)
found birds and dung beetles to be particularly good indicator
taxa, with butterflies among the better third of the 15 taxa
2010 The Authors. Journal of Applied Ecology 2010 British Ecological Society, Journal of Applied Ecology, 48, 330–339
338 M. Kessler et al.
studied by them (wasps were not included). Birds, dung beetles
and butterflies have often been advocated as good indicator
taxa for tropical biodiversity surveys (Bibby et al. 1992; Davis
et al. 2001; McGeoch, van Rensburg & Botes 2002; HornerDevine, Daily & Ehrlich 2003; Cleary 2004), and also bees,
wasps and their parasitoids have been shown to be effective
bioindicators (Tscharntke, Gathmann & Steffan-Dewenter
1998; Tylianakis, Klein & Tscharntke 2005). The reasons for
this in many cases appear to be the relative ease and effectiveness of quantitative survey techniques for these taxa in combination with manageable species numbers and a fairly well
known taxonomy, rather than their biological indicator potential as such. Thus, the selection of these taxa as high quality
biodiversity indicators is primarily driven by their low sampling costs. Accordingly, if one was to take this reasoning to its
extreme, one might argue that indicator taxa can be selected
exclusively on the basis of a simple cost calculation: how many
different taxa can one sample with the funds available? Indeed,
our study suggests that this approach might result in the best
possible selection of biodiversity indicator taxa for tropical
habitats. Our multiple regression analyses show that the
increase of the information content is primarily the result of
the inclusion of more taxa, rather than of more expensively
sampled taxa.
A sampling approach that maximizes the number of sample
taxa will also circumvent three further problems. First, the
quality of single indicator groups cannot be extrapolated from
one study to other geographical regions or habitats. Thus,
time- and fund-consuming preliminary studies along the lines
of our study would have to be conducted prior to each actual
survey, in order to select the most cost-efficient taxa for a given
situation. Second, the level of taxonomic knowledge and available expertise varies geographically and a flexible approach to
the selection of taxa would easily take this into consideration.
Third, even if a larger number of studies such as ours might in
the future identify a limited number of taxa that generally
outperform others – which may or may not be the case – an
exclusive focus on a few high quality indicator groups would
further increase the already existing taxonomic bias in tropical
biodiversity inventories (Pawar 2003; Gardner et al. 2008).
Eventually, such a one-sided approach in tropical biodiversity
inventories might lead to a biased view of tropical biodiversity
patterns and misinterpretations of how, for example, trophic
webs in tropical forests and land-use systems are built up
(Clough et al. 2007; Tylianakis, Tscharntke & Lewis 2007).
Thus, if the aim of a given study is to assess overall levels of
biodiversity, we strongly advocate that tropical biodiversity
inventories should include as many study groups as feasible
with the funds available. This approach is useful if the aim of a
survey is to compare the overall species richness and community distinctness of different habitats or different sites within a
given habitat, e.g. for the selection of conservation priorities,
or to monitor changes over time. Other, more specific applications of biodiversity indication might be better conducted with
specific taxa about which additional information, e.g. on their
ecology, is available (Grantham et al. 2008; Cowling et al.
2009; Moran, Lacock & White 2010).
Acknowledgements
This study was funded by the German Research Foundation (DFG), grant
SFB-552 STORMA (Stability of Tropical Rainforest Margins; http://
www.storma.uni-goettingen.de). We thank Pak Mann, Arifin, Daniel Stietenroth, Adam Malik, Wolfram Lorenz, Surya Tarigan, and all plantation owners
for their help in this work, and Toby Gardner, two anonymous reviewers and
the editors for valuable comments on earlier versions of the manuscript.
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Received 22 June 2010; accepted 22 November 2010
Handling Editor: Owen Lewis
Supporting Information
Additional Supporting Information may be found in the online version of this article.
Appendix S1. Description of study area, site selection, species collection, and determination.
Appendix S2. Protocols in R language used to calculate the multiple
regression analyses.
Figure S1. Figure showing the single values summarized in Fig. 4 of
the main paper.
Figure S2. Figure showing the single values summarized in Fig. 6 of
the main paper.
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